HOWARD R. MOSKOWITZ, Ph.D. MATTHIAS ...

HOWARD R. MOSKOWITZ, Ph.D. MATTHIAS SILCHER, M.A. EUGENE GALANTER, Ph.D. LINDA ETTINGER LIEBERMAN, M.Ed.

MIND GENOMICS®: THE NEW NOVUM ORGANUM VOLUME 1: PEOPLE AND GOVERNMENT

Copyright © 2013 Howard R. Moskowitz, Matthias Silcher, Eugene Galanter, Linda Ettinger Lieberman, and Moskowitz Jacobs, Inc. All rights reserved. Series Executive Co-Editors: Linda Ettinger Lieberman and Dvora Chaiet Book design and layout by Linda Ettinger Lieberman ISBN-13: 978-1470085490 ISBN-10: 1470085496

Library of Congress Control Number: CreateSpace, North Charleston, South Carolina

Company and product names mentioned herein are the copyrighted or registered trademarks of their respective owners.

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Contents Dedications Acknowledgments Foreword Preface

v vi vii ix

Part 1 – Tools 1: Frameworks and working tools.......................................................... 2:

So exactly what do we study? .............................................................

Part 2 – Experimental Design Meets the Big Issues 3:

Eat right and pay right what’s a good nutrition policy worth? ....

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The 800 pound gorilla – health care .................................................

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Education – Securing the foundation of the nation ........................

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Energy – you just can’t live without it...............................................

Part 3 – Talking to the citizen consumer and keeping secrets 7:

Giving information – What the audience wants to receive from the advertiser ...........

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Getting information – What the audience doesn’t want the advertiser to know ................................................................................

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Telling it all ... or only some of it ... and to whom .........................

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What people will and won’t tell researchers ..................................

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What information should consumer-knowledge providers to deliver to justify THEIR seat at the table?......................................................

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Dedications Howard’s Dedication It is with that delicious mix of reverence and delight that I dedicate this series of books to those who have inspired me. To my beloved wife, Arlene, the real woman of valor, the one without whose ongoing encouragement I could never have started this work, much less continued it to its completion. To my honored parents, the late Moses and Leah Moskowitz, who inspired and guided me in their individual ways, always full of heart and hope. To my teachers, Professors S.S. Stevens and William F. Reynolds, who helped me to move beyond training, to education. To my first boss, the late Dr. Harry L. Jacobs, who gave me that all-soimportant chance and encouraged me to find my own path. And finally, to my teachers of a lifetime ago, Rabbi Jacob Blachorsky of blessed memory, and Rabbi Abraham Zimels, both of whom set me on this path with their guidance and love. Thank you all. Matthias’s Dedication This book is dedicated to you Dad, on the occasion of your 70th year. I love you, Dad, and must acknowledge the fact that the good life I lead would not be possible without you. You have been and still are the Fatherly Force behind me, sometimes invisible yet always felt, as you made sure I learned good values, such as responsibility and kindness toward others. You gave me many chances to test my courage and talents and I knew you would be there to catch me and get me back on track. Eugene’s Dedication For eight incredible grandchildren—Savor intellectual effort of a high order. Linda’s Dedication For my parents, Shirley and Cyrus Ettinger, for encouraging my curiosity about the world. For my husband, Larry, and daughter, Shani, for encouraging me to return to work after a long hiatus. To Howard Moskowitz, for work that lets me apply life-long learning to new and challenging projects.

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Acknowledgments Throughout this process there have been a number of people and groups who have helped bring these experiments and the manuscript to its current state. The authors would like to extend a special thank you to Linda Ettinger Lieberman and Dvora Chaiet, Series Executive Co-Editors, Mind Genomics®: The New Novum Organum, and to Mel Lipetz who served as an early copy editor and proofreader. We would also like to say thank you to Paolo Gentile, David Moskowitz and Suzanne Gabrione, all of Moskowitz Jacobs, Inc. Paolo and David have guided the technical preparation of data and Suzanne helped prepare the graphs and tables. And finally, a thank you to Roseanne Luth and her staff at Luth Research, who have been our business partners throughout the beginnings of this project and through volumes 1-3.

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Foreword Hats off to Howard R. Moskowitz, Matthias Silcher, Eugene Galanter, and Linda Ettinger Lieberman on the forthcoming publication of their seminal work Mind Genomics®: The New Novum Organum. Some 100 years ago, the great novelist and science fiction writer H.G. Wells predicted that “statistical reasoning will some day be as important for efficient citizenship as the ability to read and write.” That day has arrived with a vengeance, and we are woefully unprepared. President Obama stated that “the United States cannot compete in a global economy when it ranks twelfth in the percentage of young adults with college degrees.” He might just as well have added that the United States cannot compete in the new information age economy when so many of its citizens are statistically, or using the modern term, quantitatively, illiterate. Indeed, to quote Lynn Arthur Steen, author of Achieving Quantitative Literacy, an Urgent Challenge for Higher Education, “The wall of ignorance between those who are quantitatively literate and those who are not is currently threatening our democratic culture.” Individual success in our new information age economy requires a set of problem solving skills that emphasize the flexible application of reasoning abilities. These skills or abilities form the basis of quantitative analysis and quantitative literacy. Contrary to popular opinion, though, quantitative analysis usually does not involve elementary reasoning with sophisticated mathematics – more often than not, it involves sophisticated reasoning with only elementary mathematics, and thus, with time, energy and purpose, it can be taught to all our citizens. We like to think that quantitatively literate people possess special pairs of glasses that enable them to see things that are simply invisible to anyone who doesn’t possess a pair of them. Here is an example of what we mean by this. In a 1970 primary election in New York State, 5,250 votes were cast and the winner’s plurality was only 62 votes. After the election, 136 votes were found to be irregular, and should not have been counted. We do not know, though, the actual breakdown of these irregular votes, i.e., how many were for the winner and how many were for the loser. In a now famous case known as De Martini vs. Power, the loser petitioned the Supreme Court of the State of New York to order a new election. The Court agreed, arguing that “it is not beyond likelihood that a small difference of 62 votes could be altered in a new election.” The Appellate Division unanimously affirmed the Supreme Court’s ruling. The case was then brought before the New York State Court of Appeals. One of the Justices possessed a pair of our glasses, and upon donning them, immediately observed that “the majority of 7

the winner would not evaporate unless at least 99, or 72.8 %, of the irregular votes were cast for the winner.” Based on this observation, the Court of Appeals reversed the decisions of the previous two Courts, stating that “it takes credulity to assume that in so close an election, such an extreme percentage (72.8%) of invalid votes would be cast in one direction.” Taking this one step further, if our Court of Appeals Justice had been fitted with a stronger prescription, then he might actually have seen an urn containing white balls (corresponding to the winner) and black balls (corresponding to the loser). There are 62 more white balls (corresponding to the winner’s plurality) than black balls in the urn, and the total number of balls in the urn is 5,250. We now randomly remove 136 balls (corresponding to the number of invalid votes) from the urn. What are the chances that there are now more black balls than white balls in the urn, i.e., that the invalid votes actually changed the outcome of this election? Looking at it through these glasses we see clearly that the chances of this happening are pretty slim – indeed, using just some elementary statistical analysis, it’s quite simple to show that the chance of the invalid votes changing the outcome of this election is less than one in ten million! Here is a typical exchange between an entering college freshman and an academic advisor that takes place countless times at college campuses across the country. Freshman: “What math course do I need?” Advisor: “It depends on what you want to major in. If you’re interested in the physical or biological sciences, then you need real Calculus. If you’re interested in business, then you only need watered down Calculus. And if you’re interested in none of the above, then today is your lucky day. You don’t need any math.” These answers are wrong, wrong and, tragically, even more wrong. No individual needs math – some disciplines require math to help clarify their principles. But, everyone needs quantitative analysis skills, and no one more so than those people in the third category above. This is why we’re so excited about the forthcoming publication of Mind Genomics®: The New Novum Organum, by Howard R. Moskowitz et al. The original Novum Organum was published in 1620 by Sir Frances Bacon and is widely considered to be the initial source of empirical reasoning, or equivalently, the initial source of the scientific method. We like to think of the Novum Organum as the initial source of quantitative reasoning, and Moskowitz et al. bring this to the next level by showing that quantitative analysis has applications everywhere! The Moskowitz Jacobs Rule Developing Experimentation (RDE) method is the penultimate tool for teaching the fundamental principles and skills of quantitative analysis. Not only does RDE combine all the buzzwords (silos, elements, patterns, 8

optimization, utilities, etc.) of quantitative analysis, but even more important, it teaches quantitative analysis in context. Here at Queens College, a unit of the City University of New York, which proudly counts Howard R. Moskowitz as one of its alumni, we are undertaking a pilot project that will embed RDE into one of our math literacy courses this coming semester, with the ultimate aim of suffusing RDE throughout our entire curriculum. We fully expect that within several years, all of our graduates will be quantitatively literate, fully prepared to function as informed citizens in a democratic society. Dr. Martin Braun Professor of Mathematics Queens College December, 2010

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Preface In 1620, Sir Francis Bacon published the Novum Organum, which was part of a larger work called Instauratio Magna. Bacon set up his system to organize the sciences, to define a method of scientific inquiry, and then to collect observations and facts systematically. Through the discipline of repeatable experiments, he sought to discover relationships that were not self-evident to naïve experience. Bacon saw himself, and is often seen by others, as a philosopher of science whose methods revolutionized how knowledge about the world is gained. Bacon called this process of systematically collecting and organizing Tables of Instances. This inductive method was a new way of extending knowledge, a new logic, a Novum Organum. His empirical method radically departed from the Aristotelian model of purely theoretical scientific thought. These hallowed methods constructed laws from presumed axioms—without ever dirtying one’s hands by actually checking anything. Bacon was convinced that the sages before him had failed to make any visible progress in the sciences because they lacked the empirical method. In Novum Organum, Bacon urged a revolution in teaching and the use of the scientific method, namely checking through experiments. By now it should be clear that the authors of this book are fans of Francis Bacon and his scientific method of induction. This book, Mind Genomics®: The New Novum Organum, explores a variety of important topics like terrorism, education, health, and even privacy, all through relatively simple experiments. We look at how people react to aspects of their daily lives, what drives them, what issues are most important. We will examine public policy based on these responses. A word of caution. You won’t see any treatment of reactions based on the existing understanding of this topic. We mean experiments, and mean them quite seriously. We will not just give you, the reader, an interesting trip through discussions of published experiments, giving you the knowledge without the tools to dig deeper. That’s not our approach at all. Rather than presenting data from the literature, the thousands of journals and millions of words, we hope to educate you in how to actually do straightforward experiments, what the results may look like, what they imply. All of our methods are accessible to you from start to finish. We do so within the frame of systematic experimentation using the methods already known and battle-tested by consumer research. But we are doing it for all the social science disciplines. Our Tables of Instances is called

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Rule Developing Experimentation (RDE), with its paradigm for systematic design which uses a variation of experimental design called conjoint analysis. With this book we will show how to create a bank of test stimuli, mix/match these stimuli creating test combinations, present the combinations to the respondent, and obtain ratings from them on a variety of scales such as Interest to help Americans become healthier, Willingness to pay additional taxes for health care reform, and When, how, to whom, and to what extent they want to communicate private information. We will then explain how to use simple modeling to deduce which specific elements within the larger concepts most strongly influence acceptance versus rejection. More than that, we will use the inductive method to find patterns, rules, and new facets within the nature of people’s everyday thinking. No other book deals with these topics in such an accessible, structured way with easy-to-do experiments and a body of knowledge about the topic. We will share new ways of thinking, exciting experiments, novel and previously known results. We are convinced that our revolutionary method of gaining knowledge will be enjoyable and enlightening for you. Now, please join us on this incredible adventure into the algebra of the everyday mind.

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CHAPTER 1 Frameworks and working tools Abstract: We wrote this book to share our view about conducting experiments using statistical design of test stimuli. We open this chapter with an introduction to three mainstream approaches to research – questionnaires, full experiments, and data mining, explaining their origins, their strengths, and their shortcomings. We describe how each works within a framework of a simple business problem. We then introduce the organizing principle of stimulus-response (S-R), and its workhorse tool, RDE (Rule Developing Experimentation), our version of conjoint analysis. With RDE the researcher creates test stimuli, mixes these stimuli in order to generate test combinations, presents the combinations to the respondent, and obtains ratings. RDE is, as the name suggests, an experiment. Through the systematic variation of tested features the researcher identifies which particular feature drives acceptance, and by how much. Using statistical analysis (regression modeling), we show how the pattern of responses allows us to trace the ratings back to the stimuli. Going one step further, we see how knowledge of which stimuli drive responses shows us the algebra of the mind, the implicit rules that people use when evaluating these test stimuli. Throughout this book we use the experimental method coupled with statistical analyses, typically regression and clustering, in order to reveal the deeper structure of what is happening inside the mind. We move beyond method to topics. Our focus is not so much on method as on using our systematic approach to map out the mind as it copes with stimuli representing daily, relevant situations. Thus, our experiments move beyond interesting one-off observations, instead evolving into the basis of a normative, descriptive, and prescriptive science. Introduction We begin this book with the discussion of its world-view and its tools. When we started writing this book, we had two choices: we could write a standard business book replete with rapid-fire vignettes, one after another, describing the topics, pointing to what is happening in today’s world, or we could give you a fun read. The only problem with the fun read approach is that it fails to give you the tools by which you can do things yourself. So, we decided to write using the second choice. However, this book will not only entertain you, it will also give you an understanding of the world of 12

experience along with the tools to conduct your own experiments. With what we’re going to share, we’ll begin to create a field whose scattered pieces reside in many different disciplines. We invite you to continue the act of creating this field when you are ready. Three worlds: Questionnaires, Full Experiments, and Half-Experiments (Data Mining) There are three ways to conduct research: Questionnaires, Full Experiments, and Half Experiments (Behavioral Data Mining). We’ll see how each works within the framework of a commonly faced business problem: For example, a beverage manufacturer wants to come out with a new can. The can has two types of attributes, or variables, which need to be explored. One variable is the color of the can (red, blue, green). The other is the text information that the can should feature as a splash (e.g., in a big circle), to highlight the information. So, how does the manufacturer go about finding out what to find out: 1. What should the color be: red, blue, green? 2. What ought to be the splash message: new, taste, low calorie? The world of questionnaires Questionnaires have a longstanding importance of measuring individuals’ attitudes toward public policy. A rather large industry deals with the measurement of these changing attitudes [1]. Industry typically uses questionnaires as the workhorse tool [2]. The questionnaire comprises a set of statements that the respondent rates on some type of anchored scale (e.g., agree versus disagree; irrelevant versus important). The nature of the questions, the types of scales used, and the subsequent analysis all vary by the problem and by the predilections of the researcher [3, 4]. In the end, however, the data generate a profile of the respondent on key measures. With the help of that profile, it becomes possible to place the respondent into one of a limited number of groups, i.e., to classify them. This paradigm of questionnaire and analysis typifies a great deal of the research done to understand people’s minds. Researchers use questionnaires to discover the respondents’ attitudes by asking direct questions about the topic at hand. We could ask people: what color should the soda can be? Or, which of these three splashes should we feature on the can? You’re probably thinking that these questions sound perfectly reasonable. 13

If we want to make sure that we get it right, we might move beyond asking a person to tell us the color they prefer and actually show them a can with the proper color or the actual splash, so he can answer more intelligently. There’s absolutely nothing wrong with asking people what they want, whether in words or by actually showing them the stimulus. What could be missing from this approach? With questionnaires, we face these problems: rationality, political correctness, and the like [5]. When we work with questionnaires we ask the respondent to reflect inward, to tell us what he feels. We may show the respondent a stimulus in order to clarify the questionnaire, and make the meaning of the question more obvious. After all, when we talk about a blue can, it makes sense to show the exact blue about which we are talking. At the end of the day, however, we end up asking the respondent to tell us what he thinks. The problem? The respondent may tell us what he truly believes he thinks. Or, the respondent may tell us what he thinks we want to hear, or should hear. In the latter case, the respondent acts as editor of his own feelings. Such editorship is the bane of questionnaires because intellectual judgment may cloud how the respondent actually feels about the product. We’ll never know, of course, because the questionnaire format doesn’t allow us to observe behavior. Rather, it instructs the respondent to describe what we need to measure. We are missing the choice, the action. The world of full, live experiments The opposite of a questionnaire is a full live experiment where you create a stimulus and record the respondent’s behavioral reaction. Nothing beats the understanding that you get when you hold the stimulus in your hand and look at it. We talked about stimuli before in the section on questionnaires, but there we used a picture instead of the words to help clarify the question. We really didn’t ask the respondent to react to the visual of the soda can per se, although we could have. If you re-read the section on questionnaires above, you’ll notice that the picture of the soda can with the correct color was simply to ensure that the respondent could properly visualize the color. Before we begin a full experiment, we must lay out the different combinations that we are going to test. Rather than ask the respondent to tell us what he or she wants at an intellectual level, we will show the stimulus (or stimuli) to the respondent, instruct the respondent to make some indication of their 14

acceptance/preference, and then watch and/or record the behavior. Table 1.1 below shows the fifteen combinations that the beverage company is considering for the color and message on the can. In a full experiment, the manufacturer would create the fifteen cans, bottle the soda in the cans, and send them out to different stores. Each store would have only one of the test can combinations. The cans would be shelved in the usual place for the product and priced in the usual way. That is, the only difference would be that the can is a prototype. After a while, say a week, or even a month, the storeowner would report how many cans were sold. By comparing the different sales figures, the company would figure out which combination of color and messaging was best. There’s even more to the story, however. After all the data is in, the company could determine, through statistics, how each of the colors and messages contributed to the sales. The statistics will add to the company’s knowledge. Statistics, or in today’s jargon analytics, will convert the observations to patterns. The patterns lead to knowledge, to rules, and to corporate intellectual property. Table 1.1. The fifteen combinations of color and messaging made for a soda product. Soda# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Variable 1 Color – Symbol Blue Blue Blue Green Green Green Red Red Red Blue Green Red None None None

Variable 2 Text Info New Great Taste Low Calorie New Great Taste Low Calorie New Great Taste Low Calorie None None None New Great Taste Low Calorie

These full-blown experiments cost a great deal of money. Imagine how many products you would need to create when you want to test ten or more variables. Although one would like to test all combinations in the stores, and thus really capture the solution, reality intrudes rather abruptly. Companies interested in conducting full experiments generally select a handful of products from the whole set - ones they believe to be the most successful. That is, through some process of judgment, the companies whittle down the array of products to a small enough number to be considered affordable, often eliminating options until the 15

one product that they believe will succeed remains. In that case, the store test with actual purchases of real products by real consumers becomes a way to avoid a costly mistake, should the corporate judgment be wrong (and often it turns out to be that way, dead wrong). The bottom line here is simple: in an ideal world, you learn the most from real-world experiments, whether the experiments are run in stores with actual consumers, or in setups such as store simulations. Experiments happen all the time because, quite simply, they work. For example, before Disney did a complete overhaul of its stores, it did the experiment. Disney launched a single prototype store. It worked out the kinks in its new model with this store before sinking down its whole investment, a costly endeavor that most retailers skip [6]. In general, companies minimize the cost of these studies because companies are in the business of making profits, not in the business of learning and educating themselves. The unhappy consequence of that reality constraint is a limited experiment that produces limited knowledge of what wins and what loses. This experiment fails; it does not teach principles and does not, therefore, make the company wiser for the future. We don’t advocate such limited experiments, unless one has no other choice. Keep in mind that the corporate data libraries are filled with experiments. For the most part, they are simply one-off tests, comprising one to three products to navigate a momentary intersection. A larger-scale test which we just described, a complete study with systematically varied products, comprises a source of data from which the company could learn for the future. What the company chooses, however, is to run these small, one-off tests. Consequently, the data libraries look more like collections of disparate test reports than the source of scientific and business wisdom. Whereas robust, full experiments generate a true knowledge base, they are very difficult to conduct within the constraints of reality. With sufficient resources, one could create different hotel rooms or different museum exhibits, for example, present these stimuli to consumers, get their reactions, and write up the results. There’s a lot to be said about live experiments; they are very hard to conduct and very expensive. The social experiment is one type that is very expensive, but closely related to our work. In their book Digest of Social Experiments, David Greenberg and Marc Shroder describe all social experiments conducted to-date in the United States. When large-scale social experiments were first introduced in the 1960s they were a departure from the normal proactive efforts of policy research [1]. Social experiments randomly assign people either to a group that is subject to one or more policy “treatments” or to one that continues to be subject to the prevailing policy norm (“controls”). A 16

social experiment might test the efficacy of a welfare-to-work program by randomly assigning welfare applicants either to a new program such as an intensive, coached job search combined with the provision of services like transportation assistance and subsidized child care, or to the status quo where the welfare recipient is entirely left alone to find a job [7]. The world of half-experiments, behavioral data mining Between the full experiment and the questionnaire lies a third realm, behavioral data mining. Data mining is the process by which one gleans patterns from existing data. For example, companies may analyze past buyer behavior in order to predict future demand. Behavioral data mining is an outgrowth of two very unlikely parents, but as we will see in a moment, they are not as different as one might think. The grandparent of behavioral data mining is ethnology, a tool of anthropologists. Ethnology is one of the major branches of cultural anthropology. It studies cultures by comparatively examining their historical development [8]. In the nineteenth century, ethnology was offering explanations for extant cultures, languages, and races in terms of diffusion, migration, and other historical processes. In the twentieth century, ethnology focused on the comparative study of past and contemporary cultures. Since cultural phenomena can seldom be studied under conditions of experiment or control, the ethnologist requires comparative data from the total range of human behavior. That array of information effectively avoids many assumptions about human nature that may be implicit in the mores and behaviors of any single culture [9, 10, 11]. Today, ethnologists learn about contemporary cultures through ethnography, the parent of behavioral data mining. Ethnography is a holistic strategy that does not prescribe any particular method (e.g., observation, interview, questionnaire), but instead tries to describe a culture through writing [12]. An ethnographer is an observer who lives among the people to be studied, becomes part of the group, records the activity, and eventually creates a coherent narrative which tells their story. The ethnographer attempts to capture the essence of the people he or she observes, while still recognizing that he or she is an outsider who is trying to blend in. This holistic approach to data gathering grew out of the ethnologist’s needs for comparative data across cultures. By gathering data about a culture firsthand and drawing the best possible conclusions, the ethnographer provides an evidence-based story about a contemporary group. The other parent of behavioral data mining is the Internet itself. This marvelous web of sites, of people interacting with their computers, searching for information, buying things, communicating with friends, provides a behavioral 17

record to be mined. Using the ethnographer’s principles to understand people and the statistician’s tools to discern pattern, the behavioral data miner develops an understanding of what’s happening. There’s no experiment here, and there’s no questionnaire either. There are simply reams and reams of data, waiting to be organized into a coherent whole. Returning to our soda can problem, our data mining researcher would endeavor to find existing data about soda sales or parallel topics. He would aim to find data that could be extracted and applied directly to the topics of chief concern, which are the color and the messaging on the can. If he were fortunate enough to find a recent study comparing soda can colors and messaging, the story might end here. However, at the heart of the soda can question is an implicit understanding that the parts may be separate variables, while consumers buy the whole product. The researcher needs to know how text and color act together to drive the response. How do the parts work? Finding data that replicates the variables and the final product may not always be possible. Enter the Fourth World – Our World of Stimulus-Response If questionnaires leave a lot to be desired, because they may allow the respondent to game the system, and be politically correct, if full experiments are ideal but almost impossible to execute because of effort and time, and if existing data do not provide fruitful answers, then what are we to do? Can we develop a fourth approach to provide us a sense of what consumers want, without necessarily being held prisoner by the respondent’s mental policeman (that politically correct editor which always operates), or by the impossibility of doing full experiments? The answer to this question is a qualified YES. There are methods which allow us to explore the mind of the respondent, overcome some of the political correctness, and reveal, at a deeper level, what is going on. Like all good investigators and students, we’re going to borrow from all three worlds: questionnaires, experiments, and data mining. However, we’re going to create our own world of data, a world where experiments are easy to do (within days), where data is crisp and crystal clear (through experimental design and modeling), and where the interpretation is straightforward (the impact of inter-ocular trauma – hitting us right between the eyes). A stop on our journey of understanding is to the rather straightforward, but powerful stimulus-response experiments. These experiments are simple. Take different phrases or pictures, mix them together in a systematic way, present them to consumer respondents, obtain ratings, and then determine, through simple statistics, how the rating is constructed. That is, we have mixed together elements 18

and presented those elements. How much of the rating can be attributed to each element? The description you just read may sound so simple as to seem almost trivial. It is simple, but far from trivial. When, in fact, we have a selection, and mix together different components from that selection and present the combination to the respondent, we’re actually simulating nature. For nature is nothing more than the combination of stimuli. When we do this simple mixing, presenting, rating, deconstructing we actually end up with the algebra of the consumer mind. We learn, for example, how different propositions or ideas drive a person’s response. We learn how ideas influence each other. And, perhaps most importantly, the participant, the respondent in the study, does not sit there in the test situation consciously giving ratings in a politically correct, but ultimately false, manner. The participant does not and cannot game the system. In the end, we figure out just what’s important to the respondent. Behavioral psychology, with its background in experiment, introduced the notion of S-R or stimulus-response [13]. Who has not at one time or another heard the story of Pavlov's most famous experiment; conditioning dogs to salivate at the ring of a bell? The subject is given a stimulus, and responds. What then are the rules that make the dog respond? The experimenter doesn’t stop with knowing that the dog will salivate. That’s interesting. But there’s more. The experiment teaches the researcher a great deal about the behaving organism, especially when the researcher systematically varies the test stimulus and measures the changes in response, looking for patterns that correlate with the changed stimulus. This is S-R, stimulus-response, cause-effect, action-reaction. The power of S-R analysis is substantially greater than the power of standard questionnaires. In questionnaires the stimuli are questions. In S-R analysis the stimuli come from the real world. They are visuals, phrases, sounds, anything that can be varied. The output is responses, but then later on rules, engineering rules for behavior, what to do to create a response. Virtual stimuli, real consumers, experiments using the computer Let’s move on to the exciting world of experiments. The power of S-R can be unleashed cheaply and expertly with today’s arsenal of technological tools. An approach called statistical design of experiments, or experimental design, allows us to work with the computer to present the test stimuli.

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The background of experimental design How do you plant a field to maximize the yield of crops? How do you fertilize the crops to maximize the yield? If you’re wondering what crops, yields, planting, farmers, and the like have to do with the mind of consumers, then you’re on the right track. A century ago, a lot of agronomy was conventional wisdom, hit and miss coupled with some tricks of the trade. Then, in the latter part of the nineteenth and the early part of the twentieth century, statisticians became involved. The statisticians developed efficient designs (i.e., sets of test combinations). These were combinations that the farmer, or better, the agronomist, would follow when planting and fertilizing. The experimental design specified which field would receive which treatment. It seems logical to us today, but the notion of a designed experiment a century ago was not as clear, nor was it obvious what it could deliver. And, what happened? Simply put, some fields yielded more when harvested, and some fields yielded less. The statistician could relate the yield to the treatment, and thus identify which particular treatments increased the yield, and which decreased the yield. This was the full experiment. It’s like the experiment we described above. Make the various combinations, watch what people buy, and determine the underlying rules. Let’s bring this approach to the computer. We’re not going to plant fields, but we are going to create fifteen different pictures of our soda cans, varying in color and in ‘splash.’ We’ll present each of these pictures on the screen, and instruct the respondent to rate interest in buying the soda. We won’t measure actual purchase. Rather, we’ll do something intermediate; we’ll present the combination and let the respondent rate degree of interest. We’ll do a partial experiment. The stimuli will represent the actual products, but will be ‘virtual stimuli’ on the computer screen. The response will be a rating of immediate feeling or an expectation of future action, e.g., degree of interest, likelihood of buying, and so forth. In order to better understand the results, we would like to explain the setup of our little experiment in more detail. Do not worry; it is very simple to understand. We see the data from one person in Table 1.2. Our table is set up in order to enable fast and easy statistical analysis. Let’s look at the table and see what we can do with the data.

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The Data – Setup 1. Let’s look at Table 1.2. You will see that we expanded the information shown in Table 1.1, so that you see a much fuller array of information. The table shows the data prepared for statistical analysis. Look at the number of rows. There are fifteen of them, one per soda. Each soda can has only one color and only one text. 2. The specific color is shown in columns 1 through 3. When there is a ‘1’, the soda can is color #3, which we assign to the color blue. Thus, in row ‘1’ (soda 1, or concept 1) we see that there is a ‘1’ for the color ‘Blue’, and a ‘1’ for the text ‘New.’ This is precisely the combination we saw for soda 1 in Table 1.1. The only difference here is that we have expanded the number of columns. 3. Instead of writing the color and writing the text in the columns, we ‘coded’ the columns with a ‘1’ when the element is present or a ‘0’ when the element is absent. This is called dummy coding. The 1s and the 0s are numbers. So, what we did, in effect, was transform the list of products into a matrix of numbers that we could analyze by statistical methods, specifically multiple linear regression, also called ordinary least squares (OLS) by which name we will refer to the method here). 4. We also collected “Purchase interest” ratings for every vignette or test concept. (We will use those two terms interchangeably throughout this book.) Column 8 could be the rating of one person or the average rating from 100 consumers who evaluated each of the fifteen soda cans on the computer, by looking at the can and rating it. Good experimental method dictates that when we do this experiment on the computer we should randomize the order of the fifteen cans. The coding will stay the same; just the order of rows will change. 5. If you look closely at the experimental design in Table 1.2, you will see that concepts ten through fifteen are lacking something. They do not have a color (the can is white), or they do not have a text. These are called zero cases. There’s a reason for these zero cases. The reason is statistical. When we omit these zero cases, we will encounter a nagging problem in statistics called multi-collinearity, which plagues a lot of research, and destroys a lot of good analyses. 6. A short statistical digression: Multi-collinearity is a statistical phenomenon, in which two or more variables that serve as predictors of another variable are correlated to one another. Multi-collinearity makes it difficult to 21

determine the relative importance of each predictor on the dependent variable. We can’t discover the exact contribution of a color because the colors are not independent. Knowing the ‘state’ of two colors automatically tells us what the third color will be. The same goes for splash. Knowing the ‘state’ of two text splashes tells us what the third text splash will be. We can only estimate the contribution of two colors or two text splashes. That’s not satisfactory. We don’t want that multi-collinearity. So, to avoid it, we resort to incorporating ‘true zeros’ (i.e., combinations which don’t have any of the three colors, and (other) combinations which don’t have any of the three texts). If we did not have these ‘zeros,’ it would mean that every combination must have had one of the three colors. And, in turn, if the combination did not have a red or a green colored can, it had to have been a blue can. Which, in turn, means that the three ‘colors’ aren’t really independent of each other. Knowing the condition of two can colors means automatically knowing the condition of the third can color. These precautions allow us to estimate the absolute impact or value (coefficient) of each of our six elements, three colors and three texts for the ‘splash.’ We can do so because, in truth, all three colors are truly independent of each other, and all three texts are truly independent of each other, in a statistical sense. Knowing that the can is not blue or green does not automatically make the can red. It could be colorless. Table 1.2. The fifteen combinations for a soda product shown in ‘dummy variable’ format, with 1 when the element is present and 0 when the element is absent. 1

2

3

Color

1 2 3 4 5 6 7 8 9 10 11 12 13

Variable Color

Variable Text

Blue Blue Blue Green Green Green Red Red Red Blue Green Red None

New Great Low New Great Low New Great Low None None None New

4

5

6

Text

Blue

Green

Red

New

Great taste

Low calorie

1 1 1 0 0 0 0 0 0 1 0 0 0

0 0 0 1 1 1 0 0 0 0 1 0 0

0 0 0 0 0 0 1 1 1 0 0 1 0

1 0 0 1 0 0 1 0 0 0 0 0 1

0 1 0 0 1 0 0 1 0 0 0 0 0

0 0 1 0 0 1 0 0 1 0 0 0 0

7 Rating of Purchase Interest 1 Not at all 9 Very Interested 7 6 4 4 2 5 9 4 6 8 2 5 7 22

14 15

None None

Great Low

0 0

0 0

0 0

0 0

1 0

0 1

5 4

Now, we’re ready for the analysis. As you read through this book you will find that it’s the analysis that tells us a lot about what we are investigating. When you read the results of studies, you may first feel that you want to know the topline, the bigger story, what’s really going on. That curiosity will, of course, remain with you. However, as you become more experienced you’ll want to see the numbers, and draw your own conclusions. That being said, let’s move to the data. The Data – How to analyze what RDE measures Table 1.3 shows us the analysis of our data. We used multiple linear regression (OLS) to relate the presence/absence of the six different can features (three colors, three splashes) to the rating assigned by our one respondent. You will see a standard output from a regression program. It’s fairly straightforward. Table 1.3. Results from a study of sodas for one respondent showing how the three splashes and three texts drive the rating. Squared multiple R: Adjusted squared multiple R: Effect Additive Constant CBLUE CGREEN CRED TNEW TGREAT TLOW

0.64 0.38

Coeff. 5.11 0.97 -2.03 0.72 1.72 -0.78 -0.28

Std. Error 1.40 1.24 1.24 1.24 1.24 1.24 1.24

Student t 3.66 0.79 -1.64 0.58 1.39 -0.63 -0.22

P (2 Tail) 0.01 0.45 0.14 0.58 0.20 0.55 0.83

Multiple regression is typically used for learning more about the relationship between the independent variables (in our case the color and text) and the dependent variable (the rating score). In social and natural sciences, multiple regression procedures are very widely used in research. In general, multiple regression allows the researcher to ask (and hopefully answer) the general question ‘what is the best predictor of … ?’ For example, educational researchers might want to learn the best predictors of success in high school. Psychologists may want to determine which personality variable best predicts social adjustment. Sociologists may want to find out which of the multiple social indicators best predict whether or not a new immigrant group will adapt and be absorbed into society. 23

The Squared multiple R gives us the goodness of fit. This statistic has a range from a low of 0 to a high of 1. The value we obtain for these data, 0.64, means that 64% of the variability in the ratings (from this one person) can be predicted by knowing the presence/absence of the six elements. Now move to the second row, the Adjusted squared multiple R. In statistics, the more variables you add as predictors (even the weather!), the better your prediction is going to be. That’s a statistical matter of fact. However, you can add irrelevant variables. There has to be some method for penalizing when we add nonsense variables. Quite simply, this is the Adjusted squared multiple R. The adjustment corrects for the fact that we’re adding more nonsense predictors that don’t do much. The Adjusted squared multiple R will drop down when we have a certain number of cases (here fifteen), and we have (in relative terms) a lot of meaningless predictors. If we were to have, say, 1,000 cases, the Adjusted multiple R would be very close to the actual Squared multiple R, because six predictors for 1,000 are not a lot. It’s all a matter of how many cases or observations we have (here 15), how many predictors we have (here 6, a sizeable 40% percent of 15), and the number of irrelevant predictors (for which we are penalized). The essence of the experiment comes from the analysis of the data showing how each independent variable (Color and Text) drives the dependent variable (Rating). Moving to the actual results for our six individual colors and texts, let’s look at what each color and text contributes. Looking at the output, we see five columns: 1. Effect (element or variable in the model) 2. Coeff (coefficient) 3. Std. Error (standard error of the prediction) 4. Student t (a measure of signal to noise) 5. P-value (probability that this signal/noise value came from a distribution whose mean value was actually 0) Keep in mind that these are the standard statistics to emerge from most regression modeling. We will focus on these five statistics for now. However, when it comes time to look at the results of the different studies and find out the

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story that nature is trying to tell us, we’ll concentrate primarily on the value of the coefficient. The coefficients for the different elements really tell us the story! 1. The Effect Within this column we see the Color (Blue, Green, Red) and Text (New, Great, Low) elements, as well as the additive constant. The additive constant tells us the expected value of the rating when all six variables, three colors and three text elements, take on the value 0, i.e., not present in the test stimulus. As you can see from Table 1.2, none of the respondents actually saw a test combination with no color and no text. The OLS regression model estimates what this combination would have scored. The regression analysis fits an equation of the form: Rating = Additive Constant + k1(CBLUE) + k2(CGREEN) … k6(TLOW). Since the constant reflects the situation where no element (color or text) was shown, we interpret it as the starting point or base line interest. A high constant means that people are already very positively opinionated about the topic and that the tested messages only have a limited influence. The test stimuli would start out by getting a high score without any elements. On the other hand, a low constant means that it will be the messages that have to do the work; there is no predisposition for the respondent to start out with a high rating. The messages have a lot of room to move the needle, to inform, manipulate, and hopefully convince the potential consumer. As you read through this book you will get a better feeling about what is a high versus low constant; they vary by topic. 2. Coefficient Each element generates its own coefficient showing the number of points brought to the rating by that particular element. Even though we let each element appear equally often, there’s no reason why the elements have to generate the same persuasive power. The beauty of experimental design is that it allows each element to act as an independent agent. Look now at the column of coefficients (Coeff) for the six elements. The real story begins here. We see that CBLUE, the color blue, decreases the rating by 0.97 points. So, blue isn’t the color we want. CGREEN, the color green, performs even more poorly decreasing the rating by 2.03 points. But we hit pay dirt and increase the rating by 0.72 points when we use the color red (CRED). Knowing nothing else, we could then begin with the additive constant (5.11) and 25

get an additional 0.72 points with a red can. The effect would be to create a combination scoring 5.83 (i.e., the sum of the additive constant and the element). It’s that simple. 3. Standard Error The Std. Error represents the estimated variability of the coefficient. If we were to run the study 100 times, what variation would we expect to observe in the coefficient? In this experiment, the actual scores we see for the coefficients vary by +/- 1.40 for the additive constant and +/- 1.24 for every element. 4. Student t & P value The Student t helps us determine whether or not the coefficient which emerges from the multiple regression analysis differs from 0. We calculate the Student t statistic by computing the ratio: (Coefficient/Std. Error). Let’s look at the ratio of the coefficient to its variability (measured by the standard error). The ratio for the blue color (CBLUE) is (0.97/1.24) or 0.79. Now, knowing the number of cases (let’s assume we have a sample of N=100) and the score of the predictor for CBLUE (i.e., 0.97), the computer program calculates the odds of getting this ratio of 0.79 if the true coefficient for the blue can were to be 0 instead of 0.97. The odds of us getting this ratio 0.79 in such a case (true value = 0) are pretty high, around 45% (see the P-value of 0.45). So, we conclude that our observed coefficient for BLUE, namely 0.97, might easily have come from a distribution whose true value is actually 0! For this book we usually talk about coefficients that correspond to a Pvalue of 0.10 or lower. That P-value means that we would expect to see the coefficient we obtain less than 10% of the time if we were to repeat the study and were dealing with a true coefficient of 0. In a sense we create a straw man, a coefficient of 0, and try to determine how frequently we would observe our coefficient if it were actually equal to that straw man. The foregoing paragraphs about the student t test and the likelihood of seeing what we observe (the coefficient) given the variability (standard error) come from the world of inferential statistics. A lot of researchers base their conclusions about the way the world works on hypotheses that are tested by just such methods as the student t test. We will not focus our attention very much on the question of whether or not the coefficients we observe are truly different from 0, in a statistical sense, but rather focus on the patterns in the data. That is, we will focus on the different coefficients (e.g., those for color, those for text) as we search for the story that nature is trying to tell us.

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Contemporary Applications of the Stimulus-Response Approach Conjoint analysis, the study of systematically varied stimuli, represents one increasingly popular application of the S-R approach. Instead of colors and splash messaging, the stimuli in conjoint analysis are phrases or a combination of phrases and visuals; the responses are ratings on a criterion scale, such as interest, believability, uniqueness. In conjoint analysis, the researcher creates a bank of test stimuli (e.g., a set of phrases and visuals), mixes/matches these stimuli creating test combinations, presents the combinations to the respondent, and obtains ratings. Each respondent rates a relatively large set of concept combinations and the researcher then uses regression modeling to deduce which individual elements within the larger concepts most strongly influence acceptance versus rejection. The statistical analysis is quite simple, generally one or another form of regression modeling. Besides its operational elegance, another benefit of conjoint analysis is that it forces the respondent away from the role of evaluating separate stimuli and into the role of evaluating mixtures, which is the more normal case in the everyday world, both for soda cans and for public policy initiatives. Finally, the respondent cannot adopt a stance of being politically correct because in the combinations (so-called test concepts) there are different messages, some of which the respondent may like and others which he or she may dislike. The respondent must make an immediate judgment and move on to the next test combinations. The elements inside the concept fight it out to drive the respondent’s rating. In the world of consumer research, conjoint analysis methods occupy a venerable role. Tracing the history back to 1964 [14], conjoint approaches have become increasingly popular among researchers, both in academia and in the business world. For the most part, conjoint analysis has seen its greatest acceptance among those researchers who seek to define the features of products or services. Through the systematic variation of these features the researcher rapidly identifies which particular feature drives up acceptance and by how much. It should come as no surprise, therefore, that conjoint analysis is becoming increasingly popular among applied researchers who avail themselves of these powerful methods to identify an optimal product rather quickly and efficiently [15, 16] The heritage of conjoint analysis lies in consumer-oriented research in the for-profit sector. Conjoint has been used in product creation ranging from relatively simple physical products [17, 18] to entire systems, such as hotels [19]. Conjoint analysis also finds use in advertising, where it can be a powerful method for identifying messages that break through the clutter in a deconstruction of advertising messages [20]. Finally, conjoint analysis has been used to better 27

understand the way people process messages, with dependent variables being both rating of acceptance and actual behavior response time [21]. In all of these examples, however, the effort has focused on the creation of stimuli or messaging which fit different needs and can be sold to consumers. During the past several years, conjoint analysis has also begun to find a home in public sector research where the issues pertain to public policy. For example, one can use the same approach of systematic variation to identify what particular features of an anxiety-provoking situation drive the respondent to state that he or she is more anxious [22]. Other applications in public policy deal with the charitable giving sector where the research has focused on the drivers of charitable giving and the differentiation of those drivers by the different charities, and different mind-sets of respondents. One work by the senior author deals with the application of conjoint analysis to political messaging and to the treatment of the presidential candidate as a consumer product [23]. Another study on privacy issues is done in this spirit of research on public policy and continues some of the earlier privacy-focused research for Internet privacy [24]. We will use conjoint measurement through this volume and companion volumes. Our version of conjoint measurement, called RDE (Rule Developing Experimentation) allows us to create databases showing how consumers respond to different aspects of ordinary life. The totality of what we do with conjoint analysis/RDE will create an algebra of the mind for everyday life, or what will end up being a new science that we call Mind Genomics®. Summary We opened this chapter with an introduction to three mainstream approaches to research – questionnaires, full experiments, and data mining – and explanations of their origins, their strengths, and their shortcomings. We then reviewed the idea of stimulus-response. Seeing how people react to a combination of stimuli achieves success by simultaneously disarming the inner policeman (which governs the responder of a questionnaire) and avoiding the complications and costs associated with conducting a full experiment. We discussed how the computer assists us in all steps of the S-R process, from gathering data to rapidly outputting the statistical measures that answer our questions. We went through an agonizing analysis of experimental design, from setup to analysis, and to some degree, interpretation. For the most part, we are not going to do this type of meticulous analysis for our topics. The book would be an 28

impossibility to write, and certainly unreadable. It is sufficient to understand the basic principles which combine definition of the stimulus, definition of the rating, experimental design, study implementation, statistical analysis, and interpretation. Finally, we closed with a discussion of how experimental design is applied to research across industries, from consumer research in the private sector to individuals’ complex views on public policy. We introduced the notion of RDE as a version of conjoint analysis and how RDE will help us create an algebra of everyday experience, a science of Mind Genomics®. These tools have armed you with the necessary foundations for navigating the upcoming chapters. You are now ready to take the next steps on your journey. References: [1] Fischer, F., Miller, G., & Sidney, M. S. (2006). Handbook of public policy analysis: Theory, politics, and methods. Boca Raton, FL: CRC Press. [2] Zikmund, W. G., & Babin, B. J. (2012). Essentials of Marketing Research. Stamford, CT: Cengage Learning. [3] Saris, W. E., & Gallhofer, I. N. (2007). Design, Evaluation, and Analysis of Questionnaires for Survey Research. New York, NY: Wiley & Sons. [4] Saris, W. E., & Sniderman, P. M. (2004). Studies in public opinion: Attitudes, Non-attitudes, Measurement Error and Changes. Princeton, NJ: Princeton University Press. [5] Munoz, C. G. (2008). Can Polls Measure Anti-American Sentiment? World and I Magazine, 23(6). [6] Barnes, B. (2009, October 12). Disney’s Retail Plan Is a Theme Park in Its Stores. The New York Times. Retrieved March 28, 2013 from the World Wide Web: http://www.nytimes.com/2009/10/13/business/media/13disney.html?_r=2& [7] Greenberg, D. H., & Shroder, M. (2004). The Digest of Social Experiments. Washington, D.C.: Urban Institute Press. [8] Gaillard, G. (2003). The Routledge Dictionary of Anthropologists. New York, NY: Routledge. [9] Lowie, R. H. (1938). The History of Ethnological Theory. New York, NY: Farrar and Rinehart. 29

[10] Mead, M. (1959). People and Places. New York, NY: Bantam Books. [11] Geertz, C. (1973). The Interpretation of Culture. New York, NY: Basic Books. [12] Emerson, R. M., Fretz, R. I., & Shaw, L. L. (2011). Writing Ethnographic Fieldnotes. Chicago,IL: University of Chicago Press [13] Wilson, J. T., Ford, C. S., Skinner, B. F., Bergmann, G., Beach, F. A., & Pribram, K. (1954). Current Trends in Psychology and the Behavioral Sciences. Pittsburgh, PA: University of Pittsburgh Press. [14] Luce, D. & Tukey, J. (1964). Simultaneous conjoint measurement: a new type of fundamental measurement. Mathematical Psychology 1, 1-27. [15] Cattin, P. & Wittink, D. R. (1989). Commercial use of conjoint analysis: An update. The Journal of Marketing, 53(3), 91-96. [16] Wittink, D. R. & Cattin, P. (1982). Commercial use of conjoint analysis: A survey. The Journal of Marketing, 46(3), 44-53. [17] Green, P. E. & Srinivasan, V. (1978). Conjoint Analysis in Consumer Research: Issues and Outlook. Journal of Consumer Research, 5(2),103-123. [18] Moskowitz, H. R. & Martin, D. (1993). How Computer Aided Design and Presentation of Concepts Speeds Up the Product Development Process. Proceedings of the 46th ESOMAR Marketing Research Congress, Copenhagen, Netherlands, pp. 404-424. [19] Wind, J., Green, P. E., Shifflet, D., & Scarbrough, M. (1989). Courtyard by Marriott: Designing a Hotel Facility with Consumer-Based Marketing Models. The Institute of Management Sciences Interfaces 19(1), 25-47. [20] Moskowitz, H. R., Itty, B., Shand, A., & Krieger, B. (2002). Understanding the consumer mind through a concept category appraisal: Toothpaste. Canadian Journal of Market Research 20, 3-15. [21] Moskowitz, H. R., Cohen, D., Krieger, B., & Rabino, S. (2001). Interest and reaction time analysis of credit card offers: Managerial implications of high level research procedures. Journal of Financial Services Marketing 6(2), 172-189.

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[22] Ashman, H., Teich I., & Moskowitz, H. R. (2004, May). Migrating consumer research to public policy: Beyond attitudinal surveys to conjoint measurement for sensitive and personal issues. ESOMAR Conference: Public Sector Research. Berlin, Germany. [23] Moskowitz, H. R., Gofman, A., Tungaturthy, P., Manchaiah, M., & Cohen, D. (2000, April). Research, politics and the Internet can mix: considerations, experiences, trials, tribulations in adapting conjoint measurement to optimizing a political platform as if it were a consumer product. Proceedings of ESOMAR Conference: Net Effects. Dublin, Ireland, pp. 109-130. [24] Moskowitz, H.R., Rabino, S., Ciacco, V., Hjelleset, T. & Asami, R. (2003). The Alphabet of Privacy –What are Communications About Privacy that Interest Internet Users. Canadian Journal of Marketing Research 21(1), 31-45.

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Chapter 2 So exactly what do we study? Abstract: In this first book of the Mind Genomics® series we focus on the important subject area of public policy. We want to measure psychological aspects in public policy. In this chapter we discuss how people respond to anxiety-provoking situation of terrorism, specifically responses to the described emotional consequences of terrorism. There already is a lot of information about how governments deal with terror threats, but there is an urgent need to develop effective tools to measure the impact of psychological, social, and political responses to terrorism. The U.S. federal government has undertaken unprecedented efforts to increase the nation’s ability to respond to terrorism, including the establishment of the Department of Homeland Security (DHS) and the passing of the PATRIOT Act. Through RDE (Rule Developing Experimentation) we redefine the problem in a way that allows us to systematically investigate psychological responses to terrorism. Our data gives us a sense about how people perceive the likely effectiveness of existing government policies and programs. Do these policies reduce or, perhaps even the opposite, increase, the anxiety about terrorism? Our data reveal some elements of protection that we might believe would reduce anxiety (international cooperation, United Nations, Centers for Disease Control and Prevention, Homeland Security), but actually work in the opposite direction, unexpectedly increasing anxiety. Introduction We spent Chapter 1 going over the mechanics of experimental design. The real question is: how can experimental design be employed as a tool to explore new subject areas? For the most part, experimental design is applied to relatively large-scale problems, with many variables. It often is used for one experiment at a time. The focus is on the specific learning gained from the experiment. Since we deal with only one experiment at a time, we really don’t get the overall view of how the world works. The analogy is the archaeology of a single part of a wall versus the understanding we get from seeing an entire city uncovered and restored by the archaeologist. When researchers deal with experimental design they typically uncover a single wall and from there they try to generalize. This book will discuss how we conduct experiments in a series of important subject areas until the whole city has been uncovered, one wall at a time, and its story has unfolded.

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In this second chapter we will discuss how the method will be used to unveil a single wall, or to illuminate our understanding of our very first real-world topic: the emotional consequences of terrorism. How do we think and what do we do? Unlike many studies in science where the topics, as well as the methods, are different, we have chosen to create a system whereby the studies appear to have the same structure, the same format. There are skeptics who argue that such adherence to a single structure is not productive; nature is so varied that one method cannot suffice to understand it. We accept those critiques. Yet, we feel that with a single structure governing the way we think, we can cover a great deal of ground, uncover many aspects of how nature works, and be in a position to understand experienced reality more profoundly, at least from our perspective. Despite Oscar Wilde’s famous epigram from "The Relation of Dress to Art" in Pall Mall Gazette (2/28/1885):

Consistency is the last refuge of the unimaginative. and Ralph Waldo Emerson’s line from “Self Reliance” in Essays: First Series (1841):

A foolish consistency is the hobgoblin of little minds, adored by little statesmen and philosophers and divines. we believe that in the case of science, and especially when breaking new ground, it is always easier to follow standardized approaches in gathering the data. The topic of inquiry may be new, but the methods should be comfortable and accommodating. Defining a universe to study, creating silos, creating elements Often researchers, especially those young and new to the field of research, believe that the research that they perform taps into universal ideas, universal notions. For these researchers there is a feeling that it has to be somehow the right research. Not to be cynical or jaded, but the notion of the right research can mislead. Yes, when we read many research reports we will find some which are on the mark and others which miss the boat entirely. We’re not talking here about the specific execution of the study, which can be sloppy, or the analysis, which can be incorrect. We’re saying that there is no right research.

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What should we do? Our approach parses the problem into pieces that we can handle. We won’t deal with mega problems and mega answers. Those problems are attractive to some because they allow general, not particular, answers. One can pontificate and declaim without saying anything specific. We’re going to do things differently. We are going to redefine the problem in a way that allows it to be approached through experimental design. We might be accused of answering what we can rather than answering the question, an old trick of college students and engineers. And, in fact, that’s pretty true. With the problems that we will address, it is probably more judicious to answer them in the way we know can lead to knowledge rather than to tackle the issue head on and end up with generalities which fail to add to human understanding. Let’s look at the way we can address big issues. 1. Identify a specific part of the problem. Define the issue in a way that makes sense. There is nothing specific to do in Step 1 except to think. For example, we are going to deal with nutrition policy and education. This is a big problem around the world. We can’t address the entire problem; we’d be here for years. So, we will identify a specific part of the problem – namely, what types of actions can the government take to restore nutritional adequacy to the diet? It sounds like a general question, but you may get a sense that we’ve gone from nutrition policy down to government action, and even further, down to specific actions. 2. Identify the components of the problem. Our brief introduction to the soda can study gives us the way. We didn’t look at the soda can as a large, monolithic problem. Rather, and perhaps a bit artificially, we reworked the problem to be the combination of features. With that systematic approach in mind, using the notion of experimental design as the structure, we will approach the problem in a new way. Rather than facing it head on, we will restructure the problem as a series of relevant topics or silos, each with a set of alternatives called elements. 3. Use Rule Developing Experimentation (RDE). Now that we accept the strategy of silos and elements (i.e., investigating responses to components), we have the system with which to attack the problem. Like our efforts with the soda can, we will mix and match the elements of the topic, create vignettes, present these vignettes to the respondent, and acquire the ratings. The analysis of the results by regression analysis will reveal which particular elements drive the response to the topic [1, 2]. From the pattern of responses, we should be able to stand back and get a sense of what influence the element has on the problem. We certainly were able to do that when we dealt 34

with the colors and the texts of the splash on the soda can. When it comes to the problems we address, we may also find deeper patterns in the data, not just winning and losing elements. 4. Define a rating scale(s). Respondents can evaluate a short vignette on many different rating scales to which any market researcher or sensory analyst can well attest [3, 4, 5.] The important thing is to capture the impression of the respondent on relevant, meaningful, and exciting dimensions. So, for example, when it comes to issues of public policy, we will use questions about the policy being able to deliver or restore specific benefits to citizens. We will also use monetary scales reflected in the amount of money a person is willing to pay for a certain vignette, or even more interestingly, the amount of taxes a person is willing to pay for the vignette when the topic is social or public policy. The stuff of knowledge; how should we think about public policy? There is a puzzling quality about the way an apparently innocuous term like ”public policy” is often used. On the one hand, it frequently seems fairly obvious to what public policy is meant to refer, yet, as soon as we start trying to define it, it becomes difficult. This leads academics to rightly suggest that ‘public policy is a deceptively simple term which conceals some very complex activities’ [6]. In general, policy-makers are engaged in a range of activities related to discovering and trying to solve problems. For example, when we talk about social policies, we are referring to ‘human beings living together as a group in a situation requiring that they have to deal with each other’ [7]. To discover and solve problems, policy-makers evaluate programs, draft legislation, and make funding decisions. In their book Talking Policy: How Social Policy Is Made, Judith Bessant, Rob Watts, Tony Dalton, and Paul Smyth vividly describe how policy-makers conduct research in order to discover certain things about the world – the scale and causes of social problems like poverty, homelessness, crime, or unemployment; the needs of people with disabilities; how single mothers cope, or don't cope, with the cost of house rental. They also describe how time is spent on research that is intended to evaluate the effectiveness of existing government policies and programs [8]. At the end of the day, public policy is much more concerned with practicalities than it is with ideas. There are good reasons why this should be so. It is not always necessary to understand a problem in order to do something about it. Whether or not we do understand why problems occur, the 35

way into a problem is not always the way out of it. There is a strong pragmatic tradition in policy which basically says that one uses what works and drops what does not [9]. Today’s business and academic environments have seen the growth of interest in all kinds of policy making. Go into any bookstore in the U.S., or look in the news section in business magazines - Time, Newsweek - and you’re likely to see policy topics. Go on the Web, look at blogs, and you’re equally likely to see comments on today’s news in talk back sections, with many of these comments being points of view about current policies. Policy is big news, big business. What the foregoing short tour tells us is that the world is awash in information, and a lot of that information deals, in one way or another, with policy, with the way governments deal with people, with responses to current events within the framework of the government-person axis. It’s no longer a world of bread and circuses of ignorant, poverty-stricken individuals seeking only to survive and desiring a bit of amusement. People are vitally interested in the way their government treats them and what the government does in its role as protector of the people. Dealing with Terrorism Let’s now turn to a world awash in anxiety about terrorism and the search by governments for citizen-meaningful measures to allay this anxiety. Most definitions of terrorism recognize that beyond the physical damage caused by the event itself, it is intended to have a psychological effect. One of the various definitions of terrorism that have been offered is the following: The illegal use or threatened use of force or violence; an intent to coerce societies or governments by inducing fear in their populations. [10]. From the impact of an attack or threat and its consequences (e.g., death or anxiety), to the impact of personal preparedness by each citizen (e.g., behavioral and social procedures), or acts of counterterrorism by governments (e.g., new security institutions), there is an urgent need to develop effective tools that measure the impact of psychological, social, and political responses to terrorism [11]. The U.S. federal government has undertaken unprecedented efforts to increase the nation’s ability to respond to terrorism, including the establishment of the Department of Homeland Security (DHS) and the passing of the USA PATRIOT Act. However, national counterterrorism policy has not addressed the 36

emotional consequences of terrorism. And, whether the current administration in the U.S. as of this writing (2013) labels the War on Terror what it is, or uses some other convenient circumlocution, it only took one major incident to return Americans to the fears they had years ago when nineteen terrorists destroyed the World Trade Center in New York City and part of the Pentagon in Washington, and caused the deaths of over 3,000 people who were simply going about their normal business on that day. The pressure cooker bombings at the Boston Marathon (April 15, 2013) caused major panic and shutdowns following the explosions, while worries about new terrorist plots spread. In the days and weeks following that event, the country was pushed back into some of the same feelings it had experienced on 9/11/01. A nationwide survey of Americans after September 11, 2001 (9/11), revealed that Americans’ common coping strategies on that day and several days later included ‘talking with others’ and ‘religious thoughts or actions.’ The most common single coping behavior was to ‘check on the safety of close family members’ [12]. From this and similar observations, researchers concluded that a key component of local, state, and federal planning strategies should be to provide citizens with ways to communicate and to participate in rescue and recovery efforts [13]. Many of the lessons learned from those observations lead to better communications following the Boston Marathon bombing and the pursuit of its perpetrators. The available data reveal that terrorism can exert different effects on vulnerable populations. Studies on terrorism conducted immediately after 9/11, or the terrorist bomb attack on the Alfred P. Murrah Federal Building in downtown Oklahoma City on April 19, 1995, found a wide range of emotional and behavioral reactions, both in the cities where the attacks occurred and across the country [14, 15]. DiGiovanni [16] reported that individuals in cities, locations more likely to be a terrorism target, show different reactions from individuals in rural areas when both groups were asked about their feelings regarding management of such an incident. For example, individuals in the terror-affected areas say that they are ‘more willing to return to their families immediately,’ regardless of official advice or orders to stay in place. In comparison, respondents from the countryside said that they were less likely to return to their families. Other studies of residents of New York City after 9/11 found differences in anxiety levels and traced these differences to cultural and ethnicity factors [17]. Opinion polls also show that occasionally the anxiety simply disappears. For example, one study suggested that domestic terrorism such as the Oklahoma City bombing is likely to be seen as important in general and in the abstract, but with low personal risk and little impact on individuals' routine behavior, consequently having low political salience [18]. 37

With this introduction to the reality of terrorism and the different emotional reactions, how then do we systematically investigate psychological responses to terrorism in the way that seemed so effective and straightforward when we dealt with the splash color and splash text on soda cans? Are we able to cross the gap between soda and fear? If so, then by what means, and even more importantly, can we convert that successful crossing into a knowledge-producing paradigm that would be useful for policymakers? Our basic strategy – Divide into parts and move forward When we engage in projects that focus on products or services that one could consume, whether it means eat/drink or use services offered by for-profit organizations, it is fairly simple to approach the problem in a systematic way. A product or service comprises a number of parts, some major, some minor. They also comprise specific benefits which accrue to the user. Finally, the product or service has some emotion and/or guaranteeing authority. Marketers dealing with the evaluation of systematically varied test concepts have no problem breaking apart a complex topic, dividing the topic into components, and then testing the combinations. Then, the combinations are created by experimental design, as in our investigation of the optimal combination of colors and texts for splashes on cans of soda. Social issues are an entirely different topic, with a different intellectual history and with problems less tractable than those faced by marketers tasked with improving products and services. We’ll start by identifying the general issue: how people respond to anxiety-provoking situations. Terrorism is merely a single example. Figure 2.1. The Fifteen Deal With It!™ studies. Courtesy of It! Ventures, LLC

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Anxiety itself is a widely studied topic. Research on anxiety has accelerated since the 1980s [19]. In the spirit of our empirical approach, we began by considering fifteen different topics of anxiety. Figure 2.1 shows how we broke down this very large topic into concrete areas. Simply compartmentalizing the project into discrete topics helped a great deal because it focused the subsequent research. Applying the discipline of silos and elements As you read our approach, do not for a minute think that this is the only way, or even one of the right ways. We already mentioned that there is no right research. Rather, it is simply a strategy, an approach, to better understand a complex topic using the methods of scientific inquiry. For each of our decisions about what constitutes a silo and what constitutes an element, there might be hundreds, perhaps even thousands of alternatives. We have to start somewhere though, and we don’t want the perfect to be the enemy of the good. So here’s our best guess. In the spirit of our soda can where we gained understanding simply by looking at colors and texts of splash information on the can, we will understand terrorism a bit more by identifying the silos and the elements. We begin with four silos, each silo comprising nine elements (see Table 2.1). Rather than randomly choosing the silos and then filling them in with nine elements, we chose the four silos so that the silos would be appropriate to each of the fifteen topics about anxiety-provoking situations as shown in Figure 2.1. Beyond having common silos, however, we also created a specific rationale for each of the 36 elements. The rationale for each element appears in the first text column. This structured approach gives us a template to fill in. In the words of poet Robert Frost, ‘that has made all the difference’ (quote from the 1920 poem “The Road Not Taken”). Of course, one might argue that we lose some of the individual granularity of each topic area by following a template, but even so, we end up with a workable database that can be studied both in isolation and in comparison with the other anxiety-provoking situations.

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Table 2.1. Silos and elements for the terrorism study. Rationale Element Silo #1 – What A1 Media talking about it The media talking about potential terrorist acts A2 A threat A bomb threat for a building that is a false alarm A3 More intense description A bomb under your car … A4 More intense description Bombs blowing up in the middle of a building … A5 More intense description Fire raging through a building … A6 More intense description Contamination of the food supply … A7 More intense description A deadly disease like smallpox/anthrax let loose A Computer virus let loose that impacts your A8 More intense description everyday businesses … A9 More intense description A dirty nuclear bomb set off … Silo #2 – Impact B1 No one is affected In a non - populated area … B2 More affected In a heavily populated area … B3 Kids An area crowded with children … B4 Parents/ seniors An area crowded with senior citizens … B5 Others An area filled with tourists … B6 Warning level 1 When you least expect it … B7 Warning level 2 During a Yellow alert … B8 Warning level 3 During an Orange alert … B9 Warning level 4 During a Red alert … Silo #3 – Feelings and Locale C1 Alone and helpless You are all alone … and you feel helpless… You think about it, you just can't stop thinking about it C2 Can't stop thinking about it … and you feel uneasy ... C3 Get away You'd drive any distance to get away from it … C4 Scared You are scared … inside and out C5 Sensory You experience it all … seeing, smelling, tasting C6 Overwhelmed All the stress just builds up … you feel overwhelmed Memory loss - maximum You experience temporary memory loss because C7 depression there's just too much to take in ... C8 Family and friends While surrounded by family and friends ... C9 Turning point At a special moment … in your life Silo #4 – Remedies D1 Highest global authority You trust that God will keep you safe You believe that international cooperation in the D2 Global authority United Nations will keep you safe D3 Global authority You think UN Forces will keep you safe D4 Global authority Believe Homeland Security will keep you safe You believe that the Centers for Disease Control and D5 Global authority Prevention will keep you safe D6 Local authority You think that Local police will keep you safe D7 Local authority You think that Local hospital will keep you safe D8 Media keeps you informed It’s important for the Media will keep you informed You need to contact your friends and family to make D9 Contact with family and friends sure they are OK …

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Creating short vignettes The thrust of our approach will be to test these ideas about terrorism and the other topics through experimental design. The experimental design mixes and matches the 36 elements. Experimental design creates combinations of two to four elements, in the form of short vignettes. There’s a reason for this approach. When asked to focus on only one element, we pay attention to it as if it were the only stimulus in the world. As a consequence, the natural interplay among different ideas - competition and survival of the best - cannot come into play. The respondent pays close attention to each element. In the end, we don’t know whether an element scores strongly because the element is truly important or anxiety-provoking, or if the element scores strongly because when evaluated by itself it is important, but it cannot compete against other elements. We would never know, in a one-at-a-time world. For the RDE studies reported in this book we will rely on experimental design. Following the recipes dictated by the design, the computer mixes and matches the concept elements. Each element appears independently of every other element so that in some concepts, or vignettes, one or even two silos are entirely absent. In the previous chapter we saw that experimental design generates combinations and that statistical analysis of the combinations, done correctly, can determine the contribution of each specific element to the vignette. We follow that approach here, only with a more complex case than the soda can. Master the specifics and you will understand many of the experimental designs in this book. There are two specific ideas to keep in mind and profoundly understand: 1. Experimental design works with the specific set of silos. Typically for RDE each silo comprises the same number of elements. 2. The experimental design can be applied to the vignettes that will be shown to each individual respondent. The basic structure of the design remains the same, but the specific combinations differ across the different respondents. Each person ends up seeing different combinations of the same set of 36 elements. The benefit of this cannot be overstated. RDE’s experimental design and permutation strategy ensures that there are no biases due to the possible and unsuspected interaction of pairs of elements. There are just too many pairs of elements. The permutation is a true torture test of the elements. Any element which performs well against so many backgrounds must be a strong element. 41

Creating concepts is fairly straightforward on the computer. As we see in Figure 2.2, the computer program (IdeaMap.net®) simply puts together the elements according to the experimental design. The respondent is not even aware of the systematic combinations other than perhaps reporting that she or he has seen some of these elements before. Some of the concepts will comprise two elements, others will comprise three elements; most will comprise four elements. Figure 2.2. Example of a test concept as a respondent would see it, along with the rating scale shown at the bottom.

Although it might seem that the concept is hard to read because it comprises standalone phrases rather than complete sentences, the opposite is really the case. Exit interviews with respondents over the years have never pointed to this standalone format as producing any problems. In fact, respondents have no problem filling in the blanks for the missing connectives. Their comments make a lot of sense. People typically don’t read every word in an article or advertisement. Rather, they skim, skipping around, grazing, picking up relevant words. In a recent study conducted at the University of Hamburg, Germany, by Harald Weinreich and his colleagues, they reported that, on average, people have time to read 28% of the words if they devote all of their time to reading. More realistically, people read about 20% of the text on the average page [20]. As Alistair Nicol of Alistair Nicol PR and Design, specializing in newsletter design and production, puts it: ‘The average time spent reading an advertisement or billboard is a few seconds. Excellent for instant brand awareness, but not long enough to impart important or detailed messages that will engage your clients or staff and provoke the desired response.’ [21]. And so, the same thing occurs here. The respondent skims through the text and responds at the end.

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Of course, respondents who go through the task are not the clients who authorize the studies. Often, clients demand that the concepts be written as text (hard to follow for respondents), with full sentences (often ignored), and presented in paragraph form (unappealingly dense). Other clients demand that each test stimulus comprise one and only one element from each vignette. Their (incorrect) rationale is that otherwise the concept does not make sense. The reality of the situation is the opposite. Despite believing that the typical respondent is an uptight homo economicus needing all the relevant information, biological evolution has created in humans an organism that functions very well on partial information. The rating question(s) Beyond the test stimuli lie the rating questions. There is a great deal of literature about how to ask the right question [22, 23]. But what do you ask a person about anxiety? How do you phrase it? Is there an art to asking questions, or do respondents simply get the gist of the question and proceed onward? The answers to the foregoing questions are not so clear, nor are any written in stone. There are a couple of general guidelines, however: 1. Make the question meaningful. Respondents need to understand what you are asking in order to answer your questions. Yes, respondents are able to deal with incomplete paragraphs of information with missing connectives and truncated ideas. Nevertheless, they are not mind readers. 2. The rating scale must go in a familiar direction. When you phrase your question so that the scale goes in the opposite direction from what is expected, the counter-intuitive way, the respondents will still give you the appropriate answer. They understand the switch. On the other hand, the counter-intuitive nature of the rating scale (e.g., going in the wrong direction) may produce some bias. The respondent may shrink the range of the ratings assigned simply because the rating scale goes in this unfamiliar direction. That shrinkage could change some of the parameters of the model you will build with the data. Now that we have dealt with the issues involved with rating questions, what specific question did we choose to address the issue of anxiety, and more importantly, why? Look at Figure 2.2, but this time, pay attention at the bottom of the figure where the rating question is placed. The question is fairly simple: How well can you deal with this TERRORISM ISSUES situation? (1 = Easily ... 9 = Not at all). 43

There are at least four points worth making here: 1. We phrased the question in the positive; dealing with a problem rather than asking the respondent to rate anxiety. We weren’t sure that the respondent could actually rate anxiety levels. On the other hand, respondents are accustomed to dealing with problems, and they do so a lot of the time. Thus, framing the question in terms of dealing with an issue seemed to be more logical and easier. 2. Nonetheless, we were interested in anxiety. We solved that problem by reversing the order of the scale. Successful dealing with a situation was to be assigned the lowest value on the scale, 1, rather than being assigned the highest value. We were measuring anxiety (high end of the scale), but couching the question in terms of a positive act (dealing with the issue) rather than a negative emotional state (anxiety). Of course we compromised a bit here by changing the direction of the scale. 3. We used a simple, anchored 9-point scale. Some practitioners like to label every scale point [24]. We do not, and did not. Labeling scale points may confuse people. The way we set up the scale seemed to produce no problems or questions from respondents. 4. Our rating scale was generic. We phrased the rating question in a way that could be immediately applied to the remaining fourteen studies (see Figure 2.1). Comparability across studies is also a good idea. Comparability adds to the value of the research. Running the study A study can only be as good as the field execution. In most studies using experimental design, field execution can be botched in any number of ways, and has been, both by the authors and their colleagues. This was especially true in the period before computer presentation of stimuli, acquisition of data, and automatic modeling. In the late 1980s, most of the work had to be done by hand. The different combinations of elements had to be created by experimental design. At the time, there was only one experimental design, painfully crafted by hand. The hand-crafted, hand-edited experimental design ensured that all of the concepts comprised two to four elements, that each individual test concept or vignette comprised at most one element from each silo, and that the elements appeared in a randomized fashion uncorrelated with any other element. This almost magic array of test combinations was then printed out on paper, pictures were pasted 44

onto the paper when called for, and in turn, the concepts were divided into booklets. Each person then rated a certain number, or even all of the booklets, in a randomized order. Those were the good old days; lots of work, with an experiment taking at least two to three weeks, and straining the patience of many of the folks involved. Nostalgic memories – certainly, yes. Happy days – maybe. Error-free studies – probably not. Today, the steps to this dance are a bit easier. The computer generates the combinations following a program, presents the combinations (test vignettes) on the screen to respondents sitting at home or elsewhere, collects the data and analyzes the results virtually immediately, and creates a model for each respondent showing how the individual elements drive the ratings. The process is far simpler, far more automatic, and faster thanks to the Internet on the one hand and to the increasingly available computer power on the other. Furthermore, people are no longer afraid of computers, or the Internet, making it a lot easier to run experiments with people scattered far and wide, even around the world. Electronic and online surveys provide the ability to conduct large-scale data collection [25] less expensively than conducting studies through the postal mail [26, 27]. With online surveys, as the sample size increases the costs per response decrease instead of increasing significantly as they do with postal mail [28]. Online surveys are becoming increasingly common [29], and research comparing online versus postal surveys is starting to confirm that online survey content results may be no different than postal survey content results, yet they provide strong advantages of speedy distribution, response cycles, and cost [30]. Recruiting Respondents In the early days of the Internet it was easy to get respondents. Everyone wanted to participate in studies; everything was new and magical at the time. One might be as easily connected to Tartu, Estonia, as to Pittsburgh, Pennsylvania. The studies included fancy images, funny animations or other color enhanced survey presentations [30]. So, in the spirit of these pioneers, people volunteered to participate. The researcher (or more likely the research company) merely put a banner up, a pop-up announcement, and many people would rush to participate. One of the pioneers of the field, Dennis Gonier, founder of DMS (Digital Marketing Services, in Texas) called this the ‘stream’ effect. One simply needed to put one’s fishing pole in the stream to catch willing fish. Everyone seemed to be biting. Nevertheless, the process of self-selection in online access panels is accompanied by criticism [31]. Under particular scrutiny are not only the methods of recruiting, such as active versus passive recruiting, but also the 45

motivation of the panelists themselves when joining in online panels. But ample research shows that actively recruited panelists do not differ from those recruited passively regarding their personal characteristics [32]. Such days ended, however, fairly quickly [33]. By early 2000 or so, company after company recognized that the happy fishing hole was being depleted; the seemingly inexhaustible supply of willing respondents was dwindling. And thus arose companies specializing in acquiring respondents by sending out emails to willing participants. These participants had, in one way or another, signed up to participate in market research surveys. They gave their permission by checking off a specific box where they agreed to receive such invitations. These were called opt-in respondents because they opted-in to participate. Let’s examine the email invitation that was sent for this study, not so much because of what it will tell us about the study, but because of what it will tell us about the information needed to secure participation. Figure 2.3 shows the email invitation. The invitation begins with a subject line which will also appear as the subject or topic on the actual email invitation. The subject line must be sufficiently intriguing to get the respondent to participate. It’s especially difficult when dealing with problem areas such as issues generating anxiety (e.g., terrorism). It’s a lot easier with foods and not so easy with credit cards (because credit cards and other financial services are perceived as annoyances unless immediately needed by the respondent). The invitation body text tells the respondent enough about the study to secure participation, but not much more. We don’t want to bias respondents. It is a very fine line between telling respondents what we want to accomplish and tipping them off about the actual purpose of the study so that they answer in the way they believe the experimenter wants them to answer.

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Orienting the respondent who accepts the invitation We just completed the first hurdle in fielding our study. We created the invitation; some respondents clicked on the link and were guided to the study. Typically, they would see the wall of studies shown in Figure 2.1, from which each respondent would select a particular study that interests him. Returning to the study, let’s look at the orientation screen which comes up immediately after the respondent selects the study on Terrorism (Figure 2.4). The screen tells the respondent just a bit more about the study, explaining how to use the computer to answer questions, explaining the scale, and then telling the respondent the reward.

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About half of the respondents who begin the study drop out during the course of the interview. The rule of thumb is that even with highly motivated panelists it’s a good idea to mail about 20 times the number of respondents whose data you want. So, when you want 100 respondents, it’s a good idea to mail 2,000 to 2,500 invitations. The actual RDE interview The RDE interview begins after the orientation screen. For this particular study on terrorism each respondent evaluated a set of 60 unique concepts or vignettes. The concepts, created by means of experimental design, present each of the 36 elements in small combinations of two to four phrases. Across all 60 combinations every element appears three times. With a respondent required to evaluate 36 elements randomized across 60 combinations, it is impossible for any respondent to game the system. There is simply too much happening to remember. Most respondents begin by consciously trying to be consistent, but soon become frustrated and just let their gut feeling take over. In a sense they are bored to truth. At the end of evaluating the 60 vignettes, the RDE portion of the interview completes and the respondent moves into the second part of the interview. The respondent completes a questionnaire on geo-demographics (e.g., gender, age, income, market, family status), as well as attitudes towards the various aspects of anxiety (e.g., Figure 2.5). Traditionally, questionnaires of this type are called classification questionnaires.

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Analyzing the data – getting to the heart of the issue Analysis of data can be lots of fun, especially when you have set up the stimuli so that they conform to an experimental design. You don’t have to root around in the results hoping for some statistically significant finding to emerge. And when your results are not statistically sigificant you don’t have to worry that your study was a failure. Disabuse yourself of those negative feelings; experimental designs don’t fail. Of course you can screw up the study, but with today’s computerized systems you have to make very clever mistakes. Your experiment must be flawed from the very beginning to make a severe error when the stimulus presentation and the data acquistion and data analysis are run automatically. Of course, all this presumes that you have the correct experimental design, that you have thought about the problem, and that you are using the appropriate design to answer the problem. There’s a lot of latitude, however, even in those latter considerations. We begin with the notion of experimental design which we already described in Chapter 1 for the topic of soda cans. Our experimental design is a bit more complicated here. We see one set of combinations in Table 2.2 and its binary expansion in Table 2.3. These two tables present only a small fraction of the combinations that will be evaluated by a single respondent. Fortunately, the entire set of combinations are created and analyzed by computer.

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Table 2.2. Experimental design for one respondent, showing eight of the test combinations. Vig1 = vignette or test concept1, and so forth. Vig1 Vig2 Vig3 Vig4 Vig5 Vig6 Vig7 Vig8 Silo A 5 0 0 0 6 0 1 0 Silo B 0 8 6 2 3 4 9 3 Silo C 5 9 2 0 0 5 0 0 Silo D 8 9 8 9 0 2 3 4

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Our data are set up to allow us an analysis of each person. Keep in mind that each respondent evaluated a unique set of 60 vignettes. What’s important is that we create an individual model for each person, showing the number of rating points added to the rating when the element is present. Our tool is OLS, ordinary least squares regression, the workhorse of most off-the-shelf statistical software. Look at Table 2.4. You will see two columns of results for the elements from Silo #1 – What shown in Table 2.1. The first column is labelled Persuasion Model, the second column is labelled Interest Model. Both results come from applying OLS to the data. However, they come from different intellectual traditions. The results tell the same story (Figure 2.4), but the interpretations of the results differ.

Let’s now dive into the data, to see what we have, and what we learn. As we do so, we’ll also discover some handy tips about what to do to the data to make the story emerge more clearly. With 36 elements, there is always the problem of a wall of data. The problem doesn’t go away by plotting the results because there are 36 relatively unconnected elements. So, it’s finding a pattern among a group of discrete elements that will be our challenge.

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First some housekeeping issues: 

The Persuasion Model relates the presence/absence of the elements to the 9-point rating scale. We used the same regression modeling in Chapter 1 when we explored the fifteen soda cans.

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The Interest Model comes from a different intellectual history and transforms the 9-point rating scale to a binary scale. Consumer researchers focus on the membership of an individual in a group and not on the intensity of feeling. Thus the 9-point scale, a measure of feeling, must be converted to membership versus non-membership in a group. The conversation cuts the nine points into two locations. High numbers (7, 8, 9) are defined to mean that the person cannot cope with the situation as described by the vignette. Low numbers (1, 2, 3, 4, 5, 6) are defined as can cope with the situation. Keep in mind that these cut-points and numbers are arbitrary. On the other hand, they have been used in thousands of studies [34].

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The dependent variable for the Persuasion Model is the 9-point rating. The dependent variable for the Interest Model is the binary value, 0 (corresponding to ratings 1 through 6), or 100 (corresponding to ratings 7 through 9).

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When creating the model we can either run 121 separate equations and average the parameters to get an estimate of the coefficient (a.k.a. impact) for each element, or we run one large model that puts all of the data together. With one large model we end up with (121)*(60) or 7260 rows of data on which we run one regression.

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Since a great deal of this book focuses on the issue of what the consumer/citizen will do, we will adopt the perspective of the sociologist. That means that we will generally report the data in terms of the Interest Model, which, as you recall, uses 0 or 100 as the dependent variable.

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We will use the Persuasion Model for segmenting people since the Persuasion Model is more granular. That is, we don’t lose information by recoding the responses to a binary scale.

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When dealing with more than 20 or so respondents and combining their results, the Interest and Persuasion Models generate highly correlated results (Figure 2.6).

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Figure 2.6. The 36 coefficients for the Interest Model plotted against the corresponding coefficient for the Persuasion Model. Both approaches lead to the same conclusion, but approach the issue of meaning differently.



We interpret the Additive constant of the Persuasion Model, k0, to be the 9-point rating that we would obtain were we to have no elements in the test vignette. Clearly that never happens; people need something in the vignette to which they react. So the additive constant is an estimated parameter. In turn, the coefficients k1 ... k36 (in this case) show the number of rating points contributed by each element, respectively. For the Interest Model data shown in Table 2.4, the additive constant is 19, meaning that in the absence of any elements we estimate that 19% of the respondents will rate the vignette 7 through 9, corresponding to not being able to deal with the situation. When we begin to add elements, however, the proportion of respondents increases. Thus, element A4 (bombs blowing up in the middle of a building) adds another 15%.



For the Interest Model the ratios of values are meaningful. That is, when we find or develop a vignette whose total impact value is 60, we interpret this to be twice the proportion of people as a vignette with a total impact of 30.



Statistical analyses from many hundreds of these studies suggest the following norms for the Interest Model:     

Greater than 15 = extremely important 10-15 = very important 5-10 = relevant (with 8 significant at 95% confidence level) 0-5 = adds to anxiety but really not relevant Less than 0 = begins to detract from the dependent variable.

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Finer-Grained Analysis: The Terrorism Study In the data we just presented, there’s a world lying just below the surface, waiting for us researchers to open up. Based on Table 2.5, we learn a great deal about what drives a person to say she or he cannot deal with a particular terrorism situation versus what drives a person to say she or he can deal with the situation. Again, rather than a questionnaire asking a person her or his reactions to terrorism, we choose to break down terrorism to a series of silos, and within those silos to a set of elements. These are descriptions of terrorism situations. They are not necessarily as richly described as we might read in a news story, but they are also not fleshless abstractions that we would encounter in a questionnaire. When looking at the Total Sample we see that our study on terrorism uncovered a lot of elements that drive anxiety. Interestingly, there are some elements of protection that we might otherwise think would reduce anxiety (international cooperation, United Nations, Centers for Disease Control and Prevention, Homeland Security). The data tell us differently. These so-called ameliorating elements actually seem to work in the opposite direction of what we might think. These elements increase anxiety, not decrease it.         

A bomb under your car… 21 A dirty nuclear bomb set off … 20 You believe that international cooperation in the United Nations will keep you safe 20 You think United Nations Forces will keep you safe 19 Bombs blowing up in the middle of a building … 15 A deadly disease like smallpox or anthrax let loose ... 14 You believe that the Centers for Disease Control and Prevention will keep you safe 12 Contamination of the food supply … 10 You believe that Homeland Security will keep you safe 10

Only two elements reduce the anxiety from terrorism:  

It’s important that the Media will keep you informed You need to contact your friends and family to make sure they are OK …

-5 -6 54

Table 2.5. Some of the detailed findings from the Deal With It!™ Terrorism Study. Data courtesy of It! Ventures, LLC. The utilities or impact values are sorted from high to low for the total panel. Shaded cells denote significant positive impact values (i.e., anxiety generators). Total Sample Base Size 121 Constant 19 Elements which increase anxiety A3 A bomb under your car … 21 A9 A dirty nuclear bomb set off … 20 You believe that international cooperation in the United Nations will keep D2 20 you safe D3 You think United Nations Forces will keep you safe 19 A4 Bombs blowing up in the middle of a building … 15 A7 A deadly disease like smallpox or anthrax let loose ... 14 You believe that the Centers for Disease Control and Prevention will keep D5 12 you safe A6 Contamination of the food supply … 10 D4 You believe that Homeland Security will keep you safe 10 D6 You think that your Local police will keep you safe 7 B3 An area crowded with children … 6 D7 You think that your Local hospital will keep you safe 6 Elements which have minor effects on anxiety A5 Fire raging through a building … 4 B9 During a Red alert … 4 B2 In a heavily populated area … 3 B6 You never expected it to happen to you or someone close to you ... 3 C6 All the stress just builds up … you feel overwhelmed 3 B7 During a Yellow alert … 2 B8 During an Orange alert … 2 C5 You experience it in all your senses … 2 A2 A bomb threat for a building that is a false alarm … 1 B5 An area filled with tourists … 1 B4 An area crowded with senior citizens … 0 C1 You think about it when you are all alone … and you feel so helpless 0 C2 When you think about it, you just can't stop ... 0 You experience temporary memory loss because there's just C7 0 too much to take in ... B1 In a non populated area … -1 C4 You are scared … inside and out -1 A1 The media talking about potential terrorism acts … -2 C3 You'd drive any distance to get away from it … -2 C8 Family and Friends play a big role in your life … -2 C9 At a turning point in your life ... -2 A8 A Computer virus let loose that impacts your everyday businesses -3 D1 You trust that God will keep you safe -3 Elements which decrease anxiety D8 It’s important that the Media will keep you informed -5 D9 You need to contact your friends and family to make sure they are OK … -6 55

Now, let’s look at some subgroups, not so much for our general learning, but to pick and choose little tidbits which stand out. Gender is always an interesting subgroup. Gender differences are rarely found in these types of projects, unless it is the topic of study. Terrorism is not. When we look down Table 2.6. at the responses of males and females, we should be looking for two things. The predisposition to be anxious (cannot deal with the terrorism issue) is the same for men (constant score of 18) and for women (constant score of 19). We see that males are far more anxious and concerned when the element presents a solution. Females, in contrast, are far more anxious when the situation is described in concrete terms. Anxiety among men is more likely to be relieved by the ability to contact friends than it is among women. That is, for men, the ability to contact a friend is an exceptionally strong palliative of anxiety. Table 2.6. The big differences in effect on gender among the elements.

D1 D2 C4 D3 A6 B7 C2 A1 D9 A5 A4

Constant Males far more anxious and concerned You trust that God will keep you safe You believe that international cooperation in the United Nations will keep you safe You are scared … inside and out You think United Nations Forces will keep you safe Females far more anxious and concerned Contamination of the food supply … During a Yellow alert … When you think about it, you just can't stop ... The media talking about potential terrorism acts … You need to contact your friends and family to make sure they are OK … Fire raging through a building … Bombs blowing up in the middle of a building …

Total 19

Male 18

Female 19

M-F

-3

13

-8

21

20

28

17

11

-1 19

6 25

-4 17

10 8

10 2 0 -2

4 -4 -6 -8

12 4 2 0

-8 -8 -8 -8

-6

-13

-4

-9

4 15

-5 4

7 18

-12 -14

For the next analysis we chose the two coasts of the United States, the Northeast and the Northwest in Table 2.7. There are clear differences. For example, if we had to characterize our respondents who live in the Northeast, we would say that they are far more responsive to direct statements of a terrorist attack, such as a bomb under one’s car. As for the respondents who live in the Northwest, it’s really a matter of distrusting authority. Just from this short analysis we begin to see radical differences in what we call mind-sets.

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Table 2.7. Comparison of elements which best differentiate respondents from the Northeast from respondents from the Northwest. U.S. U.S. Northeast Northeast Northwest Northwest Elements which frighten Northeast respondents much more than Northwest respondents A3 A bomb under your car … 26 2 24 A9 A dirty nuclear bomb set off … 24 7 17 Bombs blowing up in the middle of a 19 3 16 A4 building A deadly disease like smallpox or anthrax 16 7 9 A7 let loose ... You think that your Local police will keep 12 -5 17 D6 you safe You think that your Local hospital will 8 -1 9 D7 keep you safe

D2 D4 A2

B5 B6 C6 B1 D1

Elements which frighten Northwest respondents much more than Northeast respondents You believe that international cooperation in the United Nations will keep you safe You believe that Homeland Security will keep you safe A bomb threat for a building that is a false alarm Elements which frighten one group but ameliorate the anxiety in the other group An area filled with tourists … You never expected it to happen to you or someone close to you ... All the stress just builds up … you feel overwhelmed In a non-populated area … You trust that God will keep you safe

22

30

-8

8

17

-9

3

12

-9

7

-7

14

5

-6

11

5

-3

8

3 -11

-8 6

11 -17

Matters become a bit more difficult when we deal with groups that are defined in a statistical fashion to be radically different from each other. We see this case of different groups when we segment the respondents. We’ll deal with the HOW of segmentation in a separate chapter. Right right now we’re just looking at the results in order to understand how to approach these data and extract the most that we can.

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We realize that the groups are radically different from the plot of the utilities or impacts. We see that in Figure 2.7. It’s not that the segments are opposite each other so that what makes one group anxious relieves the anxiety of the other group. Nothing of the sort. It’s rather that the segments respond to different messages. Figure 2.7. Comparison of the utility or impact value of 36 elements when the utility values are broken out by mind-set segments.

Clearly, the segmentation reveals two different groups of people who must be communicated to in different and segment-appropriate ways. Since we divided our 121 respondents into groups based strictly on statistical considerations, we call them S1 and S2. Segment S1 is very anxious to begin with (additive constant = 27). Segment S1 is most anxious when the vignette talks about remedies to the hazard, like the United Nations or the Department of Homeland Security! Segment S1 is most reassured when we talk about God or contact with friends. Contrast this type of person with those in Segment S2. This person is not particularly anxious when the notion of terrorism itself is raised (additive constant = 9), but gets exceptionally anxious when specific terrorist incidents are mentioned. And very little reassures him.

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Table 2.8. Comparison of elements which best differentiate respondents from the the two mind-set segments. S1 S2 Base Size 67 54 Constant 27 9 Anxiety provoking – Segment 1 D3 You think United Nations Forces will keep you safe 34 0 You believe that international cooperation in the United Nations will keep D2 33 3 you safe D4 You believe that Homeland Security will keep you safe 20 -3 You believe that the Centers for Disease Control and Prevention will keep D5 18 3 you safe D6 You think that your Local police will keep you safe 14 -1 D7 You think that your Local hospital will keep you safe 12 -1 A3 A bomb under your car … 8 38 Anxiety provoking – Segment 2 A9 A dirty nuclear bomb set off … 5 39 A3 A bomb under your car … 8 38 A4 Bombs blowing up in the middle of a building … 2 31 A7 A deadly disease like smallpox or anthrax let loose ... 3 28 A6 Contamination of the food supply … 3 19 B3 An area crowded with children … 2 11 B2 In a heavily populated area … -3 10 A5 Fire raging through a building … 0 9 B5 An area filled with tourists … -4 8 Anxiety reducing – Segment 1 D9 You need to contact your friends and family to make sure they are OK … -11 -1 D1 You trust that God will keep you safe -10 5 Anxiety reducing – Segment 2 A8 A Computer virus let loose that impacts your everyday businesses -2 -4

Types of information from standard questionnaires Through this chapter and the book as a whole, we will gain most of our knowledge from the pattern of responses to the systematically varied stimuli (i.e., the test vignettes or the concepts). On the other hand, we should not negate the value of the questionnaire, the second part of the RDE study. The respondent completed this section after rating the 60 different test vignettes. We learn a great deal about the respondent from the self-profiling questionnaire, as we see in Table 2.9. The table shows the summary percents for the respondents from the study. Scanning through the table gives a sense of who these people are and the values that they say they hold. Of course, the classification tells us about who the people say they are, but does not tell us the pattern of differences in their mind-sets with respect to specific issues. Only the RDE portion, the experiment itself, will do that. 59

Table 2.9. Distribution of respondents who participated in the Deal With It!™ study on terrorism. Classification Question How worried are you right now? Extremely Moderately Slightly Not at all Which three attributes MOST influence your ability to cope with this Situation? [Select three] Knowing you have a plan to deal with it Knowing your government has a plan to deal with it Watching less TV or reading less Focusing on the positives Recognizing how I feel and dealing with it Talking to friends Helping others deal with it Doing something I enjoy Staying connected in my community Demographics Please tell us your gender. Male Female Please tell us where you live. United States - Northwest United States - Southwest United States - Mountain United States - Central Plains United States - South United States - Northeast United States - Southeast United States - Alaska/Hawaii

Base size 10 29 52 30

23 53 26 72 47 43 16 32 13 Base size 28 93 13 14 7 14 8 40 14 1

Table 2.9 can be re-created according to many different criteria. For example, we could compare the distribution of responses for the two segments or for people in the different markets. Yet, it would be virtually impossible to get the insights about the segments from this self-profiling exercise. Those segments emerge only when we confront the respondents with stimuli and force the study participant to respond to the test vignettes.

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Summary In this chapter we showed how to use the power of experimental design. We did this on the basis of a real-world topic: the emotional consequences of terrorism. To achieve our understanding we restructured the issue to create it as an experiment. We recast the issue of emotional responses in terms of a series of relevant topics or silos, each with a set of alternatives that we called elements. We mixed and matched the elements, created vignettes, presented these vignettes to the respondent, and instructed the respondent to rate the vignette. That systematic approach using experimental design provided a new way to deal with the issue of terrorism, more in terms of reactions to test stimuli than selfreported feelings. From the pattern of those responses we got a sense of what influence the element has. Does it either reduce or enhance the anxiety which accompanies the experience of terrorism? The pattern of responses from the Total Sample shows many elements which drive anxiety (like different types of bombs or deadly diseases). Paradoxically, some elements of protection that we might think would reduce anxiety (international cooperation, United Nations, Centers for Disease Control and Prevention, Homeland Security) actually work in the opposite direction and increase expected anxiety. The predisposition of how to deal with terrorism is the same for men and women. But there are some differences. Males are far less anxious than women when the situation is described in concrete terms. Furthermore, anxiety among men is more likely to be relieved by the ability to contact friends in case of a terrorist action. This contact with friends is less effective among women. There are clear differences between people living in the Northeast versus the Northwest. Respondents who live in the Northeast are far more responsive to direct statements of a terrorist attack such as a bomb under one’s car. As for the respondents who live in the Northwest, it’s really a matter of distrusting authority. In our final step, we segmented our panel, but not according to conventional factors such as socio-demographic variables like gender or where they live. Rather, we grouped them based on their mind-sets (i.e., their response patterns to different messages). These mind-sets emerge from the pattern of

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responses to the vignettes and represent different patterns of reactions to specifics about terrorism. We discovered two segments based upon the patterns of reactions: 

Segment S1 is already very anxious to begin with and becomes even more anxious when the vignette talks about remedies to the hazard, like the United Nations or the Department of Homeland Security!



Segment S2 is not particularly anxious when the notion of terrorism itself is raised, but becomes exceptionally anxious when the vignette talks about specific terrorist incidents painting a word picture of what happens.

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Introduction to Part 2 Experimental Design Meets the Big Issues All experiments are slices of reality, slices that we cut ourselves, investigate, and then discuss. We can be aficionados of the hypothetico-deductive method where we have grand ideas about how the universe works. Yet, it’s the specifics that matter, for they are the stuff of knowledge. In the end, we’re stuck with small experiments that either confirm or deny the prediction. Grand theories are fun, but they’re pretty hard to work with. We may want to approach nature in the here and now. We as authors of this volume will take the less romantic, but more productive, path. We will build up our picture of reality from small experiments, small efforts to make sense of it all. This is the inductive approach that we favor and, indeed, why we named this book The New Novum Organum. We are fans of Sir Francis Bacon and his methods of induction. Induction isn’t quite as grand nor is it particularly elegant as the sweeping theory. Rather, it’s the hard slog to get enough data so that we get an idea of what the world may be like. To understand deduction, think about your knowledge of Microsoft products; say Word, PowerPoint or Excel. You may begin by reading the user’s manual/guide which tells you how this world works. Now that you know the world, you make predictions about what will happen. You formulate hypotheses all the time when you use features to accomplish objectives. That is, you say to yourself: if I do ‘X’, I should get ‘Y’. Without thinking, you do the experiment. You may be successful, in which case you retain the hypothesis as knowledge of how the program works. Or you may fail, and the keystrokes don’t work, in which case you reject the hypothesis and try another experiment to get working knowledge. In the end, you may even go back to reading some specific instructions again and repeat the process. When you learn how to use a program you don’t begin with specific goals to prove or disprove. And, if you’re a typical user, you probably don’t even read the user’s manual particularly thoroughly except perhaps to get started. You do the cursory reading and then you start playing. You make mistakes, take other actions, muck around, make more mistakes, but eventually through your actions you build up a sense of how the program works. That is a good analogy of what we will do here. We’ll do our mucking around with experiments and, we hope, good science. In the end, our experiments will help us understand how slices of reality work.

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What reality should we / do we / can we seek? We live in a world that seems real to us. We do certain things, expect certain outcomes, and generally get those outcomes. Writing this book, for example, is one of those actions with intention. We wrote the book, published it, and here it is. Our goal in writing is to probe the reality of everyday life, the granularity of being. We want to explicate reality at the level we live it, at the level of the specific. We want to address a big question: How do we make public the reality in which we live, the reality of our experience? This book is not a philosophical treatise. Instead, this book comprises a series of experiments which use the tool of experimental design to reveal experience. To understand the approach we use, think for a moment of the wellknown Rorschach tests and other so-called projective tests. These methods try to get to the underlying mind of the person by having the person react to ambiguous stimuli (in the case of the aforementioned Rorschach test), or tell a story (in the case of projective techniques). The assumption is that the underlying mind will organize the response to the stimuli or to the unfilled theme. The external response will, in turn, reveal to the discerning eye and ear what may be going on behind the scenes. That projective approach, recast as experiments with meaningful stimuli relevant to the topic, is what this book is all about. Armed with this point of view we now move from discussing methods to exploring a variety of different areas. Our first topic area will be public policy. As this book is being written, there is, in the United States, an outcry for change. It’s not clear what the change should be, who should make the change, nor even how to measure when change has occurred. What is clear, however, is that there are problems.

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Chapter 3 Eat right and pay right … what’s a good nutrition policy worth? Abstract: Good nutrition is essential for growth and development, but often people do not know how to eat; they have to be educated to one or another degree. This chapter focuses on education. We used experimental design to identify the specific components of nutrition education and nutrition counseling that interests consumers. Interest, however, wasn’t sufficient. We went a step further, putting homo economicus (economic man) under our RDE microscope. We considered the nutrition goals as part of government policy and asked ‘How much more should the Federal government tax the average citizen to fund these healthier American goals?’ Rule Developing Experimentation (RDE) showed us the specific features of programs which people say they are willing to fund through their taxes. RDE furthermore helped us to identify how much in taxes each program feature and benefit was worth. We thus put an expected price tag on a nutritionally healthier America. RDE showed that people are willing to pay more in taxes for nutrition education and nutrition policy that they believe will help. If it’s likely to work, then it’s worth paying for. We analyzed several groups and also looked for mind-sets having radically different points of view. Most people agreed with each other. We didn’t uncover dramatic group-to-group differences with radically different points of view. Nutrition may be important to life, but nutrition isn’t quite as polarizing as politics. When it comes to nutrition, it’s a matter of emphasis, not a matter of polarization. Introduction How many of us have mothers? Of course, we all do! And the function of mothers? Well, for the most part, it’s to bring us up healthfully, guide us to get to adulthood, and, of course, make sure we eat our vegetables. The last reason is, perhaps, a bit off the cuff, somewhat facetious, yet the meaning is clear. Our mothers want what’s best for us and, in turn, many of us will remember how our mothers tried to instruct us in the most healthful way to eat, whether that was right or wrong. Unfortunately, mothers aren’t always around to tell us that we’re eating poorly. We all know nutrition is essential for growth and development, health, and well-being. Nutritional, or dietary, factors contribute substantially to the burden of preventable illnesses and premature deaths in the United States [1, 2]. Indeed, dietary factors are associated with four of the ten leading causes of death: 68

coronary heart disease (CHD), some types of cancer, stroke, and Type 2 diabetes [3]. The Integrated Benefits Institute, which represents some of the nation’s biggest employers, including Chevron, Google, Microsoft, as well as unions and universities, says poor employee health costs the U.S. economy $576 billion a year [4]. With regard to nutrition, one thing that comes immediately to mind is obesity. Obesity is a serious health concern for children and adolescents. Data from National Health and Nutrition Examination Survey (NHANES) surveys (1976–1980 and 2003–2006) show that the prevalence of obesity has increased: for children ages 6-11 the increase is from 7% to 17%; and for those ages 12-19 the increase is from 5% to 18%. NHANES is a major program of studies designed to assess the health and nutritional status of adults and children in the United States. The surveys are unique in that they combines interviews and physical examinations. They are conducted by the National Center for Health Statistics, part of the Centers for Disease Control and Prevention, and has the responsibility for producing vital and health statistics for the Nation[5]. The literature suggests many reasons for the increase in the number of overweight individuals and obesity in the United States [2]. One reason is that people do not know how to eat; somebody has to teach them [6]. The primary concern is that people are consuming too much saturated fat, but too few vegetables, fruits, and grain products that are high in vitamins and minerals, carbohydrates (starch and dietary fiber), and other substances that are important to good health (USDA & HHS, 2000)[7]. Another reason is less physical activity, particularly among children [8]. The literature suggests that establishing healthful dietary and physical activity behaviors should start in childhood. Educating school-aged children about nutrition establishes healthful eating habits early in life [9, 2]. ‘Teaching our children the importance of eating well at a young age is crucial to reversing the trend of obesity in this country. Everyone has a role to play in this fight to prevent childhood obesity,’ said Steven Galson who launched an initiative called Healthy Youth for a Healthy Future. The goal of the program is to highlight local communities that are coming together to motivate kids to exercise and eat nutritious foods, as well as teach them to make healthy choices. Although health promotion efforts should begin in childhood, they need to continue throughout adulthood. In particular, public education is necessary, with a focus on long-term health risks and consequences associated with overweight. 69

Public education can help people learn how to achieve and maintain a healthy weight. Whereas many people attempt to lose weight, studies show that within five years a majority regain it. To maintain weight loss, healthful dietary habits must be coupled with decreased sedentary behavior and increased physical activity, and become permanent lifestyle changes [2]. Policymakers at the national, state, and community levels can and should provide important leadership in fostering healthful diets and physical activity patterns among people in the United States [2]. Some states are currently considering the merits of tax credits to encourage physical activity among youth. In Illinois and Maryland the proposal is to make families eligible for $500 tax credits for qualified physical fitness programs targeted towards youth. The Maryland bill is part of a broader legislation that also includes tax credits for adults and seniors, including $500 tax credits for health club membership dues. In 2007, the Canadian government began providing parents with a $500 children’s fitness tax credit for eligible expenses for children under age 16. Whereas the U.S. federal government doesn’t offer such an incentive, the Personal Health Investment Today Act of 2013 (H.R. 956) would allow parents to pay for children’s physical fitness expenses with money from their family’s pre-tax savings account, which could prevent diseases associated with obesity. The pre-tax savings would grant many families access to youth fitness activities such as little league and soccer [Advocate for passage of the PHIT Act at campaign4health.org]. We believe that, in the long run, these plans of paying people to take care of themselves may work, but they are utopian. They need specifics, concretes. We believe that new ways and programs are necessary to improve access to nutrition information, nutrition education, nutrition counseling or other related services. But just what are the specific action steps for the here and now that we have been talking about? The devil is in the details. And so, our RDE study. We focus on the types of programs in which people are interested and, more importantly, whether people are willing to pay more taxes for these programs or services. Running the experiment Our study on nutrition policy follows the RDE approach and analysis that we outlined in Chapters 1 and 2. However, we will have two questions, not one question. First: Q 1:

How well do these goals spark YOUR INTEREST to help Americans become healthier?

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Question 1 asks the question of interest. However, we weren’t simply focusing on an individual’s interest in a nutrition vignette. We want to add a sense of gravitas, of importance, to the issue. To do so, we added the latter phrase to help Americans become healthier. Thus, we have a second question: Q 2:

How much more should the Federal government tax the average citizen to fund these healthier American goals?

Question 2 is phrased as if there were an objective tax in mind. The respondent had to select a specific amount of incremental tax dollars to apply to the solution. Beginning the experiment When you read this and subsequent chapters, you will see that the studies look similar. Once an investigator develops a system which works, provides data of high quality in a reliable, valid fashion, and produces usable results, there’s no reason to reinvent the method. Therefore, we developed and followed a structured approach comprising the following: 

Two rating questions, the first dealing with interest and emotion, the second dealing with taxes in stated dollar amounts.



Six silos, each with six elements, representing what the government is communicating. We favor that 6 x 6 design, as well as a 4 x 9 design (four silos, nine elements). Those two designs give us 36 elements with which to work and a lot of granular information about the topic that we explore using RDE.



A fairly standardized classification questionnaire with additional questions pertaining to nutrition.

We developed a specific composition for the respondent panel for these studies on consumer reactions to public policy . The objective was to understand how, on average, people respond to the elements, and then to understand how genders react (do males respond differently from females), as well as how ethnicities and, finally, ages react. We were aided immeasurably in advice and service by our research partner and panel provider Luth Research in San Diego, California. Working with Luth, we created a screener (a set of questions which determine whether a respondent is qualified for the survey) which allowed us to balance our sample of respondents. Adhering to those quotas gave us readable samples. A readable sample comprises a sufficient number of respondents of a specific type (e.g., gender, ethnic group, age) so that we could look at the results in more detail. The 71

quotas that we imposed will allow us to understand nutrition in a far more profound, granular way at the level of the types of ideas involved and granular at the level of the nature of the respondent. Figure 3.1 shows the orientation screen. As you will see throughout this book, the orientation page is written to introduce the study and to make the respondent feel comfortable. The screen presents just enough information to the respondent so that the ambiguities are resolved in terms of what to look at. Figure 3.1. The orientation screen for the nutrition policy study.

The keys to the kingdom; setting up the data We will see throughout this book that a lot of the skill in making sense of the results comes from setting up the data properly so that it can be analyzed readily. The actual analysis takes seconds; the effort is in creating the right data structures, the right matrices. Table 3.1 shows an example of one respondent’s (#744960) ratings for the first seven concepts. There’s really very little to see in this initial setup, but in the end, the setup will be most of what’s needed to extract the insights. Each row corresponds to a specific combination of respondent (UID) and one of the 48 vignettes evaluated by the respondent. We saw this before in the first two chapters. It never hurts to repeat, however, because mastering this little bit of statistical wizardry makes everything else happen.

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Table 3.1. The first six concepts (C-1 to 6), showing the elements in the vignettes (coded 1), and the ratings assigned by the respondent (R1 = interest to help Americans become healthier; R2 = additional amount of taxes).

Looking at our data – macro versus micro patterns We can approach our data in at least two different ways. The first way looks at the general patterns. Are interest in nutrition policy and taxes willing to pay linked together? We simply look for a general relation between interest and willingness to paying more taxes for it. Of course, when answering this macro question we really have little substantive content at a granular, specific level. Knowing that person will pay more for what he finds to be interesting doesn’t tell us specifically what is interesting. And so we look at the data with a more microscopic lens. We look at the performance of individual elements and of specific groups in the population that we identify. We try to answer the following questions of more substance. What elements win, and do the various subgroups of individuals in our test population differ in what they like? Macro analysis – do people pay more for what interests them? Whenever we deal with people, opinions, and money, we ought to ask ourselves a simple question. Do people say that they will pay additional taxes for policies that they like? One way to answer the question is to average a person’s ratings. Each individual respondent generates two averages. The first average comes from the ratings of interest on the 9-point scale. This is called an individual’s average 9-point rating, on the Persuasion Scale. The second average comes from the selection of tax amounts that would be paid. We call this average tax would pay. We can plot the average tax dollar on the ordinate (Y axis) against the average 9-point rating on the abscissa (X axis). We see that plot (called a scattergram) in Figure 3.2.

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Figure 3.2. Scattergram showing the relation between average rating on the 9-point scale for interest (emotional response) and the average amount of tax a person would pay. Each filled circle corresponds to one of the 256 respondents. There are a number of respondents who would pay no additional taxes. These are shown at the bottom right panel. Even discounting those respondents who would not pay any additional taxes we find no clear relation between interest and tax. RELATION BETWEEN GREATER INTEREST AND GREATER TAXES

LOGARITHMIC PLOT FOR TAXES

Looking closely at our plot in the left panel of Figure 3.2, we see a lot of people whose average tax selection is close to 0 even though we see that some of these individuals show high average ratings on the 9-point scale, whereas others show low average. We can separate the people near 0 from the others by plotting the tax in logarithmic spacing (see the right panel of Figure 3.2). People who would pay little taxes appear as filled circles at the bottom of the graph. The other people who would pay additional taxes are scattered above them at various Y values in the figure. We see no relation at all, in general, between the average rating on the 9-point interest scale and the amount of tax one would pay. Knowing how a person rates an entire proposition (the test vignette) does not predict how much the person would be willing to pay in taxes! What’s the real lesson to be learned here? Certainly we are disappointed that we cannot find a relation between these variables. On the other hand, there is something very critical that we learn and which will stand us in good stead. What we just did with our data was to look across many people (cross-sectional) to find a pattern. Crosssectional studies are carried out at one time point or over a short period to estimate the prevalence of the outcome of interest for a given population. In this way, researchers can take a 'snapshot' of the outcome and the characteristics associated with it, at a specific point in time. Most of the U.S. National Health Surveys are crosssectional studies which try to identify health problems and provide data from which 74

many useful inferences can be made and hypotheses generated. Cross-sectional studies are often used as a basis for health policy decisions [10,11]. But cross-sectional analyses only scratch the surface. One simply lets each person be a point, computes two statistics for each person (mean for interest; mean for paying taxes) and looks for a relation to emerge. In the upcoming chapters, we will often analyze the data like this, in cross-sectional terms, but only as a first step. Crosssectional analysis often provides a helpful overview, but its heritage from sociology and economics does not let us delve into the respondent’s mind to understand reasons and motivations. We believe that cross-sectional analysis proves to be very noisy. Often, that noise, the unwanted person-to-person variability, hides some additional important information that needs experimentation, such as RDE, in order for it to emerge to the surface. In our case, we might not observe the expected relation between the 9-point rating and the taxes willing to pay because of person-to-person differences. Some people might be simply against paying taxes, whereas others would not have this aversion or anger to additional taxes. Distributing these two types of people randomly in the population would introduce enough extraneous variability to mask any slight relation between the 9-point interest rating and tax willing to pay. If cross-sectional analysis doesn’t work, then how about each person as her or his own control? In the previous section, we pointed to the very real possibility that there is so much variability, so much noise in the data across people that we’re likely to miss patterns that exist. You can demonstrate the effect of such noise very easily. Do the following experiment, and you’ll quickly see how noise masks patterns. Simply create four squares of clearly different sizes, from small to large. Show only one of the squares to a person and ask the person to rate the area of that single square on a 9-point scale (1= very small ... 9 = very large). Remember: A person gets to rate one and only one of the four squares. You’ll need twelve people. Each square will be rated by three of the twelve people. Now plot the average rating of the size with area of the square on the X axis, and average rating of the perceived area on the Y axis. You will probably see that the data tracks, on average, but the plot is fairly flat. Now do the same experiment with three NEW people. Let each person see all four squares, in a random order. The person will rate the four squares using the same 9-point scale. Now compute the average rating of area for each of the four squares and make the same plot. This time the plot will show visibly that your respondents clearly perceived and rated the different areas. 75

What’s the difference between the two approaches and why should that concern us? Simply, the first approach is the cross-sectional approach; each person sees a different stimulus and rates it. You need a lot of people to cancel out the noise. The second approach allows each person to be her or his own control. Even with three people the pattern emerges clearly. Let’s look for the pattern using each person as her or his own control. The first step is to understand what we will look for. We know that we want to have each person as her or his own control. This means that we are going to analyze the relation between two numbers that a person provides. We did that earlier with average tax versus average interest ratings and were not successful. Our failure comes from looking at each person as one point and trying to assemble these points. We change our focus now. We know that each individual evaluated 48 different vignettes, and for each vignette the individual rated both interest on the 9point scale, as well as tax willing to pay. For each person we ask the simple question: can we uncover a linear relation between the person’s rating of interest and the amount of tax willing to pay? Let us express this question in terms of an equation: Taxes willing to pay = k0 + k1(9-point rating) Our equation says that, in theory, the amount a person would pay in additional taxes should be proportional to the rating on the 9-point scale. Each person may have a different slope, k1, which shows the number of tax dollars per one point change on the scale. With this organizing principle in mind, let’s investigate the slopes of our 256 respondents. Just how many dollars in taxes is one point worth? We lined up the data in the format shown in Table 3.1. The computer is instructed to analyze the ratings for each person, one person at a time, computing the individual’s own additive constant k0 and the slope k1. We’re particularly interested here in the resulting 256 slopes. What does the distribution look like? Our answer comes quickly from the regression program. Look at Figure 3.3. Each one of the small circles corresponds to one of the 256 respondents. The abscissa, X axis, shows the value of the slope k1, or the number of dollars corresponding to one point on the 9-point scale. There are a few respondents showing illogical results; these appear on the left in Figure 3.3.

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Figure 3.3. Distribution of the slope relating amount of taxes willing to pay versus one unit of interest on the 9-point scale. Each point corresponds to one of the 256 respondents.

What could be more important, however, is the fact that there are different opinions. Most respondents cluster between 0 and 50 dollars per point on the 9-point scale, but they don’t all lie on the same dollar point. We also see a few individuals at the outside who are willing to pay a lot more for a better nutrition policy (i.e., between $100 and $200). What do we learn about people? The simple answer is that people are willing to pay more in taxes for those nutrition education and nutrition policies that they believe will help. We learn this because we have positive slopes. Positive slopes mean more dollars willing to be paid for policies that work. Furthermore, we learn that people differ. Not everyone feels the same about the amount of money to be paid. Delving inside the data – what specifically works? In the previous chapters we dealt with the difference between the Interest Model and the Persuasion Model. For reporting our results we use the output from the Interest Model. Briefly, the Interest Model is a simple linear model of the form: Binary Rating of Interest (0/100) = k0 + k1(A1) + k2(A2) … k6(F6) The Interest Model looks at the responses from each individual. Rather than focusing on the actual numbers, 1-9, which denote intensity of feeling, the Interest Model focuses on whether or not, for a particular vignette, the respondent disliked the vignette (rating of 1-6, transformed to 0), or liked the vignette (rating of 7-9, transformed to 100). We estimate the parameters of the Interest Model for each respondent, one respondent at a time. We can do so because each respondent evaluated a unique set of 48 vignettes. The set was designed so that each respondent’s data would comprise a single analytical unit 77

complete in and of itself. That is, the level of analysis goes down to the individual respondent. The transformation of a 9-point scale to a binary 0/100 scale means that the model or equation above reveals what each element does to drive this simple binary response. The additive constant, k0, tells us the conditional probability or proportion of respondents who would rate the vignette as interesting (7-9) in the absence of elements. While the additive constant is an estimated parameter, it is a good baseline nonetheless. Look for high additive constants, on average across respondents, to show a high level of basic interest in the general idea of the study. High values of +50 or higher for the additive constant mean that the idea starts out interesting to at least 50% of the respondents; the elements don’t have to do a lot of work to increase acceptance. There’s no requirement that the additive constant be high Often for products like credit cards the additive constant can be very low, around +20 on average across respondents, meaning that the elements must do all the work to drive acceptance since only a paltry 20% of the respondents will be interested in the idea of a credit card without any additional specifics. We will find an additive constant of +39 for our data, but that will be dealt with in the next section. The second half of the Interest Model comprises the impact value of the individual elements. Recall that the elements appear independently of each other. The individual impact values are estimated for each element and for each respondent, one respondent at a time. We can do that micro-level analysis because of the individual-level modeling. We present group averages in the table, however. The impact value for an element in the Interest Model tells us the additive proportion of respondents who would switch their rating from indifferent/dislike (rating of 1-6) to interested/like (rating of 7-9) when the particular element is inserted into the vignette. Thus, we should look for impact values of +8 or higher for truly strong performers, i.e., elements which do well. Statistical analyses suggest that when the impact value of an element reaches approximately +8 and higher we will find that the element drives external positive responses, for example, increasing sales when we are able to relate sales data to impact values. In this volume and others in the series, we use impact values of +8, or occasionally +10 and higher, to denote elements which drive the response we study, such as interest in the nutrition education program. What specifically excites people and what taxes will they pay?

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Let us look at the results from the total panel in Table 3.2. People are clearly interested in nutrition goals as government policy. The additive constant 39 tells us that even without any specific messaging, almost two in five people are ready to say ‘yes, I’m interested.’ People are also willing to pay taxes for a healthier America through nutrition, to the tune of $149, on average, just for the idea alone. Let’s now move on to the specific messages. Our first question is whether or not we can discover any big ideas which spark a lot more interest, beyond our in-going interest, and whether or not our data set comprises ideas for which people are willing to pay a lot more in added taxes. Table 3.2 tells us that our best opportunities are:   

Decrease in costs for drugs (diabetes, blood pressure, cholesterol lowering) School cafeterias required to sell nutritious foods (low sugar beverages, fat free milk/dairy, fresh fruits/vegetables, whole grains, salads, yogurt, vegetarian meals) ... or lose funding Decrease in health insurance premiums

Respondents are willing to pay $15 to $20 more in taxes for these actions. They each add 4-6 percent more interested respondents. We conclude from these strongest performing elements that there are some elements that interest people, in general, and that people are willing to pay a modest amount in taxes for them. There is nothing spectacular here. Something else comes out of the data, however, and it is of great relevance. People don’t want to pay extra taxes for fast food that may not be quite as healthful as one would like. At the bottom of the list is the tax on high fat, high calorie fast foods. This is similar to the recently-touted soda tax in New York City. People are not interested in a policy dealing with those foods (interest = -3) and certainly don’t want to pay taxes (tax = -10). 

Taxes on high fat, high calorie fast foods (french fries, chips, pizza, burgers ...) to pay for health nutrition messages ... to be sent through computers, TV, cell-phone, Twitter, Facebook

In December 2009, in a nationwide poll on behalf of CBS, Americans were asked whether they support or oppose putting a special tax on junk food like soda, chips, and candy. A majority of 60% said they would oppose such a policy; men 66%, much stronger than women 54%. The reason for the refusal was that nearly three quarters believed that putting a special tax on junk food would not encourage more people to lose weight (www.pollingreport.com/health.htm).

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Looking for patterns – Interest versus tax at the individual element level When we looked for patterns at the macro level we plotted average taxes versus average 9-point interest ratings on a person by person basis. We found a great deal of variability in the data, which clouded any patterns, making it hard to discover them. We also found that we did better when we plotted tax versus 9point interest ratings on an individual by individual basis and looked at the slope. With that second analysis we quickly saw that some people would pay more tax for a one-point increase, whereas other people would pay less tax for the same one-point increase. Let’s pursue our analysis, this time looking at the pattern of tax versus element. Our analysis will be very simple. We will simply compute the utilities for the 36 elements for interest and tax, as we see in Table 3.2, and plot the relation using a scatterplot. Our results appear in Figure 3.4. We have done this plot for total panel (top left), for gender (separate plots of males versus females), and for ethnicity (separate plots for Whites, Blacks, and Hispanics). Each filled circle corresponds to one of the 36 elements. It’s clear from the six plots in Figure 3.4 that no matter how we slice the data, the respondents clearly will pay more for what they like. It’s now a matter of degree. The steeper the slope in Figure 3.4, the more tax dollars a one-unit increase in the impact or utility of the nutrition message/program will generate. All the slopes are fairly steep, but there are differences among the groups. A one-unit increase in interest among males generates more tax dollars to be given than a one-unit increase in interest among females. Both genders may be interested, but it’s the male who is more generous. Although the ethnic groups are all fairly similar, when pushed, we might say that Whites are the least generous among the three ethnic groups, and the Hispanics are the most generous.

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Figure 3.4. How incremental tax increases with incremental interest in a government nutrition program. Results are shown by total panel, by gender, and by ethnicity.

Let’s follow the same analytic strategy, this time for one’s political party. Our respondents were asked to describe their political affiliations by choosing one of five points ranging from: strongly Republican - weakly Republican - neither Republican nor Democrat - weakly Democrat - strongly Democrat. Let’s look at the two strong affiliations (Republican and Democrat), and the middle (neither). We see the tax versus interest plots in Figure 3.5. The differences in the slope of the relation are stunning. Remember, we’re not talking here about which elements perform the strongest, but rather the difference in the patterns. For Republicans, large changes in interest co-vary with relatively small changes in tax willing to pay. Apparently, Republicans may feel strongly about a policy issue, but do not want to pay for that issue despite the strength of their feeling. Democrats and the non-aligned are different from the Republicans. When these two groups feel strongly, they back up their feelings with a greater willingness to pay more taxes. Figure 3.5. How incremental tax increases with incremental interest in a government nutrition program. Results are shown by stated party affiliation and strength of the affiliation.

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We finish this first study by looking at three mind-sets which emerged from the analysis that groups people together based upon their response patterns. We will deal with the specifics of the segments below. For now, keep in mind that these segments represent groups of individuals who show radically different patterns of elements that drive acceptance. These segments differ profoundly in what they believe to be important. The specific elements that would spark interest to the mind-sets are quite different. However, to reiterate, our focus here is simply on the form of the tax versus interest relation. Figure 3.6 suggests that the three segments show the same pattern. That is, despite the strong mind-set differences in what is important (see next section), the three segments actually show the same general relation between taxes willing to pay and interest in the element. Figure 3.6. How incremental tax increases with incremental interest in a government nutrition program. Results are shown by mind-set segment.

Three mind-sets - approaching the possibility of different mind genes Up to now, we looked at the differences among the groups from the point of view of macro (i.e., large scale) patterns. To a great extent, the power of experimental design comes from the specific elements which float to the top. It is to that topic that we now turn. In this study we deal with two evaluative criteria: interest in the specific policy element and tax willing to pay. Furthermore, we deal with different ways of cutting the data. The richness of the data comes from the reality that among 36 elements there are bound to be some differences among the elements. When we deal with different groups of people, looking for the stories will often uncover insights. We will begin with the three mind-set segments. We divide the respondents by the pattern of their impact values from the Persuasion Model. That model relates the presence/absence of the 36 elements to the 9-point rating. We 83

use the impact values or coefficients for the different respondents as the basis for clustering. We cluster respondents so that respondents with similar patterns of coefficients across their 36 elements are placed into the same cluster or segment. For our analysis we focus attention on the three-segment solution which can be interpreted fairly easily and makes intuitive sense. As one might guess, nutrition policy is not the most provocative topic. It does not produce massive differences in mind-sets. In nutrition, it’s a matter of emphasis, not polarization (see Table 3.3). We continue our analysis by putting all the analytical groups together in one table for interest and in another table for tax willing to pay, but we will only look at elements which exert a moderate or stronger effect on those measures. For Table 3.4 Interest we only show the elements which drive interest towards the policy (greater than +5), and for Table 3.5 Tax willing to pay we will only look at the elements which push towards higher individual taxes (greater than $20 per person per year). In both cases we will not look at numbers, but only at the elements themselves.

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What drives interest among key subgroups? We see some recurrent patterns among the elements, but at the same time, in most cases what appeals to one subgroup does not necessarily turn off the other subgroups. Our first conclusion, therefore, from looking at subgroups, is that we are dealing with a situation where the keyword is emphasis. Among the elements which appeal to several groups are the following:   

School cafeterias required to sell nutritious foods (low sugar beverages, fat free milk/dairy, fresh fruits/vegetables, whole grains, salads, yogurt, vegetarian meals) ... or lose funding Decrease in costs for drugs (diabetes, blood pressure, cholesterol lowering) Decrease in health insurance premiums

The pattern of strong performing elements suggests that when it comes to nutrition policy, differences across groups are not really the result of different mind-sets that are polarized, but rather different emphases on the same issues. Where one group emphasizes nutrition and education, a second group emphasizes financial cost of treatment, and a third group emphasizes a decrease in premiums. These groups simply pay attention to different aspects. A good analogy to this situation is pizza with different toppings. People don’t typically hate pizza, but people like some toppings far more than they like other toppings. Table 3.4. Strongest performing elements across key subgroups for nutrition policy. The table shows the elements based on the Interest model. Cells showing an ‘X’ are those which generate the moderate or higher levels of positive interest for the subgroup (impact >+5). The same element may appeal to different subgroups. Ethnicity Additive constant School cafeterias required to sell nutritious foods (low sugar beverages, fat free milk/dairy, fresh fruits/vegetables, whole grains, salads, yogurt, vegetarian meals) ... or lose funding Decrease in costs for drugs (diabetes, blood pressure, cholesterol lowering) Decrease in health insurance premiums Mandatory in-school nutrition education NOW ... reduce future health insurance costs to taxpayers Individuals ... receive price breaks ... gym memberships, weight loss programs and exercise equipment Individual/family income tax deductions ... for enrollment/completion of nutrition course at ... work, community centers, medical facilities For each health goal met ... employee receives $500 off deductibles Improve school food nutrition guidelines and learning tools ... U.S.Dept of Agriculture ... for Pre-K through college

Gender

White Black Hisp 37 53 48

M 38

F Rep Dem 43 45 54

X

X

X

X

X

X

X

X

X

X

X

X

Political Persuasion

X

X X X

X

X

X

X

X

X

X

X

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Increase subsidies for free and reduced-cost school breakfast and lunch ... cafeterias to serve less processed/healthier food Households with children receive vouchers towards ... free annual screenings for – diabetes, obesity, high blood pressure Good eating habits ... help children ... maintain a healthy weight Evaluate annual health trends (obesity, high blood pressure, high cholesterol) ... by ... U.S. Depts. of Health and Human Services, Agriculture Provide food handlers with ... training/new equipment to cook healthier, local foods in school/company cafeterias Home Economist/Dietician teams ... give public ... healthy food preparation/cooking lessons Doctors required to take continuing education courses ... weight management, nutrition, and vitamin supplements

X

X

X

X

X X

X

X X X

What drives the amount of added taxes among key subgroups? Now let’s move to the world of taxes. In Table 3.5, we first look at the additive constant which shows the base level of taxes that respondents would pay for a nutrition education program. We begin with the total panel. On average, respondents say that they would pay $149, without messages about the specific communication regarding nutrition. However, this $149 is the aggregate of many different groups. Table 3.5. Strongest performing elements across key subgroups for nutrition policy. The table shows the elements based on the Tax Model. Cells showing an ‘X’ are those which generate a high level of taxes willing to be paid by the subgroup. The same element may generate high expected taxes (>$20) among several different subgroups. Ethnicity Gender White Black Hisp M

Additive constant Decrease in costs for drugs (diabetes, blood pressure, cholesterol lowering) Decrease in health insurance premiums Increase subsidies for free and reduced-cost school breakfast and lunch ... cafeterias to serve less processed/healthier food School cafeterias required to sell nutritious foods (low sugar beverages, fat free milk/dairy, fresh fruits/vegetables, whole grains, salads, yogurt, vegetarian meals) ... or lose funding Good eating habits ... help children ... maintain a healthy weight

108

148

195

17 6

F

12 3

Political Persuasion Rep Dem

85

245

X

X

X

X

X

X

X

X

X

X

X

X

X 86

Individuals ... receive price breaks ... gym memberships, weight loss programs and exercise equipment Households with children receive vouchers towards ... free annual screenings for – diabetes, obesity, high blood pressure Mandatory in-school nutrition education NOW ... reduce future health insurance costs to taxpayers Improve school food nutrition guidelines and learning tools ... U.S. Dept of Agriculture ... for Pre-K through college Add yearly assessment ... school nutrition education Provide food handlers with ... training/new equipment to cook healthier, local foods in school/company cafeterias Food establishments offer healthier food options and products such as Weight Watchers, Atkins, South Beach, Jenny Craig Insurers ... have "Health Help Lines" ... call policy holders to encourage/ensure healthy lifestyle

  

X

X

X

X

X

X

X

X X

X

X

X

Ethnicity: Hispanics will pay more than Blacks, who say that they will pay more than Whites in base tax. Gender: Males will pay a lot more than females. Political Persuasion: Strongly committed Democrats say that they will pay a lot more than those who are unaffiliated, who will pay a lot more than strongly committed Republicans.

Looking at the different specific statements, we’re left with the feeling that there are only two very strong elements which appeal across the different groups in terms of taxes. The remainder of the elements distributed in an idiosyncratic way, with no pattern beyond the most popular topics. These two generally strong-performing messages are, unsurprisingly:  

Decrease in costs for drugs (diabetes, blood pressure, cholesterol lowering) Decrease in health insurance premiums

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Summary The response pattern shows that nutrition policy is not the most provocative topic. It does not produce massive differences in mind-sets. What appeals to one group does not necessarily turn off complementary groups. Differences across groups are not really the result of different mind-sets that are polarized, but rather different emphases on the same issues. Where one group emphasizes nutrition and education, a second group emphasizes financial cost of treatment, and a third group emphasizes a decrease in premiums. That polarization, which we see in many other studies, is not the case with nutrition policy. There’s not a lot of emotion in nutrition. Yes, there are some topics which drive respondents to say that they are interested in the topic, but we only get about five to eight points of additional interest, not very much. The strongest performing elements are:  

School cafeterias required to sell nutritious foods (low sugar beverages, fat free milk/dairy, fresh fruits/vegetables, whole grains, salads, yogurt, vegetarian meals) ... or lose funding Decrease in costs for drugs (diabetes, blood pressure, cholesterol lowering)

It’s not interest, however, where we see the important activity. It’s paying taxes. When it comes to taxes willing to pay, we begin to see more concrete evidence of what is important. Action speaks louder than words. Not everyone wants to pay taxes. Some individuals don’t want to pay taxes even when they think that the issue is important. That desire not to pay additional taxes manifests itself in about one out of three people checking off the 1 answer for each vignette: do not want to pay any additional taxes. On the other hand, two thirds of the respondents do say that they will pay additional taxes (defined as beyond $10 on the average across the different vignettes). The elements which drive the willingness to pay more taxes are what one might expect – strong health care benefits, wrapped up in the guise of nutrition education and policy:  

Decrease in costs for drugs (diabetes, blood pressure, cholesterol lowering) Decrease in health insurance premiums

The key to the entire approach is that experimental design lets us go beyond conventional public opinion polls. Because we use sentences that have meaning, we endow the policy statements with some meat which adds to the meaning. This attenuated response to public policy issues when it comes to

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nutrition is quite different from other situations, such as health care and energy, which we will see in the next chapters. References: [1] Danaei, G., Ding, E., Mozaffarian, D., Taylor, B., Rehm, J., Murray, Ch., & Ezzati, M. (2009). The Preventable Causes of Death in the United States: Comparative Risk Assessment of Dietary, Lifestyle, and Metabolic Risk Factors. Public Library of Science (PLOS). Retrieved from http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000058 [2] Roth, R. A. & Townsend, C. E. (2003). Nutrition and Diet Therapy (8th Edition). Clifton Park, NY: Delmar Learning. [3] U.S. Department of Health and Human Services (USDHHS) (2000). Healthy People 2010: Understanding and improving health. Washington, DC: U.S. Department of Health and Human Services. [4] Japsen, B. (2012, September 12). U.S. Workforce Illness Costs $576B Annually From Sick Days To Workers Compensation. Retrieved from http://www.forbes.com/sites/brucejapsen/2012/09/12/u-s-workforce-illness-costs576b-annually-from-sick-days-to-workers-compensation/ [5] Hedley, A. A., Ogden, C. L., Johnson, C. L., Carroll, M. D., Curtin, L. R., & Flegal, K. M. (2004). Prevalence of overweight and obesity among U.S. children, adolescents, and adults, 1999–2002. In Journal of the American Medical Association, 291(23), 2847-2850 . [6] Kessler, D. (2009). The End of Overeating: Taking Control of the Insatiable American Appetite. New York, NY: Rodale Books. [7] U.S. Department of Agriculture and U.S. Department of Health and Human Services (2000). Dietary Guidelines for Americans (5th edition). Home and Garden Bulletin (232). Washington, D.C.: USDA. [8] Freemark, M. (2010). Pediatric Obesity: Etiology, Pathogenesis, and Treatment. New York, NY: Springer. [9] Allender, J., Rector, C. & Warner, K. (2010). Community health nursing: Promoting and protecting the public’s health (7th ed.). New York, NY: Lippincott, Williams & Wilkins.

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[10] Abramson, J. H. & Abramson, Z. H. (2000). Survey Methods in Community Medicine (5th edition). London, U.K.: Churchill Livingstone. [11] Kelsey, J. E., Whittemore, A. S., Evans, A. S., & Thompson, D. (1996). Methods in Observational Epidemiology (2nd edition). New York, NY: Oxford University Press.

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Chapter 4 The 800 pound gorilla – health care Abstract: In this chapter we address the issue of reshaping some of the U.S. health care system by using Rule Developing Experimentation (RDE) to understand the citizen’s mind. We framed the approach as an exercise in consumer-driven public policy. That is, we instructed the respondent to think about the issues from the point of view of the public good, not from the point of view of individual interests. We focused on discovering those specific health care features the American respondent believed would succeed and how much incremental tax the respondent was willing to pay for the features. Our data suggest that at least at the time of the study (late 2009), American respondents didn’t believe that health care specifics as we described them would succeed, but said that they were willing to pay significant extra taxes to help make the policy and its specifics a success. The analysis investigated the balancing act between hope in improving a social service - health care - and taxes someone would pay. We see that the respondents take both of these into account. As the level of basic belief in success increases, we found a corresponding increase in the basic tax willing to pay. Once again with issues of public policy, we don’t find those dramatically different mind-sets which emerge so very strongly when the topic is specific goods and services that people buy specifically for their own needs and pleasures. On the other hand, when the topic involves public policy and the question is likelihood of success, the individual elements in the test vignettes generate only modest impact values. The corresponding mind-set segmentation is modest at best. Public policy about health care is certainly a topic of conversation, but the specifics of policy are far less susceptible to mind-set segmentation than the public policy topic of health care in general. Introduction First, a question: when in American history did life expectancy improve the most? Was it the late 1800s when anesthesia made surgery easier and far more common? Was it the 1930s when antibacterial medicines became available? Or was it in recent decades when CAT scans and heart bypasses proliferated? The correct answer (from two-time Pulitzer Prize winner Nicholas D. Kristof) is: ‘None of the above. While data differ and the statistics aren’t fully reliable, the best answer is the 1940s. In that period, life expectancy increased about seven years … A prime reason is that with the war mobilization, Americans got much better access to medical care. Farmers and workers who had rarely 91

seen doctors now found themselves with medical coverage through the military, jobs in industry, or New Deal programs. In short, great health care is often less about breakthrough technologies than it is about access’ [1]. Unfortunately, the current system is increasingly inaccessible to many poor and lower-middle-class people. On September 12, 2012, the Census Bureau released data on health insurance coverage and the uninsured for 2011. Although there are several major government surveys that produce estimates of health insurance coverage, the Current Population Survey (CPS) is the most widely cited and receives national media attention. During 2011, it showed that an estimated 48.61 million people (16%) were without insurance [2]. Those lucky enough to have coverage are paying steadily more and/or receiving steadily fewer benefits. Over the past decade, premiums have risen around 130 percent while wages have increased just about 40 percent and inflation has gone up about 30 percent [3]. Direct comparisons of health statistics across nations are complex, but the Commonwealth Fund, in its annual survey Mirror, Mirror on the Wall, compares the performance of the health care systems in Australia, New Zealand, the United Kingdom, Germany, Canada, and the U.S. Its 2011 study found that although the U.S. system is the most expensive, it consistently underperforms compared to the other countries. The result is that the United States pays roughly twice as much per capita for health care as Canada, France, and the United Kingdom, yet experiences lower qualitative care and life expectancy than those countries and significantly higher infant mortality [4]. The problems inherent in the U.S. health care system are literally killing people. Harvard Medical School researchers found in an analysis released in September 2009 that nearly 45,000 people die in the United States each year— one every 12 minutes—in large part because they lack health insurance and cannot get good care [5]. A major difference between the U.S. and the other countries in the study is that the U.S. is the only country without universal health care [4]. This issue has often been debated, but the option of a universal single-payer system in which the government regulates health care seems unlikely because the political mainstream presumes that Americans would never accept this type of health care. A Gallup poll in November 2012 showed that Americans still widely prefer a system based on private insurance (57%) to one run by the government (36%) [6]. Health care reform in the United States has a long history [7]. Reforms have often been proposed, but have rarely been accomplished. However, two 92

landmark reforms were passed in 2010: The Patient Protection and Affordable Care Act (commonly called Obamacare) and the Health Care and Education Reconciliation Act. Together they represent the most significant government expansion and regulatory overhaul of the U.S. healthcare system since the passage of Medicare and Medicaid in 1965 [8]. The Affordable Care Act is aimed at increasing the affordability and rate of health insurance coverage for Americans while reducing the overall costs of health care (for individuals and the government). It provides a number of mechanisms— including mandates, subsidies, and tax credits— to employers and individuals to increase the coverage rate and health insurance affordability [9]. We all know that health care costs money and the current health care reform is no exception. However, whereas some politicians focus on the current costs, others perceive the cost as investments that will lay the foundation for future cost reductions. A preliminary cost estimate of the final health care legislation that was discussed in March 2010 showed a $940 billion price tag for the new insurance coverage provisions in the bill. Certainly, nobody knows what the right number will be, whether this $940 billion, or even the more positive, yet staggering, estimated $1.3 trillion in deficit reduction over the next two decades that will come about through this new health care bill [10]. With all that in mind, we were eager to understand what type of health care and what specific health care features the American people want, and how much THEY, the people, are willing to pay for it. If health care is such a big issue, can we explore it in the same way we explored nutrition education in Chapter 3 and terrorism in Chapter 2? Can we bring to bear the power of RDE to address the problem? The answer is obvious: yes - provided that we operationalize health care in the same way that we operationalized terrorism and nutrition education. That is, we do not talk about health care per se, but rather the components of health care. It is the responses to those components which inform and instruct rather than the generalized, diffusive response to the notion of health care. Doing the experiment We begin with the set of elements. We look at current messaging about health care taken from different print and Web sources. With today’s brouhaha and concern about health care, there is an abundance of messaging. The silos and elements in Table 4.1 merely scratch the surface of what seems to be an everexpanding sea of information. In contrast to the very mild - sounding messaging for nutrition education, the messaging for health care has a somewhat more insistent tone and overtones of demands for justice and equality. Those overtones 93

are no accident; one can go bankrupt from lack of medical care. One can get sick from poor nutrition, but the economic effects are neither so immediate nor so drastic, although perhaps ultimately just as severe. Table 4.1. The six silos and six elements for each silo. The elements were taken from current communications about the topic found in print media and on the Internet. Silo A: REDUCING SUPERFLUOUS COST A1 Reduced high-cost prescriptions ... GENERIC only PRESCRIPTIONS! A2 Eliminate insurance based pricing … ONE PRICE FITS ALL! UNIVERSAL care ... UNIVERSAL cost ... shared pricing for medical services across A3 country A4 Eliminate deductible and co-payment cost A5 Subsidize health care for low and moderate income individuals A6 Eliminate emergency room coverage ... HIGHER COSTING COVERAGE Silo B: IMPROVING QUALITY OF CARE B1 Provide quality medical care for every INDIVIDUAL and FAMILY! B2 Reduce quick-DISCHARGE surgical care ... provide safe recovery time periods B3 Public option health care ... you choose the Doctor you want B4 Reduce crowded emergency rooms ... reduce long waiting periods for care B5 Comprehensive coverage ... full coverage for ALL! B6 Provide GOVERNMENT run, along with Private, health insurance options! Silo C: COMPASSION IN ACTION/ETHICAL COVERAGE C1 Improve conditions of hospitals in lower economic areas C2 UNIVERSAL health care ... eliminate uninsured cancer patients C3 Stop FOOD OR MEDICAL CARE life decision ... feed UNIVERSAL health care! C4 Allow voluntary MEDICARE ... eliminate AGE restrictions C5 Eliminate LOW INCOME/LOW QUALITY health care C6 Eliminate BANKRUPTCY due to SUDDEN astronomical medical expenses Silo D: FIT FOR LIFE D1 Provide national FITNESS GUIDELINES at yearly physical for all D2 Provide EARLY cancer-detection screening for all individuals at yearly physical EARLY CANCER, DIABETES AND HEART DISEASE DETECTION ... reduces HIGH D3 COST CARE D4 Provide nutritional guidance at yearly physical D5 UNIVERSAL HEALTH CARE ... prevent DISEASE with covered yearly physicals for all PROVIDE universal health care to ... INCREASE education on QUALITY OF LIFE D6 HEALTH OPTIONS Silo E: TAX INCREASES & COLLECTIVE COVERAGE E1 Support public option as ESSENTIAL for health care reform! E2 Increase taxes ... provide UNIVERSAL health care E3 Pool together resources through UNIVERSAL HEALTH CARE Universal MENTAL HEALTH CARE ... eliminate costly alternatives (e.g., crime, E4 incarceration, emergency room treatment) Public options = CHOICE OF MEDICAL COVERAGE ... eliminate health insurance E5 MONOPOLIES! E6 Universal health care reduces BILLIONS spent on MEDICARE AND MEDICAID Silo F: REDUCING NON-MEDICAL/PERSONNEL COST F1 Mandated training ... recertification for all health care providers! F2 NORMALIZE salaries...(Health Insurance CEO's, Doctors and other health care 94

F3 F4 F5 F6

providers) Government will learn more about what treatments work best ... using that information to guide Doctors Government audits will prevent unnecessary non-medical treatment Government audits will prevent unnecessary medical treatment Prevent expensive treatment if a lower cost one is available

Orienting the respondent and showing the test concepts We are exploring terra incognita here, new lands. In our previous chapters on package design (Chapter 1), on terrorism (Chapter 2), and on government policy towards nutrition and nutrition education (Chapter 3), we dealt with the respondent’s emotions. Our question was couched on a personal level: How well do these goals spark YOUR INTEREST to help Americans become healthier? In this study, we looked at what people believe will succeed. Health care is so fraught with opinions that it seemed much more interesting to investigate perceived future success. (See Figure 4.1 for the orientation page.) Figure 4.1. Orientation page to the Healthcare System study.

Our first rating question was: If the American HEALTH CARE system were reformed to include these ideas, how likely is HEALTH CARE to SUCCEED? We investigated health care in this way because, in fact, no one really knows what’s likely to succeed. Many people are confused by what they read in terms of what will actually work in the United States. 95

As part of our investigations, we again wanted to put a dollar value on these different ideas. The dollar value is not what a person would pay for the individual feature. Rather, the question instructed the respondent to put herself or himself in the position of the U.S. government and decide how much additional taxes would be appropriate for this vignette. Our second rating question was: If this were the future of American HEALTH CARE, how much should the Average American Citizen be willing to pay in additional taxes to support this reform? The test concepts for the standard 6 x 6 design (six silos, six elements per silo) comprise 48 combinations of two to four elements. As before, each test vignette is incomplete. Such incompleteness makes it possible later on to do very powerful regression analyses without worrying about multi-collinearity. (An unwelcome statistical phenomenon, multicollinearity occurs when two or more predictor variables in a multiple regression model are highly correlated, preventing us from estimating the impact of each element separately.) Figure 4.2. Example of a test concept and the first rating question.

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Do people differentiate among the test vignettes in terms of belief in success and in tax? Let’s first compute the average belief in success and then compute the average tax. Each of our respondents evaluated 48 vignettes, so our average for a single respondent comes from these ratings. A set of 48 vignettes provides a rich source of information from which one can compute a fair average. There’s a lot of stimulus material in these 48 vignettes. We compute both averages very easily (Figure 4.3). Despite the range of averages from near 1 to near 9 on the succeed - scale, there are a lot of people whose averages cluster near 1. These correspond to people who do not believe that the average health care vignette will succeed, no matter what is presented. And, in turn, there is a large range of average taxes. In contrast, very few people are near $0 in their incremental taxes. We immediately learn that there are a lot of people who don’t believe that the health care initiative will succeed, but there are also a lot of people who are willing to pay taxes if the initiative does succeed. People will put their money where their mouth is. Let’s inspect the plots and draw our conclusions from the distribution of averages. Look at Figure 4.3; each filled circle corresponds to one respondent. The left panel in Figure 4.3 shows the average tax is plotted in linear coordinates (as is the average 9-point rating). The right panel shows the average tax plotted in logarithmic coordinates to separate out the low averages for taxes (near 0) from the higher non-zero averages. The effect is to isolate those individuals whose averages for taxes are near 0. We have very few of these near - zero averages for incremental taxes devoted to health care. Compare what we find here to nutrition education where the 9-point rating was interest in the policy. We should be struck by the difference. Nutrition Education: People are interested in the nutrition education policy (lots of individual averages lying in the middle and high end of the 9 - point range), but people are not interested in paying more for the policy (lots of individual averages lying near 0). Health Care: When it comes to health care we see the opposite. People don’t believe that health care will succeed (not exactly the same as interest, of course), but are willing to pay extra taxes to make it succeed.

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Figure 4.3. Scatterplot of ratings for average tax willing to be paid by a respondent (ordinate) versus the average 9 - point rating of belief in the likelihood of success (abscissa). Each point corresponds to the average from one of the 248 respondents.

RELATION BETWEEN GREATER INTEREST AND GREATER TAXES

LOGARITHMIC PLOT FOR TAXES

Just what is a unit of confidence in health care worth in taxes? Let’s continue our analysis of belief in success versus taxes willing to pay. This time, however, we will look at the relation as an equation. We know that people differentiate vignettes dealing with health care. Some vignettes are assigned greater likelihood of success, while others are assigned less likelihood of success. Furthermore, some vignettes may command higher incremental taxes, while other vignettes may command lower incremental taxes, or even no incremental taxes. Given the foregoing variation at the individual respondent level, can we discover a relation between likelihood of success (the independent variable) and incremental tax willing to pay (the dependent variable)? We begin with the hypothesis that a person is willing to pay more money in taxes when she or he believes that the health care program will succeed. We can test this hypothesis by relating the amount of taxes willing to pay as the dependent variable to the belief in success as the independent variable. We write this equation as: Taxes = k0 + k1 (Belief in Success) The foregoing equation is a simple linear model. The really important parameter of this linear model is the slope, k1. The slope tells us the number of tax dollars corresponding to a 1-unit change in belief that the program will be successful. Each of our respondents will generate one point; an average 9-point rating for belief, and an average amount of taxes willing to pay. The average from each 98

respondent will be based on the 48 vignettes that the respondent evaluated during the RDE portion of the interview. Now let’s look at the models that we can create from these points. We will look at complementary groups of respondents, such as males versus females. With, say, 100 respondents in a subgroup, we will have 100 points, or cases, from which to create the model. Each case will have a single number for belief in success (average from 48 vignettes) and a single number for increment in taxes (also averaged from the same 48 vignettes). The results appear in Table 4.2. The table comprises four columns. The first column shows the specific group. The second column shows the intercept, k0, which is the value of taxes where belief is 0 (i.e., none). The third column is the slope, k1, the parameter that really interests us. Finally, at the right, in the last column, is the goodness of fit indexed by the Pearson r. The Pearson product-moment correlation coefficient (sometimes referred to as the PMCC and typically denoted by r) is a measure of the correlation (linear dependence) between two variables, X and Y, giving a value between +1 and -1 inclusive. The Pearson correlation is widely used in the sciences to measure the strength of a linear relation between two variables. When there is absolutely no linear relation between the two variables, the Pearson r is 0. When there is a perfect linear relation between the variables, the Pearson r is either +1.00 for a positive fit or -1.00 for a negative fit. We first look at the data from the total panel, constructed from all 248 respondents (i.e., 248 cases used to create the linear equation). The slope is 45 for the total panel; a 1-unit increase in belief of success corresponds to an extra $45 of taxes one would pay. From the self-profiling classification at the end of the interview, respondents defined who they are, both in terms of geo-demographics (e.g., gender, age) and in terms of attitudes (e.g., strong Democrat, weak Democrat, neither, weak Republican, strong Republican). The self-profiling classification questionnaire lets us divide the respondents into complementary groups. In the study, we opted for approximately equal numbers of males and females, equal numbers of Whites, Blacks and Hispanics, and so forth. The equal numbers give us a chance to compare the responses of these complementary groups. The results are quite interesting: the same unit of belief in a health care program calls forth quite different amounts of taxes one would pay. 

Gender: Men will pay more than women ($51 versus $39 for the 1-unit of belief in success); 99



Age: As a respondent ages there seems to be a rough inverted U shaped curve. The same 1-unit increase in belief in success calls forth less taxes from the younger respondents (groups under 29 and 38, meaning from 18 to 29, and from 30 to 38). The middle-aged respondents (groups 44 and 52, corresponding to 39 to 44, and 45 to 52) are willing to pay more taxes for that 1-unit of belief. Finally, the older respondents (groups 64 and 70, corresponding to 53 to 64, and 65 to 70) are willing to pay less again for that 1-unit of belief. And this makes sense. The older respondents, 65+, are the individuals who are highly concerned about good health care, but they know they are going on Medicare. Yes, they are concerned, but they are covered and they need to guard their resources. Why then pay more taxes?



Ethnicity: Whites are willing to pay $60 per unit, whereas Blacks and Hispanics are willing to pay a lot less.



Political affiliation: Although there were five categories for political affiliation (neither, strong versus weak Democrats and Republicans), we looked at the two that we thought to be most polarizing— strong Democrats and strong Republicans. They are quite different; a self-styled strong Democrat is willing to pay $71 whereas a self-styled strong Republican is willing to pay only $52 for that same 1-unit of belief in success.



We divided the group of 248 into two mind-set segments. The first segment, which we call Economics, focuses primarily on affordable health care and safety from bankruptcy. The second segment, Health, focuses on getting the appropriate needed health care. Both mind-set segments show similar slopes.

Table 4.2. Parameters of the equation relating taxes willing to pay versus belief in the success of the health care program (Taxes = k0 + k1 (Belief in Success)).

-8

45

Goodness of fit (Pearson r) 0.36

Male Female

-19 1

51 39

0.40 0.33

Age 29 Age 38 Age 44 Age 52

18 40 -55 -64

40 37 53 66

0.34 0.29 0.45 0.43

Group Total

k0

k1

100

Age 64 Age 70

-14 5

49 32

0.40 0.30

White Black Hispanic

-24 -17 -11

60 43 40

0.20 0.33 0.34

Strong Democrat Strong Republican

-96 -60

71 52

0.43 0.56

-7 -9

46 44

0.13 0.37

Mind-set segment 1 – Economics Mind-set segment 2 - Health

To summarize this part, it’s quite clear that even without knowing the messages, we observe radically different responses to health care. Each respondent serves as his own control assigning both the rating of belief in success and the amount willing to pay in taxes. There are really no surprises. In this analysis we are measuring the balancing act between hope in improving a social service—health care—and the taxes someone would pay for that improvement. We see that the respondents take both of these into account. Now for specifics: What do people believe will succeed and what taxes do they say they will pay? We now move from looking at the pattern of responses to looking at specific elements. Our analysis of these elements follows the same pattern as we already explained in greater detail in Chapters 1 and 2. 

Transform the first evaluative rating (likelihood to succeed) into binary values (1 through 6  0; 7 through 9  100), and then run an ordinary least squares regression.



Transform the second rating into dollars, and then run an ordinary leastsquares regression.

In many of these studies the evaluative rating shows a higher range and more discrimination than the money rating. For example, when we work with ideas about the sensory experience provided by hotel rooms, we find a very wide range of evaluative ratings (interested versus not interested in the features), but in contrast, only a very small range in the economics (relative amount of money willing to pay). Indeed, in many studies dealing with products and services that the individual consumes, the evaluative rating shows far more discrimination, whereas the economic attribute shows far less discrimination across elements. Homo economicus in the world of products and services seems to be far more conservative than attitudes would have us believe. 101

When it comes to public policy and to taxes that people will pay, this pattern of heightened sensitivity to evaluative attributes just doesn’t seem to hold. Homo economicus is much stronger. Consider the results in Table 4.3 which show the utility values for both evaluative (likelihood to succeed) and economic (taxes would pay). These utility or impact values come from the Interest Model. The additive constant shows the estimated percent of respondents who believe in the success of the health care program, even without elements, or in the amount willing to pay for health care, again without elements. The individual impact or utility values show the expected incremental or decremental values for each element. When we deal with belief, the impact value shows the percent of respondents who would change their vote from the program will not succeed (rating 1-6) to the program will succeed (rating 7-9) when the element is inserted into the test vignette or concept. When we deal with taxes, the impact value shows the number of dollars that would be added to the base tax to show the incremental tax one is willing to pay for health care when the element is inserted into the vignette. Likelihood to succeed Only one person in three thinks that health care, in general, can be reformed. The additive constant is 33. The impact values for the tested elements on the rating scale chance to succeed are low, most between +5 and -5. This means that when the element is included in the vignette, most of the time we may expect to see a change of +/- 5% of the respondents rating the vignette as likely to succeed. There are no blockbuster elements. Only one element moves beyond +/-5. This element generates an impact or utility value of +6, hardly any improvement: 

Public option health care…you choose the Doctor you want.

Changing policy about the emergency room is perceived to be a nonstarter with a low chance to succeed: 

Eliminate emergency room coverage … HIGHER COSTING COVERAGE

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Taxes someone would pay The average additional tax a person is willing to pay is $184 without any additional information. For comparison purposes, nutrition education policy generated an additional $149 in taxes. Three of the 36 elements drive at least $20 more in taxes that a person would pay: 

Comprehensive coverage … full coverage for ALL!



Public option health care … you choose the Doctor you want



EARLY CANCER, DIABETES AND HEART DISEASE DETECTION … reduces HIGH COST CARE

Table 4.3. How the elements in health care are perceived in terms of their chance to succeed and in terms of the additional tax that they can command. Succeed Tax Additive constant 33 184 B5 Comprehensive coverage … full coverage for ALL! 3 28 B3 Public option health-care … you choose the Doctor you want 6 24 EARLY CANCER, DIABETES AND HEART DISEASE DETECTION D3 4 20 … reduces HIGH COST CARE C4 Allow voluntary MEDICARE … eliminate AGE restrictions -1 19 A5 Subsidize health care for low and moderate income individuals 1 18 A4 Eliminate deductible and co-payment cost 5 17 UNIVERSAL care … UNIVERSAL cost … shared pricing for A3 0 16 medical services across country Public options = CHOICE OF MEDICAL COVERAGE … eliminate E5 2 16 health insurance MONOPOLIES! Provide EARLY cancer-detection screening for all individuals at D2 3 13 yearly physical C2 UNIVERSAL health-care … eliminate uninsured cancer patients -3 12 E1 Support public option as ESSENTIAL for health-care reform! -1 11 B1 Provide quality medical care for every INDIVIDUAL and FAMILY! 4 11 Eliminate BANKRUPTCY due to SUDDEN astronomical medical C6 2 11 expenses Reduce crowded emergency rooms … reduce long waiting periods B4 1 10 for care NORMALIZE salaries … (i.e., Health Insurance CEO's, Doctors and F2 -2 10 other health-care providers) Provide GOVERNMENT run, along with Private, health insurance B6 0 10 options! E2 Increase taxes … provide UNIVERSAL health-care -5 9 Universal MENTAL HEALTH-CARE … eliminate costly alternatives E4 -1 8 (i.e., crime, incarceration, emergency room treatment) A2 Eliminate insurance based pricing ONE PRICE FITS ALL! 0 7 103

F5 F6 E3 B2 E6 F1 F3 D5 D6 C5 C3 C1 F4 D4 A1 D1 A6

Government audits will prevent unnecessary medical treatment Prevent expensive treatment if a lower cost one is available Pool together resources through UNIVERSAL HEALTH-CARE Reduce quick-DISCHARGE surgical care … provide safe recovery time-periods Universal health-care reduces BILLIONS spent on MEDICARE AND MEDICAID Mandated training ... recertification for all health-care providers! Government will learn more about what treatments work best ... using that information to guide Doctors UNIVERSAL HEALTH-CARE ... prevent DISEASE with covered yearly physicals for all PROVIDE universal health-care to ... INCREASE education on QUALITY OF LIFE HEALTH OPTIONS Eliminate LOW INCOME/LOW QUALITY health-care Stop FOOD OR MEDICAL CARE life decision … feed UNIVERSAL health-care! Improve conditions of hospitals in lower economic areas Government audits will prevent unnecessary non-medical treatment Provide nutritional guidance at yearly physical Reduced high-cost prescriptions … GENERIC only PRESCRIPTIONS! Provide national FITNESS GUIDELINES at yearly physical for all Eliminate emergency room coverage … HIGHER COSTING COVERAGE

-2 0 -1

6 6 5

1

5

-1

5

0

4

-1

4

2

4

0

2

1

2

-1

-2

1

-3

-3

-3

0

-3

-1

-4

1

-6

-4

-11

Just how much taxes would people pay for health care anyway? Our first regression analysis revealed the baseline tax that people would be willing to pay, which corresponds to the additive constant in the regression model. That additive constant is never explicated directly by the respondent, but emerges from the pattern of ratings (0/100 values, one per vignette), and how those ratings co-vary with the element. The additive constant is estimated from the pattern of the responses and is what remains after the contributions of the 36 elements are accounted for. In other words, the basic tax (additive constant for the Tax Model) is the estimated tax dollars that a person would pay for the health care program even without elements. The constant, the baseline tax, was $184. We now want to do the regression analysis at the individual respondent level. We are able to do individual-level modeling quite easily because of the effort that we took at the start of the study. RDE’s design ensures that at the level of the individual respondent the 36 elements are systematically combined in different vignettes so that they comprise an experimental design for each person. Each 104

person evaluates a different set of combinations, but the basic design remains the same. At the individual respondent level, the additive constant now tells us the base amount of money that each individual respondent is prepared to pay. Figure 4.4 shows the basis, the underlying value, once the messaging has been stripped away. Each person corresponds to a filled circle. Keep in mind that the additive constant is based on 48 different combinations, so we don’t have to worry about random errors for one or two ratings. Each individual respondent gives us plenty of information from which to deduce the basic amount of tax he or she would be prepared to pay. Figure 4.4: Distribution of 248 individual additive constants for tax willing to pay for health care. Each additive constant emerges after the deconstruction of a respondent’s ratings into the basic contribution (additive constant) and the part-worth contributions of each of the 36 elements. This figure is based on first transforming the 9-point rating into a tax value. Each point on the 9-point scale has a tax dollar value. We then relate the presence/absence of the 36 elements to the tax dollar.

As we can clearly see, people differ in the amount of taxes they would pay. The additive constants vary. It’s clear that most people fall between $0 and about $500. There are some who would be ready to pay higher, and some, though not many, who would not pay at all and whose additive constants are negative. Basic beliefs in success versus basic taxes willing to pay; How groups differ from each other Now that we have a structure within which we can measure the basic tax a person will pay (i.e., the additive constant), let’s look at the different values of this basic tax across groups of respondents, as well as how that basic tax co-varies with the group’s basic belief in success. Let’s look at Table 4.4. The table shows different groups ranked by the additive constant for belief in success. That additive constant is the basic belief in success, similar to the basic tax one would pay, but computed for the Interest Model and for the first rating question. 105

We begin with a very high additive constant of 47 for those respondents who define themselves as strongly Democratic. Since the additive constant for the Interest model shows the proportion of respondents expected to rate the vignette 7 through 9 (believe) in the absence of elements, we conclude that among those who define themselves to be strong Democrats, 47% would rate the likelihood of success as 7 through 9 on the 9-point scale. At almost that level are those respondents aged 53 to 65, with 43% believing in the success. Contrast these positive numbers with the bottom groups. We end up with two age groups, Age 38 to 44 and Age 45 to 52. Less than a quarter of them believe that the program can succeed. The additive constant for those two groups is 22 for age 44 (38 to 44 years old) and 23 for age 52 (45 to 52). Now let’s contrast this with the taxes the respondents are going to pay. Again, we are using the additive constant of the Tax Model as this index. We list the values on the right of Table 4.4. If we stare long enough at the numbers in Table 4.4, we might at some point come to the conclusion that there is a relation between the basic tax one would pay and the percentage of people who feel that the health care program could be a success. One very good way to uncover the relation between success and tax is to plot one number against the other as we see in Figure 4.5. This is the scatterplot of two variables, one of the easiest tools in the analyst’s toolkit and a source of remarkable insights, again and again. Table 4.4. Additive constants (basic belief and tax) for the health care program for total panel and key subgroups. The table is sorted by the additive constant for taxes willing to be paid, the second rating question. For ages, we use the top value from the group. Group Strong Democrat Black Age 21 to 29 Age 53 to 64 Male White Age 35 to 44 Age 45 to 52 Age 29 to 34 Segment 2 - Health Oriented Total panel

Basic belief 47 39 36 43 37 27 22 23 31 33 33

Basic tax

Code

265 228 205 201 198 196 196 191 188 187 184

A C E B D N P O I F G 106

Segment 1 - Economics -oriented Female Age 65+ Strong Republican Hispanic

31 28 30 29 31

183 169 152 149 126

H M K L J

We plotted the tax dollars on the ordinate and the success percent on the abscissa. Since we are interested in the different groups, we labeled each group with a letter referring back to Table 4.4. The scatterplot helps the pattern to reveal itself. All in all, there is a clear relation between belief in success and tax willing to pay. As the level of basic belief in success increases, we see a corresponding increase in the basic tax willing to pay. Keep in mind that these numbers are the additive constants derived from the pattern of the ratings and not from the respondent’s direct statements. It is rather remarkable that, for the most part, the relation conforms to a straight line. The exceptions are: 

Hispanic (J) – the basic tax to be paid should be far higher (around $150) than it is ($126), given the level of belief in success of the health care initiative. Conversely, for the basic amount of tax that the Hispanic respondent is willing to pay, the belief in success should be far lower, around 20 rather than 31.



For respondents aged 35 to 44 (P) and 45 to 52 (O), the basic tax they will pay is too high for their belief in the success of the health care initiative. They are willing to pay around $195. That high amount of taxes corresponds to a belief value of 35 to 40, not 22 to 23. If belief in success is that low, then we would expect them to be willing to pay about $100 in taxes.

Figure 4.5. How basic tax willing to pay co-varies with belief in the success of the health care initiative. Each letter corresponds to a group of respondents in Table 4.4.

107

Who believes what? Let us finish this chapter by looking a bit more deeply at the elements. Through regression analysis we identified the top three elements for the specific groups. We list the strongest performing elements in Table 4.5. In general, the recurring themes are: 

Public option health care … you choose the Doctor you want



Eliminate deductible and co-payment cost



EARLY CANCER, DIABETES AND HEART DISEASE DETECTION … reduces HIGH COST CARE



Public options = CHOICE OF MEDICAL COVERAGE … eliminate health insurance MONOPOLIES!

108

Table 4.5. Elements for health care generating the highest level of belief in success. Top 3 Male Public option health care … you choose the Doctor you want 3 Eliminate deductible and co-payment cost 3 EARLY CANCER, DIABETES AND HEART DISEASE DETECTION 2 … reduces HIGH COST CARE Female Public option health care … you choose the Doctor you want 8 Eliminate deductible and co-payment cost 7 Comprehensive coverage … full coverage for ALL! 7 Age 29 Public option health care … you choose the Doctor you want 8 Improve conditions of hospitals in lower economic areas 7 Eliminate deductible and co-payment cost 6 Age 38 EARLY CANCER, DIABETES AND HEART DISEASE DETECTION 10 … reduces HIGH COST CARE Eliminate BANKRUPTCY due to SUDDEN astronomical medical 8 expenses Public option health care … you choose the Doctor you want 7 Age 44 Provide quality medical care for every INDIVIDUAL and FAMILY! 8 Public option health care … you choose the Doctor you want 8 Provide EARLY cancer-detection screening for all individuals at 5 yearly physical Age 52 Eliminate deductible and co-payment cost 10 EARLY CANCER, DIABETES AND HEART DISEASE DETECTION 9 … reduces HIGH COST CARE Public options = CHOICE OF MEDICAL COVERAGE … eliminate 8 health insurance MONOPOLIES! Age 64 Public options = CHOICE OF MEDICAL COVERAGE … eliminate 4 health insurance MONOPOLIES! Eliminate deductible and co-payment cost 3 Comprehensive coverage … full coverage for ALL! 2 Age 70 Eliminate deductible and co-payment cost 7 Public option health-care … you choose the Doctor you want 6 109

Provide EARLY cancer-detection screening for all individuals at yearly physical White Comprehensive coverage … full coverage for ALL! Public option health care … you choose the Doctor you want EARLY CANCER, DIABETES AND HEART DISEASE DETECTION … reduces HIGH COST CARE Black Eliminate deductible and co-payment cost Public options = CHOICE OF MEDICAL COVERAGE … eliminate health insurance MONOPOLIES! Public option health care … you choose the Doctor you want Hispanic Public option health care … you choose the Doctor you want Provide quality medical care for every INDIVIDUAL and FAMILY! Reduce quick-DISCHARGE surgical care … provide safe recovery time-periods Strong Democrat Eliminate deductible and co-payment cost Public option health care…you choose the Doctor you want Public options = CHOICE OF MEDICAL COVERAGE…eliminate health in insurance MONOPOLIES! Strong Republican UNIVERSAL care … UNIVERSAL cost … shared pricing for medical services across country Eliminate deductible and co-payment cost Public option health care … you choose the Doctor you want Belief Segment 1 – Economics of health care is the most important thing Public option health care … you choose the Doctor you want Eliminate BANKRUPTCY due to SUDDEN astronomical medical expenses Provide quality medical care for every INDIVIDUAL and FAMILY! Belief Segment 2- Health is the most important thing Eliminate deductible and co-payment cost EARLY CANCER, DIABETES AND HEART DISEASE DETECTION … reduces HIGH COST CARE Public option health care … you choose the Doctor you want

6 5 4 4 6 5 4 9 7 6 11 8 6 13 13 13 7 5 5 5 5 5

110

Summary Anyone listening today to the rather raucous and not so well-intentioned debate about health care quickly realizes that we’re dealing with an area of great emotions. Those who want the public option recognize that health care in the United States is expensive, serves some far better than others, and may eventually be a victim of its own costs. Over time, as the population ages, health-related expenses are demanding an ever increasing proportion of the national budget. Yet, as the data in this chapter suggest, there are many dimensions to the problem. There are no single answers. Perhaps the easiest to deal with are the differences in primary focus. We see about half the respondents falling into the segment focusing on economics and the other half falling into the segment focusing on good health care that works. That’s the easy part. The hard part is the differences in what’s important by age and by ethnicity. From this chapter we begin to get a sense that dealing with issues of public policy have the two general aspects of: 

what should be in the policy



what are the taxes that someone is willing to pay

These are not necessarily the same, but they have to be investigated together. References: [1] Kristof, N. D. (2010, March 18). Op-Ed piece: Access, Access, Access. New York Times, pp. A31. [2] U.S. Department of Commerce: US Census Bureau (2012). Retrieved from: http://www.census.gov/hhes/www/cpstables/032012/health/toc.htm [3] Colliver, V. (2009). Health premiums up 131% in last 10 years. Retrieved May 30, 2010, from http://articles.sfgate.com/2009-09-16/news/17204841_1_insurance-employerscoverage [4] Davis, K., Schoen, C., & Stremikis, K. (2010, June 23). Mirror, Mirror on the Wall: How the Performance of the U.S. Health Care System Compares Internationally 2010 Update, The Commonwealth Fund, June 2010. Retrieved 111

from: http://www.commonwealthfund.org/Publications/FundReports/2010/Jun/Mirror-Mirror-Update.aspx?page=all [5] Reuters.com. (2009, September 17). Study links 45,000 U.S. deaths to lack of insurance. Retrieved March 16, 2010 from http://www.reuters.com/article/idUSTRE58G6W520090917 [6] Gallup. In U.S., Majority Now Against Gov't Healthcare Guarantee. ((2012, November 28) Retrieved from: http://www.gallup.com/poll/158966/majority-against-gov-healthcare-guarantee.aspx [7] Quadagno, J. (2005). One Nation, Uninsured: Why the U.S. Has No National Health Insurance. New York, NY: Oxford University Press [8] Manchikanti, L., Caraway, David., Parr, A., Fellows, B., & Hirsch, J. (2011). Patient Protection and Affordable Care Act of 2010: Reforming the Health Care Reform for the New Decade. Pain Physician, 14 (1), 35-67. [9] Pear, R. (2012, July 7). Brawling Over Health Care Moves to Rules on Exchanges. New York Times. Retrieved July 7, 2012, from http://www.nytimes.com/2012/07/08/us/critics-of-health-care-law-prepare-tobattle-over-insurance-exchange-subsidies.html?_r=1&. [10] Pulizzi, H. J., & Adamy, J. (2010, March 18). Obama: CBO Report Shows Health Overhaul Cuts Deficit. The Wall Street Journal [Web blog comment]. Retrieved May 30, 2010, from http://blogs.wsj.com/washwire/2010/03/18/17342/

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Chapter 5 Education – Securing the foundation of the nation Abstract: Studies comparing the performance of the school systems in the U.S. and other industrialized countries show that although the U.S. system is heavily funded, it consistently underperforms as compared to those of other countries. A variety of comprehensive reforms have been undertaken. In most of the cases, the reform has been combined with higher levels of spending. We used RDE (Rule Developing Experimentation) in order to identify particular features of a school reform that would drive the belief that an education reform program would be successful. At the same time, we also used RDE to determine how much incremental tax people are willing to pay for specific program features. Interest in education is certainly present in the minds of respondents, but our specific features for reform generate relatively little beyond the basic interest. Furthermore, this lack of very strong response to specific features applies to taxes as well. Our respondents are willing to pay a fair amount of taxes, but are not readily willing to pay for public educational programs despite the fact that the respondents consider these programs to be important. The message here is that education is deemed to be critical, and so, it generates a high level of basic interest and a high tax that people are willing to pay. What surprises, however, is the absence of reform elements which are able to move interest and taxes much beyond this basic level. In people’s responses to education the devil IS NOT in the details. Introduction Across countries, education and democracy highly correlate with each other [1]. Literature on education shows again and again a direct and repeated linkage of a strong educational system to a more stable society and to a productive economy [2]. Therefore, the issue of schools is one which all Americans can agree must be resolved in our best interest if the United States is to continue to prosper and succeed in the years to come, as other countries invest in educating their populations to become more competitive [3]. For many years standardized test results have been showing that American students learn less about science and mathematics than do their counterparts in other industrialized countries [4]. One important study in this area is the Program for International Student Assessment (PISA), a triennial worldwide appraisal of 15 year-olds in 30 industrialized countries by the Organization for Economic Cooperation and Development. In the 2006 study, the tests were taken by more than a quarter of a million students representing a total population of about 23 113

million in the participating countries. PISA scores the students along a gradation of seven proficiency levels in increasing order of skill, from "below Level One" to the “top Level Six”. Within the top countries, Finland, South Korea, and Hong Kong, more than 50% of the 15 year-olds reached at least Level Four. In comparison, three quarters of the American students reached only Level Two. At the 2006 PISA study, the U.S. placed 21 out of 30 countries [5]. This low level of performance has often been a catalyst for educational reform. In 1983, the National Commission on Excellence in Education called attention to the poor performance of American schools in its widely cited report A Nation at Risk [6]. Since that year, a variety of comprehensive reforms have been undertaken. Spurred by the piecemeal approach to school reform that had produced little change in the nation's test scores, New American Schools (NAS) launched its efforts for whole-school reform in 1991. This initiative was based on the premise that high-quality schools are established when external providers (design teams) assist schools which then implement the designs. The vision, or design, offers schools a focal point for their improvement efforts along with guidance regarding what students need to know and be able to do and how to get there [7]. In conjunction with President Bush's America 2000 initiative, the New American Schools Development Corporation (NASDC) was established as a nonprofit corporation. It was funded by the private sector in order to create and support design teams capable of helping existing elementary and secondary schools transform themselves into high performing organizations using whole-school designs [7]. The STEM [Science, Technology, Engineering, Mathematics] Coalition, a diverse group comprising more than 500 workforce, business, education, and science and technology organizations and businesses, has worked for strong K through college education in the STEM fields. It believes that effective policies and practices that improve student performance in these subjects increases diversity in these fields and ensures a well-educated STEM workforce which is critical to our nation’s future. [8] The Obama administration lobbied to replace the school grading system as part of Race to the Top, which measures individual students’ academic growth. The new system judges schools based on the traditional test scores, but augments them with indicators such pupil attendance, graduation rates and learning climate [9]. Education is already one of the most important and most costly, activities of American government. In most cases, reform has been combined with higher levels of spending. Public spending on education totaled $918 billion in 2012, or fifteen percent of national spending. That means that educational expenditures 114

were substantially larger than spending on national defense ($851 billion) or welfare ($587 billion) (www.usgovernmentspending.com). According to the U.S. Census Bureau, school districts in the U.S. spent an average of $10,615 per student in the fiscal year 2010, a slight increase of $116 from 2009. The year to year number is deceptively low. There was a larger increase when we look across a decade and a half. Compared to 1995, when federal spending on public schools amounted to $6,147 per pupil enrolled, the increase in spending is much larger, 73% (www.census.gov). When public schools are financed by larger budgets, taxpayers pay heavier taxes. That’s a simple fact of life. For schools, this increase in taxes requires that the deliverables are actually provided and the benefits measured and communicated. Parents should be sure that they and their children are well served, teachers need to be able to reassure the public (and themselves) that good results justify budgets and salaries, and taxpayers should be sure they get their money's worth [3]. The education study Let’s now return to RDE and apply it to education, which like the nutrition policy and health care studies, was run with 36 elements, shown in Table 5.1. With such a popular and contentious topic it’s quite a task to limit oneself to 36 elements. There are literally hundreds, perhaps thousands, of different ideas floating around, but discipline is necessary for RDE, and the results emerging at the other end of the effort are a tribute to that discipline. The elements are expressed as stand-alone phrases with a bit of complexity. For example, the notion of higher salaries by itself could be an element. However, it’s more important to combine the idea with a benefit for highly professional teachers. People understand the idea of higher salaries, but that phrase alone lacks cogency. The unmodified phrase higher salaries sounds more like a bullet point from a PowerPoint presentation. Such unmodified phrases may be acceptable for strategy studies, but they don’t work very well within more complex studies like this. Thus, we have the rationale for the more complex phrase, richer in meaning and more filled with feeling.

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Table 5.1. The elements for the education study. Silo 1: SPEND NOW/SAVE LATER Higher salaries … for highly professional teachers Improve quality of education NOW... reduce future cost to tax-payers Universal guidelines and universal learning tools … for students ... pre-K to grade 4 ... shared across country Invest in our children's future ... lobby for college enrollment tax deductions Increase taxes … increase funding for our education system Increased taxes... provide funding for more effective teaching Silo 2: IMPROVE STRUCTURAL CONCERNS Improve conditions of our schools … structurally safe environments Best-fit environments ... maximizes student learning Reduce overcrowding in schools … produces … effective learning environment Eliminate voucher programs Satisfy special education needs Create national guidelines for classroom teacher/student ratios Silo 3: ENHANCE MEASUREMENT TESTING Better measurement criteria of learning … age/grade appropriate testing Add national curriculum standards Improve curriculum through better measuring criteria Create better measures and tests for English as a second language Measure curriculum needs for individual grade levels Add yearly assessments for curriculum modification Silo 4: BENEFITS OF A QUALITY EDUCATION Teach children to think creatively and critically … reduce rote learning Better educated citizens produce better future leaders for country Better educated citizens elect better leaders Pool together our resources NOW... reduces cost later Quality supervised schools ... maximize parents’ involvement in schools Encourage music and arts in education curriculums Silo 5: ETHICS AND VALUES THROUGH EDUCATION Strengthen tolerance for racial, cultural differences Strengthen education curriculum for children with special needs Quality education ... strengthen community values Better integrate children with special needs Democracy-centered education helps the country Education … increase awareness of global issues Silo 6: QUALITY OF LEARNING Redesign national public school curriculum to improve learning Provide after school programs for children who need/want extra help 116

Healthy breakfast and lunch options … remove fatty, and sugary food Reduce teacher burnout … circulate teachers within their school districts Ensure quality teachers with higher mandated education requirements Consolidate information, data, and programs ... standardize primary school reading, writing, etc. Running the RDE study for education We begin with the orientation page, shown in Figure 5.1. This page tells the respondent what the study is about, motivates the respondent to participate by emphasizing the importance of knowledge, and then tells the respondent what is expected. Figure 5.1. Orientation page for the education system study.

It’s important to customize the language of the orientation page so that it is appropriate for the particular RDE interview. Recall, for a moment, the orientation page for health care. We couched that page in terms of success of the health care program for the simple reason that health care is undergoing dramatic change and is perceived by many to simply not work. Education, however, does work. It’s not a question of believing that education will work. Rather, it’s a question of regaining greatness. Regaining is the key word. Today’s education is perceived to be not quite in the same league as that provided to previous generations. Every respondent evaluated unique sets of 48 combinations of elements, each set comprising 3-4 elements, with a maximum of one element appearing from each silo. The experimental design ensures that each element appears three times 117

against different backgrounds. Figure 5.2 shows an example of one of these test vignettes. Figure 5.2. Example of a test vignette for the education study. The upper part of the screen shows the specific test combination. The lower part of the screen shows one of the two scales, Interest. Each screen had two associated questions; the first dealing with greatness, the second dealing with additional tax willing to pay if the education policy included the elements on the screen.

When someone likes the education policy will that person pay more tax? As we already saw in the previous chapters when the respondent evaluates 48 different vignettes the respondent rates some higher on a scale, and some others lower. You will see this range of responses occurring for the different 118

rating scales for our study on education deal with belief in success (the first rating attribute), and then amount of tax willing to pay (the second rating attribute). Across the 48 vignettes each respondent will generate a unique average for the first rating attribute and another unique average for the second rating attributes. With these 48 vignettes most people end up answering fairly honestly, simply because they cannot game the system. There’s just too much happening. Now let’s look at the average ratings and see what we can learn. The higher the average rating for belief, the more the respondent believes in the future success of the educational program. The higher the average for tax, the more tax the person says he or she is willing to pay. The 48 unique vignettes evaluated by each person ensure that we are dealing with a solid base of data underlying the average rating for each attribute for each person. Plotting the data allows us to see what’s really going on. Do the individuals generate a straight line so that a person who is more interested in the education policy is likely to pay more money? The answer is a qualified yes, at least from the data in Figure 5.3 (left panel). We see somewhat of an upward sweep. From that upward sweep and from the empty top-left corner we take away the following: 

When a person has low confidence that the educational program will be successful, it’s very unlikely that the person will want to pay a lot of additional tax. No one wants to pay taxes for something that just doesn’t seem to work.



When a person has medium to high confidence that the program will be successful, it’s a toss-up whether the person wants to pay additional taxes or not. It’s just as likely that the person will choose to pay low taxes as pay high taxes.



This far-less-than-perfect correlation between belief in an education program and taxes willing to pay differs from what happens when we deal with payment for services that a person chooses, rather than with taxes that are imposed. Other studies of this type, dealing with goods and services of a discretionary nature such as pet care, suggest that people will more readily pay for services that they choose. People will not readily pay for public programs that they find important. More simply, the economics of taxes is not the economics of payment for goods and services, even when one likes the policy for which the tax is being levied.

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Figure 5.3. How amount of tax willing to pay co-varies with degree of interest in the education program. Each point is the average for a single respondent, computed from ratings of interest and ratings of additional tax for each of 48 vignettes. The left panel shows the data plotted in linear coordinates. The right panel shows the ordinate plotted in logarithmic coordinates, emphasizing the separation of averages near 0 from the rest of the data. RELATION BETWEEN GREATER INTEREST AND GREATER TAXES

LOGARITHMIC PLOT FOR TAXES

What’s a single unit of liking really worth for education? Each of our respondents evaluated 48 different vignettes, rating each on both interest and on willingness to pay additional taxes. How do these two ratings relate to each other for each person? Again, we expect that as interest increases, we will see an increase in willingness to pay taxes. We could run simple correlations to show the strength of a linear relation. However, as we saw in the previous studies, a better approach estimates the nature of the linear relation between taxes as the dependent variable and interest as the independent variable. We can learn a lot from this approach, which is called R-R or response-response analysis. When the slope of the equation is high, a 1unit change in interest corresponds to a large change in taxes paid. When the slope of the equation is low, a 1-unit change in interest corresponds to a small change in taxes paid. With that in mind, let’s look at the slope of the equation relating taxes to interest: Tax to be paid = k0 + k1(Interest) 120

Our equation says that, in theory, the amount a person would pay in additional taxes should be proportional to the rating on the 9-point interest scale. Each person’s data may be described by a slope, k1, which shows the number of tax dollars per 1-point change on the scale. It’s clear from Figure 5.4 that people differ from each other. The same 1unit increase in interest can generate increase of tax payment from as little as $0 (and occasionally a few negatives) to as much as $250. Figure 5.4. How tax to be paid co-varies with 1-unit increases in interest in the education program. Each point is the slope of a single respondent for the linear equation, k1, relating tax to rated interest on the 9-point scale.

What is a 1-unit interest in education policy worth in tax dollars? Instead of looking at the total sample only, we created the tax versus interest model for different groups. In a sense we are looking at different average slopes, depending upon the subgroup (see Table 5.2). Table 5.2. Parameters of the equation relating taxes willing to pay versus interest in the education program (Taxes = k0 + k1(Interest)). The groups are ranked from high to low in order of the dollar value of a 1-unit of interest in education. Goodness Group k1 k0 of Fit (R2) 3 Children 97 -256 0.57 Raise taxes for education – YES 82 -179 0.45 4+ Children 78 -112 0.56 High School 76 -132 0.54 2 Children 72 -154 0.58 Strong Democrat 69 -132 0.42 1 Child 68 -127 0.48 Finished College 66 -129 0.51 121

Black Male Post Grad Hispanic Total Panel Prefer Public School Prefer Private School White No Children Female Some College Raise taxes for education – NO Strong Republican

63 62 61 59 58 58 58 51 46 45 45 32 18

-95 74 -69 -101 -95 -108 -71 -79 -52 -51 -61 -42 -22

0.45 0.51 0.4 0.44 0.45 0.46 0.44 0.44 0.38 0.39 0.39 0.36 0.45



The Total Panel shows a slope of 58.



Gender: Men show a higher slope than do women (62 versus 45).

 Ethnicity: Blacks show the highest slope (63), and whites show the lowest (51). 

Education: Those respondents whose highest academic degree was high school graduate show, by far, the greatest interest in supporting education. Their slope, 76, suggests that education is very important to these individuals. In contrast, those who finished only part of college, and thus dropped out for one or another reason, show the lowest slope, 45. The message here is that education policy much more strongly resonates among those who stopped before college, and resonates least among those who behaviorally left the educational path as they were on it, for one or another reason.



Having children makes a real difference: Those with no children are the least interested in supporting education with their tax money (46). Even when they find a program that interests them, they aren’t willing to support it with more taxes. In contrast, those with three children are dramatically interested in supporting education with their taxes (97).

Granularity: Which educational ideas win and which ones can increase taxes? Let’s move now to a more granular analysis of what happened. We begin with the total panel, and data shown in Table 5.3. 122

We start with the additive constant, interest and tax dollars in the absence of any elements. This is our baseline or fundamental level. The additive constant for interest is 44, meaning that even without messaging, 44% of our respondents would say that they are interested in helping American education regain its greatness. The additive constant for tax is $235. For comparison purposes, the additive constant for health care (Chapter 4) is $184. We interpret this to mean that respondents are a lot more willing to pay additional taxes for education than for health care. We sorted the elements in Table 5.3 by the value of interest. The elements themselves do not perform particularly well. That is, our interest in education is certainly there, based on the additive constant, but the elements contribute relatively little more. Education is something we want, but we don’t differentiate beyond basic education. The real story is on the tax side. It’s in taxes that the emotions emerge. For example, two items can generate the same interest, but one will be acceptable for increased taxes, and one will be just the opposite: 

Provide after school programs for children who need/want extra help (Interest: 4 versus Tax +$16)



Improve quality of education NOW... reduce future cost to tax-payers

(Interest: 4 versus Tax -$ 2)

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Table 5.3. Which elements in educational policy are perceived to interest citizens in helping America to regain its education greatness, and what additional taxes are people willing to pay. Interest Tax Additive Constant 44 235 Teach children to think creatively and critically … reduce 5 21 rote learning Provide after school programs for children who need/want 4 16 extra help Improve quality of education NOW... reduce future cost to 4 -2 tax-payers Invest in our children's future ... lobby for college enrollment 4 9 tax deductions Quality supervised schools ... maximize parents’ involvement 3 9 in schools Improve conditions of our schools … structurally safe 3 10 environments Healthy breakfast and lunch options … remove fatty, and 3 11 sugary food Ensure quality teachers with higher mandated education 3 3 requirements Quality education ... strengthen community values 3 2 Redesign national public school curriculum to improve 3 0 learning Strengthen tolerance for racial, cultural differences 3 3 Reduce overcrowding in schools … produces … effective 2 8 learning environment Better educated citizens produce better future leaders for 2 9 country Encourage music and arts in education curriculums 2 9 Pool together our resources NOW... reduces cost later 2 -3 Strengthen education curriculum for children with special 2 -4 needs Consolidate information, data and programs ... standardize 2 0 primary school reading, writing, etc. Universal guidelines and universal learning tools ... for students 1 -2 ... pre-K to grade 4 ... shared across country Better measurement criteria of learning … age/grade 1 -6 appropriate testing Better integrate children with special needs 1 10 Higher salaries … for highly professional teachers 1 13 Education … increase awareness of global issues 0 1 Satisfy special education needs 0 8 124

Best-fit environments ... maximizes student learning Improve curriculum through better measuring criteria Create national guidelines for classroom teacher/student ratios Better educated citizens elect better leaders Measure curriculum needs for individual grade levels Add national curriculum standards Reduce teacher burnout … circulate teachers within their school districts Create better measures and tests for English as a second language Add yearly assessments for curriculum modification Democracy-centered education helps the country Eliminate voucher programs Increased taxes ... provide funding for more effective teaching Increase taxes …increase funding for our education system

0 0

0 -1

0

1

0 -1 -1

-2 -11 -7

-1

-16

-1

-19

-2 -2 -5 -7 -8

-10 -3 -26 -22 -19

The two mind-set segments We saw dramatic segmentation when we dealt with terrorism (Chapter 2); some individuals were afraid of the actual situation, whereas others were far more afraid of intervention by supposed helpful agencies such as the government. Let’s search for segments in education in the same way. We will cluster the 253 respondents into groups based on their Persuasion Model. Recall that the Persuasion Model relates the actual 9-point ratings to the presence/absence of the 36 elements. We compute the 36 coefficients, one per element, per respondent. Then we cluster the respondents into groups or segments, such that the pattern of 36 coefficients or utilities is similar for the different people who fall into a segment, but show large differences compared to others. We identified two segments that perfectly fit that approach. When we segment on personal goods and services, we often see very large differences between segments in the impact values for the same element. The story is different for education, as it tends to be for most public policy topics that we investigate. Looking at the results from the segmentation, we see once again the segmentation is a matter of emphasis (see Table 5.4). The winning elements for each segment show fairly modest values for their utilities. 

Segment 1 focuses on education and responds to statements about better 125

learning for the student. These are learning-focused individuals. They are also willing to pay extra taxes for the better learning. They are not interested in paying higher taxes for generalities.  Segment 2 focuses on general process and wants to get everyone involved. They are, colloquially, cheerleaders for the process, rather than for the specifics. And, they are not willing to pay extra taxes for what they say interests them. Table 5.4. Interest and taxes corresponding to the winning and losing elements from the two mind-set segments for education policy. Interest Tax Key Elements Segment 1 – Focus on the education (constant) 42 234 Teach children to think creatively and critically … reduce rote 5 25 learning Quality supervised schools ... maximize parents’ involvement in 4 15 schools Improve conditions of our schools … structurally safe 4 12 environments Increased taxes ... provide funding for more effective teaching -9 -33 Increase taxes …increase funding for our education system -12 -27 Segment 2 – Focus on the general process (constant) 51 242 Redesign national public school curriculum to improve learning 8 -8 Improve quality of education NOW... reduce future cost to tax7 -1 payers Education … increase awareness of global issues -6 -25 Eliminate voucher programs -6 -31 Measure curriculum needs for individual grade levels -6 -34 Add yearly assessments for curriculum modification -6 -40 Interest in education policy versus willing to pay taxes – correlated or not? Now that we have a procedure to understand basic interest and basic taxes (i.e., by means of the additive constant), we can look at the relation between the two. Our study of health care showed us that basic belief in success was related linearly to the basic amount of tax one would pay, with a few exceptions (see Chapter 4). The strategy was to list the two additive constants, side by side, and compare them. The relation jumped out at us when we plotted the data, always a good strategy when one explores data in the quest for meaningful patterns. Let’s do that for education.

126

We see the list of additive constants in Table 5.5. We have the different groups as well as a letter code. They are needed for the plot in Figure 5.5 when we plot the data. As stated several times before, it looks like interest, or belief in success, is not the operative, discriminating, and newsworthy rating for these studies of public policy. Interest may be important for the private sector, but the clearest index of citizen involvement and sentiment looks like taxes willing to pay, based upon instructing the respondent to select the taxes. Those respondents who have many children (4+) are willing to pay far more base taxes for education ($526) than respondents who have no children ($198), and so forth. Thus, even within a study of feelings toward different aspects of education policy, experimental design of ideas generates a new, powerful index of which ideas grab the citizen and which do not. Table 5.5. How basic tax willing to pay co-varies with belief in the success of the education initiative. Each letter corresponds to a subgroup of respondents. Basic Basic Group Code Interest Tax 4+ Children 43 526 O Raise taxes for education – YES 64 378 R 3 Children 20 299 N Education – High School 41 288 G Education – Graduate or Professional School 39 281 J Males 45 280 B Hispanic 48 274 E Black 50 262 F 1 Child 54 255 L Segment 2 – Focus on social process 51 242 Q Total Panel 44 235 A Prefer Public Schools 47 234 U Segment 1 – Focus on the education 42 234 P Education – Some College 45 217 H 2 Children 36 214 M Education – Finished College 47 213 I No Children 45 198 K Female 42 190 C White 33 177 D Prefer Private Schools 34 152 V Raise taxes for education – NO 33 133 S Now let’s plot the additive constant for tax (basic tax willing to pay without elements) versus the additive constant for interest (basic interested in 127

education without elements). Just to redefine the abscissa: the basic interest in the education program in terms of ability to restore American education. We see the plot in Figure 5.5. Each key subgroup appears as a separate letter which can be decoded through Table 5.5. Figure 5.5. How basic tax willing to pay co-varies with basic interest in the education program.

Our first observation should be that, again, the additive constants generate a more or less positively correlated pattern. The pattern in Figure 5.5 isn’t a nice straight line, as we saw for health care, but the truth of the matter is that, with a few exceptions, the relation could be taken as being linear. The two exceptions are subgroup O (respondents having 4+ Children), and subgroup N (respondents having 3 Children). Subgroup O (with 4+ Children) is willing to pay a lot more taxes ($526) than might be surmised on the basis of their interest in the program as it restores American education. Either they should be willing to pay about $250 to $300 in taxes, or their basic interest in the program should be above 90%. Subgroup N (with 3 Children) shows just the opposite pattern. The respondents are willing to pay $300 in taxes, but they have the lowest level of interest in the program (constant = 20). They should be willing to pay either a lot less, or have much greater confidence in the program. The fact that both outliers are respondents with a relatively large number of younger children gives us pause. We don’t know what’s happening, but it appears that with many children, a person’s view of the education system is quite different than for respondents with fewer children. What’s working in the different subgroups in terms of interest? 128

Let’s finish our analysis by looking at each subgroup in terms of the three highest performing elements for the first question: belief that the element will help American education regain its greatness. Do these different silos share the same ideas, or are there big differences? For each subgroup, we will look at the three elements that score highest, isolate them, and show their utilities. We see these winning elements in Table 5.6. To make things easier, we list the strongest elements at the top of Table 5.6. The results are quite clear. There are just four messages that resonate among the groups. These four messages come from different types of ideas thinking, governing, empowering, and helping: 

Thinking: Teach children to think creatively and critically … reduce rote learning



Governing: Improve quality of education NOW ... reduce future cost to tax-payers



Empowering: Invest in our children's future ... lobby for college enrollment tax deductions



Helping: Provide after school programs for children who need/want extra help

Table 5.6. The elements about education policy that continue to reappear as the best performers for the different subgroups (top of the table) and the three best performing elements for each key subgroup (bottom of the table). Performance is based on the utility or impact value of the model for interest in the test vignette (i.e., the first rating question). Summary – In how many subgroups does the particular element appear as one of the three highest scoring elements (i.e., most interesting to respondents in that subgroup)? Element Teach children to think creatively and critically … reduce rote learning Improve quality of education NOW... reduce future cost to taxpayers Invest in our children's future ... lobby for college enrollment tax deductions Provide after school programs for children who need/want extra help Which three elements score most highly in each key

Frequency 10 10 9 8 Utilities 129

subgroup? Total Panel Teach children to think creatively and critically … reduce rote learning Provide after school programs for children who need/want extra help Improve quality of education NOW... reduce future cost to taxpayers Gender: Male Invest in our children's future ... lobby for college enrollment tax deductions Improve quality of education NOW... reduce future cost to taxpayers Healthy breakfast and lunch options … remove fatty, and sugary food Gender: Female Provide after school programs for children who need/want extra help Redesign national public school curriculum to improve learning Teach children to think creatively and critically … reduce rote learning Ethnicity: White Provide after school programs for children who need/want extra help Quality supervised schools ... maximize parent's involvement in schools Teach children to think creatively and critically … reduce rote learning Ethnicity: Hispanic Pool together our resources NOW ... reduces cost later Ensure quality teachers with higher mandated education requirements Improve conditions of our schools … structurally safe environments Ethnicity: Black Improve quality of education NOW ... reduce future cost to taxpayers Healthy breakfast and lunch options … remove fatty, and sugary food Higher salaries … for highly professional teachers Subgroup G – Finished High School Pool together our resources NOW... reduces cost later

Interest 5 4 4 5 5 5 5 4 4 7 7 7 5 5 4 5 5 4 9 130

Quality supervised schools ... maximize parents’ involvement in schools Strengthen tolerance for racial, cultural differences Subgroup H – Some College Improve quality of education NOW... reduce future cost to taxpayers Provide after school programs for children who need/want extra help Improve conditions of our schools … structurally safe environments Subgroup I – Finished College Teach children to think creatively and critically … reduce rote learning Quality education ... strengthen community values Invest in our children's future ... lobby for college enrollment tax deductions Subgroup J – Graduate or Professional School Teach children to think creatively and critically … reduce rote learning Provide after school programs for children who need/want extra help Quality supervised schools ... maximize parents’ involvement in schools Subgroup K – No Children Teach children to think creatively and critically … reduce rote learning Invest in our children's future ... lobby for college enrollment tax deductions Encourage music and arts in education curriculums Subgroup L –1 Child Improve quality of education NOW... reduce future cost to taxpayers Strengthen tolerance for racial, cultural differences Teach children to think creatively and critically … reduce rote learning Subgroup M – 2 Children Strengthen education curriculum for children with special needs Healthy breakfast and lunch options … remove fatty, and sugary food Quality education ... strengthen community values

8 6 5 5 4 5 4 4 11 9 9 4 3 3 7 7 7

8 7 6 131

Subgroup N – 3 Children Ensure quality teachers with higher mandated education requirements Provide after school programs for children who need/want extra help Improve quality of education NOW... reduce future cost to taxpayers Subgroup O – 4 or more Children Invest in our children's future ... lobby for college enrollment tax deductions Create better measures and tests for English as a second language Consolidate information, data and programs... standardize primary school reading, writing, etc. Mind-Set Segment 1 – Focus on the education Teach children to think creatively and critically … reduce rote learning Quality supervised schools ... maximize parent's involvement in schools Improve conditions of our schools … structurally safe environments Mind-Set Segment 2 – Focus on the general process Redesign national public school curriculum to improve learning Improve quality of education NOW... reduce future cost to taxpayers Invest in our children's future ... lobby for college enrollment tax deductions Self Profiling - Raise taxes for education: Yes Teach children to think creatively and critically … reduce rote learning Better educated citizens produces better future leaders for country Consolidate information, data and programs ... standardize primary school reading, writing, etc. Self Profiling - Raise taxes for education: No Improve quality of education NOW... reduce future cost to taxpayers Invest in our children's future ... lobby for college enrollment tax deductions Quality supervised schools ... maximize parent's involvement in schools Self Profiling - Send children to public school Teach children to think creatively and critically … reduce rote

23 22 19 10 9 9 5 4 4 8 7 7 7 5 4 5 5 5 6 132

learning Improve quality of education NOW... reduce future cost to taxpayers Invest in our children's future ... lobby for college enrollment tax deductions Self Profiling - Prefer private school Invest in our children's future ... lobby for college enrollment tax deductions Better measurement criteria of learning … age/grade appropriate testing Better integrate children with special needs Self Profiling Strong Republican Invest in our children's future ... lobby for college enrollment tax deductions Improve quality of education NOW... reduce future cost to taxpayers Provide after school programs for children who need/want extra help Self Profiling Strong Democrat Provide after school programs for children who need/want extra help Strengthen tolerance for racial, cultural differences Healthy breakfast and lunch options … remove fatty, and sugary food

5 5 10 8 7 7 4 3 8 6 6

Summary It’s clear that education is important to people. The basic interest in education programs is high, as are the taxes that people are willing to pay. Everyone believes in education; there’s nothing to dispute. What’s important, however, is what education is all about. Is it about helping the student learn and think better? Is it about preparing the student to be a better citizen? Or is it about helping all people to become equal by preparing them when they are young? Our data suggests all three views are shared, albeit among different people. That is, the respondents view education as a relatively undifferentiated blob. They don’t really fall into dramatically polarized groups. References:

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[1] Glaeser, E. L., Ponzetto, G. A. M., & Shleifer, A. (2007). Why does democracy need education? Journal of Economic Growth, 12(2), 77-99. [2] Huntington, S. P. (2006). Political Order in Changing Societies. New Haven, CT: Yale University Press [3] Ester, P., Muffels, R., Schippers, J., & Wilthagen, T. (Eds.). (2008). Innovating European Labour Markets: Dynamics and Perspectives.Northampton, MA: Edward Elgar. [4] Hechinger, J. (2010, Dec 7). U.S. Teens Lag as China Soars on International Test. Bloomberg. Retrieved January 10, 2011 from http://www.bloomberg.com/news/2010-12-07/teens-in-u-s-rank-25th-on-math-testtrail-in-science-reading.html [5] PISA (2006). Top of the Class. High Performers in Science in PISA 2006. Washington, D. C.: Organisation for Economic Co-operation and Development Publishing. [6] Cookson, P. W., Sadovnik, A. R., & Semel, S. F. (Eds.). (1992). International Handbook of Educational Reform. New York, NY: Greenwood Press. [7] Berends, M., Bodilly, S. J., & Kirby, S. N. (2002). Facing the Challenges of Whole-School Reform: New American Schools after a Decade. Santa Monica, CA: Rand. [8] STEM Education Coalition. (2011) STEM Education Coalition Core Policy Principles. Retrieved October 25, 2013 from http://www.stemedcoalition.org/wp-content/uploads/2013/10/Letter-DearColleague-Recruit-Letter-FINAL-V2.pdf [9] Dillon, S. (2010, May 13). Obama Calls for Major Change in Education Law. The New York Times. Retrieved May 16, 2010 from http://www.nytimes.com/2010/03/14/education/14child.html

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Chapter 6 Energy – you just can’t live without it Abstract: In this chapter we use RDE (Rule Developing Experimentation) to better understand what features of a new energy policy for America drive interest, as well as the amount of tax that people would be willing to pay for those features. From the data, at the level of both the total panel of all respondents and key subgroups, we see that although there are concerns about energy, there is no sense of fright nor an urgent sense that the crisis is perceived to be immediate. Although RDE typically reveals ideas that perform well, the energy study did not reveal any breakthrough ideas. We did not discover a consumer-relevant proposal for energy that could be said to break the bank. With the elements showing only medium impact, we conclude that these elements just don’t excite the respondents. Winning elements include features such as padding bank account … saving money and improving your health and your overall quality of life. RDE suggests that a successful new energy policy appeals to the nobility of the respondent and adds a little financial plug at the end. The key message from this chapter on new energy, as well as from the other three chapters on good nutrition policy, health care and education reform, is that when the topic is positioned as a government policy, people react by emotionally distancing themselves. It looks like the government policies effectively squelch a lot of the internal emotional reaction, at least as far as we can tell from the utility or impact values of the elements. When people have to respond to complex messaging about public policy, they don’t react from the gut, with their innermost feelings. Government is, in a phrase, Big Daddy, whether or not that feeling is articulated. Introduction We need energy, the vital lifeblood of our very existence. Most energy is produced by burning fossil fuels (coal, oil, and natural gas), which can be extracted more easily and cheaply than most other types of energy. Overusing this nonrenewable source of energy leads to its exhaustion and to a significant increase in its cost, as we can see from angry customers at gasoline pumps [1]. For decades, U.S. political leaders warned that Americans needed to use energy more efficiently while also seeking independence from unstable foreign energy suppliers by deriving more of it from domestic sources. Yet, despite the more than three decades of such efforts, the United States has not achieved the goal of energy independence. The opposite is the case. Instead, America has grown to become the world's largest oil consumer by a considerable margin, while its domestic oil production has rapidly diminished. In 2012, oil imports have filled 135

the expanding gap and accounted for 45 percent of total U.S. oil consumption; up from 22 percent in 1970 [2, 3]. America's dependence on oil from insecure or politically unstable countries has required extensive diplomatic and military efforts which incur huge costs to be borne by energy users and taxpayers. Besides the sure exhaustion of fossil fuels, there are other important reasons why we have to reduce burning them. Burning fossil fuels affects the environment, leading to air pollution [4], global warming [5, 6, 7, 8], severe climate changes [9] as well as health problems such as asthma [10] and cancer [11]. The United States also continues to see an increasing demand for electricity. It consumed about 60 percent more electricity in 2012 than in 1970 [www.eia.gov]. This demand for electricity will continue to grow. The Energy Information Administration forecasted in 2005 that electricity use will increase at a rate of 1.9 percent annually through 2025. This rate requires a doubling of electricity production in about 37 years [12]. Aggravating the concern about increasing demand for electricity is the possibility that today's nuclear power plants could be retired over the next 50 years as current licenses expire. This ongoing obsolescence of nuclear power plants will inevitably deprive the nation of one of its key non-carbon energy sources. At the same time, public opposition related to security problems and waste disposal makes the use of nuclear technology a somewhat tenuous, problem-plagued proposition [13, 14]. So what about other non-carbon energy sources? Clean coal technologies remain years away from commercial viability. Despite some impressive federal and state efforts to promote them, renewable sources of energy such as biomass, geothermal, wind, and solar accounted for only 13% of the domestically produced electricity used in the United States in the first ten months of 2012 [15]. In order to push the use of renewable energy, President Obama put forward a New Energy For America plan. It calls for a federal investment of $150 billion over the next decade to catalyze private efforts to build a clean energy future. Specifically, the plan calls for renewable energy to supply 10% of the nation's electricity by 2012, rising to 25% by 2025 [16]. While some may be wary of the costs and effectiveness of the age of alternative energy, we should not let such cautions halt the beginnings of the energy revolution. Countries like Germany and Denmark, which have invested significantly in alternative energy sources, are now beginning to experience the benefits that alternative energy can provide for its users [1]. When the alternate energy projects that are still in the making reach their fruition, we may find 136

ourselves in a world with less pollution-related illness, cheaper and safer energy, and the potential for a brighter future. In order for new technologies, fuels, and processes to have an effect on the energy intensity of the U.S. economy, businesses, consumers, and governments must make a conscious choice to use them. Let us now explore how people think about the government’s energy policy. The energy study Our energy study deals with interest in aspects of public policy coupled with willingness to pay incremental tax. As such, we are dealing, for the fourth time now, with a combination of emotion (Interest in a new energy policy for America) and economics (Willingness to pay taxes). The specific orientation for this study appears in Figure 6.1. Figure 6.1. Orientation page for the energy study explaining it in general and explicating the two rating questions.

The study elements appear in Table 6.1. They relate to today’s problems and future opportunities rather than consumption of energy. The elements focus on green and sustainability. Table 6.1. The six silos and 36 elements for the energy study. Silo 1: Problems with the energy system A1 Rising energy prices means higher cost of oil Inadequate supply of energy (i.e., shortage of oil supplies) ... leads to A2 electric grid overloads A3 Unmaintained, aging power plants ... unable to handle peak energy loads A4 High energy costs causes businesses to relocate to other regions A5 Increasing NYS and National average energy prices A6 Global warming ... due to increased environmental impacts 137

B1 B2 B3 B4 B5 B6 C1 C2 C3 C4 C5 C6 D1 D2 D3 D4 D5 D6 E1 E2 E3 E4 E5 E6 F1 F2 F3 F4

Silo 2: Improve ecological impact Increase the use of renewable energy (i.e., wind, solar, and other alternatives) Manage America's renewable energy assets ... secure future resources Energy efficient methods reduce the loss of America's resources Reduce reliance on imported energy resources Increase America's ecological resources Less energy consumed = Less pollutants and greenhouse gas production = Less ozone damage Silo 3: Change Change unsustainable status-quo Change environmental threats ... such as coal, nuclear plants Improved air quality ... reduce harmful emissions There are increasing non-renewable energy consumption patterns Global impact ... increased environmental damage Fossil fuels ... have a negative contribution to climate change Silo 4: Going Green State ‘green incentive’ programs Green utility cash rebates for high-energy equipment replacement Efficiency improvements for homes by local utility provider incentive programs Transitional renewable energy service plans Measured cost savings transition service plans Free solutions for energy efficiency ... buildings and businesses eligible for incentives to be gas and energy efficient Silo 5: Tax increase accompanying energy policy Increase taxes by 10% for renewable energy development services Increase taxes ... provide GREEN ENERGY programs (i.e., wind, solar, and other types of alternatives) Increase taxes by 20% ... accelerate renewable energy transition services Year-end renewable energy development tax bill … fund new service programs Quarterly renewable energy development tax bill … fund new energy policy programs Separate ‘going green’ carbon fuel tax increase Silo 6: It’s everyone’s responsibility Every thing we do every day has an impact on the planet You have the power to control most of your choices ... impact what you create You can have a global impact We all have a large personal stake in the health and vitality of places far and near 138

Embracing a greener lifestyle means improving your health and your overall quality of life Embracing a greener lifestyle means padding your bank account ... saving money

F5 F6

Based on an experimental design, we combined the elements into easy-toread combinations for the test vignettes (see Figure 6.2). Our approach ensures that: 

The elements are statistically independent of each other, meaning that the ratings can be analyzed by regression analysis to estimate the impact of each of the 36 elements. They act as independent agents even though the respondent evaluates combinations.



The concept is incomplete. That is, there are six silos, each with six elements. A purist might demand that each concept or test vignette comprise exactly one element from each silo. Anything else would make the concept incomplete and meaningless to the respondent. The truth of the matter is respondents have no problem in daily life, or in these types of studies, responding to incomplete vignettes which are comprised of only part of the full set of information. People, in general, are wired to acquire information and react to that information. We make decisions on partial information all the time, as with these test combinations. Unless there is contradictory information in a vignette which confuses the respondent, we find no problem reported by respondents about their ability to respond to the concepts.



The concepts are short and easy to read. We get a sense of this simplicity from Figure 6.2 which shows one of the test vignettes.



The respondent rates each test vignette twice. The first question deals with interest, the second question deals with tax. During this process the vignette remains on the screen; only the rating question changes.

139

Figure 6.2. Example of a test vignette showing the combination of elements and the rating scale for interest at the top and incremental tax at the bottom.

If they like the energy policy, then will they pay? The answer is definitely maybe! We found the same, and clearly now, recurring pattern for energy just as we did for health care and education. On average, it looks like respondents will pay more if they like the ideas, but a great number of respondents won’t pay much more at all. Here’s how we arrived at the answer. It’s the same strategy that we used before. We average the 48 different 9-point ratings assigned by each respondent to get one average rating of interest in the energy policy on the 9-point scale. In turn, we average the amount of taxes people are willing to pay for those 48 combinations. The result is one average point for each of our respondents. We plot them in linear coordinates in the left part of Figure 6.3. However, there is the recurrent phenomenon: there are a lot of zeros. Quite a number of respondents may like what they read, but they say that nevertheless they will not pay for the new energy policy. We see that on the right hand plot, where we plot the logarithm of the taxes rather than the taxes themselves. The logarithmic plot for the ordinate (taxes) has the convenient 140

property of spreading the taxes apart, with a lot of room on the scale devoted to the low end (i.e., taxes near $1.00 to $10.00) versus taxes near $200 to $210. The plot in Fig 6.3 shows that our supposed relation between greater interest and greater taxes, at least for the total panel, is a perceptual trick. There is a slight, not dramatic, pattern suggesting one will pay more for programs in which one believes, but the reality is that the pattern is just a slight trend. Take away all of the individuals at the bottom, who don’t want to pay more taxes, put the tax on logarithmic coordinates, and we see more clearly that we are dealing with a modest slope, and possibly even an artifact. Figure 6.3. How amount of tax willing to pay co-varies with degree of interest in the energy program. Each point is the average for a single respondent, computed from ratings of 48 vignettes, on interest and additional tax, respectively. RELATION BETWEEN GREATER INTEREST AND GREATER TAXES

LOGARITHMIC PLOT FOR TAXES

What’s a 1-unit increase in interest worth by key subgroups? As we did before for the other topics, let’s look at the linear relation between tax dollars as the dependent variable and interest in the energy program as the independent variable. We will do this for the total sample as well as subgroups. In this study we also uncovered three mind segments for energy which we will discuss in detail below. For now, the names of the segments should be enough explanation of their focus:   

Segment 1: Energy Sources Segment 2: Green Segment 3: Doom and Gloom.

Let’s now look at the simple linear equation relating tax willing to pay to interest rated by the respondent. Our starting data will be the average from each 141

respondent of his 9-point rating and the average from the same respondent of his incremental taxes. These averages will be computed for each person based upon the 48 vignettes that he evaluated in the RDE portion of the interview. To repeat, for our analysis the independent variable is the average rating of interest in the energy policy on the 9-point scale. The dependent variable is the average tax willing to pay. We write the equation as: Tax Willing to Pay = k0 + k1(Interest in energy policy, 9-point scale) The key parameter here is the slope, which tells us the number of dollars corresponding to each unit of rated interest in the energy policy. Higher slopes mean that the individuals in the subgroup will pay more tax for a unit increase in interest. Our results appear in Table 6.2. We should not be surprised by the order of the subgroups in terms of how much a unit of interest is worth in tax. Segment 3: Doom and Gloom shows a relatively enormous slope, 50, meaning that for each unit of interest, this segment is willing to pay an additional $50. On the other hand, there are the different groups who don’t want to pay more than $15 per point of interest:    

Age 70 Age 52 Market: East South Central States Strong Republican

We conclude from this first analysis that the range of taxes people are willing to pay is large. It is not, however, as large as what we saw in education (Chapter 5), perhaps because with education, the tax is perceived to be for a personal benefit, educating one’s children (or at least one’s relatives). Table 6.2. Parameters of the equation relating taxes willing to pay versus interest in the energy policy (Taxes = k0 + k1(Interest). Goodness Slope Constant Group of Fit (k1) (k0) R Segment 3 – Focus on ‘Doom and Gloom’ 50 -39 0.38 Raise taxes for the economy: Yes 44 -64 0.40 Raise taxes for new forms of energy: Yes 42 -78 0.35 Education: Completed High School 38 -57 0.41 Market: New England 36 -98 0.29 Age: Top Age 64 34 -48 0.37 Party: Strong Democrat 33 -51 0.34 Gender: Male 32 -35 0.36 Education: Completed Graduate/Post 32 -45 0.34 142

Graduate Age: To Age 38 Ethnic: White Market: Pacific States Market: East North Central States Market: Mountain States Ethnic: Black Education: Completed College Total Panel Age: Top Age 29 Segment 1 – Focus on Energy Sources Market: Southern Atlantic States Ethnic: Hispanic Segment 2 – Focus on ‘Green’ Market: West North Central States Age: Top Age 44 Market: Middle Atlantic States Gender: Female Education: Some College Raise taxes for the economy: No Age: Top Age 70 Age: Top Age 52 Market: East South Central States Raise taxes for new forms of energy: No Party: Strong Republican

30 29 26 25 25 24 24 23 23 23 22 21 20 19 18 18 16 15 15 14 13 13 9 7

-30 -20 -37 -28 -31 -33 -25 -19 -3 -30 -7 -21 -13 -22 -9 18 -11 10 -9 -7 19 -16 2 -1

0.30 0.34 0.33 0.34 0.48 0.29 0.30 0.29 0.29 0.27 0.24 0.27 0.29 0.42 0.27 0.22 0.24 0.22 0.25 0.22 0.20 0.27 0.20 0.23

Which elements do well and which do poorly Let’s move to the heart of the results, looking once again at the performance of the different elements (see Table 6.3). These performance numbers come from the Interest Model. Recall that the Interest Model relates the presence/absence of the 36 elements to a binary response: 0, i.e., not interested in the energy policy (original ratings = 1-6), or 100, i.e., interested (original ratings = 7-9). Let’s examine Table 6.3 for a closer look at the individual elements.

143

taxes.

F1 F6 B1 F5 D6 A6 B2 B3 B5 B6 D2 D3 B4 C1 D1 F2 F4 A1 A4 C2 C6 F3

Table 6.3. How the 36 elements perform in terms of interest and

Additive Constant Everything we do every day has an impact on the planet Embracing a greener lifestyle means padding your bank account ... saving money Increase the use of renewable energy (wind, solar, and alternatives) Embracing a greener lifestyle means improving your health and your overall quality of life Free solutions for energy efficiency ... buildings and businesses eligible for incentives to be gas and energy efficient Global warming ... due to increased environmental impacts Manage America's renewable energy assets...secure future resources Energy efficient methods reduces the loss of America's resources Increase America's ecological resources Less energy consumed = Less pollutants and greenhouse gas production = Less ozone damage Green utility cash rebates for high-energy equipment replacement Efficiency improvements for homes by local utility provider incentive programs Reduce reliance on imported energy resources Change unsustainable status-quo State ‘green incentive’ programs You have the power to control most of your choices ... impact what you create We all have a large personal stake in the health and vitality of places far and near Rising energy prices means higher cost of oil High energy costs cause businesses to relocate to other regions Change environmental threats ... such as coal, nuclear plants Fossil fuels ... have a negative contribution to climate change You can have a global impact

Interest Tax 39 119 6

3

6

5

5

8

5

6

4

4

3

1

3

2

3

1

3

2

3

8

3

-2

3

4

2 2 2

11 -4 -6

2

1

2

-2

1

-2

1

-9

1

-6

1

-8

1

-4 144

A2 C3 C4 D5 A3 C5 D4 A5 E2 E4 E5 E6 E1 E3

Inadequate supply of energy (i.e., shortage of oil supplies) ... leads to electric grid overloads Improved air quality ... reduce harmful emissions There are increasing non-renewable energy consumption patterns Measured cost savings transition service plans Unmaintained, aging power plants...unable to handle peak energy loads Global impact ... increased environmental damage Transitional renewable energy service plans Increasing NYS and National average energy prices Increase taxes ... provide GREEN ENERGY programs (i.e., wind, solar, and other types of alternatives) Year-end renewable energy development tax bill … fund new service programs Quarterly renewable energy development tax bill…fund new energy policy programs Separate ‘going green’ carbon fuel tax increase Increase taxes by 10% for renewable energy development services Increase taxes by 20%...accelerate renewable energy transition services

0

-8

0

-4

0

-12

0

-5

-1

-5

-1 -1 -4

-10 -9 -12

-4

-9

-4

-3

-4

-10

-5

-5

-9

-13

-14

-19

We begin with the additive constant. The additive constant tells us that about two in five people will say that they are basically interested in a new energy program, even without knowing the specifics. Furthermore, the respondents feel that the baseline additional tax should be about $119. For the total panel there are no strong positive messages. Impact values of 5-6 are approaching statistical significance. With the highest element showing an impact value of +6 we are again left to conclude that the elements don’t excite the respondents. It may well be because in this study, as with the three previous ones measuring interest and willingness to pay tax, we are dealing with ideas that are not actually personal. We may think that energy, health care, education, and nutrition policy are personal in the way we write the elements, but they may be impersonal because we’re dealing with government policy, not personal choice. Even though there are no strong positive performers, there are two strongly consistent negative ones. As one might expect, these deal with stated increase in taxes. Increasing taxes by 20% shows a much higher negative effect (impact -14) than increasing the taxes by 10% (impact -9). We can feel comfortable about the quality of the data because they show differences in the 145

correct direction despite the fact that the elements were presented in different combinations and never with each other in the same vignette.



Increase taxes by 10% for renewable energy development services



Increase taxes by 20% ... accelerate renewable energy transition services

-9 -14

The same sense of narrowness occurs for the taxes. Since we’re dealing with tax dollars rather than with percent interested, the numbers are slightly higher and the range is slightly greater. The biggest positive element for taxes (i.e., the element for which people would pay more tax) is the one dealing with selfreliance: 

Reduce reliance on imported energy resources

+11

Different mind-sets With all of today’s focus on green and energy independence, perhaps the reason for the narrow range of impacts and tax dollars may be found in countervailing forces. That is, within the total panel there may be radically different groups of respondents with dramatic highs and lows in the impact values of individual elements. In turn, perhaps the reason we don’t see those highs and lows comes from the fact that, in the total panel, we unknowingly mix these different groups so that their individual patterns cancel each other. Using the methods of cluster analysis, we segmented the respondents into three clearly different groups. The individuals falling into the same cluster show similar patterns in evaluating the elements. We expect to see a large range of impacts within a single segment because, presumably, we have eliminated the countervailing forces, the mutual cancellation. Is this the case? Or do we see a narrow range because we’re dealing with government policy rather than an individual-selected product or service? Our answers come out in Table 6.4, which show the three mind-set segments. The segments themselves are reasonably clear: Table 6.4. Strongest performing elements for the three mind-set segments. Segment – Segment – Interest Incremental 146

S 1: Focus on Energy Sources S 2: Focus on Green Total 1 S 3: Focus on Doom and Gloom Additive constant 39 43 Everything we do every F1 day has an impact on the 6 5 planet Embracing a greener lifestyle means padding F6 6 3 your bank account ... saving money Increase the use of renewable energy (i.e., B1 5 8 wind, solar, and other alternatives) Embracing a greener lifestyle means improving F5 5 4 your health and your overall quality of life Energy efficient methods B3 reduce the loss of 3 6 America's resources Manage America's B2 renewable energy assets ... 3 6 secure future resources Increase America's B5 3 5 ecological resources Global warming ... due to A increased environmental 3 1 6 impacts A Rising energy prices means 1 -4 1 higher cost of oil Unmaintained, aging power A plants ... unable to handle -1 -10 3 peak energy loads

Tax 2

3

Total 1

37 40

2

119 152

3 96 232

7

1

3

-12

8

-5

8

0

5

-9

8

5

4

3

8

5

8

9

5

5

6

3

6

11

2

3

1

-5

0

12

2

1

2

-9

6

-5

2

6

2

-9

0

31

2

9

1

-9

-2

50

2

6

-2

-24

3

3

0

10

-5

-40

-1

49

147



Segment 1: Focus on Energy Sources – constant of 43. This segment is best described by the element Increase the use of renewable energy (i.e., wind, solar, and other alternatives). The impact value is only +8. So, for this first segment, we again experience the narrowness of impact values which now appears to be a hallmark of responses to government policy. Segment 1 is willing to pay a moderate amount of additional tax ($152). However, there are no elements which drive them to pay a lot more tax beyond that basic $152. Segment 1 appears to be interested in new sources of energy, will pay a bit more, but then nothing else.



Segment 2: Focus on Green – constant of 37. Segment 2 is described by the element Everything we do every day has an impact on the planet. The impact value here is only +7. Segment 2 will pay the least taxes, an additional $92 for the base. It’s hard to draw money out of this segment.



Segment 3: Focus on Doom and Gloom – constant of 49. Segment 3 is described by the element Unmaintained, aging power plants ... unable to handle peak energy loads. This element has an impact of +10. Segment 3 is willing to pay the most amount of money ($232), and will pay up to $50 more when reading about global warming and aging power plants. Segment 3 appears to be the strongest responder to energy issues.

When they are basically more interested … will they pay more? In this study as well as others, we looked at the relation between the two additive constants of interest and willingness to pay taxes. We saw a positive relation for health care (Chapter 4), but a noisier, less clear pattern for education (Chapter 5). The hypothesis that emerged was that perhaps, as the topic became less immediate (i.e., education) and frightening (i.e., health care), the strong linear relation would become noisier. That is, when we feel something very strongly and it frightens us, like health care issues, we tend to want to escape the anxiety by paying more in taxes, hoping that this placation by payment will somehow save us. Let’s explore the nature of basic willingness to pay taxes versus basic interest, focusing on new energy policies. Looking at the data from the total panel and subgroups suggests that while energy concerns may be present and vocalized in the news, they are not frightening and immediate, except for Segment 3, our Doom and Gloom segment. Following this line of reasoning and looking at the different subgroups, we expect to see a modest, somewhat noisy relation.

148

We see this relation plotted out in Figure 6.4, which is clearly noisy. Each one of the key subgroups is defined by a letter or number. We can decipher these by looking at Table 6.5, which lists the basic willingness to pay taxes and basic interest of these subgroups. Figure 6.4 shows that Segment 3 Doom and Gloom (represented as 4) is willing to pay the most money in taxes, followed, in turn, by the Strong Democrat (represented as 1 in the plot). Yet neither of these two groups shows the level of interest in the energy policies compared to their willingness to pay taxes for it. Figure 6.4. How basic tax willing to pay co-varies with basic interest in the energy program.

Table 6.5. How basic tax willing to pay co-varies with belief in the success of the energy initiative. Each letter corresponds to a subgroup of respondents. Group Raise taxes for new forms of energy: Yes Age: 30-38 years old Market: East South Central States Market: Southern Atlantic States Ethnicity: Hispanic Education: Completed Graduate/Post Graduate Age: 65-70 years old Market: Pacific States Education: Completed College Segment 1 – Focus on Energy Sources Age: 53-64 years old Market: Middle Atlantic States

Interest Tax Code 53 206 7 52 149 E 52 96 Q 50 163 O 46 74 L 46 156 X 45 71 I 43 116 T 43 136 W 43 152 2 42 150 H 42 25 N 149

Raise taxes for the economy: No Segment 3 – Focus on ‘Doom & Gloom’ Total Panel Market: East North Central States Party: Strong Democrat Raise taxes for the economy: Yes Gender: Male Gender: Female Raise taxes for new forms of energy: No Ethnicity: Black Market: New England Education: Completed High School Segment 2 – Focus on ‘Green’ Age: 18-29 years old Education: Some College Ethnicity: White Age: 45-52 years old Party: Strong Republican Market: West North Central States Age: 39-44 years old Market: Mountain States

41 40 39 39 39 39 38 38 38 37 37 37 37 35 34 33 30 24 23 17 15

81 232 119 87 209 209 142 93 50 125 115 101 96 112 88 157 120 -10 130 99 89

6 4 A P 1 5 B C 8 K M U 3 D V J G Z R F S

Our approach to plotting basic taxes willing to pay versus basic interest provides a new tool for policy planners. Looking back at the plots from this study on energy and at the others on education, health care, and nutrition policy, we get a sense that we are dealing with two processes. One process is the fundamental linear relation between basic interest/belief and taxes willing to pay. If a person believes or is interested, then that person is willing to pay more. But beyond that basic pattern is another tendency to want to spend that seems above and beyond the basic belief. That deconstruction itself, into the reasonable linear relation and the exception to that relation, is worthy of further investigation. Does this mean that the Democrats are far more willing to pay/raise taxes, independent of their own personal beliefs? And, in turn, have we created a new empirical tool to understand more profoundly the mind of the citizen? What continues to interest the mind of the energy consumer? We looked at the winning and losing elements by total panel, deconstructed the group of 251 individuals into three mind-sets, and then developed models for these groups and for others. After that effort, what can we say about the messages that intrigue and interest our respondents? The summarized results appear at the top of Table 6.6. The strongest three elements 150

for each key subgroup appear in the middle and further down. Here are some recurrent themes and what they might suggest:  

  

The subgroups show different messages, but there are some which recur from group to group. People want to have a greener lifestyle, but it’s not clear from the most popular element (Embracing a greener lifestyle means padding your bank account ... saving money) whether we’re talking about green or talking about money. We know that people are interested in themselves. Winning elements include padding bank account … saving money, as well as improving your health and your overall quality of life. Yet there is a more noble aspect, perhaps best expressed by the personalization of what the individual does: Everything we do every day has an impact on the planet. It’s not just money. Certainly it makes a difference, but the key here is to talk about the individual, quality of life, and weave in green concerns.

Table 6.6. Most frequently appearing elements for energy among the top three, across subgroups, and the winning three elements for each key subgroup. Most frequently appearing elements when the top three elements per subgroup are identified Embracing a greener lifestyle means padding your bank account ... saving money Everything we do every day has an impact on the planet Increase the use of renewable energy (i.e., wind, solar, and other alternatives) Embracing a greener lifestyle means improving your health and your overall quality of life Increase America's ecological resources Free solutions for energy efficiency ... buildings and businesses eligible for incentives to be gas and energy efficient Green utility cash rebates for high-energy equipment replacement Specific elements which score highest in interest for each key subgroup Total Panel Embracing a greener lifestyle means padding your bank account ... saving money Everything we do every day has an impact on the planet Increase the use of renewable energy (i.e., wind, solar, and other alternatives)

Frequency 19 18 17 11 6 5 5

6 6 5 151

Gender: Males Free solutions for energy efficiency ... buildings and businesses eligible for incentives to be gas and energy efficient Embracing a greener lifestyle means improving your health and your overall quality of life Everything we do every day has an impact on the planet Gender: Females Embracing a greener lifestyle means padding your bank account ... saving money Everything we do every day has an impact on the planet Increase the use of renewable energy (i.e., wind, solar, and other alternatives) Age: 18-29 years old Inadequate supply of energy (i.e., shortage of oil supplies) ... leads to electric grid overloads Green utility cash rebates for high-energy equipment replacement Increase the use of renewable energy (i.e., wind, solar, and other alternatives) Age: 30-38 years old Embracing a greener lifestyle means padding your bank account ... saving money Increase the use of renewable energy (i.e., wind, solar, and other alternatives) Free solutions for energy efficiency ... buildings and businesses eligible for incentives to be gas and energy efficient Age: 39-44 years old Change unsustainable status-quo Fossil fuels ... have a negative contribution to climate change Green utility cash rebates for high-energy equipment replacement Age: 45-52 years old Embracing a greener lifestyle means improving your health and your overall quality of life Global warming ... due to increased environmental impacts Increase America's ecological resources Age: 53-64 years old Everything we do every day has an impact on the planet Free solutions for energy efficiency ... buildings and businesses eligible for incentives to be gas and energy efficient Embracing a greener lifestyle means padding your bank account ... saving money

4 3 3

10 10 8 6 6 6 10 6 5 10 10 9 10 10 8 10 7 6 152

Age: 65-70 years old Embracing a greener lifestyle means padding your bank account ... saving money Everything we do every day has an impact on the planet Increase the use of renewable energy (i.e., wind, solar, and other alternatives) Ethnicity: Hispanic Embracing a greener lifestyle means padding your bank account ... saving money Green utility cash rebates for high-energy equipment replacement Embracing a greener lifestyle means improving your health and your overall quality of life Ethnicity: Black Embracing a greener lifestyle means padding your bank account ... saving money Everything we do every day has an impact on the planet Increase America's ecological resources Ethnicity: White Everything we do every day has an impact on the planet Embracing a greener lifestyle means improving your health and your overall quality of life Increase the use of renewable energy (i.e., wind, solar, and other alternatives)

Market: New England Increase the use of renewable energy (i.e., wind, solar, and other alternatives) Embracing a greener lifestyle means improving your health and your overall quality of life Increase America's ecological resources Market: Middle Atlantic States Everything we do every day has an impact on the planet We all have a large personal stake in the health and vitality of places far and near Green utility cash rebates for high-energy equipment replacement Market: Southern Atlantic States Embracing a greener lifestyle means padding your bank account ... saving money Increase America's ecological resources

7 6 5 7 6 5 8 7 6 8 7 6

24 22 17 7 7 6 7 7 153

Energy efficient methods reduce the loss of America's resources Market: East North Central States Embracing a greener lifestyle means padding your bank account ... saving money Everything we do every day has an impact on the planet Increase the use of renewable energy (i.e., wind, solar, and other alternatives) Market: South Central States High energy costs causes businesses to relocate to other regions Measured cost savings transition service plans Improved air quality ... reduce harmful emissions Market: West North Central States Embracing a greener lifestyle means improving your health and your overall quality of life Inadequate supply of energy (i.e., shortage of oil supplies) ... leads to electric grid overloads Everything we do every day has an impact on the planet Market: Mountain States Manage America's renewable energy assets ... secure future resources You can have a global impact Increase the use of renewable energy (i.e., wind, solar, and other alternatives) Market: Pacific States Free solutions for energy efficiency ... buildings and businesses eligible for incentives to be gas and energy efficient Increase the use of renewable energy (i.e., wind, solar, and other alternatives) Embracing a greener lifestyle means padding your bank account ... saving money Education: Completed High School Everything we do every day has an impact on the planet Embracing a greener lifestyle means improving your health and your overall quality of life High energy costs cause businesses to relocate to other regions Education: Some College Embracing a greener lifestyle means padding your bank account ... saving money Embracing a greener lifestyle means improving your health and your overall quality of life Everything we do every day has an impact on the planet

6 10 9 7 5 5 4 14 11 11 12 11 10 7 5 5 8 5 5 10 9 8

154

Education: Completed College Increase America's ecological resources Less energy consumed = Less pollutants and greenhouse gas production = Less ozone damage Green utility cash rebates for high-energy equipment replacement Education: Graduate/Advanced Degree Embracing a greener lifestyle means padding your bank account ... saving money Increase the use of renewable energy (i.e., wind, solar, and other alternatives) Embracing a greener lifestyle means improving your health and your overall quality of life Party: Strong Republican State ‘green incentive’ programs Manage America's renewable energy assets ... secure future resources Increase the use of renewable energy (i.e., wind, solar, and other alternatives) Party: Strong Democrat Increase the use of renewable energy (i.e., wind, solar, and other alternatives) Everything we do every day has an impact on the planet Embracing a greener lifestyle means padding your bank account ... saving money Segment 1 – Focus on Energy Sources Increase the use of renewable energy (i.e., wind, solar, and other alternatives) Manage America's renewable energy assets ... secure future resources Energy efficient methods reduces the loss of America's resources Segment 2 – Focus on Green Embracing a greener lifestyle means padding your bank account ... saving money Everything we do every day has an impact on the planet Embracing a greener lifestyle means improving your health and your overall quality of life Seggment 3 – Doom and Gloom Unmaintained, aging power plants ... unable to handle peak energy loads Global warming ... due to increased environmental impacts

6 6 6 6 4 3 9 9 5 9 9 8 8 6

8 7 5 10 9 155

Increase America's ecological resources Believe in raising taxes for the economy: Yes Increase the use of renewable energy (i.e., wind, solar, and other alternatives) Everything we do every day has an impact on the planet Embracing a greener lifestyle means padding your bank account ... saving money Believe in raising taxes for the economy: No Embracing a greener lifestyle means padding your bank account ... saving money Everything we do every day has an impact on the planet Increase the use of renewable energy (i.e., wind, solar, and other alternatives) Raise taxes for new forms of energy: Yes Increase the use of renewable energy (i.e., wind, solar, and other alternatives) Embracing a greener lifestyle means padding your bank account ... saving money Everything we do every day has an impact on the planet Raise taxes for new forms of energy: No Energy efficient methods reduce the loss of America's resources Embracing a greener lifestyle means padding your bank account ... saving money Embracing a greener lifestyle means improving your health and your overall quality of life

6 9 9 8 7 6 6 9 7 7 6 6 5

Summary Throughout this study, and the three preceding ones, we have seen a very big difference between responses to messaging from government as public policy versus responses to messages about something that will happen to the individual (Chapter 2). We will also see these very strong responses later for other topics that are positioned as being personal to the respondent rather than being government policy. The key message that we take away from these studies is that the impact values are small when the topic is positioned as a government policy. Yes, there are hues and cries, tremendous outbursts of emotion about energy, about health care, somewhat about education, and occasionally about nutrition. Yes, people are willing to pay more taxes for programs in which they believe. On the other hand, we come away with a sense of emotional distance.

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When put to the test and people have to respond to complex messaging about public policy, they don’t react from the gut. Instead, we get a sense of a modulated, attenuated, significantly weakened reaction. Later on, when we deal with similar types of issues, but with the individual more personally involved, and without the aegis of the government in the background, we will get a far more emotional reaction. References: [1] Woloski, A. (2006). Fuel of the Future: A Global Push Toward New Energy. Harvard International Review, 27(4), 40-43. [2] Crooks, E. (2012, March 1). US crude oil imports fall to 12-year low. The Financial Times. Retrieved March 16, 2012 from http://www.ft.com/cms/s/0/4611795a-63bb-11e1-968600144feabdc0.html#axzz2afC08Phr [3] U.S. Energy Information Administration, Independent Statistics and Analysis. (2008). Retrieved March 26, 2010, from http://tonto.eia.doe.gov/country/index.cfm [4] Akimoto, H. (2003). Global Air Quality and Pollution. Science, 302(5651), 1716-1719. [5] Houghton, J. (2005). Global warming. Reports on Progress in Physics, 68(6), 1343-1403. [6] Philander, S. G. (2008). Encyclopedia of Global Warming and Climate Change. Thousand Oaks, CA: SAGE Publications. [7] Nordhaus, W. D. & Boyer, J. (2003). Warming the World: Economic Models of Global Warming. Cambridge, MA: The MIT Press. [8] Houghton, J. (2005). Global warming. Reports on Progress in Physics, 68(6), 1343-1403. doi:10.1088/0034-4885/68/6/R02 [9] Wuebbles, D. J., & Jain, A. K. (2002). Concerns about climate change and the role of fossil fuel use. Fuel Processing Technology, 71(1-3), 99-119. [10] Brunekreef, B., & Holgate, S. T. (2002). Air pollution and health. The Lancet, 360(9341), 1233-1242. 157

[11] Lazaroff, C. (2002, March 6) Tiny Pollution Particles Linked To Lung Cancer Retrieved February 16, 2010 from http://www.ens-newswire.com/ens/mar2002/2002-03-06-07.asp [12] Brown, M. A., Sovacool, B. K., & Hirsh, R. F. (2006). Assessing U.S. Energy Policy. Daedalus: Journal of the American Academy of Arts and Sciences, 135(3), 5-11. [13] Bushnell, J., & Wolfram, C. D. (2005). Ownership Change, Incentives and Plant Efficiency: The Divestiture of U.S. Electric Generation Plants. University of California Energy Institute CSEM Working Paper 140. [14] Sackett, J. I. (2001). The future of nuclear energy. Fuel Processing Technology, 71(1-3), 197-204. [15] U.S. Energy Information Administration, Independent Statistics and Analysis. (2013). Electric Power Monthly. Retrieved January 26, 2013, from http://www.eia.gov/electricity/monthly/epm_table_grapher.cfm?t=epmt_1_1 [16] EERE Network News. (2009). Retrieved January 21, 2009, from http://apps1.eere.energy.gov/news/news_detail.cfm/news_id=12194

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Chapter 7 Giving information – what the audience wants to receive from the advertiser Abstract: The exponential growth in information and the use of new media such as the Internet have fragmented the advertiser’s buying audience. Media fragmentation also overloads the buyer with information and promotional messages. The combination generates a perfect storm. More than ever before, the customer is educated, in control, and able to tune out unwanted information and undesirable advertising. The new media promotes user-generated content, leaving behind advertising corporations’ carefully manufactured messages. In this chapter, we deal with an analysis of what the audience wants from the advertiser – How do consumers view advertising practice today? and What would consumers like to see more/less of? The data suggest that when consumers think about goods and services, advertising remains an important source of information. But what consumers want more of is simple: truthfulness, less invasiveness, and respect for the consumer’s privacy. The main finding of this study is the discovery of a pact between advertisers and consumers. The pact governs the implied contract: I’ll give you my time, attention, and money as long as you respect me and you act honorably. Introduction One of the major implications of the shift in advertising from traditional media channels to digital media is that communications professionals can observe consumer behavior with unprecedented detail. Compare the widespread availability of conventional Web metrics on all analytics programs such as number of visits to a Web site, number of page views, and origin of visits. Then recall the arduous task of calculating how many people saw a TV commercial or heard a radio spot. One is a spreadsheet of data easily imported into Excel, and the other involves a great deal of guesswork [1]. The availability of these statistics has enabled organizations to assess the impact of their advertising at a granular level. In response, it is possible to create and critically evaluate strategy that addresses the consumer’s wants and needs. Never has the material of advertising practice been so amenable to measurement, control, and modeling as today. In our previous chapters, we dealt with the government sending messages to consumers. Now we are shifting focus, and our goal will be to determine the location, qualitative nature, and content of messages that consumers want to receive from advertisers. Keep in mind that advertisers send consumers messages to entice them to buy. But a consumer doesn’t have to buy most things. So we’re 159

now dealing with a different situation where desire for a product or service must be created by the company rather than imposed by the government or tapped from a clear, pre-existing, and unfulfilled need within the consumer. What do we know about the messages? Today’s world of media is evolving. There is no one medium, no one channel. For example, media consumption has shifted from just a few major television networks, radio stations, magazines, and the local newspaper in the 1960s to many more television stations, cable, satellite, hundreds of radio stations, millions of Web sites, scores of RSS (Rich Site Summary) feeds, and hundreds of social networks. In 1960, a single ad that aired simultaneously on CBS, NBC, and ABC could reach 80 percent of American women. Today that same ad would have to be run on 100 channels multiple times to come even close to that level of penetration [1]. There are a number of different currents and countercurrents going on. Just to name a few, we see: 

Media and audiences have fragmented



Interrupt-and-repeat advertising is giving way to brand experience



Consumers are in control



Word-of-mouth is most important



Consumers reject conventional advertising on social media sites



Relevance is king



Advertising as service

Individuals seek the entertainment and information sources that suit their personal preferences because they are able to do so, thereby shattering the mass market into millions of small fragments. This has led people to feel increasingly overloaded by information and promotional messages. In 1965, 35% of people could recall an ad from a television show that they just watched. Since then, that number has declined to 10% [1]. People now consume their content according to their personal schedule and preference. Devices such as TiVo, DVRs, MP3 players, and satellite radio have given consumers the power to silence advertisers on television and radio to a great extent. Interruptive advertising is now more likely to be suppressed or ignored.

160

What about the trustworthiness of advertisers? As we just mentioned, increased access to information has made consumers ever choosier about how they spend their time. An additional consequence is that large bodies of information allow consumers to gather information about products easily, from reviews to pricing in retail stores, all by conducting searches from their home computers. According to a Vizu Market Research report conducted in March 2008, trust in advertising is low [2]. This study also indicates that 42% of respondents would pay $20 a month, in addition to their cable bill, to avoid all TV advertisements [2]. A lower willingness-to-pay for Internet ad removal does not suggest that Internet ads are more tolerable, but that it is easier to avoid Internet advertisements with a pop-up blocker or other tools. In fact, 48% of the study’s respondents indicated that Internet advertising was the most invasive and annoying, as compared with 27% who picked TV [2]. Greg Stuart summarized the situation best in the Ad Week follow-up article titled You Can’t Avoid Ad Avoidance “What is the future of a business where the consumer hates your product — in this case advertising?” [3]. Social media, the replacement for advertising? More recently, the Internet has provided a wealth of opportunities for consumers to interact with one another. When the term word-of-mouth was coined, it referred exclusively to oral conversations between people. The adoption of communication-oriented technology has expanded the word-of-mouth to include any form of human-to-human communication including text messages, e-mails, telephone, and social media updates. All of these communication forums facilitate human interaction. Social media allow people searching for information to ask entire networks, from friends to strangers (who may live halfway across the world and share a common interest), for assistance. Thus, consumers are having more conversations about companies’ products through social media, and their conversation logs are often in the public domain. Companies’ products’ social media platforms allow them to insert themselves into these conversations with varying levels of success. Consumers are apt to interact with a company at a personalized level, and each positive interaction that a company has can build up awareness among influential consumers. Many advertisers are still struggling to crack the code of the new marketing environment. They have learned to create ads that are effective even when

161

consumers are fast-forwarding through commercials on television, and they are learning how to listen to their consumers talk on social networks [4]. About the study In this chapter we deal with an analysis of what the audience wants from the advertiser. In previous chapters, we dealt with other big picture topics, namely the attitude of consumers toward government-sponsored programs such as education, health, nutrition, and energy. There we dealt with the government informing the citizen. Now we deal with private advertisers trying to sell to the citizen. This chapter deals with three major questions concerning advertising: 

How do consumers view advertising practices today?



What would consumers like to see more of?



What would consumers like to see less of?

Answering these questions with respect to advertising gives us a sense of what is and what should be. Doing the study We followed the same approach for this experiment on advertising as we followed for government policy and, indeed, for the soda can study. We used experimental design with six silos, each of which provided a different part of a snapshot about advertising practice. Each silo comprised six elements (i.e., a total of 36 elements). Those elements describe specific experiences of advertising and have concrete meanings to consumers. Table 7.1 presents the six silos, and under each silo, the elements. Table 7.1. Silos and elements Silo 1: Media Environment A1 Big media companies CONTROL ... what you watch, read, and listen to A2 All media seem like TELEVISION A3 You spend most of your time ... with ONLINE media You read, listen, or watch ... whatever, whenever, and wherever YOU A4 WANT A5 You use PROFILE PAGES as media centers A6 MEDIA IS MEDIA ... offline or online doesn't matter 162

B1 B2 B3 B4 B5 B6 C1 C2 C3 C4 C5 C6 D1 D2 D3 D4 D5 D6 E1 E2 E3 E4 E5 E6 F1 F2 F3 F4 F5 F6

Silo 2: Reaching Consumers and Targeting Ads Advertisers want their ads seen ... by the LARGEST AUDIENCES Most ads you see ... relate to YOUR interests Advertisers reach you ... EVERY WAY and EVERY WHERE Advertisers show ads to you ... based on YOUR searches ... behavior ... friends ... psychology You CONTROL the information advertisers use ... to communicate with you Advertisers target SHOPPING ROBOTS ... you use Silo 3: Advertising Creative Your job as a consumer ... LEARN about brands Advertising aims to ... get you TALKING about brands Brands PERSONALIZE their ads ... to you Ads incorporate CONSUMER ... photos ... videos ... music, etc. Ads become more like MOVIES ... with stories and effects It's hard to tell ads ... from CONTENT Silo 4: Consumers’ Behavior Most advertising ... just WASHES OVER you You COMBINE MEDIA ... to do whatever you want to do You expect that most advertising you see ... directly RELATES to your interests What people you trust SAY about brands ... matters more than advertising SOCIAL NETWORKS and advertising ... don't mix You control the ads you see ... through SOFTWARE and PERMISSIONS Silo 5: Brand Advertising Advertising INTERRUPTS you ... to get noticed Advertisers ENGAGE you in a brand EXPERIENCE Advertising provides SERVICE to you ... info, details, help, etc Brands LISTEN to you ... and RESPOND accordingly Advertising goes where YOU go ... stores, malls, waiting areas, etc. Advertisers join the CONVERSATIONS ... you have with friends Silo 6: Advertising Industry Madison Avenue ... CENTER of the advertising industry Content, advertising, shopping, buying ... SEAMLESSLY INTEGRATE You PAY-FOR-CONTENT ... more often You expect advertisers ... to BEHAVE responsibly in all ways You find new digital technology ... CONFUSING Marketing and advertising ... becomes more INTENSE than ever

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Running the experiment Respondents received an e-mail invitation, clicked on a link, and were directed to the interview. It doesn’t hurt to reiterate that the more information of a general, task-related nature one provides to the respondent, the better the interview will be in terms of a motivated respondent who knows what to do. On the other hand, it is important to avoid setting up expectations about what is right or wrong. We presented the respondent with the following orientation page describing the general nature of the study and the scales to use (Figure 7.1). Figure 7.1. The introduction or orientation page for the advertising practices study. The respondent saw this page when he or she clicked on an embedded link starting the actual interview. Welcome to the survey of Today’s Advertising World! Websites, blogs, iPhones, Facebook, email, IM and twitter provide advertisers with new ways to market to you … and for you to experience their brands. Advertising is changing. We’d like your opinions on today’s advertising world. In this survey, you will be presented with sets of statements about advertising and marketing. Some sets may appear similar, but in fact, each one is unique. You will be asked to rate each set on the following two questions: 1) “How does this paragraph match YOUR VIEW of advertising in Today’s World?” (1 = Not at all … 9 = Perfectly) 2) “Would YOU LIKE advertising to be Less Like This or More Like This?” (1 = Less Like This … 9 = More Like This) After you complete the sets of statements, the survey concludes with a few questions for classification purposes; they are not used to personally identify you in any way. All of your answers are confidential. Thank you for taking the time to complete the survey and share your opinions with us. Click on the >> button to begin.

Afterwards, the respondent evaluated 48 test screens, rating each one on the two rating questions: 1. Does it match your view of advertising in today’s world? and 2. Would you like advertising to be More/Less like this? Although this type of study may seem long, in fact most respondents enjoy it. Each consumer saw 48 different vignettes. The stimuli were assembled on the respondent’s computer very quickly and changed automatically after the respondent assigned the ratings. Thus the task was quick, engaging, not boring.

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The test stimuli comprised short, easy-to-understand phrases, in bulleted form, i.e., simple, direct phrases without any connectives. The respondent read the combination and assigned a rating to it. The set of 48 combinations was unique for each respondent, ensuring no bias due to creating just a few representative stimuli that may have incorporated unexpectedly strong or weak performing combinations. An example of a test concept or vignette appears in Figure 7.2. The concept screen appeared with the first rating scale. Once the respondent selected an answer, the first rating scale disappeared to be followed by the second rating scale. Figure 7.2. An example of a single test concept and the two sequentially presented rating scales at the bottom of the screen.

After the respondents finished the RDE (Rule Developing Experimentation) portion of the interview comprising the test vignettes, they completed a self165

profiling questionnaire telling us more about themselves. This part of the study provides a richer sense of what is important. We will draw insights from those answers in conjunction with our element data analysis. What types of media do people consume? The sample in our study is heavily media-involved. Our respondents watch TV and Online Video, listen and read both offline and online, create and contribute to content, and control media with TiVo and DVRs. Blogs are often sources of product news and opinions. In our sample, 40% of the respondents read blogs, and 31% contributed to them regularly. See Table 7.2 to see these results. The activities give a snapshot of what Americans were doing at the end of 2008. Table 7.2. Activities offline and online. Which of These Activities Do You Do Regularly? Watch, Read, Listen Contribute Content Watch TV 80% Texting Short Messages Listen to the radio 61% Chat Watch Online Video 59% Upload photos Read books 58% Update your profile page Read magazines 53% Upload music Listen to music online 52% Post or comment to blogs Read a newspaper 48% Upload video Read blogs 40% Use a file sharing service Manage a blog Control Viewing Create music or video Record or watch TiVo or 32% Other DVR

55% 52% 51% 46% 36% 31% 23% 22% 19% 16% 12%

How do consumers interact with advertising? We asked consumers a series of questions using a 5-point scale, with scale point 5 being strongly agree and scale point 4 being agree. The typical practice is to combine the two highest scale points (strongly agree, agree), to arrive at the percent of the respondents who fall into the specific group of individuals represented by that question. By research convention in consumer and market research, the combination of these two scale points is called the Top 2 Box. We now highlight a few of the key results from this self-profiling classification with the 5-point disagree-agree scale. The numbers attached to each element are the percent Top-2 Box scores.

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Consumers are interested in brand knowledge and exchanging it with friends, family, colleagues, and communities. These conversations, together with tools like search and blogs, are an important source for learning about new products. ‘I mainly learn about brands ... from friends, searches, blogs, posts, Web sites, etc.’ 55% Consumers have confidence in the information they gather from all of their different sources. ‘I trust product information I find myself ... more than from any other source’

53%

Advertising is one of those sources – an important source – that consumers factor in. However, about one-third of the respondents felt that advertising will become less important. ‘I expect that advertising will become less important to me’

37%

Consumers express concern over the ways advertisers use their personal information. ‘Companies collect and use my personal information to benefit me’

24%

What does RDE tell us about what people want from their advertisers when they are exposed to advertising? When we talk about the results, we show impact or utility values of our tested elements. As was the case in the previous chapters, we used ordinary least squares regression (OLS) which treats each of the 36 elements as separate, independent predictors of the consumer response. This approach of treating elements as independent means that the structure we impose (see Table 7.1: Silos and elements) on advertising practice need not be correct to do its job. The silos are merely bookkeeping devices. They ensure that mutually contradictory elements, or elements which carry the same type of message but with different content, never appear together in a single test stimulus. When we analyze the data, we are confronted with a very surprising picture. If advertising practice today were to be changed into a fast moving packaged good, then our data suggest that advertising would be rather unremarkable, sitting innocuously on a lower shelf, passed by, seldom examined.

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Consumers scored most elements around neutral, which surprised us. That is, they did not see those elements as being a general description of advertising practice today. However, two elements did stand out in terms of what advertising today is NOT like. The new digital technology is not confusing, and people don’t want to pay for content. 

You find new digital technology ... CONFUSING

-8



You PAY-FOR-CONTENT ... more often

-7

Users expressed comfort with using digital media, which makes sense given the levels of media consumption and production we reported above. Increasing digital media penetration means user comfort has likely increased, or, at minimum, stayed the same since this study was conducted. In 2008, consumers did not see themselves as paying more frequently for the programs, videos, articles, postings, and entertainment they enjoy today. We note that since then, media companies have started to build pay-walls. A major turning point occurred in 2009, when Rupert Murdoch, CEO and chairman of News Corporation, threatened to remove all of his publications’ stories from Google search results. Following this threat, Murdoch announced in August of 2009 that all News Corporation publications would begin charging for access. The Wall Street Journal and Financial Times have pay-for models. The Times and Sunday Times of London joined this group by charging customers for online access in 2010. These two newspapers separate their online offerings from their print offerings [5]. The New York Times implemented a metered payment system in early 2011. Web subscriptions that include mobile apps range from $15 to $35 every four weeks. What do today’s consumers want more of, and less of? Given all the professional focus on advertising, we expected to see strong consumer feelings. We were surprised again by the extent of neutral reactions to most of the elements. Where we expected to see strong preferences for elements concerned with one or more of the newer mental models such as consumer control, the desire for relevant ads, engagement, advertising as service, and so forth, we didn’t find any. Instead, consumers felt very strongly about more conventional concerns. Here are the details. Consumers want LESS of 168

This analysis looks at the bottom of the scale. So, the numbers in the body of the table reflect the conditional probability of a respondent saying ‘I want LESS of this.’ Consumers are saying: Don’t make me pay, don’t make it hard or confusing, don’t interrupt me, and give me some alternatives to big media. 

You PAY-FOR-CONTENT ... more often

10



You find new digital technology ... CONFUSING

9



Advertising INTERRUPTS you ... to get noticed

7



Big media companies CONTROL ... what you watch, read and listen to 6

The less doesn’t only appear in the quantitative portion of the interview, as the verbatims, the answers to the open-ended questions, suggest: 

‘It is important that it doesn't take my personal time, doesn't make me pay for it (text messages and phone calls).’



‘Be more direct and clear about what you are selling. No more pop-ups online.’



‘Advertising is so dense right now that it's almost suffocating. When I watch TV, I get about five minutes of whatever show I'm watching, then I have to wait another ten minutes through commercials before it comes back on. It is VERY frustrating.’



‘I would like advertising to appear not as advertising. I like it better when advertising is done within the media content so that one doesn't have the blatant interruption of the show you are watching.’



‘I do not like when advertising and content seem to mix too much. It is irritating when all TV shows are filled with product placements.’



‘I really dislike when it interrupts me. If I am already online looking something up, advertise on the side and I will look at it, pop it up and I will never look at it.’

Consumers want MORE of What consumers want more of is simple: Advertisers should be forthright, honest, and good stewards.

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

You expect advertisers ... to BEHAVE responsibly in all ways

-7

The following verbatims give a little more texture to the meaning of this notion of responsibility: 

‘All the facts and no false advertising make me want to read what they have to say.’



‘Advertisements completely clear of any spyware or malware.’



‘Truthfulness in advertising. It's especially annoying when advertisements incorporate blatant lies in order to exaggerate their image.’



‘That it is responsible and located in appropriate places. Also, remove all billboards – they are inappropriate and only pollute the sky.’



‘Advertisers should be ethical and not infringe on privacy without explicit permission.’

The advertising pact At the start of this research, it seemed perfectly feasible to measure consumer interest in the so-called newer advertising practices, especially those which seem to flourish in the world of fractionated media. Experimental design and RDE which seem to work so well for other aspects of public policy and consumer sentiment, appeared to be a natural approach to the problem of what consumers want. It seemed perfectly reasonable that consumers would be as aware as professionals are about what was happening and that there would be strong involvement and great emotion. To some degree, our expectations paralleled the expectations we had with the social policy research and taxes. All four topics — nutrition, energy, education, and health care—are subjects of vociferous arguments (pro and con), numerous public opinion polls, many op-ed pages, and, of course, relevance to the citizenry. So we felt about the world of advertising—not quite public policy with taxes, yet not so different, to us as observers, as to be located in a different world. It is surprising, then, that the data did not immediately uncover a group of excited, vocal, opinionated consumers ready to spill their guts and tell us how it is. Consumer respondents seemed rather indifferent to newer advertising practices. Of course, the respondents were not totally indifferent. They were vocal on a handful of elements that had to do with THEIR ability to watch, listen, or read 170

and enjoy it. That is, it’s about THEM, not about ADVERTISING PRACTICE, per se! Perhaps this is the most important finding of all. It’s our mental models, but it’s about the THEM that reside far away from our rarified, intellectualized abstractions. The data reveal another dynamic. It became increasingly clear that there may be a transaction going on and that we had happened upon a quid pro quo situation. Consumers understand that content in any medium costs money to produce and distribute, and that advertising support makes it available for consumption in almost any form. Ed Keller, currently the CEO of the Keller Fay Group, but formerly head of Roper Worldwide, stated: ‘Most consumers consider advertising a fair price to pay for media’ – a Roper finding that has held up for 30 years. Notice the word pay. It is not about wanting to be involved. Involved is the professional’s language. It’s about paying with their time in order to get something. That something is content. It’s all about homo economicus, economic man in a new situation, with a new technology. The elements consumers want more of and the elements that consumers want less of create, in ePoll CEO Gerry Philpott’s phrase, a ‘pact between advertisers and consumers.’ The pact governs the implied contract: I’ll give you my time, attention, and money so long as you respect me and act honorably. What does the pact sound like? The verbatims in the open-ended questions once again point to the nature of this pact. The majority of writers offered constructive comments, relating their issues and suggesting ways that advertising practice could be more acceptable to them. They did not trash advertising nor did they say that advertising has no value. 

‘What is important to me is that they are considerate, they don't ’interrupt’, but incorporate products into life as it is.’



‘It is important that I retain control over what I watch, read, and listen to. I don't mind advertising if it is interesting to me and gives me enough information to make informed decisions.’



‘I do not like ads that use personal information to tailor ads to me. Ad tailoring should be done in the same way it is on TV. By studying the target market of a Web page and using that information, not by going through the type in my email, etc.’ What happens when the pact is broken? Here’s one outcome:

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

‘Advertising is so invasive that it has become annoying. I find myself NOT buying products because of ad campaigns. I especially resent PAYING to see ads. For example, I get extremely irritated by the ads at the beginning of movies. I don't hate advertising but I see it as something unavoidable like taxes and death. I do feel that boundaries have been crossed about where and when ads are displayed. I also feel that many ads are sexist.’

Two mind-set segments The segmentation sought those respondents who showed similar patterns, based on the pattern of their impacts, and specifically individuals who showed similar patterns of coefficients in their Persuasion Models. The Persuasion Model is the regression model whose dependent variable is the 9-point rating and whose independent variables are the 36 elements. At the end we identified two mind-sets. Segment 1, with approximately 70% of the respondents, perceives Advertiser in control and important. Segment 2 can be called Indifferent, with approximately 30% of the respondents. Segment 1: Advertiser in control and important In today’s world of advertising practice, this segment sees advertisers as using whatever means to reach consumers and controlling the agenda. Segment 1 expects advertisers to behave responsibly, ostensibly to make advertising practice more acceptable. These are the elements that Segment 1 sees as happening today: 

Advertising INTERRUPTS you ... to get noticed

10



Advertisers target SHOPPING ROBOTS ... you use

9



Advertisers want their ads seen ... by the LARGEST AUDIENCES

7



You expect advertisers ... to BEHAVE responsibly in all ways

6

Three verbatims reflect consumer views in this segment: 

‘I hate ads! If I am interested in a product, I'll seek the info I need ... STOP BOMBARDING ME WITH BULL!’



‘I am really fatigued with not being able to walk down a street without being bombarded, not just by ads, but by MOVING ads (TV screens on every subway entrance, et cetera).’

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 ‘Basically, I think our lives are controlled by advertising because we advertise for houses, foods, and even school. TV is just a list of advertisements …’ In other words, this segment perceives being in the sights of a commercial onslaught and wants relief. But they’re also saying something else that’s very important: I’m going to buy products, maybe yours, but please reach me and talk with me in ways agreeable to me. 

It is important for advertising to be relevant to my interests, but not suffocating in its presentation or integration in content.

Segment 2: The Indifferent This second segment does not express any clear, strong reactions. About two-thirds of their elements generate impacts near 0, with the remainder describing advertising practice as not like this. The Indifferent just don’t want anything. It’s as if they are asleep when it comes to what they want. The overall sense of the Indifferent is that, while they distinguish between ads and content, they don’t see advertising as tailored or personalized, nor do they see the capabilities to control their advertising experience. These are the elements that Segment 2 sees as NOT HAPPENING today: 

You use PROFILE PAGES as media centers

-5



You CONTROL the information advertisers use to communicate with you -5



Brands PERSONALIZE their ads ... to you

-5



It's hard to tell ads ... from CONTENT

-5



SOCIAL NETWORKS and advertising ... don't mix

-5



Madison Avenue ... CENTER of the advertising industry

-5



Most ads you see ... relate to YOUR interests

-6



Advertisers target SHOPPING ROBOTS ... you use

-6

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

You expect that most advertising you see directly RELATES to your interests -6



You control the ads you see ... through SOFTWARE and PERMISSIONS -6



You PAY-FOR-CONTENT ... more often

-6



You find new digital technology ... CONFUSING

-8

These verbatims give more insight into the mind of the Indifferent segment: 

‘All advertising does for me is show me what the world has created, some of it is interesting but most of it seems quite boring to me.’



‘There is a wide variety of advertisements. It seems that a lot of the advertisements are similar and by the same companies.’



‘Most ads are boring. I really don't pay attention to most of them ... I either leave the room, talk to friends or family ... I buy what I think I want for me and ads do not influence my mind.’

What do the segments want LESS of and MORE of? Despite the differences in the mind-sets, both segments appear to want the same things, and both perfectly reflected the elements we observed among the total sample in the more of and less of categories. This is a very important result. It means that there is probably a single, general pact, even when the respondents come to it from different mind-sets. Segments want LESS of: When it comes to practices the segments would like to see less of, both segments have the same view: Keep it simple, keep it free, don’t interrupt me, and give me some options. It’s a matter of degree, however. Segment 1 (Advertiser in control and important) shows a stronger reaction to what they want less of. This segment does not want advertisers interjecting into their conversations, which adds some additional granularity to the general desire for not being interrupted. The Advertiser in control and important segment wants less of: 

You PAY-FOR-CONTENT ... more often

14

174



You find new digital technology ... CONFUSING

13



Advertising INTERRUPTS you ... to get noticed

8



Big media companies CONTROL ... what you watch, read and listen to 7



Advertisers join the CONVERSATIONS ... you have with friends

5

The Indifferent segment wants less of: 

You PAY-FOR-CONTENT ... more often

6



Big media companies CONTROL ... what you watch, read, and listen to 5



Advertising INTERRUPTS you ... to get noticed

5



You find new digital technology ... CONFUSING

5

Segments want MORE of: When it comes to wanting more of certain practices, both segments agree on just one: Advertisers should conduct their affairs with a high level of integrity. The Advertiser in control and important segment wants more of: 

You expect advertisers … to BEHAVE responsibly in all ways

8

The Indifferent segment wants more of: 

You expect advertisers … to BEHAVE responsibly in all ways

7

Across both segments, every other element not mentioned above came up with neutral values. We interpret this to mean that while our study participants accept current advertising practices, they don’t feel strongly about them. Two Routes Lead to the Same Destination: The Pact Advertising practice is not at all like studying taste, product features, or package design. In those areas, sensory preferences vary, product features appeal

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to different people, and package designs are in the eyes of the beholders. Satisfying difference is what that game is about. For advertising, people want the same product but take different routes to get there. The segmentation shows the route, not the goal. In some ways, this is like a sister and brother who want to visit grandma. One likes to drive and the other likes to fly. Each uses different means and has different experiences –one the road, with its routing, accommodation, and hospitality options; the other with the choices available to the jet traveler. Yet both share the goal of reaching grandma. The same is true for advertising practice. The pact, we feel, teaches that practitioners can use most of the nonirritating tactics to get their sales messages across. Consumers will listen, they may become convinced, and they may even buy. Occasionally they will shut out the advertising with technology, or simply tune it out psychologically. Yet consumers are clearly aware that there is a pact. They do not ask the advertiser to jump through hoops, to create new realities, to amuse and entertain while selling. Consumers ask only that most content be free and that they be allowed to enjoy that content in exchange for the most precious possession they have —the moments of their lives. The Pact provides new guidance for practitioners 1. Companies should clarify their values and practices. Although this may sound boilerplate, clearly, consistently communicating a stance helps consumers understand the brand and company, and helps them identify where the common ground is. In one respondent’s words … 

‘[I want to know] the company's social responsibility and the ethics involved in their advertising campaigns.’

2. 2. Ensure that the values and practices are aligned with those of the target consumers. The pact provides guidance, but it isn’t generic. Brands need to understand the terms and provisions best suited to their customers and prospects. Brands may need to create a portfolio of pacts that address different products or segments. For example, a soft drink company might have different pacts for its flagship brand and youth brands. 176

Contrast these two statements. Each statement can become one term of a pact for a particular product. 

‘I would prefer to see advertisements that are able to get their point across without using sexuality. For me, it distracts from the selling of the product, rather than enhances it.’



‘The people that are used during the advertisement MUST BE EXTREMELY SEXY.’

3. The pact guides new advertising practices. Inventing new practices (like advertising as service, enabling consumer control, promoting word of mouth, tying into the green movement) makes sense when informed by the pact’s brands form with their customers and prospects. In one respondent’s words … 

‘I would like advertising to be more open and truthful, lay the product out there for all who are interested in buying whatever is being advertised.’ Or practices may be dead wrong, as this verbatim shows:



‘I really detest ’green washing’ in ads. We all know this type of advertising is hogwash, very untruthful, and disgusting … A lot more actual fact and truth in advertising would be a blessing.’

Practical implications and Summary The pact goes beyond the transactional contract inherent in the fair price to pay exchange of advertising for content. That contract (we’ll give you popular content, and you’ll tolerate the advertising) reflected a simple seller-buyer relation. Articulating a handful of straightforward, but profound, ground rules, the pact upends the old contract. The pact doesn’t start with content. Instead it says, assure me that you’re a good brand and company, and I’ll consider your offering and enter into a relationship with you … as a customer, supporter of your cause, etc. Be as creative and innovative as you can be with my interests at heart. But violate the pact and I’ll turn away. Acknowledgements 177

The substance of this chapter was coauthored by Steve Rappaport of the Advertising Research Foundation and Howard Moskowitz. The material was presented at the November, 2008 Conference on Mental Models in Advertising (Wharton School, University of Pennsylvania). The authors also wish to acknowledge the ongoing inspiration of Professor Yoram (Jerry) Wind of Wharton. Lauren Saul contributed tremendously by gathering primary materials and contributed insight and understanding for the research and analysis. The authors wish to thank Stacey Hall of Peanut Labs, and Becky Wu of Luth Research for their assistance in fielding the research. In this study, we worked with a new panel supplier, Peanut Labs. This supplier is known for sourcing respondents from social networks and working with people on the younger side, typically age 35 or younger. We were concerned that our RDE study to understand the mind of the consumer with respect to advertising might be too long for the younger respondent. We were pleasantly surprised to find that we had the same type of cooperation that we enjoyed with other, more traditional sample suppliers. We are also gratified that our approaches to experimental design were well-received by individuals who will be the next mainstream. References: [1] Chaney, P. (2009). The Digital Handshake. Hoboken, NJ: John Wiley & Sons. [2] Stuart, G. (2008, September). Why Consumers Hate Advertising & What They Are Doing About It. Retrieved May 14, 2010, from http://answers.vizu.com/solutions/pr/pdf/Why_Consumers_Hate_Ads.pdf [3] Stuart, G. (2008, September). You Can’t Avoid Ad Avoidance. Retrieved May 14, 2010, from http://www.adweek.com/aw/content_display/community/columns/othercolumns/e3id9a975e26c8545c5a020bb0908182476?pn=1 [4] The Economist. (2010, April 29). The Great Survivor: TV has coped with technological change. Other media can learn from it. Retrieved May 14, 2010, from http://www.economist.com/node/16009155

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[5] Saltmarsh, M. (2010, March 26). Murdoch Finalizes Paywall for Two British Papers. The New York Times. Retrieved May 14. 2010, from http://www.nytimes.com/2010/03/27/business/media/27paper.html?scp=1&sq=Murdoch%20Fina lizes%20Paywall%20for%20Two%20British%20Papers&st=cse

[5] Sulzberger, Arther Ochs, Jr. (March 17, 2011). A Letter to Our Readers About Digital The New York Times. Retrieved March 17, 2011, from http://www.nytimes.com/2011/03/18/opinion/l18times.html.

Subscriptions.

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Chapter 8 Getting information – What the audience doesn’t want the advertiser to know Abstract: Data mining tools, statistical workhorses of the information economy, allow us to learn a lot about people. Companies assemble databases of extensive consumer information that they then use when marketing to specific target populations. For that reason, individuals have become wary of disclosing personal information on-line. Frankly, the feeling is it’s my private business, not yours. Despite the fact that today’s social media platforms encourage open sharing, privacy remains an important concern. . To alleviate the privacy concerns of users in the face of evermore powerful analytics, the on-line industry has responded to public concern with posted privacy statements. They are often complex and hard to understand. In the fine print of the contract, they are not between people, but between the Internet site and the visitor/user. By using an extended version of RDE (Rule Developing Experimentatio), our tool to understand the algebra of the mind, we evaluated 83 Internet privacy messages from different companies. These were the companies’ best efforts. But did they work? Did they sound reassuring and ease people’s fears? RDE showed us some of the Internet customer’s thoughts. There’s one overriding message from these data: privacy is important to various audiences, but for vastly different reasons. What one subgroup feels fits them, another subgroup feels doesn’t fit them. The operating rule is: privacy means different things to people with opposing mind-sets. Manufacturers and other product originators publish lots of messages to assuage privacy concerns. Yet, when the creative platform does not properly hit the mark, and when words simply don’t resonate, it’s likely that the privacy concern will remain and perhaps be even more acute and disturbing. Introduction In 1890, in a landmark paper published in the prestigious Harvard Law Review, Louis Brandeis and Samuel Warren argued that individual citizens should be free from having intimate information published by an increasingly powerful press. They argued that basic human dignity should give individuals a right to privacy [1]. More than a century later, that powerful press has taken on new proportions with the growth of new media technologies. To this end, privacy has become the subject of innumerable articles [2]. The topic of Internet privacy looms larger and larger each week as more people go on-line, and as the technology and speed of information delivery and storage improves [3].

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In December 2009, Facebook caused an uproar among privacy advocates and attracted the scrutiny of lawmakers when it redefined as public information many items on users’ profiles, including friends, current city and job, their school, interests, and Web sites they liked. The degree to which users wanted to keep the information private did not matter [4]. The reason that actually caused the uproar was that, Facebook argued, no matter how much you were able to hide your information, ALL your information is out there and susceptible to being mined. Personal data is now a tradable commodity in capitalist societies [5]. One unintended consequence is that the free market economy and privacy are inherently at odds with one another [6]. The information storage capabilities of new media technologies can facilitate the collection and exchange of customer information, often without the knowledge or permission of the consumer. In addition, because digitally stored data can have an indefinite life span, public concern over the ability to control our own information is evident in consumer reluctance and wariness in disclosing personal data to on-line businesses [7,8,9]. Whether or not consumer anxiety about information gathering is warranted, the on-line industry has responded to public concern and consumer advocacy efforts with voluntarily posted privacy statements to alleviate those concerns. Although frequently governed by suggested industry guidelines, as specified by TRUSTe or similar industry coalitions, privacy statements seldom provide explicit reassurance that consumer information will be kept confidential and will not be exploited. Instead, they frequently outline how companies intend to use private customer information so that, in the event of consumer complaints, the companies are absolved of responsibility [6]. These privacy statements are placed inconveniently at the bottom of the page and are tedious, complex, and replete with legal language that the average Web user finds difficult to comprehend. Anne Kandra [10] found that many of the statements sound reassuring, but offer very little protection to the individual consumer. In addition, Web users often find privacy policies difficult to trust [11]. A Pew Internet and American Life Project report [8] revealed that trust is a vital issue for Web users. More than half of the Internet users find on-line tracking of personal information to be harmful and 86% of on-line users favor "optin" policies that require Web sites to ask for permission before collecting or using personal data. Nearly all of the Internet users (94%) want disciplinary action taken against privacy violators. It may well turn out that as people become increasingly accustomed to sharing information on the Internet, the concern with privacy will go away. 181

Perhaps, as the examples of Facebook and other social media show, people will share anything with anybody, or at least move in that direction. Another Pew Internet and American Life Project report also showed that many users are neither sufficiently proficient nor sufficiently willing to use available methods that can protect their privacy. For example, only 10 percent of Internet users have set their browsers to reject cookies; only 5 percent use anonymizing software to mask their computer identity [9]. Still, privacy matters. People have thought about confidentiality for a long time and continue to think about it each time they buy something on the Web [12]. However, the Web privacy policy information that companies offer is drafted by lawyers and has complicated twists. These specifications for information covered in privacy guarantees are not generated by consumers, although there are some analyses of what specifics in privacy are of consumer concern [13]. In this chapter, we evaluate consumer responses to such lawyerdrafted messages about Internet privacy. Specifically, we examine actual privacy statements featured in on-line portals to determine what specific messaging allays people’s fears. We use RDE to dive down into the granularity of experience, the everyday where people live and experience their individual lives and realities. Using experimental design to understand the components of privacy The privacy messages we are studying provide a good reservoir of ideas about privacy because they have been selected and used by actual corporations. Thus, we assume that the aspects of privacy are legally meaningful since, presumably, they have been approved by corporate legal counsel. Given the variety of messages presented on the Internet, it is easy to amass a set of different concept elements simply by visiting those sites. Table 8.1 provides some examples. There are a lot of messages about privacy. Even within the same topic there may be different ideas, expressed in different ways. These expressions are derived from a variety of different sources. In this context, it is important to note that the mode of presentation of an idea, as well as its wording, might be as important as the idea itself.

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Table 8.1. Eight different elements dealing with Internet privacy and individual choice about the nature of information available to companies. CH1 Mask your user identity ... and bypass lax security policies CH2 YOU now have the freedom to choose to ‘opt-in’ or ‘opt-out’ of collection practices about your behavior on the Web CH3 YOU now decide what information on-line companies can retain CH4 YOU now decide whether your name and other personal information is placed on mailing lists CH5 YOU now have the ‘ultimate’ say in how your information should be used CH6 You CAN SAY NO! when tracked by on-line advertisers CH7 YOU retain control of how your personal information is used CH8 YOU can now access your personal information ... the power to ensure accuracy Obtaining the test stimuli is fairly straightforward. The Internet is an almost inexhaustible fount of information about privacy in the form of privacy statements. Downloading these privacy statements provides the raw material, the grist for the RDE mill. In practice, the task is more one of winnowing and classifying than of discovering. It is simply downloading and editing, with an eye toward maintaining tonality. A representative set of Web sites was first identified, the privacy communications downloaded, and then parsed to obtain key messages. The key messages were slightly revised to fit the RDE format of simple declarative statements. The key messages were edited and polished using language as close as possible to the language actually present in the communication. Occasionally, a specific element was modified to make it conform to proper English, especially in cases where the phrase was used as part of a larger sentence. Dealing with lots of elements At this point, it’s important to emphasize that there are literally hundreds, perhaps thousands, of phrases on the Internet dealing with privacy. In any experiment, there is a limited amount of time, space, and money to run a study. Thus, we must winnow down the rich array of material into something that can be handled. In the previous chapters we solved the problem by limiting ourselves to 36 elements. We will expand our range beyond 36, to 83, and now illustrate a method by which it becomes possible to handle arrays of up to 300 elements.

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Restricting elements – the key to meaningful test stimuli We begin our investigation into privacy by the same classification method that we used before: 

We categorized elements into larger sets called silos. There were seven such categories: Emotional, Privacy Regulation, Legal, e-Commerce, Consumer Awareness, Consumer Protection, and Choices.



Each silo comprised eight to thirteen elements. The silo is a device that allows the creation of concepts, with the property that similar elements do not appear together in the same concept since no two elements from one silo can ever appear together in the concept.



As part of the setup, we reviewed the elements, and selected which pairs of elements should not be included in the same concept. Pair-wise restriction of elements from different silos ensures that the concepts make sense, and do not present information from different silos that contradict each other.

Dimensionalizing elements – the key to working with many elements With the large number of elements, it is likely that any single respondent will only test some, but not all, of the elements in the test vignettes. However, it is possible to estimate how the respondent would have evaluated elements not tested. One way to accomplish this objective is to apply a statistical forecasting procedure to estimate the impacts of elements not tested by a respondent. The approach has been described in previous publications [14, 15]. This estimating approach uses semantic differential scales (or dimensions), which locate each element as a specific point in the semantic space. This method is called dimensionalization. Dimensionalization proceeds in a straightforward manner. A small group of respondents, comparable to the full respondent sample, rates every element on a set of six to ten non-evaluative semantic scales. 1= A legal issue ………………... Versus 9 = A personal privacy issue 1= About consumer awareness …. Versus 9 = About consumer protection

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We then use a mathematical algorithm to estimate the impact of an untested element from the average impacts of the eight closest elements. The algorithm is applied repeatedly to a data set, always re-estimating the utilities of untested elements, but never changing the utility of tested elements. The algorithm generates a stable set of impact values by the time it is applied 50 to 100 times to the data set [14]. The interview We have already seen a number of orientation pages. This study on privacy followed the same format. The respondent read an orientation page which outlined the purpose of the study (viz., to understand privacy on the Internet). The orientation page told the respondent that he or she would read a short paragraph providing a vignette about privacy, and would then be instructed to rate the vignette on degree to which the vignette fitted the respondent. The scale ranged from 1 = definitely does not fit me … to 9 = definitely does fit me. It’s worthwhile spending a moment here to deal with the rating scale. The scales are used to ferret out various types of information. In this study, we wanted to get the respondent’s personal feeling about privacy. To do so, we instructed the respondent to think of himself only. In other chapters we focused on belief, or interest, or feelings of ultimate success. We could run half a dozen studies simply on the differences in response patterns as a function of the rating question. It’s not productive to do so here, but is worth being aware that there are different tonalities and sensibilities being tapped by the language of the rating question. The respondent evaluated a unique set of 60 different concepts on the rating scale. This task goes fairly quickly because the design of the vignettes is such as to permit rapid grazing of information. Finally, the respondent completed a detailed classification questionnaire, providing additional information. Classification questions dealt with issues such as Internet usage (reasons for using the Internet, hours per week, etc.), general media habits, as well as standard geodemographics (age, income, gender, etc.). Typical RDE studies follow a standard choreography: introduction, followed by the test vignettes (the actual experiment), and concluded by a selfprofiling classification. The combination of experimentation and self-report provides a powerful database from which to glean insights about the person. The fact that the consumer responds to existing ideas and language means that at the end of the exercise the investigator discovers what specifically works. In contrast, a lot of research efforts in the world of questionnaires ask the respondent to provide information. Yet, at the end of the exercise it’s not clear what to do; no 185

experiment has taken place. RDE avoids that problem, doing so at the very beginning by forcing one to think of alternative stimuli rather than alternative questions. The learning, the actionability and the ultimate value, lie in the stimulus itself, not in the array of questions. What’s the scoop with Internet privacy? We followed the standard analytics which begin by transforming the ratings. Ratings of 1-6 are recoded as 0 and ratings of 7-9 are recoded as 100. Using ordinary least-squares regression, we then related the presence/absence of each of the elements tested to the individual’s recoded rating (0 or 100). Keep in mind that each person did not test all of the elements. For elements that a person did not test, we estimated the impact or utility value (see Dimensionalization above). Even though a person did not test all of the elements, we could still estimate or fill in the missing values based upon the pattern of the elements that the respondent actually tested. It’s hard to get an impression of what’s happening from a table alone. A sense for the range of the impacts comes from looking at a dot density diagram, as shown in Figure 8.1. This type of plot is very useful when we deal with a large number of elements in a study. A density plot lets us see whether or not ideas are breaking through in terms of fitting the respondent. The range in this study is very narrow (between 5 and –5), suggesting that most of the elements do not fit the respondent. This disappointing finding will later be shown to result from the combination of responses from different groups of consumers with mutually contradictory opinions. A mediocre utility from the total panel may well result from the combination of two segments, one of which feels that the element definitely fits, whereas the other segment feels that the element definitely does not fit. The first would generate a high positive impact, the second would generate a high negative impact. The resulting impacts, when combined, would cancel each other. Figure 8.1. Density function for the utility of the 83 different elements. Each circle is an element. The density function is for the entire panel. The impact value shows the probability that the element fits the respondent.

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A sense of the nature of elements which fit the respondent and those that don’t fit comes from Table 8.2. We begin with the additive constant. The additive constant is the estimated conditional probability that the privacy concept will be rated 7-9 when there are no elements. Clearly this additive constant cannot be obtained from actual experience since all concepts comprise elements. The additive constant is an estimated output from the regression analysis. It can serve us as a baseline. Table 8.2. Strongest and weakest elements for privacy for the attribute of fits me. The data are from the total panel. Utility Silo Additive Constant 68 Fits me Emotional Your concerns ... your e-mail address being added 3 to mailing lists without your knowledge Does not fit me eWe give your on-line company a competitive edge -3 Commerce with our strong privacy policy Consumer There is a new initiative coming from the industry... it -3 Protection a SELF-REGULATORY system that protects the online customer Legal Needed ... ‘New Agreement Information Act’... on-line -3 data cannot be used for off-line transactions Legal FTC protection is here ... FTC has been given -3 authority to investigate on-line companies suspected of misusing consumers’ personal information Legal You are protected ... The Digital Signature Act of 1999 -3 makes it harder for on-line fraud to occur Privacy Privacy policies are important for consumers, -3 Regulation businesses, and national security. Now a selfregulatory organization of on-line advertisers will see to it that there are universal guidelines Consumer Federal legislation will ensure better protection -3 Protection 187

Privacy Regulation Emotional

Government involvement aids the creation of privacy policies Eliminate your concerns about filing your personal tax returns on-line

-4 -4

The high of 68 means that 68% of the respondents would say that the Internet privacy policy fits them. However, the particular elements neither add nor subtract very much from that high base. There is a mere 7-point range between the highest and lowest scoring elements. That range is very narrow for projects of this type, and certainly narrower than the ranges we have seen for the different projects on public policy. This narrow range may be due either to the irrelevance of the elements, or, more likely, to the fact that the total panel comprises subgroups having different mind-sets, and therefore, different value structures. Let’s look at the subgroups which are shown in Table 8.3. These subgroups comprise the different, quite standard demographics groups defined by who the person is, as well as mind-set segments. Again, those mind-sets are an output of a cluster analysis. The cluster analysis assesses the pattern of impact values for each individual respondent and groups together respondents who show similar impact values for elements, after going through the RDE exercise. Table 8.3. The table shows the subgroup, the base size, the additive constant, the range of impacts, the highest impact, and the element of highest impact value.

Total Sample

Base Size:

698

Additive Constant

Range

Max

68

7

3

Males

299

63

7

4

Females

399

72

8

2

35 or younger

307

62

8

3

Element that most strongly fits the subgroup

Your concerns ... your e-mail address being added to mailing lists without your knowledge Your concerns ... your e-mail address being added to mailing lists without your knowledge Your concerns ... your e-mail address being added to mailing lists without your knowledge Your concerns ... your e-mail address being added to mailing lists without your knowledge

188

36 or older

391

72

7

3

Beginner on the Web

56

62

14

7

Intermediate on the Web

341

68

8

4

Advanced on the Web

301

68

9

3

10 or less hrs/wk on the Web 11 or more hrs/wk on the Web

198

62

9

4

497

70

7

3

Purchase on the Web >1 /month

321

67

8

4

Purchase on the Web 1/month or less Income less than $50k

377

69

6

3

397

68

5

2

Income greater than $50k Internet News from net most often Internet News from

301

67

10

5

372

71

7

3

257

67

7

3

Needed ... A standard ‘opt-in’ privacy policy ... consumers must give consent about how their personal data is used You're concerned about cookies... a technology that makes it possible for anyone to follow you on-line You have the right to expect your personal information to be protected We will alert consumers if their personal information is likely to be used for mailing lists You shouldn't have to worry about junk e-mail ... soon you won't have to Your concerns ... your e-mail address being added to mailing lists without your knowledge Your concerns ... your e-mail address being added to mailing lists without your knowledge Your concerns ... your e-mail address being added to mailing lists without your knowledge Your concerns ... your e-mail address being added to mailing lists without your knowledge You shouldn't have to worry about junk e-mail ... soon you won't have to Your concerns ... your e-mail address being added to mailing lists without your knowledge Your concerns ... your e-mail address being added to 189

net not most often

mailing lists without your knowledge

Overall, the constants of those subgroups are very high, hovering around 70. The high constant suggests that the base idea of Internet privacy is important to the consumer. Table 8.3 shows the maximum utility values achieved by the best performing elements. It is important to note that the maximum level is low and, for the most part, the element range (maximum minus minimum) is small, except for the Beginner and for the High-Income respondents. Different mind-sets for privacy The cluster analysis generates three key subgroups showing radically different mind-sets. Differences among these segments can be clearly seen in Table 8.4 which shows the winning elements for the three segments. Keep in mind that ‘winning’ means the elements that the respondent feels most closely fit him or her. 

Segment 1, Interested but not discriminating, comprises individuals who may be interested in privacy, but don’t pay attention to the particulars. They want a global view rather than focusing on anything specific. These individuals generate impact models with a very high constant (90), but little differentiation among most of the concepts. High raters with low elementto-element discrimination appear in other studies as well. But Segment 1 comprises almost 40% of the sample, a far higher proportion than typically occurs in product studies in the commercial realm. We conclude that many respondents are vitally interested in the topic of Internet privacy, but show no reaction to the messages that companies put out to deal with privacy issues.



Segment 2, Appeal To Authority, suggests a different type of person. First, their additive constant is lower, 56. However, there are a number of messages to which this segment strongly reacts. People in this segment are clearly most responsive to appeals to authority and least by appeals to their own individual power.



Segment 3, Libertarian, is the smallest segment, comprising only 28% of the total. Their additive constant is 54. Segment 3 want to control their own privacy. They appear to be far more self-sufficient. They do not like the government interference even if it leads to the same end.

Despite the radically different mind-sets of the three segments, and especially segment 2 (Authoritarian) and segment 3 (Libertarian), these segments are 190

interspersed in the same population. Table 8.5 shows that it is very difficult to predict membership in a segment from information known about the respondent. As we will see in the discussion section, this difficulty will be very important later on.

Table 8.4. Strongest performing elements for the three segments. Tota l

Base size Silo

Additive Constant Segment #1 – Interested but not discriminating Emotional You're concerned about ‘cookies’... a technology that makes it possible for anyone to follow you on-line Segment #2 – Appeal authority Legal Needed ... Stiff penalties for companies that violate your privacy privileges Consume On-line consumers are protected by the FTC r (Federal Trade Commission) Protectio n Privacy Tighter on-line consumer privacy is necessary Regulatio to ensure security n eProtection ... A company that protects its Commerc customers will be more likely to keep its e customers Consume Now you can shop on-line more since security r is guaranteed Protectio n Segment #3 – Libertarians Emotional Your concerns ... your e-mail address being added to mailing lists without your knowledge Emotional You love the Internet but want privacy too Consume Your key need ... what information marketers r collect about you and who they're sharing it Awarenes with s

Segments 2 245

3 197

68

1 25 6 90

56

54

1

-3

1

7

1

-4

8

-1

-1

-6

7

-6

0

-5

7

-1

2

-4

7

2

0

-5

7

-1

3

-5

2

15

2 1

-7 -7

1 0

14 14

698

191

Choice

YOU now have the freedom to choose to optin or opt-out of collection practices about your behavior on the Web Choice YOU now have the ultimate say in how your information should be used Choice You CAN SAY NO! when tracked by on-line advertisers Emotional Your privacy will not be invaded anymore Legal Goal ... protect your privacy, preserve your anonymity Choice YOU retain control of how your personal information is used Choice YOU now decide whether your name and other personal information is placed on mailing lists Emotional You shouldn't have to worry about junk e-mail ... soon you won't have to Consume We will alert consumers if their personal r information is likely to be used for mailing lists Awarenes s Consume You have the right to expect your personal r information to be protected Protectio n Consume We believe it is important to know what r information is collected about you while on-line Awarenes s Consume Prior to collecting your private information we r will give you advanced notice Protectio n

0

-9

-1

13

1

-7

1

12

0

-6

-3

12

1 1

-6 -7

-1 0

12 12

0

-8

0

12

2

-5

2

12

2

-6

3

12

2

-6

3

12

2

-6

3

11

1

-7

0

11

2

-7

3

11

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Table 8.5. Partial classification of respondents in segments, geodemographics and self-stated attitudes. Segments Total 1 2 3 Base size 698 256 245 197 Familiarity with the Web: Individual Considers Self To Be Beginner 8 9 7 8 Intermediate 49 48 50 48 Experienced 43 43 43 44 Gender Male 43 38 45 46 Female 57 62 55 54 Age Under 25 11 9 14 11 25-30 19 18 18 20 31-35 14 14 15 13 36-40 19 18 20 18 41-45 15 13 13 18 46-50 10 12 9 11 51-55 6 9 4 5 Over 55 6 7 7 5 Average Age 37 38 37 38 Marital Status Single 17 16 19 15 Single, but in a couple relationship 11 13 8 13 Married 65 63 66 66 Separated 1 2 0 1 Divorced 5 5 6 5 Widowed 1 2 1 1 Education Some High School or less 2 2 1 2 High School graduate 17 22 12 17 Some College 34 30 40 32 Completed College 29 28 28 30 Graduate School 13 11 12 16 Other education beyond High School 5 6 6 4 (Technical School, etc.) Employment Employed full time – 55 55 54 56 193

35 hours a week or more Employed part time – less than 35 hours a week Not employed Student Retired

16

13

17

18

16 7 6

20 5 7

15 8 7

13 7 6

Summing up – what do these data mean from the practical perspective? We can view concerns about privacy in much the same way we view a consumer making a decision about product features and benefits. It’s clear from the high additive constant that privacy concerns are relevant to people. It’s also clear that there are no elements about which the respondent feels strongly, in either way. Privacy does not appear to be something to which an individual reacts instantaneously at a gut level when the right chord is struck. What does emerge is that privacy is personal and that individuals differ in what they feel to be relevant for them. The segmentation is stronger for privacy than it was for the various government policies of health, nutrition, education, and energy. Yet even among the segments, only one segment responds in a form that we might call dramatic. This is Segment 3, the Libertarian segment. The other two segments differ in what they believe describes their own feelings, but there is no evidence of the dramatic passion that is evidenced by the group we call the Libertarian. Note Part of the material in this chapter was included in Howard Moskowitz and Samuel Rabino (2002). The Alphabet of Privacy−What Are Communications About Privacy That Interest Internet Users? CASRO Journal, 99-109. References: [1] Warren, S. D., & Brandeis, L. D. (1890). The Right to Privacy. Harvard Law Review, 4, 193. [2] Bray, H. (2002, January 3). Spam Filter? Try a Bucket. The Boston Globe, C1. [3] Chander, A., Gelman, L., & Radin, M. J. (2008). Securing Privacy in the Internet Age. Stanford, CA: Stanford University Press.

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[4] Wortham, J. (2010, May 25). Facebook will introduce simpler privacy controls. The New York Times. [5] Hamelink, C. J. (2000). The Ethics of Cyberspace. London, U.K.: Sage Publications. [6] Papacharissi, Z., & Fernback, J. (2005). Online Privacy and Consumer Protection: An Analysis of Portal Privacy Statements. Journal of Broadcasting & Electronic Media, 49(3), 259+. [7] Nam, Ch., Song, Ch., Lee, E., & Park, Ch. (2006). Consumers’ Privacy Concerns and Willingness to Provide Marketing-Related Personal Information Online. Advances in Consumer Research, 33, 212-217. [8] Fox, S., & Lewis, O. (2001). Fear of online crime: Americans support FBI interception of criminal suspects' e-mail and new laws to protect online privacy. Pew Internet & American Life Project. Retrieved September 1, 2005, from http://www.pewinternet.org/Reports/2001/Fear-of-online-crime.aspx [9] Fox, S. (2000). Trust and privacy online: Why Americans want to rewrite the rules. Pew Internet & American Life Project. Retrieved September 1, 2005, from http://www.pewinternet.org/Reports/2000/Trust-and-Privacy-Online.aspx [10] Kandra, A. (2001, May 18). Consumer Watch: The myth of secure eshopping [Electronic version]. PC World, 19(7), 29-32. [11] Majdalawieh, M. (2010). The Integrated Privacy Model: Building a Privacy Model in the Business Processes of the Enterprise. International Journal of Information Security and Privacy, 4(3), 1-21. [12] Katz, J. E., Rice, R. E., & Aspden, P. (2001). The Internet 1995–2000: Access, Civic Involvement, and Social Interaction. American Behavioral Scientist, 45(3), 404-419. [13] Moskowitz, H. R., Rabino, S., Ciacco, V., Hjelleset, T., & Asami, R. (2003). The alphabet of privacy – what are communications about privacy that interest Internet users? Canadian Journal of Marketing Research, 21(1), 31-345. [14] Moskowitz, H. R. (1996). Segmenting consumers worldwide: An application of multiple media conjoint methods. In Proceedings of the 49th ESOMAR Congress (pp. 535-552). Istanbul, Turkey.

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[15] Moskowitz, H. R. & Martin, J. G. (1993). How computer aided design and presentation of concepts speeds up the product development process. In Proceedings of the 46th ESOMAR Conference (pp. 405-419). Copenhagen, Denmark.

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Chapter 9 Telling it all ... or only some of it ... and to whom Abstract: Data miners use large quantities of data to extract an array of information about individuals. On the opposite pole, consumers feel they are losing control over their sensitive personal information which is being released to third parties. Naturally, many consumers are becoming reluctant to share information that they consider private. Using RDE (Rule Developing Experimentation), we designed a 30-topic mega study to understand what types of information individuals would be willing to share with another specifically defined individual (such as a doctor, lawyer, casual acquaintance, employee at a local health spa) or institution (such as health or car insurance). Each RDE study followed the same design structure: a wall to guide respondents to the topic of interest, an RDE study on the topic, and then a selfprofiling questionnaire following the classic questionnaire model. The identical setup for all 30 topics allows easy comparisons and a larger-scale conclusion to be drawn about patterns of responses. Cross-sectional analysis of the study data revealed that people have a clear sense about what to share and what not to share. There is a clear hierarchy of individuals with whom one wishes to share information. Some individuals (lawyers, doctors) are, by their position, very trusted advisors. On the other hand, real estate brokers and the health insurance brokers are kept at arm’s length. However, it is all about the context that identifies the person with whom the information will be shared. RDE showed the algebra of the mind far more explicitly, and in substantially greater depth, than did the self-profiling classification. This suggested to us that experiments may be valuable, giving information that questionnaires cannot. Conventional classification questionnaires may not allow us to go into depth about people’s reactions to situations. Polls don’t make people give away confidential information. Classification questionnaires let people be the judge and the jury, mentally editing what they will share and what they will not. RDE, in contrast, forces responses to stimuli. By combining different types of information in the RDE vignettes, the experiment forces the respondent to give answers that are less mentally edited and less politically correct. Introduction In this chapter we will expand on our research dealing with privacy. Privacy is an elusive, value-laden concept, changing over time, subject to the vagaries of what current technologies may promise or threaten, and formed in part by the emotional zeitgeist of the time. Thus, it is hard to reach consensus on

197

a definition of privacy that will satisfy all due to the great variety of situations that privacy touches. Privacy comprises many facets: individual privacy regarding the integrity of the body; privacy regarding individual behavior; privacy regarding personal communication; privacy regarding individual data (see the previous chapter). Academic literature includes contributions from many different disciplines addressing the real meaning of privacy. However, a consensus does exist for those elements of privacy which relate to the collection, maintenance, use, disclosure, and processing of personal information [1]. Each of these topic areas of privacy is increasingly threatened in the Information Age, which is generally agreed to have begun in the 1970’s [http://en.wikipedia.org/wiki/Information_Age, 2]. Surveys conducted over the past several years suggest an emerging concern with privacy: 

The Gallup Organization reported that as many as 78% of the people felt that it is very important that their medical records be kept confidential [3].



A survey from Consumer Reports found that 71% of the 1,017 adults polled said they were very concerned that online firms are selling or sharing their personal data without their permission [4].

People are increasingly encountering their private information in the public domain and then articulate that it be kept out. Individual privacy rights stand in conflict with the capabilities and motivations of our information society. Each day we hear about new businesses which use computer technologies to gain this organized data to economize their operations. Inevitably, these new technologies are accompanied by public worry about loss of privacy and the ability to control information about themselves. This anxiety is only likely to increase in the wake of the U.S. Federal Government’s plans for an overhaul of surveillance laws which would make it easier for agencies to wiretap people who communicate using the Internet rather than by traditional phone services. The Federal Bureau of Investigation’s ability to carry out court-approved eavesdropping on suspects is compromised, as communications technology evolves. Since 2010, it has pushed for a legal mandate requiring companies like Facebook and others to build into their instant-messaging and other systems capabilities of complying with wiretap orders. The arguments for ability to monitor Internet communications are bolstered by the role of intelligence agencies in fighting terrorist activities within the borders of the United States [5].

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Privacy conflicts with corporate data mining In today’s global economy, information is currency that companies are willing to pay for[6]. Businesses want information about their customers because that information has value. There are two major uses of customer information that make the information valuable. First, businesses use it to learn what customers want and need. Secondly, they use it to offer their products to customers more efficiently. The more a business knows about its customers, the more able it is to satisfy them [7]. Data mining is one of those magic terms. It refers to the action of going through a set of data in order to identify patterns that are relevant to marketers. Data miners use an array of statistical tools, with common sense, to pose questions in order to identify key patterns and variables that co-vary with those patterns. Thus, a data mining problem might be to identify the pattern of preferences of an individual based upon geo-demographic variables (age, income, gender, and market), previous purchase behaviors, or attitudinal behaviors. In some respects, the data miner is a quantitative sociologist looking for determinants of specific behaviors so that the marketer can increase the chances of a successful next launch by using the patterns that the data miner uncovers [8]. Marketers count on consumers’ openness, permissiveness, and honest communication to optimize the communication and the product. For example, the availability of medical information has many benefits. Free-flowing medical information allows faster discovery of which treatments work and which do not. Medical information allows epidemiologists to locate and arrest disease outbreaks and environmental illnesses [9]. The benefits of freedom of information in the financial services sector include reduced risk of fraud, improved customer service, lower cost of credit, better prices and special offers, and reduced junk mail [10]. However, consumers who presume their privacy to be a sacred constitutional legacy may be put off when addressed, by name, with any message, even an accurately targeted one. They may be appalled to learn the degree to which their personal and commercial information has been gathered for data mining [11], or offended to learn that ad agency anthropologists are stalking them when they shop and when they communicate on the Internet in chat rooms [12]. A sharp debate is raging over the ultimate effect to be obtained when consumers give personal information in exchange for various benefits. On one side of the debate stand pragmatists who feel that, for the consumer, the benefits of technology outweigh the fears about privacy. On the other side of the debate stand defenders of personal freedom who denounce information-for-benefits 199

programs as a dangerous threat to on-line privacy. These critics warn that highly personal profiles could be created by these information-supply programs and that this information could be passed along to other companies, used for “unwanted” e-marketing and even seized by government investigators [13]. According to new reports from the Guardian and the Washington Post in June 2013, America's National Security Agency has been mining data from the servers of major American Internet companies, including Microsoft, Yahoo, Apple, Facebook and Google since 2007. The top-secret PRISM program allows the U.S. intelligence community to gain access from Internet companies to a wide range of digital information, including e-mails and stored data [14]. As reported in The New York Times (October 31, 2013), in response, companies are building technical fortresses intended to make the private information in which they trade inaccessible to the government and other suspected spies. Yet even as they take measures against government collection of personal information, their business models rely on collecting that same data in order to sell personalized ads. As long as they remain ad companies, they will be gathering a trove of information that will prove tempting to law enforcement and spies. [15] What is the bottom line for all of this? Combine the zeitgeist of the new age of technology with business aggressiveness of data mining and the message is clear: consumers are becoming less willing to give up some information. Marketing, which has always been about an exchange of information, is faced with a post-Information Age challenge. The marketer’s task is made that much harder when consumers actively, or even passively, turn away from the fray and let their willingness to participate in the marketing dialogue fade into indifference or grow into outright hostility and activism. Marketers engaged in consumer data mining must strike the appropriate balance between having a dataset as rich as possible to drive personalization efforts and at the same time ensure, demonstrate, and communicate that they do not overstep the boundaries of privacy concerns. A commonly used and extremely powerful stance on privacy was submitted by Alan Westin who defined privacy as an individual’s right “to control, edit, manage, and delete information about them[selves] and decide when, how, and to what extent information is communicated to others.” [16,17] And with this chapter, we will find out what consumers want other people and businesses to know about them. The mind of the consumer with respect to privacy

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Genomics, the science of how genes express themselves in physiology and behavior, provides an interesting organizing principle that has promise to advance consumer and public policy research. In genomics, the researcher performs simultaneous parallel tests on many thousands of genes in order to develop and then understand the pattern of responses across the different tests. With thousands of genes, and with a specific limited set of behaviors, the learning becomes more profound when the researcher can identify the specific gene and behavior correlated with an issue, such as obesity or specific types of birth defects [18]. The genomics approach is a powerful metaphor for the type of research presented here, providing both an inspiration and an organizing principle. This metaphor has been adapted to consumer and public policy research through the IT! studies [19]. This revolutionary set of 30 simultaneously conducted online conjoint studies helps us to see into the consumer mind as never before. The IT! approach uses a set of linked conjoint studies, each of which has the same type of elements and the same classification questionnaire. The wording of the element in a particular study varies by the nature of the topic. But generally, the same set of elements can be used for all of the individual studies. We saw an example of the It! approach in Chapter 2 dealing with terrorism. A key benefit of the IT! approach comes from the ability to compare results directly from one study to another. Rather than having the studies comprise independent, unconnected units whose results must be interpreted and synthesized and then comparisons made on the synthesized general results, the IT! studies allow easy comparison of specific elements, especially when the elements are identical. At the same time, however, each study within the IT! framework provides its own set of data, with conclusions, but there are larger-scale conclusions to be drawn from the pattern of responses across all of the studies. The IT! approach, with elements modified to be appropriate for the topic, has been previously presented for issues dealing with Anxiety – see Chapter 2 [20], Charitable Giving [21, 22], and Food/Beverages [23,24]. The present study represents a foray into privacy, a social issue, using the same exact set of elements for different studies. Designing the IT! project for privacy Most of what you will see in the study design will be familiar since it follows the approach we have used in the previous chapters. Our study 201

objective was to identify the types of information that the respondent would be willing to share with another specifically defined individual. We designed a mega study including 30 groups of individuals with whom one will share information (see Table 9.1). Each group of individuals is the subject of one study. The groups range from people considered to be very important in one’s life (e.g., one’s doctor), to individuals considered almost casual acquaintances (e.g., employees at a local health spa). In this way we get a sense of the range of information that a respondent would share with a specific type of individual, and in turn a sense of the span of privacy. Table 9.1. The topic areas with whom one will share information. Group 1 – Medical Doctor Dentist Pediatrician Hospital Group 2 – Insurance Health Insurance Life Insurance Car Insurance Group 3 – Financial Investment Real Estate Bank Credit Card Broker Broker Mortgage Accountant Broker Group 4 – Children’s Issues Child's Daycare Child's School University-College Group 5 – Casual Acquaintances Health Club Health Spa Hairstylist Library Video Rental Auto Mechanic Store Group 6 – Stores Local Wholesale Club Department Pharmacist Supermarket Store Store Group 7 – Miscellaneous Religious Dept. of Motor Lawyer Employer Institution Vehicles Five categories of information and one category of visuals Each of the 30 studies comprised six silos of information that could be provided by an ordinary individual to the professional who was the topic of the study. In turn, each silo comprised six elements (Table 9.2). The six silos ranged from visual stimuli to information about health, purchasing patterns, financial status, etc. Each element within a silo was created to be a stand-alone phrase that 202

could be incorporated into a test vignette. The research strategy mixes these elements into small, easy-to-read vignettes or test concepts. Table 9.2. The elements for the privacy study, divided into six silos of six elements Silo 1 – Visuals (Pictures) Set of forms Executives Family Woman at a computer Office Building Red Forms Silo 2 – Personal Details Family assets and property value Family members ... names, e-mail addresses, and telephone numbers Life insurance policy details ... designated beneficiaries and death benefits Personal identification about family members ... social security numbers and physical descriptions Number of people in household ... gender, ages Annual household income Silo 3 – Medical Complete medical history ... family diseases, past surgeries, and other ailments Results of physical examinations and tests Family history of alcohol or substance abuse Medical prescriptions filled Treatment for a particular ailment or medical condition Victim of abuse, neglect, or domestic violence Silo 4 – Family Financial Deposits and withdrawals from financial accounts Personal details from loan applications or insurance policies Credit line information, credit reports showing debt level Mortgage, home equity loans home refinancing Copies of bank statements and IRA statements showing information about your current financial status 401K plan or company profit sharing portfolio Silo 5 – Housing Property transactions, names of landlords, mortgage lending institutions, and payment information Homeownership … family home, vacation home Housing expense information ... mortgage payments, real estate taxes, and insurance premiums Income verification statements or letters 203

Home address Number of years living in house Silo 6 – Credit Card and Purchasing Behaviors Credit card information to various vendors Contact specifics … billing address, e-mail address, work and home telephone numbers Bonus card benefits … rebate offers and discounts Suggestive selling ... information disclosed on-line about your past purchases to other customers Details revealed on-line to other customers or other vendors about your frequent purchases Details disclosed after your participation in on-line surveys concerning shopping behavior The e-mail invitation This study was conducted over the Internet using respondents provided by a third party specializing in mailings (Digital Arrow, Inc.). Internet-based studies rely on e-mail invitations that provide a URL link. Thus, a key to a successful study is to create an attractive invitation that promises an interesting interview and some reward. Figure 9.1 shows the e-mail invitation sent out to respondents. As in other studies, the invitation does not tell the respondents too much about the interview, but provides sufficient information for the respondent to go to the next step of clicking the link. Approximately 5% of the respondents who receive the invitation actually participate, making it important to find a service that can handle large numbers of e-mail invitations in a cost-effective, rapid fashion. It is also important to specify the starting and closing times for the interview. The researcher has to give the respondents enough time to receive the e-mail and participate at their leisure. Our observations of the process suggest that most of the responses occurr within 24 to 48 hours of receiving the e-mail invitation. Figure 9.1. The e-mail invitation to participate in the privacy issues study.

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Subject Line: Talk back about Your Privacy Issues and tell it like it is! Invitation text: How much information about your life and your family’s life are you willing to share, and with whom? At I-Novation, an independent research organization, we’re trying to find out what aspects of privacy, people are willing to share with their doctor, a bank, and other institutions or companies with which you might do business. To connect to the study, simply click on the link (if your email does not support hotlinks, cut and paste the link into your browser) and choose one of the easy-to-answer surveys. http://12.109.160.54/ESMR01/privacy.asp Depending on your connection speed, each survey should take between 15 and 20 minutes to complete. Each survey you take will count for one entry in the prize drawing featuring a first prize of $150, and a second prize of $50. The more surveys you take, the more chances you have to win! Please participate soon, as the study will close at 9 p.m. (Eastern Time) on Sunday, May 23rd. Please be assured that any information you provide will be held in the strictest confidence. You will not be contacted by any sales or other research organization as a result of your participation in this survey. Thanks in advance for your input, and good luck! The I-Novation team.

Selecting the study The respondent who selects the link is immediately directed to a wall of studies shown in Figure 9.2. The wall presents the different studies to maximize the chances of the respondent choosing the least popular study. That is, the position of each study on the wall changes. The least-populated study appears at the top left where the eye alights first on the page, and the most populated study appears at the bottom right. In this way, we attempt to balance the studies, by giving the less popular studies a chance to be more visible. It is important to stress that, even at this point of study selection, the respondent does not know very much about the study topic other than the information conveyed in the invitation. Figure 9.2. The wall showing the different privacy studies. The studies are arranged in order of popularity, giving the least popular study a greater chance of being selected.

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Welcome to the PRIVACY Survey

Please select the survey that you would like to participate in by clicking on o You can participate in as many surveys as you wish. (You can participate in each survey only once) You may also send this link to two of your closest friends.

Disclaimer: If you are under age 18, please do not complete this survey. The invitation wa

Department Store

Wholesale Club Store

L

Auto Mechanic

DMV

V

Library

Hairstylist/Barber

H

Health Club

Religious/Institution

U

Child’s Children School

Child’s Children Daycare

E

Lawyer

Accountant

M

Real Estate Broker

Credit Card Company

I

Bank

Car Insurance

L

Health Insurance

Pharmacist

H

Pediatrician

Dentist

D

The interview The interview begins immediately after the respondent selects the study. The structure of the interview is identical across all of the 30 studies, with two exceptions: The orientation page: Modified slightly according the study topic. The rating question: ‘How willing are you to share this information with your… (study topic)?’ 206

The respondent begins with an orientation page which introduces the study (Figure 9.3). This page describes the purpose of the study, the different tasks, and the subject matter. Every effort is made to keep the information neutral. Even though the invitation presented the idea of telling one’s mind, the tonality of the actual interview itself is muted in order to let the respondent’s own feelings emerge. Figure 9.3. Orientation page to the survey dealing with sharing information with one’s doctor. Welcome to the Privacy survey. This survey will take about 15-20 minutes to complete. During the course of the survey, we will show you concepts (vignettes) regarding different aspects of privacy … things that you would want to share with your DOCTOR and information you certainly would not want to share. Each concept is a packet of information that you might be asked to share. Based upon the issues in our ‘vignette’ please tell us:

‘How willing are you to share this information with your DOCTOR?’ 1=Not willing … 9=very willing. Some of the concepts (vignettes) may appear similar, but in fact they are all different, so please read each one carefully. Please rate the concepts as a whole and use the entire rating scale. At the end of the concept section of the interview you will be asked some demographic questions. As our way of saying ‘Thank You’ you will be entered in a prize drawing with a first prize of $150 and a second prize of $50. At the end of the study you can go back to the main study page and take additional surveys. The more surveys you take, the more chances you have to WIN … so take as many surveys as you like (up to 30 chances). You will not be able to take any one particular survey more than once. Please press continue to start the survey.

Test vignettes designed for individual-level modeling Each respondent evaluated 48 different test vignettes, with a concept comprising 3-4 elements. A concept could have either one or no element from each silo. Figure 9.4 shows an example of a test vignette comprising three text elements and a visual.

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The rationale for working with as many as 48 test vignettes, or even more on occasion, is worth discussing in a little more detail. Much of the received wisdom in consumer research holds that the shorter the interview can be made, the better the data will be. To some extent, this point of view is quite correct. Short interviews do not bore as much as long interviews. However, short interviews do not provide very much information either, and so, they are not particularly valuable to answer complex questions that need patterns of responses. Longer interviews may not be interesting, but can provide a lot of information, assuming that the respondents pay attention and do not lose interest. Figure 9.4. Example of a test vignette comprising one element from four of the six silos. The rating scale is at the bottom of the concept.

Family Members … names, e-mail addresses and tel

Mortgage, home equity loans, home refin

Bonus card benefits … rebate offers and di

How willing are you to share this information with your DOCTOR?

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For Stimulus-Response (S-R) research of the type done here, using the RDE approach, the issue of interview length versus results quality also holds true, but is even more important. In RDE, much of the useful information is obtained from the pattern of responses to systematically varied vignettes. The greater the number of concept elements the more valuable the information, everything being held equal. The outcome is the perennial dynamic tension between incorporating more elements into the study to create better data versus one’s desire to shorten the interview. Self-profiling questionnaire (Classification) The self-profiling questionnaire generates the necessary personal information separate from the responses to the test stimuli. The self-profiling questions deal both with standard geo-demographics and with attitudes towards privacy and media. Each of the 30 studies used the same self-profiling questionnaire so that the data could be compared from one study to another. Figure 9.5 shows an example of one of the questions from the classification: the question dealing with a checklist of individuals or groups with whom the respondent would feel comfortable sharing personal information. Figure 9.5. Screen shot of classification question, specifically dealing with a checklist of individuals or groups with which the respondent would feel comfortable sharing personal information.

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Making sense of a truly rich database – how to handle 30 studies The database generated by a mega study is quite large, so the most productive strategy is to look at the issues in a systematic way. We will use the following set of analyses to make the database coherent. 1. Likely and unlikely recipients of private information: With whom would respondents say that they would feel comfortable sharing information? 2. Participation statistics: What do log-ins and drop-outs tell us about privacy issues? 3. What types of information are shared: What are the limits of information that a person will give? 4. Visuals as passive indices of information sharing: In light of the importance of emotional elements, how do visuals perform in these concepts and do they add much, if anything? Question 1: Likely and unlikely recipients of private information Two of the questions in the self-profiling classification dealt with a respondent’s comfort in sharing information with outside individuals or groups. One question was ‘Which of the following people do you feel comfortable sharing personal information about yourself with?’ The second question was about sharing family information. Table 9.3. Percent of respondents saying that they feel comfortable sharing personal information with an individual of a certain type (e.g., doctor). Data averaged across the 30 privacy studies, from the selfprofiling classification portion of the interview. Sharing data with… Doctor Lawyer Health Insurance employees Dentist Pharmacist Hospital staff Pediatrician

Sharing data about myself family 74% 69% 37% 31% 34% 28% 26% 23% 25% 21% 25% 24% 22% 25% 210

Religious Institution staff Life Insurance Broker Accountant Employer Real Estate Broker Mortgage Broker Child's/Children's School staff Bank employees Credit Card employees Investment Broker Car Insurance Broker Dept. of Motor Vehicles employees University/College staff Child's/Children's Daycare staff Hairstylist/Barber Health Club employees Health Spa employees Library staff Warehouse or Wholesale Club Store employees Local Supermarket employees Department Store employees Video Rental Store employees Auto Mechanic

17% 16% 15% 13% 10% 9% 8% 8% 8% 7% 7% 6% 5% 4% 3% 1% 1% 1% 1% 1% 1% 1% 1%

15% 12% 10% 9% 6% 6% 11% 5% 5% 4% 5% 4% 4% 7% 3% 1% 1% 1% 1% 1% 1% 1% 1%

The table of percents shows us clearly that respondents perceive a hierarchy of individuals with whom they would feel comfortable sharing information. This hierarchy emerges without the type of information being specified by the question. At the top of the hierarchy we find, not surprisingly, is the Doctor, with 74% of the respondents feeling comfortable sharing information about oneself, and 69% feeling comfortable sharing information about one’s family. Dropping to half or less are the Lawyer and Health Care Provider. At the very bottom of the list are casual contacts, those with whom respondents do not want to share confidential information. Measuring readiness to share: Baseline interest in sharing personal information (experiment) versus the direct response (questionnaire) Rather than asking the respondents to identify those with whom they would share information, as done in the classification questionnaire, let’s instruct the same individuals to respond to the test vignettes. That is, our respondents will participate in an experiment with test stimuli and then we will measure their 211

responses. We ask the respondents to rate the likelihood of sharing all the information presented in the scenario. RDE thus moves beyond simple classification questionnaires to a more concrete scenario and observes stated choice behavior (degree of feeling one would share information transformed to not share versus share). Armed with this point of view, we look once again at results, but this time we create a mathematical regression model based on the experiment. We run the experiment and then create the 30 regression models, one for each RDE study. Our research strategy generates two measures of sharing: 1. Self-profiling questionnaire (see above): The percentage of people who would share based upon checking off a box I would share confidential information with this individual. 2. Experiment – Regression Analysis: The Additive Constant is an output from the regression model. The additive constant can be construed as basic likelihood of sharing information without any additional information being presented about the nature of the information to be shared. The actual Additive Constants appear in Table 9.4 (first column). We see they differ by the type of person receiving the information, which is what one might expect. As the data shows, a respondent would be most willing to share information with a Lawyer (Additive Constant = 61), less so with an Accountant (50), and significantly less so with a Doctor (40).

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Table 9.4. Additive Constant (propensity to share information), log-ins, complete, and ratio of log-ins to completes. Study Name:

Additive Log- Comple Constant ins ted

Lawyer Accountant Mortgage Broker Child's/Children’s Daycare staff Doctor Hospital staff Religious Institution staff Bank employees Investment Broker Real Estate Broker Health Club employees Wholesale Club Store employees Life Insurance Broker Dentist Department Store employees Pharmacist University/College staff Pediatrician Hairstylist/Barber Dept. of Motor Vehicles Health Insurance employees Child's/Children’s School staff Employer Credit Card employees Car Insurance Broker Library staff Local Supermarket employees Video Rental Store employees Health Spa employees Auto Mechanic

61 50 49 41

60 48 46 68

34 33 34 40

Ratio of complet es to logins 57% 69% 74% 59%

40 39 37 35 34 32 27 26

129 116 142 96 67 56 66 87

85 71 85 59 33 34 43 50

66% 61% 60% 61% 49% 61% 65% 57%

26 25 24 21 20 20 18 18 17 14 14 14 13 12 9 7 7 5

47 70 80 64 73 57 82 77 78 111 109 88 61 106 129 93 72 58

36 41 51 38 41 35 52 58 47 62 69 57 42 69 75 60 40 37

77% 59% 64% 59% 56% 61% 63% 75% 60% 56% 63% 65% 69% 65% 58% 65% 56% 64%

Does the experiment agree with the questionnaire? The easiest way to answer is by plotting the Additive Constants against the percent saying they would

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share. When these two measures agree, we ought to see a reasonable facsimile of a straight line. Let’s look at the scatterplot in Figure 9.6. Figure 9.6. Comparison of direct rating of share information by selecting those with whom one would share (abscissa; classification questionnaire) versus propensity to share obtained using the Additive Constant from conjoint analysis (ordinate)

The RDE experiment with systematically varied vignettes reveals a range of Additive Constants whose pattern differs from the pattern one might have expected from the classification questionnaire. Some of the Additive Constants are low. These correspond to the incidental groups of people who have no right to an individual’s private information. Thus, it is not surprising that individuals say they would not share information with Health Spa employees or their Auto Mechanic. In some other cases, such as one’s Accountant or Mortgage Broker there is a willingness to share private information even though the self-profiling classification would suggest that a person would not share information (see Table 9.4).

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The differences might well be due to the mental framework in which the question is answered and the depth of information on which the statistical index is based. In polling questions of the type found in the classification section of the interview, the respondent is not provided with a scenario, but rather simply asked a general question to the effect of ‘would you share information with so-and-so?’ Question 2: Log-ins, completes, and the Additive Constant One of the features of the mega study is the freedom to choose a study and to drop out of it. Given this freedom, how many people select a study, and how many people complete a study once they enter the interview? Let’s return to Table 9.4 and look at the right hand columns. We see the number of log-ins and the number of completes. Finally, we can compute the ratio (i.e., the proportion of people who, having started the particular study, actually complete it). Without knowing anything more about the topic, we can hypothesize that the more frequently chosen study should be one in which there is either the greatest latent interest or, perhaps, is the least emotionally threatening. We see some verification of this hypothesis in Table 9.4. The studies with the greatest number of log-ins are: Religious Institution staff (142), Doctor (129), Local Supermarket employees (129), Hospital staff (116), and Child’s/Children’s School staff (111). These may be interesting for two reasons: 1. The Doctor, Hospital staff and Child’s/Children’s School staff deal with a person’s own welfare, or the welfare of those close to that person. Welfare here can be religious, health, or children. 2. Participation in the privacy study with supermarkets may be done out of curiosity. When it comes to the topic chosen least frequently, we see three financial groups emerging at the bottom: Accountant (48), Life Insurance Broker (47), and Mortgage Broker (46). Completion rates are also interesting. The highest proportion of completion occurs with: Mortgage Brokers (74%), Life Insurance Brokers (77%), and Dept. of Motor Vehicles (75%). The studies with the lowest proportion of respondent completions were: University/College staff (54% complete), and Child’s/Children’s School staff (56% complete).

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Why the difference between these two groups of receiving individuals? The answer may be in the nature of the receiving individual. 

Mortgage Brokers and Life Insurance Brokers provide something that a respondent wants, namely an economic benefit. The respondent knows he or she has to surrender information. Dept. of Motor Vehicles employees deal with the respondent’s fears. In all three cases, the typical respondent who will be involved with these individuals spends time rehearsing a plausible story. There is greater emotional involvement and more play acting. Some of that play acting includes trying to anticipate questions whose correct answers can gain a mortgage, life insurance policy, or avoid punishment.



In contrast, with schools and education there is probably not as much anticipatory play-acting going on to ensure that one says the right thing at a meeting.

Question 3: Limits on sharing specific types of information The real substance of the data comes from the RDE portion, the systematic variation of the elements. As we will see below, there are limits to the type of information that people will share. The complexity comes from the two aspects of the study: 1. The specific information which itself varies in level of personal intimacy. 2. The recipient may or may not be the appropriate individual with whom to share the specific information. A detailed analysis of each element is beyond the scope of this chapter. A more productive approach to showing differences comes from designing a specific vignette and then seeing how the respondents would react to it and to the elements that comprise it. Let us consider the utilities of six different elements across the 30 studies. We could have chosen any of six elements from each silo. The choice here is arbitrary. 1. Visual – Visual of family 2. Standard geo-demographics – Number of people in household, their gender and ages 3. Medical – Specific medical prescriptions filled 4. Financial – Credit line information, credit report showing debt level 5. Housing – Housing expense information, such as mortgage payment, real estate taxes, and insurance premiums 216

6. Store purchase behavior – Details disclosed after your participation in on-line surveys concerning shopping behavior Now, the question is what patterns we can discover from these six elements signifying information to be shared with 30 receiving people? The results appear in Table 9.5. The columns show the Additive Constant (column A) and the impact scores for the visual element (column B) and for one element from each of the five types of information (columns C, D, E, F and G, respectively). The final column shows the sum of the additive constant and the six elements. The rows correspond to the individuals with whom the information is shared. Positive numbers mean that the respondent will share the specific information in a column with the specific person in the row. Negative numbers, in contrast, mean that there is a decreased likelihood of sharing that particular information with that particular individual. Thus, for a Lawyer, and without any other information being conveyed, the conditional probability of sharing information is 61, or 61% (i.e., the Additive Constant). However, there is a reduced likelihood of sharing information about the number of people in the household, their gender and their ages (-7), so the net effect is about 54 for a lawyer with that particular information. In contrast, there is an increased likelihood of sharing information with one’s lawyer about credit line information or credit reports showing debt level (+7), so the net effect is about 68 for a lawyer with that particular information. We see from this analysis that there are two aspects of privacy: 

Basic predilection: The first aspect is one’s predilection to share information with a specific individual. It is revealed by the additive constant.



Contribution of content: The second aspect deals with the content and is shown by the utility value. The utility value reveals the additional increase or decrease in likelihood of sharing information and must be added to the constant to arrive at an overall measure of total likelihood.

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Table 9.5. Additive Constant (propensity to share information), and utility values for one element for each of the six types of information. A B C D E F G H Stand Sum ard Store Add Visual Geo Purch . of Medi Fina Hou Demo ase Con Famil cal nce se Behav st y ior graphi cs Lawyer 61 -1 -7 -1 7 1 -7 53 Accountant 50 -2 4 -12 -3 12 -6 43 Life Insurance 26 3 9 4 -4 6 -8 36 Broker Health 17 6 8 14 -8 -5 -6 26 Insurance Dentist 25 2 2 1 -1 -1 -4 24 Investment 34 4 -3 -15 2 4 -8 18 Broker Religious 37 -2 2 -2 -6 -6 -5 18 Institution staff Doctor 40 1 3 3 -13 -15 -2 17 College staff 20 3 3 -1 -7 0 -2 16 Bank employees 35 1 2 -16 -2 0 -5 15 Department Store 24 2 0 -5 -2 -6 -1 12 employees Real Estate 32 -6 7 -19 -5 11 -9 11 Broker Wholesale Club Store 26 -1 1 -5 -6 -3 -1 11 employees Credit Card 14 1 2 -7 0 1 -1 10 employees Health Spa 7 2 1 2 -2 -1 1 10 Pediatrician 20 -1 2 12 -9 -12 -2 10 Supermarket 9 3 1 0 -3 -1 0 9 employees Mortgage 49 -5 2 -26 6 -1 -17 8 Broker Pharmacist 21 -6 4 4 -9 -6 -3 5 218

Auto Mechanic Hospital staff Health Club Car Insurance Broker Library staff Hairstylist Employer Dept. of Motor Vehicles Child's/Children ’s School staff Video Rental Child's/Children ’s Daycare staff

5 39 27

1 -7 -2

0 2 -4

-2 3 -10

0 -10 -3

0 -12 -2

0 -11 -3

4 4 3

13

1

4

-3

-6

-5

-2

2

12 18 14

-2 -2 -3

2 4 0

-3 -2 0

-5 -8 -3

-1 -6 -4

-2 -3 -4

1 1 0

18

-1

-1

-4

-7

-7

-3

-5

14

-2

2

-3

-10

-4

-2

-5

7

-2

0

-2

-5

-5

-1

-8

41

-2

-10

-13

-19

-11

-7

-21

The patterns that emerge from Table 9.5 are quite simple but persuasive. 

There is a clear hierarchy of individuals with whom one wishes to share information. We see this from the Additive Constant, as well as from the sum of the additive constant and the six impact values. Lawyer is at the top, and a number of other people are at the bottom, depending upon whether the key measure is the additive constant or the additive constant together with the impacts of each of the six elements. However, we find no surprises here.



Respondents clearly know the types of information that they would like to share with other individuals. The respondents want to share information about medical issues with the Life Insurance Broker or Health Insurance Broker and definitely not share information about medical issues with the Financial Sector people.



Some individuals are basically very trusted advisors, such as a Lawyer or a Doctor, but even for these trusted advisors there can be information that a respondent is simply not willing to share.



And then there are some individuals who are very trustful advisors, yet with whom a person would ‘definitely not’ share personal information. Two good examples of these trusted advisors to be kept at arm’s length are the Real Estate Broker and the Health Insurance Broker. 219

What type of information makes people think twice before sharing? It’s becoming clearer that people have a sense of what to share and what not to share. Some information is very sensitive and rarely shared with anyone except the right person. Other information may be so irrelevant as to be shared with almost anyone. A key benefit of a mega study such as our privacy study is the ability to compare the impact values of the same element across all the studies. It’s hard to look at a wall of numbers. We have to summarize the finegrained information. First, we will look at how often the element received a positive impact value, meaning that the element (i.e., the specific type of information) will be shared with the receiving individual. And, while we are doing that count, we can also look at the maximum and minimum impact values across the 30 studies. The maximum impact value means the greatest incremental proportion of people with whom one would share the specific information in the element. The minimum impact would mean the least incremental (usually negative) proportion of people with whom one would share the information. With that analysis in mind, let’s proceed to the data in Table 9.6. No element is always negative. This means that the respondents are willing to share each of the types of information with someone. However, an analysis of the impact values of the statements across the 30 studies suggests that most of the values are negative, meaning that the respondents don’t want to share the information. Only three statements show a positive utility score in more than 50% of the studies. Respondents are prepared to share the information about: Number of people in house, gender and age, Number of years living in the house, and Home address, and contact specifics. This type of information tends to be the most innocuous. Yet the information is not universally shared. Some elements are highly sensitive, but will be shared with the right people. An example is victim abuse, neglect, or domestic violence. This information is very willingly shared (+17) or very strongly kept to oneself (-28). It is all in context, i.e., dependent upon the person with whom the information will be shared. One’s purchase behavior and credit card behavior is something rarely desired to be shared, as seen by the very low positive impact values and the moderate negative impact values. This desire to keep one’s purchase patterns private is not surprising since there are numerous privacy laws in e-commerce. People don’t want to be tracked. For example, consider the element Suggestive selling … information disclosed on-line about your past purchases to other customers. This 220

information is part of the backbone of companies like Amazon, and a basis for collaborative filtering. It has virtually NO positive aspect at all (maximum impact of +1), but a significant negative aspect (minimum impact of -17). The bottom line here is that in many instances, incorrectly offering the information can be quite damaging in terms of respondents. The rule of thumb here is when in doubt, don’t. Table 9.6. Percent of times across 30 RDE studies where a specific element has an impact value greater than 0 (i.e., would be shared).

Information to be shared

Number of people in household ... gender, ages Number of years living in house Home address Contact specifics … billing address, e-mail address, work and home telephone numbers Annual household income Homeownership … family home, vacation home Complete medical history ... family diseases, past surgeries, and other ailments Family assets and property value Medical prescriptions filled Treatment for a particular ailment or medical condition Housing expense information ... mortgage payments, real estate taxes and insurance premiums

Impact value from the Interest Model % of times in 30 studies that the element Max. Min. Range achieved an impact >0 (would share) 83%

9

-10

19

67% 63%

10 9

-12 -16

22 25

50%

6

-6

12

43%

15

-8

23

40%

7

-8

15

37%

16

-21

37

33% 33%

11 14

-17 -26

28 40

33%

8

-29

37

30%

12

-15

27 221

Income verification statements or letters Victim abuse, neglect, or domestic violence Results of physical examinations and tests Family history of alcohol or substance abuse Mortgage, home equity loans, home refinancing Family members ... names, e-mail addresses, and telephone numbers Life insurance policy details ... designated beneficiaries and death benefits Copies of bank statements and IRA statements showing information about your current financial status 401K plan or company profit sharing portfolio Bonus card benefits … rebate offers and discounts Credit line information, credit reports showing debt level Property transactions, names of landlords, mortgage lending institutions, and payment information Personal identification about family members ... social security numbers, and physical descriptions Deposits and withdrawals from financial accounts Credit card information to various vendors Details disclosed after your participation in on-line surveys concerning shopping behavior Personal details from loan applications/insurance policies Details revealed on-line to other customers or other vendors about your frequent purchases Suggestive selling … information disclosed on-line about your past purchases to other customers

30%

11

-12

23

27%

17

-28

45

23%

10

-22

32

23%

12

-27

39

23%

13

-17

30

20%

5

-10

15

20%

9

-17

26

20%

8

-21

29

20%

9

-15

24

20%

2

-11

13

17%

7

-19

26

17%

6

-12

18

13%

7

-13

20

13%

4

-22

26

13%

2

-13

15

10%

1

-17

18

7%

7

-17

24

7%

3

-13

16

3%

1

-14

15

222

Question 4: What do visuals add to vignettes? The rules underlying the usage of visuals are not clear. No one knows what the visuals are supposed to add, but there is the assumption that the respondent’s emotions may be more readily tapped with visuals. This study on privacy provides an opportunity to go beyond the text and explore visuals. The experimental design comprised six different visuals: Forms (E1, E6), People shots (E3, E4) and Business/Building shots (E2, E5). These six visuals appear in Figure 9.7 in black and white. In the actual study, these elements were inserted as color pictures into the concepts as elements, meaning that the pictures were treated in the same way text elements were treated. The pictures appeared in the vignettes simply as another part of the experimental design. We estimate their impact along with estimating the impacts of the different text elements.

Figure 9.7. The six visuals used in the privacy concepts.

Despite the conventional wisdom that emotion and information are often well conveyed by pictures, even perhaps better than text, we don’t find any evidence for that claim, at least in our privacy data. Looking at the results from the study in Table 9.7, we see it’s not the visuals but the text that works.

223

Table 9.7. How the six specific visuals perform in test vignettes for the 30 different privacy studies. The numbers in the body of the table are the impact values for each picture (E1-E6), when tested in the different studies. The numbers come from the Interest Model. Positive numbers denote increased interest in sharing, negative numbers denote decreased interest in sharing. E1

Average impact of the specific picture across the 30 studies Health Insurance employees Local Supermarket employees Car Insurance Broker Investment Broker Credit Card employees Health Spa employees Department Store employees Child's/Children’s School staff Pediatrician Real Estate Broker Accountant Lawyer University/College staff Auto Mechanic Dentist Life Insurance Broker Mortgage Broker

E2

Array of Forms

E3

E4

Woman Family At Computer

Executives

E5

E6

Office Building

Single Red Form

-2

-1

-1

-1

-2

-2

5

5

6

1

2

1

5

4

3

0

0

1

3

2

1

-1

6

-1

1

7

4

0

-1

-4

3

2

1

1

-1

4

0

-4

2

3

2

3

-2

0

2

-1

2

6

-3

0

-2

0

2

4

3 5 -4 -6

2 -1 0 1

-1 -6 -2 -1

-1 1 5 -1

-2 1 -7 1

-3 -2 2 1

-1

2

3

-3

-5

1

-1 -4

0 -4

1 2

0 1

1 -4

-2 -1

-7

5

3

-5

-5

2

-8

1

-5

3

-2

2 224

Health Club employees Library staff Video Rental Store employees Dept. of Motor Vehicles employees Doctor Bank employees Employer Child's/Children’s Daycare staff Religious Institution staff Wholesale Club Store employees Pharmacist Hairstylist/Barber Hospital staff

-3

-1

-2

0

-1

-2

-1

0

-2

-1

-1

-2

-1

-1

-2

-1

0

-3

1

-2

-1

-2

-3

-5

-1 -7 -3

-3 -1 -2

1 1 -3

-4 -3 -2

-5 -4 -4

-1 0 -3

-4

-3

-2

1

-3

-6

-3

-1

-2

0

-5

-3

-4

-3

-1

-1

-3

-5

-4 -2 -2

-4 -4 -5

-6 -2 -7

-3 -4 -4

-1 -3 -5

-2 -4 -9

It is clear from Table 9.7 that, on average, no visual scores well across the different studies. There are some specific combinations of visual and study that stand out, such as a visual of the family in the study on Health Insurance, or a visual of an office building and the study on Car Insurance. These positives mean that the respondents would share more information with a health insurance company if the company were to feature a picture of a family. There are some noticeable negatives as well, where the picture actually reduces the likelihood of personal sharing of confidential information. For example, combinations such as the visual of the family with the study on the Real Estate Broker, the visual of an office building with Accountant, or the visual of an array of forms with the Mortgage Broker all diminish the desire to share confidential information. There is no clear pattern, however, which shows one visual always or never working. The effect of visuals in concepts deserves further study in a more systematic manner in order to determine the role that visuals play in driving responses in concepts. Unintended consequences – can one desensitize a person to privacy concerns? 225

Today’s world of the Internet seems to be filled with people offering their most secretly guarded information to hundreds of friends using social media such as Facebook and Twitter. They, and others, allow us to show and tell a coterie of friends about every move we make. It’s not enough that we provide valuable information about ourselves to these sites; we’re even encouraged to keep blogs, and share our inner feelings with thousands of people we don’t know. Has the Internet destroyed privacy by appealing to our desire to exhibit ourselves? It’s not, of course, clear. On the other hand, we have some interesting, albeit quite unexpected, results from our mega study on privacy that may foretell this desensitization. We can view the RDE portion of the study as an exercise which got the respondent to think about sharing information with the specific recipient of the privileged information.. For example, when the recipient was the Doctor, the entire focus of the 48 vignettes was on sharing information with a physician. We can imagine that the respondent might either be sensitized or desensitized to sharing with the physician. One question that arises is whether after such an introductory exercise with 48 vignettes, the respondent would say he feels more or less likely to share information with the person who is the focus of the particular study (e.g., Doctor). In this example, would more respondents than average say in the self-profiling classification that they would like to share personal information with the physician? Let’s approach the issue of desensitization by looking at the percent of respondents in the classification portion of the study who say that they would share private information with the person who is the specific topic of the particular study. Thus, to return to our example of the doctor, what happens to one’s self-profiled willingness to share information with the doctor after he or she has just gone through the 48 vignettes. Is self-profiled willingness the same? Does it decrease because the respondent is desensitized? Or does it increase because thinking about sharing information makes the respondent more aware of how important it is to share? A fairly simple analysis For a particular study, we expect desensitization to occur when MORE respondents say that they would share confidential information with the person who is the specific focus of the study than they would for that same individual in other studies answering the same question. In the case of sharing information with the doctor, desensitization occurs when we see a higher willingness to share information with the physician (self-profiling classification) in the Doctor study compared to sharing information with the physician (again self-profiling) in the 226

other studies (for instance, Lawyers). Having gone through the 48 vignettes with physicians makes the respondent say ‘OK, I’ll share information.’ We define desensitization to occur when the proportion of respondents saying ‘yes’ to sharing information (self-profiling classification) is higher when the RDE topic and the self-profiling question (e.g., doctor) are the same. Table 9.8 shows that there is an inadvertent decrease in the frequency of concern, based on the classification questionnaire. This decrease in frequency of concerns occurs both for information about oneself and for information about one’s family, respectively. Table 9.8. Desensitization (shaded cells), operationally defined as showing an increase of 10% or more in willingness to share. The experience rating share information with receiving person in the first part of the interview (rating vignettes) increases the percent of respondents saying directly that they are willing to share information with the same receiving individual in the self-profiling classification. Willing to share info about oneself Study Name: None/ I do not like to share personal information Doctor Lawyer Pediatrician Dentist Religious Institution staff Pharmacist Accountant Health Insurance employees Life Insurance Broker Hospital Staff University/College staff Child's/Children's Daycare staff Real Estate Broker Credit Card employees Bank Employees

Same Study

Average % from other studies

Willing to share info about one’s family Same Study

22% 79% 65% 49% 46% 40% 39% 39% 38% 20% 19% 17% 15% 14% 14% 14%

74% 36% 21% 26% 17% 24% 14% 34% 16% 25% 5% 4% 7% 4% 8%

Average % from other studies

26% 71% 62% 54% 41% 45% 29% 30% 21% 9% 21% 12% 20% 10% 9% 11%

69% 30% 24% 22% 15% 20% 9% 28% 12% 24% 4% 7% 4% 3% 4% 227

Child's/Children's School staff Investment Broker Mortgage Broker Car Insurance Broker Hairstylist/Barber Health Club employees Employer Wholesale Club Store employees Health Spa employees Dept. of Motor Vehicles employees Video Rental Store employees Department Store employees Library staff Local Supermarket employees Auto Mechanic

13% 12% 12% 10% 10% 7% 6% 6% 5%

8% 7% 8% 6% 3% 1% 13% 1% 1%

15% 6% 6% 7% 12% 0% 1% 2% 0%

10% 4% 5% 5% 2% 1% 9% 1% 1%

3%

7%

2%

4%

2% 2% 1% 1% 0%

1% 1% 1% 1% 1%

0% 2% 1% 1% 0%

1% 1% 1% 1% 1%

For example, the 29 studies excluding the Lawyers study show that 36% of the respondents feel comfortable about sharing personal information. (The highest percentage from one of the studies is 52%). However, when the study concerns Lawyers and the respondent evaluates 48 vignettes about information to be shared with attorneys, the exercise reduces concern. Now 65% of the respondents feel that they would be comfortable sharing private information with a lawyer. The same pattern continues to recur, most clear for Pediatricians, Dentists, Religious Institution staff, Pharmacists, and Accountants. Some of the other individuals show smaller effects. The reversal, namely more frequent concern, occurs with one’s Employer and with the Department of Motor Vehicles, both of which represent potentially punitive individuals. 

Thus, an up-front exercise conducted with individuals who are not inherently punishing or who have no power may reduce privacy concerns. That is, more people say that they would be willing to share information with this individual after the up-front exercise.



An up-front exercise dealing with individuals who are perceived to be powerful or punitive may, in contrast, increase the frequency of privacy concerns. That is, fewer people say that they would be willing to share information with this individual after the up-front exercise.

228

What’s the bottom line here? Systematic RDE moves beyond conventional polling and questionnaires Anyone who looks at television, a Web site, or to a lesser extent, reads a newspaper or magazine, can see the popularity of polls. During election season, in particular, we’re inundated with polls. Yet, despite its popularity, polling (e.g., those akin to the classification questionnaire) provides only partial answers to issues involving privacy. For instance, when asked to select individuals with whom they can share information, the majority of respondents choose their physician. Yet, when the respondents participate in the RDE study, which deals with responses to concrete statements about privacy, the dominance of Doctors as recipients of private information is not so clear. Lawyers emerge as more likely to receive private information. What we conclude is that we may not be able to learn as much from classification as we might from direct experiments of the RDE type. Conventional questionnaires, and thus polling, suffer from the problems that don’t allow it to go into depth and truly understand the consumer mind. Conventional polling and questionnaires leave this specification and granularity up to the respondent’s imagination. The question is presented in the most general form, without specification. RDE, in contrast, deconstructs the responses into two portions: general predisposition to share information shown by the Additive Constant, and the additional effects of individual elements that add or subtract from that general predisposition as shown by the utility values. S-R analysis tortures, tests, and in so doing, uncovers, reveals Marketers are well aware of the fact that consumers may not be able to articulate what they want in a product, but they may ‘know it when they see it’ [24]. Indeed the very immediacy of a reaction towards a product is far different from the considered opinions that respondents provide in focus groups, in-depth interviews, and even in questionnaires with open-ended questions. The disconnect between what people say they want in a product and to what they react most strongly when presented with the product often leads to marketing hand-wringing, frustration, and occasional despair because the predictions and direction provided by research fail to materialize in the actual product. Whereas in the questionnaire method the respondent gets to answer each question once, in RDE the respondent evaluates combination after combination. The task is torturous and indeed can be considered a stretch-goal for ideas. But 229

after a while, the respondent doesn’t try to be precise; he or she answers by intuition, by immediate sense. That non-considered opinion, that gut feel, that almost automatic response, is a lot like what happens in daily life. We react. And it’s RDE that helps us understand how we evaluate things quickly, or in best-selling author Malcom Gladwell’s words, in a ‘blink’ [26]. Summary Privacy is a big buzz word today. Whether we deal with medical information, buying data, identity theft, or a myriad of other topics touching a person’s private life, the issue is what’s sacred? Of course sacred could be relative. People like to share information with each other. But they don’t want to share every bit of information, and certainly not with everyone. Strangers who pass in the night may share confidences, but put those same strangers in contact with each other for business or social purposes and that momentary intimacy evaporates. Walls go up. And so, this chapter shows those walls laying out the boundaries, if ever so rough, but boundaries still, of what people view as private and as public. The first key result emerges from comparing the self-profiling data with the data from the Stimulus-Response Model. Self-profiling from the classification section of the study suggests that most respondents feel comfortable sharing personal information with someone from the medical health sector, especially with the Doctor. Another person with whom it is deemed appropriate to share confidential information is the Lawyer. Even individuals who are casual acquaintances are considered appropriate receivers of private information. The S-R approach embodied in RDE suggests a somewhat different pattern from experimentation than the one emerging out of self-profiling. Self-profiling asks, in a general way, about sharing information. RDE works with concrete vignettes in an experiment. RDE uncovers a baseline interest in sharing (Additive Constant). The Additive Constant suggests that respondents would be most willing to share information with the Lawyer, less with an Accountant and significantly less with a Doctor. In some cases, especially with respect to the Accountant or Mortgage Broker, the respondents are more willing to share personal information even though they say, at first glance in self-profiling, that they would not be interested. These differences between the direct comparison of the Additive Constant (from the S-R analysis) and the percents (from the self-profiling) resulted from the mental context in which the questions were asked. Simple and general questions generate

230

possibly incorrect answers, or at least answers that do not hold up in specific situations comprising concrete actions and specific types of information. A second key benefit of the RDE mega study on privacy is the ability to compare the impact values of the same element across all the studies. No element in the study is either always negative (not willing to be shared) or always positive (always willing to be shared). This means that the respondents are willing to share each of the types of information with someone. However, there is an asymmetry in the elements. Nearly all of the elements, except two (Contact specifics … billing address, e-mail address, work, and home telephone number and Annual household income) have more negative utilities than positive utilities. This seems to indicate that the rule for privacy is simply when in doubt, don’t share. The real bottom line – what did we just accomplish and why are the accomplishments valuable? 

The approach is systematic. We use RDE with the same stimuli across the different studies, allowing for comparisons of the same element across them, and comparisons of different elements within a single business. The mega study uses a common self-profiling questionnaire attached to each study allowing further comparisons across receiving individuals and within a study across people.



The scope is large. We look at 30 different individuals in professions and businesses so that we can identify meta-analysis patterns across businesses for personal data that consumers are willing to have companies know. We identified specific types of information that may be too sensitive to share.



The approach is scalable. The study can be conducted in many countries and repeated year after year to identify changing attitudes of consumers towards private information and its shareability. The approach is further scalable because it uses Internet-based interviews allowing for affordable base sizes and ease of execution.

Acknowledgments Some of the material here is based on: The Calculus of Consumer Privacy, apaper by Howard Moskowitz, Barbara Itty, Jeffrey Ewald, & Jacqueline Beckley, which was presented at the ESOMAR Congress, Lisbon, Spain, September, 2004. The authors acknowledge the help of David Bloom in framing the introduction and helping to draw conclusions from the results.

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[11] Givens, B. (2003, August 18). RFID and the Public Policy Void (Testimony before the California Legislature Joint Committee on Preparing California for the 21st Century). Retrieved from http://www.privacyrights.org/ar/RFIDHearing.htm [12] Tischler, L. (2004, April 1). Every Move You Make. In Fast Company (No. 81). Retrieved from http://www.fastcompany.com/magazine/81/everymove.html [13] Rifon, N. J., LaRose, R., & Choi, S. M. (2006). Your Privacy Is Sealed: Effects of Web Privacy Seals on Trust and Personal Disclosures. Journal of Consumer Affairs (Vol. 39, No. 2, pp 339-362). [14] Gellman, B., & Poitras, L. (2013, June 6). U.S. intelligence (NSA) mining data from nine U.S. Internet companies in broad secret program. Retrieved from http://www.washingtonpost.com/investigations/us-intelligence-mining-data-fromnine-us-internet-companies-in-broad-secret-program/2013/06/06/3a0c0da8-cebf11e2-8845-d970ccb04497_story.html [15] Westin, A. F. (1967). Privacy and Freedom. New York, NY: Atheneum. [16] Miller, C.C. (2013, October 31). Angry Over U.S. Surveillance, Tech Giants Bolster Defenses. The New York Times. Retrieved from http://www.nytimes.com/2013/11/01/technology/angry-over-us-surveillance-techgiants-bolster-defenses.html?ref=us [17] Bracy, Jedidiah. "Westin’s Privacy Scholarship, Research Influenced a Generation". Retrieved from http://en.wikipedia.org/wiki/Alan_Westin on 2013-05-07. [18] Moskowitz, H. R., German, J. B., & Saguy, I. S. (2005, April 1). Unveiling health attitudes and creating good-for-you foods: The genomics metaphor and consumer innovative web based technologies. Critical Reviews in Food Science & Nutrition. Retrieved from http://www.highbeam.com/doc/1P3-865329781.html [19] Beckley, J. H. P. & Moskowitz, H. R. (2002, June 15-19). Databasing the consumer mind: The Crave It!, Drink It!, Buy It! & Healthy You! Databases. Paper presented at the Annual Meeting, Institute of Food Technologists, Anaheim, CA. [20] Ashman, H., Teich, I., & Moskowitz, H. R. (2004). Migrating consumer research to public policy: Beyond attitudinal surveys to conjoint measurement for sensitive social and personal issues. Paper presented at the ESOMAR Conference, Public Sector Social Research, Berlin, Germany. 233

[21] O’Grady, K., Beckley, J., & Moskowitz, H. R. (2003). Give It! A database for not-for-profit contributions. Unpublished database. [22] Galanter, E., Moskowitz, H. R., & Silcher, M. (2010). People, preferences & prices: Sequencing the economic genome of the consumer mind. Chicago, IL: Bentham Science eBooks. [23] Ashman, H., Beckley, J., Adams, J., & Mascuch, J. (2002, June 15-19). Teens versus adults: The 2001 Teen Crave It! Study. Paper presented at the Annual Meeting, Institute of Food Technologists, Anaheim, CA. [24] Luckow, T., Moskowitz, H. R., Beckley, J., Hirsch, J., Genchi, S. (2005). The four segments of yogurt consumers; preferences and mind-sets. In Journal of Food Products Marketing (Vol. 11, No. 1, pp 1-22). [25] Moskowitz, H. R., & Gofman, A. (2007). Selling Blue Elephants: How to make great products that people want before they even know they want them. Philadelphia, PA: Wharton School Publishing / Pearson Education. [26] Gladwell, M. (2005). Blink: The Power of Thinking Without Thinking. New York, NY: Little, Brown & Company.

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Chapter 10 What people will and won’t tell researchers Abstract: In this chapter, we used the same set of elements as we did in the previous one on sharing sensitive information with specific individuals or institutions. But instead of asking the respondents how comfortable they felt providing sensitive information to dentists or lawyers, we asked respondents how they felt about providing this kind of information to consumer and opinion research professionals. Both groups of researchers use various interview techniques in order to obtain the information they want. Our RDE assessed eight types of interviews: In-depth, Internet, Gallup Survey, Focus Group for New Products, Focus Group for Social Issues, Mail, Phone, and Shopping Mall. These were our eight different RDE topics, all investigated in the same type of experiment. We wanted to determine whether the nature of the interview had anything to do with the types of information that a person would be willing to share. RDE showed clearly that there are some types of information that will be either shared (positive impact) or concealed (negative impact). And, probably to no one’s surprise, the nature of the interview made a difference. What one might tell an interviewer one might not tell other participants in a focus group. The key takeaway is that survey methods which reduce the possible influence of the interviewer are likely to yield more honest or candid answers. Introduction – The privacy and the insights business In the last chapter, we let respondents rate the degree to which they would share information with various groups of people (such as Dentist, Mortgage Broker, Lawyer, or Department of Motor Vehicles employees). In this chapter, we will use the same set of elements for the type of private, sensitive information. We report the results of a new, separate, mega study. This time, we ask the respondent to rate how comfortable he feels providing this information to a research professional in a specific venue. That is, instead of looking at a specific type of individual as the recipient of the inforamtion (previous study; Dentist or Lawyer), we will look at consumer and opinion research venues as the locations for receiving the information. Gauging public opinion is critical to the political process. Understanding public opinion is important to the media, political parties, leaders, and interest groups as part of public policy development. Market researchers evaluate

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consumer opinions and provide business with the essential information the business needs to offer focused, well-targeted products and services. Consumer and public opinion researchers are in the business of understanding what’s in the mind of their respondents. Both use various interview techniques in order to obtain the information they want. In many instances, the necessary information is very sensitive. The interview technique either leads to no, or even worse, to false information. Direct probing perhaps makes the respondent feel uncomfortable about expressing an opinion or giving sensitive information because of the vulnerability created by the interview. Interview techniques have different styles and procedures, and therefore, different characteristics. All may influence how people feel during the interview process, but what is the granularity of those differences? Let’s use RDE to fill out this interview reality by giving us a sense of what the interviewee will and will not provide. In a sense, we now use RDE to flesh out the interview experience. In this study we assess eight types of interviews: In-depth interview, Internet, Gallup Survey, Focus Group for New Products, Focus Group for Social Issues, Mail, Phone, and Shopping Mall. We evaluate how willing the respondents are to communicate private information within these eight types of interviews. For example, are people more willing to share sensitive information in an Internet interview versus an interview at the Shopping Mall? Self administered interviews When conducting an Internet or e-mail survey, the respondent receives an e-mail invitation at home or at an office computer and clicks on a link which directs the respondent to the interview. Before the widespread use of the Internet, many polls were conducted via Postal mail with questions sent to personal home or business addresses. In both cases, individuals answered the survey on their own and mailed or e-mailed it back to the pollsters. Because individuals would complete these surveys in the privacy of their homes, Internet/e-mail or Postal mail formats are generally good for probing sensitive issues compared to Shopping Mall or Telephone interviews where an interviewer is either present or at least heavily involved. Much research has demonstrated that surveys administered without an interviewer being involved, are characterized by less socially-desirable responses [1, 2], an increased willingness to answer sensitive questions [3], and higher levels of self-disclosure 236

[4]. For example, Booth-Kewley, Larson and Miyoshi [5] compared selfadministered interviews to interviewer-administered interviews. Their data showed that respondents reported more alcohol consumption when interviews were self-administered. Following this, Conrad and Schober [6] reported that participants reported more abortions when interviews were self- administered. Gerich [7], concluded that respondents find self-administered interviews to be more private. In summary, survey methods that reduce the level of question administration by human interviewers increase responses to sensitive personal questions. Absent a human interviewer, the survey is likely to yield more honest, candid answers. In the present study, we investigate the effect of different survey methods on the likelihood that respondents say that they will disclose sensitive information to market researchers. Researchers, unlike those individuals in the previous chapter, are in the business of obtaining information for business-driven insights. What happened? We want to determine whether the nature of the interview had anything to do with the types of information that a person would reveal. The nature of the interview itself has a great deal of impact on the types of information that an individual wishes to share. Let’s jump into the results, in Table 10.1. Table 10.1. What type of information will a person share in a research situation with a knowledgeable professional asking the questions or guiding. A B C D E Self- profiling

Interview Type In-depth interview Internet Gallup Survey Focus Group for new products Focus Group for social issues Mail Don't Share

Share information about Self

26% 24% 24%

Selfprofiling Share information about Family

Summary Statistics For The 30 Text Elements From The RDE Portion Additive Constant

Minimum Element Value

Maximum Element Value

17% 14% 18%

24 31 30

-15 -15 -14

10 0 3

20%

13%

27

-14

10

19%

15%

31

-16

3

16% 10%

11% 15%

22

-12

5 237

information Phone Shopping Mall

9% 9%

7% 6%

24 15

-10 -8

5 3

The first column on the left shows the type of interview. Recall that each interview was a separate RDE study. The first data column, labeled A, shows the percent of the respondents who say that they are willing to share personal information (not specified) in the particular interview venue about themselves. We see clearly that the interview type plays a role in the proportion of respondents willing to share information. In-depth interviews, Internet-based interviews, and Gallup interviews are selected as the formats in which respondents would provide personal information. The popularity of the Internet-based interview may be an artifact due to selfselection because the study was done on the Internet. The middle tier comprises the Focus Groups and Mail surveys. The lowest tier involves the Telephone and Mall interviews. These findings are cause for alarm since, in the United States, Telephone and Mall interviewing has been a mainstay of the consumer research business. Moving to column B, we now see the same respondents this time checking off the interview venues where they would provide private information, not about themselves, but rather about their family. More respondents say that they would share information about themselves than share information about their families, a result we already saw for the main study as well as reported above. Let’s move now to the results from the RDE study. Recall that we presented the respondent with test vignettes and instructed them to rate likelihood of providing information. Each of the eight studies dealt with a different interview method. Thus, we can see the power of an element to elicit information when that element is positioned as part of a specific type of interview. The data show us the basic propensity to share information (additive constant) and the contribution of each element where positive numbers mean will share and negative numbers indicate will not share. We start by looking at the Additive Constant (column C). It is an estimated value based on the pattern of all of the ratings and how those ratings covary with the presence/absence of the elements. It tells us about the likelihood of the respondent to share personal information in that particular interview format, but without the influence of the elements.

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•

At the top we see the Internet-based interviews, the Focus Groups for social issues, and the Gallup interviews. These findings are similar to the results from the self-profiling.

•

The more surprising results come from the reduced interest in sharing information through In-depth interviews. When the respondent directly rated the likelihood of providing information in an In-depth interview, the likelihood was higher when he was asked directly, and the likelihood was lower based on the RDE portion. Self-profiling suggests that the In-depth interview would be selected most frequently and accepted as the most likely venue to share information.

•

Another surprise comes from the increased likelihood to share information when the venue is the Telephone interview.

•

Finally, we see a poor performance for Shopping Mall interviews, confirming the concern about this possibly-fading venue for consumer research.

The 30 text elements provide a cross-section of information that the respondent might be asked to provide. Instead of going through all 30 of these elements for each of the eight venues we will summarize the results. Those summary statistics include the minimum impact value (column D) and the maximum impact (column E). The positive extreme is the most interesting. Here we find the type of information the respondent clearly wants to share. The strongest performing elements occur in In-depth interviews and Focus Groups for new products, with impacts of +10. The strongest performing element for the In-depth interview is Number of years living in house. The strongest element for the Focus Group for new products is Number of people in household ... their gender and ages. These two elements do well across virtually all eight interview venues (i.e., this information is most likely to be shared everywhere), perhaps because they are standard pieces of information that respondents are accustomed to providing. The two elements don’t engender any emotion, and so, respondents are willing to share them with others. Of course that willingness does not extend to other venues. Both of these elements show an impact value of 0 for the Internet-based interview. So, we might say here that these two pieces of information, on average, really don’t raise anyone’s defenses no matter where they are asked. They are innocuous, except perhaps to the overly suspicious respondent. 239

Following the same pieces of information through different interviews In the previous chapter we learned a lot by selecting specific pieces of information where we took one element from each silo and followed it. The strategy is easy and enlightens us. So we will do the same thing here. We pick specific pieces of information to represent different things that people might or might not want to share and then follow those specific elements through the eight different interviews. Table 10.2 shows respondent propensity to share information and the impact values of each element. . First we look at the average impact for a single element across the eight interview situations. This gives us a sense of how shareable the piece of information is thought to be, independent of interview situation. The higher the average impact value, the more people feel that they can share that piece of information. We find that information about Number of people in household ... their gender and ages is the most shareable element. And, not surprisingly, Credit line information and credit reports showing debt is the least shareable information. People have no problem sharing census information, but they are careful with whom they share it. This is especially the case for the element Number of people in household ... their gender and ages. This information is readily shared in Focus Groups for New Products (impact of 10), but neither shared nor hidden in the Internet interview (impact of 0). People don’t want to share information about personal finances or about health information. For example, the information about Credit lines and credit reports showing debt level is kept private in Phone interviews (impact of -6), and kept extremely private in Internet interviews (impact of -12). There are some types of information that will either be shared (positive impact) or concealed (negative impact) depending upon the interview situation. An example of this information is Medical prescriptions filled. This information is kept private on Internet surveys (impact of -3), but shared in In-depth interviews (impact of +4).

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Table 10.2. Additive Constant (propensity to share information) and impact values for one element for each of the five types of information. Each box shows the impact value for the type of information and for the particular individual with whom the information is to be shared. Positive impact means people will share, negative impact mean that people will not share.

Additive Constant Number of people in household ... their gender and ages Medical prescriptions filled Details disclosed after your participation in online surveys concerning shopping behavior Housing expense information ... mortgage payments, real estate taxes and insurance premiums Credit line information and credit reports showing debt level

Av Focus g Prod uct

Foc us Soci al

We Gall b up

Pho ne

Dep th

M ail

M all

27

31

31

30

24

24

22

15

4

10

3

0

3

5

7

5

2

0

2

-1

-3

1

-1

4

-1

0

-1

-2

0

-4

-2

-2

4

0

-1

-2

-4

-2

-2

-4

-1

3

-1

-1

-9

-12

-8

-12

-11

-6

-7

-6

-8

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Summing up The interview venue provides respondent information needed for either gauging public judgments or evaluating consumer opinions. This chapter used RDE to discover how critical the interview is for the consumer and opinion researcher. We proved that the type of interview plays a role in the proportion of respondents willing to share sensitive information. Internet-based and Gallup interviews are the interview formats which respondents indicated they would provide the most personal information. A market researcher should be aware that people do not like to share sensitive information when interviewed in a Shopping Mall The key point to take away from this chapter: self-administration had a clear impact on the level of reporting sensitive information. Respondents in the self-profiling computer interviews would be more willing to disclose sensitive information than would a matched group of respondents participating in interviews involving an interviewer. Interviews without an interviewer increase willingness to answer sensitive questions Overall, people don’t want to share information about personal finances, nor about their health condition. Only standard information which respondents are accustomed to providing, such as Number of years living in house or Number of people in household ... their gender and ages, do well across all interview venues. References: [1] Frick, A., Bachtiger, M.-T., & Reips, U.-D. (2001). Financial incentives, personal information and drop-out in online studies. In U.-D. Reaps & M. Bosnjak (Eds.), Dimensions of Internet Science (pp. 209–219). Lengerich, Germany: Pabst Science Publishers. [2] Joinson, A. N. (1999). Anonymity, disinhibition and social desirability on the internet. In Behaviour Research Methods, Instruments and Computers (Vol. 31, pp. 433–438). [3] Tourangeau, R. (2004). Survey research and societal change. In Annual Review of Psychology (Vol. 55, pp. 775–801).

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[4] Greene, J., Speizer, H., & Wiitala, W. (2008). Telephone and web: Mixedmode challenge. In Health Services Research 43(1), pp.230–248. [5] Booth-Kewley, S., Larson, G., & Miyoshi, D. (2007). Social desirability effects on computerized and paper-and-pencil questionnaires. In Computers in Human Behavior (Vol. 12, pp. 463-477). [6] Conrad, F. G., & Schober, M. F. (2007). Envisioning the Survey Interview of the Future. New York, NY: John Wiley & Sons. [7] Gerich, J. (2007) Visual analogue scales for mode-independent measurement in self-administered questionnaires. In Behavior Research Methods. 39(4), pp.985-992.

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Chapter 11 What information should consumer-knowledge providers deliver to justify THEIR seat at the table? Abstract: This chapter focuses on the consumer insights business, which provides managers and other decision makers with useful information about the mind and behavior of the consumer. We investigated two topics with RDE: First, what information do managers expect to learn about their customers? Second, from whom do managers expect to learn this information? In our experiment we asked managers to tell us what information they expect from three types of professionals: Market Researcher, Brand Planner, and Graphics Designer. These professionals provide information, often called insights, to business managers. It helps developers to understand target groups and to design better packages or better messages for an ad campaign. We were surprised to see that the market managers felt they got more insights from the agency than from the designer and expected the fewest insights from the market research supplier. RDE (Rule Developing Experimentation) suggests that the Market Researcher is rewarded on process rather than on insight. Introduction In this chapter we’re going to focus on the so-called consumer insight business. Managers need insights (including trends and research) to make better informed business decisions, enabling them to develop better products and market them more effectively to their target audience. There are two questions we try to answer: 

What specific information do managers expect to learn about their customers?



From whom do managers expect to learn this information?

The RDE experiment presents managers with different types of information to determine what is expected from three professionals who are, at one or another time, associated with that most vague of labels, consumer insight. Those professionals are the Market Researcher at a research company, the Brand Planner at an advertising agency, and the Graphics Designer at a design company.

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The nature of consumer insights First, we would like to understand where the widely used term consumer insight actually originated. Valdés [1] put it quite succinctly: Any experienced marketer will agree that in order to identify categories that may offer opportunity for growth, you must first identify which categories are more important to your target consumer than others. Marketers who recognize important lifestyle nuances can tailor their marketing programs to reflect these important variables. Most people in the research business are somewhat aware of this definition, and if not this specific phrasing, then the general idea. In other words, you have to know what’s going on in the mind of the consumer to get anywhere, or rely on chance, but only for a short time, until you fail and move on to the next job. Cagan and Vogel explain that insights can be traced to the confluence of business needs and consumer demand which pulls ever more on the insight of the business to build excitement, create needs, and then offer their products and services as solutions. People use products to improve their experience while doing tasks. They relate these experiences to their fantasies and dreams. Successful products fulfill a higher emotional value state, whether it is the excitement and security of driving in an SUV, the comfort and effectiveness of cooking in the kitchen, the relaxation and escapism of sipping coffee in a coffeehouse, or the independence and adventure of using a two-way communication device. The mantra that form follows function is no longer relevant; we are now finding ourselves in a period where form and function must fulfill fantasy[2]. They continue with this line of thinking. The demand by consumers for better products has continued to increase during the last three decades. During the 1980s and early 1990s, quality development programs, re-engineering and concurrent design were the initiatives that drove American and international companies constantly to improve their products. At the beginning of a

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new century, the emphasis has shifted from the back end to the front of the product development process[2]. Following this line of reasoning, Cagan and Vogel suggest that the knowledge worker, the provider who delivers the consumer insights, recognize four key factors for success in this new world of excitement, need/creation, and solution: 1. Expanded vision – The ability to identify product opportunities. As cultures continue to change, opportunities emerge for new products. These products do not just solve existing problems; they also create possibilities for new experiences. 2. Fast recognition – The ability to know what will work. Heightened understanding of customer needs translates into actionable insights. In turn, these insights should lead to product attributes of form and features. In order for products to be successful, they must have features and forms that consumers quickly recognize as useful, usable, and desirable. 3. A true integration of engineering, industrial design, and marketing. Merely putting teams together in a multidisciplinary context will not suffice. Team participants and the emergent team work product must be supported and managed effectively in an atmosphere in which each discipline respects and appreciates the perspective of the other. 4. Support – Do it with the corporation’s blessing. Identifying product opportunities should be the driving force for companies that manufacture products, supply services, and process information. Today’s reality is not a particularly happy one. In fact, managers and other decision makers are not equipped to learn about consumer insights and conduct this research. They must outsource to gain wisdom. The current complex business environment pushes them to hire experts to uncover these golden nuggets. In the following pages, we will introduce those experts large corporations use to learn about consumer behavior, enabling them to develop better packages or new products or get an understanding about the target groups for an ad campaign. These experts all have access to many alternative sources of information and they bring insights along with their professional activities.

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Who needs to provide what in order to earn a seat at the table Today, management welcomes consumer insights as a strategic, competitive business advantage. Maverick CEOs no longer feel confident about making decisions instinctively, at least not publicly. Anyone providing key systematic, actionable information is invited by management to share that information. Figuratively speaking, this recognition is called sitting at the management table. The phrase alludes to growing acceptance by management of the importance of valid information in the decision-making process. A major issue for management today in confronting insights is simply ‘can I use these results to do something specific that will move my business ahead?’ Over the past decade, the traditional market researcher, or at least the person who was called Market Researcher (before the name was changed to insights manager or some similar term), has been valued and well compensated for his contribution of knowledge about consumer insight. According to the European Society for Opinion and Market Research (ESOMAR), a world association for market, social and opinion researchers, in 2012, businesses in the United States spent $11.2 billion dollars on market research. Looking more globally, we see that the world relies on knowledge to move business ahead, and that market research expenditures soared to $33.5 billion dollars. The numbers keep growing year after year [3]. Consumer data are the stock-in-trade of the Market Researcher. This professional is expected to track consumers, identify emerging issues, measure responses to products, segment the marketplace, and perform a host of other knowledge tasks [4, 5]. In the last several years, the Market Researcher’s occupation has been publicly described as one which creates insight. The notion of insight is itself not particularly clear. Market Researchers are recognized for their ability to execute studies on a timely, cost-effective basis, producing data to be acted upon by other individuals. The researcher is, by vocation, a process person. The notion of insights as a part of the data is ambiguous. So what is the raison d’etre of the researcher? Is the flawless execution of the study the important factor, with insights left to the researcher’s clients who commissioned the study? What are the driving factors behind hiring a market research company – good data or good insights? We will find out.

Marketing consultants and agency Brand Planners

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Two professionals, not traditionally trained as researchers, would like to be employed in the insights business as well in order to get their share. Marketing consultants or Ad agency brand planners now actively promote their use of research and, of course, insight as core benefits that they provide to the corporation. However, Ad agency involvement in the insight business is not new. Advertising agencies were among the first to recognize the value of information. During the golden era of the 1960s to the mid-1980s, many of them boasted of their large research departments and whose services they eagerly offered and billed back to their clients. A number of today’s legendary market research leaders came from those agencies and got their start in the golden era some twenty-five to forty years ago. There are two key types of insights provided by these agency researchers and now their successors, Ad Agency Brand Planners [6]. These insights are not presented as facts per se, in the research mode, but rather embedded in work products: 1. Brand Positioning Statement: Brand positioning can be defined as the way the customer has to think about the brand relative to its competitors. Positioning comprises six essential elements: target groups, need, competitive framework, benefit, reason why, and brand character. 2. Ad Strategy: The ad strategy provides guidance and direction for the development of the brand’s advertising campaign. It consists of the who, what, and why in addressing a specific brand marketing issue or objective. Designers The last group we studied using RDE comprises the more artistic Graphic/Package Designers. Ironically, this group of professionals did not discover the value of consumer insights until recently, when they became a key competitive advantage as it had been previously in the advertising agency business. For the most part, Graphic/Package Designers were content to use their intuition about what was relevant to the consumer. Consumer research was often used only as damage control. Scott Young, President of Perception Research Services, was himself a graphics designer. He has reiterated the importance of up-front research work, the need for insights, and the fact that these insights make the work better. Young recounts the story of design for Listerine, and how insights helped [7].

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In a recent Listerine study, brand users consistently depicted the ‘barbell’ shape and the stacked black brandmark accurately, yet were often unable to identify other elements of the brand's visual identity. This insight guided branding and design strategy, as the ‘barbell’ became the focus of a new global packaging structure and the brandmark served as the foundation for extensions into new product categories. The insights role of Market Researcher, Ad Agency Brand Planner, and Graphic Designer Let’s contrast the roles of the Market Researcher, Brand Planner and Graphics Designer involved in the insight business: 

The Market Researcher’s main purpose is to bring insights to the business. In contrast, the Brand Planner and Designer use insights in their job. Insights are not their job, but rather a byproduct of or an input to their job.



The research activity or function can be performed by a non-researcher (e.g. the Brand Planner or the Designer) as part of his job.



Researchers are generally limited to the insights function; they don’t deploy them in substantive ways. Knowledge, not product, is their work result. Although the Researcher provides insights, he may not necessarily be appreciated for taking over the planning or design function. A Researcher must be satisfied offering the insights to someone else who will use them rather than immediately moving forward with applying these insights to an ad campaign, product promotion, or package/product design.

Here is the evolving world and the reason for our RDE study. We know that marketing research professionals proclaim their capability to offer systematized data collection and then refined analysis called insights. We also know that the traditional role of research in agencies was to identify what consumers wanted and bring this information to the creative professionals in order to make better advertising. It used to be that design firms prided themselves on their artistic talents and that their offerings were mini works of art that could not, for reasons never really explained, be tested. We now hear, however, in increasingly loud voice from Graphics Designers that they use consumer insights to guide them in their creative work and that the design merges together, quite seamlessly, consumer needs/requirements with art and with business savvy. What is the truth here? 249

With this background in mind, our topic for this chapter becomes very simple: Specifically, what do managers want or expect today in the way of insights about consumers from the insights-oriented Market Researchers at the supplier company, from the Brand Planners at the agencies, or from the Graphics Designers at the design house? Each of these groups uses consumer insights directly in its professional activities. They each occupy a unique niche:; the Brand Planner is an advisor, the Graphics Designer an artist, the Market Researcher is a knowledge specialist (see Table 11.1). Table 11.1. Profile of three professions studied here. InsightsOriented Graphics/ Ad Agency Market Package Brand Planner Research Designer Professional Physical product/ Work product Report Recommendation design For right For actual information For correct Accountability graphics or process leading to ad package campaign Very high, Low, may attend Involvement in major Contracts it out some focus process involvement groups Provider of Creator, artist in Knowledge Seen as insight and a business worker direction setting Yes, constitute Professional awards artistic and Few None in the field professional recognition Research Independent supplier Type of work Works within an designer or outside, or environment ad agency corporate corporate employee employee

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Using RDE to get inside the client mind An ongoing assumption to guide this research has been the mantra that consumers don’t necessarily know what they want, but they will know it when they see it. Although often used as a raison d’être for conjoint measurement [8, 9] and now RDE, the same assumption and mantra might just as well be applied to the world of professionals. That is, knowledge users may not necessarily know what they want from market researchers, agencies, or graphic designers, but they may well recognize it when they see it. Thus, there is a potentially important application of RDE in the study of professional services. The application of RDE to understanding aspects of professionalism is, furthermore, not new. There are numerous papers on the use of conjoint analysis using professionals (i.e., travel agents) as respondents[10, 11]. Building ideation into RDE We began this study with only some of the ideas well formed. Rather than waiting to better understand what clients want, a possibly thankless and unending task, we decided go to the clients themselves, identify what they wanted to learn, and then, through RDE, determine which of their wishes corresponded to very important performance criteria and which did not. Thus, our study, and in turn this chapter, answer two questions: first, what are the features of information desired by business users; second, how do we measure the importance of those features? Study #1 – Identifying information needs through Internet-enabled ideation We began this project not really knowing what kinds of elements to test in the RDE portion of the project. Yet, we knew that people know a lot about their own topic areas and have lots of ideas and opinions. Discovering what to say using the wisdom of crowds is a hot topic and a well-accepted approach to gathering material that one tests later on. There is a world of practitioners who specialize in crowdsourcing, finding out from a large group of people specific information that no one individual may realize he has, but which the crowd or group definitely has. Crowdsourcing seemed the easiest way to approach the topic and get the first task done (assemble the raw material). In Study #1 we recruited 320 decision makers, who work in this business and use insights, to participate in an Internet-facilitated ideation session. The study was set up with three parallel questions: 251

1. What kind of information about consumers/customers do you want from your Graphics Design company? 2. What kind of information about consumers/customers do you want from your Market Researcher? 3. What kind of information about consumers/customers do you want from your advertising agency Brand Planner? The managers rated the three questions in a unique order to reduce potential order bias. That is, rotating the order of the three questions ensured that the respondents wouldn’t pay the most attention to the same first profession (e.g., graphics design company) and the least attention to the same third profession (e.g., advertising agency Brand Planner). Figure 11.1 shows one of the interview screens where the respondents can select from ideas that were previously offered and also offer their own. This method is called Brand Delphi™. Figure 11.1. An interview screen for Brand Delphi™ for features about consumer insights desired from Package Designers.

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A Brand Delphi™ exercise generates a lot of data. Let’s look at a number of the hundreds of elements that emerged from this automated Internet-enabled technology of crowdsourcing ideas. Table 11.2 shows some of the unedited elements that emerged from the ideation session. Approximately 80 unique elements, with most elements partially relevant to insights, emerged. These were subsequently edited for the conjoint portion of the study. Table 11.2. Elements emerging from the Brand Delphi™ from crowdsourcing is to find those appropriate to a marketing research supplier. Brand Delphi™ Question: Beyond what you normally get from your MARKETING RESEARCH SUPPLIER, what type of additional information on consumer/customer insight would you like? Ideas/Opinions What are the times that they prefer to shop online. Have a clear quality policy or statement. I'd like the surveys to be a little bit shorter and more direct, that way more people will want to do them. Rate of success/failure. I would also like to know the extent of social responsibility of each company Why is the money not being distributed to the employees where it belongs? Company employees should be consulted more on what they need to do their job more efficiently. I would rather not participate in this question. Thank you. Background knowledge in our field. I always want the best goods or service for the least money. I like to deal with a company that cares. I'm really looking for more ‘get into their head’ type information -- Statistics on likes, dislikes, hobbies, memberships, etc. Study #2 – Identifying the important elements The 36 elements were divided into four logically distinct silos (Partnership and Philosophy; Insights; Tools; Deliverables and Fees). Each silo comprised nine elements. The elements dealt with either knowledge/insight or tools to acquire that insight. Following the standard RDE design, each respondent evaluated every element three times in a set of 60 vignettes. The vignettes, constructed by experimental design, differed across respondents. The same elements were tested, but the specific combinations differed from one respondent to another. 253

The different sets of designs, built on the same set of elements, ensured that no individual combination could bias the results. That is, if the test stimuli comprised only one set of 60 combinations rated by everyone, there might be one or two combinations that performed unexpectedly well or poorly because the particular elements go well or poorly together. This would bias the results even without the researcher being aware of it. The use of multiple designs as a research strategy strengthens the conjoint results and prevents bias due to a specific combination. Running the study and discovering what’s important to business Even though the study involved business people rather than consumers, there’s no reason to think that a business person is any more savvy than a consumer respondent would be. It never hurts to spell out the requirements of the survey, as we see in Figure 11.2. We see the familiar test stimulus in Figure 11.3.

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Figure 11.2. Orientation screen for insights study run with business respondents. Welcome to the B2B Partnership Study! We want to learn more about the attributes that are important to you when you work with your Marketing Research partner(s) firms. On the following screens you will be presented with 2-4 statements describing a Marketing Research partner firm. Please consider these statements in total and tell us

‘How important are these attributes to you when working with a marketing research supplier?’ (1=not at all important … 9=very important). There are a total of 60 screens and the study should take about 15-20 minutes of your time. You will see each statement presented several times, each time in a different combination. No screen is exactly repeated. After you complete the Partnership section of the survey, you will be asked some additional classification questions. This information will be used for research purposes only and only in aggregate with other respondents, ensuring confidentiality. As our way of thanking you for completing this survey, you will be entered in the monthly SurveySpot prize drawing with prizes totaling $10,000. In addition, you will be offered the opportunity to receive a copy of the results of the research which will shed light on what today’s business professionals are looking for in partner companies. You will be able to download the article in 2 months from our online library at: http://www.i-novation.com/ Good luck and thank you for your time! Please press continue to start the survey

We actually ran the study three times, albeit with different respondents: the Marketing Researcher, the Brand Planner, and the Designer. With three studies we can compare the same exact element across the three insight providers to learn how these groups are perceived to differ and to be the same.

255

Figure 11.3. Screen showing a typical concept and the rating scale.

Big creative ideas from our think tank to jumpstar

What motivates your customers to make Cost, convenience, habit or something

Our Total Human Analysis connects attitudes, behaviors to better understand why people do wha

Vivid scenario planning can help you see your consumers in the co

How important are these attributes to you when working with a marketing research supplier?

What insights do business people want? Table 11.3 shows the results from the total panel of respondents for the strongest and the weakest elements for each of the three professionals studies. The additive constant shows us the basic level of interest by a respondent in the insights provided by the specific professional (Market Researcher, Brand Planner, Graphics Designer). In technical terms, the additive constant shows the conditional probability that a respondent would be interested in the insights from the professional if no other elements were present. 256

At a basic level, professionals were most interested in insights from the advertising agency (additive constant of 59) and the graphics design house (53) and least interested in insights from market research (44). The value from the additive constant is only part of the story. The elements show the greatest variability from high to low for the market research department meaning that although the Market Researcher starts out with a lower additive constant, it can occasionally reach greater interest by incorporating the proper elements. To reach a high utility value, with a great deal of interest for business people, the specifics or elements promised by the Market Researcher must do a lot of the work. In contrast, the elements for the Brand Planner or the Graphics Designer need to do less work because they start out higher in their additive constant. Table 11.3. Winning and losing elements for the three insight professionals. Advertising Agency Brand Planners – Strongest (want this information) What motivates your customers to make a choice? Cost, convenience, habit or something else? Consumer insight is a blend of art and science … we strive to provide the best of both All our fees are quoted upfront ... never a surprise Advertising Agency Brand Planners – Weakest (don’t want this information) Anyone can provide "qualitative insights." We deliver hard data to test your hypotheses and drive actionable decisions Our planners go undercover and live with your consumers to truly understand them in their real world We focus just on secondary research … there is already a world of information out there … you just have to know where to look Geo-demography helps you to know where they live so you can find others like them Vivid scenario planning can help you see your consumers in the context of alternative future states Graphics Design winners (want this information) After all, actionable consumer insight isn't about what consumers think TODAY… it's about what they will think TOMORROW One flat monthly fee provides you with access to our full staff when you need them All our fees are quoted upfront ... never a surprise We are confident in providing you with powerful actionable results … so our fees are based on your success in the marketplace Anyone can provide "qualitative insights." We deliver hard data to test

Impact

4 4 4 -6 -6 -7 -7 -10 9 7 7 7 6 257

your hypotheses and drive actionable decisions A partner that can help you understand how technology is changing the way your customer makes decisions Graphics Design losers (don’t want this information) We focus just on secondary research … there is already a world of information out there … you just have to know where to look The most sophisticated data mining tools available … running on Cray Supercomputers … to tease out new insights from all your data Market Research winners (want this information) Learn what products your customers use ... the brands they prefer … and why What prompts your customers to act? A partner that does more than just describe your customer … they help you to understand the implications to your business All our fees are quoted upfront ... never a surprise Our statisticians can perform advanced analyses on data and find the story that you did not even know was there What motivates your customers to make a choice? Cost, convenience, habit, or something else? Market Research losers (don’t want this information) Geo-demography helps you to know where they live so you can find others like them The most sophisticated data mining tools available … running on Cray Supercomputers … to tease out new insights from all your data We focus just on secondary research … there is already a world of information out there … you just have to know where to look Specializing in creating names and logos for your company, products, and services

6 -6 -7 14 11 9 8 8 6 -7 -8 -10 -11

A sense of the extreme range of winning and losing elements can be seen in Table 11.3. These results suggest that Brand Planners in advertising agencies show the smallest range of positive and negative elements, whereas Market Research suppliers show the largest range. Furthermore, with the proper selection of elements there is the possibility of creating a strong concept for market research, but one has to be sure not to select poor performing elements. The risks of misstating the types of, and reasons for, underlying insights are greatest for the Market Researchers. The respondent’s job determines which insights are important Now that we have RDE to tell us what types of insights are important, what about a respondent’s job? Does it affect what the respondent wants to learn? 258

Our thinking was: the position you hold in the corporation drives what type of information you need to know. We’re able to identify each person who participated by job title in the self-profiling portion of the RDE study. We instructed the respondents to check the best fitting job title. Since we are looking at patterns, we’re going to confine our analyses to job titles checked by ten or more individuals. Furthermore, we pooled the data from all individual models of all three studies. That is, we assumed that the individual respondent’s needs were driving the ratings rather than the specific insight provider. We didn’t pay attention to the subject of the particular study (Market Researcher versus Brand Planner versus Graphics Designer). The result can be found in Table 11.4. Keep in mind that because we pooled the three studies, the additive constant now reflects the likelihood of the respondent to accept insights from a third party supplier. The additive constants vary by the job the respondent holds. The additive constant is highest for Administrators (65) and lowest for Information Technology (IT) and Research and Development (R&D) (36 and 33, respectively). 

Administrators, the most open, respond to general ideas such as learning what motivates the customers.



Managers, slightly less open, respond to statements about process with known costs.



Salespeople show inconsistent patterns. They respond to known fees, to actionable insights, and to high tech solutions.



Marketing responds strongly to statements about a blend of art and science, a phrase that turns off a number of the other groups.



IT responds to specifics such as price, specific actions to drive responses, and hard data.



R&D wants to know the why, to control the present and predict the future.

Table 11.4. The three winning elements for respondents in each job description. Base (number of respondents) Additive Constant Administrator What motivates your customers to make a choice? Cost, convenience, or something else?

Admin. Manager Sales Market IT

25 68

11 58

28 51

8

0

-9

RD

51 15 10 45 36 33 7 13

4

259

Learn what products your customers use ... the brands they prefer … and why One flat monthly fee provides you with access to our full staff when you need them Management One flat monthly fee provides you with access to our full staff when you need them All our fees are quoted upfront ... never a surprise We are confident in providing you with powerful actionable results … so our fees are based on your success in the market place Sales All our fees are quoted upfront ... never a surprise Helps you to understand what making a single change to a product or service would do to improve a customer’s day Statisticians perform advanced analyses on data & find the story that you did not even know was there Marketing A partner that does more than just describe your customer … they help you to understand the implications to your business Learn what products your customers use ... the brands they prefer … and why Consumer insight is a blend of art and science … we strive to provide the best of both Information Technology All our fees are quoted upfront ... never a surprise Helps you to understand what making a single change to a product or service would do to improve a customer’s day Anyone can provide "qualitative insights." We deliver hard data to test your hypotheses and drive actionable decisions

7

6

-2

6

13

9

0

5

-7

6

13

9

0

5

-7

4

12

14

7 17

-5

5

10

1

1 12

1

4

12

14

7 17

-5

-1

-3

13

1 17 12

4

4

10

3 12 10

-4

-2

0

11 13 12

7

6

-2

10 15 15

-2

3

-5

10 -14

-4

4

12

14

7 17

-5

-1

-3

13

1 17 12

-6

4

4

10 15 15

1 15

-1 260

R&D After all, actionable consumer insight isn't about what consumers think TODAY… it's about what they will think TOMORROW A partner that can help you understand how technology is changing the way your customer makes decisions Learn what products your customers use ... the brands they prefer … and why

1

1

1

6

-6 19

-5

0

4

0

5 16

7

6

-2

10 15 15

Different mind-sets and what each wants from insights providers Throughout this book we have seen the value of segmenting individuals by their mind-sets. Such segmentation is not based on conventional information or even on what people say about themselves, but on how they react in experiments when they have to decide whether to buy or not buy, to pay taxes or not to pay taxes. People don’t necessarily know the segment to which they belong. People, especially those in business, may fancy themselves as rational decision makers, but often the segmentation comes from responses to more emotional stimuli, or at least to rationale stimuli covered with an emotional veneer. We’re moving beyond our study of citizens and consumers to a study of business people. The segmentation method is the same; just the people and topic differ. Three clear segments emerged: 1. Segment 1 (Relationship) comprises individuals interested in relationships with the insights supplier. They show a very high additive constant (59), with the elements adding a moderate amount of draw to the supplier. These respondents are predisposed to insights providers. 2. Segment 2 (Price) comprises the price/performance or smart shopper segment. They are process oriented with an eye on the bottom line. The elements have to work a bit harder because they begin with an additive constant of 50. 3. Segment 3 (Technological Empowerment) comprises the technological empowerment group. They are interested in the latest processes to produce insight. The elements have to work even harder because their additive constant is 43. Fortunately, for Segment 3 many elements convince the insights buyer. 261

Table 11.5. Strongest performing elements for the three mind-set segments.

Impact

Winners – Segment 1 (Relationship) – Additive Constant of 59 Learn what products your customers use ... the brands they prefer … and why A partner that does more than just describe your customer … they help you to understand the implications to your business Consumer insight is a blend of art and science … we strive to provide the best of both A partner that can help you understand how technology is changing the way your customer makes decisions Winners – Segment 2 (Process, Price, Performance) – Additive Constant of 50 All our fees are quoted upfront ... never a surprise We are confident in providing you with powerful actionable results …so our fees are based on your success in the marketplace One flat monthly fee provides you with access to our full staff when you need them Winners – Segment 3 (Technology, State of the Art Empowerment) – Additive Constant of 43 Our statisticians can perform advanced analyses on data and find the story that you did not even know was there Our Total Human Analysis connects attitudes, behaviors and societal influences to better understand why people do what they do Our behavioral analysis focuses only on activities and programs which impact sales The most sophisticated data mining tools available … running on Cray Supercomputers … to tease out new insights from all your data A partner that can help you identify unmet needs and wants among consumers Through our custom proprietary research tools, we help you gain competitive advantage A partner that makes you feel that you truly know the customers … everything about them … hobbies, passions, fears, dreams … and more

10 9 8 8

17 13 10

20 13 11 10 10 9 9

The concept response segments transcend conventional demographics and job descriptions (see Table 11.6).

262

Table 11.6. Proportions of respondents falling into the segments by key classification question. All numbers are percents. The rows add to 100%. (Departures from 100% are due to rounding.) Segment 1 Relationship

Total Panel Study in which the respondent participated Market Research Study Ad Agency Study Design Firm Study Gender Female Male Age Age