Direct-to-Consumer Pharmaceutical Advertising: Building and Testing a Model for Advertising Effectiveness RICK T. WILSON
Hofstra University
Using a large-scale database, we present, test, and refine a model for direct-to-consumer (DTC) advertising effectiveness via structural equation modeling.
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Results suggest that consumers who are greatly involved in their healthcare and BRIAN D. TILL
Saint Louis University
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possess positive attitudes toward DTC advertising appear to be more likely to contact a doctor about the prescription drug after viewing a DTC advertisement. While individuals that are poor in health and/or hold more favorable attitudes toward the healthcare system do appear to respond to DTC advertising, the effect Is quite small. The results of this study provide a comprehensive overview of DTC advertising's effect on behavior.
INTRODUCTION
Pharnidceutical companies spend in the neighborhood of $2.6 billion a year on the advertising and promotion of prescription drugs (Thomaselli and Teinowitz, 2004). In fact, it appears that massmarket advertising has become a necessity rather than a luxury for many pharmaceutical companies as massive research and development costs and an increasingly competitive marketplace have forced companies to spend enormous sums of money on promotion to quickly gain market share before patents expire (Crain, 2005) or before offering it over-the-counter or in generic form (Whyte, 1993). Recent evidence also suggests that the more a firm spends on the advertising of a particular drug, the more likely it is that a physician will diagnose and prescribe said drug (Zachry et al., 2002). Advertising of prescription drugs is often the greatest when drugs are new, when they are of high quality, and when the untreated population is large (Iizuka, 2004). Yet prescription drugs are a product that is often designed to appeal to a small and specific group of consumers. Heavily advertising a drug without mass-market appeal can be quite costly, especially devoid of a thorough understanding of which consumers will respond most favorably to 2 7 0 JflUROflL Of eOUERTISHlG RESEfiflCH September 2 0 0 7
direct-to-consumer (DTC) advertising. In a report given to the Food and Drug Administration (FDA) in 2003 by Rodale Inc., publishers of Prevention and Men's Health magazines, the publisher noted that the percentage of consumers who have asked a doctor about an advertised drug has remained flat at around 30 percent from 1997 to 2002 (Rodale Inc., 2003). However, for all the importance of DTC advertising, little research is found detailing consumer characteristics that will enable advertisers to better hone their mass marketing. In fact, missing from the DTC advertising literature is a comprehensive and empirically validated model of consumer response to DTC advertising. Our purpose is not to debate the merits of DTC advertising (for a critical review of the debate surrounding DTC advertising, see Auton, 2004), but rather to fill an important gap by building upon previous work in DTC advertising to present and test a comprehensive model of consumer response to DTC advertising. LITERATURE REVIEW
Much of the prior research on DTC advertising has focused on governmental regulation (Cady, 1976; Vatjanapukka and Waryszak, 2004), presence of risk infonnation in advertisements (Menon, DOI: 10.2 50 i/S002184990 7070304
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Heavily advertising a drug without mass-market appeal can be quite costly, especially devoid of a thorough understanding of which consumers will respond most favorably to DTC advertising.
Deshpande, Perri, and Zinkhan, 2003; Morris, Ruffner, and Klimberg, 1985), the doctor-patient relationship {Murray et al., 2004), doctor prescription writing behavior (Petroshius, Titus, and Hatch, 1995; Walton, 1980), or assessing the awareness of and affective reactions to DTC advertising (Alperstein and Peyrot, 1993; Everett, 1991; Mehta and Purvis, 2003; Morris et al., 1986; Roth, 2003). As DTC adverHsing appears not to be a passing fad, but rather here to stay, a greater emphasis has been placed in determining the variables most predictive of the desired behavioral outcome—seeking additional information about the advertised drug that is important, considering that consumers cannot legally purchase pharmaceuticals without a prescription (Williams and Hensel, 1995). Several variables have been used to predict a consumer's likelihood to request additional information about an advertised prescription drug. Attitude toward DTC advertising is a frequently used measure and, not surprisingly, the results confirm that a positive attitude toward DTC advertising is a viable predictor of behavior (Bell, Kravitz, and Wilkes, 1999; Mehta and Purvis, 2003; Mintzes et al., 2002; Peyrot, Alperstein, Van Doren, and Poli, 1998). Another popular predictor variable is general health status, with individuals who report a lower health status being more likely to seek additional information on an advertised drug (Bell, Kravitz, and Wilkes, 1999; Perri and Dickson, 1988). Additionally, studies have demonstrated
that consumers who are currently taking prescription drugs {Bell, Kravitz, and WiUces, 1999; Mehta and Purvis, 2003) and those who are subsequently diagnosed with the specific ailment (Perri and Dickson, 1988) are more likely to inquire about an advertised drug. While most of these studies have analyzed a few predictor variables to advertising effectiveness, only a handful have attempted to take a more comprehensive examination of the items that lead a consumer to seek additional information from a doctor, pharmacist, or friend (Bell, Kravitz, and Wilkes, 1999; Huh and Becker, 2005; Menon, Deshpande, Zinldian, and Perri, 2004; Williams and Hensel, 1995). In a study of 329 adults in Sacramento County, California, Bell, Kravitz, and Wilkes (1999) analyzed such predictive variables as faith in regulation, attitude toward DTC advertising, and health status as well as a number of demographic items. Using logistic regression, the authors determined that people who take action upon seeing DTC advertisements hold a positive attitude toward DTC advertising, evaluate their health status as low, have a positive attitude toward healthcare, and overestimate the governmental oversight of DTC advertising. Of the demographic items analyzed (age, gender, education, income, and race), only female respondents appeared to be a trait significantly correlated with a behavioral indicator. While uncovering impressive results, this studv did not include such
potentially relevant constructs such as a consumer's involvement in one's own healthcare, which has been shown to be an important variable influencing both attitudes toward healthcare and one's general health (Gould, 1988). Furthermore, the use of logistic regression inherently limits the complexity of the relationships that may be tested. Another study by Williams and Hensel (1995) evaluated information-seeking behavior among older adults in retirement communities in southwestern Ohio and the community at-large in rural western Pennsylvania. Key predictive variables included attitude toward DTC advertising, health status, and healthcare knowledge as well as demographic items such as education, occupation (e.g., healthcare professional), and current prescription drug use. Using univariate and path analysis, the authors found that healthcare knowledge and positive attitudes toward DTC advertising influenced information-seeking behavior. A study by Huh and Becker (2005) also evaluated information-seeking behavior and found that greater exposure to DTC advertising, a poor health status, prescription drug use, females, and white Americans were more likely to seek additional information on advertised drugs. Like the previous model by Bell, Kravitz, and Wilkes {1999), Huh and Becker (2005) and Williams and Hensel {1995} did not include consumer involvement with their own healthcare in their respective models. Furthermore, the generalizability of Williams and Hensel's study is likely limited by the geographic representation of the subjects as well as the narrowly defined age group. Finally, Menon, Deshpande, Zinkhan, and Perri (2004) presented a model that incorporated the traditional hierarchy-ofeffects framework along with a number of audience predictor variables: health status, involvement with one's health, and
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Missing from the DTC advertising literature is a comprehensive and empirically validated model of consumer response to DTC advertising, and a model that includes consumer involvement as a key factor in information-seeking behavior. demographic and psychographic (e.g., attitude toward healthcare, etc.) items. The authors also included predictor variables that directly related to characteristics of the advertisement such as the source (FDA versus a drug manufacturer), message (message appeal, length, format, etc.), and channel (broadcast versus print media). By explicitly bringing in the traditional hierarchy-of-effects framework, Menon, Deshpande, Zinkhan, and Perri (2004) added awareness, knowledge, attitudes, and behavior to their model. Awareness in the model is defined in terms of consumer attention to tlie advertisement whereas knowledge is defined as ability to process and u nderstand DTC advertising, which can often be ambiguous and clandestine in its presentation of risk information. Tiie authors characterized attitudes in their model as affect toward DTC advertising. Finally, the authors classified behavior as any number of outcomes including seeking additional information, making an appointment with a physician, requesting the drug from a physician, refilling a prescription, and adhering to tlie drug regimen. While the model presented by Menon, Deshpande, Zinkhan, .uid Perri (2004) is a good step forward, there are some additional factors that we believe are relevant to assessing the effectiveness of DTC advertising. Importantly, the Menon, Deshpande, Zinkhan, and Perri (2004) model appears not to have been empirically tested. And, indeed, the somewhat generalized nature of the way the Menon, 272
Deshpande, Zinkhan, and Perri (2004) model is specified complicates its testing. In the following section we present our model and consequent hypotheses. Our goal is to present a model of DTC advertising effectiveness that is both comprehensive and more easily tested. Indeed, we also undertake empirical validation of the model. MODEL AND HYPOTHESIS DEVELOPMENT
Based on the previous research and additional development on our part, we present in Figure 1 our model of DTC advertising
Education
effectiveness. The hypotheses for the constructs in our model are presented below and discussion begins with demographics. Interestingly enough, demographic variables have often heen used in DTC advertising research and with conflicting results. Mehta and Purvis (2003) found that age and income had no effect on information seeking behavior. Similarly, Morris et al. (1986) found no difference in attitudes toward DTC advertising based upon gender, race, and marital status. However, Morris et al. (1986) did find that consumers with less education did have more favorable attitudes toward DTC advertising. Contrary to Morris et al. (1986), several studies found that higher educated consumers are more likely to notice DTC advertising (Alperstein and Peyrot, 1993; Schommer, Doucette, and Mehta, 1998) and seek additional information (Peyrot, Alperstein, Van Doren, and Poli, 1998). In terms of age, Doucette and Schommer (1998) found that younger consumers are more apt to attend to DTC advertising
Age
Involvement with One's Healthcare
Figure 1 Hypothesized Model of DTC Advertising Effectiveness
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than are older consumers. Therefore, in support of Alperstein and Peyrot (1993), Schommer, Doucette, and Mehta (1998), Peyrot, Alperstein, Van Doren, and Poli (1998), and Doucette and Schommer (1998), more highly educated and younger consumers are expected to be both more involved with their own healthcare and hold more positive attitudes toward the healthcare system. Hla: Consumers who have more education will be more involved with their healthcare than consumers who have less education, Hlb:
Hlc:
Consumers who are younger will be more involved with their healthcare than consumers who are older. Consumers who have more education will have a more positive attitude toward the healthcare system than consumers who have less education.
Hid: Consumers who are younger will have a more positive attitude toward the healthcare system than consumers who are older. Health characteristics such as health status or healthcare practices of the consumer have been found to influence involvement with one's healthcare (Perri and Dickson, 1988). In their study of patients who viewed a mock advertisement, Perri and Dickson determined that consumers who had low self-reported health status levels and/or had the disease to which the advertised drug referred were more apt to attend to the advertisement and thus displayed greater involvement with their healthcare. Contrary to this, Gould (1988) found that consumers who had low self-reported health status levels indicated that they were not putting
This study indicates that involvement in one's healthcare and attitudes toward DTC advertising directly impact the action-taking behavior of individuals who view DTC advertising.
enough time into taking care of their health. Yet Gould also noted that consumers with lower self-reported health status levels were also more likely to insist that their doctors keep up with the latest scientific information. Consequently, consumers with more favorable healthcare characteristics are expected to be less involved with their own healthcare. H2:
Consumers with more favorable healthcare characteristics will be less involved with their healthcare than consumers with less favorable healthcare characteristics.
A consumer's health characteristics are also reported to affect a consumer's attitude toward healthcare (Gould, 1988). Gould found that consumers with lower self-reported health status levels reported having little trouble finding good doctors and were quite satisfied with the quality of medical care they received. Additionally, Conul, Carter, and Wind (2000) found that consumers in poor health are more likely to trust their doctor's judgment. Following these findings, we expect that consumers with less favorable healthcare characteristics will have more favorable attitudes toward the healthcare system while consumers with more favorable healthcare characteristics will have less favorable attitudes toward the healthcare system. H3:
Consumers with more favorable healthcare characteristics will hold less favorable attitudes toward the
healthcare system than will consumers with less favorable healthcare characteristics. Similar to a consumer's health characteristics, a consumer's involvement with their healthcare has also been found to influence attitudes toward the healthcare system (Gould, 1988). Gould found that consumers who are more involved in their healthcare are more preventative in their outlook, more likely to use altemative forms of medical treatment, and less likely to challenge the authority of their doctor than consumers who are less involved in their healthcare. Consequently, consumers who are more involved in their own healthcare are expected to have more positive attitudes toward the healtlicare system. H4:
Consumers who are more involved in their own healthcare will hold more positive attitudes toward the healthcare system than consumers who are less involved in their own healthcare.
While Menon, Deshpande, Zinkhan, and Perri (2004) posit that involvement in one's healthcare is an important predictor of behavior in DTC advertising effectiveness, little empirical work has verified this. What work has been done found no significant results based on level of consumer involvement with their own healthcare (Perri and Dickson, 1988). However, considering that only one study has looked at involvement's effect on DTC
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action-taking behavior and that this variable is a component of Menon, Deshpande, Zinkhan, and Pern's (2004) conceptual model, we leave this construct in our model. Consistent with Menon, Deshpande, Zinkhan, and Perri's (2004) predictions, it is expected that consumers with higher levels of involvement with their own healthcare are more likely to seek additional information about an advertised drug than are consumers with lower levels of involvement. H5:
Consumers who are more involved with their own healthcare are more likely to seek additional information about an advertised drug than are consumers who are less Involved with their own healthcare.
Consumers who hold a more positive attitude toward the healthcare system are said to be more likely to seek additional information about an advertised drug (Menon, Deshpande, Zinkhan, and Perri, 2004). Bell, Kravitz, and Wiikes (1999) found that consumers who believe that their health insurance provided good coverage for prescription drugs were more likely to seek additional information about an advertised drug. Furthermore, consumers who possess more traditional views of the doctor-patient relationship were also more likely to request additional information (Peyrot, Alperstein, Van Doren, and Poli, 1998). Accordingly, consumers with a more positive attitude toward the healthcare system are more likely to request additional information on an advertised drug than are consumers with a negative attitude toward the healthcare system. H6:
Consumers with a more positive attitude toward the healthcare system are more likely to seek additional information about an advertised drug than are consum-
ers with a negative attitude toward healthcare. Attitudes toward advertising are comprised of both positive and negative affects (MacKenzie, Lutz, and Belch, 1986). DTC advertising is no different (Mehta and Purvis, 2003). In fact, past research has demonstrated that attitudes toward healthcare do affect attitudes toward DTC advertising differently Those consumers who have a more traditional view of the doctor-patient relationship and/or hold a more positive view of the healthcare system have been found to possess negative attitudes toward DTC advertising (Gonul, Carter, and Wind, 2000; Menon, Deshpande, Perri, and Zinkhan, 2002). These authors suggest that consumers who trust their doctors may not need to seek out additional information from alternative sources such as DTC advertising, but rather see their doctors as primary sources of information. Therefore, it is expected that consumers with positive attitudes toward the healthcare system will possess negative attitudes toward DTC advertising. H7:
Consumers with a more positive attitude toward the healthcare system are more likely to possess negative attitudes toward DTC advertising than are consumers with a negative attitude toward the healthcare system.
Attitudes toward DTC advertising serve as an important predictor for informationseeking behavior (Peyrot, Alperstein, Van Doren, and Poli, 1998). In fact, a number of authors have found that those consumers holding positive attitudes toward DTC advertising are more likely to seek additional information about the advertised drug (Bell, Kravitz, and Wiikes, 1999; Mehta and Purvis, 2003; Williams and Hensel, 1995). Consequently, consumers who possess a positive attitude toward
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DTC advertising are expected to seek additional information about an advertised drug. H8:
Consumers with a more positive attitude toward the DTC advertising are more likely to seek additional information about an advertised drug than are consumers with a negative attitude toward DTC advertising.
METHOD Sample
The source of the data was taken from the Interuniversity Consortium for Political and Social Research (ICPSR) database. Subjects were adults in the continental United States and were surveyed regarding DTC advertising of prescription drugs (Lo, 2004). The research, which took place between March 2000 and March 2001, was commissioned by the University of California, San Francisco and carried out by Harris Interactive Inc. A computerized random dialing procedure of a stratified sample of persons with telephones produced a total sample of 15,400. The sample was weighted by age, race, education, health insurance status, and gender to represent the composition of the U.S. population as defined by the March 2000 Current Population Sur-
vey from the U.S. Census Bureau. To ensure a random selection of respondents at the household level, participants were selected based on the person with the most recent birthday. To encourage respondents to participate in the study, threefourths of the respondents received a cash uicentive ranging from $10 to $50. In some cases, nonrespondents were sent a letter detailing the purpose of the survey and encouraging their participation as a method to further increase the response rate. The sample total of 15,400 was reduced to 6,917 due to nonworking numbers (49 percent) and other issues including
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language barriers or no one in the house being 18 years or older. The overall response rate was 46.4 percent resulting in 3,209 interviews. For purposes of this study, we included only those respondents who recalled seeing a DTC advertisement in the past 12 months (2,702). Missing data for some of the key variables of interest yielded a usable sample of 2,290. While several articles have been written using this data set, none have used the data to build a predictive model of advertising effectiveness. Rather, these studies have examined some aspect of the doctor-patient relationship (Murray et al., 2003a, 2003b, 2003c, 2004). Measures
A summary of the variables and their associated items are foimd in Table 1. The demographic variables of age and education are single item measures and were assigned an error variance of 0.0 to reflect that respondents are expected to accurately reveal their age and level of education. Age is categorized into four ranges to reflect how most media and advertising firms group consumers for targeting and rating purposes. The mean age is 25-34 years with a standard deviation of 0.89. Education is categorized into seven levels ranging from less than high school education to completed graduate school. The mean level of education is completed some college, but having no degree with a standard deviation of 1.97. Health characteristics are defined as the number of visits the respondent has made to a doctor or clinic in the past 12 months with fewer visits being seen as more favorable. Additionally, health characteristics are defined as the number of different doctors the respondent has seen in the past 12 months with fewer different doctors being seen as more favorable. White visiting doctors more frequently and visiting a greater variety of different doctors
It is our supposition that our study is the first empiricai study that has shown that greater invoivement in one's healthcare has a significant and positive effect on DTC action-taking behavior.
can be seen as taking a proactive step in maintaining one's health, other studies have found poor health to be a major contributor to frequent doctor visits (Rohrer, 1999). Consequently, health characteristics for this study are defined as visiting doctors more frequently as well as visiting a greater number of doctors. Menon, Deshpande, Zinkhan, and Perri (2004) define involvement in one's healthcare as having a greater motivation to process and a greater need for cognition concerning health-related information. The need for cognition often motivates individuals to seek out additional information on the relevant topic (Cohen, Stotland, and Wolfe, 1955). The involvement construct for this study consists of two items measuring how frequently an individual seeks out information from a doctor or other healthcare professional using a 4-point Likert scale ranging from never (1) to often (4). The attitude toward healthcare construct consists of two items using a 4-point Likert scale ranging from never (1) to often (4). Respondents were asked if they thought doctors personally spend enough time with patients and how receptive doctors are to concerns and issues that patients mention to them. Attitude toward DTC advertising was measured via two items using a 4-point Likert scale ranging from strongly disagree (1) to strongly agree (4). Respondents were asked whether they believed that DTC advertising improved people's understanding of medical condi-
tions as well as gave patients the confidence to talk to their doctor about their concerns. Finally, the dependent variable, DTC action, is a single-item measure to assess whether the respondent took action after seeing the DTC advertisement. Taking action is defined as asking a doctor for a prescription, making an appointment with a doctor, or talking/emailing/calling a doctor about an advertised prescription drug. Because this variable is a single-item measure, the error variance must be set based upon reliability measures from prior research. However, as no prior research was found by the authors, the error variance was set to 0.1 times the variance of the DTC action item (Anderson and Gerbing, 1988). Methodology
The constructs and the model were tested using weighted least squares and LISREL 8.7 structural equation modeling software. Measurement models for the observed variables and theoretical constructs indicate strong reliability with Cronbach's Alpha ranging from 0.72 to 0.91 (Nunnally, 1978). The theoretical constructs also possess strong divergent validity with the highest correlation between constructs being 0.375. Two structural models were created— the hypothesized model (1) and a revised model (2), which removes nonsignificant paths and paths with small effect sizes below 0.15 (Schumacker and Lomax, 2004,
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TABLE 1 Summary of Variables and Measures Cronbach 99"?VHfr*
Scale
Alpha
Heallh characteristics (HLTHCAR) NUMViSP
0.91
In the last 12 months, bow many times did you go to a doctor's office
1 - 6 + visits
or clinic to get care for yourself?
2 - 4-5 visits
0.95
3 - 2-3 visits ^-0^1 NUMDRSP
visits
In the last 12 months, how many different doctors or health care
1 * 4 + doctors
professionals have you seen?
2 - 3 doctors
0.88
3 - 2 doctors 4 - 0 - 1 dpct_ors tnvoivement with one's healttioare (tf^VOLV) INFDRSP
0.78
Thinking about all the different places where you get information about
1 to 4
specific medical conditions or treatments, how often do you get
(never to often)
0.98
J[)f^9.'