and synthesize novel interpretations that move their projects in new directions. ... multiple intelligences, social capital and work environments interact as a ...
WHERE POTENTIAL LEADS
By Stephen S. Brand
Submitted in fulfillment of the requirements for the degree of Executive Doctor of Management
CASE WESTERN RESERVE UNIVERSITY
May, 2004
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WHERE POTENTIAL LEADS
Abstract By Stephen S. Brand
This study explores the influence of the personal characteristics of engineers and the nature of their work environment on innovation, characterized as generating revenue from innovative outputs. Using the paradigm of multiple intelligences developed by Gardner and its related literature, in concert with academic achievement in high school and the presence of a mentor, we empirically tested these relationships with over 250 engineers from one Fortune 500 corporation. The results show that although theory suggested direct positive influences of musical, spatial, logical and physical intelligences on revenue, only two of these intelligences had any direct effect and the effect was negative. However, social capital skills were able to mediate the influence they have on gross revenue, except in the case of spatial intelligence. Also, while having a mentor was generally a negative influence on gross revenue, social capital was able to mediate this into a positive and significant relationship. In the environment studied, overall academic achievement in high school influenced gross revenue positively, while academic achievement in science courses had a negative influence on gross revenue.
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Introduction “Continued innovation of products, services, technology and the organization itself, is one way to keep a business on its feet during these turbulent times.” (Senge, 1992). Innovation is crucial to the ongoing stability and growth of a corporation. Companies must determine the most effective formula that will maintain their level of innovation and excellence. This formula combines quality employees with the design of an environment in which these individuals deliver innovation and expertise. The most important element of the formula is the make-up of their most valued asset – their human capital. To compete with their innovative rivals, corporations use a variety of criteria to predict success and assess the value that employees bring to their company. During the selection process, most corporations evaluate previous work experiences, academic credentials and impressions made during interviews. Some look at high school and college grades (Brass, 1995; Guion, 1992) as well as other criteria traditionally viewed as benchmarks for predicting success. However, as we will see, this may not be the most effective approach to predicting success. This research is designed to examine at individual characteristics (such as academic achievement, levels of multiple intelligences and social capital) in tandem with the nature of the work environment, the combination of which provides a truer predictor of an engineer’s potential to provide strategic innovative and economic value to their companies. Personal Impact In analyzing successful personal impact on innovation, Steiner stresses 108
. . . the importance of individual experience, the importance of unconventional (free) interpretations, and the importance of respect for individual uniqueness. The magic moments of innovation arise when scientists or engineers assert their individuality, break free from the structured thinking of their traditional methods, and synthesize novel interpretations that move their projects in new directions. (Steiner, 1995) Achieving the ambitious goal of quantifiable personal innovation requires developing a complex tapestry of organizational elements and identifying stellar human potential that can thrive in a corporate environment that provides the support and latitude necessary for excellence. To achieve innovative breakthroughs, we must look beyond traditional benchmarks and explore other options for the selection of potential innovators. [Scientists and engineers] are often the sources of innovation in both products and processes, yet routine science and engineering is typically carried out in an environment that stresses structure, order, and adherence to prescribed methods—exactly the wrong setting for successful innovation. (Steiner, 1995) Work Environment The traditional corporate environment does not always lend itself to creating structure-free, disorderly, ad hoc methods of discovery and breakthrough thinking. There is a challenge for corporations to better understand the needs of the innovators they rely on for breakthroughs—those who may not want to play by the rules. Employing creative visionaries who may not be able to adapt to the corporate structure could actually decrease their innovative output. . Meanwhile, the competition may have figured out better ways to select and leverage talent. If corporations want to best leverage the creative powers of their research engineers, they must first determine whether a carefully orchestrated work environment is necessary to support the desired level of innovative output. Then they must understand the type of individuals who thrive within the
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environment they create. They also need to develop an employee screening process that identifies those with the most potential to deliver value. Our major objective is to look at how constructs of academic achievement, multiple intelligences, social capital and work environments interact as a function of determining an individual’s level of innovative output. We will evaluate the impact of some of these factors within one Fortune 500 engineering and manufacturing firm. Currently, the company evaluates a wide range of characteristics that may yield the best range of innovative engineers. This study will test a number of characteristics theorized within the literature that may or may not be part of the present evaluative process of this employer. The Question With this in mind, the overall research question is: what combination of work environments, social networking (capital) skills, academic success (grades) and levels of multiple intelligences (efficacies) lead someone to become innovative (as defined by revenue they bring to the company) during his or her tenure in a corporate engineering position? A focus on innovation does not necessarily lead to economic results, however, in the context of this study we will focus on those innovations that yield financial benefits to the company. The objectives for this research include: •
Providing insight for our targeted company as they develop a more accurate process for determining the innovative success of their future engineering pool.
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Providing insight for those who are developing teams of engineers as to what characteristics might prove to be effective in delivering results in the future.
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Providing those who support students as they determine future careers that would yield success for them. If corporations, schools and career counselors can benefit from this research, they
may be able to better coach future engineers with the skills, talents, interests and passion to innovate toward choosing careers and work environments that are more appropriate— careers in which they can be successful. This study will not provide an exhaustive analysis of all possible criteria for hiring or career coaching, as this is not possible in one such research effort. It will, however, provide important insights and begin to make connections between the mentioned personal characteristics and behaviors, and successful careers. It will also look at the mediating role of social capital skills and impression of the work environments in the stated relationships, although it will not provide a comprehensive understanding of social capital and work environment as it relates to innovative output. While it would ultimately be important to understand the relationship between raw innovation and the impact one’s work has on his or her company’s financial success, this is beyond the scope of this effort. However, since corporations are typically very interested in the financial impact of their team of innovators, we will be using revenue as a measure of innovative output. Literature Review and Conceptual Model Figure 1 demonstrates the relationships among the defined constructs that will be empirically tested in this study. The hypothesized antecedents to an individual’s
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innovative output include academic achievement, levels of multiple intelligences, and the presence of a mentor. We will also test the hypothesized mediation effects of both social capital and work environment from the independent variables to innovative output. Figure 1 further shows the relationships among the constructs of academic achievement, efficacies (multiple intelligences), social capital, work environment and gross revenue. The initial dependent variable of personal innovation will be measured and labeled in this study by the gross revenue that one engineer has achieved for the company through his or her most innovative accomplishment. Other innovation measures were theorized (defined later), but lack of a normal distribution in the other measures of innovation led us to focus only on gross revenue. The model shows the hypothesized influences of the independent variables, academic achievement and efficacies (theorized as multiple intelligences, now relabeled efficacies) on gross revenue, possibly mediated through social capital and work environment. As will be indicated later, spatial efficacy, which was hypothesized and defined based on the literature, will be removed due to the absence of significant relationships with the other constructs. Also, musical efficacy will divide into two entities, based on the results of a factor analysis suggesting distinct behaviors. The rationale and justification for use of the independent variables of academic achievement and efficacies are explained in more in Table 1. Using grades to predict professional success is an ongoing debate amongst cognitive and educational researchers. Research also suggests that certain multiple intelligences would be effective measures when recommending career direction and predicting adult success in specific careers. Additionally, the research suggests that having strong social skills and working within an environment supports and enhances innovative output. The 112
literature review presented in Table 1 shows the theoretical background for why these constructs are included and tested within this model. The definitions provide a sense of where the theories are supported. Academic Achievement Research has found that the path many people take from school to career is based on one success after another. When people do well in school, their self-esteem is boosted and they are in positions to excel in the areas that provide them the most external recognition. Yomada and Tom (1996) suggest that “higher intelligence is necessary for publicly recognized creative achievements.” Lubinski, et al. discuss the role of academic and career success in a longitudinal study of highly gifted students. “Adolescents identified before the age of 13 (N = 320) as having exceptional mathematical or verbal reasoning abilities (top 1 in 10,000) were tracked over 10 years. They pursued doctoral degrees at rates over 50 time’s base-rate expectations, with several participants having created noteworthy literary, scientific, or technical products by their early 20s. Early observed distinctions in intellectual strength (viz., quantitative reasoning ability over verbal reasoning ability, and vice versa) predicted sharp differences in their developmental trajectories and occupational pursuits.” (Lubinski, et. al., 2001) Achieving academic success is the foundation of what our society values when determining potential. However, some scholars are critical of the way our present system identifies early achievers. Abraham Tannenbaum of Teachers College at Columbia University suggests that “those who are successful in school may be masters of trivia, straight ‘A’ retrievers and dispensers of other people’s knowledge, who masquerade as gifted even though they can never generate a worthwhile idea of their own.” (Blohowiak, 1992).
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One cannot assume that a student in high school or college who is deemed gifted or ‘smarter’ than others will be an unqualified success. Although many of these students continue to deliver on their identified promise, some researchers (Winner, 2000; Csikszentmihalyi, 1996; Subotnik and Arnold, 1994) question whether this criterion and approach in identifying this select group provide true indications of their ability to achieve well into adulthood. Winner (2000) suggests that “few gifted children go on to become adult creators because the skills and personality factors required to be a creator are very different from those typical of even the most highly gifted children.” Runco discusses one particular career choice study that reinforces this in the math and science domain: The math-science participants, for example, would be expected to choose careers that involve math and science. This prediction is, however, simplistic in the sense that it is based solely on cognitive potential. Career choice and many significant developmental decisions reflect preferences and personality perhaps more than cognitive abilities. (Runco, 1999) Traditional hiring criteria may not be the best way to predict career and creative success. As Bandura et al. (2001) point out, “children’s perceived efficacy rather than their actual academic achievement is the key determinant of their perceived occupational self-efficacy and preferred choice of work-life.” By using the criteria presently in use for college acceptance and for identifying potential in the workplace, we may be passing over individuals who have the ability to be the breakthrough innovators of tomorrow. Milgram and Hong (Subotnik and Arnold, 1994) are also not convinced that academic achievement is crucial to a successful professional career. Their findings suggest that “creative thinking and creative performance are better predictors of adult life accomplishment than intelligence or school 114
grades ... school grades in adolescence predicted academic achievement in adults. However, grades in school were unrelated to any accomplishments in adult life outside the academic arena.” Torrance, one of the most highly regarded researchers in the area of creative development and accomplishment, discussed some of his work during his later years in an interview conducted by Shaughnessy (1998). Torrance states that: In our 30-year longitudinal study of predicting creative behavior, we identified two special groups of subjects. I called the first group ‘beyonders.’ The number and quality of their publicly acknowledged creative achievements were extremely high. The other group was called the ‘great expectations’ group. They were sociometric stars when they were in high school and when the predictor data were collected. The great expectations group had been high achievers, but they had not persevered the way that the beyonders had. Teachers, high school administrators and parents spend a great deal of time boosting the self-esteem of the great expectations group. They reinforce this group’s successful academic achievements. They look to secure places for this group in the most prestigious universities. They decide how the “great expectations” child is going to become the best at what he or she excels in during the school years. What they sometimes miss are the “beyonders” who are under the “success potential” radar. With these individuals, adults tend to either try to fix them and compensate for their lower performance or help them find jobs that demand a lower level of thinking and performance. In spite of this approach, some beyonders break out of these low expectations and surpass their friends who demonstrated more obvious potential according to traditional standards. We will look to see if any of these beyonders are present in the corporate engineering community where we conducted our research.
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H1: Academic success (overall and science grades) in high school will have no effect on gross revenue (innovative output). Multiple Intelligences (efficacies) The ideal engineer is a composite ... He is not a scientist, he is not a mathematician, he is not a sociologist or a writer; but he may use the knowledge and techniques of any or all of these disciplines in solving engineering problems. Dougherty, N. W. (1955) If we cannot solely use presently accepted academic achievement (grades), traditionally identified intelligence and skills that predict innovative success, we must seek out alternative measurements that provide more effective predictors. “Broader tests of intelligence, such as those being proposed and explored (e.g., Gardner, 1983; Sternberg, 2001) offer possibilities for increasing levels of prediction” (Libinski et al., 2001). One option to explore might be the work of Gardner, who suggests that taking a much broader view of intelligence could assist in evaluating cognitive excellence that may currently be overlooked. The theory of multiple intelligences was developed by Howard Gardner (1999) in response to observing the impact of brain damage on human cognitive capacities. By identifying the specific location within the brain that was damaged, he was able to isolate a range of these capacities. This isolation allowed him to see how losing one particular cognitive skill may not have any impact on another. For example, Gardner writes, “a person may be skilled in acquiring foreign languages, yet be unable to find her way around an unfamiliar environment or learn a new song or figure out who occupies a position of power in a crowd of strangers.” His theory suggests that the traditional measures of intelligence are narrow in reporting anything more than the skills of
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linguistic and mathematic cognition. Gardner’s theory has been primarily utilized in the context of designing educational programs that support people with a diversity of “intelligences,” allowing students to be recognized for their range of innate talents. Based on Gardner’s work, it appears that the spectrum of an individual’s intelligences could help define in what type of career he or she may succeed with greater accomplishment. Gardner’s (1983) paradigm for intellectual analysis looks at a range of skills and behavioral biases of individuals. He calls these skills multiple intelligences, and in his first presentation of this notion he proposed seven intelligences: mathematical/logical (number smart), verbal/linguistic (word smart), visual/spatial (picture smart), musical (music smart), bodily/kinesthetic (physically smart), interpersonal (people smart) and intrapersonal (self smart). Later, Gardner added natural (nature smart) and existential (spiritually smart). Although Gardner never suggested measuring these intelligence levels as an assessment tool, many others have expanded on his work to develop ways to determine what levels of these intelligences each person exhibits and what careers these levels suggest. In the context of his theory of multiple intelligences, Gardner introduced three distinct uses of the term “intelligence”: •
A property of all human beings (All of us possess these . . . intelligences)
•
A dimension on which human beings differ (No two people—not even identical twins—possess exactly the same profile of intelligences)
•
The way in which one carries out a task in virtue of one’s goals (Joe may have a lot of musical intelligence but his interpretation of that piece made little sense to us) (Gardner, 2003)
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Gardner’s notion of adding intrinsically generated goals to a definition of intelligence reflects the nature of what Runco (1999) and Bandura, et. al. (2001) suggest regarding the importance of including personality, self efficacy and interests in the intelligence assessment. Gardner presents a wide and comprehensive view of intelligence, which, McClelland (1973) suggests, “allows us to help individuals explore careers that are appropriate to their innate and acquired competencies in contrast to a more abstract and limited look at the types of abilities that traditional IQ tests measure.” Since the creation of Gardner’s paradigm of multiple intelligences, many researchers have looked at how each one affects an individual’s education, effectiveness in the workplace and even day-to-day life. In the context of this study, we are looking for links between the individual’s levels of multiple intelligences and his or her path to innovative success. Before we look at possible constructs that mediate this path, we will explore some direct connections between Gardner’s delineation and an individual’s professional output. Literature and other career oriented indicators identify specific intelligences as having a strong connection with characteristics of engineers and scientists: logical, spatial, physical, and musical. We will use the label efficacies, because the focus of the multiple intelligence literature and instruments reflect propensity and desire to participate in these activities rather than skills and excellence, which would suggest that intelligence, would be more appropriate. The remaining hypotheses in the research reflect these theories and notions. Musical intelligence (efficacies). A number of researchers perceive a strong link between excellence in science and musical acumen. Root-Bernstein (2001) explains, “Music and science are two ways of 118
using a common set of ‘tools for thinking’ that unify all disciplines. Levenson (1994) argues, “in Bach’s day ... music ... was a tool that could describe and discover patterns, order in nature, in human experience. The mathematical precision of rhythm and harmony suggested metaphors and the real thing, all at once – hence Kepler’s polyphonic solar system, Newton’s rainbow, mapped onto a scale of colors.” “The very best engineers and technical designers in the Silicon Valley industry are, nearly without exception, practicing musicians.” (Venerable, 1989) Rauscher (1994) writes, “Music acts as an ‘exercise’ for exciting and priming the common repertoire and sequential flow of the cortical firing patterns responsible for higher brain functions.” This impact on developing higher brain functions may have led to the breakthrough discoveries created by so many of the inventors inducted into the National Inventors Hall of Fame. These innovators gravitated towards musical exploration and expression for both professional and entertainment purposes. For instance, Robert Rines, inventor of sonar and high definition radar, was the composer of an off-Broadway musical in New York City’s theater district. Ray Damadian, inventor of MRI, studied violin at the Juilliard School of Music. Charles Kaman “used his knowledge of vibration, gained from building helicopters, to design a guitar made from aerospace composites but that still had a natural sound” (www.invent.org, 2004). Both the literature and these anecdotes suggest that those skilled in scientific discovery or invention may also have some aptitude within the domain of music. H2: A high score in the musical efficacy will have a positive effect on gross revenue (innovative output).
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Spatial and physical intelligence (efficacy). “Scores on a spatial-visualization composite would probably add incremental validity to verbal and math scores, which are currently being used for identifying students with exceptional talent for engineering” (Humphreys et al., 1993). Being able to visualize the unseen in one’s mind is highly valued in engineering and creative scientific endeavors. Traditional IQ tests make some assessment of the ability to visualize 3-D images from 2-D patterns, yet Humphreys, et al. (1993) suggest that test scores and courses in high school do not stress visualization ability as much as would be helpful in identifying the stellar engineers of the future. Spatial and physical efficacies presented the following innovative individuals with a formula for success. Gardner (1983) writes, “Einstein had an especially welldeveloped set of (spatial) capacities . . . he became mesmerized in first reading Euclid: and it was to the visual and spatial forms, and their correspondence, that Einstein was most strongly drawn.” Einstein is among a list of inventors with highly developed spatial skills. Others include: Kekule (benzene ring), Watson and Crick (DNA), and daVinci. Gardner concludes, “The engineer, the technician, or the inventor does not merely use materials in culturally established ways but actually rearranges materials in order to create an object better fitted to the task that he is confronting” – thereby leveraging his or her physical intelligence (efficacy). This idea fits well with the definition of an engineer as “a person who uses scientific knowledge to solve practical problems” (hyperdictionary.com, 2004). The different types of engineers range from mechanical to electrical to software and beyond, and these scientists use diverse skills to solve the problems that are posed to them or that are uncovered in the course of their work. In addition to the obvious logical skills, a good 120
engineer needs both spatial and (as Gardner suggests) bodily (physical) intelligence to excel. Gardner (1983) points out that no intelligence stands alone. The best performer in each domain uses a cluster of intelligences to accomplish tasks effectively. “Confined to spatial intelligence, he (the engineer) may understand a mechanism reasonably well and yet have no idea of how actually to manipulate or operate the object in which it is housed: restricted to bodily intelligence, he may be able to execute the appropriate motions yet fail to appreciate the way in which the apparatus or the procedure works.” No other literature could be found that suggests that engineers need skills in physical intelligence. We will test the presence of this intelligence in our sample to determine if there is a reason to look more closely at how engineers use a good sense of physical movement to design and invent. H3: A high score in spatial efficacy will have a positive effect on gross revenue (innovative output). H4: A high score in the bodily/kinesthetic (physical) efficacy will have a positive effect on gross revenue (innovative output). Logical intelligence (efficacies). To one who knows what it takes to be an engineer, it is apparent that logic and mathematics are essential for the individual who travels on the track to success. “Admissions requirements for undergraduate engineering schools include a solid background in mathematics (algebra, geometry, trigonometry, and calculus).” (http://www.bls.gov/oco/ocos027.htm, 2004).
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Engineering text books emphasize the need to think logically as well as to use the key elements of the laws of mathematics. Day-to-day work as an engineer invariably involves the use of these two skills in both formal and intuitive explorations. At face value, logical efficacy appears to be the backbone of all highly technical innovations. As Mhadeshwar (2004) argues, “Logic is the very foundation of engineering. Engineers are supposed to be the most logical people around.” H5: A high score in the logical efficacy will have a positive effect on gross revenue (innovative output). Although the aptitude of logical and mathematical thinking is essential for a strong engineering career, Mhadeshwar (2004) is concerned that with engineers focusing only on their logic and math skills, “we rarely get flamboyant ideas. This is because when you are searching for ideas, excessive logical thinking can short-circuit your creative process. The remedy: It's good to have a logical approach in the labs, but when you are hunting for certain ideas, try to break free from it.” This warning about the consequences of focusing on one characteristic to the exclusion of others supports the notion that a spectrum of efficacies is crucial for the well-rounded engineer who can be technical and innovative at the same time or on the same project. Social Capital Historically, many inventors, engineers and scientists have been stereotyped as anti-social, loners and classic individualists. Yet, practitioners and researchers have determined that effective social and networking skills are necessary for productive collaboration with others who can add value to an engineer’s work. These researchers 122
suggest that dynamic communication skills are essential in allowing an idea to come to life. If the hypotheses posited so far in this study are accurate, then we could argue that an individual could possess only the non-personality-related efficacies (spatial, physical, and logical) and be a highly successful innovator. However, additional research suggests that the ability to communicate one’s discovery or innovation effectively (interpersonal intelligence) is essential for success. With the proper personal interaction skills, an individual can collaborate with others, communicate discoveries and influence decision makers who may not understand the power of their innovations at first glance. Stored in people, tacit knowledge is actuated (shared) though trust formation. Trust develops in predictable network patterns that by their nature run counter (are mis-aligned) to hierarchical organization. If one treats tacit knowledge as a natural resource embodied in humans (or human resource), then knowing where and how to mine the networks for tacit knowledge is the turn-key solution for rapid innovation. (Stephenson, 2004) A key component of interpersonal intelligence is the ability to work through networks to maximize the value of knowledge that one brings to a project. In his seminal article, “The Strength of Weak Ties,” Granovetter (1973) looks at social networks by exploring “the degree of overlap of two individuals’ friendship networks that varies directly with the strength of their tie to one another.” Granovetter shows how strategic relationships that depend on weak ties have more lasting impact than those with strong ties. Building on Grannovetter’s work, Burt (2000) “identified those who act as the glue that connects those in weak tie relationships as structural holes.” Burt’s most consistent empirical finding has been that “dense networks are associated with 123
substandard performance.” “Entrepreneurial networks” or “broker networks,” as Burt discusses, “are large sparse, non-hierarchical networks rich in opportunities to broker connections across structural holes.” This type of network is related to outcomes of more “creativity and innovation, (more) positive job evaluations, early promotion and higher earnings.” Social capital could have significant impact on our respondents’ career success. Engineers with strong social capital may have the ability to take their innovations further than an innovator that has no ability to connect with others during the development or influencing processes. Based on Burt and Grannovetter’s thinking, regardless of efficacy or academic achievement, those who create large networks of weak ties have a better chance of achieving innovative outcomes in their careers than do those with dense social networks. To many entrepreneurs and corporations, secrecy is the expected protocol in developing new ideas and attempting to push the technological envelop. However, those who move faster and act efficiently in an open environment tend to use strong capital skills, extending their tentacles to others in their field, while still playing the competitive game. Saxenian (1994) saw this contrast in comparing the development of Silicon Valley to the technology corridor (Route 128) in Boston. “Silicon Valley has a regionalnetwork-based industrial system.... The region's social networks and open labor market encourage entrepreneurship and experimentation. Companies compete intensely while learning from one another about changing markets and technologies through informal communication and collaboration.” These observations support Grannovetter’s theory concerning the strength of weak ties. In contrast, Saxenian argues, “the Route 128 region is dominated by a small number of relatively vertically integrated corporations that keep 124
largely to themselves. Secrecy and corporate loyalty governed relations between companies and their customers, suppliers, and competitors, reinforcing a regional culture that encourages stability and self-reliance.” This closed world that depended on strong ties led to a very slow growth curve for Boston high-tech companies, who achieved only incremental success versus their fast-growing, vibrant colleagues in the west. Technical innovation does not depend solely on hiring those with technical skills. The challenge is more complex than placing a creatively brilliant engineer in a lab by her- or himself and simply expecting breakthrough innovations to emerge. Social capital theory explains the relationship between the individual’s networking and social talents and his or her intellectual and technical skills. Trust, reciprocity, shared values, networking, and norms are all things that, according to social capital theory, add value in a firm, or between firms, by speeding the transfer of information and the development of new knowledge. Social innovation capital (SIC) is therefore a necessary precondition (or antecedent) to the production of all forms of IC (Intellectual Capital), including valuable intellectual property (IP), such as patents, trademarks, and copyrights. In the absence of SIC – or in the presence of weak SIC – even the most valuable IP is merely ephemeral. (McElroy, 2001) H6: A strong set of social capital skills mediates the relationship between each efficacy and gross revenue (innovative output). H7: A strong set of social capital skills mediates the relationship between academic success and gross revenue (innovative output). Work Environment Ideas about how to create effective work environments are numerous, and opinions about how to create inspiring spaces that increase creative accomplishments are even more abundant. One element in creating a work environment that welcomes innovative creations is the balance between strong direction and ‘tinkering’ with 125
scientific phenomena. In his discussion of how pure scientific research is often more fruitful than is heavily-directed, project-based science, Albert Szent-Gyorgyi (1894) has his own thoughts. The most brilliant project is worthless in the hands of a poor scientist, while, conversely, a good scientist has a good chance to come up with something valuable whatever he touches. Pasteur went to Germany to study brewing beer and came home with the discovery of optical isomerism.... Find (good scientists) and support them, enable them to work. That's all there is to it. Don’t torture them with senseless projects. (Szent-Gyorgyi, 1894) It takes a very patient and insightful executive to support the efforts of what Chargaff (1980) suggests is the most successful imaginative scientist. To harvest the best work of every engineer, scientist and inventor in their midst, executives must create an environment of trust and respect. Just when they think one of their engineers is daydreaming or totally off track, a great idea comes out to invigorate an entire new line of thinking and innovation. “As in most sciences the boundary between imagination and rational perception often is obscure, and intuition will sometimes be concealed by [the scientist/engineer] who had it, being supplied ad hoc with an experimental underpinning out of which the inspiration will be claimed to have grown.” Based on this insight, patience and not control could be the most effective way to manage an engineer, counterintuitive as this approach may sound to some managers. Individual creativity emanates from the interaction of expertise in one’s domain, task motivation and creative skills. Finding an environment that welcomes exploration of ideas while holding onto the individual’s internal motivation is crucial. Amabile (1997) points out, “managers who learn these lessons will recruit for people who already have that spark of passion for their work (as well as the requisite skills and experience), but 126
they will also nurture that spark by creating a work environment that downplays the obstacles and fosters the stimulants to creativity. Only then will their organizations be poised to lead through innovation.” Lapierre and Giroux (2003) determined the dimensions of a work environment that foster a high level of creativity in a high-tech organization: “work atmosphere; vertical collaboration; autonomy/freedom; respect; alignment; and lateral collaboration. They are valid, reliable predictors of the creativity achieved in high-tech organizations.” Our construct of social capital will measure an engineer’s perception of an innovative environment that may include both vertical and lateral collaboration. The design of a workplace that has a sense of freedom, where engineers are given respect and where all are aligned to the vision of the organization, will yield more innovative outputs. H8: Work environments will mediate the relationship between efficacy and gross revenue (innovative output). H9: Work environments will mediate the relationship between academic success and gross revenue (innovative output). Based on anecdotal support from a previous study (Brand, 2003), having a mentor was included as an independent variable. Questions in the survey were added to test the hypothesis that having a mentor increases one’s creative output. H5: Having a mentor will have a positive effect on gross revenue (innovative output). Methodology Participants
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We surveyed engineers from one company to determine the way in which certain predetermined characteristics influence the financial outputs of their work. Currently, some company executives suggest that passion, previous tinkering, and having a relative who is an engineer are more important predictors of success than academic achievement and a strong résumé. The firm being researched has as a pool of approximately 900 engineers spread across facilities in over 40 countries. All had the opportunity to participate in this study with total anonymity. Based on a review of the relevant literature on innovation and associated domains, an internet survey was developed founded in the appropriate grounded theory. The survey was placed on a web site independent of the corporation being researched. The instrument consisted of five core constructs as well as additional demographic questions. Emails announcing the purpose of the survey and means of participation were sent to 120 engineering general managers throughout the company. These individuals were asked to forward the survey information to engineers on their team. Two additional reminder emails were sent during the three-week data collection period. In total, 385 surveys were received: 91 after the first email, 209 after the second and 85 in the third wave. Respondents participated in the survey in the first quarter of 2004. Dependent Variable – Innovation The existing scales designed to evaluate technically innovative outputs have limited consistency, because innovation, from an empirical standpoint, is hard to define. One study evaluated creative output by asking participants to rate themselves in everyday problem solving as they made comparisons to friends, family and colleagues (Lapierre 128
and Giroux, 2003). Another uses a self report suggesting how their innovation would be recognized by peers (Fodor and Greenier, 1995). A third, which is the closest to one of the scales that we have chosen, uses a self-reported level of perceived novelty and uniqueness of an individual’s output (Landry and Amara, 2002). In this study, we used three variables to define the construct, technical innovation: 1.
the number of patents awarded to an individual
2.
a report of revenue that has been generated from his or her innovations; and
3.
the level at which the respondent perceives that his or her innovation impacts the world (levels of innovation). Patents. Comparing the number of patents an individual has been awarded during his or
her career could provide some insight into his or her innovative output. To receive a patent, an inventor (engineer) must use his or her unique personal combination of creativity and technological know-how, or even intuition, to invent a “process, machine, or article of manufacture, composition of matter or improvement of any of the above” (USPTO, 2003). Receiving a patent suggests that no one else has invented or applied for a patent for that exact innovation. Some suggest that looking solely at the number of patents generated might have little value; some innovations have many patents, some patents are never brought to market, and many innovators and companies do not patent due to the requirement to disclose trade secrets. However, we will review the number of patents each respondent received and determine if there is a correlation with the other innovation measures or constructs. Revenue. 129
Measuring pure innovation and creativity is an interesting academic exercise. However, from a corporate standpoint, the ultimate bottom line is the bottom line. Therefore, the evaluation of an engineer’s value to his or her company could be made more concrete by calculating the financial success of their output. Therefore, respondents were asked to report gross and net revenues for the innovation they created that has had the most impact. Levels of innovation. Genrich Altshuller, a patent examiner for the Russian navy over 50 years ago, was determined to quantify the value of a patent in terms of its impact. His work spurred a significant range of literature and practice, some of which has led to the TRIZ process of innovation. Altshuller examined a large (some suggest over 1 million) number of patents, looking for the hallmarks of truly creative inventions.... By removing the subject matter (or specific industry), Altshuller was able to elucidate the problem solving process. He categorized the patents' solutions into five levels.... With each succeeding level, the knowledge required of the inventor, as well as the potential profit from the invention, increases. (Savransky, 1996) Altshuller’s evaluative system and subsequent tools for innovation have been used worldwide in the most successful corporations, recently using Six Sigma. In his analysis (1949), each patent was placed in a particular category and the percentage of patents in each category was identified: the standard solution (32%); change of a system (45%); solution across industries (19%); solutions across sciences (4%); discovery (.3%). The adapted version of Altshuller’s scale measures six levels of innovation: unique to the individual, unique to the team, unique to the company, unique to the industry, unique across industries and new to the world (breakthrough). 130
Reflecting this measure, three specific questions were asked: 1. evaluate the level of innovation achieved by your most successful innovation; 2. evaluate the overall tenor of your work; and 3. evaluate your most successful work prior to coming to your present company. Independent Variables Academic achievement. The construct of academic achievement is divided into two self-reported achievements in high school: overall grades and science-only grades. Multiple intelligences (efficacies.) Gardner (1999) developed his theory of multiple intelligences as a result of a long-held fascination with human development. His research was focused on braindamaged adults and gifted children. He attempted to make sense of his notions about development and cognitive capacity. His observations “were cluing him into the same message: that the human mind is better thought of as a series of relatively separate faculties” (p 31). Gardner’s theory has been used mostly to help educators become more aware of the diverse types of cognitive explorations used by their students. Although Gardner never fully embraced the notion of testing individuals to discern their levels of multiple intelligences, many others have attempted this type of application. Gardner’s major concern was the viability of asking people to rate their understanding of themselves or to take a short-answer test to determine their bodily-kinesthetic intelligence (p. 136) or other intelligences.
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Our scale regarding multiple intelligences (efficacies) will provide us with selfreported insights as to the individual’s propensity to enjoy various activities that fall into the separate intelligences, participate in them, or believe they are good at them. Again, because the nature of the items in the instrument does not address talent or excellence in a domain, we have decided to refer to these “intelligences” as efficacies. Gardner suggests that there may be more correlations within the multiple intelligences construct versus the highly defined statistical definitions highlighted by factor analysis. A behavior-based evaluation might be more appropriate if the insights we collect suggest some benefit to using this construct qualitatively. We have chosen this construct to present an alternative view of intelligence levels, as broadly defined by Gardner and those who follow in his research. Shearer (1999) has been creating a database of validated connections between his multiple intelligence survey and careers. Because his instrument is fairly long, using it in this study would limit our data collection only to multiple intelligences, which is too narrow for our purposes. However, we did use elements. On a scale of 1-100, Shearer suggested that engineers generally tested in the following intelligences: Logical Mathematical – 68, Spatial – 67, Intrapersonal – 65, Linguistic – 51, Bodily Kinesthetic – 42, Interpersonal – 45, and Musical – 37. The scale we ultimately used was created by Betts and Jolene (1996). It includes 42 items divided into 7 specific intelligences (6 items each) that were initially identified by Gardner in his early research: Linguistic, Logical/ Mathematical, Physical, Intrapersonal, Musical, Spatial, and Interpersonal. Six items, taken from Shearer’s instrument (1999), were added that were focused on evaluating natural intelligence. 132
(Gardner’s later additions of spiritual and existential intelligences were not included in this study.) Later in the process, we removed the intelligences that were not fully supported by the theory found in the literature. Mediators Social capital. Within this research, social capital is defined as the networking behavior that allows individuals to leverage the support of others in an attempt to produce highly innovative technical results. Some insights regarding this definition of social capital are drawn from the work of McElroy (2002), who has coined the term social innovation capital… which refers to the structural manner in which whole social systems (i.e., firms) organize themselves around – and carry out – the production and integration of new knowledge.” (McElroy, 2002) Although McElroy’s definition of social capital would be the most aligned with our goal for assessing engineers and their innovative results, he has not developed a fully validated scale for distribution. In addition, McElroy was looking more closely at the system of individuals operating within the network, while we are looking for the skills of an individual to better leverage the system. Foret and Dougherty’s (2001) work is more supportive of the goals of this research. They looked at social capital in the context of networking behavior, which they defined as “individuals’ attempts to develop and maintain relationships with others who have the potential to assist them in their work or career.” Therefore, we are using a 33item scale with five elements that emerge out of Foret and Dougherty’s (2001) factor analysis. The factors and the alpha values derived from their research are: maintaining 133
contacts (5 items, alpha=.79); socializing (7 items, alpha=.77); engaging in professional activities (8 items, alpha=.73); participating in church and community (4 items, alpha=.75); and increasing internal visibility (4 items, alpha=.65). In contrast to Foret and Dougherty, who used this social capital construct as a dependent variable, we expect social capital to be a force in mediating our independent variables on the dependent variable, innovation. Work environment. For the purpose of this study, we are looking to see if one’s work environment has the ability to spark innovative output. Specifically, we want to determine if work environments mediate the independent variables. Amabile, working with the Center for Creative Leadership, has developed one of the better-known measures of innovative work environments, KEYS (Center for Creative Leadership, 2004). This is a 78-item survey used in a range of settings. Ekvall’s (2001) Situational Outlook Questionnaire is also implemented extensively “to effectively discern climates that either encourage or discourage creativity and the ability to initiate change.” Although these and other instruments for evaluating the ability of a work environment to support innovation are valid and effective, they are primarily used in total, making it difficult for us to combine with other constructs as we did in this study. We used Prather’s (1996) scale, which is based on Ekvall’s scale and definition of the work environment. The problem of innovation in organizations is rooted in the nature of creative processes and creative persons and that the two different kinds of creativity (radical and revolutionary or adaptive and confirmatory) are differently facilitated by organizational conditions” (Ekvall, 1994, p. 204) 134
Challenge, freedom, idea support, trust, dynamism, order/structure/plan, goal clarity, risk taking, playfulness/humor, debates, conflicts, idea-time, livefulness/dynamism are construct delineations (factors) that Ekvall extracted from his well-used instrument. An adapted version of these factors taken from both Ekvall and Prather was used in this study to determine the level of innovative support within the work environment.
Findings Over 380 on-line surveys were collected during the 3-week participation window. We removed respondents that did not complete a substantial portion of the survey, resulting in 282 cases to analyze. Because gross revenue was the only hypothesized dependent variable with a normal distribution, it was determined to be the strongest choice for measuring innovative output. Although the company being researched suggested that most of their engineers would know the gross and net revenues of their individual innovation’s impact on the company, only 169 of the 282 respondents being analyzed answered the question relating to gross revenue. When comparing the 169 to 282 samples, one can see only minor differences in the results. These differences will be addressed within this discussion and set of tables. Unless there are substantial differences between the two samples, we will present the results of the largest possible sample—282 for most tests and 169 when considering regressions on gross revenue. This dual-n approach was utilized to compare
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the two levels of analyses and determine the validity of the regressions on gross revenue threatened by a low n. Demographics The data in Table 2 provide us some insight into the demographics of our respondent sample. You’ll notice that most of our respondents were from middle income families who came from the suburbs. You’ll also see a very strong gender imbalance, with males comprising more than 90% of the sample. This imbalance was expected based on the national statistics of the number of female engineers in the US, which, in 2002, was stated by the National Science Foundation at 9% (www.nsf.gov). One of the company executives with whom we spoke suggested that having a relative as an inventor may have some impact. Based on that idea, we included this question in our engineer’s survey. We found that approximately fifty percent of the participant engineers have a relative who is also an engineer. Upon conducting an independent sample t-test, we noticed some significant differences between engineers who had a relative who is an engineer and those who didn’t. The differences were noted within the variables spatial (p=.042), physical (p=.013), mentor (p=.023) and social capital (p=.027). We also saw that there was a significant difference in the means in gross revenue that could suggest between $1-3 million difference between these two groups (p=.067). Since approximately 50% of the respondents suggested that their innovations were new to the world, it made sense that approximately 50% said that they had at least one or more patents to their name.
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Factor Analysis A confirmatory factor analysis was conducted using maximum likelihood estimation and Promax Rotation within SPSS. The results are presented in Table 3a and 3b. The forced dimensions were derived from the literature. Factor loadings ranged from .49-.91. All cross loadings were less than .36. Social capital divided into five distinct factors (eigenvalues are presented as (n=169/n=282): presenting at conferences (2.50/2.04); attending professional development activities (1.39/1.66); going to social events with business associates (1.33/1.37); seeking increased internal visibility (1.23/1.25); and participating in civic activities outside of work (1.12/.99). Work environment divided into 2 distinct factors: a work environment responsive to employees (7.26/6.68) and the respondent perception of the informality in the work environment (1.03/.77). Differences between the lower and higher respondent loading levels were primarily between .01-.19, with only two items showing differences above .20. Because of the relatively low level of cross loadings and high levels of construct loading, we can be confident in our constructs having both convergent and discriminate validity. Efficacy, which is an adapted version of multiple intelligences, shows 5 distinct dimensions: creation of music (3.40/3.52), music sensitivity (1.66/2.44), spatial (1.22/1.11), logic (.96/1.18) and physical adeptness (.77/.86). Although music was expected to load as one factor, the analysis discovered two as identified above. Of the efficacy factors, inter-item reliability, as seen in table 4 was relatively high with the following results: creation of music (.90), sensitivity to music (.70), spatial (.69), logic (.66) and physical adeptness (.67). Both social capital and work environment 137
factors were consolidated for the regression analysis with crohnbach alpha at .82 and .87, respectively, establishing an acceptable level of inter-item reliability. Most of the factor correlations (see table 4) in this model are quite low. The strongest relationships exist between age and number of patents (.34***), and age and gross revenue (.32***). On face value, this makes sense as it takes a new engineer a number of years in the field in order to both generate patents as well as deliver revenue to their company. Number of patents and logic (.22**) also has a relatively high correlation, which is appropriate based on what the role of an engineer is all about: using logic to invent and discover. Table 5 presents the factor loading correlations taken from the factor analysis and compares the distinction when n=169 vs. n=282. Due to low to moderate correlations, we can again confirm that there is high discriminate validity among our constructs and factors. Because those correlations of factors to be consolidated were slightly higher than other correlations, we can feel somewhat confident that we have achieved convergent validity as well. However, to be more confident, we would have liked to see higher correlations within these factors. Because of the relatively high coefficients in the music factors, we would expect to see some common variable behavior. As we will discuss later, however, the two factors behaved quite differently. An independent sample t-test was conducted comparing the 169 respondent sample with the remaining 113 from the full 282 respondent sample. Although most of the constructs are quite similar, the results suggest that the sample means are different for spatial (.485***), mentor (-.345**) and overall grades (.405***), suggesting that for spatial and overall grades, those not
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responding to the question regarding gross revenue have a higher mean than those who did, and conversely for the question regarding mentors. Hypothesis Testing Table 6 (which is visualized in the revised conceptual model in figure 2) shows the estimated coefficients that resulted from regressions of IVs and hypothesized mediators on gross revenue. The color shaded paths on the table indicate significant results. Of the two hypothesized mediators, only social capital (.325***) had any influence on gross revenue. Because r2=.200***, there is an 80% chance that these results are random. The influence of grades (H1) on gross revenue was surprisingly significant. Although moderate in scope, influence of science grades (-.160*) was negative, while the influence of overall grades on gross revenue was positive (.259**). And since the literature suggests that grades have little influence on innovation and “success,” it was surprising to see that good science grades in high school actually have a negative influence on an engineer’s innovative financial output. This could explain Csikszentmihalyi’s (1996) suggestion that “school threatened to extinguish the interest and curiosity that the child had discovered outside its walls.” The education literature states that there is a strong relationship between the multiple intelligence constructs we identified and success in engineering and science (Mhadeshwar, 2004, Humphreys et al., 1993, Venerable, 1989, Gardner, 1983). As stated before, we have redefined this overarching construct as efficacy. In figure 2, the revised conceptual model suggests that, of the four efficacy constructs identified in the hypothesized conceptual model, only physical efficacy (H4) (-.213***) and creation of 139
music (H2) (-.169*) have any direct influence on gross revenue and it is negative. The results also suggest that social capital (H6, H7) fully mediates the efficacies of music sensitivity and logic. It is interesting to note that creating music (.214**) has a significant and positive influence on social capital; yet being sensitive to music (-.182*) has a significant but negative influence. With r2 being.33***, there is a 67% chance that these results are due to randomness. Of the remaining efficacy constructs, only logic (.12*) has a significant, yet minor influence on social capital. In contrast to the efficacy relationships towards social capital, only music sensitivity (.196**) and logic (.171**) have significant influence on the respondents’ perception of an innovative work environment. The regression analysis of these hypothesized mediating constructs suggests that social capital fully mediates (H6, H7) the relationship between both music sensitivity and logic as it impacts gross revenue. There is some anecdotal evidence that mentorship has an impact on success in the workplace. We found that having mentors (H10) had a moderate (.38***) influence on social capital but a negative direct influence on gross revenue (-.173**) Discussion This of the research data colleted presents many contradictions with the data of other researchers as found in the literature. One of the most surprising results of this work is that of the role academic achievement plays in determining one’s impact on innovative output in the company we observed. Although mixed in the impact of grades on innovation and success, the literature leans toward suggesting that academic success is more likely to predict academic success than professional success. Since we are only 140
surveying those from one company, this could mean that the company uses academic achievement as a criterion for hiring, which would preclude anyone with less than stellar grades from being part of the engineering effort at the company. Also, in looking at the specific relationships, it is interesting to note the positive impact of overall academic achievement on gross revenue as compared to the negative impact of science grades on revenue. This could mean that the science skills taught in school are incongruous with the skills needed for an engineer to be successful in this situation. Additionally, this could mean that those with strong science skills don’t allow themselves to be tied down to the approach or even discipline being used in the school environment. Additional data could help us become more certain about this issue. The role of multiple intelligences (or efficacies, as we have redefined it) produced a very different picture than we expected. Literature suggests that efficacy in music, logic, spatial and physical activities influences success in engineering and science. More specifically, it suggests that students who exhibit these intelligences are directed toward fields in science and engineering by many educators and counselors. Our data suggests that efficacies in these areas will not directly lead to generating revenue, and that physical efficacy and the creation of music can have a negative direct influence. However, all of the efficacies (except for spatial) are mediated by social capital skills. This makes a point that human resources managers would be well-advised to consider if they are evaluating efficacies: make sure your potential employees also have the social skills to best leverage their strength in these areas. The disappearance of spatial efficacy in predicting success in engineering on its own or through either of the mediators is also puzzling. The relationship between 141
possessing spatial skills and becoming a successful engineer seems intuitive; however, it is unclear why this notion did not reveal itself within the analysis. It was hypothesized that logic and spatial intelligence would have a direct and significant influence on innovation. We did, however, see a significant, yet small correlation between logic and spatial skills, and gross revenue. So there is a relationship, but gross revenue did not depend solely on logic and spatial skills. This is not a result that makes intuitive sense. Looking at the distinct factors of creating music vs. sensing or enjoying music and teaching at professional events vs. going to these events provides some interesting nuances of interpretation. One could surmise that both creating music and teaching at professional events is more proactive and dynamic than enjoying music and just attending conferences. (The factor analysis provided empirical evidence for these distinctions.) These proactive constructs had a more significant and stronger correlation with most of the other antecedents and constructs. This was especially pronounced in their correlation with gross revenue: create music -.12** vs. enjoy museum -.10 and presenting at conferences .22*** vs. professional development .13***. When looking at these numbers, it is interesting to see that the more actively involved someone is with music, the more significant and negative the relationship is with gross revenue. Conversely, presenting at conferences seems to have a higher correlation with gross revenue than those who simply attend. This may suggest that performing musicians (those who create music) may not focus on the bottom line as do those who speak at conferences and who may, therefore, be able to communicate more ideas that will generate revenue.
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There are a number of reasons why the findings here might contradict those in the literature. When Gardner talks about the role of spatial and bodily kinesthetic intelligence on innovative success, he is discussing the likes of Einstein, Watson and Crick (DNA), and da Vinci, not your typical corporate engineers. Also, the definition other researchers use for innovative success may be different from that which we used in our hypotheses. We conjectured that creative success suggested innovations that were viable economically. Winner (2002) looked at success as “becoming adult creators”, not adult, financially- focused creators. Furthermore, Csikszentmihalyi (1996) suggested that “school threatened to extinguish the interest and curiosity that the child had discovered outside its walls.” However, school may not have threatened to extinguish the interest in creating ideas that will deliver financial results to their company. Although not hypothesized in our original model, having a mentor became important in establishing social capital skills, yet it seems to have no influence on one’s perception of an innovative work environment or gross revenue. This suggests that, although having a mentor is a positive element in one’s development, developing a more company-aligned mentorship program may better serve the financial objectives of the company. If those who are hiring do not clearly communicate their expectations regarding revenue generation, engineers will not make that a priority during their tenure at the company. Also, the company has no method for screening out those engineers who are only interested in pure research and innovation for innovation’s sake. This research could suggest that companies look closely at their hiring practices to make sure that the results
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they are looking for are embedded in the criteria for first interviewing and then engaging engineers for employment. Overall, it would have been preferable to provide deeper global understanding of the hypothesized issues with stronger significance and more powerful relationships. However, the data have provided some insights that deserve discussion and future consideration. Limitations There are a number of limitations in using this data in practical applications. One of the challenges is that these instruments are neither effectively validated nor specifically designed for this type of study. Based on availability, we chose a multiple intelligence/efficacy scale that was not strong; it is unclear as to whether the lack of direct influence was due to behavioral issues or a research design issue. The method of distributing surveys electronically to managers who were asked to forward them to their engineer employees was not as successful as we would have liked. While we were able to secure what we think is up to 30% of the engineers within the corporation, there is no clear indication as to what the response rate of the survey was. There was a substantial drop off from the 395 individuals who started the survey to the 282 surveys that we were able to use in the analysis. A better system of pre-testing the survey in this particular research environment may have uncovered this challenge, allowing us to initiate some changes that may have increased the response rate and level of survey completion. One of the other challenges in this type of research, which is targeted to individuals, is that it does not take into account the fact that many engineering projects 144
(especially in the corporate arena) are group efforts. As such, a number of items allowing us to compare working in groups vs. working alone within the context of this research effort would be insightful. Future Research One of the most intriguing outcomes of this work is the quest for a better definition of innovation, and the relevance this definition could provide for those looking to secure engineering talent that can drive the corporate creative process towards financial success. Future research could first survey how companies measure their own success and how these measures are utilized for identifying those who can be the drivers of innovative output. Imagine what it would mean for corporate executives to have a formula for identifying those who could propel the future innovative output of their companies. We have seen in this research a few elements that have some impact on innovative output, including musical acumen, skills in physical movement, social skills, academic achievement, etc. Future research could expand this list of fundamentals and expose a more comprehensive profile of who might best benefit the company. We should explore those elements that, on face value make sense, but also find elements that could be both significant and surprising. After refining the survey instruments, I recommend looking at comparisons of similar characteristics to efficacies and their influence on gross revenue or other innovation output measures in corporate vs. independent innovators. In my personal research comparing inventors in these two arenas, I did notice some substantial differences that support much of the anecdotal evidence outlined above that did not 145
emanate from the data (i.e. grades predicting innovation) in this study. For example, those with poor grades, who could have contributed to the bottom line, would not be present at the company unless grades were not used as hiring criteria. Some of these differences may also be influenced by the structure of working within an organization vs. working more independently or in a small firm. I also recommend comparing a number of large corporations to see if the one surveyed in this study was an accurate representation or an aberration. Conclusions This study could provide new insights and present a range of ideas to guide additional studies concerning multiple intelligences (efficacies), engineering and innovative output. Howard Gardner developed his theory of multiple intelligences for two purposes: to better understand some of the greatest success stories in our time, and to influence our education system to improve the learning process for those who possess great potential but few of the traditional academic skills that schools and businesses use for personal evaluation. Although Gardner did not intend to develop an assessment tool to measure levels of multiple intelligences, many educators and psychologists use his paradigm to explain new ways of looking at human potential. As previously mentioned, Gardner and others have developed some ideas concerning how people with certain multiple intelligences would excel in particular domains and careers. We hope that this study can help begin the development of an empirical correlation between these intelligences and professional accomplishments, not just between these intelligences and career choices. In addition, this study can provide the impetus for looking at the
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interaction of social capital skills and the work environment with multiple intelligences (efficacies), grades and having mentors. A number of researchers are emphatic about the lack of relationships between grades and success (Csikszentmihalyi, 1996, Subotnik and Arnold, 1994, Shaughnessy, 1998, Blohowiak, 1992). Therefore, the influence we found grades to have on gross revenue was somewhat unexpected. The positive, yet small influence overall grades have on gross revenue suggests that those with good grades do excel in engineering, at least in the environment that this study explored. The negative influence science grades have on gross revenue brings up a range of questions. Maybe the approach to science education in our schools does not encourage the kind of creative thinking that could lead to revenuegenerating innovations. Of course, this could be an overstated conclusion of this significant but small finding. The influence of overall grades vs. science grades would be intriguing to study in future research, especially if companies continue to use grades as criteria for hiring. In the end, this study asks more questions than it answers and poses many contradictions. One contribution this work can offer is for those who use multiple intelligences to coach students in career options, encouraging them to look for better ways to ensure security in the advice they give students. Just because an individual enjoys a type of experience or excels in it—and just because a certain set of skills suggests a direction in career choice—doesn’t mean that this skill set will lead to that individual’s adding financial value to the company. This research will hopefully inspire those in career coaching to look more closely at the antecedents of being exceptional rather than acceptable. 147
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153
Tables and Figures Hypothesized Conceptual Model Figure 1 HS Overall Academic Achie ve ment HS Scie nce Academic Achie ve ment Efficacies (Multiple Intelligences)
H1
H1
Gross Revenue
H2
Musical H3
Spatial
H4
H5
Phys ical
H6-H7
H10
H8-H9
Social Capital Logical/ Mathe matical Work Environment
Mentor
154
Figure 2 Revised Conceptual Model Overall HS Academic Achie ve ment Scie nce HS Academic Achie ve ment
.259** -.160*
Efficacy
Gross Revenue .200***
-.169*
Cre ate Music .214** .147** -.213***
.325***
Music Sensitivity
-.173** -.182** no sig
Logic
.121** .141* .375*** .419***
Phys ical
Mentor
155
Social Capital .327*** .300***
Figure 3 Corporate Career Innovation Survey 2004
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Table 1 Construct Definitions Academic Achievement Efficacies/Multi ple Intelligences
Definition Grades in HS Overall and Science • A property of all human beings (All of us possess these . . . intelligences) • A dimension on which human beings differ (No two people—not even identical twins— possess exactly the same profile of intelligences)
Citation n/a
Scale n/a
Gardner, H (2003). Paper presented at the American Education Research Association. Chicago, Illinois. April 21, 2003.
• The way in which one carries out a task in virtue of one’s goals (Joe may have a lot of musical intelligence but his interpretation of that piece made little sense to us) (Gardner, 2003) Musical Spatial Physical Logical
Mentor
Social Capital
Work Environment
Gross Revenue
music smart picture smart physically smart number smart
Gardner, H (1983). Frames of mind: The theory of multiple intelligences. New York: Basic Books.
• spend time with someone you would consider a mentor? • contact someone outside of your close-knit professional sphere for insight or advice? n/a • connect with an external mentor (someone who helps you and does not work in your company) to help solve problems related to work?
the networking behavior that allows individuals to leverage the support of others in an attempt to produce highly innovative technical results.
work environment that has the ability to spark innovative output
gross revenues for the innovation they created within their company that has the most impact on the world.
167
Betts, George and Kercher Jolene. (1996) Multiple Intelligences & LifeLong Learning. Unpublished.
n/a
Foret, M. L. & Dougherty, T. W. (2001). Correlates of networking behavior for managerial and professional employees. Group & Organizational Management, 26(3), 283-311. McElroy, Mark W. (2002). Social innovation capital. Journal of Intellectual Capital, v.3, n. 1, pp. 30-39. Ekvalll, G. (1997) Organizational conditions and levels of creativity. Creativity and Innovation Management, 6(4), p. 200. n/a
Prather, C. (1996) How’s your climate for innovation. R&D Innovator, 5(5) n/a
Table 2 Demographics n percent Gender
Number of Patents
169 %
282 %
Male
93.5%
91.1%
Female
6.5
8.9
0
55.6
64
1
13
10.5
Some College
4.3
7
10.1
Two Year Degree
3.7
3.7
65.2
60.9
23.2
24.4
Doctorate
3.7
3.7
Individual
4.7
5
2
Age
Income as Family
Type of Neighborh ood
9.5
n percent Relatives as engineers
Last level of school
169 %
282 %
none
47.9%
51.1%
at least one
52.1
48.9 0.4
High School
3
6.5
5.2
Four Year Degree
4
5.3
3.4
Master's
5-10
3.6
4.1
10+
3.6
3.1
25-30
14.2
16.3
Team
8.3
7.1
31-40
35.5
31.6
Company
7.7
7.4
41-50
29.6
33.7
Industry
6.5
7.8
51-60
17.2
14.9
Across Industry
18.9
17.7
61+
3.6
3.5
Breakthrough
50.3
49.3
very low
5.3
4.6
low
15.6
18.3
middle upper middle
55.1
upper remote non farm
Most impact
n/a
3.6
5.7
Individual
6.5
6.7
52
Team
8.9
9.2
22.2
22.7
Company
8.9
7.4
1.8
2.2
Industry
8.3
8.2
Tenor
6
4.6
Across Industry
14.8
14.9
rural farming
18.6
20.5
Breakthrough
46.7
45.7
suburban
62.3
58.2
n/a
5.9
7.8
urban
13.2
16.5
168
Table 3 Factor Loadings for Efficacy (IV), Social Capital (Mediator) and Work Environment (Mediator)
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Table 3 (Continued) Factor Loadings for Efficacy (IV), Social Capital (Mediator) and Work Environment (Mediator)
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Table 5 Factor Loading Correlations* as taken from SPSS Factor Analysis Output 1
2
3
4
5
6
7
8
9
10
11
12
13
1.00
.57
.29
.26
.28
.42
.24
.23
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.31
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1.00
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.16
.18
Spatial
.23
.22
1.00
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.18
.20
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.30
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.12
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.25
.36
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1.00
.38
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.12
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.10
.39
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.27
.18
Physical
.33
.30
.36
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1.00
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.27
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.12
.06
Mentor Present at Conferences
.38
.27
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1.00
.23
.23
.33
.34
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.29
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.15
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1.00
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.37
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.13
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.13
.03
.17
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.23
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1.00
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.31
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.06
.15
Civic Events Increased Internal Visibility
.29
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1.00
1 2
Create music Music Sensitivity
3 4 5 6 7 8 9
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*lower left is when n=282 upper right is when n=169 1-5 Efficacy factors, 6-11 Social Capital factors and 12-13 Work Environment Factors
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