Entrepreneurial profiling: a decision policy analysis of the influence of entrepreneurial self-efficacy on entrepreneurial intent.

Brice, Jeff, Jr. ; Spencer, Barbara

ABSTRACT

An unresolved issue in the study of entrepreneurs is what factors do individuals consider before attempting to establish new ventures? Also, which of these factors are most influential to a decision after deliberation is complete? Previous studies have examined similar questions by developing inquiries of entrepreneurial self-efficacy that are based solely on discrete business functions. However, not only are functional assessments too venture-specific for the general nature of most entrepreneurial self-efficacy research, they are usually operationalized with self-report direct surveys that are highly susceptible to social desirability response bias.

In this study, we apply a decision modeling methodology to empirically assess the influence of human competencies in the entrepreneurial self-efficacy assessment process. Decision modeling is a within-subjects analytical procedure that is resistant to external biases. A significant finding is that self-efficacy assessments utilizing entrepreneurial competencies are able to successfully discriminate individuals with strong entrepreneurial intentions from others. In fact, the resultant decision profile of those with high entrepreneurial intentions parallels that of actual entrepreneurs. Results, limitations, and implications for future research are presented.

INTRODUCTION

A principle inquiry in the research in entrepreneurship is what factors enhance the probability that someone will decide to start and manage a new business enterprise? This query is important because, depending it's the resolution, we will develop diverse methods of training and supporting individuals to create their own businesses. Therefore, this vein of study should generate significant insights not only for academics, but also to practitioners, policy makers, and oliticians.

Early research on the decision to start a new business tended to focus either on contextual factors such as job displacement (Shapero & Sokol, 1982), prior work experience (Mokry, 1988) or on individual personality factors such as the need for achievement (McClelland, 1965), internal locus of control (Begley & Boyd, 1987), acceptance of risk (Brockhaus & Horowitz, 1986), and the tolerance of ambiguity (Schere, 1982). More recent models of entrepreneurial decision have adopted a perspective in which the individual is an intentional decision maker and actor, engaging in the rational appraisal of situational and personal factors (Bird, 1988, Krueger, 1993). Thus, from the newer cognitive perspective, external factors and personality factors still influence the entrepreneurial decision, but only insofar as they are perceived and interpreted by the potential entrepreneur.

Krueger, Reilly, and Carsrud (2000) compared two models of entrepreneurial decision-making based on the premise that intention to start a new venture is the major predictor of entrepreneurial behavior. In both models (Shapero & Sokol, 1982; Ajzen, 1991), self-efficacy emerged an important influence on intention. In essence, the belief that one can personally execute the behaviors needed to create a new venture is professed to enhance the intent to do so (Boyd & Vozikis, 1994; Krueger & Brazeal, 1994). The purpose of the present paper is to build on this cognitive approach by profiling how individuals weigh different criteria when judging entrepreneurial efficacy.

LITERATURE REVIEW

Entrepreneurial Self-Efficacy

While relatively new to research on entrepreneurship, self-efficacy is widely recognized as a key construct in social learning theory (Bandura, 1977), a perspective which assumes that behavior, cognitions, and the environment continually influence each other in the mindset of individuals (Bandura, 1977, 1986). Self-efficacy refers to people's judgments regarding their ability to perform a given activity (Bandura, 1977, 1982, and 1986) and is proposed to influence individual choices, goals, emotional reactions, effort, ability to cope, and persistence (Gist, Stevens, & Bavetta, 1991). Hackett and Betz (1981) proposed that Bandura's (1977) theory of self-efficacy provides a useful conceptual framework from which to predict the occupational preferences of individuals. Based on this foundation, Boyd and Vozikis (1994) and Krueger and Brazeal (1994) helped lodge the notion of self-efficacy firmly in the entrepreneurship literature by suggesting that perceptions of entrepreneurial self-efficacy could contribute significantly to an individual's deliberations about whether, or not, to pursue an entrepreneurial career.

Even before the appearance of these seminal pieces, Chandler and Jansen (1992) conducted research on business founders' self-assessments of "proficiency in the entrepreneurial function." A strength of this research was their development of a scale measuring five human competencies associated with the entrepreneurial, managerial, and technical-functional roles of business founders (Mintzberg & Waters, 1982; Pavett & Lau, 1983; Schein, 1987). Chandler and Jansen (1992) demonstrated that founders of the most successful firms in their sample rated themselves higher than others on capabilities associated with all three of these roles.

More recently, Chen, Greene, and Crick (1998) operationalized entrepreneurial self-efficacy (ESE) as self-assessed "certainty" in dealing with 26 specific tasks identified from prior literature and interviews with several local entrepreneurs concerning key entrepreneurial roles. After gathering self-ratings on these tasks from students and business owners/executives, they used factor analysis to combine them into five categories including marketing, innovation, management, risk-taking, and financial control. They also created an overall "ESE" measure, by taking the mean over all 26 items. Their findings showed that among students, overall ESE was significantly correlated with the stated intention to start a business. Among business executives, those who were founders rated themselves higher on total ESE and particularly, on innovation and risk-taking, than did non-founders.

While Chandler and Jansen's (1992) and Chen, Greene and Crick's (1998) results are enticing, further research on entrepreneurial self-efficacy and the intention to start a new business is needed. For instance, what criteria do people use when deciding about their aptitude to start a business? Are some efficacy criteria more important than others in making this evaluation? Chandler and Jansen's (1992) most successful entrepreneurs rated themselves highly on all competencies, while Chen, Greene and Crick's (1998) founders rated their abilities on innovation and risk-taking more highly than did non-founders. But neither study tells us which criteria people consider most, or least, important when judging their ability to start a new venture. Such information would be particularly important if it helped us to understand the decision-making processes of prospective entrepreneurs.

A second nagging unresolved issue regarding entrepreneurial self-efficacy is the problem of social desirability bias in self-assessments. Because the notion of self-efficacy inherently involves people's judgments about their ability to perform given activities (Bandura, 1982), the use of self-reported survey evaluations make sense. Yet, in such circumstances, individuals may be tempted to inflate their ratings (i.e., to impress study evaluators, among other reasons). In fact, Chen, Greene, and Crick (1998) noted that the high interfactor correlations among their component entrepreneurial self-efficacy scores may well have been caused by social desirability response bias. They stated that future researchers should think of ways to reduce social desirability.

The study described here is an effort to advance the research on entrepreneurial self-efficacy and entrepreneurial intentions and to address the social desirability limitation encountered in Chen, Greene, and Crick (1998). A decision modeling approach is applied to assess how individuals weigh several key entrepreneurial competencies in deciding whether someone would be capable of pursuing a promising business venture. This method avoids the social desirability dilemma because respondents make hypothetical decisions based on specified cues. Decision modeling diminishes the misrepresentation of social desirability response biases that might be uncovered by asking respondents to make judgments about ambiguous situations in a seemingly external world, which serves to expose their genuine sensitivities (Fischer, 1993). Results are then compared across those who intend to start a new venture and those who do not. The following general research questions were addressed in this study:

1. How do individuals weigh specific entrepreneurial abilities when judging someone's capability to start a new venture? Do some efficacy criteria matter more than others in making such judgments?

2. Can prospective entrepreneurs be discriminated from others based upon their application of efficacy criteria in assessing fitness for entrepreneurial behavior?

Since our research employs a human competency description of entrepreneurial self-efficacy, we first review the development of the construct. We then present the decision modeling technique and its effect on decreasing social desirability response bias. Finally, we empirically evaluate the effect of entrepreneurial self-efficacy on the intention to pursue an entrepreneurial career.

Operationalizing Entrepreneurial Self-Efficacy

Bandura (1982) defined self-efficacy as the task-specific consideration of perceived fitness to perform a particular activity. In the case of entrepreneurship, entrepreneurial self-efficacy may be comprised of deliberation of those tasks that relate to the initiation and development of new ventures, which is considered emblematic of the entrepreneurial act (Livesay, 1982). One way to identify these tasks is to think about the basic functional areas of business.

For instance, a study by Scherer, Adams, Carley, and Weibe (1989) operationalized entrepreneurial self-efficacy as expertise in accounting, production, marketing, human resources, and general organizational skills. A limitation of this approach is that proficiency in all of areas may not be required for all new venture efforts. For instance, while a prospective manufacturer of industrial equipment may have to consider whether he or she is competent in all of the aforementioned functional responsibilities before attempting to develop a new venture, an independent hot-dog cart operator may only have to consider his or her basic accounting and marketing skills before launching a new hot-dog cart operation. As this example demonstrates, the assessment of specific functional abilities before new venture initiation is dependent on the scope and scale of the particular venture being considered.

Moreover, an entrepreneurial self-efficacy construct based solely on functional capabilities ignores the fact that co-opting from external sources may solve some functional shortcomings, on the part of the prospective entrepreneur. For example, an individual who lacks accounting/bookkeeping skill can easily and inexpensively purchase that service from an independent contractor. Knowing this, a prospective entrepreneur without sufficient accounting expertise may still be willing to undertake the development of a new venture. Because a negative perception of one's fitness in some functional capacities may not have the predicted effect on entrepreneurial behavior, it seems likely that a functional capability description of entrepreneurial self-efficacy may not have a decisive influence on whether or not one decides to pursue an entrepreneurial career.

Instead of considering narrow functional tasks, a different approach to clarifying entrepreneurial efficacy is to consider the broader human competencies associated with new venture development since human competency assessments are less dependent on the specification and complexity of particular new venture entry domains. Drawing from writings by Mintzberg and Waters (1982); Pavett and Lau (1983); and Schein (1987), Chandler and Jansen (1992) identified five such competencies based on the three primary roles of the entrepreneur: the entrepreneurial, managerial, and technical-functional. The idea is that both an industrial manufacturer and a hot-dog cart operator must assume all of these roles while initiating their firms, regardless of the scope or scale of their ventures.

In the entrepreneurial role, business founders examine their environment and listen to their customers to find new opportunities, and devise methods to exploit opportunities for the benefit of a new firm (Mintzberg & Waters 1982). Two competencies are involved here. First, entrepreneurs must possess the human/conceptual competency to recognize unique opportunities, and second, they require the drive to take the venture from conceptualization through to fulfillment (MacMillan, Siegel, & SubbaNarisimha, 1985; Timmons, Muzyka, Stevenson, & Bygrave, 1987; Chandler & Jansen, 1992). In the managerial role, there are also two broad competencies: leadership and organizational skills (Pavett & Lau 1983; Schein 1987), and the political competence to procure the support of network members (Pavett & Lau 1983). In the technical-functional role, business founders must have some specialized expertise in the industry within which the firm will operate (Pavett & Lau 1983; Chandler & Jansen, 1992).

In their research, Chandler and Jansen (1992) operationalized each of these five competencies with multiple items. For our purposes, each competency had to be simplified and worded as a single cue to fit into vignettes concerning prospective entrepreneurial decisions. To this end, we simplified their descriptions into the following five competency statements:

1. Has strong leadership and organizational skills (LEAD/ORG SKILLS)

2. Has good sense of what customers want & need (OPP RECOGN)

3. Is willing to make sacrifices to avoid failure (DRIVE)

4. Has specific work-related technical or functional expertise (EXPERTISE)

5. Has political savvy needed to enlist support of key people (POLITICAL).

The next section explains how these cues may be related to entrepreneurial intentions.

Entrepreneurial Self-Efficacy (Competencies) and Entrepreneurial Intentions

Self-efficacy is a construct indicating that behavior, cognition, and the environment influence each other in a dynamic fashion, thus allowing individuals to form beliefs about their ability to perform specific tasks (Bandura, 1977). Entrepreneurial self-efficacy (ESE) is, therefore, viewed as having the capabilities that can modify a person's belief in his or her likelihood of completing the tasks required to successfully initiate and establish a new business venture (Bandura, 1986). More specifically, entrepreneurial self-efficacy is defined as the degree to which one believes that he or she is able to successfully start a new business venture.

Past research can be used to link entrepreneurial self-efficacy and entrepreneurial intentions. Hackett and Betz (1981) projected that Bandura's (1977) theory of self-efficacy may be applied to determine the vocational inclinations of individuals. Empirical findings indicate that self-efficacy is highly involved in the career decision-making process. In fact, career self-efficacy was found to be the most important predictor of males' intentions to pursue careers in traditionally female occupations (Giles & Rea, 1999). In relation to entrepreneurship, individuals with high levels of entrepreneurial self-efficacy may also have strong occupational intentions for an entrepreneurial career. Lent, Brown, and Hackett (1994) applied self-efficacy in a social cognitive framework (Bandura, 1986) to explain three aspects of generalized career development: (1) the formation of career-relevant interests, (2) selection of a career choice option (intentions), and (3) performance and persistence in the selected occupation. Lent, et al (1994) found that self-efficacy was significantly related to career interests, career choice goals (intentions), and occupational performance. However, Lent, et al (1994) also found that self-efficacy is the sole mediator between a person's abilities and his or her career interests. These three findings taken together can be interpreted as meaning that self-efficacy may be used to predict the intended career-related intentions and behavior of individuals. It has been established that self-efficacy is the major influence on career-related behavior in Bandura's (1986) social cognitive theory (Lent, et al, 1994). Since social cognitive theory proposes that individuals choose to undertake tasks in which they are confident, comfortable, and perceive competence (Bandura, 1986), this study hypothesizes that individuals who maintain relatively high entrepreneurial intentions will place significant weight on their perception of fitness for entrepreneurial competencies (highly entrepreneurial self-efficacious). Thus,

Hypothesis 1: Individuals who maintain strong entrepreneurial intentions will place significant weight on considerations of fitness in the evaluation of entrepreneurial competencies.

Perceived Relative Value of Competency Components of Entrepreneurial Self-Efficacy

When contemplating which entrepreneurial competency criteria might be weighed more heavily in the self-perception analysis, it is helpful to review past research. As explained previously, Chandler and Jansen (1992) identified and tested five competencies pertaining to three roles from a sample of entrepreneurs (business founders). These entrepreneurs placed significance on the competencies that were evaluated in a distinctive order. Entrepreneurs placed significance on human/conceptual competence (LEAD/ORG SKILLS), first; ability to recognize opportunity (OPP RECOGN), second; drive to see the venture through to fruition (DRIVE), third, technical/functional competence (EXPERTISE), fourth, and political competence (POLITICAL), last. While individuals with strong entrepreneurial intentions are not actual entrepreneurs, it is likely that they might value these competencies in a similar order. Intent is a dependable predictor of human behavior in an assortment of circumstances, and has been deemed by many to represent the most successful forecaster of human attitudes and action (Ajzen, 1991; Ajzen & Fishbein, 1980; Krueger, 1993; Krueger, 2000). Intentions are assumed to capture the essence of stimulating factors that influence behavior. They are signals of how intensely individuals are prepared to perform and how much effort they are prepared to commit to carry out the expected behavior. Basically, the more robust the intent, the more probable it is to be able to foretell the anticipated behavior (Ajzen, 1991). Past research (Kim & Hunter, 1993) found that intentions explained sixty-seven percent of the variance in behavior and path analysis confirmed that the association between attitudes and behavior is fully explained by the attitude--intention and intention--behavior links (Krueger, 2000). It is, therefore, foreseeable that individuals with strong entrepreneurial intentions will hold similar attitudes to entrepreneurs when evaluating the relative importance of entrepreneurial competencies. While it is impossible to forecast, with any confidence, the exact order of relative competency significance, it is hypothesized that the most and least valued competencies should be parallel in both groups. Thus,

Hypothesis 2: Individuals who have strong entrepreneurial intentions will value human/conceptual competence (LEAD/ORG SKILLS) as most important.

Hypothesis 3: Individuals who have strong entrepreneurial intentions will value political competence (POLITICAL) as least important.

The next section explains how these entrepreneurial self-efficacy competencies (cues) were used in a decision modeling procedure.

METHODOLOGY

The Decision Modeling Technique

The typical method for conducting entrepreneurial self-efficacy studies is the self-reported direct survey. However, direct surveys are susceptible to the effects of social desirability response bias (Fisher, 1993), which is the result of the unfortunate human propensity to present oneself in the best possible light. Respondents are often reluctant to respond truthfully to probing questions due to ego defensive or impression management motivations (Fisher, 1993). This phenomenon may result in information that is scientifically prejudiced toward respondent's belief of what is desired by the researcher or otherwise socially acceptable to others. Social desirability response bias can lead to misleading research results and may be responsible for questionable conclusions about individual attitudes, intentions, and behaviors (Mensch & Kandel, 1988; Chen, Greene, & Crick, 1998).

A valuable remedy utilized by researchers to alleviate the consequences of social desirability response bias is by the application of an indirect questioning methodology. Indirect questioning is a projective technique that requires individuals to respond to questions that are presented from the viewpoint of another person or group (Anderson, 1978). By employing this method, respondents are allowed to project their unconscious biases in varying situations and disclose personal attitudes and beliefs (Campbell, 1950). Thus, indirect questioning allows respondents to express their true feelings under the pretense of role-playing.

This study used the indirect questioning method of decision modeling (Slovic & Lichtenstein, 1971) to examine individual assessment of human competency criteria for entrepreneurial self-efficacy. This method has also been identified under the more general terminology "conjoint analysis" (Shepherd & Zacharakis, 1999). While this technique has been frequently used to examine individual differences in decision processes (Klaas & Wheeler, 1990; Spencer & Crosby, 1997; Powell & Mainiero, 1999), venture capitalist assessments (Shepherd & Zacharackis, 1999, Shepherd & Zacharackis, 2002, Zacharackis & Meyer, 2000), and consumer purchase decisions (Lang & Crown, 1993), it has never been utilized in studies of entrepreneurial intentions or entrepreneurial self-efficacy. Further, no entrepreneurial self-efficacy studies have scrutinized individual assessment of entrepreneurial self-efficacy variables; rather, they have examined the assessments made by groups of individuals whose perceptions are averaged together across the factors being studied.

In decision modeling methods, researchers methodically vary the cue content on a series of conditions to produce a large number of cue combinations. Each participant makes judgments about a sufficient quantity of these conditions to permit individualized (within-subject) regression analyses. The regression weights of the cues in the conditions create a decision policy for each individual, which can be interpreted to determine which cues hold the most importance for a particular set of decisions. In the present study, each respondent will be examined for his or her application of targeted human competency criteria in the assessment of entrepreneurial self-efficacy. Then, these cue condition assessments will be examined for their relation to individual's entrepreneurial intent.

Sample

In entrepreneurial decision research, it is important to uncover occupational intentions at a time when respondents are wrestling with important career decisions (Krueger, Reilly, & Carsrud, 2000). The sampling of only successful, current, or openly prospective entrepreneurs introduces prejudices that influence data erratically, especially in the case of highly ambiguous, and uncommon, phenomena (Krueger, Reilly, & Carsrud, 2000). For this research, a general sample of upper-class college students was utilized because while the exact details of a new venture may have not been fully developed in the minds of most of those with an entrepreneurial interest, global career intentions and evaluations of efficacy should have been (Scherer, Adams, Carley, & Weibe, 1989). Therefore, it is fitting to investigate entrepreneurial intent and self-efficacy utilizing a sample of upper-class college students. In keeping with this instruction, the study participants in this research were 140 volunteer graduating undergraduate university business students from a large southeastern university.

The students represented a diverse variety of business disciplines and were all enrolled in he university's capstone business policy/strategy course as was required immediately before graduation. The researcher visited each business policy/strategy class midway into the semester of the study (after prior approval of each course instructor) and made a brief appeal seeking study volunteers. Those who agreed to participate were then given the survey in its entirety. Instructions on the survey stressed the seriousness of academic research and beseeched each participant to analyze each scenario and answer each question as truthfully as possible.

Entrepreneurial Self-Efficacy Decision Cues

In this research, participants were asked to make judgments about the likelihood of hypothetical prospective entrepreneurs being able to establish a new business venture based, solely, on vignettes which highlighted differing combinations of the five decision-making cues described above. Each of the five cues had two possible values, no or yes (coded 0, 1) resulting in 2 x 2 x 2 x 2 x 2 = 32 vignettes representing all combinations of decision cues. After consideration of the cue combination profile presented in each vignette, the participants recorded their opinion whether, or not, a fictional character that possessed those particular cue attributes would be able to establish a new business venture. Each decision was recorded as very unlikely to very likely on a seven-point Likert continuum. Figure 1 shows a sample decision outcome scenario.

Entrepreneurial Intent

The entrepreneurial intent of each student was measured on a five-point Likert scale, which was adapted from an entrepreneurial decision scale (alpha = 0.92) in Chen, Greene, and Crick (1998). Principal components factor analysis with a varimax rotation revealed that all five of the entrepreneurial intent items in the scale loaded on only one factor, which demonstrates the unidimensionality of the construct. The internal consistency reliability of the scale was assessed with Cronbach's alpha, which was registered at 0.89. This measure is indicative of high scale internal consistency reliability (Nunnally & Bernstein, 1994). Entrepreneurial intent scores were calculated by averaging the five items for each respondent. Low entrepreneurial intent was characterized by those respondents registering a mean of 1.0--2.4, with high entrepreneurial intent corresponding to means of 3.6-5.0. Figure 2 shows the entrepreneurial intent scale.

Demographic Variables

Each participant provided information on their gender (coded female=0, male=1), race (coded Black=0, White=1, Asian-Pacific Islander=2), and age.

Analysis

The analysis of the data in a decision modeling study is a within-subjects analysis. The decision modeling approach "models" in a mathematical computation, such as a regression equation, the process a subject uses to bring together information to make a judgment (Zedeck, 1977). The regression equation illustrates the decision maker's policies for integrating and evaluating information (Zedeck, 1977).

For each of the 132 participants who completed the survey, their decision solution in each vignette was regressed on each of the five cues using ordinary least squares regression within a general linear model. This analysis is different from the usual treatment of regression, in which one regression equation is determined to represent an entire sample. In decision modeling, the focal point is exclusive to one individual, and analyses are performed to discriminate the decision policy of that one individual instead of a general equation for a sample of subjects. This within-subjects treatment of regression serves to diminish the measurement error attributed to individual differences (Stahl & Harrell, 1981). Therefore, one equation was estimated for each subject (132 regression estimates).

For the regressions, the participant's decision choices in the 32 vignettes was the dependent variable and the decision cues, the independent variables, were coded and randomized for each vignette by the researchers. The regressions were processed in batches of 10 utilizing the multivariate general linear model function in SPSS 11.0. Raw beta coefficients were examined to assess the importance of each independent variable in explaining each participant's decisions. Raw regression coefficients are appropriate to interpret in decision modeling since they do not change significantly as a function of decision cue structure, unlike semipartial correlations and standardized regression weights (Lane, Murphy, & Marques, 1982). These coefficients revealed the participants decision policy, with the most important cues to each participant's group of decisions registering a significant effect on decision outcome (p < 0.05). The sample size for each participant in regression analyses was the number of decisions made (n = 32). Since each participant made 32 decisions in the experiment, 4,244 decisions were analyzed in this study.

Before analyzing the decision policy of participants, the reliability of their decisions must be assessed (Stahl & Harrell, 1981). The multiple correlation coefficient squared (R-squared) from each participant's regression equation is a measure of the reliability of the decision that was made. These values represent the consistency with which each participant made decisions. Nonsignificant multiple correlation coefficients represent that the participants were making random decisions, as opposed to following logical decision rules, and should be dismissed from further analyses (Butler & Cantrell, 1984).

To test the relative effect of decision cues between participants, mean beta values for each decision cue were calculated for the entire sample. An F-test was performed to test the equality of means between each of the five decision cues. The test for equality of the mean values for each quality cue was written as Ho: n1 = n2 = n3 = n4 = n5 where n1 is the mean raw beta weight derived for decision cue 1, n2 is the mean raw beta weight derived for decision cue 2, and so on. If the test hypothesis was rejected (p < .05), paired comparisons were performed on each combination of decision cues utilizing paired-sample t tests. The paired-sample t test procedure compares the means of two decision variables within a single group. It computes the differences between values of the two variables for each case and tests whether the average differs from zero. From the significance indices of each combination, we were able to distinguished which decision cues were the most, and least, significant for each group of respondents.

RESULTS

As noted earlier, the sample initially included 140 soon to be graduating business students. Eight of these were excluded from the analysis because they failed to complete the research questionnaire. Of the remaining 132 respondents, 58% were male and 42% female, 83% Caucasian, 15% African-American, and 2% Asian-Pacific Islander. The mean age for the sample was 22.6 yrs (median = 22) with the range extending from 20 to 32 years of age.

Each participant's decision policy regarding the likelihood of an individual establishing a new business venture was determined with ordinary least squares regression within a general linear model. The first step in analyzing these decision policies was to examine the reliability of each individual's regression equation (Stahl & Harrell, 1991). Of the 132 regressions, 14 were deemed nonsignificant after reviewing their squared multiple correlations (p > .05). Since a nonsignificant value indicates that the subject was recording random decisions, these 14 subjects were xcluded from the study leaving a between-subjects sample of 118.

General Research Question 1 asked how important each of the entrepreneurial efficacy decision cues would be to a sample of individuals assessing the likelihood of someone being able to establish a new business venture. The decision modeling technique produced raw beta coefficients attributable to each human competency decision cue for each participant. The raw beta coefficients were then averaged for each decision cue, revealing an overall decision policy for the sample. An F-test and paired-samples t test analysis revealed a significant difference between the mean regression weights placed on the five human competency decision cues (F = 5.16; p = 0.0004). Specifically, having a good sense of customer wants and needs (mean = 1.237) and leadership/organizational skills (mean = 1.228) had the most influence on decisions about entrepreneurial efficacy. Willingness to make sacrifices was second (mean = 1.098); and technical/functional expertise (mean = 1.047) and political savvy (mean = 0.999) were judged lowest in importance, respectively.

General Research Question 2 asked whether individuals with strong intentions to begin a business would utilize these human competency decision cues differently from others when judging entrepreneurial capability. From this general research question, three study hypotheses were generated. Hypothesis 1 predicted that individuals with strong entrepreneurial intentions would more seriously evaluate their fitness for each entrepreneurial competency relative to others. Hypotheses 2 and 3 anticipated that individuals with strong entrepreneurial intentions would mirror actual entrepreneur's judgment of the most and least valued entrepreneurial competencies. In order to evaluate these three hypotheses, sample decision policies were re-analyzed after the assignment of participants to three groupings based on the magnitude of their entrepreneurial intentions.

Results across these three subgroups strongly support the three study hypotheses. As forecasted in Hypothesis 1, only those individuals with high entrepreneurial intentions (n = 35) placed significantly different weights on the five competencies when making judgments about entrepreneurial efficacy (F = 5.43; p = .0004). In contrast, the mean beta values for the five decision cues did not vary significantly among those with low (n = 46; F = 0.468; p = 0.758) or neutral (n = 37; F = 1.71; p = 0.151) entrepreneurial intentions. Apparently, these respondents did not bother to differentiate among the cues as much as did those individuals with a strong desire and interest to start a business of their own.

As shown in Figure 3, the paired t test analysis demonstrated that individuals who have strong entrepreneurial intentions judged leadership/organizational skills and having a good sense of customer needs the most important indicators of entrepreneurial efficacy; technical/functional competency and the willingness to make sacrifices was second, and political competency was the least important. These results support hypotheses 2 and 3.

The result of the grouping analysis demonstrates that individuals with high entrepreneurial intentions can be discriminated from other individuals and other groups based on their application of human competency decision criteria. Respondents with low or moderate interest in entrepreneurship weighed all of the capabilities about equally when making judgments about entrepreneurial efficacy. However, those with high entrepreneurial intentions weighed some capabilities significantly more than others. We interpret this finding to mean that these criteria are particularly relevant to individuals possessing serious entrepreneurial intentions.

DISCUSSION

The purpose of this study was to examine the criteria used by participants, who possess different levels of entrepreneurial intent, when assessing their ability to establish a new venture. By learning how diverse people evaluate specific human competencies related to entrepreneurship, we hope to learn more about the factors influencing personal self-efficacy evaluations. Instead of straightforwardly asking respondents about their own entrepreneurial self-efficacy, we used a projective technique to capture their unconscious biases and personal attitudes about the construct (Campbell, 1950).

To create our research instrument, we manipulated factors from Chandler & Jansen's (1992) entrepreneurial competency study. These factors included leadership and organizational skills, knowledge about what customers want and need, the willingness to make personal sacrifices to avoid failure, specific technical/functional expertise, and the political savvy to enlist support of key stakeholders. An attempt was then made, utilizing decision-modeling methodology, to determine which of these criteria are most relevant to the entrepreneurial self-efficacy assessments of individuals with strong entrepreneurial intentions. In addition, we compared the decision policies of respondents with varying levels of entrepreneurial intent to determine if these criteria were applied differently in their judgments of overall entrepreneurial efficacy.

It was found that knowledge of customer wants and needs and whether, or not, one possesses strong leadership and organizational skills were judged as most important in the assessment of entrepreneurial self-efficacy by the overall sample. These considerations were followed in importance by the willingness to make sacrifices to avoid failure, work-related technical/functional expertise, and political savvy, respectively. Certainly, these are critical attributes that all individuals should consider (Chandler & Jansen, 1992) when judging whether, or not, to attempt entrepreneurial activity. Therefore, self-efficacy research instruments that include these criteria should be useful to discern prospective entrepreneurs from others. In this respect, the experimental manipulation of human competency decision cues was successful.

When comparing the decision policies of groups in the sample that were separated by their level of entrepreneurial intent, it was demonstrated that only the group with strong entrepreneurial intent placed significance on any of the entrepreneurial competency decision cues. Individuals who professed strong entrepreneurial intentions valued strong leadership and organizational skills the most. This was followed by knowledge of customer wants and needs, work-related technical/functional expertise, willingness to make sacrifices to avoid failure; and political savvy, respectively. It is obvious from these results that the significance displayed by our overall sample emanated from the high intent subgroup. Therefore, it can be concluded that the decision cues in this study were able to differentiate those individuals who had strong entrepreneurial intentions from others. These preliminary findings should provide encouragement for future research to utilize human competency components of entrepreneurial self-efficacy.

Accordingly, the decision policy for those with strong entrepreneurial intentions exhibited in this study closely matches the factor magnitude pattern of actual entrepreneurs found in Chandler and Jansen (1992). In their study, the investigators surveyed actual entrepreneurs and had them divulge which human competencies they felt were most important to successfully initiate a new business venture. Our study performed a similar analysis, except couched in terms of self-efficacy assessment for prospective entrepreneurs. Previous attempts to correlate prospective and actual entrepreneur's self-efficacy component significance haven't fared as well. For instance, Chen, Greene, and Crick (1998), developed an entrepreneurial self-efficacy scale that incorporated five expected entrepreneurial roles and tasks (marketing, innovation, management, risk-taking, and financial control) that should have been relevant to both the prospective and actual entrepreneurs in their study. However, the prospective entrepreneurs (entrepreneurship students) registered significantly higher efficacy scores for marketing, management, and financial control while the actual entrepreneurs (business founders) were highly efficacious for innovation and risk-taking. In short, the researchers found no common factors that were important to the full spectrum of entrepreneurs under investigation. Our examination is an attempt to apply entrepreneurial self-efficacy components that are relevant to all types and levels of the entrepreneur continuum.

There are several implications of entrepreneurial self-efficacy that merits further emphasis. First, self-efficacy is a wide-ranging evaluation of perceived fitness for the performance of a specific activity (Bandura & Wood, 1989; Wood & Bandura, 1989; Gist & Mitchell, 1992). In a real-world entrepreneurship context, information derived exclusively from the individual (cognitions), the particular venture creation and development task (behavior), and the network of supporting individuals and organizations involved in a specific entrepreneurial effort (environment) may possibly add to estimated capability judgments on the part of the prospective entrepreneur. However, in the context of global entrepreneurial self-efficacy research, it may be more useful to examine self-efficacy for general entrepreneurial tasks (such as nonspecific new venture initiation) through the mechanism of universal assessment criteria (e.g. human competencies) instead of relying on functional criteria that is too specific to be applied to all forms of planned venture initiations. Since we know that entrepreneurship is a planned phenomenon (Bird, 1988, Katz & Gartner, 1988), it only makes sense that we examine self-efficacy perceptions with universally appealing assessments unless we regulate ourselves to research questions designed for specific types of planned ventures (e.g., self-efficacy of initiating an industrial equipment manufacturing operation). Second, self-efficacy may be labeled as an evolving phenomenon since the efficacy estimation may be modified over time as new information and know-how are obtained (Bandura & Wood, 1989; Wood & Bandura, 1989; Gist & Mitchell, 1992). In the context of entrepreneurship, it is possible that individual perception of one's suitability to be an entrepreneur changes periodically as one transforms from a nascent entrepreneur with a high-quality new business concept to a veteran entrepreneur who has intimate familiarity with the hardships and successes of a series of new venture start-ups, failures, growths, and harvests. We can only test this proposed phenomenon longitudinally, however, if we apply research questions that revolve around recurrent considerations, such as human competencies, instead of venture-specific discrete functions. Last, self-efficacy research is vulnerable to the social desirability bias of the sample (Chen, Greene, & Crick, 1998). In order to alleviate this threat to the validity of entrepreneurial self-efficacy research, this study applied a decision modeling indirect questioning methodology that is resistant to social desirability biases (Fischer, 1993). Future self-efficacy research should apply other indirect questioning methodologies to protect the viability of research results and conclusions.

The present research is limited by a number of issues. The first limitation pertains to the decision modeling methodology used herein. Although the self-efficacy decision cues were derived from Chandler and Jansen's (1992) entrepreneurial competency analysis, the decision cues are not an identical match for the factors that the researchers developed. We summarized each dimension of their multidimensional scale into a one-sentence description of the major emphasis of the factor. In doing so, we transformed each multi-item factor into a single item measure. There is the possibility that we misrepresented the factors by being either too vague or too specific. Second, the importance of each cue may vary depending on the experience of the rater. For example, the political savvy decision cue was consistently the least important factor for all of the groups studied. However, the ability to garner support and gain consensus among a network of key supporters is vital to new venture success (Chandler & Jansen, 1992). It is apparent from this study that not all individuals with strong entrepreneurial intentions know exactly what is necessary to establish a new business. This is indicative of potential entrepreneurs who operate with little information of possible obstacles (Krueger & Brazeal, 1994) and may be a primary cause for the high failure rate of entrepreneurial start-ups. The caveat here, though, is that our strong entrepreneurial intent subgroup mirrored the perspective of actual business founders. Last, the exclusive use of students may limit the generalizability of our results.

In conclusion, entrepreneurship researchers have developed entrepreneurial self-efficacy measures mainly along narrowly applicable functional assessments utilizing self-reported direct surveys. Decision modeling approaches can be used to rank decision criteria that underlie universal entrepreneurial competencies. Using such an approach, this study has made an initial attempt to validate human competency criteria used in making judgments about entrepreneurial self-efficacy. Further research is warranted to examine these criteria by applying indirect questioning methodologies on other samples drawn from the broad-spectrum of entrepreneurs.

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Jeff Brice, Jr., Texas Southern University

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FIGURE 1: SAMPLE DECISION MODELING SCENARIO

DECISION MODELING EXERCISE

Directions: In each scenario, please indicate how likely it is that
someone will be able to start a promising business.

Circle (1) if Very unlikely
Circle (2) if Unlikely
Circle (3) if Somewhat unlikely
Circle (4) if Neither likely nor unlikely
Circle (5) if Somewhat likely
Circle (6) if Likely
Circle (7) if Very likely

Person A:
--has strong leadership and organizational skills... No
--has a good sense of what customers want and need... Yes
--is willing to make personal sacrifices to avoid failure... Yes
--has specific work-related technical or functional expertise.. No
--has political savvy needed to enlist support of key people.. No

1) With these factors in mind, how likely is it that A will be able to
establish a new business venture?

Very 1 2 3 4 5 6 7 Very
Unlikely Likely

FIGURE 2: ENTREPRENEURIAL INTENTIONS SCALE *

Directions: Please circle the appropriate number based on your response
to the questions below.

1) I am interested in setting up my own business.

Strongly 1 2 3 4 5 Strongly
disagree agree

2) I have considered setting up my own business.

Strongly 1 2 3 4 5 Strongly
disagree agree

3) I am prepared to set up my own business.

Strongly 1 2 3 4 5 Strongly
disagree agree

4) I am going to try hard to set up my own business.

Strongly 1 2 3 4 5 Strongly
disagree agree

5) How soon are you likely to set up your own business (select the
response that most closely matches your plans).

 1 2 3 4 5

 Never after within within within
 10+ yrs 6-10 yrs 1-5 yrs 1yr

* Cronbach's alpha = 0.89

FIGURE 3: RESULTS OF DECISION MODELING ANALYSIS:
HIGH ENTREPRENEURIAL INTENTIONS SUBGROUP (DECISION POLICY MAP)

Decision Cue Profile: L/O OP TF DR PO

Means: 1.32 1.25 1.03 1.04 0.86
Decision Cue Groupings *: [L/O OP]
 [OP TF DR]
 [DR PO]

Most Important Least Important

Entrepreneurial Self-Efficacy Decision Cue Key:

L/O = Leadership/Organizational: Strong leadership and organizational
skills.

OP = Opportunity Recognition: A good sense of what customers want and
need.

TF = Technical/Functional: Specific work-related technical or
functional expertise.

DR = Drive: Willing to make personal sacrifices to avoid failure.

PO = Political: Political savvy needed to enlist support of key people.

* The means of decision criteria grouped within the same brackets are
not significantly different.