ENTREPRENEURIAL SELF-EFFICACY: THE DEVELOPMENT OF A MEASURE AND ITS RELATIONSHIP TO ENTREPRENEURIAL ACTION

Alex F. De Noble, San Diego State University
Dong Jung, San Diego State University
Sanford B. Ehrlich, San Diego State University


CHAPTER MENU

ABSTRACT
INTRODUCTION
ENTREPRENEURIAL INTENSIONS AND ACTIONS
RESEARCH QUESTIONS
METHOD
RESULTS
DISCUSSION AND CONCLUSIONS
IMPLICATIONS
TABLE 1
TABLE 2
TABLE 3
CONTACT
REFERENCES


ABSTRACT

In this paper, we developed a measure of entrepreneurial self-efficacy (ESE) and tested its psychometric properties using exploratory and confirmatory factor analyses. This measure, consisting of six subscales, was tailored to the venture creation process consistent with Bandura’s (1986) recommendations regarding the development of domain-specific self-efficacy measures. We found that mean scores of four dimensions differentiated between entrepreneurship and non-entrepreneurship students, and two mean differences (“developing new opportunities” and “unexpected challenges”) were statistically significant. Students who were entrepreneurship majors had significantly higher levels of overall ESE than their counterparts. We offer several implications for future research, practice, and entrepreneurship education.

INTRODUCTION

This paper develops a measure of entrepreneurial self-efficacy (ESE) and examines its relationship with commitment to entrepreneurial intentions and actions. ESE is a construct that measures a person’s belief in their own abilities to perform on the various skill requirements necessary to pursue a new venture opportunity. Recent literature applying ESE has shown promise in differentiating between entrepreneurs and non-entrepreneurs (Chen, et al., 1998; Golden & Cooke, 1998). In this paper, we build on these prior studies by developing a more refined measure of the ESE construct that incorporates a comprehensive set of demands placed on the startup entrepreneur.

Earlier studies focusing on antecedents to entrepreneurship have been devoted to evaluating the extent to which a person’s traits and personality characteristics (e.g., internal vs. external locus of control, extraversion vs. introversion, achievement motivation, affiliation needs) lead to entrepreneurial actions (Lumpkin & Dess, 1996; Ahmed, 1985; Begley & Boyd, 1987; Miner, et al., 1989; Lumpkin & Erdogan, 1999). Static personality characteristics, traits, or predisposition at the individual level of analysis however, have not proven effective at consistently predicting entrepreneurial activity (Sandberg & Hofer, 1987). A more promising approach is emerging with recent literature that adapts cognitive constructs such as self-efficacy from the literature on organizational behavior (Bandura, 1995) to entrepreneurship. In these papers, it is asserted that that individuals might be more inclined to pursue entrepreneurship if they believed that they possessed the necessary skills to function in such an environment (Chen, et al., 1998; Golden & Cooke, 1998, Boyd & Vozikis, 1994; Krueger & Brazeal, 1994). To extend this approach, it is necessary to develop a more refined measure of self-efficacy, one that includes the entrepreneurial skills that are uniquely different from managerial skills so often cited in the literature.

The Construct of Self-Efficacy

Self-efficacy refers to a judgement of one’s capability to accomplish a certain level of performance or desired outcomes (Bandura, 1986). According to Bandura (1986), individuals gradually accumulate their self-efficacy through prior cognitive, social, and physical experiences (Gist, 1987). As such, prior successful enactment of a task can change one’s expectations and help further reinforce his or her self-efficacy. Bandura (1990) argued that self-efficacy affects an individual’s thought patterns that can enhance or undermine performance. Specifically, if one has a high level of self-efficacy, he or she is more likely to set a higher or challenging goal, which in turn raises the level of motivation and performance attainments. A high level of self-efficacy can help individuals maintain their efforts until their initial goals are met (Gist, 1987).

Self-efficacy has a number of practical and theoretical implications for entrepreneurial success because initiating a new venture requires unique skills and mind sets, which may be far different from those required for managers in a fully established organization (Chen et al., 1998). Sometimes, roles for an entrepreneur may not be clearly defined, and many uncertainties may exist regarding the success of one’s venture. One of the strongest barriers that an entrepreneur has to overcome is the anxiety about his or her success throughout the initial startup process. By definition, an entrepreneur with a high level of self-efficacy, who truly believes in his or her capability to execute all of the requirements to perform a task successfully is more likely to see the positive potential outcomes that might accrue from a new venture. As a result, the entrepreneur may sustain more effort through the entrepreneurial process to achieve these positive outcomes.

Prior research on self-efficacy has supported the positive effect that self-efficacy has on individuals’ motivation and performance. For example, Stajkovic and Luthans (1998) recently conducted a meta-analysis based on 114 previous studies of self-efficacy and found a significant weighted average correlation between self-efficacy and work-related performance (r = .38). However, it wasn’t until the early 1990s when entrepreneurial researchers became interested in self-efficacy as a bridging concept, which could explain an entrepreneur’s, initial effort to set up and grow a new venture.

As it has been employed in the entrepreneurial literature, ESE has generally focused on one’s belief in their ability to take entrepreneurial actions based on their assessment of the managerial, functional (e.g., marketing, financial, accounting), and technical skills that they possess. In much of the literature, researchers have concentrated on describing the capabilities of entrepreneurs in terms that appear remarkably similar to the roles of effective managers. Chen, et al. (1998) conducted the first empirical study of ESE by developing a measure that included individual’s assessments of their marketing, innovation, management, risk-taking, and financial control skills. In addition, using a sample of students and small business executives, Chen, et al. (1998) controlled for variables such as age, gender, educational level, the number of entrepreneurial friends and relatives, and the number of entrepreneurial courses that they had taken. Their findings indicated that ESE had a significant and positive effect on the likelihood of being an entrepreneur. Moreover, ESE was positively related to internal control and negatively related to chance control. From our perspective, one of the most significant findings was that innovation and risk-taking differentiated entrepreneurs from managers. Consistent with our assertions, these cognitive capabilities appear to be more important components of ESE than assessments of one’s technical, functional, or managerial skills. Therefore, it is our contention that the existing literature has been too narrowly focused on managerial, technical, or functional skills as opposed to specific skills that comprise the entrepreneurial startup experience.

In a similar vein, Chandler & Jensen (1992) completed a study in which individuals were queried about their competence in executing skills necessary for effectiveness in entrepreneurial, managerial, and technical-functional roles. While their study did not directly address the concept of ESE, it focused on similar factors used in the Chen, et al. (1998) research. One such factor, human conceptual competence contains items that measure the ability to supervise, influence and lead; maximize results in allocating resources; and keep the organization running smoothly. While these measures may apply to some successful entrepreneurs, most do not adequately differentiate between successful entrepreneurs and effective managers. However, among the specific roles defined by Chandler and Jensen (1992), the entrepreneurial role is addressed. In this role, abilities such as recognizing opportunities and driving the venture through to fruition are seen to be critical in the entrepreneurial process. As in the Chen, et al. (1998) findings, these entrepreneurial skills can be used to create an expanded measure of ESE that may successfully differentiate between entrepreneurs and managers.

Miner (1990) posited that the entrepreneur and manager possess different motivations. The entrepreneur is more task-motivated while the manager is more hierarchically motivated. The role of the entrepreneur is driven by five motive patterns summarized as follows: 1) desire to achieve through one’s own efforts, 2) maintain personal control over outcomes by avoiding risk and leaving little to chance, 3) obtaining feedback on the level of results of one’s performance, 4) desire to introduce innovations, and 5) desire to think about the future. In the empirical portion of Miner’s study (1990), he found support for these differences. Using a sample of high growth entrepreneurs and managers, Miner (1990) found that there were statistically significant differences between the two groups providing support for his assertions. By incorporating these motive patterns in the development of an ESE measure, we might improve the discrimination between entrepreneurs and managers.

ENTREPRENEURIAL INTENSIONS AND ACTIONS

In the context of studying the entrepreneurial process, we believe that a refined measure of ESE can serve as a useful research tool in predicting entrepreneurial intentions and actions. Drawing from the field of social psychology, Kim and Hunter (1993) used meta-analysis to empirically demonstrate that intentions successfully predict behavior and attitudes successfully predict intentions. Thus, Krueger et al. (1999) suggest that since the act of initiating a new venture is typically a planned behavior, then the construct of entrepreneurial intention can be used as a reliable predictor of the individual taking further entrepreneurial actions. Accordingly, we contend that a person’s perceived self-efficacy to undertake the demands of new venture initiation will positively influence entrepreneurial intention and ultimately entrepreneurial actions.

RESEARCH QUESTIONS

Given that the objective of this research was to devise a more comprehensive measure of ESE, we created the following research questions to guide our approach:

  1. What are the unique skills that entrepreneurs believe they must possess during the startup phase of their new venture?
  2. Does a person’s ESE on these skills lead to entrepreneurial intentions?
  3. Do entrepreneurial intentions lead to entrepreneurial actions?
  4. Is there a difference in overall levels of ESE between graduate students concentrating in entrepreneurial studies and those concentrating in other business subject areas?

METHOD

Participants and Procedures

Our primary objective in the current study was to develop a reliable and valid measure of entrepreneurial self-efficacy. Such a measure can be used to identify individuals who actually commit to marshalling the necessary financial and human resources needed to pursue a venture opportunity. These individuals would be quite distinct from those who merely think about setting up their own business but never initiate the necessary actions. A measure of entrepreneurial self-efficacy therefore needs to be developed by identifying the unique qualities of the entrepreneurial context to attain high levels of content and criterion-related validity. We initiated our scale development process by inviting 8 local entrepreneurs for a 2-hour brainstorming session where they freely discussed some of the most critical issues to become a successful entrepreneur. Each individual’s responses to a stimulus question were aggregated in real-time and projected to the entire group by means of a local area network.

Upon being faced with the following stimulus question: “What critical issues did you face during start-up and early development of your company?” The participants generated over 100 comments. Many indicated that they “must have a strong can do attitude” as the most important or critical factor contributing to entrepreneurial success during the start-up stage. For example, one person said, “I must believe in my ability to persevere in spite of all indications that failure is imminent.” Even if we did not use these “can do” related statements for item generation directly, this “can do” attitude or “self-efficacy” reinforced the importance of developing such a scale. Some core factors identified by the local entrepreneurs included management ethics, marketing, recruiting key people, and raising capital. We combined this information with findings from relevant literature (Gartner, 1989; Miner, 1990; Robinson, et al., 1991) to create a list of 35 skills and behaviors, which served as a basis for our questionnaire.

Next, we converted these 35 skill items into sentences for use in a Q-sort procedure. The Q-sort involves categorizing and sorting these sentences into logical groupings of similar items. A total of 5 were created and mailed to 5 local entrepreneurs. They were asked to categorize the items according to conceptual similarity based on past experience and professional judgement. Among the 5 local entrepreneurs, two returned worksheets useable for subsequent analysis.

Based on the results of the Q-sort and the entrepreneur’s categorization, we selected 6 theoretical dimensions for our ESE measure and incorporated these items into a questionnaire to collect data for further modification and refinement. We tentatively labeled these six core dimensions as follows: 1) Risk and uncertainty management skills (“I can work productively under continuous stress, pressure and conflict.); 2) Innovation and product development skills (“ I can originate new ideas and products.”); 3) Interpersonal and networking management skills (“I can develop and maintain favorable relationships with potential investors.”); 4) Opportunity recognition (“I can see new market opportunities for new products and services.”); 5) Procurement and allocation of critical resources (“I can recruit and train key employees.”); and 6) Development and maintenance of an innovative environment (“I can develop a working environment that encourages people to try out something new.”). All items were rated on a 5-point scale ranging from Strongly disagree (1) to Strongly agree (5) based on a question “How capable do you believe you are in performing each of the following tasks?”

We also measured participants’ intention to start their own business using a four-item scale developed by Krueger, et al., and (1999). An example of one item is: “How likely it is that you are going to start your own business in the next five years? All four items were measured based on a 5-point scale ranging from Extremely unlikely (1) to Extremely likely (5). In addition, we measured participants’ actual time they spent on activities related to starting their own business by asking them “In the last 3 months, how much time on average did you spend on activities related to start up your own business in a week?”

We initiated the quantitative validation procedure by collecting data from 272 undergraduate students taking various introductory business classes in a large public university in the Southwestern United States. Additional data were collected from 87 MBA students taking various entrepreneurship and other management courses in the same university. From the undergraduate students, we randomly selected 115 students (sample 1) for initial exploratory analysis and held the remaining 157 undergraduate students (sample 2) for confirmatory factor analysis. Graduate student data were used for testing discriminant and predictive validity of the proposed entrepreneurship self-efficacy scale.

The mean age of sample 1 was 25.4 (SD = 5.4) and 56.5 percent of them were male. Their academic majors were evenly distributed among management (20%), marketing (10.4%), finance (21.7 %), information/decision science (13.9%) and accounting (13.9%). The mean age of sample 2 was 25.0 (SD = 5.2) and 48.4 percent of them were male. Their academic majors showed a similar pattern as compared with sample 1 and their majors were management (19.7%), marketing (10.6%), finance (20.4 %), information/decision science (14.6%) and accounting (12.7%). There were no entrepreneurship major students among the undergraduate samples. The mean age in the graduate sample was 29.3 (SD = 5.9) and 64.4% of them were male. Approximately 24% of the graduate students indicated that they were concentrating in entrepreneurial studies in their MBA or MSBA program.

RESULTS

Results of Exploratory Factor Analysis with Sample 1

An exploratory factor analysis was run with the initial 115 undergraduate students to examine the underlying factor structure of the instrument using the Principal components method with Varimax rotation. Based on an Eigenvalue of 1 as a cut-off point for factor extraction, this initial factor analysis produced a 10-factor solution, which explained 66.5 % of the total variance. However, careful examination of the initial factor loadings suggested that there were only six meaningful factors based on a guideline we used (each item should load on its proposed factor above .40, while it should not load on other factors above .40). Based on this initial result of factor analysis with sample 1, we retained a total of 29 items for further refinement of the instrument.

Results of Exploratory Factor Analysis with Graduate Sample

Additional exploratory factor analysis with the 29 items was run based on 87 graduate students. The results clearly supported a six-factor structure. Out of 29 items, 22 loaded on its underlying factor at least at the .40 level. We retained one additional item because of its conceptual relationship to the human resources dimension. This six-factor model accounted for 61.9% of the total variance. These final items are shown in Table 1 with their factor loadings. We also changed our tentative labels for these factors based on content analysis of individual items under each scale. These final labels for six dimensions are also shown in Table 1.

Results of Confirmatory Factor Analysis with Sample 2

To further test the psychometric properties of our newly developed measure of ESE, we used the undergraduate sample 2 for confirmatory factor analysis (CFA). CFA is a widely used technique for testing the psychometric properties of measurement instruments. CFA tests a pre-specified factor structure against an empirically derived structure, and provides goodness of fit indices for the resulting solution (Bobko, 1990; Bollen, 1989; Kenny & Kashy, 1992). The fit indices generated by LISREL to test our six-factor model included the Goodness of Fit Index (GFI), Adjusted Goodness of Fit Index (AGFI) and Root Mean Squared Residual (RMSR). GFI is the ratio of the sum of the squared discrepancies to the observed variances. A value of .90 or above generally indicates an optimal level of fit (Bentler, 1990). The AFGI adjusts GFI scores based on degrees of freedom. A value of .90 or above generally indicates an optimal level of fit. The RMSR is the square root of the mean squared discrepancies between the implied and observed correlation (or covariance) matrices, and generally values less than .05 are considered an acceptable or optimal fit (Joreskog & Sorbom, 1989).

We did not conduct comparisons among various factor models (e.g., 3-factors model vs. 6-factors model) because we did not feel that there was sufficient theoretical basis to do so. CFA was run based on a correlation matrix using maximum likelihood estimates. The result indicated reasonable fit considering that this measurement was newly created. The coefficient of determinant, an indication of how well the observed variables in combination serve as measuring instruments for all latent variables jointly, was remarkably high (.978). Because the coefficient of determinant is considered a generalized indicator of reliability (Byrne, 1989), the high coefficient score indicates that the measurement model is excellent. However, fit indices provided mixed support for the 6-factor model in that, while RMSR was within an acceptable range (.05), other indices such as GFI (.82) and AGFI (.77) were below the optimal level of fit.

Reliability and Validity of the Final Measure of Entrepreneurial Self-Efficacy

InTable 2, descriptive statistics and intercorrelations among subscales of ESE based on the graduate samples are displayed. Reliabilities of four out of seven scales exceeded the recommended cut-off point of .70 for a newly created measure (Nunnally, 1978). Reliabilities of the remaining three scales (Core purposes, Unexpected challenges, and Critical human resources) also were close to the cut-off point ranging between .66 and .69. Intercorrelations between entrepreneurial self-efficacy scales and two measured criterion variables (Entrepreneurial intention and Actual preparation) showed consistent positive relationships. For example, Developing new opportunities, Innovative environment and Unexpected challenges had significant positive correlations with Entrepreneurial intention. In addition, Unexpected challenges had a significant positive correlation with actual number of hours that participants spent on setting up their own business. Thus, the stronger the person’s perception that they were capable of coping with unexpected challenges, the more time they actually spent on preparing for their own business. We then combined all six subscales to create an overall measure of entrepreneurial self-efficacy, and ran a correlation analysis among self-efficacy, intention, and actual time they spent. As expected, ESE had a significant positive correlation with intention (r = .37, p < .001) and a marginally significant correlation with time actually spent (r = .20, p = .08).

We also tested the discriminant validity of the instrument by comparing scores of entrepreneurial self-efficacy between Entrepreneurship (n = 21) and non-entrepreneurship MBA students (n = 66) with a series of one-way analysis of variance. One underlying assumption we made for this comparison was that entrepreneurship major graduate students should have higher levels of interest in setting up their own business in the future. Previous entrepreneurship studies often made this assumption (e.g., Chen, et al., 1998) for testing discriminant validity of newly developed instruments. Indeed, entrepreneurship major students had a significantly higher level of intention to become an entrepreneur than non-major students did (4.38 vs. 3.51, F (1, 85) = 11.01, p < .001). They also spent substantially more hours on preparation than non-entrepreneurship majors (9.28 vs. 2.76, F (1, 78) = 10.92, p < .001). Means for entrepreneurial self-efficacy scales for the two groups and the results of one-way ANOVAs are shown in Table 3.

Four out of six mean scores for entrepreneurship majors were higher than non-entrepreneurship majors, and two mean differences (Developing new opportunities and Unexpected challenges) were statistically significant. In addition, students who were entrepreneurship majors had significantly higher levels of overall ESE than their counterparts.

DISCUSSION AND CONCLUSIONS

In developing a refined measure of ESE, we wanted to incorporate only those items that specifically related to the skill requirements for the startup entrepreneur. While the measures proposed by Chen, et al. (1998) and Chandler & Jensen (1992) do reflect skill requirements that would challenge an entrepreneur, several of their items do not adequately differentiate between entrepreneurs and effective managers. This study demonstrates that cognitive measures of ESE represent a fruitful direction for further research and analysis. We derived measures from both the literature and qualitative brainstorming sessions that represent activities germane to the entrepreneurial process. These measures were used to address each of our research questions.

Research Question 1

The results of the factor analysis showed the emergence of six distinct factors that appear to capture the skills comprising an improved measure of ESE. These skills are necessary to sustain an individual’s beliefs in their capabilities to respond to the social, physical, and cognitive demands of the startup process. While technical and functional skills inevitably will be employed during the startup process, we believe that self-efficacy in these areas is not a key factor driving a call to action. As Miner (1990) found, the motivations of entrepreneurs and managers are quite different suggesting that entrepreneurs are much more motivated to achieve through their own efforts. The individual(s) contemplating a startup are more likely to examine their beliefs about capabilities in dealing with initiating innovations, forming internal and external key relationships, establishing a vision, and remaining adaptable to change. During the entrepreneurial process, the individual must establish a social and physical context in which to execute action—this context is solely based on their capabilities since the corporate organizational umbrella and safety net are not present. In the corporate environment, the individual may believe in their capability to execute in similar ways, however, the downside risks have considerably less personal impact.

The first factor, developing new product or market opportunities, includes a set of skills related to opportunity recognition. This skill would be particularly important for an individual considering the pursuit of an entrepreneurial venture. For example, individuals who would most likely believe in their capability to start a company would have to be creative in spotting opportunities for incremental or discontinuous changes in products/services or markets. They must believe that the product or market-based opportunity that they have identified can serve as a solid foundation upon which to launch a venture. This opportunity recognition dimension is well known and researched in the literature (Krueger, 1999), and is included in other measures of ESE (Chen, et al., 1998; Chandler & Jensen, 1992).

The second factor, building an innovative environment, refers to the individual’s capability to encourage others to try new ideas, initiate novel actions, and take responsibility for their own outcomes. As presented earlier, Chen, et al. (1998) found support for similar dimensions referred to as risk-taking and innovation. Our dimension extends this notion by evaluating the entrepreneur’s perceived capability to foster innovative actions among other potential member’s of the founding team. The challenge of building such a working environment from scratch in a new venture situation is much different from that of a manager trying to instill innovation in an established corporate setting. In the former case, an entrepreneur must believe that he/she can set up such an environment where none had existed before.

The third factor, initiating investor relationships, has been found to be important activities to obtain sufficient funds to capitalize the startup company (Ehrlich, et al., 1994). This type of networking activity is an integral part of what an entrepreneur must do to realize and sustain the vision. The demands of breaking into and maintaining this network are often underestimated by startup entrepreneurs, yet when the venture process begins, these activities can often be the most time consuming and demanding of activities which require significant skills.

The fourth factor, defining core purpose, serves to clarify and focus the entrepreneur on the essential vision that their proposed company will need to attract key management personnel, employees, and investors. If an individual believes that he or she is not capable of settling on a core purpose, it is unlikely that they will feel motivated to initiate a startup venture. A focus on the vision and values of the company was reported to be a critical skill by high-growth entrepreneurs in the research conducted by Eggers, et al. (1994).

The fifth factor, coping with unexpected challenges, deals with the ambiguity and uncertainty that encompasses the life of a startup entrepreneur. Transitioning from the comfort of an existing company into a world of venture creation requires someone to tolerate the lack of information, equivocal messages, and rejections that will be faced in the process. These types of challenges will occur with feedback from potential investors, fluctuations in market conditions, requirements for cash infusions, and other similar issues.

The sixth factor, developing critical human resources, represents the ability of the entrepreneur to attract and retain key individuals as part of the venture. An individual considering an entrepreneurial startup must recognize the need to involve others in the creation process. Believing that one has the capability to attract and retain talented individuals is an important component of startup activities. This human resources component has also been found to be a critical self-reported skill of high-growth entrepreneurs in the Eggers, et al., (1994) study.

Through these six dimensions, we have attempted to come up with a set of skill requirements that uniquely reside in the domain of entrepreneurs rather than a combination of entrepreneurs and managers. Measuring a person’s perceived self-efficacy on these unique dimensions will enable researchers to gain better insights into those factors that are more robust in their ability to predict subsequent entrepreneurial intentions and actions.

Research Question 2

In our study, we next attempted to correlate the above six skill factors to empirical measures of entrepreneurial intention and actual preparation activities. We found that three of these skill factors, developing opportunities, innovative environments, and dealing with unexpected challenges, were positively and significantly correlated with entrepreneurial intention. We did not find support for the other measures, though we expected these to also be significantly correlated with intentions. This finding may be inconclusive based on our limited sample of students. In future research, we will seek to replicate this study with a sample of employees working in larger and more established technology-based organizations. In the current sample, these findings may reflect the inexperience of students who have yet to develop an appreciation for the myriad of activities necessary to raise capital, attract critical human resources, and define the company’s core purpose. The areas where significant correlations were found center around locating a key opportunity to exploit in initiating a venture and coping with the unexpected challenges in doing so.

Research Question 3

In examining the relationship between entrepreneurial intention and actual preparation that one would undertake in starting a venture, we found that there was a significant positive correlation with actions. Furthermore, the skills associated in dealing with unexpected challenges also are positively correlated with entrepreneurial actions. These findings are consistent with Krueger, et al., (1999) who show that intentions are the single best predictor of planned behavior. However, the skills associated with developing opportunities and innovative environments were not significantly correlated to action. These results may indicate that entrepreneurial intention is an intervening variable between ESE and action because not all of the dimensions of ESE lead one to undertake the action steps. Skills such as opportunity recognition may lead people to think about or intend to start a business, however such skills may not always constitute a strong enough pull to cause someone to mobilize into action. By the same token, a person’s belief in their ability to create an innovative environment may influence their intent to start a business, but once again may not serve as the catalyst to initial action. However, if a person believes that they are capable of coping with the stress that comes with drastic change, then taking actions to go down the entrepreneurial path represents a logical next step in the process.  Thus antecedents to action appear to be based on intentions, but not all antecedents to intention necessarily translate into follow-on actions. Further research is required to assess which ESE dimensions lead to action versus the mere intention to commit to action. Skills associated with coping with change and uncertainty showed promise in filling this role as a robust predictor to entrepreneurial action.

Research Question 4

Finally, in this study, we examined for differences in ESE between graduate students concentrating in entrepreneurial studies and graduate students concentrating in other areas of business. We wanted to see if our measure of ESE was able to discriminate between entrepreneurial and non-entrepreneurial oriented people. As expected, we did find significant differences in intention and subsequent actions. We also found two of the six dimensions of ESE (opportunity recognition and dealing with unexpected challenges) were significantly different in the expected direction. The intention and action findings are not surprising since the entrepreneurship students consciously made the decision to pursue this program of study and therefore would have ample opportunities to engage in action planning. Also, since programs in entrepreneurship usually start off with an introductory course emphasizing opportunity recognition and evaluation as well as the need to cope with constant changes in the environment, students with training in these areas are more likely to have a higher ESE on these types of skills. In two of the ESE dimensions, defining core purposes and maintaining investor relationships, the mean scores for the non-entrepreneurship students were higher (but not significantly different) than those of the entrepreneurship students. In both samples, building and maintaining investor relationships came out the lowest on the ESE measures. Neither group exhibited high levels of confidence in their abilities to establish and work with investors in entrepreneurial companies.

IMPLICATIONS

The current study can offer several practical and theoretical implications. Theoretically, ESE helps us understand what makes potential entrepreneurs sustain their initial efforts to materialize new business opportunities. Given the recent research findings by Krueger, et al., (1999) that demonstrated a positive relationship between perceived self-efficacy and entrepreneurial intention, we can use ESE developed in the present study to explain cognitive characteristics of entrepreneurs. Our preliminary results in the present study showed that individuals who are high on ESE had higher levels of entrepreneurial intention and actually spent more time on initial preparation.

For years, entrepreneurship researchers have spent considerable amounts of time to find unique personal orientation and other attitudinal variables that would explain individual characteristics among successful entrepreneurs (Chen, et al., 1998). Several studies have also attempted to develop measures of self-efficacy for entrepreneurs. However, relatively few measures developed in prior studies are as much entrepreneurial specific as the ones we developed in our study. This is also consistent with Bandura’s (1986) recommendation of developing self-efficacy tailored to the specific tasks (in our case, tasks should be related to the various skills required to set up a company) for higher predictive validity (Gist, 1987). In addition, none of the previous studies in this area utilized an empirically derived set of skills obtained from practicing entrepreneurs. Therefore, our newly developed ESE can be used in future entrepreneurship research to uncover a more complete process of entrepreneurship activities.

Practically, there are various ways that employers and venture capitalists might be able to utilize our ESE measure. For example, venture capitalists can use ESE as a way to evaluate members of the key management team in companies they are considering placing an investment in. Since several studies have found self-efficacy to be a better predictor of subsequent performance than an individuals’ past behavior (Gist, 1987), our measure may serve to increase their confidence level in an investment decision. In a corporate environment, measuring ESE might be useful for decision-makers to identify individuals who can champion efforts to pursue new product or market opportunities. The ability to identify potential entrepreneurs with appropriate skills to initiate and execute new venture creation is critical in both of the above contexts.

These results also indicate that it may be possible through entrepreneurship coursework and training to build confidence among students and nurture those skill requirements essential to the entrepreneurial process. Thus, with better measures of ESE, Entrepreneurship educators can be better informed in the design of course content and overall curriculum development. In the case of entrepreneur/investor relations, for example, educators must strive to devise ways of demystifying the ambiguous networks and processes of raising equity capital.

In this study, we attempted to build upon prior work in the area of entrepreneurial self-efficacy by developing a set of skills that more closely resemble the actual demands and requirements placed upon the would-be entrepreneur. Future researchers can now build upon our work by further refining a measure of ESE and by applying this and other related measures to a wide variety of research contexts. Although we recognize that entrepreneurial self-efficacy is not the only factor affecting an entrepreneur’s success, it will shed more light on our ability to understand antecedents to entrepreneur actions.

CONTACT: Alex De Noble, Department of Management, San Diego State University, San Diego, CA 92182; (T) 619-594-4890; (F) 619-594-3272; adenoble@mail.sdsu.edu

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