Comparative analysis of the predictors of entrepreneurial intent and self-efficacy of UK engineering undergraduates
Donna Murray
Presented at the Evidence-Based Policies and Indicator Systems Conference
12 July 2006
London
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Comparative analysis of the predictors of entrepreneurial intent and self-efficacy of UK engineering undergraduates
Abstract
UK government funding for the enhancement of entrepreneurship and enterprise activity has been a priority for a number of years. The higher education sector has responded by increasing the number and variety of activities to prepare students for entrepreneurship, but its assessment lacks comparative metrics. As a consequence, except for waiting a decade or more to count the start-ups that have resulted, their revenue and the jobs that have been created, there is little evidence to guide public policy. By default, most enterprise activities are assessed by determining participant satisfaction with the different programmes and anecdotal reference to early successes starting companies without knowing their outcomes. This paper reports on the use of the concepts of venturing self-efficacy and entrepreneurial intent as trajectory metrics, measures that the literature suggests would indicate the individual is well established on the path that leads to starting a company. The value of these measures for the comparative evaluation of educational activities is demonstrated by showing that, after controlling the influence of gender, industry experience related to the course of study of undergraduate engineers is the strongest predictor of higher levels of venturing self-efficacy.
The analysis then shows that how lecturers use industry examples and talk about start-up experiences has a measurable impact, and more intense enterprise education courses can both be shown to have an effect on entrepreneurial intention above and beyond the influence of gender and having a father who runs his own business. A closing discussion highlights the importance of having appropriate metrics that enable this analysis.
Introduction
The UK Government has placed the issue of entrepreneurship on the political agenda, introducing many different policies for the enhancement of entrepreneurship and enterprise activity. The Office of
Science and Technology alone has introduced several knowledge transfer activities in the last few years. The Cambridge-MIT Institute (CMI) was founded in 2000, the following year saw the introduction of the Higher Education Innovation Fund (HEIF), the Science Enterprise Challenge (SEC), and the University Challenge (UC). In 2003 a separate fund for the provision of training for Knowledge transfer practitioners was established.
HEIF is one of the main areas where Government is attempting to facilitate universities in responding to the needs of industry. The HEIF project is currently in its 3 rd round of funding, HEIF 1 began in
January 2002 and awarded £78 million to 89 projects, ranging from £250k up to £5m. HEIF 2 then started in August 2004, funding 124 awards, 46 of which were collaborative, an increase from HEIF 1 which had only 16 collaborative awards. HEIF 3 starts in the 2006/2007 academic year and offers
£238 million, 75% of which was awarded by formula, while 25% was reserved for competitive bids.
The successful HEIF 3 bids include activities such as projects to increase the competitive advantage of UK creative companies doing business in China and India and to identify entrepreneurial talent at an early stage with the aim of students creating their business while still studying. HEIF 3 focuses on projects which are of economic benefit to the UK, but which do not generate significant levels of income and are therefore difficult for universities to justify.
Early analysis of the HEIF 1 programme indicates that many of the projects have faced difficulties with extending Knowledge transfer widely in their institutions (SQW consultants, Interim Evaluation of
Knowledge Transfer Programmes Funded by the Office of Science and Technology through the
Science Budget, 2005). HEIF 3 attempts to overcome this problem by offering a more permanent stream of funding to enable universities to develop best practice in the area of knowledge transfer.
The other OST activities all have specific aims, and attract significant amounts of funding. The
Science Enterprise Challenge aims to develop a network of centres in UK universities, specialising in the teaching and practice of commercialisation and entrepreneurialism in the field of science and
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tec hnology. To date, the SEC programme has been allocated £43.9 million of government funding.
The University Challenge (UC) provides seed funds for early stage ventures, and aims to bridge the funding gap which can prevent University research from being translated into commercialisation. The
UC has been allocated £60 million in two funding rounds. The provision of training for knowledge transfer practioners aims to address the lack of suitable training in this area, and to support the other funding initiatives by ensuring that there are sufficient trained practioners to undertake HE commercialisation. The training fund is set at £1 million. Finally the Cambridge-MIT Institute (CMI) was formed in 2000, and combines the expertise of Cambridge and MIT to act as a catalyst to improve economic competitiveness and encourage entrepreneurship in higher education. CMI received £65 million of government funding.
Through these policies the UK Government intends to “encourage programmes of education which focus on raising awareness and understanding of the entrepreneurial sector and help individuals to identify opportunities to engage” with the SME community (Cooper et al. 2004). Overall, these programmes all have laudable aims, however, the question remain – how best to measure the efficacy of these policies and of policies from other agencies. Significant amounts of public funding have been allocated to the promotion of knowledge transfer and entrepreneurship yet there appears no agreement on how to judge the success of the programmes. While the amounts involved remain small compared to overall HE funding, they still appear highly significant to the general community.
In addition, to the increase in government polices, there has been a fundamental shift in the UK economy which has resulted in a decline in the number of large enterprises and a marked increase in the number of small and medium sized enterprises (SMEs) (Cooper 1998). In 1999 there were 3.7 million enterprises in the UK 24,000 of which were medium sized (50 to 249 employees) and there were only 7,000 large firms (250 or more); SMEs accounted for 38% of national turnover (Hawkins
2001). The HE sector understands that for many of their students, the future lies not in academia or large corporations, but in SMEs as small firms play an increasingly important role in economic development and growth (Cooper 1997). The rate of technological and economic change will also lead to individuals having a greater variety of careers as well as employers; thus, the concept of the portfolio career is likely to become much more common (Henderson and Robertson 2000). Such trends imply that the world of work, which today’s graduates are entering, is very different from that into which their counterparts stepped a decade ago (Lucas et al. 2006).
In response to the number of Government initiatives, and the changing world facing graduates, the higher education sector has responded by increasing the number and variety of activities to prepare students for entrepreneurship, but its assessment lacks comparative metrics. In time, perhaps a decade into the future, one might return and count the start-ups that have resulted, their revenue and the jobs that have been created. One could then associate the yields of different kinds of programmes, and identify the types and the specific activities that were most successful. There is, however, little evidence to guide public policy in the present, other than participant satisfaction with the different programmes and anecdotal reference to early successes starting companies without knowing their outcomes. This paper reports on early findings from a study, supported by the Cambridge - MIT
Institute (CMI), into the impact of industrial experience on these output measures.
Entrepreneurial Self-efficacy as a Common Denominator
In a comparative assessment of diverse programmes, one finds that the skill sets being taught and pedagogical approaches may differ from one offering to the next, requiring some common metric. The metric offered here for that purpose is venturing self-efficacy.
Selfefficacy, as defined by Bandura (1997) is founded on “people’s judgement of their capabilities to organise and execute courses of action required to produce given attainments”. It is not so much whether an individual will receive a benefit from performing a task, but rather whether the task itself can be successfully completed. Research has shown with great regularity that those with self-efficacy in a specific domain will be successful in such varied areas as being able to quit smoking, overcome fear of spiders, endure cancer pain, and work more effectively with computers. The spiralling effects
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of self-efficacy mean that once it is well-established it leads individuals to make choices and attempt tasks which reinforce and further increase their sense of confidence in their capabilities. It is also the case that self-efficacy generalises across domains, such that success within one field of activity may result in an enhanced sense of capability within others.
One of the concept’s most important uses in public policy in the United States was to bring about an understanding of why young women abandoned interest in science and engineering as a career. Mau
(2003) used 1988 Longitudinal Survey data to study math self-efficacy; after four years, 1988 math self-efficacy was as important a predictor as 1988 measured math proficiency in predicting whether students originally interested in pursuing science and engineering careers were continuing to pursue that goal in 1992. Typically, women have lower self-efficacy levels compared with their male counterparts, particularly in areas such as math; thus, it can be seen why they are less inclined to persist in science and engineering.
Thinking more specifically about the enterprise domain, the probability that a student will engage in innovation and entrepreneurial actions is directly related to their level of entrepreneurial self-efficacy and will be influenced in part by their levels of confidence with respect to certain abilities. Behaviours such as innovation, opportunity identification and entrepreneurship have been linked to self-efficacy
(Ardichvili et al. 2003) as has career persistence. This implies that the likelihood of a student pursing a career path characterised by innovation and enterprise will be closely linked to their perceived capability in related skills areas. Individuals who believe that they have capabilities in certain areas will be more likely to initiate new behaviours in those areas and persist in related activities. It is, however, important that individuals have a realistic sense of their abilities, since failure can result in negative outcomes which can erode self-efficacy. Those who do not fully appreciate their strengths and abilities may be more inclined to act within their capabilities, and in so doing forego opportunities to enhance their self-efficacy through stretching themselves to act beyond their perceived ability levels.
Bandura (1986, 1997) has developed a listing of the factors that increase self-efficacy. First and most important is the actual performance of the task. Direct experience and subject mastery through the successful performance of a task dominate the factors that predict the development of enhanced selfefficacy (Bandura et al. 1982; Gecas 1989; Pajares 1996). Other sources include learning through modest levels of personal failure or that of others; vicarious experience, where observation of others, or hearing them talk of their experiences, provides a basis for changing perceptions of one’s own abilities; and learning through the success of others (Bandura 1997).
In the context of education self-efficacy for entrepreneurship can be conceptualised as being enhanced through teaching and learning approaches which encourage students to learn through the experience of others, as well as their own experience (Rae and Carswell 2000). Students may learn through examples in lectures, while analysis of case studies which explore the entrepreneurial event or other aspects of the venture creation process offer a slightly more interactive learning opportunity
(Krebner 2001). The introduction of guest entrepreneurs into class enables students to learn directly from their experience (Cooper et al. 2004). Practice-based projects and in-company placements present increasingly intensive levels of interaction which provide deeper and more powerful learning opportunities. Arguably, “the most powerful learning situation is achieved where experiential learning, through active involvement with an entrepreneurial company, enables students to acquire knowledge about the business environment, and develop questioning and problem-solving skills in a real-life setting” (Cooper et al. 2004).
Building on this concept of enhancing authentic mastery, periods of work experience provide individuals with the opportunity to see how they are able to contribute positively to the activities of an organisation, and influence its development and growth. The nature of the period of work experience will in turn influence the possible sources of self-efficacy to which the individual is exposed. Providing the individual with a range of experiences(tangible periods which permit task accomplishment and mastery), working with different individuals (role models for vicarious learning), on a range of activities
(multiple opportunities for the development of authentic mastery), and exposure to work which is
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difficult and challenging, provide opportunities for stretch and development beyond currently perceived levels, a view supported by Train and Elkin (2001) who argue that effective learning is grounded in experience, which in turn is central to the development of self-belief and self-efficacy (Ndoye 2003).
Research isolating the relative performance of other predictors such as vicarious performance by watching others perform the task of concern, social influence and emotional states, offers mixed results.
Method and Results
The Education and High Growth Innovation group is an informal research activity sponsored by the
Cambridge - MIT Institute, with participation from the Universities of Cambridge, Edinburgh, Lancaster,
Sheffield, Strathclyde, and York, and MIT. The team began work in spring 2004 with some pilot research that among other findings determined that the positive influence of industry work placements were not consistent with the anecdotal evidence. In autumn 2004 the team fielded a revised questionnaire that completed 2711 surveys at MIT and the participating UK universities, the source of the data presented here.
Given a focus on Science, Engineering and Technology (SET) students, this work draws on the results from 492 engineering undergraduates who in October were starting their third and fourth years, and who as a consequence have two or three full years of university experience to be studied.
The first subject here deals with the question of measurement, discussing the approach used to measure the impact of formal education, university culture, entrepreneurship and business courses and industry placement. Additional questions capture gender and family background in areas known to be important predictors of self-efficacy and entrepreneurial intention. Then an analysis is provided that defines the outcome measures of self-efficacy for venturing and separately for technology applications, and entrepreneurial intention. The following analysis first presents the correlations among these variables, and then uses regression analysis of the variables significantly related to the outcomes to estimate their comparative contributions to the outcomes by comparing their standardized regression coefficients.
Measurement of Policy-relevant variables
The survey instrument included questions that sought to determine for each individual their backgrounds, and a series of questions that experiences and perceptions of some of the content of lectures and laboratories, what business or management courses they might any work experience they might have had characteristics, the university classroom culture, entrepreneurship activities, work experience, and other factors.
Role models and classroom culture. Many believe that professors and lecturers serve as both role models and an important source of career information and cultural values (Scherer et. al. 1989). The undergraduates were asked how often lecturers used industry example in lecture, how often they talked about industry jobs, any start-up experiences they might have had and other personal experience in business. It is found that there is a substantial classroom culture pointing these engineering students towards industry with 73.4% reporting the use of industry examples at least weekly or more often, followed by 48.2% who say that their lecturers talked in some way about jobs in industry. Factor analysis found these items constituted an acceptable component (Table 1,
Component 1). The alpha coefficient was acceptable for three items shown, but a decision was made to use a simpler two item scale that had a higher alpha of .830 and seemed more interpretable since it contained the two items specific to industry (d and e).
The evidence suggests that the engineering lecturers did not discuss personal start-up experiences very often, with only 6.5% reporting that their lecturers had talked about this subject weekly or more often. In the factor analysis this falls on its own component, and will be used as a single item variable.
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Pedagogy. There is a general belief that independent thought is fostered by the use of assignments that involve problems without a simple right and wrong answer. Going further, confidence that one is mastering the skills necessary to a future career is strengthened if the tasks taken on are not abstract, but rather are what might well assigned to the individual in the world of work. Questions asked about this domain found that (Component 2, bold font) the frequency of their occurrence was similar, with
26.8% of the students reporting that they worked on open-ended problems at least once a week, to
19.8% working weekly or more on projects set around industry problems being used to set projects, and 28.2% working weekly in the laboratory on industry-related problems.
The answers to these three items are found to constitute a separable component found in the factor analysis. When the items are tested further, they are found to constitute a scale of teaching that involves industry-related and practical pedagogy. The alpha co-efficient of reliability is somewhat marginal but acceptable at .664, and is used in the analysis below.
Summative impact of the third year. In addition to this specific content, a related dummy variable is created by comparing the third and fourth year students. Given that the survey was conducted in
October, the 50.8% of the respondents who are fourth year students are returning after having completed their third year, which at many UK universities includes the completion of a substantial project. If the third year students who are just starting at that level are treated as a baseline for comparison, the difference between the third and fourth years can be used in what is often called a pseudo-longitudinal study to estimate the over-all impact of the third year.
Table 1
Factor analysis of formal and informal educational content d. A lecturer/professor used industry-based examples to illustrate technical principles. e. A lecturer/professor talked about jobs in industry. c. A lecturer/professor talked about examples from other kinds of work environment, e.g. the public sector. a A lecturer/professor talked about his/her personal experience in business. i You worked on an open-ended problem, e.g. with no single best answer or known preferred outcome. j You worked individually on a project which focused on a real industry problem.
At least weekly
73.4%
48.2%
35.5%
39.3%
26.8%
19.8%
I
.856
.812
.648
.562
Component
II
.792
.791
III h. You worked on a lab problem which was based on a real problem. g. You went on a company visit and talked with an employee about his/her job.
28.2%
1.6%
.716
.783 f . A guest speaker from industry talked about the application of a technical principle in the commercial world.
13.6% .782 b. A lecturer/professor talked about a start-up company s/he is/has been involved in.
6.5%
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser
Normalization, with pairwise deletion. Total extracted variance = 66.2%.
IV
.545
.866
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Business and entrepreneurship courses. Attendance in business-related courses among these engineering students is an indication both of interest in and subsequent effect of courses in business, management and entrepreneurship. Two dummy variables are employed that report whether or not the student had taken such courses, with 43.9% of all the engineering students saying they had taken at least some kind of business of management courses, and 16.7% saying that they had participated specifically in entrepreneurship activities. Given that the nature and length of entrepreneurship courses in particular can vary considerably from a few evenings to an extended course of study, but are often rather intense short events, a further question for each activity was asked about the level of activity in terms of the hours per week the course was offered, with 6.3% saying one or two hours a week and 7.1% saying three to five hours a week, accounting for all but 3.2% who participated in more intense courses.
Industry work experience. One source of information about industry engagement is also found in the battery of questions used in the factor analysis. Guest speakers are reported by 13.6% of the students to have made presentations at the universities once a week or more often. Another question asked about the frequency of industry visits, and 86.4% said almost never were there any visits.
Twelve percent reported visits were made once or twice a month, and 1.6% reported that they went on visits at least weekly. These frequencies may be explained by students with part-time work at local compan ies. In any event, the two items together form a scale with an unacceptable Cronbach’s alpha of .501 and are dropped from consideration.
A detailed presentation of this work using this data set was presented in Lucas et. al. (2006) that focused on the role of summer placements, and analysis is available on a variety of aspects of work experience. Here the analysis focuses first on just the fact of whether the student did or did not have an industry work experience in the prior year, creating a dummy variable with 52.0% reporting they had worked in business or industry. Among the variables studied in the earlier research, the dominant predictor of self-efficacy and other outcomes among the characteristics of work experience was the relationship between their academic course of study and the work they were given. The variable is a three step ordinal variable, and for these students, 50.6% reported that they had given work not at all related to their course of study, 20.6% said that the work was somewhat related, and only 28.8% said it was closely related to their course of study.
Co-variates. In this as in many domains, men consistently report higher levels of self-efficacy than women. As a consequence, almost any relationship that includes self-confidence in task performance should control in some way control for the possible presence of the confounding effects of gender.
Similarly, those whose fathers run their own businesses are widely and consistently more interested themselves in starting companies, and more often do so. Both of these factors are examined in the analysis.
Outcome metrics
Pivotal to any comparison of the impact of diverse programmes and activities is the identification of a common outcome measures. Given an average age of entrepreneurs as 35, while with patience and deep funding one might wait ten or twenty years to determine whether undergraduates start their own businesses, policy horizons tend to be a bit closer. To contribute to current policy discussions, EHGI and CMI gen erally have pursued the notion of “trajectory” metrics that are on their face indicators of a strong predisposition to pursue a path towards the successful start-up companies that are the ultimate policy goal.
Self-efficacy in particular domains provides a valuable yardstick because one knows that when the tasks being asked about are specified in some detail, they are good predictors of future performance
(Stajkovic and Luthans 1998). At the heart of the self-efficacy concept is the notion of persistence, a characteristic particularly important to entrepreneurship, and it seems an appropriate measure to determine whether different educational experiences increase the persistence in desired directions.
EHGI was concerned with skills and domain self-confidence that would support technology-based
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innovation, and the 2004 survey included a pool of items that asked the students if they could perform a series of tasks that would help them persist in the pursuit of that goal. The items included a series of representative skills in new venture development that included hiring, sales, marketing and opportunity recognition; as well as technical tasks that they might need to perform. The results are factor analysed to reveal a two factor solution, with venturing self-efficacy and technology applications falling out as different dimensions. Here the focus is the venturing component of self-efficacy. The six items are used to create a scale with an alpha statistic of .904.
Table 2
Venturing Component of a Factor Analysis of Self-efficacy Items
I
Know the steps you would take to place a financial value on a new business venture.
Work with a supplier to get better prices that help a new venture become successful
Pick the right marketing approach for the introduction of a new kind of service.
.828
.816
.801
Recruit the right employees for a new project or venture.
Estimate accurately the costs of running a new project.
.728
.704
.702 Recognise when an idea is good enough to support a major business venture.
Varimax rotation method. Taken from a factor analysis in Lucas et al (2005) where a 23 items with general venturing and technology-based applications produced a two component solution that explained 63.0% of the total variance. The venturing component explained 33.7% of the variance.
Cronbach’s alpha for six items = .904.
The second trajectory metric used in this paper is entrepreneurial intention. The scale used here is made up of four items spread in a larger battery of work-related attitudes assessed with a seven point
Likert agreedisagree format. The items include statements about the eventual future, “At least once I have to take a chance and start my own company;” and a closer opportunity, as in, “If I see an opportunity to join a start-up company in the next few years, I will take it.” A third item addresses a continuing awareness, “I often think about ideas and ways to start a business;” and the fourth addresses risk and reward, “The idea of high risk/high pay-off ventures appeals to me.” This scale has been used for a number of studies with an alpha statistic that has varied from .780 to .810, with the exception of one study that it was .680.
The most important use of the scale for the current paper is found in a study of 1400 MIT undergraduates in spring 2003. In that survey, 7% of the undergraduates had an average score of 6 or higher, which is to say they on average agreed with all four statements (with a 7 being strongly agree.) The interpretation of this score was used at that time to characterize those that could be considered intent on becoming entrepreneurs. In 2005, the graduated alumni in the Class of 2003 were interviewed in a broader study of MIT alumni, and it was found that 18 months after graduation
7% had started companies, providing some evidence of the validity of the scale as a measure of current intention to start a company.
Results
The outcomes of venturing self-efficacy and entrepreneurial intention are related as would be expected (r = .409, p < .001), and both background variable are important. The self-efficacy literature suggests that men generally have higher self-confidence that they can perform tasks, including in circumstances when skills are actually equal. The results here are consistent with that view. In this study, a dummy variable for gender with men =1 relates to the venturing self-efficacy scale (r = .158, p
< .001) and to entrepreneurial intention (r = .196, p < .001). Having a father that runs his own business is similarly related to both outcomes, correlating with venturing self-efficacy (r= .127, p < .01) and more strongly with entrepreneurial intent (r = .210, p < .001).
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No significant relationships are found for the general impact of the third year, with it relating for example to venturing self-efficacy r = .036 (n.s.). Relationships are found, however, for some of the more specific classroom variables. The use of industry-based examples and talking about industry jobs is seen to relate to venturing self-efficacy (r = .113, p < .05), and a lecturer talking about start-ups related to entrepreneurial intention (r = .188, p < .001). There is some ambiguity of interpretation of the direction of a relationship here since some of this effect might be explained by those interested in entrepreneurship noting and remembering the occasions a lecturer talked about these issues.
Entrepreneurial intent is also related to the use of open ended and real examples in engineering courses (r = .110, p < .05).
Taking courses in entrepreneurship but not business courses related to higher levels of entrepreneurial intent. The fact that they did or did not participate in a entrepreneurship course has a relationship with intent (r = .119, p < .05), and taking more intense enterprise courses of a substantial number of hours per week is related more strongly yet (r = .217, p < .001).
The role of industry work experience is shown by the dummy variable that the individual did or did not work in industry in general, which does not relate to either of the outcome metrics used here. By contrast, when their reported work experience was closely related to their major course of study, this experience turns out to be the largest relationship among the predictor of venturing self-efficacy (r =
204, p < .001).
Correlations have ambiguous interpretations, particular when background variables are correlated with both the variable considered the predictor and the outcome are both correlated to some common variable. As noted, gender as is related to venturing self-efficacy and to entrepreneurial intent. In addition to the relationship with gender and father’s background, as seen in Table 3, gender is also related to whether these undergraduates reported that they were given more open ended questions and problems framed as real industry examples in class (r = .108, p < .05). Those that had taken an enterprise course were more likely to have been placed in industry work closely related to their course of study (r = .157, p < .01). Under these circumstances there is a problem of knowing how best to attribute variance and identify the stronger predictor variables which is solved by the use of regression analysis. Table 4 shows a regression equation that uses as predictor variables those found to be significantly correlated for each of the two outcome metrics.
Predicting venturing self-efficacy Gender, father owning a business, the heightened use of industry examples in lecture, and having a close relationship between one’s work and academic course of study are included in a regression model to predict together the scale of venturing self-efficacy. Taken as a whole, the model explains 11.3% of the total variance in this form of self-efficacy, and has a predictive power that could only have happened by chance less than one time in a thousand. While it is generally held that the regression coefficient b is the more stable predictor of effect sizes across different samples (Blalock 1960), when comparing variables within a given sample one should focus on the standardised beta coefficients (Pedhazur 1982). Here one finds that for these engineering undergraduates, all four of the included variables have an independent predictive relationship with venturing self-efficacy. Of the four, with a beta of .225, the strongest predictor is the relationship between the student course of study and the work assigned.
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A. Men
B. Father run bus
C. 4 th year students
D. Open ended, industrybased problems
E. Frequency lecturer used industry examples
F. Lecturer talked often about start-ups
G. Took enterprise course
H. Hours/week of enterprise course
I. Took business or management course
J. Industry work
K. Close relationship with course of study
L. Venturing self-efficacy
M. Entrepreneurial intent
A B C D
E F G H I J K L
---
(492)
.015
(488)
-.022
(492)
.108*
(482)
.081
(481)
.060
(439)
-.064
(382)
.007
(492)
.000
(386)
.001
(492)
---
(488)
.055
(488)
.030
(478)
-.039
(477)
.056
(436)
-.044
(380
.091*
(488)
-.005
(383)
.001
(488)
---
(492)
.103* ---
(482) (482)
.046 .111*
(481) (479)
.046 -.057
(439) (431)
.101* .058
(382) (372)
.060 .021
(492) (482)
.118* .131*
(386)
.056
376
.014
(492) (482)
---
(481)
-.032
.020
.002
.001
.017
(481)
---
(430) (439)
.006
.152
.086
.090
(439)
---
(371) (361) (382)
-.140
(481) (439) (382)
.692
(375) (365) (381)
.022
(382)
---
(492)
.155
(386)
.148***
(492)
---
(386)
.086 ---
(386) (492)
.033 .054 .120* .078 -.152** .076 .157** .081 .154** .190*** ---
(393) (389) (393) (385)
.158*** .127** .036 0.072
(383)
.113*
(347)
.031
(305)
-.051
(456) (452) (456) (446) (445) (410) (356)
.196*** .210*** -.017 .110*
(478) (474) (478) (468)
(393)
.084
(456)
(308) (393) (393)
-.008 .033 .204*** ---
(360) (456) (371) (456)
.048 .188*** -.119* .217*** -.035
(467) (426) (370) (478) (374)
.080
(478)
.077
(384)
.409***
(448)
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Table 4
Regression Model Predicting Venturing Self-efficacy
(Constant)
B
24.017
Std. Error
2.878
Beta t
8.346
Sig.
.000
Men 3.353 1.233 .137 2.718 .007
Father runs own business
Lecturer often used industry examples to illustrate principles, talked about industry jobs
Work had close relationship with course of study
2.998
.847
2.535
1.050
.288
.574
.144
.150
.225
2.855
2.940
4.413
.005
.003
.000
Dependent Variable: Venturing self-efficacy. Multiple R = .336, R 2 = 11.3%, F = 11.192, p < .001, degrees of freedom = 4, 352.
Predicting entrepreneurial intent. The variables found to correlate significantly with entrepreneurial intent are used in another regression model to determine their separate contributions. Gender, father owning a business, setting problems in an industry context, taking an entrepreneurship class, more specifically taking a more concentrated entrepreneurship class estimated in hours pre week, and having a close relationship between one’s work and academic course of study are used to predict together the scale of venturing self-efficacy. Taken as a whole, the model explains 16.5% of the total variance in entrepreneurial intent, and has a predictive power p < .001. With the explained variance assigned, simply taking an enterprise course and the use of problems set in an industry context. It is particularly interesting to note that the dominant predictive factor for self-efficacy, the student having a work placement experience close to their course of study, has no direct effect on entrepreneurial intent.
Table 5
Regression Model Predicting Entrepreneurial Intention
(Constant)
Men
Father runs own business
Close relationship to course of study
Lecturer set open-ended and problems drawn from industry
Lecturer talked often about start-ups
Took enterprise/entrepreneurship course
B
3.955
.502
.407
.113
.006
.297
.121
Std. Error
.367
.165
.142
.078
.027
.095
.085
Beta
.170
.160
.082
.013
.171
.080 t
10.787
3.045
2.878
1.455
.240
3.112
1.412 sig.
.000
.003
.004
.147
.810
.002
.159
Entrepreneurship course hours/week .079 .026 .175 3.092 .002
Dependent Variable: Entrepreneurial intent. Multiple R = .406, R 2 = 16.5%, F = 7.891, p < .001, degrees of freedom = 7, 280. When estimated without the three predictors with beta < .1, the multiple R = .372, R 2 = 13.8%, F = 18.617 and df = 4, 464.
This estimation model is used further to estimate the predictive strength, and to demonstrate further the tie between venturing self-efficacy and entrepreneurial intention. As a first step, the model in
Table 5 is recalculated (not shown) with only the four variables with betas over .1. The result is only a small drop in the R square to 13.8%, suggesting that by far the largest proportion of explanatory power is found in the two background variables of men and having a father who runs his own
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business; the classroom culture created when lecturers talk about their start-up experience, and taking more intense entrepreneurship courses (but not just the fact that one takes such a course.)
This model is shown in Table 6 after the further step is taken of introducing venturing self-efficacy as an additional predictor of entrepreneurial intent. The result shows that these factors together have a multiple R of .512, which is to say they explain 26.2% of the variance in entrepreneurial intent. One might note that the stronger relationships in Table 5 remain as independent and consequential predictors of intent, but by far the strongest predictor of entrepreneurial intent is venturing selfefficacy.
Table 6
Regression Model including Venturing Self-efficacy to Predict Entrepreneurial Intention
B Std. Error Beta t Sig.
(Constant)
Men
Father runs own business
Lecturer talked often about start-ups
Entrepreneurship course hours/week
2.832
.370
.393
.274
.083
.284
.126
.106
.077
.023
.123
.155
.147
.152
9.982
2.930
3.699
3.542
3.625
.000
.004
.000
.000
.000
Venturing self-efficacy .042 .005 .345 8.142 .000
Dependent Variable: Entrepreneurial intent. Multiple R = .512, R 2 = 26.2%, F = 30.760, p < .001, degrees of freedom = 5, 438.
Discussion
Looking at the substance of these results suggests that background factors of gender and having a father who runs a business are important sources of entrepreneurial confidence and motivation.
These results are consistent with a substantial literature on both subjects, and add further support to their findings, while showing that the undergraduates included here are like others studied elsewhere.
Thinking about the methodology used in future work, any study of entrepreneurship needs to include these background factors as controls when studying aspects of entrepreneurship development.
The other two factors that predict venturing self-efficacy represent authenticity of experience. Selfefficacy is often thought of as mastery of authentic performance, which is to say being confident that one can successfully perform tasks in the context and under the conditions one will face in the future.
The use of industry examples in lecture to illustrate technical principles and hearing about what industry jobs would entail might be understood as adding a sense of authenticity to the student’s studies. Of course the ultimate authenticity for those going on to work in industry is to be given industry work while they are undergraduates, but note that the simple fact of working in industry correlates almost not at all with venturing self-efficacy. Authenticity would seem to require a strong tie between the nature of the industry work the student is given and their likely career path.
An intriguing finding is that work experience is a very strong predictor of self-efficacy, but that it explains very little of the differences found in entrepreneurial self-efficacy. Then when self-efficacy is shown to be strongly related to entrepreneurial intent, it leads one to hypothesise a causal model: authenticity and task performance lead to self-efficacy, and efficacy along with other variables leads to entrepreneurial intention. The effects of authenticity are almost entirely captured by the self-efficacy variable so there are no direct effects of authenticity on intention. The further study of these data with a statistical method to test alternative causal models is called for.
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Conclusion
The failure of the broader category variables to predict higher levels of self-efficacy or entrepreneurial intent strongly suggest that one cannot treat categories of programmes as having similar effectiveness. The impact of the third year in engineering has no effect in general, but engineering courses that have a prevalence of tangible examples drawn of enterprise activities have a measurable impact. Industry placement on average has little or no effect on these outcome variables, but if the placement work is tied to their course of study quite strong effects are found.
These results suggest a need to drill down to the elements in different approaches to entrepreneur development and do not contribute to improvement in the knowledge, skills, self-efficacy and intent of potential entrepreneurs.
Looking at the comparative impact of the quite different education activities addressed here, this research suggests that the area for immediate attention is the improvement of the use of industry work placements in the larger educational process. There are thousands of UK students placed in business and industry every year, which is a great but daunting opportunity, for it is neither easy nor inexpensive to find appropriate placements for such large numbers of students. But the data here suggest that when work placements are done well by linking student work to their courses of study, it is by far the dominant predictor of venturing self-efficacy. Given that innovative pursuits follow from such self-confidence in all walks of life, this research would suggest that if one wishes to improve enterprise education, the use of industry placements may have greater leverage than most other programme areas.
For those concerned with the study and formulation of education policy, the principal conclusion is that little can be done with assessment measures specific to one or a few programmes. Nor can much useful guidance be found in measures that, however excellent they might be, cannot be observed within the 3 to 5 year life cycle of many public initiatives. If one can identify, measure and validate trajectory metrics, however, it enables comparative research to support public policy.
Whether the metrics of venturing self-efficacy and entrepreneurial intent used in this demonstration of comparative analysis will stand up under scrutiny is less important than the sure knowledge that trajectory metrics are needed, and their development is central to intelligent policy evaluation. The availability of generally accepted and widely used outcome measures enables comparative evaluation across programme and activities, which in turn allows the systematic accumulation of knowledge to guide action.
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