VOL. 1, NO 1, JULY 2010, 51-62

UNDERSTANDING ENTREPRENEURIAL MINDSET:

A STUDY OF ENTREPRENEURIAL SELF-EFFICACY,

LOCUS OF CONTROL AND INTENT TO START A BUSINESS

Andrew Borchers* and Sung-Hee Park**

Kettering University

Abstract—With growing interest in entrepreneurship in higher education, faculty

are facing increased calls to instill an “entrepreneurial mindset” in students. The

authors base this study on a rich literature history on self-efficacy and expectancy

theory. Starting with work by Chen, Greene, and Crick (1998) and Rotter (1966), the

study measures entrepreneurial self-efficacy (ESE) with 22 items, locus of control

(LOC) with 23 items and intention to start a business (ITSB) with five items among a

sample of engineering and business students (n=191). We also collected paired data

from students before and after taking a course in innovation and new ventures. Along

with basic analyses using correlation and paired sample t-tests, we performed

confirmatory factor analysis and a Multi-Group SEM to test the effects of LOC on the

link between ESE and ITSB. We support Chen et al.’s (1998) work in showing that ESE

and ITSB are in fact related, and we demonstrate a moderating role for LOC in the

relationship between ESE and ITSB. The authors discuss conclusions and further areas

for study.

1. Introduction Many observers have recognized entrepreneurship as a rising star in higher education over the

past ten years. Interest has expanded from business schools to many academic domains including

engineering. Student interest in the field has fueled demands for practical education in business

formation, which, in turn, has led to a rising concern with the relevance of traditional business

education (Jones & English, 2004, pp. 416-423; Bennis & O’Toole, 2005, 96-104). The success

of entrepreneurs, like Jeff Bezos and Bill Gates, is one reference point that sparks student interest

in entrepreneurship. Also widely reported are the entrepreneurial developments in both e-

commerce and in the biomedical fields. Writers like C.K. Prahalad (2004) have also advanced

entrepreneurship as a vehicle for social change in the developing world.

Entrepreneurial education is not solely the domain of business schools. Increasingly, there is

interest in innovation and new ventures within technical disciplines, such as engineering and

science. A study among Canadian universities shows support (although not always through

formal courses) among deans in science, medicine and engineering (Menzies, 2004). Organizations like the National Collegiate Inventors and Innovators Alliance (NCIIA) have

sparked interest in entrepreneurship among science and engineering students through a variety of

programs. Notably, a significant numbers of non-business school researchers and practitioners

*Interim Dept Head Business Department; **Asst. Prof Business Dept

Kettering University; 1700 W. Third Ave; Flint, MI 48504; www.kettering.edu

A. Borchers and S.-H. Park

52

attended the recent NCIIA conferences in Portland (2006), Tampa (2007), Dallas (2008),

Alexandria, VA (2009) and San Francisco (2010). The conference schedules for these events

show significant interest in topics ranging from engineering and scientific innovation to projects

in the developing world and environmental sustainability.

Along with the rise of interest in entrepreneurial education have come calls both in the popular

and academic press to instill an ―entrepreneurial mindset‖ in students. Many authors, including

Timmons (1989), Timmons and Spinelli (2007), and McGrath (2000), speak to unique personal

attributes that differentiate entrepreneurs from other leaders. McGrath (2000) identifies the

difference between conventional management and entrepreneurial leadership in five key

elements: 1. Continuous search for opportunity – Entrepreneurial leaders create a climate to support

this search.

2. Framing – Entrepreneurial leaders define success on different terms and articulate

strategic direction in different ways from conventional managers.

3. Stocking an opportunity register – Entrepreneurial leaders continuously seek out and

identify opportunities.

4. Focus – Entrepreneurial leaders focus their thinking by making small investments that

allow them to make better decisions on which opportunities to pursue later on.

5. Promoting adaptive execution – Entrepreneurial leaders use discovery driven planning,

where leaders learn as they go and adapt their execution to market realities.

In his work with Spinelli, Timmons (2007) identifies a set of core attributes that he believes are

central to entrepreneurial behavior. These include:

Commitment and determination

Leadership

Opportunity obsession

Tolerance of risk

Ambiguity and uncertainty

Creativity

Self-reliance and adaptability

Motivation to excel

Courage

This call for ―entrepreneurial mindset‖ creates an interesting challenge for researchers in

identifying exactly what one means by the term. In this study, we address this challenge by

building on two lines of research that focus on ―self-efficacy,‖ the belief one has in his or her own

ability to complete a task and ―locus of control,‖ which refers to how individuals attribute the

results of their lives to internal or external forces. In our work, we examine the relationship

between these two constructs and the dependent variable ―intent to start a business.‖

We explore these constructs in the context of Kettering University, a small, private engineering

focused university in Flint, MI. The institution employs a co-operative model of engineering

education, where students complete alternate terms of work and study. Currently, the university

works with over 500 co-operative employers. Over the years, student co-op experiences have

become much more diverse and now range from work with large corporate to small

entrepreneurial firms. This transition, along with a general trend in engineering education to focus

on innovation, is a strong motivator for the institution to incorporate entrepreneurial education in

its programs.

Understanding entrepreneurial mindset

53

2. Literature

We based this study on two literature sources. First, the work of Chao Chen, Patricia Green, and

Ann Crick (1998) establishes the idea of entrepreneurial self-efficacy (ESE) as an antecedent to

intent to start a new business (ITSB). In their study, Chen et al. seek to identify characteristics

that are entrepreneurial in nature. They propose a measure of entrepreneurial self-efficacy (ESE)

that contains 22 items in five factors: marketing, innovation, management, risk taking and

financial controls. Chen et al. define ESE as ―the strength of an individual’s belief that he or she

is capable of successfully performing the roles and tasks of an entrepreneur‖ (pp. 295-316). They

also note that prior research consistently shows that self-efficacy is a strong predictor of future

performance.

The research of Chen et al. is deeply rooted in literature regarding the concepts of self-efficacy

and expectancy theory. Additionally, their data collection, the concept of ―entrepreneurial

mindset,‖ comes into plain view. The researchers developed the ESE construct using two study

groups. One group (n=140) was a set of mid-career MBA students in two entrepreneurship

classes. Here they established that self-efficacy predicts a student’s intention to start a business.

Chen et al. studied a second group of entrepreneurs (n=281) to confirm that the factors they

identified in the first group did, in fact, support a differentiation between managers and

entrepreneurs.

It is interesting that Chen et al. conclude that ESE is a moderately stable belief that requires

systematic effort to change. Entrepreneurial education, according to Chen et al., all too often

focuses on technical skills and not on developing attitudes and perceptions. Pretorius, Nieman,

and Vuuren write in a similar vein, noting two models of entrepreneurial education used in South

Africa. In the entrepreneurial performance education model, the focus is on motivation and

entrepreneurial and business skills. Pretorius et al. state that this model postulates a direct linear

relationship between entrepreneurial performances as functions of motivation and skills. The

second model, called the entrepreneurial education model, is a more holistic approach that

includes five factors:

1. entrepreneurial success themes

2. business knowledge and skills

3. business plan utilization, learning approaches

4. the facilitator

5. the program’s context

Both of these models clearly go beyond mere technical skills as a base for entrepreneurial

success.

The second major literature source is the work of J. B. Rotter (1966) on locus of control. Locus of

control refers to how individuals attribute the results of their lives to internal or external forces.

Some individuals show a high internal locus of control: they believe that events largely result

from their own behavior. Other individuals have a high external locus of control in believing that

forces outside of their control, including fate or chance, determine events. A number of

researchers working on entrepreneurship (including Diaz-Bretones & Rodriguez, 2003; Timmons

& Spinelli, 2007) have referenced this classic work. Further, this literature draws a close

connection between self-efficacy (ESE) and locus of control. Timmons (1989; 2007) states:

Successful entrepreneurs believe in themselves. They do not believe fate, luck, or

other powerful, external forces or persons the success or failure of their venture

A. Borchers and S.-H. Park

54

will be governed by. They believe that their accomplishments and setbacks lie

within their own control and influence, that they personally can affect the

outcome. This attitude is also consistent with the self-confident desire to take

personal responsibility—so long as this does not lead to overconfidence,

arrogance, or lack of humility.

These authors have found self-efficacy to be a key psychological attribute in understanding an

individual’s decision to be an entrepreneur.

3. Research Questions As Kettering University begins to offer academic and co-curricular activities in entrepreneurship,

we set out to explore entrepreneurial education in an engineering school context. In particular, we

posit the following questions:

a) Among Kettering University students, will Chen et al.’s (1998) ESE construct correlate

with intentions to start a new enterprise (ITSB)?

b) What role does locus of control play in student’s ESE and intent to start a new enterprise?

c) Can data reduction identify underlying concepts among Chen et al.’s (1998) 22

measures? Are these underlying constructs consistent with Chen et al.’s (1998) original

work?

d) Will students report higher levels of ESE and ITSB after completing a course in

innovation?

4. Methodology

We sought to understand entrepreneurial thinking among students at Kettering University. The

authors surveyed a sample of students (n=191) in nine classes during 2006-2009. Seven of the

classes (n=166) were sections of an upper level elective course in innovation and new venturing.

One class was a freshmen introduction to business class (n=12) populated by business and

engineering students. The final class was a senior design capstone class (n=13) populated with

mechanical engineering students.

In a first round of data collection (n=44) students in four of these classes were asked to complete

a 33 question survey. Participation was voluntary, and we explained the importance of the study

as part of the university’s plans to add coursework in entrepreneurship. Questions included the 22

items identified by Chen et al. (1998) in their ESE construct, five items to measure ―intention to

start a business‖ (ITSB) and six items for demographics. We conducted additional rounds of data

collection in 2007, 2008 and 2009. With these rounds, the sample size grew to 191. During these

rounds, we asked students to answer all the items in the first round in addition to a standard

measure of locus of control using 23 items. Further, these students completed a post survey

measuring the ESE and ITSB concepts after completion of the new venture course.

5. Findings As a first step, we checked the reliability of the measures included in the study. The ITSB

(intention to start a business) measure showed a Cronbach alpha of .91. The ESE (entrepreneurial

self-efficacy) measure showed a Cronbach alpha of .92. The five component measures

(marketing, innovation, management, risk taking and finance) showed Cronbach alpha scores of

.77 to .87. Hence, it appears that in the current data collection efforts the constructs advanced by

Chen et al. were reliable.

Understanding entrepreneurial mindset

55

Second, we used factor analysis to examine the 22 items included in Chen et al.’s (1998)

work in an effort to identify underlying concepts in the instrument. The factor analysis with

Varimax rotation identified five identifiable factors consistent with Chen et al.’s (1998) work (see

Table 1). The author labels the first one as ―Marketing,‖ the second as ―Innovation,‖ the third as

―Management,‖ the fourth as ―Risk Taking,‖ and the fifth as ―Finance.‖ The value of this analysis

is that it allows one to break ESE into manageable pieces. Further, from a teaching perspective,

this breakdown allows instructors to conceptualize topics to cover and analyze for possible

improvement. Table 1 - Rotated Component Matrix(a)

Component

1 2 3 4 5

Set/meet market goals 1 .736 .179 .253 .117 .121

Set/meet sales goal 1 .821 .271 .110 .111 .169

Set/attain profit 1 .831 .231 -.011 .194 .110

Est position in mkt 1 .616 .310 .268 .178 .097

Conduct mkt analysis 1 .341 .419 .329 .425 -.127

Expand business 1 .421 .574 .143 .323 .026

Create new venture/ideas 2 .236 .829 .109 .123 .165

Create new product/service 2 .222 .830 .010 -.004 .173

Expand mkt/geography 2 .306 .578 .096 .223 .302

New methods prod/mkt/mgmt 2 .255 .529 .323 .157 .176

Reduce risk/uncertainty 3 .217 .186 .207 .487 .304

Perform strategic planning 3 .025 .388 .503 .224 .164

Manage time by setting goals 3 .150 .059 .807 .021 -.004

Est/achieve goals & obj 3 .227 .045 .800 .107 .257

Define org roles/resp/policy 3 .173 .072 .736 .131 .186

Take calculated risks 4 .265 .122 .268 .183 .639

Decisions under uncertainty 4 .153 .198 .005 .212 .812

Take resp for ideas/decisions 4 -.138 .308 .534 .066 .427

Work under pressure/conflict 4 .056 .154 .311 .072 .705

Perform fin analysis 5 .112 .059 .025 .864 .179

Develop fin systems/control 5 .077 .210 .000 .879 .056

Control Costs 5 .221 .055 .266 .687 .160

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with

Kaiser Normalization. A Rotation converged in 7 iterations.

Q1, 2, 3, 4, 5, 6 – Marketing (items noted with a (1))

Q 7, 8, 9, 10 – Innovation (items noted with a (2))

Q11, 12, 13, 14. 15 - Management (items noted with a (3))

Q16, 17, 18, 19 – Decision making under uncertainty and pressure (items noted with a (4)

Q20, 21, 22 - Financial/Market analysis and control (items noted with a (5)

A. Borchers and S.-H. Park

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Third, we ran correlations on multiple variables from the student survey including ITSB,

ESE, locus of control, demographic elements, along with the five factors identified by factor

analysis (see Table 2). Notably, the constructs of ESE and ITSB were correlated with an R value

of .310 and a significance of .000, which was consistent with Chen et al. (1998). We found locus

of control correlates with both ITSB and ESE in the expected direction (negative). Note: we

scaled our measure of locus of control so that high external locus of control scales close to one;

and high internal locus of control scales close to zero. This indicates that individuals with high

internal locus of control (and hence a low locus score in our measurement) have higher ESE and

ITSB than individuals with high external locus of control. In addition, we found that none of the

demographic variables –including class, GPA, gender or family/friends in entrepreneurship—

significantly correlated with ITSB. Consistent with the findings of Chen et al. (1998), these

variables correlate, at best, weakly with the ITSB construct. Table 2 - Correlations

EFFICACY INTENT LOCUS PEFFICA PINTENT

EFFICACY Pearson

Correlation 1 .310(**) -.230(**) .316(**) .137

Sig. (2-tailed) . .000 .005 .001 .168

N 188 188 144 103 103

INTENT Pearson

Correlation .310(**) 1 -.170(*) .199(*) .759(**)

Sig. (2-tailed) .000 . .042 .044 .000

N 188 188 144 103 103

LOCUS Pearson

Correlation -.230(**) -.170(*) 1 -.108 -.114

Sig. (2-tailed) .005 .042 . .276 .250

N 144 144 144 103 103

PEFFICA Pearson

Correlation .316(**) .199(*) -.108 1 .310(**)

Sig. (2-tailed) .001 .044 .276 . .001

N 103 103 103 106 106

PINTENT Pearson

Correlation .137 .759(**) -.114 .310(**) 1

Sig. (2-tailed) .168 .000 .250 .001 .

N 103 103 103 106 106

**Correlation is significant at the 0.01 level (2-tailed). *Correlation is significant at the 0.05 level (2-tailed).

Fourth, we conducted paired t-tests for the 2007, 2008 and 2009 observations in order to see

if ESE (and its components) or ITSB changed between the pre and post measures (see

statistics in Appendix I). While our work lacks experimental controls, such as randomization and

placebo groups, our test on ESE was significant and showed the hoped for sign (positive) while

the test on ITSB is not significant. With respect to ESE and its components, our results suggest

that students exposed to entrepreneurship in our course came away with a stronger

entrepreneurial self-efficacy. The change in ITSB was not significant, but has interesting

implications for entrepreneurship education that we discuss later. Table 3 - Paired Samples Test

Paired Differences t df Sig. (2-tailed)

Mean

Std.

Deviation

Std. Error

Mean

95% Confidence Interval

of the Difference

Lower Upper

Pair 1

INTENT - PINTENT .009 .5901 .0581 -.107 .124 .150 102 .881

Pair 2

PEFFICA - EFFICACY .6516 .7014 .0691 .5145 .7887 9.428 102 .000

Understanding entrepreneurial mindset

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6. Advanced Analysis We extended our initial analysis to include confirmatory factor analysis while testing ESE as

second order constructs using LISREL 8.80. For the observed variables, Table 5 shows factor

loadings and their corresponding t values. All except five items—i.e., Q5; Q6; Q11; Q12; Q22;

and Q27—have large and significant loadings—> 0.70—on their corresponding factors, which

indicates evidence of good construct validity, as described by Hair, Anderson, Tathan, and Black

(1998). All the items have statistically significant relationships with their factors, with most of the

t-values exceeding a critical value at the 0.05 significance level (some of them are significant at

the 0.10 level).

We assessed the goodness-

of-fit of the posited

measurement model using a

two-step approach described

by Hu and Bentler (1998;

1999): the maximum

likelihood technique. Hu

and Bentler (1998; 1999)

argue that one should

simultaneously use two fit

indices in order to reduce

discrepancy across fit

indices. The 2 statistic is

657.63 (df = 319) which is

significant at the 0.01 level.

The problem with using the

chi-square statistic is that it

is sensitive to large sample

sizes and will thus indicate

a statistically significant

difference even when the

practical difference is

relatively small. To

compensate for the

sensitivity of statistics to

large sample sizes, we

analyzed fit indices to

assess the model more

effectively.

Following Hu and Bentler’s

(1998; 1999) suggestion, as

the first step, we assessed

the fit of the model by

investigating the

standardized root mean

square residual (SRMR).

The SRMR 0.08 meets the

maximum suggested cutoff

value (0.08 or less) of the

Latent Variable Item # Loading T-values

ESE – Marketing Q01 0.75 - 1

Q02 0.89 12.60

Q03 0.85 12.10

Q04 0.76 10.62

Q05 0.65 8.89

Q06 0.34 4.48

ESE - Innovation Q07 0.85 -

Q08 0.84 13.66

Q09 0.75 12.72

Q10 0.70 10.72

ESE - Management Q11 0.55 -

Q12 0.58 6.36

Q13 0.79 7.70

Q14 0.92 8.27

Q15 0.77 7.63

ESE – Risk Taking Q16 0.73 -

Q17 0.79 10.22

Q18 0.72 9.37

Q19 0.78 10.07

ESE – Financial Control

Q20 0.90 -

Q21 0.86 13.36

Q22 0.68 10.45

Behavioral Intention Q23 0.89 -

Q24 0.91 18.80

Q25 0.79 14.13

Q26 0.92 19.12

Q27 0.15 1.97

Table 5: Completely Standardized Loadings and T-values

A. Borchers and S.-H. Park

58

model. Next, we compared the fit of the model to the independence model with the comparative

fit index (CFI). The CFI 0.97 suggests that the model fit data comparatively better than the

independence model in a ―good‖ level value (0.90 or greater). Additionally, the Tucker-Lewis

Index (NNFI) 0.97 suggests a good fit of the model as well. We also calculated the root mean

square error of approximation (RMSEA). The RMSEA 0.074 suggests that the model fits the data

well (the suggested cutoff value for a good fit is usually 0.10 or less). Overall, the results suggest

that the model is accurately measuring the constructs in the theorized manner while the data

supports the theory.

The overall structural model fit is good. We examined the same criteria used in the assessment of

the measurement model again while following the two-step approach Hu and Bentler (1998;

1999) recommended. The 2 statistic is 647.69 (df = 318) which is significant at the 0.01 level.

The SRMR is 0.08. The CFI is 0.97. Additionally, NNFI is 0.97 and RMSEA is 0.079. We show

the path coefficients and explain variances for the model (see Figure 1).

Figure 1: LISREL Model Result

We tested the moderating effects of LOC by assessing invariance between two groups with

differing levels of LOC. We separated subjects into two groups: the low external LOC group

(individuals with low scores in our measure) and the high external LOC group (individuals with

high scores by our measure). The two groups were mean-separated by the scores of the CFO (i.e.,

separated at the LOC value of 0.40). The method for testing moderator effects is multi-group

SEM (MGSEM), which Vandenberg and Lance (2000) discuss, and essentially follows Byrne’s

(1998) multi-group invariance test paradigm. Researchers often use multi-group invariance tests

for assessing equivalencies across groups. However, multi-group invariance tests can also assess

the role of moderator variables, if the score of the moderator variable can create two groups.

Since the role of moderator variables has been defined by Sharma et al. (1981, pp. 291-300) as

one that systematically modifies the form and/or strength of a relationship between a predictor

and a criterion variable. Differences in relationships among constructs between the two groups

could indicate an interaction effect of the moderator variable.

ESE

MKT

INN RSK

FIN

ITSB (52%)

.84

.84

.82

.88

.67

.64 *

MGT

Understanding entrepreneurial mindset

59

A multi-group invariance test begins by fitting a model to the data for each of the two groups

considered separately with none of the parameters constrained to be equal across groups.

Statisticians often call this the baseline model. Next, researchers increase constraints on the

baseline model by specifying the parameters of interest to be constrained across groups. Then,

one compares the chi-square difference test between the baseline model and the more restrictive

models in order to determine whether the model and the individual parameter estimates are

invariant across the samples. A significant difference in chi-square represents a deterioration of

the model, and researchers should reject the null hypothesis that parameters are equal (as

suggested by Koufterosa & Marcoulides, 2006).

Per Koufterosa and Marcoulides (2006), actual tests of the moderating role of LOC involved

comparison of the measurement invariance model against the structural coefficient invariance

model, where measurement invariance (i.e., parallel invariance) means setting all factor loadings

and error variances equal across groups; and structural coefficients invariance means setting all

structural coefficients equal across groups (pp. 286–307). Establishing measurement model

invariance prior to examining invariance of structural model invariance is essential because any

potential differences in these measurement model parameters may contribute to structural model

non-equivalency. We also should note that we are looking at the differences in 2 as well as

various goodness-of-fit indexes in order to test for invariance. Table 6 presents the results of our

invariance tests using MGSEM:

Table 6: Invariance Tests across Other Orientation Levels

As shown in Table 6, the 2 difference between the parallel model and the structural coefficients

invariance model is 7.75 with 1 degrees of freedom, which is statistically significant at 0.01

levels. This indicates that the additional constraints imposed led to a deterioration of the model

(i.e., set the path between ESE and ITSB equal across the two groups). In other words, the

structural coefficients are different across the level of LOC. Thus the result supports the notion

that LOC has a general moderating effect on the links shown in Figure 2. We found the path

coefficient between ESE and ITSB was changed (i.e., from 0.48 with the low external LOC group

to 0.15 with the high external LOC group) simply by comparing two MGSEM results. The

significance of the path coefficients also changed (3.73: significant to 1.12: non-significant).

Model 2 d.f. CFI NNFI RMSEA Delta 2

Delta

(d.f.)

Sig.

Level

Parallel 1002.80 695 0.90 0.90 0.080 - - -

Structural Coefficients

Invariance 1010.55 696 0.90 0.90 0.080 7.75 1 0.0005

A. Borchers and S.-H. Park

60

©

7. Conclusion and Further Areas for Study This work is a quantitative study of entrepreneurial education in an engineering institution. The

results show a positive correlation between ESE and ITSB, which confirms Chen et al.’s (1998)

earlier work. Further, we have two new and interesting findings that may be of interest for

entrepreneurial faculty:

I. The results suggest that among students completing a single course in innovation and

new ventures, self-efficacy (ESE) changes in a positive direction. However, changing

students’ intention to start a business (ITSB) appears to be more difficult. In fact, we

found ITSB actually remained largely unchanged between pre and post measurers.

Simply increasing the belief that one can do the tasks of an entrepreneur appears to be

insufficient in altering students’ intentions to start a business. We frequently observed

this among students in their closing presentations. Typically, we ask students ―would you

pursue your project as a business venture?‖ and ―do you think you will start a new

venture some day?‖ A surprising number of students respond to this question by saying

that our innovation course convinced them that starting a new enterprise is difficult and

not for them.

II. Our findings on locus of control provide additional insight into understanding the

relationship of ESE (entrepreneurial self-efficacy) to ITSB (Intention to start a business).

Among students with low external locus of control (e.g. students who believe they

control their own destiny), the relationship between ESE and ITSB is significant.

Teaching those students entrepreneurial skills should result in higher self-efficacy and,

hence, higher intent to start a business. However, in order to affect students with high

external locus of control (e.g. students who believe external forces set their fate), we must

do more than teach entrepreneurial skills. We need to find ways to alter these students’

locus of control and to help them believe they can change their circumstances. This

echoes the work of Chen et al. (1998) and Pretorius et al. (2005) both of whom call for

entrepreneurial education to go beyond technical details and to address student attitudes.

We are pursuing another line of work: to understand better ―entrepreneurial mindsets‖ as a part of

a ―shared mental model.‖ Mental models allow people to understand and interpret phenomena, to

draw inferences, to decide what actions to take, to control system execution, and to make

predictions (Johnson-Laird, 1987). The reason we are interested in measuring shared mental

ESE ITSB

0.15 (1.12)

ESE ITSB

0.48 * (3.73)

Group 1 – Low External LOC

Group 2 – High External LOC

Figure 2: MGSEM Results

Understanding entrepreneurial mindset

61

model is that it can be a good indicator of our programs’ performance. The ultimate outcome

sought from our entrepreneurial program is to create an entrepreneurial mindset among our

students that contributes either to innovation or to new venture creation later in their careers.

Through measuring shared mental model between successful entrepreneurs and our students, we

believe we can assess whether or not our program creates an entrepreneurial mindset among the

students now rather than being forced to wait years to see what students actually do. Figure 3

(below) represents the expected shared mental model between successful entrepreneurs and the

students in our program. As an indicator of our program’s success, we expect to see the mental

models of students progressing through the program become increasing similar to the mental

models of experienced entrepreneurs.

Figure 3: Shared Mental Model in Entrepreneurship Across the Curriculum

According to Langan-Fox, Code, and Langfield-Smith (2000), there are three available methods

for developing shared mental models (pp. 242-271):

1. pair wise ratings,

2. repertory grid technique, and

3. casual mapping.

Each method involves work with a small group of experts (experienced entrepreneurs in our case)

prior to measuring students. We are currently working on this approach as a next step in our

research.

Acknowledgements Authors’ Note: The authors wish to acknowledge generous support for this research from the

Kern Family Foundation

Notes Bennis, W. G., & O’Toole, J. (2005). How

Business Schools Lost Their Way.

Harvard Business Review, 83(5), 96-

104.

Byrne, B.M. (1998). Structural Equation

Modeling: Basic Concepts, Application,

and Programming. Mahwah, NJ:

Lawrence Earlbaum Associated, Inc.

Chen, C., Greene, P. G., & Crick, A. (1998). Does

entrepreneurial self-efficacy distinguish

entrepreneurs from managers? Journal of

Business Venturing, 13, 295-316.

Diaz-Bretones, F, & Rodriguez, A. (2003). Locus

of control, Nach and values of

community entrepreneurs. Social

Behavior and Personality, 31(8), 739-

748.

Hair, J.F., Anderson, R.E., Tathan, R.I., & W.C.

Black. (1998). Multivariate Data

Analysis with Reading. Englewood

Cliffs, NJ: Prentice Hall.

Hu, L., & Bentler, P. M. (1998). Fit indices in

covariance structure modeling:

Sensitivity to underparameterized model

misspecification. Psychological

Methods, 3. 424-453.

Hu, L. & Bentler, P. M. (1999). Cutoff criteria for

fit indexes in covariance structure

analysis: Conventional criteria versus

Entrepreneurs’

Mindset

Students’

Mindset

Interactions

(Entrepreneurial

Education

Programs)

Shared

Mental Model

at pre- EAC Shared

Mental Model

post EAC

A. Borchers and S.-H. Park

62

new alternatives. Structure Equation

Modeling, 6(1), 1-55.

Johnson-Laird, P. N. (1987). Mental Models:

Toward a Cognitive Science of

Language, Inference, and

Consciousness. Cambridge, MA:

Cambridge U P.

Jones, C., & English, J. (2004). A contemporary

approach to entrepreneurship education.

Education and Training, 46(8/9), 416-

423.

Koufterosa, X., & Marcoulides, G. A. (2006).

Product development practices and

performance: A structural equation

modeling-based multi-group analysis.

International Journal of Production

Economics, 103, 286–307.

Langan-Fox, J., Code, S., & Langfield-Smith, K.

(2000). Team mental models:

Techniques, methods, and analytic

approaches. Human Factors, 42(2), 242-

271.

McGrath, R. G., & MacMillan, I. C. (2000).

Entrepreneurial Mindset: Strategies for

Continuously Creating Opportunity in an

Age of Uncertainty. Cambridge, MA:

Harvard Business Press Publishing.

Menzies, T. (2004). Are universities playing a

role in nurturing and developing high

technology entrepreneurs? The

administrators’ perspective.

International Journal of

Entrepreneurship and Innovation, 5(3).

Prahalad, C. K. (2004). The Fortune at the

Bottom of the Pyramid: Eradicating

Poverty Through Profit. Upper Saddle

River, NJ: Wharton School Publishing.

Pretorius, M., Nieman, G., & Vuuren, J. (2005).

Critical evaluation of two models for

entrepreneurial education: An improved

model through integration. The

International Journal of Educational

Management, 19(5), 413-427.

Rotter, J.B. (1966). Generalized expectancies of

internal versus external control of

reinforcements. Psychological

Monographs, 80(609).

Sharma, S, R., Durand, & Gur-Arie, O. (1981).

Identification and analysis of moderator

variables. Journal of Market Research,

18(3), 291-300.

Timmons, J. (1989). The Entrepreneurial Mind.

Andover, MA: Brick House Publishing.

Timmons, J., & Spinelli, S. (2007). New Venture

Creation. Boston: McGraw Hill.

Vandenberg, R. J., & Lance, C. E. (2000). A

review and synthesis of the measurement

invariance literature: Suggestions,

practices, and recommendations for

organizational research. Organizational

Research Methods, 3(1), 4-69.

Appendix I – Statistical Results Table 4 - Descriptive Statistics

N Minimum Maximum Mean Std. Deviation

ESE 188 1.3636 4.9090 3.2953 .6121

ITSB 188 1.0 4.8 3.174 .8516

MARKET 188 1.0 5.0 3.074 .8198

INNOVATE 188 1.00 5.00 3.2440 .8310

MANAGE 188 1.2 5.0 3.480 .6973

RISK 188 1.75 5.00 3.7409 .66197

FINANCE 188 1.0000 5.0000 2.8989 .8450

Post ESE 106 2.5000 4.8181 3.8983 .5034

Post ITSB 106 1.0 4.8 3.139 .8427

Post MARKET 106 2 5 3.84 .557

Post INNOVATE 106 2.25 5.00 3.8868 .59775

Post MGMT 106 1.8 12.6 4.090 1.0291

Post RISK 106 2.50 5.00 4.1580 .54590

Post FIN 106 1.0000 5.0000 3.4874 .8827

Valid N (listwise) 103