Technological Forecasting And Social Change

The impact of entrepreneurship education on the entrepreneurial
intention of students in science and engineering versus business studies
university programs
Daniela Maresch a
, Rainer Harms b,⁎, Norbert Kailer c
, Birgit Wimmer-Wurm c
a Institute for Innovation Management, Johannes Kepler University Linz, Austria
b IGS/NIKOS, University of Twente, The Netherlands
c Institute for Entrepreneurship and Organizational Development, Johannes Kepler University Linz, Austria
a b s t r a c ta r t i c l e i n f o
Article history:
Received 30 September 2014
Received in revised form 13 May 2015
Accepted 3 November 2015
Available online 23 December 2015
Keywords:
Entrepreneurship education
Entrepreneurial intention
Student entrepreneurship
Technology entrepreneurship
Theory of planned behavior
TPB
Academic research has shown that Entrepreneurship Education (EE) increases Entrepreneurial Intention (EI).
However, this does not happen uniformly in all contexts, as specific contexts may require different EE action.
In this paper the authors investigate the context-specific questions in two separate categories of students. If con-
text is important, we should see different outcomes from similar EE classes provided to different student groups.
The authors' results suggest that there is a contextual difference. The results indicate that EE modified to suit a
particular target group could address the issue of subjective norms separately for business students and science
and engineering students. Their principal results show that EE is generally effective for business students and sci-
ence and engineering students. However, the EI of science and engineering students is actually negatively affect-
ed by subjective norms, whereas that effect is not apparent among the business student sample. The authors
suggest that future research is needed on effective didactic approaches in EE for science and engineering
students.
© 2015 Elsevier Inc. All rights reserved.
1. Introduction
The importance of entrepreneurship to society has been identified
and discussed since at least the fifteenth century (Schumpeter, 1912),
and that discussion remains topical (Kirchhoff et al., 2013; Grichnik
and Harms, 2007). The questions of whether and how entrepreneurial
skills and competences can be fostered during education were posed
by Cotrugli (1990), and later followed up by Cantillon (1931). From
these historical roots, Entrepreneurship Education (EE) has evolved to
become a prominent field. This field is born of diverse disciplines,
which include economics, management, education, and technical stud-
ies (Davidsson, 2008).
The authors embrace the concept that EE is based on the realization
that successful entrepreneurship is positively affected by the disposi-
tions, skills, and competences of the founders of an enterprise (Rauch
et al., 2005; Unger et al., 2011). We suggest that these dispositions,
skills, and competences can be shaped by education (Kuratko, 2005),
and cite recent meta-analyses (Bae et al., 2014; Martin et al., 2013) indi-
cating that EE is generally effective. We seek to enhance the knowledge
in this field by investigating the outstanding question of what makes EE
effective, and for whom.
The question of “what makes EE effective” has been discussed in a liter-
ature stream on intention-based models for entrepreneurship education
(Kuehn, 2008). Kuehn (2008, p. 87) states: “If entrepreneurial intentions
precede entrepreneurial behavior, then entrepreneurship educators should
benefit from intentions-based research in entrepreneurship”. If this is so,
then EE should investigate the drivers of this Entrepreneurial Intention
(EI). Theory, and a recent meta-analytical assessment (Schlaegel and
Koenig, 2014), both suggest that the drivers of EI are attitudes, subjective
norms, and perceived behavioral control. These elements of the Theory of
Planned Behavior (TPB) also influence the effectiveness of EE (Kuratko,
2005; Gorman et al., 1997; Rauch and Hulsink, 2015).
EE research further investigates when EE can most effectively influ-
ence students' EI. We analyze two such conditions. First, we examine
the extent to which students possess the attitudes, subjective norms,
and perceived behavioral control considered prerequisites of becoming
an entrepreneur. Here we add to the literature by investigating not only
the direct effects of TPB constructs, but, in treating them as moderators
of the EE–EI relationship (Ho et al., 2014), and we also examine the re-
lationship in the context of specific fields of study.
Second, it is science and engineering students in particular whose
entrepreneurial activities create new, high-quality firms (Åstebro
et al., 2012) that ultimately contribute to job growth (Kirchhoff,
1994). Strengthening this human capital basis for technology-based en-
trepreneurship may be vital, especially for regions affected by an eco-
nomic crisis (Harms et al., 2010; Heitor et al., 2014; Fink et al., 2012).
Technological Forecasting & Social Change 104 (2016) 172–179
⁎ Corresponding author.
E-mail addresses: [e-mail] (D. Maresch), [e-mail]
(R. Harms), [e-mail] (N. Kailer), [e-mail]
(B. Wimmer-Wurm).
http://dx.doi.org/10.1016/j.techfore.2015.11.006
0040-1625/© 2015 Elsevier Inc. All rights reserved.
Contents lists available at ScienceDirect
Technological Forecasting & Social Change

However, with few exceptions (Phan et al., 2009; Yanez et al., 2010), the
literature on the EE offered to science and engineering students is quite
thin. We address the call from Rauch and Hulsink (2015) for more re-
search into the specific effects of EE programs on students from different
disciplines, particularly from science and engineering disciplines. We
investigate the specific situation of students of technical sciences, as
they are the most likely to start up technology-oriented ventures. Our
analysis is relevant as it shows which drivers in which target groups ed-
ucators can address to nurture EI.
2. Theoretical framework and hypotheses
2.1. Affecting entrepreneurial intention through entrepreneurship
education — a discussion of the literature
We refer to the definition of EI as the “self-acknowledged conviction
by a person that they intend to set up a new business venture and con-
sciously plan to do so at some point in the future” (Thompson, 2009,
p. 676). EI has become a vibrant field in entrepreneurship research
(Fayolle and Linan, 2014), as “intentions have proven the best predictor
of planned behavior, particularly when that behavior is rare, hard to ob-
serve, or involves unpredictable time lags” (Krueger et al., 2000, p. 411).
Most recently, a longitudinal study by Kautonen et al. (2015) confirmed
that EI predicts entrepreneurial action. Thus, the question of what influ-
ences EI is a relevant one for policy makers, practitioners, and educators.
Research into the role of EE in the formation of EI is based, first of all,
on TPB (Ajzen, 1991), which provides a strong theoretical foundation
(Schlaegel and Koenig, 2014; Krueger and Carsrud, 1993). It posits that
a person's future behavior is preceded by intention: the stronger a
person's intention to engage in a specific behavior, the more likely it is
that the actual behavior will be performed. Furthermore, the intention
to perform a given behavior is the result of three cognitive antecedents:
(i) attitude toward behavior; (ii) subjective norms; and (iii) perceived
behavioral control.
Second, EE is seen as a strong antecedent of EI. Two theoretical con-
cepts have been developed that support this relationship: (i) human
capital theory (Becker, 1964); and (ii) entrepreneurial self-efficacy
(Bae et al., 2014; Chen et al., 1998). Human capital theory holds that
human capital represents “the skills and knowledge that individuals ac-
quire through investments in schooling, on-the-job training, and other
types of experience” (Bae et al., 2014, p. 219–220). It is regarded as a de-
terminant of EI. A meta-analysis by Martin et al. (2013) found that EE is
associated with higher levels of EI. Entrepreneurial self-efficacy refers to
“the strength of a person's belief that he or she is capable of successfully
performing the various roles and tasks of entrepreneurship” (Chen et al.,
1998, p. 295). Chen (2010) found entrepreneurial self-efficacy to be a
positive moderator of the relationship between EE and EI.
Research on EI has brought together TPB and EE in various ways
(Martin et al., 2013). In earlier studies, education was merely the context
in which TPB constructs and EI were evaluated (Autio et al., 2001; Liñán,
2004; Lüthje and Franke, 2003). Apart from the direct effects of EE on EI,
another group of studies assumes that the effect of EE on EI is (partially)
mediated through its effect on TPB's intervening constructs (Rauch and
Hulsink, 2015). As the direct and mediated influences of EE via TPB
have meta-analytical support, research has begun to investigate a fourth
model variant, which is that the effect of EE on EI may be moderated by
the three cognitive antecedents posited under TPB (Ho et al., 2014).
In this study we provide an integrated model of the relationship be-
tween EE and EI that brings together both direct and indirect effects. The
following section reports the development of the hypotheses.
2.2. Hypotheses
We begin by hypothesizing a direct impact of TPB constructs on EI,
based on the findings of previous studies (Krueger et al., 2000;
Kautonen et al., 2015; Lüthje and Franke, 2003; Kolvereid, 1996;
Souitaris et al., 2007). We add to the literature by providing hypotheses
on why this impact may differ between science and engineering stu-
dents and other students.
First, the term ‘attitudes toward behavior’ refers to a person's favor-
able or unfavorable evaluation of the target behavior. The more positive
a person's evaluation of the outcome of starting a business is (Krueger
et al., 2000; Autio et al., 1997; Pruett et al., 2009; Segal et al., 2005;
Van Gelderen and Jansen, 2008), the more favorable his or her attitude
toward that behavior should be, and consequently the stronger his or
her intention to start a business should be. Second, the term ‘subjective
norms’ relates to a person's perception of the opinions of social refer-
ence groups (such as family and friends) on whether the person should
perform a certain behavior. The better the reference group's opinion is,
the more encouragement for starting a business a person receives from
this reference group, and the higher the person's motivation to comply
with it is, the stronger the person's intention to start a business should
be. Third, the term ‘perceived behavioral control’ reflects the perceived
ease or difficulty of performing the behavior. It is based on whether the
person believes that the required resources can be obtained, and that
opportunities for performing the behavior exist (Bandura, 1986; Swan
et al., 2007). Perceived behavioral control not only predicts the forma-
tion of intentions, but also supports the prediction of actual behavior
by serving as a proxy for actual control (Ajzen, 1991).
In the context of entrepreneurship, the empirical results broadly
confirmed TPB predictions with respect to the positive relationship be-
tween attitudes toward behavior, subjective norms and perceived be-
havioral control, respectively, and EI (Krueger et al., 2000; Kautonen
et al., 2015; Lüthje and Franke, 2003; Kolvereid, 1996; Souitaris et al.,
2007). In line with these findings, we propose the following hypothesis:
H1a. There is a positive relationship between (1) pro-entrepreneurial
attitudes, (2) subjective norms, and (3) perceived behavioral control,
and a person's EI.
The fact that recent graduates from science and engineering are pro-
viding the gross flow of new, high-quality firms—over and above those
of other academic entrepreneurs (Åstebro et al., 2012)—highlights the
importance of these students as targets of EE. Thus, the fact that the ma-
jority of studies into student EI are based on business students or on un-
defined student populations (Bae et al., 2014; Martin et al., 2013),
indicates a gap in the literature arising because this student population
might differ from others with regard to entrepreneurship. This differ-
ence may be based on education content (Kuckertz and Wagner,
2010) and on social identity theory (Obschonka et al., 2012).
Business students have received more education in business matters
than other students. This may cause a weakening of the relationship be-
tween pro-entrepreneurial attitudes, subjective norms, perceived be-
havioral control and a person's EI. Kuckertz and Wagner argue that
(Kuckertz and Wagner, 2010, p. 529): “learning about the facts of busi-
ness causes [business students] to evaluate entrepreneurial opportuni-
ties more vigorously”. This additional knowledge may not only reduce
the level of EI per se, but also the degree to which initially favorable
TPB components influence EI.
Obschonka et al. (2012) draw on social identity theory. They argue
that social identity – “the aspect of a person's self-image that is derived
from membership of social groups” (Obschonka et al., 2012, p. 137) –
influences the “cognitive processes that [...] underlie the formation of
entrepreneurial intentions” (Obschonka et al., 2012, p. 137). Here,
Obschonka et al. (2012) show that the strength of group identification
can affect the relative strength of the TPB drivers of EI. We argue that
it may not only be the strength of group identification that leads to
differences in the strength of TPB drivers—between business students
and science and engineering students—but that the group differences
themselves lead to differences in the strength of TPB drivers. For exam-
ple, science and engineering students may perceive that legitimate
group behavior in their case includes the exploration of science and
173D. Maresch et al. / Technological Forecasting & Social Change 104 (2016) 172–179

engineering matters (Jungert, 2013). Hence, they may regard subjective
norms relating to entrepreneurship as rather negative. This perception
may lead to a weak relationship between TPB drivers and EI, particularly
in the context of high group identification.
In one of the first empirical studies into EI among science and engi-
neering students, Lüthje and Franke (2003) show that EI is significantly
related to pro-entrepreneurial attitudes. Souitaris et al. (2007) show
that EE can impact positively on pro-entrepreneurial attitudes of science
and engineering students, a finding that was later confirmed by
Kuckertz and Wagner (2010). These studies confirm the importance of
EE, and pro-entrepreneurial attitudes toward EI, for science and engi-
neering students. So, while in general the effect of TPB components
may also be applicable to business students, theoretical arguments sug-
gest that a differentiated perspective may be warranted. This leads us to
propose H1b.
H1b. The degree to which pro-entrepreneurial attitudes, subjective
norms, and perceived behavioral control affect EI, differ with the type
of study.
In addition to these three motivational drivers, EE research proposes
that there is a positive relationship between EE and EI. Robinson et al.
(1991) argue that entrepreneurial attitudes may be influenced by edu-
cators and practitioners. Dyer (1994) suggests that training in how to
start a business, or specialized courses in entrepreneurship, might give
some people the confidence that they are sufficiently in control of
their own behavior to start their own business. Similarly, Krueger and
Brazeal (1994) argue that EE increases students' knowledge, builds
their confidence, and fosters self-efficacy, which should, in turn, en-
hance their perception that entrepreneurship is a feasible option for
them. Moreover, EE shows students the intrinsic rewards involved in
starting a new business, which should increase the perceived desirabil-
ity of entrepreneurship. In research relating specifically to science and
engineering students, Souitaris et al. (2007) tested the effect of EE pro-
grams on entrepreneurial attitudes and EI, and found that science and
engineering programs increase overall EI. A recent meta-analysis of
the link between EE and EI (Bae et al., 2014) supports the positive link
between the two. Finally, EE not only promotes entrepreneurial behav-
ior, but also intrapreneurial behavior (Bjornali and Støren, 2012). Thus,
we propose the following hypothesis:
H2a. The higher the extent of EE, the stronger the person's EI.
The strength of the impact of EE may differ between business stu-
dents and science and engineering students. This study highlights two
competing lines of arguments. On the one hand, the impact of EE on EI
may be greater for science and engineering students than for students
in other disciplines. Education might have a diminishing rate of return.
It may be most effective in changing intentions when the initial level of
EE is low. That might well be the case for science and engineering
students, who often learn about entrepreneurship and business in detail
for the first time via EE. By contrast, the incremental effects of EE
on business students may be low. The findings of Frederick and
Walberg (1980) indicate that the time spent on instruction may have
a diminishing rate of return.
On the other hand, Walberg and Tsai (1983) argue (referencing
Simon (1979)) that prior experience of a subject allows a person to ac-
quire and process new knowledge more efficiently than those with less
exposure to the subject. Hence, science and engineering students may
have a different mental framework from that which is suited to quickly
process information on entrepreneurship. This may make EE more ef-
fective for business students.
H2b. The degree to which EE affects a person's EI is affected by the type
of study.
We now look at the moderating effects EE has on the three cognitive
antecedents of EI. EE affects how students evaluate the consequences of
entrepreneurship. According to Prospect Theory (Kahneman and
Tversky, 1979), a certain gain is valued more highly than an uncertain
equal or greater gain. Similarly, people will assess a certain loss to be
more damaging than an uncertain equal or greater loss. Logically, the
gains and losses induced by the same stimulus (e.g., starting a business)
will be evaluated against the background of a future without that
stimulus.
This expectation bias has three effects on the impact of EE on stu-
dents' EI. First, as EE typically frames entrepreneurship positively in
terms of gains compared against other career options, it will strengthen
students' positive attitudes rather than any negative ones and therefore
enhance the positive impact of attitudes on EI. As the effects proposed
by Prospect Theory are expected to hold generally, we do not propose
a differentiated set of hypotheses for business students and science
and engineering students. We propose the following hypothesis:
H3a. The higher the extent of EE, the stronger the positive impact of at-
titudes on EI.
Second, the more students know about entrepreneurship, the clear-
er will be their expectations of how entrepreneurship will influence
their lives, which in turn will make their decisions less reliant on the en-
trepreneurship opinions of their social reference groups (Kautonen
et al., 2015).
H3b. The greater the extent of EE, the weaker the positive impact of
subjective norms on EI.
Third, EE aims to help students develop the skills and competences
to seize entrepreneurial opportunities. Thus, as students receive more
EE, they should become more confident in their ability to create and
evaluate entrepreneurial opportunities, and in their ability to secure
the resources required to seize them. This leads to potential entrepre-
neurship gains becoming more likely, while at the same time the losses
arising from the risk involved in entrepreneurial activity become less
likely. We propose the following hypothesis:
H3c. The greater the extent of EE, the weaker the positive impact of
perceived behavioral control on EI.
Fig. 1 illustrates the hypothesized relationships.
3. Method
3.1. Data collection and description of the sample
The data from this study are derived from the 2011 Austrian study
(Kailer et al., 2012) of the GUESSS project [Global University Entrepre-
neurial Spirit Students' Survey] (Sieger et al., 2011). The data for the on-
line survey were provided by Austrian students at 23 institutes of higher
education, with the express support of their senior faculty. The survey
attracted 4548 responses, representing a response rate of 4.3%. The allo-
cation by field of study, as well as by the level of study, shows a distribu-
tion approximating to the Austrian student population.
When an empirical analysis is based on cross-sectional data collect-
ed with just one method (Lindell and Karagozoglu, 1997), and with the
key variables captured as self-reported continuous values (Harrison
et al., 1996) the threat of common method bias (CMB) cannot be
discounted. CMB refers to false conclusions that result from “variance
that is attributable to the measurement method rather than to the con-
structs the measures represent” (Podsakoff et al., 2003, p. 879, Williams
and Brown, 1994). If methodical triangulation is impossible, Podsakoff
et al. (2003) suggest a variety of measures to identify and correct
CMB. However, according to Spector (2006) and Richardson et al.
(2009), the suggested measures to protect studies from CMB are unreli-
able and often misleading. Thus, this study focuses on strategies that
help to avoid CMB in the first place. To reduce evaluation apprehension,
we assured that their input would be anonymous (Podsakoff et al.,
174 D. Maresch et al. / Technological Forecasting & Social Change 104 (2016) 172–179