Technological Changes and Their Effects on Students: COMP640 Project

This capstone project, submitted by Utsav Ban and Sachin Kumar for COMP640 – Forecasting and Management Technology at the University of the Potomac, investigates the impact of technological changes on students. The research addresses the problem of technology overuse and its increasing dependency in modern life, examining how technology influences students' learning. The project includes an introduction outlining the problem, research questions, hypotheses, and the study's purpose, significance, and organization. The methodology section details the research approach, while the findings and conclusions sections present the results and their implications. The study aims to provide insights into how technology affects students' cognitive abilities and overall learning experiences, referencing the use of AI and e-learning in the classroom. The project also acknowledges the role of technology in improving the learning ability of students and saving time, while highlighting the potential negative impacts of excessive technology use.

The impact of entrepreneurship education on the entrepreneurial
intention of students in science and engineering versus business studies
university programs
Daniela Marescha, Rainer Harmsb,⁎, Norbert Kailerc, Birgit Wimmer-Wurmc
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 speci fic contexts may require different EE action.
In this paper the authors investigate the context-speci fic questions in two separate categories of students. If c
text is important, we should see different outcomes from similar EE classes provided to different student group
The authors' results suggest that there is a contextual difference. The results indicate that EE modi fied to suit
particular target group could address the issue of subjective norms separately for business students and scien
and engineering students. Their principal results show that EE is generally effective for business students and
ence and engineering students. However, the EI of science and engineering students is actually negatively aff
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 lite
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 shoul
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-analyticalassessment (Schlaegeland
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
entrepreneurialactivities 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 entrepreneurialintention 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 m
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
entrepreneurialintentions” (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 controlof
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 incrementaleffects 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

2003), and we also counterbalanced the question order in the question-
naire (Chang, 2010).
The authors took several measures to avoid nonresponse bias (NRB),
including carefully designing the questionnaire, managing its length,
and establishing the importance of the survey (Yu and Cooper, 1983).
However, since NRB cannot be ruled out in view of the achieved return
rate, we employed archival and wave analysis (Rogelberg and Stanton,
2007). The first approach helps to verify whether externalfactors
prevented the recipient from returning the completed questionnaire
on time (passive NRB), by comparing the characteristics of the sample
with the characteristics of the population (Rogelberg and Stanton,
2007).The second approach looks for active NRB resulting from the
recipient's conscious decision not to respond, by comparing early and
late responses (Rogelberg et al.,2003).Neither of the tests suggests
that NRB is an issue in this dataset.
3.2. Operationalization and method of analysis
Entrepreneurial Intention, as our dependent variable, was measured
with a 7-point Likert scale, anchored with I never thought about founding
(1), and I have already started on the realization (7). Conceptually and
empirically, the measure is based on the entrepreneurial ladder (van
der Zwan et al., 2012). The subsequent analysis excluded responses at
the far end of the realization scale (number 7 on the Likert scale) in
order to exclude actual founders of enterprises (Thompson, 2009).
Attitude was based on Ajzen (1991), and measures the respondent's
attitude toward entrepreneurship. The measure used was a 7-point, 4-
item scale whose single factor explains 81.79% of variance.It has a
Cronbach's alpha (α) of .925.
The measurement of subjective norms used a 7-point scale to capture
opinions on the respondent starting a business, from family, friends, and
people generally important to the respondent (Kolvereid, 1996). The
higher the value, the more positive was the subjective norm supporting
entrepreneurship. Its single factor explains 74.14% of variance. It has a
Cronbach's α of .821.
Perceived behavioral control was measured in accordance with the
construct of the locus of control scale by Levenson (1973). The study
adopts 8-item, 7-point scale aggregated to a formative construct.
While perceived behavioral control focuses on a more specific behavior
(in this case a startup), the locus of control reflects a more general view
on whether a person can actively influence his or her life. The locus of
control is less suitable for predicting a specific behavior (Ajzen, 1991),
but was part of the dataset.
Entrepreneurial education was measured by the number of entrepre-
neurship courses that each student had taken; examples included Busi-
ness Planning,Creativity,Entrepreneurial Marketing,and others.To
differentiate education tracks, we used the self-reported study speciali-
zation. Specifically, we compared students from technical disciplines
(engineering and natural sciences) with students from business studies
(business administration and economics). As control variableswe chose
age and gender.
The descriptive statistics suggest that there are few differences be-
tween science and engineering students and business students. A key
difference is that science and engineering students have a higher degre
of EE (Table 1).
The chosen method of analysis was ordered logistic regression, as th
dependent variable was highly skewed. Group 1 contains those that had
never considered an entrepreneurial career, and group 2 contains those
who had considered entrepreneurship to at least some degree. Within
the ordered logistic regression we took a stepwise approach, in that
we first entered the controls, then the direct relationship that reflects
the impact of the TPB components, and finally the moderators. Moder-
ation is assessed with a two-way interaction of centered variables.
These stepwise analyses were carried out twice, once for science and
engineering students, and once for business students.
4. Findings
The results of the analysis are summarized in Table 2. The R2 values
and the percentage of correctly classified cases indicate a good overall
model fit. The increase in R2 and the percentage of correctly classified
cases from step one to step two, and finally to step three, indicate that
each step contributed to explaining EI.
The control variables suggest that older students have a higher de-
gree of EI. Female students, however, have a lower degree of EI. These
findings show that the inclusion of the controls was warranted.
Pro-entrepreneurial attitudes are in all cases positively related to EI.
This is in line with previous findings. Subjective norms are negatively
related to EI for science and engineering students, and significant for
the whole group.This finding contrasts with previous findings.Per-
ceived behavioral control is positively related to EI for the full sample,
but there is no significant relation for science and engineering students,
Fig. 1. Research model.
175D. Maresch et al. / Technological Forecasting & Social Change 104 (2016) 172–179

or for business students. This gives partial support for H1a and support
for H1b.
We find significant positive relations between EE and EIfor all
groups. This lends support to H2a. The coefficient for the business stu-
dent sample is larger than the coefficient for the other groups. This find-
ing supports H2b.
Only one finding is significant with regard to the moderated rela-
tionship between EE and TPB components: the greater the extent of
EE, the weaker the positive impact of subjective norms on EI.This
lends support to hypothesis H3b (Table 3).
5. Discussion and conclusion
The goal of this study was to analyze two sets of conditions under
which EE may be most effective for enhancing EI.We analyzed the
role of motivational drivers and type of prior education. We found gen-
eral support for a positive effect of EE on EI. Further, we found mixed
Table 1
Operationalization.
Full sample Science and
engineering
students
Business
students
Diff.
FL Mean SD Mean SD Mean SD
Control variables
Age 28.45 1.43 28.01 5.02 27.99 5.73
Gender (0 = male; 1 = female) 0.64 .48 .30 .46 .60 .49 ***
Independent variables
Attitude (Cronbach's alpha = .925; % var. expl.: 81.79%)
Being an entrepreneur suggests more advantages than disadvantages to me. .831 3.98 1.58 4.16 1.47 4.28 1.56
A career as entrepreneur is attractive for me. .936 4.08 1.85 4.39 1.69 4.53 1.81 #
If I had the opportunity and resources, I would become an entrepreneur. .919 4.40 1.92 4.67 1.77 4.82 1.85
Being an entrepreneur would bring me great satisfaction. .927 3.83 1.90 4.00 1.83 4.33 1.86 **
Subjective norms (Cronbach's alpha = .884; % var. expl.: 81.75%)
If I became an entrepreneur, my … would react:
Parents and other family members .832 5.26 1.36 5.39 1.30 5.42 1.34
Friends and other students .880 5.39 1.19 5.51 1.11 5.57 1.12
Other persons important to me .864 5.40 1.18 5.46 1.12 5.56 1.13 #
Perceived behavioral control (formative)
When I get what I want, it is usually because I am lucky. (r) 4.78 1.45 4.09 1.42 4.87 1.41
I have often found that what will be, will be. (r) 3.40 1.54 3.51 1.54 3.40 1.52
It is not always wise for me to plan too far ahead because many things turn out to be a matter of good or bad fortune. (r)4.78 1.57 4.80 1.54 4.82 1.46
My life is chiefly controlled by powerful others. (r) 5.59 1.42 5.56 1.47 5.65 1.35
I feel like what happens in my life is mostly determined by powerful people. (r) 5.77 1.38 5.75 1.42 5.75 1.34
In order to make my plans work, I make sure that they fit in with the desires of people who have power over me. 5.81 1.37 5.70 1.45 5.76 1.35
I am usually able to protect my personal interests. 5.24 1.19 5.34 1.20 5.30 1.13
I can pretty much determine what will happen in my life. 5.77 1.18 5.75 1.24 5.82 1.13
Moderator variables
Entrepreneurship Education (single item) 1.33 2.43 1.33 2.30 0.95 1.68 ***
Dependent variable
Entrepreneurial Intention (single item)
Please indicate if and how seriously you have thought about founding your own company. 1.66 1.56 1.87 1.63 1.82 1.51
(r) recoded; FL: factor loading; SD: standard deviation.
⁎ p b .05.
⁎⁎ p b .01.
# p b .1.
Table 2
Correlations.
(1) (2) (3) (4) (5) (6)
(1) Age
(2) Gender −.144⁎⁎
(3) Attitudes .027# −.172⁎⁎
(4) Subj. norms −.094⁎⁎ −.011 .490⁎⁎
(5) PBC .074⁎⁎ −.069⁎⁎ .051⁎⁎ .092⁎⁎
(6) EE −.022 .031⁎ .023 .017 −.013
(7) EI .124⁎⁎ −.136⁎⁎ .517⁎⁎ .252⁎⁎ .086⁎⁎ .080⁎⁎
⁎⁎ p b .01.
⁎ p b .05.
# p b .1.
Table 3
Results of the moderated regression.
Full sample
n = 3581
Science and
engineering
students
n = 635
Business
students
n = 859
B Sig B Sig B Sig
Step 1
Constant −.707 .000 −.385 .465 −.098 .805
Age .037 .000 .029 .117 .028 .038
Gender −.482 .000 −.527 .007 −.758 .000
C&S-R2; Nagelk. R2; % corr. class..029; .039;
58.4%
.020; .027;
59.4%
.042; .056;
60.1%
Step 2
Constant −.1.474 .000 −.971 .108 −1.517 .002
Age .049 .000 .035 .091 .057 .001
Gender −.331 .000 −.206 .352 −.739 .000
Attitudes .223 .000 .209 .000 .232 .000
Subjective norms .105 .487 −.335 .009 .084 .423
Perceived behavioral control .013 .011 −.001 .959 .000 .002
C&S-R2; Nagelk. R2; % corr. class..281; .374;
74.2%
.199; .267;
70.2%
.291; .390;
75.2%
Step 3
Constant −1.480 .000 −.864 .154 −1.421 .004
Age .050 .000 .032 .125 .055 .001
Gender −.351 .000 −.268 .233 −.759 .000
Attitudes .225 .000 .210 .000 .234 .000
Subjective norms .104 .035 −.322 .012 .058 .590
Perceived behavioral control .014 .014 −.003 .839 .000 .997
EE .098 .000 .091 .048 .124 .024
EE ∗ Attitudes .000 .936 −.006 .527 .008 .459
EE ∗ Subj. norms .001 .958 .058 .281 −.131 .047
EE ∗ PBC .001 .502 .006 .324 .000 .968
C&S-R2; Nagelk. R2; % corr. class..287; .382;
75.1%
.209; .280;
70.4%
.299; .400;
76.5%
176 D. Maresch et al. / Technological Forecasting & Social Change 104 (2016) 172–179

evidence with regard to different conditions,such as motivational
drivers and type of prior education.
EE seems to positively affect EI when controlled for age, gender, and
motivational drivers. This finding is in line with theory and previous
findings (Souitaris et al., 2007; Kolvereid and Moen, 1997), and under-
scores the importance of EE for educators and policy makers seeking
to enhance EI. We note two issues, as follows.
First, the coefficients for EE—while positive and significant in all
cases—are rather low. This may indicate that the didactics of EE could
be improved. The search for the most effective didactic forms for EE is on-
going. The current situation is marked by little consensus on the type of
didactics necessary to deliver the most effective EE (Martin et al., 2013),
and by new emerging forms of EE (Xanthopoulou and Papagiannidis,
2012; Harms, 2015). Second, our findings indicate that business students
may profit more from EE, as indicated by the larger coefficient. This lends
support to the “Matthew effect” thesis in EE (Walberg and Tsai, 1983).
This thesis postulates a positive impact of prior educational background,
current education, and motivation on academic achievements. Students
who have previously received a business education are therefore more
likely to acquire and process knowledge related to entrepreneurship.
If it is recent engineering graduates whose entrepreneurial activities
create new, high-quality firms (Åstebro et al., 2012), then our findings
give cause for concern. While the level of EE for science and engineering
students is significantly higher than for business students, in absolute
terms it is still quite low. It also seems that current EE is less effective
in raising their level of EI, potentially based on the “Matthew effect” in
education. As extending the time commitment for EE is not often an op-
tion, we suggest that educators should investigate whether they can
create EE didactics that tap into the cognitive schemata of science and
engineering students.One promising contender may be the Lean-
Startup based classes (Harms, 2015; Harms et al., 2015), as this didactic
approach draws heavily on the empirical circle (Ries, 2011) that all sci-
ence and engineering students should be familiar with. The same ap-
proach also builds on the design approach that science and
engineering students ought to be familiar with (Mueller and Thoring,
2012).
The effectiveness of EE does not seem to be affected by most TPB as-
pects, as we only find one moderating relationship. Hence, we find that
EE is also effective for students with a TPB set that is initially unfavorable
to EI. This is in line with the findings of Rauch and Hulsink (2015), who
showed that EE may change TPB aspects in the course of education, as it
positively affects attitudes and perceived behavioral control. However,
we did not learn much about the conditions under which EE is more ef-
fective. Bae et al. (2014)examined other likely moderators of the EE–EI
relationship—such as the specificity of the education, its duration, and
the gender, family background, and culture of the students—and found
that only supportive cultural contexts positively affected the EE–EI
relationship.
One moderation was significant: that of EE and subjective norms for
business students. We hypothesized that the greater the extent of EE,
the weaker the positive impact of subjective norms on EI would be.
We expect that the role models presented in EE education may actually
outweigh the impact of the subjective norms affecting students. This
would account for the negative coefficient.
While not central to this study, the results of the analysis of the di-
rect effects of TPB drivers warrant discussion. While the positive rela-
tionship between pro-entrepreneurial attitudes and EI was expected,
three findings stand out. First, the negative impact of subjective norms
on the EI of science and engineering students warrants explanation.
The more strongly their peers value entrepreneurship, the more deter-
minedly science and engineering students reject entrepreneurship.
The phenomenon might be explained by social identity theory
(Obschonka et al., 2012), and by the notion that science and engineering
students construct a social identity for themselves that is science-driven
and not necessarily entrepreneurial (Jungert, 2013). Group members
can react defensively to threats to their social identity (Branscombe
et al., 1999) in that they resist “perceived group differences in values,
beliefs, and attitudes” (de Hoog, 2013, p. 362). Hence, science and eng
neering students may react adversely to social pressure in favor of en-
trepreneurialism, even when they take courses in entrepreneurship. It
follows that educators should strive to counter the threat posed by so-
cial identity, perhaps by including teaching on how entrepreneurialism
is central to the identity of science and engineering students, for exam-
ple by highlighting successful engineer-entrepreneur role models (Sun
and Lo, 2012).
Second, the missing connection between “subjective norms” and EI
for the full sample may be the source of methodological artifacts. This
might be a result of balancing the positive and negative effects of sub-
samples (e.g.,science and engineering students versus business stu-
dents). Alternatively, it might be the result of a confounding effect: as
subjective norms, attitudes, and EI are positively correlated, part of the
effect of subjective norms on EI may be masked (for a similar observa-
tion see Schlaegel and Koenig, 2014). Third, the absence of the expecte
impact of “perceived behavioral control” could be explained by the fact
that the items available to the research reflected a general locus of inte
nal control, rather than a domain-specific construct. Using a more gen-
eral measure tends to reduce predictive power (Chen et al.,1998).
Domain-specific alternatives for future studies could be entrepreneurial
self-efficacy (Chen et al., 1998) or a domain-specific locus of control
scale (Schjoedt and Shaver, 2012).
The findings of this study must be viewed in light of its limitations.
First, as we use a cross-sectional design, the temporal nature of cause-
and-effect cannot be incorporated in the models. We suggest pre- and
post-test designs on the antecedents, processes, and effects of EE on EI
(Rauch and Hulsink, 2015). Second, the effectiveness of EE is highly de-
pendent on the particular didactics that are used. By pooling data from
23 universities, with an even larger variety of entrepreneurship courses
we were able to show a general trend. However, the effect of particular
dactics on EI merits further inquiry. Third, although the tests implement
ed did not indicate any issues arising from the response rate, it was rath
low, and we cannot completely discount the threat that the respondents
may have self-selected into the survey as well as into EE. Fourth, we ne
to point out the time lag between the formation of EI and its translation
into entrepreneurial action. Although recent engineering graduates have
been shown to create high-quality new firms (Åstebro et al., 2012), the
average age of the founders is in the mid-thirties. This creates a conside
able time gap, and we have yet to see if the EI of students translates int
higher startup rates among more mature adults.
Finally, when assessing the effectiveness of EE, the intention–action
gap in entrepreneurship has to be taken into account. A recent longitu-
dinal study in the same geographic context, also relying on the theoret-
ical framework of the TPB, showed that within a one-year time frame
only about 30% of intenders took steps toward entrepreneurship
(Kautonen et al., 2015). In another study, the same authors identified
action fear, action uncertainty, and competing interests as the main bar
riers against turning EI into entrepreneurial action (van Gelderen et al.,
2013). These volitional factors can be addressed by EE.
The findings and limitations of the current research present a num-
ber of promising opportunities for future research. While highlighting
the general effectiveness of EE,the findings also reveal the need for
didactic approaches in EE to be tailored to the specific needs of distinct
groups of students. However, educators could only develop such target-
group specific didactics in EE if they had a profound understanding of
the challenges and barriers these specific target groups face in develop
ing EI, and also of the issues involved in translating them into entrepre-
neurial action. Progressing to that level of understanding would require
far more research to be conducted, particularly in a form based on lon-
gitudinal studies tracking students for a considerable time beyond the
end of their formal EE. Research on entrepreneurship in later phases
of life shows that general education has a long-term impact on entre-
preneurship (Hatak et al.,2013,2015; Harms et al.,2014).Thus,EE
might also be expected to show such long-term effects.
177D. Maresch et al. / Technological Forecasting & Social Change 104 (2016) 172–179

The results presented in this paper offer some justification for the
importance many universities attach to EE. The findings suggest that
EE is generally effective for both business students and science and en-
gineering students. However, differences between business students
and science and engineering students are evident with regard to the im-
pact of subjective norms on EI: while subjective norms have a negative
impact on science and engineering students' EI, this effect is not present
in the business student sample. This result implies that EE should be
target-group specific and thus address the issue of subjective norms
separately for business students and science and engineering students.
References
Ajzen, I., 1991. The theory of planned behaviour. Organ. Behav. Hum. Decis. Process. 50,
179–211.
Åstebro, T., Bazzazian,N., Braguinsky, S.,2012.Startups by recent university graduates
and their faculty: implications for university entrepreneurship policy.Res.Policy
41, 663–677.
Autio, E., Keeley, R.H., Klofsten, M., Ulfstedt, T., 1997. Entrepreneurial intent among stu-
dents: testing an intent modelin Asia, Scandinavia,and USA. In: Sexton, D.L.,
Kasarda,J.D. (Eds.),Frontiers of Entrepreneurial Research.Babson College Publica-
tions, Babson, MA.
Autio, E.,Keeley, R.H.,Klofsten, M., Parker,G.G.C., Hay, M.,2001. Entrepreneurial intent
among students in Scandinavia and in the USA.Enterp.Innov. Manag. Stud. 2,
145–160.
Bae, T.J., Qian, S., Miao, C., Fiet, J.O., 2014. The relationship between entrepreneurship ed-
ucation and entrepreneurial intentions: a meta-analytic review. Enterp. Theory Pract.
38, 217–254.
Bandura, A., 1986. Social Foundations of Thought and Action: A Social Cognitive Theory.
Prentice-Hall, Englewood Cliffs.
Becker, G.S., 1964. Human Capital: A Theoretical and Empirical Analysis, with Special Ref-
erence to Education. National Bureau of Economic Research, New York, N.Y.
Bjornali, E.S., Støren, L.A., 2012. Examining competence factors that encourage innovative
behaviour by European higher education graduate professionals. J. Small Bus. Enterp.
Dev. 19, 402–423.
Branscombe, N., Ellemers, N., Spears, R., Doosje, B., 1999. The context and content of social
identity threat. In: Ellemers, N., Spears, R., Doosje, B. (Eds.), Social Identity: Context,
Commitment, Content. Blackwell, Oxford, UK, pp. 35–58.
Cantillon, R., 1931. Essai sur la nature du commerce en général, ed. and with an English
translation by Henry Higgs for the Royal Economic Society. Macmillan & Co. London.
Chang, S.-J., 2010. A.v. Witteloostuijn, L. Eden, From the editors: common method vari-
ance in international business research. J. Int. Bus. Stud. 41, 178–184.
Chen, Y., 2010. Does entrepreneurship education matter to students' entrepreneurial in-
tention? A Chinese perspective. Second International Conference on Information Sci-
ence and Engineering, China.
Chen, C.C., Greene, P.G., Crick, A., 1998. Does entrepreneurial self-efficacy distinguish en-
trepreneurs from managers? J. Bus. Ventur. 13, 295–316.
Cotrugli, B., 1990. Il Libro Dell'Arte di Mercatura, a Cura di Ugo Tucci, Arsenale.
Davidsson, P., 2008. The Entrepreneurship Research Challenge.Edward Elgar,
Cheltenham.
de Hoog, N., 2013. Processing of social identity threats: a defense motivation perspective.
Soc. Psychol. 44, 361–372.
Dyer Jr., W.G., 1994. Toward a theory of entrepreneurial careers. Enterp. Theory Pract. 19,
7–21.
Fayolle, A., Linan, F., 2014. The future of research on entrepreneurial intentions. J. Bus. Res.
67, 663–666.
Fink, M., Lang, R., Harms, R., 2012. Local responses to global technological change — con-
trasting restructuring practices in two rural communities in Austria. Technol. Fore-
cast. Soc. Chang. 80, 243–252.
Frederick, W., Walberg, J., 1980. Learning as a function of time. J. Educ. Res. 73.
Gorman, G., Hanlon, D., King, W., 1997. Some research perspectives on entrepreneurship
education,enterprise education,and education for small business management: a
ten-year literature review. Int. Small Bus. J. 15, 56–77.
Grichnik, D., Harms, R., 2007. Zur Zukunft der deutschsprachigen
Entrepreneurshipforschung - Strategien und thematische Schwerpunkte.Zeitschrift
für Klein- und Mittelunternehmen und Entrepreneurship 55, 266–275.
Harms, R., 2015. Self-regulated learning, team learning and project performance in entre-
preneurship education: learning in a lean startup environment.Technol.Forecast.
Soc. Chang. 100, 21–28.
Harms, R., Wdowiak, M., Schwarz, E.J., 2010. The role of entrepreneurship- and technolo-
gy transfer oriented initiatives in improving the innovation system in the Western
Balkans. Int. J. Bus. and Globalization 4, 18–34.
Harms, R., Kraus, S., Luck, F., Walsh, S.T., 2014. On the motivational drivers of gray entre-
preneurship: an exploratory study. Technol. Forecast. Soc. Chang. 89, 358–365.
Harms, R., Marinakis, Y., Walsh, S.T., 2015. Lean Startup for materials ventures and other
science-based ventures: Under what conditions is it useful? Trans.Mater.Res.2.
http://dx.doi.org/10.1088/2053-1613/2/3/035001.
Harrison,D.A.,McLoughlin,M.E.,Coalter,T.M.,1996.Context,cognition and common
methode variance: psychometric and verbal protocol evidence. Organ. Behav. Hum.
Decis. Process. 68, 246–261.
Hatak, I., Kautonen, T., Fink, M., 2013. Senior-Unternehmertum: Empirische Evidenz aus
27 europäischen Ländern. Die Betriebswirtschaft 73, 7–26.
Hatak, I., Harms, R., Fink, M., 2015. Age, job identification, and entrepreneurial intention.
J. Manag. Psychol. 30.
Heitor, M., Horta, H., Mendonça, J., 2014. Developing human capital and research capaci-
ty: science policies promoting brain gain.TechnologicalForecasting and Social
Change 82, 6–22.
Ho, Y.-P.,Low, P.-C., Wong,P.-K., 2014. Do university entrepreneurship programs influ-
ence students' entrepreneurial behavior? An empirical analysis of university students
in Singapore. Advances in the Study of Entrepreneurship, Innovation, and Economic
Growth 24, 65–87.
Jungert, T., 2013. Social identities among engineering students and through their transi-
tion to work: a longitudinal study. Stud. High. Educ. 38, 39–52.
Kahneman, D.,Tversky,A.,1979.Prospect Theory: An Analysis of Decision Under Risk,
Econometrica, XL. pp. 263–291.
Kailer, N., Gruber-Mücke, T., Wimmer-Wurm, B., Blanka, C., 2012. Entrepreneurial Intentions
and Activities of Students at Austria Universities — Global University Entrepreneurial
Spirit Student's Survey 2011 — National Report. Johannes Kepler University Linz.
Kautonen, T., van Gelderen, M., Fink, M., 2015. Robustness of the theory of planned behav-
iour in predicting entrepreneurial intentions and action.Enterp.Theory Pract.39,
655–674.
Kirchhoff, B.A., 1994. Entrepreneurship and dynamic capitalism. The Economics of Busi-
ness Firm Formation and Growth, Praeger, Westport, Conn.,.
Kirchhoff, B.A., Linton, J.D., Walsh, S.T.,2013. Neo-Marshallian equilibrium versus
Schumpeterian creative destruction: its impact on business research and economic
policy. J. Small Bus. Manag. 51, 159–166.
Kolvereid,L.,1996.Prediction of employment status choice intentions.Enterp.Theory
Pract. 21, 47–57.
Kolvereid, L., Moen, O., 1997. Entrepreneurship among business graduates: does a major
in entrepreneurship make a difference? J. Eur. Ind. Train. 21, 154–160.
Krueger, N.F., Brazeal, D.V., 1994. Entrepreneurial potential and potential entrepreneurs.
Enterp. Theory Pract. 18, 91–104.
Krueger,N.F.,Carsrud,A.L.,1993.Entrepreneurial intentions: applying the theory of
planned behaviour. Entrep. Reg. Dev. 10, 315–330.
Krueger, N.F., Reilly, M.D., Carsrud, A.L., 2000. Competing models of entrepreneurial inten-
tions. J. Bus. Ventur. 15, 411–432.
Kuckertz, A., Wagner, M., 2010. The influence of sustainability orientation on entrepre-
neurial intentions — investigating the role of business experience. J. Bus. Ventur. 25,
524–539.
Kuehn,K.W., 2008. Entrepreneurial intentions research: implications for entrepreneur-
ship education. J. Entrep. Educ. 11, 87–98.
Kuratko, D.F., 2005. The emergence of entrepreneurship education: development, trends,
and challenges. Enterp. Theory Pract. 29, 577–597.
Levenson, H., 1973. Reliability and validity of the I, P, and C scales — a multidimensional
view of locus of control. American Psychological Association Convention, Montreal.
Liñán, F., 2004. Intention-based models of entrepreneurship education. Piccola Impresa/
Small Bus. 3, 11–35.
Lindell, M., Karagozoglu, N., 1997. Global strategies of US and Scandinavian R&D-intensive
small- and medium-sized companies. Eur. Manag. J. 15, 92–100.
Lüthje, C., Franke, N., 2003. The “making” of an entrepreneur: testing a model of entrepre-
neurial intent among engineering students at MIT. R&D Manag. 32, 135–147.
Martin, B.C., McNally, J.J., Kay, M.J., 2013. Examining the formation of human capital in en-
trepreneurship: a meta-analysis of entrepreneurship education outcomes.J. Bus.
Ventur. 28, 211–224.
Mueller, R.M., Thoring, K.,2012. Design thinking vs. lean start up: a comparison of two
user-driven innovation strategies. International Design Management Research Con-
ference, Boston, MA.
Obschonka, M., Goethner, M., Silbereisen, R.K., Cantner, U., 2012. Social identity and the
transition to entrepreneurship: the role of group identification with workplace
peers. J. Vocat. Behav. 80, 137–147.
Phan, P., Siegel, D., Wright, M., 2009. New developments in technology management ed-
ucation: background issues, program initiatives, and a research agenda. Acad. Manag.
Learn. Educ. 8, 324–336.
Podsakoff, P.M., MacKenzie, S.B., Podsakoff, N.P., Lee, J.-Y., 2003. Common method biases
in behavioral research: a critical review of the literature and recommended remedies.
J. Appl. Psychol. 88, 879–903.
Pruett, M., Shinnar, R., Toney, B., Llopis, F., Fox, J., 2009. Explaining entrepreneurial intentio
of university students: a cross-cultural study. Int. J. Entrep. Behav. Res. 15, 571–594.
Rauch, A.J., Hulsink, W., 2015. Putting entrepreneurship education where the intention to
act lies. An Investigation into the Impact of Entrepreneurship Education on Entrepre-
neurial Behaviour, Academy of Management Learning & Education http://dx.doi.org/
10.5465/amle.2012.0293.
Rauch, A., Frese, M.,Utsch, A., 2005. Effects of human capital and long-term human re-
sources development and utilization on employment growth of small-scale busi-
nesses: a causal analysis. Enterp. Theory Pract. 29, 681–698.
Richardson, H., Simmering, M., Sturman, M., 2009.A tale of three perspectives: examining
post hoc statistical techniques for detection and correction of common method vari-
ance. Organ. Res. Methods 12, 762–800.
Ries, E., 2011. The Lean Startup. Crown Publishing, New York.
Robinson, P.B., Stimpson, D.V., Huefner, J.C., Hunt, H.K., 1991. An attitude approach to the
prediction of entrepreneurship. Enterp. Theory Pract. 15, 13–31.
Rogelberg, S.G., Stanton, J.M., 2007. Introduction: understanding and dealing with organi-
zational survey nonresponse. Organ. Res. Methods 10, 195–209.
Rogelberg, S.G., Conway, J.M., Sederburg, M.E., Spitzmüller, C., Aziz, S., Knight, W.E., 2003.
Profiling active and passive nonrespondents to an organizational survey.J. Appl.
Psychol. 88, 1104–1114.
Schjoedt, L., Shaver, K.G., 2012. Development and validation of a locus of control scale for
the entrepreneurship domain. Small Bus. Econ. 39, 713–726.
178 D. Maresch et al. / Technological Forecasting & Social Change 104 (2016) 172–179

Schlaegel, C., Koenig, M., 2014. Determinants of entrepreneurial intent: a meta-analytic
test and integration of competing models. Enterp. Theory Pract. 38, 291–332.
Schumpeter,J., 1912.Theorie der Wirtschaftlichen Entwicklung.Duncker & Humblot,
Leipzig.
Segal, G., Borgia, D., Schoenfeld, J., 2005. The motivation to become an entrepreneur. Int.
J. Entrep. Behav. Res. 11, 42–57.
Sieger,P., Fueglistaller,U., Zellweger,T., 2011.International Report GUESSS 2011,in,
GUESSS, St. Gallen,.
Simon, H.A., 1979.Information processing models of cognition. Annu.Rev. Psychol.30,
363–396.
Souitaris, V., Zerbinati, S., Al-Laham, A., 2007. Do entrepreneurship programmes raise en-
trepreneurial intention of science and engineering students? The effect of learning,
inspiration and resources. J. Bus. Ventur. 22, 566–591.
Spector, P.E., 2006. Method variance in organizational research: truth or urban legend?
Organ. Res. Methods 9, 221–232.
Sun, H., Lo, C.C.T., 2012. Impact of role models on the entrepreneurial intentions of engi-
neering students. IEEE International Conference on Teaching, Assessment, and Learn-
ing for Engineering, Hong Kong.
Swan, W., Chang-Schneider, C.,McClarity, K., 2007. Do people's self-views matter? Am.
Psychol. 62, 84–94.
Thompson,E.R., 2009.Individual entrepreneurial intention: construct clarification and
development of an internationally reliable metric. Enterp. Theory Pract. 33, 669–694.
Unger, J., Rauch, A., Frese, M., Rosenbusch, N., 2011. Human capital and entrepreneurial
success: a meta-analytical review. J. Bus. Ventur. 26, 341–358.
van der Zwan, P., Verheul, I., Thurik, A.R., 2012. The entrepreneurial ladder, gender, and
regional development. Small Bus. Econ. 39, 627–643.
Van Gelderen, M., Jansen, P., 2008. Autonomy as a start-up motive. J. Small Bus. Enterp.
Dev. 13, 23–32.
van Gelderen, M.,Kautonen, T., Fink,M., 2013. The moderating role of volitional condi-
tions and trait self-control on the entrepreneurial intention–action relationship.
Babson College Entrepreneurship Research Conference, Lyon.
Walberg,H.J., Tsai, S.-L.,1983.‘Matthew’ effects in education.Am. Educ. Res.J. 20,
359–373.
Williams, L.J., Brown, B.K., 1994. Method variance in human resource research: effects on
correlations, path coefficients, and hypothesis testing. Organ. Behav. Hum. Decis. Pro-
cess. 57, 185–209.
Xanthopoulou, D., Papagiannidis, S., 2012. Play online, work better? Examining the spill-
over of active learning and transformational leadership. Technol. Forecast. Soc. Chang.
79, 1328–1339.
Yanez, M., Khalil, T.M., Walsh, S.T., 2010. IAMOT and education: defining a technology and
innovation management (TIM) body-of-knowledge (BoK) for graduate education
(TIM BoK). Technovation 30, 389–400.
Yu, J., Cooper, H., 1983. A quantitative review of research design effects on response rate
to questionnaires. J. Mark. Res. 20, 36–44.
Dr. Daniela Maresch is Assistant Professor at the Institute for Innovation Management
(IFI) at the Johannes Kepler University Linz. After her studies in International Business at
WU Vienna University of Economics and Business and at ESC Lyon (France) Daniela gaine
a PhD from WU Vienna and WWU Münster (Germany). Daniela gained practical experi-
ence in financial reporting working for a major Austrian utility. Due to her interest in lega
topics, Daniela completed an LL.M. (WU) in Business Law while working as a Senior Lec-
turer at the Department of Finance, Accounting and Statistics of WU Vienna. Before joinin
the IFI team in March 2014, Daniela worked in corporate law for a renowned Viennese law
firm. Daniela has published in the area of auditing and financial reporting and will now
employ her interdisciplinary expertise in research into topics at the intersection of innova
tion, finance and business law. Her research at the IFI will focus on the role of trust in ban
lending, the social impact of disruptive technologies such as additive manufacturing and
the protection of intellectual property rights in business angel investments.
PD Dr. Rainer Harms is Associate Professor for Entrepreneurship at NIKOS, University of
Twente, where he is heading the research direction of International Entrepreneurship.
He is Associate Editor of Creativity and Innovation Management and Zeitschrift für KMU
und Entrepreneurship. He was visiting professor at the Vienna University of Economics
and Business (Wirtschaftsuniversität Wien), and at the Universitat Autònoma de Barcelo-
na, and held positions at University Klagenfurt (Habilitation) and WWU Münster (Doctor-
ate). His research interests are technology entrepreneurship, firm growth, and innovation
management.
Prof. Dr. Norbert Kailer is Full Professor and Head of the Institute for Entrepreneurship
and Organizational Development at Johannes Kepler University Linz (JKU) and member
of the board of the academic pre-incubator Akostart in Linz. He was professor for HRM
at the Institute for Work Science at the Ruhr-University Bochum. Research interests: de-
velopment of SME, entrepreneurship education, university–business cooperation, and
technology entrepreneurship.
Mag. Birgit Wimmer-Wurm is university assistant at the Institute for Entrepreneurship
and Organizational Development at JKU Linz. Currently she is JKU program manager for
the Federal Program “Knowledge Transfer Centers and Exploitation of IPR — Knowledge
Transfer Centre West”. Research interests: technology and knowledge transfer.
179D. Maresch et al. / Technological Forecasting & Social Change 104 (2016) 172–179