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Q Academy of Management Learning & Education, 2017, Vol. 16,
No. 2, 277–299. https://doi.org/10.5465/amle.2015.0026
........................................................................................................................................................................
The Impact of EntrepreneurshipEducation in Higher Education:
A Systematic Review andResearch Agenda
GHULAM NABIManchester Metropolitan University, United
Kingdom
FRANCISCO LIÑÁNUniversity of Seville, Spain, and Anglia Ruskin
University, United Kingdom
ALAIN FAYOLLEEM LYON Business School, France
NORRIS KRUEGERUniversity of Phoenix, United States, and
Entrepreneurship Northwest, United States
ANDREAS WALMSLEYPlymouth University, United Kingdom
Using a teaching model framework, we systematically review
empirical evidence on theimpact of entrepreneurship education (EE)
in higher education on a range of entrepreneurialoutcomes,
analyzing 159 published articles from 2004 to 2016. The teaching
model frameworkallows us for the first time to start rigorously
examining relationships between pedagogicalmethods and specific
outcomes. Reconfirming past reviews and meta-analyses, we find
thatEE impact research still predominantly focuses on short-term
and subjective outcomemeasures and tends to severely underdescribe
the actual pedagogies being tested. Moreover,we use our review to
provide an up-to-date and empirically rooted call for less obvious,
yetgreatly promising, new or underemphasized directions for future
research on the impact ofuniversity-based entrepreneurship
education. This includes, for example, the use of novelimpact
indicators related to emotion and mind-set, focus on the impact
indicators related tothe intention-to-behavior transition, and
exploring the reasons for some contradictory findingsin impact
studies including person-, context-, and pedagogical model-specific
moderators.
........................................................................................................................................................................
Since the first entrepreneurship course at HarvardBusiness
School was delivered in 1947, entrepre-neurship education (EE)
programs in higher educa-tion have grown rapidly and globally
(Kuratko, 2005;Solomon, 2007). This growth reflects increasing
rec-ognition that university-based EE programs (here-after referred
to as EE programs) promise to supporta range of potential
entrepreneurial outcomes (Nabi
Prof. Fayolle, Prof. Krueger, and Prof. Walmsley made an
equalcontribution to the paper. The authors thank Associate Editor
Prof.Siri Terjesen and the anonymous reviewers for providing
con-structive and helpful guidance throughout the review process.
Wealso thank Prof. Béchard, Prof. Henry, and Prof. Solomon for
theircomments on earlier drafts of this paper, and Dr. Christina
Purcelland Imran Akhtar for their technical support.
Address all correspondence to: Dr. Ghulam Nabi, Departmentof
Management, Business School, All Saints Campus, OxfordRoad,
Manchester, M15 6BH, UK. Email: [email protected]
277
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https://doi.org/10.5465/amle.2015.0026mailto:[email protected]
-
& Liñán, 2011; Rideout & Gray, 2013). For
example,enhanced student venture creation skills, knowl-edge, and
attitudes (Greene & Saridakis, 2008) andgraduatebusiness
start-upsandoverall job creation(Greene, Katz, & Johannisson,
2004; Rideout & Gray,2013) ultimately contributing to economic
growthand development (Bosma, Acs, Autio, Coduras, &Levine,
2008).
Synthesizing this fast-growing body of empiricalresearch and
reviews on EE outcomes suggeststhree main patterns. First, reviews
highlight a focuson short-term, subjective impact measures such
asentrepreneurial attitudes and intentions, ratherthan longer term
ones such as venture creationbehavior and business performance, and
call forfuture research to address this gap (e.g., Garavan&
O’Cinneide, 1994; Henry, Hill & Leitch, 2005;Pittaway &
Cope, 2007). Promoting and implement-ing EE programs entails
substantial investment oftime and resources, so it is critically
important totake stock of what we currently know about therange of
EE outcomes and provide benchmarks forfurther research.
Second, recent reviews suggest that the impact ofEE programs on
attitudes and behavior is equivocalbecause studies suggest both
positive and negativeoutcomes (Dickson,Solomon,&Weaver,
2008;Fayolle,2013; Martin, McNally, & Kay, 2013; Thompson,
Jones-Evans, & Kwong, 2010). These reviews tend to arguethat
the contradictory findings of EE impact studiesmay be due in part
to methodological or statisticalartifacts such as cross-sectional
survey methodologyand lack of control groups; notably, Rideout
andGray’s (2013) review and recent meta-analyticalstudies by Martin
et al., (2013) and Bae, Qian, Miao,and Fiet (2014). However, also
likely are other sub-stantial reasons for the contradictory
findings in EEimpact research that can be teased out with
singlestudies/interventions: for example, the nature andcontext of
pedagogical interventions as well as con-textual factors. In their
extensive 1970–2004 review ofEEresearch,PittawayandCope
(2007)concludethere isa lack of research that directly links
student/graduateentrepreneurial outcomes to different
pedagogicalmethods and call for deeper investigation. Pedagogi-cal
methods may emphasize, for example, “explora-tion, discussion, or
experimentation (e.g., library, webor other interactive searches,
labs, field trips, simula-tions)” (Béchard & Grégoire,
2005:111).
As well as examining a range of EE impact mea-sures, it is
therefore necessary to examine the dif-ferent pedagogical methods
that underpin them, notjust methodological issues. Confusion
regarding the
impact of EE may result from the wide diversity ofpedagogical
methods employed in EE programs(Fretschner & Weber, 2013). This
is further compli-cated by the lack of detail on pedagogical
in-terventions studied (Martin et al., 2013), and the needfor a
stronger, more theory-driven framework forassessing the impact of
such interventions (cf.Baptista & Naia, 2015; Fayolle &
Gailly, 2008;Krueger, 2015; Lackéus, 2015; Neergaard,
Tanggaard,Krueger, & Robinson, 2012). It is therefore important
totakestockofresearchonthepedagogy-entrepreneurialoutcomes link
within a coherent framework.Third, few reviews focus on EE
specifically in
higher education. Notable exceptions are Pittawayand Cope (2007)
and Rideout and Gray (2013), butthe former is limited to data
fromover a decadeagoand the latter focuses on articles until
2010/2011.We cover 100 articles published in the past 5 years,which
have not been covered in previous reviewsof university-based EE
impact (e.g., Rideout &Gray, 2013) or meta-analyses of EE
outcomes ofeducation in general (e.g., Martin et al., 2013).There
is still, therefore, a need for a current reviewthat focuses on EE
pedagogy and outcomes inhigher education.These three distinct yet
related research gaps
form the rationale for this article. Our aim is to re-view
systematically the empirical evidence on theimpact of higher
education-based EE published inthe last decade. Using the teaching
model frame-work outlined below, we focus on assessing therangeof
EEoutcomes in impact studies.A secondaryaim is to examine the
extent of the relationship be-tween the pedagogical methods used
and the spe-cific outcomes achieved. While the former offersa broad
overview of the evidence of EE impact, thelatter explores whether
the mixed results in impactstudies are related to different
pedagogicalmethods. To advance understanding of how to re-search EE
impact, we need both.Webelieve that themain strength of
ourworkhere
is the adoption of an integrated teaching modelframework (Figure
1) to offer a coherent, overarchingtheoretical structure that
covers both a broad rangeof entrepreneurial outcomes and
pedagogicalmethods (Béchard & Grégoire, 2005; Fayolle
&Gailly, 2008). Our teaching model framework in-tegrates a
range of impact measures and peda-gogies. This is particularly
useful here because forthe first timewecannowevaluate not only the
rangeof EE outcomes in higher education impact studies,but also any
patterns that connect specific types ofpedagogical methods and
impact measures. Our
278 JuneAcademy of Management Learning & Education
-
framework therefore permits empirical review witha pedagogical
slant and responds to calls for morerigorous research to explore
reasons for the contra-dictory findings in EE research (cf. Martin
et al.,2013). The teaching model approach provides criti-cal
grounding for researchers and practitioners inthe field of EE.
Conceptual Framework
Pedagogical research highlights how the evalua-tion of impact
should be a key dimension of anyteaching program and therefore
needs to be con-sidered at the program design stage (Fayolle
&Gailly, 2008). In our research, types of EE impacthave been
integrated into the broader context ofa teaching model framework
(Béchard & Grégoire,2005, 2007; Fayolle & Gailly, 2008).
We explore twodimensions in our review—types of impact and
un-derpinning pedagogy—given the paucity of researchthat directly
links student/graduate entrepreneurialoutcomes
todifferentpedagogicalmethods (Pittaway& Cope, 2007).
In the absence of a single impact measure withinthe teaching
model framework, Henry, Hill, andLeitch (2003, building on Jack
& Anderson, 1998)propose an impact classification system
(incor-porating several types of impact measures) that canbe
employed to assess the level of impact of EEprograms. This
classification system draws on ear-lier research on
entrepreneurship (Block & Stumpf,1992) andeducational impact
(Kirkpatrick, 1959), andcomplements the impact dimension of the
teachingmodel framework because it highlights a range
ofimpactmeasures from thebeginning to the endof anEE program and
beyond (see Figure 1 for a more
detailed explanation), thereby providing a basis forthe
systematic evaluation of EE impact studies.Reflection on different
types of EE impact mea-
sures raises the issue of underpinning pedagogicalmethods.
Béchard and Grégoire (2005) address thisissue through identifying
three “archetypical”teaching models in higher education: the
supplymodel, the demand model, and the competencemodel, plus two
hybrid teaching models. The sup-ply model focuses on pedagogical
methods high-lighting a behaviorist paradigm, in terms of
the“transmission and reproduction of knowledge andapplication of
procedures” (e.g., lectures, reading,watching/listening; Béchard
& Grégoire, 2005: 111).The demand model focuses on
pedagogicalmethods highlighting a subjectivist paradigm,involving
personalized meaning through partici-pation in terms of
“exploration, discussion andexperimentation” (e.g., library use,
interactivesearches, simulations; Béchard & Grégoire,
2005:111). The competencemodel focuses on pedagogicalmethods,
highlighting an interactionist theoreticalparadigm, in terms of
active problem solving in real-life situations, where “teaching is
conceived asa strategic intervention to allow for—and
influen-ce—how students organize the resources at theirdisposal
(e.g., knowledge,abilities) intocompetencesthat can be mobilized
for action” (Béchard &Grégoire, 2005: 115–116). This model
focuses onmethods emphasizing “communication and dis-cussion”
(e.g., seminar, presentations, debates) andknowledge “production”
(e.g., essays, modeling,portfolios).In contrast to the
supplymodel,which emphasizes
a behaviorist perspective, both the demand andcompetence models
fit within the constructivist
Nature of EE Pedagogical Methods (Béchard& Grégoire, 2005;
Fayolle & Gailly, 2008)
• Supply model focusing on reproductionmethods such as lectures,
reading, and soforth.
• Demand model focusing on personalized/participative methods
(e.g., interactivesearches, simulations).
• Competence model focusing oncommunication, discussion, and
productionmethods (e.g., debates, portfolios).
• Hybrid models (i.e., mixture of above).
Impact Indicators (Jack & Anderson, 1998)Operational
Level
• Level 1: Current and on-going measuresduring the program
(e.g., interest andawareness).
• Level 2: Pre- and postprogram measures(e.g., knowledge,
entrepreneurialintentions).
• Level 3: Measures between 0 and 5 yearspostprogram (e.g.,
number and type ofstart-ups).
• Level 4: 3 to 10 years postprogram (e.g.,survival of
start-ups).
• Level 5: 10 years plus postprogram (e.g.,contribution to
society and economy).
FIGURE 1An Integrated Teaching Model Framework Encompassing EE
Impact and Underpinning Pedagogy
2017 279Nabi, Liñán, Fayolle, Krueger, and Walmsley
-
approach to EE (Löbler, 2006; Neergaard et al.,
2012).Behaviorism assumes learning is primarily thepassive transfer
of knowledge from the teacher tothe student, while constructivism
assumes thatlearning involves actively participating in the
con-struction of new understanding. Often, pedagogicalmethods in EE
in higher education are highly be-haviorist: lectures, homework,
quizzes, and so forth,that focus on knowledge acquisition, rather
than thedeeply experiential approaches of the
constructivistperspective (Neergaard et al., 2012). Béchard
andGrégoire (2005) apply these teaching models (sup-ply, demand,
competence) in EE to a higher educa-tion context. This allows us to
classify and analyzevarious pedagogical models and review
empiricalevidence on the link between EE pedagogy andimpact.
Systematic Review Methodology
We analyze 159 EE impact studies published from 1February 2004
to 2 January 2016, continuing wherePittaway and Cope’s (2007) study
left off. Followingbest practice from the methodological
(Tranfield,Denyer,&Smart, 2003), synthesis (Cooper, 1989;
Fink,2009), and entrepreneurship literature (Pittaway &Cope,
2007; Wang & Chugh, 2014), we use a “sys-tematic review
process.” Initially, we use the rootword “education” to search
through all 11 entrepre-neurship journals listed in the Association
of Busi-ness Schools (ABS) as medium- and
high-rankingentrepreneurship journals (Harvey, Kelly, Morris,
&Rowlinson, 2010).1We then use three databases (ABIProQuest,
Emerald, and Science Direct) to searchfor a broader range of
keywords/search terms. Thehighest number of hits were from search
terms in-cluding “entrepreneurship education,” “higher ed-ucation,”
“pedagogy,” “educational interventions,”“graduate,”
“undergraduate,” or Boolean variationsof these terms and an
extensive range of others.
Only article citations that met the following cri-teria were
included: (a) empirical in nature rather
thanpurely conceptual; (b) peer-reviewedpublishedjournal
articles rather than working/conferencepapers or unpublished
material; (c) primarily fo-cused on higher education in terms of
entrepre-neurship education (or elements thereof) and itsempirical
impact on entrepreneurship outcomes(broadly defined to include both
attitudinal andbehavioral outcomes); (d) sampled recipients of
EEfrom higher education institutions (rather thanprimary/secondary
school, or nonhigher educationlevel); and (e) analyzed primary
rather than sec-ondary data (Bae et al., 2014 and Martin et al.,
2013were included because of their use of meta-analysis,but reviews
or research agendas were excluded).We also added searches for
articles from bibli-
ographies, key authors, andGoogle Scholar, aswellas checking
relevant references in recent reviews ofEE outcomes (e.g., Bae et
al., 2014; Martin et al., 2013;Rideout &Gray, 2013).We screened
these additionalcandidates using our selection criteria. For
exam-ple, Martin et al. (2013) includes articles that
areunpublished or focus on schoolchildren, and weretherefore
excluded from our review.2 Two coauthorsindependently read the
original collection of arti-cles. We identified two first-order
themes: (1) Typesof Impact and (2) Pedagogical Methods. We
thenidentified second-order themes by mapping our ar-ticles onto
Henry et al.’s (2003) classification for im-pact measures (Levels 1
to 5) and Béchard andGrégoire’s (2005) framework of pedagogical
models(e.g., supply, demand, and competence). For exam-ple,
traditional lectures and business plan writingsuggested a supply
model, active participation inseminars, events or out-of-class
projects reflecteda demand model, and real-life entrepreneurial
sit-uations indicated a competence model.
REVIEW FINDINGS:THEMES AND TRENDS
Webegin by examining background characteristicsof our articles.
This is useful when interpretinggeneral patterns, for example, the
most prominentjournal outlets, country contexts, and types
ofstudents/graduates. We then analyze our articlesregarding types
of EE impact and relationships be-tween types of impact and
different pedagogicalmethods.
1 The ABS incorporates blind peer-reviewed journals for
rankingentrepreneurship journals and expert assessment of
journalquality (Harveyetal., 2010).Our 11ABS journals include:
JournalofBusiness Venturing, Entrepreneurship Theory and Practice,
Jour-nal of Small Business Management, International Small
BusinessJournal, Small Business Economics, Entrepreneurship and
Re-gional Development, Strategic Entrepreneurship Journal,
FamilyBusiness Review, Journal of Small Business and Enterprise
De-velopment, International Journal of Entrepreneurial Behaviourand
Research, and Venture Capital: An International Journal
ofEntrepreneurial Finance.
2 Further examples of excluded articles (with reasons for
exclu-sion) are available from the authors.
280 JuneAcademy of Management Learning & Education
-
Background Characteristics of the Data Set
Our sample covers research published in 61 jour-nals,
predominantly in entrepreneurship and smallbusiness journals (39%)
and management and edu-cation journals (47%). The eight journals
publishingthe most EE impact articles account for 86 out of the159
articles (54%).3
Overall, the majority of our articles were pub-lished in the
last 5 years and are dominated byEuropean, undergraduate, and
entrepreneurship/business student samples. A majority are from
2011onward (100 articles, 63%) and were not covered inprevious
reviews or meta-analyses (e.g., Martinet al., 2013; Rideout &
Gray, 2013). Data comes from38 countries, dominated by Europe (81
articles, 51%,especially the UKwith 28/18%); US (27/17%); Asia
(26/16%); and then followed by Africa (16/10%); Australia(2/1%);
and international comparisons (5/3%). Stu-dents in our sample
aremostly undergraduate (53%)or postgraduate (12%), or alumni or
unspecifieduniversity students. The majority studied
entrepre-neurship and business (35%) or business combina-tion
courses (24%).
Types of Impact
In the articles reviewed (see Table 1), we distinguishbetween
studies focusing largely on our frame-work’s (see Figure 1) lower
level impact indicators(typically short-term/subjective indicators
at Levels1 and 2) and on higher level ones (typically
longerterm/objective indicators at Level 3 or above).
Morespecifically, themost common impact indicators arerelated to
lower level indicators of subjective/personal change: attitude (32
articles), skills andknowledge (34 articles), perceived feasibility
(42 ar-ticles), and entrepreneurial intention (81 articles).
Bycontrast, higher level indicators of longer term, ob-jective, or
socioeconomic impact are much less fre-quent: 21 articles study
start-ups and 8 articlesconsider venture performance, both
typically within10 years of the program. Last, 41 articles report
re-sults not falling into any of these categories. Thesearticles
measure impact in terms of other variables,such as subjective norms
(Souitaris, Zerbinati, &
Al-Laham, 2007), dispositionaloptimism(Crane,2014),or
satisfaction with the EE program (Rae & Woodier-Harris,
2012).Most articles in the review claim a positive link
between an EE programand subjective (e.g., personalchange) or
objective (e.g., business start-up activity)impact indicators (205
instances overall, see Table 1).Regarding lower level impact
indicators, the mostcommon indicator by far is entrepreneurial
intentions(Level 2 in our framework). Most of the reviewed
arti-cles (61 articles out of 81, 75%) report a positive
linkbetween EE and participants’ start-up intentions.Nonetheless,
several studies report mixed, negative,or nonsignificant/ambiguous
results for the link withentrepreneurial intentions (18 articles or
22%, seeTable 1). Of these, some articles suggest that EE re-duces
entrepreneurial intention for certain groups, forexample, male
German students (Packham, Jones,Miller, Pickernell, & Thomas,
2010), female Finishstudents (Joensuu, Viljamaa, Varamäki &
Tornikoski,2013), Greek students (Petridou&Sarri, 2011),
studentswith previous entrepreneurial exposure (Fayolle,Gailly,
& Lassas-Clerc, 2006b), or students withaweaker entrepreneurial
university culture (Wang& Verzat, 2011). Our results suggest we
know con-siderably more about the direct EE-intentions
re-lationship in general than about the moderatingrole of gender
(e.g., Joensuu et al., 2013; Shinnar,Hsu, & Powell, 2014),
culture- (e.g., Bernhofer &Han,2014; Crane, 2014), or
context-specific patterns(e.g., Piperopoulos & Dimov, 2015;
Turker & Selçuk,2009), with only nine studies focusing clearly
onsuch relationships.Further, using a meta-analysis of 73 studies,
Bae
et al. (2014) report a small but significantly
positiveEE–entrepreneurial intentions relationship, but
thatcultural values act as a moderator. For example,a high
collectivistic culture or a low uncertaintyavoidance culture
reinforces the impact of EE. Theyalso report that after controlling
for pre-educationentrepreneurial intentions, the EE-intentions
re-lationship is not significant nor is gender a signifi-cant
moderator. Although their research does notfocus specifically on
the impact of EE in higher ed-ucation (they look at average effects
across all ed-ucation levels), we include them here because
theirfindings provide some indicative evidence.Compared to
entrepreneurial intentions (51%), far
fewer studies exist on the relationship between EEand other
subjective impact indicators (Levels 1 and 2of our framework)
including psychological variablessuch as attitude (20%, e.g.,
Boukamcha, 2015; Chang,Benamraoui, & Rieple, 2014; Vorley
&Williams, 2016);
3 Education1 Training (31 articles), The International Journal
ofManagement Education (12), Journal of Small Business
andEnterprise Development (10), International Journal of
Entre-preneurial Behavior & Research (9), Journal of Small
BusinessManagement (7), International Entrepreneurship and
Manage-ment Journal (6), International Small Business Journal
(6),Academy of Management Learning & Education (5).
2017 281Nabi, Liñán, Fayolle, Krueger, and Walmsley
-
TABL
E1
MainTy
pesof
Impa
ctsin
Impa
ctStud
ies
Person
alch
ange
Busine
ss
Other
(41articles
,26%
)1.Attitud
e(32articles
,20%
)
2a.S
killsan
dkn
owledg
e(34articles
,21
%)
2b.F
easibility
(42articles
,26%
)2c
.Entrepren
eurial
intention
(81articles
,51%
)3.Bu
sine
ssstart-
up(21articles
,13%
)
4/5.
Performan
ce&
Socio-ec
on.
(8articles
,5%)
Bako
tic&Kruzic,
2010
P;Ba
su,201
0P;
Bouk
amch
a,20
15P;
Byab
asha
ija&
Katon
o,20
11P;
Can
zian
ieta
l.,20
15P;
Cha
nget
al.,20
14P;
Fayo
lle&Gailly,
2015
P;Fretsc
hner
&W
eber,201
3P;
Friedrich&Visse
r,20
06P;
Gerba
,201
2P;
Harriset
al.,20
07A;H
enry
etal.,20
04P;
Hietane
n,20
15P;
Idog
ho&Ba
rr,
2011
P;Izqu
ierdo&
Buelen
s,20
11P;
Karlsso
n&Mob
erg,
2013
P;Kas
sean
etal.,20
15P;
Ken
ny,
2015
P;Kirby
&Hum
ayun
,201
3P;
Lane
roet
al.,20
11A;
Liñá
n,20
04P;
Men
toor
&Friedrich,
2007
N;
Pack
ham
etal.,20
10M;P
etrido
u&Sa
rri,
2011
M;P
ittaway
etal.,20
15P;
Shariffe
tal.,20
10P;
Solesv
ik,
2013
P;So
uitariset
al.,20
07A;
Stam
boulis
&Ba
rlas
,201
4P;
Vorley&W
illiam
s,20
16P;
Walter&
Doh
se,201
2P;
Walteret
al.,20
13P
Brink&Mad
sen,
2015
M;B
urrows&
Wragg
,201
3P;
Cha
ng&Rieple,
2013
M;C
hang
etal.,2014
P;Collins
etal.,20
06PI;
DeT
ienn
e&
Cha
ndler,20
04P;
Diaz-Cas
eroet
al.,
2012
PI;
Dom
ingu
inho
s&
Carva
lho,
2009
P;Fa
oite
etal.,20
04N;
Galloway
etal.,
2005
P;Garalis
&Strazd
iene
,200
7P;
Gielnik
etal.,2015
P;Gilbe
rt,2
012P;
Gon
dim
&Mutti,
2011
A;G
undryet
al.,20
14P;
Harms,
2015
P;Hen
ryet
al.,
2004
P;Jone
s&
Jone
s,20
11P;
Kirkw
oodet
al.,
2014
P;Klapp
er,
2014
P;La
nset
al.,
2013
A;L
eeet
al.,
2005
P;Martinet
al.,
2013
P;Morriset
al.,
2013
P;Mun
ozet
al.,
2011
P;Ohlan
det
al.,20
04P;
Prem
and
etal.,20
16P;
Tan&
Ng,
2006
P;Th
ursb
yet
al.,20
09P;
Toun
èset
al.,20
14P;
Ulven
blad
etal.,
2013
PI;v
on
Aba
hoet
al.,20
15P;
Arm
strong
,201
4P;
Baraka
teta
l.,20
14P;
Basu
,201
0P;
Bouk
amch
a,20
15P;
Burrow
s&
Wragg
,201
3P;
Byab
asha
ija&
Katon
o,20
11P;
Diaz-Cas
ero
etal.,20
12PI;
Fayo
lle&Gailly,
2015
P;Gerba
,20
12P;
Gielnik
etal.,20
15P;
Gilbe
rt,2
012P;
Harms,20
15P;
Harriset
al.,
2007
A;H
attab,
2014
N;H
eino
nenet
al.,20
11A;
Hen
ryet
al.,20
04P;
Izqu
ierdo&
Buelen
s,20
11P;
Jone
s&Jone
s,20
11P;
Karim
iet
al.,20
16P;
Karlsso
n&
Mob
erg,
2013
P;Kas
sean
etal.,
2015
N;K
irkw
ood
etal.,20
14P;
Lane
roet
al.,20
11P;
Laviolette
etal.,20
12P;
Limaet
al.,20
15N;
Liñá
n,20
04P;
Men
toor
&Friedrich,
2007
N;
New
bold
&
Ahm
edet
al.,20
10N;
Alm
obaireek
&Man
olov
a,20
12P;
Arm
strong
,201
4P;
Aslam
etal.,20
12P;
Azim
&Akb
ar,201
0P;
Bako
tic&
Kruzic,2010
P;Ba
raka
tet
al.,20
14P;
Basu
,201
0P;
Bernho
fer&Han
,201
4P;
Bouk
amch
a,20
15P;
Byab
asha
ija&Katon
o,20
11P;
Can
zian
ieta
l.,20
15P;
Cha
ng&Rieple,
2013
M;
Che
nget
al.,20
09N;
Cod
uras
etal.,20
08P;
Crane
,201
4P;
DeClercqet
al.,20
13P;
DeGeo
rge&
Fayo
lle,
2008
P;Diaz-
Cas
eroet
al.,20
12PI;
Farash
ah,201
3P;
Fayo
lle
&Gailly,20
15M;F
ayolle
etal.,20
06aP;
Fayo
lleet
al.,
2006
bM;F
lorinet
al.,20
07P;
Fran
coet
al.,20
10P;
Friedrich&Visse
r,20
06P;
Gerba
,201
2P;
Gielnik
etal.,20
15P;
Gilbe
rt,201
2P;
Ham
idie
tal.,
2008
P;Hattab,
2014
P;Hen
ryet
al.,
2004
P;Heu
er&Kolve
reid,
2014
P;Hyttiet
al.,20
10A;
Ismaile
tal.,
2009
P;Joen
suuet
al.,20
13N;Jon
eset
al.,20
08P;
Jone
set
al.,
2011
P;Ba
eet
al.,20
14A;
Karim
ieta
l.,20
16P;
Karlsso
n&Mob
erg,
2013
P;Kas
sean
etal.,20
15P;K
eat
etal.,20
11P;
Kirby
&Hum
ayun
,201
3P;
Lane
roet
al.,20
11P;
Laviolette
et
Burrow
s&W
ragg
,20
13P;
Con
nolly
etal.,20
06P;
Dag
hbas
hyan
&Hårsm
an,201
4P;
Dom
ingu
inho
s&
Carva
lho,
2009
P;Don
nellon
etal.,
2014
P;Dutta
etal.,20
10P;
Gielnik
etal.,
2015
P;Gilbe
rt,
2012
P;Hen
ryet
al.,20
04P;
Jans
enet
al.,20
15P;
Karlsso
n&
Mob
erg,
2013
P;La
ngeet
al.,20
14P;
Martinet
al.,
2013
P;McA
lexa
nder
etal.,20
09P;
Pei-Le
e&Che
n-Che
n,20
08P;
Poblete&
Amoros
2013
A;
Prem
andet
al.,
2016
P;Rau
ch&
Hulsink
,2015P;
Støren
,201
4A;
Vince
tt&Fa
rlow
,20
08P;
Wilso
net
al.,20
09P
Alarape
2007
P;Don
nellon
etal.,
2014
P;Gordo
net
al.,20
12P;
Hen
ryet
al.,20
04P;
Lang
eet
al.,20
14P;
Martinet
al.,20
13P;
Matlay20
08P;
Voise
yet
al.,20
06P
Azim
&Akb
ar,201
0P;
Bell,2
015,P;
Burrow
s&W
ragg
,20
13P;
Crane
,201
4P;
Crane
&Mey
er,
2007
P;Cruzet
al.,
2009
P;Don
nellon
etal.,20
14P;
Gilbe
rt,201
2P;
Gordo
net
al.,20
12P;
Groen
ewald
2012
P;Ham
idi
etal.,20
08P;
Harris&Gibso
n,20
08N;H
egarty,
2006
P;Heino
nenet
al.,20
11A;
Hus
sain
etal.,20
10N;K
irby
&Ibrahim,2
011P;
Lack
eus,20
14P;
Lane
roet
al.,20
11P;
Lean
,201
2P;
Li&
Liu,
2011
P;Lo
uren
ço&
Jaya
warna
,201
1PI;
Louren
çoet
al.,20
13PI;M
artin
etal.,20
13P;
Matlay,
2011
P;McC
rea,
2013
P;Millm
anet
al.,
2008
P;Mue
ller
&And
erso
n,20
14P;
New
bold
&Erwin,
2014
P;Ohlan
det
al.,20
04P;
Pittaw
ayet
al.,
2011
P;Pittaw
ayet
al.,20
15P;
(table
continues)
282 JuneAcademy of Management Learning & Education
-
TABL
E1
Con
tinu
ed
Person
alch
ange
Busine
ss
Other
(41articles
,26%
)1.
Attitud
e(32articles
,20%
)
2a.S
killsan
dkn
owledg
e(34articles
,21
%)
2b.F
easibility
(42articles
,26%
)2c
.Entrepren
eurial
intention
(81articles
,51%
)3.Bu
sine
ssstart-
up(21articles
,13%
)
4/5.Pe
rforman
ce&
Socio-ec
on.
(8articles
,5%)
Graev
enitzet
al.,
2010
M;V
orley&
William
s,2016
P;W
atts
&W
ray,
2012
P
Erwin,201
4P;
Pei-
Lee&Che
n-Che
n,20
08P;
Pipe
ropo
ulos
&Dim
ov,2
015P;
Rau
ch&Hulsink
,20
15P;
Saee
det
al.,
2015
P;Sá
nche
z,20
11P;
Shinna
ret
al.,20
14M;
Solesv
ik,201
3P;
Souitariset
al.,20
07A;T
oled
ano&
Urban
o,20
08A;
Wilso
net
al.,20
07P;
Wilso
net
al.,20
09P;
Zainud
din&Rejab
,20
10P;
Zainud
dinet
al.,20
12P
al.,20
12P;
Leeet
al.,20
05P;
Limaet
al.,20
15N;L
iñán
,20
04P;
Martinet
al.,20
13P;
Milleret
al.,20
09P;
Moh
amad
etal.,20
14N;
Moh
amed
etal.,20
12P;
Muo
fhe&du
Toit,2
011P;
New
bold
&Erwin,2
014P;
Pack
ham
etal.,20
10M;
Petridou
&Sa
rri,20
11M;
Pipe
ropo
ulos
&Dim
ov,
2015
P;Rap
osoet
al.,20
08P;
Rau
ch&Hulsink
,201
5P;
Sánc
hez,20
11P;
Shariffe
tal.,20
10P;
Smith&
Beas
ley,
2011
A;S
oles
vik,
2013
P;So
lesv
iket
al.,20
13P;
Solesv
iket
al.,20
14P;
Souitariset
al.,20
07P;
Støren
,201
4P;
Turker
&Se
lcuk
,200
9P;
Varam
äkie
tal.,20
15A;V
onGraev
enitz
etal.,20
10M;W
alter&
Doh
se,201
2P;
Waltere
tal.,
2013
M;W
ang&Verza
t,20
11M;W
esthea
d&
Solesv
ik,201
5M;W
ilso
net
al.,20
09P;
Yag
hmae
ieta
l.,20
15PI;Zainu
ddin
&Rejab
,20
10P;
Zainud
dinet
al.,
2012
P;Zh
anget
al.,20
14P;
Zhao
etal.,20
05P
Prem
andet
al.,20
16P;
Rae
&W
oodier-
Harris,2012
PI;
Sánc
hez,20
11P;
Souitariset
al.,20
07P;
Tan&Ng,
2006
P;Vince
tt&Fa
rlow
,20
08P;
Wee
,200
4P;
Man
&Fa
rquh
arso
n,20
15P;
Woo
dier-H
arris,
2010
PI;Y
usoffe
tal.,
2012
P
P=26
P=25
P=32
P=61
P=19
P=8
P=34
PI=0
PI=3
PI=1
PI=2
PI=0
PI=0
PI=4
M=2
M=3
M=1
M=9
M=0
M=0
M=0
N=1
N=1
N=4
N=5
N=0
N=0
N=2
A=3
A=2
A=4
A=4
A=2
A=0
A=1
Total=
32To
tal=
34To
tal=
42To
tal=
81To
tal=
21To
tal=
8To
tal=
41
Note:In
firstrow
,num
bero
fpap
ers(and
percen
tage
oftotal)indica
ted.
Percen
tage
sroun
dedup
.Som
earticles
cons
ider
morethan
oneim
pact
mea
sure,a
ndare,therefore,
includ
edmorethan
once
inthetable.
Find
ings
:P=po
sitive
;PI=
positive
indirect;M
=mixed
;N=ne
gative
;A=am
bigu
ous/no
tsignifica
nt.
2017 283Nabi, Liñán, Fayolle, Krueger, and Walmsley
-
perceived feasibility (26%, e.g., Rauch&Hulsink, 2015;or
skills and knowledge (21%., e.g., Burrows&Wragg,2013; Premand,
Brodmann, Almeida, Grun, & Barouni,2016). Most studies suggest
a positive link betweenthe program and these variables, but some
articlesreport results that are not significant or negative.These
include, for example, the absence of a signifi-cant link between EE
and entrepreneurial attitudesamongSpanish students (Lanero,
Vázquez,Gutiérrez,&Garcı́a, 2011), andanegative
linkbetweenEEandat-titudes toward entrepreneurship among South
Africanstudents (Mentoor & Friedrich, 2007), or
perceivedentrepreneurial and management skills amongBritish
students (Chang & Rieple, 2013). So again,limited studies
explore the context-specificity ofEE’s impact.
Novel ways of assessing EE impact in higher ed-ucation are
limited. Only four studies explore emo-tion or related approaches
to assessing EE impact.For example, inspiration (not learning)
emerges asthe most important benefit of EE, implying a “changeof
heart” as well as a positive link to entrepreneurialintentions
(Souitaris et al., 2007). A few other studiesalso suggest a
positive EE-outcomes link regardinguncertainty and ambiguity
tolerance (Lackéus, 2014);dispositional optimism (Crane 2014); and
sense ofpsychological ownership (Man & Farquharson,2015).
Similarly, four studies focus on EE impacton intention-to-nascent
start-up activity or entre-preneurial identity. These suggest
either a non-significant impact of EE on nascency (Souitaris et
al.,2007), or a positive link through a dynamic process ofinternal
self-reflection and social engagement(Donnellon, Ollila, &
Middleton, 2014; Lackéus, 2014),and personal development, for
example, a multiplesense of responsibility, independent thinking,
andconnecting to one’s ownand others’ needs (Mueller &Anderson,
2014). Other emotion- or transition-basedindicators are also
completely absent from our re-view. For example, outside of our
review, researchhighlights EE’s role in developing the importance
ofentrepreneurial passion (intense positive emotionand drive, see
Cardon, Wincent, Singh, & Drnovsek,2009), yet it is strikingly
missing from the articles inour review.
Our reviewsuggests 29 instances (corresponding to25 articles,
see Table 1) focusing on objective impactindicators, typically over
a longer timeframe corre-sponding to the higher Levels 3 (0–5
years), 4 (3–10years), or 5 (over 10 years) in our framework.
Becausethese types of studies are limited in our review,
someexamplesaregiven.Suchstudies include thepositiveimpact of
undergraduate (Pei-Lee&Chen-Chen, 2008)
and postgraduate (Dominguinhos & Carvalho, 2009)EE programs
on start-up rates at Level 3 of ourframework. Furthermore, Lange,
Marram, Jawahar,Yong, and Bygrave (2014) provide a notable
exampleof the long-term positive impact of EE on Babsongraduate
performance over a 25-year period, in-cluding a major economic
contribution, for example,1,300 new full-time businesses were
started, withaverage annual revenues of $5.5 million and an
av-erage of 27 employees. Last, using a meta-analyticalapproach
(includingpre-andposteducationdata,N516,657), Martin et al. (2013)
found small but positiverelationships between EE and
entrepreneurial out-comes incorporating nascent behavior, and
start-upand venture performance (e.g., financial success
andpersonal income).AswithBaeetal., (2014), theydonotspecifically
focus on higher education (they look ataverage effect across all
educational levels), but weinclude them here because their findings
providesome indicative evidence. Most of our higher impactstudies
report a positive link between EE and objec-tive indicators, but
one suggestsa relationship that isnot significant. Using a sample
of 2,827 universitygraduates in Norway, Støren (2014) reports
graduateswho have had EE are not more frequently self-employed than
other graduates. Thus, our reviewsuggests high-impact studies are
scarce andneednotshow positive impact.A final finding relates to
the measurement meth-
odology of the articles. Typically, articles use cross-sectional
survey methodology (68%). Nonetheless,some notable exceptions
employ a longitudinaldesign and/or a control group. These generally
dem-onstrate a pattern of positive EE impact for entrepre-neurial
intentions (Souitarisetal., 2007), competencies(Sánchez, 2011),
and start-ups (Karlsson & Moberg,2013). However, even in more
methodologically rigor-ous studies, a few still report a lack of
significant re-sults for entrepreneurial self-efficacy (Souitaris
et al.,2007) or significantly negative impact on entrepre-neurial
attitudes (Mentoor & Friedrich, 2007). Overall,the review
suggests reasonable evidence of positiveEE impact. This holds
especially for entrepreneurialattitudes and intentions (impact
Levels 1 and 2 of ourframework), but even here some examples
demon-strate differential impact depending on context
andthebackgroundofparticipants (Fayolle&Gailly, 2015;Fayolle et
al., 2006b).
Pedagogical Methods Underpinning Impact
Next, we examine the extent of the relationshipbetween the
pedagogical methods used and the
284 JuneAcademy of Management Learning & Education
-
TABL
E2
Ove
rview
ofAlterna
tive
Peda
gogies,
Com
pariso
nStud
ies,
andTy
pesof
Impa
ct
Type
sof
Impa
ctb
1.Attitud
e2a
.Skillsan
dkn
owledg
e2b
.Fea
sibility
2c.E
ntrepren
eurial
intention
3.Bu
sine
ssstart-up
4/5.Pe
rform.
&so
cioe
con.
Other
Type
sof
Teac
hing
Mod
elPe
dago
gya
Supp
lySá
nche
z,20
11P;
Shinna
reta
l.,20
14M
Crane
2014
P;Sá
nche
z20
11P;
Solesv
iket
al.,
2013
P;So
lesv
iket
al.,20
14P
Crane
2014
P;Sá
nche
z20
11P
Supp
ly-
Dem
and
Fretsc
hner
&W
eber,
2013
P;Hen
ryet
al.,20
04P;
Izqu
ierdo&
Buelen
s,20
11P;
Liñá
n,20
04P;
Shariffe
tal.,
2010
P;Stam
boulis
&Ba
rlas
,201
4P
Hen
ryet
al.,20
04P;
Klapp
er,201
4P;
Thursb
yet
al.,
2009
P
Hen
ryet
al.,20
04P;
Izqu
ierdo&
Buelen
s,20
11P;
Liñá
n,20
04P
Ham
idie
tal.,
2008
P;Hen
ryet
al.,20
04P;
Liñá
n,20
04P;
Shariffe
tal.,
2010
P
Hen
ryet
al.,20
04P
Hen
ryet
al.,
2004
PCrane
&Mey
er,200
7P;
Ham
idie
tal.,
2008
P
Dem
and
Bouk
amch
a,20
15P;
Fayo
lle&Gailly,
2015
P;Kirby
&Hum
ayun
,201
3P;
Souitariset
al.,
2007
A
Lans
etal.,20
13A;
Mun
ozet
al.,20
11P;
Prem
andet
al.,
2016
P
Bouk
amch
a,20
15P;
Fayo
lle&Gailly,
2015
P;So
uitaris
etal.,20
07A
Bouk
amch
a,20
15P;
Fayo
lleet
al.,
2006aP;
Fayo
lle&
Gailly,
2015
M;
Kirby
&Hum
ayun
,20
13P;
Millere
tal.,
2009
P;So
uitaris
etal.,20
07P;
Varam
äkie
tal.,
2015
A
McA
lexa
nder
etal.,
2009
P;Prem
and
etal.,20
16P
Bell,2015P;
Millm
anet
al.,20
08P;
Mue
ller
&And
erso
n,20
14P;
Pittaw
ayet
al.,
2011
P;Prem
and
etal.,20
16P;
Souitariset
al.,
2007
P
Dem
and-
Com
pet.
Friedrich&Visse
r,20
06P;
Harris
etal.,20
07A;
Hietane
n,20
15P;
Kas
sean
net
al.,
2015
P;Ken
ny,201
5P;
Vorley&
William
s,20
16P
Burrow
s&W
ragg
,20
13P;
Cha
ng&
Rieple,
2013
M;
DeT
ienn
e&
Cha
ndler,20
04P;
Garalis
&Strazd
iene
,200
7P;
Gon
dim
&Mutti,
2011
A;H
arms,
2015
P;Jone
s&
Jone
s,20
11P;
Kirkw
oodet
al.,
2014
P;Morris
etal.,20
13P;
Toun
èset
al.,2014
PVorley&
William
s,2016
P
Aba
hoet
al.,20
15P;
Arm
strong
,201
4P;
Burrow
s&W
ragg
,20
13P;
Harms,
2015
P;Harris
etal.,20
07A;Jon
es&Jone
s,20
11P;
Kas
sean
etal.,
2015
N;K
irkw
ood
etal.,20
14P;
Pipe
ropo
ulos
&Dim
ov,2
015P;
Rau
ch&Hulsink
,20
15P
Arm
strong
,201
4P;
Cha
ng&Rieple,
2013
M;D
eGeo
rge
&Fa
yolle,
2008
P;Florin
etal.,20
07P;
Friedrich&Visse
r,20
06P;
Kas
sean
etal.,20
15P;
Pipe
ropo
ulos
&Dim
ov,2
015P;
Rau
ch&Hulsink
,20
15P
Burrow
s&W
ragg
,20
13P;
Dutta
etal.,
2010
P;Jans
enet
al.,20
15P;
Rau
ch&Hulsink
,20
15P
Burrow
s&W
ragg
,20
13P;
Man
&Fa
rquh
arso
n,20
15P;
Tang
&Ng,
2006
P;W
ee,200
4P
(table
continues)
2017 285Nabi, Liñán, Fayolle, Krueger, and Walmsley
-
TABL
E2
Con
tinu
ed Typ
esof
Impa
ctb
1.Attitud
e2a
.Skillsan
dkn
owledg
e2b
.Fea
sibility
2c.E
ntrepren
eurial
intention
3.Bu
sine
ssstart-up
4/5.Pe
rform.
&so
cioe
con.
Other
Com
pet.
Cha
nget
al.,20
14P;
Pittaw
ayet
al.,
2015
P
Brink&Mad
sen,
2015
M;C
hang
etal.,20
14P;
Gielnik
etal.,20
15P;
Gilbe
rt,2012P
Gielnik
etal.,20
15P;
Gilbe
rt,2
012P;
Toleda
no&
Urban
o,20
08A
Baeet
al.,20
14A;
Gielnik
etal.,20
15P;
Gilbe
rt,201
2P
Don
nellon
etal.,
2014
P;Gielnik
etal.,20
15P;
Gilbe
rt,2
012P;
Vince
tt&Fa
rlow
,20
08P
Don
nellon
etal.,20
14P;
Gordo
net
al.,20
12P
Don
nellon
etal.,
2014
P;Gordo
net
al.,20
12P;
Lack
eus,20
14P;
McC
rea,
2013
P;Pittaw
ayet
al.,
2015
PCom
par
ison
sW
alter&Doh
se,
2012
PW
alter&Doh
se,
2012
P;W
ang&
Verza
t,20
11M
Lang
eet
al.,20
14P
Lang
eet
al.,
2014
P
Note:Articleswitho
utteac
hing
mod
elinform
ationno
tsho
wn(13forL
evel
1,13
forL
2a,21forL
2b,53forL
2c,9
inL3
,4in
L4/5an
d22
inothe
r).S
omearticles
cons
ider
morethan
oneim
pact
mea
sure,a
ndare,
therefore,
includ
edmorethan
once
inthetable.
aBa
sedon
ourfram
eworkdraw
ingon
Béch
ard&Grégo
ire(2005).
bBa
sedon
ourfram
eworkdraw
ingon
Hen
ryet
al.’s
(200
3)clas
sifica
tion
.See
Table1forde
tailson
thesign
ofim
pacts(pos
itive,
nega
tive
,mixed
,oram
bigu
ous).F
orthe
compa
riso
nstud
ies(Lan
geet
al.,20
14;W
alter&Doh
se20
12;W
ang&Verza
t201
1),s
upplymod
elsareco
nsistently
foun
dto
have
less
positive
impa
ct.
286 JuneAcademy of Management Learning & Education
-
specific outcomes achieved (see Table 2). In our re-view,
studies that provide sufficient pedagogicaldetail are limited. Only
72 of our 159 articles (45%)provide enough detail for us to
determine theirpedagogical approach. The following section fo-cuses
on these 72 articles.
Supply and Supply–Demand Model Pedagogy
Only five articles can be classified in terms ofsupply model
pedagogy. These are positively re-lated to self-efficacy (Sánchez,
2011) and entrepre-neurial intentions (e.g., Crane, 2014; Solesvik
et al.,2013, 2014). For example, Sánchez (2011) focuses
ontransmitting knowledge to students so that they“know about
entrepreneurship,” and this mainlybehaviorist course has a positive
impact on a rangeof student perceptions (at Level 2 of our
framework,e.g., intention, self-efficacy). This suggests a
supplymodel link to lower level impact indicators, al-though
Shinnar et al., (2014) find mixed results, pri-marily at Level 2,
based on a moderating effect ofgender. In turn, programs that
combine pedagogiesfrom the supply and demand model tend to be
pos-itively related to lower levels of our framework. Ofthe 12
supply–demand articles, only one (Henryet al., 2004) addresses
impact at higher levels. Atypical example of a supply–demand
article is theprogram analyzed by Hamidi, Wennberg, andBerglund
(2008) which despite concentrating onknowledge transmission,
includes some experien-tial learning, in this case, creativity
developmentexercises whereby the authors report a positive linkwith
entrepreneurial intentions.
Demand and Demand–CompetenceModel Pedagogy
Fifteen articles analyze interventions adhering todemand model
pedagogy. These typically focuson short-term intensive experiential
programs(e.g., Fayolle & Gailly, 2015), or longer
experientialresidential-based programs (e.g., Boukamcha, 2015).They
also include student-led entrepreneurshipclubs that allow students
to work on collaborativeprojects and gain awareness from
experienced entre-preneurs (Pittaway, Rodrı́guez-Falcon, Aiyegbayo,
&King, 2011), and a pedagogical method that goesbeyond formal
classroom teaching, incorporating,for example, network events and
interaction withentrepreneurs (Souitaris et al., 2007). All these
stud-ies share a focus on exploration, discussion,
andexperimentation, with a preoccupation on students’
needs and interests.Moreover, these studies largelysuggest a
positive link of this model’s pedagogywith lower level impact
indicators—our frame-work’s Level 2 indicators (entrepreneurial
intention,Fayolle et al., 2006a; Souitaris et al., 2007), or
otherpersonal change, such as satisfaction with thecourse or
participation (Millman,Matlay, &Liu, 2008;Pittaway et al.,
2011).Of the EE programs studied in the review, 27 are
consistent with demand–competence model peda-gogy. They share
the inclusion of an important ele-ment of realism, such as
real-life problems tobe solved. This is powerful, because despite
thechallenges to the learner, the learning is moretransferable to
the real world (cf. outside our re-view, Neergaard et al. 2012). In
the articles in thisstream, the pedagogical methods are
experi-ential and entail working side by side with, forexample,
entrepreneurs (e.g., Chang & Rieple,2013); realistic
entrepreneurial exercises (e.g.,Gondim & Mutti, 2011); starting
and running a“real” business (e.g., Burrows & Wragg, 2013);
andproblem-based learning (e.g., Kirkwood, Dwyer,& Gray, 2014).
Again, these studies report a posi-tive link with lower level
impact measures(skills and knowledge, and feasibility, e.g.,
Jones& Jones, 2011). However, ambiguous or mixed re-sults are
also found for intention and feasibility(Chang & Rieple, 2013;
Harris, Gibson, & Taylor,2007). Overall, the pattern suggests a
positivelink between demand and demand–competencemodel pedagogy and
primarily lower level impactindicators.
Competence Model Pedagogy
Twelve articles fall into this category. Pedagogicalmethods
entail students who are starting up busi-nesses by consulting
external experts, typically forlegal, accounting, and sales help
(Vincett & Farlow,2008) or dealing with real-world problems or
oppor-tunities in industry-engaged environments to en-hance social
interaction and deeper learning(Gilbert, 2012). These articles are
positively relatedto Level 2 (skill development, learning; Gilbert,
2012),Level 3 (actual start-ups; Gilbert, 2012; Vincett
&Farlow, 2008), and Level 4 of our framework (positivechanges
in the person andbusiness that run 5 yearsafter the course: e.g.,
increase in social capital andsocioeconomic bonds; Gordon,
Hamilton, & Jack,2012). Given the limited number of articles in
thiscategory, we see our results as indicative ratherthan
confirmatory.
2017 287Nabi, Liñán, Fayolle, Krueger, and Walmsley
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Comparison Studies
Only three articles compare EE programs usingcompeting
pedagogical methods. Lange et al.(2014) suggest that experiential
courses (featuringdemand and competence models) better
predictmultiple entrepreneurial behaviors: The rare be-haviorist
courses in their study (“how to writea business plan”) are
essentially a negative pre-dictor. They measure impact at the
highest im-pact level of our framework (Level 5) and showa positive
socioeconomic impact up to 25 yearspostprogram. Similarly, Walter
and Dohse (2012) com-pare active learning (constructivist) to
traditionallearning (behaviorist) in locations with eitherweak or
already-strong entrepreneurial cultures,finding the constructivist
model to have a strongerimpact in terms of, for example,
entrepreneurialintention.
Overall, our review highlights that each categoryof pedagogical
methods (supply, demand, compe-tence, hybrids) has some positive
relationship withthe lower level impact indicators of our
teachingmodel framework (e.g., attitudes and intentions).However,
the demonstrated pattern of pedagogyimpact depends to an extent on
the aims of re-searchers. Although articles featuring fewer
experi-ential programs (supply, supply–demand, demand)focusmore
onbasic or lower levels of our framework,articles examining more
experiential programs(demand–competenceandcompetence)also
focusonimpact at higher levels (e.g., actual start-ups
andsocioeconomic impactover time).These latter studiesask more from
their programs and typically obtainhigher impact.
DISCUSSION
Guided by a unique, theory-driven teachingmodelframework, we
undertook a systematic review ofa range of EE impacts in higher
education, draw-ing on empirical evidence published since 2004.This
entailed a thematic analysis of the evidenceusing our adopted
teaching model framework toclassify different types of outcomes and
peda-gogies. We also explored the extent of the re-lationship
between pedagogical methods andoutcomes achieved.
Reaffirmation of Past Reviews
Despite the increase in the amount of research onEEand
entrepreneurial outcomes in higher education
over the past 12 years (nearly two thirds of our 159articles are
published in the last 5 years), there isstill a general focus on
lower level, short-term,subjective impact indicators, especially
the EE–entrepreneurial intentions link (51%), and the lack
ofspecifying even minimal pedagogical detail (55%).Hence, in
general, we reconfirm the findings andrepeat the calls of previous
reviews for more re-search on entrepreneurialbehavior (e.g.,
Pittaway&Cope, 2007) and greater pedagogical detail (cf.Martin
et al., 2013). Our teaching model frameworkurges a focus on higher
level impacts such as start-ups, firm survival rates, business
performance, andsocietal contribution. Furthermore, it also
meansthat future researchers provide detailed informationabout the
pedagogical methods, so we can un-derstand the impact of
pedagogical designs andmethods.Extending previous reviews, our
findings lead us
to focus on new or underemphasized calls for futureresearch. As
a general pattern from our findings,progress on the previous calls
outlined above hasbeen slow, and EE impact research continues to
belimited. For example, in our review, it is rare to seearticles on
novel EE impact measures or exploringthe reasons behind the
contradictory findings inhigher education-based EE research that go
beyondstatistical/artifactual reasons (cf. Martin et al.,
2013;Rideout & Gray, 2013). Table 3 presents our
recom-mendations for future research and these are dis-cussed in
more detail below.
Types of EE Impact
Focus on Novel Impact Indicators Related toEmotion-Based
Approaches
Given the dominance of entrepreneurial intentionsas an impact
indicator in our research, we suggest itis important to understand
alternative impact mea-sures. Although entrepreneurship is
considereda “journey of the heart” and the importance of
un-derstanding entrepreneurial emotion (affect, emo-tions,
feelings), especially during the new venturecreation process is
acknowledged (Cardon, Foo,Shepherd, & Wiklund, 2012), there is
surprisinglylittle empirical research in our review that focuseson
emotion-based impact indicators. We thereforeurge scholars to
pursue the following importantavenues.First, we are surprised by
the scarcity of research
that addresses emotion or affect. Given the growingconsensus on
their importance in entrepreneurial
288 JuneAcademy of Management Learning & Education
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thinking, for example, passion (Cardon et al.,2009, 2012;
Gielnik et al., 2015), this is startling.For example, only one
empirical study in our sam-ple measures EE program-derived
entrepreneurialinspiration (Souitaris et al., 2007) that
identifiesemotional inspiration (not learning or
incubationresources) as the most important EE “programmebenefit”
with inspiration also positively related toentrepreneurial
intentions (Souitaris et al., 2007).Moreover, they define it as “a
change of hearts(emotion) and minds (motivation) evoked by eventsor
inputs from the programme and directed towardsconsidering becoming
an entrepreneur” (Souitaris
et al., 2007: 573). Thus, we consider it of central im-portance
as both an impact indicator in its own right(i.e., if EE increases
inspiration), and as a predictorof other impact measures. Indeed,
Souitaris et al.(2007: 587) conclude: “Universities that want to
as-sess the effectiveness of their programmes shouldcapture not
only how much their students learnabout entrepreneurship or whether
they are satis-fied with the courses, but also whether they are
in-spired from theprogramme.”Despite its importance,inspiration
from EE programs in higher educationremains an under-researched
phenomenon andwarrants further research attention.
TABLE 3Future Research Directions: Types of EE Impact and
Pedagogical Models
Reaffirmation of past reviews
1. Ongoing requirement for increased research on higher level
impact indicators by examining objective and higher level measures
atLevels 4 and 5 of our teaching model framework (see Figure 1)
including entrepreneurial behavior.
2. More detail about the specifics of the pedagogy in impact
studies.
New or underemphasized research directions
1. Types of ImpactA. Focus on novel impact indicators related to
emotion-based and mind-set approachesi. Explore role of EE
program-derived inspiration in higher education as an impact
indicator and a mediator between EE and a range of
other impact measures. For example, does inspiration mediate the
EE-behavior relationship?ii. Examine the development of the
entrepreneurial mind-set in higher education such as dispositional
optimism, uncertainty and
ambiguity tolerance.B. Focus on impact indicators related to the
intention-to-behavior transitioni. Build on Souitaris et al. (2007)
to generate new knowledge about why there is (or is not) a
transition from entrepreneurial intentions into
nascent or start-up behavior, specifically for example, why do
some recipients of higher education-based EEwith high
entrepreneurialintentions start up their own businesses after
graduating, while others (despite high intentions) do not?What is
the role of EE in highereducation in this process?
ii. Explore the development of entrepreneurial identity in
higher education.C. Explore contextual reasons for some
contradictory findings in impact studiesi. Explore individuals’
background in terms of previous entrepreneurial exposure and
pre-educational intentions to clarify the impact of
higher education-based EE.ii. Directly examine if the impact of
EE programs in higher education on a range of entrepreneurial
outcomes is gender-specific and for
which outcomes.iii.Consider contextual factors in higher
education, e.g., type of course, type of institution.iv. Expand
existing research by looking at relationship between culture and
national context in EE impact studies. For example, how do
cultural valuesmoderate the impact of EE on outcomes?What
outcomes are culture specific? Our teachingmodel framework could
beexpanded to incorporate culture-specific frameworks (e.g.,
Hofstede, 2003; Schwartz, 2004).
v. Explore underexamined fast-growing/emerging
countries/continents in our sample e.g., Brazil, Russia, Africa,
and Australia.vi.Examine double-moderator interaction effects. For
example, does EE impact outcomes as a function of culture and
gender?2. Pedagogical methods underpinning impactA. Investigate
competence model-related pedagogical methods to determine if they
are truly more effective than other models, and why
they are effective.B. Building on our teaching model framework,
directly compare and contrast a broad range of pedagogical models
(supply, demand,
competence, and hybrids) in terms of their impact on a range of
impact indicators (from Levels 1 to 5).
General recommendations
1. Explore EE at other levels, i.e. other than higher
education.2. Explore impact of university-based EE on stakeholders
other than students and graduates. For example, university faculty,
donors/
investors, and community.
2017 289Nabi, Liñán, Fayolle, Krueger, and Walmsley
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A second key knowledge gap centers on impactmeasures focusing on
the development of the en-trepreneurial mind-set, defined here as
cognitivephenomena deeper than intent4 (Krueger, 2007,
2015;Lackéus, 2015). Few studies in our review even ref-erence
this phenomenon. One rare example (Crane,2014) suggests
dispositional optimism as a key in-dicator of EE impact because of
its self-regulatoryfunction anddealingwith uncertainty and
setbacks.They find their program improves such optimism,suggesting
another fruitful avenue to explore. Sim-ilarly, under OECD’s
Entrepreneurship360 initia-tive, Lackéus (2015) identifies the
importance ofuncertainty/ambiguity tolerance as impact indica-tors
for action-based EE programs that tie back tothe issue of emotions
in entrepreneurial thinking.
Focus on Impact Indicators Related to theIntention-to-Behavior
Transition
Our findings also suggest a paucity of studies of EEin higher
education that bridge the transition fromintention to behavior,
that is Levels 2 to 3 in ourteaching model framework. This is an
importantavenue because intention does not always translateinto
entrepreneurial behavior and little is knownabout this transition.
Indeed, Pittaway and Cope(2007: 498) conclude “what isnot known . .
. iswhetherpropensity or intentionality is turned into
‘entre-preneurial behavior’, either in its broad sense orwhen
focused narrowly on venture creation.” Al-though we re-emphasize
their claim here, we alsoextend their call, by suggesting two
specific ave-nues that we encourage more scholars to pursue.
First, our review suggests very little empiricalattention on
analyzing how entrepreneurial in-tention translates into nascent or
start-up activities.Although this relationship is examined in our
re-view regarding start-up activities for nascency afteran EE
program (e.g., Souitaris et al., 2007), the lack ofa positive
significant relationship (albeit via entre-preneurial intentions)
suggests more research isrequired on how intention follows through
to action(or not). For example, why do some recipients of EEwith
high entrepreneurial intentions start up theirown businesses after
graduating, while others (de-spite high intentions) do not? What is
the role ofEE in this process? Second, very few studies in
ourreview analyze the development of entrepreneurial
identity, although we see hints that EE relates topersonal
development beyond knowledge and skillacquisition, for example, by
a change in thinkingstyle (Mueller & Anderson, 2014), internal
self-reflection, and external engagement (Donnellonet al., 2014;
Lackéus, 2014). Given how little weknow of how intent becomes
behavior, this is ex-ceptionally important for further
research.
Explore Contextual Reasons for ContradictoryFindings:
Background, Gender, and Culture
As our results report, most papers suggest positiveresults
between EE and a broad range of impact in-dicators, but with some
contradictory studies (con-sistentwithMartinetal.,
2013).Theseauthorsadvancemethodological concerns as an explanation
of suchcontradictory results; however, it would be remiss notto
also assess person- and context-specific factors.Concerning student
backgrounds, for those who
have less exposure to entrepreneurship, the generaleffect tends
to be positive, because they usuallyincrease their entrepreneurial
intention, attitudes,and self-efficacy by participating in the
programs(e.g., Fayolle &Gailly, 2015; Fayolle, Gailly, &
Lassas-Clerc, 2006a; Sánchez, 2011). In contrast, for
thosestudents who already have entrepreneurial experi-ence, family
background, or high previous entrepre-neurial intention,
theeffectsaregenerallyweakerandmay even be negative (see, e.g.,
Fayolle et al., 2006b;Von Graevenitz, Harhoff, & Weber, 2010).
Similarly,Bae et al. (2014) found that after controlling for
pre-educational entrepreneurial intentions, the relation-ship
between EE and postprogram entrepreneurialintentions is not
significant. However, given that Baeet al.’s (2014) meta-analysis
did not focus specificallyon higher education, we encourage more
studies tofocuson the roleof studentbackground in this
context.Regarding students’ background, gender-specific
differences are also an important source of contra-dictory
findings. Few studies in our review focus onthe differential impact
of EE for male and femalestudents/graduates, although those that
did identifygender-specific effects. For example, Wilson,
Kickul,andMarlino (2007) showthatEEhasastronger impacton
self-efficacy among females than males. Otherstudies also suggest
the impact of EE on entrepre-neurial intentions is gender-specific
(e.g., Joensuuet al., 2013; Packham et al., 2010), although there
aretoo few studies to indicate if this favors males orfemales. A
controversial finding in Bae et al.’s (2014)article concludes that
gender does not signifi-cantly moderate the EE–entrepreneurial
intention
4 Education researchers often refer to “noncognitive skills”
todifferentiate from more surface level learning such as facts
androte-learned skills (e.g., Krueger, 2015).
290 JuneAcademy of Management Learning & Education
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relationship. However, Bae et al. (2014) did not spe-cifically
examine studies of EE in higher education(aswedo), but rather
lookedataverages fromameta-analysis across educational levels.
Furthermore,unlike Bae et al. (2014), we look at higher level
impactin terms of entrepreneurial behavior. Although wedid not find
any reported gender-specific effects atthis level, in our view,
this doesnotmean that theydonot exist, merely that studies have not
specificallyfocused on these effects.
Looking at further aspects of context (e.g., type ofprogram:
optional or compulsory; type of institution),there is evidence from
our review that initial positiveattitudes toward entrepreneurship,
which are, how-ever, not fully formed, change once they are
con-frontedwith the complexities and pitfalls of businessstart-up
during EE. In our review, Hytti, Stenholm,Heinonen, and
Seikkula-Leino (2010) analyze themotivations of students taking a
compulsory EE pro-gram, finding that students with intrinsic
motivationreport lower learning and less satisfaction with
thecourse (they expected more). Those taking the pro-gram with
extrinsic motivation express a greaterdegree of satisfaction.
Similarly, Petridou and Sarri(2011) find that attitudes and
intentions are raised byanEEprogram inageneralist university, but
loweredin a technology institute. The latter can be explainedby the
realization of the complexities involved instarting up a technology
venture.
Similarly, culture and national context are likelysignificant
factors but rarely tested directly becausealmost all studies in our
review focus on a single-country or culture (or at least do not
investigateculturaldifferences).However, Baeetal.’s
(2014)meta-analysis suggests some salient cultural dimensions,at
leastwith respect to entrepreneurial intentions. Forexample, some
national or cultural contexts may behigher on some cultural
dimensions, on average, likeuncertainty avoidance (level of
comfortableness withuncertainty and ambiguity; Hofstede, 2003, also
seeKrueger, Liñán, & Nabi’s, 2013 Special Issue in thisarea).
This suggests culture-specific moderators areworthy of further
consideration. In addition, oursample is dominated by studies in
the United King-dom,UnitedStates, andAsia, but only 5%are from
thefast-growing emerging BRIC (Brazil, Russia, India,and China)
economies. There are no studies fromRussia or India, and Africa and
Australia are alsounder-represented, suggesting such countries
andcontinents are largely absent from studies.
Moreover, culture is also likely to exhibit in-teraction
effectswithother impact factors likegenderas implied in a handful
of our articles regarding
culture- and gender-specific findings. Packhamet al.(2010), for
example, suggest findings that EE nega-tively relates to
entrepreneurial intentions for maleGerman students. This
double-moderator effect isconsistent with limited research outside
our review,for example, Shneor, Camgöz, and Karapinar (2013),who
look at gender effects in two cultural settings,while analysis of
Culture x Gender effects is absentfrom the studies reviewed
here.Considering our discussion on how student back-
ground and context (the “audience” dimension of theteaching
model; Fayolle & Gailly, 2008) seem to ex-plain contradictory
findings in previous studies, fu-ture research in this field is
especially promising.Knowing the background and the profile of the
stu-dents (e.g., prior entrepreneurial knowledge andskills,
motivators, gender) and context (e.g., type ofprogram, type of
institution, program and countrycontext) can also lead to better
design and imple-mentation of EE programs, and ultimately to
moreefficient learning processes, environments, andhence, impact
(Béchard & Grégoire, 2005; Fayolle &Gailly, 2008, 2015).
It also opens the door for futureimpact research that is more
mindful of potentialmoderating factors and exploring a range of
rela-ted questions. For example, to what extent is the im-pact of
EE programs in higher education on a rangeof entrepreneurial
outcomes gender-, culture-, andcontext-specific? Which impact
indicators in ourframework are dependent on moderator effects
andwhich are more universally applicable? Our teach-ing model
framework could also be expanded to in-corporateculture-specific
frameworks (e.g.,Hofstede,2003; Schwartz, 2004) allowing further
considerationof the impact of higher education-basedEEprogramsin
different international and cultural contexts.
Pedagogical Methods Underpinning Impact
Pedagogical Reasons for Contradictory Findings:Differences in
Pedagogical Methods
Our review suggests that all the pedagogicalmethods (supply,
demand, competence, hybrids)have positive impact at Levels 1 and 2
of our teach-ingmodel framework (e.g., attitudes and
intentions).However, our reviewed studies suggest that peda-gogical
methods based on competence are bettersuited for developing higher
level impact. The evi-dence suggests that competencemodel pedagogy
isassociatedwith both subjective measures at Level 2(e.g.,
entrepreneurial intention), and objective onesat Levels 3 (e.g.,
actual start-ups up to 5 years
2017 291Nabi, Liñán, Fayolle, Krueger, and Walmsley
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postprogram) and 4 (longer term impact on businessup to 10 years
postprogram). To put it more simply,such deeper, more experiential
pedagogies seem tohave the most potential to have impact at
higherlevels because students focus on developing be-havioral
competency in solvingproblems in real-lifeentrepreneurial
situations.
Our findings suggest that the use of different
ped-agogicalmethods is at least partially responsible forthe
inconsistent findings in impact studies. However,given that our
findingsare based onapartial sampleof our population of articles,
they are indicativerather than confirmatory.5 To the best of our
knowl-edge, this is the first systematic review that usesa teaching
model framework to assess the impact ofEE. In our view, this
provides novel and meaningfulinsights. EE makes strong claims to
have significantimpact and a strong bias toward experiential
peda-gogies. This review confirms that we need to focusstrongly in
this direction. For example, it is essentialto expand research on
competence-model-relatedpedagogical methods. Do they really have
strongerimpact than othermodels, especially at higher levelsof our
teachingmodel framework? How do they workregarding underlying
processes?
Focus on Comparison Studies to ComparePedagogical Methods
Our review reveals very few comparison studies thatdirectly
compare the impact of different pedagogicalmethods. Considering the
growing number of EEprograms and the growing demand to assess
them,should we not ask for evidence of what pedagogicalmethods
work, desired impact, and actual impact?We thus encourage
researchers to compare typesof impact across different teaching
pedagogicalmethods. This is the onlyway for us to understand
EEimpact in an incremental and meaningful way.
Our review includes comparison studies thatlink EE pedagogical
methods in higher educationto a broad range of impact measures
usinga teaching model framework. However, compari-son studies in
our review only tend to comparepedagogical methods in a limited way
(e.g., supplyversus competence; Lange et al., 2014; Walter
&Dohse, 2012; Wang & Verzat, 2011). In our review,we
identify five different pedagogical models
including hybrid versions (supply, supply–demand,demand,
demand–competence, competence). Weurge scholars of future
comparison studies to di-rectly compare the impact of a broader
rangeof pedagogical methods using a teaching modelframework. We
believe that such a comparativeapproach offers great opportunities
to explorea number of theoretically, practically, and empiri-cally
meaningful research questions that mayhelp to explain the
contradictory findings on theimpact of higher education-based EE
programsand increase generalizability. For example, whatpedagogical
models work for which types of im-pact and in which contexts? We
encourage futureresearchers to rigorously isolate the impact of a
ped-agogical intervention, controlling for the context-
andperson-specific factors outlined earlier.
Limitations and General Recommendations
Three limitationsof our reviewarenoteworthy. First,we only cover
EE in higher education, although EEalso flourishes in high school
programs, and adult(nondegree and non-academic) education.
Focusingon other educational levels and means of deliveryoutside
higher education was outside the scope ofour research, but our
findings do open the door forassessing EE impact at other
levels.Second, data onwhether an individual is exposed
to multiple training before, during, and after highereducation
is limited. However, some articles in ourreview do use more
sophisticated research designs,for example, adopting a
pretest–posttest controlgroup design (e.g., Souitaris et al.,
2007), or control-ling for prior entrepreneurial exposure (e.g.,
Fayolle& Gailly, 2015). Although focusing on methodologi-cal
designs is outside the primary scope of our re-search and is
covered elsewhere (e.g., Rideout &Gray, 2013), we still include
a range of articles withdifferent methodologies in our research,
and ourfindings confirm those of existing reviews with anemphasis
onmethodological rigor (e.g.,Martinet al.,2013; Rideout & Gray,
2013). Rather than reiteratethe methodological weaknesses that
other reviewsfound, we sought to identify perhaps less obvious,yet
greatly promising new or underemphasized di-rections for future
research.Third, our review focuses on the recipients of
university-basedEEprogramsandtheirentrepreneurialattitudes,
knowledge, skills, and behaviors. How-ever, such programs obviously
also influenceawider set of stakeholders, such as the
instructorsthemselves and, in the case of field projects, the
5 Reduced from 159 to 72 due to insufficient pedagogical
in-formation from 55% of our articles. Further, we suspect that
itcould be extremely valuable to assess the quality of pedagogy,not
just its intended characteristics.
292 JuneAcademy of Management Learning & Education
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individuals and organizations involved. For exam-ple, “real-life
cases”where students work on variousconsultancy tasks (such as
market validation stud-ies). The impact of EE can be on
entrepreneurialbehavior of staff and lecturers, when teaching
en-trepreneurship influences academics to become en-gaged in it
themselves (whether in commercializingresearch or in
nonresearch-based entrepreneurialactivity at the side of academic
work). EE programswhere students engage in market validation
studiesand so forth also expose students to the entrepre-neurial
community. This can be built into higherlevels of our teaching
model framework to examinestakeholder impact. For example, we can
assess thevalue of EE to university faculty, donors/investors,and
communities at Levels 3, 4, and 5 of our frame-work (cf.
Duval-Couetil, 2013).
CONCLUSIONS
While confirming the weaknesses in EE impact stud-ies (e.g.,
dominance on lower level attitudinal andintentionality impact
measures, and a lack of keydetail concerning pedagogy), wealso
identify threemainways ofmoving forward. First, as indicated
inTable 3,weaddvaluebyprovidinganup-to-date andempirically rooted
call for future research in highereducation. Second, by applying a
teaching modelframework, we offer several intriguing and
under-emphasized suggestions for improving EE research.Last and
relatedly, we provide some critical insightsinto the reasons for
the contradictory findings in EEresearch (e.g., rarityof
cross-cultural, gender-specificand pedagogical-comparison research)
that can befurther teased out through single
studies/interven-tions, so we can understand how EE really works
intheory and practice.
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