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Journal of Business Venturing Design 1 (2021) 100004
Contents lists available at ScienceDirect
Journal of Business Venturing Design
journal homepage: www.elsevier.com/locate/jbvd
Design science in entrepreneurship: Conceptual foundations and guiding
principles
Christoph Seckler
a
,
∗
, René Mauer
a
, Jan vom Brocke
b
a
ESCP Business School, Jean-Baptiste Say Institute for Entrepreneurship, Heubnerweg 8-10, Berlin 14059, Germany
b
University of Liechtenstein, Fürst-Franz-Josef-Strasse, Vaduz 9490, Liechtenstein
Keywords:
Design science
Entrepreneurship
Conceptual foundations
Guiding principles
Design science in the entrepreneurship eld holds the promise of developing relevant knowledge with scientic
rigor. Yet despite the promise of this approach, the entrepreneurship eld still lacks guidance on how to plan,
conduct, and assess design science work. In order to develop theoretically grounded principles, we rst make our
perspective on design science explicit. We characterize design science in entrepreneurship as a specic scientic
approach that shares the values of practice (i.e., usefulness) and uses the methods of science (i.e., scientic method
plus more specic, scrutable methods). We conceptualize design knowledge as a body of scientic knowledge
that comprises both design object knowledge (e.g., situated artifact, and design principles), and design evaluation
knowledge (e.g., usefulness, and social worth). Drawing on these foundations, we provide guidance on (1) how
to make design knowledge contributions explicit, (2) how to position design science work, (3) how to eectively
utilize prior knowledge, and (4) how to use tting methods in design science work. The article contributes by
further developing the conceptual foundations of design science in entrepreneurship and providing guidance on
how to conduct and assess design science work in the entrepreneurship eld.
Introduction
Design science presents the entrepreneurship eld with a promise.
The promise is that entrepreneurship scholars can develop practically
relevant knowledge with scientic rigor ( Berglund, Dimov & Wennberg,
2018 ; Dimov, 2016 ; Romme & Reymen, 2018 ). The promise is that
entrepreneurship scholars can be at the vanguard of generating new
knowledge, not only to report on ‘what is’ but to develop knowledge on
how things ‘should be’ ( Voss, 2020 ). The promise is that entrepreneur-
ship scholars can help address the grand challenges of our time
( George, Howard-Grenville, Joshi & Tihanyi, 2016 ) not only to under-
stand them, but also to design solutions to tackle them ( Romme, Ansell,
Buck, Choi, van der Eyden & Figueroa Huench, 2018 ). We believe
it is this signicant promise of design science that accounts for so
much traction among entrepreneurship scholars ( Berglund et al., 2018 ;
Berglund, Bousha & Mansoori, 2020 ; Dimov, 2016 ; Sarasvathy, 2003 ;
Talmar, Walrave, Podoynitsyna, Holmström & Romme, 2020 ; van Burg
& Romme, 2014 ; Zhang & Van Burg, 2020 ).
While many have already embraced this promising research ap-
proach, there is still little guidance in the entrepreneurship eld on
how to plan, conduct, and assess design science. The initial design
science work in the entrepreneurship eld has mostly paved the way
by outlining what design science is and how it is related to sci-
∗ Corresponding author.
E-mail address: cseckler@escp.eu (C. Seckler).
ence and design (
Berglund et al., 2018 ; Dimov, 2016 ; Romme & Rey-
men, 2018 ; Sarasvathy, 2003 ). Sarasvathy (2003) was one of the
rst to introduce the notion of entrepreneurship as a design science
through her engagement with the ideas of Herbert Simon and par-
ticularly his work The Sciences of the Artificial ( Simon, 1996 ). Draw-
ing on these ideas, Dimov (2016) put forward a strong argument out-
lining why entrepreneurship phenomena lend themselves to a design
perspective. He also discussed the dierent nature of design knowl-
edge and highlighted its future-oriented nature. Building on this work,
Berglund et al. (2018) initiated a special issue on design and en-
trepreneurship. In the editorial, the authors explained how design
knowledge is a specic, pragmatically oriented body of knowledge
that may in fact constitute the ‘missing link’ between theory and prac-
tice. Additionally, contributing to this special issue, Romme and Rey-
men (2018) introduced a framework on ‘how entrepreneurship can be
conceived as a discipline at the interface of science and design’ ( Romme
& Reymen, 2018 : 5).
The aim of this article is to complement this previous work by provid-
ing comprehensive and practical guidance for entrepreneurship scholars
in conducting design science research. To theoretically ground this guid-
ance, we rst make explicit the foundations of our perspective on design
science. Drawing on Bunge’s philosophy of technology ( Bunge, 1985 ,
1996 , 2017 ), we characterize design science as a scientic approach that
https://doi.org/10.1016/j.jbvd.2022.100004
Received 18 September 2021; Received in revised form 8 February 2022; Accepted 8 February 2022
2667-2774/© 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )
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C. Seckler, R. Mauer and J. vom Brocke Journal of Business Venturing Design 1 (2021) 100004
shares the values of practice (i.e., usefulness) and uses the methods of
science (i.e., scientic method plus more specic, scrutable methods).
While the former makes design knowledge relevant, the latter makes
it rigorous. Furthermore, we conceptualize design knowledge as a sci-
entic body of knowledge that comprises both the knowledge of de-
vising courses of action (i.e., design object knowledge) and the values,
criteria, and value judgements related to its quality (i.e., design eval-
uation knowledge). Building on these foundations and drawing on in-
sights from the design science literature in the information systems (IS)
eld (e.g., Gregor & Hevner, 2013 ; Hevner, March, Park & Ram, 2004 ;
vom Brocke, Winter, Hevner & Maedche, 2020 ), we outline four specic
guiding principles: (1) how to make the contributions to design knowl-
edge explicit, (2) how to position design science work within previous
literature, (3) how to eectively use prior scientic knowledge, and (4)
how to use tting methodical approaches.
This article contributes in three ways to the development of a design
science approach in the entrepreneurship eld ( Berglund et al., 2018 ;
Dimov, 2016 ; Romme & Reymen, 2018 ). First, we complement the dis-
cussion on the three bodies of knowledge –theory, practice, design –
introduced by Berglund et al. (2018) . More specically, we link the three
bodies of knowledge to their underlying approaches. Making this link
explicit helps in explaining why design knowledge can be considered a
body of scientic knowledge, and why design science is a scientic ap-
proach. We believe this is important in establishing the legitimacy of the
design science approach in the scientic entrepreneurship community.
Second, we contribute to the discourse on the nature of design knowl-
edge in entrepreneurship ( Berglund et al., 2018 ; Dimov, 2016 ; Romme &
Reymen, 2018 ). While previous research has focused mainly on the de-
sign object knowledge component (e.g., focusing on the role of artifacts,
design principles, and design theories), we shift the discourse toward the
design evaluation knowledge components (i.e., values, standards, and
value judgements related to the quality of design object knowledge).
This shift allows us to further dierentiate the kinds of theoretical and
empirical contributions that design science research is able to make.
Third, we outline theoretically grounded guiding principles to help de-
ne good practice for design science in entrepreneurship. It is our hope
that these guidelines may help entrepreneurship scholars to eectively
plan, conduct and assess design science work.
Design science as a bridge between basic science and practice in
entrepreneurship
We argue that design science in entrepreneurship serves as a bridge
between basic science and practice in entrepreneurship (see Fig. 1 ).
While design science shares the ultimate value of practice (i.e., useful-
ness), it uses the methods of science (i.e., scientic method plus more
specic, scrutable methods). By basic science in entrepreneurship , we re-
fer to an approach to entrepreneurship that aims at developing explana-
tory knowledge (i.e., explaining, describing and potentially predicting
entrepreneurship phenomena) using the scientic method. We employ
the term design science in entrepreneurship to denote an approach simi-
larly based on the scientic method that seeks to develop and expand
design knowledge. Finally, by practice in entrepreneurship , we refer to an
approach to entrepreneurship premised primarily on taking action or
decisions rather than on developing scientic knowledge. We draw on
Bunge’s philosophy of technology ( Bunge, 1967 , 1985 , 1996 , 1999 ) to
further characterize the three approaches and show their interrelations
along ve dimensions: value, aim, background knowledge, problem , and
methods ( Bunge, 1996 : 79).
Design science in entrepreneurship as a specific scientific approach
To provide an initial characterization of design science in entrepreneur-
ship , we will introduce a few examples of design science work. One
good example of this approach has recently been published in Sci-
ence ( Campos, Frese, Goldstein, Iacovone, Johnson & McKenzie, 2017 ).
Campos et al. (2017) addressed the question of how to design en-
trepreneurship training to educate small business owners in West-
ern Africa. Positioned within the economic literature interested in en-
trepreneurship, the study was motivated by the authors’ observation
that psychological entrepreneurship training may be more eective
than more business-oriented entrepreneurship training ( Campos et al.,
2017 ). Drawing on action theory ( Frese & Zapf, 1994 ), they devel-
oped a novel form of psychological entrepreneurship training and
evaluated its eectiveness in a randomized controlled trial. Other
good examples include research on how to make decisions under
uncertainty ( Sarasvathy, 2001 ), how to create university spin-os
( van Burg, Romme, Gilsing & Reymen, 2008 ), and how to design busi-
ness models ( Osterwalder, 2004 ).
A shared key characteristic of the above examples of design sci-
ence studies is their ultimate value of usefulness. By usefulness, we
refer to the utility of the design object. The training designed by
Campos et al. (2017) is intended to be useful in educating entrepreneurs.
The principles suggested by Sarasvathy (2001) set out to be useful in
guiding the decision making of entrepreneurs. The principles developed
by van Burg et al. (2008) seek to be useful in creating university spin-
os. Finally, the framework developed by Osterwalder (2004) is de-
signed to be useful in developing business models. While usefulness can
Fig. 1. Three approaches in the entrepreneurship domain.
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C. Seckler, R. Mauer and J. vom Brocke Journal of Business Venturing Design 1 (2021) 100004
be considered the ultimate value of design science work, it can appear in
dierent forms, such as eectiveness and eciency (e.g., Hevner et al.,
2004 ; Holmström, Ketokivi & Hameri, 2009 ; Venable, Pries-Heje &
Baskerville, 2016 ).
Design science in entrepreneurship can further be characterized
by its aim . We suggest that the main aim of design science in en-
trepreneurship is to contribute to the body of design knowledge in
the entrepreneurship domain (for similar views from other elds, see:
Holmström et al. 2009 ; vom Brocke et al. 2020 ). In general terms, design
knowledge can be dened as knowledge which ‘devises courses of action
aimed at changing existing situations into preferred ones’ ( Simon, 1996 :
129). Similarly, others have characterized design knowledge as prag-
matically oriented knowledge ( Berglund et al., 2018 ), prescriptive
knowledge ( Gregor, Chandra Kruse & Seidel, 2020 ; Niiniluoto, 1993 ),
knowledge about means-ends relationships ( vom Brocke et al., 2020 ),
or as teleological knowledge ( Evered, 1976 ; Whetten, 2009 ). Design
knowledge contributions can take dierent forms which are either the-
oretical (e.g., situated artifact, design principle, design theory) or em-
pirical (e.g., realized artifact) (see, for example, Baskerville, Baiyere,
Gregor, Hevner & Rossi, 2018 ; Berglund et al., 2018 ; Bunge, 2003 ;
Gregor et al., 2020 ).
Design science in entrepreneurship is further characterized by spe-
cic background knowledge . Background knowledge here refers to ‘the
body of knowledge used, and taken for granted until new notice’
( Bunge, 2003 : 29). Design science in entrepreneurship makes use of
existing knowledge both in relation to design knowledge and explana-
tory knowledge ( Drechsler & Hevner, 2018 ). While design knowledge
is about devising action, explanatory knowledge seeks to describe, ex-
plain, and potentially predict social facts in the entrepreneurship do-
main ( Bunge, 1996 ). Both types of background knowledge are equally
important in design science. For instance, in developing entrepreneur-
ship training, Campos et al. (2017) drew on both the literature on train-
ing design (i.e., design knowledge) and action theory (i.e., explanatory
knowledge). While the point of departure for design science projects is a
problem in the body of design knowledge, design science projects make
use of both types of knowledge in all major phases of a design science
project (e.g., analysis, design, evaluation phase).
The problems that design science addresses are design problems
–that is, issues in the body of design knowledge ( Bunge, 2017 ;
Holmström et al., 2009 ). Design problems can be formulated as ‘how
to’ questions. While the ‘how’ refers to the actions, the ‘to’ refers to
the preferred situations in Simon’s terminology ( Simon, 1996 ). In other
words, the ‘how’ refers to means, the ‘to’ refers to some kind of end
( vom Brocke et al., 2020 ). For example, Campos et al. (2017) consid-
ered the question of how to design training to educate entrepreneurs in
Western Africa eectively. Whereas many design science studies start
from a practical problem, it is important to note that not every practical
problem is a design knowledge problem (e.g., Holmström et al., 2009 ).
Design knowledge problems are only those practical problems for which
no design knowledge exists (e.g., Holmström et al., 2009 ). Just because
an entrepreneur does not know how to act, it does not mean there is
no knowledge in literature on this topic. Furthermore, a design science
study does not have to be motivated by problems, but might also by
initial conjectures about novel opportunities or new means-ends rela-
tionships that represent a ‘clear departure from the accepted ways of
thinking and doing’ ( Gregor & Hevner, 2013 : 346).
The methods used by design science in entrepreneurship are the gen-
eral scientic method and a collection of special, scrutable procedures
( Bunge, 1996 , 2017 ). The general scientic method can be described
as a structured procedure involving three main phases ( Bunge, 2003 ,
2017 ).
1
The scientic method starts by posing a problem against a body
of scientic background knowledge. It continues by using special and
1 Here we follow Bunge’s notion of the scientic method to be consistent with
our perspective. We acknowledge, however, that there are dierent opinions on
scrutable methods to develop novel knowledge which, in turn, needs to
be checked against the existing body of knowledge to justify a contribu-
tion to knowledge ( Bunge, 2017 , 2003 ). As part of the scientic method,
special and scrutable procedures are used which refer to more spe-
cialized methods, e.g., for collecting and analyzing data ( Bunge, 2017 ,
2003 ). The example of Campos et al. (2017) illustrates both the use of
the scientic method and special, scrutable methods. The authors po-
sitioned this study against the body of existing scientic knowledge on
entrepreneurship training (in economics) and asked whether a psycho-
logical training design could be more eective than a standard business
training program. They developed theoretically grounded psychological
training drawing on action theory, and subsequently used a random-
ized controlled trial to test the eectiveness of the designed training.
They discuss how the designed training is more eective than business-
oriented training programs ( Campos et al., 2017 ).
How design science is different from basic science and practice in
entrepreneurship
A fundamental dierence between design science and basic science
is their ultimate value . Whereas design science ultimately strives for use-
fulness, basic science strives to make true claims about the world. Fur-
thermore, the aims of the two approaches are dierent. Design science
aims to contribute to the body of design knowledge ( Berglund et al.,
2018 ; Romme & Dimov, 2021 ), while basic science aims to contribute
to explanatory knowledge. Additionally, the two approaches draw on
dierent background knowledge . While design science draws on both de-
sign knowledge and explanatory knowledge in addressing design issues,
basic science draws mainly on existing explanatory knowledge. A third
dierence is that design science addresses problems on how to devise ac-
tion, whereas basic science in entrepreneurship addresses mainly prob-
lems of describing or explaining and asks why, how and what questions
(e.g., Dubin, 1969 , 1976 ; Whetten, 1989 ). While these characteristics
dierentiate the two approaches, they nevertheless share a fundamen-
tal characteristic of scientic approaches: both design science and basic
science make use of the scientic method ( method) in their search for
knowledge. This, according to Bunge (1996 , 2003 , 2017), is what makes
both approaches scientic.
We also must dierentiate design science from practice in en-
trepreneurship. While design science shares with practice the value of
usefulness, design science is dierent in all four of the other dimen-
sions. A rst subtle but nevertheless important dierence is the main
aim of the two approaches. The aim of practice is to perform or imple-
ment entrepreneurial actions or decisions, whereas the aim of design
scientists is to guide or devise such action or decision making, yet not
necessarily to perform it. This is similar to an entrepreneurship professor
developing a scientically grounded entrepreneurship training design
that is subsequently implemented by local trainers (e.g., Campos et al.,
2017 ). The background knowledge of design science and practice is also
dierent. While practice draws mainly on practice knowledge, design
science is primarily grounded in scientic knowledge. Practice knowl-
edge is developed through experience and intuition, and indeed is often
idiosyncratic and tacit ( Selden & Fletcher, 2019 ). Such practical knowl-
edge includes perceived wisdom such as ‘never start a startup with a
friend’ or pragmatic advice such as ‘10 slides you need in your pitch’
( Kawasaki, 2021 ). While practical knowledge is also prescriptive in na-
ture, a key dierence from design knowledge is the way it is developed.
While design science uses the method s of science (i.e., scientic method
and special, scrutable procedures), practice relies mainly on personal
experience and intuition ( Bunge, 1985 , 2017 ). While entrepreneurial
practice may benet from using special, scrutable procedures (e.g.,
Camuo, Cordova, Gambardella & Spina, 2020 ), entrepreneurial prac-
whether a single scientic method exists. For a recent debate see, for instance,
Treiblmaier (2018) .
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tice does not aim at addressing or solving a problem in the scientic
body of knowledge. This is why Bunge (1985) does not consider prac-
tice in entrepreneurship to be a scientic approach. This does not mean
that the ideas and knowledge developed in practice are not useful, just
that they are not scientic knowledge ( Bunge, 2017 ).
How the three approaches are interrelated
Despite being distinguishable, the three approaches are interrelated
and connected to each other in important ways (see arrows between
boxes in Fig. 1 ). First, design science and basic science in entrepreneur-
ship are interrelated through the exchange of both knowledge and prob-
lems. On the one hand, design science draws on explanatory knowledge
produced through basic science research. For instance, the entrepreneur-
ship training designed and evaluated by Campos et al. (2017) draws on
explanatory knowledge such as action theory ( Frese & Zapf, 1994 ). On
the other hand, design science is connected to basic science by revealing
novel explanation problems ( Bunge, 2017 ). Such explanation problems
may spark novel basic science inquiries. For example, a novel expla-
nation problem raised by Campos et al. (2017) may account for why
some entrepreneurship trainers are better at implementing the training
design.
Similarly, design science and practice in entrepreneurship are in-
terrelated through both knowledge and problems. On the one hand,
design science informs entrepreneurial practice by guiding its ac-
tions through design knowledge . For example, previous design sci-
ence work provides guidance on making decisions under uncertainty
( Sarasvathy, 2001 ), designing a business model ( Osterwalder, 2004 ;
Osterwalder & Pigneur, 2010 ), or developing an ecosystem strategy
( Talmar et al., 2020 ). On the other hand, entrepreneurial practice can
inspire design science research by revealing novel design problems –that
is, practical problems for which no design knowledge yet exists. Exam-
ples of such design problems include how best to design a global gov-
ernance system to address some of the grand challenges of the present
day ( Romme et al., 2018 ), or how to develop a thriving entrepreneurial
ecosystem in Europe or the United States. Similarly, artifacts from en-
trepreneurial practice can be studied by design scientists in their search
for novel design knowledge ( vom Brocke & Maedche, 2019 ). For ex-
ample, the heuristics used by expert entrepreneurs can be studied to
develop knowledge on how to make good entrepreneurial decisions un-
der uncertainty. Thus, entrepreneurial practice and design science are
also mutually interrelated.
Design knowledge as a specific body of scientific knowledge in
entrepreneurship
Related to the previous discussion, we also consider design knowl-
edge to be a specic scientic body of knowledge. We conceptualize
design knowledge as a relational concept that emerges at the intersec-
tion of a problem space and a solution space (see Fig. 2 ). While the
problem space includes knowledge on what kind of practical problem
exists, for whom, where, and when (e.g., how to foster small business
growth in Western Africa), the solution space comprises knowledge on
solution options (e.g., business training, psychological training, nan-
cial support, networking opportunities). While problem space and solu-
tion space can exist independently, it is only through relating them to
each other that design knowledge emerges ( vom Brocke et al., 2020 ).
More specically, we suggest that design knowledge emerges through
iterative design and evaluation processes ( Hevner et al., 2004 ; March &
Smith, 1995 ; Romme & Reymen, 2018 ; Simon, 1996 ; vom Brocke et al.,
2020 ). The two processes give rise to two specic types of knowledge:
one of devising courses of action (i.e., design object knowledge) and one
evaluating the devised courses of action (i.e., design evaluation knowl-
edge). In the following, we further discuss these two design knowledge
categories that together make up the body of design knowledge.
Design knowledge comprises design object knowledge
The rst category of design knowledge to emerge at the intersection
of the problem and the solution space is design object knowledge. De-
sign object knowledge is the knowledge prescribing courses of action on
how to change an existing situation into a preferred one ( Simon, 1996 ).
In general terms, design object knowledge can be described in the form
of ‘Do X in order to Y’ ( Berglund et al., 2018 ; Evered, 1976 ; Gregor et al.,
2020 ; Whetten, 2009 ), where X refers to some kind of action, process,
or event(s) and Y refers to a desired outcome. The design object knowl-
edge component in Campos et al. (2017) can be sketched as follows:
Implement psychological entrepreneurship training (X) to make small
business owners more successful (Y). A key characteristic that dieren-
tiates design object knowledge from explanatory knowledge is the tem-
poral orientation ( Dimov, 2016 ; Evered, 1976 ; Whetten, 2009 ). While
design object knowledge is oriented toward a future state of the world,
explanatory knowledge involves examining the past to explain a present
situation. While design object knowledge prescribes means to achieve
an outcome (X in order to Y), explanatory knowledge explains an eect
through a cause (Y because of X) ( Whetten, 2009 ). This future orienta-
tion of design object knowledge is closely in sync with the knowledge
that practitioners in entrepreneurship are seeking ( Dimov, 2016 ).
It is important to note that, while design knowledge can be
highly specic, it can also be found at increasing levels of abstrac-
tion ( Berglund et al., 2020 ; Gregor & Hevner, 2013 ; Holmström et al.,
2009 ). More specic design object knowledge can be seen, for in-
stance, in a situated artifact. Situated artifacts are detailed blueprints
that devise specic courses of actions ( vom Brocke et al., 2020 ). In
Campos et al. (2017) , the entrepreneurial training design to be re-
alized by the trainers is an example of such a situated artifact. On
a more abstract level, design object knowledge can take the form
of entrepreneurial design principles ( Berglund et al., 2018 ; Gregor
& Hevner, 2013 ; Gregor et al., 2020 ; vom Brocke et al., 2020 ). De-
sign principles abstract and generalize knowledge on how and why
artifacts work. For instance, design principles following the CIMO
logic ( Denyer, Traneld & Van Aken, 2008 ) specify the context
(C), the intervention (I), the mechanism (M) and the desired out-
come (O) to devise action. To illustrate, a design principle based on
Campos et al. (2017) might be to increase small rm prots (O) in West-
ern African countries (C), conduct a personal initiative training (I) that is
eective because it (among other things) increases business innovative-
ness, diversication of product lines, and access to nance (M). On an
even more abstract level, design object knowledge can be found in the
form of well-developed design theories. Design theory can be dened
as an abstract system of ideas that devises action ( Bunge, 2003 ). More
abstract forms of design object knowledge are similar to the notion of
what Holmström et al. (2009) call formal theory.
Design knowledge comprises design evaluation knowledge
A second category of design knowledge is what we in this study re-
fer to as design evaluation knowledge. Design evaluation knowledge is
knowledge about criteria, values, and value judgments related to the
quality of the design object, including its social worth ( Bunge, 1985 ).
Design evaluation knowledge relates to the quality of the devised ac-
tion rather than the devised action itself ( Bunge, 1996 ). It is about ad-
jectives, not nouns. It focuses on the usefulness and social worth of the
devised action. Design evaluation knowledge is the outcome of the eval-
uation process undertaken as part of design science ( Hevner et al., 2004 ;
Romme & Reymen, 2018 ; Simon, 1996 ). For example, the design evalua-
tion knowledge contribution of Campos et al. (2017) is that it rigorously
evaluated the eectiveness of the proposed entrepreneurship training.
While the process of evaluation is often seen as important for design sci-
ence ( Venable et al., 2016 ), design evaluation knowledge captures the
outcome of this evaluation process.
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Fig. 2. Design knowledge as a relational concept.
Design evaluation knowledge is arguable more crucial for design sci-
ence than for basic science in entrepreneurship. Basic science also makes
evaluations, but they are mostly internally oriented ( Berglund et al.,
2018 ; Bunge, 1985 ), meaning that they focus exclusively on scien-
tic objects such as data, hypotheses, and methods. In other words,
basic science in entrepreneurship does not primarily evaluate objects
outside the scientic realm ( Bunge, 1985 ). By contrast, design sci-
ence explicitly evaluates situations in the external world by thinking
about ‘real-life actions’ that change an existing situation into a pre-
ferred one ( Simon, 1996 ). For example, design scientists claim that it
is good for entrepreneurs to use eectuation principles to make deci-
sions under uncertainty ( Sarasvathy, 2001 ). They claim that personal
initiative training is good for creating economic prosperity in Western
Africa ( Campos et al., 2017 ). And they claim that the world should be
governed in a certain way ( Romme et al., 2018 ). For this reason, de-
sign evaluation knowledge is an important second design knowledge
category.
Particularly relevant for entrepreneurship scholars working on
design-oriented research are two types of design evaluation knowl-
edge. The rst is about the eectiveness and eciency of the de-
vised action ( Bunge, 1985 ). Here, eectiveness refers to whether a
plan is achieving its goal, while eciency is related to the eval-
uation of input/output ratios. Most journals publishing design sci-
ence research in the IS eld require this type of functional evalua-
tion of a designed artifact ( Venable et al., 2016 ; Venable, Pries-Heje
& Baskerville, 2012 ). However, there is also a second, often over-
looked evaluation that is about the social worth of the devised ac-
tion (e.g., Bunge, 1985 ; Myers & Venable, 2014 ). This second ques-
tion is also important because design knowledge can be both eec-
tive and ecient in guiding action but simultaneously socially worth-
less or even harmful (e.g., Bunge, 1985 ; Myers & Venable, 2014 ).
McMullen, Brownell and Adams (2021) provided the following exam-
ple: ‘Consider entrepreneurs engaged in the production and sale of
methamphetamines. They may provide a product to customers that of-
fers value such that customers receive more perceived immediate ben-
et from consuming the drugs than they pay to purchase them. Cus-
tomers may be satised, and entrepreneurs may prot, but the external
costs of these transactions on society in terms of additional health care
costs, crime, and so on are not fully accounted for’ ( McMullen et al.,
2021 : 6). Thus, we consider knowledge on the social worth of a
devised action to be a second, relevant type of design evaluation
knowledge.
In summary, we suggest that design knowledge can be conceptual-
ized as a relational concept that emerges at the intersection of a problem
and a solution space and comprises both design object knowledge and
design evaluation knowledge.
Guiding principles for doing design science in entrepreneurship
In this section, we build on the previously discussed conceptual foun-
dations to provide practical guidance on how to conduct good design
science in entrepreneurship (see Table 1 ). We formulate four guiding
principles grounded in the dimensions which we have identied as inte-
gral to design science as a specic scientic approach: (1) aim, (2) prob-
lem, (3) background knowledge , and (4) methods ( Bunge, 1996 : 79). For
each of these dimensions, we provide one overarching guiding principle
as well as more specic guiding questions to provide guidance for plan-
ning, conducting, and assessing design science research in entrepreneur-
ship. To develop the guiding frameworks, we draw considerably on work
from other elds, and particularly the IS eld (e.g., Baskerville et al.,
2018 ; Gregor & Hevner, 2013 ; Gregor et al., 2020 ; Hevner et al., 2004 ;
Venable et al., 2012 ; vom Brocke et al., 2020 ).
Guiding principle 1: be explicit in outlining the contributions to the body of
design knowledge in entrepreneurship
The rst guiding principle is to make the contribution to the en-
trepreneurship eld explicit. The aim of design science is to contribute to
the body of design knowledge (e.g., Holmström et al., 2009 ; vom Brocke
et al., 2020 ) and hence the aim of design science in entrepreneurship
is to contribute to design knowledge in the entrepreneurship eld. Ex-
plicitness about the intended contribution is useful in planning, com-
municating, and assessing design science work. However, the question
of how to be explicit is more easily asked than answered (e.g., Corley
& Gioia, 2011 ). In the design knowledge contribution framework, we
provide a taxonomy that may help to make the types of contributions
explicit (see Fig. 3 ). We outline four distinct types of contributions
along two axes, denoting the type of design knowledge and type of
contribution. As discussed above, we dierentiate two types of design
knowledge: design object and design evaluation knowledge. Further-
more, we draw on an often-used dierentiation of the type of contri-
bution by distinguishing between theoretical and empirical contribu-
tions (e.g., Corley & Gioia, 2011 ; Whetten, 1989 ). By theoretical con-
tributions, we refer to the development or extension of ideas –that
is, concepts, propositions, classications, theories, methods and what-
ever else can be thought of ( Bunge, 2003 ). By empirical contributions,
we refer to the characterization of objects of experience (e.g., observ-
ing, describing, measuring) as well as the realization of artifacts (e.g.,
constructing a material artifact or realizing a training design) ( Bunge,
1967 ).
A rst contribution to the entrepreneurship literature that should be
made explicit is the theoretical design object contribution. By theoretical
design object contributions, we refer to the design or extension of sit-
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Table 1
Guiding principles for planning, conducting, and assessing design science.
Dimension Guiding principle Guiding questions
1. Aim Be explicit in outlining the contributions to the
body of design knowledge in entrepreneurship
• In which ways is the developed design knowledge novel
and advances the entrepreneurship eld?
• Have you outlined the relevant contributions advancing
design object knowledge in entrepreneurship?
• Have you discussed design evaluation contributions to the
entrepreneurship eld?
2. Problem Be specic in positioning the design science study
within the existing entrepreneurship literature
• Is the design problem in the entrepreneurship literature
clearly stated that motivates this study?
• Is the respective entrepreneurship discourse adequately
represented or is it a strawman?
• Is the
type of design science study made explicit?
3. Background
Knowledge
Be comprehensive in drawing on the best available
scientic knowledge in analyzing, designing, and
evaluating
• Have you utilized state-of-the-art explanatory knowledge
in analyzing, designing, and evaluating?
• Do you draw on the best available design knowledge in all
main phases of your design science study?
• Are relevant concepts clearly dened?
4. Methods Be rigorous in using tting methods depending on
the state of prior knowledge
• Do the methods t the state of prior knowledge in the
respective phase of the project?
• Are the methods rigorously performed?
• Is the process transparently documented?
Fig. 3. Design knowledge contribution framework.
uated artifacts, design principles or design theories ( Baskerville et al.,
2018 ; Bunge, 2003 ; Gregor & Hevner, 2013 ; Holmström et al., 2009 ).
For example, Campos et al. (2017) contributed a novel situated arti-
fact by designing psychological entrepreneurship training. On a slightly
higher level of abstraction, the same design science study may have also
contributed to the development of design principles by explaining how,
why, and under what conditions the training is useful (e.g., Denyer et al.,
2008 ; Gregor et al., 2020 ; van Aken, 2004 ). Follow-up research could
then extend these design principles –for example, by identifying novel
mechanisms or by extending the breadth of the context in which this psy-
chological entrepreneurship training is eective. Furthermore, another
contribution could be to build on this evolving body of theoretical design
knowledge and develop even more abstract and general entrepreneur-
ship design theories ( Holmström et al., 2009 ). It is important to note
that such theoretical design object contributions can also be made in
the form of process models ( Mohr, 1982 ), which specify the sequence
of events or means that lead from an existing situation into a preferred
one ( Simon, 1996 ).
Second, the intended empirical design object contribution should also
be made explicit in a study. Empirical design object contributions re-
fer to the realization of artifacts or their empirical characterization
( Bunge, 1967 ). A rst main empirical contribution can be made by con-
structing a material artifact. In the IS eld, the realization of a design
idea is also referred to as an instantiation ( March & Smith, 1995 ). In the
entrepreneurship domain, such an empirical design object contribution
could be, for example, a built prototype, a realized training program, or
a realized startup. Making or realizing a theoretical artifact is an empir-
ical contribution to design knowledge, which can also be necessary to
empirically evaluate a theoretical design object ( Venable et al., 2012 ).
A second, still underrepresented empirical design object contribution
is describing an action or artifact and its eects ( vom Brocke & Maed-
che, 2019 ). The description of an artifact and/or its eects provides an
empirical understanding of the artifact. Such empirical knowledge can
be a rst step toward developing theoretical design object knowledge
( vom Brocke & Maedche, 2019 ). In medicine, for example, it is often
not understood why a particular treatment has positive health eects
on patients. In some cases, the underlying mechanism is not theorized
until many years later, often based on empirical cases that describe the
intervention and its eects under dierent conditions ( Romme, 2003 ).
Third, contributions to design evaluation knowledge should also be
made explicit. A theoretical design evaluation contribution refers to the
development or extension of ideas on criteria, standards, and techniques
for evaluating design objects. So far, we have stressed mainly the func-
tional criteria of eectiveness and ecacy. However, for any given de-
sign object, other criteria for evaluation –such as usability, t with the
organization, simplicity, and beauty –can also be relevant ( Hevner et al.,
2004 ; Venable et al., 2012 ). For example, many startups are currently
working on lithium-ion batteries, which are used in electric vehicles, cell
phones, etc. The ability to store as much energy as possible while also
being inexpensive, durable, and sustainable are often among the desir-
able criteria or design requirements for batteries of this kind. Creating
novel relevant design criteria constitutes a theoretical evaluation knowl-
edge contribution. Furthermore, design science can also contribute by
weighting design object knowledge in terms of given criteria. For exam-
ple, a theoretical contribution to the body of design evaluation design
knowledge would be a normative argument on whether personal ini-
tiative training in Western Africa creates social value. At issue here is
the question: does such training provide more social value than harm
to the aected communities? Finally, theoretical design evaluation con-
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tributions that develop novel techniques for evaluation should be made
explicit. For example, the development of novel computational simula-
tion techniques to evaluate the eectiveness of entrepreneurship train-
ing would constitute such a contribution.
Fourth, the research project should make explicit whether it intends
to make an empirical design evaluation . Such a contribution can be the
empirical evaluation of a design object or the empirical description of
relevant design criteria. The most prominent empirical evaluation con-
tribution is probably the evaluation of realized artifacts ( Venable et al.,
2012 , 2016 ). This is a key contribution of Campos et al. (2017) . They
used empirical methods to test the eectiveness of the implemented
training in a randomized controlled trial with 1500 participants. How-
ever, there are other relevant empirical evaluation contributions to be
outlined. A design science study may contribute by empirically explor-
ing and describing criteria and standards for evaluating artifacts, such as
the empirical description and exploration of design requirements along
customer segments (e.g., Peers, Tuunanen, Rothenberger & Chatter-
jee, 2007 ; van Aken & Berends, 2018 ). Such empirical design evalua-
tion contributions should be made explicit if they go beyond the body
of knowledge and inform future design work.
Guiding principle 2: be specific in positioning the design science study within
the existing entrepreneurship literature
The second guiding principle is to be specic about how the design
science study is positioned within the existing design knowledge in the
entrepreneurship eld. It is important to be explicit about the design
problem or design opportunity that motivates the design science project.
While being specic about the positioning of the design study is impor-
tant because it claries the purpose of the study (and largely inuences
the way the design problem/opportunity is addressed), the question of
how to position a study is challenging for every design science project. In
the design science positioning matrix, we propose four types of design
science positionings: evaluation, improvement, exaptation, and explo-
ration (see Fig. 4 ) . The matrix builds on Gregor and Hevner (2013) and
draws on the conceptualization of design knowledge as a relational con-
cept premised on means-ends relationships and their evaluation. We
distinguish between known and unknown means or ends to outline
the four resulting positionings, which we outline in more detail in the
following.
A rst way of positioning a design science study in the existing
entrepreneurship literature is to position it as an evaluation study.
For evaluation studies, the key question is whether an already known
Fig. 4. Design science positioning matrix.
means-ends relationship is in some way useful. For example, Campos
et al. (2017) positioned their study as an evaluation study. They sug-
gested that psychologically grounded entrepreneurship training could
be eective for small business owners. The authors positioned their
study against this backdrop, stating that ‘few attempts have been made
to experimentally evaluate the success of teaching such attributes to own-
ers of small-scale businesses in developing countries’ ( Campos et al.,
2017 : 1287, emphasis added). While such evaluation studies can test
the artifact, they can also evaluate more abstract design principles
( Gregor et al., 2020 ; van Aken, 2004 ) or design theories ( Gregor &
Hevner, 2013 ; Holmström et al., 2009 ). In these cases, there are par-
allels with theory-testing research in basic science entrepreneurship re-
search projects (e.g., Edmondson & McManus, 2007 ). If some theoretical
knowledge exists, the question is whether the theory is indeed true (ba-
sic science) or useful (design science). A design science study framed
as an evaluation study can employ a variety of evaluation methods,
including observational (e.g., eld study, case study), analytical (e.g.,
static analysis, optimization, dynamic analysis), experimental (e.g., con-
trolled experiments), testing (e.g., functional and structural testing), or
descriptive (e.g., informed argument, case-based reasoning, scenarios)
( Hevner et al., 2004 ). For a comprehensive framework of evaluation
methods, see also Venable et al. (2012) .
A second way to position a design science study in the entrepreneur-
ship literature is as an improvement study. In an improvement study,
the goal or the end can be dened suciently from the beginning,
while the means for achieving that end need to be developed ( Gregor
& Hevner, 2013 ; Hevner & Gregor, 2020 ). This is the case for the ques-
tion of how to make a ‘machine bureaucracy’ ( Mintzberg, 1980 : 322)
more entrepreneurial. In this case, the ‘end’ of becoming more en-
trepreneurial is rather clearly dened. The question is how to nd means
for achieving that end. Such improvement projects are very common in
both the management and IS elds ( Peers et al., 2007 ; van Aken &
Berends, 2018 ), and both elds have developed methodologies for ad-
dressing such improvement projects (e.g., Peers et al., 2007 ; van Aken
& Berends, 2018 ). These methodologies both propose a rather structured
process from (1) analyzing the problem to (2) designing solutions and
(3) evaluating them ( Peers et al., 2007 ; van Aken & Berends, 2018 ). In
the example of ‘machine bureaucracy’, a design scientist might, say, rst
try to analyze the problem by understanding the underlying reasons for
why the ‘machine bureaucracy’ is not suciently entrepreneurial. Build-
ing on this analysis, the scientist would then develop a design for how
to reorganize the rm. Finally, the solution would be evaluated using
appropriate methods.
While the previous two positionings are rather common, a less ex-
plored type of positioning is exaptation studies. In exaptation studies, the
means are typically well known, but the associated ends have yet to be
explored ( Gregor & Hevner, 2013 ; Hevner & Gregor, 2020 ). For exam-
ple, many imagine that blockchain technology (i.e., means) has many
potential uses in entrepreneurship; however, in many areas, the actual
use – i.e., the end –has yet to be explored. In the IS eld, an impor-
tant stream of research investigates the aordances, or benets, of new
technologies –such as big data analytics technologies ( Dremel, Hert-
erich, Wulf & Vom Brocke, 2020 ; Lehrer, Wieneke, Vom Brocke, Jung
& Seidel, 2018 ), in-memory computing ( vom Brocke, Debortoli, Müller
& Reuter, 2014 ) or blockchain technology ( Werner, Basalla, Schneider,
Hays & Vom Brocke, 2021 ). This kind of design science positioning is
likely to require more creativity if it is to suggest novel ends. Research
in IS has started to develop methodologies for supporting exaptation in
the sense of identifying and evaluating use cases of technology in con-
text ( Thomas & Vom Brocke, 2010 ; vom Brocke, Sonnenberg, & Simons,
2009 ). We think that further developing such approaches for the eld
of entrepreneurship oers great promise.
Finally, a design science project can be positioned as an explo-
ration project. Exploration projects are characterized by an open search
for novel means-ends relationships. Exploration projects may be the
most entrepreneurial of the four types of design science research
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projects ( Gregor & Hevner, 2013 ; Hevner & Gregor, 2020 ). Gregor and
Hevner (2013) described these projects as follows: ‘True invention is
a radical breakthrough –a clear departure from the accepted ways
of thinking and doing’ ( Gregor & Hevner, 2013 : 346). They continue:
‘This type of work does not t neatly with some models of DSR where
the rst step is shown as “dene the research problem and justify the
value of a solution ”(see Peers et al. 2008). Here, a recognized prob-
lem may not necessarily exist, and the value of a solution may be un-
clear. As Simon (1996) says, the researcher may be guided by nothing
more than “interestingness ”’ ( Gregor & Hevner, 2013 : 346). Examples
of such research in the entrepreneurship eld include the rst thinking
about eectuation principles ( Sarasvathy, 2001 ) or, in the IS eld, ‘the
rst thinking on decision support systems (DSS) by Scott-Morton (1967)’
( Gregor & Hevner, 2013 : 346). These design science projects are likely
to progress in a highly iterative and creative fashion, with the problem
space and the solution space being explored ( vom Brocke et al., 2020 ).
Novel problems are explored for which novel solutions are designed,
which may further improve the understanding of the problem and, in
turn, provide new insights into solutions and so forth. Arguably, this
approach is entirely consistent with an entrepreneurial mindset.
Guiding principle 3: be comprehensive in drawing on the best available
scientific knowledge in analyzing, designing, and evaluating
A third guiding principle for conducting good design science is to
draw on the best scientic knowledge available in analyzing, design-
ing, and evaluating. Building on the existing body of scientic knowl-
edge is one of the hallmarks that dierentiates design science from prac-
tice ( Drechsler & Hevner, 2018 ). However, the question of how to ef-
fectively leverage the existing knowledge base is by no means trivial
( Drechsler & Hevner, 2018 ). In the knowledge base utilization frame-
work (see Fig. 5 ), we provide guidance on how to leverage the knowl-
edge base (i.e., explanatory knowledge and design knowledge) for three
typical sub-problems of a design science project: analysis, design and
evaluation (e.g., Peers et al., 2007 ; Sein, Henfridsson, Purao, Rossi
& Lindgren, 2011 ; van Aken & Berends, 2018 ). By analysis, we refer
to the sub-problem of dening and understanding a practical problem
( van Aken & Berends, 2018 ). By design, we refer to the deliberate cre-
ation of an artifact or design object ( van Aken & Berends, 2018 ). By eval-
uation, we refer to the assessment of the usefulness (and social worth)
of the created artifact ( van Aken & Berends, 2018 ; Venable et al., 2012 ).
In the following, we discuss in more detail how the knowledge base can
be leveraged for each of these three sub-problems.
For the