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Research Policy
journal homepage: www.elsevier.com/locate/respol
Goal heterogeneity at start-up: are greener start-ups more innovative?
Brigitte Hoogendoorn
a,⁎
, Peter van der Zwan
b
, Roy Thurik
c
a
Assistant Professor of Entrepreneurship, Erasmus School of Economics, Department of Applied Economics, Erasmus University Rotterdam, Burg Oudlaan 50, 3062 PA
Rotterdam, The Netherlands
b
Associate Professor of Entrepreneurship, Leiden University, The Netherlands
c
Professor of Entrepreneurship and Economics, Montpellier Business School, France, and Emeritus Professor of Economics of Entrepreneurship, Erasmus University
Rotterdam and Free University in Amsterdam, The Netherlands
ARTICLE INFO
Keywords:
Goal heterogeneity
Start-ups
Green entrepreneurship
Environmental regulations
Innovation
Global Entrepreneurship Monitor
ABSTRACT
Start-ups differ in the extent to which they introduce innovations to markets and, hence, in their potential
contribution to society. Understanding the heterogeneous character of start-ups is key to explaining the varia-
bility in innovation. In this study, we explore whether start-ups that place more emphasis on environmental
value creation versus economic value creation (‘greener start-ups’) are more innovative. We also examine how
environmental regulations at the country level affect this relationship. We theorize that the fundamental dif-
ference between economic value creation (private wealth generation, i.e., self-regarding interest) and en-
vironmental value creation (environmental gains for society, i.e., other-regarding interest) influences en-
trepreneurial opportunity identification and exploitation. When considering the regulatory context, we draw on
the innovation inducement effect of environmental regulations and expect these regulations to be most effective
for entrepreneurs with a strong emphasis on economic value creation. Performing multi-level ordered logit
regressions r with 2,945 start-up entrepreneurs in 31 countries (Global Entrepreneurship Monitor data), we find
that ‘greener start-ups’ are more likely to engage in product and process innovations. We find some evidence of a
positive moderation effect for environmental regulations. We advance research on innovative entrepreneurship
by theorizing and finding evidence that other-regarding goals are relevant in explaining start-up innovativeness.
1. Introduction
Innovation is a central aspect of entrepreneurship
(Schumpeter, 1934) and an important goal for policymakers. Start-ups
have been recognized as the engine behind innovative behaviour,
leading to increased competition, employment generation, and, ulti-
mately, economic growth (Hébert and Link, 1989;Schumpeter, 1934).
Recently, there is increased interest on the potential contribution of
start-ups in bringing solutions to environmental challenges such as
climate change and biodiversity loss (Shevchenko, Lévesque, and
Pagell, 2016;York and Venkataraman, 2010). However, en-
trepreneurial firms, particularly start-ups, differ in the extent to which
they introduce innovations to markets (Davidsson and Wiklund, 2001;
Bhave, 1994). Understanding heterogeneity among start-ups is the key
to explaining their variability in innovation and, subsequently, their
potential contribution to the economy and society
(Colombelli, Krafft, and Vivarelli, 2016). The central question of this
study is whether the goals pursued by start-ups, in particular their drive
to realize environmental gains for society, influence their innovative-
ness. In other words: are greener start-ups more innovative?
Previous research has addressed why some firms are more in-
novative than others by focusing on contextual, firm, and individual-
level factors (Autio et al., 2014;Block, Fisch, and Van Praag, 2017;
Cohen, 2010;Galende, 2006), with particular interest on the regulatory
context where environmental innovations
1
are concerned (Jaffe et al.,
2002 and 2005;Rennings, 2000). A fundamental assumption under-
lying this literature is that organizations are singularly driven by eco-
nomic self-interest (Van de Ven et al., 2007;Cohen et al., 2008).
However, by assuming economic self-interest, the existing research
neglects the possibility that entrepreneurs are motivated by other-re-
garding interests such as the drive to contribute to a better environ-
ment. Differences in pursued goals may have consequences on
https://doi.org/10.1016/j.respol.2020.104061
Received 24 June 2020; Accepted 24 June 2020
⁎
Corresponding author.
E-mail address: bhoogendoorn@ese.eur.nl (B. Hoogendoorn).
1
Environmental innovations can be defined as those innovations that “consist of new or modified processes, techniques, systems, and products to avoid or reduce
environmental damage” (Kemp et al. (2001) in Horbach (2008: 163)). In this study, we consider innovativeness in general terms without explicitly referring to the
impact of these innovations on the natural environment.
Research Policy xxx (xxxx) xxxx
0048-7333/ © 2020 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/).
Please cite this article as: Brigitte Hoogendoorn, Peter van der Zwan and Roy Thurik, Research Policy,
https://doi.org/10.1016/j.respol.2020.104061
entrepreneurial judgement, behaviour, and outcomes such as innova-
tion (Shane et al., 2003;Van de Ven et al., 2007). For instance, an
entrepreneur who is determined to improve the quality of the en-
vironment may decide to invest in sustainable energy sources despite a
negative business case. Therefore, considering heterogeneity in goals
may increase our understanding of what drives variability in innova-
tion.
This study examines the relationship between start-up goals and
innovativeness and how environmental regulations affect this re-
lationship. In particular, we consider a start-up's drive to create en-
vironmental value relative to economic value as a source of goal het-
erogeneity. Our argumentation draws on the fundamental difference
between economic value creation, which concerns private wealth
generation (i.e., self-regarding interest), and environmental value
creation, which relates to environmental gains for society (i.e., other-
regarding interest) (Van de Ven et al., 2007). Although it has been
stressed that environmental entrepreneurs are driven by economic (self-
regarding) and environmental (other-regarding) motives
(Thompson et al., 2011;Leno and York, 2011;York et al., 2016), few
studies have examined the consequences of this combined logic.
We argue that the relative importance of environmental value
creation over economic value creation influences the entrepreneur's
opportunity identification and incentive to innovate. In line with pre-
vious research on other-regarding behaviour (De Dreu and Nauta, 2009;
Grant and Berry, 2011;Meglino and Korsgaard, 2004), we reason that
pursuing other-regarding goals requires empathy for others’ viewpoints,
which demands a full understanding of the preferences and needs of
others and encourages the development of useful and innovative ideas.
In addition, the drive to contribute to environmental improvement is
likely to be fuelled by dissatisfaction with prevailing practices, which
serves as an additional motivator to discover innovative opportunities
(Cliff, Jennings, and Greenwood, 2016; Shepherd and DeTienne, 2005).
In addition to opportunity identification, pursuing environmental goals
other than economics ones may help overcome the appropriation pro-
blem inherent in the process of innovating when expected private re-
turns are low but expected societal returns are prevalent. When con-
sidering the effect of environmental regulations, we draw on the
inducement effect of such regulations on innovation (Jaffe et al, 2005;
Porter and Van der Linde, 1995) and argue that environmental reg-
ulations appeal to economic incentives to innovate (i.e., innovating to
avoid increased costs of production and profit from increased customer
demand and reduced risk) (Ambec et al., 2013;Wagner, 2003).
We make use of the 2009 round of the Global Entrepreneurship
Monitor (GEM) that provides information about entrepreneurs’ goals
including environmental value creation goals or “green goals.”
Performing multi-level regressions for 2,945 start-up entrepreneurs in
31 countries, we find that the value-creating goals of start-ups are im-
portant for the probability of adopting innovations. That is, start-ups
that place stronger emphasis on environmental value creation relative
to economic value creation (“greener start-ups”) are more likely to be
involved in product innovation and in process innovation than start-ups
that focus primarily on the creation of economic value. Furthermore,
this relationship between start-up goals and innovation is rather uni-
form across economies. We find some evidence of a moderating role for
environmental regulations in that greener start-ups are more likely to
innovate at the product level in countries with stricter environmental
regulations.
Our study makes the following contributions. First, we advance re-
search on entrepreneurship by addressing the consequences of other-
regarding motives as a source of heterogeneity among start-ups.
Although the pursuit of other-regarding goals is increasingly addressed
in non-traditional forms of entrepreneurship such as social, sustainable,
and environmental entrepreneurship, the consequences of pursuing such
goals are less well-researched. Whereas others have studied the con-
sequences in terms of organizational challenges, organizational design
principles, and start-up success (Battilana and Lee, 2014;Renko, 2013;
Parrish, 2010), we explore the consequences of other-regarding goals
for a start-up's innovativeness. We predict and find a significant and
positive relationship between environmental (relative to economic)
value-creation goals and innovativeness such that “greener start-ups”
are indeed more innovative.
Second, we contribute to the discussion on the inducement effect of
environmental regulations on innovation; we theorize that the in-
ducement effect may play out differently for different types of firms.
Past research assumes that environmental regulations are needed to
push profit-maximizing firms to overcome market failures, behavioural
shortcomings, or organizational inertia to address overlooked profitable
opportunities for innovation (Ambec et al., 2013;Kozluk and
Zipperer, 2015). We theorize that non-economic motives, such as the
pursuit of environmental value creation, may alter the inducement
mechanism. Entrepreneurs who pursue other-regarding motives may
innovate without the additional economic incentives from regulatory
interventions. While we did not find convincing moderation effects
across the board, we did find some evidence for a moderating role of
sturdier environmental regulations. We believe our sample of start-ups
drives this result and more research is warranted to explore the in-
ducement effect for different types of firms.
Third, this study adds to our understanding of the influence of en-
vironmental regulations by investigating the relationship between en-
vironmental regulations, goal heterogeneity among start-ups, and dif-
ferent types of innovation in interaction. Other studies on this
intersection are mainly single-country and single-sector studies
(Kammerer, 2009;Horbach, 2008;Cleff and Renning, 1999) or lack a
hierarchical structure (Triguero et al., 2013). Our multi-level approach
addresses the point that “individual differences (as well as cultural
contexts) are likely to influence the relative balance between self- and
collective interests in explaining entrepreneurial behaviour”
(Van de Ven et al., 2007, p. 367). Our results suggest that in countries
with sturdier environmental regulations, greener start-ups are more
likely to innovate at the product level.
This paper is structured as follows. The next section provides a lit-
erature background, followed by an introduction to our hypotheses.
The paper continues with a description of our data and the methods
applied. After the results are presented, a discussion and conclusion
follow in the final section.
2. Literature review
We first introduce the concept of environmental entrepreneurship
relative to traditional entrepreneurship and to other forms of en-
trepreneurship characterized by other-regarding start-up motives (i.e.,
social and sustainable entrepreneurship). Subsequently, we discuss the
innovativeness of new enterprises and in particular how entrepreneurs’
individual characteristics shape the process of opportunity identifica-
tion and exploitation. After discussing the regulatory environment and
its inducement effect on innovation, we theorize how heterogeneity in
goals, opportunity identification, and the regulatory environment
combine to affect start-up innovativeness by formulating hypotheses in
the subsequent section.
2.1. Environmental entrepreneurship and other-regarding goals
The scholarly field of entrepreneurship concerns “how, by whom,
and with what effects opportunities to create future goods and services
are discovered, evaluated, and exploited” (Shane and
Venkataraman, 2000, pp. 218). The entrepreneur, defined here as
someone who starts, owns, and leads a business on his or her own ac-
count and risk (Reynolds et al., 2005;Sternberg and Wennekers, 2005),
is a central agent in the entrepreneurial process of identifying and ex-
ploiting these opportunities and introducing innovations. The dominant
assumption that prevails in the entrepreneurship literature is that the
entrepreneur is driven by self-interested profit-seeking motives
B. Hoogendoorn, et al. Research Policy xxx (xxxx) xxxx
2
(Van de Ven et al., 2007;Cohen et al., 2008). Although it is widely
acknowledged that the motives of individual agents to start and run a
venture are multiple (Shane et al., 2003;Hessels et al., 2008), they
mainly reflect self-regarding interests. There is convincing support for
non-economic start-up motives such as lifestyle considerations, being
independent, and gaining status (see Parker, 2009 for an overview).
However, people are driven by both self- and other-regarding goals
(Piliavin and Charng, 1990), and they differ in the extent to which they
pursue these goals (Meglino and Korsgaard, 2004). Nevertheless, pur-
suing other-regarding interests is less well-researched and increasingly
finds its expression in social, environmental, and sustainable en-
trepreneurship literature (Dacin et al., 2010;Thompson et al., 2011;
Van de Ven et al., 2007).
Social, environmental, and sustainable entrepreneurship all offer an
alternative paradigm to traditional entrepreneurship. Compared to
traditional for-profit entrepreneurship, they represent other-regarding
motives and outcomes that exist in the exploitation of opportunities
that relate to societal relevant issues (Cohen et al., 2008). What dis-
tinguishes environmental entrepreneurs from social and sustainable
entrepreneurs are (1) their environmentally relevant motivations, (2)
their seizing of opportunities that render both economic and environ-
mental benefits, and (3) their exclusive focus on environmentally re-
levant market failures (Thompson et al., 2011). Environmental en-
trepreneurs address environmental degradation through the creation of
financially profitable organizations while social and sustainable en-
trepreneurs exploit these opportunities through for-profit, community-
based, and nonprofit organizations (York et al., 2016, p. 695).
2
En-
vironmental entrepreneurs are, next to self-regarding profit motives,
directly driven by the motivation to contribute to environmental gains
for others in the society even when there is no sound “business case”
(Hockerts and Wüstenhagen, 2010;Pachecoet al., 2010;Shepherd and
Patzelt, 2011).
Although environmental entrepreneurs combine economic and en-
vironmental goals, and thereby pursue self- and other-regarding inter-
ests, they do so to varying degrees. Hence, environmental entrepreneurs
form a diverse group that entails individuals who aim to change the
world and improve the quality of the environment at the expense of
economic objectives and individuals who seize environmental oppor-
tunities primarily for private wealth generation purposes
(Anderson and Leal, 2001;York et al., 2016). The line between “green”
and “non-green” entrepreneurs is empirically difficult to draw. There-
fore, we treat environmental value creation as a continuum and explore
the relative emphasis on environmental versus economic value creation
at start-up as our variable of interest.
2.2. Innovative entrepreneurship and individual characteristics
Firms differ in the extent to which they introduce innovations to
markets (Bhave, 1994;Davidsson and Wiklund, 2001). Various per-
spectives that explain this variation exist including the resource-based
view, with a focus on internal resources and capabilities (Barney, 1991;
Teece, 2006); industrial organization, which stresses market and in-
dustry characteristics (Douma and Schreuder, 1992); and the evolu-
tionary approach, which emphasizes accumulation of knowledge and
path-dependency over time (Nelson and Winter, 1977).
3
The innova-
tiveness of new enterprises finds its expression in the entrepreneurship
literature and, more specifically, the innovative entrepreneurship lit-
erature (Block et al., 2017), which traditionally stresses the character-
istics of entrepreneurs and sources of opportunities (Autio et al., 2014;
Shane, 2003).
New enterprises are more likely to be innovative when the en-
trepreneur possesses certain personality characteristics. Notably,
Schumpeter (1934) stresses individual creativity, and Kirzner (1973)
emphasizes the importance of entrepreneurial alertness in the process
of opportunity recognition. The entrepreneurs’ individual character-
istics that have been addressed more recently are experiences, beliefs,
capabilities, and socio-economic characteristics (Block et al., 2017). For
example, Koellinger (2008) demonstrates that self-confidence and
educational attainment relate to innovative entrepreneurship, and
Cliff et al. (2006) show that entrepreneurs with greater experience in
other industries are more likely to act as innovative entrepreneurs.
Entrepreneurs’ individual characteristics shape the process of op-
portunity identification and exploitation (Shane, 2003). Put differently,
the act of identifying opportunities and the extent to which resources
and capabilities are allocated to innovation are acts of individual jud-
gement and decision-making (Cliff et al., 2006). Changing market
conditions (e.g., consumer preferences, available technologies, and
demographics) produce new information that serves as a source of
entrepreneurial opportunity (Eckhardt and Shane, 2003). However,
individuals differ in their access to such information (e.g., effort put
into acquisition or networking), beliefs about the information, and their
ability to cognitively process the information for opportunity identifi-
cation (Dyer et al., 2008;Shepherd and DeTienne, 2005).
The pursuit of other-regarding interests affects how individuals ac-
quire and process information and, hence, how entrepreneurial op-
portunities are identified (Grant and Berry, 2011;Van de Ven et al.,
2007). The desire to help or contribute to others in society encourages
empathy for others’ viewpoints. Being sensitive to the needs of others
requires the consideration of the perspectives of multiple others and
stimulates the understanding of the preferences, needs, and values of
others (Grant and Berry, 2011;Meglino and Korsgaard, 2004). Taking
multiple perspectives influences one's information-acquiring behaviour
by intensely observing, questioning, and maintaining diverse social
networks to assess what others need and value (De Dreu and
Nauta, 2009). Being open to the viewpoints of others, as generated by
the desire to benefit others, has been found to stimulate creativity and
results in ideas that are novel and useful to others (Grant and
Berry, 2011). Additionally, questioning prevailing practices, percep-
tions of what is considered appropriate, and dissatisfaction with ex-
isting conditions motivate the discovery of innovative opportunities
(Shane and Venkataramen, 2000;Shepherd and DeTienne, 2005).
Empirical evidence suggests that “founders who more strongly question
the functional or ethical legitimacy of prevailing practices are also more
likely to do things differently” (Cliff et al., 2006, p. 634).
2.3. Innovation, appropriation, and regulation
In addition to individual characteristics and opportunities is the
context of influence on the innovativeness of new enterprises, with two
prevailing foci: clusters, networks, and alliances; and the regulatory
environment (see Block et al., 2017 for a review).
The regulatory environment legitimizes enterprise behaviour and
provides incentives that influence the direction of industrial sectors and
the creation of new enterprises (Audretsch et al., 2007;Meek et al.,
2010). In the realm of environmental challenges, the importance of the
regulatory environment is twofold. First, severe environmental damage
to society resulting from market failures (i.e., public goods, ex-
ternalities, monopoly power, inappropriate government intervention,
and imperfect information) (Dean and McMullen, 2007) provides a
strong economic rationale for public intervention (Rennings, 2000;
Jaffe et al, 2002 and 2005). Second, market failures in the innovation
process stimulate governments to adopt policies to encourage the de-
velopment and adoption of environmentally beneficial innovations
(Jaffe et al., 2005;Stenholm et al., 2013). A prominent market failure
concerns the existence of positive externalities in terms of knowledge
spillovers inherent in the entrepreneurial process of innovation due to
2
See Lenox and York (2011);Thompson, Kiefer, and York (2011); and
Belz and Binder (2015) for an extensive description of the distinctions and
commonalities among social, environmental, and sustainable entrepreneurship.
3
See Galende (2006) and Cohen (2010) for an overview of these approaches.
B. Hoogendoorn, et al. Research Policy xxx (xxxx) xxxx
3
the public good characteristics of the assets produced (new knowledge)
(Aghion et al., 2005;Audretsch et al., 2007). Knowledge spillovers (i.e.,
other firms benefitting from such new knowledge) imply that the pri-
vate returns to innovation are smaller than the social returns. Moreover,
the adoption of (environmental) innovations that gradually replace
older, less environmentally friendly products and processes produce
large societal benefits; however, the entrepreneur can only capture a
small portion of the value created for private gains (Jaffe et al., 2002).
Empirical studies establish that the social returns to innovation are
generally at least twice as high as the private returns (Teece, 2018). Due
to knowledge spillovers, appropriation (i.e., the degree to which a firm
is able to capture rents from its innovations) is almost always proble-
matic despite the existence of value-capture mechanisms such as pa-
tenting and licensing (Teece, 2006 and 2018)
4
. Hence, the prospect of
low private returns to innovation results in a lack of incentive to invest
and justifies government intervention.
Market failures associated with environmental damage interact with
market failures associated with innovation (Jaffe et al., 2005). En-
vironmental regulations directly reduce environmental damage and
indirectly induce firms to innovate (Porter, 1991;Porter and Van der
Linde, 1995). The introduction of environmental regulations, such as a
carbon tax, increases a firm's cost of production. This increase in costs
induces the firm to substitute inefficient and costly environmentally
unfriendly production methods and stimulates the development of new
products and services. Porter and Van der Linde (1995) were among the
first to suggest that the returns of such innovations might partially or
even more than fully offset the costs of compliance.
5
Environmental regulations serve as a trigger to overcome market
failures in the innovation process and to alter the appropriability of
innovations (Ambec, et al., 2013;Kozluk and Zipperer, 2015;
Wagner, 2003). For example, whereas knowledge spillovers cause en-
trepreneurs to be reluctant to invest in innovation due to low appro-
priation, sufficiently stringent environmental regulations may trigger
entrepreneurs to introduce new environmentally superior technologies
or replace existing production processes to overcome the cost of com-
pliance (Ambec et al., 2013). Additionally, environmental regulations
may serve to overcome asymmetric information that hinders consumers’
ability to correctly value environmentally superior offerings
(Ambec and Barla, 2002). As “the result of the state's selection and
enforcement of acceptable or preferred practices” (Meek et al., 2010, p.
495), environmental regulations signal the legitimization of environ-
mental issues as a broad societal goal, increase environmental con-
sciousness among consumers and, as a result, influence home market
demand and appropriation (Kostova and Roth, 2002;Scott, 1995).
Entrepreneurs can reduce the uncertainties involved in introducing
innovations by addressing these broadly accepted environmentally re-
levant goals (Meek et al., 2010;Aguilera-Caracuel and Ortiz-de-
Mandojana, 2013). Abundant empirical literature confirms that en-
vironmental regulations stimulate innovation, although the results de-
pend, at least in part, on the proxy used for innovation (mostly mea-
sured as research and development (R&D) expenditures or (green)
patents), on the sector analysed and on the environmental regulations
under scrutiny (Ambec et al., 2013;Barbieri et al., 2016;Ghisetti and
Pontoni, 2015).
In the next section, we hypothesize how heterogeneity in goals at
start-up (i.e., economic and environmental goals) relates to opportunity
identification and exploitation and how the regulatory context influ-
ences this relationship.
3. Hypotheses
3.1. Heterogeneity in goals and innovativeness
We now theorize how heterogeneity in goals and the act of identi-
fying and exploiting opportunities combine to affect start-up innova-
tiveness. Environmental entrepreneurs seize opportunities that render
both economic benefits for private gains as well as environmental gains
for society (Thompson et al., 2011). We argue that the motivation to
contribute to environmental gains for others in the society (i.e., serving
other-regarding interests) stimulates entrepreneurs to take multiple
perspectives (Van de Ven et al., 2007). In line with previous research
(Grant and Berry, 2011;Meglino and Korsgaard, 2004), taking multiple
perspectives results in different and more complete views of opportu-
nities and stimulates opportunity identification.
Moreover, entrepreneurs strongly driven by the desire to achieve
environmental improvements are likely to be dissatisfied with the
current conditions of the natural environment or the detrimental be-
haviour of prevailing business practice (Pinkse and Groot, 2015;
Meek et al., 2010) including prevailing ethical and moral standards
(York and Venkataraman, 2010). Hence, we expect that environmental
entrepreneurs, more so than traditional entrepreneurs, question pre-
vailing practices, have deviating perceptions of what is considered
appropriate, and are more dissatisfied with existing circumstances.
These conditions motivate the discovery of innovative opportunities
(Shane and Venkataramen, 2000;Shepherd and DeTienne, 2005).
Next, we argue that an entrepreneur's drive to create environmental
value relative to economic value also influences his or her incentive to
innovate. The decision to allocate resources to innovation activities
depends on the expected degree to which economic value or private
rents can be captured by the investing firm (Audretsch et al., 2007;
Jaffe et al., 2002). However, the existence of knowledge spillovers
poses appropriation problems and decreases the likelihood that a profit-
maximizing entrepreneur will invest in innovative activities
(Jaffe et al., 2002 and 2005). Moreover, entrepreneurs with a strong
drive to create environmental value differ in their effort to appropriate
economic value from their entrepreneurial activities (Van de Ven et al.,
2007) and may strive to improve the quality of the environment at the
expense of economic objectives. A strong drive to create environmental
value as an integrated part of the business logic will reduce reluctance
to invest when, despite appropriation problems, societal benefits can be
realized, resulting in a stronger incentive to innovate.
Hence, entrepreneurs who are characterized by a strong drive to
create environmental gains for others in the society deviate in their
opportunity identification and their incentive to innovate. Based on
these arguments, we formulate the following hypothesis:
H1: Start-ups that pursue environmental (relative to economic) value-
creation goals are more innovative.
3.2. The moderating role of environmental regulations
We argue that environmental regulations appeal to the economic
incentives of appropriation. As costs of production increase due to en-
vironmental regulations, firms will seek to offset such cost increase by
innovating into less costly ways of production or alternative production
methods. Environmental regulations also create opportunities as the
demand for more efficient and environmentally friendlier products and
services is likely to increase (Meek et al., 2010;Aguilera-Caracuel, and
4
The appropriation of innovations differs across sectors and industries. It is
beyond the scope of this study to elaborate on this. See Breschi, Malerba, and
Orsenigo (2000) and Teece (1986,2006,2018) for appropriation conditions
and regimes.
5
The relationship between environmental regulations and innovation is also
known as the Porter hypothesis (Porter and van der Linde, 1995). Three ver-
sions of this hypothesis can be distinguished: a “weak” version (where en-
vironmental regulation does not have a predetermined effect on competitive-
ness but always stimulates certain types of innovations), a “narrow” version
(where only certain types of environmental policies are actually able to sti-
mulate innovations and overall competitiveness) and a “strong” version (where
efficiency gains due to induced innovation effects are able to completely offset
the loss of competitiveness) (Jaffe and Palmer, 1997). In this study, we mainly
focus on the weak version of the Porter hypothesis.
B. Hoogendoorn, et al. Research Policy xxx (xxxx) xxxx
4
Ortiz-de-Mandojana, 2013). Avoiding increased production costs,
turning a profit on increased demand, and reducing uncertainty all
relate to economic incentives and profit maximization. Hence, we ex-
pect that environmental regulations mainly induce economically mo-
tivated firms to innovate while environmentally motivated firms may
be motivated to innovate without the additional economic incentive.
Therefore, we expect the relationship between a start-up's environ-
mental drive (relative to its economic drive) and innovativeness to be
weaker in countries with sturdier environmental regulations. In a
context characterized by sturdy environmental regulations, economic-
ally driven start-ups and environmentally driven start-ups innovate,
albeit for different reasons.
Hence, the difference in the likelihood of innovating between eco-
nomically and environmentally driven start-ups is expected to be
smaller in countries with strict environmental regulations than in
countries with lax environmental regulations. Therefore, we hypothe-
size the following moderation effect:
H2: Environmental regulations negatively moderate the positive re-
lationship between a start-up's environmental (relative to economic) value-
creation goals and innovativeness.
Next, we explore the relationship among environmental regulations,
goal heterogeneity among start-ups, and two types of innovation: pro-
duct innovation and process innovation.
The decision-making process for product or process innovations are
based on different reasoning (Halme and Laurila, 2009;Hockerts and
Wüstenhagen, 2010). Product innovations are mainly driven by market
demand while process innovations are more motivated by cost-savings
(Horbach, 2008;Triguero et al., 2013). Product innovations with a
reduced environmental impact not only result in societal benefits but
most likely also translate into benefits for the consumer. Although for
environmental product innovations, this may not always be the case
(e.g., green energy) (Krammerer, 2009), the market will reward addi-
tional investments in new or supplementary product features through
the consumers’ willingness to pay a premium. Contrary to product in-
novation, innovations at the process level are less likely to confer ad-
ditional benefits for the consumer and, hence, rewards in the market are
limited or absent (Cleff and Rennings, 1999;Kammerer, 2009). How-
ever, this argument is less likely to hold for services where process
innovations result in more efficient and better service delivery that
directly benefits customers. Nevertheless, process innovations tend to
be internally motivated by cost savings through, for example, more
efficient use of resources.
As product and process innovations are based on different rea-
soning, the influence of environmental regulations on decision-making
processes for both types of innovation are also likely to differ. Most
environmental regulations, although they can be very diverse, directly
appeal to cost savings (Coglianese and Anderson, 2012). For example,
the introduction of performance standards, as well as environmental
taxes and emission trading, puts a price on the release of pollution and
hence, demands firms to limit their emission levels to save on costs. The
same regulations only indirectly influence market demand for product
innovation through increased environmental consciousness and con-
sumers’ willingness to pay a premium for environmental benefits.
Therefore, we expect that environmental regulations directly influence
process innovation whereas the same regulations only indirectly influ-
ence market demand for product innovation. The differing effects of
environmental regulations on product and process innovations are also
reflected in empirical literature, albeit with mixed results
(Triguero et al., 2013;Horbach, 2008;Rehfeld et al, 2007;Cleff and
Rennings, 1999;Green et al., 1994).
Hence, based on the above reasoning, we not only expect the re-
lationship between a start-up's environmental (relative to economic)
value-creation goals to be weaker in countries with sturdier environ-
mental regulations, but we also expect this effect to be stronger for
process innovation compared with product innovation. Thus, we hy-
pothesize the following:
H3: The moderation effect of environmental regulations on the re-
lationship between a start-up's environmental (relative to economic) value-
creation goals and innovativeness is weaker for process innovation than for
product innovation.
4. Data and method
4.1. Data sources
Individual-level data are used from the 2009 round of the GEM
(Schøtt and Jensen, 2016). GEM is the largest international data col-
lection effort on entrepreneurial activity. GEM conducts interviews
with representative samples of the adult population to obtain in-
formation about their entrepreneurial propensity, attitudes, and opi-
nions (Reynolds et al., 2005). Bosma et al. (2012) provide details on
GEM and country-specific information such as sample sizes and sam-
pling methodologies. We focus on the 2009 GEM data because this is
the only year when GEM included specific questions about green en-
trepreneurial activity.
These individual-level data have been supplemented with country-
level data that reflect a country's institutional arrangements regarding
green entrepreneurship. That is, we use data from the Organization for
Economic Co-operation and Development (OECD) concerning en-
vironmental taxes, and we use data from the World Economic Forum on
the stringency of environmental legislation (see below).
Our estimation sample contains 2,945 start-up entrepreneurs from
31 countries. An overview of the countries is provided in Table 1 to-
gether with the average values of the innovation variables (columns 1
to 3), the average value of the dependent variable (column 4), and the
values of the country-level variables (columns 5 to 7). See below for an
elaboration on the independent, dependent, and country variables.
4.2. Variables
4.2.1. Goals
We focus on owner-managers, that is, respondents who answer af-
firmatively to the following question: “Are you, alone or with others,
currently the owner of a company you help manage, self-employed, or
sell any goods or services to others?” We concentrate on a specific
subsample: start-ups, that is, owner-managers of relatively “young
businesses.” For this purpose, we include owner-managers who have a
business that is at least three months old but no more than 42 months
old. We follow GEM's convention in using the thresholds of three and 42
months.
To measure the goals pursued by these start-ups, we use the 2009
GEM question asking respondents to allocate 100 points according to
three organizational goals of value creation, namely, environmental,
societal, and economic. The exact wording of this question is as follows:
“Organizations may have goals according to the ability to generate
economic value, societal value, and environmental value. Please allo-
cate a total of 100 points across these three categories as pertaining to
your goals.” We define our independent variable as the difference in
allocated points between environmental and economic goals. A positive
(negative) value of this variable means that a start-up entrepreneur
allocated more points to environmental (economic) goals than to eco-
nomic (environmental) goals. This implies that the higher the score, the
greener we consider the start-up to be. Table 1, column 4, reveals that
on average, start-up entrepreneurs allocate more points to economic
goals than to environmental goals in each country (given the negative
signs).
4.2.2. Innovation
We use a subjective measure of innovation that “… is fully in line
with the Oslo Manual on collecting and interpreting innovation data”
(Horbach et al., 2012, p. 113). Thus, whether activities qualify as in-
novative depends on the perspective of the entrepreneur. This is in line
B. Hoogendoorn, et al. Research Policy xxx (xxxx) xxxx
5
with the measure used by Koellinger (2008) who claims that “[f]rom an
economic point of view, a product, service, or production process does
not need to be new to the world to have economic impact”
(Koellinger, 2008, p. 22). Our measures focus on the novelty of the
products, services, and processes introduced by the start-up. The in-
novation measures in our study have been included in many earlier
studies (e.g., Koellinger, 2008;Stephan and Uhlaner, 2010;Schøtt and
Jensen, 2016;Young et al., 2018).
We consider three types of innovation: 1) an overall innovation
index, 2) product innovation, and 3) process innovation. Thus, we
follow a large set of earlier studies and the often-used Community
Innovation Survey that distinguishes between product and process in-
novations (Lee et al, 2015;Morris, 2018;Schøtt and Jensen, 2016). An
overall innovation index, being a combination of product and process
innovation, has also been used in earlier research (Schøtt and
Sedaghat, 2014;Morris, 2018).
The three types of innovation are constructed based on the fol-
lowing items included in the GEM questionnaire:
Item 1: “Do all, some, or none of your potential customers consider
this product or service new and unfamiliar?” with answers all (value 2),
some (value 1), or none (value 0).
Item 2: “Right now, are there many, few, or no other businesses
offering the same products or services to your potential customers?”
with answers no (value 2), few (value 1), or many (value 0).
Item 3: “Have the technologies or procedures required for this
product or service been available for less than a year, between one to
five years, or longer than five years?” with answers less than a year
(value 2), between 1 to 5 years (value 1), or longer than 5 years (value
0).
Items 1 and 2 reflect the newness of the product/service and basi-
cally distinguish between innovations that are new to the firm (but
already available on the market) and innovations that are new to the
market (before competitors introduced the product). This two-
dimensional approach to measuring product innovation is also captured
by the Community Innovation Survey (see also Lee et al. (2015) and
De Jong and Vermeulen (2006)). The two product innovation items are
averaged to construct an index of product innovation (column 2,
Table 1).
Item 3 reflects process innovation. We use the original questionnaire
item and corresponding answer categories (with values 0, 1, and 2;
column 3, Table 1).
We construct a general index of innovation, and this index is cal-
culated as the average of the three items (following Schøtt and
Sedaghat, 2014; column 1, Table 1). For our three innovation measures
it holds that larger values indicate higher propensities to innovate.
4.2.3. Country variables
We focus on environmental taxes (source: OECD) and the stringency
of environmental legislation (source: Global Competitiveness Report,
World Economic Forum). Environmental taxes reflect environmentally
related tax revenue as a percentage of a country's GDP. The environ-
mental aspect of legislation is captured by the question “How stringent
is your country's environmental regulation?” This question originates
from the World Economic Forum's Executive Opinion Survey and has
been assessed by a panel of experts in each country. More information
about the sampling methodology of the Executive Opinion Survey and
the composition of the panel of experts is revealed in Chapter 3.1 of the
Global Competitiveness Report (Schwab et al., 2006, pp. 125-135). Low
(high) values indicate a relatively lax (stringent) environmental regime.
The advantage of this measure is clearly its availability for multiple
years. There are few alternatives, of which one is an OECD measure that
reflects “the degree to which environmental policies put an explicit or
implicit price on polluting or environmentally harmful behaviour.”
However, this measure is available for only 20 countries in our sample.
The correlation coefficient between this OECD measure and the mea-
sure used in the present study is 0.79. To avoid issues of reverse
Table 1
Overview of countries and key characteristics.
Country Innovation (1) Product innovation
(2)
Process innovation
(3)
Environmental value
creation (4)
GDP per
capita (5)
Environmental taxes
(6)
Stringency
legislation (7)
Argentina 0.53 0.67 0.23 -39.63 17.71 1.13 3.2
Belgium 0.51 0.49 0.56 -38.69 38.13 2.17 6.1
Brazil 0.44 0.34 0.63 -78.86 13.26 0.93 5.1
Chile 0.83 0.99 0.51 -42.72 16.55 0.93 5.1
China 0.57 0.60 0.50 -42.78 7.64 0.81 3.0
Colombia 0.46 0.41 0.55 -48.21 10.13 0.98 4.3
Denmark 0.58 0.76 0.23 -35.63 41.28 4.35 6.6
Dominican Republic 0.46 0.56 0.27 -44.98 10.04 2.59 3.6
Finland 0.33 0.37 0.24 -48.22 39.97 2.62 6.4
France 0.56 0.58 0.50 -36.72 35.16 1.85 5.8
Germany 0.38 0.44 0.27 -62.72 38.03 2.14 6.7
Greece 0.46 0.51 0.36 -47.96 30.86 1.89 4.1
Guatemala 0.70 0.73 0.64 -53.47 6.52 0.79 3.4
Hungary 0.23 0.28 0.15 -95.51 20.68 2.89 5.1
Iceland 0.42 0.47 0.31 -43.16 42.68 1.95 5.7
Israel 0.45 0.48 0.38 -60.58 27.40 3.05 4.7
Italy 0.46 0.56 0.24 -34.88 35.40 2.56 5.0
Japan 0.54 0.56 0.50 -10.00 34.80 1.61 6.0
Korea 0.34 0.42 0.19 -43.12 28.66 2.81 4.6
Malaysia 0.40 0.39 0.42 -35.53 20.16 0.24 5.3
Netherlands 0.49 0.62 0.22 -40.37 45.84 3.49 6.2
Norway 0.39 0.54 0.10 -49.32 61.76 2.35 6.3
Peru 0.62 0.74 0.37 -57.61 8.96 0.50 3.8
Slovenia 0.42 0.48 0.32 -24.73 29.62 3.01 5.3
South Africa 0.84 0.76 1.00 -19.29 11.52 1.50 4.8
Spain 0.42 0.42 0.44 -46.62 33.46 1.68 4.6
Switzerland 0.37 0.41 0.29 -55.92 52.58 1.82 6.5
Tunisia 0.44 0.52 0.28 -92.09 9.61 1.28 5.2
United Kingdom 0.41 0.48 0.25 -37.09 36.26 2.25 5.9
United States 0.33 0.39 0.22 -49.34 48.40 0.79 5.4
Uruguay 0.62 0.65 0.56 -47.47 14.71 1.60 4.2
B. Hoogendoorn, et al. Research Policy xxx (xxxx) xxxx
6
causality, we use lagged measures for these two country-level en-
vironmental variables (2008 data for environmental taxes and 2007
data for the environmental legislation variable).
4.2.4. Control variables
We control for an individual's gender (1=male; 0=female) and his
or her age (at least 18 years old), which are common controls to take
into account when studying the individual-level determinants of in-
novativeness (Baron and Tang, 2011;Ahlin et al., 2014;Schøtt and
Jensen, 2016). Educational attainment is also included. Several studies
find that education is positively related to innovation (Koellinger, 2008;
Schøtt and Jensen, 2016). Education is defined as the highest level of
education an individual has completed.
We also include a proxy for wealth by retrieving information about
whether someone is a business angel: “You have, in the past three years,
personally provided funds for a new business started by someone else ex-
cluding any purchases of stocks or mutual funds.” A value of 1 is assigned
when an individual has provided funds and 0 otherwise.
6
Entrepreneurial experience may be important for the firm's level of
innovativeness (Cliff et al., 2006). Although we do not have specific
experience measures, for example, in terms of industry experience, we
include a general experience measure (see also Koellinger, 2008). That
is, we control for whether the entrepreneur recently experienced an
entrepreneurial exit, where the exact questionnaire item reads as fol-
lows: “You have, in the past 12 months, shut down, discontinued, or quit a
business you owned and managed, any form of self-employment, or selling
goods or services to anyone.”
An individual's motivation to start a business is also controlled for,
which can be either opportunity-based – someone started a business
because of a lucrative business opportunity – or necessity-based in a
case in which someone did not have alternative options for work.
Opportunity-based motivation seems to be related to innovativeness,
particularly in terms of product innovation rather than process in-
novation (Schøtt and Jensen, 2016).
Furthermore, we include the firm's size in terms of the number of
employees working for the business (a logarithmic transformation is
applied). Firm size has been found to be positively associated with in-
novativeness (Baron and Tang, 2011;Ahlin et al., 2014). Reichstein and
Salter (2006) find that firm size is positively related to process in-
novation.
Another characteristic at the firm level, sector orientation
(De Jong and Vermeulen, 2006), has not been included as a control
variable because of the substantial reduction in the estimation sample
(there are too many missing values for this variable in the GEM da-
taset). An analysis with sector orientation included as a control variable
is provided in the section with robustness checks.
At the country level, we control for a country's GDP per capita,
based on Purchasing Power Parity, in US dollars (2008 data), with the
World Bank as data source. We refer to earlier work that includes GDP
per capita as a control variable (e.g., Koellinger, 2008).
An overview of all variables is provided in Table 2. The descriptive
statistics are shown in Table 3.Table 3 reveals that the average allo-
cated difference between environmental and economic points amounts
to -48.06. This means that, on average, start-up entrepreneurs allocate
substantially more points to economic value than to environmental
value. Additional calculations reveal that start-up entrepreneurs allo-
cate 14.54 points, on average, to environmental value, and 62.61
points, on average, to economic value.
Table 4 shows the Pearson correlation coefficients between all
micro-level variables. No concerns for multicollinearity are detected.
This is confirmed on the basis of an inspection of the (non-reported)
variance inflation factors (VIFs). That is, the VIFs do not exceed 1.78,
and this is well below the common threshold value of 10 (Hair et al.,
2010). The country-level correlations – based on 31 observations
(countries) – are 0.49 between GDP per capita and environmental taxes
(p=.01), 0.77 between GDP per capita and stringency of environmental
legislation (p<.001), and 0.46 between environmental taxes and
stringency of environmental legislation (p=.01). The VIFs for the
country-level variables do not exceed 2.66.
4.3. Method
Van de Ven et al. (2007, p. 367) acknowledge that “individual dif-
ferences (as well as cultural contexts) are likely to influence the relative
balance between self- and collective interests in explaining en-
trepreneurial behaviour.” Hence, in the current research, we integrate
two levels of analysis into one framework, that is, the micro level – the
start-up entrepreneur with his or her entrepreneurial endeavour – and
the country level. In other words, in answering our research question,
we make use of hierarchical (nested) data and, thus, we explicitly re-
cognize that start-up entrepreneurs – at the micro level – are nested
within countries – at the highest level (Aguinis et al., 2013). A multi-
level analytical approach allows for such research designs.
Given the ordered nature of our three innovation variables, we
make use of multi-level ordered logistic regressions. The innovation
index contains 7 categories, product innovation contains 5 categories,
and process innovation contains 3 categories. To enhance our inter-
pretation, we also show the marginal effects (averaged across all ob-
servations in the estimation sample) corresponding to our main in-
dependent variable.
To compare a multi-level regression framework with a conventional
regression framework, one usually assumes in the ordered logit case
that the probability of Y
ij
taking value kdepends on
β
0j
+ β
1
X
1ij
+ … + β
p
X
pij
, where jis a subscript for countries, and ifor
start-up entrepreneurs; k(k=1, …, K) represents the category of the
dependent variable; pdenotes the number of independent variables.
The difference with the usual regression model is that we assume that
each country jhas a different intercept coefficient β
0j
. One may specify
β
0j
= γ
00
+u
0j
to indicate the variation across countries (country-level
variables can be added as well; see below), u
0j
being a residual term at
the country level.
Moreover, a set of cut-points κ
1
, …, κ
K-1
is estimated. Specifically,
the probabilities can be denoted as Prob(Y
ij
=k) = F(κ
k
–β
0j
–β
1
X
1ij
–…
–β
p
X
pij
) – F(κ
k-1
–β
0j
–β
1
X
1ij
–…–β
p
X
pij
), where F(⋅) is the logistic
cumulative distribution function. The model can also be extended to
have different slope coefficients β
1j
, …, β
pj
(we impose such a country-
dependent slope for our independent variable below).
7
There are two major advantages of performing multi-level regres-
sions compared with conventional multiple regressions. First, multi-
level regressions take into account the data's hierarchical structure. If
this higher level in the data is ignored, standard errors would be too
small, resulting in spuriously significant results (Peterson et al., 2012)
and an increase in the risk of making Type I errors (Stephan et al.,
2015). Second, conventional regression models assume independence
across observations. However, in our hierarchical set-up, we expect
interdependence across individuals within countries, for example, as
individuals within a country share similar cultural values. Below, we
show that a considerable amount of the total variance in our dependent
variables resides between countries; this indeed justifies the use of
multi-level modelling (Hox et al., 2017).
Hypotheses 2 and 3 focus on moderation effects. Specifically, they
form expectations about how the relationship between environmental
6
Adding household income to the regressions rather than the current proxy
would lead to a substantial reduction of our estimation sample (from 2,945 to
2,381 observations, a reduction of almost 20%).
7
Covariance terms between the random parts can also be included. Note that
the ordered logit model does not contain a residual term e
ij
at the individual
level.
B. Hoogendoorn, et al. Research Policy xxx (xxxx) xxxx
7
value creation and innovation – lower level variables in our multi-level
setup – change as a function of higher-order moderator variables
(Aguinis et al., 2013). Interaction terms are added to the model
specifications to test for such cross-level moderation effects; a random
slope for environmental value creation is included to properly model
the cross-level interactions (Heisig and Schaeffer, 2019).
We follow Hox et al. (2017, p. 52) in that “… grand mean-centering
of variables that have random slopes or that are involved in an inter-
action is always helpful.” Given that we add interaction terms between
our independent variable and country-level variables in Hypotheses 2
and 3, and the fact that the country level variables are also included, we
use standardized versions for our environmental variable and country-
level variables (Stephan et al., 2015).
We show the deviance, which is a measure of model fit, in our re-
gression tables. Model specifications with a lower value for the de-
viance have a better fit than models with a higher deviance value.
5. Results
First, we determine the amount of variation of the dependent
variables at the country level. Multi-level ordered logistic regressions
without control variables are performed for three dependent variables:
our innovation index, our measure of product innovation, and our
measure of process innovation (the results are not tabulated). The intra-
class correlations (the ICC values) are 0.07 for the innovation index and
product innovation and 0.08 for process innovation.
8
These values are
sufficiently high to justify the use of multi-level modelling (Hox et al.,
2017) – usually a threshold value of 0.05 is taken in earlier research
(Heck et al., 2010).
Table 2
Definitions of variables.
Variable Data source and questionnaire item Coding
Dependent variables (micro level)
Innovation Combination of 3 items:
1) Do all (value 2), some (value 1), or none (value 0) of your potential customers
consider this product or service new and unfamiliar? (GEM)
2) Right now, are there many (value 0), few (value 1), or no (value 2) other
businesses offering the same products or services to your potential customers?
(GEM)
3) Have the technologies or procedures required for this product or service been
available for less than a year (value 2), between one to five years (value 1), or
longer than five years (value 0)? (GEM)
Average of items 1, 2, and 3.
Product innovation Combination of item 1 and 2 above. Average of items 1 and 2.
Process innovation Item 3 above. Item 3.
Independent variable (micro level)
Environmental value creation
(points difference)
Organizations may have goals according to the ability to generate economic value,
societal value and environmental value. Please allocate a total of 100 points across
these three categories as pertaining to your goals. (GEM)
Points allocated to environmental value minus
points to economic value.
Control variables (micro level)
Gender What is your gender? (GEM) 1 if male, 0 if female.
Age What is your current age (in years)? (GEM) Age in years.
Education What is the highest level of education you have completed? (GEM) None or some secondary education (reference);
secondary education; post-secondary education.
Business angel You have, in the past three years, personally provided funds for a new business
started by someone else, excluding any purchases of stocks or mutual funds. (GEM)
1 if Yes, 0 if No
Entrepreneurial experience You have, in the past 12 months, sold, shut down, discontinued or quit a business you
owned and managed, any form of self-employment, or selling goods or services to
anyone. (GEM)
1 if Yes, 0 if No.
Opportunity-based Are you involved in this firm to take advantage of a business opportunity or because
you have no better choices for work? (GEM)
1 if “to take advantage of a business opportunity”, 0
otherwise o
Firm size Right now how many people, not counting the owners but including exclusive
subcontractors, are working for this business? (GEM)
Log(number of employees + 1)
Variables at the country level
GDP per capita Purchasing Power Parity, in US dollars (2008 data). (World Bank) Continuous
Environmental taxes Environmentally related tax revenue, as a percentage of a country's GDP. (OECD) Continuous
Stringency legislation How stringent is your country's environmental regulation? (World Economic Forum) Continuous
Table 3
Descriptive statistics individual-level and country-level variables.
Mean SD Minimum Maximum
Dependent variables
Innovation 0.47 0.44 0 2
Product innovation 0.52 0.53 0 2
Process innovation 0.38 0.64 0 2
Micro level variables
Environmental value creation (points
difference)
-48.06 37.68 -100 100
Male 0.59 0.49 0 1
Age 39.75 11.28 18 82
None/some secondary education 0.25 0.43 0 1
Secondary education 0.34 0.47 0 1
Post-secondary education 0.41 0.49 0 1
Business angel 0.09 0.28 0 1
Entrepreneurial experience 0.08 0.26 0 1
Opportunity-based 0.48 0.50 0 1
Firm size (log) 0.79 0.97 0 10.88
Country level variables
GDP per capita (divided by 1,000) 26.37 13.51 6.52 61.76
Environmental taxes 1.76 0.78 0.24 4.35
Stringency legislation 4.90 1.05 3.00 6.70
Table is based on 2,945 observations in 31 countries. Reference category edu-
cation in regressions: none/some secondary education. Values for environ-
mental value creation and the country-level variables are shown before stan-
dardization.
8
Note that the first-level variances are fixed at π
2
/3 in the multi-level ordered
logit case. Given that the variances at the country level are estimated at 0.25,
0.24, and 0.28 for the innovation index, product innovation, and process in-
novation, respectively, we arrive at ICC values of 0.07 for the innovation index
and product innovation, and 0.08 for process innovation.
B. Hoogendoorn, et al. Research Policy xxx (xxxx) xxxx
8
Table 5 shows the estimated coefficients of our control variables for
the three innovation measures. Higher probabilities of displaying in-
novative behaviour are found for female start-up entrepreneurs and
those with more education, at least for our innovation index and pro-
duct innovation. Entrepreneurial experience is positively associated
with the innovation index and with process innovation but not with
product innovation. Furthermore, opportunity-motivated entrepreneurs
are significantly more likely to bring innovative products and services
to the market than necessity-motivated entrepreneurs (Schøtt and
Jensen, 2016). Surprisingly, firm size has a non-significant coefficient
across the board for which earlier research found a positive relationship
(Baron and Tang, 2011;Ahlin et al., 2014;Reichstein and Salter; 2006).
At the country level, we note that GDP per capita is negatively asso-
ciated with each innovation measure.
Table 6 adds the environmental variables to the specification of
Table 5. Note that our independent variable at the individual-level
measures the difference in allocated points between environmental
goals and economic goals and that the variable has been standardized.
We observe a significant and positive relationship between environ-
mental value creation and our measure of innovativeness in column 1 of
Table 6. Indeed, column 1 of Table 6 reveals that start-ups that pursue
environmental (relative to economic) value-creation goals are sig-
nificantly more innovative, thereby supporting H1. Columns 2 and 3 of
Table 6 show that environmental value creation is also significantly and
positively related to product innovation and process innovation, re-
spectively.
Regarding the country-level environmental variables in Table 6, we
do not observe significant relationships between the environmental
policy variables and our innovation index (column 1 of Table 6). For
process innovation, we find – in addition to the significant and negative
coefficient of GDP per capita – a significant negative relationship for
environmental taxes. That is, in countries with high environmental
taxes, start-ups are significantly less likely to engage in process in-
novation than start-ups in countries with relatively low environmental
taxes. With respect to process innovation, Cleff and Rennings (1999)
and Green et al. (1994) find a positive correlation with environmental
regulations. We, however, do not find a significant relationship for the
stringency variable in our study. The relationship between environ-
mental regulation and product innovation remains disputed (Cleff and
Rennings, 1999;Kammerer, 2009;Triguero et al., 2013). For example,
Kammerer (2009) indicates that regulatory stringency is positively re-
lated to environmental product innovations that are novel to the firm;
Table 4
Correlation matrix micro-level variables.
1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.
1.Innovation 1.00
2.Product innovation 0.88*1.00
3.Process innovation 0.62*0.17*1.00
4.Environmental value creation 0.12*0.13*0.04*1.00
5.Male -0.08*-0.08*-0.03 -0.03 1.00
6.Age -0.04*-0.00 -0.08*0.07*0.01 1.00
7.None/some secondary educ. -0.03*-0.05*0.01 -0.02 -0.06*0.05*1.00
8.Secondary education 0.04*0.04*0.02 -0.01 -0.01 -0.08*-0.42*1.00
9.Post-secondary education -0.01 0.01 -0.02 0.03 0.06*0.03*-0.48*-0.59*1.00
10.Business angel 0.03*0.03*0.02 0.05*0.04*0.02 -0.05*-0.00 0.05*1.00
11.Entrepreneurial experience 0.06*0.04*0.06*0.00 0.03 -0.01 0.03 -0.01 -0.02 0.04*1.00
12.Firm size 0.04*0.03*0.02 0.05*0.12*0.04*-0.07*-0.01 0.07*0.14*0.07*1.00
13.Opportunity-based 0.04*0.03*0.04*0.03 0.06*0.02 -0.13*0.02 0.10*0.04*0.01 0.07*
⁎
p<0.10. Table is based on 2,945 observations in 31 countries. Reference category education in regressions: none/some secondary education.
Table 5
Multi-level ordered logit regressions with innovativeness as the dependent variable; control variables only.
Innovation (1) Product innovation (2) Process innovation (3)
Coeff. SE Coeff. SE Coeff. SE
Micro level
Male -0.255
⁎⁎⁎
0.069 -0.302
⁎⁎⁎
0.071 -0.071 0.085
Age -0.002 0.003 0.0007 0.003 -0.007*0.004
Secondary education 0.231
⁎⁎
0.094 0.211
⁎⁎
0.096 0.177 0.114
Post-secondary education 0.261
⁎⁎⁎
0.092 0.285
⁎⁎⁎
0.094 0.173 0.113
Business angel 0.182 0.121 0.169 0.122 0.134 0.142
Entrepreneurial experience 0.311
⁎⁎
0.126 0.161 0.128 0.416
⁎⁎⁎
0.147
Opportunity-based 0.210
⁎⁎⁎
0.069 0.182
⁎⁎
0.071 0.234
⁎⁎⁎
0.085
Firm size 0.042 0.035 0.041 0.036 0.053 0.042
Country level
GDP per capita -0.328
⁎⁎⁎
0.087 -0.234
⁎⁎
0.095 -0.371
⁎⁎⁎
0.095
Random part
Variance country level 0.158 0.201 0.155
Diagnostics
Deviance 9,012 7,679 4,478
SE=standard error. Table is based on 2,945 observations in 31 countries. Reference category education: none/some secondary education. The cutpoint estimates are
available upon request.
⁎
p<0.10
⁎⁎
p<0.05
⁎⁎⁎
p<0.01
B. Hoogendoorn, et al. Research Policy xxx (xxxx) xxxx
9
however, this result cannot be supported when these innovations are
new to the market. All in all, we find tentative evidence that environ-
mental regulation is more strongly related to process innovation than to
product innovation (see also Cleff and Rennings, 1999;
Rennings, 2000). In our discussion below, we elaborate more on the
relationships of our two country-level variables with innovation. We
can also measure the “explanatory power” (R
2
) of the environmental
country-level variables, measured in terms of the proportion of variance
explained at the country (Hox et al., 2017). When comparing the
country-level variances for each dependent variable in Tables 5 and 6,
we conclude that the explanatory power is approximately 31% for our
general innovation variable, approximately 16% for product innova-
tion, and 45% for process innovation.
Table 7 shows the average marginal effects corresponding to our
main independent variable at the individual level (i.e., environmental
value creation) and the country-level environmental variables. Hence,
these marginal effects inform us about the estimated change in the
probability of belonging to each category of the dependent variable as
the result of a one-standard deviation change of the environmental
variable. To assess the magnitude of the marginal effects, we also report
the predicted probabilities of belonging to each category of the de-
pendent variable. This way, the marginal effects can be denoted as a
percentage of this predicted probability. For example, when focusing on
environmental value creation for our innovation index (panel 1), the
marginal effects are 1.6, 1.9, 1.1, 0.3, and 0.1 percentage points for
categories 3, 4, 5, 6, and 7, respectively. Although these marginal ef-
fects may not seem substantial, their magnitudes are approximately 8%,
15%, 20%, 21%, and 22% of the predicted probability, which is sub-
stantial. Additionally, for product innovation (panel 2) and process
innovation (panel 3), we find several sizeable marginal effects for en-
vironmental value creation, and also for the environmental taxes vari-
able in case of process innovation.
Finally, Table 8 adds the interaction terms between environmental
value creation and the three country-level variables. Generally, we do
not observe significantly different relationships between environmental
value creation and innovation across countries given the non-sig-
nificance of the interaction terms in column 1 of Table 8. Thus, H2 is
not supported. However, we find a significant and positive coefficient of
the interaction term for stringency of environmental legislation in case
of product innovation (column 2 of Table 8). In countries with a strict
environmental regime, environmental value creation is more strongly
associated with product innovation than in countries with a more lax
regime. We find non-significant coefficients of the interaction terms for
process innovation.
Note that each specification in Table 8 includes a random slope for
environmental value creation (Heisig and Schaeffer, 2019) because we
allow for a country-dependent relationship between environmental
value creation and innovativeness. In general, we find that random
slope specifications for environmental value creation do not have a
better fit than specifications without the random slope (as in Table 6,
likelihood ratio tests result in χ
2
=4.33; p=.50 for the innovation index
and χ
2
=6.01; p=.31 for product innovation), indicating a relatively
stable relationship between environmental value creation and innova-
tion across countries. However, there is one exception. We find that
there is unexplained variance at the country level in terms of the be-
tween-country relationship between environmental value creation and
process innovation (LR χ
2
=18.35; p=.002). We are not able to explain
this unexplained variance across countries for process innovation with
our specification in column 3 of Table 8 given the non-significant in-
teraction terms. Future research should thus focus on an extended array
of environmental regulation variables to further investigate the be-
tween-country relationship between environmental value creation and
process innovation.
5.1. Robustness checks
Industry. The analyses above do not include industry orientation as a
control variable. Adding this variable would reduce the estimation
sample substantially (from 2,945 observations in 31 countries to 2,039
observations in 21 countries). Table 9 repeats the exercises of Table 8
but with a SIC-1 industry variable added. In general, the conclusions are
qualitatively similar to those in Table 8. A model formulation without
the cross-level interactions included reveals a significant and positive
relationship between environmental value creation and our three
Table 6
Multi-level ordered logit regressions with innovativeness as the dependent variable; control variables and environmental variables included.
Innovation (1) Product innovation (2) Process innovation (3)
Coeff. SE Coeff. SE Coeff. SE
Micro level
Environmental value creation 0.220
⁎⁎⁎
0.037 0.213
⁎⁎⁎
0.037 0.160
⁎⁎⁎
0.043
Male -0.255
⁎⁎⁎
0.069 -0.298
⁎⁎⁎
0.071 -0.066 0.084
Age -0.003 0.003 0.0002 0.003 -0.008
⁎⁎
0.004
Secondary education 0.252
⁎⁎⁎
0.094 0.236
⁎⁎
0.096 0.198*0.114
Post-secondary education 0.274
⁎⁎⁎
0.092 0.304
⁎⁎⁎
0.094 0.183 0.113
Business angel 0.156 0.121 0.144 0.123 0.109 0.143
Entrepreneurial experience 0.326
⁎⁎⁎
0.126 0.173 0.128 0.419
⁎⁎⁎
0.147
Opportunity-based 0.210
⁎⁎⁎
0.069 0.182
⁎⁎
0.071 0.234
⁎⁎⁎
0.085
Firm size 0.038 0.035 0.036 0.036 0.053 0.042
Country level
GDP per capita -0.343
⁎⁎⁎
0.121 -0.223 0.142 -0.326
⁎⁎⁎
0.125
Environmental taxes -0.111 0.091 -0.014 0.106 -0.291
⁎⁎⁎
0.097
Stringency legislation 0.062 0.115 -0.033 0.136 0.116 0.113
Random part
Variance country level 0.109 0.168 0.085
Diagnostics
Deviance 8,975 7,647 4,458
SE = standard error. Table is based on 2,945 observations in 31 countries. Reference category education: none/some secondary education. The cutpoint estimates are
available upon request.
⁎
p<0.10
⁎⁎
p<0.05
⁎⁎⁎
p<0.01
B. Hoogendoorn, et al. Research Policy xxx (xxxx) xxxx
10
Table 7
Marginal effects corresponding to ordered logit regressions from Table 6.
Innovation (1) Product innovation (2) Process innovation (3)
Predicted
probability
ME Environmental
value creation
ME
Environmental
taxes
ME Stringency
legislation
Predicted
probability
ME Environmental
value creation
ME
Environmental
taxes
ME Stringency
legislation
Predicted
probability
ME Environmental
value creation
ME
Environmental
taxes
ME Stringency
legislation
Category 1 0.312
⁎⁎⁎
-0.045
⁎⁎⁎
(-14.8%)
0.023
(7.5%)
-0.012
(-4.2%)
0.381
⁎⁎⁎
-0.047
⁎⁎⁎
(-12.7%)
0.003
(0.8%)
0.007
(2.0%)
0.710
⁎⁎⁎
-0.031
⁎⁎⁎
(-4.6%)
0.057
⁎⁎⁎
(8.3%)
-0.022
(-3.3%)
Category 2 0.253
⁎⁎⁎
-0.006
⁎⁎⁎
(-2.6%)
0.003
(1.3%)
-0.002
(-0.7%)
0.304
⁎⁎⁎
0.004
⁎⁎⁎
(1.3%)
-0.0002
(-0.1%)
-0.001
(-0.2%)
0.206
⁎⁎⁎
0.019
⁎⁎⁎
(9.8%)
-0.035
⁎⁎⁎
(-17.8%)
0.014
(7.1%)
Category 3 0.223
⁎⁎⁎
0.016
⁎⁎⁎
(7.5%)
-0.008
(-3.8%)
0.004
(2.1%)
0.213
⁎⁎⁎
0.024
⁎⁎⁎
(11.9%)
-0.002
(-0.8%)
-0.004
(-1.8%)
0.084
⁎⁎⁎
0.012
⁎⁎⁎
(14.6%)
-0.022
⁎⁎⁎
(-26.5%)
0.009
(10.5%)
Category 4 0.133
⁎⁎⁎
0.019
⁎⁎⁎
(15.3%)
-0.010
(-7.7%)
0.005
(4.3%)
0.075
⁎⁎⁎
0.013
⁎⁎⁎
(18.2%)
-0.001
(-1.2%)
-0.002
(-2.8%)
Category 5 0.057
⁎⁎⁎
0.011
⁎⁎⁎
(19.6%)
-0.006
(-9.9%)
0.003
(5.5%)
0.027
⁎⁎⁎
0.006
⁎⁎⁎
(20.7%)
-0.0004
(-1.3%)
-0.001
(-3.2%)
Category 6 0.016
⁎⁎⁎
0.003
⁎⁎⁎
(21.4%)
-0.002
(-10.8%)
0.001
(6.0%)
Category 7 0.005
⁎⁎⁎
0.001
⁎⁎⁎
(21.9%)
-0.001
(-11.1%)
0.0003
(6.2%)
ME=Marginal Effect (averaged across all observations). Numbers between parentheses represent marginal effects as a percentage of the predicted probability for each category. Innovation contains 7 categories; product
innovation contains 5 categories; process innovation contains 3 categories.
⁎
p<0.10
⁎⁎
p<0.05
⁎⁎⁎
p<0.01
B. Hoogendoorn, et al. Research Policy xxx (xxxx) xxxx
11
measures of innovativeness, as is also found in Table 6. These results are
available from the authors upon request.
Social value creation as a control variable. Our main results do not
take into account the third objective that is included in the original
GEM questionnaire (points allocated to social goals next to environ-
mental and economic goals). As a robustness check, we add the points
allocated to social value creation to our model formulations. To do so, it
is necessary to subtly change the definition of our independent variable.
Rather than subtracting the points allocated to environmental value
from the points allocated to economic value, we take the fraction of
environmental points (environmental points/(environmental
points + economic points)). We do so because the ceteris paribus con-
dition inherent in regression models is satisfied when including this
adjusted variable. It is indeed possible for the adjusted variable to in-
crease by 1 while social value creation is held constant (because the
total number of points allocated to both environmental and economic
value does not necessarily change). Table 10 repeats the exercises of
Table 8 but with social value creation added (and the revised version of
our independent variable incorporated). The results remain qualita-
tively similar. In a model formulation without the cross-level interac-
tions (results not shown) we find that environmental value creation
again has a significant and positive relationship with each innovation
measure (available upon request).
System estimation. The specifications for product and process in-
novation are estimated separately, not jointly. Because the set of in-
dependent and control variables is identical for product and process
innovation, system estimations (seemingly unrelated regressions)
would result in similar estimates in the traditional linear regression
case. In multi-level ordered logistic regressions, the estimates are not
necessarily the same. Nevertheless, because of the inability to reach
computational convergence for the system estimation – including
interaction terms and the random slope – in Table 8, we decided to
perform separate regressions for product and process innovation
throughout. We feel comfortable with this decision because system
estimations were performed as an alternative to the results in Tables 5
and 6(without interactions and random slopes), and non-significant
covariances were found between the country-level intercepts of product
innovation and process innovation (0.031, p=.49 in Table 5; -0.005,
p=.87 in Table 6). In addition, the coefficient estimates deviated only
slightly from the present results in Tables 5 and 6. For example, en-
vironmental value creation is significantly and positively associated
with product innovation (ß=0.214; p<0.001) and process innovation
(ß=0.161; p<.001).
6. Discussion and conclusion
Start-ups differ in the extent to which they introduce innovations to
markets and, hence, in their potential contribution to society.
Understanding the heterogeneous character of start-ups is key to ex-
plaining this variability in innovation. We investigated whether the
goals pursued by start-ups are related to start-up innovativeness. In
particular, we studied if the motivation of start-ups to create environ-
mental value for society relative to economic value influences their
innovativeness. Additionally, we studied how environmental regula-
tions influence this relationship. In other words: are greener start-ups
more innovative, and how does this relationship differ across regulatory
contexts?
We theorized how the relative importance of environmental value
creation (i.e., other-regarding interest) over economic value creation
(i.e., self-regarding interest) influences the entrepreneur's opportunity
identification and incentive to innovate. We predicted and found a
significant and positive relationship between environmental (relative to
Table 8
Multi-level ordered logit regressions with innovativeness as the dependent variable; interaction terms included.
Innovation (1) Product innovation (2) Process innovation (3)
Coeff. SE Coeff. SE Coeff. SE
Micro level
Environmental value creation 0.243
⁎⁎⁎
0.047 0.231
⁎⁎⁎
0.038 0.146*0.074
Male -0.251
⁎⁎⁎
0.069 -0.291
⁎⁎⁎
0.071 -0.054 0.085
Age -0.003 0.003 0.0006 0.003 -0.008
⁎⁎
0.004
Secondary education 0.243
⁎⁎⁎
0.094 0.232
⁎⁎
0.097 0.212*0.115
Post-secondary education 0.270
⁎⁎⁎
0.092 0.299
⁎⁎⁎
0.095 0.200*0.114
Business angel 0.156 0.121 0.150 0.123 0.109 0.144
Entrepreneurial experience 0.330
⁎⁎⁎
0.127 0.175 0.128 0.409
⁎⁎⁎
0.149
Opportunity-based 0.212
⁎⁎⁎
0.069 0.185
⁎⁎⁎
0.071 0.234
⁎⁎⁎
0.086
Firm size 0.034 0.035 0.037 0.036 0.054 0.043
Country level
GDP per capita -0.340
⁎⁎⁎
0.121 -0.238*0.141 -0.288
⁎⁎
0.124
Environmental taxes -0.107 0.091 -0.021 0.105 -0.245
⁎⁎
0.096
Stringency legislation 0.052 0.113 -0.027 0.135 0.062 0.111
Cross-level interactions
Env. value × GDP/capita -0.025 0.083 -0.064 0.064 -0.001 0.125
Env. value × Env. taxes 0.059 0.061 0.040 0.055 -0.030 0.096
Env. value × Stringency legislation 0.050 0.073 0.113
⁎⁎
0.057 0.003 0.113
Random part
Variance country level 0.101 0.164 0.066
Variance env. value creation 0.012 0.0004 0.074
Covariance 0.019 -0.008 0.008
Diagnostics
Deviance 8,971 7,641 4,440
SE=standard error. Table is based on 2,945 observations in 31 countries. Reference category education: none/some secondary education. The cutpoint estimates are
also available upon request.
⁎
p<0.10
⁎⁎
p<0.05
⁎⁎⁎
p<0.01
B. Hoogendoorn, et al. Research Policy xxx (xxxx) xxxx
12
economic) value creation and innovativeness such that “greener en-
trepreneurs” are indeed more innovative. Moreover, we theorized that
the inducement effect of environmental regulations on innovation
mainly appeals to economic incentives (i.e., costs-savings, increased
demand, and reduced risk) with different effects for product and pro-
cess innovation. We argued that sturdier environmental regulations are
especially effective for economically driven start-ups, thereby weak-
ening our main relationship. Except for the role of the stringency of
environmental regulation for product innovation, our predictions are
not confirmed. Given the country-level regulatory variables at hand, we
did not find convincing moderation effects across the board (see below
for a nuanced discussion).
Our findings contribute to the entrepreneurship and innovation
literature in several ways. First, we advance research on entrepreneur-
ship by addressing the consequences of other-regarding motives as a
source of heterogeneity among start-ups. Taking both self- and other-
regarding interests into account acknowledges the natural instincts of
human beings (Piliavin and Charng, 1990;Meglino and
Korsgaard, 2004) and thus is more accurate in explaining en-
trepreneurial behaviour and outcomes. The pursuit of other-regarding
goals is increasingly addressed in non-traditional forms of en-
trepreneurship such as social, sustainable, and environmental
entrepreneurship. However, the consequences of pursuing such goals are
less well-researched. Where others have focused on the consequences of
other-regarding goals in terms of organizational challenges
(Battilana and Lee, 2014), organizational design principles
(Parrish, 2010), and start-up success (Renko, 2013), we extend this
research to the consequences for start-up innovativeness. The idea that
being open to the viewpoints of others stimulates creativity, cognitive
processing, and hence, innovativeness has been applied to organiza-
tional behaviour (Grant and Berry, 2011;De Dreu and Nauta, 2009).
Extending this approach to entrepreneurship is still rare (Cohen et al.,
2008). An exception is a study by Renko (2013), which, based on a
sample of nascent social entrepreneurs, arrives at similar conclusions:
the pursuit of other-regarding goals at start-up positively relates to
entrepreneurial innovativeness. Thus, our study demonstrates that
other-regarding motives have consequences for entrepreneurial out-
comes and are not restricted to the context of social entrepreneurship.
We extend this idea to environmental entrepreneurship characterized
by a focus on both economic profit and provisioning environmental
benefits (Thompson et al., 2011). Our findings confirm that future re-
search is warranted on how other-regarding motives are related to
entrepreneurial cognition and the consequences for opportunity iden-
tification, venture creation, growth, and social impact (Mitchell et al.,
Table 9
Multi-level ordered logit regressions with innovativeness as the dependent variable; interaction terms included. Including SIC-1 industry information.
Innovation (1) Product innovation (2) Process innovation (3)
Coeff. SE Coeff. SE Coeff. SE
Micro level
Environmental value creation 0.269
⁎⁎⁎
0.059 0.240
⁎⁎⁎
0.051 0.171*0.094
Male -0.259
⁎⁎⁎
0.087 -0.319
⁎⁎⁎
0.089 -0.068 0.104
Age -0.003 0.004 0.001 0.004 -0.009
⁎⁎
0.005
Secondary education 0.289
⁎⁎
0.113 0.318
⁎⁎⁎
0.117 0.101 0.137
Post-secondary education 0.272
⁎⁎
0.115 0.369
⁎⁎⁎
0.118 0.072 0.140
Business angel 0.057 0.145 0.047 0.146 0.042 0.170
Entrepreneurial experience 0.437
⁎⁎⁎
0.150 0.205 0.153 0.484
⁎⁎⁎
0.174
Opportunity-based 0.251
⁎⁎⁎
0.084 0.260
⁎⁎⁎
0.086 0.176*0.102
Firm size 0.033 0.042 0.029 0.043 0.052 0.050
Mining, construction -0.324 0.242 -0.403 0.251 -0.177 0.296
Manufacturing 0.429*0.223 0.382*0.225 0.187 0.264
Transport, storage, communications -0.043 0.224 0.015 0.228 -0.376 0.280
Wholesale trade -0.600
⁎⁎
0.264 -0.653
⁎⁎
0.272 -0.207 0.332
Retail trade, hotels, restaur. 0.159 0.182 0.095 0.183 0.146 0.217
Financial services 0.407 0.259 0.313 0.258 0.314 0.306
Business services -0.150 0.286 -0.204 0.293 0.100 0.341
Social services 0.033 0.204 -0.084 0.207 0.090 0.248
Personal/consumer service 0.229 0.242 0.135 0.248 0.173 0.289
Country level
GDP per capita -0.370
⁎⁎
0.146 -0.205 0.180 -0.364
⁎⁎
0.148
Environmental taxes -0.102 0.135 -0.063 0.160 -0.142 0.138
Stringency legislation 0.090 0.134 -0.024 0.170 0.109 0.124
Cross-level interactions
Env. value × GDP/capita -0.074 0.106 -0.081 0.084 -0.136 0.150
Env. value × Env. taxes 0.131 0.092 0.067 0.088 0.148 0.136
Env. value × Stringency legislation -0.015 0.085 0.073 0.068 -0.055 0.131
Random part
Variance country level 0.106 0.199 0.053
Variance env. value creation 0.008 0.0005 0.072
Covariance 0.014 -0.010 0.044
Diagnostics
Deviance 6,127 5,162 3,119
SE=standard error. Table is based on 2,039 observations in 21 countries. Reference category education: none/some secondary education. Reference category
industry: Agriculture, forestry, hunting, fishing. The cutpoint estimates are also available upon request.
⁎
p<0.10
⁎⁎
p<0.05
⁎⁎⁎
p<0.01
B. Hoogendoorn, et al. Research Policy xxx (xxxx) xxxx
13
2007).
Second, we contribute to the discussion on the inducement effect of
environmental regulations and the appropriation of rents from in-
novations by introducing entrepreneurs’ non-economic motives.
Theoretical explanations for the existence of the inducement effect of
environmental regulations assume that profit opportunities are over-
looked until the introduction of environmental policy pushes firms to
reconsider their production processes and product offerings
(Kozluk and Zipperer, 2015). Contrary to arguments on why the in-
ducement effect exists,
9
we argue that non-economic motives, such as
the pursuit of environmental value creation, may alter this mechanism.
Entrepreneurs who pursue other-regarding motives may innovate
without the additional economic incentives from regulatory interven-
tions. We expected that in countries with sturdier environmental reg-
ulations the relationship between goals at start-up and innovativeness is
weaker. While we did not find moderation effects across the board, we
did find that in countries with strict environmental regulations, greener
start-ups are more strongly associated with product innovations. We
believe that our focus on start-ups may explain these moderate findings.
Although we know very little about how the inducement effect of
environmental regulations on innovation plays out differently for dif-
ferent types of firms, some authors suggest that the inducement effect is
smaller for smaller firms. Aghion et al. (2015) put forward that the
positive knowledge spillover inherent in the process of innovation is
likely to be smaller in small firms that operate in niches, which seems to
be particularly the case for the context of environmental en-
trepreneurship (Hockerts and Wüstenhagen, 2010). Additionally,
Ambec et al. (2013) suggest that organizational failure or inertia due to
misaligned incentives or asymmetric information is more likely to occur
in large firms compared with small firms. Nevertheless, empirical evi-
dence that firm size or age is relevant for the inducement effect of
environmental regulation is scant. Whereas Lanoie et al. (2011) find no
effect of size (or age) on the effect of environmental regulations on R&D
budget in a sample of 4,200 facilities across seven OECD countries,
other studies add size and age as control variables when assessing the
effect of regulations on some measure of innovativeness and find mixed
results (Kammerer, 2009;Rehfeld et al., 2007;Triguero et al., 2013).
Future research could test our expectations more generally and explore
how the effect of environmental regulations on innovation may play out
differently for other types of firms such as family firms or nonprofit
firms. For example, there is some evidence that the regulatory en-
vironment has a different effect on family firms compared to non-family
firms as the former are willing to sacrifice economic value to preserve
non-economic socioemotional value such as image and reputation
(Berrone et al., 2010;Cruz et al., 2014).
Third, the present study adds to our understanding of the influence
of environmental regulations by investigating the relationship between
environmental regulations, goal heterogeneity in terms of environ-
mental and economic value creation among start-ups, and different
Table 10
Multi-level ordered logit regressions with innovativeness as the dependent variable; interaction terms included. Social value creation added as control variable.
Innovation (1) Product innovation (2) Process innovation (3)
Coeff. SE Coeff. SE Coeff. SE
Micro level
Environmental value creation (percentage) 0.689
⁎⁎⁎
0.189 0.566
⁎⁎⁎
0.185 0.666*0.357
Social value creation 0.154
⁎⁎⁎
0.040 0.175
⁎⁎⁎
0.041 0.054 0.050
Male -0.248
⁎⁎⁎
0.070 -0.282
⁎⁎⁎
0.071 -0.058 0.085
Age -0.003 0.003 0.0004 0.003 -0.008
⁎⁎
0.004
Secondary education 0.234
⁎⁎
0.094 0.218
⁎⁎
0.097 0.211*0.115
Post-secondary education 0.247
⁎⁎⁎
0.093 0.274
⁎⁎⁎
0.095 0.199*0.114
Business angel 0.155 0.122 0.144 0.124 0.125 0.145
Entrepreneurial experience 0.330
⁎⁎⁎
0.126 0.178 0.128 0.412
⁎⁎⁎
0.149
Opportunity-based 0.214
⁎⁎⁎
0.069 0.187
⁎⁎⁎
0.071 0.225
⁎⁎⁎
0.086
Firm size 0.040 0.035 0.041 0.036 0.057 0.043
Country level
GDP per capita -0.320
⁎⁎⁎
0.115 -0.169 0.149 -0.307*0.179
Environmental taxes -0.149 0.096 -0.046 0.118 -0.204 0.140
Stringency legislation 0.035 0.108 -0.116 0.143 0.088 0.164
Cross-level interactions
Env. value × GDP/capita -0.134 0.325 -0.376 0.318 0.015 0.592
Env. value × Env. taxes 0.118 0.262 0.093 0.258 -0.273 0.447
Env. value × Stringency legislation 0.150 0.288 0.458*0.272 -0.016 0.534
Random part
Variance country level 0.069 0.161 0.171
Variance env. value creation 0.053 0.0006 1.608
Covariance 0.060 -0.010 -0.413
Diagnostics
Deviance 8,921 7,591 4,426
SE=standard error. Table is based on 2,931 observations in 31 countries (there are 14 observations for which the independent variable in this table is not defined
because 0 points are allocated to environmental and economic value). Reference category education: none/some secondary education. The cutpoint estimates are also
available upon request.
⁎
p<0.10
⁎⁎
p<0.05
⁎⁎⁎
p<0.01
9
Theoretical explanations for overlooking profit opportunities concern the
existence of market failures (i.e., knowledge spillovers, asymmetric informa-
tion, and imperfect competition); behavioral arguments (i.e., managers suffer
from shortcomings in rationality such as limited information processing capa-
city or cognitive abilities, are risk averse, or rely on habits and routines); and
organizational failure arguments (e.g., misaligned incentives, imperfect in-
formation, moral hazard, and hidden action) (Ambec et al., 2013;
Wagner, 2003).
B. Hoogendoorn, et al. Research Policy xxx (xxxx) xxxx
14
types of innovation. Whereas other studies are predominantly single-
country and single-sector (Kammerer, 2009;Horbach, 2008;Cleff and
Renning, 1999), or lack a hierarchical structure (Triguero et al., 2013),
our multi-level approach enables us to address whether individuals
within a country share similar cultural values that may influence the
extent to which start-ups pursue environmental over economic goals.
Based on differing decision-making logics for process and product in-
novations (Halme and Laurila, 2009;Hockerts and
Wüstenhagen, 2010), we theorize and find that the influence of en-
vironmental regulations on decision-making processes for both types of
innovations differs. Although we predicted a dampening effect for
process innovation compared with product innovation, we find no
moderation effect of regulations for process innovation, and, con-
versely, we find a positive moderation effect for product innovation.
This interesting result suggests that in countries with stricter environ-
mental regulations, greener start-ups are more likely to innovate at the
product level. This result is in line with Triguero et al., (2013), based on
a sample of small and medium enterprises (SMEs) across 27 EU coun-
tries, who find a moderately significant positive relationship between
existing regulatory standards and product innovation and no relation-
ship with process innovation. A potential explanation is provided by
Jaffe et al. (2002) who suggest that environmental regulations have two
competing effects: a direct effect on increasing costs as per the induced
innovation hypothesis and an indirect one on product output. Higher
production costs may result in higher prices, reducing product output
and subsequently in the incentive to engage in research and develop-
ment. Another explanation for our results may be related to the mea-
sures we use for environmental regulations. Whereas other studies use
self-reported perceived environmental stringency (Cleff and
Renning, 1999;Horbach, 2008;Kammerer, 2009;Triguero et al.,
2013), we measure the stringency of environmental regulations at the
country level by the assessment of a panel of national experts that aims
to capture the multidimensionality of regulations (Aguilera-
Caracuel and Ortiz-de-Mandojana, 2013). This measure includes as-
pects such as flexibility, clarity, consistency, stability, enforcement of
regulations, and concern for specific pressing environmental issues in a
given country. This manner of measuring stringency, more so than
perceived stringency, may signal the importance of environmental is-
sues in a society (Kostova and Roth, 2002;Scott, 1995) without ne-
cessarily being related to increased production costs at the firm level.
This may suggest that the stringency of environmental regulations at
the country level is an indicator of market demand for environmental
products and services and hence relates to product innovation.
What is evident from all our analyses is that environmental value
creation significantly and positively relates to both product and process
innovation but that the effects of some other determining factors differ
substantially between product and process innovation. For example, the
entrepreneur's gender seems to matter for product innovation but not
for process innovation while the opposite is true for an entrepreneur's
age. These results remain unexplained and warrant further investiga-
tion for policy makers to be able to stimulate innovation in a desired
direction and with impact on society.
Limitations and future research. The GEM is a rich dataset cov-
ering a large set of countries; however, it has some limitations. For
example, we controlled for a wide array of micro-level variables;
however, some characteristics of the start-up firms are generally not
included in the GEM survey such as financial indicators.
While our innovation measures have been used previously, they are
not specific to the environmental activities of the firm. For example,
being innovative does not necessarily imply contributing substantially
to improving the natural environment. In other words, we include
general innovation measures but not measures of eco-innovation. In the
context of environmental innovations, other types of innovations have
been identified to be of importance such as social and institutional in-
novations (Rennings, 2000). Our dataset does not allow us to include
these types of innovation. It would be interesting to assess how start-up
goals are related to these types of innovation. In addition, being in-
novative at start-up does not necessarily imply a substantial or even
positive impact on the natural environment. Greener start-ups may
choose a particularly small-scale niche that matches their personal
environmental standards without having any impact outside their
target group (“bioneers”) (Hockert and Wüstenhagen, 2010). The im-
pact of innovations on the natural environment and their antecedents
offer ample room for future research.
Finally, we could not make use of the full potential of the dataset
because country-level data were available for 31 of the 50 countries.
Hence, future research may want to extend the present analysis to a set
of countries that contain a larger variation in economic development. In
general, the findings of this study can serve as a starting point for more
quantitative research in the area of green entrepreneurship because the
definition of green entrepreneurship is fairly simple to include in future
questionnaires.
Policy implications. Start-ups are a particularly interesting target
group for policymakers as they have relatively high probabilities of
innovating (Huergo and Jaumandreu, 2004), those that innovate have
higher survival chances (Colombelli et al., 2016), and their innovations
are likely to breed future innovations (Teece, 1986;Galende, 2006).
Hence, understanding and stimulating the emergence of innovative
start-ups is highly relevant, particularly in the context of current en-
vironmental challenges.
Our study offers a few suggestions for policymakers. First, the goals
pursued at start-up are relevant for the level of novelty introduced by
start-ups. Being empathetic to the viewpoints of others and taking
multiple perspectives into account are behaviours that can be en-
couraged. Education, in particular higher education, has been found to
have a motivation-shaping effect on start-up entrepreneurs to pursue
other-regarding interest relative to self-regarding interest (Estrin et al.,
2016). Policymakers could actively stimulate this motivation-shaping
effect of education programmes. Second, a sharp increase in environ-
mental regulation in the past four decades (United Nations, 2012) in-
dicates a strong conviction on the part of policy makers that their in-
tervention serves as an effective mechanism to curb environmental
degradation. Although we acknowledge that, with regard to the mod-
eration effects of environmental regulations, we do not find robust
evidence across the board, we see some potential in the relevance of
environmental regulations. At the same time, we note that we found a
significant cross-country variation of the relationship between en-
vironmental value creation and our measure of process innovation.
While with the country variables at hand, we could not explain this
cross-country relationship, this area provides ample opportunities for
future research to analyse policy-relevant country variables that influ-
ence the association between environmental value creation and in-
novation.
Overall, our study is an important step towards understanding
variability in innovativeness among start-ups and the relationship be-
tween environmental entrepreneurship and innovation. We found ro-
bust evidence in our multi-country dataset that greener start-ups are
more innovative. We explored the role of environmental regulations
and found that they are related mainly to product innovation rather
than process innovation. In understanding the relationship between
environmental value creation and innovation across countries, we
found a stronger positive relationship in countries with strict environ-
mental legislation. Thus, an important source of heterogeneity among
start-ups in terms of innovativeness is the entrepreneurs’ motivation at
start-up, and future research is needed to further understand this re-
lationship.
Declaration of Competing Interest
We wish to confirm that there are no known conflicts of interest
associated with this publication and there has been no significant
B. Hoogendoorn, et al. Research Policy xxx (xxxx) xxxx
15
financial support for this work that could have influenced its outcome.
Acknowledgements
The authors are grateful for the valuable comments of Joern Block
and Jolanda Hessels, and our anonymous reviewers on earlier versions
of this paper. Roy Thurik is member of the LabEx Entrepreneurship
(University of Montpellier, France), funded by the French government
(Labex Entreprendre, ANR-10-Labex-11-01).
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