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In the present paper we investigate whether and to what extent green innovations significantly differ from non-green ones, in terms of i) inter and intra-organizational relationships leading to their development and ii) technological characteristics, as complexity and novelty. Then, we study the impact of these organizational factors and technological features on the value of green innovations. In particular, we focus on a specific type of green innovations, as green technological innovations, and consider green patents as proxy for them. Analyzing green and non-green patents developed by a sample of companies included in the Dow Jones Sustainability World Index, we find that green innovations have important peculiarities compared to conventional ones. Specifically, developing green innovations requires establishing collaborations with external actors as well as among internal actors to a greater extent, while the technologies underling green innovations seem to be characterized by a higher degree of complexity and novelty. With regard to the value of green innovations, results show that the most valuable ones are those that more highly rely on collaborations among internal actors, whereas higher levels of novelty seem to be detrimental, at least in the short-medium term.
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Electronic copy available at: http://ssrn.com/abstract=1879861Electronic copy available at: http://ssrn.com/abstract=1879861
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Organizational factors and technological features in the development of
green innovations: evidence from patent analysis
Antonio Messeni Petruzzelli*1, Rosa Maria Dangelico§, Daniele Rotolo*, Vito Albino*
*Dipartimento di Ingegneria Meccanica e Gestionale, Politecnico di Bari, Viale Japigia 182, 70126 Bari, Italy
§Dipartimento di Ingegneria per l’Ambiente e lo Sviluppo Sostenibile, Politecnico di Bari, Via Alcide De Gasperi, 74100 Taranto, Italy
Abstract
In the present paper we investigate whether and to what extent green innovations significantly differ from
non-green ones, in terms of i) inter- and intra-organizational relationships leading to their development and ii)
technological characteristics, as complexity and novelty. Then, we study the impact of these organizational
factors and technological features on the value of green innovations. In particular, we focus on a specific type of
green innovations, as green technological innovations, and consider green patents as proxy for them. Analyzing
green and non-green patents developed by a sample of companies included in the Dow Jones Sustainability
World Index, we find that green innovations have important peculiarities compared to conventional ones.
Specifically, developing green innovations requires establishing collaborations with external actors as well as
among internal actors to a greater extent, while the technologies underling green innovations seem to be
characterized by a higher degree of complexity and novelty. With regard to the value of green innovations,
results show that the most valuable ones are those that more highly rely on collaborations among internal actors,
whereas higher levels of novelty seem to be detrimental, at least in the short-medium term.
Keywords: green innovation, environmental technologies, patents, intra-organizational collaborations, inter-
organizational collaborations, technological complexity, technological novelty
1. Introduction
The growing global attention towards environmental sustainability issues (World Commission on
Environment and Development, 1987; United Nations Environment Programme, 2009) determined an increase
in the interest towards green innovations as a key factor to achieve the targets of sustainable development
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1 Corresponding author. E-mail: a.messeni.petruzzelli@poliba.it
Electronic copy available at: http://ssrn.com/abstract=1879861Electronic copy available at: http://ssrn.com/abstract=1879861
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(OECD, 2009), as well as in the effort of scholars to investigate the main antecedents of green2 innovation
development process (Fussler & James, 1996; Jaffe et al, 2002; Chen et al., 2006). Specifically, existing studies
focused on several dimensions favouring the development of green innovations, such as process and activities
(e.g. Dangelico & Pujari, 2010; Pujari, 2006), organizational factors (e.g. Foster & Green, 2000; Lenox &
Ehrenfeld, 1997), marketing issues (e.g. Ottman et al., 2006; Reinhardt, 1998), eco-design tools and methods
(e.g. Rennings, 2000; Waage, 2007), and public policy instruments (e.g. Parry, 2003; Popp, 2006; Rehfeld et al.,
2007; Johnstone et al., 2010). Other studies paid attention to the outcomes of green innovations, contributing to
the debate of being green and competitive (e.g. Chen et al., 2006; Klassen & Whybark, 1999; Lefebvre et al.,
2003; Shrivastava, 1995). Despite such a growing interest towards the integration of environmental issues into
the innovation process, little attention has been devoted in the literature, especially by means of quantitative
studies, on how inter- and intra-organizational relationships influence the development of green innovations, on
their main technological features, and on their success factors. The increasing attention towards sustainability is
transforming the competitive landscape, so forcing companies to change the way they think about products,
technologies, processes, and business models (Nidumolu et al., 2009). Thereby, it would be relevant to
understand organizational and technological peculiarities of green innovations, as well as the drivers of their
value.
In particular, in this paper we focus on a specific type of green innovations, namely environmental
technologies. Following the definition provided by the Commission of European Communities, (2004, p. 2),
these “encompass technologies and processes to manage pollution (e.g. air pollution control, waste
management), less polluting and less resource-intensive products and services and ways to manage resources
more efficiently (e.g. water supply, energy-saving technologies)”. First, we investigate whether and to what
extent green innovations significantly differ from non-green ones, in terms of i) inter- and intra-organizational
relationships sustaining and characterizing the creation of innovations and ii) technological characteristics, as
complexity and novelty. Then, we study the impact of these organizational factors and technological features on
the value of green innovations. Following a well-established tradition in the literature, we use patents as a proxy
for innovations (e.g. Ratanawaraha & Polenske, 2007), and, specifically, green patents as a proxy for green
innovations. To this aim, we considered a sample of companies included in the 2004 Dow Jones Sustainability
World Index and belonging to the following sectors: basic materials, industrials, technology, and utilities. We
defined a set of keywords characterizing green patents, on the basis of which we identified 151 green patents
developed by the sample companies and registered at the U.S.PTO from 1973 to 2003. Then, we identified a
control sample of non-green patents developed by the same companies. Using patent-based data, we defined
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2 In this paper, we will use the word ‘green’ as a synonym of the words ‘eco’, ‘environmental’, ‘environmentally friendly’.
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suitable proxies for the analyzed organizational and technological variables and collected relevant information
for both green and non-green patents.
We found that developing green innovations requires establishing collaborations with external actors as
well as among internal actors to a greater extent compared to the development of conventional innovations.
Similarly, the technologies underling green innovations seem to be characterized by a higher degree of
complexity and novelty. With regard to the value of green innovations, results show that the most valuable ones
are those that highly rely on collaborations among internal actors. On the contrary, green innovations
characterized by a higher level of novelty seem to be less valuable.
The paper is structured as follows. In Section 2, we present the theoretical background of the study,
focusing on the extant literature on the role of inter and intra-organizational collaborations for the development
of green innovations, and on two main technological dimensions of innovation, namely complexity and novelty.
In Section 3, we report methodology details, in terms of data collection, sample, and measures. In Section 4, we
present results from the study, while, in Section 5, discussion and conclusion are reported.
2. Theoretical Background
2.1 Organizational Factors in the Development of Green Innovations
2.1.1 Inter-organizational collaborations
The creation of collaborative networks plays an important role in the innovation development process (e.g.
Bossink, 2002; Verona, 1999). By means of collaborations, firms can successfully innovate by sharing
complementary resources and competencies (Grandori & Soda, 1995; Powell, 1998; Wissema & Euser, 1991).
To this aim, firms can create alliances, joint ventures, inter-firm networks, R&D consortia or partnerships (e.g.
Doz et al., 2000; Tidd, 1995). This is the basic idea underlying the open innovation paradigm proposed by
Chesbrough (2003), which assumes that firms ‘‘can and should use external ideas as well as internal ones, and
internal and external paths to market’’ to make the most out of their technologies (Chesbrough, 2003, p.24). This
model is based on the recognition that innovation partly depends on firm-specific knowledge resources and
strongly depends on determinants that are external to the firms, because these are often specialized on one field
of knowledge and rarely have all the required resources internally (Christensen et al., 2005; Vanhaverbeke,
2006). In the development of green innovations, collaboration and information exchange with external
organizations may be even more crucial. In fact, environmental issues do not represent core competences for
most firms. In addition, they often do not internally possess all required knowledge and competences to develop
green innovations. For example, if firms aim at reducing the environmental impact of their products, given that
this takes place at many points in the supply chain and firms are not themselves involved in all phases of the
product life cycle, collaboration with other companies in the product’s value chain as well as with other actors
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(such as government or NGOs) is essential (Roy & Whelan, 1992). Furthermore, the complexity of
environmental issues requires that firms aiming at developing green innovations create links with a wide range
of external parties (Foster & Green, 2000; Lenox & Ehrenfeld, 1997; Ngai et al., 2008) and include a broad
range of stakeholders in the development process (Hart, 1995; Polonsky & Ottman, 1998). These stakeholders
can be a source of environmental knowledge and competences outside the firm’s main domain. The relevance of
integrating external environmental knowledge and competences to develop green innovations is clearly stressed
by Rand Waddoups (senior director of Business Strategy and Sustainability for Wal-Mart Stores Inc.), who
stated3 that “one of the best things about sustainability is that as soon as you start into it, you realize that it’s
impossible to know everything. It’s impossible to truly be an expert in sustainability because it’s huge… We are
innovating differently than we’ve ever done before, because we get to hear from our stakeholders, and they teach
us, and they give us new perspectives that really are advantageous to our business.” Similarly, external
collaborations were also very important for Clorox Company, a leading manufacturer and marketer of consumer
products, with headquarter in California, which created several partnerships to develop more environmentally
friendly products. The company website explicitly reports: “We know we can’t achieve our environmental goals
alone. That’s why we work with a variety of organizations dedicated to environmental stewardship and
sustainability.”4
2.1.1 Intra-organizational collaborations
External collaboration is not the only important organizational factor that firms can lever with to develop
innovations. In fact, cross-functional collaboration has been highlighted as a key factor too (e.g.Luo et al., 2006;
Pinto & Pinto, 1990; Pinto et al., 1993 Song et al., 1997). Integrating environmental issues into firm’s innovation
activities may necessitate even more cross-disciplinary coordination and integration (Shrivastava & Hart, 1995).
In fact, the integration of the natural environment adds complexity to organizational processes (Hart, 1995), so
requiring the collaboration of environmental specialized functions with other functions, such as R&D,
manufacturing, and marketing. Furthermore, different functions and departments within the firm represent
important knowledge sources (Cabrales et al., 2008) and their integration plays a fundamental role in developing
green innovations (Foster & Green, 2000; Pujari, 2006). For instance, manufacturing function is knowledgeable
about the environmental impact of production processes, pollution prevention, and environmentally conscious
manufacturing, while marketing function is knowledgeable on customers’ requirements about the environmental
performance of products. Several companies recognized that cross-functional integration between environmental
specialized units and other functions within the firm is very important to develop sustainable solutions. For
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3Interview“WalMart'sJourneyTowardSustainabilityandGreaterValue”publishedonlineonMay18th,2009at
http://www.greenerdesign.com/podcast/2009/05/18/walmartssustainabilityjourney?page=0%2C2.
4http://www.thecloroxcompany.com/community/ourenviropgs/partners_affl.html
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example, Hewlett-Packard established an energy supply chain function, representing a formal, cross-functional
bridge between traditional procurement and environmental responsibility teams.
2.2 Technological Features in the Development of Green Innovations
2.2.1 Technological complexity
The term complexity stems from the pioneering definition provided by Simon (1969: p.195) as “a large
number of parts that interact in non-simple ways, _ _ _ [such that] given the properties of the parts and the laws
of their interactions, it is not a trivial matter to infer the properties of the whole”. Thus, complexity has two
separate dimensions, as system size (number of variables) and interactions (correlation of neighbouring points)
(Sommer & Loch, 2004). Complexity represents a fundamental issue of green innovations, being these the result
of the integration and combination of several and heterogeneous technologies and knowledge resources.
Specifically, this tight relationship occurring between green innovations and complexity depends on the same
complexity of environmental issues, as well as on the many different ways they can be managed (pollution
prevention, waste handling, and emission cleaning). Thereby, green innovations are often dedicated to perform
several functions simultaneously, and tend to be applied at different phases of products/processes’ lifecycle, thus
requiring the integration of multiple interacting competencies and capabilities
A complex technology may be defined as an applied system whose components have multiple interactions
and constitute a non-decomposable whole. Therefore, it is generally associated with knowledge that is
sophisticated and difficult to understand (Gopalakrishnan & Bierly, 2001). Such knowledge sophistication makes
complex technologies difficult to be imitated, mainly because competitors need to have an understanding of the
whole set of different subunits, as well as of their multiple interactions. Furthermore, complex technologies
generally embody original and architectural knowledge (Henderson & Clark, 1990; Lengnick-Hall, 1996).
Following these arguments, it is possible to argue that complex technologies tend to act as a source of
competitive advantage (Barney, 1991), being rare, valuable, and imperfectly inimitable.
However, the development of complex technologies presents three main difficulties. First, it requires the
combination of components that draw from different knowledge bases (Dodgson, 1992). Second, it involves high
commercialization costs, since complex technologies comprise many components, subsystems, and interactions,
and consequently their commercialization must also take into account such many differently organized but
closely integrated subunits (Burns & Stalker, 1961; Lawrence & Lorsch, 1967; Singh, 1997). Third, the higher
the complexity associated with a specific technology, the more uncertainty exists (e.g. Rosenkopf & Tushman,
1994; Tushman & Rosenkopf, 1992).
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2.2.2 Technological novelty
Sustainability has been recognized as the innovation’s new frontier (Nidumolu et al., 2009). Given the
recent emergence of this trend, we believe in the importance of investigating the novelty of the technologies
underlying green innovations, analyzing how they differ from previous technological paradigms.
Novel technologies involve the introduction of a new approach to technical practices (Reinganum, 1981),
especially referring to green technological innovations that have been proved to act as a major source of change
(Azzone & Noci, 1998). These technologies are associated with the first stage of the technology life-cycle (S-
shape evolution), i.e. with the introduction of a new technology, generally characterized by great market
uncertainty and R&D efforts (e.g. Abernathy & Utterback, 1978; Callon, 1980). If successful, technologies
coming through this phase and moving to the growth stage are destined to become radical innovations, breaking
existing technological paradigms (Shane, 2001), involving a shift towards new trajectories, and representing the
basis on which further innovations are built (Gupta et al., 2006). Therefore, novel technologies may represent
rare, valuable, and inimitable sources of competitive advantage (Barney, 1991), allowing business growth and
new business development (see also Phene et al., 2006).
Such an advantage mainly derives from the benefits associated with learning economies (Lilien & Yoon,
1990), causal ambiguity (Reed & Defillippi, 1990), switching costs (Schmalensee, 1982), and consumer learning
(Carpenter & Nakamoto, 1989). Referring to consumer behaviour, environmentalism of consumers (Chen et al.,
2006) is increasing in the world nowadays, and thereby drives firms to pay more attention in environmental
issue, because consumers are more willing to choose green products and even pay relatively high prices for
environment-friendly products (Henriques & Sadorsky, 1996). Moreover, introducing novel environmentally
friendly technological solutions contribute to enhance the green image and reputation of firms, which may
positively support the diffusion of these innovations (Chen, 2007).
Nevertheless, introducing a novel technology does not always lead to the achievement of a competitive
advantage, because it is risky (Dewar & Dutton, 1986) and contextual factors, such as the pace of technology
and market evolution (Suarez & Lanzolla, 2007), may significantly affect the value of such novel technical
solutions. In addition, green innovative technologies strongly depend on legislative frameworks (Weber and
Hemmelskamp, 2005), whose change may undermine their effective and efficient development.
Rapid technology evolution may also negate possible “experience curve” advantages (Lieberman, 1989),
rendering a firm’s knowledge base obsolete and destroying existing competences (Tushman & Anderson, 1986;
(Henderson & Clark, 1990; Leonard-Barton, 1992; Schilling & Steensma, 2002; Tushman & Rosenkopf, 1992).
In addition, a fast changing technology influences the effectiveness of patents and other forms of intellectual
property protection, since it may give latecomers plenty of opportunities to “invent around” a patent and come
up with improved products that do not necessarily infringe on patent rights. Finally, technological evolution
affects key antecedents of buyers’ switching costs, such as domain expertise (Wernerfelt, 1985) and consumer
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preference formation (Carpenter & Nakamoto, 1989). In fact, this “technological uncertainty” makes buyers
reluctant to invest in product specific competencies (Carpenter & Nakamoto, 1989), with the consequent risk of
introducing an “underdeveloped” product that will make final customers more willing to switch to alternative
products (Kalish & Lilien, 1986).Similarly, market evolution may imply changes in consumer tastes or
preferences, emergence of new regulations, and degree of market fragmentation (Agrawal & Bayus, 2002;
Fallters & Willmott, 2009), which may impact on the diffusion and success of a novel technology.
3. Methodology
3.1 Data and Sample
In this study, we focus on a specific type of green innovations, i.e. green technological innovations,
meeting the requirement of being patentable. The choice to use patents to describe innovations is widely
accepted in the literature (Ratanawaraha & Polenske, 2007), and it is due to the following main reasons. First,
patent data are readily available in most countries. Second, the extensiveness of patent data enables researchers
to conduct both cross-sectional and longitudinal analysis. Third, patent data contain detailed useful information,
such as technological fields, assignees, inventors, and some other market features.
We carried out our analysis on green patents developed by a sample of firms included in the Dow Jones
Sustainability World Index in 2004. Specifically, we focused on four sectors, basic materials, industrials,
technology, and utilities (see Table 1), where the strong appropriability regime makes patents an effective and
critical means to protect innovations (Levin et al., 1987; Mansfield, 1986). Furthermore, relying on a cross-
industrial sectors analysis allows us to obtain more general findings that go beyond the specificity of a single
sector.
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The geographical locations of the sample companies are reported in Figure 1.
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Insert Figure 1 about here
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On the basis of an in-depth literature review (Brunnermeier, 2003; Hart, 1997; Jaffe & Palmer, 1997;
Klassen & Whybark, 1999; Nameroff et al., 2004), as well as by considering the definition of environmental
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technologies5 (Commission of the European Communities, 2004; p. 2), we defined a set of keywords (Table 2)
useful to identify green patents, i.e. innovations potentially leading to same form of environmental benefits.
Specifically, we performed a lexical query on the claims text for each patent the firms in the sample granted at
the U.S.PTO from 1973 to 2003. Accordingly, patents were classified as green when the query returned a
positive match between at least one of the selected keywords and claims text. Successively, in order to avoid the
selection of false green patents, we checked the aim of the patented technology through the abstract reading.
This process was independently performed by each of the authors and results were compared to avoid potential
bias in the sample selection process. Thus, we identified a sample of 151 green patents. Table 1 reports some
statistics concerning the green patents sample. In particular, 40 firms granted at least one green-patent and 83
firms have no patents that could be classified as “green”. Then, we collected for each green patent bibliographic
data about the citing and cited patents. Specifically, 680 patents cited the green patents and 1,068 patents were
cited by the green patents.
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Insert Table 2 about here
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Afterwards, we defined an equal size control sample of non-green patents. Specifically, for each green
patent a counter non-green patent was identified according to the following matching criteria. First, the green
and non-green patents had to be granted by the same assignee. Second, in order to control for the time cohort the
two patents had to be filed in the same year (fling year). Third, we matched the counter patent by considering the
main U.S. three-digit technological class to which the two patents were assigned. Finally, since the multinational
nature of the firms included, we matched for the nationality of the first inventor. Whether the counter sampling
match returned more than one non-green patents for the given green one, the selection was random. As for the
green patents sample, for each of the 151 non-green patents we gathered bibliographic data for all forward and
backward patent citations. More precisely, the non-green patents in the control sample were cited by 594 patents
and cited 906 patents.
As presented above, our data rely on patents granted as the U.S.PTO. In particular, a patent represents the
grant of a property right to the inventor, issued by the U.S. PTO. Generally, the term of a new patent is 20 years
from the date on which the application for the patent was filed in the United States or, in special cases, from the
date an earlier related application was filed, subject to the payment of maintenance fees. U.S. patent grants are
effective only within the United States, U.S. territories, and U.S. possessions. Furthermore, in accordance with
the U.S. patent law, the right conferred by the patent grant is the right to exclude others from making, using,
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5 Environmental technologies encompass “technologies and processes to manage pollution (e.g. air pollution control, waste management), less polluting
and less resource-intensive products and services and ways to manage resources more efficiently (e.g. water supply, energy-saving technologies)”.
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offering for sale, or selling the invention in the United States or importing the invention into the United States.
The patent law specifies the general field of subject matter that can be patented and the conditions under which a
patent may be obtained. Any person who invents or discovers any new and useful process, machine,
manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent, subject
to the conditions and requirements of the law. Specifically, the patent law argues that the subject matter must be
useful. The term useful refers to the condition that the subject matter has a useful purpose and also includes the
notion of operativeness. Beyond usefulness, in order for an invention to be patentable it must be both new, i.e.
not previously patented or described in a printed publication, and non obvious, i.e. sufficiently different from the
prior art.
A patent is requested by filing a written application at the U.S.PTO (U.S.PTO, 2008). The person or
company filing the application is referred to as the applicant, which may be the inventor or its assignee. The
application contains a description of how to make and use the invention that must provide sufficient details for a
person skilled in the referring technological area to make and use the invention. The application also includes
one or more claims, which define the scope of protection. After filing, an application is often referred to as
patent pending. This serves to provide warning to potential infringers. For a patent to be granted, that is to take
legal effect in a particular country, the patent application must meet the above three patentability requirements:
usefulness, novelty, and non-obviousness. If the application does not comply, objections are communicated to
the applicant or patent agent or attorney and one or more opportunities to respond to the objections in order to
bring the application into compliance are usually provided. Finally, once granted the patent is subject to renewal
fees to keep it in force.
3.2 Measures
Dependent Variable: We measured the value of green and non-green innovations (InnValue) by the total
number of citations the specific patent received within five years of the filing date, excluding self-citations
belonging to the focal company and (if any) to other assignees involved in the same patent. We rely on this
measure following the pioneering study by Trajtenberg (1990), who demonstrates the existence of a positive and
significant correlation between the returns to innovation and the citation indicators. Specifically, forward
citations indicate “that the cited patents opened the way to a technologically successful line of innovation. [...]
Thus, if citations keep coming, it must be that the innovation originating in the cited patent had indeed proven to
be valuable” (Trajtenberg, 1990: p. 174). This patent value measuring approach has been widely adopted and
validated along subsequent researches (Albert et al., 1991; Hall et al., 2005; Harhoff et al., 1999).
Each patent has an equal five-year time window to be cited, which allows us to eliminate any bias in the
number of forward citations received (e.g. Griliches, 1979; Henderson & Cockburn, 1996). In fact, patents
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granted in earlier years are exposed to risk of being cited by other patents for a longer period. In addition, we
chose a five-year time window because, as reported by Griliches (1979), the knowledge capital depreciates
sharply, losing most of its value within five years (e.g. Argote, 1999; Katila & Chen, 2008).
Independent Variables.
Organizational factors. Two organizational factors were considered as: i) inter-organizational
collaborations and ii) intra-organizational collaborations. The inter-organizational collaborations (InterOrg) were
measured as the number of co-assignees with whom the sample companies registered the patents. This
information captures active research collaboration and/or contractual research between two or more
organizations (Sapsalis & Van Pottelsberghe, 2007). On the other hand, we measured intra-organizational
collaborations (IntraOrg) counting the number of focal company’s inventors involved in the patents.
Specifically, in the case of patents assigned to two or more organizations, we identified the focal company’s
inventors by matching the address of the inventors with the focal company and its subsidiaries’ one (Singh,
2005).
Technological features. We considered two main technological features affecting the value of green
innovations, as complexity and novelty. The complexity of technologies (Complexity) was measured by the time
invariant count of the number of three-digit patent classes in which the U.S.PTO assigns the patent (Lerner,
1994). In fact, the more the classes to which a patent is assigned, the broader is the set of different technologies
that have been integrated, hence providing a measure of the complexity of the innovation.
The novelty of technology (Novelty) was measured as the number of three-digit patent classes in which
previous patents cited by the given patent are found, but the patent itself is not classified (Rosenkopf & Nerkar,
1999; Shane, 2001). The assignment of a patent to a specific class determines what previous innovations must be
cited in a patent, representing the technological basis upon which the patent is built. Thus, when a patent cites
previous patents in classes other than the ones it is in, that pattern suggests that the patent builds upon different
and novel technological paradigms from the one in which it is applied.
Control Variables. In order to avoid biases from other effects on the value of innovations, we introduced
several control variables. First, scholars stressed that patent value may also depend on the number of claims (e.g.
Lanjouw & Schankerman, 2001; Tong & Frame, 1994). Hence, we included as a control variable the number of
claims (Claims). Second, some patents may cite more the prior scientific art. Thus, we controlled for the number
of non-patent references (ScBackCit), i.e. scientific publications, that a patent cited in the prior art. In fact, there
is a strong recognition in the literature about the value of non-patent references as an indicator of science and
technology interplay (e.g. Cassiman et al., 2008; Van Looy et al., 2004). Third, the value of patent has been also
traditionally related to the number of backward citations (Narin et al., 1997). Therefore, we controlled for the
number of U.S. patents (USBackCit) and foreign patents (ForBackCit) cited in the prior art. Fourth, the focal
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companies may have different levels of expertise, capability, and propensity to innovate. Then, we included a
variable to control for the companies’ technological capital (TechCap), measured as the number of patents
successfully filed by the firms during the five years prior the filing date of the given patent (Phene et al., 2006).
Finally, we inserted industrial sector dummies, country dummies, and year dummies to control for the
unobserved heterogeneity. In particular, country dummies are included to control for firms located in U.S.,
Japan, which count for about 70% of the total number of companies, and other countries (Germany, France, UK,
Sweden, and Taiwan). Table 3 reports the construction of the variables.
---------------------------------------------------
Insert Table 3 about here
---------------------------------------------------
3.3 Estimation
The dependent variable (InnValue) is a non-negative integer count variable. Then, the linear regression’s
main assumption is violated. In fact, this variable cannot be normally distributed. Generally, count models
represent an improvement relative to the linear regression models. The simplest estimation is the Poisson, but it
assumes equity between the conditional mean and the variance. However, patent data on citations are generally
over-dispersed (Hausman et al., 1984). Therefore, an extension of the Poisson estimation was considered, i.e. the
negative binomial one. We deemed it more suitable to our data as it allows the variance to differ from the mean,
and thus can handle over-dispersion (Cameron & Trivedi, 1986; Hausman et al., 1984). Furthermore, the alpha
test on the over-dispersion reported in the regression results tables confirms that the negative binomial
estimation is more suitable than the Poisson estimation, since the alpha parameter is significantly different from
zero.
4. Results
The descriptive statistics and correlation matrix are reported in Table 4 (green innovations’ sample), Table
5 (control sample of non-green innovations), and Table 6 (whole innovations sample). All correlations between
the independent variables fall below the 0.70 threshold for both the green innovation’ sample and control
sample, thus indicating an acceptable discriminant validity (Cohen et al., 2003). Furthermore, the examination of
the variance inflation factors (VIFs) associated with each regression coefficient shows a range from 1.89 to 2.42,
thus suggesting that multicollinearity is not a problem in any of these models (Belsley et al., 1980).
The comparison of the value of green innovations with the control sample presents no significant
differences in terms of number of forward citations a patent received (t-test on the sample mean of InnValue:
12‐
p>0.1). However, considering the organizational factors, it seems that to develop green innovations companies
establish more external collaborations (proportion test on InterOrg: p<0.01), as well as tend to combine internal
competencies and resources to a greater extent (t-test on the sample mean of IntraOrg: p<0.05). Similarly,
technological factors significantly differ between green and non-green innovations in terms of both complexity
and novelty. In fact, the technologies underling green innovations seem to be characterized by a higher degree of
novelty (t-test on the sample mean of Novelty: p<0.01) and complexity (t-test on the sample mean of
Complexity: p<0.01) compared to non-green ones.
---------------------------------------------------
Insert Table 4 about here
---------------------------------------------------
---------------------------------------------------
Insert Table 5 about here
---------------------------------------------------
---------------------------------------------------
Insert Table 6 about here
---------------------------------------------------
Table 7 presents the coefficient estimates for the negative binomial regression models. Specifically,
Model 1 is the baseline model that only includes the control variables. In Model 2, we introduce inter-
organizational and intra-organizational collaborations. Model 3 contains the impacts exerted by technological
complexity and novelty. Finally, in Model 4 all the effects are simultaneously analyzed. Below, we discuss the
results obtained with the full model (Model 4).
---------------------------------------------------
Insert Table 7 about here
---------------------------------------------------
Findings show that the coefficient for inter-organizational collaborations is positive but not significant,
whereas intra-organizational collaborations present a positive and significant value (β=0.700; p<0.05).
Regarding technological features, it is possible to notice that that novelty of technologies underlying green
innovations has a negative effect on their value (β=-0.063; p<0.001), while complexity seems to have no effect
on the value of green innovations. We have also tested the quadratic effects of the independent variables.
However, any significant results emerge. With regard to control variables, coherently with previous studies, the
number of claims and the number of U.S. backward citations are positively related with the value of green
innovations.
13‐
Furthermore, we run the regression analysis also in the sample of non-green patents, to compare the
effects exerted by organizational factors and technological features on the value of innovation in the two
samples. As reported in Table 8, both IntraOrg and Novelty do not have any significant effect on the innovation
value in the sample of non-green patents, in which, on the contrary, InterOrg and Complexity display a positive
effect. This result suggests that success factors of green innovations significantly differ from those of non-green
ones.
---------------------------------------------------
Insert Table 8 about here
---------------------------------------------------
5. Discussion and Conclusion
This paper studies green innovations, by investigating the role played by organizational factors (inter-
and intra-organizational relationships) and main technological features (complexity and novelty). To this aim,
green patents developed by a sample of firms belonging to the Dow Jones Sustainability World Index have been
analyzed.
On the one hand, we compared to what extent green innovations differ from “conventional” innovations
in terms of organizational factors and technological features. Regarding this, findings reveal that green
innovations are characterized by higher levels of both inter- and intra-organizational collaborations compared to
other innovations developed by the same firms. This result is coherent with previous studies, highlighting the
relevance of environmental information exchange and integration among a wide variety of actors both within
and outside the firm (e.g. Foster & Green, 2000; Lenox & Ehrenfeld, 1997), and shows that developing green
innovations requires significantly higher organizational capabilities than developing conventional innovations. In
addition, referring to technological features, our study reveals that green innovations are characterized by higher
levels of complexity and novelty than others. The higher level of complexity can be explained by the fact green
innovations require several pieces of knowledge to be integrated, due to several types of environmental impact
they reduce, such as materials, energy, pollution, waste, as well as to different phases of products/processes’
lifecycle in which they may be applied. Differently, the higher level of novelty points out that green innovations
are really innovative compared to previous innovations on which they are based. This result is very interesting,
suggesting that the integration of the natural environment into the innovation process leads to the creation of new
knowledge, significantly different from the one upon which it is based.
Furthermore, we investigated to what extent the organizational factors and technological features affect
the value of green innovations. In particular, our findings reveal as the most valuable green innovations those
highly relying on collaborations among internal actors. This higher level of intra-organizational collaborations
14‐
may reflect on a higher quality of innovations that on turn leads to a higher value of them. On the contrary, green
innovations characterized by higher levels of novelty seem to be less valuable. This counterintuitive result may
be explained by the fact that, when green innovations are too new, more time is needed for their understanding,
diffusion, and adoption within the markets. In addition, we conducted the same analysis on the control sample of
non-green innovations finding that different organizational and technological factors influence innovation value.
In particular, both inter-organizational collaborations and technological complexity have a positive effect. This
result suggests that developing successful green innovations requires investing in different organizational and
technological drivers compared to conventional innovations.
In terms of managerial implications, our study offers interesting insights. First, it emerges that the
development of green innovations, as well as their value, is influenced by the establishment of inter- and intra-
organizational relationships. Thus, we encourage managers and corporate executives to invest in the
creation/strengthening of external and, especially, internal networks, through which relevant environmental
knowledge can be exchanged. Moreover, these relationships may allow firms to access heterogeneous
technological competencies, whose integration provides the complexity characterizing green innovations
development process. Second, being the first to introduce a green innovation has been proved to be not always
the best solution. This result depends on the costs and risks going along with the first move, which, under certain
circumstances, make wait and see the most suitable strategy. This is the case of the introduction of green
innovations, which may suffer from high demand uncertainty and market opposition, that significantly reduce
the pace of their diffusion. Thereby, in these situations, government support may play a key role in favouring
market acceptance and sustaining firms to introduce innovative green technological solutions.
This study is the first one to explicitly and quantitatively compare green innovations and conventional
innovations features. Furthermore, to our knowledge, so far no study investigated the effect of both
organizational factors and technological features on green innovations’ value, providing a comparison with a
sample of non-green innovations. However, this study has some limitations that should be acknowledged. First,
analyses have been conducted on a patent sample characterized by a smaller dimension compared to other
studies based on patents. This is due to the fact that the number of green patents (and consequently of non-green
patents) has not been a priori established, but is the result of the patents registered by a selected sample of
companies with a recognized commitment towards environmental issues (companies listed in the Dow Jones
Sustainability Index). Nevertheless, despite of the small size of our sample, the accuracy and the conservative
character of the selection process allows us to be confident about the inclusion of only patents presenting green
properties. In addition, the small sample size is also due to the fact that patent data collection stops in 2004, in
order to have each patent with an equal time window of five years to be cited. Thus, this approach does not
account for the increasing policy interest towards sustainability issues, characterizing recent years. Second, not
all possible organizational factors and technological features that could play a relevant role in the innovation
15‐
development process have been considered. For example, through patent analysis, we were able to study intra-
organizational collaborations, but we were not able to evaluate the extent to which these collaborations were
cross-functional. Other factors supporting the green innovation development process may be specific
environmental training courses attended by R&D employees, investments in environmental R&D, top
management commitment toward environmental issues, an environmental culture widespread at all levels of the
firm, as well as regulatory standards and legislative changes. Finally, some specifications should be done
referring to the proxies we used. In particular, the innovation process is described by means of patents, which
represent a specific subset of all potential innovations. Thus, other indicators of innovation could be considered
in future studies, such as new products, especially in low-tech industries, where patents do not represent a
suitable proxy to capture innovative dynamics.
6. Acknowledgments
We gratefully acknowledge thoughtful comments from the two anonymous reviewers.
16‐
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21‐
8. Tables & Figures
Table 1. DJSI’s industrial sectors and number of firms.
Industrial Sectors Number of firms Number of firms with at
least one green patent
Basic Materials 22 3
Industrial 58 21
Technology 27 11
Utilities 16 4
Total 123 40
Figure 1. Firms’ geographical locations.
22‐
Table 2. Set of keywords adopted for green patent identification.
Green keywords
acid rain
alternative energy
decontamination
disposal
emission
energy conservation
energy efficiency
energy saving
environment
material reduction - reduce materials
pollution – pollutant
recycle – recycling – recyclable
renewable energy
reuse – reusing
toxic
waste
23‐
Table 3. Dependent, independent, and control variables.
Variable Measure
Dependent variable
InnValue Count of the number of citations the focal company received within five years from the filing date.
Independent variables
Organizational Factors
InterOrg Count of the number of co-assignee(s) with whom the company granted the focal patent
IntraOrg Count of the number of focal company’s inventors involved in the focal patent.
Technological Features
Novelty Count of the number of U.S.PTO technology-classification system’s three-digit classes in which
previous patents cited by the given focal patent are found, but the focal patent itself is not classified.
Complexity Count of the number of U.S.PTO technology-classification system’s three-digit classes to which the
focal patent is assigned.
Control variables
Claims Count of the number of claims made by the focal patent.
ScBackCit Count of the number of scientific references cited in the prior art of the focal patent.
USBackCit Count of the number of U.S. patents cited in the prior art of the focal patent.
ForBackCit Count of the number of non-U.S. patents cited in the prior art of the focal patent.
TechCap Count of the number patents that the focal company’s successfully filed for during the previous five
years.
Industrial Sector
dummies
Dummy variables indicating the industrial sector in which the focal patent’s assignee operates
(default = Industrial Goods & Services).
Country dummies Dummy variables indicating a particular country of the companies in the sample (default = U.S.).
Year dummies Dummy variables indicating a particular year in the observed period 1998-2003 (default = 1998).
24‐
Table 4. Descriptive statistics and bivariate correlation matrix for green innovations’ sample (N=151).
Variables Mean Std.Dev. Min Max 1 2 3 4 5 6 7 8 9 10
1. InnValue 5.503 5.969 1 51 1.000
2. InterOrg 0.106 0.309 0 1 0.003 1.000
3. IntraOrg 3.192 2.103 0 13 -0.048 -0.350 1.000
4. Novelty 4.675 3.841 0 18 -0.112 -0.100 0.057 1.000
5. Complexity 2.066 1.159 1 7 0.089 0.185 -0.178 -0.067 1.000
6. Claims 16.669 13.019 1 69 0.062 -0.069 -0.024 -0.038 0.090 1.000
7. ScBackCit 1.623 4.031 0 26 -0.007 -0.075 0.215 -0.078 -0.113 0.178 1.000
8. USBackCit 7.079 5.067 0 27 0.044 -0.014 0.023 0.574 -0.082 0.047 0.007 1.000
9. ForBackCit 3.570 3.962 0 17 -0.087 0.108 0.236 0.132 0.066 0.290 0.133 0.264 1.000
10. TechCap 4154.01 4162.89 0 12911 -0.052 -0.135 -0.053 -0.037 0.098 0.074 0.193 -0.072 -0.050 1.000
Table 5. Descriptive statistics and bivariate correlation matrix for non-green innovations’ sample (N=151).
Variables Mean Std.Dev. Min Max 1 2 3 4 5 6 7 8 9 10
1. InnValue 4.934 4.090 1 32 1.000
2. InterOrg 0.033 0.180 0 1 0.039 1.000
3. IntraOrg 2.808 1.825 0 10 -0.008 -0.245 1.000
4. Novelty 3.762 3.046 0 22 0.105 0.027 0.047 1.000
5. Complexity 1.788 1.037 1 5 0.108 0.038 0.066 0.111 1.000
6. Claims 14.868 11.760 1 67 0.166 0.037 -0.029 0.203 0.027 1.000
7. ScBackCit 1.940 5.288 0 35 0.101 -0.005 -0.024 0.275 0.093 0.213 1.000
8. USBackCit 5.993 4.495 0 30 0.191 0.025 0.002 0.639 0.031 0.239 0.305 1.000
9. ForBackCit 2.762 3.984 0 23 0.089 0.058 0.247 0.201 0.120 0.463 0.206 0.334 1.000
10. TechCap 3819.39 4216.90 0 12911 0.032 -0.057 -0.011 -0.055 0.045 0.111 0.214 0.116 0.170 1.000
25‐
Table 6. Descriptive statistics and bivariate correlation matrix for the whole sample (N=302).
Variables Mean Std.Dev. Min Max 1 2 3 4 5 6 7 8 9 10
1. InnValue 5.219 5.116 1 51 1.000
2. InterOrg 0.070 0.255 0 1 0.021 1.000
3. IntraOrg 3.000 1.975 0 13 -0.027 -0.291 1.000
4. Novelty 4.219 3.491 0 22 -0.028 -0.040 0.065 1.000
5. Complexity 1.927 1.106 1 7 0.101 0.148 -0.058 0.023 1.000
7. Claims 15.768 12.418 1 69 0.105 -0.021 -0.019 0.071 0.071 1.000
6. ScBackCit 1.781 4.697 0 35 0.040 -0.046 0.082 0.093 -0.006 0.191 1.000
8. USBackCit 6.536 4.812 0 30 0.104 0.016 0.025 0.606 -0.017 0.143 0.146 1.000
9. ForBackCit 3.166 3.987 0 23 -0.009 0.100 0.248 0.172 0.103 0.379 0.174 0.305 1.000
10. TechCap 3986.72 4186.37 0 12911 -0.015 -0.096 -0.029 -0.039 0.077 0.093 0.205 0.021 0.065 1.000
26‐
Table 7. Impact of the organizational factors and technological features on the value of green innovations a, b.
Dependent variable:
InnValue Model 1 Model 2 Model 3 Model 4
Independent variables
InterOrg 0.186 (0.241) 0.008 (0.238)
IntraOrg 0.086 (0.030) *** 0.074 (0.029) ***
Novelty -0.064 (0.026) *** -0.062 (0.026) **
Complexity 0.082 (0.071) 0.081 (0.070)
Control variables
Claims 0.013 (0.006) ** 0.013 (0.005) ** 0.012 (0.006) ** 0.012 (0.005) **
ScBackCit -0.013 (0.018) -0.022 (0.017) -0.015 (0.018) -0.023 (0.017)
USBackCit 0.029 (0.013) *** 0.030 (0.012) *** 0.054 (0.018) *** 0.055 (0.017) ***
ForBackCit 0.001 (0.021) -0.010 (0.021) -0.001 (0.020) -0.009 (0.020)
TechCap 2.74E-5 (1.83E-5) 3.12E-5 (1.77E-5) 2.41E-5 (1.87E-5) 2.79E-5 (1.82E-5)
Industrial Sector
dummies (3) Included Included Included
Country dummies (2) Included Included Included
Year dummies (27) Included Included Included
Log pseudo-likelihood -376.40 -372.96 -370.94 -368.05
Alpha test 0.251 (0.046) *** 0.239 (0.045) *** 0.218 (0.039) *** 0.205 (0.040) ***
No. of Obs. 151 151 151 151
a Huber-White robust standard errors are reported in parentheses.
b *p < 0.1; **p < 0.05; ***p < 0.01.
27‐
Table 8. Impact of organizational factors and technological features on the value of green and non-green-innovations a, b.
Dependent variable:
InnValue Model 5a
(green innovations) Model 5b
(non-green innovations)
Independent variables
InterOrg 0.008 (0.238) 0.411 (0.259) *
IntraOrg 0.074 (0.029) *** 0.030 (0.030)
Novelty -0.062 (0.026) ** -0.006 (0.023)
Complexity 0.081 (0.070) 0.071 (0.048) *
Control variables
Claims 0.012 (0.005) ** 0.010 (0.004) **
ScBackCit -0.023 (0.017) 0.005 (0.010)
USBackCit 0.055 (0.017) *** 0.026 (0.015) *
ForBackCit -0.009 (0.020) 0.003 (0.014)
TechCap 2.79E-5 (1.82E-5) -2.95E-5 (1.62E-5)
Industrial Sector
dummies (3) Included Included
Country dummies (2) Included Included
Year dummies (27) Included Included
Log pseudo-likelihood -368.05 -338.75
Alpha test 0.205 (0.040) *** 0.112 (0.057) ***
No. of Obs. 151 151
a Huber-White robust standard errors are reported in parentheses.
b *p < 0.1; **p < 0.05; ***p < 0.01.
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