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Collaborations between actors from different sectors (governments, firms, nonprofit organizations, universities, and other societal groups) have been promoted or mandated with increasing frequency to spur more innovative activities. This article argues that there is an essential gap in evaluating the issues of these collaborative arrangements on innovation and a need to theorize the costs of these arrangements systematically. This article identifies three implicit assumptions in current research that prevent a sound analysis of the costs of collaborative innovation and advances a new cost theory based on the integration of studies from several research fields and explanations provided by three main economic theories: transaction cost economics, game theory, and the knowledge-based view. In particular, four overarching factors are posited to impact the effectiveness of collaboration for innovation: governance (the number of collaborators and the hierarchical relationships among them); compactness (the degree of relationship formality that binds collaborators together); reliability (the quality of the relationships); and institutionalization (the extent to which the relationships have been pre-established by practice). We discuss the importance of leveraging these factors to determine an optimal governance structure that allows collaborating actors to minimize transaction, cooperation, and knowledge costs, and to reward participants proportionally to the cost they bear, in order to foster conditions of reciprocity, fair rates of exchange, and distributive justice.
The Journal of Technology Transfer
1 3
The costs ofcollaborative innovation
RobertoVivona1 · MehmetAkifDemircioglu2 · DavidB.Audretsch3
Accepted: 4 March 2022
© The Author(s) 2022
Collaborations between actors from different sectors (governments, firms, nonprofit organi-
zations, universities, and other societal groups) have been promoted or mandated with
increasing frequency to spur more innovative activities. This article argues that there is
an essential gap in evaluating the issues of these collaborative arrangements on innovation
and a need to theorize the costs of these arrangements systematically. This article identifies
three implicit assumptions in current research that prevent a sound analysis of the costs of
collaborative innovation and advances a new cost theory based on the integration of studies
from several research fields and explanations provided by three main economic theories:
transaction cost economics, game theory, and the knowledge-based view. In particular, four
overarching factors are posited to impact the effectiveness of collaboration for innovation:
governance (the number of collaborators and the hierarchical relationships among them);
compactness (the degree of relationship formality that binds collaborators together); reli-
ability (the quality of the relationships); and institutionalization (the extent to which the
relationships have been pre-established by practice). We discuss the importance of leverag-
ing these factors to determine an optimal governance structure that allows collaborating
actors to minimize transaction,cooperation, and knowledge costs, and to reward partici-
pants proportionally to the cost they bear, in order to foster conditions of reciprocity, fair
rates of exchange, and distributive justice.
Keywords Collaborative innovation· Cross-sectoral collaboration· Transaction cost
economics· Game theory· Knowledge-based view· Governance
JEL classification O39
* Roberto Vivona
Mehmet Akif Demircioglu
David B. Audretsch
1 Nord University Business School, Bodø, Norway
2 Lee Kuan Yew School ofPublic Policy, National University ofSingapore, Singapore, Singapore
3 Institute forDevelopment Strategies, School ofPublic andEnvironmental Affairs, Indiana
University-Bloomington, Bloomington, IN, USA
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R.Vivona et al.
1 3
1 Introduction
Innovation is increasingly pursued through the collaboration of a vast array of stakehold-
ers, including private sector firms, public sector organizations (such as government agen-
cies, state administrations, and local governments), and nonprofit organizations (the third
sector), along with groups and individuals from the civil society (Barrutia & Echebar-
ria, 2019; Moore & Hartley, 2008; Sørensen & Torfing, 2011) and from universities and
research institutions (Demircioglu & Audretsch, 2019; Miller, 2016; Walsh etal., 2016).
This type of collaborative innovation, based on cross-sectoral collaboration, is becoming
increasingly important in norm and practice, since it is expected to be necessary to address
contemporary grand challenges and wicked problems (e.g., Agranoff & McGuire, 2001;
Demircioglu & Vivona, 2021; Gazley, 2014; McGuire, 2006; Waardenburg etal., 2020). In
addition, current research argues and finds evidence that cross-sectoral collaborations lead
to more innovation (Barrutia & Echebarria, 2019; Demircioglu & Audretsch, 2020; Torfing
& Triantafillou, 2016). Hence, cross-sectoral collaboration has become a “key innovation
strategy” for innovation (Hartley etal., 2013, p. 826), and organizations aim to promote
more collaborative activities with an extended range of partners.
Underlying the theoretical positioning of this strategy stands the assumption that col-
laboration has a positive impact on organizational innovativeness, which, in practice,
results in the proposition that collaboration is often “perceived as a value in itself” and as
an “objective to be met” (Voorberg etal., 2015, p. 15). Nevertheless, collaboration for the
sake of collaborating may be highly detrimental for organizations: collaboration is costly,
as it requires greater use of resources of money, time, and effort (Torugsa & Arundel,
2016; Wegrich, 2019), and, additionally, there may be operational, technological, perfor-
mance, and legal barriers to effective collaboration (Agranoff & McGuire, 2004; McGuire
& Agranoff, 2011).
This gap is unfortunate because, as Audretsch and Belitski (2019, p. 22) argue, “research
is needed on innovation collaboration costs”, and, although some studies exist regarding
the barriers or costs of collaborative arrangements (e.g., Cummings & Kiseler, 2007; Noot-
eboom, 2008; Terjesen & Patel, 2017; Torugsa & Arundel, 2016; Van Knippenberg etal.,
2004), these studies do not comprehensively discuss these costs.
By showing the contingencies under which collaboration is preferred, some scholarly
efforts have aimed to address this gap. For instance, Felin and Zenger (2013) examine how
a specific governance form (i.e., closed vs. open and collaborative forms) can be chosen by
private enterprises on the basis of the attributes of the innovation problem. Hartley etal.
(2013) compare innovation strategies for public sector organizations, and they illustrate the
conditions that make collaborative innovation superior to “in-house” innovation (i.e. New
Public Management market-driven innovation and neo-Weberian bureaucratic innovation),
while O’Toole (1997) points out the importance of understanding and evaluating the cost
of interagency collaboration, as collaboration may be a significant cost to governments.
Similarly, Fallis (2006) compares how analyses of collaboration systematically differ
across scholars in different fields (e.g., science vs. philosophy), illustrating the characteris-
tics of scholarly research that make collaboration successful.
While these studies provide relevant insights, they are deeply rooted in their own sec-
toral traditions. Private sector studies (e.g., Audretsch & Belitski, 2019; Terjesen & Patel,
2017) focus on commercial, market, industrial, and technological innovations where the
primary aim is to create value through increased profits or market share, while public and
third sector studies (i.e., nonprofit organizations) focus on public and social innovation to
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The costs ofcollaborative innovation
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create public and social value (e.g., increased legitimacy and equity) (Torfing & Trian-
tafillou, 2016). We intend to overcome this limitation by taking a sector-neutral perspec-
tive on innovation, which is more suitable to the analysis of cross-sectoral collaborations.
Moreover, while most studies explore the conditions favoring collaboration (e.g., Moore &
Hartley, 2008; Sørensen & Torfing, 2011), there is also a considerable gap in the system-
atic analysis of the specific costs of collaborative innovation, which is required in order
to reject or confirm collaborative approaches to innovation on the grounds of cost–ben-
efit analyses (Bommert, 2010). This is particularly important in light of recent works sug-
gesting that innovation and collaboration exhibit diminishing marginal returns (Audretsch
& Belitski, 2020; Denicolai etal., 2016; Kobarg et al., 2019); in other words, as Torfing
(2019, p. 5) states, “[t]he Achilles heel of collaborative innovation is the inherent tension
between collaboration and innovation”.
To address this gap, we employ three theoretical perspectives, namely transaction cost
economics, game theory, and knowledge-based view, to integrate studies from several
research fields, and we develop a new cost theory which systematizes said insights accord-
ing to four main factors: governance (the number of collaborators and the hierarchical
relationships among them); compactness (the degree of relationship formality that binds
collaborators together); reliability (the quality of the relationships); and institutionaliza-
tion (the extent to which the relationships have been pre-established by practice). In doing
so, this article focuses on cross-sectoral collaborative innovation. Undeniably, collabora-
tive innovation also takes place within sectors (i.e., intra-sectoral collaborative innovation);
the reader should be mindful that intra-sectoral collaborative innovation may, in some
cases, simply exhibit a subset of the costs discussed here, but, in other cases, be inherently
distinct, such as the case of collaboration with competitors – or coopetition (see Ritala
etal., 2016). Moreover, we focus on a generic cross-sectoral collaboration model (which
is explained in the next section) that is less specific than other models advanced in the lit-
erature (e.g., open innovation models, Triple and Quadruple Helix models) in order to shed
light on a broader range of costs for innovation incurred by collaborative arrangements.
The remainder of the article is organized as follows. The following section presents the
framework of our study and its underlying concepts in order to illustrate how collabora-
tion is linked to innovation in the literature. Then, we investigate the reasons for the lack
of rigorous cost–benefit analysis approaches regarding collaborative innovations. Building
on previous research, we then advance an integrated cost theory that encompasses several
phenomena related to cross-sectoral collaborative innovations. We also discuss the impli-
cations of our cost classification for the general literature on collaboration and innovation.
We conclude by highlighting paths for future research.
2 Cross‑sectoral collaborative innovation
2.1 Innovation andvalue creation
While the concept of innovation is multifaceted, subjective, and can be defined in various
ways, a simple and straightforward definition states that an innovation is something new
and useful (Mulgan, 2007; Mulgan & Albury, 2003): it comprises both the concepts of nov-
elty—“something new”, such as a new product, process, practice, postulation, or policy—
and of utility, or value creation—“and useful”, since innovation should yield results and
organizations and users should be able to extract some value from them (OECD/Eurostat,
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R.Vivona et al.
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2018; Vivona etal., 2020). The latter concept is particularly interesting for the purpose of
this article. The idea that innovation should be useful, and thus create some value, has been
discussed widely in innovation studies: some researchers are concerned with whether the
value created by the innovative process can be appropriated by the innovator (Baldwin &
Henkel, 2012; Jacobides etal., 2006; Laursen & Salter, 2014; Ritala & Hurmelinna-Lauk-
kanen, 2009) while others are more concerned with measuring the results and impact of the
innovation (Arundel etal., 2019; Bloch & Bugge, 2013; Smith, 2005).
Most importantly, the concept of value creation is coupled with the goal of innovation:
indeed, “innovation can have different goals and therefore can be directed to obtain differ-
ent results” (Vivona etal., 2020, p. 3). Goals and objectives are profoundly different across
sectors. For example, public and private sectors have different values: while the former is
typically concerned with equity and enhancing citizen participation in public services, the
latter focuses on the profit maximization of firms (Rainey, 2009; van der Wal etal., 2008).
In other words, when the goal of an innovation is to increase profits or the market share
of a firm (i.e., private sector organization), it is usually referred to as a “market innova-
tion” or “industrial innovation” since it is directed to create market value (Jacobides etal.,
2006). When a public sector organization seeks innovation with the goal of creating pub-
lic value (e.g., by enhancing citizen participation), innovation is usually framed as “public
sector innovation” (De Vries etal., 2016; Demircioglu, 2017; Verhoest etal., 2007). Other
innovations aim at creating social value by reducing socio-economic vulnerabilities (i.e.,
“social innovation”) (Murray etal., 2010; Phills etal., 2008; Van der Have & Rubalcaba,
While this conceptualization of the innovation goal is important for understanding the
primary objective of an innovation, several scholars argue that innovation is multifaceted
and can simultaneously create various forms of value; that is, innovation is rarely either
one form or the other but more often a “blend” that creates shared value (Porter & Kramer,
2011). For instance, Emerson (2003) integrates the creation of financial and social values
into a blended value proposition, stating that all firms generate returns not only in terms
of better profits but also in terms of social performance. This proposition is particularly
insightful to better understand cross-sectoral collaborative innovation.
2.2 Cross‑sectoral dimension ofcollaborative innovation
Collaborative innovation has traditionally been advanced in the private sector, as the “crea-
tion of innovations across firm (and perhaps industry) boundaries through the sharing of
ideas, knowledge, expertise, and opportunities” (Ketchen etal., 2007, p. 372). However,
this phenomenon has been successfully applied to other sectors. For instance, Torfing
(2019, p. 2) notes that “collaborative innovation offers an alternative approach to innova-
tion that is particularly suited to the public sector. The public sector aims to produce public
value, and both public and private actors (including service users and citizens) can contrib-
ute to the production of public value and are likely to be motivated to collaborate in its pur-
suit”. Bolton and Savell (2010) describe a particular form of cross-sectoral collaboration
known as “social impact bond,” which blends the efforts of public, private, and third sector
organizations for the delivery of innovative social programs (social value) with innovative
investments that ease the monetary burden on public budgets (public value) and provide
valuable financial returns to private investors (market value). Additionally, Lichtenthaler
(2017) illustrates how a mobility service innovation (i.e., car-sharing) creates value, which
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The costs ofcollaborative innovation
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is shared with the manufacturer (profits) and society at large (reduction of environmental
Our interest resides in studying the cross-sectoral dimension of collaborative innova-
tion, that is, collaborative innovation which takes place across sectors and creates diverse
values for the multitude of actors collaborating in the innovation process. This term also
refers to the evolution of collaborations beyond the public–private sector dichotomy.
Indeed, while “sector” was traditionally used to identify a distinction based on the owner-
ship of an organization (public vs. private), this division is no longer appropriate, as other
sectors have emerged.1 For instance, from the 1970s onwards, “third sector” organizations
emerged, neither owned by the state nor seeking profits but instead motivated by the goals
of serving the community and by the primacy of people (and labor) over capital (Defourny,
2013; Salamon, 2002). Additionally, some scholars advance the idea of a “fourth sector,
which includes organizations that simultaneously pursue the creation of social and finan-
cial value (e.g., Sabeti, 2011). As stated in the previous section, however, research suggests
that such proposals fail to recognize that all organizations pursue multiple value creation
(Bozeman, 2018; Emerson, 2003; Moore, 1995). In a minority of studies, the term “fourth
sector” is instead used to refer to tertiary education (Benseman etal., 1996; Tobias, 1997).
As education, knowledge, and learning are highly relevant to the context of innovation dis-
course (see e.g., Darroch, 2005; Dudau et al., 2018), we include the educational sector,
along with research centres and institutions, as the “fourth sector.” We also include as a
“fifth sector” residual organizations and groups of individuals that pursue other distinct
values in regards to innovation (e.g., generic society; social media; interest groups for envi-
ronmental protection, the protection of animal rights, or the advancement and regulation of
artificial intelligence).
Thus, this five-sector distinction (the private sector, the public sector, the nonprofit
sector, the educational sector, and the society at large) is relevant to collaborative inno-
vation; while innovation was traditionally studied primarily in terms of the private sec-
tor domain (e.g., Aghion & Howitt, 1990; Romer, 1987, 1994; Teece, 1986), substantial
research has shown that the other sectors are innovative and important to the study of inno-
vation. Research has shown the importance of innovation in the public sector (Arundel
etal., 2019; Demircioglu & Audretsch, 2017) and the role played by third sector organiza-
tions in social innovation (Westley etal., 2014). The literature also reveals the impact of
educational institutions on innovation systems and the role of knowledge in the innovation
process (Etzkowitz & Leydesdorff, 2000; Lundvall, 1992), as well as the role of society at
large in innovative user-led designs (von Hippel, 2006) or as the primary stakeholders in
innovations for environmental protections (Rennings, 2000). In sum, studying all sectors
1 In order to take a sector-neutral perspective on innovation, it is also important to grasp the concept of
sector, a task which is complicated by the variety of concepts associated with the term. The Cambridge
Dictionary defines sector as “one of the areas into which the economic activity of a country is divided.” In
this sense, economic activity is often split into employment segments such as the primary sector (i.e. jobs
related to the extraction of raw materials), the secondary sector (i.e. jobs related to the transformation of
raw materials into products), the tertiary sector (i.e. jobs related to service provision), and the quaternary
sector (i.e. jobs related to research and development—innovation) (Pettinger, 2019). Another common (mis)
use of the term is to refer, as the Collins Dictionary suggests, to industry, which is a deeper categoriza-
tion of businesses based on the type of activities (e.g. the electricity industry, the tourism industry, and
the manufacturing industry). The term sector is also used beyond the differentiation of businesses and can
instead refer to a traditional distinction based on the ownership of an organization (public vs. private). Thus,
sector refers, as in the Merriam-Webster Dictionary, to “a sociological, economic, or political subdivision
of society.”.
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R.Vivona et al.
1 3
and how collaboration unfolds among them is crucial to understanding collaborative inno-
vation and its effects.
2.3 The rationale forcollaborative innovation
Collaboration is defined as “the process through which two or more actors engage in a con-
structive management of differences in order to define common problems and develop joint
solutions based on provisional agreements that may coexist with disagreement and dissent”
(Hartley, 2013, p. 826). It is different from coordination, which is the “orderly arrangement
of group effort to provide unity of action in the pursuit of a common purpose” (Mooney,
1953, p. 86), and from cooperation, which is the “joint pursuit of agreed-on goal(s) in a
manner corresponding to a shared understanding about contributions and payoffs” (Gulati
etal., 2012, p. 537). Collaboration can be seen as merging cooperation (the commitment
of actors to work towards the same end) with coordination (the organizational complex-
ity of making actors work together effectively) (Gulati etal., 2012). When organizations
collaborate, they aim to obtain more resources while achieving their goals and interests
(Dias & Selan, 2022; Minson & Mueller, 2012; Tseng etal., 2020). Aiming to obtain more
resources and working together with common interests makes organizations more innova-
tive because with collaboration, different organizations can learn from each other (Demir-
cioglu & Audretsch, 2020; Martínez-Costa etal., 2019; Tseng etal., 2020). Thus, collabo-
ration has become highly relevant to innovation, particularly when there are agreements of
goals, interests, and values across different organizations (Van der Voet & Steijn, 2021).
Traditionally, innovation was conceived entirely as an in-house process carried out in
private R&D labs (Romer, 1994). In the early 2000s, however, researchers started real-
izing that these closed models were obsolete and that innovation was increasingly pursued
through open channels where firms (or, more generally, organizations) sourced and shared
knowledge to increase their innovation outputs (Bogers etal., 2018; Chesbrough, 2003;
Hameduddin etal., 2020). Although open innovation is not directly related to collabora-
tion, Felin and Zenger (2013, p. 914) clarify that the “underlying mechanisms for access-
ing external knowledge and fostering open innovation have, in turn, encompassed a range
of alternatives including contests and tournaments, alliances and joint ventures, corporate
venture capital, licensing, open-source platforms, and participation in various development
communities.” Likewise, in public management, scholars recognized that “bureaucratic
(closed) ways of innovating do not yield the quantity and quality of innovations necessary
to solve emergent and persistent policy challenges” (Bommert, 2010, p. 15).
Therefore, the concept of open innovation, initially defined by Chesbrough (2006, p. 1)
as “a paradigm that assumes that firms can and should use external ideas as well as internal
ideas, and internal and external paths to market, as the firms look to advance their technol-
ogy”, is relevant as cross-sectoral collaborative innovation can be understood as a sub-case
of the open innovation model (Gallaud, 2013). Indeed, Gallaud (p. 236) defines collabora-
tive innovation and its rationale as:
“an organization cooperates with other firms (suppliers, customers, competitors, and
consultants) or other organizations (such as universities or public research organism)
to develop or commercialize a new innovation. The organizations agree to pool their
resources or to share information and knowledge to develop one project, at the end
of the project, they keep independent from the legal point of view. The main goal of
such collaborative innovation is to gain access to the partner’s knowledge and com-
petences especially to tacit knowledge”
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The costs ofcollaborative innovation
1 3
Moreover, collaborative innovation can happen in different forms, contexts, and part-
nerships across sectors: for instance, in the Triple Helix model (university-industry-gov-
ernment), which postulates the dynamism through continuous reorganizations of the inno-
vation collaboration due to cultural and technological evolution (Audretsch & Belitski,
2021a; Etzkowitz & Leydesdorff, 2000), or in the Quadruple Helix model, which adds to
this dynamism media-based and culture-based public relations (Carayannis & Campbell,
2009; Miller etal., 2018).
3 On thecosts ofcollaborative innovation: atriple fallacy
Despite these advantages, collaboration demands a more tremendous amount of resources
(in time, money, and efforts) and can be unappealing due to several potential costs (Con-
nelly etal., 2014). Furthermore, the risk of collaboration failure is significant: Gulati etal.
(2012) report that over 50 percent of collaborative arrangements fail to deliver. Thus, when
deciding to collaborate, one should ask whether pursuing innovation through a specific
cross-sectoral arrangement is beneficial; that is, whether the benefits outweigh the costs.
Since the nature of innovative activities is uncertain, organizations cannot entirely calcu-
late the costs (including risks) and benefits of innovation. However, cost analyses in the
context of collaborations for innovation are not well developed in the literature for other
three core reasons: the value attributed to collaboration, a presumption of the superiority of
collaboration, and an assumption of the inevitability of collaboration.
This first reason has already been mentioned in the introduction: collaboration is
increasingly seen as a value in itself (Sørensen & Torfing, 2011; Voorberg etal., 2015). In
many instances, collaboration is not a means to achieve objectives but an objective in itself
that must be pursued regardless of its merit. Certainly, there are underlying rational reasons
to push for collaboration which are related to the uncertainty of innovation. For instance,
due to the impossibility of giving an expected value to and to calculate innovation outputs,
and due to information asymmetries between actors and sectors, organizations start collab-
oration to develop trust and get to know each other better (Audretsch & Belitski, 2021b).
However, decision on whether—and how—to collaborate to develop innovations should be
taken on the basis of rational decision-making focused on processes, an not on ideological
presuppositions (Bommert, 2010).
Second, there is a widespread presumption that collaboration is a superior way to pur-
sue strategic objectives (such as innovation) based on an “implicit” cost–benefit analysis;
indeed, it is not uncommon that researchers assume that, if we see organizations collaborat-
ing, then it means that the benefits of collaboration do outweigh the costs (see Fallis, 2006;
Tartari & Breschi, 2012). However, there are at least two significant problems with this
approach. One is related to actors’ cognitive biases. Individuals and organizations act under
bounded rationality; not only do they not possess perfect information (so that they might
not know all the costs and benefits related to collaboration), but they might underestimate
known costs (Simon, 1976). For instance, Buehler etal. (2005) demonstrate that collabo-
ration exacerbates fallacies in planning; that is, they find that group planning—compared
to individual planning—reinforces a bias through which task completion schedules are
unrealistically predicted, which results in greater delays. The other problem lies in assum-
ing that the collaborative activity in these cases is discretionary (Tartari & Breschi, 2012),
as discretionary activity is associated with net benefits. As mentioned, collaboration is
increasingly becoming a mantra, so that researchers reported on several cases of mandated
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R.Vivona et al.
1 3
collaboration in which the collaborative activity was compulsory rather than discretion-
ary (McNamara, 2016; Rodriguez etal., 2007). Thus, as collaboration is increasingly man-
dated—more or less formally—assuming the positivity of net benefits on the basis that
actors are “choosing” to collaborate becomes a contradiction in terms.
Third, cost analyses are sometimes disregarded because collaboration is deemed to be
inevitable in order to achieve innovation objectives. This is not always the case, because
some organizations may not be willing to collaborate (i.e., ability-willingness paradox),
for example family firms that prefer to preserve the affective endowment towards their
business (Rondi etal., 2021), or because collaboration may not be necessary when exter-
nal knowledge can be sourced from other channels without much efforts, such as knowl-
edge spillovers (Audretsch & Belitski, 2020). However, there is a persistent assumption
that innovation problems stemming from high degrees of complexity (wicked problems
or grand challenges) can be solved only through collaboration (Edmondson, 2016; Head,
2019; O’Toole, 1997). This belief is reinforced by the fact that cross-sectoral arrangements
are already complex, and measuring benefits, costs, and performance in these kinds of
settings is difficult (O’Toole, 1997). Nevertheless, collaboration is multifaceted and can
include diverse arrays of stakeholders: classifying collaboration as “inevitable” reinforces
a black-and-white fallacy that suggests that only two opposing options are available. In
fact, collaboration is a broad concept as it encompasses a great variety of arrangements;
indeed, “[t]he phenomenon has been given a variety of names—including alliances, coali-
tions, community-based collaboratives, networks, and partnerships—and includes a variety
of different ways to collaborate” (Connelly, 2014, p. 18).
Various arrangements can be classified based on their degrees of formality and hierar-
chy (Gazley, 2008). First, arrangements can be formal when based on contracts (such as
joint ventures and strong partnerships) or informal when based on noncontractual relation-
ships built between individuals and organizations (Simard & West, 2006). Second, arrange-
ments can vary on the basis of how control is exercised, ranging from cases in which the
decision-making power is completely shared (non-hierarchical, horizontal collaboration)
to cases that resemble the structure of an organization (hierarchical, vertical collaboration)
(Gazley, 2008; Gulati & Singh, 1998; Schuppert, 2011). Thus, “collaboration” can be seen
as a continuum of various arrangements with diverse intensities of collaboration rather
than as a dichotomous choice, and cost analyses can still serve the purpose of identifying
the optimal collaborative solution to an innovation problem.
As innovation studies have already described the benefits of collaborative innovation in
great detail, in this article, we tackle the abovementioned gap by developing a comprehen-
sive theory on the costs related to cross-sectoral collaboration for innovation.
4 Towards acost theory ofcollaborative innovation
In order to answer our research question on the costs of collaborative arrangements for
innovation, we employ three competing explanations—namely, (i) transaction cost eco-
nomics, (ii) game theory, and (iii) the knowledge-based view—to account for, analyze and
integrate relevant findings from innovation studies—such as innovation management, tech-
nology management, knowledge management, open innovation, and collaborative inno-
vation research—with literature on collaborative efforts related to network management,
collaborative governance, public administration, and organizational theory, as well as with
insights from behavioral economics and psychological science studies. As most studies
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The costs ofcollaborative innovation
1 3
focus on collaboration and innovation within sectors, we instead emphasize sector-specific
studies to the case of cross-sectoral collaborative innovation. Adaptions are needed since
findings are not always valid across sectors; nonetheless, “this does not mean that one can-
not make inferences about risks and issues from related theories” (Bommert, 2010, p. 26).
4.1 Transaction cost economics ofcollaborative innovation
Transaction cost economics postulates that agents incur coordination costs to monitor, con-
trol, and manage transactions (Hoetker & Mellewigt, 2009; Lee etal., 2015). They will
seek the optimal governance structure that can minimize these costs (Williamson, 1981,
1989). As transaction costs are not directly measurable, Williamson (1981, pp. 552–553)
explains that a transaction cost analysis considers “the comparative costs of planning,
adapting, and monitoring task completion under alternative governing structures”. Trans-
action cost theory can be employed by assuming that (i) “the governance structure that best
fits a particular transaction (one with low transaction costs) performs better than one that
does not (one with higher transaction costs) (Jobin, 2008, p. 442), and that (ii) collabora-
tive innovation can be approximated to a set of transactions.
By doing so, we argue that differences in cross-sectoral collaborative arrangements will
impact the ability of coordinating actors to working together effectively, thus generating
differences in transaction costs in the form of higher or lower coordination costs. Coordina-
tion is costly, and coordination costs have been found to “constitute an important barrier
to the effectiveness and efficiency of joint work in innovative settings” (Vural etal., 2013,
p. 134). For instance, Batkovskiy etal. (2015) find that coordination costs are one of the
primary challenges and risks in organizational networks to develop innovations in the Rus-
sian high-tech industry. Bel and Warner (2015), while comparing European and US studies
on inter-municipal cooperation, single out how improper governance structures increase
the risk of high coordination costs, which make cooperation between local governments
expensive and ineffective. Cummings and Kiesler (2007) also find that coordination costs
are detrimental to research outcomes in multi-university research projects.
Coordination costs are thus a significant barrier to collaboration for innovation within
and across sectors. However, not all coordination costs are the same. We classify coor-
dination costs under two broader categories: costs related to the nature of the innovation
problem and costs related to the structure of the collaboration. Autonomy, communication,
and waiting costs, along with managerial efforts, are part of the former category (coordi-
nation costs related to the innovation problem), while formality, hierarchy, size, trust, and
geographical location are part of the latter category (coordination costs related to the col-
laboration structure). We explain these in detail below.
The first element shaping coordination costs is the nature of the innovation prob-
lem itself: some problems are much more complex than others (e.g., grand challenges
or wicked problems) and require more coordination (Krogh & Torfing, 2015). This is
particularly true in cross-sectoral collaborative arrangements, in which the innovation
problem is usually complex across many dimensions (e.g., technical, economical, and
social) (Edmondson & Reynolds, 2016). Problem complexity can also be expressed
by the degree of problem decomposability, that is, the extent to which the innovation
problem can be decomposed into independent and non-related tasks (Argyres & Silver-
man, 2004; Baldwin & von Hippel, 2011). Complex problems exhibit low degrees of
decomposability due to a high degree of task interdependence: the more interdependent
tasks are, the less autonomy actors have in carrying out their own activities, the more
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R.Vivona et al.
1 3
coordination is required among collaborators, the higher will be the transaction costs
(Diener etal., 2015). Thus, in complex innovation problems, coordination costs arising
from low autonomy tend to be highly relevant.
Task interdependence is also related to factors that produce other specific costs, which
can be categorized as coordination costs. First, when tasks are interdependent, collabora-
tors need to communicate with each other more frequently and intensively; thus, monetary
and non-monetary (e.g., time) communication costs arise (MacMillan etal., 2004). Second,
due to task interdependence, it is common that an actor, before working on his own task,
must wait for other collaborators to finish their activities; again, the cost of waiting is com-
posed of both non-monetary (e.g., time that an actor needed to wait) and monetary terms
(e.g., the salary paid to an actor waiting unproductively) (Camacho, 1991). Third, interde-
pendencies among tasks must be directly managed by coordinators, and this coordinating
activity requires greater managerial effort (Rawley, 2010) so managerial effort is another
cost of collaboration.
The second major element that impacts the coordination costs of cross-sectoral arrange-
ments is the structure of the collaboration. Collaboration among firms, public agencies,
governments, nonprofit organizations, research institutions, and/or other societal actors can
employ various structures. For instance, Nissen etal. (2014) present the case of a pub-
lic–private innovation partnership for the development of a prototype trolley in a Danish
hospital where the strong and centralized leadership of the private consultant resulted in
an effective management of the innovation process. Conversely, Li etal., (2012, p. 61),
analyzing cases of cross-sectoral collaboration for social innovation, propose the example
of the CDSs (formal bodies called “common denominator subjects”) in Hangzhou city in
China where “[t]he relations between agents are equal, not a hierarchical one … actors
are relatively autonomous in the decision-making process. They act as a new configuration
of social agents.” Ansari etal. (2001), within the context of health professionals’ educa-
tion in South Africa, suggest that cross-sectoral structures must be clear and well-defined
in order to secure effective functioning. However, in other cases, collaboration is looser
and less rigid. For instance, exploring cross-sectoral collaborations for homeless services,
Berman and West (1995) find that most innovative services are provided through informal
As mentioned, these collaborative structures can thus be differentiated based on their
degrees of formality and hierarchy (Gazley, 2008), which are related to transaction costs.
First, collaborative agreements with high degrees of formality can reduce coordination
costs because “[f]ormalization makes the division of labor and the interactions between
partners more predictable” (Gulati & Singh, 1998, p. 786). Additionally, Felin and Zenger
(2013) suggest that formal and institutional ties reduce coordination costs as they are
essential when upfront investment and commitment are needed to tackle complex prob-
lems. However, in organization-like settings, when formality is associated with rigidity,
the relationship may be negative. While upfront coordination costs may be lower in formal
arrangements, Rawley (2010, p. 9) finds that formality is associated with higher costs of
re-coordination since “routines and contracts are costly to change once they are institution-
alized.” Second, in collaborative arrangements with high degrees of hierarchical control,
boundaries are clearer so that tasks, activities, and decisions do not overlap, and decision-
making is simplified (Galbraith, 1977). Similarly, Gulati and Singh (1998, p. 784) sug-
gest that hierarchical controls are “superior information-processing mechanisms that result
from the increasing division of labor and the uncertainty originating from the need to coor-
dinate interdependent subtasks”; thus, hierarchical governance structure can help reduce
transaction costs.
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The costs ofcollaborative innovation
1 3
In addition to formality and hierarchy, size, industry type, trust, and institutional location
can impact overall transaction costs. The size of the collaboration (i.e., number of actors)
is relevant, as large collaborations are costlier to coordinate (Camacho, 1991). Transaction
costs are also affected by industry type, such as high technology and knowledge-intensive
organizations have become more important than manufacturing and mining sectors, so
dynamism and density of transactions have been changing dramatically in recent decades
(Hoetker & Mellewigt, 2009). Moreover, transaction costs are different not only across
industries but also across sectors due to cognitive distance (Balland et al., 2015). The
governance structure, through its rules and controls, can shape trust among collaborators,
which deeply affects transaction costs (Becker & Murphy, 1992; de Zubielqui etal., 2019;
Gulati & Singh, 1998). Finally, transaction costs are affected by the institutional location
of the collaboration; indeed, in countries with more stable and efficient laws, collaborative
arrangements tend to exhibit lower coordination costs (Becker & Murphy, 1992). On the
contrary, geographical location seems to be irrelevant; for instance, private sector research
finds that “[f]irms who collaborate within close proximity will not experience higher inno-
vation than firms collaborating with international partners, illustrating that the limits to
collaboration do not increase with geographical proximity” (Audretsch & Belitski, 2019,
p. 21).
4.2 Game theory ofcollaborative innovation
Provided that no single actor is able to advance innovative solutions to complex problems
without collaborating, game theory can be employed to understand: (i) what is the optimal
way actors can combine resources? (ii) how are they going to form a coalition? and (iii)
how do they intend to split the benefits (payoff). While in ordinary games payoff is pre-
determined, the nature of cross-sectoral collaborative innovation makes it harder to ascer-
tain. First, innovation is a risky activity, which may end in failure (zero payoff), and whose
outcome is highly unpredictable, thus causing frequent disagreements over the innova-
tion strategy (Walsh etal., 2016) and conflicts over competing goals (Torugsa & Arundel,
2016). On the other hand, in cross-sectoral coalitions actors will assign diverse values to
the actual payoff, as they seek innovation for different reasons. Thus, what can be highly
valuable to one actor (e.g., profit to a firm) may be less valuable to others (e.g., a public
agency) (Nissen etal., 2014). Therefore, collaborative innovation can be interpreted as a
cooperative cost game (Curiel, 2013) in which actors will tend to form a grand coalition to
share the costs related to an activity (i.e., innovation).
Analyzing the costs of cross-sectoral collaborative innovation in light of coopera-
tive game theory thus means to answer the question “provided that actors will share the
costs of the innovation process, what are the factors impacting their decision-making and
negotiations on how to split them optimally?”. These factors will relate to all the ex-ante
measures (e.g., monetary and non-monetary incentives) needed to make different and idi-
osyncratic values and ambitions compatible. The magnitude of these costs is dependent
on several characteristics of the collaborators, such as power, trust, reputation, and com-
mitment. Cooperative game theory suggests that powerful actors (e.g., actors that possess
fundamental resources to solve the innovation process, or so called “veto players”) will
mostly impact the cost allocation, thus making divergence among them costly to the overall
process (Schoon & York, 2011). However, in order for cooperation to emerge and endure
among all players, trust is a primary requirement. Indeed, collaborations are effective only
if actors have “the confirmation that participants in a collective endeavor are trustworthy
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R.Vivona et al.
1 3
and credible, with compatible and interdependent interests” (Emerson etal., 2012, p. 14).
When trust is lacking, perceived risks related to opportunism demand that organizations
put in place higher levels of control towards partners’ activities using pragmatic mecha-
nisms in order to align the divergent interests of the collaborators (Helper etal., 2000).
These measures divert organizational resources away from other uses, thus increasing the
total cost of the collaboration.
Another factor that can impact the total cost is reputation, which can be defined as “his-
torical trustworthiness” (Christiansen & Vandelø, 2003, p. 310). Reputation is complex to
build and requires active and prolonged engagement with stakeholders along with the moti-
vation towards co-decisional processes (Romenti, 2010). Reputation can also be formed in
the initial phases of a collaboration but requires organizations to invest resources in repu-
tation-building activities that demonstrate collaborators’ skills and competences and fos-
ter professional interactions (Christiansen & Vandelø, 2003). Santos etal. (2018) find that
when reputation building is too costly, cooperation can still emerge if actors report their
interactions, thus creating a reporting cost for organizations (e.g., time and effort to write
web pages).
Moreover, collaboration effectiveness is also deeply dependent on the commitment of
collaborators to work towards common and shared goals (Gazley, 2010). Roxenhall and
Andrésen (2012) find that commitment2 is crucial when starting new collaborations. Ham-
marord and Roxenhall (2017), linking commitment and innovation, demonstrate that
commitment positively mediates the relationship between collaboration and innovation,
such that organizations tend to be more innovative when commitment is higher. More spe-
cifically, commitment has been operationalized as whether organizations “feel strongly
associated with the network”, “have positive feelings for the network”, “ought to continue
to be part of the network”, and the organization believes that “the network problems almost
feel like our own problems" (Hammarord & Roxenhall, 2017, p. 30). Similarly, however,
as seen from the studies of commitment and its operationalization, commitment is not easy
to establish and maintain, and highly subjective (Hammarord & Roxenhall, 2017).
A cooperative cost game can be analysed also through non-cooperative game theory,
which suggests that within a coalition, each individual will aim at maximising its payoff. In
this sense, collaboration presents indirect costs which are the risks associated with reduced
effectiveness (including the extreme case of innovation failure) due to incompatibilities.
Indeed, divergent interests can lead to the risk of innovation failure; Krogh and Torfing
(2015, p. 103) explain concisely that “[w]hile minor conflicts may be fruitful in the sense
that they force the actors to sharpen their ideas and arguments and revise their proposed
solutions, serious conflicts may destroy collaboration and create insurmountable deadlocks
that prevent innovation.” For instance, Rodriguez etal., (2007, p. 173) report that, in the
context of an organizational innovation for improving elderly services in urban region of
Canada, a “great deal of energy was expended in committees, information sessions, and
training programs. However, 6months after the so-called ‘D-day’ when the program was
2 Commitment can be divided into three components that differently affect collaborative costs: affective,
calculative, and normative commitment (Roxenhall & Andrésen, 2012). The affective (or emotional) com-
ponent is based on common values and relationships of trust, so that in long-established collaborations,
actors would have higher levels of affective commitment. The calculative component is based on indi-
vidualistic, business-like standards: organizations would have higher levels of calculative commitments if
prior investments in the collaboration created situations of “lock-in” or if there are no feasible alternatives.
Finally, the normative (or moral) component is based on feelings of responsibility towards the collabora-
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The costs ofcollaborative innovation
1 3
due to be implemented, a senior Regional Board official declared it ‘dead.’” Divergent
values and goals can also compromise consolidated collaborations, undoing all previous
investments (such as setting up a coordinating structure or building relations of trust) (Con-
nelly etal., 2014) and thus compromising systemic innovation processes.
Finally, another kind of cost occurs when the innovation does not fail, and its effective-
ness is not impaired, but the innovation benefits are appropriated by a single actor, or when
incentive mechanisms set up to align interests perversely contribute to further misalign-
ment. For instance, Bommert (2010, p. 26) explains that, in the context of private–public
collaborations, “actors have the possibility to manipulate the elements of the innovation
cycle to exert their particular interests over the goal of innovating public value.” Likewise,
Hartley etal., (2013, p. 826) find that “collaborative innovation may be at risk when par-
ticular private actors are able to capture the collaborative arena and exploit the process of
innovation and its result to their own advantage.”
4.3 Knowledge‑based view ofcollaborative innovation
A third major explanation of the costs associated with cross-sectoral collaboration for
innovation is the knowledge-based view of collaborative innovation, which suggests that
competing collaborative arrangements will impact knowledge flows, occurring “whenever
an idea generated by a certain institution is learned by another institution” (Peri, 2005,
p. 308). While learning and knowledge sharing remain some of the primary rationales
for innovation (Du Plessis, 2007), costs that are incurred for the acquisition of relevant
external knowledge (i.e., knowledge inflows) and for the retention of confidential internal
knowledge (i.e., knowledge outflows) cannot be disregarded when deciding to collaborate.
Undoubtedly, knowledge sharing is an essential feature of collaborative innovation set-
tings; traditionally, it has been the driver that pushed researchers and practitioners to move
away from in-house R&D-based innovation models and to look beyond firm’s boundaries
for the development and implementation of innovations (Baldwin & von Hippel, 2011;
Chesbrough, 2003). In particular, primary importance has been given to external knowl-
edge sourcing, which is defined as an organization’s “tendency to use knowledge from
beyond its boundaries through a wide range of external channels” (Asimakopoulos etal.,
2020, p. 123). Consistent with the economics theory of bounded rationality (Simon, 1976),
knowledge sourcing and acquisition are expensive and somewhat unexpected processes:
agents have limited resources when looking for relevant external knowledge and selecting
for potential partners and appropriate collaborations. This limitation can be exacerbated by
the hiddenness of knowledge: the location of the knowledge required to solve an innovation
problem is not always revealed, and it is typical that actors are unaware of the location of
relevant knowledge (Felin & Zenger, 2013). Thus, a first cost related to knowledge flows is
associated with the cost of knowledge sourcing, which comprises all the costs relating to
identifying and acquiring knowledge.
Subsequently, even when collaborators have been found and knowledge acquired, it may
still be the case that some important knowledge for the innovation process resides beyond
the “collaboration’s boundaries”; that is, collaborators may not possess all significant infor-
mation, skills, and ideas to effectively develop, implement, or diffuse the innovation. In
other words, the question becomes: is all relevant knowledge needed to solve the innova-
tion problem contained within the boundaries of the cross-sectoral collaboration? In this
regard, Minson and Mueller (2012, p. 200) find that, when collaborating, actors tend to
feel more effective, and this induced feeling tends to inhibit the exploration of external
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R.Vivona et al.
1 3
knowledge since “the mere act of collaborating enhances confidence, and thereby limits
receptivity to outside advice,” which reduces effectiveness. A second cost related to knowl-
edge flows is thus a confidence cost, which is related to the cognitive risk of limiting the
knowledge search to the collaboration’s boundaries, thus increasing the risk of omitting
relevant information needed for the innovation process.
Copious research has also pointed out that in order to be beneficial to the innovation
process, external knowledge acquisition must be coupled with internal absorptive capacity
(see e.g. Cohen & Levinthal, 1990; Tsai, 2001; Vanhaverbeke etal., 2008). This concept
has traditionally been a private sector construct (Van den Bosch etal., 2003) since it is
defined as the “ability to recognize the value of new information, assimilate it, and apply
it to commercial ends” (Cohen & Levinthal, 1990, p. 128). Nevertheless, new information
is required for the innovation process in all kinds of organizations, regardless of their sec-
tor, since “[a]ll organizations are to some extent knowledge intensive” (Willem & Buelens,
2007, p. 582). Therefore, all organizations need to invest in and build a capacity to first
absorb and then exploit external knowledge, and these investments are costly, multifaceted,
and difficult to evaluate (Cohen & Levinthal, 1990; Kostopoulos etal., 2011). Investments
in absorptive capacity also represent an example of the higher costs incurred by cross-sec-
toral collaborations, as actors collaborating within the same sector usually have general
experience and knowledge to sufficiently understand external knowledge flows.
Indeed, in the context of cross-sectoral collaborative innovation, even if organizations
already have significant absorptive capacities, further investments may be required, as
extant capacity may be incoherent with the acquisition of extra-sectoral knowledge, which
is knowledge pertaining to different sectors. For instance, in the context of industrial inno-
vation, Cohen and Levinthal (1990, p. 144) suggest that “one important determinant of the
ease of learning is the degree to which outside knowledge is targeted to a firm’s needs and
concerns” and that “the ability to evaluate and utilize outside knowledge is largely a func-
tion of the level of prior related knowledge” (p. 128). Across sectors, knowledge is differ-
ent in nature. It is mainly targeted and related to sectoral needs; for instance, knowledge
sourced from universities is typically targeted to educational and research needs, so that it
is less concerned with firms’ purposes (Vega-Jurado etal., 2009). This is highly relevant
for cross-sectoral collaborations; in order to effectively exploit extra-sectoral knowledge,
diverse expertise is required and must be built since, in the absence of appropriate knowl-
edge bases, organizations may not be able to absorb new knowledge (Terjesen & Patel,
2017). However, although the effects of knowledge diversity from collaboration contrib-
utes to generating new knowledge, some studies find that its marginal benefit to innova-
tion may decrease due to process losses, integration, and capacity (Cummings & Kiseler,
2007; Nooteboom, 2008; Van Knippenberg etal., 2004). Therefore, a third cost related to
knowledge flows is identified as the cost of capacity building for exploiting extra-sectoral
external knowledge.
Once collaborators are identified and adequate capacity is built, transferring knowl-
edge within a collaborative arrangement may be more or less costly, depending on several
factors. For instance, Wathne etal. (1996) list four main factors that facilitate knowledge
flows. First, knowledge transfer is found to be ineffective when collaborators do not exhibit
an open and transparent attitude. Within the context of cross-sectoral collaboration, collab-
orators may have different to divergent values, so that openness and transparency become
crucial elements to limit the costs of collaborative innovation. Second, costs related to
knowledge transfer arise whenever there are misinterpretations. Wathne etal. (1996) clas-
sify this factor as the channel of interaction: when knowledge is transferred through chan-
nels that allow information to be shared the most clearly (e.g., face-to-face interaction vs.
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The costs ofcollaborative innovation
1 3
email interaction), collaboration is more efficient. In this regard, Felin and Zenger (2013)
find that the governance structure of formality is positively associated with the richness of
the channels of interactions, such that within informal structures knowledge transfers may
be more costly. Third, to secure effective knowledge transfers, collaborators must be in a
relationship based on mutual efforts and trust (Hoetker & Mellewigt, 2009). Ring and Van
de Ven (1992, p. 489) explain that, in order for trust to emerge, at least three conditions
must be met:
(1) reciprocity, by which one is morally obligated to give something in return for
something received … (2) fair rates of exchange between utilitarian costs and ben-
efits … and (3) distributive justice, through which all parties receive benefits that are
proportional to their investment.
If these conditions are unmet, collaborators do not perceive themselves as equals.
When equity and trust are lacking, actors may be obliged to take preventive measures that
increase the costs of knowledge transfer. Fourth, Wathne etal. (1996) conclude that prior
experience in collaborating with specific partners eases knowledge transfers. Therefore,
newly formed collaborative arrangements are, on average, more costly than consolidated
ones regarding the transfer of knowledge.
Finally, knowledge flows entail costs not only in letting relevant knowledge “flow in” or
“flow within” (e.g., sourcing, absorbing, and transferring) but also in preventing confiden-
tial knowledge from “flowing out.” We categorize these costs (and risks) as exposure costs,
which have been reported across sectors in various fields. One example is the risk of rel-
evant and valuable in-house R&D leaking out not only to collaborators, but also to “com-
petitors through common suppliers or customers” (Cassiman & Veugelers, 2002, p. 1179),
thus requiring additional control costs. Laursen and Salter (2014) refer to this problem as
the paradox of openness: although firms need openness to develop innovations, they also
need to put in place strategies to appropriate the monetary benefits deriving from innova-
tion and to protect themselves from competitors, thus incurring costs and risks. Referring
to the public sector context, Hartley etal., (2013, p. 826) state that some public authorities
must deal with needs for confidentiality, as “collaboration may compromise public secu-
rity, compromise the privacy of private firms and citizens, or harm the interests of public
enterprises.” Similarly, in the context of research institutions, Macfarlane (2017) recog-
nizes that while scholars need collaborations for various ends (e.g., increasing productiv-
ity), knowledge sharing can also be counterproductive as scholars are pressured to develop
independent research and individual achievements in order to be promoted.
4.4 Integrated cost theory ofcollaborative innovation
Based on the integration of the theoretical explanations presented above, we argue that four
main factors and their interplay need to be analyzed to determine the optimal collaborative
arrangement that can minimize the costs incurred by collaborative innovations:
Governance, in terms of the number of collaborators and the hierarchical relationships
among them.
Compactness, in terms of the degree of relationship formality that binds collaborators
Reliability, in terms of the quality of the relationships (e.g., level of trust, commitment,
and reputation among collaborators).
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R.Vivona et al.
1 3
Institutionalization, in terms of the extent to which the relationships have been pre-
established by practice.
Table1 summarizes these main factors constituting the integrated cost theory of col-
laborative innovation (ICT), their explanation provided by the transaction cost economics,
game theory, and knowledge-based view of collaborative innovation, and their expected
impact on the costs of collaborative innovation.
5 Discussion andimplications
When attempting to innovate, organizations can choose whether to do so internally or
through collaboration. Moreover, organizations can choose among several collaborative
arrangements and prioritize some over others when collaborating with various actors. Col-
laboration is costly; cross-sectoral arrangements may particularly present even higher costs
(e.g., due to knowledge diversity and interest divergence). Thus, rigorous analyses on the
merits of such collaborations should be advanced. However, we observe a loose matching
between this need and the current positioning of collaborative innovation in the literature.
The purpose of this article was to obtain a better understanding of the process underly-
ing collaboration for innovation and to identify the potential costs incurred in these col-
laborative settings specifically. In this regard, we believe that our article has several impli-
cations. First, we developed and advanced a cost theory for cross-sectoral collaborative
innovation that encompasses, merges, and integrates studies from different research fields
in light of three major economic theories (transaction cost theory, game theory, and knowl-
edge-based theory). This approach reflects the many diverse phenomena involved in the
management of cross-sectoral collaborations and thus shares the “epistemic value of hav-
ing theories that unify a wide range of phenomena” (Fallis, 2006, p. 201). Our concep-
tualization of the main factors affecting collaborative innovation costs (i.e., governance,
compactness, reliability, and institutionalization) adapts and models extant knowledge on
the costs of collaborative arrangements and makes it explicit on an abstract and conceptual
level, which allows scholars to examine broader research questions.
A second epistemic implication of this study resides in the explicit acknowledgement of
the weaknesses and limitations in the current discourse on collaborative innovations. While
“[i]dentifying problems in the empirical literature can serve a valuable scientific function”
(Baumeister & Leary, 1997, p. 312) as it allows researchers to recognize knowledge gaps
and to correct its direction, we also explain the reasons for this gap—namely, the assump-
tions of the value attribution, superiority, and inevitability of collaborative innovation. Fur-
thermore, this article avoids positing a further dichotomous “black-or-white” fallacy; that
is, our approach dismisses divisive arguments on whether to collaborate or not and brings
into focus the continuum of the various “gray” alternatives, as cross-sectoral collaborative
arrangements can take completely different forms (i.e., have diverse degrees of collabora-
tion based on the extant governance, compactness, reliability and institutionalization) and
thus have different costs within the innovation process. Moreover, within this approach, a
crucial question becomes how to reduce the costs or limitations of collaborative innova-
tion. Audretsch and Belitski (2019) suggest that focusing on human capital, prioritizing a
few partners for collaboration (instead of aiming to collaborate with too many actors), and
better coordination can help to reduce barriers and costs. In this regard, policymakers may
focus on reducing the costs of collaborative innovation as well as understanding the costs
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The costs ofcollaborative innovation
1 3
Table 1 Main factors of the Integrated Cost Theory (ICT) of collaborative innovation, their impact, and explanations
ICT factors Transaction cost economics Game theory Knowledge-based view
Overall Impact: unclear The number
and type of actors, and the hierarchical
relationships among them (vertical or
horizontal) will shape the costs differ-
The higher the number of collaborators,
the greater the costs to coordinate them
will be (Camacho, 1991)
However, vertical relationships can
improve the division of labor and
reduce costs of overlapping (Galbraith,
1977; Gulati & Singh, 1998)
The chosen governance structure (size,
hierarchy) can also impact costs via
higher/lower reliability (Becker & Mur-
phy, 1992; de Zubielqui etal., 2019)
The costs related to governance will
depend on the nature of the grand
coalition in terms of total number of
players, numerosity of veto-players, and
the divergence of interest among these
powerful actors (the higher their diver-
gence, the greater the costs) (Schoon &
York, 2011)
The type and origins of collaborators, in
terms of their absorptive capacities)
will impact the costs of capacity build-
ing: even if organizations already have
significant absorptive capacities, invest-
ments may be required as extant capacity
may be incoherent with the acquisition
of extra-sectoral knowledge, which is
knowledge pertaining to different sectors
(Cohen & Levinthal, 1990)
Overall impact: positive Compact agree-
ments reduce the costs related to coor-
dination, opportunism, and knowledge
The more compact relationships are (e.g.,
a stipulation of binding agreements,
formation of a new legal entity), the
lower the costs of coordination due to
savings in communication, waiting, and
managerial efforts (Gulati & Singh,
The costs related to compactness will
depend on the characteristics of the
game (i.e., of the innovation problem):
when cooperative game theory holds,
binding agreements are possible; under
non-competitive game theory, it is not
possible to form binding agreements, so
that costly controls against opportunism
are necessary (Krogh & Torfing, 2015)
Compactness improves the richness of
channels of interactions among collabo-
rators, reducing the costs of transferring
knowledge (Wathne etal., 1996)
Moreover, binding agreements can reduce
the pressures of exposure and confidenti-
ality (Cassiman & Veugelers, 2002)
Overall Impact: positive Quality relation-
ships reduce collaboration costs and
have a reinforcing effect on compact-
Quality relationships (e.g., based on trust
among collaborators) are associated
with lower coordination costs (de
Zubielqui etal., 2019)
Quality relationships will ease the
process of negotiation for the allocation
of collaboration costs. (Emerson etal.,
Moreover, trust among players will ease
the application of cooperative game
theory, reducing costs via improved
compactness (Christiansen & Vandelø,
2003; Emerson etal., 2012)
Quality relationships (i.e., based on trust
and transparency) will ease knowledge
transfers, reducing overall costs of col-
laboration (Hoetker & Mellewigt, 2009)
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R.Vivona et al.
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Table 1 (continued)
ICT factors Transaction cost economics Game theory Knowledge-based view
Overall impact: unclear Institutionaliza-
tion may cause rigidity and over-confi-
dence, but can also increase commit-
ment and reinforce the reliability
Institutionalized relations may exhibit
rigidity, which increases the coordina-
tion costs when tackling a new innova-
tion problem (Rawley, 2010)
However, pre-established formal and
institutionalized relations can have a
positive impact on costs via improved
reliability (Felin & Zenger, 2013)
Institutionalized relations will ease
the process of negotiation for the
allocation of costs, via higher levels
of players commitment (e.g., affective,
calculative) and improved reputation
(Gazley, 2010; Romenti, 2010)
Institutionalized relations will present a
confidence bias, making the costs of
knowledge sourcing higher (Minson &
Mueller, 2012)
However, pre-established relations can ease
knowledge transfers (Wathne etal., 1996)
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The costs ofcollaborative innovation
1 3
of these types of innovations. For instance, different types of costs may be incurred dur-
ing different stages. Transaction costs may occur after deciding to collaborate while coop-
eration and knowledge costs may occur in early stages. Additionally, some coordination is
required in the beginning to decide how to divide tasks and may be relevant when deciding
whether to collaborate. Other knowledge costs (such as confidence costs) should be man-
aged during the collaboration to ensure that all relevant knowledge is acquired. Neverthe-
less, all three types of costs may occur at any stage of the collaboration process.
Additionally, this study contributes to the literature by providing a systematic and
coherent framework for making sense of the empirical findings of recent research by
demonstrating that there are tradeoffs in terms of costs and benefits to be considered. For
instance, while knowledge sourcing pushes organizations beyond their boundaries to inno-
vate, Asimakopoulos etal. (2020) show that sourcing externally is an efficient strategy only
for moderate levels of collaboration and that in some situations the actual costs of sourc-
ing outweigh the benefits, so that sourcing externally is not beneficial and other strategies
are to be preferred (e.g. internal development). Thus, this article explains why collabora-
tion is not a panacea for organizations, and policymakers should recognize its costs and
We mentioned that one of the arguments in favor of collaborative innovation is that
alternatives (e.g., closed modes of innovation) are said to be inappropriate to solving
wicked problems (e.g., Bommert, 2010)—that is, the assumption that collaboration is inev-
itable. This study contributes to this debate. We, along with the analysis resulting from the
three economic theories employed in this study, suggest that the problem resides in the
identification of the optimal conditions of collaboration for a specific innovation problem.
In other words, the question should be which collaborative arrangements (among the many
possibilities) solve the complex innovation problem while minimizing the costs of collabo-
ration. We identified the salient factors that can shape the cost structure of a cross-sectoral
arrangement, such as the formality and hierarchy of the governance structure or the level
of trust among collaborators. However, as shown, collaboration itself is not inevitable as
collaborative arrangements do impose higher costs when solving complex problems. For
instance, coordination becomes costlier (e.g., due to greater communication costs, waiting
times, and managerial efforts). While communication costs have been drastically reduced
by the diffusion of ICT (Shin, 1997), the costs of waiting and directly managing interde-
pendencies are still highly relevant in the context of collaborative arrangements for innova-
tion, as innovation problems are generally complex. Recognizing that complex problems
incur greater costs to be coordinated allows us to ask whether a specific collaborative
arrangement is feasible or new solutions must be advanced. Indeed, recent practices can
help reduce these coordination costs for highly complex problems; for instance, modulari-
zation aims at “innovatively transforming previously complex, non-decomposable prob-
lems into simpler, more decomposable problems” (Felin & Zenger, 2013, p. 923).
We argue that collaboration should be critically evaluated and not pursued for its own
sake. However, this article also has implications for the question of whether we should
systematize and pursue collaboration even when costs outweigh benefits. Scholars fre-
quently call for the systematization of collaborations due to the benefits of experience (e.g.
Cummings & Kiesler, 2008; Murphy etal., 2015), and our cost conceptualization provides
some theoretical justifications for this call. Indeed, institutionalized collaboration seems
to be comparatively more efficient than impromptu modes; for instance, as Wathne etal.
(1996) and Ring and Van de Ven (1992) suggest, prior experience in collaborating with
an actor reduces costs of knowledge transfer and enhances trust, which in turn reduces all
three major categories of costs (transaction, knowledge, and cooperation). Due to these
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R.Vivona et al.
1 3
factors, when collaborations are consolidated, we may expect to see reduced collaboration
costs and systemic collaborations that are relatively more advantageous, which may justify
the positioning of collaboration as having a “value in itself”; even if the present costs of
collaboration are higher than the benefits, the promise of declining collaboration costs over
time may be enough to justify an investment in collaborative assets.
Nevertheless, regardless of the attractiveness of these conclusions, our integrated model
debunks these assumptions. While it is true that in systemic collaborations several costs
are reduced, we shed light on the actuality that even institutionalized collaborations are
not free of costs and are likely to be more expensive. For instance, when collaboration is
systematized, new coordination costs may occur due to rigid routines, offsetting the gains
in coordination due to enhanced trust. Again, wicked problems may need fast adaptions
and require frequent re-coordination, which may be less likely to happen in a consolidated
collaborative arrangement. Furthermore, systematized collaborations may incur higher
knowledge costs due to increasing confidence costs: once a specific arrangement is set,
actors may be too reliant on their own capacities and miss external learning opportunities
that may be crucial to solve new and complex problems. When flexibility and permeability
are crucial to solving an innovation problem, a careful comparative cost analysis suggests
that seeking a novel, fresh, “innovative” collaboration may be preferable to extant arrange-
ments, even if trust was created.
6 Conclusions
There is growing interest in analyzing collaborative innovation because innovation is pur-
sued through the cross-sectoral collaboration of different sectors and organizations. This
interest is primarily justified by the (untested) assumptions that collaboration is conducive
to innovation, that it is able to solve organizational and socio-economic problems, and that
it can deal with grand challenges or wicked problems. However, there are not enough stud-
ies that provide empirical evidence regarding the costs of such collaborative engagement.
In fact, collaboration can be detrimental and costly, and the main contribution of this arti-
cle is to provide a theoretical framework for the costs associated with collaborative innova-
tions. We provide a framework analyzing why and how explanations of transaction cost
theory, game theory, and knowledge-based theory constitute the main costs of collabora-
tive innovation. More specifically, we suggest that transaction costs derive from the nature
of the innovation problem and the structure of the collaboration, cooperation costs derive
from the costs of making idiosyncratic interests compatible and the indirect costs of inno-
vation failure or opportunism, and knowledge costs derive from the need to manage both
inflows and outflows of knowledge.
We recommend that future studies evaluate collaboration using rigorous models, and
the framework we present in this article is a first step toward developing these models.
Future research may need to systematically analyze when collaborative innovation is ben-
eficial in order to develop explicit models that simultaneously evaluate the costs and ben-
efits of collaborations for innovation and advance the measurement of such collaborations.
Additionally, this study identified the costs, or disadvantages, of innovating through cross-
sectoral collaborations. Future research may integrate these two aspects and evaluate, both
theoretically and empirically, the conditions and cases under which collaboration benefits
outweigh costs, making cross-sectoral collaborative arrangements beneficial for innova-
tion. Finally, in this article we addressed the “how” to collaborate in order to reduce costs
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The costs ofcollaborative innovation
1 3
of collaborative innovation; future studies may consider the issue of extent of the collabo-
ration (i.e., intensity) to categorize collaboration into heterogeneous modes and ask “how
much” or “how less” collaborative innovation could be pursued.
Collaborating actors should be aware of the importance of determining an optimal gov-
ernance structure that allows them to minimize transaction, knowledge, and cooperation
costs, and they should design the innovative process such that each participating actor is
rewarded proportionally to the cost they bear in order to foster conditions of reciprocity,
fair rates of exchange, and distributive justice (Ring & Van de Ven, 1992). This can only
be achieved with a proper understanding of the costs of collaboration and how they are dis-
tributed across collaborators.
Funding Open access funding provided by Nord University.
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... Analyzing collaborative innovation in the literature is a great challenge even if the focus on technologies is defined because various aspects and applications of collaboration to innovate invade the academic literature in many forms. For instance, Zhou and Ren (2021) On the other hand, technological collaborative innovations has its own dark side for firms: it has been costly, it demands money, efforts, and time (Torugsa & Arundel, 2016;Wegrich, 2019), and, further, it provokes operational adjustments, technological reconfiguration, and legal barriers to overcome to be effective for innovation (McGuire & Agranoff, 2011;Vivona et al., 2022). To address this side of collaborative innovation, Vivona et al. (2022) developed the cost theory to systematize all insights from the literature in four main factors: governance, compactness, reliability, and institutionalization to shed light on a broader range of costs for innovation incurred by collaborative arrangements. ...
... For instance, Zhou and Ren (2021) On the other hand, technological collaborative innovations has its own dark side for firms: it has been costly, it demands money, efforts, and time (Torugsa & Arundel, 2016;Wegrich, 2019), and, further, it provokes operational adjustments, technological reconfiguration, and legal barriers to overcome to be effective for innovation (McGuire & Agranoff, 2011;Vivona et al., 2022). To address this side of collaborative innovation, Vivona et al. (2022) developed the cost theory to systematize all insights from the literature in four main factors: governance, compactness, reliability, and institutionalization to shed light on a broader range of costs for innovation incurred by collaborative arrangements. Governance refers to relationships in hierarchical level and the number of collaborators involved, reliability refers to relationships' quality; compactness is about the degree of formality in relationships that connect collaborators; and institutionalization that measure what the extent the relationships in practice have been pre-established. ...
... Technological collaborative innovation is not a merely coordination of an orderly arrangements of efforts to pursue a common technological purpose (Mooney, 1953), or a merely cooperation to join agreed-on goals into a share comprehension about design systems or reconfigure technological resources (Gulati et al., 2012). It merges cooperation (commitment towards same end) with coordination (complexity to work together effectively) (Vivona et al., 2022). This view may be much more explored by the researchers to enhance the practical aspects of this perspective. ...
Full-text available
Collaborative innovation become one of the most strategy decision across firms and a well-defined phenomenon that became popular among practitioners and researchers (A. S. Cui O’Connor, 2012; Liu et al., 2017). Many theories were considered to explain collaboration phenomena such as resources-based view, organization theory, strategy, information processing theory, the economic theory of complementarities among others (Barney, 1991; Cassiman Veugelers, 2006; Daft Lengel, 1986; Milgrom Roberts, 1995; Tushman Nadler, 1978). However, technology advances provide new variations in collaboration to innovativeness. For example, collaborative activities with suppliers and customers (Karhade Dong, 2021), community source projects (Liu et al., 2017) or collaboration with distant partners (T. Cui et al., 2020), corporate engagement with startups (Shankar Shepherd, 2019), innovation networks (Aarikka-Stenroos et al., 2017), and innovation ecosystems (Granstrand Holgersson, 2020).Collaborative innovation takes over the existence of an inter-organizational activities executed by people that together perform with high level of interdependence something innovative (T. Cui et al., 2020; Davis Eisenhardt, 2011). Some authors (Adner Kapoor, 2010; T. Cui et al., 2020; Rico et al., 2008) highlight that this interdependence is characterized along two dimensions: technological and behavioral. Technological interdependence is linked to knowledge and the exchange of resources for research and development, and behavioral interdependence is associated with the field of communication, social interaction between collaborative actors and the coordination of these relationships to innovate.Other perspectives in the literature explain and theorize about collaborative innovation as knowledge-sharing trajectories (Majchrzak Malhotra, 2016; Trkman Desouza, 2012), or multi-actor collaboration (Torfing, 2019), or building collaborative capabilities (Swink, 2006) among other approaches. In this editorial, we bring some thoughts and idea about collaborative innovation under a technological perspective to incentive researchers to go beyond in innovative technologies research embedded in collaboration.Collaboration efforts also became a common way of firms to enhance innovations and its technological development with clear determinants about their beneficial effects, and therefore, the literature is well stablished in this subject (Pereira et al., 2018). However, collaboration only succeeds when technological resources and capabilities are combined, and parties define jointly how to enhance and use them accordingly (Snow, 2015).Collaborative innovation as a new technological paradigm refers to a network innovation model supported by interactions of multiple parties such as enterprises, universities and research institutions as core elements and government, financial institutions, nonprofit organizations, intermediaries as auxiliary elements (W. Zhang et al., 2021). Notwithstanding, collaboration networks operating in different organizational levels are present in various patterns and characteristics of evolution, they require different actors and capabilities in the network composition to become a remarkable asset in developing technologies to be patented afterwards in some cases (Gomes et al., 2017).In facing of risks of failures during innovative trajectories, firms invest in collaborative initiatives as an attempt to mitigate cost impacts, share responsibilities and greater technical performance in the process of technology lifecycle development. Thus, technological alliances are useful means to attend these goals (Kim Song, 2007). Technological alliances are critical to enable digital transformation and innovation. Briefly, Zhang et al. (2021) highlight technological alliance as a voluntary interfirm cooperation involving codeveloping technologies through sharing and exchanging of these technologies to meet business needs (W. Zhang et al., 2021).The collaborations in various technological domains help to bring heterogenous knowledge, complementary resources, and capabilities for a better innovation performance (Swink, 2006; W. Zhang et al., 2021). Under the perspective that innovation is essentially knowledge creation (Nonaka, 1994), collaborative innovation through a technological perspective may be configured by different activities, processes, or routines of generation, sharing, integration, and utilization of knowledge produced during the innovation process lifecycle (Nonaka, 1994; W. Zhang et al., 2021). Further, this configuration of activities, processes, or routines support the development of evolutionary technological capabilities (Sampson, 2007).In the field of technological innovations, the evolution now is more collaborative in nature (J. Zhang et al., 2019). Collaboration is a trend for technological prosperity. Analyzing collaborative innovation in the literature is a great challenge even if the focus on technologies is defined because various aspects and applications of collaboration to innovate invade the academic literature in many forms. For instance, Zhou and Ren (2021) analyzed low-carbon technology collaborative innovation in industrial cluster; Shen et al. (2021) studied collaborative innovation in supply chain systems; Wan et al. (2022) highlight that blockchain application intensify collaborative innovation through distributed computing, cryptography and game theory; Li and Zhou (2022) researched on the mechanism of Government–Industry–University–Institute collaborative innovation in green technology; and Fan et al. (2022) pointed out that collaborative innovation also may act as a driver to mobilize and coordinate scientific and technological resources within a city, further promoting innovative development among cities.On the other hand, technological collaborative innovations has its own dark side for firms: it has been costly, it demands money, efforts, and time (Torugsa Arundel, 2016; Wegrich, 2019), and, further, it provokes operational adjustments, technological reconfiguration, and legal barriers to overcome to be effective for innovation (McGuire Agranoff, 2011; Vivona et al., 2022). To address this side of collaborative innovation, Vivona et al. (2022) developed the cost theory to systematize all insights from the literature in four main factors: governance, compactness, reliability, and institutionalization to shed light on a broader range of costs for innovation incurred by collaborative arrangements. Governance refers to relationships in hierarchical level and the number of collaborators involved, reliability refers to relationships’ quality; compactness is about the degree of formality in relationships that connect collaborators; and institutionalization that measure what the extent the relationships in practice have been pre-established. This cost perspective may be explored empirically.The decentralization of technological collaborative innovation, its nonlinear, globalized, and networked form transformed its process to more collaborative approaches among entities (Fan et al., 2020). Lopes and Farias (2022) showed that technology tools support the establishment of relationships of trust promoted by leaders committed to well-established goals, being a characteristic of governance that has a positive influence on collaborative innovation processes. Hwang (2020) mentioned that several countries have implemented policies to facilitate technological convergence by supporting collaborative innovations. The author also mentions that collaborative innovation is a crucial strategy to facilitate technological convergence. In sum, firms have been increased collaboration in technological activities and collaboration works as an enabling to learn about turbulent technological change and uncertainties to enhance the ability to deal with innovations (Dodgson, 1993).Technological collaborative innovation is considered essential to promote the flow of resources, knowledge, and technology among entities, considering that innovation is no longer a closed and isolated system. The main premise is technologies do not exist in isolation. Only by exchanging materials, energy, and information with the environment the innovation system be renewed and developed. Therefore, the integrator condition of technological collaborative innovation is also conducive to a more comprehensive disclosure of the collaborative mode and overall performance of technological innovation activities (Fan et al., 2020).Technological collaborative innovation is not a merely coordination of an orderly arrangements of efforts to pursue a common technological purpose (Mooney, 1953), or a merely cooperation to join agreed-on goals into a share comprehension about design systems or reconfigure technological resources (Gulati et al., 2012). It merges cooperation (commitment towards same end) with coordination (complexity to work together effectively) (Vivona et al., 2022). This view may be much more explored by the researchers to enhance the practical aspects of this perspective.In general, collaboration itself does not survive in the face of inevitable behavioral problems which requires an establishment of trust characterized by receptive organizational cultures, community of interest, and continually supplement knowledge for the purpose of collaboration in highly successful technological innovations (Dodgson, 1993). Thus, this can be a new chapter for technological collaborative innovations.
... For example, how legitimacy is fundamental to reach the signing of the contract (Taubeneder et al., 2022); some reflections on the need to corroborate the possibility of successfully transferring knowledge in the specific case of franchises to form this kind of alliance (Brookes & Altinay, 2017); the importance of the bargaining process before the signing of the contract (Oliveira Cruz & Cunha Marques, 2013). We also find theoretical developments on how different theories (mainly the transaction cost theory and the resource-based view) explain the choice of collaboration (as opposed to other governance mechanisms) according to various features, such as trust, legitimacy, etc. (Carvajal-Camperos, 2022;Vivona et al., 2022). And more recently, Vivona et al. (2022) reviewed and tested a comprehensive framework of barriers that prevent collaboration agreements (in the specific case of university-industry collaboration). ...
... We also find theoretical developments on how different theories (mainly the transaction cost theory and the resource-based view) explain the choice of collaboration (as opposed to other governance mechanisms) according to various features, such as trust, legitimacy, etc. (Carvajal-Camperos, 2022;Vivona et al., 2022). And more recently, Vivona et al. (2022) reviewed and tested a comprehensive framework of barriers that prevent collaboration agreements (in the specific case of university-industry collaboration). ...
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Strategic alliances in the biotechnology sector are key to achieving the sustainable development goals of the Agenda 2030 and to boosting technology transfer, value creation and firms’ survival. This paper focuses on a momentum that has scarcely been analysed in the literature; from the moment a firm decides to form an alliance, to the moment the contract is signed. There are many combinations of antecedents that may influence the pre-alliance phase and lead either to the final signing of the alliance or to its cancellation. We base on the transaction cost theory and analyse how different combinations of possible impediments and firm-specific attributes affect the final contract signature. We use a qualitative comparative analysis on 69 firms in the Spanish biotechnology sector that belong to the Spanish Bioindustry Association (AseBio). The results help to identify and understand the different scenarios that may lead biotech firms to sign the strategic alliance contract.
... Furthermore, diversity of organizational cultures, which is stimulated through collaborative diversity, might incite tensions between actors (Diamond and Vangen, 2017). Hence, increased actor diversity might stifle the innovation process because of the increased transaction costs (Vivona et al. 2022). ...
... As collaborations with external stakeholders are complex endeavours in which perspectives, interests and resources have to be aligned, substantial efforts arise in managing these collaborations and controlling their transaction costs (Klijn and Koppenjan, 2016;Vivona et al. 2022). These complexities might result into underperforming processes and might damage the innovation process (Huxham, 2003;Diamond and Vangen, 2017). ...
Full-text available
Public service innovation involves a process of creative exploration of new ideas, knowledge and perspectives. The article poses that creative exploration emerges from the combination of a climate for creativity that is active inside the organization, and collaborations with diverse actors that are present outside the organization. We test the effect of these conditions on innovation using data from the Australian Public Service. Our findings demonstrate that both a climate for creativity and collaborative diversity are positively related to innovation, yet a tipping point exists at which the positive effects of collaborative diversity on innovation turn negative.
... Collaborative innovation is not merely a coordination of orderly arrangements of efforts to pursue a common technological purpose or merely a cooperation to join agreed-on goals into a share comprehension about design systems or reconfigure technological resources towards innovativeness. Collaboration to innovate merges cooperation (commitment towards the same end) and coordination (complexity to work together effectively) (Vivona et al., 2022). This takes place for RC as an antecedent of innovation management. ...
Full-text available
Relational capabilities (RC) draw upon the relational view incorporated into different approaches in competing ways. To provide a clarification of concepts to identify trends, perspectives, and future research opportunities, we conducted a literature review through a bibliometric analysis from 1979 to 2021 publications. Additionally, we highlight the most influential papers that extensively discuss RC and its influences and variations to manage innovation. Findings identify some descriptive insights about publications and the turning point article in the literature that influenced the evolution of RC under the innovation management perspective. RC intertwines into various aspects of innovation management, such as knowledge-based co-creation, learning value, collaborative strategy, innovative ecosystems, strategic management, managing partners, and organizational growth. Therefore, the stream of innovation management leads us to conceptualize RC, highlighting opportunities for further research to solidify RC’s significance. By reviewing and identifying the most influential papers and their authors and systematizing existing knowledge on RC, this research produces theoretical contributions for dynamic capabilities and innovation theory. From a practitioner standpoint, managers likely look at relational resources and strategies towards innovativeness from a new perspective understanding that relational rents exist.
... In particular, collaborating on innovation can be seen as a collaborative cost game, actors weighting between forming (larger) networks with particular structures, to share the costs related to the activity and to increase (at least implied) benefits of collaboration (Curiel, 2013;Vivona et al., 2022); or not collaborating. The latter consequently entails forming differently structured networks, due to the perception of potential costs of collaborating outweighing its potential benefits (Connelly et al., 2014), and actors thus pursuing opportunistic behaviors (Das & Rahman, 2010;Williamson, 1985). ...
Full-text available
We study the structure and evolution of networks of inventors involved in university licensing and patenting. In particular, we focus on networks of inventors that have successfully licensed a university patent (i.e., licensing networks ), and investigate levels of their fragmentation, cliquishness, and whether they exhibit the small world phenomenon. We find that these licensing networks are more fragmented and cliquish than the networks of inventors engaged in all (not necessarily licensed) patents (i.e., patenting networks ), and that they are not small worlds. Additionally, by comparing the created licensing networks to random subnetworks of the patenting networks, we find that concerns in regard to the potential effects of opportunistic behavior are, to some degree, justified. We detect an interesting collaboration behavior of inventors who license, which we designate as dualistic opportunistic behavior.
... In other words, this is where skills, knowledge, as well as ideas interact at all levels. Inter-sectoral collaboration, seen as productive, is becoming a major source of innovation, and its importance is growing, innovation processes are increasingly organized according to distributed knowledge bases across economic sectors (Bennat & Sternberg, 2020;Vivona, Demircioglu & Audretsch, 2022). Research conducted by universities and other publicly funded institutions is also an important part of the innovation process, from basic to applied and translational research. ...
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Our paper develops theoretical and methodological principles of grounding assessing the dominant trends in intersectoral academic research related linked to the main tools and instruments of funding innovations in business companies. We employ the analytical approach as well as the two-stage bibliometric analysis of scientific articles published in the past 80 years and indexed in Scopus abstract and citation database which has been selected for its clarity, coverage, as well as its scope. In addition, we employ the outcomes of analytical analysis conducted using with the help of Google Trends tool. This approach described above allowed allows us to compare the peak periods for the changing the search queries of main concepts on this problem with the periods of the most significant events in the innovation sphere and financial policy. Moreover, we apply the VOSViewer software for identifying the dominant trends in intersectoral research related to funding innovation of business companies, as well as for finding out which instruments for the implementation of the financial policy implementation are more relevant for academics and scholars. Our results from the first stage demonstrate that the researchers’ focus on funding innovation of business companies and financial regulation was targeted on such topics as tax, monetary, budget, and investment instruments. Additionally, our results from the second stage additionally helped us to determine the dominant trends in intersectoral research connected to each group of the identified instruments. Thence, our findings contributed to the clustering of the theory of funding innovation of business companies by structure and main instruments. These results can be useful for by the multidisciplinary scientists, entrepreneurs, investors, innovators, as well as other relevant stakeholders and practitioners in making their everyday decisions on funding innovations in business companies.
... In the case of ULLs, social innovation and effective and broad collaboration are clear principles for their characterization as a locus of co-creation of solutions that bring public value [38], notwithstanding that part of the literature sees collaboration, especially in collective projects, as a "value" or superior principle to be followed. It should be noted that every process of participation in social innovation has significant costs, and many consider it necessary to carry out an analysis of the costs and benefits of the open governance process, stating that "collaboration must be critically evaluated and not sought for itself" [75]. ...
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TThe challenges to public policy brought by climate change are some of the biggest challenges for cities around the world. These challenges are costlier and more substantial for low-income communities given the existence of their greater social and economic vulnerability. Among the existing tools, this paper highlights the role played by urban living labs (ULLs), which have been discussed in the literature as a booster of urban resilience in a more sustainable direction. By considering ULLs as strategic institutional arrangements that seek resilience for the critical urban infrastructure challenges of climate change, the main target of this paper is to analyze ULLs as a strategy for increasing critical infrastructure resilience in the region of the Global South. These labs were initiated in developed countries, so we can ask: How are developing countries adapting this strategy in order to mitigate the problems of climate change? To achieve this goal, we reviewed previous literature on ULLs, specifically looking for case studies with ULL projects and highlighting the processes of public innovation policies and transfers of knowledge between countries; in order to complement our empirical analysis, we carried out a case study on Brazil. Despite the limitations of the sample, the data suggest the existence of different barriers to the implementation of ULL projects in Brazil compared to those in European cities
In recent years, collaborative innovation of public services has become a growing research field. However, how collaborative arrangements lead to innovation remains quite unclear. We propose that collaborative innovation is dependent on processes of divergence and convergence, which are enhanced by four conditions of collaborative innovation: diversity of ideas and perspectives, learning through interaction, consensus building, and implementation commitment. The combination of these conditions is explored through qualitative comparative analysis (QCA) in 19 European eHealth partnerships. The results suggest a combined effect of these conditions on service innovativeness, which rejects contemporary views on the dichotomous nature of divergence and convergence.
Full-text available
Amidst severe global crises, governments are under pressure to deliver appropriate outcomes to society and create a resilient future. Therefore, public managers started to consider the benefits that entrepreneurial leadership may offer; however, some scholars argue that entrepreneurial leaders act anti-democratically. Using data from Australia (n = 104,471), this study investigates whether entrepreneurial leaders enhance the effectiveness of public organizations, while also upholding democratic principles. The results suggest that the adoption of entrepreneurship by public managers positively influences the ability of both achieving organizational goals and enacting a democratic culture where staff is consulted and participates openly in decision-making.
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Collaborative innovation presents itself as a promising method for crafting innovative solutions to wicked problems. While the barriers and drivers of collaborative innovation have been studied extensively in the expanding collaborative innovation literature, there is still a need for more empirical studies of the role of public leadership in overcoming the barriers and strengthening the drivers of collaborative innovation. In order to contribute to this endeavour, this chapter conducts a cross-case analysis of 14 cases of collaborative innovation aimed at curbing gang violence in the city of Copenhagen. The chapter provides empirically informed answers to the questions of when and where leadership is particularly needed, what public leaders should be aware of when leading collaborative innovation processes, and how they should go about developing innovative solutions to wicked problems such as the current Danish gang problem.
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From an exclusive national sample of Brazilian university laboratories, this study examines the relationship between the intensity of university-industry collaboration (UIC) and the scale of research resources and the scope of activities at the laboratory level. We defned the scale of the laboratories’ research resources in terms of staff and capital stock. The staff of laboratories was proxied by the number of permanent researchers, non-permanent researchers, and non-researchers who made up their personnel structure. The capital stock was proxied by the value of the laboratories’ equipment infrastructure. The scope of activities conducted in the laboratories covered teaching, research, technological development, the provision of technological services, and technological extension. Results of the ordered logit model indicated that the number of non-researchers in the laboratory staff is positively associated with the UIC intensity, emphasizing the relevance of the non-academic experience for the establishment of links between laboratories and industry. A new type of evidence showed that the latent variable for the UIC intensity increased with the scale of the equipment infrastructure. We also found a positive relationship between UIC and technological activities at the laboratory level. Idiosyncrasies concerning the knowledge fields of the laboratories are discussed.
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In his seminal 1921 book, Risk, Uncertainty, and Profit , Frank Knight distinguished uncertainty and risk. This paper applies Knight's concept of uncertainty to knowledge generated in incumbent organizations to explain the inherent difficulty in assessing potential innovations along with the key role played by knowledge spillover entrepreneurship as a conduit for transforming new knowledge created by an incumbent organization but ultimately commercialized through the creation of a new firm and innovation. Knowledge is inherently uncertain and constitutes what is characterized as the knowledge filter impeding innovative activity in the context of incumbent firms and organizations. The organizational and institutional context and market uncertainty can either facilitate or impede the spillover of knowledge from the firm where it was created to the entrepreneurial startup where it is transformed into innovation. The empirical evidence based on a large, unbalanced panel of 9,126 UK firms constructed from six consecutive waves of a community innovation survey and annual business registry survey during 2002–2014. Implications for managers, scholars, and policymakers are provided.
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The Open Innovation in Science literature suggests university knowledge creation should be followed by knowledge dissemination to industry and the public. Although several entrepreneurial university models have been proposed in the literature explaining the role of knowledge production, extant studies generally assume that the elements required by and involved in university outbound innovation are automatically aligned. This conceptual piece introduces the corporate-inspired strategic alignment framework for entrepreneurial universities. In addition, this paper examines the strategic congruence among the individual, organisational and system levels and the functional congruence between knowledge and entrepreneurial capitals. It demonstrates how they can fulfil the increasingly complex role that they must play in science, industry, and society.
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Services constitute strategic components of firms’ value proposition, specifically for manufacturing firms currently called to servitize their products to develop product-service systems. In order to develop new services, they need to acquire, assimilate, transform and exploit external knowledge, thereby partnering with external stakeholders, a strategy labelled open service innovation. Yet research on innovation management in general and open innovation in particular has mostly focused on product innovation, leaving this area of research scantly understood. This is particularly true for manufacturing firms involving a family in the business, namely family manufacturing firms, acknowledged for adopting distinctive innovation behavior. With the intention of addressing this gap, we conceptually investigate open service innovation in family manufacturing firms by embracing a relational perspective. In so doing, we identify drivers and contingencies of family manufacturing firms’ innovation behavior that might trap them in their own net(work) and suggest managerial solutions to escape from such trap.
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Innovation and Innovativeness for the Public Servant of the Future: What, Why, How, Where, and When This chapter aims at clarifying the concepts of innovation and innovativeness for public servants. It first explains what public sector innovation is and how it differs from private sector innovation (what question). Then, it discusses why public sector employees and public organizations need to innovate (why question). It also highlights how to support innovativeness of public servants, by addressing the innovative skills that can be improved (how question). In particular, are there any specific conditions and drivers for innovation in public service? This chapter also discusses which types of jobs and workplaces require more innovative skills for the public servant of the future (where question) and briefly explores evolving opportunities and challenges in the workforce (when question) to predict what the public servant of the future will look like.
Rising and changing citizen expectations, dire fiscal constraints, unfulfilled political aspirations, high professional ambitions, and a growing number of stubborn societal problems have generated an increasing demand for innovation of public policies and services. Drawing on the latest research, this book examines how current systems of public governance can be transformed in order to enhance public innovation. It scrutinizes the need for new roles and public sector reforms, and analyzes how the gradual transition towards New Public Governance can stimulate the exploration and exploitation of new and bold ideas in the public sector. It argues that the key to public innovation lies in combining and balancing elements from Classic Public Administration, New Public Management and New Public Governance, and theorizes how it can be enhanced by multi-actor collaboration for the benefit of public officials, private stakeholders, citizens, and society at large.
Social norms regulate actions in artificial societies, steering collective behavior towards desirable states. In real societies, social norms can solve cooperation dilemmas, constituting a key ingredient in systems of indirect reciprocity: reputations of agents are assigned following social norms that identify their actions as good or bad. This, in turn, implies that agents can discriminate between the different actions of others and that the behaviors of each agent are known to the population at large. This is only possible if the agents report their interactions. Reporting constitutes, this way, a fundamental ingredient of indirect reciprocity, as in its absence cooperation in a multiagent system may collapse. Yet, in most studies to date, reporting is assumed to be cost-free, which collides with many life situations, where reporting can easily incur a cost (costly reputation building). Here we develop a new model of indirect reciprocity that allows reputation building to be costly. We show that only two norms can sustain cooperation under costly reputation building, a feature that requires agents to be able to anticipate the reporting intentions of their opponents, depending sensitively on both the cost of reporting and the accuracy level of reporting anticipation.
Immigration is a hotly debated and politicized policy area, one in which governments confront fierce opposition from populist parties and negative media narratives altering citizens’ perceptions of the issue. There is also growing scholarly interest in migration; new migrant integration research advocates for a conceptual shift away from focusing on migrant populations and towards rethinking host communities. At the same time, public sector innovation research is developing new approaches for how governments and public organizations can be innovative in dealing with grand challenges such as migration. The aim of this article is to merge these two subfields in order to answer a guiding research question: how can public organizations be innovative to promote integration for migrants? We suggest a typology of different innovation strategies that governments can adopt regarding integration, and we present five illustrative cases from European nations to examine how governments can innovate in order to integrate migrants. We find that governments can use multiple tools to promote integration and respond to grand challenges; governments can use various sources of innovation to address major barriers to migrant integration (e.g. language barriers, negative media and public opinion, and the difficulties of providing concrete assistance).