Network Governance in Action: Functions and Practices to Foster Collaborative Environments

Abstract

Collaborative networks attract the attention of researchers, practitioners, and policymakers as an alternative to solve complex problems. However, there are gaps regarding the day-to-day activities network leaders perform to foster collaborative environments. We propose a research framework for the micro-governance of collaborative networks by analyzing how contextual factors influence the use of governance functions and practices. Our study contributes to the nascent theory of network governance by proposing relationships among contextual factors, functions, and practices. We also offer insights for practitioners and policymakers who want to improve the effectiveness of collaborative networks composed of public and private members.

Keywords

wicked problems collaborative networks network governance collaboration collaborative environment

Introduction

Developing and developed countries face wicked problems (Rittel & Webber, 1973) in diverse areas such as pollution and watershed protection, mobility, refugee integration, education, and health care (McCrea, 2020). The coronavirus pandemic has exposed the vulnerability of isolated actions to prevent the spread of the disease. Most of these problems can no longer be solved by states or by organizations working in isolation (Escobar & Deshpande, 2019; Head, 2008). Single organizations lack the financial resources, knowledge, and legitimacy to deal with the dynamic and complex problems that affect society (Bianchi et al., 2021). As a response, over the last decades, collaborative networks have flourished in many forms and with many characteristics to engage stakeholders such as organizations, states, and civil society in initiatives that aim to propose and implement solutions for these problems (Bitterman & Koliba, 2020; Butterfield et al., 2004; Krogh, 2020).

Networking is a widespread phenomenon both in the public and in the private sector, to the extent that scholars refer to a network society (Castells, 2011) and a society of networks (Raab & Kenis, 2009). Collaborative networks and cross-sector collaborations (DiVito et al., 2020) are formed to solve complex social and environmental problems (Bodin et al., 2017; Yahia et al., 2019), to raise the competitiveness of private companies, and to foster innovation (Dagnino et al., 2015). These are sets of interconnected and interdependent actors that pool their resources to achieve individual and collective goals (Kickert et al., 1997). In collaborative networks, stakeholders from the public, private, and nonprofit sectors commit to policy making, policy implementation, and service delivery tasks (Koski et al., 2018). The strength of collaborative networks consists of coordinating the organizations’ activities to face wicked problems while their members keep functioning autonomously (Isett et al., 2011; Sørensen & Torfing, 2009).

However, governing collaborative networks is challenging due to the diversity of stakeholders involved, the asymmetry of power and information, and conflicting goals that may arise among members (Smith, 2020). Several studies have been conducted to understand how networks can be governed and how network effectiveness can be improved (Ansell & Gash, 2008; Assens & Lemeur, 2016; Klijn & Koppenjan, 2016; Moretti, 2017; Provan & Kenis, 2008). According to Stoker (1998, p. 17), governance is concerned with creating the conditions for ordered rule and collective action. Nevertheless, governance is not only making the rules but also enforcing the rules. Getting members to comply with network rules is a hard task. Despite the significant advances concerning the modes of network governance (Provan & Kenis, 2008), there are still gaps in understanding how governance is expressed in day-to-day activities that promote transparency, trust, learning, fairness, and power symmetry (Agranoff & McGuire, 2001; Ansell & Gash, 2008; Bingham, 2009; Huxham & Vangen, 2005; Purdy, 2012; Ran & Qi, 2018).

In this article, we make a contribution that goes beyond the current approaches to network governance by proposing a micro-governance of collaborative networks, that is, the functions and practices performed by network leaders that stimulate cooperation among network members. This micro-governance approach aims to open the “black box of cooperation” (Albers et al., 2016; Diaz-Kope et al., 2015), enlightening the micro process and the operational aspects that keep network governance in action. The purpose of this article is threefold. First, it identifies a set of functions and practices to govern collaborative networks identified by the literature. Second, it analyzes these functions and practices in light of the contextual factors and network outcomes. Third, it organizes functions and practices into a framework for the micro-governance of collaborative networks. From a theoretical perspective, the article contributes to overcoming gaps about the influence of internal and external contextual factors in the micro-governance of networks. From a managerial point of view, the article contributes to qualifying network governance and minimizes the difficulties faced by network leaders. Finally, understanding how the governance of collaborative networks can be performed also contributes to the formulation of public policies.

The remainder of this article is organized as follows: The next section discusses the governance of collaborative networks. This section is followed by a presentation of the main elements that compose our research framework and the propositions of our study. The fourth section presents the implications for research and practice, as well as suggestions for future research.

The Governance of Collaborative Networks

While individual organizations usually rely on rigid bureaucratic and hierarchical mechanisms to guarantee the authority and the execution of activities, collaborative networks rely on flexible structures and intense communication to nurture collaboration and stimulate collective action (Wachhaus, 2012). According to Smith (2020), a collaborative network refers to a group of three or more interdependent organizations that come together using collective decision-making practices to achieve a specific goal. Collaborative networks demand governance efforts, that is, governing collaborations refers to the governance of collaborative entities per se (Vangen et al., 2015). Conflicts are managed through repeated interactions that encourage and foster norms of trust and reciprocity (Smith, 2020). This type of network governance comprises the design of a coordination structure (Mariani, 2016; Provan & Kenis, 2008) for organizing collective actions and achieving common objectives (Albers, 2005, 2010).

The governance of collaborative networks involves “designing, managing, and controlling networks to reduce uncertainties and improve competitive position” (Grabher & Powell, 2004, p. xiii). It also aims to foster goal consensus among partners to provide the impetus for collaboration (Vangen & Huxham, 2011) according to specific rules and procedures (Dal Molin & Masella, 2016). Building on these definitions, Vangen et al. (2015) propose that “the governance of collaborations entails the design and use of a structure and processes that enable actors to set the overall direction of the collaboration, and that coordinate and allocate resources for the collaboration as a whole and account for its activities” (p. 8). In the last 15 years, the literature has offered different theoretical approaches for governing collaborations (Ansell & Gash, 2008; Emerson et al., 2012; Provan & Kenis, 2008).

In a seminal paper, Provan and Kenis (2008) propose three basic modes of network governance from which hybrid models can be generated: shared governance, lead-organization governance, and network administrative organization.

A second approach to the governance of collaborative networks was proposed by Ansell and Gash (2008). The authors conducted a meta-analytical study of 137 cases of collaborative governance and presented a contingency model of collaborative governance. Their model identifies critical variables that influence whether governance will produce successful collaboration, including prior history of conflict or cooperation, incentives for stakeholders to participate, power and resource imbalances, leadership, and institutional design.

Emerson et al. (2012) proposed a third approach to governing collaborative networks. Following Bryson et al. (2006), they define governance as a set of coordinating and monitoring activities that enable the survival of the collaborative partnership or institution. While Ansell and Gash (2008) restrict governance to only formal, state-initiated arrangements and engagement between government and nongovernmental stakeholders, the model of Emerson et al. (2012) also encompasses multipartner networks. They also outline an integrative framework with three nested dimensions, representing the general system context, the collaborative governance regime, and its collaborative dynamics and actions.

The three frameworks proposed by Provan and Kenis (2008), Ansell and Gash (2008), and Emerson et al. (2012) offer insights relevant to the understanding of the governance of collaborative networks. However, these frameworks also lack a fine-grained understanding of the micro-governance of collaborative networks, that is, the day-to-day functions and practices performed by one or more network leaders to reach common objectives. In the next section, we present the theoretical dimensions and propositions concerning the micro-governance of collaborative networks.

The Micro-Governance of Collaborative Networks: Functions and Practices

Network leaders have the difficult task of promoting collaboration among a set of organizations who may hold different perceptions about the reasons for collaborating (Cepiku & Mastrodascio, 2019; McGuire & Silvia, 2009; Reypens et al., 2017). Although these organizations collaborate on common goals, they may not agree on the best way to achieve them. The literature suggests that network leaders perform six different functions using practices to govern collaborative networks.

The first function performed by network leaders consists of aligning the interests of the participants. The effectiveness of collaborative networks largely depends on the capacity of coordinators and leaders to identify the challenges faced by the network members, align their goals, and propose solutions (Damgaard & Torfing, 2010). Network leaders identify and define the direction of the activities performed by the collaborative network and coordinate which outcomes should be attained through joint effort (Acar et al., 2008). By doing so, network leaders may be able to develop mission-based management (Brinckerhoff, 2009) sustained by shared values and a common purpose that fosters network development (Mariani, 2016). The function mobilizing aims to stimulate network members to undertake the efforts necessary to reach collective goals. The governance of the network sets the allocation of decision-making rights, stimulates the exchange of information between the members, and defines incentives, providing the conditions to mobilize participants for collective action (Van Veen-Dirks & Verdaasdonk, 2009).

Organizing helps put into practice the goals already lined up by the collaborative network. It refers to providing and organizing the human, financial, technological, and legal resources to stimulate network development (Sørensen & Torfing, 2005). It may also refer to organizing the routines and processes (Cristofoli et al., 2015) that guide network members. The expected outcome of network governance is establishing an environment that provides favorable conditions for productive interactions and helps the members achieve common goals (Agranoff & McGuire, 2001).

Collaborative networks leverage the knowledge and resources of different members, such as organizations and public agencies, to solve complex problems. Therefore, the fourth function of network governance consists of integrating participants and their resources. According to Sørensen and Torfing (2005), the integration of network members offers benefits, for instance, sharing the knowledge, plans, and activities of the collaborative network, and helps to align joint decisions. It refers to engaging the members who have already joined the network and identify their resources and capabilities, but also attracting and integrating new members who may contribute to the collective goals (Lehtonen, 2014).

Arbitrating complements the function of integrating, especially as conflicts and disagreements frequently occur in relationships that require negotiation and deliberation in cooperative and nonhierarchical contexts. In collaborative networks, conflicts are heightened by the inherent governance tensions highlighted by Provan and Kenis (2008). In this sense, one of the functions of governance is to react to critical situations to reconcile conflicting networking relations (Cristofoli et al., 2015). The last function consists of monitoring the actions of the participants and the outcomes achieved. A sound monitoring system is essential to ensure that the collective goals have been achieved and, eventually, promote corrections (Van Veen-Dirks & Verdaasdonk, 2009).

Based on the abovementioned governance functions, we propose that

Proposition 1: Network leaders perform the functions of aligning, mobilizing, organizing, integrating, arbitrating, and monitoring to govern collaborative networks.

These six governance functions represent essential roles network leaders perform when governing collaborative networks. To perform the governance functions in day-to-day activities, network leaders use the support of a set of collaborative governance practices (Booher, 2004). As the complexity of a collaborative network increases—either through the number of participants or the collective objectives established—a broader combination of practices is used to support the relationships (Grandori & Soda, 2006). The number of governance practices is potentially unlimited, and the same practice may be used to perform different functions (Wegner et al., 2017). Hence, for the purposes of this article, myriad governance practices have been gathered into three groups, namely, agreement, arrangement, and engagement, based on the types of network managerial behaviors proposed by Klijn et al. (2010).

Network leaders use practices of agreement for partner selection and integration to reduce the risks of cooperation (Arranz & de Arroyabe, 2007) and facilitate the alignment of goals between network members (Henttonen et al., 2016). Formalization practices, such as contractual agreements, contribute to the definition of roles and responsibilities and to the organization of network decision-making processes and communication channels (Cristofoli & Markovic, 2016). Vermeiren and Raeymaeckers (2020) showed that agreement practices fulfill a mediating role between network members and create connections among the stakeholders. Practices of conflict mitigation and resolution also facilitate the construction of shared meaning between stakeholders (Crosby & Bryson, 2010).

Network leaders use practices of arrangement to reach consensus among network participants and facilitate the coordination of the activities (Mariani, 2016). In collaborative networks, participative decision-making processes are considered essential to ensure the alignment of interests and goals of the participants (Lavikka et al., 2015). The literature also highlights the use of practices for controlling, monitoring, and evaluating to stimulate collective action (Dimitratos et al., 2010; Roberts, 2011). Moreover, the definition of roles and responsibilities of each partner, based on the goals set, is a practice that contributes to the network control system (Arranz & de Arroyeba, 2007).

Network leaders use practices of engagement to connect members (Page, 2010). Social and interactional practices, characterized as interpersonal encounters, both mediated by technologies and face-to-face, enacted by and between the stakeholders’ key people (Tidström et al., 2018) with the aim of delivering relevant, pertinent and current information (Attfield et al., 2010) are often employed by network leaders. Alvarez et al. (2010) report that practices such as field visits, forums, and informal meetings are activities that promote interaction and consequently strengthen relationships and build trust among members. Practices for the division of results are also important to keep stakeholders committed and interested in the cooperation. The sense of the equity of the distribution of benefits and costs of collaborative decisions depends on the stakeholders’ interpretations (Page, 2010). Thus, collaborations fail when the partners realize an unsatisfactory relationship between their contribution and the compensation received (Park & Ungson, 2001).

Based on the abovementioned group of governance practices, we propose that

Proposition 2: Network leaders perform practices of agreement, arrangement, and engagement to govern collaborative networks.

The Antecedents of Micro-Governance: Contextual Factors

The context in which the collaborative network operates may affect the design and operation of its governance (Raab et al., 2015). Ansell and Gash (2008) feature three contextual factors that influence the collaboration and the dynamics of relationships: the asymmetry of power-resources-knowledge, the incentives and restrictions for participation, and the previous history of cooperation or conflict among network members.

Asymmetry of power-resources-knowledge among network members is a contextual factor that may pose significant challenges to network governance (Cowan et al., 2015). When some actors do not have the capacity, organization, status, or resources to participate under similar conditions in network activities, governance is likely to be manipulated by the strongest actors (Ansell & Gash, 2008). Another line of research suggests that in such situations, governance needs to be modified to reduce asymmetry among network members (Wilding et al., 2012) and guarantee their access to information and participation in decision-making processes.

A second contextual factor refers to the members’ incentives and restrictions for participation. According to Ansell and Gash (2008), incentives to participate depend on the expectations of each actor on the outcomes that membership in the collaborative network can generate, particularly in comparison with the time and resources that the collaboration requires. Incentives increase as the actors realize that membership positively influences their outcomes (Page, 2010). Moreover, mandated networks (Saz-Carranza et al., 2016) also require incentives for members to become involved in a collaborative environment.

The interplay of competition and cooperation among the members can also have relevant implications for network governance (Hoffmann et al., 2018). When previous relationships between the network participants have been characterized by low cooperation and high conflict, the collaborative network will probably face difficulties due to low levels of trust and commitment among its members (Ansell & Gash, 2008). On the contrary, a history of successful collaboration fosters commitment and trust, which provides a virtuous cycle for collaboration.

Based on these arguments, we propose,

Proposition 3: Internal, as well as external contextual, factors influence the use of governance functions and practices to govern collaborative networks.

The Micro-Governance Results: Intermediate Outcomes

There is a long academic tradition analyzing network effectiveness (i.e., the instrumental goals a collaborative network pursues) and how governance contributes to this effectiveness (Provan & Milward, 1995, 2001; Provan & Kenis, 2008). However, few studies consider how micro-governance contributes to intermediate outcomes. We argue that micro-governance fosters intermediate outcomes and creates an environment that supports collaborative actions among network members (Emerson & Nabatchi, 2015). Micro-governance does not guarantee the achievement of the instrumental goals of a collaborative network, but it helps network members in developing intermediate outcomes, that is, an adequate atmosphere to work cooperatively toward the network’s instrumental goals. Good micro-governance may also foster a collaborative atmosphere at the network level, as network effectiveness and outcomes for the individual members depend on the coordinated actions of different members (Provan & Milward, 2001).

The outcomes of whole networks’ micro-governance can be assessed either with an emphasis on the structure or an emphasis on leadership (Smith, 2020). In the first case, outcomes are measured by the network’s structural components, such as density, centralization, overlap, and system stability. The second emphasis assesses the ability of network leaders to develop, guide, and facilitate the actions of network members (Smith, 2020). According to Provan and Lemaire (2012), the leader here must be considered as a manager of a network and not as a manager in a network. So, the outcomes of micro-governance are assessed by how well the network fosters a collaborative environment characterized by trust, legitimacy, learning, power, and fairness.

The first outcome, trust among members, has widely been recognized as a relevant condition for collaboration (Bryson et al., 2006, 2015; Zhong et al., 2017). Trust among members has the potential to reduce transaction costs, increase the likelihood of inner network stability, promote knowledge sharing, and stimulate innovation (Klijn et al., 2010). It is especially relevant in collaborative networks because the uncertainties of collaboration cannot all be managed through hierarchical power, surveillance, and contracts (Edelenbos & Klijn, 2007). By promoting the integration of network members in a collaborative environment, network leaders stimulate the communication and connections that lead to trust. We follow the concept of collaborative trust proposed by Getha-Taylor et al. (2019) as

an individual perception that is the product of one’s assessments, experiences, and dispositions, in which one believes, and is willing to act on, the words, actions, and decisions of others. This can include a reliance on principles, rules, norms, and decision-making procedures that articulate collective expectations. (p. 51)

For instance, aligning, integrating, arbitrating, and monitoring are functions that may foster trust among members and therefore support a positive environment for collaboration.

The second outcome, legitimacy, indicates whether micro-governance is appropriate for the purpose and precepts of the constituted collaborative network. It can be assessed with an internal or an external approach. Internal legitimacy refers to the validation of the network’s credibility to its members and is strongly associated with transparency and clear rules, as well as participation and representation in decision-making processes (Koski et al., 2018). External legitimacy refers to the validation to outside stakeholders that the outcomes of the network are valuable and worthwhile (Persson et al., 2011). Both approaches can be supplemented by Fawcett and Daugbjerg’s (2012) proposition of input and output legitimacy. Input legitimacy refers to the process through which network decisions are reached, and output legitimacy refers to the extensions of the network outcomes.

The third micro-governance outcome, network learning, has been extensively studied in the literature. It is learning by a group of organizations as a group (Gibb et al., 2017; Knight, 2002). Network learning is a mechanism through which past experiences can shape future functions and practices of governance (Smith, 2020). The members of a network need to learn about joint decision-making processes and how they can work together and combine resources in a collaborative environment. Thus, micro-governance plays an important role in fostering network learning by offering a trustworthy environment, transparency, and opportunities to share ideas and information about the resources and capabilities of each partner.

Power is related to the capacity of a member to influence others’ actions and behaviors in an intentional way (Huxham & Beech, 2008). Although power imbalances are a potential tension for the micro-governance of collaborative networks, they have often been disregarded in theoretical frameworks (Smith, 2020). The effect of power imbalances on network effectiveness is contingent on a variety of factors, such as the strength of the institutional environment, degree of urgency of the goals, previous experience in power-sharing, and the cost-benefit calculus for participants, among others (Smith, 2020). Purdy (2012) proposed a framework for assessing power in collaborative networks based on three sources of power (authority, resources, and legitimacy) and three arenas for power use: the participants, the process design, and the content of collaborative governance processes. He advises, “conducting an assessment of power could reveal mistaken beliefs and hidden sources of power that may reduce overconfident, defensive, or domineering behaviors during collaborative processes” (Purdy, 2012, p. 415).

The last outcome—fairness —refers to the perception of network members regarding the governance rules and appropriate distribution of the benefits provided by the collaborative network. Grandori and Neri (1999) highlight that networks perceived by their members as governed through fair rules have higher chances to promote a collaborative environment. Moreover, a fair distribution of outcomes occurs when network members receive benefits proportional to their contributions (Park & Ungson, 2001). Although the collaborative network creates extra value, and the participants are willing to distribute it fairly, there may be difficulties in reaching a consensus on how to divide this extra value (Page, 2010). Tuusjärvi and Blois (2004) propose that equity assessment should consider the different interpretations of members regarding the benefits of participating in a network. A perception of fairness may also be enhanced in collaborative networks through the functions and practices of micro-governance, playing an essential role in developing a collaborative environment, as summarized in the following proposition:

Proposition 4: The governance functions and practices performed by the network leaders influence the generation of a collaborative environment (i.e., an internal environment characterized by trust, legitimacy, learning, power symmetry, and fairness).

Finally, previous studies suggest that there is a recursive interplay between the intermediate outcomes of collaborative networks and how network leaders perform the micro-governance functions and practices. For instance, trust among network members is an outcome of governance functions which may also affect the use of such functions. Once relationships based on trust have been established, network leaders find it easier to align goals and organize activities (Provan & Kenis, 2008). Learning is another outcome of micro-governance functions and practices that can influence micro-governance itself (Gibb et al., 2017; Wegner & Mozzato, 2019). A similar effect must be considered for each intermediate outcome of micro-governance as there is an ongoing relationship among these elements, as we summarize in the following proposition:

Proposition 5: There is a recursive interplay between micro-governance intermediate outcomes (the network collaborative environment characterized by trust, legitimacy, learning, power symmetry, and fairness) and micro-governance functions and practices performed by network leaders.

We summarize the micro-governance of collaborative networks, the contextual factors, and the collaborative environment (intermediate outcome) generated by the micro-governance in Figure 1.

Figure 1.

The micro-governance of collaborative networks.

Implications and Directions for Future Research

The micro-governance of collaborative networks plays an important role in fostering intermediate outcomes, that is, an environment that stimulates cooperation among network members. Such a collaborative environment, based on trust, learning, legitimacy, power symmetry, and fairness, provides the abilities to marshal and focus the necessary actions to reach the instrumental goals for which the collaborative network has been created. The collaborative environment represents the intermediate outcome of collaborative networks as it indicates whether the network can generate relational value for its members. We follow Austin and Seitanidi (2012) to define relational value “as the transitory and enduring benefits relative to the costs that are generated due to the interaction of the collaborators and that accrue to organizations, individuals, and society” (p. 5) Therefore, relational value refers to the ultimate goals a collaborative network has been created for. While instrumental goals (e.g., public service delivery, joint R&D, market share) may continuously change due to the context, relational value remains a general goal pursued by the members of collaborative networks.

Relational value represents the ultimate goal of collaborative networks. Micro-governance has the hard task of fostering an environment that promotes collaboration among members and allows for the generation of relational value. Governing collaborative networks has been a challenge for managers and leaders, especially when stakeholders with different perceptions join the network and when complex problems arise that must be solved through collaboration (Ansell & Gash, 2008; Assens & Lemeur, 2016; Klijn & Koppenjan, 2016; Moretti, 2017; Provan & Kenis, 2008).

Previous studies offered relevant insights about how to organize the governance mode of collaborative networks (Provan & Kenis, 2008) and about the dynamics of collaboration (Ansell & Gash, 2008; Emerson et al., 2012). However, these studies do not offer advice about the day-to-day activities that network leaders need to perform to foster a collaborative environment and create relational value. Our framework goes a step further by analyzing what we call “the micro-governance of collaborative networks,” that is, the functions and practices of network leaders that produce collaborative environments.

The framework has implications both for theory and practice. It contributes to the existing body of research about network governance by proposing relationships between contextual factors, governance functions and practices, and intermediate outcomes that foster relational value. Furthermore, the relationships we propose shed new lights on the “black box of collaboration” (Albers et al., 2016; Diaz-Kope et al., 2015; Thomson & Perry, 2006) and help explain the mechanisms behind network operation and its effectiveness. We welcome empirical studies to test the theoretical relationships we proposed and improve our comprehension of the micro-governance of collaborative networks. We especially recommend that researchers develop qualitative case studies to describe how network leaders perform governance functions and which governance practices they use. Comparative analysis can also contribute to a deeper understanding of the combination of governance functions that support intermediate outcomes. As collaborative networks strongly vary in terms of the size, membership, and context in which they operate, researchers should consider these conditions when comparing different networks.

Future studies can also analyze how the positive environment fostered by micro-governance may support network members in generating relational value. However, other conditions (i.e., the resources each network member offers to the collaborative network) may also play an essential role in generating relational value and should be considered by researchers. Thus, we encourage researchers to test and expand our framework by considering how micro-governance and intermediate outcomes influence relational value.

In terms of practical implications, we believe our framework and the empirical results derived from this proposal can help network leaders and network members to better understand how such complex and nonhierarchical organizations can be governed to solve problems and generate relational value. Although we do not expect to provide solutions, we believe that deep comprehension of the conditions that explain how to generate collaborative environments may be an important outcome for practitioners. Moreover, policymakers may also benefit from this research framework in several ways. Public agencies often act as meta-governors or recommend the use of different governance modes when supporting collaboration. By doing so, they want to make collaboration efficient and effective. Therefore, our framework can support public agencies in making collaborative networks more likely to reach good outcomes and offer services to society.

Footnotes

Acknowledgements

The authors would like to thank to the anonymous reviewers for their effort in increasing our paper quality. The authors also thank CAPES for the partial support for this paper (CAPES PROEX grant aux. 1636/2018).

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Douglas Wegner

Author Biographies

Douglas Wegner holds a PhD degree in business administration from the Federal University of Rio Grande do Sul (2011) and was visiting scholar at the University of Dortmund–Germany (2019) and University of Sevilla (2016). He is full professor of interorganizational relations at the Business and Management School of Unisinos University and the co-leader of GeRedes/CNPq (Brazilian Group for Interorganizational Network Studies). He has published academic papers on interorganizational networks and cooperation at international conferences (SMS, ISIRC) and in journals such as Journal of Management and Governance, Creativity and Innovation Management, Journal of Knowledge Management, International Journal of Entrepreneurial Behaviour & Research, International Review of Applied Economics, Journal of Cleaner Production, and Journal of Small Business Management. His current research interests include interorganizational relations, interorganizational networks, network governance, and network management.

Jorge Verschoore holds a PhD degree in business administration from the Federal University of Rio Grande do Sul (2006) and was visiting scholar at the University of California, Berkeley (2015–2016) and Poitiers University (2012–2015). He is full professor of strategy at the Business and Management School of Unisinos University and the leader of GeRedes/CNPq (Brazilian Group for Interorganizational Network Studies). Currently, he is the CAPES coordinator of master and doctorate professional programs (2017–2021). He has published numerous academic papers on networks and cooperation at international conferences (SMS, EGOS, and Academy of Management) and in journals such as Project Management Journal; Journal of Management and Governance; Journal of Construction Engineering and Management; European Business Review; Journal of Environment and Sustainable Development; International Journal of Technology, Policy and Management; and Journal of Management in Engineering. His current research interests include interorganizational relations, network cooperation, coopetition, strategic platforms, and blockchain.

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