Public Organiz Rev DOI 10.1007/s11115-015-0320-4
From Fragmentation to Comprehensiveness in Network Governance Martina Dal Molin 1 & Cristina Masella 1
# Springer Science+Business Media New York 2015
Abstract Public networks are increasingly implemented at different government levels and across policy areas to increase coordination of services, decision-making and services delivery. Network governance is one promising theoretical perspective through which networks have been studied by different scholars and schools of thought. However, the literature on network governance is still fragmented, sectorial and issues-based. An overarching framework for the comprehensive analysis of the accumulate knowledge is missing. To address this limitation, we propose a comprehensive framework for analyzing the development of the findings in the field. The framework includes four building blocks that reflect the main issues debated in literature: the conditions affecting the choice of a mode of network governance, the modes of network governance, the dimensions of meta-governance and the outcome evaluation. The framework would support academics and policy makers who deal with network governance in different policy domains. The article concludes with a discussion of the proposed framework and its applications in future research. Keywords Network governance . Meta-governance . Literature . Framework . Policy makers
Introduction Networks of public, private and societal actors have been implemented to enhance integration and coordination between services, to ensure a more effective system of services provision, revitalizing, at last, the overall public sector efficiency (Jessop 2002; Provan and Milward 2001; 1995; Thompson 2003; Koppenjan and Klijn 2004a; * Martina Dal Molin
[email protected] Cristina Masella
[email protected] 1
Department of Management, Economics and Industrial Engineering, Politecnico di Milano, Milano, Italy
M.D. Molin, C. Masella
McGuire 2006; Löfgren and Ringholm 2009; Provan et al. 2009; Turrini et al. 2010; Isett et al. 2011). Today networks for services delivery have been implemented in different public domains (Provan and Milward 2001; Raab and Milward 2003; Graham et al. 2009; Robins et al. 2011; Provan et al. 2013). The widespread implementation of networks for services delivery has led to the development of the concept of network governance that has attracted the attention of academics and practitioners (see Torfing 2005a, b; Provan et al. 2007; Meuleman 2008a, b; Haveri et al. 2009; Sørensen and Torfing 2009; Klijn et al. 2010a; Meuleman 2010; Christopoulos et al. 2012). Network governance has been defined as B a stable articulation of mutually dependent, but operationally autonomous factors (…), who interact through conflict-ridden negotiations that take place within an institutionalized framework of rules, norms, shared knowledge and social imaginaries (…) and contribute to the production of public values^ (Sørensen and Torfing 2005, p.197; 2009, p. 236). Closely connected with network governance, the concept of meta-governance has also emerged. Meta-governance can be defined as Bthe governance of governance (Meuleman 2008a, b; Bell and Hindmoor 2009) and represents the efforts of public authorities in steering networks through a different set of rules or other strategies and it represents the way in which networks are empirically governed (Haveri et al. 2009; Baker and Stoker 2012)^. Network governance is a promising perspective for the field, but still offers ground for further research. The literature on network governance has, in fact, blossomed fragmentarily, often based on a single and sector-specific case study (see Ball 2008; Hendriks 2008; Damgaard and Torfing 2010; Graham 2011; Baker and Stoker 2012) or focused on specific features of network governance (e.g. collaboration, Ahujia 2000; Bryson et al. 2006; democratic accountability, Aarsæther et al. 2009; Klijn and Skelcher 2007; participation, Bingham et al. 2005; democracy and effectivity Sørensen and Torfing 2009; forms of network governance, Provan and Kenis 2008; Rodriguez et al. 2007; Milward and Provan 2006). An overarching framework of network governance that could connect these dispersed findings is still missing. Therefore, we develop an analytical framework that (1) is based on the most relevant works on the topic, (2) analyzes the effects of contextual factors in implementing a form of network governance and the way in which it should be metagoverned, (3) allows systematic and longitudinal comparison of network governance experiences. Our framework is Blongitudinal^ since it covers the different developmental stages of network governance implementation; and is Bsystematic^ since it builds from the main empirical and theoretical studies on network governance and corporate governance (Imperial 2005) to identify the key themes debated in literature. In chasing this goal, the present work contributes to the body of literature in two ways. First, we review the literature on network governance, mapping the fragmented knowledge and suggesting directions for future research. Secondly, we propose a comprehensive framework that might help policy-makers and academics to analyze network governance. The paper is organized as follows. The next section provides a description of the methodology, the research and selection of criteria used for the systematic literature review. The second section provides the descriptive analysis of the review’s findings and the third section presents the comprehensive framework for analyzing network governance. We conclude with a final discussion to stimulate future debates.
From Fragmentation to Comprehensiveness in Network Governance
Systematic Literature Review: Method and Data Collection We designed a multiple-step literature review, to collect the most relevant studies on network governance, meta-governance and collaborative governance. The approach to literature review was systematic to ensure transparency and replicability (Bakker 2010; Müller-Seitz 2012). First, we selected 14 English-speaking journals dealing with Political Science and Public Administration with an Impact Factor higher than 1.00 (Table 1). We excluded journals dealing with specific policy issues, e.g. Armed Forces & Society, Communist and Post-Communist Studies, Electoral Studies, International Journal of Conflict Management, Climate Policy. We used selected keywords to collect articles with unrestricted date of publication. The use of an unrestricted date expanded the possibility of finding out the most important articles dealing with the topic investigated (Denyer and Tranfield 2009). We then adopted the following inclusion criteria (1) empirical and theoretical articles, (2) focused on national or sub-national level, (3) adopting a whole network perspective (i.e. not focusing only on a specific issue) and (4) without restriction to a specific policy domain. We carried out the final paper selection after reading the full paper. Second, we identified in the full papers other relevant articles published in journals we had not considered before (Table 1). We then extended our review approach to those journals, applying the same keywords and the same inclusion criteria. At the end of this process we collected 84 relevant articles.
Findings Findings are organized into two sub-sections. First, we present the historical development of publications on the topic. Second, we discuss the key themes used to develop the framework.
Table 1 Literature review: method and criteria 1st step selected Journals
Annals of the American Academy of Political and Social Science, Annual Review of Political Science, British Journal of Political Science, Comparative Political Studies, European Journal of Political Research, Journal of European Public Policy, Journal of European Social Policy, Journal of Policy Analysis and Management, Journal of Public Administration Research and Theory, Journal of Social Policy, Policy Sciences, Policy Studies Journal, Politics & Society, Public Administration
2nd step selected Journals
Administration & Society, Administrative Science Quarterly, European Journal of Political Research, European Political Science, European Political Studies, Local Government Studies, Policy studies, Political Studies Review, Political Studies, Public Administration and Development, Public Administration Review, Public Management Review, Public Organization Review, Scandinavian Political Studies
Keywords
Network governance; meta-governance; collaborative governance AND Research, features, public sector
M.D. Molin, C. Masella
Historical Development The topic of network governance is relatively new within the public sector literature. The trend of publications reveals a growing attention from the beginning of the 2000s since 2005; publications on network governance then decrease until 2008 and they rise from the end of 2000s up to now (Fig. 1). In an initial phase, network governance was studied as a new way for providing public services (Torfing 2005a, b; Sørensen and Torfing 2007). Research questions typically focused on the reasons behind network development, how they should work, which actors are involved and which coordination mechanisms are in place. One example is the work of Peters and Pierre (1998), who consider network governance a way to re-structure and re-think public administration and the public sector in line with the process of Bgoverning without government^ (Kickert et al. 1997; Peters and Pierre 1998; Kooiman 2003). Starting from the middle of the 2000s, network governance is analyzed through the paradigm of efficiency: research typically focuses on how network governance should be efficiently managed, which factors affect its success or failure and what is the impact on effectiveness and democracy (Teisman and Klijn 2002; Torfing 2005a; Klijn and Skelcher 2007; Sørensen and Torfing 2007; Provan and Kenis 2008; Lewis 2011). Three distinct schools of thoughts – the Danish, the Dutch and the U.S. school - have emerged. The Danish (Sørensen and Torfing) and the Dutch schools (Kickert, Klijn and Koppenjan) focused on the compatibility between network governance and democratic values (Klijn 2005; Sørensen 2005; Sørensen and Torfing 2005, 2007; Klijn and Skelcher 2007) and on the efforts on the (meta) governing processes of network (Sørensen 2006; Sørensen and Torfing 2009). On the other hand, the U.S. school (Kenis, Milward and Provan) focuses on the identification of network governance forms (Provan and Kenis 2008) and on the relationship between network structure and network effectiveness (Milward and Provan 2006; Provan et al. 2007). Recently, network governance research has entered into a third phase. Three topics of attention emerged. First, some research focuses on the outcome of network governance, in terms of its conceptualization and relevant dimensions of evaluation (Sørensen 2005; Sørensen and Torfing 2005; Mathur and Skelcher 2007; Klijn et al. 2010a, b). Second, Norwegian, Finnish and Swedish case studies have investigated how networks are empirically meta-governed (Fotel and Hanssen 2009; Haveri et al. 2009; Sørensen and Torfing 2009; Baker and Stoker 2012). Third, some scholars embraced the proposition of Lewis (2011), i.e. BWhat theoretical frameworks will
Fig. 1 Network governance: publications’ trend
From Fragmentation to Comprehensiveness in Network Governance
ensure a robust, interesting and productive future for this sub-field?^ (Lewis 2011, p.1221) and focus on developing useful frameworks for studying network governance (see Wachhaus 2012). A Comprehensive Framework for Network Governance: Building Blocks We identified four main issues debated within literature: (1) starting conditions; (2) form of network governance; (3) meta-governance and (4) the outcome of network governance. Starting Conditions The choice of a form of network governance is affected by a set of starting conditions (Ansell and Gash 2008; Provan and Kenis 2008; Robins et al. 2011; Emerson et al. 2012). These starting conditions, often described sparsely within literature, may be grouped into six categories: (1) trust, (2) size, (3) goal consensus, joint action and motivation, (4) leadership and commitment, (5) embeddedness and (6) diversity. Trust refers to the positive expectation that one factor A will consider the interest of another factor B (Provan and Kenis 2008; Klijn et al. 2010b; Milward et al. 2009). Trust therefore allows participants to go beyond their personal interests, reducing uncertainty about opportunistic behavior (Provan and Kenis 2008; Klijn et al. 2010b). It is thought to have a positive impact on network governance since it generates mutual understanding, creates stability and favors the development of a strong basis for cooperation. Trust also facilitates the exchange of information and resources, reducing transaction costs (Ansell and Gash 2008; Provan and Kenis 2008; Milward et al. 2009; Emerson et al. 2012). Size concerns the number of factors involved in the network. It is related to the number of possible activities and connections among factors (Provan and Kenis 2008). In fact, when their number increases, the possible lines of coordination and their coordinated activities rise disproportionately, adding complexity to the governance activities within the network (Donahue 2004; Milward and Provan 2006). The effects of size on network governance cannot be described as positive or negative, but rather in terms of higher or lower complexity to governance activities. In other words, when network size increases, network governance activities are more complex; when it decreases, governing activities becomes less articulated. Goal consensus, joint action and motivation are related to both the degree of factors’ agreement about the network’s goals and to the strategies implemented to ensure the goals’ achievement (Provan and Kenis 2008; Røiseland 2011; Emerson et al. 2012 ). There is a relationship between the three factors. Specifically, goals consensus supports the development of joint action and shared strategy to Bgenerate desired outcomes together that could not be accomplished separately^ (Emerson et al. 2012, p. 14). Motivation is enhanced by goals consensus and joint activities, but motivation itself may support joint activities and strategies (Emerson et al. 2012). The presence of these three conditions has a positive impact on network governance, since it enhances the quality and the strength of relationships among factors involved (Rodriguez et al. 2007; Provan and Kenis 2008; Emerson et al. 2012).
M.D. Molin, C. Masella
Leadership and commitment refers to the presence of a leader who supports governance activities (Emerson et al. 2012) and brings Bstakeholders together and getting them to engage each other in a collaborative spirit^ (Ansell and Gash 2008, p. 554). Specifically, strong leadership affects network governance in two ways. First, it facilitates the ability of attracting different stakeholders (Ansell and Gash 2008). Second, the leader facilitate collaboration among factors and the overall success or failure of the overall network governance (Emerson et al. 2012). Embeddedness is a relational concept that can be distinguished into two meanings: relational embeddedness and structural embeddedness. Relational embeddedness refers to the relation among pairs of network participants that takes into account their respective goals and needs. Structural embeddedness refers to the extent to which members of dyadic shared partners are connected to another (Robins et al. 2011). The concept of embeddedness thus highlights the fact that the network’s actions and outcomes are affected by dyadic relations and by the overall structure of the network (Jones et al. 1997). It is thought to positively affect network governance for two reasons: at one hand, it improves the development of collaborative and reciprocated relations and, on the other hand, it sustains mutual social monitoring and influence (Donahue 2004; Robins et al. 2011). Diversity refers to the nature (public, private, NGOs) of factors involved (Donahue 2004; Røiseland 2011). Diversity is related to the complexity of network governance: as the diversity of participants increases, the diverse interests, goals and expectations make the identification of a shared strategy a hard work (Røiseland 2011). Form of Network Governance In order to classify the existing modes of network governance, it is possible to distinguish between two of contributions. On one hand, there are scholars who identified the characteristics useful to categorize the forms of network governance, but do not provide an actual taxonomy of network governance (Donahue 2004; Røiseland 2011). On the other hand, Provan and Kenis (2008) offer a threefold classification of network governance forms that is adopted by different scholars (see Cristofoli and Markovic 2013; Macciò and Cristofoli 2013; Raab et al. 2013). Provan and Kenis (2008) identified two dimensions to classify network governance. First, the brokered dimension refers to the degree of centralization-decentralization of network activities. Within the centralization-decentralization continuum, the authors identified a situation in which there is an organization taking some relevant governance activities, leading the others to participants. Second, the governed dimension refers to whether the network is governed by an internal or external factor. On the contrary, externally governed networks are characterized by the presence of an external authority specifically established for governing the network (Provan and Kenis 2008). Based on these dimensions and on their empirical analysis on four mental health networks in Arizona, Provan and Kenis (2008) identified three forms of network governance: Shared form (also called Bself-governance^ or Bparticipant-governed^), Lead Organization form and NAO (Network Administrative Organization) form. In Shared form the decision-making power is diffused among participants and governance activities are defined and administered by participants themselves. As a
From Fragmentation to Comprehensiveness in Network Governance
result, network participants are responsible for managing internal and external relationships, decision-making power is (more or less) symmetrical and the network acts collectively as a single unit. Governance can occur through formal (e.g. regular and defined meetings) or informal (e.g. spontaneous and uncoordinated efforts) mechanisms. Lead Organization form is characterized by the presence of an internal administrative entity that takes all network-level governance activities and decisions, leaving operational activities to the free initiative of participants. Its role can be established by the participants themselves or mandated. NAO form is characterized by an external governed dimension and a centralized brokered dimension. A separate administrative entity is specifically established outside the network’s boundaries with the tasks of governing, coordinating and sustaining the network. NAO has the full decision-making power over all the network’s activities, both strategic and day-to-day tasks. Meta-Governance In recent years, the concept of meta-governance has increasingly attracted scholars’attention. This is particularly true for empirical research from 2009 onwards (Baker and Stoker 2012; Haveri et al. 2009). Meta-governance represents the Bgovernance of the governance^ (Sørensen and Torfing 2009; Bell and Hindmoor 2009; Meuleman 2008a, b) or the Bgoverning of collaboration^ (Haveri et al. 2009). It is broadly defined as the effort of legitimized public authorities in steering networks through rules and other strategies, with the ultimate goal of shaping and directing particular forms of network governance (Sørensen 2006; Sørensen and Torfing 2009; Baker and Stoker 2012). Governments have a fundamental meta-governance role since they define the juridical regulations between organizations, mediate dialogue between actors, solves disputes and, finally, define the strategic context in which actors are expected to act (Jessop 2003). Three relevant contributions can be identified within literature on meta-governance. Kooiman (2003) defined meta-governance as a third-order governance (p. 170), i.e. a governance mechanism that guides the governing processes of the whole governance system (Kooiman 2003). From a managerial point of view, Koppenjan and Klijn (2004a, b) formulated the concept of meta-governance as a dilemma for public managers that have the task of managing complex public networks (Koppenjan and Klijn 2004a, b). More recently, Sørensen and Torfing (2009) offer a Bpolitical science view^ of meta-governance, conceived as the means through which public authorities can mobilize resources and capabilities of a set of heterogeneous factors, while they retain the ability to influence the scope and the outcome of a public networks. Sørensen (2006) and Sørensen and Torfing (2009) identify four classes of instruments through which networks can be meta-governed: design, framing, management and participation. These instruments can be grouped into hands-off tools and hands-on tools. Network design and framing are typically hands-off tools since they are implemented at a distance from network participants and they are related to the definition of specific economic and political frames; network management and participation are instead hands-on tools because they are characterized by a direct interactions with
M.D. Molin, C. Masella
network participants (Sørensen and Torfing 2005, 2009; Sørensen 2006; Aarsæther et al. 2009; Haveri et al. 2009; Fotel and Hanssen 2009; Baker and Stoker 2012). Network design is aimed at influencing the scope, the characteristics and the institutional procedures of networks. Empirically, network designed can (1) encourage the formation of specific governance arrangements, (2) define specific rules of exclusion and inclusion, and (3) empower certain factors by giving them additional resources. Network framing is used by public authorities to determine political goals, fiscal conditions and legal basis of the network. Network framing empirically deals with (1) the formulation of political goals to be achieved, (2) the allocation of resources, (3) the definition of legal rules and (4) story telling about relevant societal problems and the promulgation of best practices. Network management aims at reducing tensions that may appear between participants. Network participation aims to influence the policy agenda and the range of possible alternatives at the hands of governments and the policy outputs related to networks. Outcome of Network Governance Conceptualizing and evaluating the outcome network governance is challenging because there is share agreement on it. There are instead different academic positions, related to efficiency and effectiveness (the U.S. school), efficiency and trust (the Dutch school) or effectiveness and democracy (the Danish school). Different measures have thus been proposed to measure the effectiveness of service delivery networks (see Provan and Milward 1995, 2001; Kenis and Provan 2009; Turrini et al. 2010) but they are said to be insufficient for assessing the overall outcome of network governance. In fact, since network governance (1) is thought to enhance the effectivity of the governance process, e.g. by establishing a framework for consensus-oriented decision (Sørensen 2005), (2) it is also affected by the problem of democratic accountability, i.e. how to ensure that network governance is in compliance with democratic values. Sørensen and Torfing (2009) propose effectivity and democracy as relevant dimension upon which outcome of network governance should be assessed. While past research suggests that effectivity might represent a suitable dimension for assessing network governance outcome, there is a lack of clear understanding of what it actually means. Furthermore, effectivity cannot be assessed with the dimensions traditionally used to evaluate hierarchical governance (e.g. cost efficiency of public programs, operational effectiveness of these programs) or market governance (e.g. Pareto optimal allocation of costs and benefits). Rather, its assessment requires taking into account three dimension: cost efficiency, operational effectiveness and allocative efficiency (Sørensen and Torfing 2009). Specifically, a network governance is effective when it delivers what it is supposed to deliver (Sørensen and Torfing 2009) in terms of (1) quality of networked-based policies, (2) ability to solve relevant public problems, (3) costs of network solutions and (4) enhancement of democratic legitimacy. The question of democratic accountability, despite its relevance (Aarsæther et al. 2009), has received limited attention. Sørensen and Torfing stated that, in order to ensure its democracy, network governance should be anchored to legitimized elected politicians and to democratic values (Sørensen 2005; Sørensen and Torfing 2009). Accordingly, they propose the model of Democratic Anchorage to measure to what
From Fragmentation to Comprehensiveness in Network Governance
extent network governance is actually democratic. The model considers network governance to be democratic when it fulfills four criteria (Sørensen 2005; Sørensen and Torfing 2009). First, network governance should be controlled by democratically elected politicians to ensure that decisions and services are in compliance with the popular will. Second, it is necessary that affected population and organization are represented inside the processes of network governance. Third, network governance has to be accountable to citizens indirectly affected by decisions or services. Finally, internal processes of network governance should be based upon commonly accepted democratic standards and values.
A Conceptual Framework for Network Governance The underlying assumption of our framework is that the choice of a network governance form is affected by a set of starting conditions. Once established, each form network governance is meta-governed through different tools, that have a different impact on the outcome, generally assessed in terms of effectivity and democracy (Sørensen and Torfing 2005, 2009; Mathur and Skelcher 2007; Hendriks 2008; Aarsæther et al. 2009; Fotel and Hanssen 2009; Nyholm and Haveri 2009; Damgaard and Torfing 2010). We distinguish three dimensions within the framework (Fig. 2): the choice dimension, the meta-governance dimension and the evaluation dimension. The choice dimension refers to decision-making process through which a particular form of network governance is implemented. At this stage public authorities should consider a set of starting conditions to implement the form of network governance that is thought to be more effective in that context. The meta-governance dimension refers to the steering process carried out through a set of tools implemented to ensure the goals’ achievement. The choice between these tools is not purely technical, but it is affected by the form of network governance that in turn depends on the diffusion of decision-making power within the network (see Sørensen and Torfing 2005, 2009; Aarsæther et al. 2009;
Meta-tools Starting conditions
Hands-off Network design
Trust Size
Modes of network governance – power distribution
Effectivity Network framing
Goal consensus
Shared form
Leadership and commitment
Lead Organization form
Network management
NAO form
Network participation
Embeddedness Diversity
Fig. 2 A conceptual framework for network governance
Outcome evaluation
Hands-on
Cost efficiency, operational effectiveness, allocative efficiency Democracy
Democratic anchorage
M.D. Molin, C. Masella
Baker and Stoker 2012). Finally, the evaluation dimension refers to the final stage in which the outcome should be assessed. The Choice Dimension Literature identifies a set of starting conditions that could affect the choice of a form of network governance (see Sørensen 2005; Milward and Provan 2006; Klijn and Skelcher 2007; Rodriguez et al. 2007; Ansell and Gash 2008; Provan and Kenis 2008; Klijn et al. 2010b; Milward et al. 2009; Robins et al. 2011; Emerson et al. 2012) . To date, public authorities have to identify the form of network governance that is thought to be more effective according to the starting conditions. Trust, being considered to favor mutual understanding, cooperation and information exchanges, affects the choice of network governance in a sense that when factors feel deeply confident to each other, shared form of network governance could perform better, allowing to maintain a high degree of cooperation and low level of transaction costs (Klijn et al. 2010b). When the level of trust is low, instead, Lead Organization or NAO model may be a solution since the threat of opportunistic behaviors and one-shot cooperation is avoided by any superior factors with the task of monitoring every-day network activities (Provan and Kenis 2008; Milward et al. 2009; Emerson et al. 2012). Size is connected to the coordination pathways between factors and to the complexity of governance activities (Donahue 2004; Milward and Provan 2006; Provan and Kenis 2008). When the network is small it could be linked to Shared form, but when size increases a Lead Organization or NAO form could better manage the grown number of factors. In fact, autonomous coordination between factors is feasible if the number of participants is relatively low; when it increases a separate administrative entity could ensure the goals’ achievement and activities coordination. Situations characterized by a high level of goal consensus, motivation and joint action are suitable to implement Shared governance model. When there is low agreement on goals, NAO or Lead Organization models are more suitable to guide others’ behavior and ensuring goals’ achievement (Rodriguez et al. 2007; Provan and Kenis 2008; Røiseland 2011). In fact, the presence of external and independent factors, with the task monitoring network activities, can avoid the risk that participants pursue their own interests. Similarly, strong commitment and leadership may be associated to Lead Organization or NAO, in which the role of leader is clearly defined. When commitment and leadership is relatively low, shared form of network governance could perform better since decision-making power is diffused among participants. The effect of embeddedness is similar to that of trust. In fact when embeddedness is high, factors are supposed to govern the network by themselves through shared governance model. On the contrary, when relational and structural embeddedness are low, the presence of an upper-order organization can cat as a glue, enhancing the quality and the intensity of network participants’ relationships (Jones et al. 1997; Robins et al. 2011). Finally, a high degree of diversity could be better governed through Lead Organization or NAO model, in which a single organization has the power of coordinating the others’ activities, behaviors and goals, avoiding opportunistic conducts. On the opposite case, when the diversity is limited, a Shared governance model can be
From Fragmentation to Comprehensiveness in Network Governance
implemented: factors can coordinate their activities by themselves, enhancing trust and mutual monitoring (Røiseland 2011). The Meta-Governance Dimension The analysis of the meta-governance dimension starts from the tools’ classification proposed by Sørensen and Torfing (2009). We focus on this classification since it provides generic classes of instruments and not a specific type of regulation, giving a more comprehensive picture of the met-governing process. The comprehensiveness of this is also in line with our goals. The proposed relationship is derived from theoretical comparisons between the features of each form of network governance and the characteristics of the meta-tools. It has no prescriptive or predictive character and it should be tested with empirical cases. The distinction between hands-off or hands-on tools can be related to the governed dimension as it fits with Provan and Kenis’ (2008) description of governing factors that are inside or outside network boundaries. Similarly, Sørensen and Torfing (2009) distinguish between tools implemented at distance (hands-off) and tools implemented with a close interaction with network’s participants (hands-on tools). Shared form of governance might be metagoverned by Bnetwork participation^, a tool that focuses on enhancing trust, cooperation and collectively building decisionmaking processes. This type of governance, in fact is implemented when the levels of trust and goal consensus are high, two conditions that facilitate collaborative decisionmaking processes and the development of shared goals. The idea of network participation as an efficient tool for Shared governance form is also supported by small number of network participants, limited diversity among factors and the strong embeddedness. Lead Organization form might be instead metagoverned through a mix of hands-off and hands-on tools. Specifically, it may focus on network management and network framing. The Lead organization could reduce tensions and resolve conflict, but it could also supervise the circulation of relevant information. On the other hand, however, the leader organization has the power to define lower-order goals and evaluating participants according to the goals’ achievement. Finally, NAO form of network governance might opt for hands-off tools. It may be meta-governed through network design and network framing. The use of network design is justified by the fact that the NAO is constituted specifically for governing the network. Accordingly, the definition of scope, composition and institutional procedures are activities related to the structural aim of the NAO. On the other hand, use of network framing is a direct consequence of the implementation of network design: if the NAO can establish the boundaries, the scope and the constitution of the network, it can also define fiscal conditions and legal basis of the network. Finally, the fact that it is specifically built with the task of governing the network, makes the evaluation of factors another structural feature of its activities. The Evaluation Dimension The evaluation dimension deals with the relationship between meta-tools and outcome of network governance, in terms of effectivity and democracy. According to Sørensen
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and Torfing (2009), each meta-tool contributes to increase effectivity and democracy of network governance. Sørensen and Torfing identify five measures to assess network governance effectivity: (1) degree of goals’ achievement, (2) alignment of goals and political context, (3) reinforcement of interdependence, (4) mobilization of key resources and (5) institutionalization of cooperation between factors involved. The first two measures, that are strictly related public programs objectives, derive from hands-off tools, specifically from network design and network framing. Mobilization of key resources is related to network management, since the identification of key factors and key resources is a fundamental task of every network manager (see Agranoff and McGuire 2001). The last measure is linked to network participation tools and it focuses on the degree to which stable cooperation is established between factors involved. Democracy in network governance can be assessed through six criteria: (1) process transparency, (2) resource allocation based on performance evaluation, (3) empowerment of key factors, (4) increased participation and (5) negotiation in decision-making process. Resource allocation and improvement in transparency are respectively related to network framing and network design. These two criteria, in fact, should only be established at the distance since they seem to involve a certain degree of authoritative, but legitimized, power. Empowerment of weak factors derives from network management and, along with the identification of key stakeholders, represents a central task for network managers. Similarly, increased participation could be promoted by networks managers who push to involve more factors. Finally, negotiation-based decision making is linked to network participation since it is based on involving more and more factors to defined agreed solutions and shared strategy.
Discussion The framework of analysis we proposed is aimed at providing support to scholars and policy-makers who deal with the implementation of network governance. Specifically, we proposed to analyze the implementation of network governance starting from the existing contextual factors that affect their choice. The presence of different types of contextual factors affects the choice of network governance in two ways. At one hand, when the level of trust is high and the network is composed by a relatively few factors that share a strong goal consensus with a high degree of embeddedness, a Shared form of network governance may be able to achieve an effective and democratic outcome. Once implemented, the Shared form of network governance should be governed through network participation that use a direct involvement of network’s participants. Finally, the use of network participation to metagovern Shared form contributes to the outcome’s effectivity and democracy. It enhances effectivity since it leads to a stable and institutionalized cooperation between participants, and it favors democracy since it establishes a negotiated and consensus-based decision making process, in which decisions are taken collectively by participants. On the contrary, when the context in which network governance will be implemented is characterized by low levels of trust, goal consensus and low embeddedness between a high number of participants, Lead Organzation or NAO form of network
From Fragmentation to Comprehensiveness in Network Governance
governance can be more appropriate to achieve the desired effective and democratic outcome. While the contextual factors supporting choice are similar, the way in which these forms are metagoverned is different. Lead Organization form should metagoverned through a combination of hands-on and hands-off meta-tools, specifically network framing and management. Network framing contributes to effectivity since (1) it favors the alignment between network goals and the political context and (2) it reinforces interdependencies and collaboration between factors involved. On the other hand, network framing enhances democracy of network governance because it ensures that resources allocation is based upon a process of performance evaluation. Network management, instead, contributes to network effectivity by mobilizing key resources and key factors and to democracy by empowering weak factors. Finally, NAO form of governance calls for hands-off tools, i.e. network framing and network design. Network framing, as in the case of Lead Organization, contributes to effectivity and democracy by aligning network goals and political context, by reinforcing interdependencies between factors and by ensuring that resources allocation is based upon a process of performance evaluation. Network design, on the other hand, enhances effectivity by supporting a strong focus on the goals’ achievement and by orienting the factors’ behavior in this direction and it contributes to democracy by improving transparency in governing process.
Conclusion In recent years network governance became a key topic in public literature and it has attracted the attention of different scholars and practitioners. However, despite the growing attention in this topic, the literature is fragmented and a comprehensive framework for its analysis does not exist. Starting from these gaps, we proposed a comprehensive framework that can support both scholars in understanding the functional dynamics of network governance and public authorities in providing a sort of guide for choosing a form of network governance according to different factors and, once implemented, in metagoverning it. We started by considering which conditions affect the choice of the form of network governance between the three ideal-type identified by Provan and Kenis (2008). Secondly, since these forms are characterized by different governing factors and by a heterogeneous degree of decision-making power centralization, they should be metagoverned through different tools that reflect their power allocation. Each of these tools may improve network governance outcomes, evaluated in terms of effectivity and democracy. The framework is theoretical in nature and for the most part it is deductively developed. Accordingly, it needs to be applied on empirical studies to test its applicability, to stimulate future debates and to refine it by empirically-based findings.
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