Theorizing on the social dynamics within collaborative policy

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innovation and collaborative policy innovation (networks). Then the paper goes .... means that wicked policy problems typically transcend the portfolios of individual public sector ..... analysis-and-crucial-cases-svpw-conference-2012.pdf]. 15.

Theorizing on the social dynamics within collaborative policy innovation networks Vidar Stevens ([email protected]) and prof. dr. Koen Verhoest ([email protected])

Keywords: collaborative policy innovation, policy networks, actor behaviour, strategic approach, normative approach, strategic-relational approach, congruence analysis.

Conference Paper prepared for the 2015 ICPP conference in Milan, T12P03


Introduction Over the past two decades, research on policy networks has become increasingly fashionable. During these years, the locus of research has shifted several times. The first generation of research looked at the contribution of policy networks to effective policymaking (Provan and Milward, 1995; Rhodes, 1997; Scharpf, 1997; Koppenjan and Klijn, 2004). The second generation was more interested in the role of policy networks for democratizing policymaking by enhancing empowered participation, democratic deliberation and democratic ownership (Benz and Papadopoulos, 2006; Klijn and Skelcher, 2007; Warren, 2009). A third generation is now emerging which focusses on the innovative capacities of policy networks (in this paper called ‘collaborative policy innovation networks’, see section 2) and aims to explore when, how and why policy networks can contribute to innovation in public policies (Carstensen and Bason, 2012; Sørensen and Waldorff, 2014). Various studies that originate from this third ‘research generation’ have classified the behaviour of public sector organizations as a fundamental factor influencing the innovative capacity of these policy networks (Van Buuren and Loorbach, 20091; Morgan, 2010; Marsh and Edwards, 2009). Despite this consensus in the literature, theoretical knowledge on the origins and the different behavioural manifestations of public sector organizations in collaborative policy innovation networks is still lacking. As a consequence, the ability of developing causal arguments linking the sort of behaviour with processes and outcomes of innovation in public policies has so far been like trying to nail a pudding to the wall (Mahroum, 2013). Therefore, the prime purpose of this conference paper is to provide more theoretical clarity on the manner in which public sector organizations commonly behave in collaborative policy innovation networks, interact with each other and eventually come to an agreement2. The paper advances as follows. First attention is devoted to the concepts of innovation, policy innovation and collaborative policy innovation (networks). Then the paper goes into more detail on what so far has been written on the behaviour of public sector organizations in collaborative processes of innovation and the manner in which agreement is reached between involved organizations. Here the paper touches upon what we call the ‘normative’ perspective and the ‘strategic’ perspective. After that we make the case for a strategic-relational approach as a valuable addition to the contemporary views in the literature. We end the conference paper by reflecting on a methodology that will allow us to determine the relative explanatory strength of each of the theoretical perspectives that is discussed. So, let us proceed.

1. The concept of innovation Innovation is one of the magic concepts that over the years has been embraced by many OECD governments as a modernization strategy for the public sector (Borins, 2008; Osborne and Brown, 2011; OECD, 2014). Confronted with major budgetary pressures and grand societal challenges, these governments have felt the need to step beyond their conventional wisdoms and their sedimented practices of organizing public services, and instead come up with innovative solutions to address unmet public needs. ‘Innovation’ is, however, quite an ‘elusive concept’ that often lacks a precise definition (Lloyd-Reason, Wall and Muller, 2002), and with the growing attention that it receives in the public sector, there is the risk that the notion of innovation loses its distinct meaning and becomes synonymous with other terms, like e.g.: ‘reform’, ‘change’ or ‘new ideas’ (Sørensen and Torfing, 2011: 849). To avoid this, Sørensen and Torfing (2011: 849-851) have done a lot of effort to come up with a definition that captures the exact gist of the concept. They defined innovation as, 1

Van Buuren and Loorbach, for example, underscore in their article that looks at the governance arrangements to generate policy innovations, that if organizations remain to act according to the differences in the innovation process, the process itself will be paralyzed. The authors refer to it as ‘inter-group anxiety’ (2009:381). 2 This means that we primarily focus on ‘successful’ innovation processes of collaborative policy innovation, that is to say, innovation processes in which the actors managed to create unison. The reason to do so is that we believe we first must be clear on under which conditions a particular research phenomenon (read: agreement on an innovation) is likely to occur, before the academic literature advances to the conditions under which the phenomenon is not likely to occur. We acknowledge that both sorts of analysis are necessary to set up a firm theoretical foundation in the research domain of collaborative policy innovation (cf. Yin, 2003:48). So we see this first focus on successful innovation attempts as a part of a diptych-investigation.


“the intentional and proactive process of actors that involves the generation and practical adoption and spread of new and creative ideas, which aim to produce a qualitative change in a specific context.” This definition ascribes five key aspects to innovations that make them distinctively different from the other, aforementioned, analytical terms. The first aspect being the intentional and proactive action of involved actors. As Sørensen and Torfing (Idem: 849) argue, “although the process of innovation is an open and unpredictable process, involved actors will deliberately try to change, or even improve, the current state of affairs.” Secondly, the definition makes clear that innovation is not merely about generating a new idea. A ‘creative’ or ‘new’ idea only becomes an innovation when it is implemented and therewith able to produce some significant effects. Thirdly, Sørensen and Torfing foresee with their definition that innovations are not about delivering more or less the 3 same kind of goods, services or solutions , but rather about changing the form, content, repertoire 4 of goods, services and organizational routines or even transforming the underlying problem 5 understanding, objectives and program theory . Such a radical transition is by Sørensen and Torfing seen as ‘qualitative’ rather than ‘quantitative’ change. Fourthly, innovation is always relative to a specific context. The new is not necessarily novel to the world but merely perceived to be new in a particular context or domain (Zaltman, Duncan and Holbek, 1973; Sørensen and Torfing, 2011:850). Fifthly, although innovation carries a positive connotation, the concept itself is not about whether the consequences of an innovation are good or bad (Hartley, 2005). There are different ways in which governments can ‘innovate’. The academic literature makes 6 mention of administrative process innovations (Daft, 1978; Meeus and Enquist, 2006 ), technological 7 process innovations (Damanpour and Gopalakrishnan, 2001; Edquist et. al., 2001 ), product or 8 service innovations (Damanpour et. al., 2009 ), governance innovations (Moore and Hartley, 2008; Bekkers et. al., 2011), conceptual innovations (Bekkers et. al., 2011) and policy innovations (Sørensen and Waldorff, 2014). This article is interested in the latter category. Policy innovation is by Sørensen and Waldorff (2014) defined as the radical transformation of problem understandings, policy visions, objectives, strategies and/or policy instruments for solving a specific policy problem.

2. (Collaborative) policy innovation (networks) Whereas the definition given by Sørensen and Waldorff is relatively ‘new’, the phenomenon of ‘policy innovation’ itself definitely has some history in the public sector. For example, public officials have under the slogan of ‘reinventing government’ in the 1980s and 1990s tried to render public policies more efficient (Osborne and Gaebler, 1992; Sørensen, 2014). What makes contemporary policy innovations, nonetheless, distinct from their predecessors is the collaborative manner in which they tend to emerge. That is to say, it is not uncommon that the generation of a policy innovation is the outcome of an innovation process that involves a multitude of (public) actors (Van Buuren and Loorbach, 2009). The collaborative character of recent policy innovations 9 has for most part been a consequence of the ‘wickedness’ of many of today’s policy issues and the inability of ‘traditional’ policy responses to get a hold on these problems.


This is by Hall (1993) understood as a first order change. This is by Hall (1993) understood as a second order change. 5 This is by Hall (1993) understood as a third order change. 6 The creation of a ‘one-stop shop’. 7 The digital assessment of taxes. 8 The creation of youth work disability benefits. 9 Apart from wicked policy issues, many governments are confronted with rising expectations of citizens to the quality, availability and effectiveness of public policies (Sørensen and Torfing, 2010) and are additionally stimulated to rethink their institutional designs due to the decreasing amount of available resources in the public sector as a consequence of the financial and economic crises (Bekkers et. al., 2013; Keast and Mandell, 2014). 4



'Wicked issues ' is a term that was coined by Rittel and Webber in 1973. Herewith they referred to policy issues that possess 10 specific properties, to mention: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

Wicked problems have no definitive formulation; Wicked problems have no stopping rule; Solutions to wicked problems are not true or false, but good or bad; There is no immediate test of a solution to a wicked problem; Wicked problems do not have a well-described set of potential solutions; Each wicked problem is essentially unique; Every implemented solution to a wicked problem has consequences; Each wicked problem can be considered a symptom of another problem; The causes of a wicked problem can be explained in numerous ways; The policy planner has not right to be wrong.

In more laymen’s terms, wicked problems can be defined as policy problems that are persistent and generally dealt with in a context of great uncertainty with regard to the nature of the matter and possible solutions (Rittel and Webber, 1973). Causal relations underlying these policy problems are often numerous and difficult to identify. Developments in one seemingly unrelated policy field can impinge in unpredictable and intricate ways on realities of another policy sector (Ney, 2009). This means that wicked policy problems typically transcend the portfolios of individual public sector organizations. ‘Traditional’ public management responses to complexity and uncertainty, like 11 technical (expert-driven) solutions and routine administrative solutions , for most part take place in the hierarchical silo structures of individual public sector organizations and do often not consider the involvement of other actors (Hartley, 2005). Hence, public sector organizations have had great difficulty in taming the cross-cutting nature of wicked policy problems. In consequence, governments have set up collaborative governance arrangements as a means to tackle these daunting and wicked policy problems. Here the rationale was that through collaboration across the conventional borders in the public sector, innovative policy solutions would emerge that better fit the wicked policy context, as more stakeholders and thus more knowledge, information, resources and experiences are included in the policymaking process (Bekkers et. al., 2013:13). Carstensen and Bason (2012) have called these processes in which multiple public sector organizations interact and participate to come up with policy innovations, ‘collaborative policy innovations’ (CPIs). In similar fashion, we use the term ‘collaborative policy innovation networks’ (CPINs) to explicitly refer to the multi-actor collaborative governance arrangements that are central in these innovation processes.

3. Mutual learning collaborators or strategic agents? When talking about the establishment of collaborative policy innovations, authors have acknowledged that these innovation processes are open and relatively messy (Van de Ven et. al., 2008). At the outset, involved organizations are merely loosely-coupled participants, whilst through interaction, more enduring relations and rules will evolve that make the innovation processes more stable and predictable (Van Buuren and Loorbach, 2009). None of the (collaborative) policy innovation scholars have, however, focused on how governmental actors commonly behave in these interactions and how they eventually arrive at this stable ‘social equilibrium’ 12. In contrast, the 10

There are many examples of such kinds of wicked issues, like e.g.: youth unemployment, population ageing, obesity, intermodal transport, poverty, energy, sustainability, immigration, security, etc. 11 Like e.g.: markets, outsourcing or regulatory prescription. 12 In the repository of the Web of Science, only 28 articles, from the period of 1977 to 2015, can be retrieved that either carry the phrase of ‘policy innovation(s)’ or ‘collaborative policy innovation(s)’ in their title (Carley, 2011; Davis, 1996; Flanagan, Uyarra and Larangja, 2010; Gómez-Mera, 2011; Graefe and Levesque, 2015; Green and Orton, 2012; Grinstein-Weiss, Wagner, Edwards, 2005; Hoyman and Weinberg, 2006; Jaffe, 2000; Jenkins, 2014; Lambright and Teich, 1979; Mahroum, 2013; Marsh and Edwards, 2009; Mintrom and Vergari, 1998; Mintrom, 1997; Morgan, 2010; Orellana, 2010; Schut et. al., 2013;; Van Buuren and Loorbach, 2009; Wu and Ramesh, 2013; Zhu, 2014; Sapat, 2004; Tolbert, Mossberger and McNeal, 2008; Sørensen and Waldorf, 2014; Rainey and Kline, 1979; Carstensen and Bason, 2014). Only 6 out of these 28 articles were interested in the governance of policy innovations that had to be established in multi-actor constellations or devoted attention to the dynamics of collaborative policy innovations (Flanagan, Uyarra and Larangja, 2010; Van Buuren and Loorbach, 2009; Carstensen and Bason, 2012; Sørensen and Waldorf, 2014; Morgan, 2010; Marsh and Edwards, 2009). Quite generally stated, these articles looked at the promise and potential of collaborative policy innovations.


more generic literature on ‘collaborative innovation in the public sector 13’ gives some clues on both of these aspects. Within this stream of literature two camps can be distinguished. The scholars in the first camp, which we call the ‘normatives’, are rather positive about the behaviour of public sector organizations. According to these scholars, organizations that engage in collaborative innovation processes want to protect their organization’s interests but at the same time have an intrinsic open attitude towards a trust-based circulation and cross-fertilization of creative ideas across conventional boundaries in the public sector (Sørensen and Torfing, 2010; Bommert, 2010). Additionally, these actors are, in due course, believed to be impartial about the formation of joint ownership and the responsibility for the selection and the eventual implementation of the innovative ideas (Bommert, 2010; Sørensen and Torfing, 2011). Trough processes of mutual learning, in which ideas are discussed in an atmosphere of trust, involved organizations get a shared understanding of the fundamental problems (Vinke-de Kruijf et. al., 2014) and eventually arrive, in a transparent manner, on the most-suited solution for the targeted policy problem (Sørensen and Torfing, 2011:852). The ‘activity’ of mutual learning, which is central in this perspective, closely aligns with what Argyris (1977) has called ‘double-loop learning’ and what Piaget (1976) has defined as ‘accommodative cognitive processes’. Just like these two concepts, mutual learning asserts that the basic assumptions and understandings of involved organizations (‘the learners’) are challenged through the interactions with other organizations. Ultimately this leads to ‘cognitive convergence’ (Dwyer, Schurr and Oh, 1987) or a common position-taking on a certain issue. According to Karthik (2002) four evolutionary phases can be identified though which a mutual learning process progresses. In the first phase, which is called awareness, learning is considered to be unilateral. Organizations begin to get a notion of the intentions, concerns and goals of the other involved actors. Within the second phase, seen by Karthik as exploration, the ‘partners’ tentatively start preparing for collaboration by ‘setting ground rules for future interactions’ (Dwyer, Schurr and Oh, 1987:17). Learning is still unilateral and experiential; however, elements of mutual learning will begin to emerge. The third phase, that of expansion, is characterized by greater trust and an increased ‘investment’ for mutual benefit between the actors. In the fourth phase, by Karthik classified as commitment, the actors move beyond ‘probing each other’, towards the mutual development of new ideas and solutions for severe policy problems. The second camp, which can be denoted as the ‘strategists’, are convinced that such a way of ‘collaborative’ behaving is not something apparent. These scholars expect it to be inevitable that public sector organizations act strategically as a means to protect their organization’s interests and at a minimum secure an outcome that safeguards the continuity of the practices of their organization (Torfing, 2013: 30814; Gray, 1989; Ansell and Gash, 2008). The ‘strategists’ base their arguments on the fact that a collaborative innovation process in itself is full of uncertainty with regard to the development of the process and the exact outcome (Osborne and Brown, 2011; Bernier and Hafsi, 2007; Bommert, 2010; Koppenjan and Klijn, 2004). The only certainty that involved organizations have upfront is that the eventual outcome will act as a game-changer and alter the way in which affected organizations operate, relate to and interact with each other 15. Unison, or agreement on a collaborative policy innovation, is in this regard considered to be an outcome of a strategic game or ‘a battle of interests’. Teisman elaborates in his 2000-article on the peculiarities of such a strategic game. In his so-called ‘rounds model’, he understands ‘the strategic interactions’ as an intertwined clew of a series of decisions taken by the various actors (2000:939). The process is assumed to consist of different decision-making rounds. In each round, all actors will bring forward their problem perceptions, possible solutions and political judgments (Teisman, 2000:939). All organizations can score points in each round, in terms of a leading definition of the problem and the (preferred) solution. At the same time, a new round can rapidly ‘change the direction of the match’ (Teisman, 2000:938-939). 13

The literature of collaborative innovation in the public sector looks at the interaction between relevant and affected actors irrespective of the type of innovation (Nambisan, 2008; Sørensen and Torfing, 2011:15). As such, the scholars of this stream of research argue that their insights quite generally apply to the different types of innovation in the public sector. 14 Torfing (2013:308) for example writes that “the truth is that the link between interaction, collaboration and innovation is contingent and there is a persistent risk that we will not be able to reap the fruits of collaborative innovation.” 15 Remember the second and third order change that innovations envision.


Despite the strategic intentions of the individual organizations, the actors will during the interactions have some sort of recognition of their mutual dependencies. That is to say, in the back of the heads of the single actors there will be the awareness that none of the public sector organizations has sufficient action potential to unilaterally solve the complex, dynamic and diversified policy problems (Rhodes, 1996:657). Therefore, actors will in the course of the strategic game look for a compromise that represents the most optimal outcome for their individual interests. According to Teisman (2000:946), “progress is thus made when a ‘compromised’ solution is adopted and supported by a majority of relevant actors.” This statement induces that there will be certain winners and losers at the end of the strategic game. Yet, the outcome cannot be considered as final or permanent. The compromise will only last until one or more of the actors are dissatisfied with the outcome and starts a new decision-making round (Termeer, 1993:44-51; Teisman, 2000:947). Both perspectives have gained traction among collaborative innovation scholars. This is remarkable seen the fact that there is some rivalry between the two perspectives. Actually, the perspectives can be considered as the two extremes of the same continuum. The leading rationale of the ‘normatives’ is quite prescriptive. It inhabits a strong view about how public sector organizations ought to behave in collaborative innovation networks. Actor behaviour and the interactions between the actors are guided by certain norms (like e.g.: mutual learning, joint ownership, etc.) and it is not appropriate if the actors challenge these. At the other end of the spectrum, the ‘strategists’ put much emphasis on the individual gains of the actors and conceive them as strategic agents that seek to defend or even improve their position no matter the cost. Arguably, ‘strategists’ tend to regard free-riding and shirking behaviour of actors as conventional in processes of collaborative innovation. The eventual outcome is a power struggle that results in a certain amount of winners and losers.

4. A strategic-relational approach We perceive both perspectives as useful focal lenses to further ‘explore’ the behaviour of public sector organizations and the social dynamics within collaborative policy innovation networks. Yet, we also see room for a third way or some kind of intermediary perspective that falls within the grey zone of the aforementioned continuum. A perspective that both considers the institutional forces of the network constellation as well as the strategic intentions of the relevant actors. Such an intermediary perspective aligns with the rationale of the strategic-relational approach of Hay (2002) and Jessop (2001). To our knowledge, this framework has so far not been employed in the literature on collaborative (policy) innovation or the study on the social dynamics within collaborative policy innovation networks. Yet, the approach has been used to explain behaviour of actors in, and the social dynamics of, other public policy constellations, like e.g.: the politics of the Indian highway development (Chettiparamb, 2007), the privatization of the Mexican oil sector (Heigl, 2011) and the responses of agencies to termination threats in the UK public sector (Dommett and Skelcher, 2014). The strategic-relational theory has emerged in response to the ontological tension between absolute structural determinism and totally free-willed actors (Dommett and Skelcher, 2014:543). It resolves the tension by arguing that structure and volition are analytical rather than empirical categories, and thus the focus of scholarly effort should be on investigating the social relations between them (Hay, 2002). The relationship is understood as that between a strategic selective context that favours certain action strategies over others, and actors who calculate and select their possible moves in the light of an appreciation of that context. This reflexivity of actors is what within the strategic-relational theory is called ‘strategic selectivity’. The theory does not have a priori assumptions about actor interests nor does it presuppose that actors are rational agents. The approach merely brings the ‘agency into structure’ – producing a structured action setting – and the ‘structure into the agency’ – creating a contextualized actor or what Hay (2002, 128) has called a ‘situated agent’. When applying this rationale to the setting of collaborative policy innovation networks, it can be argued that:


the ‘network constellation’ [quite abstractly formulated] influences the action repertoire of involved public sector organizations, and thereby gives structure to the interactions in the collaborative policy innovation processes. At a more micro-level, this means that the involved public sector organizations calculate their strategic action regarding the outcome of the innovation process in relation to the ‘institutional dynamics’ of the network constellation. Within the governance and network literature, the interplay between the ‘institutional dynamics’ of the network constellation and ‘strategic intentions’ of the agents has already received some attention (Scharpf, 1997; Agranoff, 2006; Koppenjan and Klijn, 2004; Rhodes, 1997). A much heard proposition in the literature is that actors occupy different ‘power’ positions in networks; or stated differently, positions that vary in how much influence an organization ‘can’ exert on the interaction processes (e.g.: Rhodes, 1996:657; Agranoff, 2006; Kooiman, 1993:4). Due to these power asymmetries, unison is, in this branch of literature, regarded as a compromise between the vested interests of the more ‘powerful’ actors in the network constellation. A common point of departure of these scholars is that complex policy problems require a combination of essential resources in order to be tamed (Rhodes, 1996:658). Unison is thus reached when the essential combination of resources for taming the policy issue is acquired with the support of the actors that possess the resources. Here a resource 16 is understood as a supply that is necessary to produce a certain benefit (Aldrich, 1979; Benson, 1982). The essential resources are within network constellations, however, generally not owned by a single actor but rather spread over a multitude of organizations (Koppenjan and Klijn, 2004; Scharpf, 1997). Hence, the resource(s) an actor possesses, determines the amount of ‘leverage’ an organization has in steering the course of action of the innovation process. Among the network scholars there exists the agreement that the resource of ‘competencies’, or what by some has been called ‘realization power’, is the most important resource for the emergence of a policy solution (Agranoff, 2006; Koppenjan and Klijn, 2004). These scholars argue that without competencies, a policy idea does not have the support to become a policy solution. Characteristic for collaborative policy innovation networks is that all actors possess certain competencies. Though, some actors have more ‘realization power’ than others (Ansell and Gash, 2008; Koppenjan and Klijn, 2004). This latter is a consequence of the width of the competence. Therefore, organizations with wide competences will be more in the position, or have more ‘weight’, to influence the outcomes of the collaborative policy innovation processes than organizations that possess small competences. With regard to the importance of the other resources, network scholars have not made a specific ordering. Indeed, it can be argued that the resource of (tacit) knowledge is very valuable within collaborative policy innovation networks, since the principal idea of using these networks as policy vehicles is to internalize ‘external’ knowledge in order to get a better understanding of the crosscutting policy phenomena (Bommert, 2010). Yet, in a similar fashion kind-a-like arguments can be made for the other types of resources. Additionally, the essential mixture of resources differs per policy solution, which makes the ranking of the remaining resources mere guesswork. Therefore, Scharpf (1978) introduced another measure to differentiate between ‘critical actors’ and ‘noncritical’ actors in network constellations, that is to say: he also considered the substitutability of a resource to play a role in the network constellation. With the latter Scharpf (1978) meant the possibility that a specific resource of an organization can be acquired by another organization in the network constellation. He argued that if a resource is substitutable, the position of the actor in the network constellation will be weaker than if this is not the case (Ibidem).


Within the literature a variety of resources are distinguished, to mention: financial resources, production resources, competencies, legitimacy and knowledge (Aldrich, 1979; Benson, 1982; Koppenjan and Klijn, 2004:144). For most of these categories it is obvious what they entail. Perhaps the ‘vaguer’ resources are production resources and legitimacy. Production resources are necessary for enabling policy initiatives. One can think of, for instance, owning land in an urban restructuring issue. In other cases, production resources entail technology, personnel, equipment, etc. (Ibidem). Legitimacy is synonymous for authority or support. It should be separated from the concept of ‘legality’, as there is the possibility that a government action can be legal whilst not being legitimate. In this sense, the concept of legality more aligns with the competencies that an organization possesses and legitimacy denotes whether an organization is in the position to perform a certain task or action (Locke, 1689).


Based on these two contingencies, four archetypical positions can be distilled that organizations can occupy in collaborative policy innovation networks (see figure 1). In ranked order (from stronger to weaker positions), we have called these archetypical positions: barking dogs, biting dogs, calm dogs and sleeping dogs. This classification is quite similar to the one used by Rowley (1997) to cluster the different responses of firms to stakeholder pressures. To this end, the article of Rowley can be used to shed more light on how actors that hold one of these archetypical positions are likely to behave. Following the work of Rowley, we consider biting dogs (wide competences, non-substitutable resources) to be the core players of the collaborative policy innovation networks. These actors will largely be able to resist pressures from other actors and exploit their powerful position to control network exchanges (Rowley, 1997:903). This kind of behaviour comes perhaps closest to the freeriding kind of behaviour as was envisioned by the ‘strategists’. Barking dogs (wide competences, substitutable resources) are, in contrast, perceived to be less able to manipulate and/or control the interactions in the network constellation. Rowley (1997:904) even ascribes ‘Hermit-like[1]’ behaviour to these types of organizations. Barking dogs face the danger of having little ‘leverage’ to steer the development of the collaborative policy innovation, as they do not possess non-substitutable resources. Given these dynamics, barking dogs are likely to collaborate if the debates on the collaborative policy innovation align with their interests, though they will paralyze the interactions when there is a growing incongruence of objectives. By stifling the policy integration, these type of organizations hope that the other actors will make amends and still incorporate their interests in the eventual agreement on the collaborative policy innovation Calm dogs (small competences, non-substitutable resources) refer to organizations that have a rather advantageous position in the network constellation. They do have some edge as they possess resources that are necessary for the establishment of the collaborative policy innovation and which cannot be obtained in any other way. These organizations are with regard to their competences relatively small players. Therefore, it can be expected that these organizations will try to get the most out of their position by navigating between the different interests of the ‘stronger’ actors in the innovation process. By striving for parity among competing interests, these organizations create room to articulate their own concerns in the deliberations on the collaborative policy innovation (Rowley, 1997:902; Oliver, 1991:157).

Figure 1: Archetypical positions of actors in collaborative policy innovation networks


Hermit-like behaviour refers to an Avoidant Personality Disorder (AvPD). This is a cluster C personality disorder recognized in the Diagnostic and Statistical Manual of Mental Disorders. Individuals afflicted with the disorder are often described as people that feel unwanted and isolated from others. They possess feelings of inadequacy, are extremely sensitive to negative evaluation and quite often avoid social interaction. When translated to organizational behaviour, Hermit-like behaviour should be understood as the likeability to adopt a solitarian role and position in the interactions on a collaborative policy innovation.


Sleeping dogs (small competences, substitutable resources) are perceived to be mere subordinates in the collaborative policy innovation networks. Rowley elaborates on the position of these organizations by zooming in on the asymmetric relationship between General Motors and its network of small suppliers. He (1997:904) indicates that the suppliers furnish virtually 100 percent of their output to General Motors, but each supplier contributes only a small proportion of General Motors’ inputs. Thus, suppliers are unable to individually resist pressures exerted from more central actors, like General Motors. Following these arguments, sleeping dogs are expected to show little strategic behaviour and simply conform to the plans and procedures agreed upon by the other, more influential, public sector organizations in the network constellation. We acknowledge that the archetypical positions and corresponding behavioural manifestations are quite abstractly formulated. We have tried to be more specific by allgining various organizational strategies to the positions, like e.g.: the typology constructed by Olivier (1991) or the one used by Kickert et. al. (1997). However, this proved to be very difficult as for the reason that such a coupling would not be based on theoretical judgements but rather be mere speculation. The various typologies can be utilized in the further analysis to cluster and map the organizational responses and thereby get a better understanding in the specificities of the behavioural manifestations of the involved actors. Though, most importantly, the core assumptions of the strategic-relational approach, of which the advocates can be called ‘the positionists’, make a third perspective17 which can be used in a further analysis on the social dynamics within collaborative policy innovation processes.

5. Congruence analysis and backward mapping The aim of such a further analysis should be to give an adequate indication of the relative explanatory power of each of the three discussed perspectives. Only then the research niche of collaborative policy innovation is really able to theoretically advance. In our view, the best way to do this is to make use of the theory-oriented approach of congruence analysis (Blatter and Blume, 2008a and 2008b, George and Bennett, 2005; Haverland, 2006; Haverland, 2007; Yin, 2003 18), as the principal goal of congruence analysis is to make a contribution to the scholarly discourse on the relevance and relative importance of specific theories and/or general paradigms (Blatter, 2012). A researcher applying the research strategy of congruence analysis uses case studies to provide empirical evidence for the explanatory relevance or relative strength of one theoretical approach in comparison to other theoretical approaches. The latter is achieved by deducing sets of specific propositions and observable implications from abstract theories in a first step and then by comparing a broad set of empirical observations with these implications drawn from diverse theories (Blatter, 2012:11). The prototypical research question of a congruence analysis are therefore does theory A provide a better explanation in comparison to other theories, and, does theory A provide relevant explanatory insights that no other theory has revealed (Blatter and Haverland, 2012; Blatter, 2012:11)? These two research questions represent two slightly different approaches on how to conduct a congruence analysis. Within the literature, scholars talk about the competing theories approach and the complementary theories approach. The work of Scott Sagan (1993) on risk-management concerning the safety of nuclear weapons is by many seen as a good example of the competing theories approach. In this work, Sagan is interested in which of the theories best explains the dynamics under investigation. Graham T. Allison and Philip Zelikow’s study on the Cuban Missile Crisis (1999) is, in contrast, seen as a good representative of the complementary theories approach. This approach implies that theories lead to complementary implications in the real world and that a plurality of theories is not a source of confusion and uncertainty but rather provides the basis for more comprehensive explanations and understandings (Blatter, 2012:12). In addition, the central assumptions of the complementary theories approach legitimize the search for theories that are able to provide new or neglected explanatory insights. When using the latter approach, however, it is essential that the used theories share a common point of departure (Blatter, 2012).

17 18

Alongside the perspectives of the ‘normatives’ and ‘strategists’ as discussed in section 3. Yin calls this pattern matching.


All aspects considered, we perceive the complementary theories approach as the best way to get an optimal understanding of the ‘black-box’ on the social dynamics in collaborative policy innovation networks, since the three discussed perspective all depart from the view that the actors have the intrinsic motivation to protect their organization’s interests, but have different understandings on how the organizations behave and interact with each other. In this way, the three theories can be ‘merged’ into a new perspective that better represents the social dynamics within collaborative policy innovation networks. Despite the differences in orientation, both ‘congruence-analysis’ approaches start, as noted in the first paragraph of this section, with deducing specific propositions and predictions from abstract theories. When we do such an exercise for the three discussed theories and relate their premises to the (expected) social dynamics of collaborative policy innovation networks, we get a template as presented in table 2. The columns represent the 3 different perspectives. The rows are filled with propositions that are distilled from the normative, strategic and strategic-relational approaches. The ‘T’ in the table means that the deduced proposition is true according to the theory that aligns with the column. Vice versa, an ‘N’ asserts that the proposition is not valid according to the theory that aligns with the column. A ‘P’ refers to partial and means that according to the theory the proposition can partially be confirmed. The second step of the congruence analysis is to conduct an in-depth case study to get a decent notion of the empirics of the object under study (Blatter, 2012:12). In the study on collaborative (policy) innovation there is no commonly accepted framework for analyzing empirical cases. Though, there have been a few scholars that made use of a rather similar research tactic called backward mapping. Backward mapping is a research tactic that, based on a certain outcome, intends to trace back the origins of the development of a specific process (Elmore, 1979:602). This research tactic is very useful to get an insight in the social dynamics of collaborative policy innovation networks, as it allows to connect a successful attempt of actors to create unison on a collaborative policy innovation (‘outcome’) with the interactions between the multiple actors in the collaborative policy innovation networks (‘process development’). The backward mapping tactic is in its core comparable with the general aspects of research strategy of theory building processtracing (hereafter referred to as TBPT). Just like TBPT, backward mapping has the ambition to trace (theoretical) causal mechanisms through an in-depth analysis of the process development (Beach and Pedersen, 2013). To fully explore the empirics, we believe that a backward mapping process should consist of three types of analysis: an actor analysis, a network analysis and a game analysis. The added value of an actor analysis is that it will show us who the (active) participants were in the collaborative policy innovation process, what the initial problem perceptions were of the involved actors and what ‘resourceful’ positions the organizations occupied. The network analysis will, subsequently, shed more light on the general contact patterns between the actors, as it will collect relational data on the various (formal and informal) interactions between the actors. In this way, researchers can get a good understanding of the morphology 19 of the network constellation. Finally, the game analysis will zoom in on the specific stagnations and breakthroughs of the innovation process and enlighten how the actors (strategically) behaved, how they reciprocally interacted with each other and in what manner deadlocks were eventually overcome and unison was created. All three analysis combined will, on the whole, assure that scholars are able to compare the deduced (theoretical) expectations with the necessary empirical observations in the last stage of the congruence analysis (Blatter, 2012:14). Consequentially, the strength of a theory in explaining a particular part of the collaborative policy innovation process, will depend on the degree of fit between the implications of the proposed theory and the empirical data, relative to the degree of fit between the specified predications of the ‘rival theories’ and the data (Haverland, 2010:5).


The general shape of the network (which actors have a central place in the network, who are the gatekeepers, which actors have a peripheral position, which actors had frequent contact and which met rather sporadic, how frequent did the actors meet, were the contacts rather formal or informal?) and the interactions between the nexus of actors.


Figure 2: Formula determining the degree of fit between (proposed) theory and empirical observations

6. Closing remarks To sum up, the purpose of this conference paper was to theoretically reflect on how public sector organizations commonly behave in processes of collaborative policy innovation and eventually manage to secure an innovative outcome that to a certain degree is supported by (a majority of) the involved parties. We have seen that so far scholars devoted little attention to these social dynamics within collaborative policy innovations. The literature on collaborative innovation, howbeit, has given us some indication on both the behavioural manifestations of organizations within innovation processes in the public sector as well as the ways in which unison can be achieved. Apart from the distilled approaches of the ‘normatives’ and the ‘strategists’, we came up with a third perspective, a strategic-relational approach, that can be used for further analysis on how collaborative policy innovation networks can foster innovation. The latter will be our next step when we turn our scholarly effort to the investigation of the empirical dynamics of collaborative policy innovations. Here we will follow the proposed methodology of section 5.

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‘The Normatives’ 1.



The way in which the public sector organizations behave in collaborative policy innovation networks: a. The organizations have an intrinsic open attitude towards a trust-based circulation of ideas, concerns and solutions. b. The organizations have an intrinsic open attitude towards the cross-fertilization of ideas and solutions. c. The organizations are impartial about the selection and eventual implementation of ideas. d. The organizations want to protect their organization’s interests. e. The organizations act as free-riders; they do not consider the position-taking of the other involved actors. f. Certain organizations actors are more in ‘the position’ to deploy a dominant style of behaving, whereas other actors use more mundane ways to bring their ideas across. g. Organizations that have wide competencies and possess non-substitutable resources are able to resist pressures from the other actors and control network exchanges. h. Organizations that have wide competencies and possess substitutable resources show signs of Hermit-like behaviour. i. Organizations that have small competencies and possess non-substitutable resources navigate between the interests of the stronger actors in the network and strive for parity among the competing interests in order to create space to articulate their own interests. j. Organizations that have wide competencies and possess substitutable resources are mere subordinates that show little strategic behaviour. The interactions in the collaborative policy innovation process: a. The interactions take place in an atmosphere of mutual trust. b. The organizations put much emphasis on their individual gains and losses in the interactions. c. Through processes of mutual learning involved organizations get a shared understanding of the fundamental problems and possible solutions. d. The interactions proceed in a sequential order, where actors first get an awareness of the different position-takings, subsequently set ground rules for further exploration, and eventual move beyond ‘probing each other’ towards the mutual development of new ideas and solutions for the severe policy problems. e. The interaction process is a ‘battle of interests’ f. The decision-making on a collaborative policy innovation is an intertwined clew of a series of decisions taken by the various actors in different decision-making rounds. g. All actors aim to score points in each round of decision-making, in terms of leading definition of the problem and the (preferred) solution. h. The interaction process is a ‘match’ of which the direction can change per decision-making round. i. The interaction process is a strategic game, where involved organizations use different action repertoires as a consequence of their position in the network constellation. Unison is….: a. The collaborative policy innovation represents the most-suited solution for the targeted policy problem. b. The collaborative policy innovation is supported by all actors. c. Unison is the outcome of a compromise between the majority of actors; meaning that at the end of the interaction process there are certain winners and losers. d. Unison is above all a compromise of the vested interests of the stronger actors in the constellation. e. The agreement on the collaborative policy innovation can be considered as final or permanent. f. Unison is reached when the combination of essential resources for taming the issue is acquired. g. The agreement only lasts until one or more of the actors start a new decision-making round.

‘The Strategists’


































Table 1: Template of deduced propositions from discussed theories and related to the dynamics of collaborative policy innovation network


‘The Positionists’

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