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Record W2776433109

Revisiting governance tools: a network level perspective

2012· article· en· W2776433109 on OpenAlexaboutno aff
Martina Dal Molin, Cristina Masella

Bibliographic record

VenueVirtual Community of Pathological Anatomy (University of Castilla La Mancha) · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Policy and Administration Research
Canadian institutionsnot available
Fundersnot available
KeywordsPerspective (graphical)Corporate governanceComputer scienceBusinessArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

RATIONALE Networks, defined as a set of organizations connected to each other by resource dependencies and pursuing repeated and enduring exchanges (Agranoff and McGuire, 2011; Podolny and Page, 1998), are increasingly being implemented in public sector. In fact, according to managerial and academic debates, networks seem to represent the most suitable answer to the diffusion of wicked problems, fragmentation, complexity and dynamic character of contemporary society.  But also the paucity and the diffused character of resources make the occurrence of network more relevant: if individual actors do not have all the needed resources for answering to complex need of clients, networks, taking together different organizations and pooling their resources, can enhance the efficiency of the overall system. As a result  of the networks’ diffusion, several researchers began to study them focusing on several issues: effectiveness, integration, control, enablers, measurement (e.g. density and centrality) and governance. However, only in a small number of cases networks are studied using a governance perspective and, usually, the focus is on the existing type of network governance and on the approaches being used to study it. RESEARCH CONTEXT Adopting a focus on the dependent variable and using a macro level perspective, this paper provides a theoretical framework that, based  both on the existing literature on network and governance and both on some empirical case studies, can help practitioners and policy makers in associating a specific set of governance tools to each form of network governance, identified by the seminal work of Kenis and Provan (2009). In addition, this paper adopts a macro-level perspective and, as a consequence, the focus is on explaining the key features of network governance as a whole, giving particular relevance on the instruments used to govern the network. Our approach on  network governance can be inscribed in what Torfing and Sorensen called “governability theory” (Sorensen and Torfing, 2009; Torfing, 2005), in which network governance is understood as a horizontal mechanisms of coordination between several actors.  Finally, the definition of tools we adopted is those provided  by Bemelmans-Videc (1998) and Salamon (2000), in which tools are seen as an identifiable methos, or set of techniques, through which a collective action is implemented in order to solve public problems. According to these preliminary remarks, our research questions can be formulated as follow. First, starting from the previous classification of tools, which tools are more suitable to govern a network? How can the previous classification ( Salamon, 200; Vedung, 1998; Scheneider and Ingram, 1990) be reorganized in the case of network? Second, following the taxonomy of network governance provided  by Kenis and Provan (2009) is it possible to identify a theoretical and suitable set of instruments to govern each type of network governance? DESIGN and METHODOLOGY We designed a two-step literature review. The first step was aimed at collecting the existing studies about network governance and tools of governance. To meet this goal, a set of keywords  (networks, tools of governance, governance AND performance, classification, effectiveness, approaches) was identified and entered in selected journal (Public administration, Public Administration Review, Journal of Public Administration Research and Theory, Political Studies, Policy Studies Journal, Governance: An International Journal of Policy, Administration and Institutions).  Articles were selected according to the following criteria: (1) theoretical and empirical researches on network governance and (2) theoretical and empirical works on governance tools. This research allows us to collect 45 relevant papers (30 papers about network and 15 about governance tools). Second, once the first phase of literature review was ended, we tried to highlight (1) an exhaustive classification of tools, grouping the previous work of Salamon (2000), Vedung (1998) and Schneider and Ingram (1990) and, according to the type of governance identified by Kenis and Provan (2009), (2) based on in depth case-study,  we have associated a set of suitable tools for each of this type of network governance. We have studied four experiences of healthcare network (Australia, Canada, United Kingdom and Italy). We have used documental analysis in order to (1) classify each of this experience in one of the form of governance provided by Kenis and Provan (2009) ( shared model , Lead Organization model and NAO model ) and (2) to understand which tools are empirically used to govern the network. Documental analysis focused on official documents and reports and articles in journal. FINDINGS Starting from the work of Salamon, Vedung, Scheneider and Ingram, we provide a new threefold classification of instruments. Our classification of tools can be inscribed in the so called minimalist approach, because it classifies instrument in three classes. In addition, this classification benefits of the findings of  (1) the resource-based model, that focuses its attention on the technical specification of tools and on the existing differences between them, and (2) on the policy design model, based on the assumption that instruments are substitutable, but their choice depends on the specific context they will be implemented in. This classification is no longer based on the extent of force or on the outcome to be realized, but on the structure of network in which tools will be implemented. Our classification of tools is provided in the following table. Tools Description Examples Formal - authoritative tools In implementing formal-authority tools, the basic assumption is the presence of a legitimate actors (individual or organization) having some form of coercitive power on the other actors. In other word, authority tools are related to some form of hierarchy among actors Guidelines, definition of : tariffs,  services and their provision modes, roles of the other actors and their responsibilities, coordination mechanisms among services, incentives and performance evaluation Semi-formal tools The key features of these class of tools is the fact a legitimate authoritative actor does not existing and it is replaced by a form of soft hierarchy. In this case there are some organization that, for several reasons (e.g. resources, organizational size, power), have the power of coordinating the other participants’ activities. In economic term, this situation can be identified as a sort of oligopoly of instruments. Complete contracts, regular meetings, information dissemination, agreement building Informal tools Informal tools refers to moral suasion and they focuses on the fact that network participant interact and exchanges between them among time. In this case governance occurs through informal tools, basing on the others’ perceptions Trustworthiness, reciprocity, reputation This theoretical classification allows us to associate, for each type of network governance identified by Kenis and Provan (2009) a set of instruments. In “shared form” of network governance informal tools seem to be the more suitable tools to govern the network, because there is no formal administrative entity to govern the network and decisions are taken by all actors collectively. In “lead organization” form of governance, in which there is one organization that have sufficient resources and legitimacy to play a lead role and represented by three case studies, semi-informal and formal tools are used, according to the degree of legitimacy of the lead organization. Finally, the “NAO model”, a model characterized by the presence of an external actors specifically build  for governing the network and represented by a case study, can be efficiently govern through formal tools of governance. Case studies provided three relevant findings. First, as other authors have just shown, shared form of governance is not popular in healthcare sector: healthcare networks are often made up by law and a form of institutional control is always present. Second, it seems that traditional authoritative tools (i.e. permission, prohibitions, sanctions) disappeared both in case of NAO model and in Lead Organization one and they are replaced by complete contracts, agreement building and regular meetings among network participants. The enhanced role of complete contracts is strictly related to the principal-agent theory and to the fact that controlling actors activities, when actors are territorial dispersed as happened in networks, is more difficult. Finally, in analysing network governance from a tools perspective, it seems that the nature (internal or external) of the controller does not have a relevant impact on the implemented tools. CONTRIBUTION, LIMITATIONS AND FURTHER RESEARCH The contribution of this paper is twofold. First, it adopts a macro-level perspective for studying network, enhancing the utilization of this approach. This approach, in fact, seems to be less used for the study of network, if it is compared with the micro-level perspective. Second, it tries to enhance the practical understanding of network governance providing a taxonomy of instruments and associating them to the form of governance identified. This paper has two main limitations. First of all, it does not look at the shared governance model because it is not present in healthcare sector. Second, we have used only documental analysis approach.  Our future research will focus on testing this framework with other case studies, using documental analysis, direct interviews and surveys. KEYWORDS Network governance, tools of governance, classification

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.007
Science and technology studies0.0030.032
Scholarly communication0.0170.027
Open science0.0030.006
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0050.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.154
GPT teacher head0.369
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Published2012
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