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Interpretive structural modelling of inter-agency collaboration risk in public safety networks

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)

Abstract

Inter-agency collaboration is a well-established, yet very difficult process in public governance. Despite the fact that it is often unsuccessful, collaboration risk research is still undeveloped and the impact of this risk on the effectiveness of joint activities is still underestimated. This issue is of particular significance in public safety networks, where inter-agency collaboration processes are conducted under the conditions of the complexity and uncertainty. For this reason, the article is intended to: (1) identify factors of collaboration risk in public safety networks, (2) determine the impact of individual risk factors on inter-agency collaboration outcomes, (3) identify the relationship between risk factors of inter-agency collaboration in public safety networks, and (4) analyse the growth of this risk in public safety networks. These purposes were achieved using the Systematic Literature Review based on PRISMA Group methodology and Interpretive Structural Modelling together with MICMAC analysis. The applied research approach also has some limitations resulting from the number of experts. However, the results obtained allow us to better understand issues of inter-agency collaboration risk in public safety networks. They identify key collaboration risk factors, such as inappropriate collaboration rules and inadequate allocation of tasks and resources. In consequence, they indicate risk symptoms that are worth keeping track of in order to prevent collaboration ineffectiveness.

Original languageEnglish
Pages (from-to)1193-1221
Number of pages29
JournalQuality and Quantity
Volume56
Issue number3
DOIs
Publication statusPublished - Jun 2022

Keywords

  • Collaboration risk
  • Inter-agency collaboration
  • Interpretive structural modelling
  • Public safety networks
  • Risk factors

ASJC Scopus subject areas

  • Statistics and Probability
  • General Social Sciences

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