Managing information sharing and stewardship for public-sector collaboration: a management control approach

Cuganesan, Suresh; Hart, Alison; Steele, Cara

doi:10.1080/14719037.2016.1238102

Cited by 21 publications

(15 citation statements)

References 26 publications

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“…When managers make the impact of the efforts of the collaboration transparent and enticing for key players to work together, the collaboration will be positively affected [60,61]. This seems parallel to the beneficiary contact findings that when employees see how their results impact a person, they increase performance.…”

Section: ) Socialisingmentioning

confidence: 83%

Artificial intelligence for the public sector: opportunities and challenges of cross-sector collaboration

Mikhaylov

Estève

Campion

2018

Phil. Trans. R. Soc. A.

118

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Public sector organizations are increasingly interested in using data science and artificial intelligence capabilities to deliver policy and generate efficiencies in high-uncertainty environments. The long-term success of data science and artificial intelligence (AI) in the public sector relies on effectively embedding it into delivery solutions for policy implementation. However, governments cannot do this integration of AI into public service delivery on their own. The UK Government Industrial Strategy is clear that delivering on the AI grand challenge requires collaboration between universities and the public and private sectors. This cross-sectoral collaborative approach is the norm in applied AI centres of excellence around the world. Despite their popularity, cross-sector collaborations entail serious management challenges that hinder their success. In this article we discuss the opportunities for and challenges of AI for the public sector. Finally, we propose a series of strategies to successfully manage these cross-sectoral collaborations.This article is part of a discussion meeting issue 'The growing ubiquity of algorithms in society: implications, impacts and innovations'.

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Section: ) Socialisingmentioning

confidence: 83%

Artificial intelligence for the public sector: opportunities and challenges of cross-sector collaboration

Mikhaylov

Estève

Campion

2018

Phil. Trans. R. Soc. A.

118

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“…(e.g., Harrell et al, 1989). Further studies related to information stewardship and information sharing can promote further discussion about how and what types of data-quality management and information security can enhance quality services in public management (Carter, 2017; Cuganesan et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

“…In their analysis, information sources are distinguished as (a) evaluations or assessment of performance made by internal members or groups in the organization and (b) information about performance from outside the organization. The distinctive dimension of information sources is important because this is related to the concerns that internal assessment or internal data collection processes sometimes are connected to professional objectivity and information stewardship (Carter, 2017; Cuganesan et al, 2017).…”

Section: Theoretical Foundationsmentioning

confidence: 99%

The Effects of Diverse Feedback Dynamics on Performance Improvement: A Typology of Performance Feedback Signals

Min

Brower

2020

Administration & Society

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This article illustrates managerial responses to different performance feedback signals in decision-making processes for improving performance. First, we conceptualize and illuminate distinctive dimensions of feedback signals—internal feedback, external feedback, and comparative feedback signals. Then, we test how these different feedback signals improve performance of public-sector programs based on a 10-year panel data set from the Korean Performance Assessment Rating Tool (K-PART). We find that performance signals from sources internal to individual programs and from external reference points of problem identification (social performance comparison) affect program performance. This suggests an association between internal management and social comparison mechanisms relative to performance improvement. The novel contribution of this research lies in promoting scholarship on performance management by identifying three unique sources of performance feedback signaling.

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“…A level of trust is required, but only by engaging in collaboration and, indeed, sharing information can they establish such trust (cf. Cuganesan, Hart, and Steele 2017).…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Paradoxes of collaborative governance: investigating the real-life dynamics of multi-agency collaborations using a quasi-experimental action-research approach

Waardenburg

Groenleer

Jong

et al. 2019

Public Management Review

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The literature suggests that collaborative governance efforts typically face three types of challenges: substantive problem-solving challenges, collaborative process challenges and multi-relational accountability challenges. In this article, we investigate how these challenges manifest themselves in practice and explore potential ways in which collaborators can deal with them. To do so, we studied eight multi-agency crime-fighting collaborations in the Netherlands using a quasi-experimental action-research approach. We found that the challenges present collaborations with a set of paradoxical demands. Collaborations that were able to make progress transcended the paradoxes by adopting a 'both/and' rather than an 'either/or' mindset. Our findings contribute to knowledge about designing effective multi-agency collaborations.

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Managing information sharing and stewardship for public-sector collaboration: a management control approach

Cited by 21 publications

References 26 publications

Artificial intelligence for the public sector: opportunities and challenges of cross-sector collaboration

Artificial intelligence for the public sector: opportunities and challenges of cross-sector collaboration

The Effects of Diverse Feedback Dynamics on Performance Improvement: A Typology of Performance Feedback Signals

Paradoxes of collaborative governance: investigating the real-life dynamics of multi-agency collaborations using a quasi-experimental action-research approach

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