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Exploring The Diffusion Of Innovation Among High And Low Innovative Localities

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  • Richard M. Walker
  • Claudia N. Avellaneda
  • Frances S. Berry

Abstract

Berry and Berry (1999, 2007) argue that diffusion of policy innovations is driven by learning, competition, public pressure or mandates from higher levels of authority. We undertake a first time analysis of this whole framework and present three sub-studies of innovation. First, we examine the drivers of total innovation. Second, we assess whether the factors influencing the most innovative localities are similar to or different from the factors impacting the low localities. Finally, we disaggregate total innovation into three different innovation types. Our findings, undertaken on a panel of English local governments over four years, reveal that a majority of the diffusion drivers from innovation and diffusion theory are indeed positively significant for total innovation. However, local authorities that adopt higher and lower levels of innovation than predicted do things differently while the framework has limited applicability to types of management innovation. We concluded that the Berry and Berry model is best suited to the analysis of total innovation, but not as well suited to the analysis of different types of innovation. We also outline a research agenda that might better explain the diffusion of public policy and public management innovation types than is captured by current literature.

Suggested Citation

  • Richard M. Walker & Claudia N. Avellaneda & Frances S. Berry, 2011. "Exploring The Diffusion Of Innovation Among High And Low Innovative Localities," Public Management Review, Taylor & Francis Journals, vol. 13(1), pages 95-125, January.
    • Handle: RePEc:taf:pubmgr:v:13:y:2011:i:1:p:95-125
    DOI: 10.1080/14719037.2010.501616
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    References listed on IDEAS

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    1. Pollitt, Christopher & Bouckaert, Geert, 2004. "Public Management Reform: A Comparative Analysis," OUP Catalogue, Oxford University Press, edition 2, number 9780199268498.
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    Cited by:

    1. Maxim Kotsemir & Alexander Abroskin & Dirk Meissner, 2013. "Innovation concepts and typology – an evolutionary discussion," HSE Working papers WP BRP 05/STI/2013, National Research University Higher School of Economics.
    2. Rohit Madan & Mona Ashok, 2025. "Making Sense of AI Benefits: A Mixed-method Study in Canadian Public Administration," Information Systems Frontiers, Springer, vol. 27(3), pages 889-923, June.
    3. Youlang Zhang & Hongshan Yang, 2023. "Bureaucratic politics, innovation compatibility, and the dynamic diffusion of subnational decentralization reforms in China," Review of Policy Research, Policy Studies Organization, vol. 40(4), pages 553-572, July.
    4. Aaron Deslatte & Serena Kim & Christopher V. Hawkins & Eric Stokan, 2024. "Keeping policy commitments: An organizational capability approach to local green housing equity," Review of Policy Research, Policy Studies Organization, vol. 41(1), pages 135-159, January.
    5. Murano, Angelo & Bruno, Bruna & Vespri, Vincenzo, 2026. "Blockchain barriers in public organizations: A systematic literature review," Socio-Economic Planning Sciences, Elsevier, vol. 103(C).
    6. Sun, Hao & Su, Jun & Ma, Liang, 2021. "The diffusion of the utility tunnel policy: Evidence from Chinese cities," Utilities Policy, Elsevier, vol. 72(C).
    7. repec:osf:osfxxx:ruyp3_v1 is not listed on IDEAS
    8. Yingchao Ji & Jie Yin, 2022. "Diffusion Characteristics and Driving Factors of the Smart Tourism City Policy—Event History Analysis," Sustainability, MDPI, vol. 14(11), pages 1-15, May.

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