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SME’s disinclination towards subcontracting in the public sector markets: an attributional perspective

Author

Listed:
  • Temidayo Akenroye
  • Jonathan D. Owens
  • Adekunle Sabitu Oyegoke
  • Jamal Elbaz
  • H.M. Belal
  • Fedwa Jebli

Abstract

Purpose - This study aims to examine the causes of small and medium-sized enterprises (SMEs) disinclination towards subcontracting in public sector markets. Previous studies have revealed that UK SMEs are reluctant to do business with the public sector through the subcontracting route, but the reasons for this lack of enthusiasm have not been widely researched. Design/methodology/approach - Drawing on semi-structured interviews with SMEs competing for public contracts in North West England, a qualitative study was performed, from which several themes emerged. Findings - The findings were synthesised into a framework underpinned by attribution theory, to portray situationally and dispositionally caused factors that were used to interpret SMEs’ behaviour. Social implications - The findings can guide policy development and government interventions in developed and developing countries, aimed at using public procurement as a policy tool to develop the small business sector. Originality/value - This paper contributes in a unique way to an emerging discourse on how subcontracting can facilitate the access of SMEs to government procurement spending. It adds to knowledge regarding the explanatory power of attribution theory – from its base in social psychology.

Suggested Citation

  • Temidayo Akenroye & Jonathan D. Owens & Adekunle Sabitu Oyegoke & Jamal Elbaz & H.M. Belal & Fedwa Jebli, 2022. "SME’s disinclination towards subcontracting in the public sector markets: an attributional perspective," Journal of Public Procurement, Emerald Group Publishing Limited, vol. 22(2), pages 109-127, January.
    • Handle: RePEc:eme:jopppp:jopp-05-2021-0032
    DOI: 10.1108/JOPP-05-2021-0032
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    Cited by:

    1. Sungkon Moon & Lei Hou & SangHyeok Han, 2023. "Empirical study of an artificial neural network for a manufacturing production operation," Operations Management Research, Springer, vol. 16(1), pages 311-323, March.

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