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Self-Set learning goals and service performance in a gig economy: A Moderated-Mediation role of improvisation and mindful metacognition

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  • Açıkgöz, Atif
  • Latham, Gary P.

Abstract

Drawing on goal-setting theory, the current research examines whether the indirect relationship between self-set, rather than assigned or participative, learning goals and an Uber driver’s service performance is positive and significant in an emerging work context, namely, the gig economy. In this regard, we hypothesized that there is a positive, significant relationship between self-set learning goals and a driver’s improvised ways to provide customer service. Building on metacognitive practice, we further hypothesized that a gig driver’s mindful metacognition positively moderates the relationship between improvisation and service performance. The overall hypothesis tested is that the indirect relationship between self-set learning goals and a gig driver’s service performance via improvisation is positive and significant, and this relationship is positively moderated by mindful metacognition. Data were collected from 149 gig drivers. Ordinary least squares regression-based path analyses revealed support for these hypotheses.

Suggested Citation

  • Açıkgöz, Atif & Latham, Gary P., 2022. "Self-Set learning goals and service performance in a gig economy: A Moderated-Mediation role of improvisation and mindful metacognition," Journal of Business Research, Elsevier, vol. 139(C), pages 1553-1563.
    • Handle: RePEc:eee:jbrese:v:139:y:2022:i:c:p:1553-1563
    DOI: 10.1016/j.jbusres.2021.10.074
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    Cited by:

    1. Açıkgöz, Atif & Acikgoz, Fulya & Günsel, Ayşe & Latham, Gary P., 2023. "The relationship between imperfect information and performance speed: The mediation of improvisation in new product development teams," Technovation, Elsevier, vol. 121(C).
    2. Coker, Kesha K. & Zeiss, Jessica G. & Albinsson, Pia A., 2024. "The consumer on stage: Toward a typology of improvisation in consumption contexts," Journal of Business Research, Elsevier, vol. 170(C).
    3. Jian Zhu & Bin Zhang & Hui Wang, 2024. "The double-edged sword effects of perceived algorithmic control on platform workers’ service performance," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-12, December.

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