When the gig isn’t up: The importance (and relevance) of trust on gig workers’ performance and commitment

Rachel Campagna and Jennifer Griffith

Journal of Trust Research, 2023, vol. 13, issue 2, 164-196

Abstract: Millions of employees are now classified as gig workers – a subset of contingent employees with alternative employment arrangements. This type of work arrangement can be beneficial for both managers (e.g. cost savings, specialised skillsets) and employees (e.g. work preferences such as flexibility). Yet little research has addressed how trust for a manager might factor into gig workers’ performance when compared to traditional employees, perhaps because research has implied that trust is irrelevant to gig workers. We test this prediction across four studies to show that low trust is a double-edged sword with unfavourable and favourable outcomes. On the one hand, we find that less trust in the manager leads to lower performance and commitment among gig workers. Yet, on the other, we find that lower levels of trust help to offset or mitigate the harmful outcomes of trust violations, or unexpected, negative workplace events. Our findings highlight the important role of trust in this context of gig versus traditional workers.

Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/21515581.2023.2215747 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:jtrust:v:13:y:2023:i:2:p:164-196

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RJTR20

DOI: 10.1080/21515581.2023.2215747

Access Statistics for this article

Journal of Trust Research is currently edited by Peter Ping Li

More articles in Journal of Trust Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().