A recent study published in the Journal of Management Science and Engineering offers a fresh perspective on algorithmic control, challenging the prevailing assumption that it functions solely as a negative managerial instrument. Rather than operating as a monolithic form of control, the study demonstrates that algorithmic control is multidimensional. Its three core functions—behavioural constraints, tracking and evaluation, and standardised guidance—produce markedly different effects on gig workers’ turnover intention, thereby overturning the dominant one-dimensional understanding of algorithmic management.

In essence, the findings reveal a clear divergence in outcomes across these functions. Behavioural constraints and tracking evaluation significantly increase gig workers’ turnover intention by intensifying feelings of relative deprivation. In contrast, standardised guidance not only weakens this negative pathway but also directly suppresses turnover intention. This dual role highlights the capacity of certain algorithmic functions to mitigate, rather than amplify, worker dissatisfaction.

As corresponding author Wei Cai explains, previous research has primarily examined the aggregate effects of algorithmic control while overlooking its functional heterogeneity. By introducing relative deprivation as a mediating mechanism within the Job Demands–Resources (JD-R) model, the study provides new theoretical insights. Most notably, it is among the first to empirically demonstrate that the standardised guidance embedded in algorithmic systems can act as a “buffering resource,” offering platforms a novel pathway for optimising digital management practices.

The empirical evidence is drawn from a two-wave questionnaire survey of 242 food delivery riders, with the surveys administered one month apart. This research design was intentionally adopted to reduce common method bias and to strengthen the credibility and robustness of the findings.

Crucially, the study underscores that algorithmic control does not inevitably generate adverse outcomes for gig workers. As Cai notes, algorithmic management can also produce beneficial effects when its design prioritises supportive functions. Rather than abandoning algorithmic systems altogether, platforms are encouraged to strike a balance between operational efficiency and human-centred management. By enhancing elements of standardised guidance—such as improving task allocation mechanisms and providing timely, transparent feedback—platforms may alleviate the persistent problem of high turnover rates that continues to challenge the gig economy.

More information: Shengxian Yu et al, Online platform algorithmic control and gig workers’ turnover intention in China: The mediating role of relative deprivation, Journal of Management Science and Engineering. DOI: 10.1016/j.jmse.2024.08.004

Journal information: Journal of Management Science and Engineering Provided by KeAi Communications Co., Ltd.