When Algorithms Manage Humans: A Double Machine Learning Approach to Estimating Nonlinear Effects of Algorithmic Control on Gig Worker Performance and Wellbeing

TL;DR

This paper employs a Double Machine Learning approach to uncover complex, nonlinear effects of algorithmic oversight on gig workers' performance and wellbeing, revealing that transparency and clear rules enhance positive outcomes.

Contribution

It introduces a novel application of Double Machine Learning to estimate nonlinear, conditional effects in organizational settings, specifically in the context of algorithmic management.

Findings

Supportive HR practices boost wellbeing, but their impact on performance varies with algorithmic oversight.

Transparency and explainability of oversight strengthen positive effects.

Linear models can misrepresent the true nonlinear relationships in algorithmic management effects.

Abstract

A central question for the future of work is whether person centered management can survive when algorithms take on managerial roles. Standard tools often miss what is happening because worker responses to algorithmic systems are rarely linear. We use a Double Machine Learning framework to estimate a moderated mediation model without imposing restrictive functional forms. Using survey data from 464 gig workers, we find a clear nonmonotonic pattern. Supportive HR practices improve worker wellbeing, but their link to performance weakens in a murky middle where algorithmic oversight is present yet hard to interpret. The relationship strengthens again when oversight is transparent and explainable. These results show why simple linear specifications can miss the pattern and sometimes suggest the opposite conclusion. For platform design, the message is practical: control that is only partly defined creates confusion, but clear rules and credible recourse can make strong oversight workable. Methodologically, the paper shows how Double Machine Learning can be used to estimate conditional indirect effects in organizational research without forcing the data into a linear shape.