Algorithmic Wage-Setting and Perceived Distributive (In)Justice in the Gig and Hybrid Workforce: A Conceptual Framework
Keywords:
hybrid workforce, procedural justice, pay transparency, algorithmic management, gig economy, equity theory, distributive justice, algorithmic wage-settingAbstract
Digital platforms and traditional companies are increasingly employing algorithmic systems to set wages for workers based on their behavior and performance, as well as on live data from the marketplace, rather than on a fixed or negotiated wage system. While such systems can be presented as efficient and responsive, this paper suggests that they can inadvertently create a sense of distributive injustice by creating a challenge to knowing, comparing and challenging pay. Based on the concepts of Equity Theory, Organizational Justice Theory and recent research on algorithmic wage-setting, the paper proposes a conceptual framework that connects four main characteristics (calculation opacity, wage variability, individualized pay differentiation, and limited wage voice) to perceived inequity, outcome unfairness, and procedural unfairness. They are hypothesized to affect employee and organizational outcomes such as time-based stress, intentions to turnover and switch platforms, withdrawal of effort, and collective mobilization. A framework also describes wage-calculation transparency, income diversification, regulatory wage floors, sharing information about wages with peers, occupational context and algorithmic literacy as factors that can either exacerbate or dampen these relationships. Eight propositions are tested and are advanced as guidelines for future empirical research. The framework is also expanded to cover hybrid jobs and full-time positions, where rewards systems are increasingly based on AI and dynamic bonuses, as well as on a semi-algorithmic basis. The paper ends by highlighting trust, transparency, fairness and retention as key elements of governance.
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