AI-MEDIATED PLATFORM WORK: MEANINGFULNESS UNDER ALGORITHMIC MANAGEMENT

Abstract

Artificial intelligence increasingly mediates how platform work is matched, monitored, evaluated and performed. This conceptual paper develops a meaningfulness-based framework for AI-mediated platform work. Gig and platform work provide the bounded empirical setting because automated coordination, opaque evaluation, fluctuating demand, dependence on ratings and limited possibilities for contestation make the relevant tensions especially visible. The scientific problem is the under-specification of how co-occurring substitution and augmentation within a task bundle interact with platform-level control and governance to shape the significance workers attribute to a focal work episode. The purpose is to specify that relationship. The object isthe AI-mediated platform-work episode, operationalized through itstask bundle; the subject isthe relationship among task-level AI effects, platform-level control and governance, meaningfulness pathways and worker context. Drawing on theory synthesis and abductive model development, the article integrates research on gig work, algorithmic management, task-based labour economics and meaningful work. Contextual evidence from the OECD, the European Union and the World Bank establishes the scale and relevance of the setting but does not test the proposed relationships. Substitution and augmentation are treated as non-exclusive task-level effects, and control intensification as a cross-cutting platform-level effect. Algorithmic management is treated as the governance layer surrounding task execution rather than as a synonym for AI use within tasks. Following Rosso, Dekas and Wrzesniewski (2010), the model locates experienced meaningfulness at worker level and distinguishes four pathways: individuation, self-connection, contribution and unification. Five continuous task-bundle descriptors characterize configurations in which AI may replace or support human activity. Five platform-governance arrangements are proposed to preserve or expand opportunities to enact the pathways: rule intelligibility, credible human contestability, worker voice, capability development and relational infrastructure. Worker values and cultural or interpersonal cues are proposed to shape pathway salience, while livelihood dependence and material or institutional security condition practical access to the governance arrangements. Seven propositions specify these relationships and are formulated for testing with episode-level measures nested within workers and platforms. Because experienced meaningfulness is associated with engagement, performance and retention (Bailey et al. 2019), the model also indicates where platform design decisions may carry organizational consequences. The contribution is a level-specific model for examining meaningfulness in AI-mediated platform-work episodes. Transferability beyond gig and platform work remains a question for future research.