Abstract
The delegation of managerial functions such as job allocations, performance appraisals, and disciplining working behaviors to automated, intelligent algorithms has transformed various aspects of workplace dynamics. Despite the increasing prevalence of algorithmic management in today’s workplaces, its implications for working outcomes remain underspecified. Given the contextual novelty of this research, we adopted a mixed methods approach to theorize an algorithmic management resource model and investigate its configural relationships with crowdworkers’ engagement and burnout. This was achieved by analyzing online crowdworker community narratives and subsequently developing nuanced insights into the resources that algorithmic management offers or impedes. In phase 1, drawing on Conservation of Resources (COR) theory tenets, we utilized computational text analysis to explore resource gains and losses associated with algorithmic management. Then, using configurational analysis over two studies (N=322), we identified and empirically examined the interrelationships among resource passageways and working outcomes, specifically engagement and burnout. Our results support a theoretical understanding of the algorithmic management resource model and shed greater light on several configurations of algorithmic resource passageways, sufficiently explaining crowdworkers’ engagement and burnout in distributed, dispatched work settings such as online labor platforms.
DOI
10.17705/1jais.00967
Recommended Citation
Delgosha, Mohammad Soltani and Hajiheydari, Nastaran, "Algorithmic Management Resource Model and Crowdworking Outcomes: A Mixed Methods Approach of Computational and Configurational Analysis" (2025). JAIS Preprints (Forthcoming). 210.
DOI: 10.17705/1jais.00967
Available at:
https://aisel.aisnet.org/jais_preprints/210