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Paper Number
2215
Paper Type
Completed
Description
Without a doubt, the heavy use of artificial intelligence (AI) will be involved in the future of work. Pertinent to the deployment of AI in organizations, algorithmic control is the managerial use of intelligent algorithms as a means to align individual worker behaviors with organizational objectives. While algorithmic control may facilitate efficient management of workers, it also leads to intrusive and unilateral exertion of controls over workers, also known as “algorithm as boss” phenomenon. In this study, we attempt to understand the outcomes and tradeoffs that different configurations between the AI and gig workers would produce, by conducting a randomized field experiment with one of the largest delivery rider labor unions in Asia. Overall, our study suggests that providing collaborative algorithmic control not only increases gig workers’ utility in terms of monetary rewards but also enhances their intrinsic rewards, which has the potential to benefit the gig platform as well.
Recommended Citation
Chan, Jason; Kyung, Nakyung; Yoon, Sojung; and Kim, Yeonseo, "Algorithm as Boss or Coworker? Randomized Field Experiment on Algorithmic Control and Collaboration in Gig Platform" (2023). ICIS 2023 Proceedings. 15.
https://aisel.aisnet.org/icis2023/techandfow/techandfow/15
Algorithm as Boss or Coworker? Randomized Field Experiment on Algorithmic Control and Collaboration in Gig Platform
Without a doubt, the heavy use of artificial intelligence (AI) will be involved in the future of work. Pertinent to the deployment of AI in organizations, algorithmic control is the managerial use of intelligent algorithms as a means to align individual worker behaviors with organizational objectives. While algorithmic control may facilitate efficient management of workers, it also leads to intrusive and unilateral exertion of controls over workers, also known as “algorithm as boss” phenomenon. In this study, we attempt to understand the outcomes and tradeoffs that different configurations between the AI and gig workers would produce, by conducting a randomized field experiment with one of the largest delivery rider labor unions in Asia. Overall, our study suggests that providing collaborative algorithmic control not only increases gig workers’ utility in terms of monetary rewards but also enhances their intrinsic rewards, which has the potential to benefit the gig platform as well.
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