Paper Type
Short
Paper Number
PACIS2025-1433
Description
With the growing popularity of generative artificial intelligence (GenAI), some gig economy workers have started to signal their GenAI expertise in profile descriptions, aiming to enhance their employability. Employers may perceive significant productivity gains from hiring workers who signal GenAI expertise due to the remarkable capabilities of GenAI, while employers that have concerns related to the misuse of GenAI may hesitate in assigning jobs to such workers. Due to the coexistence of productivity gains and concerns, how workers’ signaling GenAI expertise affects employers’ evaluation remains a theoretical puzzle. This study plans to empirically investigate (i) the effect of workers’ signaling GenAI expertise on employers’ evaluation, and (ii) the moderating effect of the type of work (i.e., creative vs. technical work), by leveraging data from a leading global gig economy platform. The expected findings will contribute to theoretical literature and provide valuable insights for practitioners.
Recommended Citation
Liu, Zhiwu; Zhang, Ying; Kotlarsky, Julia; and Huang, Yuxin, "Investigating the Effect of Signaling GenAI Expertise on Employers’ Evaluation of Gig Workers" (2025). PACIS 2025 Proceedings. 28.
https://aisel.aisnet.org/pacis2025/aiandml/aiandml/28
Investigating the Effect of Signaling GenAI Expertise on Employers’ Evaluation of Gig Workers
With the growing popularity of generative artificial intelligence (GenAI), some gig economy workers have started to signal their GenAI expertise in profile descriptions, aiming to enhance their employability. Employers may perceive significant productivity gains from hiring workers who signal GenAI expertise due to the remarkable capabilities of GenAI, while employers that have concerns related to the misuse of GenAI may hesitate in assigning jobs to such workers. Due to the coexistence of productivity gains and concerns, how workers’ signaling GenAI expertise affects employers’ evaluation remains a theoretical puzzle. This study plans to empirically investigate (i) the effect of workers’ signaling GenAI expertise on employers’ evaluation, and (ii) the moderating effect of the type of work (i.e., creative vs. technical work), by leveraging data from a leading global gig economy platform. The expected findings will contribute to theoretical literature and provide valuable insights for practitioners.
Comments
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