Paper Number

ICIS2025-2785

Paper Type

Short

Abstract

Generative Artificial Intelligence (GenAI) is transforming online labor markets by enhancing worker capabilities and altering work demand. Despite its rapid adoption, the impact of GenAI on matching efficiency—the ability of platforms to effectively pair employers with workers—remains underexplored. This study investigates how GenAI affects the employer–worker matching process, focusing on AI-assisted proposal creation. While access to GenAI may improve proposal clarity and increase the likelihood of a successful match, it can also obscure workers' unique attributes, potentially hindering effective matching. To identify these dual effects, we design controlled lab experiments that vary GenAI access, allowing us to track workers' behavior and output, as well as employers' decisions throughout the matching process. We further analyze how these dynamics differ between creative-intensive and routine-intensive jobs. This research offers an understanding of GenAI’s role in the matching process, highlighting its potential to reduce information asymmetry while simultaneously weakening signaling effectiveness.

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12-GenAI

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Dec 14th, 12:00 AM

A Better Matchmaker? The Impact of GenAI on Matching Efficiency in Online Labor Markets

Generative Artificial Intelligence (GenAI) is transforming online labor markets by enhancing worker capabilities and altering work demand. Despite its rapid adoption, the impact of GenAI on matching efficiency—the ability of platforms to effectively pair employers with workers—remains underexplored. This study investigates how GenAI affects the employer–worker matching process, focusing on AI-assisted proposal creation. While access to GenAI may improve proposal clarity and increase the likelihood of a successful match, it can also obscure workers' unique attributes, potentially hindering effective matching. To identify these dual effects, we design controlled lab experiments that vary GenAI access, allowing us to track workers' behavior and output, as well as employers' decisions throughout the matching process. We further analyze how these dynamics differ between creative-intensive and routine-intensive jobs. This research offers an understanding of GenAI’s role in the matching process, highlighting its potential to reduce information asymmetry while simultaneously weakening signaling effectiveness.

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