Paper Number
ICIS2025-2750
Paper Type
Short
Abstract
Drivers on food-delivery platforms are granted the autonomy to select their delivery tasks. But this autonomy often leads to distracted driving, raising safety risks for both drivers and the public. We study whether AI-driven automated dispatch systems can mitigate these risks and how safety incidents affect drivers’ adherence to such systems. Using proprietary data from Baedal Minjok, South Korea’s largest food-delivery platform, we exploit the introduction of an optional AI dispatch system as a natural experiment and apply a Difference-in-Difference framework. We find that AI dispatch adoption reduced accident rates by 60%, with stronger effects among less-experienced drivers. However, drivers who experienced accidents while using the system were less likely to engage with the dispatch system post-accident, unless they had substantial prior exposure. Our findings highlight both the safety potential and behavioral challenges of algorithmic management, offering design implications for automated dispatch systems on food-delivery platforms.
Recommended Citation
Karamshetty, Varun; Kyung, Nakyung; Lee, Chulsung; and Sim, Jiwon, "Trust under Pressure: Managing Road Safety in AI-assisted Gig Work" (2025). ICIS 2025 Proceedings. 20.
https://aisel.aisnet.org/icis2025/is_good/is_good/20
Trust under Pressure: Managing Road Safety in AI-assisted Gig Work
Drivers on food-delivery platforms are granted the autonomy to select their delivery tasks. But this autonomy often leads to distracted driving, raising safety risks for both drivers and the public. We study whether AI-driven automated dispatch systems can mitigate these risks and how safety incidents affect drivers’ adherence to such systems. Using proprietary data from Baedal Minjok, South Korea’s largest food-delivery platform, we exploit the introduction of an optional AI dispatch system as a natural experiment and apply a Difference-in-Difference framework. We find that AI dispatch adoption reduced accident rates by 60%, with stronger effects among less-experienced drivers. However, drivers who experienced accidents while using the system were less likely to engage with the dispatch system post-accident, unless they had substantial prior exposure. Our findings highlight both the safety potential and behavioral challenges of algorithmic management, offering design implications for automated dispatch systems on food-delivery platforms.
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