Harmonizing Human Touch and AI Precision in Customer Service
研究商家如何在客户服务中平衡AI自动化与人工服务，发现人机协作存在陷阱：中等劳动力成本时协作效果反而不如纯人工，且消费者对服务者身份的敏感会侵蚀利润。
Artificial intelligence (AI)-powered chatbots offer a cost-effective solution for customer service, but often fall short in delivering personalized or complex interactions. In response, many merchants invest in AI training and explore human–AI collaboration to leverage the strengths of both automation and human touch. However, this introduces a strategic tradeoff between service quality and operational efficiency. Using a game-theoretic framework, this study examines how merchants can optimally choose service strategies with AI involvement. Our analysis reveals a critical collaboration trap. Contrary to the prevailing belief that increased collaboration consistently enhances service, consumers’ sensitivity to the identity of the service agent erodes profitability, and greater collaboration will worsen this effect by accelerating task delegation to AI, thereby amplifying negative consumer perceptions. Furthermore, the study shows that a moderate-cost collaboration trap emerges: human–AI collaboration underperforms compared to human-only service when labor costs are at intermediate levels. Collaboration yields benefits only when labor costs are either very low or prohibitively high. In competitive markets, merchants can gain a strategic advantage not by enhancing their own service quality, but by capitalizing on rivals’ inefficient AI deployment. These findings challenge the assumption that more human–AI collaboration is always better and provide actionable insights for managing hybrid service strategies and optimizing AI investments.