少即是多：通过自适应激励设计管理AI采用

中文摘要 AI 辅助

我们研究AI采用中的博弈行为和自适应激励设计。一家大型医疗器械公司要求约5000名员工每月向内部AI助手提交至少200次查询。强制要求实施后，首次使用率显著增加，但使用模式显示出博弈行为：查询次数集中在阈值附近，且31%的查询是重复或偏离任务的。该公司随后修订了激励设计，将目标降至100次。利用各分支机构的分阶段实施，我们估计这一调整使查询量减少了30%，其中重复或偏离任务的查询约占下降的90%。非重复、与工作相关的查询的估计变化很小且统计上不显著。在销售人员中，月销售额增加了7%。这些发现将激励调整识别为技术采用中的一个重要边际：重新校准使用要求可以减少博弈行为并提高员工绩效。

英文摘要

We study gaming and adaptive incentive design in AI adoption. A large medical-device company required roughly 5,000 employees to submit at least 200 queries per month to an internal AI assistant. First-time use increased significantly after the mandate, but usage patterns suggested gaming: query counts bunched at the threshold, and 31 percent of queries were repeated or off-task. The firm subsequently revised incentive design and lowered the target to 100. Using staggered implementation across branches, we estimate that the adjustment reduced query volume by 30 percent, with repeated or off-task queries accounting for about 90 percent of the decline. The estimated change in non-repeated, work-related queries was small and statistically insignificant. Among sales employees, monthly sales increased by 7 percent. The findings identify incentive adaptation as a consequential margin of technology adoption: recalibrating usage requirements can reduce gaming and improve employee performance.

发表机构

• Faculty of Business and Economics, The University of Hong Kong（香港大学工商管理学院）
• HKU Centre for AI, Management and Organization（香港大学人工智能、管理与组织中心）

机构由 AI 辅助整理，请以论文原文为准。