讲座题目：Identity Disclosure and Anthropomorphism in Voice Chatbot Design：A Field Experiment

主 讲 人：代宏砚 教授

讲座时间：2024年3月7日（周四）13:00

讲座地点：MBA教育中心阶梯教室

主 持 人：刘小元 买球平台商学院教授

主讲人介绍：

代宏砚，买球平台商学院教授、博士生导师，中财MBA课程教授/导师，美国杜克大学访问学者。获得清华大学硕士学位，香港科技大学博士学位。研究方向为数据驱动的优化决策、物联网、人机交互。发表40余篇论文，包括Management Science，European Journal of Operational Research，管理科学学报，管理工程学报等国内外旗舰期刊，主持国家自然科学基金重大研究计划培育项目、国家自然科学基金面上项目等多项国家省部级科研项目。获得全国物流学会优秀论文第一名，浙江省科技进步三等奖，中国物流与采购联合会科学技术奖二等奖等多项科研奖励。为京东到家、易点云、国家电网、香港Esquel集团等多家国内国际知名企业提供数据驱动的运营管理解决方案，并获得全球华人学者管理科学与工程年会“管理科学实践奖”二等奖。

讲座摘要：

Fueled by the widespread adoption of algorithms and artificial intelligence (AI), the use of chatbots has become increasingly popular in various business contexts. In this paper, we study how to effectively and appropriately use voice chatbots, particularly by leveraging two design features: identity disclosure and anthropomorphism, and evaluate their impact on the firm operational performance. In collaboration with a large truck-sharing platform, we conducted a field experiment that randomly assigned 11,000 truck drivers to receive outbound calls from the voice chatbot dispatcher of our focal platform. Our empirical results suggest that disclosing the identity of the chatbot at the beginning of the conversation negatively affects operational performance, leading to a reduced response rate. However, humanizing the voice chatbot by adding our proposed anthropomorphism features (i.e., interjections and filler words) significantly improves response rate, conversation length, and order acceptance intention. Moreover, interestingly, even when the chatbot’s identity is disclosed along with humanizing features, the operational outcomes still improve. The magnitude of improvement is comparable to the enhancement achieved by humanizing the chatbot without disclosing its identity. This finding suggests that enhancing anthropomorphism may potentially counteract the negative effects of chatbot identity disclosure. Finally, we propose one plausible explanation for the performance improvement—the enhanced trust between humans and algorithms and provide empirical evidence that drivers are more likely to disclose information to chatbot dispatchers with anthropomorphism features. Our proposed anthropomorphism improvement solutions are currently being implemented and utilized by our collaborator platform. On a broader note, this study contributes valuable insights into the effective implementation of voice chatbots in business settings.