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Academic SeminarMachine and Human: A Field Experiment of AI Chatbot Disclosure for Conversational Commerce

  • 일시
  • 2018-11-20 ~ 2018-11-20
  • 시간
  • 09:30 ~ 11:00
  • 장소
  • 9호관 7층, 9708호 강의실
  • 주관부서
  • 경영공학부 행정팀
  • 전공대상
  • IT경영
IT경영 분야 세미나를 아래와 같이 개최하오니, 관심 있는 분들의 많은 참석 부탁 드립니다.

1. 일시: 2018년 11월 20일(화), 09:30~11:00
2. 장소: 9호관 7층 9708 강의실
3. 강사: Xueming Luo 교수 (Temple University)
4. 주제: Machine and Human: A Field Experiment of AI Chatbot Disclosure for Conversational Commerce
5. 연구분야: IT경영 분야
* Lecture will be delivered in English.

Abstract:
Empowered by AI, chatbots are surging as new technologies to facilitate firm-initiated communications and conversational commerce. However, little is known from the customer side regarding these questions: (1) Will the disclosure of AI chatbot identity negatively impact customer purchases? (2) How would customers perceive A.I chatbot as service agents facilitating purchase decisions? And (3) how to mitigate the negative impact of chatbot disclosure? We collaborate with a large fintech company to conduct a randomized field experiment. Our data analyses find that undisclosed AI chatbots are as effective as proficient human workers in engendering customer purchases. However, the disclosure of chatbot identity before or after the machine-human conversation negatively impacts the purchase conversion by 56% to 83%. We also explore the underlying mechanism. While the subjective survey data suggest that chatbots are perceived as less capable of providing product knowledge and sentiment empathy when compared to human agents, the objective voice-mining data on the conversation content suggest no such gap. These results imply that the negative impact of chatbot disclosure may be driven by human’s subjective outgroup bias against machine. Fortunately, such negative impact can be allayed or recovered by prior experiential learning of AI or a disclosure right after making the purchase decision—a disclosure timing strategy that enables customers to learn the full experience of interacting with the smart machine. These findings have profound implications for tackling AI technology adoption challenges, for consumer targeting, and for company communications.
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