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Frontiers: Machines vs. Humans: The Impact of Artificial Intelligence Chatbot Disclosure on Customer Purchases

Author

Listed:
  • Xueming Luo

    (Fox School of Business, Temple University, Philadelphia, Pennsylvania 19122)

  • Siliang Tong

    (Fox School of Business, Temple University, Philadelphia, Pennsylvania, 19076)

  • Zheng Fang

    (Sichuan University, 610017 Chengdu, Sichuan, China)

  • Zhe Qu

    (Fudan University, 20043 Shanghai, China)

Abstract

Empowered by artificial intelligence (AI), chatbots are surging as new technologies with both business potential and customer pushback. This study exploits field experiment data on more than 6,200 customers who are randomized to receive highly structured outbound sales calls from chatbots or human workers. Results suggest that undisclosed chatbots are as effective as proficient workers and four times more effective than inexperienced workers in engendering customer purchases. However, a disclosure of chatbot identity before the machine–customer conversation reduces purchase rates by more than 79.7%. Additional analyses find that these results are robust to nonresponse bias and hang-ups, and the chatbot disclosure substantially decreases call length. Exploration of the mechanisms reveals that when customers know the conversational partner is not a human, they are curt and purchase less because they perceive the disclosed bot as less knowledgeable and less empathetic. The negative disclosure effect seems to be driven by a subjective human perception against machines, despite the objective competence of AI chatbots. Fortunately, such negative impact can be mitigated by a late disclosure timing strategy and customer prior AI experience. These findings offer useful implications for chatbot applications, customer targeting, and advertising in conversational commerce.

Suggested Citation

  • Xueming Luo & Siliang Tong & Zheng Fang & Zhe Qu, 2019. "Frontiers: Machines vs. Humans: The Impact of Artificial Intelligence Chatbot Disclosure on Customer Purchases," Marketing Science, INFORMS, vol. 38(6), pages 937-947, November.
    • Handle: RePEc:inm:ormksc:v:38:y:2019:i:6:p:937-947
    DOI: 10.1287/mksc.2019.1192
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