Frontiers: Machines vs. Humans: The Impact of Artificial Intelligence Chatbot Disclosure on Customer Purchases
Xueming Luo (),
Siliang Tong (),
Zheng Fang () and
Zhe Qu ()
Additional contact information
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
Marketing Science, 2019, vol. 38, issue 6, 937-947
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.
Keywords: artificial intelligence; chatbot; conversational commerce; new technology; disclosure (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (119)
Downloads: (external link)
https://doi.org/10.1287/mksc.2019.1192 (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:inm:ormksc:v:38:y:2019:i:6:p:937-947
Access Statistics for this article
More articles in Marketing Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().