Deploying Chatbots in Customer Service: Adoption Hurdles and Simple Remedies

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Tác giả: Maqbool Dada, Brett Hathaway, Evgeny Kagan

Ngôn ngữ: eng

Ký hiệu phân loại: 006.4 Computer pattern recognition

Thông tin xuất bản: 2025

Mô tả vật lý:

Bộ sưu tập: Metadata

ID: 227073

 Comment: 39 pages, 7 figures, 6 tables
  for SSRN version with Electronic Companion (EC) see https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4283285Despite recent advances in Artificial Intelligence, the use of chatbot technology in customer service continues to face adoption hurdles. This paper explores reasons for these adoption hurdles and tests several service design levers to increase chatbot uptake. We use incentivized online experiments to study chatbot uptake in a variety of scenarios. The results of these experiments are threefold. First, people respond positively to improvements in chatbot performance
  however, the chatbot channel is utilized less frequently than expected-time minimization would predict. A key driver of this underutilization is the reluctance to engage with a gatekeeper process, i.e., a process with an imperfect initial service stage and possible transfer to a second, expert service stage -- a behavior we term "gatekeeper aversion". We show that gatekeeper aversion can be further amplified by a secondary hurdle, algorithm aversion. Second, chatbot uptake can be increased by providing customers with average waiting times in the chatbot channel, as well as by being more transparent about chatbot capabilities and limitations. Third, methodologically, we show that chatbot adoption can depend on experimental implementation. In particular, chatbot adoption decreases further as (i) stakes are increased, (ii) the human/algorithmic nature of the server is manipulated with more realism. Our results suggest that firms should continue to prioritize investments in chatbot technology. However, less expensive, process-related interventions can also be effective. These may include being more transparent about the types of queries that are (or are not) suitable for chatbots, emphasizing chatbot reliability and quick resolution times, as well as providing faster live agent access to customers who experienced chatbot failure.
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