Blame the Bot: Anthropomorphism and Anger in Customer–Chatbot Interactions

Abstract

Chatbots have become common in digital customer service contexts across many industries. While many companies choose to humanize their customer service chatbots (e.g., giving them names and avatars), little is known about how anthropomorphism influences customer responses to chatbots in service settings. Across five studies, including an analysis of a large real-world dataset from an international telecommunications company and four experiments, the authors find that when customers enter a chatbot-led service interaction in an angry emotional state, chatbot anthropomorphism has a negative effect on customer satisfaction, overall firm evaluation, and subsequent purchase intentions. However, this is not the case for customers in non-angry emotional states. The authors uncover the underlying mechanism driving this negative effect (expectancy violations caused by inflated pre-encounter expectations of chatbot efficacy) and offer practical implications for managers. These findings suggest it is important to both carefully design chatbots and consider the emotional context in which they are used, particularly in customer service interactions that involve resolving problems or handling complaints.

The impact of anthropomorphism on customer satisfaction in chatbot commerce: an experimental study in the food sector

“…Moreover, as there is only limited information available on anthropomorphic chatbots for commercial purposes, we focus on digital agents as a sales and marketing channel rather than a customer service solution. This study further differs from earlier papers as we performed a practical experiment, whereas other authors have measured effects basically by displaying illustrations [ 17 ]. Sheehan et al [ 71 ] confirmed that the validity of results can be improved by asking participants to experimentally engage with and subsequently rate a conversational agent.…”

“…The few references published to date addressing the humanization of chatbots focus mainly on those implemented in customer service-related scenarios but not on those related to commerce. The limitation detected in our search through the literature is that data have been collected via non-experimental approaches only, with the mere description or graphical illustration of a chatbot interaction, but not the replication of a real-life scenario on an e-commerce platform [ 17 ]. For testing our hypotheses, two identical food e-commerce platforms were developed with Wix Website Builder.…”

“…When the context required it, a content adjustment was made to change the wording from “avatar” or “human” to “chatbot”. C ustomer satisfaction (CS) could be captured using Chung et al’s [ 17 ] approach. Their scales were appropriate for this study because they refer to a similar scenario in their research.…”

“…Their main functions are information provision support, navigation assistance for targeted product search, and recommendation provision [ 2 ]. Customers can make use of their advantages and receive precise information, obtain guidance, and find out about the latest trends in a time-saving manner [ 17 ]. For instance, the chatbot can detect the availability of a particular product and provide information or suggestions about a potential purchase (cross-selling/up-selling).…”

Thumbs Up or Down: Consumer Reactions to Decisions by Algorithms Versus Humans

“…Study 8 examined a potential solution: anthropomorphizing the algorithm. Extant work suggests that humanizing a nonhuman agent (e.g., referring to an object with a personal name) leads people to attribute human-like abilities to it (Crolic et al 2022; Epley 2018). We proposed that humanizing an algorithm should more closely align consumers’ perceptions of a human decision maker and an algorithmic decision maker, enabling the human-like algorithm to lead to more positive reactions than the non-human-like algorithm.…”