Factors influencing users' adoption and use of conversational agents: A systematic review

Abstract

As artificially intelligent conversational agents (ICAs) become a popular customer service solution for businesses, understanding the drivers of user acceptance of ICAs is critical to ensure its successful implementation. To provide a comprehensive review of factors affecting consumers' adoption and use of ICAs, this study performs a systematic literature review of extant empirical research on this topic. Based on a literature search performed in July 2019 followed by a snowballing approach, 18 relevant articles were analyzed. Factors found to influence human‐machine cognitive engagement were categorized into usage‐related, agent‐related, user‐related, attitude and evaluation, and other factors. This study proposed a collective model of users' acceptance and use of ICAs, whereby user acceptance is driven mainly by usage benefits, which are influenced by agent and user characteristics. The study emphasizes the proposed model's context‐dependency, as relevant factors depend on usage settings, and provides several strategic business implications, including service design, personalization, and customer relationship management.

Creepiness: Its antecedents and impact on loyalty when interacting with a chatbot

“…On the one hand, consumers find the technology useful and convenient (i.e., the chatbot technology “frees” them from restrictive business hours in obtaining customer service); on the other hand, they perceive the technology as restricting their autonomy (i.e., the chatbot “enslaves” them, asking them to divulge personal information to enable its technological efficiency). In response to this paradox, consumers cope with the situation by mobilizing the privacy calculus defense tactic: “they weigh the expected benefits from online transactions against the perceived risks” (Lutz et al, 2020, p. 1169). More specifically, they accept less control over their personal information against increased personalized service.…”

“…We assume that users concerned about privacy threats may experience a sense of unease when interacting with the chatbot (Ischen et al, 2020), particularly as privacy concerns and creepiness are strongly correlated (Inman & Nikolova, 2017). Additionally, creepiness is a concept that can be understood in the context of privacy cynicism (Lutz et al, 2020), and the “privacy paradox” (Hoffmann et al, 2016). As consumers develop a feeling of uncertainty (one aspect of creepiness) and powerlessness over the decision to control disclosure of their private information, they are led to use the chatbot despite their privacy concerns, possibly heightening perceptions that intelligent technologies appear to be creepy.…”

“…When interacting with human‐like machine interfaces such as chatbots or robots, consumers report experiencing some form of creepiness, which is described as “a potentially negative and uncomfortable emotional response paired with perceptions of ambiguity toward a person, technology or even during a situation” (Langer & König, 2018, p. 3). While not much is known about the antecedents and consequences of this uneasy feeling in customer service encounters, creepiness can be seen in the broader context of privacy cynicism (Lutz et al, 2020). When confronted with new technology such as chatbots, smart speakers (Lutz & Newlands, 2021), or social robots (Lutz et al, 2019), users might face uncertainty and powerlessness.…”

“…In addition, we examined the impact of creepiness on loyalty when interacting with this emerging technology. The study is grounded in the technology paradox (Mick & Fournier, 1998), along with the privacy paradox and privacy cynicism (Lutz et al, 2020) as its theoretical underpinnings…”

Human versus chatbot: Understanding the role of emotion in health marketing communication for vaccines

“…Despite the rapid adoption of chatbots in various industries, there are limited empirical data regarding the factors influencing consumer response to chatbots. Ling et al ( 2021 ) review of the emerging conversational agent research in marketing has identified varied types of variables, from usage considerations (e.g., perceived usefulness, perceived enjoyment) to bot characteristics (e.g., anthropomorphism, empathy) and user traits (e.g., personal innovativeness), which can impact consumers' attitude and evaluation. Specific to the study focus of the emotional context of consumer–chatbot interaction, prior studies have primarily addressed the usage considerations of utilitarian/cognitive versus hedonic/arousal factors (Sivaramakrishnan et al, 2007 ; Zarouali et al, 2018 ) driving consumer attitude toward the bot.…”