Understanding the Impact of Low Personalization on Customers’ Prior Negative Experience with Virtual Conversational Agents: A Conceptual Framework

Huu Trong Nguyen ()
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Huu Trong Nguyen: University of Bradford

A chapter in Business Innovation for the Post-pandemic Era in Vietnam, 2023, pp 75-85 from Springer

Abstract: Abstract Virtual conversational agents (VCAs) powered by artificial intelligence (AI) have attracted great interest from many research scholars. Its diverse applications are widely acknowledged, and the ability to deliver personalized responses has become a key for increased service quality provided by this AI-powered tool. Building on the perspective of learning from experience theory, this research explores the prior negative experience and presents a conceptual framework to understand its effects on customers’ avoidance behavior with VCAs. Low personalization is expected as the main driver of perceived low informativeness, low credibility, low enjoyment, violation of shared language and information overload, leading to avoidance behavior and switching intention of customers. In addition, time pressure is believed to moderate the links between perceived low informativeness, violation of shared language and information overload as negative prior experiences and switching intention. The findings offer a novel way to understand customer behavior with VCAs under avoidance rather than approach perspective. Important theoretical and managerial implications are provided.

Keywords: Virtual conversational agents; Avoidance behavior; Switching intention; Time pressure; Artificial intelligence anxiety (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-99-1545-3_7

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DOI: 10.1007/978-981-99-1545-3_7

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