When Do AI Chatbots Lead to Higher Customer Satisfaction than Human Frontline Employees in Online Shopping Assistance? Considering Product Attribute Type

Yanya Ruan, Jozsef Mezei*

*Corresponding author for this work

    Research output: Contribution to journalArticleScientificpeer-review

    53 Citations (Scopus)
    260 Downloads (Pure)

    Abstract

    The increasing adoption of AI chatbots in online shopping assistance, as
    a complement or substitute for human frontline employees (HFLEs), leads
    to the question whether HFLEs perform better than AI service robots and
    why. From the perspective of product attribute type (experiential/ func-
    tional) and focusing on customer satisfaction, this study explores how the
    impact of service agent on customer satisfaction varies along with product
    attribute type. A scenario-based experiment was designed and completed
    by 567 participants. Although HFLEs lead to higher customer satisfaction
    when the product attribute is experiential, AI chatbots perform better than
    HFLEs when the product attribute is functional. We make use of perceived
    information quality, perceived waiting time, and positive emotions, three de-
    terminants of customer satisfaction, to explain the variation of the role of
    different service agent types. The findings offer useful implications for com-
    panies when selecting service agent types in online shopping assistance.
    Original languageEnglish
    Article number103059
    JournalJournal of Retailing and Consumer Services
    Volume68
    DOIs
    Publication statusPublished - Sept 2022
    MoE publication typeA1 Journal article-refereed

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