Facilitating retail customers’ use of AI-based virtual assistants: A meta-analysis

Blut, Markus; Wünderlich, Nancy V.; Brock, Christian

Abstract

Retailers rely on virtual assistants (VAs), such as Amazon's Alexa and chatbots, to deliver 24/7 customer service at low costs, as well as novel shopping opportunities. Despite improved VA capabilities due to artificial intelligence (AI), many retailers still struggle to convince customers to become repeat users of VAs. Therefore, to establish recommendations for how to facilitate VA use, this meta-analysis extracts 2,766 correlations from 244 independent samples of customers interacting with VAs. The results suggest that customer-, VA-, and shopping occasion–related factors all influence technology use. Price value is the strongest driver, followed by support, social influence, and anthropomorphism. Performance risk, competence, and trust matter to lesser extents. These factors exert strong indirect effects by triggering two customer responses: cognitive and emotional. Negative emotions emerge as a particularly important mediator. Finally, several VA types enhance or weaken the noted effects, including whether they are intelligent/less intelligent, commercial/noncommercial, voice-/text-based, and avatar-/non-avatar-based. The results suggest no one-size-fits-all approach applies for VAs, because their performance varies across customer responses. The current meta-analysis provides in-depth guidance for retailers seeking to select appealing VAs.

Citation

Blut, M., Wünderlich, N. V., & Brock, C. (2024). Facilitating retail customers’ use of AI-based virtual assistants: A meta-analysis. Journal of Retailing, https://doi.org/10.1016/j.jretai.2024.04.001

• Journal Article Type: Article
• Acceptance Date: Apr 22, 2024
• Online Publication Date: May 13, 2024
• Publication Date: May 13, 2024
• Deposit Date: May 13, 2024
• Publicly Available Date: May 14, 2024
• Journal: Journal of Retailing
• Print ISSN: 0022-4359
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1016/j.jretai.2024.04.001
• Public URL: https://durham-repository.worktribe.com/output/2438467