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A Multi-Methods Analysis of Voice Assistant-Enabled Purchase Recommendation Acceptance
Ransome Bawack, Emilie Bonhoure, Sabrine MALLEK
This research investigates the evolving role of voice assistants (VAs) in shaping consumer purchase decisions, specifically focusing on understanding the benefits of following VA-enabled purchase recommendations (PR). We employ a multi-methods approach to combine partial least squares structural equation modeling (PLS-SEM) and artificial neural networks (ANN) to analyze survey data from 418 US-based voice shoppers. The study integrates and extends the Value-Based Adoption Model (VAM). Our results reveal that consumers prioritize three dimensions of perceived value - purposive, economic, and entertainment - when evaluating VA-enabled PR. The study complements existing literature by offering a nuanced three-dimensional perspective, contributing to a deeper understanding of the factors influencing consumer acceptance in the voice shopping context. It highlights the need for a multidimensional perspective to capture the nuances of consumer perceptions in voice shopping contexts and align recommendations with consumers' self-concept to enhance perceived value.
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