A Multi-Methods Analysis of Voice Assistant-Enabled Purchase Recommendation Acceptance

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.