Understanding users' voice assistant exploration intention: unraveling the differential mechanisms of the multiple dimensions of perceived intelligence

Abstract

Purpose

The purpose of this study is to develop a framework for the perceived intelligence of VAs and explore the mechanisms of different dimensions of the perceived intelligence of VAs on users’ exploration intention (UEI) and how these antecedents can collectively result in the highest level of UEI.

Design/methodology/approach

An online survey on Amazon Mechanical Turk is employed. The model is tested utilizing the structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) approach from the collected data of VA users (N = 244).

Findings

According to the SEM outcomes, perceptual, cognitive, emotional and social intelligence have different mechanisms on UEI. Findings from the fsQCA reinforce the SEM results and provide the configurations that enhanced UEI.

Originality/value

This study extends the conceptual framework of perceived intelligence and enriches the literature on anthropomorphism and users’ exploration. These findings also provide insightful suggestions for practitioners regarding the design of VA products.

Citation

Zhao, Y., Chen, Y., Sun, Y. and Shen, X.-L. (2024), "Understanding users' voice assistant exploration intention: unraveling the differential mechanisms of the multiple dimensions of perceived intelligence", Internet Research, Vol. 34 No. 6, pp. 2096-2122. https://doi.org/10.1108/INTR-10-2022-0807