When AI Speaks: The Role of Awe and Habit in Shaping Brand Engagement through Voice Assistants in Experiential Services

Authors

  • Dhivya Lakshmi. N School of Management Studies, Vadapalani, Chennai, Tamil Nadu, India.
  • Vijayakanthan.S School of Management Studies, Vadapalani, Chennai, Tamil Nadu, India.

DOI:

https://doi.org/10.70917/ijcisim-2026-3493

Keywords:

AI-enabled voice assistants; Motivational Consumer Innovativeness; Trust; Purchase Behavior; Voice Commerce; PLS-SEM; Conversational Commerce, Behavioural Engagement, Awe Experience, Habit Generation Z; Structural Equation Modeling.

Abstract

This study examines how AI-enabled voice assistants influence consumer brand engagement in experiential service contexts, focusing on the mediating role of awe experience and the moderating effect of habit. Drawing on the Stimulus–Organism–Response (S–O–R) framework and self-transcendent emotion theory, the study conceptualizes voice assistants as emotional agents rather than functional tools. Data were collected from 471 Generation Z consumers with prior experience using AI voice assistants and analyzed using partial least squares structural equation modeling (PLS-SEM). The results reveal that voice assistant usage significantly enhances brand engagement both directly and indirectly through awe experience. Furthermore, habitual usage strengthens the impact of voice assistant interactions on brand engagement, indicating that repeated exposure stabilizes emotional responses. This study extends S–O–R theory to AI-mediated environments by identifying awe as a critical emotional mechanism and habit as a behavioral amplifier. The findings offer theoretical and managerial insights for designing emotionally engaging AI-driven brand interactions.

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Published

2026-08-17

How to Cite

Dhivya Lakshmi. N, & Vijayakanthan.S. (2026). When AI Speaks: The Role of Awe and Habit in Shaping Brand Engagement through Voice Assistants in Experiential Services. International Journal of Computer Information Systems and Industrial Management Applications, 18(17s), 35–48. https://doi.org/10.70917/ijcisim-2026-3493

Issue

Section

Original Articles