Namaste Alexa: The Impact of Non‐Native Language Queries on Voice Assistant Usage Intentions

ABSTRACT This study explores how language‐related constructs—language pride, prejudice and pragmatism—affect user perceptions and usage intentions of voice assistants (VAs) in multilingual markets. Drawing on Communication Accommodation Theory (CAT), we examine the emotional and rational responses elicited by linguistic mismatches between users and VAs, focusing on Hindi as a prominent non‐native language. Employing a mixed‐methods approach, Study 1 utilises qualitative interviews ( n = 25) to uncover users' frustrations and adaptation strategies when interacting with VAs. Study 2 quantitatively tests a conceptual model ( n = 423) using PLS‐SEM, revealing that language constructs significantly influence anthropomorphism, which in turn drives both emotional and rational responses, ultimately shaping continuance or discontinuance intentions. Our findings advance the theoretical application of CAT in human–machine interaction and offer practical guidance for VA developers and marketers in enhancing linguistic inclusivity, cultural sensitivity and user retention in emerging markets.

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Publication Details

Journal
Journal of Consumer Behaviour
Published
2026-01-21
DOI
https://doi.org/10.1002/cb.70108
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
0.00
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article

Namaste Alexa: The Impact of Non‐Native Language Queries on Voice Assistant Usage Intentions

Sumeet Kaur, Shailja Agarwal, Jaspreet Kaur, Emmanuel Mogaji
Journal of Consumer Behaviour
AI in Service Interactions
article

Namaste Alexa: The Impact of Non‐Native Language Queries on Voice Assistant Usage Intentions

Sumeet Kaur, Shailja Agarwal, Jaspreet Kaur, Emmanuel Mogaji
article en

Abstract

ABSTRACT This study explores how language‐related constructs—language pride, prejudice and pragmatism—affect user perceptions and usage intentions of voice assistants (VAs) in multilingual markets. Drawing on Communication Accommodation Theory (CAT), we examine the emotional and rational responses elicited by linguistic mismatches between users and VAs, focusing on Hindi as a prominent non‐native language. Employing a mixed‐methods approach, Study 1 utilises qualitative interviews ( n = 25) to uncover users' frustrations and adaptation strategies when interacting with VAs. Study 2 quantitatively tests a conceptual model ( n = 423) using PLS‐SEM, revealing that language constructs significantly influence anthropomorphism, which in turn drives both emotional and rational responses, ultimately shaping continuance or discontinuance intentions. Our findings advance the theoretical application of CAT in human–machine interaction and offer practical guidance for VA developers and marketers in enhancing linguistic inclusivity, cultural sensitivity and user retention in emerging markets.

Journal of Consumer BehaviourVol. 25(2)
Institute of Management Technology (IN), Jaipuria Institute of Management (IN), Fore School of Management (IN), Centre for Development Economics (IN), Keele University (GB)
Quality Education
Openalex Percentile: Top 81%
AI in Service Interactions
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