Speaking Like Home: How Regional Language Adaptations in Robots Enhance Trust and Willingness to Pay

While service industries like healthcare and elder-care increasingly use technology to address staff shortages, its potential to support consumers experiencing vulnerability remains under-explored and under-utilized. For instance, elderly people who are experiencing vulnerability due to language loss or reduced proficiency in communicating in their national language can be supported by voice-based interfaces, such as robots, that can communicate with them in their native regional language. However, responses to such regional language adaptations, as well as the potential managerial benefits of tailoring service offerings to consumers with specific linguistic needs, remain unclear. This study begins to explore this area and reveals that elderly consumers prefer to interact with robots in their regional language rather than in the national language. We also find that caregivers are willing to pay a 30% premium for a companion robot that can use a regional language, and that regional language capabilities foster trust in robots in the general population, too. The theoretical contribution of the study draws from linguistics to show that these positive effects arise because regional synthetic speech is processed more fluently. The study also highlights human-likeness as a crucial boundary condition, as regional language adaptations unexpectedly backfire when delivered with machine-like voices. For service organizations, regional adaptations in voice-based interfaces present a unique opportunity to better serve linguistically vulnerable consumers while also fostering more positive attitudes in the general population, provided there is a high degree of human-likeness.

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Publication Details

Journal
Journal of Service Research
Published
2026-04-03
DOI
https://doi.org/10.1177/10946705261432142
Citations
1
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
32.02

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article

Speaking Like Home: How Regional Language Adaptations in Robots Enhance Trust and Willingness to Pay

Jenny van Doorn, Giovanni Telussa
1 citations
Journal of Service Research
AI in Service Interactions
32.02
article

Speaking Like Home: How Regional Language Adaptations in Robots Enhance Trust and Willingness to Pay

Jenny van Doorn, Giovanni Telussa
article en
1 citations

Abstract

While service industries like healthcare and elder-care increasingly use technology to address staff shortages, its potential to support consumers experiencing vulnerability remains under-explored and under-utilized. For instance, elderly people who are experiencing vulnerability due to language loss or reduced proficiency in communicating in their national language can be supported by voice-based interfaces, such as robots, that can communicate with them in their native regional language. However, responses to such regional language adaptations, as well as the potential managerial benefits of tailoring service offerings to consumers with specific linguistic needs, remain unclear. This study begins to explore this area and reveals that elderly consumers prefer to interact with robots in their regional language rather than in the national language. We also find that caregivers are willing to pay a 30% premium for a companion robot that can use a regional language, and that regional language capabilities foster trust in robots in the general population, too. The theoretical contribution of the study draws from linguistics to show that these positive effects arise because regional synthetic speech is processed more fluently. The study also highlights human-likeness as a crucial boundary condition, as regional language adaptations unexpectedly backfire when delivered with machine-like voices. For service organizations, regional adaptations in voice-based interfaces present a unique opportunity to better serve linguistically vulnerable consumers while also fostering more positive attitudes in the general population, provided there is a high degree of human-likeness.

Journal of Service Research
University Medical Center Groningen (NL), University of Groningen (NL)
Nederlandse Organisatie voor Wetenschappelijk Onderzoek
Quality Education
Openalex Percentile: Top 1%
AI in Service Interactions
32.02
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