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Language translation effects in Chatbots: Evidence from a randomized field experiment on a mobile commerce platform

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  • Nayak, Ashutosh
  • Nair, Ashwin Aravindakshan

Abstract

This study investigates the impact of language translation innovations in artificial intelligence (AI) digital assistants. We use data from a mobile com- merce platform application in India that introduced a Hindi and English version of its previously English-only language chatbot. The data, obtained from a randomized field experiment conducted on new users, help determine the impact of introducing language translation in conversational chatbots by quantifying its effect on user metrics such as purchases and uninstalls. In the experiment, the firm only altered the language of interaction, from English- only to Hindi and English. The firm did not make any other changes in the design or purchase flow within the application. We find that language trans- lation innovations significantly increase the number of user sessions and also improve user purchases and engagement. The increase in the engagement did not emerge from an increase in the number of sessions but from an increase in interactions within a session in the bilingual app. We also observe a sharp rise in uninstalls for the population that received the bilingual app. We find that in the bilingual chatbot, uninstalls rise with increased user interactions in a high involvement product category. In sum, the results from the field experiment show that while language translation in artificially intelligent as- sistants leads to greater purchases, it could also lead to increased uninstalls. This result suggests that implementing similar language translation innova- tions in isolation, without any modifications to in-application experience, has the potential to yield negative outcomes for the firm.

Suggested Citation

  • Nayak, Ashutosh & Nair, Ashwin Aravindakshan, 2025. "Language translation effects in Chatbots: Evidence from a randomized field experiment on a mobile commerce platform," Journal of Business Research, Elsevier, vol. 190(C).
    • Handle: RePEc:eee:jbrese:v:190:y:2025:i:c:s0148296324006623
    DOI: 10.1016/j.jbusres.2024.115158
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    as
    1. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
    2. Koslow, Scott & Shamdasani, Prem N & Touchstone, Ellen E, 1994. "Exploring Language Effects in Ethnic Advertising: A Sociolinguistic Perspective," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 20(4), pages 575-585, March.
    3. Luna, David & Peracchio, Laura A, 2001. "Moderators of Language Effects in Advertising to Bilinguals: A Psycholinguistic Approach," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 28(2), pages 284-295, September.
    4. Erik Brynjolfsson & Xiang Hui & Meng Liu, 2019. "Does Machine Translation Affect International Trade? Evidence from a Large Digital Platform," Management Science, INFORMS, vol. 65(12), pages 5449-5460, December.
    5. Erik Brynjolfsson & Daniel Rock & Chad Syverson, 2018. "Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 23-57, National Bureau of Economic Research, Inc.
    6. Mariani, Marcello M. & Hashemi, Novin & Wirtz, Jochen, 2023. "Artificial intelligence empowered conversational agents: A systematic literature review and research agenda," Journal of Business Research, Elsevier, vol. 161(C).
    7. Sun, Yuan & Chen, Jin & Sundar, S. Shyam, 2024. "Chatbot ads with a human touch: A test of anthropomorphism, interactivity, and narrativity," Journal of Business Research, Elsevier, vol. 172(C).
    8. Ascarza, & Neslin, & Netzer, & Lemmens, Aurélie & Anderson, Zachery & Fader, Peter S. & Gupta, S. & Hardie, B.G.S. & Libai, Barak & Neal, David & Provost, Foster, 2018. "In pursuit of enhanced customer retention management : Review, key issues, and future directions," Other publications TiSEM 28a90d28-6daf-42f1-bd8e-e, Tilburg University, School of Economics and Management.
    9. Juquelier, Antoine & Poncin, Ingrid & Hazée, Simon, 2025. "Empathic chatbots: A double-edged sword in customer experiences," Journal of Business Research, Elsevier, vol. 188(C).
    10. Aradhna Krishna & Rohini Ahluwalia, 2008. "Language Choice in Advertising to Bilinguals: Asymmetric Effects for Multinationals versus Local Firms," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 35(4), pages 692-705, August.
    11. Chakraborty, Debarun & Kumar Kar, Arpan & Patre, Smruti & Gupta, Shivam, 2024. "Enhancing trust in online grocery shopping through generative AI chatbots," Journal of Business Research, Elsevier, vol. 180(C).
    12. Ping Jin & Humza Haider & Russell Greiner & Sarah Wei & Gerald Häubl, 2021. "Using survival prediction techniques to learn consumer-specific reservation price distributions," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-26, April.
    13. Seetharaman, P B & Chintagunta, Pradeep K, 2003. "The Proportional Hazard Model for Purchase Timing: A Comparison of Alternative Specifications," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(3), pages 368-382, July.
    14. Gabriela Gutierrez-Huerter O & Jeremy Moon & Stefan Gold & Wendy Chapple, 2020. "Micro-processes of translation in the transfer of practices from MNE headquarters to foreign subsidiaries: The role of subsidiary translators," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 51(3), pages 389-413, April.
    15. Viswanath Venkatesh & Fred D. Davis, 2000. "A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies," Management Science, INFORMS, vol. 46(2), pages 186-204, February.
    16. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, Enero-Abr.
    17. Rutz, Oliver & Aravindakshan, Ashwin & Rubel, Olivier, 2019. "Measuring and forecasting mobile game app engagement," International Journal of Research in Marketing, Elsevier, vol. 36(2), pages 185-199.
    18. David Luna & Laura A. Peracchio, 2005. "Advertising to Bilingual Consumers: The Impact of Code-Switching on Persuasion," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 31(4), pages 760-765, March.
    19. Eva Ascarza & Scott A. Neslin & Oded Netzer & Zachery Anderson & Peter S. Fader & Sunil Gupta & Bruce G. S. Hardie & Aurélie Lemmens & Barak Libai & David Neal & Foster Provost & Rom Schrift, 2018. "In Pursuit of Enhanced Customer Retention Management: Review, Key Issues, and Future Directions," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 5(1), pages 65-81, March.
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