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Measuring technology acceptance over time using transfer models based on online customer reviews

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  • Baier, Daniel
  • Karasenko, Andreas
  • Rese, Alexandra

Abstract

Online customer reviews (OCRs) are user-generated, semi-formal evaluations of products, services, or technologies. They usually consist of a timestamp, a star rating, and, in many cases, a comment that reflects perceived strengths and weaknesses. OCRs are easily accessible in large numbers on the Internet – for example, through app stores, electronic marketplaces, online shops, and review websites. This paper presents new transfer models to predict technology acceptance and its determinants from OCRs. We train, test, and validate these prediction models using large OCR samples and corresponding observed construct ratings by human experts and generative artificial intelligence chatbots as well as estimated ratings from a traditional customer survey. From a management perspective, the new approach enhances former technology acceptance measurement since we use OCRs as a basis for prediction and discuss the evolution of acceptance over time.

Suggested Citation

  • Baier, Daniel & Karasenko, Andreas & Rese, Alexandra, 2025. "Measuring technology acceptance over time using transfer models based on online customer reviews," Journal of Retailing and Consumer Services, Elsevier, vol. 85(C).
    • Handle: RePEc:eee:joreco:v:85:y:2025:i:c:s0969698925000578
    DOI: 10.1016/j.jretconser.2025.104278
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    as
    1. Artem Timoshenko & John R. Hauser, 2019. "Identifying Customer Needs from User-Generated Content," Marketing Science, INFORMS, vol. 38(1), pages 1-20, January.
    2. Kim, Taeyong & Hwang, Seungsoo & Kim, Minkyung, 2022. "Text analysis of online customer reviews for products in the FCB quadrants: Procedure, outcomes, and implications," Journal of Business Research, Elsevier, vol. 150(C), pages 676-689.
    3. Wu, Kewen & Zhao, Yuxiang & Zhu, Qinghua & Tan, Xiaojie & Zheng, Hua, 2011. "A meta-analysis of the impact of trust on technology acceptance model: Investigation of moderating influence of subject and context type," International Journal of Information Management, Elsevier, vol. 31(6), pages 572-581.
    4. Vermeer, Susan A.M. & Araujo, Theo & Bernritter, Stefan F. & van Noort, Guda, 2019. "Seeing the wood for the trees: How machine learning can help firms in identifying relevant electronic word-of-mouth in social media," International Journal of Research in Marketing, Elsevier, vol. 36(3), pages 492-508.
    5. Pantano, Eleonora & Servidio, Rocco, 2012. "Modeling innovative points of sales through virtual and immersive technologies," Journal of Retailing and Consumer Services, Elsevier, vol. 19(3), pages 279-286.
    6. Kübler, Raoul V. & Colicev, Anatoli & Pauwels, Koen H., 2020. "Social Media's Impact on the Consumer Mindset: When to Use Which Sentiment Extraction Tool?," Journal of Interactive Marketing, Elsevier, vol. 50(C), pages 136-155.
    7. Lee Cronbach, 1951. "Coefficient alpha and the internal structure of tests," Psychometrika, Springer;The Psychometric Society, vol. 16(3), pages 297-334, September.
    8. Praveen, S.V. & Gajjar, Pranshav & Ray, Rajeev Kumar & Dutt, Ashutosh, 2024. "Crafting clarity: Leveraging large language models to decode consumer reviews," Journal of Retailing and Consumer Services, Elsevier, vol. 81(C).
    9. Mochen Yang & Yuqing Ren & Gediminas Adomavicius, 2019. "Understanding User-Generated Content and Customer Engagement on Facebook Business Pages," Information Systems Research, INFORMS, vol. 30(3), pages 839-855, September.
    10. Jan Ole Krugmann & Jochen Hartmann, 2024. "Sentiment Analysis in the Age of Generative AI," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 11(1), pages 1-19, December.
    11. Rese, Alexandra & Schreiber, Stefanie & Baier, Daniel, 2014. "Technology acceptance modeling of augmented reality at the point of sale: Can surveys be replaced by an analysis of online reviews?," Journal of Retailing and Consumer Services, Elsevier, vol. 21(5), pages 869-876.
    12. 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.
    13. Joachim Büschken & Greg M. Allenby, 2016. "Sentence-Based Text Analysis for Customer Reviews," Marketing Science, INFORMS, vol. 35(6), pages 953-975, November.
    14. Pantano, Eleonora & Naccarato, Giuseppe, 2010. "Entertainment in retailing: The influences of advanced technologies," Journal of Retailing and Consumer Services, Elsevier, vol. 17(3), pages 200-204.
    15. Dixit, Saumya & Jyoti Badgaiyan, Anant & Khare, Arpita, 2019. "An integrated model for predicting consumer's intention to write online reviews," Journal of Retailing and Consumer Services, Elsevier, vol. 46(C), pages 112-120.
    16. Hua (Jonathan) Ye & Cecil Eng Huang Chua & Jun Sun, 2019. "Enhancing mobile data services performance via online reviews," Information Systems Frontiers, Springer, vol. 21(2), pages 441-452, April.
    17. Zhang, Chenxi & Xu, Zeshui, 2024. "Gaining insights for service improvement through unstructured text from online reviews," Journal of Retailing and Consumer Services, Elsevier, vol. 80(C).
    18. Bruner, Gordon II & Kumar, Anand, 2005. "Explaining consumer acceptance of handheld Internet devices," Journal of Business Research, Elsevier, vol. 58(5), pages 553-558, May.
    19. Kumari, Vandana & Bala, Pradip Kumar & Chakraborty, Shibashish, 2024. "A text mining approach to explore factors influencing consumer intention to use metaverse platform services: Insights from online customer reviews," Journal of Retailing and Consumer Services, Elsevier, vol. 81(C).
    20. Fred D. Davis & Richard P. Bagozzi & Paul R. Warshaw, 1989. "User Acceptance of Computer Technology: A Comparison of Two Theoretical Models," Management Science, INFORMS, vol. 35(8), pages 982-1003, August.
    21. Mortenson, Michael J. & Vidgen, Richard, 2016. "A computational literature review of the technology acceptance model," International Journal of Information Management, Elsevier, vol. 36(6), pages 1248-1259.
    22. Hartmann, Jochen & Heitmann, Mark & Siebert, Christian & Schamp, Christina, 2023. "More than a Feeling: Accuracy and Application of Sentiment Analysis," International Journal of Research in Marketing, Elsevier, vol. 40(1), pages 75-87.
    23. Joachim Büschken & Greg M. Allenby, 2020. "Improving Text Analysis Using Sentence Conjunctions and Punctuation," Marketing Science, INFORMS, vol. 39(4), pages 727-742, July.
    24. Alantari, Huwail J. & Currim, Imran S. & Deng, Yiting & Singh, Sameer, 2022. "An empirical comparison of machine learning methods for text-based sentiment analysis of online consumer reviews," International Journal of Research in Marketing, Elsevier, vol. 39(1), pages 1-19.
    25. Rese, Alexandra & Baier, Daniel & Geyer-Schulz, Andreas & Schreiber, Stefanie, 2017. "How augmented reality apps are accepted by consumers: A comparative analysis using scales and opinions," Technological Forecasting and Social Change, Elsevier, vol. 124(C), pages 306-319.
    26. Hausman, Angela V. & Siekpe, Jeffrey Sam, 2009. "The effect of web interface features on consumer online purchase intentions," Journal of Business Research, Elsevier, vol. 62(1), pages 5-13, January.
    27. Decker, Reinhold & Trusov, Michael, 2010. "Estimating aggregate consumer preferences from online product reviews," International Journal of Research in Marketing, Elsevier, vol. 27(4), pages 293-307.
    28. Viglia, Giampaolo & Adler, Susanne J. & Miltgen, Caroline Lancelot & Sarstedt, Marko, 2024. "The use of synthetic data in tourism," Annals of Tourism Research, Elsevier, vol. 108(C).
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