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

Daniel Baier, Andreas Karasenko and Alexandra Rese

Journal of Retailing and Consumer Services, 2025, vol. 85, issue C

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.

Keywords: Online customer reviews; Technology acceptance; Transfer models; LLMs (large language models); Transformer architecture; Generative artificial intelligence chatbots; ChatGPT (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:joreco:v:85:y:2025:i:c:s0969698925000578

DOI: 10.1016/j.jretconser.2025.104278

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