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

DOI zum Zitieren der Version auf EPub Bayreuth: https://doi.org/10.15495/EPub_UBT_00008832
URN to cite this document: urn:nbn:de:bvb:703-epub-8832-3

Title data

Baier, Daniel ; Karasenko, Andreas ; Rese, Alexandra:
Measuring technology acceptance over time using transfer models based on online customer reviews.
In: Journal of Retailing and Consumer Services. Vol. 85 (2025) . - 104278.
ISSN 0969-6989
DOI der Verlagsversion: https://doi.org/10.1016/j.jretconser.2025.104278

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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.

Further data

Item Type:	Article in a journal
Keywords:	Online customer reviews; Technology acceptance; Transfer models; LLMs (large language models); Transformer architecture; Generative artificial intelligence chatbots; ChatGPT
DDC Subjects:	300 Social sciences > 330 Economics
Institutions of the University:	Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Chair Business Administration XIV - Marketing and Innovation > Chair Business Administration XIV - Marketing and Innovation - Univ.-Prof. Dr. Daniel Baier Faculties Faculties > Faculty of Law, Business and Economics Faculties > Faculty of Law, Business and Economics > Department of Business Administration Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Chair Business Administration XIV - Marketing and Innovation
Language:	English
Originates at UBT:	Yes
URN:	urn:nbn:de:bvb:703-epub-8832-3
Date Deposited:	29 Jan 2026 13:48
Last Modified:	29 Jan 2026 13:49
URI:	https://epub.uni-bayreuth.de/id/eprint/8832

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