Beyond sentiment analysis of online customer reviews: an approach to automated measurement of technology acceptance from online customer reviews

Andreas Karasenko () and Daniel Baier ()
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Andreas Karasenko: University of Bayreuth
Daniel Baier: University of Bayreuth

Journal of Business Economics, 2025, vol. 95, issue 7, No 1, 917-955

Abstract: Abstract Recent developments in machine learning (ML), especially transformer-based discriminative and generative deep learning, transform the marketing landscape. So, e.g., marketers predict with high accuracy sentiment scores from online customer review (OCR) comments in natural language and gain valuable insights whether, when, and how apps, products, or services should be improved. However, oftentimes, OCR comments contain additional interesting information that goes beyond sentiment indications. In this work, we propose a new approach to predict – based on the well-known Technology Acceptance Model (TAM) – extended TAM construct scores from OCRs and compare the accuracy of this prediction with various ML models for this purpose. The comparison is based on a dataset with n = 5,356 OCR comments for the Ikea app, labeled by three human experts (n = 3), and 18 ML models. Following this we conduct a case study on the Ikea dataset and show how to use these TAM construct scores in conjunction with topic modeling to identify various usability issues of the Ikea app. Additionally, we propose an approach that leverages TAM constructs to identify OCRs with complex and rich content that would not be identifiable with sentiment alone.

Keywords: Technology acceptance model; Technology acceptance construct score prediction over time; Transfer model; Machine learning; Transformer-based discriminative deep learning; Transformer-based generative deep learning; Few-shot learning; Online customer reviews (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11573-025-01232-z

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