A1 Journal article (refereed)
Identifying relevant segments of AI applications adopters : Expanding the UTAUT2’s variables (2021)

Cabrera-Sánchez, J.-P., Villarejo-Ramos, Á. F., Liébana-Cabanillas, F., & Shaikh, A. A. (2021). Identifying relevant segments of AI applications adopters : Expanding the UTAUT2’s variables. Telematics and Informatics, 58, Article 101529. https://doi.org/10.1016/j.tele.2020.101529

JYU authors or editors

Publication details

All authors or editors: Cabrera-Sánchez, Juan-Pedro; Villarejo-Ramos, Ángel F.; Liébana-Cabanillas, Francisco; Shaikh, Aijaz A.

Journal or series: Telematics and Informatics

ISSN: 0736-5853

eISSN: 1879-324X

Publication year: 2021

Volume: 58

Article number: 101529

Publisher: Elsevier

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1016/j.tele.2020.101529

Publication open access: Not open

Publication channel open access:

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/72786


Artificial intelligence (AI) is a future-defining technology, and AI applications are becoming mainstream in the developed world. Many consumers are adopting and using AI-based apps, devices, and services in their everyday lives. However, research examining consumer behavior in using AI apps is scant. We examine critical factors in AI app adoption by extending and validating a well-established unified theory of adoption and use of technology, UTAUT2. We also explore the possibility of unobserved heterogeneity in consumers’ behavior, including potentially relevant segments of AI app adopters. To augment the knowledge of end users’ engagement and relevant segments, we have added two new antecedent variables into UTAUT2: technology fear and consumer trust. Prediction-orientated segmentation was used on 740 valid responses collected using a pre-tested survey instrument. The results show five segments with different behaviors that were influenced by the variables of the proposed model. Once known, the profiles were used to propose apps to AI developers to improve consumer engagement. The moderating effects of the added variables—technology fear and consumer trust—are also shown. Finally, we discuss the theoretical and managerial implications of our findings and propose priorities for future research.

Keywords: artificial intelligence; consumers; consumer behaviour; applications (computer programmes); trust; introduction (implementation); segmentation; heterogeneity

Free keywords: artificial intelligence; segmentation; technology fear; consumer trust; heterogeneity; unified theory of adoption and use of technology; UTAUT2

Contributing organizations

Ministry reporting: Yes

Reporting Year: 2021

JUFO rating: 2

Last updated on 2022-20-09 at 14:53