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Identifying relevant segments of AI applications adopters : Expanding the UTAUT2’s variables

Year of publication

2021

Authors

Cabrera-Sánchez, Juan-Pedro; Villarejo-Ramos, Ángel F.; Liébana-Cabanillas, Francisco; Shaikh, Aijaz A.

Abstract

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.
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Organizations and authors

Publication type

Publication format

Article

Parent publication type

Journal

Article type

Original article

Audience

Scientific

Peer-reviewed

Peer-Reviewed

MINEDU's publication type classification code

A1 Journal article (refereed), original research

Publication channel information

Publisher

Elsevier

Volume

58

Article number

101529

​Publication forum

68133

​Publication forum level

2

Open access

Open access in the publisher’s service

No

Self-archived

Yes

Other information

Fields of science

Computer and information sciences; Business and management

Keywords

[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Publication country

United States

Internationality of the publisher

International

Language

English

International co-publication

Yes

Co-publication with a company

No

DOI

10.1016/j.tele.2020.101529

The publication is included in the Ministry of Education and Culture’s Publication data collection

Yes