Consumers acceptance of artificially intelligent (AI) device use in service delivery

By Dogan Gürsoy, Oscar Hengxuan Chi, Lu Lu, Robin Nunkoo. International Journal of Information Management, 2019

1,272 citations recorded by the source corpus.

Abstract This study develops and empirically tests a theoretical model of artificially intelligent (AI) device use acceptance (AIDUA) that aims to explain customers’ willingness to accept AI device use in service encounters. The proposed model incorporates three acceptance generation stages (primary appraisal, secondary appraisal, and outcome stage) and six antecedents (social influence, hedonic motivation, anthropomorphism, performance expectancy, effort expectancy, and emotion). Utilizing data collected from potential customers, the proposed AIDUA model is tested. Findings suggest that customers go through a three-step acceptance generation process in determining whether to accept the use of AI devices during their service interactions. Findings indicate that social influence and hedonic motivation are positively related to performance expectancy while anthropomorphism is positively related to effort expectancy. Both performance and effort expectancy are significant antecedents of customer emotions, which determines customers’ acceptance of AI device use in service encounters. This study provides a conceptual AI device acceptance framework that can be used by other researchers to better investigate AI related topics in the service context.

Subfields: Artificial Intelligence, Information Systems and Management, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/f6b5bcadd93d14726a181baba478c07bb97d722b

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Citations in the corpus

Use of AI-based tools for healthcare purposes: a survey study from consumers’ perspectives (2020, methodology): “The control variables are age, gender, race, income, employment, education, general computer skills, technical knowledge about AI technology, and experience with an AI-based service, which are found and tested by prior research as factors affecting the adoption of AI devices e.g., [3].”
Trustworthy Artificial Intelligence: A Review (2022, background): “Before-the-loop 1 [82, 113, 208] Before-the-loop, Over-the-loop 1, 4 [82, 204] In-the-loop, Over-the-loop 3, 4 [103, 168, 203]”
Antecedents of customers’ acceptance of artificially intelligent robotic device use in hospitality services (2019, methodology): “The AIDUA model also proposes two similar factors, namely performance expectancy and e ﬀ ort expectancy (Gursoy et al., 2019).”
Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making (2021, methodology): “…technology adoption models, such as TAM and UTAUT, are not considered suitable for studying the adoption of AI because they mainly focus on the use of functional technologies and cannot fully explain the complex decision-making process involved in the context of AI adoption (Gursoy et al., 2019).”
Service robot implementation: a theoretical framework and research agenda (2019, background): “Before secondary appraisal and outcome assessment, consumers first evaluate the relevance of AI to themselves (Gursoy et al., 2019).”
The Potential of Generative Artificial Intelligence Across Disciplines: Perspectives and Future Directions (2023, neutral): “54,55 All these highlight the need for further research on generative AI adoption.”