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Promoting Healthcare Workers’ Adoption Intention of Artificial-Intelligence-Assisted Diagnosis and Treatment: The Chain Mediation of Social Influence and Human–Computer Trust

Author

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  • Mengting Cheng

    (School of Economic and Management, Anhui University of Science and Technology, Huainan 232001, China)

  • Xianmiao Li

    (School of Economic and Management, Anhui University of Science and Technology, Huainan 232001, China)

  • Jicheng Xu

    (School of Economic and Management, Anhui University of Science and Technology, Huainan 232001, China)

Abstract

Artificial intelligence (AI)-assisted diagnosis and treatment could expand the medical scenarios and augment work efficiency and accuracy. However, factors influencing healthcare workers’ adoption intention of AI-assisted diagnosis and treatment are not well-understood. This study conducted a cross-sectional study of 343 dental healthcare workers from tertiary hospitals and secondary hospitals in Anhui Province. The obtained data were analyzed using structural equation modeling. The results showed that performance expectancy and effort expectancy were both positively related to healthcare workers’ adoption intention of AI-assisted diagnosis and treatment. Social influence and human–computer trust, respectively, mediated the relationship between expectancy (performance expectancy and effort expectancy) and healthcare workers’ adoption intention of AI-assisted diagnosis and treatment. Furthermore, social influence and human–computer trust played a chain mediation role between expectancy and healthcare workers’ adoption intention of AI-assisted diagnosis and treatment. Our study provided novel insights into the path mechanism of healthcare workers’ adoption intention of AI-assisted diagnosis and treatment.

Suggested Citation

  • Mengting Cheng & Xianmiao Li & Jicheng Xu, 2022. "Promoting Healthcare Workers’ Adoption Intention of Artificial-Intelligence-Assisted Diagnosis and Treatment: The Chain Mediation of Social Influence and Human–Computer Trust," IJERPH, MDPI, vol. 19(20), pages 1-19, October.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:20:p:13311-:d:943300
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    1. Adi Alsyouf & Abdalwali Lutfi & Nizar Alsubahi & Fahad Nasser Alhazmi & Khalid Al-Mugheed & Rami J. Anshasi & Nora Ibrahim Alharbi & Moteb Albugami, 2023. "The Use of a Technology Acceptance Model (TAM) to Predict Patients’ Usage of a Personal Health Record System: The Role of Security, Privacy, and Usability," IJERPH, MDPI, vol. 20(2), pages 1-24, January.

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