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Exploring behavioural intentions toward smart healthcare services among medical practitioners: a technology transfer perspective

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  • Jinxin Pan
  • Shuai Ding
  • Desheng Wu
  • Shanlin Yang
  • Jun Yang

Abstract

Recently, a variety of artificial intelligence (AI)-driven smart healthcare services are rapidly emerging in the medical market, such as intelligent image analysis, surgical navigation systems, and aided diagnosis. However, one of the major challenges is practitioners’ hesitation and unwillingness to employ these new technologies in medical practice. This study focuses on identifying the influential factors of adoption intention of smart healthcare services for both clinicians and non-clinicians from the perspective of technology transfer. Through collecting 484 questionnaire data from doctors in Anhui, China, we find support to show that perceived usefulness (PU), attitude, and the experience of using mHealth are key factors that influence both clinicians and non-clinician’s adoption intention. Meanwhile, it is confirmed that subjective norm has a positive effect on only clinicians’ behavioural intention (BI) while perceived risk (PR) has a negative impact on only non-clinicians’ attitude. Among all the constructs, the experience of using mHealth has the strongest positive effect on doctors’ adoption intention on smart healthcare services, a positive impact on the PU and perceived ease of use, and a negative impact on the PR. This study provides an improved understanding of doctors’ BI of smart healthcare services, and practice guidance for product development and marketing strategies.

Suggested Citation

  • Jinxin Pan & Shuai Ding & Desheng Wu & Shanlin Yang & Jun Yang, 2019. "Exploring behavioural intentions toward smart healthcare services among medical practitioners: a technology transfer perspective," International Journal of Production Research, Taylor & Francis Journals, vol. 57(18), pages 5801-5820, September.
    • Handle: RePEc:taf:tprsxx:v:57:y:2019:i:18:p:5801-5820
    DOI: 10.1080/00207543.2018.1550272
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    Cited by:

    1. Pham, Phuoc & Zhang, Huilan & Gao, Wenlian & Zhu, Xiaowei, 2024. "Determinants and performance outcomes of artificial intelligence adoption: Evidence from U.S. Hospitals," Journal of Business Research, Elsevier, vol. 172(C).
    2. Chauhan, Ankur & Jakhar, Suresh Kumar & Jabbour, Charbel Jose Chiappetta, 2022. "Implications for sustainable healthcare operations in embracing telemedicine services during a pandemic," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    3. Man Lai Cheung & Ka Yin Chau & Michael Huen Sum Lam & Gary Tse & Ka Yan Ho & Stuart W. Flint & David R Broom & Ejoe Kar Ho Tso & Ka Yiu Lee, 2019. "Examining Consumers’ Adoption of Wearable Healthcare Technology: The Role of Health Attributes," IJERPH, MDPI, vol. 16(13), pages 1-16, June.
    4. Tortorella, Guilherme Luz & Saurin, Tarcísio Abreu & Fogliatto, Flavio S. & Rosa, Valentina M. & Tonetto, Leandro M & Magrabi, Farah, 2021. "Impacts of Healthcare 4.0 digital technologies on the resilience of hospitals," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    5. Tortorella, Guilherme L. & Fogliatto, Flavio S. & Saurin, Tarcísio A. & Tonetto, Leandro M. & McFarlane, Duncan, 2022. "Contributions of Healthcare 4.0 digital applications to the resilience of healthcare organizations during the COVID-19 outbreak," Technovation, Elsevier, vol. 111(C).
    6. Chong Li & Yingqi Li, 2023. "Factors Influencing Public Risk Perception of Emerging Technologies: A Meta-Analysis," Sustainability, MDPI, vol. 15(5), pages 1-37, February.
    7. Alexander Mayr & Philip Stahmann & Maximilian Nebel & Christian Janiesch, 2024. "Still doing it yourself? Investigating determinants for the adoption of intelligent process automation," Electronic Markets, Springer;IIM University of St. Gallen, vol. 34(1), pages 1-22, December.
    8. Cao, Guangming & Duan, Yanqing & Edwards, John S. & Dwivedi, Yogesh K., 2021. "Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making," Technovation, Elsevier, vol. 106(C).
    9. Jianwei Deng & Sibo Huang & Liuan Wang & Wenhao Deng & Tianan Yang, 2022. "Conceptual Framework for Smart Health: A Multi-Dimensional Model Using IPO Logic to Link Drivers and Outcomes," IJERPH, MDPI, vol. 19(24), pages 1-22, December.
    10. Lee, Jung Young & McFadden, Kathleen L. & Lee, Min Kyung & Gowen, Charles R., 2021. "U.S. hospital culture profiles for better performance in patient safety, patient satisfaction, Six Sigma, and lean implementation," International Journal of Production Economics, Elsevier, vol. 234(C).
    11. Tortorella, Guilherme Luz & Fogliatto, Flavio S. & Kurnia, Sherah & Thürer, Matthias & Capurro, Daniel, 2022. "Healthcare 4.0 digital applications: An empirical study on measures, bundles and patient-centered performance," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    12. Viswanath Venkatesh, 2022. "Adoption and use of AI tools: a research agenda grounded in UTAUT," Annals of Operations Research, Springer, vol. 308(1), pages 641-652, January.
    13. Guilherme Luz Tortorella & Anupama Prashar & Jiju Antony & Flavio S. Fogliatto & Vicente Gonzalez & Moacir Godinho Filho, 2024. "Industry 4.0 adoption for healthcare supply chain performance during COVID-19 pandemic in Brazil and India: the mediating role of resilience abilities development," Operations Management Research, Springer, vol. 17(2), pages 389-405, June.

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