IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v195y2023ics0040162523004535.html
   My bibliography  Save this article

Exploring the drivers of XAI-enhanced clinical decision support systems adoption: Insights from a stimulus-organism-response perspective

Author

Listed:
  • Dalvi-Esfahani, Mohammad
  • Mosharaf-Dehkordi, Mehdi
  • Leong, Lam Wai
  • Ramayah, T.
  • Jamal Kanaan-Jebna, Abdulkarim M.

Abstract

The concept of Explainable Artificial Intelligence (XAI) provides a clear and comprehensible explanation for the reasoning behind a system's output, allowing users to understand the context in which it operates. In the realm of Clinical Decision Support Systems (CDSS), XAI is particularly crucial, as it helps healthcare professionals (HCPs) in their decision-making processes. Without XAI, there is a risk of over-reliance on the system's output, potentially leading to subpar results. Despite the numerous benefits that XAI-enhanced CDSS hold in the healthcare industry, there has been a limited number of studies examining their implementation and acceptance. Thus, the objective of this study was to examine the adoption of XAI-based CDSS through the Stimulus-Organism-Response model. The sample consisted of 172 HCPs from Malaysian public and private hospitals, and the research model was tested using Partial Least Squares Structural Equation Modeling (PLS-SEM) in conjunction with the bootstrapping method. The results showed a significant positive correlation between the stimulus factors of informed action, transparent interaction, and representational fidelity and the positive attitude towards XAI-based CDSS as an organism factor. Additionally, the study found that attitude was a significant predictor of the intention to adopt as a response factor. The analysis also revealed a negative and significant moderation effect of perceived performance risk on the relationship between attitude and intention, while the positive moderating effect of perceived fairness was not supported. The findings of this study have significant implications for both theoretical and practical considerations and highlight the importance of XAI in the field of Clinical Decision Support Systems.

Suggested Citation

  • Dalvi-Esfahani, Mohammad & Mosharaf-Dehkordi, Mehdi & Leong, Lam Wai & Ramayah, T. & Jamal Kanaan-Jebna, Abdulkarim M., 2023. "Exploring the drivers of XAI-enhanced clinical decision support systems adoption: Insights from a stimulus-organism-response perspective," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    • Handle: RePEc:eee:tefoso:v:195:y:2023:i:c:s0040162523004535
    DOI: 10.1016/j.techfore.2023.122768
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162523004535
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2023.122768?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yuen, Kum Fai & Koh, Le Yi & Tan, Luan Yi Hazel & Wang, Xueqin, 2023. "The determinants of virtual reality adoption for marine conservation," Technology in Society, Elsevier, vol. 72(C).
    2. Duan, Yanqing & Edwards, John S. & Dwivedi, Yogesh K, 2019. "Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda," International Journal of Information Management, Elsevier, vol. 48(C), pages 63-71.
    3. Richard Berk & Hoda Heidari & Shahin Jabbari & Michael Kearns & Aaron Roth, 2021. "Fairness in Criminal Justice Risk Assessments: The State of the Art," Sociological Methods & Research, , vol. 50(1), pages 3-44, February.
    4. Torres, Russell & Sidorova, Anna, 2019. "Reconceptualizing information quality as effective use in the context of business intelligence and analytics," International Journal of Information Management, Elsevier, vol. 49(C), pages 316-329.
    5. Julia Amann & Dennis Vetter & Stig Nikolaj Blomberg & Helle Collatz Christensen & Megan Coffee & Sara Gerke & Thomas K Gilbert & Thilo Hagendorff & Sune Holm & Michelle Livne & Andy Spezzatti & Inga S, 2022. "To explain or not to explain?—Artificial intelligence explainability in clinical decision support systems," PLOS Digital Health, Public Library of Science, vol. 1(2), pages 1-18, February.
    6. Yu-Shan Chen & Stanley Y.B. Huang, 2017. "The effect of task-technology fit on purchase intention: The moderating role of perceived risks," Journal of Risk Research, Taylor & Francis Journals, vol. 20(11), pages 1418-1438, November.
    7. Andrew Burton-Jones & Camille Grange, 2013. "From Use to Effective Use: A Representation Theory Perspective," Information Systems Research, INFORMS, vol. 24(3), pages 632-658, September.
    8. Talukder, Md. Shamim & Sorwar, Golam & Bao, Yukun & Ahmed, Jashim Uddin & Palash, Md. Abu Saeed, 2020. "Predicting antecedents of wearable healthcare technology acceptance by elderly: A combined SEM-Neural Network approach," Technological Forecasting and Social Change, Elsevier, vol. 150(C).
    9. Hong, Areum & Nam, Changi & Kim, Seongcheol, 2020. "What will be the possible barriers to consumers’ adoption of smart home services?," Telecommunications Policy, Elsevier, vol. 44(2).
    10. Kamal, Syeda Ayesha & Shafiq, Muhammad & Kakria, Priyanka, 2020. "Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM)," Technology in Society, Elsevier, vol. 60(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ong, Ardvin Kester S. & Kurata, Yoshiki B. & Castro, Sophia Alessandra D.G. & De Leon, Jeanne Paulene B. & Dela Rosa, Hazel V. & Tomines, Alex Patricia J., 2022. "Factors influencing the acceptance of telemedicine in the Philippines," Technology in Society, Elsevier, vol. 70(C).
    2. Cano-Marin, Enrique & Mora-Cantallops, Marçal & Sánchez-Alonso, Salvador, 2023. "Twitter as a predictive system: A systematic literature review," Journal of Business Research, Elsevier, vol. 157(C).
    3. Jinzhi Zou & Khairul Manami Kamarudin & Jing Liu & Jiaqi Zhang, 2024. "Towards Sustainable Mobility: Determinants of Intention to Purchase Used Electric Vehicles in China," Sustainability, MDPI, vol. 16(19), pages 1-20, October.
    4. Jin P. Gerlach & Ronald T. Cenfetelli, 2022. "Overcoming the Single-IS Paradigm in Individual-Level IS Research," Information Systems Research, INFORMS, vol. 33(2), pages 476-488, June.
    5. Arfi, Wissal Ben & Nasr, Imed Ben & Kondrateva, Galina & Hikkerova, Lubica, 2021. "The role of trust in intention to use the IoT in eHealth: Application of the modified UTAUT in a consumer context," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    6. Hoda M. Aboalsamh & Laith T. Khrais & Sami A. Albahussain, 2023. "Pioneering Perception of Green Fintech in Promoting Sustainable Digital Services Application within Smart Cities," Sustainability, MDPI, vol. 15(14), pages 1-13, July.
    7. Weiling Ke & Lele Kang & Chuan-Hoo Tan & Chih-Hung Peng, 2021. "User Competence with Enterprise Systems: The Effects of Work Environment Factors," Information Systems Research, INFORMS, vol. 32(3), pages 860-875, September.
    8. Latinovic, Zoran & Chatterjee, Sharmila C., 2022. "Achieving the promise of AI and ML in delivering economic and relational customer value in B2B," Journal of Business Research, Elsevier, vol. 144(C), pages 966-974.
    9. Sami S. Binyamin & Md. Rakibul Hoque, 2020. "Understanding the Drivers of Wearable Health Monitoring Technology: An Extension of the Unified Theory of Acceptance and Use of Technology," Sustainability, MDPI, vol. 12(22), pages 1-20, November.
    10. Lim, Joon Soo & Zhang, Jun, 2022. "Adoption of AI-driven personalization in digital news platforms: An integrative model of technology acceptance and perceived contingency," Technology in Society, Elsevier, vol. 69(C).
    11. Talwar, Shalini & Kaur, Puneet & Escobar, Octavio & Lan, Sai, 2022. "Virtual reality tourism to satisfy wanderlust without wandering: An unconventional innovation to promote sustainability," Journal of Business Research, Elsevier, vol. 152(C), pages 128-143.
    12. Michael Addotey-Delove & Richard E. Scott & Maurice Mars, 2023. "Healthcare Workers’ Perspectives of mHealth Adoption Factors in the Developing World: Scoping Review," IJERPH, MDPI, vol. 20(2), pages 1-27, January.
    13. Subhodeep Mukherjee & Manish Mohan Baral & Chittipaka Venkataiah & Surya Kant Pal & Ramji Nagariya, 2021. "Service robots are an option for contactless services due to the COVID-19 pandemic in the hotels," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 48(4), pages 445-460, December.
    14. Taiga Saito & Shivam Gupta, 2022. "Big data applications with theoretical models and social media in financial management," CARF F-Series CARF-F-550, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    15. Biancone, Paolo & Secinaro, Silvana & Marseglia, Roberto & Calandra, Davide, 2023. "E-health for the future. Managerial perspectives using a multiple case study approach," Technovation, Elsevier, vol. 120(C).
    16. Cannavacciuolo, Lorella & Capaldo, Guido & Ponsiglione, Cristina, 2023. "Digital innovation and organizational changes in the healthcare sector: Multiple case studies of telemedicine project implementation," Technovation, Elsevier, vol. 120(C).
    17. Md. Alamgir Hossain & Ruhul Amin & Abdullah Al Masud & Md. Imran Hossain & Mohammad Awal Hossen & Mohammad Kamal Hossain, 2023. "What Drives People’s Behavioral Intention Toward Telemedicine? An Emerging Economy Perspective," SAGE Open, , vol. 13(3), pages 21582440231, July.
    18. Anna Langenberg & Shih-Chi Ma & Tatiana Ermakova & Benjamin Fabian, 2023. "Formal Group Fairness and Accuracy in Automated Decision Making," Mathematics, MDPI, vol. 11(8), pages 1-25, April.
    19. Islam, A.K.M. Najmul & Laato, Samuli & Talukder, Shamim & Sutinen, Erkki, 2020. "Misinformation sharing and social media fatigue during COVID-19: An affordance and cognitive load perspective," Technological Forecasting and Social Change, Elsevier, vol. 159(C).
    20. Leonie Kuen & Fiona Schürmann & Daniel Westmattelmann & Sophie Hartwig & Shay Tzafrir & Gerhard Schewe, 2023. "Trust transfer effects and associated risks in telemedicine adoption," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-22, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:tefoso:v:195:y:2023:i:c:s0040162523004535. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/00401625 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.