Medical Artificial Intelligence Information Disclosure on Healthcare Professional Involvement in Innovation: A Transactional Theory of Stress and Coping Model

Abstract

AbstractComplicated medical AI innovation activities call for healthcare professionals with more practical experience, who can propose their functional requirements for AI technology and products and provide useful feedback. Medical AI information disclosure is closely related to healthcare professionals. It is critical but may be a mixed blessing in promoting their participation in medical AI innovation. Based on the transactional theory of stress and coping, this study explored the influential mechanism of medical AI information disclosure on healthcare professional involvement in AI innovation. To examine the research model, the 356 valid responses of Chinese healthcare professionals were collected by two-stage online survey. Structural equation modeling was used to examine the effect of information disclosure on healthcare professional involvement and the mediation effect of challenge appraisal and hindrance appraisal. Ordinary least squares regression was used to examine the moderation effect of subjective norm. Results indicated that medical AI information disclosure through challenge appraisal was positively related to healthcare professional involvement, whereas it through hindrance appraisal negatively affected healthcare professional involvement. Moreover, subjective norm of AI use moderated negatively the impacts of medical AI information disclosure and challenge appraisal but moderated positively the impacts of medical AI information disclosure and hindrance appraisal. This study discussed medical AI information disclosure as a kind of stressor and expanded the application of the transactional theory of stress and coping in the smart healthcare context. It enriched the associations amongst tech-stressor, appraisal outcomes and individuals' coping strategies and contributed to the development of medical AI innovationKeywords: Medical artificial intelligencehealthcare professionalinformation disclosureuser involvementchallenge-hindrance appraisalsubjective norm Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the National Natural Science Foundation of China [Grant Nos. 72072110;72132009; 72102033]; the Shanghai Science and Technology Innovation Action Plan Soft Science Project [Grant No. 23692110000]; the China Postdoctoral Science Foundation [Grant Nos. 2022T150101; 2021M700714]; the Fundamental Research Funds for the Central Universities of China [Grant No. N2206012].Notes on contributorsWeiwei HuoWeiwei Huo is an Associate Professor at SILC Business School, Shanghai University, China. She published more than 30 research papers in international quality journals such as Information Technology & People, Journal of Business Ethics, Computer Human Behavior.Wenhao LuoWenhao Luo is a master student at SILC Business School, Shanghai University, China. His research interests include medical artificial intelligence and healthcare professional behavior.Jiaqi YanJiaqi Yan is a lecturer at School of Business Administration, Northeastern University, China. He received his PhD from Tongji University and also studied as a visiting PhD student at the University of Sydney. His research focuses on organizational behavior and human resource management.Yixin WangYixin Wang is a master student at SILC Business School, Shanghai University, China. Her research interests include medical artificial intelligence and human resource management.Yehui DengYehui Deng is an undergraduate student at SILC Business School, Shanghai University, China. His research interests include business innovation.

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