Mitigating resistance in smart health monitoring systems: the role of data governance and privacy concerns
本研究通过混合方法，基于创新抵制理论和数据治理机制，发现程序性和关系性数据治理机制（如立法保护、透明度、信任）比结构性机制更能减少用户对智能健康监测系统的抵制，隐私担忧显著影响功能障碍进而加剧抵制。
Purpose Smart health monitoring systems (SHMSs) have encountered resistance and limited adoption by various stakeholders. This study aims to investigate the impact of data governance on the associated privacy concerns in relation to barriers, thereby mitigating users' resistance to SHMSs. Design/methodology/approach This mixed-methods study draws on innovation resistance theory and data governance mechanisms. We developed a research model based on 20 qualitative interviews with individuals from multiple stakeholder groups and empirically tested the model using 277 valid responses from potential and current SHMS users, collected through an online questionnaire survey. Findings The findings reveal that data governance mechanisms–incorporating legislative protection, cultural and religious differences (procedural data governance mechanisms), transparency, and trust (relational data governance mechanisms)–are more influential than accountability and responsibility (structural data governance mechanisms) in reducing user resistance to SHMSs. Privacy concerns significantly influence functional barriers to SHMSs and ultimately positively affect users' resistance to SHMSs. Cultural and religious differences and trust mechanisms are significantly associated with privacy concerns among users with a high personal innovativeness level. Research limitations/implications The study extends innovation resistance theory by integrating data governance, showing how theoretical models can be practically adapted for diverse health information technology (HIT) contexts. The findings offer societal implications, informing policies that promote SHMS development with robust privacy protections, inclusive design and trust-building governance. Originality/value This is a pioneering study that extends innovation resistance theory by integrating data governance, demonstrating how theoretical models can be tailored to address diverse needs within the HIT domain.