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Mitigating resistance in smart health monitoring systems: the role of data governance and privacy concerns

Jingjing Zhang et al.

Internet Research2026https://doi.org/10.1108/intr-12-2024-2032article
AJG 3ABDC A
Weight
0.50

Abstract

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.

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https://doi.org/https://doi.org/10.1108/intr-12-2024-2032

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@article{jingjing2026,
  title        = {{Mitigating resistance in smart health monitoring systems: the role of data governance and privacy concerns}},
  author       = {Jingjing Zhang et al.},
  journal      = {Internet Research},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1108/intr-12-2024-2032},
}

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Mitigating resistance in smart health monitoring systems: the role of data governance and privacy concerns

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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