Mitigating resistance in smart health monitoring systems

The role of data governance and privacy concerns

Dados Bibliográficos

ID12420349
AutoresJingjing Zhang (0000-0003-0408-374X, Auckland University of Technology Department of Management, Technology, and Organisation, autor correspondente), Farkhondeh Hassandoust (0000-0001-7190-9527, The University of Auckland Department of Information Systems and Operation Management), Allen C Johnston (0000-0003-0301-4187, Culverhouse College of Business, The University of Alabama Department of Information Systems, Statistics, and Management Science)
Ano2026
Volume36
Fascículo7
Páginas82-106
Data de publicação2026-02-23
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoInternet Research (JOURNAL)
Identificadores do periódicoISSN: 1066-2243 • E-ISSN: 2054-5657
EditoraEmerald Publishing Limited (PUBLISHER • GB)
DOI10.1108/intr-12-2024-2032
OpenAlexW7131776734
IdiomaEN
Referências citadas114

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

Accountability · Corporate governance · Data governance · Information governance · Information privacy · Resistance (ecology · Stakeholder · Survey data collection · Transparency (behavior · COVID-19 Digital Contact Tracing · Ethics and Social Impacts of AI · Privacy, Security, and Data Protection