Resistance to Blockchain Adoption in Health Care Organizations: Evidence From a Cross-Sectional Study

Background: Traditional health care systems struggle to ensure the security of medical data. To address these issues, organizations are exploring blockchain-based solutions, which offer strong security for managing medical data and transactions. Despite these benefits, adoption remains limited because many health care organizations are hesitant to implement blockchain apps due to perceived risks associated with these apps. Objective: Our research aims to study the adoption of blockchain-based health care apps in health care organizations by adopting a risk-based approach. Using perceived risk theory (PRT), we developed a research model that links specific perceived risks of blockchain-based health care apps to the intention to adopt them. Methods: This study used a cross-sectional design to test the research model through an online questionnaire administered to IT professionals from health care organizations in Canada and Africa. IT professionals were selected because they influence technology adoption and possess greater blockchain knowledge than other health care staff. A total of 217 responses were collected; after removing incomplete entries, 194 valid responses remained for analysis. This exceeded the minimum recommended sample size of 100 for structural equation modeling with 8 latent variables, 40 observed variables, a value of .05, and an anticipated effect size of 0.3. Partial least squares structural equation modeling was used to test the hypothesized relationships in the research model. Results: Harman single-factor test showed that the first factor accounted for 25.376% of the total variance (below 50%), indicating that common method bias was not a concern. Partial least squares structural equation modeling identified key risks influencing the overall perceived risk of blockchain-based health care apps and their adoption in health care organizations. The findings revealed that the relationship between perceived risk and intention to adopt blockchain-based health care apps is significant in Canadian health care organizations but not in African ones. Additionally, the availability of cloud-based blockchain solutions was found to reduce organizations’ perceived risk related to insufficient resources for adopting and implementing blockchain-based health care apps. Conclusions: This study used PRT to identify risks that may hinder the adoption of blockchain-based health care apps. Two risks—medical data disclosure and loss of control over medical data—were identified as blockchain-specific and incorporated as new PRT dimensions, extending the theory for blockchain contexts. The extended PRT can guide future studies examining reluctance to adopt blockchain apps. Results also show that contextual differences between Canada and Africa influence the relationship between perceived risks and adoption intentions. Our findings are beneficial to governments, health care organizations, and blockchain development agencies, as they will be able to implement strategies to mitigate some of the risks perceived by IT professionals related to blockchain-based health care apps.