Factors Influencing the Acceptance of Distributed Research Networks in Korea: Data Accessibility and Data Security Risk
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Objectives: Distributed research networks (DRNs) facilitate multicenter research by enabling the use of multicenter data;therefore, they are increasingly utilized in healthcare fields. Despite the numerous advantages of DRNs, it is crucial to understandresearchers' acceptance of these networks to ensure their effective application in multicenter research. In this study, wesought to identify the factors influencing the adoption of DRNs among researchers in Korea. Methods: We used snowballsampling to collect data from 149 researchers between July 7 and August 28, 2020. Five factors were used to formulate thehypotheses and research model: data accessibility, usefulness, ease of use, data security risk, and intention to use DRNs. Weapplied a structural equation model to identify relationships within the research model. Results: Data accessibility and datasecurity were critical to the acceptance and use of DRNs. The usefulness of DRNs partially mediated the relationship betweendata accessibility and the intention to use DRNs. Interestingly, ease of use did not influence the intention to use DRNs, butit was affected by data accessibility. Furthermore, ease of use impacted the perceived usefulness of DRNs. Conclusions: Thisstudy highlighted major factors that can promote the broader adoption and utilization of DRNs. Consequently, these findingscan contribute to the expansion of active multicenter research using DRNs in the field of healthcare research.
Abstract
Objectives: Distributed research networks (DRNs) facilitate multicenter research by enabling the use of multicenter data;therefore, they are increasingly utilized in healthcare fields. Despite the numerous advantages of DRNs, it is crucial to understandresearchers' acceptance of these networks to ensure their effective application in multicenter research. In this study, wesought to identify the factors influencing the adoption of DRNs among researchers in Korea. Methods: We used snowballsampling to collect data from 149 researchers between July 7 and August 28, 2020. Five factors were used to formulate thehypotheses and research model: data accessibility, usefulness, ease of use, data security risk, and intention to use DRNs. Weapplied a structural equation model to identify relationships within the research model. Results: Data accessibility and datasecurity were critical to the acceptance and use of DRNs. The usefulness of DRNs partially mediated the relationship betweendata accessibility and the intention to use DRNs. Interestingly, ease of use did not influence the intention to use DRNs, butit was affected by data accessibility. Furthermore, ease of use impacted the perceived usefulness of DRNs. Conclusions: Thisstudy highlighted major factors that can promote the broader adoption and utilization of DRNs. Consequently, these findingscan contribute to the expansion of active multicenter research using DRNs in the field of healthcare research.
참고문헌 (30)
- Bredfeldt CE, "Managing protected health information in distributed research network environments : automated review to facilitate collaboration", BMC Med Inform Decis Mak, 13, pp.39~ (2013)[DOI]
- Velentgas P, "A distributed research network model for post-marketing safety studies : the Meningococcal Vaccine Study", Pharmacoepidemiol Drug Saf, 17(12), pp.1226~1234 (2008)[DOI]
- Toh S, "Comparative-effectiveness research in distributed health data networks", Clin Pharmacol Ther, 90(6), pp.883~887 (2011)[DOI]
- Holmes JH, "Clinical research data warehouse governance for distributed research networks in the USA : a systematic review of the literature", J Am Med Inform Assoc, 21(4), pp.730~736 (2014)[DOI]
- Forrow S, "The organizational structure and governing principles of the Food and Drug Administration’s Mini-Sentinel pilot program", Pharmacoepidemiol Drug Saf, 21(Suppl 1), pp.12~17 (2012)[DOI]
- McMurry AJ, "A self-scaling, distributed information architecture for public health, research, and clinical care", J Am Med Inform Assoc, 14(4), pp.527~533 (2007)[DOI]
- "Health and medical big data platform" (2023)[URL]
- "MOA project: medical record observation and assessment for drug safety" (2023)[URL]
- Venkatesh V, "User acceptance of information technology : toward a unified view", MIS Q, 27(3), pp.425~478 (2003)[DOI]
- Sandberg J, "A qualitative study of the experiences and satisfaction of direct telemedicine providers in diabetes case management", Telemed J E Health, 15(8), pp.742~750 (2009)[DOI]
- Dunn HP, "Perceived usefulness and ease of use of fundoscopy by medical students : a randomised crossover trial of six technologies(eFOCUS 1)", BMC Med Educ, 21(1), pp.41~ (2021)[DOI]
- Schnall R, "Trust, perceived risk, perceived ease of use and perceived usefulness as factors related to mHealth technology use", Stud Health Technol Inform, 216, pp.467~471 (2015)
- Rho MJ, "Factors influencing the acceptance of telemedicine for diabetes management", Cluster Comput, 18(1), pp.321~331 (2015)[DOI]
- Venkatesh V, "A theoretical extension of the technology acceptance model: four longitudinal field studies", Manag Sci, 46(2), pp.186~204 (2000)[DOI]
- Rho MJ, "Predictive factors of telemedicine service acceptance and behavioral intention of physicians", Int J Med Inform, 83(8), pp.559~571 (2014)[DOI]
- Kamal SA, "Investigating acceptance of telemedicine services through an extended technology acceptance model(TAM)", Technol Soc, 60, pp.101212~ (2020)[DOI]
- de Sena Abrahao R, "Intention of adoption of mobile payment : an analysis in the light of the Unified Theory of Acceptance and Use of Technology(UTAUT)", RAI Revista de Administracao e Inovacao, 13(3), pp.221~230 (2016)[DOI]
- Sangroya A, "Information systems, technology and management", pp.255~265 (2010), Springer
- Kim KK, "Development of a privacy and security policy framework for a multistate comparative effectiveness research network", Med Care, 51(8 Suppl 3), pp.S66~S72 (2013)[DOI]
- Goodman LA, "Snowball sampling", Ann Math Stat, 32(3), pp.148~170 (1961)
- Parker C, "Snowball sampling" (2019), SAGE Publications Ltd
- "Package ‘lavaan’: latent variable analysis" (2023)[URL]
- Nunnally JC, "Psychometric theory" (1978), McGraw-Hill
- Chin WW, "Modern methods for business research", pp.295~336 (1998), Lawrence Erlbaum Associates
- Hair JF, "Multivariate data analysis" (2010), Prentice Hall
- Curtis LH, "Design considerations, architecture, and use of the Mini-Sentinel distributed data system", Pharmacoepidemiol Drug Saf, 21 Suppl 1, pp.23~31 (2012)[DOI]
- Nadri H, "Factors affecting acceptance of hospital information systems based on extended technology acceptance model : a case study in three paraclinical departments", Appl Clin Inform, 9(2), pp.238~247 (2018)[DOI]
- Abdekhoda M, "The effects of organizational contextual factors on physicians’attitude toward adoption of Electronic Medical Records", J Biomed Inform, 53, pp.174~179 (2015)[DOI]
- Hsiao JL, "A study of factors affecting acceptance of hospital information systems : a nursing perspective", J Nurs Res, 19(2), pp.150~160 (2011)[DOI]
- Guo XT, "Understanding the acceptance of mobile health services: a service participants analysis", Proceedings of 2012 International Conference on Management Science & Engineering (ICMSE) 19th Annual Conference, pp.1868~1873 (2012)
