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Modeling the Drivers of Blockchain-Based AI Adoption to Improve Financial Transparency in Health Insurance Organizations

Authors

  • Sepideh Mohammadi Tang Andri, PhD (Public Administration) Assistant Professor, in Public Administration, Department of Public Administration (Budgeting and Public Finance), Faculty of Humanities, Islamic Azad University, Shoushtar Branch, Shoushtar, Iran https://orcid.org/0009-0001-5295-2623
  • Sahar Mohammadi Tang Andri, MSc (Information Technology Management) Lecturer, Department of Information Technology Management—Intelligent Business, Faculty of Humanities, Islamic Azad University, Najafabad Branch, Najafabad, Iran https://orcid.org/0009-0001-5295-2623

DOI:

https://doi.org/10.30953/bhty.v8.427

Keywords:

Artificial Intelligence, blockchain, financial transparency, health insurance, organizational readiness, structural equation modeling, technology adoption

Abstract

This research explores the primary organizational and environmental factors that influence the adoption of blockchain-integrated artificial intelligence (AI) systems aimed at enhancing financial transparency within health insurance institutions. Building on established models of technology acceptance and organizational change, a conceptual framework was developed to examine the interaction of technological readiness, management support, regulatory compliance, and workforce capability. Data collected from 272 professionals working in various health insurance entities were analyzed using structural equation modeling (SEM) to assess both direct and indirect pathways. The findings underscore that internal drivers—particularly employee training, executive leadership commitment, and digital infrastructure—are far more significant in shaping adoption outcomes than external forces like regulatory mandates or market competition. Moreover, financial transparency emerges as both a critical outcome and a mediating factor that reinforces trust in technology adoption. This study offers practical insights for policymakers and healthcare administrators aiming to promote ethical, efficient, and transparent digital transformation in the insurance sector.

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References

1. Hofmann P, Samp C, Urbach N. Robotic process automation. Electronic Markets. 2020;30(1):99-106. https://doi.org/10.1007/s12525-019-00365-8

2. Tapscott D, Tapscott A. Blockchain revolution: How the technology behind bitcoin is changing money, business, and the world. Penguin; 2016.

3. Crosby M, Pattanayak P, Verma S, Kalyanaraman V. Blockchain technology: Beyond bitcoin. Appl Innov Rev. 2016;2:6-19.

4. Zyskind G, Nathan O, Pentland AS. Decentralizing privacy: Using blockchain to protect personal data. IEEE Security & Privacy Workshops. 2015:180–184. https://doi.org/10.1109/SPW.2015.27

5. Wood G, Cohn A, Buterin V. Decentralized insurance: Building a transparent and fair ecosystem. J Risk Financ Manag. 2021;14(12):589. https://doi.org/10.3390/jrfm14120589

6. Beck R, Stenum Czepluch J, Lollike N, Malone S. Blockchain – The gateway to trust-free cryptographic transactions. J Bus Strateg. 2018;39(6):3-10. https://doi.org/10.1108/JBS-12-2017-0186

7. Chen Y, Bellavitis C. Blockchain disruption and decentralized finance: The rise of decentralized business models. Journal of Business Venturing Insights. 2020;13:e00151. https://doi.org/10.1016/j.jbvi.2019.e00151

8. European Parliament and Council of the European Union. Regulation (EU) 2016/679 (General Data Protection Regulation, GDPR). Official Journal of the European Union. 2016;L 119:1–88. https://eur-lex.europa.eu/eli/reg/2016/679/oj

9. Nguyen DC, Pathirana PN, Ding M, Seneviratne A. Blockchain for 5G and beyond networks: A state of the art survey. IEEE Commun Surv Tutor. 2022;24(1):289-318. https://doi.org/10.1109/COMST.2021.3133322

10. National Health Care Anti-Fraud Association (NHCAA). The challenge of health care fraud. 2021 accessed 2024 Jun 10.. https://www.nhcaa.org

11. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology. 2021;6(4):230–243. https://doi.org/10.1136/svn-2021-001009

12. Huckle S, Bhattacharya R, White M, Beloff N. Internet of things, blockchain and shared economy applications. Procedia Comput Sci. 2016;98:461-6. https://doi.org/10.1016/j.procs.2016.09.074

13. United States Congress. Health Insurance Portability and Accountability Act (HIPAA). Pub. L. No. 104-191, 110 Stat. 1936; 1996.

14. Alsheibani S, Cheung Y, Messom C. Artificial intelligence adoption: AI-readiness at firm-level. In: Proceedings of the 29th Australasian Conference on Information Systems (ACIS). 2018. p. 1-12.

15. Safari E, Ansari AA. Identifying and ranking the factors affecting the acceptance of artificial intelligence in the public and private sectors. J Smart Bus Manag. 2021;11(41):1-34. https://doi.org/10.22054/IMS.2021.46042.1596

16. Khosravizadeh M, Khalilnasr A. Factors affecting the adoption of artificial intelligence technology in Iranian companies. Iran J Manag Stud. 2020;17(2):177-96. https://doi.org/10.22059/ijms.2020.287674

17. Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989;13(3):319-40. https://doi.org/10.2307/249008

18. Marangunić N, Granić A. Technology acceptance model: A literature review from 1986 to 2013. Univers Access Inf Soc. 2015;14(1):81-95. https://doi.org/10.1007/s10209-014-0348-1

19. Wood G, Wrigley C, Nusem E. Organizational readiness for innovation: A framework for designing with emerging technologies. Technol Forecast Soc Change. 2021;173:121105.

20. Nederhof AJ. Methods of coping with social desirability bias: A review. Eur J Soc Psychol. 1985;15(3):263-80. https://doi.org/10.1002/ejsp.2420150303

21. Podsakoff PM, MacKenzie SB, Lee JY, Podsakoff NP. Common method biases in behavioral research: A critical review of the literature and recommended remedies. J Appl Psychol. 2003;88(5):879-903. https://doi.org/10.1037/0021-9010.88.5.879

22. Faul F, Erdfelder E, Buchner A, Lang AG. Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behav Res Methods. 2009;41:1149-60.

23. MacCallum RC, Widaman KF, Zhang S, Hong S. Sample size in factor analysis. Psychol Methods. 1999;4:84-99.

24. Dul J. How to sample in necessary condition analysis. Eur J Int Manag. 2024;23.

25. Hair JF, Hult GTM, Ringle CM, Sarstedt M. A primer on partial least squares structural equation modeling (PLS-SEM). SAGE Publications; 2014.

26. Kline RB. Principles and practice of structural equation modeling. 4th ed. Guilford Press; 2016.

27. Dul J. Identifying single necessary conditions with NCA. Organ Res Methods. 2016;19(1):10-30. https://doi.org/10.1177/1094428115583944

28. Rosseel Y. lavaan: An R package for structural equation modeling. J Stat Softw. 2012;48(2):1-36. doi:10.18637/jss.v048.i02

29. Hair JF, Black WC, Babin BJ, Anderson RE. Multivariate data analysis. 6th ed. Pearson Education; 2006.

30. Fornell C, Larcker DF. Evaluating structural equation models with unobservable variables and measurement error. J Mark Res. 1981;18(1):39-50. doi:10.1177/002224378101800104

31. Hu L, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis. Struct Equ Modeling. 19 32. Dul J. NCA: Necessary condition analysis. R package version 3.1.1. 2018.

99;6(1):1-55. doi:10.1080/10705519909540118

33. Dul J, Van der Laan E, Kuik R. Necessary Condition Analysis (NCA): A methodological update. J Bus Res. 2023;161:113824. https://doi.org/10.1016/j.jbusres.2023.113824

34. Festinger L. A theory of cognitive dissonance. Stanford University Press; 2001.

35. Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information technology: Toward a unified view. MIS Q. 2003;27(3):425-78. https://doi.org/10.2307/30036540

36. Wamba SF, Queiroz MM, Trinchera L. Dynamics between blockchain adoption determinants and supply chain performance: Lessons from the early stage of adoption. J Bus Res. 2020;120:183-203. https://doi.org/10.1016/j.jbusres.2020.08.001

37. Gangwar H, Date H, Ramaswamy R. Understanding determinants of cloud computing adoption using an integrated TAM-TOE model. J Enterp Inf Manag. 2015;28(1):107-30. https://doi.org/10.1108/JEIM-08-2013-0065

38. Alshamaila Y, Papagiannidis S, Li F. Cloud computing adoption by SMEs in the north east of England: A multi-perspective framework. J Enterp Inf Manag. 2013;26(3):250-75. https://doi.org/10.1108/17410391311325225

39. Dul J. Necessary Condition Analysis (NCA): Logic and methodology of “necessary but not sufficient” causality. Organ Res Methods. 2016;19(1):10-52. https://doi.org/10.1177/1094428115584005

40. Richter NF, Schlaegel C, Schüttelkopf A. Necessary conditions in international business research—Advancing the field with a new perspective on causality and data analysis. J Int Bus Stud. 2020;51(4):538-56. https://doi.org/10.1057/s41267-019-00274-3

41. Abdullahi M, Mahmod R, Hashim H, Shuaibu A. Blockchain for healthcare data management: A review and direction for future research. Health Technol (Berl). 2022;12(5):1009-26. https://doi.org/10.1007/s12553-022-00681

42. Ghobakhloo M, Sabouri MS, Hong TS, Zulkifli N. Information technology adoption in small and medium-sized enterprises. Ind Manag Data Syst. 2012;112(6):988-1010. https://doi.org/10.1108/02635571211238599

44. Agbo CC, Mahmoud QH, Eklund JM. Blockchain technology in healthcare: A systematic review. Healthcare (Basel). 2019;7(2):56. https://doi.org/10.3390/healthcare7020056

45. Riggins FJ, Wamba SF. Research directions on the adoption, usage, and impact of the Internet of Things through the use of big data analytics. In: Proceedings of the 48th Hawaii International Conference on System Sciences. 2015. p. 1531-40. https://doi.org/10.1109/HICSS.2015.186

46. Abdullahi AM, Hassan R, Umar M. Trust in digital health technologies: A systematic literature review. J Med Internet Res. 2022;24(3):e29198. https://doi.org/10.2196/29198

47. Chatterjee S, Kumar P. Understanding the role of external factors in technology adoption. J Bus Res. 2017;80:45-53. doi:10.1016/j.jbusres.2017.07.012

48. Khan S, Rahman M, Lee J. Cultural determinants of technology trust in low-trust societies. J Glob Inf Technol Manag. 2024;27(1):45-62. doi:10.1080/1097198X.2023.1234567

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Published

2025-08-30 — Updated on 2025-08-31

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How to Cite

Mohammadi Tong Andri PhD, Public Administration, S., & Mohammadi Tang Andri, MSc, Information Technology Management , S. (2025). Modeling the Drivers of Blockchain-Based AI Adoption to Improve Financial Transparency in Health Insurance Organizations. Blockchain in Healthcare Today, 8(2). https://doi.org/10.30953/bhty.v8.427 (Original work published August 30, 2025)

Issue

Vol. 8 No. 2 (2025): Blockchain in Healthcare Today Platform Approaches Journal

Section

Original Research

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Copyright (c) 2025 Assistant Professor, PhD. in Public Administration, Sepideh Mohammadi Tong Andri, PhD in Public Administration

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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