Skip to main content
Have a personal or library account? Click to login
Why Trust Matters More Than Utility: Physicians’ Acceptance of Algorithmic Management in AI-Driven Pharmaceutical Communication Cover

Why Trust Matters More Than Utility: Physicians’ Acceptance of Algorithmic Management in AI-Driven Pharmaceutical Communication

Open Access
|Jul 2026

References

  1. Bisaschi, L., Calderoni, P., Garces, I., Lechardoy, L. and Nardoni, S. (2025). Algorithmic management in the healthcare sector: Evidence from case studies in Italy and France. JRC Working Papers Series on Labour/Education and Technology, 2025(05). Seville: European Commission, Joint Research Centre (JRC).
  2. Chen, Z., Wu, T., Wu, X., Wang, J., Ke, S., Li, H., & Lin, R. (2025). The mediating effects of technology trust and perceived value in the relationship between eHealth literacy and attitude toward the usage of artificial intelligence in nursing: A cross-sectional study. BMC Nursing, 24, 989. https://doi.org/10.1186/s12912-025-03577-w
  3. Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research, pp. 295–336. Lawrence Erlbaum Associates. https://doi.org/10.4324/9781410604385-10
  4. Claggett, J., Petter, S., Joshi, A., Ponzio, T., & Kirkendall, E. (2024). An infrastructure framework for remote patient monitoring interventions and research. Journal of Medical Internet Research, 26, e51234. https://doi.org/10.2196/51234
  5. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
  6. Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015). Algorithm aversion: People erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology: General, 144(1), 114–126. https://doi.org/10.1037/xge0000033
  7. Dingel, J., Kleine, A.-K., Cecil, J., Sigl, A. L., Lermer, E., & Gaube, S. (2024). Predictors of health care practitioners’ intention to use AI-enabled clinical decision support systems: Metaanalysis based on the Unified Theory of Acceptance and Use of Technology. Journal of Medical Internet Research, 26, e57224. https://doi.org/10.2196/57224
  8. Dhagarra, D., Goswami, M., & Kumar, G. (2020). Impact of trust and privacy concerns on technology acceptance in healthcare: An Indian perspective. International Journal of Medical Informatics, 141, 104164. https://doi.org/10.1016/j.ijmedinf.2020.104164
  9. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
  10. Kamal, S. A., Shafiq, M., & Kakria, P. (2020). Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM). Technology in Society, 60, 101212. https://doi.org/10.1016/j.techsoc.2019.101212
  11. Kauttonen, J., Rousi, R., & Alamäki, A. (2025). Trust and acceptance challenges in the adoption of AI applications in health care: Quantitative survey analysis. Journal of Medical Internet Research, 27, e65567. https://doi.org/10.2196/65567
  12. Lambert, S. I., Madi, M., Sopka, S., Lenes, A., Stange, H., Buszello, C.-P., & Stephan, A. (2023). An integrative review on the acceptance of artificial intelligence among healthcare professionals in hospitals. npj Digital Medicine, 6(1), Article 111. https://doi.org/10.1038/s41746-023-00852-5
  13. Liebenspacher, F. & Siegfried, P. (2022). Pharmacy 4.0—The Potential of Integrating Digital Technologies into Daily Healthcare Processes at Pharmacies, Timişoara Medical Journal, 2022(2), 3, http://doi.org/10.35995/tmj20220203
  14. Ploegmakers, K.J., Medlock, S., Linn, A.J. et al. (2022). Barriers and facilitators in using a Clinical Decision Support System for fall risk management for older people: a European survey, European Geriatric Medicine, 13, pp. 395–405. https://doi.org/10.1007/s41999-021-00599-w
  15. Radeva, M.N., Hristova, E., Georgiev, R.T. & Zlatarova, Z.I. (2026). Awareness, trust, and expectations of AI for glaucoma care among Bulgarian ophthalmologists: Role of demographic factors. PLOS Digital Health, 5(1), e0001199. https://doi.org/10.1371/journal.pdig.0001199
  16. Ringle, C. M., Sarstedt, M., Mitchell, R., & Gudergan, S. P. (2018). Partial least squares structural equation modeling in HRM research. The International Journal of Human Resource Management, 31(12), 1617–1643. https://doi.org/10.1080/09585192.2017.1416655
  17. Ringle, C. M., Wende, S., & Becker, J.-M. (2024). SmartPLS 4 (Version 4) [Computer software]. SmartPLS GmbH. https://www.smartpls.com
  18. Rosenbacke, R., Melhus, Å., McKee, M., & Stuckler, D. (2024). How explainable artificial intelligence can increase or decrease clinicians’ trust in AI applications in health care: Systematic review. JMIR AI, 3, e53207. https://doi.org/10.2196/53207
  19. Shevtsova, D., Ahmed, A., Boot, I. W. A., Sanges, C., Hudecek, M., Jacobs, J. J. L., Hort, S., & Vrijhoef, H. J. M. (2024). Trust in and acceptance of artificial intelligence applications in medicine: Mixed methods study. JMIR Human Factors, 11, e47031. https://doi.org/10.2196/47031
  20. Shin, D. (2021). The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI. International Journal of Human–Computer Studies, 146, 102551. https://doi.org/10.1016/j.ijhcs.2020.102551
  21. Venkatesh, V., & Bala, H. (2008). Technology Acceptance Model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.x
  22. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
Language: English
Page range: 1871 - 1882
Published on: Jul 21, 2026
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2026 Carmen Rozalia GEORGESCU, Ionut Andrei MILITARU, published by Bucharest University of Economic Studies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.