Factors Influencing the Intention to Use Artificial Intelligence in Pharmaceutical Care
Article Sidebar
Main Article Content
Abstract
This study aims to investigate the factors influencing pharmacists’ intention to use artificial intelligence (AI) in pharmaceutical care and to develop guidelines for promoting the adoption of AI to support pharmacists’ professional practice. The study analyzes the relationships among factors related to technology usage intention, drawing on the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). The findings reveal that factors with a statistically significant positive influence on the intention to use AI include performance expectancy (b=0.489, p<0.001), which exerts the strongest effect, followed by social influence (b=0.312, p<0.001) and attitude toward use
(b=0.174, p=0.044). However, effort expectancy, perceived ease of use, and facilitating conditions were not found to have a statistically significant effect on usage intention. These results suggest that pharmacists place greater emphasis on treatment effectiveness and work efficiency than on the ease of using technology. This study contributes to a deeper understanding of the role of factors influencing pharmacists’ intention to adopt artificial intelligence. The knowledge gained can be applied to policy formulation and strategic planning aimed at enhancing healthcare professionals’ attitudes and confidence in the use of technology.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
บทความที่ได้รับการตีพิมพฺเป็นลิขสิทธิ์ของวารสาร
References
Srivastava R. Applications of artificial intelligence in medicine. Explor Res Hypothesis Med. 2024;9(2):138-46.
The Pharmacy Council of Thailand. สารสภาเภสัชกรรม เรื่อง ยืนยันจำนวนเภสัชกรยังขาดแคลน [Pharmacy Council Message: Confirming the shortage of pharmacists] [Internet]. 2024 [cited 2025 Oct 8]. Available from: https://pharmacycouncil.org/
Roosan D, Padua P, Khan R, Khan H, Verzosa C, Wu Y. Effectiveness of ChatGPT in clinical pharmacy and the role of artificial intelligence in medication therapy management. J Am Pharm Assoc (2003). 2024;64(2):422-8.e8.
Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989;13(3):319-40.
Venkatesh V, Davis FD. A theoretical extension of the technology acceptance model: Four longitudinal field studies. Manag Sci. 2000;46(2):186-204.
Edo OC, Ang D, Etu E-E, Tenebe I, Edo S, Diekola OA. Why do healthcare workers adopt digital health technologies: A cross-sectional study integrating the TAM and UTAUT model in a developing economy. Int J Inf Manag Data Insights. 2023;3(2):100186.
Kim YJ, Choi JH, Ngayo Fotso GMN. Medical professionals’ adoption of AI-based medical devices: UTAUT model with trust mediation. J Open Innov Technol Mark Complex. 2024; 10(1):100220.
Wei W, Prasetyo YT, Belmonte ZJA, Cahigas MML, Nadlifatin R, Gumasing MJJ. Applying the technology acceptance model–Theory of planned behavior (TAM-TPB) model to study the acceptance of building information modeling (BIM) in green building in China.
Acta Psychol (Amst). 2025;254:104790.
Domingo MAKA, Bernadas AGP, Maiquez JDP, Ong AKS, Magana MC. Assessing the acceptance of palm biometric payment systems: An integration of technology acceptance model and valence framework. Acta Psychol (Amst). 2025;259:105426.
Emon MMH, Khan T. The mediating role of attitude towards the technology in shaping artificial intelligence usage among professionals. Telemat Inform Rep. 2025;17:100188.
Marghany MNM, Elmohandes NMAA, Mohamad I, Elshawarbi NNM, Saleh MI, Ghazy K, et al. Robots at your service: Understanding hotel guest acceptance with meta-UTAUT investigation. Int J Hosp Manag. 2025;130:104227.
Napitupulu D. A conceptual model of e-Government adoption in Indonesia. Int J Adv Sci Eng Inf Technol. 2017;7(4):1471-8.
Torres R, Gerhart N. Mobile proximity usage behaviors based on user characteristics. J Comput Inf Syst. 2019;59(2):161-70.
Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information technology: Toward a unified view. MIS Q. 2003;27(3):425-78.
Davis FD, Venkatesh V. Toward preprototype user acceptance testing of new information systems: Implications for software project management. IEEE Trans Eng Manag. 2004; 51(1):31-46.
Susanty A, Puspitasari NB, Jie F, Akhsan FA, Jati S. Consumer acceptance of halal food traceability systems: A novel integrated approach using modified UTAUT and DeLone & McLean models to promote sustainable food supply chain practices. Clean Logist Supply Chain. 2025;15:100226.
Abbasi HA, Ting DH, Moughal W, Hamad S. An integrated model for encouraging consumer EV purchase: UTAUT extended with electric vehicle knowledge and advertisement. Res Transp Bus Manag. 2025;63:101448.
Rajak M, Shaw K. An extension of technology acceptance model for mHealth user adoption. Technol Soc. 2021;67:101800.
Joshi H. Integrating trust and satisfaction into the UTAUT model to predict Chatbot adoption – A comparison between Gen-Z and Millennials. Int J Inf Manag Data Insights. 2025;5(1):100332.
Chalasani SH, Syed J, Ramesh M, Patil V, Pramod Kumar TM. Artificial intelligence in the field of pharmacy practice: A literature review. Explor Res Clin Soc Pharm. 2023;12:100346.
Krejcie RV, Morgan DW. Determining sample size for research activities. Educ Psychol Meas. 1970;30(3):607–10.
Nunnally JC. Psychometric Theory. 2nd ed. New York: McGraw-Hill; 1978.
Taiwo AA, Downe AG. The theory of user acceptance and use of technology (UTAUT): A meta-analytic review of empirical findings. J Theor Appl Inf Technol. 2013;49(1):48–58.
Alenazi L, Alhalal E. Factors affecting artificial intelligence usage intention among nursing students: Unified theory of acceptance and use of technology. Nurse Educ Today. 2025;106780.
Iskandar YHP, Tangiisuran B, Ariff AM. Examining the extension of the Technology Acceptance Model (TAM) in electronic medical records among community pharmacists in Malaysia. New Emirates Med J. 2023;4(1):e090123212462.
Zhai H, Yang X, Xue J, Lavender C, Ye T, Li JB, et al. Radiation oncologists’ perceptions of adopting an artificial intelligence–assisted contouring technology: Model development and questionnaire study. J Med Internet Res. 2021;23(9):e27122.
Forster K, Strauss T. Decentralised machine learning in healthcare and life sciences: Applying the technology acceptance model. Telemat Inform Rep. 2025;18:100198.
Venkatesh V, Thong JYL, Xu X. Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Q. 2012;36(1):157-78.
Escobar-Rodriguez T, Carvajal-Trujillo E. Online purchasing tickets for low cost carriers: An application of the unified theory of acceptance and use of technology (UTAUT) model. Tourism Management. 2014;43:70-88.
