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Journals / International Journal of Health Management and Tourism / 2024 / Cilt: 9 Sayı: 3

An Examination of Factors Influencing Physicians' Acceptance and Use of the e-Nabız System

Mukadder Bektaş(KARABÜK ÜNİVERSİTESİ)0000-0002-7405-383X
Abdullah Karakaya(KARABUK UNIVERSITY)0000-0002-3214-6771
Pages
346–367
Publication Date
2024-11-27
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Conclusion: The findings indicate that Model 2 provides a more comprehensive explanation of the factors affecting the acceptance and use of the system.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Aim: This study aims to identify the factors influencing physicians' acceptance and use of the e-Nabız system by comparing two models.
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Key Findings & Data:Methods: Conducted with 388 physicians from university hospitals across Turkey, the study utilized an online survey based on the Unified Theory of Acceptance and Use of Technology (UTAUT) scale.
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Conclusion & Impact:Conclusion: The findings indicate that Model 2 provides a more comprehensive explanation of the factors affecting the acceptance and use of the system.
🔬Design:Survey & Empirical Study
📍Scope:Comprehensive Sample
⚙️Variables:IT Acceptance and Use · UTAUT · Structural Equation Modelling

Abstract

Aim: This study aims to identify the factors influencing physicians' acceptance and use of the e-Nabız system by comparing two models. Methods: Conducted with 388 physicians from university hospitals across Turkey, the study utilized an online survey based on the Unified Theory of Acceptance and Use of Technology (UTAUT) scale. Descriptive analyses, frequency and percentage distributions, reliability analysis, confirmatory factor analysis, and structural equation modelling were applied to the collected data. Results: In Model 1, the behavioral intention was influenced by performance expectancy and social influence, while usage behavior was shaped by social influence, facilitating conditions, and behavioral intention. Model 1 accounted for 75% of the variance in behavioral intention and 69% in usage behavior. In contrast, Model 2 identified performance expectancy, anxiety, habit, personal technology innovativeness, and workflow as significant predictors of behavioral intention. Usage behavior in Model 2 was influenced by habit, facilitating conditions, anxiety, personal technology innovativeness, workflow, and behavioral intention, explaining 84% of the variance in behavioral intention and 85% in usage behavior. Conclusion: The findings indicate that Model 2 provides a more comprehensive explanation of the factors affecting the acceptance and use of the system. To enhance acceptance and usage, the study suggests focusing on anxiety management, emphasizing performance benefits, aligning the system with workflow, educating users about new technologies, and implementing incentives to foster habitual use. Future research should explore other technology acceptance models in various healthcare information systems to deepen understanding.

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Cite This Article

Bektaş, M., Karakaya, A. (2024). An Examination of Factors Influencing Physicians' Acceptance and Use of the e-Nabız System. International Journal of Health Management and Tourism, Cilt: 9 Sayı: 3, 346–367. https://doi.org/10.31201/ijhmt.1568873

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