- 期刊
探討使用e化智能系統行為意圖之影響因素：UTAUT 2的應用和擴展
Factors Determining the Behavioral Intention of Using E-intelligent Systems: An Application and Extension of the UTAUT 2 Model
摘要
目的：隨著資訊科技與醫療服務的快速變遷，e化智能系統是協助護理人員工作環境中新科技的重要應用之一。因此，瞭解護理人員對該系統使用意圖的影響因素則是值得探討的重要議題。本研究旨在提出一個理論模式來預測與解釋護理人員使用e 化智能系統行為意圖之影響因素，並結合延伸型整合性科技接受模式、資訊系統成功模式、科技壓力、行動自我效能等理論，並將科技壓力視為調節變數。方法：本研究透過問卷調查法，以雲林縣某地區教學醫院護理人員為研究對象。共計發出200份問卷，回收有效問卷176份，使用結構方程模式進行資料分析。結果：分析結果顯示：護理人員對e化智能系統之（1）績效期望、社會影響、習慣、滿意度對行為意圖有顯著的正向影響；（2）努力期望、資訊品質對績效期望有顯著的正向影響；（3）系統品質、行動自我效能對努力期望有顯著正向影響；（4）研究模式中，科技壓力不具調節效果。結論：最後研究結果主要發現，在開發或優化現有e化智能系統的過程，除需考量如何提升系統整體的操作及資訊品質外，亦須將護理人員的心理感受（包含同儕及社會對其使用系統的觀感等）納入考量，以提升護理人員使用這項系統的意圖，進而提升護理人員的工作效率。
並列摘要
Objectives: With the rapid evolution of information technology and medical services, e-intelligent systems are now amongst the essential applications of modern technology being utilized in the working environments of nursing staff. Therefore, a key issue to be investigated is what factors influence the intention of nursing staff to use such systems. This study proposes a theoretical model to predict and explain the factors determining the nursing staff's behavioral intention to use e-intelligent systems. The study also incorporates various theories, including the extended unified theory of acceptance and use of technology (UTAUT), the information system success model, technostress, mobile self-efficacy, and technostress moderators. Methods: This study employed a survey to collect data. The sample hospital is located in Yunlin County, and is categorized as a district teaching hospital. The nursing staff was randomly selected, 200 questionnaires were distributed, and 176 valid questionnaires were collected. Data were analyzed using the structural equation model. Results: The results showed that: (1) performance expectancy, social influence, habit, and satisfaction of nursing staff using e-intelligent systems have significant positive impacts on the behavioral intention; (2) effort expectancy and information quality of nursing staff using e-intelligent systems have significant positive impacts on performance expectancy; (3) system quality and action self-efficacy of nursing staff using e-intelligent systems have significant positive impacts on effort expectancy; and (4) the results do not show the effects of moderation caused by technology-related pressure. Conclusions: The research findings primarily conclude that within the development/optimization processes of existing e-intelligent systems, in addition to the improvements to overall system operation and information quality, it is necessary to consider the psychological effects on nursing staff (i.e., perception from colleagues and society on their use of such systems). This will encourage nursing staff to use the system, and ultimately improve their work efficiency.