隨著ChatGPT推出後越來越多的生成式AI應用出現在大眾的視野，透過結合ChatGPT也出現了更多進階的應用，如微軟推出的Copilot就是將ChatGPT嵌入到Microsoft 365中，而到了2024年Copilot也可以嵌入更多的應用程式中了，如Tableau Copilot、GitHub Copilot、UiPath Autipilot，這些都是把Copilot嵌入應用程式中的案例，相信在未來會有更多的不同的生成式AI應用出現，企業該如何順應這個浪潮導入這些應用，而生成式AI可以為企業員工哪樣的效應，將成為企業導入生成式AI最重要的議題。
　　因此本研究將透過問卷調查法，探討企業對於生成式AI的使用意願，從工作上的績效期望、導入時需要付出的預期努力、身邊同事朋友間的社群影響及最後的促進條件這幾個構面去探討影響到我們行為意圖的程度，本研究總共收回了367份問卷，再透過SPSS 28.0版及Smart PLS 4.0版進行數據分析。
　　本研究結果表示，「績效期望」及「預期努力」都對「行為意圖」產生了顯著的影響，而「促進條件」雖然比「績效期望」及「預期努力」產生的影響小，但也有顯著影響，而「社群影響」方面反而沒有影響，雖本研究仍然有著一些限制，但希望透過此次的研究能更對於企業導入生成式AI產生一定的幫助及貢獻。

With the launch of ChatGPT, more and more generative AI applications are appearing in the public's view. Through the integration of ChatGPT, more advanced applications have emerged. For instance, Microsoft's Copilot integrates ChatGPT into Microsoft 365, and by 2024, Copilot will be embedded into more applications such as Tableau Copilot, GitHub Copilot, and UiPath Autopilot. These are examples of embedding Copilot into applications. It is believed that there will be more different generative AI applications in the future. How businesses should adapt to this trend and introduce these applications, and the effects generative AI can have on employees, will become the most important issues for businesses in implementing generative AI.
　　Therefore, this study will use a questionnaire survey method to explore the willingness of enterprises to use generative AI. It will examine the degree to which our behavioral intentions are influenced by factors such as performance expectancy in work, the expected effort required for implementation, social influence among colleagues and friends, and facilitating conditions. A total of 367 questionnaires were collected, and statistical analysis was conducted using SPSS version 28.0 and Smart PLS version 4.0.
　　The results of this study indicate that performance expectancy and effort expectancy both have a significant impact on behavioral intention. While facilitating conditions have a smaller effect compared to performance expectancy and effort expectancy, they still exhibit a significant influence. However, social influence does not have an impact. Although there are some limitations in this study, it is hoped that the findings will provide meaningful insights and contributions to enterprises considering the implementation of generative AI.

目錄
論文審定書 i
中文摘要 ii
Abstract iii
目錄 iv
圖目錄 vi
表目錄 vii
第一章 緒論 - 1 -
第一節 研究背景及動機 - 1 -
第二節 研究目的 - 2 -
第三節 研究流程 - 3 -
第二章 文獻探討 - 4 -
第一節 人工智慧 - 4 -
第二節 整合科技接受模式 - 7 -
第三章 研究方法 - 12 -
第一節 研究架構 - 12 -
第二節 研究假說 - 12 -
第三節 操作型定義 - 14 -
第四節 研究設計 - 14 -
第四章 資料分析 - 19 -
第一節 描述型統計分析 - 19 -
第二節 各構面問項分析 - 20 -
第三節 信度分析 - 21 -
第四節 收斂效度與區別效度 - 21 -
第五節 共線性分析 - 25 -
第六節 結構模型分析與假說驗證 - 26 -
第七節 分群分析 - 27 -
第五章 結論與建議 - 34 -
第一節 研究結果 - 34 -
第二節 研究貢獻 - 36 -
第三節 研究限制 - 36 -
第四節 未來研究方向建議 - 37 -
參考文獻 - 38 -
英文文獻 - 38 -
中文文獻 - 40 -
附錄一 本研究整理資料 - 41 -
附錄二 本研究發放問卷 - 49 -

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