隨著生成式人工智慧快速成熟，企業在數位轉型過程中開始導入生成式AI助理，以提升工作效率與決策品質。然而，不同部門的業務特性、員工的科技焦慮程度，以及不同世代員工的科技使用經驗與接受態度，皆可能影響生成式AI助理的使用意願，使導入成效呈現不同結果。本研究以延伸整合科技接受理論(Unified Theory of Acceptance and Use of Technology 2, UTAUT2)為主要架構，刪除較不適用於組織內部使用情境的價格價值構面，並新增科技焦慮作為負向前因。同時，本研究進一步納入部門別(行政、製造)與世代別(X、Y)之差異分析，建構一個能夠解釋企業員工採用生成式AI助理意願之整合模型。

本研究採問卷調查法，蒐集半導體產業員工在工作情境下使用生成式AI助理之經驗與認知，並以結構方程模型進行資料分析。研究內容主要檢驗UTAUT2核心構面，包括績效期望、努力期望、社會影響、促成條件、享樂動機與習慣，對行為意圖之影響，並分析科技焦慮對行為意圖的負向作用。此外，本研究亦進一步探討部門別與世代別在上述關係中是否具有群體差異，以了解不同員工群體在生成式AI接受意願上的差異。

本研究結果可提供企業在導入生成式AI工具時之實務參考，協助企業了解不同員工族群的接受特性、潛在阻礙與訓練需求。透過分析科技接受因素、科技焦慮與員工背景差異，本研究可作為企業制定分眾化導入策略之依據，進而提升生成式AI助理在組織內部的採納率與實際落地成效。

With the rapid development of generative artificial intelligence (AI), many companies have started to use generative AI assistants to improve work efficiency and support digital transformation. However, employees may accept these tools differently because of their departments, technology experience, and generational backgrounds. Therefore, understanding employees' intention to use generative AI assistants is important for successful implementation.

Based on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), this study examines semiconductor industry employees' behavioral intention to use generative AI assistants. Price value is removed because it is less relevant in an internal enterprise context, while technology anxiety is added to reflect employees' concerns toward new technology. This study also compares administrative and manufacturing departments, as well as Generation X and Generation Y.

This study collects 1,090 valid responses and analyzes the data using PLS-SEM and bootstrapping. The results show that performance expectancy, hedonic motivation, habit, and social influence have significant positive effects on behavioral intention. In contrast, effort expectancy, facilitating conditions, and technology anxiety do not have significant effects.

The multigroup analysis shows that Generation X is more influenced by social influence, while Generation Y is more influenced by effort expectancy. Facilitating conditions have a stronger effect on manufacturing employees than on administrative employees. Overall, this study suggests that companies should consider department and generation differences when promoting generative AI tools.

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