隨著生成式人工智慧 (Generative Artificial Intelligence， GenAI )逐漸成工作者於內容生成、資料整合與決策支援等重要輔助工具，其應用已深度融入知識工作情境。然而，既有研究多聚焦於科技接受與使用意願，且在生成式人工智慧技術持續演進下，對於其如何創造工作效益，以及科技能力與工作需求間配適機制之探討仍相對不足。為彌補此研究缺口，本研究以任務科技適配理論為基礎，將生成式人工智慧之科技特性視為二階構面，納入其互動與智能等新興動態科技特徵，並進一步考量任務之知識特性，建立探討科技特性、任務特性、任務科技適配與預期效益之關係，以驗證任務導向觀點於生成式人工智慧工作情境中的適用性。 本研究採問卷調查法蒐集資料，並運用描述性統計分析、信效度檢驗、變異數分析及結構方程模型進行實證分析，以檢驗研究構面間之因果關係與模型解釋能力。研究結果顯示，科技特性對任務科技適配具有顯著正向影響；任務特性對任務科技適配亦具有顯著正向影響；而任務科技適配則對預期效益產生正向影響。 研究結果顯示，生成式人工智慧所創造之工作價值，關鍵在於其是否能有效支援工作需求並融入工作流程。本研究除驗證任務科技適配理論於生成式人工智慧情境中的適用性外，亦補充現有研究較少從任務與科技配適觀點探討人工智慧工作應用效益之不足。研究成果可作為組織推動人機協作、優化工作流程及提升生成式人工智慧應用效益之參考，並為後續相關研究提供理論基礎與發展方向。;As Generative Artificial Intelligence (GenAI) increasingly serves as an important tool for content generation， information integration， and decision support， its application has become deeply embedded in knowledge-intensive work environments. Despite its widespread adoption， existing research has primarily focused on technology acceptance and usage intentions. Given the rapid evolution of GenAI capabilities， limited attention has been paid to how GenAI creates work-related benefits and how the alignment between technological capabilities and task requirements contributes to such outcomes. To address this gap， this study adopts Task-Technology Fit (TTF) theory as its theoretical foundation. Technology Characteristics are conceptualized as a second-order construct that incorporates emerging and dynamic technological features， including interactive and intelligent capabilities. In addition， Task Characteristics are examined from the perspective of task-related knowledge attributes. Based on these perspectives， this study develops a research model to investigate the relationships among Technology Characteristics， Task Characteristics， Task-Technology Fit， and Performance Expectancy， thereby evaluating the applicability of a task-oriented perspective in the context of GenAI-enabled work. A questionnaire survey was conducted to collect empirical data. Descriptive statistical analysis， reliability and validity assessments， analysis of variance， and structural equation modeling were employed to examine the causal relationships among the research constructs and evaluate the explanatory power of the proposed model. The results indicate that Technology Characteristics have a significant positive effect on Task-Technology Fit. Task Characteristics also exert a significant positive effect on Task-Technology Fit. Furthermore， Task-Technology Fit demonstrates the positive effect on Performance Expectancy. The findings suggest that the value created by Generative Artificial Intelligence depends largely on its ability to effectively support task requirements and integrate into work processes. This study not only validates the applicability of Task-Technology Fit theory in the context of Generative Artificial Intelligence， but also extends the existing literature by examining AI-enabled work benefits from a task–technology alignment perspective. The findings provide practical implications for enhancing human–AI collaboration， optimizing work processes， and improving the effectiveness of GenAI applications in workplace settings， while offering a theoretical foundation for future research.
