隨2022年11月OpenAI對市場開放以大型語言模型(LLM)為核心技術生成式人工智慧服務ChatGPT之後，人工智慧重新成為當今熱門議題，ChatGPT與其他生成式人工智慧服務之使用者群體也隨技術的日益成熟而日漸擴張。生成式人工智慧工具被應用的情境也已深入包括高等教學場域與商業營運流程之中，同時應用在不同專業領域上皆已有實例，其包括醫療、生物科學、教育、法律、財經……等等，且亦有產業領頭企業導入生成式人工智慧以最大化提高企業組織中的個人的工作效率，同時企圖透過生成式人工智慧達成人機協作與組織的數位轉型。
本研究透過問卷調查方式，應用結構方程模型(SEM)進行資料分析，從個人使用層面進行當今生成式人工智慧使用樣態探討。利用科技接受模型為研究架構主體，並且採用任務科技適配模型中的任務科技適配變項做為替換原始科技接受模型的外部變數變項，另外也將信任做為替換原始科技接受模型中的使用態度變項，以此作為研究架構來進行結構方程模型分析以預測使用者對於生成式人工智慧工具的使用行為意向。
本研究結果揭示，任務科技適配對感知易用性與感知有用性產生正向影響，並證實感知有用性為生成式人工智慧工具使用的最重要預測變數。然而，本研究亦發現信任對於生成式人工智慧的使用行為意向並未顯示出統計上的顯著關聯性。研究結果可供未來生成式人工智慧開發團隊在服務設計與模型參數調整上參考，同時也可作為需要深入瞭解生成式人工智慧相關資訊以制定教育、行政、商務、法規等領域政策規劃的依據。

Following the market release of OpenAI's generative artificial intelligence (AI) service, ChatGPT, in November 2022, AI has once again become a prominent topic of interest. The user base for ChatGPT and other generative AI services has expanded alongside the maturation of the technology. These generative AI tools are being increasingly integrated into various contexts, including higher education and business operations, with applications spanning diverse professional fields such as healthcare, biosciences, education, law, and finance. Leading enterprises are also adopting generative AI to maximize individual efficiency within organizational settings, aiming to achieve human-machine collaboration and digital transformation.
This study employs a questionnaire survey and utilizes structural equation modeling (SEM) to analyze data on the current usage patterns of generative AI from an individual user's perspective. The research framework is based on the Technology Acceptance Model (TAM), incorporating the Task-Technology Fit (TTF) model's variables as substitutes for the original TAM external variables. Additionally, trust is used as a replacement for the attitude variable in TAM. This framework is employed to conduct SEM analysis and predict users' behavioral intentions regarding the use of generative AI tools.
The findings reveal that task-technology fit positively influences perceived ease of use and perceived usefulness, with the latter being identified as the most significant predictor of generative AI tool usage. However, trust does not show a statistically significant correlation with usage behavior intentions. These results provide valuable insights for future generative AI development teams in service design and model parameter adjustments and can serve as a reference for policymakers in education, administration, business, and regulatory fields seeking to understand and formulate policies related to generative AI.

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