隨著生成式人工智慧與大型語言模型之發展，其自然語言處理正逐步受到法律領域的青睞，然而，模型運作不透明之黑盒子特性與可能產生錯誤資訊之幻覺風險，仍使使用者對其準確性、可解釋性產生疑慮，進而影響其信任與採用意圖，亦限制其於法律專業場域中之實際應用。
本研究以生成式人工智慧法律諮詢系統為情境，聚焦於「思考流程」與「引用來源」兩項常見透明化機制，並探討其對系統透明度及使用者後續感知與行為意圖之影響。研究設計上，以Gemini 2.5 Flash建置模擬平台，採2x2組間實驗操弄兩項透明化機制，並透過問卷量測系統透明度、感知隱私顧慮、感知有用性、感知易用性、信任、對系統態度與行為意圖等構念，結合二因子變異數分析與偏最小平方法結構方程模型進行整體檢驗。
研究結果顯示，思考流程與引用來源無論個別或同時呈現，皆未能顯著提升受試者之系統透明度感知，另一方面，感知隱私顧慮亦未如既有研究對其他構念展現統計上顯著之影響力。相較之下，系統透明度對感知有用性與感知易用性則具有顯著正向影響，且此二構念進一步影響信任與對系統態度之形成，並透過對系統態度之建立，進一步影響最終之行為意圖。
綜上所述，在生成式人工智慧法律諮詢系統情境中，透明度仍為重要因素，但既有常見之透明化設計並不足以有效提升使用者之透明度感知。對使用者而言，影響其採用意圖之關鍵，相較於於資訊揭露的多寡，系統是否具備良好的可理解性、易用性與整體使用體驗反而更為重要。據此，本研究除補充生成式人工智慧法律諮詢系統之使用者行為研究缺口外，亦對法律科技介面設計與透明化機制之後續發展提供實證參考與依據。

With the development of generative AI and large language models, their natural language processing capabilities have attracted increasing attention in the legal domain. However, the black-box nature of model operations and hallucination risks continue to raise concerns regarding accuracy and explainability, which may affect users’ trust and behavioral intention and limit practical application in the field.
This study examines a generative AI-based legal consultation system, focusing on two common transparency mechanisms “thinking” and “citation,” and further investigates their effects on System Transparency and users’ perceptual constructs. A platform powered by Gemini 2.5 Flash was developed for a 2x2 between-subjects experiment, and questionnaire data were collected to measure the constructs. The data were analyzed using two-way ANOVA and PLS-SEM.
The results show neither transparency mechanism, whether displayed individually or simultaneously, significantly enhanced perceived System Transparency. In addition, Perceived Privacy Concern showed no significant effects on other constructs. In contrast, System Transparency positively affected Perceived Usefulness and Perceived Ease of Use, both of which were significantly associated with Trust and Attitude Toward the System. Attitude Toward the System also had a significant positive effect on Behavioral Intention.
Overall, transparency remains important, but common transparency designs may be insufficient to enhance perceived System Transparency. Users’ Behavioral Intention appears to depend less on the amount of disclosed information and more on comprehensibility, ease of use, and overall user experience. These findings provide empirical implications for user behavior research and the design of transparency mechanisms in LegalTech interfaces.

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