隨著資通訊技術（ICTs）的持續發展，許多使用者也不經意地感受到水漲船高的壓力，我們通常稱之為「科技壓力」。這種與科技互動而產生的壓力，常以生產力下降、疲勞、與焦慮等負面效應展現。同時，人工智慧（AI）作為一種迅速發展的新興科技，一系列特殊的挑戰也應運而生。儘管在組織的決策過程中，AI的使用越來越廣泛，但AI自身決策機制並不透明，這種「黑箱」式決策降低了使用者對其的信任感，且產生的決策是否具有危險性也不得而知。有鑑於此，為了幫助AI的決策過程更加清晰，以提高使用者對其的信任，AI的可解釋性日益重要。然而，透過具可解釋性的AI（XAI）所獲得的終端使用者之信任感，是否會因為人們感受到的科技壓力而有所削弱，是一項值得討論的研究議題。因此，本研究透過分心衝突理論與認知負荷的角度，深入探討何種類別的XAI的解釋方法所帶來的信任會因為終端用戶既有的科技壓力而有所削弱。同時透過實驗與統計分析，檢測不同解釋方法對於信任的關係，以及科技壓力對該關係的調節作用。根據本研究之結果，我們發現科技壓力不會影響最終用戶對人工智慧解釋的信任，但科技壓力的存在本身與對AI的信任有顯著的負向關係。我們期許本研究可以為將來研究與實務上的應用提供一些啟發與參考。

With the continuous development of information and communication technologies (ICTs), many users inadvertently experience increasing pressure, often referred to as 'technostress'. This type of stress, arising from interactions with technology, is commonly manifested in negative effects such as decreased productivity, fatigue, and anxiety. Concurrently, artificial intelligence (AI), as a rapidly evolving technology, presents a series of unique challenges. Despite the growing use of AI in organizational decision-making processes, the opaque nature of AI decision-making mechanisms, often described as "black-box" decisions, undermines user trust. The potential risks associated with these AI-generated decisions are also uncertain. In light of this, the importance of AI explainability has grown significantly to enhance user trust in AI decision-making processes. However, whether the trust in explainable AI (XAI) among end-users is diminished due to perceived technostress is a research topic worthy of discussion. Therefore, this study delves into how certain types of XAI explanations might be less effective due to the existing technostress of end-users, exploring this through the lens of distraction-conflict theory and cognitive load. It also examines, through experiments and statistical analysis, the relationship between different explanatory methods and trust, as well as the moderating role of technostress in this relationship. Based on the results of this study, we found that technostress does not impact the end users' trust in AI explanations. However, the presence of technostress itself is significantly negatively related to trust in AI. We hope that this research can provide inspiration and references for future studies and practical applications.

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