隨著人工智慧漸漸應用於輔助消費者決策，了解消費者為何願意採用人工智慧推薦系統，已成為消費者行為研究中的重要議題。過去有關人工智慧採用的研究，多半從結果導向的角度出發，討論知覺有用性、信任與態度等因素；然而，研究較少關注消費者如何理解並參與人工智慧產生建議的決策過程。因此，本研究以內隱理論為基礎，探討消費者對能力可塑性的信念是否會透過過程導向影響其人工智慧採用意願，並進一步檢驗人工智慧之透明度是否會強化此一心理機制。
本研究採用組間實驗設計，以人工智慧旅遊規劃助理為情境，透過線上實驗最終蒐集270份有效樣本。研究中，內隱理論作為個人差異變數進行測量，並操弄人工智慧透明度之高低，同時加入人工智慧使用習慣作為控制變數。研究結果顯示，內隱理論對人工智慧採用意願並未產生顯著的直接影響。然而，具有較強成長信念的消費者，較傾向於關注人工智慧產生建議的過程，而過程導向作為中介機制，進一步提升其人工智慧採用意圖。此外，人工智慧透明度具有調節效果；在高透明度情境下，過程導向能顯著提升人工智慧採用意圖，但在低透明度情境下，此效果則不顯著。條件式間接效果亦顯示，內隱理論透過過程導向影響人工智慧採用意圖的效果，主要存在於高透明度情境中。
本研究因此總結，內隱理論並未直接影響消費者是否願意採用人工智慧，而是透過條件性的心理歷程發揮作用。此結果不僅補充了消費者與人工智慧互動的相關文獻，也說明過程導向是理解人工智慧採用的重要機制，而人工智慧透明度則是使此機制發揮效果的關鍵條件。實務上，本研究亦建議企業在設計人工智慧推薦系統時，應提供適當且易理解的過程說明，以協助消費者理解推薦邏輯並提升採用意圖。

As artificial intelligence (AI) becomes increasingly integrated into consumer decision-making, understanding why consumers adopt AI-assisted systems has become an important issue in consumer behavior research. Prior studies have mainly explained AI adoption through outcome-based evaluations, such as perceived usefulness, trust, and attitudes. However, less attention has been paid to how consumers engage with the decision-making process behind AI recommendations. Drawing on implicit theories, this study examines whether consumers’ beliefs about the malleability of human ability influence their intention to adopt AI through process-focused thinking. It further investigates whether AI transparency strengthens this mechanism by providing process-related information.
An online experiment was conducted with 270 valid U.S. participants in an AI-assisted travel planning context. Implicit theories were measured as an individual difference variable, AI transparency was manipulated as high versus low, and AI usage habit was included as a control variable. The results show that implicit theories did not directly predict AI adoption intention. However, consumers with stronger incremental beliefs exhibited higher process-focused thinking, which, in turn, increased AI adoption intention. Moreover, AI transparency moderated this effect: process-focused thinking significantly increased AI adoption intention only under high transparency. The conditional indirect effect of implicit theories on AI adoption intention, mediated by process-focused thinking, was also significant under high transparency.
These findings suggest that implicit theories influence AI adoption not through a simple direct effect, but through a conditional psychological process. This study contributes to consumer–AI interaction research by identifying process-focused thinking as a key mechanism and AI transparency as an important boundary condition. It also provides managerial implications for designing AI recommendation systems that help consumers understand how recommendations are generated.

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