隨著全球電子商務市場快速擴張，線上購物已成為主要的消費模式之一。然而，產品選擇日益多樣、資訊量持續增加，使消費者面臨資訊過載與決策不確定性，亟需可信的資訊來源協助判斷。網路口碑作為同儕經驗分享的重要管道，有助於降低購買風險；近年人工智慧技術的發展使 AI 推薦系統逐漸普及，透過演算法分析大量資料，提供個人化商品建議。兩者在電商環境中並存，但消費者如何評價與信任這兩種資訊來源，以及其對購買決策的影響，仍缺乏系統性比較。基於此，本研究旨在比較 AI 推薦與網路口碑的來源可信度差異，並檢驗可信度對購買意願的影響機制。

本研究以日本消費者為對象，採實驗設計，將受測者隨機分配至 AI 推薦組或網路口碑組，閱讀相應的產品推薦資訊後，評估其對資訊來源的專業性、可靠性與購買意願。問卷共取得 259 份有效樣本，並採獨立樣本 t 檢定、相關分析與迴歸分析進行假說驗證。

研究結果呈現三項主要發現。首先，AI推薦在專業性上顯著優於網路口碑， 但在可靠性上兩者相當。可靠性對購買意願的影響力大於專業性，在網路口碑情境中更僅有可靠性具顯著影響。最後，AI推薦的專業性優勢未能完全轉化為購買意願優勢，顯示可靠性不足限制了其購買促進效果。由此，本研究揭示 AI 推薦與網路口碑在可信度構成上的差異，並證實可靠性相較於專業性在購買決策中更具關鍵影響力。實務上，結果強調電商業者須同時提升 AI 的透明性與信任度，並在日本市場採取漸進導入策略以發揮 AI 與網路口碑的互補效益。

As the global e-commerce market continues to expand, consumers face increasing information overload, raising the need for credible information sources. While eWOM provides peer-based evaluations and AI recommendation systems offer algorithmic, personalized suggestions, consumer evaluation and trust these two information sources and the ways this trust influences purchase intention remains underexplored. This study compares the source credibility of AI recommendations and eWOM and examines its influence on purchase intention.

A total of 259 valid responses were collected from Japanese consumers. Participants were randomly assigned to either an AI recommendation or an eWOM condition and evaluated the source’s expertise, trustworthiness, and their purchase intention.

The results indicate that AI recommendations were perceived as more expert but not more trustworthy than eWOM. Trustworthiness exerted a stronger influence on purchase intention than expertise. In the case of eWOM, only trustworthiness showed a significant effect. Although AI recommendations hold an expertise advantage, this advantage did not fully transfer into higher purchase intention due to insufficient credibility. Overall, the findings highlight distinct credibility structures between AI and eWOM and underscore the central role of trustworthiness in purchase decisions. Practically, firms should improve the transparency and trustworthiness of AI systems and adopt a gradual implementation strategy in the Japanese market to leverage the complementary strengths of AI recommendations and eWOM.

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