| 摘要: | 資訊不對稱為經濟學核心理論之一，但在高價二手奢侈品市場中，其表現形式遠較傳統理論更為複雜。以勞力士手錶為代表的名錶，不僅具有消費品特徵，更具再投資財性質，其估價與交易過程仰賴非標準化的知識與專業判斷。Akerlof 所提出的「檸檬市場」理論雖指出產品品質與認證問題為資訊不對稱的核心，但本研究指出，該市場的關鍵落差更體現在「默會知識的掌握」與「人際間專業評價的不對等」。
本研究探討再投資財市場的資訊不對稱問題，聚焦於兩大問題。首先，在缺乏標準化估價制度的情境下，資訊不對稱於再投資財市場中如何具體展現？其次，買賣雙方如何因應高度仰賴專業知識與信任的雙重資訊落差？研究採用質性個案研究方法，透過訪談、平台觀察與社群參與，建構出市場參與者如何透過選擇性揭露、社群驗證與聲譽管理等方式，共同形塑信任與估價邏輯。
本研究透過個案深度訪談與分析，發現再投資財市場運作機制根本不同於傳統資訊不對稱理論假設，本研究揭示市場參與者透過默會知識形成分層信任體系，並存在雙向專業評估機制，而非單純依賴制度性中介。此發現證實資訊不對稱理論在知識密集型再投資財領域存在理論限制性，並據此建構出「知識驅動的信任概念性架構」。
本研究從再投資財角度挑戰阿卡洛夫資訊不對稱理論，探討其在奢侈品市場的適用限制， 並嘗試建構「知識驅動的信任概念性架構」。同時提出 AI Agent 作為默會知識媒介的可能解決方案之一，為高單價知識密集型市場的信任建構提供理論與實務上的參考。
;Information asymmetry has long served as a foundational concept in economics, yet its expression in the high-end second-hand luxury market proves far more intricate than traditional theories suggest. Using Rolex watches as a representative case, this study highlights the dual nature of such goods—as both consumption items and reinvestment assets—whose valuation relies not on standardized criteria, but on non-formalized knowledge and expert discretion. While Akerlof’s “market for lemons” model has emphasized product quality uncertainty and certification gaps, this research contends that the true asymmetry in this market lies in the uneven distribution of tacit knowledge and interpersonal evaluation power.
Focusing on how information asymmetry materializes in the absence of standardized pricing systems, and how market actors navigate expertise-dependent, trust-driven exchanges, this study adopts a qualitative case study approach combined with interviews, platform observation, and engagement with enthusiast communities, the research uncovers how selective disclosure, peer validation, and reputation management operate as alternative trust infrastructures that shape valuation and mitigate risk.
The findings demonstrate that trust in this market is neither fully institutional nor entirely transactional, but rather emerges through layered, experience-based assessments governed by professional hierarchies. In response, the study proposes a knowledge-driven conceptual framework of trust formation, challenging the explanatory limits of conventional information asymmetry theory. It further explores the potential of AI Agents to serve as digital mediators of tacit knowledge, offering a practical pathway to support trust-building and decision-making in knowledge-intensive, high-stakes markets. |
