台灣禮贈品產業長期面臨低價競爭與產品同質化的雙重困境，特別是在中國大陸工貿一體型業者以規模化、低成本策略大舉滲透之後，傳統以 OEM為主的商業模式，已愈來愈難以撐起應有的利潤空間與品牌差異性。儘管業界都知道「文化元素」是提升產品附加價值的關鍵，但真正落實時卻往往卡在三個地方：文化語意的定義太模糊，設計師無從下手；整個開發流程缺乏系統性的理論支撐，經常憑感覺走；市場需求難以精準捕捉，做出來的東西叫好卻不叫座。
本研究旨在探索如何將「設計思考」方法論有效導入禮贈品產業，並整合當前生成式 AI（AIGC）工具，為這個行業建構一套可操作、可複製的創新開發模型。研究以唐美雲歌仔戲團年度鉅製《臥龍：永遠的彼日》為核心個案，透過質性研究法進行深度剖析。研究採用史丹佛 d.school「五步驟設計思考框架」，提出「文化轉譯模型（Cultural Translation Model，CTM）」，整合「關鍵語意萃取→視覺符碼矩陣→情境敘事邏輯→商品設計語意」四個轉化層次，能有效將抽象的文化符碼轉譯為具體的產品語意，強化文化產品的故事張力與市場共鳴。
於本實踐專案中，生成式 AI 工具被定位為設計思考實踐過程中之輔助協作者，而非獨立之決策系統。透過 AI 在大規模文化數據提取與視覺原型快速迭代的優勢，能顯著提升 ODM 開發過程中的精準度、速度與產品細膩度。研究結果顯示，本實踐歷程不僅能緩解文化轉譯的隨機性問題，亦為同樣面對文化深度合作對象之禮贈品 ODM 廠商提供一條可參考之實踐路徑，賦予傳統製造業在數位轉型浪潮中具備高度差異化的競爭門檻；具備文化設計能力的業者，在面對客戶議價時亦展現出更強的主導優勢，得以從「價格競爭」轉向「價值競爭」。
研究結果顯示：(1)導入本框架後，授權方於開發各階段之審查意見由「方向爭議」轉為「細節優化」，未再出現結構性翻案；(2)內部設計團隊形成以 CTM 矩陣為基礎之共通設計語言，設計決策有了可對話的依據；(3)AI 渲染圖之引入，讓授權方在實體打樣前即完成方向確認，相較傳統流程明顯縮短開發週期、減少實體打樣次數；(4)商品依時程於《臥龍：永遠的彼日》演出前完成交付，並獲授權方驗收通過。本研究受限於商品開發必須於演出上檔前完成交貨之產業時序，未能於研究時程內完成消費者端之文化解碼效能與購買決策驗證，相關後續研究方向已於第六章詳述。

Taiwan's gift and premium industry has long suffered from low-price competition and product homogenization, particularly as mainland Chinese manufacturers have penetrated the market with scale-driven, cost-optimized strategies. While industry practitioners recognize that cultural elements are key to enhancing product value, the actual implementation of cultural design remains hindered by three persistent challenges: ambiguous cultural semantics that leave designers without actionable direction, a lack of systematic theoretical frameworks for the development process, and difficulty in accurately capturing market demand.
This study explores how Design Thinking methodology can be effectively integrated into the gift and premium industry, combined with generative AI (AIGC) tools, to construct a replicable and operational innovation development model. Using the Tang Mei-Yun Taiwanese Opera Company's production "Zhuge Liang: A Promise Never Forgotten" as the core case, this study conducts an in-depth qualitative analysis through an embedded single-case design. Adopting the Stanford d.school five-step Design Thinking framework, this study proposes the Cultural Translation Model (CTM), which integrates four transformation layers to systematically translate abstract cultural codes into concrete product semantics.
Furthermore, this study positions generative AI as an assistive collaborator within the design thinking process, rather than an autonomous decision-making system. By leveraging AI's advantages in large-scale cultural data extraction and rapid visual prototype iteration, the model significantly enhances the precision, speed, and refinement of the ODM development process. The study also proposes the concept of Critical Experience Nodes (CEN), integrating Kahneman's Peak-End Rule with service design touchpoint analysis.
Findings indicate that, after implementing this framework: (1) the licensor's review feedback shifted from directional disagreement to detail-level optimization, with no structural revisions required at later stages; (2) the internal design team developed a shared design vocabulary anchored in the CTM matrix, enabling more dialogue-based design decisions; (3) the introduction of AI-rendered visualizations enabled the licensor to confirm design directions before physical sampling, significantly shortening the development cycle and reducing the number of physical sampling rounds compared to traditional workflows; and (4) products were delivered on schedule and approved by the licensor before the show's premiere. Due to the industry constraint that products must be delivered before the show's opening, this study did not include consumer-end validation of cultural semantic decoding or purchasing decisions; such validation is identified as a key direction for future research in Chapter 6.

中文文獻
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台灣貿易中心（2023）。〈潮流趨勢篇：掌握市場需求，打造專屬賀禮，瞄準客製化商機〉。《經貿透視雙周刊》。取自 https://info.taiwantrade.com
袁世珮（2022）。唐美雲與吳定謙跨世代創新合作 將阿嬤的歌仔戲變成年輕
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