在全球食品供應鏈競爭加劇、原物料價格波動及食品安全法規日益嚴格之環境下，食品製造企業之競爭已逐漸由傳統產品與價格導向，轉向以顧客價值與交易成本管理為核心之策略競爭模式。尤其在B2B食品原料市場中，企業如何降低顧客交易成本、建立信任機制並深化合作關係，已成為形成持續競爭優勢之關鍵因素。
本研究以邱志聖（2020）提出之策略行銷4C成本理論為主要分析架構，從顧客決策角度探討食品製造企業如何透過降低外顯單位效益成本（C1）、資訊搜尋成本（C2）、道德危機成本（C3）及專屬陷入成本（C4），建立競爭優勢與長期合作關係。同時輔以Porter五力分析，以了解食品原料製造產業之外部競爭環境，並進一步導入人工智慧（Artificial Intelligence, AI）與擴增實境（Augmented Reality, AR）觀點，建構「4C × AI × AR」整合分析架構。
本研究採文獻分析與個案研究法，以台灣食品原料製造企業—湯武生技企業有限公司為研究對象。研究結果顯示，湯武生技目前於外顯單位效益成本（C1）與資訊搜尋成本（C2）構面已具備一定市場競爭能力，顯示其在產品品質、製造能力及技術服務方面已達產業基本競爭水準。然而，在道德危機成本（C3）與專屬陷入成本（C4）構面上，與市場領導者仍存在差距，顯示企業目前已具備被市場選擇之能力，但尚未完全形成被顧客依賴之關係型競爭優勢。
研究進一步發現，AI技術之導入可透過商情分析平台、產品推薦系統、需求預測及智慧品質管理系統，有效降低新客戶資訊搜尋成本（C2），並透過品質追溯與異常預警機制降低道德危機成本（C3）；AR技術則可透過新品研發訓練、人才培育及標準化作業教材，提高知識傳承效率與品質一致性，進一步強化顧客信任基礎。相較於AI主要改善資訊分析與決策支援能力，AR則有助於建立組織學習與品質管理能力，兩者共同促進企業知識累積與顧客關係深化。
本研究認為，AI與AR對4C成本結構之影響中，以專屬陷入成本（C4）最具策略價值。當企業能透過AI商情分析平台、需求預測系統、產品知識庫及AR數位學習平台，建立與客戶共同使用之知識體系與決策支援機制時，顧客所依賴的將不再只是食品原料本身，而是整體知識資產、數位平台及合作網絡。此種由產品依賴、知識依賴進一步發展至系統依賴之合作模式，將有助於提高顧客轉換成本，並形成長期且難以被競爭者模仿之競爭優勢。
綜合而言，本研究指出，食品製造企業若能以4C成本理論為核心，整合AI與AR數位能力，不僅可有效降低顧客交易成本，更能促使企業由食品原料供應商轉型為食品解決方案提供者（Food Solution Provider），進一步發展為客戶之策略合作夥伴（Strategic Partner）。本研究之主要貢獻在於深化4C成本理論於食品製造業之應用，並提出4C、AI與AR整合之分析架構，作為食品製造企業推動策略行銷與數位轉型之重要參考。

In an environment characterized by intensified global food supply chain competition, volatile raw material prices, and increasingly stringent food safety regulations, competition within the food manufacturing industry has gradually shifted from a traditional product- and price-oriented approach to a strategic model centered on customer value and transaction cost management. Particularly in the B2B food ingredient market, the ability to reduce customer transaction costs, establish trust mechanisms, and deepen collaborative relationships has become a critical factor in sustaining competitive advantage.
This study adopts Chiu’s (2020) Strategic Marketing 4C Cost Theory as its primary analytical framework. From the perspective of customer decision-making, the study examines how food manufacturing firms can establish competitive advantages and long-term customer relationships by reducing four types of customer costs: Cost of Utility (C1), Cost of Information Search (C2), Cost of Moral Hazard (C3), and Cost of Asset Specificity (C4). Porter’s Five Forces framework is also employed to analyze the external competitive environment of the food ingredient manufacturing industry. Furthermore, perspectives of Artificial Intelligence (AI) and Augmented Reality (AR) are incorporated to construct an integrated analytical framework of “4C × AI × AR.”
This research adopts a qualitative approach combining literature review and case study methodology, with Tang Wu Biotechnology Co., Ltd., a Taiwanese food ingredient manufacturer, serving as the focal case. The findings indicate that Tang Wu Biotechnology has already developed competitive capabilities in Cost of Utility (C1) and Cost of Information Search (C2), demonstrating that the company has reached the industry's basic competitive standards in product quality, manufacturing capability, and technical service support. However, gaps remain in Cost of Moral Hazard (C3) and Cost of Asset Specificity (C4) when compared with market leaders. This suggests that while the company possesses the ability to be selected by customers, it has not yet fully established relationship-based competitive advantages that make customers dependent on the firm.
The study further reveals that AI technologies, including business intelligence platforms, product recommendation systems, demand forecasting tools, and intelligent quality management systems, can effectively reduce information search costs (C2) for new customers while lowering moral hazard costs (C3) through quality traceability and anomaly warning mechanisms. Meanwhile, AR technologies contribute to new product development training, employee education, and standardized operational learning systems, thereby enhancing knowledge transfer efficiency and quality consistency. While AI primarily improves information analysis and decision-support capabilities, AR strengthens organizational learning and quality management capabilities. Together, these technologies facilitate knowledge accumulation and deepen customer relationships.
The study argues that among the four cost dimensions, the impact of AI and AR on Cost of Asset Specificity (C4) is strategically the most significant. By establishing AI-driven business intelligence platforms, demand forecasting systems, product knowledge databases, and AR-based digital learning platforms, firms can create shared knowledge systems and decision-support mechanisms with customers. As a result, customers become dependent not only on food ingredients themselves but also on the integrated knowledge assets, digital platforms, and collaborative networks developed through long-term cooperation. This evolution from product dependency to knowledge dependency and ultimately to system dependency increases switching costs and creates sustainable competitive advantages that are difficult for competitors to imitate.
In conclusion, this study demonstrates that food manufacturing firms can leverage the 4C Cost Theory as a strategic foundation and integrate AI and AR capabilities to reduce customer transaction costs effectively. More importantly, such integration enables firms to transform from food ingredient suppliers into Food Solution Providers and eventually into Strategic Partners for their customers. The primary contribution of this study lies in extending the application of the 4C Cost Theory within the food manufacturing industry and proposing an integrated framework that combines 4C cost management, AI, and AR as a reference for strategic marketing and digital transformation initiatives.

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