從價值鏈理論探討體育非營利組織經營之研究－－以中華帕拉林匹克總會為例

A Study on the Management of Sports Nonprofit Organizations from the Perspective of Value Chain Theory: The Case of the Chinese Taipei Paralympic Committee

本研究旨在探討中華帕拉林匹克總會於非營利運動組織治理中的角色與價值創造機制，透過價值鏈模型與社會影響力投資（SROI）分析，回應當代非營利組織在運動平權與永續治理上的挑戰。研究對象為中華帕總，採用質性訪談、次級資料分析與SROI量化方法，深入分析其組織運作與社會影響，主要訪談對象為身心障礙運動參與之教練與選手，SROI的計算結果為2.33，以帕總的年度計畫預算為投入成本，計算各項活動例如獎牌數或是醫療資源使用的減少等等所產生的效益。研究主要發現包括：(1)中華帕總價值鏈已逐漸從單一競技導向轉為融合訓練、倡議、社區參與的多元價值鏈；(2) 建立由社區參與延伸至體育實踐的價值路徑；(3)組織架構尚有整合與專業化空間；(4) SROI分析顯示非財務性價值可透過代理變數具體化，增進溝通與透明。本研究的價值在於提供非營利體育組織策略轉型、績效管理與價值溝通的參考架構，並能推廣及有助於當代NPO在面對資源競爭、使命推廣與社會信任重建時，找尋新的策略路徑與發展模式。最後的研究限制方面，本研究以單一個案為主，部分成果依賴訪談推論，未來可擴大樣本並強化長期績效資料，以提升普遍性與分析深度。

This study explores the role and value creation mechanism of the Chinese Taipei Paralympic Committee (CTPC) in nonprofit sports organization governance. Using the value chain model and Social Return on Investment (SROI) analysis, the research addresses challenges faced by contemporary nonprofit organizations in sports equity and sustainable governance. The study focuses on CTPC, employing qualitative interviews, secondary data, and SROI to analyze its operations and social impact. Main interviewees include coaches and athletes in para sports. The calculated SROI is 2.33, based on CTPC’s annual program budget, evaluating benefits such as medal achievements and reduced medical resource usage. Key findings: (1) CTPC’s value chain has shifted from competition-focused to amodel integrating training, advocacy, and community engagement; (2)a value pathway from community participation to sports practice is established; (3)organizational structure requires further integration and specialization; (4)non-financial values can be measured using proxies, enhancing communication and transparency. This study offers areference for nonprofit sports organizations in strategy, performance, and value communication, supporting NPOs in finding new development paths amid resource competition and trust rebuilding. Limitations include its single-case scope and reliance on interview-based inference. Future research should expand samples and strengthen long-term data to improve generalizability and depth.