生物辨識技術在作業基礎管理中的應用 ：基於活動成本分析的個案研究

Abstract

數位治理與智慧組織管理為當代重要趨勢，國家級研究機構亦面臨提升資訊安全與營運效率之挑戰。傳統管理機制於應對專案導向運作（Project-Based Organization, PBO）之複雜性、高人員流動性及資料機敏性時，日益凸顯其侷限性。本研究旨在探討整合「生物辨識技術」與「作業基礎管理（Activity-Based Management, ABM）」理論，應用於特定個案「某政府智庫」，以建構數位治理新模式之可行性與潛在效益。 本研究採個案研究法，首先剖析該智庫之組織特性、標準化專案生命週期（涵蓋啟動、定義、執行、變更、完成五階段）與現存管理瓶頸。繼而，深入闡述生物辨識技術（如人臉、靜脈辨識）除可強化基礎設施安全（含觀察名單預警機制）外，更能系統性嵌入專案流程各節點，提供客觀、即時之活動數據（如工時、參與頻次、系統操作記錄）。 研究發現，藉由 ABM 管理框架（含部署與衡量模式、QTCPS+Sec 績效指標），可有效轉譯生物辨識數據，從而支持活動成本分析、資源耗用追蹤與專案績效評估。考量該智庫四大核心業務組（政策研究、科研資料、科技產業、創新創業推動）之顯著異質性，本研究進一步提出差異化之部署策略（例如：整合 AI 輔助決策、嵌入 DevOps 流程、支援行動化應用、促進虛實整合）與相應之衡量焦點。 此外，本研究亦建構了成本效益分析框架，評估導入此整合系統之潛在成本、風險以及預期效益（包含有形與無形、短期與長期）。研究結論指出，此整合框架展現其應用潛力，預期能顯著提升該政府智庫之資訊安全韌性、作業流程透明度、資源配置效率及管理決策品質；惟其成功實施，需輔以周延之數據治理、隱私保護、系統整合與組織變革管理等配套措施。本研究成果可為該智庫及其他知識密集型 PBO，提供數位轉型與智慧治理之參考框架與策略建議。

In the era of digital governance and intelligent organizational management, government-affiliated research institutions are confronted with the dual imperative of enhancing information security and improving operational efficiency. Traditional management mechanisms often fall short when addressing the inherent complexities, high personnel turnover, and data sensitivity typical of Project-Based Organizations (PBOs). This study investigates the feasibility and potential advantages of integrating biometric recognition technologies with Activity-Based Management (ABM) theory, using a government-affiliated think tank as a case study, to develop a forward-looking model for digital governance.

Adopting a case study approach, this research first examines the organizational characteristics, standardized project lifecycle (comprising initiation, definition, execution, change, and closure phases), and current management bottlenecks of the selected think tank. It then explores how biometric technologies—such as facial and vein recognition—can enhance infrastructure security (e.g., through watchlist-based alert mechanisms) and be systematically embedded in each project phase to generate objective, real-time activity data (e.g., working hours, participation frequency, and system usage logs).

Findings demonstrate that the ABM framework, which includes deployment and measurement models and leverages QTCPS+Sec performance indicators, can effectively transform biometric data into actionable managerial insights. These insights support activity-based cost analysis, resource consumption tracking, and project performance evaluation. Acknowledging the functional heterogeneity among the think tank’s four core divisions (policy research, scientific data, industrial technology, and innovation promotion), this study proposes differentiated deployment strategies—such as AI-assisted decision support, DevOps integration, mobile enablement, and cyber-physical coordination—paired with tailored performance measurement approaches.

Additionally, a cost-benefit analysis framework is constructed to evaluate the anticipated costs, risks, and benefits (both tangible and intangible, short- and long-term) of implementing such an integrated system. The study concludes that this framework has strong potential to enhance organizational resilience in information security, improve workflow transparency, optimize resource allocation, and elevate decision-making quality. Nonetheless, successful implementation requires supporting mechanisms, including comprehensive data governance, robust privacy protection, technical system integration, and proactive change management. The results offer a practical reference model and strategic roadmap for digital transformation and intelligent governance within knowledge-intensive PBOs.