本研究的目的是藉由將優勢，劣勢，機會和威脅（SWOT）分析策略矩陣模擬為Web服務的一種，組成SWOT分析的方法，結合服務品質（QoS）功能的基礎上來建置業務流程/組合的過程中來選擇SWOT服務，基於網路服務具有跨平台、彈性及重複使用的特性，形成一種彈性策略；並根據我國警察職務行為的現況分析，提出一些觀點，可整合不同SWOT分析提供者的方式來建置執法和服務品質管理系統，提高工作環境品質與警察素質，以促進警察執法與服務品質管理的完善。另外，由於SWOT分析是採取客觀和中立的立場，透過積極參與警方決策前的討論會議，在官員和警察學者的見解中達成共識，雖然有助於形成一致意見的達成，但缺點是SWOT分析可能無法協助決策者訂定完善的行動計劃或策略。
為解決上述問題，本研究除考量網路服務組合流程，針對各流程重視程度去選擇網路服務，整合SWOT分析並形成從方案產生到最後進行群體選擇之網路服務選擇相關研究。故本研究提出一群體決策網路服務選擇流程(Group Decision SWOT ，GDSWOT)，首先以語意方式來考量各決策者對任務、服務品質的重視程度與全域最佳化產生多個選擇方案，再以群體決策中之TOPSIS方法為基礎，提出能讓決策者以較少的評估工作且考量群體偏好之網路服務選擇方法，以期能多利用可用之網路服務並可降低整體決策者對系統期望之落差。
預期的表現是有效提高警察素質，並根據時間和社會變化創造高質量的工作環境。本研究試圖分析其基本理論狀況，並提出相關觀點以完善其理論。

This study proposes an architecture in which a SWOT analysis is employed to consider group preference via the concept of web services. The SWOT analysis is based on unanimity, including adopting an objective and neutral stance, and creates a consensus among officers and the insights of police academics through active participation in discussions prior to police decision-making. However, because SWOT analysis may be insufficient to formulate an action plan or strategy, SWOT analysis is integrated into Group Decision Web Service Selection (GDWSS) to form a new GDSWOT for law enforcement and service quality management to rank factors by order of preference. The specific study finds that assigning linguistic variables to decision makers (DMs) is more intuitive for representing their important weights with fuzzy sets for SWOT analysis; doing so helps eliminate the drawbacks in semantic ambiguity and provide better model options. The specific study considers the preferences of DMs and ensures the consistency of the selection. Every task/policy can be finished by a set of services that expose the operations required to analyze composite process-services in multiple tasks/policies. The corresponding optimization approaches for each task/policy select a service to optimize the overall evaluation function. In an effort to provide greater consistency, different DMs assign different task/policy weights to generate global optimization parameters. Then, a new hybrid method named SWOTS-TOPSIS is used to reduce the need for a large amount of input preference data. In addition, improving the disagreement and setting preferences using GDSWOT can alleviate the DMs' workload. The combined methodology is thus a helpful tool for DMs. The expected performance is to improve the quality of police effectively and create a high-quality work environment in response to time and social changes. The study tries to analyze its fundamental theoretical basis and proposes relevant ideas to perfect its theories.

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