本研究利用資料包絡分析法所演變出來的共同權重模式，結合麥氏生產力指 數，發展多指標與多週期生產力評量技術，幫助績效管理者為各受評單位決定 每一週期之指標共同權重，讓每一期內的受評單位皆透過相同的評比標準評比，
解決以往資料包絡分析法與麥氏生產力指數之應用上的評比誤差。在階段一中，
本研究利用共同權重模式結合麥氏生產力指數，並拆解成兩個子生產力指數，
分別為個人競爭力的變化與群體競爭力消長。階段二以階段一為基礎，利用 (Caves, Christensen, & Diewert, 1982) 的數值轉換概念，賦予麥氏生產力指數循 環性質，可用以直接觀察各受評單位多週期之間的生產力變化。
本研究所發展出來的評量方法，未來可供績效評量者對其它產業進行評比，
依據不同的產業挑選適當的評量指標，進行多週期生產力評估。管理者也可使 用本研究之生產力評量技術。當管理者在評量各受評單位的生產力表現時，除 了可以利用本研究使用的共同權重模型計算各指標之權重外，亦可根據其本身 的經營理念，主觀地限制各指標權重之區間，如此評量的結果會更加符合管理 者的需求，找出管理者心中最具生產潛力的受評單位。(Liu & Peng, 2009)提出在 共同權重模式中加入虛擬權重限制之數學模型，可使得評量結果符合管理者之 需求。以投入項為例，每一項虛擬投入佔整體的虛擬投入的比重會在數學模型
中受到限制，以數學式表示為 m iU
i i io
i L io
i
B
V x
V
B x
1，其中
B
iL與B
iU 分別表示 DMUo第 i 項虛擬投入佔全體虛擬投入比重之下限及上限。
未 來 研 究 中 還 可 使 用 (Liu, Peng, & Chang, 2006) 所 提 出 之 (Most Compromising Common Weights, MCWA)來計算各週期的各組共同權重，其計算 績效值的基本概念為使得各 UOA 相互妥協，找到最折衷的共同權重使得各 UOA 的績效值最大。除此之外，當績效評量者或是管理者在進行生產力評量時，由 於各受評單位間的規模或性質可能差異過大，造成評量出來的權重有極端化的
42
情形，嚴重扭曲評量之結果。為了解決此問題，(Liu & Peng, 2010)提出在共同權 重概念下，根據各 UOA 的績效落差程度將之分為若干群，再分別計算各群內之 指標的權重值，使得整體的績效排名更為合理，其評量結果更具參考價值。最 後，可將虛擬權重限制之概念融合(Liu & Peng, 2010)之共同權重的分群概念與手 法，應用麥氏生產力指數，使得生產力表現的評量不傴符合管理者的經營理念，
也能將各受評單位依據其本身之規模分群，深入發展更為穩健之多週期生產力 評量技術。
43
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