許多研究文獻證實組織學習導致廠商或管?系統績效長期持續改善，組織所?積的學習效果能形成智慧資本，此外，組織學習可能在兩期之間存有遞延活動，動態資?包絡分析模式的發展已經可以處理遞延活動。迄今，不管是靜態和動態資?包絡分析模式皆未能處?潛在學習效果影響，因此，本研究嘗試發展?階段方式將學習效果融入資?包絡分析模式中。先將潛在學習效果以時間序?資?進??積存?估計，並將其?入靜態與動態資?包絡分析再進?相對效?評估。針對永續發展議題，本研究將應用所發展之衡?模式研究垃圾處?與資源回收系統之組織學習效果與學習遞延效果對相對效?影響。本研究以臺灣各縣市垃圾資源回收為研究對象，自2001年至2009年為研究樣本，共計9年的資料23個縣市政府為研究樣本，進行迴歸分析估計各縣市累積的學習效果，將此導入資料包絡分析模式進行各縣市政府資源回收量之相對績效評估，本研究研究目的有四個部分:首先，本研究發展新的方法將學習效果與學習遞延效果分別融入靜態與動態資?包絡分析模式中。其次，學習效果與學習遞延效果確實對MSW資源回收績效具有攸關性。再次，透過差額變數分析提供擬定各縣市政府改善空間與幅度，並透過效率值分解可發現無效率最主要來源來自於組織學習效果短缺所導致。最後，還發現各地區所得水準不同將會有不同的學習方式，導致學習累積效果有顯著的差異，各縣市政府在推動各項資源回收措施時，應該注意是否能確實傳遞並?實到民眾的生活中。此外，地方政府應繼續不斷地推動資源回收政策。The effects of organizational learning, which results in continuous improvement of organizational performance over time, have been widely discussed. The cumulative learning effect may form as a source of intellectual capital. Furthermore, organizational learning may have carry-over effects between two terms. The dynamic data envelopment analysis (DEA) model has been developed to address such carry-over effects. Thus far, neither the static nor the dynamic DEA model has been used to examine the latent learning effect. Therefore, a two-stage approach is developed together with the estimation of latent learning effect using time-series data; the estimated learning effect is then used in both static and dynamic DEA models. The proposed static and dynamic DEA models can be used to investigate the efficiency of the organizational learning effect and learning carry-over effect of Municipal Solid Waste (MSW) recycling systems.This study estimated the cumulative learning effect of 23 local governments in Taiwan from 2001 to 2009 using regression analysis; the results were incorporated into static and dynamic DEA models to evaluate the relative performance of MSW recycling systems. This work makes four major contributions: first, this study developed a novel approach that incorporates the effects of organizational learning and the carry-over effects of organizational learning into the static and dynamic DEA models, respectively; second, the learning effect and learning carry-over effect affects the MSW recycling performance; third, improved policies were provided to local governments with inefficient MSW recycling systems; finally, this study found that household disposable income levels influence learning methods, leading to a considerable difference in cumulative learning effects. In promoting recycling policies, local governments should determine whether such policies can be disseminated and implemented given the local community’s standard of living. In addition, local governments should continue to promote recycling policy.
