QualityOriented Network DEA Model for the Research Efficiency of Philippine Universities In response to the Philippines education systemu0027s development plan in strengthening the research culture of the different higher education institutions, this study was conducted to assess the research efficiency of 18 research universities in the country. Standard Data Envelopment Analysis (DEA) models, have been used in previous researches to measure the relative efficiency of research performance of universities with quantityoriented indicators inputs and outputs. This study proposed the use of a network DEA (NDEA) model considering quality indicators in assessing the efficiency of the research performance of universities. NDEA allows the measurement of efficiency per stage of a process. The main finding is that the standard DEA tends to exaggerate the research efficiency compared to that of NDEA. The use of qualityoriented publication indicator also revealed that the use of quantityoriented indicators alone exaggerates the performance of the universities as it is mainly based on the number of researches produced, not considering the impact or quality of each research. Some of the topperforming universities ranked lower with the use of quantityoriented indicators.

Some variants and extensions of dominance network analysis Abstract   The aim of this paper is to extend Dominance Network (DN) analysis, a methodology that evaluates the dominance relationships among a sample of homogeneous operating units (OUs) based on the inputs they consume and the outputs they produce. The relative efficiencies of the OUs are commonly assessed using Data Envelopment Analysis (DEA). DN complements DEA providing indicators to assess the performance of not only each OU individually but also the whole set of OUs. DN also allows visualisation of multidimensional datasets that in conventional DEA can only be done for lowdimensional datasets. The methodology builds a weighted directed acyclic network, the nodes of which are the observed OUs and the links represent all the dominance relationships among the OUs. The weight of these links measures the relative inefficiency among the linked OUs. Complex Network Analysis (CNA) techniques can be applied to this type of layered network. This provides many interesting measures and indexes to characterise the individual nodes as well as the whole sample. However, existing DN applications only involve the existing OUs and hence use a nonconvex DEA technology. In this paper, an approach to overcome this limitation and use other DEA technologies is proposed. Also, as a secondary objective, how the use of different inefficiency metrics affects the results of the analysis is studied.