This study extends data envelopment analysis (DEA) to a dynamic framework. Our dynamic DEA not only provides a measure of dynamic productive efficiency, but can also be used as a nonparametric alternative to the econometric modeling of the intertemporal behavior of a firm.
What is academic research efficiency and what determines the differences between scholars' academic research efficiency? The literature on this topic has evolved exponentially during the last decades. However, the divergence of the approaches used, the differences in the bundles of outputs and inputs considered to estimate the efficiency frontiers, and the differences in the predictors of efficiency variability among scholars that are considered in prior studies, make it interesting to have an overview of the literature dedicated to this topic. Relying on a systematic review of empirical studies published between 1990 and 2012, this article proposes and discusses a framework which brings together a set of outputs and inputs related to academic research efficiency, and the individual, organizational, and contextual factors driving or hampering it. The ensuing results highlight several avenues which would help university administrators and policy makers to better foster academic research efficiency, and researchers to better channel their efforts in studying the phenomenon.
Data Envelopment Analysis DEA evaluates the relative efficiency of decision-making units DMUs but does not allow for a ranking of the efficient units themselves. A modified version of DEA based upon comparison of efficient DMUs relative to a reference technology spanned by all other units is developed. The procedure provides a framework for ranking efficient units and facilitates comparison with rankings based on parametric methods.