Malmquist-Luenberger productivity indexes for dynamic network DEA with undesirable outputs and negative data

The data envelopment analysis (DEA) technique is well known for computing the Malmquist-Luenberger productivity index (MLPI) in measuring change decision-making units (DMUs) over two consecutive periods. In this research, we detect infeasibility of directional distance function (DDF) based DEA model MLPI under variable returns to scale technology when takes on negative values. We address problem by developing a novel DDF-based that computes an improved MLPI. extend DDF approach dynamic network structure and introduce analyzing performance DMUs time. also develop sequential shifts efficient frontiers due random shocks or technological advancements indexes comprises multiple divisions connected vertically intermediate links horizontally time carryovers. proposed models are feasible bounded with undesirable features non-negative Real 39 Indian commercial public private banks from 2008 2019 used illustrate indexes.