A Hybrid Framework for Forecasting Network Malmquist-Luenberger Productivity Index: DDF-based DEA and Machine Learning Approach

Measuring productivity is crucial for analyzing organizational performance and assessing development. Malmquist-Luenberger productivity index (MLPI) is a prominent method for evaluating Total factor productivity, accounting for both desirable and undesirable outputs. This study advances productivity analysis through the development of an advanced MLPI model within a network DEA framework (NMLPI), while employing window analysis separately to assess efficiency patterns across time periods. Using a directional distance function (DDF)-based approach, the model handles negative data and undesirable resources, assessing efficiency and productivity across overlapping periods. To advance network DEA, Machine learning algorithms, including Support vector regression (SVR), Least-squares SVR, and Twin SVR, are integrated to reduce computational complexity and enable predictive analysis. The practical applicability of this approach (three-year window width) is demonstrated on the Information Technology (IT) sector (2018{23), structured as a two-stage system encompassing operational effectiveness and financial viability. Findings indicate that operational effectiveness significantly impacts overall efficiency compared to financial viability. Sensitivity analysis reveals the inuence of variables on divisions and overall efficiency. Results demonstrate steady productivity growth in IT companies, driven by technological progress and efficiency improvements, reected in high NMLPI, TCH (Technological change), and TECH (Technical efficiency change) values. The hybrid model delivers precise efficiency predictions with minimal error rates for the window W 4 , validating its robustness and potential for strategic decision-making.