Evaluating ESG-Based Sustainability in Software Companies: A Performance Analysis Using MPSI-OPLO-POCOD Framework

Abstract

In today's world, companies are restructuring their operations with a focus on Environmental, Social, and Governance (ESG) criteria to build a sustainable economy. In this context, efforts to meet the requirements of sustainability are increasingly on the rise. By establishing a sustainable financial performance structure, companies are enhancing their efforts to gain both short-term and long-term competitive advantages. This research explores ESG-based sustainable performance analysis, supported by practical applications. Additionally, a model based on Multi-Criteria Decision-Making (MCDM) is proposed, and an approach for sustainable performance analysis is implemented in this study. The Modified Preference Selection Index-Opportunity Losses‐Based Polar Coordinate Distance (MPSI-OPLO-POCOD) method is used for performance analysis in this research. In this method, the MPSI approach applies objective criteria weighting to determine the importance of different factors, while the OPLO-POCOD method is used to derive the performance values and rankings of companies. The performance levels for 17 software companies listed in the S&P 500 index for the years 2021, 2020, 2019, and 2018 are determined using the MPSI-OPLO-POCOD method. Four applications are conducted within this scope. The decision model for the research includes ESG scores and financial ratios as the criteria. In the application results, the most important criteria for the years 2021, 2020, 2019, and 2018 are identified as Return on Equity (ROE), ROE, Tobin Q Ratio, and Earnings Before Interest, Taxes, Depreciation, and Amortization (EBITDA), respectively. In the performance rankings of software companies, the companies with the highest performance levels for the years 2021, 2020, 2019, and 2018 are identified as PTC Inc (PTC.OQ), Autodesk Inc (ADSK.OQ), Cadence Design Systems Inc (CDNS.OQ), and Autodesk Inc (ADSK.OQ), respectively. Furthermore, the research includes assessments of performance changes before and after the Covid-19 period. The application results are also validated through sensitivity analyses.