ASSESSING SOCIAL IMPACT: STRATEGIES AND FRAMEWORKS FOR EFFECTIVE IMPACT EVALUATION IN INVESTMENT

Abstract

Objective: The measurement of social impact has become increasingly important in today's age of increased concern for the welfare of others and priority placed on long-term sustainability. The need for this kind of evaluation becomes even more apparent when one considers the desire for both monetary gain and the promotion of beneficial social change. However, novel approaches for assessing social effect are required due to the complexities and diverse character of this phenomenon. Several challenges to measuring social impact in the context of financial investments are highlighted and discussed in this research. One difficulty is that different stakeholders may view the same social consequences in different ways. Method: In this paper the Natural Language Processing based Dynamic Modelling System (NLP-DMS) has been proposed to represent the dynamic interplay between economic and social forces across time. The difficulty of measuring the impact of investments on social outcomes over the long term and in the future is overcome by this method. The versatility of these methods is illustrated by practical applications in fields such as microfinance and sustainable agriculture. Result: The simulation study verifies their efficiency by weighing prospective social benefits against the costs of various hypothetical investment scenarios. The present research makes a contribution to the expanding field of impact evaluation by suggesting novel methods (NM) for improving the precision of social impact assessment in financial investments. Conclusion: The research provides stakeholders with adaptable tools to negotiate difficulties and achieve real change through investment decisions by combining NLP for quantitative analysis with System Dynamics for dynamic modelling.