PREDICTIVE ANALYTICS FOR SOCIAL BEHAVIOR: A COMPUTATIONAL APPROACH TO UNDERSTANDING ORGANIZATIONAL DYNAMICS

Abstract

Introduction: The study of social behavior is being transformed by predictive analytics, which provides businesses with creative methods to comprehend and control their psychological dynamics. The purpose of this research is to investigate the implementation of predictive analytics in forecasting and analyzing organizational dynamics and social behavior.
Literature Review: In the structures of organizations, predictive analysis integrates data and algorithms to foresee team and individual behavior, maximize performance, and arrive at strategic choices. According to a Deloitte research, companies that use predictive analytics witnessed a 25% boost in performance, thereby helping them better manage resources.
Methodology: The researchers are permitted to apply the “primary quantitative data collection method” in the current research from the 70 distinctive responses and a set of 10 questions. In addition to the design, the statistical examination includes descriptive statistics, ANOVA tests, model summaries, and coefficient evaluations.
Findings: SPSS software facilitates statistical data collection. Thus, this part emphasizes the demographic test and the test connected to the variable. It is necessary to use this research investigation to get more pertinent numerical data.
Discussion: A useful tool for comprehending whether social behavior affects organizational dynamics has emerged as predictive analytics. However, predictive models, for one instance, may detect possible disputes or poor performance early on, allowing management to take preventative action or provide focused assistance.
Conclusion: The research concludes that the potential of predictive analytics in comprehending and controlling organizational dynamics is highlighted. Future studies should concentrate on resolving biases in model predictions and enhancing computational interpretability.