Socio-Technical Systems in the Digital Era: Bridging Information Systems Engineering with Behavioral Science for Enhanced decision making

Abstract

This research discovers how combining information systems engineering and behavioral science can lead to better decisions in industrial systems using information technology. Since digital environments are becoming more complicated, learning about how humans act with technology is necessary for developing adaptable, strong and user-focused systems. To model and understand socio-technical issues, Decision Trees, Support Vector Machines, Neural Networks and Reinforcement Learning algorithms were all used. Tests proved that the hybrid strategy supported by behavioral knowledge scored an average accuracy of 89.7% compared to 77.5% for traditional technical approaches. By integrating behaviors, the system advanced in adaptability by 15% and the users became more satisfied, scoring 18%. Analysis against previous research demonstrates that blending engineering and behavioral approaches leads to better system results and sustainability. Using empirical results, this study presents a new approach to interdisciplinary challenges linked to modern digital systems. These findings show that socio-technical design can help design more effective support for making decisions at work that are both advanced and socially aware.