A Quantitative AGI-Driven Framework for Modeling Digital Organizational Citizenship Behavior and Employee Performance in Technology-Driven Organizations

Abstract

In contemporary technology-led companies, the workplace dynamics have become very fast-moving and dynamic, making D-OCB an essential predictor of employee productivity, effectiveness, and collaboration. Many performance evaluation methods cannot cope effectively with capturing adaptive behaviors in AGI-enabled settings. The paper proposes a new quantitative approach to predicting employee performance based on the AGI-TOCB-EPM (Artificial General Intelligence -Driven Technology-Oriented Citizenship Behavior and Employee Performance Model) that includes such components as Digital OCB Components, an AGI Behavior Analytics Engine, the Digital OCB Impact Index (DOII), and the AGI Behavior Adaptability Score (ABAS). The framework uses mathematical models and regression analyses in assessing employee behavior adaptability, collaboration efficiency, digital engagement level, and innovation orientation. Results obtained by simulating the proposed model based on the behavior of 100 employees working in a hybrid setting reveal that the average value of DOII is 0.822; the average ABAS is 0.844, while the Employee Performance Prediction (EPP) score is 0.864. In addition, behavioural classification reveals that 32% of employees belong to the "Excellent" performance level category, 41% to the "Good," 19% to the "Moderate," and 8% to the "Poor." Comparative evaluation proves the superiority of AGI-TOCB-EPM over traditional systems. The ability to make intelligent decisions, efficiency, and strategic planning are just a few of the advantages achieved from improving the behavioral patterns of the employees through the application of the suggested model. The findings illustrate the ability of AGI behavioral intelligence to contribute to the success and sustainability of organizations. The AGI-TOCB-EPM can be applied in the management of employee performance through a data-driven approach.