Artificial Intelligence-Based Communication and Work Performance: A Quantitative Case Study of a National Electricity Distribution Utility

Abstract

This study examines the relationship between Artificial Intelligence (AI)-based communication and employees' work performance, using a quantitative case study of the Utility Distribution Network (UDN). As AI increasingly mediates organizational communication through chatbots, dashboards, and automated alerts, understanding how it relates to employee-level outcomes has become critical, particularly in safety-critical infrastructure settings. Guided by Media Richness Theory, this study conceptualizes AI-based communication through six dimensions: information exchange, message reception, message understanding and feedback, message acceptance, persuasion, and reaction provocation. A cross-sectional survey was administered to 180 UDN executives who regularly interact with AI-based communication systems, and data were analyzed using descriptive statistics, Pearson correlation, and multiple regression. All six communication dimensions correlated positively with work performance; however, regression results indicated that only information exchange, persuasion, and reaction provocation were significant unique predictors of performance, with persuasion emerging as the strongest predictor. Findings suggest that AI-based communication is associated with higher work performance mainly where it clarifies information, influences behavior, and triggers timely action, rather than where it only delivers messages. Because the design is cross-sectional and limited to one organization, these associations should not be read as causal. The study applies Media Richness Theory to AI-mediated environments and offers preliminary practical guidance for designing AI communication systems in safety-critical organizations.