A Web-Based Socio-Technical Framework for Adaptive Organizational Intelligence Using Large Language Models and Management Theory

Organizations increasingly require integrated mechanisms to continuously interpret human, structural, and cultural signals often overlooked by conventional business intelligence and human resource systems. This study presents IntellicaHR, a web-based socio-technical Organizational Intelligence System integrating role-based workflows, normalized performance indicators, Large Language Model–supported semantic analysis and interpretation, gamification, and established management theory. The platform operationalizes the McKinsey 7S Framework for continuous organizational alignment monitoring and uses Deming’s System of Profound Knowledge to structure executive-level explanations and recommendations. Because real workplace data on emotional climate, leadership behavior, skill alignment, and conflict are ethically and practically difficult to obtain, the system is demonstrated using three literature-grounded synthetic organizational scenarios: a Healthy Organization, a Skill-Misaligned Organization, and a Toxic Management Environment. Scenario characteristics are formalized through fuzzy linguistic states and overlapping membership functions to generate synthetic users, reports, assessments, project interactions, and managerial communications over a 45-day simulation. These artifacts are processed through IntellicaHR’s native analytics and interpretation pipeline. The resulting metric profiles matched the intended scenario definitions and were clearly distinguishable. The Healthy Organization exhibited high participation, strong skill adequacy, positive emotional tone, and low conflict; the Skill-Misaligned Organization showed capability and execution deficiencies; and the Toxic Management Environment exhibited elevated conflict, negative emotional patterns, and weaker managerial responsiveness. These findings demonstrate the functional coherence and scenario-sensitive behavior of IntellicaHR as a proof-of-concept artifact but do not establish real-world diagnostic accuracy, predictive validity, or organizational effectiveness, which require future longitudinal evaluation with real users and organizational data.