Intelligent employee behavior analysis for organizational culture optimization with q rung orthopair fuzzy FUCA

Employee behavior is one of the most important factors in the creation of an organizational culture because it determines collaboration, motivation and productivity. Nonetheless, the interdependence, subjectivity and uncertainty of behavioral ratings is not well-tuned using existing multi-criteria decision-making (MCDM) and q-rung orthopair fuzzy (qROF) models, which dictate that the decision parameters are constant and fail to acknowledge adaptive expert feedback. In order to overcome these shortcomings, this paper presents the q-rung orthopair fuzzy Faire Un Choix Adéquat (qROF-FUCA), a refined decision-support system that combines human expertise with an algorithmic corrective mechanism in order to dynamically optimize the membership degrees and minimize evaluation bias. FUCA, in contrast to traditional qROF-MCDM frameworks, uses a two-layer adaptation mechanism, which consists of converting fuzzy parameters using expert feedback and optimizing membership functions using a second-layer adaptation algorithm, making it more flexible, transparent and responsive to complicated behavioral data. A simulated situation of a corporate setting, which is a hypothetical case study, is used to evaluate employee behavior, gauging it through communication, teamwork, adaptability, and involvement in decision-making. Sensitivity analysis and the comparison to classical qROF-MCDM approaches prove that it can be more consistent in its decisions and has a superior ranker, decision stability and resilience to uncertainty. The analysis reveals that the best score belongs to the job of improving organizational culture with the highest rating called Participative Decision-Making. The outcomes are witnesses to the efficacy and usefulness of the qROF-FUCA technique. Practically, there is a framework which presents a framework through which the behavioral qualities are assessed and the best way to cultural maximization is enacted. Although the model has performed well, the study recognizes that the model uses hypothetical data and can be expanded with real organizational data and fuzzy-machine learning hybrid mechanisms to enhance predictive accuracy and generalizability.