Educational assessment has evolved into a complex and interconnected system shaped by accountability demands, competency-based education, digital technologies, artificial intelligence (AI), and learning analytics. Despite these developments, existing research remains fragmented, often emphasizing psychometric measurement, predictive analytics, feedback, or isolated assessment practices while providing limited explanation of how interactions, feedback processes, and institutional conditions influence assessment outcomes over time. This integrative review examined how System Dynamics and Decision Analytics can be integrated to understand and improve educational assessment systems. Guided by Whittemore and Knafl’s integrative review framework and reported in accordance with PRISMA 2020, the review synthesized 94 studies published between 2005 and 2026 and retrieved from Scopus and Web of Science. The synthesis demonstrated that educational assessment operates through interconnected relationships involving policy, curriculum, institutional resources, technology, faculty capacity, teaching, feedback, student learning, data, and decision-making. It further identified recursive feedback mechanisms linking assessment information to analytical interpretation, educational intervention, institutional performance, and system adaptation. Based on the synthesis, the study developed the Dynamic Decision Analytics Framework for Educational Assessment Systems (DDAF-EAS). The framework’s principal contribution is an assessment-to-adaptation architecture that connects assessment, educational data, decision analytics, evidence-informed action, outcomes, and feedback across learner, instructional, institutional, and policy levels. DDAF-EAS further provides diagnostic, dynamic, prospective, and adaptive analytical capabilities and generates propositions for future empirical testing and System Dynamics simulation. The framework provides a foundation for modelling, simulation, hybrid analytics, and evidence-informed decision-making, extending educational assessment toward adaptive and continuously improving systems. Not applicable.
Quality-risk decision models require traceable links between reported judgments, numerical priorities and managerial recommendations. This single-case study audits retained assessment summaries from 40 export-oriented leather and footwear firms in Bangladesh and one reported spherical fuzzy analytic hierarchy process (SF-AHP) decision model. Descriptive screening reproduces fourteen item totals, while a computational audit compares five criterion weights and three strategy scores. Communication, compliance, resource costing and equipment monitoring account for 41.12
Competition and cooperation have been discussed and subjected to economic and business studies in terms of the behavioral causes and consequences of these strategies chosen solely or in combination by companies in compliance with their interests. Researches have revealed that not only competition but also cooperation is strategically important for individual companies and markets. If discussed in the theoretical frame, high competition conditions may drive the market or some of the companies in the market to reach a steady-state level where investments may become less advantageous, and industrial cooperation to be applied between the companies in this situation may support them in overcoming this stagnative period. This study aims to analyze the economy and business literature with respect to competition and cooperation. The research review results suggest that coopetition can reasonably be a foreseeable operational development strategy for companies in the period when they operate under intense market competition conditions. Not applicable.
This paper examines the effect of tax education on tax compliance behavior among small businesses in Uganda. Using a cross-sectional survey of 460 licensed small businesses drawn from a population of 16,614 firms in Kampala Capital City and Mukono Municipality, data were collected through structured questionnaires and analyzed using Ordinary Least Squares (OLS), Feasible Generalized Least Squares (FGLS) and logistic regressions. The findings reveal that tax education has a significant and positive effect on tax compliance, with businesses exposed to tax education exhibiting higher levels of compliance than those without such exposure. The results are robust across alternative econometric specifications. The study further identifies tax knowledge and perceived fairness of the tax system as important drivers of compliance, while socio-demographic characteristics exert a considerable influence as well. These findings underscore the importance of strengthening taxpayer education programs as a strategy for enhancing voluntary tax compliance among small businesses. For broader policy insights, future research could extend the analysis to a nationally representative sample, unlike the current study which only focuses on Kampala and Mukono. Clinical trial number: Not applicable.
Industrial safety management and hazard-response optimization have become increasingly challenging in modern large-scale industrial systems due to the growing complexity of manufacturing processes, interconnected operational environments, diverse hazard sources, and the presence of uncertain, incomplete, ambiguous, and imprecise information. Consequently, effective industrial safety planning and emergency response require advanced decision-making frameworks capable of simultaneously evaluating multiple, often conflicting, criteria, including hazard severity, operational reliability, response effectiveness, worker safety, environmental impact, resource allocation, system resilience, and mitigation efficiency. To address these challenges, this study introduces a novel Circular Complex Fermatean Fuzzy Set (CrC-FFS) framework, specifically designed to model uncertainty, ambiguity, hesitation, and complex-valued information associated with large-scale industrial hazard-risk assessment and safety management problems. The proposed Circular Complex Fermatean Fuzzy Set extends the capabilities of existing complex Fermatean fuzzy sets and circular Fermatean fuzzy sets by providing greater flexibility for representing higher-order uncertainty in practical industrial environments. Furthermore, several refined algebraic operational laws, including direct sum, direct product, and scalar multiplication based on generalized t-norms and t-conorms, are established to ensure consistent manipulation of Circular Complex Fermatean Fuzzy information. Based on these operational laws, novel weighted averaging, ordered weighted averaging, weighted geometric, and ordered weighted geometric aggregation operators are developed to effectively aggregate uncertain multi-criteria safety information. Their fundamental mathematical properties and special cases are thoroughly investigated to demonstrate their validity, robustness, and flexibility for industrial hazard-risk evaluation. These aggregation operators are subsequently incorporated into a multigranulation-based multi-criteria decision-making framework to prioritize hazard categories, evaluate mitigation strategies, optimize emergency response plans, and improve industrial safety performance under uncertain environments. To validate the effectiveness of the proposed methodology, a practical case study involving large-scale industrial hazard-risk assessment and safety response optimization is presented. Comparative analyses and sensitivity investigations demonstrate that the proposed CrC-FFS framework provides superior performance in handling uncertainty, complex-valued information, and conflicting safety criteria compared with existing approaches. Overall, the proposed methodology offers a flexible, robust, intelligent, and reliable decision-support framework for industrial safety management and hazard-response optimization under uncertain environments, thereby contributing to resilient industrial operations, improved risk mitigation, and informed strategic decision-making.
