https://jdaid.qom.ac.ir/ Journal of Data Analytics and Intelligent Decision-making en daily 1 Tue, 30 Jun 2026 00:00:00 +0330 Tue, 30 Jun 2026 00:00:00 +0330 https://jdaid.qom.ac.ir/article_4582.html The aim of this study is to explore the role of sports law in the responsibility of coaches and to explain the challenges and new approaches in this area in the context of Iranian sports. To this end, in addition to collecting demographic data of coaches, variables such as education level, coaching experience, championship records, and possession of specialized skills such as first aid were examined. The main research tool was a questionnaire that assessed various dimensions of coaches' responsibility in areas such as training and supervision, safety and equipment, medical care, player coordination, and recognition of dangerous conditions. The data were collected by distributing questionnaires among 700 active coaches in sports fields. The findings showed that educational and skill factors play a significant role in promoting coaches' legal awareness and the proper fulfillment of their responsibilities, while mere experience or coaching degree alone does not guarantee legal understanding. The results also indicated that having a championship record and specialized training in subjects such as first aid can lead to improved performance of coaches in protecting the rights and health of athletes. These results reveal the need to rethink the legal and professional training of coaches and design new programs to promote their responsibility within the framework of sports law. https://jdaid.qom.ac.ir/article_4583.html This research examines the micro-foundations of individual ambidexterity as a managerial dynamic capability for addressing the tension associated with pursuing dual strategies. It empirically investigates the coevolutionary intertwining effects of different levels of integrative complexity (IC) and business complexity to identify the optimal composition of cognition–risk interaction that enhances ambidexterity performance. A multidisciplinary approach was adopted to address dualities and ambidexterity through a managerial cognition lens. The application of artificial neural networks has helped to gain a deeper understanding of the role of the cognitive dimension related to dualities. The results clarify that risk and business complexity can have both negative and positive effects. Specifically, in low-risk settings, high levels of IC are not capable of inducing higher ambidexterity performance, suggesting that risk should stimulate the cognitive complexity of CEOs. Moreover, the findings reveal that a high degree of cognitive complexity cannot be viewed as the sole dominant factor in successful strategy renewal, as it is exogenously fueled. An investigation of different scenarios indicates that, at higher levels of risk, differentiation-dominant IC, encompassing a perceptual decision-making style can enhance ambidexterity despite limited integration. In contrast, in low-risk settings, integration supported by analogical thinking may be more effective. https://jdaid.qom.ac.ir/article_4584.html Proportional representation is a foundational concept in social choice theory, seeking to ensure that the preferences of distinct groups are reflected fairly in collective decisions. As algorithmic systems increasingly shape high-stakes decisions in society, there is a growing need for principled methods that enable artificial intelligence (AI) to account for heterogeneous human values and preferences. This article explores how proportional representation can be extended beyond its classical role in voting and elections to address key challenges in modern AI. We focus on two central domains. First, we examine clustering, when data points naturally represent individuals or agents with diverse characteristics or preferences. We review recent advances that reinterpret clustering as a representation problem, introduce formal notions as a representation problem for both centroid-based and non-centroid-based clustering, and highlight algorithmic guarantees ensuring that large, cohesive groups receive influence proportional to their size. Second, we consider AI alignment, particularly reinforcement learning from human feedback (RLHF) in the presence of heterogeneous preferences. We argue that learning a single global reward function is fundamentally insufficient to capture population-level diversity and may violate basic social choice principles. To address this, we present a framework based on committees of reward functions, designed so that pairwise preferences induced by the committee proportionally reflect those of human annotators. We discuss theoretical guarantees showing that small committees suffice to achieve low proportionality error, as well as empirical evidence demonstrating substantial improvements over any single deterministic reward model. https://jdaid.qom.ac.ir/article_4585.html The rapid development of artificial intelligence (AI) technology and its major effects on the economy and productivity of countries have made policymaking and regulation in this area more urgent than ever. One of the issues that is important for decision-making, regulation, and legislation in artificial intelligence is the capacity of artificial intelligence to impact various jobs and productivity growth, particularly how AI will reshape jobs, limit and create employment opportunities, and ultimately affect productivity. The purpose of this study is to investigate the impact of artificial intelligence on the medical science landscape and labor market processes in medical occupational groups. This study collected data using the library method, drawing on reliable scientific and research sources. Data gathered from scientific articles, theses, and reputable sources available in databases such as Google Scholar, PubMed, and Scopus were analyzed. The results show that artificial intelligence can significantly increase the accuracy and speed of disease diagnosis by employing machine learning algorithms and analyzing complex medical data. In addition, AI can play an effective role in predicting and preventing diseases by analyzing health data collected from millions of patients. https://jdaid.qom.ac.ir/article_4586.html The emergence of Industry 5.0, characterized by the synergistic collaboration between human intelligence and advanced digital technologies, has created new expectations for higher education and highlighted the need to redesign university learning environments. Accordingly, this study investigates the key dimensions and components required for designing intelligent and human-centered learning environments in fifth-generation universities. The research adopts a qualitative approach based on library research and narrative literature review. Relevant scholarly sources on smart learning environments, human-centered learning, artificial intelligence (AI) in education, learning analytics, and higher education transformation were examined and synthesized. The findings indicate that such environments are shaped by several interrelated components, including intelligent and personalized learning platforms, interactive technologies such as augmented and virtual reality, flexible and collaborative learning spaces, and mechanisms that support students' self-efficacy, creativity, and problem-solving skills. The study contributes by integrating intelligent technologies with human-centered educational principles and by proposing a conceptual framework to guide the design of learning environments aligned with Industry 5.0 requirements. https://jdaid.qom.ac.ir/article_4587.html This study develops an integrative model to examine how digital media networks-comprising smart networks, social networks, and digital media-collectively influence insurance firm performance, with the Metaverse conceptualized as a strategic mediating platform. While prior studies have explored digital transformation in insurance, limited empirical research has examined how immersive technologies translate digital network resources into performance outcomes, particularly in emerging markets.Using a descriptive survey design, data were collected from 486 insurance professionals across two major Iranian insurance firms. Partial Least Squares Structural Equation Modeling (PLS‑SEM) was employed to test the hypothesized relationships and assess the model’s reliability, validity, and predictive power.The results indicate that smart networks (β = 0.437, p < 0.001), social networks (β = 0.323, p < 0.001), and digital media (β = 0.292, p < 0.001) have significant positive effects on insurance performance. Moreover, the Metaverse exhibits a strong partial mediating effect (β = 0.472, p < 0.001; VAF = 59%), amplifying the performance impact of digital networking infrastructures. The structural model demonstrates strong explanatory and predictive capability (R² = 0.75, Q² = 0.41, GOF = 0.449, SRMR = 0.078, NFI = 0.86).The findings highlight how insurers can leverage AI‑ and IoT‑based smart networks, social media engagement, and digital platforms to enhance operational efficiency and customer experience. The Metaverse emerges as an enabling environment for immersive interaction, virtual training, and innovative insurance services in emerging markets.This study is among the first to empirically validate the Metaverse as a mediating mechanism between digital networks and firm performance in the insurance sector. By integrating the Resource‑Based View (RBV) with technology adoption theories (TAM and UTAUT), the research advances digital transformation theory and offers actionable insights for insurance firms operating in emerging economies. https://jdaid.qom.ac.ir/article_4588.html Database design for knowledge-centric systems suffers from fragmentation: relational models provide integrity but implicit semantics; knowledge graphs offer explicit relationships but weak transactions; ontology-based approaches (OBDA) suffer mapping complexity; multi-model systems lack conceptual coherence. This paper proposes a unified relational-semantic architecture that bridges these gaps. The model introduces five principles: term-entity independence (context-neutral Terms), triadic contextualization (Term–Domain–Module tuples for polysemy resolution), relationships as first-class citizens (explicit Relations with typed RelationTypes), governance through typed schemas (module-relation compatibility MRT and declarative Relation Constraints), and service-oriented access control. Evaluation on a realistic dataset (5,000 terms, 18,547 relations) in Islamic sciences compares the proposed model against pure relational, Neo4j, and OBDA baselines. Results indicate that the model provides native support for contextualized term definitions across domains and modules—a feature absent from all baselines. Low overhead (45–99%) guarantees 99.4% constraint accuracy. For deep transitive closure, Neo4j is 4–5× faster (mitigable by materialized paths). The model outperforms OBDA by 3–8× and achieves a conceptual coherence score of 4.7/5, compared to 2.2–3.1/5 for baselines. The architecture offers a practical, empirically validated alternative for digital libraries, encyclopedias, and scholarly knowledge systems where contextual meaning and semantic relationships are paramount. https://jdaid.qom.ac.ir/article_4597.html Cryptocurrency markets are highly volatile, making return forecasting both valuable and difficult. This study develops a machine learning framework for predicting cryptocurrency return trends, formulated as a three-class problem (negative, near-zero, and positive next-day returns) rather than the conventional binary one. XGBoost, Random Forest, and Support Vector Machine (SVM) were evaluated on Bitcoin, Ethereum, and Solana using daily data from December 2024 to July 2025. The features combined technical indicators with macroeconomic variables, and hyperparameters were tuned via Bayesian Optimization using the Optuna framework. Performance was assessed by classification accuracy and per-class precision, recall, and F1-score. Average accuracies generally exceeded 60%. SVM achieved the highest average accuracy, while XGBoost was the most stable across assets and return classes. Positive returns were predicted more reliably than near-zero and negative returns, though strong positive-class recall was not always matched by precision. The results suggest the potential of combining machine learning, technical indicators, and macroeconomic variables for return-trend prediction. As they derive from a single test window and three assets, they should be read as indicative rather than definitive.