درخت حوزه‌های تخصصی

Identification of the strategic parameters of Iran’s energy-policy model; • Sensitivity analysis of the entire dynamic model considering system stability in uncertainty conditions; • Machine process including sentence-mining, analysis of frequent patterns, prediction of time series, and formation of dynamic analysis blocks in dynamic systems analysis; • Extraction of the strategic parameters of the carrying capacity in the energy-policy model.

This study investigates the multifaceted challenges of implementing digital transformation within Iranian oil and gas companies, focusing on organizational barriers and managerial solutions. Despite the global momentum toward digitalization, Iran’s oil and gas sector faces unique obstacles, including resistance to change, skill gaps, legacy infrastructure, and insufficient alignment between technology providers and large enterprises. Utilizing a mixed-methods approach, we conducted a survey of 210 managers and IT professionals across upstream and downstream organizations, complemented by in-depth interviews with key stakeholders. Exploratory factor analysis (EFA) and structural equation modeling (SEM) were employed to identify and validate critical barriers and their interrelations. Findings reveal that organizational inertia, lack of digital competencies, and inadequate investment in digital infrastructure are the most significant impediments, while leadership commitment and targeted training programs emerge as effective managerial remedies. The study contributes actionable insights for policymakers and executives by outlining a roadmap for overcoming digital transformation hurdles and achieving sustainable competitive advantage. The results underscore the necessity of a holistic strategy integrating technological, human, and process dimensions to facilitate successful digital transformation in Iran’s oil and gas industry.

Understanding farmers' mental models (MM) is crucial for interpreting their behavior; however, these models, are shaped by technical, economic, and social factors, have often been overlooked. Using a case study from Kangavar County, Western Iran, this study investigates the MM of farmers and experts regarding wheat seed usage and proposes optimization strategies using a mixed-methods approach. Qualitatively, phenomenological analysis of interviews with 16 participants (selected via purposive and snowball sampling) identified five key factors shaping farmers' mental models (MM): work context, climate, experienced risks, available resources, and subjective norms. Experts' rationale for seed recommendations centered on three themes: academic knowledge, professional experience, and user characteristics. Quantitatively, a structured questionnaire administered to a separate sample of 255 farmers (stratified proportional sampling) and 50 experts (complete enumeration) revealed significant disparities. Independent samples t-tests revealed statistically significant differences (p < 0.01) across all major mental model (MM) domains. Responses were initially scored as percentages (0–100) based on item agreement, then standardized to a 100-point scale. Mean scores were substantially higher among farmers in experienced risks (74.02 vs. 15.80), subjective norms (66.24 vs. 18.93), and work context (65.67 vs. 35.30), indicating stronger perceived influence of experiential and social factors. Conversely, experts scored higher on academic knowledge (75.28 vs. 41.71), reflecting greater reliance on scientific evidence.The Delphi method identified and prioritized key strategies for optimizing seed use, with "preparing a suitable seedbed" (weighted average: 0.148) and "using certified seeds" (weighted average: 0.143) ranked highest. These findings underscore the critical gap between farmer and expert models and emphasize the need for targeted interventions that integrate experiential knowledge with scientific evidence to bridge this divide, fostering more sustainable seed usage practices.

Poultry production is one of the fastest-growing industries worldwide, and poultry meat has become a primary protein source for many populations. To maximize growth in the shortest possible time, producers often rely on disease-preventing antibiotics and growth promoters. However, numerous studies have detected antibiotic residues in poultry meat, raising concerns about their contribution to antibacterial resistance in human pathogens. Consequently, consumer demand for healthier products—particularly antibiotic-free poultry—is steadily increasing. Although this trend is advancing globally, it is still in its early stages in Iran. Accordingly, in order to move towards the development of the market for healthy products, the first essential step is to identify the factors that influence household consumption patterns. This study employs a combination of three decision-making tools, including Interpretive Structural Modeling (ISM), MICMAC analysis, and Fuzzy BestWorst Method (BWM), to evaluate factors influencing the purchase of antibiotic-free poultry in Mashhad, Iran, a key market where such products are available and consumer trends are emerging, in 2025. The process begins with the ISM method, which constructs a hierarchical model to illustrate the influence and interdependence of these factors. This step is crucial for understanding their collective impact on consumer purchase intention. Subsequently, the MICMAC analysis categorizes these factors based on their levels of influence and dependence, identifying the most critical elements that create a competitive advantage. Finally, the fuzzy Best–Worst Method (BWM) is applied to assign weights to these key factors. The results indicate that consumer awareness is the highest weight, followed by attitude and subjective norms. Identifying these key factors and their interrelationships provides actionable insights for decision-makers and stakeholders for strategic planning and prioritization. Based on these findings, it is recommended that policymakers and marketers focus on increasing consumer awareness, shaping positive attitudes, and leveraging subjective norms to encourage the purchase of antibiotic-free poultry. Targeted educational campaigns, informative labeling, and marketing strategies tailored to consumer preferences can help enhance adoption and support healthier consumption patterns.

This study conducts a comprehensive examination of the interlinkages among four key financial markets—crude oil, gold, currency, and equities—over the July 23, 2013, to March 12, 2025. This study employs the Contemporaneous and Lagged R2 Decomposed connectedness approach recently introduced by Balli et al. (2023), which allows us to decompose the connectedness among variables into lagged and contemporaneous components. As the previous approaches only show overall or contemporary results, this novel approach fills the gap in the literature. The findings reveal that most spillovers occur contemporaneously, with the U.S. dollar playing a central role in the propagation of contemporaneous shocks. In contrast, gold emerges as a significant transmitter of long-term shocks, underscoring its relevance for strategic risk management. Moreover, crude oil is the main receiver of shocks in the average and lagged connectedness in the network. Dynamic analyses indicate heightened market sensitivity during periods of geopolitical tension, particularly under intensified sanctions and currency volatility in Iran. By uncovering the evolving structure of cross-market dependencies, this research offers valuable insights for portfolio allocation, systemic risk assessment, and the formulation of responsive economic policies in times of financial stress.