درخت حوزه‌های تخصصی

This study examines the key drivers of the Islamic debt securities market (Sukuk). This Sharia-compliant financial instrument is gaining recognition for its role in promoting economic development through ethical, non-usurious financing. As it is being used more in sectors such as infrastructure, energy, housing, and development, Sukuk has emerged as a significant financing tool for both Islamic and international markets. Following a two-step research strategy, the study first identified and categorized key factors influencing Sukuk market development by conducting a qualitative analysis of academic literature, scientific reports, and institutional documents. Twenty-one main components were distilled at this stage. Experts' views were gathered and analyzed at the second stage, based on linguistic variables, a fuzzy ranking technique, and Excel-based modelling, to prioritize factors uncovered while managing uncertainty in expert judgment. The results indicate that increasing the liquidity of Islamic finance instruments is the most significant method for improving Sukuk market growth, followed by the Management of Issuance Costs of Islamic Financial Securities (Sukuk) and Risk management (including exchange rate, interest rate, and credit risk) within the Sukuk structure. The study offers policy relevance to policymakers, regulators, and stakeholders who aim to improve the Islamic finance ecosystem by promoting the development and efficiency of the Sukuk market through targeted, evidence-based policies.

The primary objective of this study is to develop a predictive model for money laundering risk in Iranian listed firms. Initially, firm-level money laundering risk is measured using auditor assessments of anti-money laundering (AML) activities disclosed in annual audit reports. Subsequently, a quantitative modeling approach is employed, using financial and governance-related variables identified in prior research. To validate the quantitative findings, a qualitative approach based on grounded theory is also applied to identify additional explanatory factors. This research follows a mixed-methods design, incorporating both quantitative and qualitative phases. In the quantitative phase, a panel logit regression model is estimated using data from 1,680 firm-year observations covering the period 2012–2023. Independent variables include firm size, return on equity, leverage, investment opportunities, board independence, and board size. In the qualitative phase, semi-structured interviews were conducted with 10 experts to identify key risk factors, followed by the design and administration of an 18-item questionnaire distributed to 110 professionals. Exploratory factor analysis was then used to extract latent variables. The quantitative analysis reveals significant relationships between money laundering risk and several variables, such as firm size (positive), return on equity (negative), leverage (positive), and board independence (negative). The qualitative analysis identifies three core factors: (1) organizational culture and employee training, (2) corporate governance, and (3) a composite factor comprising compliance, organizational complexity, financial performance, firm size, and capital structure. Together, these factors explain over 50% of the variance in expert responses. The convergence of results from both methodological approaches confirms the robustness of the proposed model. Corporate governance indicators—particularly board size and independence—alongside financial attributes such as firm size, profitability, and capital structure, are found to be significant predictors of firm-level money laundering risk. The findings underscore the importance of strengthening internal control mechanisms and compliance structures in reducing money laundering risk.

Financing upstream oil and gas projects in Iran—particularly for non-governmental companies—faces numerous obstacles, including the lack of effective guarantee mechanisms, absence of credible collaterals, limited access to sovereign guarantees, and the skepticism of financial institutions. This paper, adopting a descriptive-analytical approach and grounded in the study of existing legal and institutional frameworks, explores the legal capacities for designing and utilizing alternative instruments to traditional guarantees in the financing process of high-risk projects. Within this framework, and based on the “Production and Infrastructure Financing Act”, the executive bylaw on the establishment of non-governmental guarantee funds, and other upstream legal documents, the establishment of an institution titled the "Oil Guarantee Fund" is proposed as an innovative solution. This fund is designed to mitigate default risk, facilitate project credit assessment, and replace bank or property-based guarantees for companies active in the oil and gas value chain. Its legal structure is envisioned based on the model of non-governmental institutions and private joint-stock companies. The proposed model has been formulated through a legal analysis of the relevant documents and informed by the outcomes of expert panels comprising specialists in energy law, finance, and public policy. Findings of the study indicate that the establishment of such a fund, capable of issuing valid guarantees based on contractual commitments, provides a practical tool to strengthen institutional trust and facilitate investment in upstream oil and gas projects. The article also presents a conceptual and institutional framework for the fund and offers recommendations for its policy-level and legal implementation.

Iran's gas industry plays a strategic role in supplying and distributing natural gas energy to society. The project-based, operational, and service-oriented nature of this industry, combined with its large workforce, underscores the necessity to address the risks confronting its human resource management system. This study was conducted to identify and analyse these human resource management risks. This study employed a mixed-methods approach. In the qualitative phase, the researcher's lived experience was utilized to identify and categorize 35 human resource management risk factors in Iran's gas industry. For the quantitative phase, a risk management framework was applied to evaluate each identified factor based on two key dimensions: risk probability and risk impact. The assessment results were subsequently visualized through a heat map. The study results demonstrated that none of the indicators fell within the low-risk zone, with 15 indicators classified as medium-risk and 20 as high-risk, while two critical indicators - imposition of salary and benefit restrictions with a risk rating of 9 and dependence on political affiliations for filling key positions with a risk rating of 8 - were identified as the most substantial challenges facing the human resource management system in Iran's gas industry.

Purpose- This study aims to identify and analyze innovative digital marketing strategies in rural tourism, with a specific focus on leveraging Augmented Reality (AR) and intelligent algorithms within technological ecosystems. The main research question is: How can augmented reality and intelligent algorithms be utilized to enhance digital marketing in rural tourism to effectively attract tourists and promote sustainable local development? This research addresses a gap in the integration of AR and artificial intelligence in rural contexts, where prior studies have often examined these technologies in isolation and with less emphasis on rural settings. Design/methodology/approach- Employing a qualitative exploratory approach and thematic analysis method, data were collected through semi-structured interviews with 16 experts in digital marketing, information technology, and management. Snowball sampling was used for participant selection, with theoretical saturation achieved after the 16th interview. Findings - The analysis revealed seven overarching themes: technological transformation of the tourism experience (e.g., AR virtual tours and AI personalized recommendations), empowerment of local communities (through digital literacy training and local content production), redefinition of marketing strategies (e.g., location-based marketing and algorithm-optimized advertising), cultural and environmental sustainability (preservation via virtual museums and reduced ecological impact), implementation barriers (e.g., weak infrastructure and ethical concerns), horizons of innovation and research (e.g., gamification and metaverse), and digital identity reconstruction (through cultural branding with emerging technologies). Research limitations/implications- Limitations include the focus on a specific cultural context (e.g., rural Iran), lack of longitudinal data for long-term change analysis, and reliance on qualitative data potentially influenced by researcher bias. Implications suggest longitudinal studies to assess long-term technology impacts and comparative research across diverse regions. Practical implications - Rural tourism managers can develop localized AR platforms, implement digital literacy programs for local populations, and deploy AI-powered location-based marketing systems to boost tourist attraction and enhance local sustainability. Originality/value- This study provides the first comprehensive framework for integrating AR and intelligent algorithms in rural tourism marketing, emphasizing rural-specific challenges like infrastructure deficits and cultural resistance, adding value for sustainable development in developing regions.