bluuebunny/crossref_metadata_2025 huggingface dataset:
Design and Explanation of Portfolio Optimization Model Using Similarity and TOPSIS Methods
Authors: Biglari Sohrab , Fathi Zadollah , Fallahshams Layalestani Mir feiz
Published: February 2025

This study examines and optimizes investment portfolios using Similarity and TOPSIS methods. The aim of this study is to identify and rank assets in order to create a diversified and low-risk portfolio that can provide the best possible returns. The Similarity method is used to identify the correlation between assets and find more diversified combinations, while the TOPSIS method ranks assets based on their proximity to the positive ideal by evaluating multiple criteria. This research is applied in nature and is classified as descriptive-survey research in terms of data collection method. The statistical population of the study includes companies listed on the stock exchange during the period from 2011 to 2020. A judgmental sampling method is employed in this research. The results indicate that the combination of these two methods with the Markowitz model can lead to improved portfolio performance and offer better results under different market conditions. This practical approach enables investors to create an optimized portfolio resistant to market fluctuations through detailed analysis based on multiple data points.
Presenting a Model of Investors' Emotions and Behavioral Reactions Regarding Stock Portfolio Risk-Taking During the COVID-19 Era
Authors: Khajavi Mehdi , Nakhaei Karim , Nakhaei Habib Alah , Talebinia Ghodrat alah
Published: February 2025

Classical theories are based on the assumption that investors act completely rationally when making decisions, aligning with the theory of the rational economic person. The aim of this study is to present a model of investors' emotions and behavioral reactions concerning stock portfolio risk-taking during the COVID-19 era. The research is an exploratory mixed-method study. The first phase, using an interpretive paradigm and thematic analysis approach, involved interviews with 16 purposefully selected participants until theoretical saturation was achieved. The interview data were analyzed using open coding, and a thematic network of investors' emotions and behavioral reactions concerning stock portfolio risk-taking during the COVID-19 era was developed. Based on the study's findings, the themes were categorized into 46 basic themes, 15 organizing themes, and 4 global themes: cognitive biases (including framing, limited accessibility, and attention), judgmental biases (including self-serving bias, reference point, and overconfidence), emotional (affective) biases (including regret aversion, justification, loss aversion, increasing risk-taking, and optimism), and cultural factors (including locus of control, investment habits, and value system). In the quantitative phase, the research components were quantified using the structural equation modeling technique. According to the results of the quantitative phase, the validity of all four global themes—cognitive biases, judgmental biases, emotional biases, and cultural factors—was confirmed. Moreover, the ranking of the global themes showed that judgmental bias had the highest rank (5.13) in terms of the likelihood of occurrence and influence and exerted the greatest impact on investors' emotions and behavioral reactions regarding stock portfolio risk-taking during the COVID-19 era. Similarly, the other indicators—cognitive biases, cultural factors, and emotional biases—were ranked in subsequent positions.
Applying the Exploratory Factor Analysis Approach in Ranking Key Indicators of Tax Avoidance in Industries
Authors: Dejlani Javad , Shorvarzi Mohammad Reza , Masih Abadi Abolghasem , Mehrazin Alireza
Published: February 2025

One of the key topics in accounting is the investigation of factors influencing tax avoidance and its metrics, which are closely related to the concept of evaluating corporate tax planning. The statistical data for calculating 12 effective tax rates included companies listed on the stock exchange over a five-year period (2011 to 2015). During this period, the relative stability of laws, which is essential for a uniform assessment environment, was more prevalent than in the following years. Considering the tax figures presented in the tax returns and those finalized through tax audits, the results of exploratory factor analysis revealed that the highest repetition of factor loadings occurred with long-term accrued and paid taxes. Therefore, by taking into account the finalized tax figures, the difference between the cash effective tax rate (TA_Cash_ETR) had the highest explanatory power, while the difference between accounting profit and taxable income (Perm) had the lowest. When considering the declared tax figures, the two criteria of cash effective tax rate difference (TA_Cash_ETR) and the three-year cash effective tax rate (Cash_ETR3) had the highest, and permanent differences based on discretionary accruals (DTAX) had the lowest explanatory power. In other words, these criteria have the highest and lowest relevance concerning the measurement of tax avoidance in companies operating in Iran’s economic environment. Given that tax regulations in a specific industry may differ from those in another, it is preferable to focus on the criterion with the highest explanatory power and greatest relevance when researching tax avoidance. The clear characteristic of these factors is that considering a multi-year long-term period, while reducing statistical errors, yields more reliable and appropriate results compared to a single fiscal year. Overall, the long-term cash effective tax rate, adjusted by the industry in which the company operates, is a more suitable criterion for measuring and evaluating tax avoidance. On the other hand, although considering finalized company figures is important for measuring the research objective, the declared figures in the tax return are also effective and reliable.
Evaluating the Efficiency of Sustainable Suppliers Based on Human and Environmental Indicators Using Data Envelopment Analysis and Gap Analysis (Case Study: Petrochemical Companies)
Authors: Karamad Somayeh , Dehghan Dehnavi Hassan , Babaei Meybodi Hamid , Rabbani Mojdeh
Published: February 2025

Evaluating the efficiency of sustainable suppliers not only raises awareness among stakeholders but also enhances competition, promotes industry dynamism, and fosters the sustainable and balanced development of society. In the present study, human and environmental indicators were utilized, and optimal input and output criteria were selected based on expert opinions. Therefore, the selection of variables (inputs and outputs) in this research was assessed through a review of similar studies, interviews, and surveys with experts and specialists. All calculations were performed using DEA Solver software. The results revealed that out of 13 companies, 12 were efficient (efficiency score equal to one), and only one company was inefficient. Furthermore, "Movaledan Chemical Company," with an efficiency score of 1.66, was found to be more efficient compared to other companies, almost twice as efficient as "Pars Pak Kimia Company," which had the lowest efficiency score of 0.858. Pars Pak Kimia Company, with the lowest efficiency score (0.858), ranked last. In Data Envelopment Analysis (DEA), when calculating the efficiency of companies, several efficient units were used as benchmarks (or references) for each inefficient company. The benchmarks for Pars Pak Kimia Company were "Iran Petrochemical Trading Company" with a lambda coefficient of 0.262, "Chelik Alborz Company" with a lambda coefficient of 0.55, "Movaledan Chemical Company" with a lambda coefficient of 0.052, and "Farzam Chemical Group" with a lambda coefficient of 0.135. The results of the paired T-test indicate a significant difference at the 95% confidence level between the scores of this variable in the current and target states.
Examining FinTech Indicators on the Performance of Equity Investment Funds
Authors: Baghmirani Roya
Published: February 2025

This research examines FinTech indicators on the performance of equity investment funds. With the development of FinTech, new tools have emerged to enhance the efficiency, transparency, and performance management of funds. In this study, the Principal Component Analysis (PCA) method was used to identify key FinTech components, followed by the Markov Switching model to analyze the variable performance patterns of the funds. The results revealed that components of FinTech infrastructure and performance management had the greatest contribution to the improvement of the composite FinTech index. The findings indicated that the FinTech infrastructure component had the most significant impact on the composite FinTech index, followed by performance management and FinTech evaluation in the institutional environment. To analyze different performance states, the Markov Switching model was applied, revealing two main performance states in the funds: a stable state with high returns and a volatile state with lower returns. Practical recommendations for fund managers include strengthening FinTech infrastructure to enhance security and reduce costs, utilizing data mining and behavioral analytics to better identify customer needs, and employing blockchain and automation technologies to improve efficiency and transparency. This study also highlights limitations, such as data restrictions and the generalizability of findings to all institutional environments. These results can assist fund managers in leveraging financial technologies to not only improve fund performance but also increase attractiveness to investors and better manage risks.
Identification and Prioritization of Environmental Pollutants in the Bistoon Petrochemical Industry, Kermanshah
Authors: Akbarpoor Narges , Khaksar Mansoor , Irandost Mansoor
Published: February 2025

The global environmental pollution crisis has led scientists to believe that if development and environmental protection are not aligned and integrated, sustaining life on Earth for future generations will not be feasible. In Iran, due to the expansion of oil and gas industries and their close connection with the social and environmental lives of surrounding residents, increased attention to the environmental and health impacts of the oil, gas, and petrochemical industries is justified. Thus, eliminating or reducing these risks to an acceptable level is of great importance. Petrochemical industries are known to generate environmental pollutants throughout various stages, including the collection of raw materials, production and transformation of intermediates, and the collection and storage of final products. The primary environmental impacts of petrochemical projects involve water, soil, air, and noise pollution, as well as socio-economic consequences. Therefore, the aim of this research is to identify and prioritize environmental pollutants in the Bistoon Petrochemical Industry, Kermanshah. The research is applied in terms of purpose and exploratory-descriptive in terms of method. The research instrument is a pairwise comparison questionnaire. The statistical population consists of the managers of the Bistoon Petrochemical Company, Kermanshah, with 10 experts selected randomly using the snowball sampling method. Data analysis was conducted using the SWARA software. The results indicate that water and wastewater-related pollutants, with a weight of 0.445, rank first. Air-related pollutants, with a weight of 0.271, rank second. Soil and waste-related pollutants, with a weight of 0.164, rank third. Noise-related pollutants, with a weight of 0.120, rank fourth. Based on these findings, the implementation of advanced technologies to improve the wastewater purification processes to reduce environmental pollution is recommended.
Women’s Promotion to Managerial Positions in Iranian Organizations: A Systematic Review Approach
Authors: Sadeghpour Dilmaghani Mahsa , Gholipour Arian , Roshandel Arbatani Taher , Mohammadesmaeili Neda
Published: February 2025

This study aims to explore the key factors Impacting the promotion of Iranian women to managerial positions in Iranian organizations. By identifying barriers and facilitators across individual, social, cultural, and organizational domains, this research seeks to provide a comprehensive understanding of the challenges women face in ascending to leadership roles and offer practical solutions to address these issues. The study employed a systematic review approach, analyzing existing literature and qualitative data from various academic sources. The research focused on identifying relevant themes through conceptual and axial coding. Articles were selected based on their focus on women’s promotion, gender roles, organizational culture, and leadership dynamics. A thematic analysis was conducted to extract key patterns related to women’s career progression, with particular attention to cultural and organizational barriers in Iran. The study identified several significant barriers to women’s promotion, including entrenched gender stereotypes, organizational cultures favoring male leadership, and the persistent glass ceiling effect. Work-family conflict also emerged as a critical impediment to women’s career growth. However, social support systems such as mentorship and organizational changes promoting diversity were found to be facilitators of promotion. Personal traits such as perseverance and assertiveness also contributed to overcoming these barriers, though broader cultural and organizational support remains essential for sustained progress. While there has been some progress in the promotion of women to managerial positions in Iranian organizations, substantial barriers persist. Gender stereotypes, organizational culture, and work-family conflict continue to limit women’s career opportunities. To foster women’s promotion, organizations must implement supportive policies, mentorship programs, and cultural shifts that promote gender equality and recognize diverse leadership styles.
Assessment of the Role of Cultural Navigation in the Development of Cultural Identity
Authors: Tabatabaei Qomi Seyed Mohammad Javad , Mohebbi Serajeddin , Ameri Shahrabi Mohsen , Shojaeifar Ali
Published: February 2025

In today’s world, the phenomenon of globalization and the expansion of communication technologies have increasingly enhanced intercultural interactions. In this context, the concept of "cultural navigation" has gained special significance as a process through which individuals and communities can navigate diverse cultural environments and adapt to communication challenges and cultural differences. This study aimed to evaluate the role of cultural navigation in the development of cultural identity. The research is applied in terms of its objective, exploratory in terms of its approach, and employs a mixed-methods (qualitative-quantitative) design for data analysis. In the qualitative phase of the study, and for the purpose of model design based on grounded theory methodology, a group of experts in cultural fields (university professors, executive managers, and consultants) were selected and underwent in-depth interviews. In this phase, purposive sampling was employed, and the process continued until theoretical saturation was achieved, resulting in a total of 15 interviews. In the quantitative phase, cultural sector managers were considered the population, and among them, 115 individuals were selected as the statistical sample through stratified random sampling with proportional allocation. In the qualitative phase of the research, due to the use of grounded theory, the data collection tool was unstructured interviews with experts. In the quantitative phase, the main data collection tool was a researcher-developed questionnaire consisting of 74 items, designed based on the initial conceptual model. In the quantitative phase, SPSS and SmartPLS software were used to conduct descriptive and inferential analyses. The results of the research led to the design of a process model (based on the structure of the paradigm model) for assessing the role of cultural navigation in the development of cultural identity, and the hypothetical relationships of the model were tested and confirmed in a large population.
Entrepreneurial Strategies in Ai-Enhanced Sports Marketing Platforms
Authors: Akbari Mohsen , Naderi Nasab Mehdi , Biniaz Seyyed Abbas
Published: February 2025

Artificial intelligence (AI) is transforming sports marketing by enabling more personalized fan experiences, enhancing customer engagement, and optimizing marketing operations. AI technologies such as machine learning, predictive analytics, and chatbots are widely used to drive innovation and create new revenue models in the sports industry. However, despite the benefits, the integration of AI in sports marketing comes with challenges related to technical implementation, economic risks, and ethical concerns. This article aims to review entrepreneurial strategies in AI-enhanced sports marketing platforms and explore their effectiveness in driving innovation, market expansion, and customer engagement. This narrative review uses a descriptive analysis method to synthesize literature from academic journals, industry reports, and case studies related to AI-driven marketing strategies in sports. The analysis focused on identifying key entrepreneurial approaches such as innovation, market penetration, partnerships, and revenue models within AI-enhanced sports marketing platforms. The study identified several successful entrepreneurial strategies enabled by AI, including innovation in product development through personalized fan engagement platforms and virtual reality experiences. AI also supports market expansion by enhancing digital advertising and enabling dynamic pricing. Strategic partnerships with technology companies are crucial for accessing advanced AI tools, while customer-centric strategies driven by AI have improved fan engagement. The analysis also highlighted the challenges associated with AI adoption, including high costs, data privacy issues, and ethical considerations. Nonetheless, AI-driven revenue models such as subscriptions and targeted advertising are creating new business opportunities. AI is reshaping sports marketing by enabling new entrepreneurial strategies that drive innovation, market growth, and customer engagement. While challenges exist, the future of AI in sports marketing holds significant potential for further growth and transformation
Examining the Impact of Deviation from Target Leverage on the Probability of Corporate Bankruptcy
Authors: Amini Far Masoud , Anvar Khatibi Saeed , Zinali Mehdi
Published: February 2025

Deviation of companies’ leverage from optimal leverage can result in the imposition of costs and an increased risk of bankruptcy. In this study, we examine the effect of companies’ deviation from optimal leverage on the probability of bankruptcy. For this purpose, data from 86 companies over a 6-year period were collected. The analysis and hypothesis testing were conducted using the statistical software EViews and logistic regression methodology. The results showed that deviation from target leverage for companies with leverage higher than the target leverage had a significant relationship with the risk of bankruptcy. In other words, the more a company's leverage exceeds its target leverage, the higher the probability of bankruptcy. However, for companies with leverage lower than the target leverage, no significant relationship was found with the probability of bankruptcy. On the other hand, leverage with a one-year and three-year lag had a significantly positive relationship with the probability of bankruptcy.
Identifying and Prioritizing Factors Affecting Supply Chain Financing in the Energy Sector
Authors: Asadi Asadabad Reza , Borhani Seyed Abbas , Safa Mojgan , Badiei Hossein
Published: February 2025

The energy sector, due to its complex and evolving nature, requires innovative financing methods. The aim of this study is to identify and prioritize factors influencing supply chain financing in the energy sector. This research is applied in orientation and quantitative in methodology. The theoretical population of the study consisted of experts active in the energy sector with a solid background in financing and supply chain financing. Judgment sampling was employed in this study. The methods used in this research included Fuzzy Delphi and CoCoSo. The Fuzzy Delphi method was utilized to screen the research factors, while CoCoSo was applied for final prioritization of the factors. The primary data collection tools were an expert assessment questionnaire and a prioritization questionnaire. This study was conducted in three stages. In the first step, 21 factors were identified through literature review and interviews with experts. In the next step, these factors were screened using expert assessment questionnaires and the Fuzzy Delphi method. Ten factors, which achieved a favorable defuzzified value, were selected for final prioritization. The selected factors were ranked using the CoCoSo method. The prioritized factors included: the degree of integration of information systems within supply chain components, the penetration rate of data-driven technologies in the energy supply chain, and the level of banks' control over the country’s financing sector. Practical recommendations were developed based on the key factors identified in the study. Some of these recommendations included: focusing on the compatibility and adaptability of technologies with existing systems during technology transfer, using fourth-generation technologies to integrate the supply chain, and enhancing the diversity of financing methods in the country through the development of fintechs and financial startups.
Examining and Comparing the Efficiency of MLP and SimpleRNN Algorithms in Cryptocurrency Price Prediction
Authors: Ahmadi Farrokh , Toloie Eshlaghi Abbas , Radfar Reza
Published: February 2025

Cryptocurrencies have been widely identified and established as a new form of electronic currency exchange, carrying significant implications for emerging economies and the global economy. This research focused on the "examination and comparison of the efficiency of MLP and SimpleRNN algorithms in predicting cryptocurrency prices" using the Python programming language. Price predictions for Bitcoin, Ethereum, Binance Coin, Cardano, and Ripple were made using two deep learning algorithms (including the MLP algorithm and the SimpleRNN algorithm) over the period from 2017 to 2023. The results of cryptocurrency price prediction using deep learning algorithms were satisfactory; and the comparison of predictions across all cryptocurrencies indicated minimal differences between the algorithms studied, suggesting that they were efficient and had low error rates. Based on the obtained results regarding Bitcoin price prediction, the best algorithm was SimpleRNN; for Ethereum price prediction, the best algorithm was MLP; for Binance Coin price prediction, the best algorithm was SimpleRNN; for Cardano price prediction, the best algorithm was MLP; and for Ripple price prediction, the best algorithm was MLP.
Application of the DEMATEL Method in Analyzing Citizen-Centric Development Strategies in Public Administration (Case Study: Zanjan Municipality)
Authors: Babaei Moghtada , Alabedin Rahmani Zain , Jafari Mostafa
Published: February 2025

Citizen-centric public administration enhances active citizen participation in decision-making processes. It encourages citizens to share their opinions, ideas, and feedback on policies and programs. Promoting citizen-centricity in municipalities can highlight the importance of civic education and capacity-building initiatives. Municipalities provide opportunities for residents to enhance their understanding of local governance processes, rights, and responsibilities. The purpose of this study was to identify and examine the influence and impact of citizen-centric development strategies in public administration. This research is applied in terms of its purpose and falls within the category of mixed-method research using a deductive-inductive approach. The qualitative segment of the study involved professors and experts in policy-making, public management, and urban management. A purposive sampling method with the snowball technique was employed, resulting in a sample size of 17 participants. The quantitative segment included managers and staff of Zanjan Municipality. Using a non-probability convenience sampling method, 52 managers participated in the study. In the qualitative section, data from the interviews led to the identification of 10 citizen-centric development strategies in public administration. The quantitative analysis was conducted using the DEMATEL method. According to the DEMATEL analysis, networking, participation mechanisms, and citizen empowerment are both highly influential and highly influenced strategies. Fluid bureaucracies, evidence-based decision-making, and professional competencies of agents are the most impactful strategies. Additionally, effective technologies, participation mechanisms, and open information are more susceptible to influence compared to other strategies.
Examining the Effects of Green Financing on Sustainable Development in Iran
Authors: Tavakoli Muhaddeseh , Momeni Vasalian Hoshang , Ismail Nia Getabi Ali Asghar , Seifipour Roya
Published: February 2025

Green financing is a crucial approach in executing all economic activities, and it is essential to formulate and adopt strategies, comprehensive environmental policies within the framework of laws and development policies, green job creation, and green financing. Given the significance and impact of green financing on the country's sustainable growth and development, this study analyzes the relationships between the green financing index and sustainable development using the Smooth Threshold Autoregressive (STAR) model. To achieve this, the green financing index is first calculated, followed by an analysis of the relationship between the green financing index and the country’s sustainable development. The results indicate that the threshold rate is 18%. Considering the differences in the coefficients obtained for the linear and nonlinear components of the model, the varying influence of sustainable growth on other variables is demonstrated. Furthermore, the findings reveal that before reaching the 18% threshold of sustainable development, the green financing index has a positive effect in the linear model. However, beyond this specified rate, its impact becomes negative and more substantial. The results of the diagnostic tests indicate that there is no serial autocorrelation or heteroskedasticity in the model, and the model has accurately captured all existing nonlinear behaviors, with the normality of the distribution being accepted.
Challenges and Barriers to Implementing Collaborative Governance for Linking Education and Industry
Authors: Masteri Farahani Fateme
Published: February 2025

The present study aims to identify the challenges and barriers to implementing collaborative governance in the linkage between education and industry to provide practical solutions for managers and stakeholders in this field. To achieve the research objectives, the theoretical literature, including the history, definitions, and theories related to collaborative governance, education-industry interaction, and existing barriers, was first reviewed. The study, based on an interpretive philosophical foundation and an inductive approach, employed a qualitative method and a grounded theory strategy. Participants were selected through purposive sampling and snowball techniques. Data were collected using interviews with 10 experts from academia, industry, and government sectors. The research data were analyzed using a three-phase coding process: open coding, axial coding, and selective coding. Based on data analysis, the barriers to implementing collaborative governance in the education-industry linkage were categorized into six dimensions: causal conditions, contextual conditions, intervening conditions, the core category, strategies, and outcomes. In causal conditions, factors such as a lack of financial resources and the absence of legal infrastructure were identified as key barriers. Contextual conditions included inappropriate organizational culture and traditional attitudes toward collaborative governance. Intervening conditions highlighted the impact of rapid technological changes and insufficient policy-making. The core category identified in this study was the inefficiency of governance structures, which serves as the central challenge in education-industry interaction. Proposed strategies included enhancing transparency, reforming regulations, and leveraging modern technologies to facilitate interactions. Positive outcomes of successfully implementing collaborative governance included increased innovation, strengthened collaborations, and improved educational effectiveness. The temporal scope of the study was 2024, and the spatial scope was higher education institutions and industries in Iran. The study results revealed that the barriers to implementing collaborative governance pertain to two levels: macro (policy-making and legislation) and micro (cultural development, planning, and operational tools). Accordingly, a combined top-down and bottom-up approach is recommended to address these barriers. This approach can foster effective interactions among academic, industrial, and governmental institutions, thereby enabling the successful implementation of collaborative governance.
Predicting Audit Failure Using Metaheuristic Algorithms
Authors: Zarrin Saeed , Mohammadi Ahmad , Zeynali Mehdi
Published: February 2025

The aim of the present study is to predict audit failure using metaheuristic algorithms in companies listed on the Tehran Stock Exchange. To achieve this objective, 1,848 firm-year observations (154 companies over 12 years) were collected from the annual financial reports of companies listed on the Tehran Stock Exchange during the period from 2011 to 2022. In this study, four metaheuristic algorithms (including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Bee Colony Optimization (BCO)) were utilized, as well as two methods for selecting the final research variables (the two-sample t-test and the forward stepwise selection method) to create the model. The results from the metaheuristic algorithms indicate that the overall accuracy of the GA, PSO, ACO, and BCO algorithms is 95.3%, 94.5%, 90.6%, and 92.8%, respectively, demonstrating the superiority of the Genetic Algorithm (GA) compared to other metaheuristic algorithms. Furthermore, the overall results from the variable selection methods indicate the efficiency of the stepwise method. Therefore, in companies listed on the Tehran Stock Exchange, the stepwise method and the Genetic Algorithm (GA) provide the most efficient model for predicting audit failure.
Providing a Framework for Identifying and Analyzing Drivers Affecting the Future of Banking with an Emphasis on Blockchain
Authors: Ghodsi Ebrahim , Poorfakharan Mohammadreza , Mohaghar Ali , Maleki Mohammadhasan
Published: February 2025

Blockchain technology, with its advantages such as reducing intermediaries, controlling costs, and increasing security, has garnered attention from the financial industry. This technology, besides centralizing banking processes, can help improve operational efficiency. This study aims to identify and analyze the drivers influencing the future of banking in Iran, with a focus on blockchain technology. The research is applied in terms of orientation and quantitative in terms of methodology. Two methods, fuzzy Delphi and Marcus, were used for data analysis. Initially, 29 drivers were extracted through a literature review and interviews with banking experts. Subsequently, nine drivers were selected for final prioritization using the Marcus method after distributing expert questionnaires and screening them with the fuzzy Delphi method. The final drivers were ranked through a questionnaire distribution. The perceived organizational benefits, the compatibility of blockchain technology with the IT systems of the banking sector, the development of RegTech in the country, and the regulatory approaches of lawmakers regarding digital financial technologies were, respectively, identified as the highest-priority drivers. The most important practical recommendations of the study include developing a tool to measure the benefits of blockchain technology, adopting a systemic view towards the implementation of new technologies, making necessary changes before transferring the technology to the banking sector, leveraging RegTech capacities to assess the risks of using digital financial technologies, and ensuring the integration of regulatory policies and comprehensive regulation.
Presenting a Qualitative Model of Behavioral Biases in Institutional Investors' Decisions in Iran
Authors: Samii Farzad , Farsad Amanollahi Gholamreza , Keyghobadi Amirreza
Published: February 2025

The purpose of this study is to present a qualitative model of behavioral biases in institutional investors' decisions in Iran. This research is applied in terms of its goal and descriptive-analytical in nature. The research method is qualitative. The statistical population of this study includes academic experts and managers of investment companies listed on the stock exchange, selected through the snowball sampling method and theoretical saturation, with a total of 12 participants. The data collection tool is a semi-structured interview based on theoretical foundations. The data analysis was conducted using thematic analysis. The results of the qualitative section were categorized into four main themes and sub-themes. The identified cognitive and psychological factors include: "overconfidence, disposition effect, herding behavior, information bias, and mental accounting." The identified amplifying factors are: "macroeconomic decisions, information asymmetry, and information overload." The identified strategies include: "reviewing and accepting mistakes, adherence to the trading plan, and avoiding bias toward specific stocks or characteristics." The identified outcome factors are: "better performance, attracting more investors, and enhancing the efficiency of the capital market."
Developing a Customer Confusion Management Model Based on the Internet of Things in the Banking Industry
Authors: Balipour Babadi Vajiheh , Rashnoodi Asghar , Omidi Fereydoun
Published: February 2025

The aim of this study is to develop a customer confusion management model based on the Internet of Things (IoT) in the banking industry. This research adopts a mixed-methods approach (qualitative and quantitative). The qualitative strategy used in this study is thematic analysis, while the quantitative strategy employed is survey-based. The statistical population for the qualitative phase consists of 17 managers of Bank Mellat branches in Tehran. In the quantitative phase, the population includes all customers of Bank Mellat in Tehran, with 384 questionnaires distributed and collected through random sampling. The findings from the qualitative thematic analysis identified key themes and descriptive codes from interview texts, including: sources of confusion, consequences of customer confusion, internet infrastructure, influential customer characteristics that increase confusion, organizational education approaches to IoT, and organizational factors impacting confusion management. The results of structural equation modeling using Smart PLS software demonstrated that the customer confusion management model based on IoT in the banking industry exhibits strong validity and fit. Therefore, IoT facilitates the appropriate investment in expanding software and hardware infrastructure across all bank branches, as well as the efforts of staff and management to provide quick and informed access to IoT services.
Developing an Intellectual Capital Model in the Computer Industry Based on Strategies and Outcomes Using the Grounded Theory Method
Authors: Baba Ahmadi Reza , Chirani Ebrahim , Mirbargkar Seyed Mozaffar
Published: February 2025

This research aimed to develop an intellectual capital model in the computer industry based on strategies and outcomes using the grounded theory method. The data collection tool was a semi-structured interview conducted with experts. To this end, using a purposive sampling approach (snowball sampling), interviews were conducted with 15 academic experts or senior managers of companies active in the computer industry listed on the Tehran Stock Exchange. The research data were analyzed using the coding method and Strauss and Corbin’s approach, extracting the main categories and concepts. The results indicate that, based on the study’s paradigmatic model, the causal factors of intellectual capital include capital structure, investment, production costs, quality of goods and services, competitive ability, innovation and creativity, professional ethics, commercialization of ideas, and the exchange of knowledge and experience. Contextual factors of intellectual capital include advanced technology, buyer behavior, and the competitive environment. Intervening factors of intellectual capital include market efficiency, general government policies, and the country’s developmental status. Intellectual capital strategies encompass company management policies, risk management, and supply chain management. Moreover, the outcomes of intellectual capital include production capability, sales capability, export capability, and profitability.
Examination of Sedimentary Facies and Diagenesis of the Eocene Deposits in Saravan, Southeastern Iran
Authors: Salehabadi Shima , Kohnsal-Qadimvand Nader , Jahani Davoud , Majidi Fard Mohammad Reza
Published: February 2025

The study area is located north of Saravan County, covering the regions from Ghasht toward Sib Soran. Stratigraphic sections of Ghasht and Sib Soran were measured and sampled to describe facies and determine the sedimentary environment. In this area, major clastic and carbonate facies groups were identified. The region includes layers of sandstone, limestone, marl, and shale. The limestones contain benthic foraminifera such as Nummulites and Alveolina, along with other skeletal and non-skeletal components. Based on the presence of key benthic foraminiferal species, an Eocene to middle Eocene age is proposed for the area. Facies include microconglomerates, sandstones, and shales, exhibiting fining-upward trends in grain size, indicative of a marine setting with turbiditic facies. Microscopic studies further reveal that the sediments in this area are composed of carbonate facies. These facies are associated with the facies belts of lagoon, shoal, and shallow open marine environments. Based on facies characteristics and the abundance of allochems, the depositional environment of this region is interpreted as a shallow marine setting with a gentle slope. Analysis of diagenetic processes in the region highlights various processes such as micritization, cementation, compaction (mechanical and chemical), dissolution, and the filling of fractures and pores. This study, based on fieldwork alongside laboratory results, aims to investigate the sedimentary facies, diagenesis, and sedimentary environment of the Saravan region in southeastern Iran.
The Impact of Macroeconomic Factors and Banking Management on Bank Deposit Attraction in Iran: Evidence from the Generalized Method of Moments Panel Approach
Authors: Shourekchali Javad Montazeri , Fattahi Majid , Bagheri Seyed Mohammad
Published: February 2025

Given the growth in the number of private banks in Iran over the past decades, competition among banks to attract resources and deposits has increased. To maintain profitability and sustain their position in the banking industry, banks must focus on customer satisfaction and offer services that align with customer needs. Accordingly, and considering the importance of identifying the factors influencing the attraction of bank deposits for decision-makers in the field of banking management in the country, this study aims to identify the factors affecting the attraction of bank deposits in Iran during the period from 2011 to 2022 using the panel Generalized Method of Moments (GMM) approach. The findings of this study indicate that among the macroeconomic factors affecting the attraction of bank deposits, inflation and macroeconomic instability have a positive effect on attracting transactional deposits (including current accounts, savings accounts, and other deposits) but a negative effect on attracting investment deposits (including long-term and short-term investments). Meanwhile, national income and interest rates have a negative effect on transactional deposits and a positive effect on investment deposits. Furthermore, among the banking variables affecting bank deposit attraction, the quality of electronic services, customer clubs, and the Return on Equity (ROE) of banks have had a positive and significant impact on the growth of both transactional and investment deposits.
Identification of Policy Indicators for Changing Transportation Modes to Reduce Environmental Pollution in the Iran
Authors: Fadakar Masouleh Amir , Eyzadpanah Norouz , Jabbari Samad
Published: February 2025

This study aims to identify and validate key policy indicators to facilitate the transition from conventional transportation modes to sustainable alternatives, with the goal of reducing environmental pollution in Iran. A qualitative approach was employed using the fuzzy Delphi method to achieve expert consensus on the critical dimensions and indicators of sustainable transportation policies. The study engaged 20 experts, including university professors and senior professionals from the transportation and environmental sectors, selected through purposive and snowball sampling methods. Data collection involved semi-structured interviews, and the analysis focused on coding and triangulation to identify relevant policy dimensions. Triangular fuzzy sets (l, m, u) were used to quantify expert opinions and address uncertainties, ensuring the reliability of the findings. The study identified six key dimensions for effective policymaking: the role of policymakers, policy approaches, mechanisms for creating dynamism, policymakers' capabilities, mastery of environmental conditions, and the provision and utilization of necessary data. Specific indicators under these dimensions include evidence-based policymaking, participatory approaches, collaboration with academic and professional experts, integration of reliable data, and alignment with contemporary scientific paradigms. The findings emphasize the importance of institutional frameworks, systematic approaches, and stakeholder engagement in fostering sustainable transportation systems. The proposed indicators achieved consensus among experts, with fuzzy scores exceeding the threshold for validation. The results provide a comprehensive framework for developing sustainable transportation policies that address environmental challenges. The identified dimensions and indicators align with global best practices and offer actionable insights for policymakers. By emphasizing participatory and evidence-based approaches, the findings can guide national efforts to reduce environmental pollution through the promotion of sustainable transportation modes.
Development of a Human Resources Processes Model Based on the AMO Approach in the Iraqi Volleyball Federation
Authors: Jabbar Alabedi Mohammed Kadhim , Moshkelgosha Elham , Abdulkareem khudhair Karrar , Zamani Nukaabadi Alireza
Published: February 2025

The aim of this study is to develop an integrated model of human resource processes based on the AMO approach in the Iraqi Volleyball Federation. This research is applied in purpose, descriptive-survey in terms of data collection, and qualitative in nature. The data analysis was conducted using thematic analysis. The statistical population of this study consisted of experts from the Iraqi Volleyball Federation and university professors in the field of sports management in Iraq. A purposive sampling method was employed, selecting 16 individuals from the Federation’s members, university professors specializing in sports management, and human resources management specialists using snowball sampling. The results of the analysis of the interviews revealed that the codes extracted from 16 interviews amounted to 315 initial codes, from which 46 primary themes were derived. Ultimately, the extracted themes were named and categorized into three final themes based on the AMO model: green ability, green motivation, and green opportunities. The AMO model, focusing on enhancing employees' capabilities, creating participation opportunities, and promoting motivation, can contribute to improving organizational efficiency. The findings also indicate that employee training and development of skills, as well as the creation of reward systems aligned with desired behaviors, are key success factors in implementing this model.
Proposing a Model for Enhancing Financial and Operational Performance in Supply Chains
Authors: Naderian Negar , Keramati Mohamadali , Amin Mousavi Seyed Abdollah
Published: February 2025

This study aims to propose a model for improving the financial and operational performance of supply chains. The participants in this study were supply chain managers with a teaching experience of at least 15 years and holding a master's degree or higher. The selection of individuals was conducted using purposive sampling based on the criterion of teaching experience at the primary educational level. A total of 22 experts and specialists participated in the sampling process. Data were collected through semi-structured interviews, which continued until theoretical saturation was reached. Thematic analysis based on the Aterid-Sterling framework was used to analyze the qualitative data. To ensure validity, several criteria were employed, including Holsti's coefficient, Cohen's kappa index, Pi-Scott coefficient, and Krippendorff’s alpha, all of which were confirmed. Thematic analysis was performed using ATLAS.ti software. The results of the thematic analysis indicate that, from the 54 indicators (themes) identified, 13 primary themes were recognized, and two major themes were derived. The identified model included two primary themes: financial performance and operational performance within the supply chain. The 13 primary themes are: inventory management, financial reporting, financial actions at the operational level, coordination, risk management, sourcing strategy, demand planning, technology management, logistics strategy, customer interaction, flexibility, supplier communication management, and world-class quality. Given the critical importance of the supply chain in organizational success, enhancing its financial and operational performance is essential and fundamental. These improvements will lead to optimizing coordination among internal and external members, efficient logistics management, and sharing financial information, ultimately resulting in increased productivity, cost reduction, and enhanced competitiveness. Moreover, identifying and managing various risks, such as price risk, supplier risk, and market risk, are key actions that help effectively address environmental challenges and opportunities.