The Decision Science and Intelligent Systems

The purpose of this research was to design an interpretive structural model of the factors affecting the phenomenon of investors' mental anchoring in the capital market. This research is a survey in terms of its developmental purpose, a survey in terms of its data collection method, and a cross-sectional study in terms of its time. The model was presented based on a mixed method with a quantitative and qualitative approach. The statistical population included academic and capital market experts, of whom 15 were interviewed as a statistical sample until the theoretical saturation of the interview. In the qualitative stage, the results of the semi-structured interviews were analyzed using the content analysis method. The content analysis of the interviews with the experts led to the identification of 18 factors affecting the phenomenon of investors' mental anchoring, which were: initial experiences, information and analysis, the influence of the media, psychological factors, economic factors, social factors, market transparency, access to information, laws and regulations, investment strategies, risk management, market analysis, investment experience, economic situation, social influence, profitability, investor behavior trends, and self-confidence. The results of fitting the interpretive structural model of these factors showed that these factors can be categorized into 6 levels in terms of influence and impact. Psychological factors, investment strategies, profitability, and self-confidence have been identified as the most influential factors, while economic factors, social factors, laws and regulations, and economic situation have been identified as the most influential factors on investors' mental anchor.

Identifying and evaluating the challenges of comprehensive supervision by the central bank refers to processes in which the country's central bank seeks to identify the problems and weaknesses present in the supervisory and financial system, as well as to assess the impact of these challenges on the performance and stability of the banking and financial system. Overall, identifying and assessing supervisory challenges is of significant importance for the central bank, as it aids in maintaining financial stability and preventing crises, ultimately safeguarding the interests of depositors and enhancing the credibility of the nation's financial system. The objective of this research is to identify and evaluate the challenges of comprehensive supervision by the central bank. In this study, 10 experts in the fields of banking and economics identified 15 key challenges and subsequently responded to a questionnaire designed for this purpose. The results of the analyses conducted using the DEMATEL method indicated that among the identified challenges, the lack of transparency in the central bank's operations and legal constraints for more precise supervision were recognized as the most influential challenges. These challenges play a crucial role in the quality of supervision and the effectiveness of the central bank, and addressing them could contribute significantly to improving the performance and credibility of the country's banking system. The findings of this research may provide valuable insights for policymakers in designing appropriate strategies to strengthen supervision within the financial system.

Government investment in development projects is a primary mechanism for creating new production and service capacities. However, these projects often face challenges, including budget deficits and incomplete funding allocation. Given the competition between current expenditures and development credits for public funds, employing efficient management mechanisms for development projects is crucial. Optimization methods and efficiency analysis models, such as Data Envelopment Analysis (DEA), can be valuable tools. DEA is a non-parametric method used to evaluate the efficiency of homogeneous decision-making units (DMUs) based on multiple inputs and outputs. This study examines the performance of capital investment credits in Lorestan Province over six years, categorized by COFOG divisions and groups, using the Fair DEA (KA) model. The results show consistent rankings for 2021, 2020, and 2018, while other years exhibit differences. A comparative analysis with other provinces using the CCR and CSW models reveals that Lorestan’s efficiency was below the national average in 2017 and 2018 but improved from 2019 to 2021, surpassing the national average.

In today’s rapidly evolving innovation landscape, managing intellectual assets and assessing their maturity plays a crucial role in gaining competitive advantage, creating value, and achieving organizational success. Therefore, this research aims to present a predictive model for the maturity of intellectual capital in knowledge-based firms located in industrial parks. This model employs a hybrid approach that integrates Multi-Layer Perceptron (MLP) artificial neural networks with genetic algorithms and firefly algorithms. The research is applied-developmental in nature, descriptive-modeling in methodology, and mixed-methods (qualitative and quantitative) in data type. The qualitative sample consisted of 12 experts and specialists selected through snowball sampling, while the quantitative segment included 212 knowledge-based companies chosen using stratified random sampling. Data collection tools included a review of scientific literature, specialized interviews, and standardized questionnaires. After assessing the validity and reliability of the questionnaires, the data were analyzed using the Delphi method, confirmatory factor analysis, MLP neural networks, and their combinations with genetic algorithms and firefly algorithms. SPSS, PLS, and Python software were utilized for this purpose. The results indicated that all models examined were capable of predicting the maturity level of intellectual capital. However, the hybrid model combining neural networks with the firefly algorithm demonstrated significantly better and more accurate performance in predicting intellectual capital maturity levels, achieving evaluation metrics of 95.35% accuracy, 94.35% precision, 95.35% recall, 94.41% F1-score, and an AUC of 0.996.

For the purpose of data storage and transmission, constrained sets are required. Thus, to convert sequences from a full shift space to a sofic shift, certain constraints must be applied. One method to address this issue is the use of finite-type codes. An (X, n)-finite-type code can be used to transform sequences from a full n-shift into sequences from the shift space X. If X is a sofic space with entropy at least equal to the logarithm of n, then an (X, n)-finite-type code exists.

Efficient management of retail warehouses is associated with multiple challenges, and identifying key criteria for evaluating the performance of these warehouses is of particular importance. The primary research question in this study is: which criteria are more important for evaluating the performance of retail warehouses? In this study, performance-related criteria were initially identified and extracted through a comprehensive review of the literature and analysis of previous research. Subsequently, these criteria were evaluated using the fuzzy Delphi method and expert opinions in the field. In the next step, the validated criteria were ranked and prioritized using the fuzzy Best-Worst Method (FBWM) to determine the relative importance of each criterion. The results of the study indicated that inventory accuracy and transfer time are of high importance and have a direct impact on warehouse productivity and cost reduction. Additionally, labor productivity, picking time, stockout cost, and transfer quality were among other important factors, highlighting the role of human efficiency in improving warehouse performance. Criteria such as transportation cost, safety, and compliance with standards were considered less important compared to others. These findings are consistent with many previous studies and emphasize that accurate inventory management and optimization of warehouse processes are essential for enhancing efficiency. These results can assist managers in making better decisions aimed at improving warehouse performance and increasing customer satisfaction.

Often, the purchasing process is considered merely as a repetitive operational task and is rarely addressed strategically. For this reason, many possibilities for using purchasing as a strategic tool to achieve the long-term goals of the organization are simply ignored. This is especially important in the automotive industry and its supply chain elements, where both the diversity and consumption of their items are high. In this regard, the present study presents an approach to classifying spare parts required by the passenger car after-sales service network and developing purchasing strategies appropriate to each group. In the proposed approach, the SWARA technique is used to evaluate and weight the criteria and then the parts under consideration are categorized using the Kraljic portfolio matrix. In this assessment, current issues such as sanctions and the effect of dependence on international supply sources are also considered in the dimensions of supply risk and profitability. The population of the present study is two large organizations supplying and distributing spare parts for Peugeot vehicles based on the after-sales service network, and among them, 50 high-consumption parts are examined. Finally, while classifying parts into the four areas of the Kraljic portfolio matrix, appropriate strategies for each category are suggested. Using the results of this study, it is possible to improve efficiency and reduce costs in the spare parts supply chain, as well as increase customer satisfaction.

Resource depletion and climate change are forcing business managers to transform towards intelligent sustainability-based models, and the pharmaceutical industry is no exception. With the aid of management approaches and technologies of industry 4.0, it is possible to become efficient with least costs, while considering environmental and social principles. Based on these issues, the present study provides some important findings. First, circular economy practices are effective on technological innovation in Industry 4.0. Industry 4.0 technologies increase the efficiency of circular economy practices. Second, innovation technology is positively related to supply performance from a sustainability perspective. Finally, by relying on the transformational style and creating plans, a way to change towards sustainability and sustainable creation can be gained. Hence, the study provides a deeper understanding of the concepts of the circular economy and Industry 4.0 technologies and provides insights into ways to improve intelligent sustainable performance in the current digital age. The statistical population of the current research is various pharmaceutical companies such as buying companies (Exir, Alborz Daro, Caspian Tamin, etc.), raw materials producer (Sina Glass Company) and glass pharmaceutical company that are related to the supply of upstream and downstream products, and this makes the present study comprehensive.