Modern Research in Decision Making

Abstract Decision-making is a foundational element in both individual and organizational contexts, drawing significant scholarly attention in the field of management. Theories in decision-making are underpinned by foundational assumptions that stem from differing ontological and epistemological paradigms. Thus, exploring the critical topic of decision-making through the lens of Iranian-Islamic texts gains particular importance in addressing these theoretical underpinnings. This study aims to examine the structural components of decision-making based on the thought of Jalal al-Din Muhammad Rumi. The research methodology integrates several layers: interpretive in terms of paradigm; exploratory in terms of logic; qualitative in terms of strategy; case study in terms of research method; descriptive-exploratory in terms of its methodological nature; document-based in terms of data collection tools; and thematic analysis as the analytical approach. Through this methodology, the research identified 224 descriptive codes, 28 interpretive codes, and 6 overarching themes: 1.Attention to Divine Will, 2.Decision and Action, 3.The Role of Divine Influence in Decision-Making, 4.Growth through Decision-Making, 5.Errors and Failures in Decision-Making, 6.Determination in Decision Implementation. The exploration of the structural components of decision-making, as inspired by the thought of Jalal al-Din Muhammad Rumi, contributes to the enrichment of existing theoretical frameworks in the field of decision-making.

Abstract The purpose of customer relationship management is to develop profitable customer relationships and increase company value. Based on this, this research identifies factors affecting competitive advantage. The current research is applied in terms of purpose and in terms of survey method with model development approach. The time frame of the research is five years (2016-2016). For this purpose, information on the indices of 33 factors affecting competitive advantage in Tejarat Bank were entered into Bayesian averaging models (BMA), (TVP-DMA) and (TVP -DMS) Based on the error rate, the BMA model had the highest accuracy. After estimating the model, 8 main variables were identified. which includes the long-term account balance; the amount of use of mobile bank; the amount of internet bank usage; real customers; legal clients; special or normality of the customer; The type of job and education. In the next step, customers are entered into the LRFM model and categorized based on these variables, finally, the effect of non-fragile variables in the Markov switching model on profitability was analyzed. The results indicated that the majority of the variables have a positive and significant effect on the level of profitability, and by moving from the high prosperity to the deep recession, the impact of the variables has increased. As a result, it can be said that profitability in the state of economic recession has a higher sensitivity to explanatory variables.

Abstract Customers are situated at the center of every business. In fact, they are the pulsating heart of any enterprise, through which revenue streams flow and new customers are attracted. Due to various factors, customer behavior is often complex and uncertain, evolving over time. Therefore, in such circumstances, it is necessary to consider the dynamic nature of customers for analyzing their behavior and devising appropriate strategies. Knowledge and predictions derived from static models are only valid for a specific period and cannot describe the complex and uncertain nature of customer behavior.

The aim of this study is to discover dominant patterns of customer dynamics across different value tiers using sequential pattern mining and big data analytics. This research is conducted using customer data from a bank over time. The study focuses on modeling customer dynamics using sequential pattern mining. This approach, by utilizing sequential pattern mining, can assist businesses in planning and improving customer relationship management processes.

Abstract Sustainability is one of the current management concepts that many companies are trying to implement. it is necessary to understand this concept from the perspective of strategy and coordinate the operational and manufacturing activities of the organization accordingly. To achieve sustainable manufacturing organizations must develop and implement a relevant strategy aligned with their processes. This research employs a descriptive approach, focusing on developmental-applicative goals, hybrid methodology, survey execution, and data collection. Data collection methods include interviews, checklists, and questionnaires. In this study, we explore sustainability within Saipa’s manufacturing strategies, presenting a three-stage process for formulation and implementation. In its initial stage, nine sustainable manufacturing strategies have been formulated for Saipa based on the model proposed by Slack and Lewis (2011). These strategies are designed to promote sustainability. In the second phase, Intuitionistic fuzzy logic was employed to assign weights to these strategies and green performance strategies, least waste and resource efficiency have had the most weight. In the third stage, the relationship matrix is formed in the QFD structure and Using the Intuitionistic fuzzy CoCoSo technique, Saipa company’s main processes are evaluated. The priority of the main process in implementing the sustainable production strategies of Saipa Company is 1) pressing and body building operations, 2) assembly operations, 3) parts orders, 4) painting operations, 5) internal logistics and 6) retouching operations.

Abstract this research, with the aim of evaluating the green innovation ecosystem and the way of green science and technology policy of selected European countries in order to ensure that Europe is moving towards minimizing Carbon, holistic sustainable development, and the green, innovative and competitive economy are on the move. This research is trying to provide a set of useful input, mediator and output indicators to determine the model indicating the stages affecting the performance of the green innovation ecosystem of the selected countries by examining the background of similar research. By collecting the available data through data banks, and preparing the ground for mathematical modeling, the mathematical model was designed by combining methods of network data envelopment analysis and game theory with the help of Lingo software to combine relative efficiency in functional areas and create a Calculate the final weighted score to evaluate the performance of the green innovation ecosystem for EU member states. The results indicate that according to the designed conceptual model including four stages of knowledge creation and development, knowledge implementation, financial and economic transformations and environmental protection, with the change in the performance of countries in the stage of financial and economic transformations, efficiency The total number of countries increases significantly;

Abstract Risk management in the supply chain and its impact on the competitiveness, dynamism and agility of large industries is considered a key factor in capturing market share, reducing the influence of competitors, and the survival of large companies. In this new approach, by introducing a systematic model to extract each risk from the subset to which it belongs, we identify the risks in the system and calculate the amount of each risk using the risk priority score method. Then, by introducing a three-level fuzzy inference system model as a powerful tool to analyze and assess the impact of each risk in supply chain management, we will analyze the results. The final model was implemented in the Guilan Tobacco Company as a case study, the fuzzy inference system model was implemented with MATLAB software, and the results obtained were compared with the weighted simple sum fuzzy decision-making method, and the accuracy of the results was confirmed. This research, by introducing a comprehensive model for controlling and extracting all potential risks in the supply chain and analyzing each risk through a fuzzy inference system, can help managers improve productivity and competitiveness due to its high performance and speed of response in uncertain conditions. One of the most important results of this research is the identification of the status of each risk, which senior managers must quickly control by taking corrective actions for these risks (risks that are at a medium or high level).