درخت حوزه‌های تخصصی

Modeling the implementation of organizational ambidexterity as an organizational ability in alignment and efficiency in response to market demands and simultaneously adapting to environmental changes through innovation and discovery of new solutions can help improve the performance of the aforementioned organizations. The purpose of this research is to design a two-level organizational model in the branches of the Tejarat Bank of Isfahan province to identify the important indicators. in current research, the research method is a descriptive survey, and researcher-made questionnaires were designed based on the Fuzzy Dimetal method and the fuzzy network analysis process, these indicators have been prioritized by 36 senior employees of Isfahan Tejarat Bank using the combined technique of Fuzzy Dimetal and fuzzy network analysis process. based on the output of the above table, it indicates that the index of organizational managerial competence and loose relations, and finally attraction and employment were introduced as the most effective positive and negative indicators. the indicators of human resources of the organizational ambidexterity model, which mainly refers to the organizational and individual culture, indicate the influential role of the internals of individuals and organizations in the implementation of a new concept. In the strategic indicators, the existence of a flexible structure for the implementation of ambidexterity has been emphasized. From the aspect of social capital, the significant point is the high priority of the index of revision of social processes and components in the total indicators of ambidexterity in the organization. This means that in the initial stages of the implementation of dualism, it is necessary to review the processes and equipment to discover exploitation and exploration opportunities, plan to follow up on these opportunities at the same time, and formulate a model of ambidexterity in different parts of the bank.

After assessing the efficiency of the units under evaluation, determining their ranking becomes a critical concern for decision-making. Ranking methodologies rely on specific unit inputs and outputs, but some variables affecting unit performance are not directly observable and are termed contextual variables. Thus, the primary objective of this research is to introduce a ranking methodology that incorporates these variables. To achieve this, a two-step approach is employed. Firstly, the Data Envelopment Analysis (DEA) technique calculates efficiency scores for each Decision-Making Unit (DMU), considering its unique inputs and outputs. Subsequently, the linear regression method is applied to assess the impact of contextual variables on efficiency. Finally, efficiency scores are modified by removing the impact of contextual variables, enabling unit ranking. To illustrate this proposed methodology, we conducted a performance evaluation of provincial gas companies in Iran. The evaluation covered four periods, from 2013 to 2016.

As the digital landscape continues to reshape the banking industry, understanding the factors influencing customer loyalty in online banking becomes paramount. This study investigates the intricate relationships between e-satisfaction, e-loyalty, and the moderating effects of consumer perceived risk and skill. Drawing on a sample of 168 customers from selected branches of Bank Mellat in Mashhad, statistical analyses were conducted using SPSS and Smart-PLS. The results reveal a direct and positive impact of e-satisfaction on e-loyalty, elucidating the fundamental role of customer contentment in fostering loyalty in the online banking domain. Moreover, the study uncovers a nuanced interplay, demonstrating that the relationship between e-satisfaction and e-loyalty is contingent on the levels of consumer perceived risk and skill. High perceived risk and low skill levels diminish the impact of e-satisfaction on e-loyalty, highlighting the need for tailored strategies for these customer segments. This research contributes to the evolving understanding of e-loyalty dynamics, emphasizing the significance of customization, security, convenience, delivery, communication, and website aesthetics in shaping customers' e-loyalty. The findings provide actionable insights for banking institutions to refine their online platforms, fostering a more personalized, secure, and engaging digital banking experience to enhance customer loyalty.

The adoption of unhealthy lifestyles by individuals can lead to the development of various health conditions, including hypertension, high blood fat levels, and diabetes, posing significant risks to their well-being. This study focuses on examining the lifestyle of patients with diabetes and high blood fat levels in the city of "bordekhoon," conducted at Health Care Centers. Diabetes is a global health concern that is rapidly increasing and is associated with substantial costs. By applying data mining techniques, early detection of diabetes can be achieved, which can help prevent the progression of the disease and mitigate complications such as cardiovascular issues, vision problems, and kidney diseases. Nowadays, data mining-based approaches are employed to predict diabetes and hypertension, aiming to enhance early diagnosis accuracy and obtain valuable insights. In this paper, a combination of classification techniques (Ensemble Method) is used to predict and identify two types of diabetes. Factors such as gender, diet, fasting plasma glucose (FPG), physical activity, cigarette consumption, age, genetic predisposition, and body mass index (BMI) are modeled and analyzed using IBM SPSS Modeler 18 software. The accuracy of the employed techniques is ultimately presented.

The purpose of this article is to present the evolutionary model of loan sales using collective intelligence and meta-heuristic algorithms (bird flight algorithm). In terms of method, this research is in the category of quantitative research, and in terms of purpose, it is included in the category of applied research. The statistical population includes all active companies admitted to the Tehran Stock Exchange. This research has been investigated between 2011 and 2019 for active companies admitted to the Tehran Stock Exchange. The method of data collection is through library study and financial data of companies admitted to the stock exchange by referring to the financial statements and explanatory notes with the financial statements, and it has also been compiled using the Rahavard Novin software. Also, with the help of EViews 9 and MATLAB software, he presented a borrowing sales model, and in the next step, with the help of MATLAB software and the flight of bird's algorithm, he presented an evolutionary model of borrowing sales, in the end, by comparing the step-by-step regression model and the borrowing sales model. The findings showed that the borrowing sales model with the help of the bird flight algorithm has a higher efficiency.

This study aims to investigate the factors influencing value chain (VC) in pharmaceutical industry, as a very important sector in Iran. The research method is descriptive-analytical in term of method. The required data have been collected from the experts of a public joint stock company. Conducting the research, first the literature of VC is reviewed to identify the initial factors affecting on VC of pharmaceutical companies. In this Phase, 34 factors were identified in 8 categories including; institutional, industry, political, economic, social, technological, legal and environmental factors. In the next step, a primal evaluation of the initial factors by 14 experts of the research, resulted to the 20 more important factors which were used in the modelling process. Due to the need for fuzzy logic regarding subjective judgments in cause-effect relationships between factors, the fuzzy cognitive mapping (FCM) method in FCM expert software is used to visualize the relationships among these factors. The results show that technological capabilities, government policies, company resilience, financial strength, medicine price, sustainable waste management, cost of raw materials, production technology, cost of energy, R&D, operational efficiency, recycling capabilities, transportation cost, VC governance (coordination/ partnerships/ integration), consumer behavior and social (market) trends, environmental concerns about waste disposal internet of things (IoT) and connected devices, non-value-adding activities, import limitations and skilled human resources, respectively are the most influencing factors on pharmaceutical VC.