sayyed mohammad reza davoodi

Financial institutions model potential counterparty distress before lending to potential counterparties in an attempt to mitigate such losses. Therefore, one of the ways to help in the correct use of investment opportunities and also to prevent the wastage of resources is to predict financial helplessness. In this regard, in this research, a new model related to the prediction of financial helplessness is presented, in which the deep learning method based on the gray wolf optimization algorithm will be used. Then the predictive power of this model is compared with the artificial neural network method. For this purpose, the information of 160 selected firms admitted to the Tehran Stock Exchange during the period of 2016 to 2022 was used. The findings of the research related to predicting the helplessness of firms based on the deep learning method based on the gray wolf meta-engineering algorithm, showed that the percentage of success of this model in predicting the helplessness of companies is equal to 98.33%, while the power of prediction the artificial neural network method was for 94.45%. Based on this, it can be concluded that the deep learning method based on the gray wolf meta-engineering algorithm has a higher level of accuracy than the artificial neural network method in predicting the helplessness of the investigated firms

The choice of the stock portfolio is one of the most important and vital decisions of legal investors in Tehran Stock Exchange, which has been one of the most important issues discussed in the past and present, and with the research that has been conducted in this field, there are models for determining the portfolio. It is presented that over time the defects of each one have been identified and another model has replaced it. Based on this, in the present research, the issue of portfolio selection has been investigated using the multi-criteria fuzzy method using the tripartite decision-making approach and cumulative perspective theory. For this purpose, the data of 30 selected companies admitted to the Tehran Stock Exchange during the period 2014 to 2022 have been used. In order to investigate the issue of portfolio selection, the multi-criteria fuzzy method was used using the tripartite decision-making approach and cumulative perspective theory through the creation of superiority relationships. The findings of the research showed that the multi-criteria fuzzy portfolio selection model using the tripartite decision-making approach and cumulative perspective theory has a better performance than the classical two-way decision-making approach and the expected utility theory in stock portfolio selection.

Process innovation is a subset of innovation related to improving or reconfiguring production methods, workflows, or techniques. Process innovation aims to optimize the production or delivery method of existing goods or services. The importance of process innovation lies in its potential to simplify operations, reduce costs, minimize resource consumption, and improve productivity and quality. In agriculture, innovation catalyzes transformation, efficiency improvement, sustainability, and flexibility in production systems. Process innovation in sugarcane production and processing is vital to address the multifaceted challenges facing the industry, including market demand fluctuations, environmental degradation, and socio-economic inequalities. By embracing innovation, stakeholders can increase productivity, optimize resource use, reduce environmental impacts, and as a result, ensure the long-term sustainability and resilience of the sugarcane sector. In this regard, the present research was designed and carried out to explain the indigenous components of sugarcane production and processing with an emphasis on process innovations.

The research adopted Sandelowski and Barso's meta-composite qualitative approach. The process started with a systematic review of 405 primary articles and finally, 33 articles related to the research purpose were selected. The inclusion criterion was the publication in the field of sugarcane production and processing between 2010 and 2023. To validate the proposed model and increase acceptability, the fuzzy Delphi method with a panel of experts was used. The results were verified and reviewed by the experts.

The fuzzy Delphi resulted in deriving 57 key local indexes within four processes, i.e. planting, growing, harvesting, and processing. According to the results, it can be concluded that the integration of indigenous knowledge systems with modern innovations is important to enhance sustainability, flexibility, and learning in the sugarcane industry. In addition, joint efforts among the beneficiaries are needed to reinforce innovation, promote knowledge exchange, and develop comprehensive strategies to advance the sustainability and prosperity of the sugarcane sector.

Conclusion/ Implications: 

It can be inferred that innovation in the process of sugarcane production is too important to be ignored because it acts as a catalyst to enhance productivity, sustainability, and flexibility in the industry. Hence, it is suggested to create an interdisciplinary platform for cooperation and knowledge exchange to integrate indigenous knowledge systems with modern innovations in the process of sugarcane production. This way, communication among researchers, policymakers, industry players, and local communities is facilitated to create and implement innovative solutions proportional to specific fields and needs of sugarcane producers.

 The present study aims to design an innovative organization model for state-owned enterprises (case study: Iraq).

 The research is applied in terms of its objective and is survey-based in terms of its method. The type of research is qualitative, and the statistical population includes corporate executive experts, academic experts, and academic-corporate executive experts in Iraq. Data analysis was conducted using thematic analysis. The research tool is a semi-structured interview.

 The research findings indicate the extraction of five dimensions for the innovative organization model for state-owned enterprises in Iraq.

 The dimensions and components include: Organizational: creating a flexible organizational structure, developing an innovation culture among organizational employees, establishing new innovation management methods within the organization, making the organization agile through innovation. Individual: developing strategic thinking among employees, enhancing employees' individual creativity, adapting employees to innovative changes, continuously upgrading employees' skills and knowledge. Technical: developing proficiency in new technologies, enhancing IT infrastructure, increasing the use of artificial intelligence, developing scalable innovative capabilities within the organization. Strategic: defining and focusing on innovative strategies, increasing investment in research and development, enhancing knowledge management within the organization, managing technological risks in the organization. Legal: updating intellectual property laws, developing and drafting updated e-commerce laws, formulating security regulations

Emotions are the main driver of all human behavior and play a very important role in shaping people's thoughts, behaviors and relationships in life and work environment. Everyone's responses and reactions to various internal and external stimuli, mainly showing emotions, are completely different and complex. Recognition of emotions in organizational behavior is of particular importance. The purpose of this research is to investigate the effect of organizational empathy on the control of employees' emotions with regard to the mediating role of organizational learning in Ahvaz Islamic Azad University and Suleiman Mosque.The method of this research is descriptive and correlational, the statistical population of the research included all the employees of the Islamic Azad University of Ahvaz and Suleiman Mosque, who were selected as a statistical sample by stratified random sampling. The main tool of this research was a questionnaire. In order to analyze the data, Pearson's correlation coefficient and regression analysis were used using SPSS and Amos statistical software.The findings showed that organizational empathy has a significant effect on the control of employees' emotions, considering the mediating role of organizational learning in Ahvaz Islamic Azad University and Suleiman Mosque. Also, organizational empathy has a significant impact on emotional control and organizational learning.The obtained results show that the creation of organizational empathy among university staff and faculty will have an impact on the process of controlling employees' emotions in different perspectives.

The return distribution of a portfolio is not constant in different periods, which is affected by the dynamics of financial markets and provides the basis for the instability of the portfolio. Distributionally Robust Portfolio Optimization (DRO) takes into account the uncertainty of the portfolio due to changes in the distribution of portfolio returns. In the current research, the objective function of the portfolio model is to maximize the Calmar ratio, which is one of the reward-risk ratios, and its calculation depends on the distribution of the portfolio returns. The research strategy to robust the return distribution parameter is to consider all the returns that are located in a neighborhood of the empirical distribution of the portfolio, which was used to determine such distributions using the Wasserstein metric. A sample portfolio of the research consists of 8 indices or industries from the Tehran Stock Exchange in the period from 1390 to 1400 and on a weekly time horizon. The test data has been divided into 5 periods, and to evaluate the DRO portfolio compared to the portfolio without this feature, the result of dividing the average of Calmar's ratios into the 5 mentioned periods by their standard deviation has been used. The results show that the DRO portfolio improves this ratio by 0.27, and in addition, the minimum ratio of Calmar in 5 periods in the DRO portfolio is higher than the basket without this property.

The purpose of the current research is to design a green supply chain pricing model with a multi-criteria decision-making approach and game theory (case study: home appliance industry). In this research, after library study and identification of key indicators in predicting green supply chain pricing, using the fuzzy Delphi method, the screening of indicators was done in three stages, and out of 20 indicators, based on the opinions of 13 experts, 13 indicators were selected and based on the priority selection index approach, company D entered the game theory as the leader company because it had the highest priority. Finally, based on the game theory, the scenario between 4 members of the supply chain was evaluated and analyzed, and the best and worst scenarios were also introduced. Also, in order to test the structural validity of the proposed model, information related to the green supply chain of 9 selected home appliance companies was implemented using MATLAB software.

Today, a large part of the company's property and assets are related to intangible assets, whose value and price have been determined in recent years for two reasons, the formation of concession contracts and the determination of the share of each partner in capital contracts. Joint ventures have been taken into consideration. In this research, a conceptual model has been presented to identify the influencing factors in technology valuation in the field of financing using foundation data theory. For this purpose, a model has been presented by using the opinion of the experts of Isfahan Regional Electricity Distribution Company and a comprehensive review of the issue in the field of technology valuation selection for the analysis of financial decisions in the field of financing and investment in the solar energy industry. To develop the presented model, a qualitative model based on foundational data theory has been followed. With regard to all the cases mentioned in this research, the technology valuation model for the analysis of financial decisions in the field of financing and investment in the solar energy industry according to the collection of experts' opinions according to three main categories (valuation model, technology organization and investment) and 9 sub-categories (valuation model, technology organization and investment) and 9 sub-categories (low cost, exclusive experience, value provided by technology, communication, technological infrastructure, agility, knowledge management, human resources, new financing model) design has been To ensure that the factors are chosen correctly or not, a content validity analysis has been performed for each of the categories, and according to the results, all the categories have the necessary sufficiency to be included in the valuation model. In the continuation of the research, using the structural equation model, the relationships of the indicators have been modeled in Lisrel software to determine the influence of each factor on each other and also on its main factors. Based on the obtained results, the relationship between the subcategory of human resources and the technology organization in the funding valuation model is identified as meaningless with an impact factor of 0.24. Therefore, this criterion cannot be included in the financing valuation model. But the relationship of other factors with their main criteria in the valuation model of financing is significant. Finally, the validity of the model has been investigated using statistical tests.

Food packaging is responsible for protection, transportation, distribution, beauty, presentation, marketing and many other things. One of the packaging parameters that has received little attention is the paradox of packaging. The purpose of this article is to identify and classify different types of packaging paradoxes in the biscuit and chocolate industries. This research aims to provide a new classification in the field of food packaging paradoxes. In this research, qualitative methods, including library study and meta-composition, have been used. The target population was the articles available between 2010 and 2023. In this research, 210 elements of paradox or open code, 16 core codes and three categories were identified. The results are presented as a model. This paper presents a new synthesis of packaging paradoxes in the food industry. Also, this study provides a new approach to understanding the complexity of packaging decisions in the food supply chain. Understanding the complexity of decisions in the field of packaging will lead to better packaging management and increased packaging performance. The findings of this research can help companies better identify packaging paradoxes. Identifying the paradoxes of packaging makes managers make better decisions in the field of packaging. As a result, strategic packaging capacity can be achieved, leading to increased product sales and company performance.

The research strategy to stabilize the return distribution parameter is to consider all the returns that are located in a neighborhood of the empirical distribution of the portfolio, which has been used to determine such distributions using Wasserstein's metric criteria and K-L divergence. A sample portfolio of the research consists of 8 indices or industries from the Tehran Stock Exchange with the largest trading volume in the time period from the beginning of 2011 to the end of 2018 and in the weekly time horizon. The test data has been divided into 5 periods, and to evaluate the results of the stable distribution basket compared to the basket without this property, the result of dividing the average ratios of Kalmar and Rachev in the 5 periods by their standard deviation has been used. The results of the optimization with the help of particle aggregation algorithm show that the distribution stable basket of squid improves the mentioned ratio by 1.27 and in addition, the minimum ratio of squid in 5 periods in the distribution stable basket is higher than the basket without this feature. The stable distribution portfolio of Rachof improves this ratio by 1.40, and in addition, the minimum ratio of Rachof in 5 periods in the stable distribution portfolio is higher than the portfolio without this feature.

The sugarcane industry is a pivotal force in the agricultural perspective. It is essential to promote process innovations in sugarcane planting for sustainable growth and competition. This study was conducted to analyze the effective key factors of process innovations in the sugarcane industry. It is applicable from an objective aspect and the required data was collected through qualitative methods and meta-synthesis. This method systematically studied thirty-three articles related to the research objective among 405 primary articles. Final articles were selected based on inclusion criteria. The validity of the research was confirmed based on the inclusion criteria, holding sessions with members of the research team, the use of an expert, and examination of the whole process for theoretical consensus. Reliability was also determined via the Critical Appraisal Skills Program. The effective dimensions and components of process innovation in the sugarcane industry include four dimensions, namely steps of innovation in the sugarcane production cycle that have been identified in the form of 45 components. The four stages of this cycle are "innovation in the process of planting", "innovation in the process of growing", "innovation in the process of harvesting" and "innovation in the process of processing". As the sugarcane industry follows innovation complexities, this research presents a map to understand the multi-faceted perspective of the process innovation. Through the classification of the components in vital stages, stakeholders achieve insight into transformational strategies and reinforce sustainable methods that are technologically advanced in sugarcane planting.

The production of digital content is the key to the success of businesses in the competitive market particularly evident in the fashion and clothing industry where turnover rates are notably high. Considering the importance of the subject in this study, the components of digital content production in marketing in the fashion and clothing industry were identified. The current study is an applied-developmental study in terms of its purpose, and it is a cross-sectional survey research in terms of the method and time frame of data collection. Semi-structured interviews with fashion industry managers and marketing professors were used to collect data. The Holstein method was used to validate the qualitative analysis and the observed agreement coefficient was estimated at 0.712. The community of research participants was experienced fashion industry managers and university professors in the field of marketing management. Sampling was done with a purposeful method and finally theoretical saturation was achieved with 21 interviews. To analyze the research data, the qualitative analysis of the theme and MaxQDA software were used. Research findings showed that technical and non-technical factors ,management, are the underlying factors of the model that affect the production of digital content. The quality, user-friendliness, popularity, security and interactivity of the media also provide the necessary platform for the production of digital content. Since the content is produced for the customer, the role of the customer should not be neglected, and customer satisfaction, loyalty, trust and participation are the consequences of this model. Finally, content production should be based on quality, aesthetics, Planning and having value to provide customer-oriented goals for fashion industry businesses in digital content production

Today, algorithmic trading is widely used in trading management. Algorithmic portfolio management is a new type of these system through which the portfolio manager helps to increase the quality of profit and reduce the risks of his portfolio using algorithmic tools. The purpose of this research is to design an algorithmic trading system based on deep reinforcement learning with the help of a neural network. In this approach, the agent or trader searches the search space to find more rewards, which is the same as more returns. The trader is faced with technical signals including relative strength index, stochastic oscillator, convergence-divergence indicator, and minimum, maximum, closing, and opening prices. Deep reinforcement learning replaces the Q value or quality function table with a neural network. Finally, upon receiving the state word, the mentioned neural network suggests one of the three actions of selling, buying, and holding. This proposal is in the form of three possibilities with a total of one, and the proposal with the maximum probability is implemented. The result of the implementation of the deep reinforcement learning trading system on the total index of Tehran Stock Exchange in the period of 2011 to 2014 shows that the research system was significantly different from the other three systems in the mean and convergence-divergence index. Also, the Sharpe ratio of the research system compared to the other three models showed growth of at least 1.4 times.

Interval-valued prediction includes the prediction of an interval that its boundaries determined by two random variables. The aim of this research is to design and optimize the mean-variance daily scalping trading portfolio based on interval-valued prediction for lowest and highest daily prices with the vector autoregression approach. In the present study, using the vector autoregression method, the interval related to the lowest and highest daily prices is predicted and then based on it, a daily scalping trading system is formed, including buying and selling in the forecasted prices. To reduce the risk of the trading system and increase the number of trading positions, the optimal daily scalping trading portfolio is developed in the mean-variance framework.The sample portfolio includes five shares of the Tehran Stock Exchange in a 190-day period, taking into account trading costs, shows that the average daily return is 0/0018 and the Sharpe ratio is 0/4809, which is better than the Sharpe ratio of individual daily scalping trading of portfolio assets. The daily average of the total index in the research period is 0/001 and the Sharp ratio is 0/0835, which shows that the trading system has a much better performance than the buy and hold strategy. Risk-averse investors who are interested in the daily scalping strategy are suggested to use the optimal portfolio approach introduced in the present study after carefully evaluating the profitability and risk on the set of stocks they want

Risk parity is one of the stock portfolio selection models that has received much attention after the American financial crisis in 2008. The philosophy of this model is to allocate the risk of the portfolio to the same extent among its constituent assets. Conditional value at risk is one of the popular and common measures of risk measurement in finance, which measures the mathematical expectation of loss of a stock portfolio for values beyond a threshold value and at a known confidence level and time horizon. The aim of the current research is to design and optimize the performance of the risk parity stock portfolio model with the criterion of conditional risk value. There are different approaches in modeling optimal portfolio selection that use different criteria and methods to calculate and estimate returns and risks. Various criteria have been proposed to measure risk in finance, each of which has its own advantages and disadvantages. One of the criteria that has been introduced with the aim of reducing the disadvantages of the common and popular measure of value at risk is the conditional value at risk or expected drop, which is used as a measure of risk in the present study. Conditional value at risk measures the average loss of the portfolio for cases where the amount of loss exceeds value at risk

The present article was carried out with the aim of presenting a supply chain sustainability model using blockchain technology based on an innovative strategy in the food industry. In terms of purpose, it is mixed, cross-sectional and research. 21 participants of the qualitative part were selected in a purposeful way until reaching theoretical saturation. In the quantitative part, 135 managers and experts of companies active in the country's food industry were used. The data collection tools are semi-structured interviews and questionnaires. Validity according to Lincoln and Goba's point of view and reliability was estimated as 0.652 based on Holstein's formula. Convergent validity and divergent validity, combined reliability and Cronbach's alpha were used to measure the validity of the questionnaire. Data analysis was performed in the qualitative part with the foundation data method and MAXQDA software, and in the quantitative part with the partial least squares method and SMART PLS software. Based on the paradigm model of the research, it was determined that blockchain technical platforms and blockchain management platforms provide the necessary background, and blockchain barriers in the supply chain play an intervening role. Innovative strategy leads to environmental, economic and social sustainability.

A strong brand differentiates the product from competitors' products, reduces search costs, minimizes perceived risks, and offers high quality from the customer's point of view. He considered that one of the most important problems of Iran's food industry is related to the brand name, especially in the field related to the internalization of awareness and association of the brand name in the customer's mind. Cognitive distortions, as psychological factors that cause perceptual errors in people, can be effective in their negative feelings and, as a result, affect the expected results of customers' behavior; Therefore, identifying the psychological components of branding in the food industry with the foundation's data approach (case study: Gaz Asari) is of great importance. The method of this research is qualitative and grounded theory method (foundation data) which has been used in this research due to some of its special features. The statistical population consists of interviews with 17 people, including all managers and expert managers in the Gaz Asari brand industry, which means analyzing the data obtained from the interviews, using the qualitative-inductive grounded theory method (including the steps of open coding, central coding, and selective coding). The results include 19 general categories in the form of a paradigm model, which include causal conditions (competitive pressure, changing customer characteristics, market uncertainty, understanding the importance of branding and lifestyle changes), central phenomenon (psychological branding), background conditions (sustainability of brand effects) flexible characteristics of the brand and branding skills, intervening conditions (customer, company capability, brand competition capacity and the challenges of Sanat Gaz administration) and strategies (differentiation strategy, modeling strategy, discovery strategy, and resistance strategy) and consequences (short consequences duration and long-term consequences).

Return and risk are significant parameters in selecting an optimal portfolio, depending on the portfolio return distribution. In a stochastic process, the Markov property causes the future distribution of a random process to be measurable according to the state-transition matrix and the initial process state. According to the main idea of the present study in the optimal portfolio selection, portfolio weights are chosen in a way that the Markov property is established for the portfolio return series and the distribution of future portfolio returns is close to the distribution of investor's expected returns; hence, K-L divergence (Kullback–Leibler divergence) is utilized as a criterion of closeness. Using this idea, an optimal portfolio selection model was designed and implemented in the present study. This optimal portfolio was optimized using a Markov approach and according to historical data of 10 indices on the Tehran Stock Exchange from 2009 to 2022 in a six-member state. The optimal portfolio performance evaluation using the Sharpe ratio and value at risk criteria indicated that the research model had a higher performance than the mean-variance and weight parity models.

In recent years, a strong move has been made towards integrating strategic and tactical decisions through the development of location-allocation models. Strategic decisions such as location allocation have long-term effects and are not easily changed, and tactical-level decisions involve medium-term planning over a one-year period such as inventory management policies. Integrating different levels of decision making in the supply chain helps reduce overall costs and improve performance. In the present study, a stochastic two-stage mean-conditional value at risk model is used to allocate locations and calculate the flow of materials and goods constructed of a multi-product-multi-level supply chain. In the research model, distribution centers can be selected in two types: reliable (without the possibility of disruption) and unreliable (with the possibility of disruption). Sources of uncertainty in the model include shipping costs, end customer demand, and the possibility of disruption in distribution centers. The research model uses the conditional value at risk along with the risk aversion factor to control the risk of long distances. The designed model is eventually transformed into a single-level linear programming with the help of Monte Carlo simulation. Finally, with a numerical example, the model is implemented and its sensitivity is analyzed.

Recycling of materials is one of the most important issues in urban waste management system today. The worn out tires are one of the most important types of solid wastes or solids, because their departure from the cycle of production and consumption means starting a commitment to the environment. Although the longevity of car tires has grown more than before, it is also unrealistic that the roads of different countries are now dominated by more cars. This study aimed to find the factors affecting the development of rubber recycling.

In this paper, the dynamics of the system is used. In this research, articles, library resources and the Internet have been used and based on it, it has been used to draw up causal, inventory and flow charts. Extracting the equations for the relationship between model variables and introducing them into Vensim ple 6.4 software led to simulation of the model; and the behaviors of the important variables of the model were implemented, which was studied in detail in the research.
Fidings: The findings of this study indicate that a combination of factors with mutual impact and effect on rubber recycling affects not all of these factors and their role, the development of rubber recycling will not occur. Thus, the recycling of rubber products are two of the increasing and deterrent factors.

The reuse of the product and the re-production of the product will affect the increase in the recycling of rubber and lead to the expansion of recycling, which, of course, will make sense over time.

Entrepreneurial mentorship is a key factor that, if its drivers are correctly identified, will have many consequences for success in the field of entrepreneurship. In this regard, the present study was conducted with the aim of investigating the antecedents and consequences of entrepreneurial mentoring. This research is descriptive-survey in terms of purpose, practical in terms of method, and time period of data collection. For this purpose, after searching the databases, in the period of 1390 to 1402 and 2010 to 2023, 23 articles in the field of entrepreneurship mentoring were evaluated and finally, 20 articles were selected and entered the analysis stage. These articles were selected according to the credibility and validity of the research, the quality of the research, the scientific criteria, and the relationship with entrepreneurial mentoring. In order to identify the antecedents and consequences of entrepreneurial mentoring, the meta-combination method and Maxqda software were used. The results showed that the culture of mentorship, competence of mentors, career development, and mentor-disciple relationships are considered antecedents of entrepreneurial mentorship, and perfectionist, self-directed, harmonious, emotional, and professional consequences are among the consequences of entrepreneurial mentorship.

The development of computer technologies and automated learning techniques can make decision-making easier and more efficient. In the field of machine learning, where computers always make decisions or propose suggestions for proper decision-making, there exist many decision-making techniques such as decision trees, neural networks, etc. Flexibility and comprehensibility are one of the advantages of the decision tree model. The decision tree can provide the possible options, goals, financial profit, and information needed for an investment for the managers better than any other tool. The decision tree is one of the most applicable data mining algorithms. On the other hand, crowdfunding in knowledge-based companies is a new financial phenomenon in online financing of innovative projects and knowledge-based businesses that reduces financing costs and problems in addition to changing the nature of the investment. There are four types of crowdfunding in knowledge-based companies namely donation-based, equity-based, lending-based, and reward-based. Reward-based crowdfunding can be considered the most publicly familiar crowdfunding model, where backers will actively participate in the product development process along with investment. Low-cost crowdfunding websites act in the projects as an online mediatory between the initiators and the sponsors. Therefore, the factors affecting the success of crowdfunding were evaluated in this research regarding the initiators' performance and the sponsors' feedback, and the significant attributes were presented in the form of a decision tree structure using the data mining technique. The results reveal that the best performance of initiators is related to the field of direct investment attraction with 92% accuracy of the decision tree with the most important attributes of "number of updates during the investment period" and "number of dynamic technical and tactical analyses".

The purpose of this research is non-parametric momentum strategy based on rank and symbol in Tehran Stock Exchange. In the present research, two approaches to implementing the non-parametric momentum strategy were introduced as momentums based on rank and sign. MATLAB software and coding were used for data analysis and model implementation. The research model is taken from the article of T. Sang et al. (2021). The results of the research on the monthly data of 16 industries from the Tehran Stock Exchange between 2013 and 2022 show that the momentum approach based on rank compared to normal and sign increases the Sharpe ratio by 30% and also the momentum of the rank in the value measure in Exposed to risk also has the lowest risk. In addition, the regression relationship shows that both the monthly rate momentum return and the monthly sign momentum return have a positive significant effect on the normal momentum monthly return at the confidence level of 0.95. The high and appropriate determination coefficient of 0.892 also shows that there is a linear relationship between the three yields.

In supply chain management, decision making for supplier selection is a key issue that has become a strategic goal for organizations in recent years. The problem of supplier selection due to multiplicity and variety of qualitative and quantitative indicators is a multi-attribute decision-making problem and can be used known techniques in solving the problem. The present study tries to explain and prioritize the indices of supplier’s assessment with a meta-synthesis approach.

In this article, the most effective evaluation indicators of suppliers are examined. The research started by reviewing 467 titles of articles and then by evaluating them, 282 articles were selected by using the meta-synthesis approach. By analyzing them, supplier evaluation indicators were classified into 5 dimensions, 19 sub-dimensions and 112 components, and their effect coefficient was determined using Shannon entropy method.

The results showed that price, quality, on-time delivery, revenue, organizational structure, human resource management, energy management, environmental management, customer orientation and green management indices were identified as the most effective indicators in evaluating the suppliers.

Originality/Value: 

Supplier selection and evaluation play an imperative role in creating an effective supply chain. The production process has changed over the years from the production of most products to the outsourcing of major accessories. Obviously, suppliers have a key role to play in producing a product. The manufacturer must be careful when choosing a supplier. Therefore, identification, evaluation, and selection of suppliers are imperative. The results showed that by identifying and evaluating the indicators of supply chain suppliers, companies can gain a significant competitive advantage.

The purpose of this study is to design an organizational performance model based on the digital status of the organization in the Corona pandemic with a theme analysis approach in the Ministry of Industry, Mine, and Trade (MIMT). This research was conducted with a qualitative approach. The research tool (data collection) was a semi-structured interview in which 27 elites and qualified specialists in MIMT (statistical population of this study) were selected using a purposive sampling approach to qualitative sampling. The results of data analysis in the qualitative section in the open coding stage identified 79 codes, which finally through axial coding achieved  3 categories (main category) and 21 indicators (subcategory). The main mission and goal of the managers of any organization is the effective and efficient use of various resources and facilities such as labor, capital, materials, energy, and information. Digital transformation, which has always been important as a fundamental and strategic action in organizations, has become more important in recent years following the outbreak of coronavirus; the pressures of the coronavirus epidemic have accelerated the efforts of organizations to create digital transformation