درخت حوزه‌های تخصصی

Objective: Due to the importance of the relationship between the state and the university, which indicates the governance model of the university, this paper first determined the characteristics of the different models of university governance and then examined and identified the models of governance of Iranian universities during six development plans. We considered three governance models (state-centered model, Humboldt model, market-oriented model). Methods: Quantitative content analysis was used in order to evaluate the six development plans of Iran regarding different governance model. Results: The results show that the most frequencies for state-centered model presented in the first Development Plan and the most emphasis of Humboldt model was on the fifth Plan. The component frequency of the market-oriented or entrepreneurial governance dimensions in the Iranian development plans indicates that this model was considered for the first time in the third plan and the emphasis on this model in the development plans shows an increasing trend. Conclusions: The estimation of the weighting indicators showed that the emphasis of the development plans on turning to the market-oriented or entrepreneurial model in decision making is increasing, so we suggest that more researcher in their study consider transition toward third-generation universities in Iran as a developing country.

EXTENDED ABSTRACT INTRODUCTION In the last decade, concern about the environment has increased increasingly around the world. One of the serious environmental challenges is urban solid waste, the management of which has been raised as one of the main concerns of human societies. According to the World Bank, by 2025, more than 1.4 billion people will live in cities around the world, and each of them will generate an average of 1.42 kilograms of municipal waste per day. Estimates show that municipal waste worldwide triples every year. The annual generation of municipal waste worldwide has increased from 0.68 billion tons to 2.2 billion tons. Based on available data from countries up to 2012, the World Bank has published a report focusing on municipal solid waste generation. Waste is materials or objects that are discarded or thrown away. Solid waste is waste or discarded materials and objects obtained from industrial, commercial, mining, agricultural, and general daily activities (Ugwu, Ozoegwu & Ozor, 2020). Solid waste is one of the serious environmental issues in developed and developing countries. Solid waste management is a major challenge in urban areas around the world, especially in developing countries. The main reason for this challenge is the rapid population growth along with the expansion of cities, the reduction of financial resources, and the weakness of urban planning. Human activities and changes in lifestyle and consumption patterns have led to an increase in waste production rates (Bovard & ilanloo, 2019). Controlling environmental pollution, including waste, is an important part of human duty in maintaining human health, which has a special place in new sciences and techniques according to economic health standards. Waste production is inevitable in human daily life and population increase will increase it. Municipal solid waste is defined as waste generated by human, commercial, and construction activities that are collected and treated by municipalities (Xiao, Dong, Geng, Tian, Liu, & Li, 2020). The main composition of these wastes is almost the same in different countries of the world. However, the amount of production waste, density, and share of each part of it is different from country to country and city to city. This difference is caused by economic development, geographical location, weather conditions, and cultural and social considerations (Afshar Kazemi, Eftekhar & Omrani, 2014). About, 2.01 billion tons of municipal solid waste is produced annually in the world, of which at least 33% is not environmentally managed. Worldwide, waste generated per person per day averages 0.74 kg but varies widely from 0.11 to 4.54 kg. Of course, it is predicted that by 2050, the amount of waste produced in the world will increase to 3.40 billion tons, which is equivalent to 2 times the population growth in that year. The East Asia and Pacific region produces the most waste in the world at 23%, while this number for the Middle East countries is about 6%. Of course, it is expected that the total waste production in this region will more than double by 2050. It is worth noting that in these areas, more than half of the waste is discarded without reuse. This waste growth will bring many adverse environmental, health, and welfare consequences. Therefore, it requires basic measures.     METHODOLOGY The purpose of this study is to evaluate the efficiency of circular economies in Persian Gulf countries in terms of Municipal Solid Waste Management. This research is applied in terms of purpose and descriptive survey in terms of data collection. Its statistical population was formed from Persian Gulf countries. In this research, the per capita value of MSW production and the dimensions of the social progress index "basic human needs", "basics of well-being" and "opportunity" are used as input and recycling as output in the model. SPI indicators show how well society can cover the needs of its members and improve their quality of life. Data on MSW generation and recycling rates were obtained from the World Bank and data on the three SPI indicators were extracted from the Essential Social Progress website. The data of this research is for the year 2021. The DEA model was implemented in LINGO Softer.   FINDINGS The findings show that the countries of Kuwait (1), United Arab Emirates (0.952), Saudi Arabia (0.654) and Iran (0.453) respectively have the highest circular economy performance compared to other countries in the Persian Gulf. The aim of this study is to analyze the efficiency of DMUs from the perspective of variable returns to scale (VRS).   CONCLUSION The main purpose of this evaluation is the efficiency of circular economy in the Persian Gulf region in terms of urban solid waste management. The results show that Kuwait and the United Arab Emirates respectively have the highest circular economy performance compared to other areas of the Persian Gulf. Data analysis shows that according to the statistics of the World Bank, these three countries recycle 21% and 20% of manufactured products respectively. They have good performance compared to other input indicators. It should be noted that these results can be partially under the conditions of the Covid-19 pandemic. As, Iran will produce more solid food with more population and monitoring of health protocols. This could have caused it to fall to fourth place. Data analysis shows that production per capita in Iran is much lower than other countries. Iran has an average performance in terms of SPI indicators. But in terms of long-term performance, it has been ranked fourth. The reason for this is its very low recycling. Policymakers should use advertising, education, etc. to increase the collection rate of recyclable products. In order to improve their performance, the system needs to produce and at the same time increase the recycling rate. Education and public investment can help in this regard. Also, it should improve its performance in terms of SPI indicators. The province also improves access to basic medical care, food, water and housing. Also, they should provide access to basic education and even advanced education for those in the country who wish to increase their knowledge and skills. According to the results, it is suggested that other Persian Gulf areas improve their performance in recycling. The country's data shows that they recycle less than 10% of their production. It is suggested to evaluate the performance of the Persian Gulf with Europe for a better explanation. It is also suggested to use other indicators of reuse in calculating circular economy performance. Since it is new for environmental management and circular economy at the study level. This means that the special indicators of recycling evaluation and SPI indicators in previous years are different from previous years and practically will not reach a comparative output. For this purpose, it is suggested that future researchers study in different years with an interval of 5 years in the same field and compare the results of different years. However, there is no validity and reliability of this research. But more thought is needed in generalizing the results.

EXTENDED ABSTRACTINTRODUCTION With the increase in population growth and the problems of the planet, researchers have paid more attention to the phenomenon and behavior of greenwashing. Greenwashing behavior is a situation according to which companies and businesses present unhealthy products and services as green and organic (Wang et al., 2019). This study generates new insights for emerging markets and provides potentially important information for academics and practitioners on greenwashing behavior. It can be shown that almost half of people's income is spent on buying food products (Mishra & Sharma, 2012), so the quality, safety and performance of such products have attracted the attention of customers, consumers and shareholders (Bhattacharya, al. , 2012).), in addition, environmental protection has faced problems in the country due to the limitations of greenwashing regulations and their unclear and imprecise implementation. In the field of food products, national regulations on organic and green labels have not been determined, so many companies and food industries can use greenwashing behavior well and show themselves to be environmentally friendly (Chen & Chang, 2012). With these explanations, it is possible that customers are skeptical about green claims and the purchase of green products in society will decrease, which in turn will have harm to people in terms of safety and health, and its consequences for the environment include It will be pollution and destruction of ecosystems (Hai et al., 2017). Due to having industrial companies, Khuzestan province is exposed to industrial and environmental pollution (Guo et al., 2014). In the past years, suggestions for minimizing the environmental effects of waste, access to green life, and maximizing the capacity of using green food industry products, which can reduce their potential effects by using the phenomenon of greenwashing, have not been presented by researchers. As a result, it is vital for consumers to review their product consumption practices and move from non-organic products to green products. By observing these cases, in this research, we will seek to investigate the phenomenon of greenwashing and explain the mechanism of its effect on the intention to buy green. METHODOLOGY The current research is in the field of descriptive and survey research from the methodological point of view. Also, from the point of view of the implementation method, the current study has two phases: library and field. In the library phase, the researchers presented the research model by using the literature review and the existing experimental background. The authors' tool for this has been the use of library resources. In the field phase, the authors distributed questionnaires in order to test the fit of the model and research hypotheses. Finally, 276 healthy questionnaires were collected and analyzed using the structural equation modeling method based on the partial least squares approach. FINDINGS The authors present the findings of this study in two basic steps. The first step of the findings is related to the fit and validity of the research model. The second group of findings of this research includes the confirmation of all research hypotheses, which are summarized in Tabel 1: Tabel 1. The results of the test of research hypotheseshypothesesPathPath coefficientT StatisticResultH1greenwash  Green Purchase Intention-0/3166/045supportedH2greenwash  Green Word of Mouth-0/81940/944supportedH3greenwash  Green confusion0/82740/476supportedH4greenwash  Green Skepticism0/77129/208supportedH5Green confusion  Green Purchase Intention-0/2695/833supportedH6Green Skepticism  Green Purchase Intention-0/1353/053supportedH7Green Word of Mouth  Green Purchase Intention0/2545/218supportedH8greenwash  Green confusion  Green Purchase Intention-0/1042/985supportedH9greenwash  Green Word of Mouth  Green Purchase Intention-0/2085/596supported  CONCLUSION The current research encourages and supports food industry marketers to create credible green claims that not only improve their business status, but also enhance their environmental programs. The results showed that some food industries have a high risk due to consumers' pessimism towards the greenness of their activities and services. Managers should focus on several environmentally friendly methods to reduce consumer cynicism. Managers should increase the transparency of environmental performance, and in addition, non-governmental organizations can audit companies' environmental reports and fine companies that engage in greenwashing. With these words, it is expected to be able to help control greenwashing behavior and green doubts to some extent.

EXTENDED ABSTRACT INTRODUCTION Scarcity and justice have always been the concerns of human beings. To make a choice between these two concepts, capitalist systems have given priority to scarce resources and the optimization of their use in the hope that economic growth will also provide justice. In recent decades, environmental degradation, increasing pollution, extinction of various animal and plant species, climate change and global warming have cast doubt on the unbridled growth approach adopted by the capitalist system. This approach has proposed the idea of ‘sustainable development’ as a solution to those problems. The concept of sustainable development, which was proposed in 1980, was actually a response to the destructive environmental and social effects and to the concept of economic growth in general, in such a way that this opinion originated from environmental trends (Zakerian et al., 2014). Based on this idea, it is possible to take advantage of development opportunities through the interaction and adaptation of social, environmental and economic goals. The approach focuses on sustainable development, which gives importance to the equitable distribution of economic growth benefits (social and environmental goals) and pays attention to scarcity (economic goals). On this basis Sustainable development is development that can continue for a long period of time without causing damage to the environment (Shafiei, 2018). These points bring one to the conclusion that, to exploit resources, not only economic interests but also social, environmental and economic goals should be taken into consideration.Since in recent years, the continuation of economic growth along with the development of urbanization and population growth has led to further destruction of the environment and the reduction of scarce natural resources, including water resources, this issue has led researchers to pay more attention to the environmental aspect of sustainable development. From the perspective of sustainable development, priority is given to activities that, while providing the most economic benefits, put the least pressure on water resources and the environment of the region (Nasrollahi & Zarei, 2017). In Iran as a developing country, environmental issues took on a wider and more visible dimension. Although Iran has devised measures to overcome environmental challenges, the gap with global indicators is large, and according to many activists in this field, Iran is approaching a multi-faceted environmental crisis (Rezayan ghayehbashi and Marzban, 2020). The location of Iran in the arid and semi-arid climatic region and the uneven distribution of rainfall have led to the aggravation of the water shortage in Iran, so that the increase in demand has faced the country with the limited supply of water resources.Therefore to prioritize the economic activities, it is necessary to pay attention to the water shortage issue and the other environmental considerations. In this regard, the present study has identified and prioritized the economic activities of Isfahan Province by using the criteria for different dimensions of sustainable development and with an emphasis on the importance of water resources. To this end, input-output models and the TOPSIS method are innovatively combined to provide a proper picture of prioritizing the economic activities in the province. The rest of this article is organized in several sections. The second section provides the research background. The third section presents the Background studies, and the forth section research method and the data. The findings of the study are put forth in the five section. The last section is dedicated to the conclusion of the study and suggestions for future research. METHODOLOGY Therefore, the present study prioritizes the economic activities in the province of Isfahan by combining an input-output model and the TOPSIS method as well as considering the six criteria of water abstraction, energy consumption, pollution, intensity of inter-sectoral relations, job creation, and value added. To achieve the objectives of the research, first, the national input-output table of 2016, the Central Bank data and the SFLQ quota method were used to prepare a twenty-sector input-output table for Isfahan Province. Then, using the internal multipliers of the province, the forward and backward linkages, direct and indirect water intensity, direct and indirect energy intensity, and direct and indirect employment of the economic sectors of the province were calculated. Shannon weighting method was also used to weight the criteria. Finally, the prioritization of the sectors in Isfahan Province was done with the TOPSIS method and the desired criteria. In the following sections, more details are provided on the methods collection in this research. Shannon Entropy MethodShannon entropy method served to determine the weight of each criterion in the study. This method is an alternative to using the opinions of experts, and it is advantageous in that the opinions and personal judgments of individuals are not involved in it; the weight of each criterion is determined based on a decision matrix (Dorostkar Ahmadi and Dehghani, 2020). There were a number of steps to determine the weights by Shannon entropy. First, all the decision matrices were normalized through Equation ‎(1) and the value of E (entropy) was estimated using Equation ‎(2)   In Equations‎(1) and ‎(2), , the values studied in the research and m and n represent the number of the sectors and indicators studied in the research.Next, the weight vector was calculated, and the weight of each criterion was determined using Equation‎(3).                Topsis ModelThe TOPSIS model, as a multi-criteria decision-making method, is a simple but efficient method of prioritization. It is one of the best methods in which m options are evaluated by n methods.This technique, which is a compensatory model based on MCDM methods, follows a mathematical logic. This logic first introduces a "positive ideal solution" and a "negative ideal solution". The positive one is a solution that increases the profit criterion and decreases the cost criterion. In other words, the negative ideal solution has the opposite value of the positive one. Then, all the examined options are compared with the best and the worst options, and the linear distance of each option from the best option and the worst option is measured. Finally, the option at the most distance from the worst option and the least distance from the best option is selected as the best or the optimal option (Khatami Firouzabadi et al., 2013).The TOPSIS method can be described as a sequence of steps as follows:1) Calculate the normalized decision matrix. This regards the formation of a decision matrix with a number of criteria and options. The criteria are placed in the columns and the options in the rows of the matrix.             2) Calculate the weighted normalized decision matrix. The weighted normalized value is calculated with Equation ‎(4) where  is the weight of j criteria, .=1 3) Determine the positive ideal and negative ideal responses: Thus, the best response (A+) is selected as the option that contains the highest values of the criteria, and the worst response (A-) contains the smallest values. In EquationError! Reference source not found.‎(6), O corresponds to the utility criteria, and I corresponds to the cost criteria.4) Calculate the Separator Dimensions: Using the Euclidean criterion, the distance of each point from the ideal response (positive and negative) is estimated through Equation‎(7): (The distance of option i from the positive ideal) (The distance of option i from the negative ideal)5) Calculate the relative proximity to the ideal response. The relative proximity of option to A+ is as Equation‎(8). There is also ∈ [0 to 1]. 6) Rank the priorities. Using Ri, the options can be ranked in a descending order. In other words, any option with a larger Ri has a higher priority (Moazzami Gudarzi et al., 2014). FINDINGS To show the importance of the simultaneous consideration of different criteria, the results of the study were prioritized once with the water intensity criterion and once without it. This was done with regard to the significance of water resources.For example, in the prioritization of the sectors according to the water intensity criterion, the agriculture sector was in the twentieth place, while this rank was the second in the prioritization without considering that criterion. Also, in the sector of "manufacture of leather apparel", this criterion was downgraded from the ninth to the thirteenth rank considering the water intensity criterion. Similarly, the sectors of "manufacture of textiles" and "manufacture of rubber and plastic products" were ranked 7th and 9th, respectively, while the ranks of these sectors without considering the water intensity criterion were fifteen and twelve. In the meantime, the priorities for the sectors of "wood and products of wood", "manufacture of machinery and equipment not classified elsewhere" and "construction" remained constant. CONCLUSION The results of this prioritization showed the importance of ranking the sectors in terms of water intensify as a criterion. As it occurred, through the TOPSIS method, the priority of most sectors emerged to be different from the results of prioritization with no water intensity considered. The difference was very significant in some sectors. For example, in the prioritization of sectors according to the water supply criterion, the agriculture sector was in the twentieth place, while it shifted to the second rank when prioritized without that criterion considered. Similarly, for the sector of manufacture of leather apparel, the involvement of the water intensity criterion downgraded the sector from the ninth to the thirteenth rank. Thus, the change in the rank of a sector depends on the water level of that sector. As it was found, the agriculture sector was in the second place in the prioritization without considering the water supply criterion, while the rank of this sector moved to twenty with the inclusion of the water supply criterion. This role of water intensity suggests the importance of paying attention to the water level. Therefore, it is necessary to prioritize the economic sectors in a region in accordance with the climatic conditions there. It is also advisable not to establish water-demanding activities such as steel industries and cultivation of highly water-consuming products in low-water areas.

The main reason for people investing in the stock market is to make a profit, which requires accurate information about the stock market, price changes and predicting its future trend. Therefore, investors need powerful and reliable tools for forecasting Stock prices in the future. The main purpose of this study is to present a method based on wavelet denoising and dynamic time warping to identify the stock price pattern in the Tehran Stock Exchange. In this regard, first, using the wavelet denoising preprocessing step, noise is removed from the stock price time series, and then the extracted data is used as input to the dynamic time warping prediction model. MATLAB software version 9.11 was used to analyze the research data. The statistical population of the present study includes 3 shares among the shares of steel industry companies of Tehran Stock Exchange. The research was conducted in the period 1395 to 1398. The results show that the predictions obtained from the dynamic time warping method equipped with the wavelet denoising preprocessing step in comparison with the predictions obtained from the dynamic time warping method without the wavelet denoising preprocessing step in all three shares studied, have been associated with much less accuracy and error.