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The purpose of this research is to investigate the prediction of corporate social responsibility reporting using financial ratios. To answer the research question, four prediction models of linear regression, K Nearest Neighbor, decision tree, and deep learning were investigated. Also, 61 financial ratios were used according to previous research using data related to listed and non-listed companies of Iran from the years 2012 to 2018. According to the re-sults obtained from the estimation of each of the proposed prediction mod-els, it can be stated that the k-nearest neighbor model has the lowest RMSE value, and in fact, this model predicts the amount of social responsibility with less error than other models. The linear regression model with the high-est RMSE value has a weaker performance than other models. LSTM model and decision tree respectively had the lowest RMSE value after the k-nearest neighbor model. As a result, since the LSTM model requires a large number of test sam-ples for deeper learning, it could not achieve high performance in the evaluated data set. Based on the investigations, it can be stated that the current research does not have a similar example inside or outside of Iran.

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.

Rice is a crucial agricultural product, and enhancing its productivity is essential for increasing production. This study aims to analyze the total factor productivity growth of rice production in Iran from 2000 to 2020. Using parametric (stochastic frontier analysis) approaches, the research evaluated the rice productivity growth and its components, including scale and technological changes. Based on the estimated Translog Cost Function, the annual total factor productivity growth was 2.1%, with positive technological change as the primary driver of these improvements. To further enhance productivity, the study recommends utilizing improved seeds, modern machinery, fertilizers, and nutritional solutions during rice cultivation. Additionally, the research suggests the application of parametric approaches in future studies to assess the impact of technological changes on crop yields. 

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.