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Financial statements are critical to users, as the increasing fraud cases have left behind irreversible impacts. Hence, this study aims to identify the appropriate financial ratios for fraud risk prediction in the financial statements of companies listed on the Tehran Stock Exchange within the 2014–2021 period. The study is based on data from 180 companies listed on the Tehran Stock Exchange, encompassing a total of 1440 financial statements. To select the most appropriate ratios for fraud risk prediction, all financial ratios were tested by three metaheuristic algorithms, i.e., genetic algorithm, grey wolf optimization, and particle swarm optimization. Metaheuristic and data mining methods were employed for data analysis, and these analyses were conducted using MATLAB R2020a (MATLAB 9.8). According to the research results, the fitness function yielded 0.2708 in particle swarm optimization (PSO). With an accuracy of 72.92% after 19 iterations, PSO was more accurate and converged faster than the other algorithms. It also extracted 11 financial ratios: total debts to total assets, working capital to total assets, stock to current asset, accounts receivables to sales, accounts receivables to total assets, gross income to total assets, net income to gross income, current assets to current debt, cash balance to current debt, retained earnings and loss to equity, and long-term debt to equity. The support vector machine (SVM) classifier was then employed for fraud risk detection at companies through the ratios extracted by the proposed algorithms. The accuracy and precision of financial ratios extracted by PSO and SVM were reported at 80,60% and 71,20%, respectively, which indicates the superiority of the proposed model to other models. Considering that the results obtained from the performance evaluation of financial ratios provided by PSO-SVM demonstrate the capability of this method in predicting the likelihood of fraud in financial statements, it can assist financial statement users. By incorporating these ratios about the performance of the target companies and comparing them with those of other companies, users can make more informed decisions in economic decision-making, investments, credit assessments, and more, ultimately minimizing potential losses and risks.

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.

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.

This research was conducted to investigate the motivations and attitudes of consumers to choose chocolate and their effects on purchase intention. For this purpose, the effects of health variables, mood, weight control, chocoholism, sensory, packing, price, and brand trust on the purchase intention of chocolate have been investigated using the structural equation model. The results of this research indicate that only the variable of mood has a positive and significant effect on chocoholism. Additionally, the variables of brand trust, packaging, and price have a positive, direct, and significant impact on the intention to purchase chocolate. Mood and packaging stood out with a notable difference, indicating that these two factors are especially important from the consumer’s perspective. Furthermore, attractive packaging has the ability to attract the attention of consumers and convey important information about the product, including taste, ingredients, and nutritional value. Also, most consumers buy chocolate products from their trusted brands, so in this case, famous and reliable brands usually have an advantage, price sensitivity is different in consumer groups. These factors are often interrelated and their importance may vary depending on individual preferences, demographics, and market trends.