Search results

This study aims to address the relationship between economic complexities (EC) and the green economy (GE) with fraud in the listed companies on the Tehran stock exchange. The authors study whether EC and GE increase the detection of financial statement fraud.

The authors used a multiple regression model based on the panel data method and fixed effect model to test hypotheses. The sample includes 1,351 companies listed on the Iranian stock exchange from 2014 to 2021.

The results show a negative and significant relationship between EC and GE with financial statement fraud.

Since this research is the first to address the mentioned topic in emerging markets, it provides helpful insights for financial statement users, analysts and legal entities. The study fills the literature gap and promotes knowledge regarding its relevant literature.

This study aims to analyze the relationship between economic complexity (EC) and the green economy (GE) with the real and accrual earnings management (REM and AEM) of the listed companies on the Iranian stock exchange. The authors study whether EC and the GE can affect REM and AEM.

The authors used a multiple regression model based on the panel data and a fixed effect model to test hypotheses. The sample includes 1,351 companies listed on the Tehran Stock Exchange from 2014 to 2021.

The results show a positive and significant relationship between EC and the GE with REM and AEM.

Considering the importance of a GE and since this research is the first to address the mentioned topic in emerging markets, it provides helpful insights for financial statement users, analysts and legal entities. Our study fills the literature gap and promotes knowledge regarding its relevant literature. Examining this relationship portrays the latest research perspectives in this field. The information from this study can assist in environmental management decision-making and relevant policymaking, promoting the movement toward sustainable development.

Contributing to the literature on movement structure in authoritarian regimes, this analysis focuses on the structure of two Iranian movements. We use a multi-method approach to analyze the organization of the student and women’s movements in Iran between 1997 and 2008. From 1997 to 2004, a reform government opened political opportunities. The period between 2005 and 2008 was characterized by increased repression. The student movement was organized during the first period as a hybrid composed of several networks linked in a federal structure. As the political context changed, the movement became less centralized. Its strategy shifted from one based in alliance with governing reformers to coalition building outside of the regime. In contrast, the women’s movement was organized as a densely linked web of noncentralized campaigns. The women’s movement overcame divisions as political opportunities closed in the mid-2000s and built a grassroots strategy during the latter part of the decade.

Iranian history, with roots in the oldest civilizations in western Asia, has provided significant heritage, both tangible and intangible, allowing the country to be considered as a major cultural tourism destination for both nationals and foreigners. The focus on Iranian history and heritage aims to confirm the country’s potential for attracting international tourists, while a negative image prevails abroad of a radical theocratic regime with a hostile internal political environment, alongside the instable geopolitical situation of the region. However, the changing political situation, now more secure and safe, allows an attempt to remove these prejudices, which may be achieved through the promotion of heritage and cultural tourism.

This study investigates how entrepreneurial leadership fosters market orientation, bank innovativeness and bank performance; it also investigates how market orientation contributes to brand orientation, bank innovativeness and bank performance.

In total, 1500 questionnaires were distributed to 100 bank branches in Indonesia (500 to managers and 1000 to employees); 300 responses (20% response rate) were used for further statistical analysis.

The results confirmed the existence of relationships among entrepreneurial leadership, market orientation, bank innovativeness, brand orientation and bank performance. The role of entrepreneurial leadership in fostering market orientation, bank innovativeness, brand orientation and bank performance demonstrates that leaders can motivate employees to complete their tasks.

The findings suggest that entrepreneurial leadership, new ideas and innovative products and services can foster bank performance.

The emerging banking industry in Indonesia has witnessed changing market conditions. Banks will benefit from being more market-driven and diverse in their customer relationships to generate value.

The purpose of this paper is to develop three methods including artificial bee colony algorithm (ABC-ANN), regression and adaptive neural fuzzy inference system (ANFIS) to predict the environmental indicators for land leveling and to analysis the sensitivity of these parameters.

This paper develops three methods including artificial bee colony algorithm (ABC-ANN), regression and adaptive neural fuzzy inference system (ANFIS) to predict the environmental indicators for land leveling and to analysis the sensitivity of these parameters. So, several soil properties such as soil, cut/fill volume, soil compressibility factor, specific gravity, moisture content, slope, sand per cent and soil swelling index in energy consumption were investigated. A total of 90 samples were collected from three land areas with the selected grid size of (20 m × 20 m). Acquired data were used to develop accurate models for labor, energy (LE), fuel energy (FE), total machinery cost (TMC) and total machinery energy (TM).

By applying the three mentioned analyzing methods, the results of regression showed that, only three parameters of sand per cent, slope and soil, cut/fill volume had significant effects on energy consumption. All developed models (Regression, ANFIS and ABC-ANN) had satisfactory performance in predicting aforementioned parameters in various field conditions. The adaptive neural fuzzy inference system (ANFIS) has the most capability in prediction according to least RMSE and the highest R² value of 0.0143, 0.9990 for LE. The ABC-ANN has the most capability in prediction of the environmental and energy parameters with the least RMSE and the highest R² with the related values for TMC, FE and TME (0.0248, 0.9972), (0.0322, 0.9987) and (0.0161, 0.9994), respectively.

As land leveling with machines requires considerable amount of energy, optimizing energy consumption in land leveling operation is of a great importance. So, three approaches comprising: ABC-ANN, ANFIS as powerful and intensive methods and regression as a fast and simplex model have been tested and surveyed to predict the environmental indicators for land leveling and determine the best method. Hitherto, only a limited number of studies associated with energy consumption in land leveling have been done. In mentioned studies, energy was a function of the volume of excavation (cut/fill volume). Therefore, in this research, energy and cost of land leveling are functions of all the properties of the land including slope, coefficient of swelling, density of the soil, soil moisture, special weight and swelling index which will be thoroughly mentioned and discussed. In fact, predicting minimum cost of land leveling for field irrigation according to the field properties is the main goal of this research which is in direct relation with environment and weather pollution.

The purpose of this paper is to evaluate the protective effects of Persian gum and gum Arabic on the survival of Lactobacillus plantarum subsp. plantarum PTCC 1896, Escherichia coli, Xanthomonas axonopodis, and Saccharomyces cerevisiae during freeze drying.

Cultures were harvested from the early stationary phase and enumerated after dilution according to the Milse Misra method. Bacterial suspensions were mixed with protective agents and frozen at –80°C before drying in a freeze dryer. Survival rates were determined both immediately during freeze drying and after 14 days of cold storage (at 4°C).

Compared to gum Arabic 5 and 10 percent (W/V) or skim milk 10 percent (W/V), Persian gum 1 percent (W/V) showed no significantly different effects on the survival of Lactobacillus plantarum subsp. plantarum PTCC 1896 (p<0.05). Similarly, no significant differences (p<0.05) were observed between Persian gum 6 percent (W/V), gum Arabic 6 percent (W/V), the combination of Persian gum 3 percent (W/V) and gum Arabic 3 percent (W/V), and skim milk 10 percent (W/V) in terms of their effects on the survival of Escherichia coli. Skim milk 10 percent (W/V) was, however, found to have significant (p<0.05) effects on the survival of Xanthomonas axonopodis and Saccharomyces cerevisiae. Statistically significant (p<0.05) effects were observed after 14 days of cold storage (4°C) by Persian gum 6 percent (W/V) on the survival of Escherichia coli and by gum Arabic 6 percent (W/V) on the survival of Xanthomonas axonopodis and Saccharomyces cerevisiae. It was concluded that protective agents could be replaced by Persian gum for its effect on the survival rate of Escherichia coli and by skim milk for its effects on the survival of Xanthomonas axonopodis and Saccharomyces cerevisiae. Persian gum 6 percent (W/V) seemed to be the best protective agent for Escherichia coli during 14 days of its storage as gum Arabic 6 percent (W/V) seemed to have the same performance for Xanthomonas axonopodis and Saccharomyces cerevisiae. Persian gum 1 percent (W/V) was also found an alternative protective agent for the freeze drying of Lactobacillus plantarum subsp. plantarum PTCC 1896.

As Iranian Persian gum is cheap due to its wide availability and seems to have effects similar to those of gum Arabic and skim milk at low concentrations, it may be considered a good candidate for industrial applications.

This study aims to investigate the effects of managerial overconfidence on conditional conservatism and real earnings management among companies listed on the Tehran Stock Exchange (TSE).

In this paper, the authors used the model of Ball and Shivakumar (2006) for measuring the effect of moderating overconfident management on conditional conservatism in accounting; moreover, the model of Roychowdhury (2006) is used for evaluating the relationship between managerial overconfidence and real earnings management. The study population consists of 1,144 observations and 143 firms listed on TSE over an eight-year period between 2008 and 2015. The statistical model used in this paper is a multivariate regression model; besides, the statistical technique used to test the hypotheses is panel data.

Consistent with the expectations, the results showed that there is a negative relationship between managerial overconfidence and conditional conservatism. Furthermore, the findings suggest that managerial overconfidence is negatively connected with real earnings management. This implies that when Iranian managers have many financial problems, they do not engage in real earnings management, as the real earnings management does not increase the value of the companies in the long run and even it cause damage to them.

This is one of the most important research that simultaneously surveys the impact of managerial overconfidence on conditional conservatism and real earnings management in a developing market called Iran. What really sets this study apart from other papers is that most Iranian firms between 2008 and 2015 because of economic sanctions faced severe financial problems. From this perspective, this study contributes to the research literature by expanding the knowledge of conservatism in the emerging economies.

This work aims to determine the best linear model using an artificial neural network (ANN) with the imperialist competitive algorithm (ICA-ANN) and ANN to predict the energy consumption for land leveling.

Using ANN, integrating artificial neural network and imperialist competitive algorithm (ICA-ANN) and sensitivity analysis (SA) can lead to a noticeable improvement in the environment. In this research, effects of various soil properties such as embankment volume, soil compressibility factor, specific gravity, moisture content, slope, sand per cent and soil swelling index on energy consumption were investigated.

According to the results, 10-8-3-1, 10-8-2-5-1, 10-5-8-10-1 and 10-6-4-1 multilayer perceptron network structures were chosen as the best arrangements and were trained using the Levenberg–Marquardt method as the network training function. Sensitivity analysis revealed that only three variables, namely, density, soil compressibility factor and cut-fill volume (V), had the highest sensitivity on the output parameters, including labor energy, fuel energy, total machinery cost and total machinery energy. Based on the results, ICA-ANN had a better performance in the prediction of output parameters in comparison with conventional methods such as ANN or particle swarm optimization (PSO)-ANN. Statistical factors of root mean square error (RMSE) and correlation coefficient (R²) illustrate the superiority of ICA-ANN over other methods by values of about 0.02 and 0.99, respectively.

A limited number of research studies related to energy consumption in land leveling have been done on energy as a function of volume of excavation and embankment. However, in this research, energy and cost of land leveling are shown to be functions of all the properties of the land, including the slope, coefficient of swelling, density of the soil, soil moisture and special weight dirt. Therefore, the authors believe that this paper contains new and significant information adequate for justifying publication in an international journal.