امیرحسین توحیدی

Saffron is one of the most valuable and important agricultural products in Iran, and, it is necessary to consider the preferences of consumers to develop its market. Consumers have different needs and wants, and by benefit segmenting the saffron market, companies can understand customers' preferences in each segment of the market correctly. Therefore, in this study, using the crisp and fuzzy clustering algorithms, the saffron market was divided into homogenous segments, and then, using the results of the conjoint analysis; consumer preferences, attitudes, and demographic characteristics were examined in each saffron market segment. The necessary data were collected from 400 respondents using a stratified sampling method from 13 districts of Mashhad, Iran. The results of this study showed that the saffron market could be divided into six homogeneous segments, and the fuzzy C-means clustering algorithm performs better at finding clusters than k-means, k-medoids, fuzzy Gustafson-Kessel and fuzzy Gath-Geva methods. According to the study findings, in most market segments, the weight of saffron and the packaging type are the most important attributes influencing purchase decisions. Based on the study results, it is suggested that saffron companies consider consumer preferences in different market segments when supplying saffron products and formulating marketing strategies.

Nowadays due to the intensity of competition and continuous changes in environmental conditions, identification of customers' needs and wants is essential to gaining competitive advantage in domestic and international markets. Saffron is one of the most valuable and important agricultural products in Iran, and for the development of its market, it is necessary to consider the preferences of consumers of this product. Therefore, the main purpose of this study was to rank and evaluate the effect of saffron attributes on the consumer utility of this product in Mashhad city. In this study, 3, 3 and 8 levels were considered for the trade name, packaging type and weight, respectively. In 2020, The data from 400 respondents were collected using a stratified sampling method (with proportionate allocation) from 13 districts of Mashhad-Iran. Using conjoint analysis, the results of the study showed that the weight of saffron marketed and the packaging type are important attributes influencing consumer preferences. In a more detailed analysis and taking into account the attribute levels, the results of the study confirmed that the highest utility values were found in saffron products with pocket packaging and weight of 4.608 gr. According to the study results, to design marketing strategies for different customer groups, saffron companies are recommended to pay more attention to consumer preferences in target markets.

The lack of a clear competitive strategy is one of the major problems of Iranian saffron companies to gain a competitive advantage in domestic and international markets. Therefore, the Picocrocin main purpose of this study is to rank the factors affecting competitive advantage in the domestic and international markets of Iranian saffron. Therefore, the importance of marketing resources and competitive strategies for one of the major producers and suppliers of saffron is investigated in this study considering market position theory and resource-based view. A pairwise comparison-based questionnaire is used to collect opinions of experts in the company. Using the analytic hierarchy process method, the results of the study showed that the differentiation strategy has the most effect in gaining competitive advantage. The results also indicate that particular attention must be paid to managerial and customer relationship capabilities in executing the differentiation strategy. Accordingly, improving financial conditions and understanding customers' needs and requirements are among the approaches that lead to development of key company capabilities and gaining competitive advantage in saffron markets.

Organic farming plays an important role in protecting the environment, maintaining non-renewable resources, improving the food quality, reducing the production of unnecessary products, and promoting market- oriented agricultural sector. In fact, organic farming make a significant contribution in improving the quality of the environment and natural resources, and also it has a positive effect on the quality of food supply and the promotion of public health. Given the many benefits of organic products, the market for these products has been increasingly considered by researchers, government officials and consumers. First step in developing the market for organic products is to meet the needs and demands of consumers. Recognizing consumer behavior and investigating the factors affecting it contributes significantly in success of any economic system. Besides, in advanced marketing studies, the process of identifying consumer choice is very crucial. Contrary to economists' views, consumers give little weight to benefits and costs in their decision making, and their choices are based on people's behavior, habits and other factors that may speed up the decision making. Consumer preferences for organic products depend on many factors and the importance of each of these factors varies among different consumers. Therefore, the main aim of this study is to rate and evaluate factors affecting the consumer preferences for organic products (fruitage, vegetables and cucurbits) in Mashhad city.

Many marketing researchers use regression models to evaluate consumer decisions. In these models, decision variables are definitive part of utility function which is used to calculate how to choose a product. Linearity of utility function is the vital hypothesis. To specify a non-linear model, it is necessary to use variables that can show non-linear effects (For example, including the quadratic term of variables). However, this requires the insertion of assumptions about the nature of the utility function which ultimately leads to specification bias, and subsequently misinterpretation and unreasonable applications in marketing studies. Modeling complex processes is one of the advantages of artificial neural networks, and in this approach, it is not necessary to specify a mathematical relationship between the variables. The nonlinear and complex interactions can be considered between system variables using artificial neural network model. In this study in order to rate and evaluate factors affecting consumers preferences for organic products (fruitage, vegetables and cucurbits) an artificial neural network has been used that is consist of three dependent or target variables. Also, in order to evaluate the importance of the explanatory variables of the artificial neural network, partial derivatives approach has been used. Therefore, the use of three output variables on artificial neural networks simultaneously and partial derivative approach was distinctive features of this study compared with previous ones. Data is collected through questionnaires from a total of 175 households living in Mashhad. Age, gender, education, household size, number of household members under 10 years, number of household members over 65 years, price, having information on organic products, product appearance, having information on the supply of organic products, nutritional values, ease of access, the supply of organic products during the year and having labels were the input variables of artificial neural network. Consumer preferences for the purchase of organic fruitage, vegetables and cucurbits were the target variables of the artificial neural network.

The results indicate that price has the greatest influence on willingness to consume organic products among all other factors. The price effect on willingness to consume organic products is different among individual consumers, and it's independent of the product. This finding suggested that the price of organic products had a significant impact on consumer purchasing decisions in comparison with other marketing mix elements.

The adoption and implementation of marketing strategies based on price play a very important role in the growth of organic products markets. The results of the study indicate that, for each consumer and each product, the price had almost the similar effects on willingness to choose. Hence, it is recommended that the similar pricing strategies be used for these three organic products.

This study was performed using data from 165 households in 13 areas of Mashhad County of Iran. A stratified sampling method was used to determine the sample size. Firstly, factors affecting the consumer's awareness of organic products were analyzed by using the Probit model in propensity score matching method. Then, the effect of consumers’ awareness on their willingness to consume the organic saffron in future was evaluated by estimating the average treatment effect on treatment group. The results from Probit model showed that education, attitude toward quality of organic product labels, environmental concerns and attitude toward price of organic products had positive effects on awareness of the organic products. After matching, the difference in average between treatment and control groups decreased for education, attitude toward price of organic products, environmental concerns and attitude toward quality of organic product labels. The results from estimating the average treatment effect on treatment group showed that the organic saffron future consumption among units of treatment group was 66 percent greater than those in control group. So, awareness of organic products had stimulus and positive effect on the willingness to consume the organic saffron in future. Therefore, in order to stimulate demand and increase sales, it was recommended to producers and suppliers of the organic products to use advertisements to inform consumers.

Saffron, a valuable agricultural product in the national and international level is valued by consumers in different ways and the price paid for it depends on different factors. Identifying these factors can be helpful to marketing saffron with the price that is consistent with consumer preferences. The major aim of this study is to evaluate the factors that affect the price of saffron in Mashhad using the Hedonic pricing model. What distinguishes this study from previous studies is using the sensitivity analysis approach in the context of artificial neural networks. The information needed for this research was collected from 120 saffron buyers in the city of Mashhad with the random sampling approach. Considering the 14 explanatory variables, the results showed that age and brand have the least impact on the price of saffron, while the consumption goals variable has a significant effect on the price of this product. Among the goals, nutritional uses has a positive effect, and medical and industrial uses have a negative effect on the price of saffron. According to the research findings, manufacturers and suppliers of saffron are recommended to price the product according to the buyers’ consumption goals, sale style and the saffron type, respectively.

In terms of quality and quantity, Iranian saffron has a considerable position at the international level and by taking advantage of the existing capacity; we can significantly increase the export earnings from it. On the other hand, sales forecasting based on time series analysis is s a very important element for the designing and implementing of marketing strategies in the international arena. However, the conventional approaches to forecasting, by ignoring the linear (or nonlinear) structure of data, do not provide accurate results. Therefore, the main objective of this study is to design a hybrid model consisting of two methods, artificial neural networks (ANN) and autoregressive integrated moving average (ARIMA), in order to overcome the deficiencies and the use of the unique features of the each of these methods. Using the data related to the export of Iranian saffron during the period 1904-2013, the results of the study showed that the ARIMA–ANN hybrid model is stronger and better performance than ARIMA and ANN individual models in order to forecasting of Iranian saffron export. Therefore, given the considerable performance ARIMA–ANN hybrid model, the use of this model is recommended in setting strategies related to the export and also in the forecasting of the forecasting of time series variables.

Packaging is a tool for recognition and differentiation of product and it plays a crucial role in consumer's purchasing decisions, and it can be used to create competitive advantages. Saffron is one of the most important agricultural crops in Iran and its packaging in accordance with consumer demand increases sales and satisfaction of the consumers. Therefore, the main objective of this study is to rank the factors affecting the packaging from the perspective of consumers of saffron in Mashhad. Data was collected in the form of 99 questionnaires that have been answered by the consumers of saffron in Mashhad in 2015. Analytic hierarchy process (AHP) was used to rank these factors. The results showed that labeling information about internal and international standards and saffron nutrients on the package have the greatest impact on consumer preferences. Also, the results of the ranking alternatives indicated that attention to the brand labelled on the packing is the most important issue from the perspective of the consumers. Hence, labelling information elements on the package can be a good way for brand differentiation and increasing its value. According to the results, it is recommended that producers and suppliers of saffron pay more attention to features and information elements in package design.

Providing an alternative route for mobilizing domestic savings, stock markets increase investment growth and, consequently, economic growth. Moreover, allocating better resources (through the concessions of labor division), the stock market plays an important role in economic growth. In addition to the positive effects of the stock market on economic growth, this market has high potential to create employment (direct and indirect). According to the existing conditions of the agriculture sector in meeting the needs of the society have increased the importance of this sector. Thus, it is necessary to use the stock exchange of the agricultural sector as one of the most important tools to solve the problems of this sector, improve market efficiency, increase productivity and provide financial requirements of agricultural companies in this market. Concerning the number of shares, stock volume and variety of products, agricultural exchange is the largest stock exchange. Cash returns can be used as a criterion to identify the economic status of agricultural firms in the stock market; identifying this trend plays an important role in financial planning and investment decisions. So, the main objective of this study was to compare the performance and accuracy of different methods to predict cash returns of agricultural companies in Tehran Stock Exchange.
The stock returns prediction models can be divided into two groups of statistical models and artificial intelligence. In this study, prediction accuracy was evaluated for linear (ARIMA and ARIMAX), non-linear (NAR and NARX) and hybrid models (ARIMA-ANN and ARIMAX-ANN) using monthly cash returns data over the period 2009(4)-2015(4) and according to the Diebold and Mariano test. ARIMA linear models have dominated many areas of time series forecasting. The linear function is based on three parametric linear components: auto regression (AR), integration (I), and moving average (MA). The ARIMA models also have the capability to include external independent or predictor variables. In this case, the model is a multivariate model and this model is represented as ARIMAX, where X represents the independent external. Artificial neural networks are capable of learning, generalizing, information parallel processing and error endurance. These attributes make the ANNs powerful in solving complex problems. Moreover, the ANN is able to adapt to the data pattern and relationship between the input and output, resulting in better prediction accuracy than the statistical method. The combined ARIMA(X)-ANN model is able to use the characteristics of both models simultaneously. Adding the explanatory variables, exchange rates, interest rates, inflation, oil prices and government spending, this study tried to answer the question whether performance prediction models improve significantly for linear, nonlinear and hybrid?
In this study, augmented Dicky Fuller tests (ADF), dickey fuller generalized least squares (DF-GLS), Phillips-Perron (PP) and Kwiatkowski–Phillips–Schmidt–Shin (KPSS) tests were used to test the unit root. According to results, time series of cash returns was stationary. Next we estimated ARIMA (2,0,0) and ARIMAX (2,0,0) models. Based on the criteria of accuracy, the results showed that the ARIMAX model had a higher predicting accuracy compared to the ARIMA. In this study, two models were designed using artificial neural networks to predict cash returns. the first model only included the first and second lag of cash returns (infact, the first model was Nonlinear Autoregressive model (NAR)); in the second model, in addition to two lags of cash returns, the exchange rates, interest rates, inflation, oil prices and government spending variables were used as the network inputs (this pattern is known as Nonlinear Autoregressive with Exogenous Input (NARX Model)). We find that the performance and accuracy of Auto Regression artificial neural networks increases by adding explanatory variables. In this study also used the ARIMA-ANN and ARIMAX-ANN hybrid models, in order to consider the linear and nonlinear characteristics in the data. After running the hybrid models and predicting, the results indicate that ARIMA-ANN and ARIMAX-ANN hybrid models have better performance in comparison with linear models (ARIMA and ARIMA) and non-linear models (NAR and NARX). Another result shows that ARIMA-ANN model is more accurate compared to ARIMAX-ANN. Finally, we show that hybrid models have greater prediction accuracy according to the Diebold and Mariano test.
The results showed that the addition of external explanatory variables could improve the prediction accuracy in linear and nonlinear models and this improvement in performance was significant statistically. The results of the study revealed that making use of hybrid models increased prediction accuracy significantly. Therefore, researchers, companies and shareholders of stock exchange are suggested to use the hybrid models in their financial planning to forecast economic variables.

Variations of exchange rates play an important role on the policies and pricing strategies of Iranian exporters in international markets. Therefore, for the total sample of 48 market destinations during the period 1992-2012, assessment of the degree of pricing to market is the main purpose of this study. In order to determine factors affecting the Iranian export price, we use advanced dynamic panel data methods (such as pooled mean group, the system generalized method of moments and the dynamic fixed effects) that are distinguishing features of this study relative to prior works, based on the autoregressive distributed lag and error correction model. In addition, to determine the effect of the exchange rates uncertainty on Iranian export price, we use the exponential GARCH in the context of panel data. Using the mentioned three methods, the results of this study in the short term showed that the degree of pricing to market is the range between 0/46 and 0/94, and therefore, we can conclude that contribution-profit pricing strategy is established in Iran's export markets. Also, the results in the long term showed that the export demand in importing countries is price inelastic. According to research findings, it is concluded that the policy of devaluation of the national currency will have little effect on increase of Iran's exports and improve the balance of trade in the short- and long-term.

Exchange rate pass-through is one of the most important issues in the international economic studies. Determining the degree of exchange rate pass-through can be used to define the effectiveness of foreign policy, market structure and exporters behavior. The main objective of this study is to investigate the exchange rate pass-through to export prices of Iranian pistachios during the period 1961-2011. In the previous studies, the exchange rate pass-through was assumed to be fixed during different years. This assumption is not consistent with reality, because many factors can influence the exchange rate pass-through. In this study, sensitivity analysis in the framework of artificial neural network is used to address this shortcoming. The results shows that exchange rate pass-through to Iran's pistachio export prices has been more than 70 percent, and its trend has been periodic. Furthermore, the results showed that there is a direct relationship between exchange rate fluctuations and Iranian pistachio export prices. Thus, by reducing exchange rate volatility, it is possible to supply pistachio with lower prices to the world markets. Considering the high elasticity of demand for the Iranian pistachio prices, a reduction in prices would increase revenues of exporters. Incidentally, given the high elasticity of export demand for Iran's pistachio, the revenues from the export of this product can be increased by reducing the cost of pistachio production.

In many studies, in order to predict economic variables, quantitative methods based on time series data or cross-section data are mostly used. Time series data or cross-section data do not control the heterogeneity of countries, and the possibility of obtaining the risk of biased results exists. Panel data provides more informative data and a more degree of freedom which lead to results and predictions that are more precise. In this study, while considering the significant role and proportion of dried fruits in non-oil exports, the synthetic artificial neural network-panel data method has been used to predict the price of pistachio, raisin and date exports. Then the predictions were compared, using the accuracy criteria, with the regression model (the two-way error component model). The data from ten target markets for each of the dried fruits from 1992 to 2012 were used, and the results of this study show that the new and synthetic artificial neural network-panel data method has a better performance in predicting the price of Iran’s pistachio, raisin and date exports than that of the regression method. Therefore, it is suggested that exporters, policy makers, and researchers use this method in order to predict economic variables.

This study estimate the effect of exchange rate fluctuations on saffron export demand. To this end، the pooled mean group (PMG) approach is used in order to implement the model of auto regressive distributed lag model (ARDL) and vector error correction model (VECM) in the context of panel data. The results showed that، relative price of exports has a negative and significant effect on Iran''s saffron export demand. In the short- and long-term، the estimated price elasticity suggests that Iranian saffron export demand is price inelastic. The results indicate that real income effect of importing countries on the export of Iranian saffron is positive. In the short-term، income importing countries has no significantly effect on Iranian saffron export demand، while the long-term effect is significant at the 0. 01 level. In both short- and long-term، the results indicate that exchange rate has a significant، positive effect on Iran''s saffron export demand. The exchange rate elasticity of export demand for Iran''s saffron is elastic in both short- and long-term. Therefore، the devaluation of the Rial، Iran''s currency، led to a significant increase in the export of Iranian saffron. Also، the results showed in the short-term، the unpredictability of exchange rate fluctuations lead to increase the degree of risk aversion of exporters of Iranian saffron and so they prefer to deal with this issue by reducing their export. However، in the long-term، the income effect dominates the substitution effect، and exchange rate fluctuations has a positive effect on the export of Iranian saffron by creating profit opportunities.

During recent years, globalization has been a substantial growth in different countries, and this event has been effective on many macroeconomic variables. In many countries, by increasing trade and financial openness, the size of government has been changed. Therefore, in this study, we have investigated the effects of trade and financial openness on government size in member countries of the MENA during the period 2002-2013. Considering endogenous explanatory variables using instrumental variables and using the error components three-stage least-squares (EC3SLS) estimator are distinguishing features of this study relative to previous studies. In accordance with the efficiency hypothesis, the study findings indicated that financial openness has a negative effect on the government size. Also, based on the compensation hypothesis, the results showed that trade openness has a positive effect on the government size. Generally, the results showed that the positive effect of trade openness on government size is greater than the negative impact of financial openness. These findings indicate that the government size in member countries of the MENA will increase in the face of economic globalization. Therefore, it is recommended that perform the privatization of the economy be implemented more quickly, and the role of government be changed from outsourcing to regulatory and policy-making.

For many years، Iran has traditionally been the largest pistachio producer and exporter in the world. In addition، pistachio is considered as one of the important sources of foreign exchange earnings and it has important role in reducing dependence of the public budget on oil revenues. Given the importance of pistachio in the Iran''s agricultural economy، it is essential to identify the factors that aaffects pistachio exports. The main purpose of present paper is to estimate the export demand function for Iranian pistachio. Using a panel of seven destination markets (including Australia، France، Germany، Japan، Malaysia، Mexico and Switzerland) over the period 1992–2012 and controlling for the endogeneity of the explanatory variables، error component two stage least squares method is used to estimate export demand function. According to the results، all estimated coefficients are significantly different from zero at the five percent level and have the theoretically expected signs. The results show that long-run elasticity of demand for Iran''s pistachio exports with respect to relative prices and real exchange rate changes are considerably greater (in absolute terms) than their corresponding short-run elasticity. The results also suggest that the demand for Iran''s pistachio exports is inelastic with respect to real foreign income in both short- and long-run. Thus، with respect to demand conditions in destination markets، it is necessary to reduce marginal cost of Iranian pistachio producers by elaboration and implementation supportive policies.

With regard the wave of globalization، the acceptance of free trade is further enhanced so that globalization is known as the fundamental principle of economic growth in the developing countries. The main purpose of this paper is to investigate the relationship between globalization and tax revenue، in the theoretically and empirically framework. By using a panel of 16 developing countries over the period 1990–2009 and controlling for the endogeneity of several of the explanatory variables، error component two-stage least squares method is used. Controll of determinants of tax revenue، the results of this study show that globalization (the share of international trade in gross domestic product) has a positive and statistically significant effect on tax revenue in developing countries. Another result is that macroeconomic policies and structural characteristics have significant effects on tax revenue and each of these factors plays an important role in determining the impact of globalization on tax revenue.

Exchange rate pass-through is a subject that has been discussed and analyzed due to its impacts on trade balance and export prices. The main purpose of this paper is to investigate empirically the relationship between exchange rate and Iran''s pistachio export prices. Using a panel of data from twenty-three destination markets over the period 1992–2010، fixed effects regression model is used to test this relationship. The results of model estimation with a common exchange rate coefficient for all destination markets indicate that Iranian exporters absorb a portion of change in the exchange rate and so pass-through to export prices is incomplete. Another result from this study is that competitive condition is not prevailed in destination markets. According to the pattern of demand in destination markets and high degree of exchange rate pass-through for pistachio، it is concluded that Iranian exporters should focus on non-price competition for increasing market share.

As the Current account balance and government budget balance are important macroeconomic variables، the relationship between them have been one of the main questions of Macroeconomic researchers over the past three decades. Therefore، the main objective of this paper is to examine empirically the relationship between budget and current account deficits. Using panel data of 50 developing countries over the period of 1990-2012، three econometrics methods (Error Components Two-Stage Least Squares، System Generalized Method-of-Moments and Dumitrescu-Hurlin granger causality test) have been employed in this study. The results showed that the twin deficit hypothesis is valid for developing countries. Therefore، due to the adverse effects of budget and current account deficits، it is suggested that developing countries can increase taxes to achieve stable revenue sources for financing the budget deficit، and to reduce their own instability and current account deficit.