فهرست مطالب mohammadali siahsarani kojouri

The purpose of the present study is to design and explain the purchase journey map of moviegoers based on contact points. In the first step, using the content analysis method, 29 indicators were extracted as the contact points of moviegoers in the three stages of the shopping trip, and based on that, a research questionnaire was designed. In the second step, quantitative approaches were used to investigate the statistical population. To describe the research population, 419 adult moviegoers were clustered and analyzed in two separate clusters using the Davis-Bouldin index. The results showed that in the first cluster, the pre-purchase stage (actresses and director's brand), during the purchase (sanitary facilities), and post-purchase (advice, silence, and bad-mouthing) contact points were most significant. In the second cluster, the pre-purchase stage (previous experience), during purchase (store quality), and post-purchase (information on upcoming movies) contact points were most significant. Naive Bayes was used to design the model predicting the audience return rate. The application of Naive Bayes showed that by using contact points, it is possible to predict the return rate of moviegoers in the first and second clusters with an accuracy of 0.910 and 0.814, respectively. The sensitivity analysis results showed that in the first cluster, the pre-purchase stage (cinema brand), during the purchase (personnel encounter), and post-purchase (discount for the next visit) were the best predictors of consumer behavior. In the second cluster, the pre-purchase stage (word-of-mouth advertising), during purchase (store quality), and post-purchase (handling complaints) were the best predictors of consumer behavior.

The present study aims to analyze the motivations of heroic sports activists for cooperation in benevolent marketing using a mixed approach. In the first step, 50 codes were extracted qualitatively (content analysis) as motivations for cooperation in benevolent marketing and categorized into 10 factors according to semantic similarities, based on which the questionnaire was designed. In the second step, we utilized quantitative approaches to data-mining (explanatory and predictive) in order to evaluate the statistical population. To this aim, 393 heroic sports activists were classified into two separate clusters using the Davies–Bouldin index. Moreover, we used artificial neural networks in order to design a model which predicts the cooperation of heroic sports activists in charity work. Results showed that reciprocation, desire for entertainment and pleasure, and emotional importance in the first cluster, and religious teachings, need for helping others, and desire for social cooperation in the second cluster were assigned the most cluster centers in that order. The use of neural networks indicated that the independent variables have high predictive potentials and can predict the changes in the dependent variable with the accuracy of 0.958. The use of neural networks indicated that the independent variables have high predictive potentials and can predict the changes in the dependent variable with the accuracy of 0.958.The use of neural networks indicated that the independent variables have high predictive potentials and can predict the changes in the dependent variable with the accuracy of 0.958.

Analysis decision-making patterns of buying behavior of customers and providing a predictive model is one of the challenges and areas of interest to researchers which can be widely used in the field of localization products. This study aimed to analyze and model the behavior of older customers decide to buy organic products with the hybrid approach. The research was done in two steps linked together. In the first step the reliability and validity of a questionnaire with 33 question was evaluated respectively by Cronbach's alpha and confirmatory factor analysis first and second order was approved. Opinion of 388 old customer using nine indicators were collected, then, using cluster analysis K-means based on Davies-Bouldin and sylvite criterion the optimal clusters was identified. And elderly clients in the two clusters were unwilling and eager to buy organic products was classified. In the second step purchasing behavior using decision tree models were analyzed and the optimum model was extracted "if-then" rules associated with each cluster were presented. The results showed that in both unwilling and eager cluster, education index predict the decisive factor in the decision to buy organic products, It also seems that the consumption of organic products among the elderly is not in good condition in this context proposals were presented for each cluster.

Identifying the problems of Knowledge-based companies is important because of their special position in the economy of the country, despite the increasing value of exports of Knowledge-based products, this companies suffer from two problems, areas of investment and unsustainable export, the issue that has challenged the competitiveness of these companies, it’s an issue that needs to be explored further. The aim of this study was to conceptualize and Prioritization of intelligent knowledge-based companies through a combination approach. The present study aimed to explore the mixed and the functional result. The first step is research in order to achieve the objectives of the research, using semi-structured content analysis and interviews with activist’s knowledge-based companies (professionals, managers and relevant officials), 95 Code as intelligent concepts competitive knowledge-based mining companies and 10 components were classified according to semantic relationships. The second step 12 experts selected by theoretical sampling their views through a questionnaire with 5-point Likert collected and analyzed data through fuzzy Delphi technique. The results of two surveys of experts led to the identification of eight main components of a competitor intelligence, financial intelligence, market intelligence, technical intelligence, intelligent management, regulatory intelligence, human intelligence and political intelligence to gain competitive intelligence in the knowledge-based companies.

The purpose of this study is to provide a predictive model of the Behavior of Iranian Brands’ Purchasers, which was carried out in two co-ordinated steps. In the first step, a 26-item questionnaire has been employed, which was developed through literature review. The comments of 856 Mazandarani customers on the purchase of Iranian and foreign brands were collected based on actual purchase behavior. After pre-processing of data, in order to achieve the optimal solution range, the genetic algorithm was used and effective characteristics were selected. In the second step, the purchase behavior of buyers of Iranian and foreign brands was analyzed using a decision tree and the optimal models, and IF-THEN rules of purchasers of domestic and foreign brands were obtained. The results showed that the perception of external threats, national production support and income, are main indicators in separating purchasers of Iranian and domestic brands, and the higher is the perception of external threats and national production support in the customer, the higher the tendency to domestic brands; meantime, the groups of people with higher income have more aptness to buy foreign brands.