The analysis of motivations of heroic sports activists for cooperation in benevolent marketing: mixed approach

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.