The aim of this paper is to investigate the impact of sponsorship perceptions and attitudes of sport team fans on their purchasing decision, as well as to investigate the significance of sport sponsorship in promoting socially responsible companies and other sustainable practices in society. This paper gives insight in possibilities and effects of promoting sustainable activities and socially responsible companies, through sport sponsorship, by targeting and educating sport team fans as a very important consumer segment. Data were collected from anonymous respondents who are fans of different sport teams. Attitudes were measured by asking the respondents to rank the statements based on a 5-point Likert scale. In order to analyze the data, structural equation modelling was conducted using R package. Results show that the attitudes towards sponsorship, as well as sponsorship perceptions, have a strong positive impact on the purchasing decisions of consumers, regarding products of sponsors, which enables the creation of a loyal base of fans as consumers. Results also reveal that sport sponsorship can be an effective way of promoting socially responsible companies and sustainable practices, giving an opportunity to create an educated and highly dedicated consumer base. In this way the positive effects of sports and sponsorship are spreading beyond the stadiums and arenas. These findings can help companies that are considering investing in this form of marketing, in adequately selecting and managing the campaign, but also can be very useful for official institutions in their efforts to foster sustainable practices in society. Although the fact that the research was conducted only in Montenegro can be stated as a limitation, it should be noted that this type of research has been done for the first time in this area, which gives special value to the research and increases the importance of its contribution to the existing base of scientific papers in this field.

Purpose: This paper seeks to identify the factors that influence the perceptions and attitudes of young people in Montenegro toward organic products. Design/methodology/approach: The results of the research, obtained using the multivariate factor analysis, showed that the vast majority of young consumers identify organic food as healthy and natural and that its higher price is a significant barrier inhibiting their likelihood of purchasing it, along with inaccessibility and the limited options offered. Findings: The data obtained provides valuable information for managers and decision-makers working in organic food production and sales in Montenegro – a country with the potential to develop in this field. Research limitations/implications: The study was carried out in the CEE transition country of Montenegro, with 300 young consumers. Further research is needed to validate the results through longitudinal study. Practical implications: The data obtained represents a meaningful contribution to the field of organic food production and sale in Montenegro (which is a country with a lot of potential in this area) and will assist managers and decision-makers in the field. Social implication: Unlike most research that has previously examined issues concerning organic production – such as its standardization, financing, environmental impact, and even its promotion – this paper provides a better understanding of the behavior of young consumers toward organic products in Montenegro by outlining their perceptions and attitudes toward this product category. Originality/value: This research represents the first academic study of customers' attitudes and perceptions in this area, alongside the main factors affecting the young consumer's choice to purchase organic food in Montenegro.

Customer response models have gained popularity due to their ability to significantly improve the likelihood of targeting the customers most likely to buy a product or a service. These models are built using databases of previous customers’ buying decisions. However, a smaller number of customers in these databases often bought the product or service than those who did not do so, resulting in unbalanced datasets. This problem is especially significant for online marketing campaigns when the class imbalance emerges due to many website sessions. Unbalanced datasets pose a specific challenge in data-mining modelling due to the inability of most of the algorithms to capture the characteristics of the classes that are unrepresented in the dataset. This paper proposes an approach based on a combination of random undersampling and Support Vector Machine (SVM) classification applied to the unbalanced dataset to create a Balanced SVM (B-SVM) data pre-processor resulting in a dataset that is analysed with several classifiers. The experiments indicate that using the B-SVM strategy combined with classification methods increases the base models’ predictive performance, indicating that the B-SVM approach efficiently pre-processes the data, correcting noise and class imbalance. Hence, companies may use the B-SVM approach to more efficiently select customers more likely to respond to a campaign.

An objective and data-based market segmentation is a precondition for efficient targeting in direct marketing campaigns. The role of customer segments classification in direct marketing is to predict the segment of most valuable customers who is likely to respond to a campaign based on previous purchasing behavior. A good-performing predictive model can significantly increase revenue, but also, reduce unnecessary marketing campaign costs. As this segment of customers is generally the smallest, most classification methods lead to misclassification of the minor class. To overcome this problem, this paper proposes a class balancing approach based on Support Vector Machine-Rule Extraction (SVM-RE) and ensemble learning. Additionally, this approach allows for rule extraction, which can describe and explain different customer segments. Using a customer base from a company’s direct marketing campaigns, the proposed approach is compared to other data balancing methods in terms of overall prediction accuracy, recall and precision for the minor class, as well as profitability of the campaign. It was found that the method performs better than other compared class balancing methods in terms of all mentioned criteria. Finally, the results confirm the superiority of the ensemble SVM method as a preprocessor, which effectively balances data in the process of customer segments classification