Cevher, Muhammed Fatih; Gülter, Erkan

This study aims to reveal the impact of ESG on the concepts of brand image, brand trust, and brand loyalty. In addition, the study aims to reveal the mediating roles of brand image, and brand trust in the impact of ESG on brand loyalty. To achieve this goal, a survey was conducted with 322 consumers in Türkiye. Statistical analysis programs SmartPLS3 and SPSS24 were used to analyze the data obtained from the surveys. The results of the analysis revealed that ESG has a statistically significant and positive effect on brand image, brand trust and brand loyalty. Additionally, it was found that brand image and brand trust do not act as independent mediators in ESG’s effect on brand loyalty but rather act together and sequentially as mediators in ESG’s effect on brand loyalty. In addition, this study highlights the growing awareness of ESG issues in both theoretical and practical applications. In addition to increasing consumer awareness, it has been observed that brands also attach importance to ESG practices in order to gain a sustainable competitive advantage.

İncele

Cevher, Muhammed Fatih; Gülter, Erkan

This study aims to investigate the predictive capabilities of machine learning algorithms in forecasting the success of digital marketing campaigns. In addition, the study aims to evaluate the performance of machine learning algorithm classification models and to determine which classification model is more effective in making this prediction. In this direction, a classification analysis was performed with machine learning algorithms using a dataset of 10,001 digital marketing campaigns obtained from the Kaggle platform. The study's theoretical background is based on Attribution Modelling, the Technology Acceptance Model, Incremental Response Modelling, and the Diffusion of Innovations Theory. As a result of the analysis, it was revealed that the success prediction capabilities of machine learning algorithms for marketing campaigns are pretty high. In the study comparing the success prediction abilities of machine learning models, the Gradient Boosting model demonstrated the highest success prediction ability (93.31%). In comparison, the Logistic Regression model has the lowest predictive success rate (53.36%). The findings revealed that machine learning algorithms should be more widely incorporated into the marketing literature and that businesses can run more successful and efficient campaigns by utilising machine learning models in their marketing efforts.

AtıfOpenAlex1

İncele