User-generated content (UGC) plays a central role in social commerce. However, existing knowledge remains theoretically fragmented across constructs, perspectives, and empirical contexts. To address this gap, this study conducts a systematic review of 60 peer-reviewed studies published between 2014 and 2024, following PRISMA 2020 guidelines. It develops an integrative conceptual perspective structured around five key dimensions: trust, authenticity, perceived risk, engagement, and loyalty. The findings demonstrate that UGC influences consumer decision-making primarily through mediating psychological and social mechanisms, including trust, satisfaction, perceived value, social presence, and community identification. At the same time, perceived risk remains insufficiently theorized, and comprehensive multi-dimensional models remain scarce in the literature. The study advances social commerce theory by consolidating fragmented evidence into a coherent conceptual framework. It also explicitly foregrounds the central explanatory role of mediating mechanisms in UGC effects. From a practical perspective, the findings highlight the strategic importance of fostering authentic and trustworthy UGC. This supports sustainable consumer–brand relationships and long-term value creation within digital platform ecosystems. The review has limitations related to database coverage and language restrictions, which may have led to the omission of relevant studies.

The rapid growth of e-commerce has transformed customer behaviors, demanding deeper insights into how demographic factors shape online user preferences. This study performed a threefold analysis to understand the impact of these changes. Firstly, this study investigated how demographic factors (e.g., age, gender, education) influence e-customer preferences in Serbia. From a sample of n = 906 respondents, conditional dependencies between demographics and user preferences were tested. From a hypothetical framework of 24 tested hypotheses, this study successfully rejected 8/24 (with p < 0.05), suggesting a high association between demographics with purchase frequency and reasons for quitting the purchase. However, although the reported test statistics suggested an association, understanding how interactions between categories shape e-customer profiles was still required. Therefore, the second part of this study considers an MCA-HCPC (Multiple Correspondence Analysis with Hierarchical Clustering on Principal Components) to identify user profiles. The analysis revealed three main clusters: (1) young, female, unemployed e-customers driven mainly by customer reviews; (2) retirees and older adults with infrequent purchases, hesitant to buy without experiencing the product in person; and (3) employed, highly educated, male, middle-aged adults who prioritize fast and accurate delivery over price. In the third stage, the clusters are used as labels for Machine Learning (ML) classification tasks. Particularly, Gradient Boosting Machine (GBM), Decision Tree (DT), k-Nearest Neighbors (kNN), Gaussian Naïve Bayes (GNB), Random Forest (RF), and Support Vector Machine (SVM) were used. The results suggested that GBM, RF, and SVM had high classification performance in identifying user profiles. Lastly, after performing Permutation Feature Importance (PFI), the findings suggested that age, work status, education, and income are the main determinants of shaping e-customer profiles and developing marketing strategies.

In the rapidly evolving landscape of digital marketing, the quest for more effective and engaging visual content is relentless. With artificial intelligence (AI) making significant strides, an important question arises: Can AI tools for design surpass the creativity and effectiveness of human graphic designers? This intriguing inquiry forms the cornerstone of our study. We aimed to evaluate the effectiveness of AI- generated versus human-designed advertisements through a practical approach. In the first phase, both a human graphic designer and an AI tool were given identical tasks and prompts to create visual ad solutions. In the second phase, the efficiency of these designs was assessed using Meta's A/B testing solution for Facebook and Instagram ads. This method allows the testing of visual variables by showing different ad versions to segmented audiences, ensuring that no individual sees both versions and that the budget is evenly distributed. The test duration is recommended to be two weeks or until 100 conversions are collected. This experiment, conducted in August 2024 as part of a campaign for FTN Business School's 2024/2025 enrollment, aims to provide a comprehensive insight into the actual capabilities of graphic solutions created by human designers versus AI tools. The outcome of this test promises to offer valuable insights into the real-world performance of AI-generated designs compared to those crafted by human designers. Will AI tools emerge as the new maestros of design, or will the nuanced touch of human creativity prevail? Our research aims to shed light on this cutting-edge debate, providing data-driven conclusions.

The rapid growth of e-commerce has transformed customer behaviours, demanding deeper insights into how demographic factors shape online user preferences. To understand the impact of these changes, this study performs a threefold analysis. Firstly, the study investigates how demographic factors (e.g., age, gender, education, income) influence e-customer preferences in Serbia. From a sample of n = 906 respondents, we test conditional dependencies between demographics and user preferences – “purchase frequency”, “the most important property when buying for the first time”, “the most important property before repeating a purchase”, and “reasons for quitting an online purchase”. From a hypothetical framework of 24 tested hypotheses, the study successfully rejects 8/24 (with p < 0.05), suggesting a high association between demographics with purchase frequency (p < 0.01) and reasons for quitting the purchase (p < 0.01). However, although reported test statistics suggest an association, understanding how interactions between categories shape e-customer profiles is lacking. As a consequence, the second part considers an MCA-HCPC (Multiple Correspondence Analysis with Hierarchical Clustering on Principal Components) to identify user profiles. The analysis reveals three main clusters : (1) young female unemployed e-customers driven mainly by customer reviews; (2) retirees and older adults with infrequent purchases, hesitant to buy without experiencing the product in person; (3) employed, highly educated, male midlife adults who prioritise fast and accurate delivery over price. In the third stage, the study uses identified clusters as labels for Machine Learning (ML) classification through the following algorithms: Gradient Boosting Machine (GBM), Decision Tree (DT), k-Nearest Neighbors (kNN), Gaussian Naïve Bayes (GNB), Random Forest (RF) and Support Vector Machine (SVM). The results suggest high classification performance of GBM (AUROC = 0.994), RF (AUROC = 0.994) and SVM (AUROC = 0.902) in identifying user profiles. Lastly, after performing Permutation Feature Importance (PFI), the findings suggest that age, work status, education, and income are the main determinants of shaping e-customer profiles and developing marketing strategies.

The rapid advancement of Artificial Intelligence (AI) has revolutionized numerous industries, with advertising being one of the most significantly impacted. This paper explores the transformative effects of AI technologies on advertising strategies, practices, and outcomes, particularly in the creation of creative advertising campaigns. By analyzing a wide range of relevant literature in the field, the study aims to understand the current trends, methodologies, and effectiveness of AI-driven campaigns. Additionally, practical examples from leading companies are examined to illustrate how AI technologies are being implemented in real-world scenarios to enhance creativity, target audiences more effectively, and measure campaign success. This research contributes to the growing body of knowledge by providing a comprehensive overview of AI's role in modern advertising, offering valuable insights for marketers and academics interested in the integration of AI in creative processes. The findings are expected to highlight the transformative potential of AI in advertising and suggest best practices for leveraging these technologies to maximize campaign impact.