Artificial Intelligence (AI) has become a pivotal force in transforming the retail industry, particularly in the online shopping environment. This study investigates the impact of various AI applications—such as personalized recommendations, chatbots, predictive analytics, and social media engagement—on consumer buying behaviors. Employing a quantitative research design, data was collected from 760 respondents through a structured online survey. The snowball sampling technique facilitated the recruitment of participants, focusing on diverse demographics and their interactions with AI technologies in online retail. The findings reveal that AI-driven personalization significantly enhances consumer purchase intentions and satisfaction. Multiple regression analysis shows that AI personalization (β = 0.35, p < 0.001) has the most substantial impact on purchase intention, followed by chatbot effectiveness (β = 0.25, p < 0.001), predictive analytics (β = 0.20, p < 0.001), and social media engagement (β = 0.15, p < 0.01). Similarly, AI personalization (β = 0.30, p < 0.001), predictive analytics (β = 0.25, p < 0.001), and chatbot effectiveness (β = 0.20, p < 0.001) significantly influence consumer satisfaction. The hierarchical regression analysis underscores the importance of ethical considerations, showing that ethical and transparent use of AI increases consumer trust and engagement. Model 1 explains 45% of the variance in consumer behavior (R² = 0.45, F = 154.75, p < 0.001), while Model 2, incorporating ethical concerns, explains an additional 10% (R² = 0.55, F = 98.25, p < 0.001). This study highlights the necessity for retailers to leverage AI technologies ethically and effectively to gain a competitive edge, improve customer satisfaction, and drive long-term success. Future research should explore the long-term impacts of AI on consumer behavior and the integration of emerging technologies such as augmented reality and the Internet of Things (IoT) in retail.

Short-form content has the potential for virality and broad sharing, allowing businesses to reach large audiences in a short period of time. This type of content has transformed traditional marketing approaches, capturing the attention and curiosity of Generation Z, thereby leading to the rise of digital marketing. As Generation Z is the next generation of consumers and their purchasing power increases as they enter the workforce, marketers need to understand the factors influencing their attitudes and purchase intentions. This study aims to explore the relationship between the growing presence of short-form advertising content in corporate marketing strategies and consumer behavioral intentions. To achieve this, the sub-characteristics of short-form content were categorized into expertise, ease of use, and entertainment value, while information reliability was set as a mediating variable. Data was collected through a survey of 256 adults residing in Busan and Gyeongnam, and analyzed using SPSS 28.0. The findings of the study revealed that most sub-characteristics of short-form content advertisements positively influenced both recommendation and purchase intentions. Additionally, information reliability was identified as a significant mediating factor between short-form content and consumer behavioral intentions. These results provide important insights for corporate marketers and advertising professionals, as they offer valuable guidance on how to influence consumer purchase intentions effectively.

This study investigates the willingness of Indonesian consumers, particularly in West Java, to pay for green products by applying and expanding the Theory of Planned Behavior (TPB). It examines how perceived green product value and willingness to pay premiums influence consumer intentions and behavior toward green purchases. The research highlights the gap between consumers’ willingness to pay for environmentally friendly products and the actual sales of such products. By incorporating perceived value and willingness to pay into the TPB framework, the study aims to find what factors that can address the gap particularly in a developing country context to contribute to shaping a pro-environmental socio-cultural community in Indonesia and mitigates country’s significant environmental challenges. In the context of 251 young consumers in Indonesia, this study finds that subjective norms do not significantly influence purchase intentions. However, attitudes and behavioral controls do effectively encourage green behavior, suggesting that societal norms for green behavior may not be fully established. In addition, while willingness to pay a premium and perceived value of green purchases can influence green behavior, consumers are generally reluctant to pay higher prices for environmentally friendly products.

This review paper delves into the intricate landscape of the digital economy, focusing on the multifaceted interplay between innovation, competition, and consumer dynamics. It investigates the transformative impact of digital technologies on market structures and consumer behaviors, spanning areas such as e-commerce, online publishing, taxation, and big data challenges. By analyzing network effects, market concentration, and the influence of key players like Google and Amazon, this study draws on insights from previous research. Furthermore, it examines evolving regulations with an emphasis on consumer protection, competition law, and privacy concerns. Through a comprehensive exploration of the digital ecosystem, this paper offers a nuanced understanding of how businesses, consumers, and policymakers navigate the complexities of the digital marketplace.

This study investigates the factors influencing the adoption of telehealth among consumers in Malaysia, aiming to understand the impact of effort expectancy, performance expectancy, computer self-efficacy, and trust on the intention to use telehealth, building on the Unified Theory of Acceptance and Use of Technology (UTAUT). A quantitative descriptive methodology was used, collecting data from 390 Malaysian consumers via an online survey. The data were analyzed using IBM SPSS software to evaluate the relationships between the variables. The analysis revealed significant positive relationships between all examined factors and the adoption of telehealth. Performance expectancy was the most influential factor, followed by trust, effort expectancy, and computer self-efficacy. The multiple regression model indicated that these variables collectively explain 82.1% of the variance in telehealth adoption intention. The findings provide valuable insights for providers and marketers, suggesting that telehealth platforms should focus on performance expectancy, trust, and ease of use. Additionally, the study emphasizes the need for supportive policies from the Malaysian government to enhance telehealth adoption. The results contribute to the literature on healthcare technology adoption, offering practical implications for improving telehealth implementation in Malaysia.