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The rapid development of artificial intelligence (AI) technology and personalization algorithms has fundamentally transformed the landscape of e-commerce and digital retail, particularly influencing the shopping behavior of female consumers. This study examines the utilization of AI-driven personalization algorithms and their impact on the research shopping phenomenon among female consumers in the digital era. Research shopping, defined as the behavior of searching for product information online while making the final purchase decision offline or across multiple platforms, has become increasingly prevalent as digital touchpoints multiply. This research employs a quantitative approach with a survey method involving 250 female respondents aged 18–45 who actively use e-commerce platforms in Indonesia. Data were collected using structured questionnaires and analyzed using Structural Equation Modeling (SEM). The findings reveal that AI personalization algorithms significantly influence female consumer decision-making processes, fostering research shopping tendencies through targeted recommendations, dynamic pricing, and adaptive content delivery. Furthermore, the study identifies trust, perceived usefulness, and digital literacy as key mediating variables between AI technology utilization and research shopping behavior. These results contribute to the growing body of literature on AI-driven consumer behavior and offer practical implications for digital marketers and e-commerce platform developers seeking to optimize user experience for female consumers.

This study aims to analyze user sentiment toward the integration of Artificial Intelligence (AI) in online learning platforms, which are increasingly expanding in the digital era. With the growing use of AI technologies in education—such as learning chatbots, material recommendation systems, and automated assessments—it is essential to understand users’ perceptions and reactions to these implementations. The research employs sentiment analysis based on text mining using user review data collected from various online learning platforms. The analysis process includes data preprocessing, sentiment classification using machine learning algorithms, and interpretation of results based on the proportion of positive, negative, and neutral sentiments. The findings indicate that most users express positive sentiments toward AI integration, as it enhances learning efficiency and personalization. However, some users raise concerns regarding data privacy and the lack of human interaction. This study is expected to serve as a reference for educational platform developers to design AI systems that are more adaptive, transparent, and user-centered

This research examines the consumptive style of Gen Z through TikTok Shop in purchasing fashion products, especially clothes. The background of the research focuses on the dominance of social media that influences the consumption patterns of the younger generation. The research uses a phenomenological approach to understand students' experiences in shopping at TikTok Shop. Data collection techniques include observation and in-depth interviews. The results show that TikTok Shop utilizes personalization algorithms, creative video content, and massive promotions to attract the attention of Gen Z. Purchasing decisions are often influenced by trends, influencer recommendations, and discounts that create a FOMO (Fear of Missing Out) effect. While offering convenience and an interactive shopping experience, this impulsive consumption pattern poses the risk of product quality disappointment and unplanned spending. The implications of this research highlight the need for consumer education to raise awareness about wise shopping. In addition, industry players are expected to design ethical and sustainable marketing strategies to support more rational consumption behavior. 

In today’s era, social media has become a driving force for increasing digital entrepreneurship. Businesses are utilizing social media sites such as Instagram, TikTok, LinkedIn, or even Facebook to brand their companies or products and interact with clients. This is great news for businesses, especially SMEs, to have low-cost access to key markets worldwide. One evident trend is the emergence of social commerce – business-to-consumer commerce without intermediaries, exclusive of other e-commerce models. However, the adoption of social media in digital entrepreneurship comes with several challenges, such as changes in algorithms that can affect content visibility and risks related to data security and user privacy. Nevertheless, social media remains useful in terms of analytics to support strategic decisions. This study shows the value of social media for entrepreneurship and technologies that help improve content personalization and consumer behavior analysis, such as artificial intelligence and big data. This study attempts to fill the gap in the literature by looking at the differences in the outcomes of social media use in developing and developed countries and the outcomes of new technologies on digital business ventures.

This study explores the integration of Artificial Intelligence (AI) into smart home systems using the Android operating system to enhance security, privacy, efficiency, and user comfort. Key security measures include data encryption, robust authentication methods, sandboxing, and AI integration, specifically leveraging Google Assistant for improved privacy controls. Maintenance strategies for smart homes emphasize energy management, device condition monitoring, and enhanced safety features. AI adaptation to user habits enhances productivity and situational awareness, while Android's role in connecting various IoT devices facilitates remote control and energy-efficient recommendations. Methods such as Eco Android, Greensource, byte-code transformations, and automated energy diagnosis tools aid in optimizing energy use. The comparison between smart and non-smart homes highlights the efficiency and convenience of smart homes despite higher installation costs and potential network issues. The development and deployment of an Android-based application, SafeHause, exemplifies practical implementation, emphasizing end-to-end testing, security updates, and user education. The findings affirm that AI integration with Android significantly improves the smart home experience by enhancing energy optimization, data security, and personalized user interaction. Furthermore, the study discusses future trends in smart home technology, such as the potential for more advanced AI algorithms and machine learning techniques to provide even greater personalization and automation. The importance of regular software updates and the role of user feedback in refining smart home systems are also highlighted, ensuring that these technologies continue to evolve and meet user needs effectively.