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This study explores the role of Integrated Marketing Communication (IMC) in McDonald's Indonesia and its impact on brand equity, consumer engagement, and market competitiveness. Given Indonesia’s diverse cultural landscape and rapidly evolving digital economy, McDonald's has implemented localized IMC strategies to enhance its market presence. By integrating traditional and digital media, McDonald's ensures message consistency across television advertisements, social media campaigns, and influencer collaborations. Findings indicate that localized menu offerings, such as Nasi Uduk McD and Ayam Spicy McD, have strengthened McDonald’s emotional connection with Indonesian consumers. Additionally, TikTok challenges and KOL (Key Opinion Leader) partnerships have amplified brand awareness and consumer interaction. These strategies align with the growing consumer preference for authentic, culturally relevant experiences, reinforcing IMC’s effectiveness in bridging global branding with local relevance. Furthermore, McDonald's adapts CSR initiatives such as environmental sustainability programs and Ramadan campaigns, mirroring consumer values and fostering brand loyalty. Comparative studies with local brands, like HokBen, suggest that McDonald's ability to align its messaging with Indonesian values provides a competitive advantage against both global and domestic competitors. The study concludes that IMC plays a critical role in McDonald's success in Indonesia, emphasizing the necessity of continuous adaptation to cultural and digital trends. Future research should explore long-term consumer behavior patterns and the effectiveness of emerging digital marketing innovations to further enhance McDonald's local engagement strategies.

The research integrates the Technology Acceptance Model (TAM) and Social Presence Theory to analyze consumer responses to ChatGPT. This study gathered data from 632 consumers staying at five-star hotels in Egypt’s major tourist attractions. The current study employed convenience sampling by relying on the electronic questionnaire approach, where the researchers selected an appropriate sample and the questionnaires were sent via the Internet, which contributed to facilitating the participation process, increasing the chances of their response, and rapid data collection. To test the study hypotheses, structural equation modeling (SEM) was used, which provided a strong statistical analysis to assess the correlations between variables and validate the suggested theoretical framework. Results indicate that ChatGPT’s emotional expression accuracy, richness, and personalization significantly enhance consumer interaction. Additionally, its availability and responsiveness foster a sense of emotional companionship, leading to increased emotional dependence and trust among users. The study results also supported that ChatGPT’s psychological attributes influence consumer interaction positively. In addition, the study found that ChatGPT’s ability to accurately express emotions and enable personalized interactions had a substantial impact on consumer interaction. This research contributes to the understanding of AI’s role, such as ChatGPT, in hospitality by identifying key emotional and psychological factors that enhance consumer trust. It provides actionable insights for luxury hotels to effectively integrate AI technologies such as ChatGPT, ultimately improving guest experiences and fostering loyalty. This study aims to examine the consumers’ ChatGPT emotional attributes, including emotional intelligence and emotional companionship, and ChatGPT psychological attributes on their interaction and investigate the influence of the consumers' interaction on their emotional dependence and trust towards ChatGPT.

The relevance of the research topic is due to the need for a scientific analysis of changes in the understanding of branding, in channels and methods of brand promotion, models of brand and consumer interaction in the context of the impact of digital technologies on the business process. The purpose of the study is to study the digital transformation of the branding concept. The authors use general scientific research methods (analysis, comparison, generalization), as well as elements of phenomenological and analytical approaches. As a result of the research, the main features of the difference between digital marketing and traditional marketing are revealed; the advantages of digital marketing in terms of more effective interaction with customers are indicated; the content of the concept of digital branding is clarified; digital branding trends are outlined.

ABSTRACTDrawing on interviews with children’s market researchers, brand managers and other market actors in North America, the UK and Europe, this study analyzes and positions children’s market professionals as knowledge brokers and moral interlocutors who transact between and among clients, colleagues and, at times, parents. The transactions – as understood by practitioners – extend beyond simply seeking to elicit ‘preferences’ for this or that product or experience and suggesting ‘market solutions’ to the immediate business problem at hand. Rather, the cultural labor exerted here resembles a continual sorting process in pursuit of the distinction between the child as a dependent economic actor from the child as a moral being worthy of recognition and commercial deference. They thereby strive to enable the continuity – i.e. erase the boundary – between markets and culture by enacting sympathy, sentiment and even intimacy in the conceptualization and execution of research. Investigating how these market professionals understand, construct and act upon children as economic actors, while situated amidst public, moral discourses to the contrary, opens possibilities to examine how value arises in the cultural practice of making social persons and how social personhood in some ways modulates and informs market exigencies.

With the rapid development of Internet technology and the widespread popularity of social media platforms, the purchasing behaviour patterns of Chinese consumers have changed significantly. Consumers can easily obtain rich product information and shopping channels through social media platforms, while paying more attention to the personalisation, emotionality and value resonance of products. Based on this, this paper aims to explore the changes in Chinese consumers' purchasing behaviour patterns and their impact on fashion brands' marketing strategies in the context of the development of Internet technology and social media platforms through literature review and case study analysis, so as to provide fashion brands with effective marketing strategy suggestions. It was found that KOLs significantly influence consumers' purchasing decisions, and the interactive experience on social media platforms closely shapes consumer brand loyalty. Through a case study, this paper further reveals how a fashion brand successfully increased brand exposure and consumer engagement by cooperating with KOLs through social media platforms. Fashion brands should make full use of the influence of social media platforms and KOLs to innovate their marketing strategies in order to meet consumers' personalised needs and enhance brand competitiveness and market share.

In recent years, with the improvement of people's quality of life, more and more people are pursuing natural, non detergent and care products. As an old brand product in China, Chinese soap should seize this opportunity. In order to strengthen the brand construction of Zhonghua Soap and continuously improve its popularity among young people, this study analyzes the problems faced by Zhonghua soap in the youth market through market research and analysis of the youth group and qualitative analysis of the industry and competitors. On this basis, feasible marketing strategies are proposed to expand the market share of Zhonghua Soap. Strengthen brand building to provide new ideas.

In maritime surveillance, UAV-based vessel detection is essential for ensuring security and safety at sea. However, limited and non-diverse annotated data often restrict model performance in complex maritime environments. This study introduces a novel data augmentation pipeline using multimodal generative models to enhance training datasets with realistic synthetic images. Scene descriptions are automatically generated from UAV imagery using Gemma, a lightweight multimodal language model, and then used to guide FLUX, a text-to-image diffusion model, in creating diverse vessel-centric scenes under varying environmental conditions. A hybrid annotation strategy combines YOLO-World for initial object proposals with manual refinement to ensure label accuracy. The augmented dataset is integrated with the original data to train a vessel detection model. Experiments on the VESSELImg benchmark demonstrate that the proposed approach improves the YOLOv11 detector’s mean average precision (mAP) from 0.775 to 0.805 at IoU thresholds of 0.50:0.95. These results validate the effectiveness of combining multimodal diffusion and language models for domain-specific data synthesis, offering improved generalization and robustness in UAV-based maritime vessel detection.

Purpose: This research explores the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in brand management. It aims to understand how AI technologies can optimize brand awareness, enhance personalized communication, and improve brand equity measurement, thereby redefining brand management strategies in the digital era. Design/methodology/approach: A systematic literature review was conducted, analyzing research studies published between 2014 and 2024. The methodology focused on articles that delve into the integration of AI and ML within brand management contexts. This approach was chosen to consolidate and synthesize a broad spectrum of findings on AI’s impact on brand management practices. Findings: The review identified significant enhancements in brand management facilitated by AI, particularly in consumer engagement and customization of consumer interactions through data-driven insights. AI's capability to analyze big data has enabled more precise consumer segmentation and targeting, thereby influencing brand loyalty and overall brand equity positively. Research limitations/implications: The primary limitation of this study is its reliance on data from articles indexed only in Scopus, potentially omitting relevant studies published in other languages or databases. Future research should expand the scope to include these sources and explore empirical validations of the proposed theoretical impacts of AI on brand management. Practical implications: This research highlights the practical applications of AI in improving brand management strategies, offering insights into effective AI integration into marketing practices. Businesses are encouraged to adopt AI-driven tools for better market segmentation, consumer behavior predictions, and enhanced customer relationship management. Social implications: The findings could influence public attitudes towards brand interaction by fostering greater acceptance of AI in consumer relations. The research supports enhanced corporate social responsibility through AI's ability to provide more targeted and meaningful consumer interactions. Originality/value: This paper contributes to the academic and practical understanding of AI’s role in brand management by synthesizing existing research and identifying future research directions. It is valuable to academicians, marketing professionals, and policymakers interested in the implications of AI technology in brand strategies. Keywords: Brand Management, Artificial Intelligence, Machine Learning, Consumer Engagement, Marketing Strategies. Category of the paper: Literature review.

The rapid evolution of digital marketing has prompted businesses to adopt immersive technologies that enhance consumer experiences and foster stronger brand relationships. Among these, augmented reality (AR) has emerged as a transformative tool that integrates digital content with physical environments, enabling interactive and personalized consumer engagement. This review paper provides a comprehensive synthesis of existing literature on AR applications in digital marketing, focusing on their impact on consumer interaction, decision-making, and purchase intention. A systematic search across major databases and publisher platforms, including Elsevier, Springer, Wiley, IEEE, and Taylor & Francis, initially identified 158 studies. After applying inclusion and exclusion criteria, 34 peer-reviewed studies were selected for in-depth review and analysis. Key areas of exploration include virtual try-on solutions, interactive advertising, gamified brand experiences, and AR-enabled retail environments. The findings highlight how AR reduces perceived risk, strengthens trust, and creates memorable experiences that positively influence consumer attitudes and buying behavior. Furthermore, the review identifies current challenges related to technological adoption, privacy concerns, and integration with existing marketing strategies, while also discussing future opportunities in AI-driven personalization and omnichannel retailing. By consolidating insights from these diverse studies, this paper provides a clear understanding of how AR can be strategically leveraged to enhance consumer engagement and drive sustainable growth in digital commerce.

Hair editing is a long-standing problem in computer vision that demands both fine-grained local control and intuitive user interactions across diverse modalities. Despite the remarkable progress of GANs and diffusion models, existing methods still lack a unified framework that simultaneously supports arbitrary interaction modes (e.g., text, sketch, mask, and reference image) while ensuring precise editing and faithful preservation of irrelevant attributes. In this work, we introduce a novel paradigm that reformulates hair editing as proxy-based hair transfer. Specifically, we leverage the dense and semantically disentangled latent space of StyleGAN for precise manipulation and exploit its feature space for disentangled attribute preservation, thereby decoupling the objectives of editing and preservation. Our framework unifies different modalities by converting editing conditions into distinct transfer proxies, whose features are seamlessly blended to achieve global or local edits. Beyond 2D, we extend our paradigm to 3D-aware settings by incorporating EG3D and PanoHead, where we propose a multi-view boosted hair feature localization strategy together with 3D-tailored proxy generation methods that exploit the inherent properties of 3D-aware generative models. Extensive experiments demonstrate that our method consistently outperforms prior approaches in editing effects, attribute preservation, visual naturalness, and multi-view consistency, while offering unprecedented support for multimodal and mixed-modal interactions.

While multimodal intelligent interaction systems integrate visual, textual, and voice information to enhance user experience, they often encounter problems such as excessive computational load and response delays in practical applications. This study proposes an optimization architecture for collaborative reasoning and reduces system delays through a three-step optimization strategy: integrating a deadlineaware intelligent scheduling algorithm, constructing a shared computational cache scheme, and deploying a reinforcement learning-based deadline prediction module to achieve real-time dynamic optimization of the model calling sequence. Tests on the standard MM-IMDB and CMU-MOSEI datasets show that this architecture can reduce response time by 38-42% compared with traditional serial processing methods, and the system stability remains at the level of the commercial-grade interaction standard. It is particularly noteworthy that this solution demonstrates excellent scalability across devices with different computing power. User research results show that the significant improvement in response speed and the slight loss in quality have an acceptable balance in engineering practice. This study highlights the fundamental value of dynamic collaborative optimization in real-time intelligent systems, laying the technical foundation for the further integration of hardware planning and deep multimodal fusion strategies.

This study explores the role of sentiment analysis as a predictive tool for understanding and forecasting product launch success in the digital market. Sentiment analysis involves the classification of consumer sentiment expressed on social media platforms such as Twitter and Instagram, and it can significantly impact businesses by predicting consumer behavior and product performance. The research highlights the relationship between social media sentiment and product success, demonstrating that positive sentiment is strongly correlated with higher sales and consumer engagement, while negative sentiment can lead to declines. Machine learning models, including Support Vector Machines (SVM) and Random Forest, were employed to classify sentiment from large volumes of social media data and correlate it with product performance indicators such as sales volume and consumer interaction. The study found that sentiment analysis models were highly effective in predicting product success, with positive sentiment generally driving product profitability and negative sentiment posing a potential threat to brand reputation. Moreover, the analysis showed that social media sentiment provides real-time insights into consumer perceptions, enabling businesses to quickly adjust marketing strategies and product development plans. These findings underscore the importance of integrating sentiment analysis into product launch evaluations and strategic decision-making. Future research should explore the integration of sentiment analysis with other predictive market models and investigate the effects of fake reviews and post-purchase consumer behaviors on product success.

ABSTRACTThe aim of this study is to conduct a systematic review of the literature on consumer curiosity and its impact on consumer behavior. The “Scientific Procedures and Rationales for Systematic Literature Reviews” (SPAR‐4‐SLR) methodology and the “Theory, Context, Characteristics, and Methodology” (TCCM) framework were employed to analyze 122 papers published between 1992 and 2024. Articles were selected from the Web of Science database using key terms related to consumer curiosity. Consumer curiosity is a complex phenomenon that influences various aspects of consumer behavior, including purchase decisions, consumer engagement, and adaptation to new technologies. Curiosity serves as a significant moderator and mediator in consumer interactions with the market, especially in the context of new technologies such as AI and VR. The findings of this review indicate a growing interest in studying consumer curiosity in recent years, as reflected by the increasing number of publications. The practical implications of the analysis are significant for various stakeholders. Businesses can leverage these findings to develop more effective marketing strategies that engage consumers by stimulating their curiosity. Understanding how curiosity influences decision‐making can also aid in the development of innovative products and services that better meet consumers' unmet needs. Additionally, academic researchers can build on the theoretical frameworks related to consumer curiosity and design future research based on identified gaps. Finally, managers and marketing professionals can apply these insights to personalize shopping experiences and enhance consumer engagement, which can lead to increased brand loyalty and competitive advantage. This review emphasizes the need for further research on the role of curiosity in consumer behavior and its impact on product innovation and marketing strategies and provides recommendations for future research directions that could contribute to a deeper understanding of how curiosity shapes consumer interactions with brands and products.

This study investigates the influence of music in social media marketing on consumer engagement metrics, focusing on selected provinces in Northern Vietnam. Utilizing a mixedmethod approach combining Partial Least Squares Structural Equation Modeling (PLS-SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA), the research examines how various musical elements impact engagement on social media platforms. Data collected from 460 participants through surveys and social media data extraction reveal that music presence, congruence, familiarity, and tempo significantly affect engagement metrics, including overall engagement, brand attitude, content sharing, and viewing duration. The study extends the Stimulus-Organism-Response model and Elaboration Likelihood Model to the context of music in social media marketing, offering novel insights into the complex interplay between auditory elements and consumer behavior in digital environments. The findings provide valuable theoretical contributions to the field of digital marketing and offer practical implications for social media marketers seeking to enhance consumer engagement through strategic use of music in content creation