Search Results (83)

The article explores how the design of informational and promotional messages from financial institutions influences their reception by young people. The study combined eye tracking, individual in-depth interviews (IDIs), and text mining analysis to examine both visual attention and participants’ conscious reactions. The aim was to identify young users’ preferences, determine factors influencing content perception, and assess the effectiveness of visual and audiovisual communication strategies. The main hypothesis proposed that minimalistic and visually attractive messages, enhanced with dynamic graphics, more effectively shape attitudes and elicit positive emotions. Specific aspects examined included the role of infographics, color schemes, message dynamics, and references to financial institutions in attracting attention and engagement. The results indicate that young people operate primarily in virtual space and express limited interest in traditional media such as television or print. They favor short, clear, and visually structured messages. Excessive textual content and lack of clarity provoked negative reactions and discouraged further engagement. Elements like infographics, colors, and logos were found to be strongly associated with brand recognition and memorability. Full article

In this work, the utility of multimodal vision–language models (VLMs) for visual product understanding in e-commerce is investigated, focusing on two complementary models: ColQwen2 (vidore/colqwen2-v1.0) and ColPali (vidore/colpali-v1.2-hf). These models are integrated into two architectures and evaluated across various product interpretation tasks, including image-grounded question answering, brand recognition and visual retrieval based on natural language prompts. ColQwen2, built on the Qwen2-VL backbone with LoRA-based adapter hot-swapping, demonstrates strong performance, allowing end-to-end image querying and text response synthesis. It excels at identifying attributes such as brand, color or usage based solely on product images and responds fluently to user questions. In contrast, ColPali, which utilizes the PaliGemma backbone, is optimized for explainability. It delivers detailed visual-token alignment maps that reveal how specific regions of an image contribute to retrieval decisions, offering transparency ideal for diagnostics or educational applications. Through comparative experiments using footwear imagery, it is demonstrated that ColQwen2 is highly effective in generating accurate responses to product-related questions, while ColPali provides fine-grained visual explanations that reinforce trust and model accountability. Full article

World Heritage plays a vital role in promoting sustainable urban development. Cultural and creative industries (CCIs) have gained recognition as an effective instrument for urban revitalization in recent years. The Cities, Culture, and Creativity (CCC) framework introduced by the United Nations Educational, Scientific and Cultural Organization (UNESCO) and the World Bank emphasizes the core role of culture and creativity in enhancing urban competitiveness, attractiveness, and sustainability. Based on that framework, this study takes Pingyao as a case study, using a literature review and non-participatory observation, systematically examines its assets and resources, assesses the outcomes at the spatial, economic, and social levels, and explores how CCIs, with the support of enabling factors, contribute to urban revitalization. The findings indicate that Pingyao, relying on its historical and cultural heritage, promotes the development of CCIs, resulting in significant spatial optimization, economic growth, and social benefits, while also shaping unique cultural brands. This study verifies the applicability of the CCC framework in analyzing the urban revitalization mechanism, further reveals the role of CCIs in the revitalization of World Heritage cities, enriches the urban regeneration theory, and offers theoretical and practical reference for the revitalization and sustainable development of other World Heritage cities. Full article

Personal Branding (PB) has gained significant attention in recent years, especially in career advancement and business success. This study addresses the research gap in Personal Brand Equity (PBE) measurement by developing and validating a standardized framework. Using mixed-methods research combining interviews with 10 professionals and surveys of 396 individuals across diverse professional categories, the study identifies and validates three dimensions of PBE: Brand Appeal, Brand Differentiation, and Brand Recognition. Factor analysis revealed six critical attributes influencing PBE (visibility, credibility, differentiation, online presence, professional network, and reputation) and distinguished between external- and self-Personal Brand Equity components. Data were analyzed using exploratory and confirmatory factor analyses (EFA and CFA), with reliability assessed through Cronbach’s alpha (>0.7). Findings demonstrate significant correlations between high PBE scores and positive career outcomes including job satisfaction, salary progression, and advancement opportunities. The resulting Personal Brand Equity Scale (PBES) provides both a measurement tool for professionals seeking to enhance their personal brands and a validated framework for future academic research on personal branding effectiveness. Full article

Brand typicality is crucial in shaping consumer perceptions of brands and poses challenges for novice designers to capture due to their limited tacit knowledge. Using Weka’s image classification, we developed a brand product classification model. A dataset with 600 images was obtained from Asus and MSI, the leading eSports brands, covering various products such as controllers, mouse devices, headsets, and PC gaming components. The random forest classifier achieved an accuracy of 81 to 85%, slightly higher in the PC gaming category. The design features from Asus ROG and MSI game series products were extracted to generate 36 test images. We used keywords as prompts in Midjurney and Stable Diffusion to generate 36 test images. The developed brand product classification model in this study correctly classified 30 images. However, in the OP category, two graphics card images and one casing image were misclassified. In the PC category, two mouse images and a laptop picture were misclassified. Discrepancies between AI-generated images and personal expertise were improved in terms of the efficiency of the model for new designers. The developed model deepens the understanding of brand characteristics, maintains brand coherence, and strengthens product design innovation and market competitiveness. The model effectively assesses brand characteristics in product appearances using AI, highlighting its role in improving early design processes and new product development strategies. Full article

The growing volume of textual data generated on digital media platforms presents significant challenges for the analysis and interpretation of information. This article proposes a methodological approach that combines artificial intelligence (AI) techniques and statistical methods to explore and analyze textual data from digital media. The framework, titled DAFIM (Data Analysis Framework for Information and Media), includes strategies for data collection through APIs and web scraping, textual data processing, and data enrichment using AI solutions, including named entity recognition (people, locations, objects, and brands) and the detection of clickbait in news. Sentiment analysis and text clustering techniques are integrated to support content analysis. The potential applications of this methodology include social networks, news aggregators, news portals, and newsletters, offering a robust framework for studying digital data and supporting informed decision-making. The proposed framework is validated through a case study involving data extracted from the Google News aggregation platform, focusing on the Israel–Lebanon conflict. This demonstrates the framework’s capability to uncover narrative patterns, content trends, and clickbait detection while also highlighting its advantages and limitations. Full article

Since ginger has characteristics of both food and medicine, it has a significant market demand worldwide. To effectively store ginger and achieve the drying and color enhancement effects required for better sales, it is often subjected to sulfur fumigation. Although sulfur fumigation methods can effectively prevent ginger from becoming moldy, they cause residual sulfur dioxide, harming human health. Traditional sulfur detection methods face disadvantages such as complex operation, high time consumption, and easy consumption. This paper presents a sulfur-fumigated ginger detection method based on natural image recognition. By directly using images from mobile phones, the proposed method achieves non-destructive testing and effectively reduces operational complexity. First, four mobile phones of different brands are used to collect images of sulfur- and non-sulfur-fumigated ginger samples. Then, the images are preprocessed to remove the blank background in the image and a deep neural network is designed to extract features from ginger images. Next, the recognition model is generated based on the features. Finally, meta-learning parameters are introduced to enable the model to learn and adapt to new tasks, thereby improving the adaptability of the model. Thus, the proposed method can adapt to different devices in its real application. The experimental results indicate that the recall rate, F1 score, and AUC-ROC of the four different mobile phones are more than 0.9, and the discrimination accuracy of these phones is above 0.95. Therefore, this method has good predictive ability and excellent practical value for identifying sulfur-fumigated ginger. Full article

Packaging design plays a critical role in brand recognition and cultural dissemination, yet the traditional design process is time-consuming and dependent on the designer’s technical skills, making it difficult to quickly respond to market changes and consumer demands. In recent years, advancements in machine learning, particularly in the field of natural language processing (NLP), have paved the way for novel methods in other areas, such as image processing and packaging design. This study draws inspiration from advanced NLP techniques and proposes an improved fully convolutional network (FCN) model for image semantic segmentation, which is applied to packaging design. The model integrates superpixel technology, multi-branch networks, dual-attention mechanisms, and edge knowledge distillation in a manner analogous to the approach taken by NLP models in the context of semantic segmentation and context understanding. The experimental results showed that the model achieved significant improvements in accuracy, inference efficiency, and memory usage, with an average accuracy of 96.84% and a false-alarm rate of only 2.78%. Compared to traditional methods, the proposed model achieved over 96% accuracy across 50 packaging design images, with an average segmentation error rate of only 1.42%. By incorporating machine learning techniques from NLP into image processing, this study enhances the overall quality and efficiency of packaging design and provides new directions for the application of advanced technologies across different fields. Full article

The territorial personality of a region is defined by its diverse elements and complex interrelations, evolving over time. This scientific endeavour, particularly prevalent in regional geography, aims to understand and leverage these identity elements for regional branding and development. Such studies focus on areas known as “lands”, among which the Land of Maramureș in Romania is notable for its historical depth and cross-border nature with Ukraine. Characterized by its unique culture and the intricate relationships between its people and the land, Maramureș showcases significant multiculturalism. This paper investigates the key elements that constitute the regional identity of Maramureș, ranking them by their perceived importance among local residents. By identifying and understanding these elements, the research seeks to enhance their recognition and utility as drivers of regional development. The findings aim to serve both academic readers and local government authorities, guiding investments in identity-aligned initiatives to foster regional growth and improve community well-being. This approach underscores the critical role of territorial identity in shaping regional strategies and enhancing the quality of life for inhabitants. Full article

As economic growth and societal shifts reshape urban environments, cultural and creative industrial parks are emerging as vital contributors to sustainable urban development. The design of these landscapes plays a pivotal role in enhancing user satisfaction, increasing spatial attractiveness, and promoting eco-friendly urban practices. This study examines visitor landscape perception preferences in the Textile and Garment Cultural and Creative Industrial Park, located in Haizhu District, Guangzhou, through a novel methodology combining user-generated content (UGC), deep learning models, outdoor electrodermal activity (EDA) measurements, and questionnaire surveys. The UGC-based landscape recognition model achieved an accuracy of 86.8% and was validated against user preferences captured through questionnaires. Results demonstrate that visitors prefer areas featuring cultural landmarks and natural elements, while spaces dominated by human activity and transportation infrastructure are less favored. Key landscape elements, such as signage, thematic sculptures, brand logos, and trees, were identified as highly preferred features within the park. While EDA experiments revealed significant variations in physiological responses across different spatial settings, no strong correlation was observed between EDA indicators and subjective questionnaire scores. This integrative approach enables a comprehensive, objective assessment of landscape perception, providing a data-driven, user-centered framework for improving landscape design in cultural and creative industrial parks. Full article

In the face of a highly competitive tourism market, when tourists hold positive brand equity towards a destination, it enhances the destination’s ability to differentiate itself from competitors. This study focuses on the brand equity of organic agricultural tourism, using multidimensional scaling (MDS) to explore the factor structure of brand equity and the perceptual positioning of various tourism destination brands. The research targets tourists engaging in organic agricultural tourism in the Hualien and Taitung regions, with 220 valid questionnaires collected. The research findings indicate the following: 1. Among the 22 brand equity items, “loyalty to organic agricultural tourism”, “awareness of organic agricultural products”, “quality of organic agricultural products”, “environmental sustainability”, “image of healthy tourism”, and “recognition of organic agricultural development” scored the highest. 2. The analysis revealed that the brand equity factors are ranked in the following order: BIHS, BACI, BPQ, BLO, and BAW. 3. Through MDS analysis, the five organic agricultural tourism destinations were categorized into high, medium, and low brand equity groups, illustrating the differentiated competitive relationships among these destinations. The top three factors influencing the brand perceptual maps were BAW, BIHS, and BPQ. The results of this study can serve as a reference for future research on brand equity in organic agricultural tourism and provide a scientific basis for the practical application of shaping brand equity and formulating competitive strategies. Full article

Advergames represent a novel product placement strategy that surpasses traditional advertising methods by fostering interaction between brands and their target audiences. This study investigates the unique engagement opportunities provided by video games, focusing mainly on the ‘flow experience’, an intensified state of immersion frequently encountered by players of computer games. Such immersive experiences have the potential to significantly influence a player’s perception, offering a new avenue for advertisements to impact and engage audiences effectively. The primary objective of this research was to examine the influence of advergames on players who are deeply immersed in the gaming experience, with a specific focus on the subsequent effects on brand recognition over time. The study involved 44 software developers, who were evenly divided into two groups for the experiment. Both groups were exposed to an identical gaming environment with the task of locating a designated product within the game. However, one group interacted with an enhanced version of the game, which included additional stimuli—such as dynamic music, an engaging narrative, time constraints, a competitive leaderboard, and immersive voice acting—to intensify the gaming experience. The experiment strategically placed various products within the game, and their detectability was assessed using eye-tracking technology. Following gameplay, participants completed questionnaires that measured their experience with flow state and brand recall. The data were analyzed using the Mann–Whitney U test and correlation analysis to facilitate comparisons. The findings indicated that the product associated with the primary task achieved the highest recall rate between both groups. Furthermore, eye-tracking technology identified the areas in the game that attracted the most attention, revealing a preference for mid- and high-level placements over lower-level ones. Full article

As e-commerce continues to expand, understanding the factors that drive consumer traffic to business-to-consumer (B2C) websites is crucial. This study investigates the interplay between website technological sophistication, brand recognition, and business model innovation in influencing website traffic among Korean B2C companies. Drawing on data from 9003 companies across seven key sectors—finance, retail, healthcare, technology, food, education, and media—we employ Ordinary Least Squares (OLS) regression analysis to test our hypotheses. Our findings reveal that website technological sophistication is positively associated with monthly website visits. This relationship is particularly pronounced for companies with innovative business models, highlighting the synergistic effect of advanced website features and novel business strategies in attracting consumers. Conversely, the positive impact of website technological sophistication on traffic is less significant for well-established brands with high recognition levels, indicating that strong brand equity can mitigate the need for highly sophisticated websites. These results align with the Technology Acceptance Model (TAM), Innovation Diffusion Theory (IDT), and Signaling Theory (ST), providing a nuanced understanding of how technology, branding, and innovation intersect to drive online consumer behavior. Our study offers valuable insights for e-commerce firms seeking to optimize their digital presence and underscores the importance of investing in advanced website functionalities, particularly for lesser-known brands and companies with innovative business models. Future research should explore these dynamics in different cultural and industry contexts to enhance the generalizability of our findings. Full article

This review addresses the issues of low consumer engagement and market development difficulties in intangible cultural heritage (ICH) products. Dietary ICH products are selected as research materials to discover contemporary commercial survival paths for ICH through the psychological effects of advertising. Firstly, this study examines the respective advantages of rational and emotional ICH advertisement in terms of emotional responses, cognitive responses, attitudes, recall, and recognition. Then, it explores the effects of different ICH advertisement types (rational advertisement, emotional advertisement) and different degrees of situational involvement (purchasing for oneself, purchasing gifts for others) on the advertising effectiveness, aiming to identify factors influencing the psychological effects of ICH advertisement. Through statistical analysis, the main conclusions are as follows: (1) Rational ICH advertisement prompts consumers to consider the actual attributes of ICH products, leading to a more positive purchasing attitude. (2) Emotional ICH advertisement is more effective in eliciting positive emotions from consumers and enhancing brand memory. (3) Under the scenario of purchasing a gift for others, emotional ICH advertisement has a more positive impact on consumers’ attitudes towards advertising. (4) Under different degrees of situational involvement, rational ICH advertisement has a more positive impact on consumers’ purchasing attitudes. This study not only provides guidance for optimizing ICH advertising strategies but also offers new directions for market expansion, contributing valuable insights into cultural heritage preservation, as well as the development and protection of ICH. Full article

The task of trademark text recognition is a fundamental component of scene text recognition (STR), which currently faces a number of challenges, including the presence of unordered, irregular or curved text, as well as text that is distorted or rotated. In applications such as trademark infringement detection and analysis of brand effects, the diversification of artistic fonts in trademarks and the complexity of the product surfaces where the trademarks are located pose major challenges for relevant research. To tackle these issues, this paper proposes a novel recognition framework named SwinCornerTR, which aims to enhance the accuracy and robustness of trademark text recognition. Firstly, a novel feature-extraction network based on SwinTransformer with EFPN (enhanced feature pyramid network) is proposed. By incorporating SwinTransformer as the backbone, efficient capture of global information in trademark images is achieved through the self-attention mechanism and enhanced feature pyramid module, providing more accurate and expressive feature representations for subsequent text extraction. Then, during the encoding stage, a novel feature point-retrieval algorithm based on corner detection is designed. The OTSU-based fast corner detector is presented to generate a corner map, achieving efficient and accurate corner detection. Furthermore, in the encoding phase, a feature point-retrieval mechanism based on corner detection is introduced to achieve priority selection of key-point regions, eliminating character-to-character lines and suppressing background interference. Finally, we conducted extensive experiments on two open-access benchmark datasets, SVT and CUTE80, as well as a self-constructed trademark dataset, to assess the effectiveness of the proposed method. Our results showed that the proposed method achieved accuracies of 92.9%, 92.3% and 84.8%, respectively, on these datasets. These results demonstrate the effectiveness and robustness of the proposed method in the analysis of trademark data. Full article