Search Results (52)

Social media platforms have become essential tools for businesses aiming to engage audiences through innovative communication, particularly in the food and beverage industry. This study explores the impact of three core digital marketing strategies, namely, social media advertisements, electronic word of mouth, and digital influencers, on customer responsiveness in the Lebanese food and beverage sector. Based on a cross-sectional survey of 400 participants, the findings reveal that social media advertisements significantly and positively influence customer responsiveness (β = 0.227, p < 0.001). Likewise, electronic word of mouth strongly predicts customer responsiveness (β = 0.453, p < 0.001), affirming the power of customer-generated content in shaping brand perceptions. Furthermore, the presence of digital influencers emerged as a significant predictor of consumer reaction (β = 0.236, p < 0.001), suggesting that consumers regard influencers as credible sources when making food-related decisions. Among all predictors, electronic word of mouth demonstrated the strongest effect. Control variables such as gender, age, and social media usage intensity showed no significant effect on customer responsiveness. These findings underscore the strategic value of rich media content and peer influence in shaping consumer behavior in the food and beverage industry. The study offers practical insights for marketers seeking to enhance customer engagement and brand responsiveness in digital spaces. Full article

This study aims to analyze the differentiation strategies developed by sectoral brands in the global wine industry and how these strategies interrelate. A sectoral brand is defined as having a distinctive brand name accompanied by a visual identity, with or without a slogan. Through an analysis of thirty-three sectoral brands developed by wine-producing countries, seven clusters of differentiation strategies and three clusters of differentiation attributes were identified, using quantitative and qualitative methods. The findings highlight the alignment between the differentiation strategies employed by sectoral brands and the underlying theoretical concepts, as well as overlaps between differentiation strategies and specific attributes. The results identify unrevealed opportunities for wine-producing countries that have not yet developed sectoral brands. This study’s main contribution consists of the application of a cluster analysis approach, which enabled the identification and interpretation of relationships among sectoral wine brands based on their differentiation strategies. Accordingly, the research addresses a notable gap in the existing literature by providing an integrative perspective on how sectoral brands differentiate within the world wine market. The practical implications of this study include offering valuable guidance to countries currently lacking sectoral wine brands and presenting a structured framework to effectively leverage unique national attributes. Full article

When it comes to wine communication, winery names can serve as multidimensional communication mechanisms. Their various aspects of communication concern the historical context, tourism geography, socio-economic tourism development, wine knowledge, and even gastronomy. On this basis, a whole communication strategy could be developed, which could lead to progressive interaction and networking with consumers. Quality needs to be fully and properly communicated through multiple channels. This paper aims to reveal the importance of the way in which wine producers in Northern Greece construct their brand identity through strategic naming choices—such as Estate, Ktima, Château, or the producer’s family name—as opposed to more neutral or descriptive labels. The findings suggest that winery names contribute significantly to consumer perception, place branding, and the development of wine tourism. The choice of a winery name influences regional identity, strengthens the connection with local traditions, and enhances the commercial success and tourism appeal of the region. Full article

Background: The evaluation of internationally comparable indicators of medicine use is important to devise strategies to promote the rational use of medicines (RUM). Methods: A cross-sectional study was conducted in a tertiary care hospital from January to June 2024. Prescriptions were collected from the outpatient pharmacy using systematic random sampling and analyzed for WHO/INRUD core prescribing indicators, index of rational drug prescribing (IRDP) and completeness (general, treatment and prescribers’ details). Results: Out of 844 prescriptions collected, 607 were analyzed. A total of 1837 drugs were prescribed, with a mean (SD) of 3.03 (1.51) drugs per prescription; 1378 (75%) drugs were prescribed as generic names; 125 prescriptions (20.59%) had an antibiotic prescribed; and injectables were given in 7 (1.15%) prescriptions. Of the total 1837 drugs, 1018 (55.4%) were mentioned in the National List of Essential Medicines (NLEM) 2022, while 934 (50.8%) were included in the Haryana state essential medicines list (2013–2014). The IRDP was calculated as 3.86. The mean (SD) completeness score of the prescriptions was 10.33 (0.8) (range 5 to 11). Conclusions: There was a high incidence of polypharmacy, brand name and non-essential drug prescribing, while antibiotic and injection use were in accordance with WHO standards. Full article

This research investigates linguistic hybridization in a commercial corridor of Armenia, Colombia, focusing on the usage of Spanish and English in public signage, particularly business names. Utilizing a quantitative methodology, we conducted a statistical analysis employing Chi-square tests to explore the relationship between symbolic language choice and variables such as location and type of establishment. The results demonstrated a significant association between location and language choice (Χ² = 39.353, p < .001), revealing that commercial zones with high tourist traffic exhibited a pronounced preference for English (46.55%), reflecting branding strategies aimed at attracting a younger, cosmopolitan audience. Conversely, traditional sectors such as health services (74.24% in Spanish) and religious institutions (80% in Spanish) predominantly utilized Spanish, emphasizing the community’s need for accessible communication. Additionally, establishments in the most commercial area highlighted the presence of hybrid names, indicating a blending of languages. Our findings suggest that the hybridization of English and Spanish serves as both a reflection and reinforcement of cultural identity and social hierarchies, emphasizing the role of linguistic capital in shaping social dynamics within the urban landscape of Armenia, Colombia. 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

Globally, fruit breeders and marketers create trademarked brand names for new varieties which can be protected indefinitely, extending returns on breeding investments. Brand names help promote and differentiate fruits, acting as quality signifiers and simplifying consumer choices. This study introduces brand name evaluation criteria, identifies name classification frameworks, and audits North American and international apple names, covering plant varietal denominations and both trademarked and non-trademarked names. Key criteria for a good brand name include trademarkability, memorability (simplicity, distinctiveness, meaningfulness, sound associations, mental imagery, and emotional impact), and marketability (appropriate brand image and marketing support). Two modified frameworks were used to classify apple names. The audit revealed that the prevalence of using ‘Namesake’ names associated with ‘Real or Fictitious Persons/Places’ has significantly decreased (North America: 4.9 times since the 1920s). The use of ‘Compounding’ names has remained frequent (North America: 25% in the 2020s). Some categories have seen an increased usage as follows: ‘Product Unrelated—Metaphoric’ (North America: 17.5 times) and ‘Unusual Spellings’ (not recorded until the 1980s, recently 6%) names. Since the 1960s, the following categories have remained consistent: ‘Sensory’, ‘Product/Benefit Related’, ‘Product Unrelated—Non-Metaphoric’, and ‘Blending’ names. The findings support fruit and vegetable industries in distinguishing their products through effective brand naming. Full article

This study examines how digital marketing characteristics—namely interactivity, ubiquity, and credibility—affect green consumer brand engagement and loyalty in Nigeria’s five-star hotel industry. This study advances relationship marketing and social exchange theory by demonstrating how interactivity, ubiquity, and credibility in digital marketing foster green consumer brand engagement, ultimately boosting brand loyalty. The analysis, based on responses from 304 hotel guests, reveals a strong positive relationship between each digital marketing element and green consumer brand engagement, which subsequently enhances brand loyalty. Our findings demonstrate that green consumer brand engagement serves as a mediator between digital marketing characteristics and brand loyalty, emphasizing that engagement with environmental values plays a crucial role in fostering loyalty in the luxury hotel sector. These insights inform digital marketing strategies aimed at maximizing sustainable consumer engagement, providing a foundation for loyalty-driven practices within hospitality. Full article

Social media is an important driver of firm success by providing an avenue for stakeholder engagement. Operating in a highly complex and competitive environment, firms in the ship management industry can utilise social media platforms to engage with their stakeholders, which can enhance stakeholder satisfaction and loyalty. However, stakeholder engagement rates can vary, with some posts generating more engagement than others. Drawing on the perceived value and word-of-mouth psychological motivation theories, this study introduces a theoretical model to identify and examine factors influencing stakeholder engagement on LinkedIn in the ship management industry. A hierarchical regression analysis is conducted on the posts of ten ship management firms to study the influence of content type and message characteristics variables on engagement rates. The results revealed nine variables that can significantly influence stakeholder engagement. They are links, corporate brand names, call-to-actions, message length, tangible resources, social content, emotional content, first-person texts, and emojis. The findings provide recommendations for firms in the ship management industry in terms of the message strategies to incorporate into their posts to encourage higher engagement rates. This study also enriches literature for stakeholder engagement on social media. Full article

Respiratory Syncytial Virus (RSV) continues to pose a significant challenge, contributing to elevated hospitalization rates among children up to 5 years old, with a disproportionate burden on newborns and infants under 6 months old. The unique characteristics of the young immune system make it prone to altered responses to infections and vaccinations, requiring a tailored approach to disease prevention. The recent approval of the maternal RSV vaccine (brand name ABRYSVO) represents a pivotal advancement in preventive strategies among newborns and infants, marking a milestone in RSV research as the first market-approved maternal vaccine. The present review examines clinical trial data on both recent and previous vaccine candidates, as well as the licensed vaccine, focusing on the prevention of RSV disease in newborns and young infants through the passive acquisition of antibodies following maternal immunization. Additionally, it evaluates the safety profile of these vaccines. Full article

Accurate automobile-demand forecasting can provide effective guidance for automobile-manufacturing enterprises in terms of production planning and supply planning. However, automobile sales volume is affected by historical sales volume and other external factors, and it shows strong non-stationarity, nonlinearity, autocorrelation and other complex characteristics. It is difficult to accurately forecast sales volume using traditional models. To solve this problem, a forecasting model combining trend extrapolation and causality analysis is proposed and derived from the historical predictors of sales volume and the influence of external factors. In the trend-extrapolation model, the historical predictors of sales series was captured based on the Seasonal Autoregressive Integrated Moving Average (SARIMA) and Polynomial Regression (PR); then, Empirical Mode Decomposition (EMD), a stationarity-test algorithm, and an autocorrelation-test algorithm were introduced to reconstruct the sales sequence into stationary components with strong seasonality and trend components, which reduced the influences of non-stationarity and nonlinearity on the modeling. In the causality-analysis submodel, 31-dimensional feature data were extracted from influencing factors, such as date, macroeconomy, and promotion activities, and a Gradient-Boosting Decision Tree (GBDT) was used to establish the mapping between influencing factors and future sales because of its excellent ability to fit nonlinear relationships. Finally, the forecasting performance of three combination strategies, namely the boosting series, stacking parallel and weighted-average parallel strategies, were tested. Comparative experiments on three groups of sales data showed that the weighted-average parallel combination strategy had the best performance, with loss reductions of 16.81% and 4.68% for data from the number-one brand, 25.60% and 2.79% for data from the number-two brand, and 46.26% and 14.37% for data from the number-three brand compared with the other combination strategies. Other ablation studies and comparative experiments with six basic models proved the effectiveness and superiority of the proposed model. Full article

The Unmanned Aerial Vehicle (UAV) tracking tasks find extensive utility across various applications. However, current Transformer-based trackers are generally tailored for diverse scenarios and lack specific designs for UAV applications. Moreover, due to the complexity of training in tracking tasks, existing models strive to improve tracking performance within limited scales, making it challenging to directly apply lightweight designs. To address these challenges, we introduce an efficient attention mechanism known as Macaron Attention, which we integrate into the existing UAV tracking framework to enhance the model’s discriminative ability within these constraints. Specifically, our attention mechanism comprises three components, fixed window attention (FWA), local squeezing global attention (LSGA), and conventional global attention (CGA), collectively forming a Macaron-style attention implementation. Firstly, the FWA module addresses the multi-scale issue in UAVs by cropping tokens within a fixed window scale in the spatial domain. Secondly, in LSGA, to adapt to the scale variation, we employ an adaptive clustering-based token aggregation strategy and design a “window-to-window” fusion attention model to integrate global attention with local attention. Finally, the CGA module is applied to prevent matrix rank collapse and improve tracking performance. By using the FWA, LSGA, and CGA modules, we propose a brand-new tracking model named MATrack. The UAV123 benchmark is the major evaluation dataset of MATrack with 0.710 and 0.911 on success and precision, individually. Full article

The impact of companies’ social strategies, such as corporate social responsibility (CSR) and brand activism, on corporate advantages like economic and reputational gains has been well documented in the literature. However, research becomes scarcer regarding the broader impacts of these social strategies, namely the promotion of attitudes in favor of society and the environment on the part of company stakeholders. Furthermore, there is still little research on the variables that can mediate this impact, particularly at an identity and emotional level. To cover this gap, this article aims to study the impact of environmental brand activism on employee pro-environmental attitudes, and the possible mediating effects of identification with the organization and moral elevation by analyzing the brand activism of a Portuguese retail company. The results showed that environmental brand activism can directly affect the pro-environmental attitudes of employees and have also indirect impacts on the pro-environmental attitudes of this group of stakeholders, but only through the mediating role of identification with the organization, thus contributing to a better perception of the potential of brand activism to promote social change and sustainable development. Full article

The traditional approach of single-word brand names faces constraints due to trademarks, prompting a shift towards fusing two or more words to craft unique and memorable brands, exemplified by brands such as SalesForce^© or SnapChat^©. Furthermore, brands such as Kodak^©, Xerox^©, Google^©, Häagen-Dazs^©, and Twitter^© have become everyday names although they are not real words, underscoring the importance of brandability in the naming process. However, manual evaluation of the vast number of possible combinations poses challenges. Artificial intelligence (AI), particularly natural language processing (NLP), is emerging as a promising solution to address this complexity. Existing online brand name generators often lack the sophistication to comprehensively analyze meaning, sentiment, and semantics, creating an opportunity for AI-driven models to fill this void. In this context, the present document reviews AI, NLP, and text-to-speech tools that might be useful in innovating the brand name generation and evaluation process. A systematic search on Google Scholar, IEEE Xplore, and ScienceDirect was conducted to identify works that could assist in generating and evaluating brand names. This review explores techniques and datasets used to train AI models as well as strategies for leveraging objective data to validate the brandability of generated names. Emotional and semantic aspects of brand names, which are often overlooked in traditional approaches, are discussed as well. A list with more than 75 pivotal datasets is presented. As a result, this review provides an understanding of the potential applications of AI, NLP, and affective computing in brand name generation and evaluation, offering valuable insights for entrepreneurs and researchers alike. Full article

The objective of this study was to (1) assess via cross-sectional survey the prevalence of food insecurity among African Americans [AAs] after their most recent grocery shopping trip, and (2) examine the grocery shopping factors of importance and characteristics of food-insecure AA grocery shoppers. Most (70.4%) were food-insecure. Food-insecure grocery shoppers were significantly more likely to be younger, less educated, who often skipped meals and/or practiced fasting, accessed a food pantry, were SNAP recipients, were considered to not be in ”good” health, and who had higher BMI compared to food-secure shoppers (p ≤ 0.03 * for all). Our data showed that AAs shopped for groceries a mean 2.20 ± 1.29 times per week, for low prices (72.1%), without a weekly budget (58.9%), with a grocery list (44.6%) or using an app (27.6%), for high-quality vegetables (27.5%), for good customer service (22.9%), for store brands (20.8%) and name brands (17.9%).Food-insecure shoppers were significantly more likely to grocery shop more times per week, have a weekly budget, and use an app, but were significantly less likely to report store brands, name brands, good customer service, and high-quality vegetables as grocery factors of importance (p ≤ 0.03 * for all). Grocery strategies such as shopping with a grocery app and/or grocery list could help food-insecure AAs reduce grocery trips, promote meal planning to save money, and avoid skipping meals/fasting, while eating healthier. Full article