Search Results (614)

Supply chain management (SCM) involves complex coordination among multiple actors under demand uncertainty. However, most existing studies focus on simplified network structures that fail to capture all relevant dimensions of real-world supply chains or assume deterministic demand. This study proposes a comprehensive stochastic bi-level optimization framework for a multi-factory, multi-retailer, multi-customer, and multi-product supply chain network. The model captures the hierarchical interaction between decision-makers, where the production facility owner acts as the leader and the retailer as the follower, and jointly optimizes profit across both levels. To efficiently solve the resulting bi-level problem, two tailored metaheuristic solution approaches—a two-tier genetic algorithm (TT-GA) and a two-tier particle swarm optimization (TT-PSO)—are developed. Computational experiments across multiple scenarios demonstrate that TT-PSO outperforms TT-GA in Scenarios 1 and 2, achieving overall profit improvements of 6.46% and 0.76%, respectively, while TT-GA yields superior performance in Scenario 3 with a 2.80% profit improvement. The proposed framework provides decision-makers with a robust and practical tool for improving profitability and operational efficiency in complex, uncertain supply chain environments. Full article

Background: Hepatitis E virus (HEV) is an increasingly recognized cause of acute viral hepatitis in Thailand as the burden of hepatitis A, B, and C has declined. HEV is a positive-sense RNA virus in the family Hepeviridae with three major open reading frames encoding replication proteins (ORF1), the capsid protein (ORF2), and an accessory protein involved in viral egress (ORF3). Unlike highly endemic regions where genotypes 1 and 2 are linked to waterborne outbreaks, infections in Thailand are reported mainly as sporadic cases associated with zoonotic transmission, most commonly genotype 3. Objectives: This review summarizes the epidemiology, transmission routes, and public health implications of HEV infection in Thailand. Methods: Peer-reviewed studies on HEV seroprevalence, molecular epidemiology, and transmission in Thailand were identified through PubMed using combinations of the keywords “HEV” and “Thailand”. Two investigators independently screened titles, abstracts, and full texts. Eligible studies were synthesized qualitatively. Results: Earlier studies suggested low population exposure, but more recent evidence indicates substantial cumulative risk. A nationwide survey among blood donors reported anti-HEV IgG seroprevalence of about 30%, with geographic variation and increasing prevalence with age. Detection of HEV RNA in pigs, slaughterhouse environments, and retail pork products, together with links to raw or undercooked pork consumption, supports pigs as the principal reservoir and foodborne exposure as an important route. Transfusion-associated infection has also been documented. Conclusions: In Thailand, HEV infection is linked mainly to zoonotic and foodborne transmission involving genotype 3. Stronger surveillance, food safety measures, and risk-based blood safety policies are needed. Full article

Retail environments are increasingly discussed within social sustainability frameworks, yet the in-store experiences of families with children remain underexamined in empirical retail design research. This study investigates how families with children experience department store interiors from an inclusive design perspective. A cross-sectional mixed-methods survey was conducted with 100 parents who had previously visited a department store with their child. The survey questionnaire generated both quantitative and qualitative data through 15 closed-ended Likert-type items and open-ended written responses. The findings indicate that family-inclusive retail experience should be understood as a multidimensional phenomenon shaped by accessibility and perceptibility; physical circulation and access comfort; auditory and sensory comfort; and visual perception and lighting. Across the results, physical circulation emerged as the most persistent friction domain, especially in relation to stroller maneuverability, waiting areas, and resting provision. Qualitative responses reinforced this pattern, highlighting congestion, circulation bottlenecks, sensory overload, and wayfinding difficulty. Overall, this study reframes family retail experience through the concept of spatial friction and proposes an exploratory framework for experiential inclusivity in department store interiors aligned with social sustainability objectives. Full article

This study examines how investor sentiment and information transparency jointly shape informational efficiency in China’s A-share market. Using a monthly panel of major CSI stock indices from 2008 to 2023, we measure market efficiency through a price delay framework that captures the speed of information incorporation into prices. The results show that heightened investor sentiment is associated with greater price delay, suggesting that sentiment-driven trading can impede informational efficiency in a retail-dominated market. Importantly, this effect is attenuated in environments with higher information transparency: the interaction between sentiment and transparency indicates that improved disclosure quality weakens the extent to which sentiment distorts price discovery. These findings are robust to instrumental-variable estimation and a range of additional checks. Overall, this study highlights information transparency as a key institutional condition that moderates sentiment-driven inefficiency and provides evidence on the role of disclosure reforms in supporting more efficient price formation in emerging equity markets. Full article

This study explores the intricate behavioral consumer psychology dynamics of how certain elements—color, price, gender differences, and the concept of the frequency illusion—affect emotions, brand awareness, and consumer decision-making in a digital environment. Going beyond conventional analyses, this study also explores the intersection of sustainable business practices, elucidating the potential for ethical, environmentally conscious, and business-sustainable decision-making. Utilizing a quantitative method and survey data from 207 respondents, this research contributes to a more profound level of understanding of consumer decision-making in the Lebanese retail sector, offering strategic insights for organizations seeking to enhance brand recognition, while aligning with responsible and sustainable practices in today’s dynamic and competitive environment. The study found that psychological cues—color, price, gender differences, and frequency illusion—significantly influence emotions, brand awareness, and consumer decision-making in retail. Future research should examine the tensions in consumer decision-making, where brand awareness and emotional cues can simultaneously facilitate and bias choices, with effects contingent on exposure, demographic characteristics, digital fluency, and cultural context. Full article

There is a gap in knowledge regarding the school food environment in small-sized municipalities. Thus, this study aims to analyze the anthropometric status of schoolchildren and the school and community food environments in a small Brazilian municipality. This is a cross-sectional, exploratory, and ecological study conducted in elementary schools and food retail outlets in Jerônimo Monteiro, Espírito Santo, Brazil. Anthropometric indicators were assessed using the students’ weight and height. The school food environment was analyzed by evaluating the National School Feeding Program (PNAE) menu using the IQ-COSAN index, classifying foods brought in lunchboxes and sold at schools according to the Brazilian Dietary Guidelines, and auditing food retailers outside schools using the ESAO-S and ESAO-R instruments. Food establishments were categorized according to the Locais-Nova classification and scored using the Healthy Food Store Index (HFSI) and Healthy Meal Restaurant Index (HMRI). The study included 2 schools and 266 schoolchildren (5–11 years), of whom 33.1% had excess weight. The PNAE menu was classified as “needing improvement,” and 81% of schoolchildren’s lunchboxes contained processed/ultra-processed foods. In the external food environment around schools, low levels of access to healthy foods and predominance of ultra-processed food sales were observed. Full article

As social media campaigns become increasingly important in grocery and supermarket retail communication strategies, there is little research on how consumers view campaign performance throughout their decision-making process, rather than isolated behavioral outcomes. This study examines how the five-stage decision-making process is influenced by consumer-perceived social media performance effectiveness (CP-SMPE), grounded in consumer decision-making theory and social media performance literature. The study uses a mixed-methods research design, combining qualitative interviews with the consumers and a quantitative survey of 300 grocery shoppers in Greece. Perceived return on investment, revenue contribution, lead generation, engagement, reach, cost efficiency, and quality of electronic word-of-mouth are components of social media performance conceptualized as a multidimensional construct. Exploratory factor analysis and PLS-SEM were employed to analyze quantitative data. The findings show that high perceived social media campaign performance influences all stages of the consumer decision-making process, both directly and indirectly, through sequential intermediate stages. It ultimately enhances purchase decisions and post-purchase outcomes. By adopting a consumer-centric, process-based perspective, this study contributes to research on digitally mediated retail decision-making by demonstrating how effective social media communication can support more informed, structured consumer choices. The findings suggest that social media communication can lead to more informed and potentially responsible consumption choices by improving information environments and decision support, even though sustainability outcomes are not directly measured. Full article

This study examines the relationships between technology readiness, Augmented Reality Consumer Experience Scale (ARCES), and purchase intention in digital retail environments. Unlike prior augmented reality studies that primarily focus on technology adoption or isolated experiential effects, this study integrates technology readiness, multidimensional AR-based consumer experience, and purchase intention within a single correlational framework. Data were collected from 385 university students using a correlational research design. The factor structure of the adapted measurement scale was assessed through exploratory and confirmatory factor analyses, and the relationships among the variables were examined using correlation analysis. The findings indicate significant positive relationships: technology readiness is positively associated with AR-based consumer experience, and AR-based consumer experience is positively associated with purchase intention. From a sustainability-oriented perspective, these findings suggest that AR-enabled retail experiences may support more informed and reflective pre-purchase evaluation processes in digital environments. Full article

The rapid deployment of surveillance cameras in urban, industrial, and domestic environments has intensified the need for intelligent systems capable of analyzing video streams beyond the limitations of single-camera setups. Unlike traditional single-camera approaches, multi-camera systems expand spatial coverage, reduce blind spots, and enable consistent tracking of people and objects across non-overlapping views, thereby improving robustness against occlusions and viewpoint changes. This article presents a comprehensive review of multi-camera vision systems published between 2020 and 2025, covering application domains including public security and biometrics, intelligent transportation, smart cities and IoT, healthcare monitoring, precision agriculture, industry and robotics, pan–tilt–zoom (PTZ) camera networks, and emerging areas such as retail and forensic analysis. The review synthesizes predominant technical approaches, including deep-learning-based detection, multi-target multi-camera tracking (MTMCT), re-identification (Re-ID), spatiotemporal fusion, and edge computing architectures. Persistent challenges are identified, particularly in inter-camera data association, scalability, computational efficiency, privacy preservation, and dataset availability. Emerging trends such as distributed edge AI, cooperative camera networks, and active perception are discussed to outline future research directions toward scalable, privacy-aware, and intelligent multi-camera infrastructures. Full article

The fashion industry’s business is becoming increasingly complicated and active. This industry is expected to be highly competitive, particularly in the retail sector. Therefore, this research aims to examine the impact of supply chain information sharing and quality management on firm performance, with supply chain agility as a mediating variable, in the Asian fashion industry. A total of 169 participants from the fashion sector in a developing country were surveyed. The proposed hypotheses were examined using a quantitative approach, employing Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS to assess and validate the measurement model. The results indicate that supply chain information sharing and quality management have a significant impact on a firm’s performance. Similarly, the sharing of supply chain information and quality management has a significant impact on firm performance by mediating supply chain agility. The study offers actionable insights for managers in volatile fashion supply chains. Firms can enhance performance by sharing real-time demand and inventory information, strengthening key quality practices, and adopting flexible, data-driven production processes. Integrating information sharing, quality management, and agility enables faster responses to shifting consumer trends, thereby improving overall competitiveness in fast-fashion environments. This study offers valuable guidance for supply chain professionals seeking to enhance practices within their networks. The results underscore the strategic importance of information sharing and quality management in promoting agility, an essential capability for achieving a competitive advantage. Additionally, the insights generated are relevant to practitioners, policymakers, and industry leaders aiming to strengthen supply chain responsiveness and resilience. Full article

Context. The grocery sector is undergoing a massive shift in consumer behavior, with global chatbot usage projected to reach 8.4 billion units by 2024—surpassing the total human population—and online grocery revenue per shopper expected to hit USD 449.00 by 2023. In this competitive landscape, small grocery stores must adopt AI-driven tools to modernize their operations. However, these businesses often face significant inefficiencies in manual inventory management, resulting in errors and reduced competitiveness. Objective. This research aims to develop and validate a chatbot application using Large Language Models and Retrieval-Augmented Generation (RAG) for operational management of grocery stores. Method. The method employed a quantitative experimental approach with a five-component system architecture: a web interface, a FastAPI API, a Mistral-7B-Instruct-v0.2 model, a dynamic SQL generator, and a custom RAG application with an FAISS vector database, all integrated through SQLAlchemy 2.0.40. Results. The results demonstrate that a chatbot achieves an average response time of 0.08 s with 80% overall accuracy, showing a 96.2% improvement in information query time and a 92.9% reduction in operational errors. Conclusions. Major conclusions suggest that the chatbot system is effective for retail environments and has the potential to enhance the operational efficiency of grocery stores, serving as a foundation for future research in applied conversational assistance. Full article

This study investigates and compares Food Safety Management System (FSMS) compliance in retail shops across Mazovia (Poland) and Kerala (India). A structured visual inspection checklist with 51 indicators across seven FSMS sections was used in 500 shops per country: design and layout, general food safety, food handling and storing practices, display, personnel hygiene practices, sanitation and cleanliness, and pest control. Each section was scored using a four-point ordinal scale. Compliance scores were analyzed using the Mann-Whitney U test, Kruskal–Wallis test, Principal Component Analysis (PCA), and Cluster analysis to identify influencing factors and compliance patterns. The results demonstrate significant differences between the two countries, with Polish retail shops showing notably higher compliance (p < 0.001). No significant difference was observed in the design and layout section (p = 0.103). None of the assessed shop categories in either country achieved full compliance with all food safety requirements. Retail format, location, and number of employees were significantly associated with compliance levels. This is the first comparative study to examine FSMS compliance in retail shops in Mazovia, Poland, and Kerala, India, using a standardized visual inspection method. The findings contribute to a better understanding of FSMS performance in retail environments under different economic and regulatory conditions. Identifying how variations in retail format, staffing, and operational practices influence FSMS compliance can support the development of context-specific strategies to improve food safety performance. Full article

Self-supervised contrastive learning has become an effective approach for visual representation learning when large-scale annotation is impractical. In this study, we evaluate three widely used methods—SimCLR, MoCo v2, and BYOL—for large-scale stock keeping unit (SKU) recognition in retail environments. Experiments are conducted on the RP2K benchmark and a domain-specific in-house dataset (InSKU) using both linear probing and full fine-tuning. Under the original RP2K configuration with extended self-supervised pre-training, SimCLR achieves the highest Top-1 accuracy under linear evaluation (94.98%). In contrast, BYOL attains the highest performance under full fine-tuning (99.22% Top-1 accuracy). After filtering and deduplicating the dataset to reduce class imbalance and near-duplicate samples, MoCo v2 achieves competitive, and in some cases superior, linear performance under a reduced training budget. Cross-domain evaluation on InSKU indicates that SimCLR generalises more effectively under frozen-encoder constraints, whereas BYOL and MoCo v2 require full adaptation. These results highlight the sensitivity of contrastive representations to dataset composition, optimisation regime, and domain shift, providing practical guidance for deployment in dynamic retail settings. Full article

Urban commercial restructuring, driven by the closure of traditional supermarkets and the expansion of new-format superstores, creates a large-scale spatial reallocation challenge requiring scientific location-allocation methods. Traditional heuristic algorithms such as Genetic Algorithm (GA) struggle with discrete spatial optimization under 400+ candidate sites and complex geographic mask constraints: they converge slowly and easily fall into local optima. This study proposes a Deep Reinforcement Learning (DRL) framework named GeoPPO (Geospatial Proximal Policy Optimization) to address this gap. Using Xi’an’s retail restructuring as a case setting—427 candidate locations and multidimensional geographic features—the approach models spatial constraints via a gridded environment encoded as a five-channel state tensor. Key innovations include a dynamic action-constraint mechanism that masks invalid actions based on boundary rules and competition avoidance, and a curriculum learning strategy that enables stable convergence. The framework fills the need for methods that handle hard spatial constraints in large-scale location-allocation. Tests demonstrate rapid convergence within 1,000 epochs, achieving 75% average demand coverage—2.7% and 5.5% higher than GA and Particle Swarm Optimization (PSO), respectively. Ablation experiments confirm that Vanilla PPO without dynamic action masking fails to produce feasible solutions. The framework offers a feasible technical path for handling highly dynamic urban facility spatial configuration with geographic mask constraints. Full article

The growing integration of digital technologies with physical consumption spaces has led to the emergence of phygital environments, fundamentally transforming consumer decision-making processes. At the same time, sustainability has become an increasingly important normative and strategic context shaping contemporary consumption. While phygital solutions are often associated with sustainability-oriented claims, empirical evidence explaining how consumer behavior in phygital environments relates to sustainability remains limited. This study examines consumer behavior in phygital purchasing contexts through the prism of sustainability, focusing on the decision-making mechanisms that may support sustainability-oriented choices rather than treating phygital behavior as sustainable consumption per se. Using a two-stage analytical approach, the study first identifies key purchasing dimensions characterizing consumer behavior in phygital environments and then empirically tests the direction and strength of their relationships within a theoretically grounded structural model. Based on survey data collected from 2160 consumers, Exploratory Factor Analysis (EFA) was employed to identify latent purchasing dimensions, followed by Confirmatory Factor Analysis (CFA) and covariance-based structural equation modeling (CB-SEM) to validate the measurement model and examine hypothesized relationships. The results reveal four interrelated purchasing dimensions—purchase pragmatism, emotional commitment to the purchase, purchase comfort, and purchase pleasure—that shape consumers’ engagement in phygital purchasing processes. The findings suggest that phygital environments may foster sustainability-oriented decision-making by enhancing information access, decision efficiency, emotional engagement, and experiential value. However, the study does not directly measure environmental or sustainability outcomes; instead, it clarifies how established dimensions of consumer decision-making operate within phygital environments when analyzed from a sustainability-oriented perspective. The study offers theoretical implications for research on phygital consumer behavior and sustainability-oriented marketing, as well as managerial insights for designing phygital customer experiences that may support more informed and responsible consumption choices. Full article