Search Results (164)

Packaged discretionary foods that are energy-dense and nutrient-poor are widely available in the current food environment, potentially contributing to overconsumption and excessive energy intake over time. Factors such as on-pack visual cues (for example, front-of-pack image and food units per serving) and structural features (for example, package transparency) have an important role in nudging consumers towards better portion control. As little is known regarding the presence of these features on packaged discretionary foods in the current retail context, this study aimed to examine the presence of such cues on packaged discretionary foods in Australian supermarkets. Six common packaged snacks were selected: ice-cream, chocolate, lollies, sweet biscuits, savoury biscuits and crisps. Data were collected by in-store visits and using retail websites. A total of 1930 products were included; the majority were share packs (n = 1419, 73.5%), followed by multipacks (n = 385, 19.9%) and single packs (n = 126, 6.5%). Less than half of the share pack products (47%) had front-of-pack images aligned with the manufacturer-suggested serving sizes on the Nutrition Information Panel. Structural features, including transparency, partitioning and resealability, were less common and identified in less than 30% of packaged snacks. Overall, the findings showed that on-pack visual cues and structural features are not commonly used for portion control in packaged discretionary foods in Australian retail settings. Opportunities exist to improve on-pack cues and guides to support better portion size decisions. Full article

Staphylococcus aureus has been recognized as an important foodborne pathogen and methicillin-resistant S. aureus (MRSA) can cause fatal infections worldwide. Of great concern is that MRSA have been found in animals and non-healthcare settings; however, knowledge about the prevalence and genetic characteristics of S. aureus, especially MRSA from animals, retail meats and market shared shopping vehicles in the same district, is limited. In this study, we collected 423 samples including handrail swabs (n = 226) of shopping trolleys and baskets from 18 supermarkets, retail meats (n = 137) and swine nasal swabs (n = 60) between 2018 and 2020 in China. S. aureus isolates were isolated and identified by PCR, and then the mecA was used to confirm the MRSA. The antibiotic resistance and virulence genes among S. aureus were also analyzed, followed by whole genome sequencing (WGS). S. aureus isolates were widely distributed in shared shopping vehicles (8.0%, 18/226), retail meats (14.6%, 20/137) and swine (18.3%, 11/60). In total, 49 S. aureus were obtained and 20 of the 49 isolates were MRSA. We firstly reported a high prevalence of MRSA in shared shopping vehicles (7.5%, 17/226), followed by raw meats (2.2%, 3/137), and 44.4% (8/18) of the 18 supermarkets possessed MRSA-positive shopping vehicles. All 20 MRSA isolates were SCCmec IVa MRSA clones. Enterotoxin genes (sea/seb) associated with S. aureus food poisoning were present in 45.0% of the 20 S. aureus isolates from retail meats and 25.0% of the 20 MRSA isolates carried enterotoxin genes. Retail meats in this study carried ST6-MRSA, a common ST type of S. aureus from food-poisoning outbreaks in China. WGS showed that the MRSA from meats harbored enterotoxin gene sea and immune evasion genes (sak and scn) associated with human infections, and were clustered with previously reported MRSA isolates from animals and humans. The MRSA isolates carrying multiple virulence genes from shopping vehicles were also clustered with previously reported MRSA isolates from humans and animals, suggesting that the exchange of MRSA isolates might occur among different niches. Our results highlighted the risk of retail meats and shared shopping vehicles in spreading antimicrobial-resistant pathogens including MRSA. To our knowledge, this is the first report of the wide spread of MRSA in shared shopping vehicles in China. Full article

The digitalization of business activities is already a reality in most developed countries. India, driven by strong information technology, is rapidly digitalizing across business industries. In the retail industry, this shift is visible in the adoption of omnichannel strategies to enhance value for customers, loyalty and trust, retailer’s image, overall shopping experience, and operational productivity. The present study assesses the extent of omnichannel transformation in the five leading Indian retail sectors viz. Consumer Electronics, Fashion and Apparel, Furniture and Home Decor, Grocery and Supermarkets, and Personal Care and Hygiene. The research design uses mystery shopping approach to collect data from 166 nationally present retailers to analyze their level of omnichannel implementation and digital adoption. The authors performed all statistical analyses and visualizations in R using the ggstatsplot package. The results highlight a less-than-ideal picture of channel integration, suggesting that while top retailers in each sector dominate channel integration, most others have made limited progress. Among the sectors, the Furniture and Home Decor sector leads in channel integration. Other contributions of this study include the enhancement of the existing measuring tool by introducing new indicators. The study reveals gaps in omnichannel implementation to help managers plan strategic improvements. Full article

The concept of sustainability in the supermarket sector has emerged as a strategic priority, as companies are required to reduce their environmental footprint and enhance their social and economic performance. The aim of this literature review is to identify, document, and analyze the key performance indicators (KPIs) applied in the sector, with emphasis on environmental, social, and economic dimensions, and to investigate the extent to which technical energy interventions are linked to business and consumer benefits. The methodology was inspired by the general logic of organized search and selection procedures, and for this reason, elements of the PRISMA framework were used, with a search conducted across multiple international scientific databases and selection criteria ensuring the validity and relevance of the sources. The analysis classified the indicators into the following three categories: environmental (e.g., CO₂ emissions, energy consumption), social (e.g., customer satisfaction, corporate image), and economic (e.g., ESG score, return on investment). The study revealed substantial progress made by supermarket chains globally in adopting energy-efficiency technologies, such as LED lighting and renewable energy with proven benefits in reducing consumption and consequently, improving environmental performance. However, a lack of holistic integration between technical interventions and social-economic indicators was identified, limiting the use of KPIs as a strategic tool for guiding specific sustainability strategies. This research concludes that there is a need to develop unified, sector-specific measurement frameworks that integrate environmental, social, and economic parameters, as well as empirical research that quantitatively connects energy strategies with business and consumer performance through comparable indicators in the context of supermarket operations, thereby opening ground for further exploration of the field. Full article

Breakdown of cold-chain integrity drives the persistence of foodborne pathogens in poultry supply chains in warm, mountainous climates. This study used Al-Mandaq (Saudi Arabia) as a model to assess genetic diversity and contamination in bacteria from poultry storage units using 16S rRNA sequencing, VITEK 2, selective culturing, and ISSR/RAPD fingerprinting on 150 swabs. The Salmonella enterica complex comprised 15/29 isolates (51.7%), followed by Escherichia spp. 6/29 (20.7%) and Bacillus spp. 3/29 (10.3%). Five Salmonella serovars were identified: Enteritidis (8), Waycross (3), Minnesota (2), Typhimurium (1), and Dublin (1). S. Enteritidis accounted for 8/29 isolates (27.6%) and predominated among Salmonella in supermarket retail samples in Al-Mandaq. Combined ISSR and RAPD cluster analysis revealed highly clonal S. Enteritidis groupings, consistent with cross-contamination and prolonged survival in refrigeration equipment. In resource-limited settings, the combined ISSR and RAPD approach enhanced identification and differentiation of bacterial contamination sources within refrigeration equipment, providing superior strain-level discrimination compared to single-marker systems and improving epidemiological traceability of cross-contamination events. These results highlight the risk of clonal pathogen persistence in poultry cold-chain environments and the value of integrated molecular fingerprinting for surveillance in challenging climates. Full article

This study proposed a methodology for obtaining a valid database of food retail establishments and characterized the community food environment, understood as the distribution and type of food outlets, in a Brazilian medium-sized municipality after the collapse of a mining tailings dam. An ecological study was conducted with establishments selling food for home consumption (butcher shops, fish markets; fruit and vegetable specialty markets; large- and small-chain supermarkets; bakeries and local markets) and immediate consumption (bars, snack bars, and restaurants). For home-consumption establishments, data were requested from governments and completed with website/app searches, virtual audits (Google Street View), and on-site audits. For immediate-consumption establishments, only on-site audit was used due to the low quality of the secondary databases. Agreement between databases was assessed with the Kappa statistic. Density (d) was calculated by the area (in km²) of the sampling stratum. Public databases presented low validity (23.0%; Kappa −0.388; p = 1.000), even after virtual auditing (31.4%; Kappa 0.37; p < 0.001). 96 establishments for home consumption and 261 for immediate consumption were identified, with predominance of local markets (35.4%), bars (35.2%), and snack bars (29.1%). The region with the highest density of establishments was the “Other Areas” stratum (d = 4.7 for home-consumption establishments and d = 13.2 for immediate-consumption establishments). Audit proved most effective, especially for small establishments. The lack of governmental databases and the identified food environment should inform municipal policies to promote food and nutrition security and reduce inequalities after the disaster. Full article

Currently, supermarket-run shopping centers, known as “Big Marts,” monitor sales information for every single item in order to predict potential customer demand and update inventory management. Anomalies and general trends are commonly discovered through data warehouse mining using a range of machine learning techniques, and businesses such as Big Marts can use the obtained data to forecast future sales volumes. Compared to other research publications, this one forecasted sales with higher accuracy using machine learning models including KNN (K Nearest Neighbors), Naïve Bayes, and Random Forest. To adapt the proposed business model to anticipated outcomes, the sales forecast is based on Big Mart sales for various stores. Using different machine learning methods, the data that is produced may then be used to predict potential sales volumes for retailers such as Big Marts. The projected cost of the suggested system includes the following identifiers: price, outlet, and outlet location. In order to facilitate data-driven decision-making in retail operations and help Big Marts optimize their business models and effectively satisfy anticipated demand, this study emphasizes the importance of incorporating cutting-edge machine learning approaches. Full article

E-commerce growth has reshaped consumer behavior and retail services, driving parcel demand and challenging last-mile logistics. Existing research predominantly relies on survey data and global regression models that overlook intra-urban spatial heterogeneity in shopping behaviors. This study bridges this gap by analyzing e-commerce demand’s spatial distribution from a retail service perspective, identifying key drivers, and evaluating implications for omnichannel strategies and logistics. Utilizing waybill big data, spatial analysis, and multiscale geographically weighted regression, we reveal: (1) High-density e-commerce demand areas are predominantly located in central districts, whereas peripheral regions exhibit statistically lower volumes. The spatial distribution pattern of e-commerce demand aligns with the urban development spatial structure. (2) Factors such as population density and education levels significantly influence e-commerce demand. (3) Convenience stores play a dual role as retail service providers and parcel collection points, reinforcing their importance in shaping consumer accessibility and service efficiency, particularly in underserved urban areas. (4) Supermarkets exert a substitution effect on online shopping by offering immediate product availability, highlighting their role in shaping consumer purchasing preferences and retail service strategies. These findings contribute to retail and consumer services research by demonstrating how spatial e-commerce demand patterns reflect consumer shopping preferences, the role of omnichannel retail strategies, and the competitive dynamics between e-commerce and physical retail formats. Full article

Corporate environmental claims often neglect the substantial ecological impact of land-use changes. This case study examines the spatial dimension of retail-driven land-use transformation by analyzing supermarket expansion in the Veneto region (northern Italy), with a focus on a large grocery retailer. We evaluated its corporate environmental claims by assessing land consumption patterns from 1983 to 2024 using Geographic Information Systems (GIS). The GIS-based methodology involved geocoding 113 Points of Sale (POS—individual retail outlets), performing photo-interpretation of historical aerial imagery, and classifying land-cover types prior to construction. We applied spatial metrics such as total converted surface area, land-cover class frequency across eight categories (e.g., agricultural, herbaceous, arboreal), and the average linear distance between afforestation sites and POS developed on previously rural land. Our findings reveal that 65.97% of the total land converted for Points of Sale development occurred in rural areas, primarily agricultural and herbaceous lands. These landscapes play a critical role in supporting urban biodiversity and providing essential ecosystem services, which are increasingly threatened by unchecked land conversion. While the corporate sustainability reports and marketing strategies emphasize afforestation efforts under their “We Love Nature” initiative, our spatial analysis uncovers no evidence of actual land-use conversion. Additionally, reforestation activities are located an average of 40.75 km from converted sites, undermining their role as effective compensatory measures. These findings raise concerns about selective disclosure and greenwashing, driving the need for more comprehensive and transparent corporate sustainability reporting. The study argues for stronger policy frameworks to incentivize urban regeneration over greenfield development and calls for the integration of land-use data into corporate sustainability disclosures. By combining geospatial methods with content analysis, the research offers new insights into the intersection of land use, business practices, and environmental sustainability in climate-vulnerable regions. Full article

The adoption of sustainable food products by consumers is often hindered by both perceived and actual barriers within retail environments. This study investigates the interaction between the objective availability of sustainable food, its in-store visibility, and consumer perceptions of and discourses about these aspects, specifically examining how these factors contribute to socio-spatial disparities in access within an urban context (Turin, Italy). The research combined qualitative interviews with 50 consumers—to understand their perceptions and purchasing criteria—with quantitative observations of the presence and presentation of products in 56 supermarkets and 28 open-air markets across different socio-economic areas. The findings indicate that while sustainable products are more widely available than commonly perceived, their visibility (shelf positioning, signage) is significantly lower in socio-economically disadvantaged areas. This “invisibility” creates a crucial perceptual barrier, particularly for consumers who rely on immediate environmental cues and efficient shopping strategies, thus limiting purchases despite the actual presence of the products. The study concludes that in-store presentation strategies are critical mediators of perceived availability, disproportionately affecting consumers in lower socio-economic contexts and highlighting an innovative dimension of food access inequality that calls for targeted interventions at both the retail and policy levels. Full article

Product engagement detection in retail environments is critical for understanding customer preferences through nonverbal cues such as gaze and hand movements. This study presents a system leveraging a 360-degree top-view fisheye camera combined with two perspective cameras, the only sensors required for deployment, effectively capturing subtle interactions even under occlusion or distant camera setups. Unlike conventional image-based gaze estimation methods that are sensitive to background variations and require capturing a person’s full appearance, raising privacy concerns, our approach utilizes a novel Transformer-based encoder operating directly on 3D skeletal keypoints. This innovation significantly reduces privacy risks by avoiding personal appearance data and benefits from ongoing advancements in accurate skeleton estimation techniques. Experimental evaluation in a simulated retail environment demonstrates that our method effectively identifies critical gaze-object and hand-object interactions, reliably detecting customer engagement prior to product selection. Despite yielding slightly higher mean angular errors in gaze estimation compared to a recent image-based method, the Transformer-based model achieves comparable performance in gaze-object detection. Its robustness, generalizability, and inherent privacy preservation make it particularly suitable for deployment in practical retail scenarios such as convenience stores, supermarkets, and shopping malls, highlighting its superiority in real-world applicability. Full article

In the context of retail logistics, achieving operational efficiency and cost reductions requires distinguishing between value-added (VA) and non-value-added (NVA) activities. VA activities are those that bring products closer to their correct position on the shelf, with their price updated, guaranteeing their availability to customers. All other activities are considered as NVA activities. NVA activities include activities such as unnecessary handling, waiting, excessive movement, and stock mismanagement. This study is based on an on-site experience conducted in a Modelo supermarket, part of the Sonae group and one of Portugal’s largest retailers, which reinforces the practical significance of its findings. By analyzing various aspects of internal retail logistics, this research challenges traditional definitions of value and waste—typically applied in manufacturing—and proposes a new approach tailored to retail operations. Six specific types of NVA activities were identified in this context. Applying this classification model, a multi-moment analysis was conducted to quantify the labor utilization in VA tasks, offering insights into process inefficiencies. The proposed model provides a systematic framework for categorizing retail logistics operations, supporting decision-makers in streamlining workflows, improving productivity, and optimizing resource allocation. Beyond academic discourse, this model serves as a practical tool for retailers aiming to enhance their internal logistics efficiency. Full article

This paper introduces an integrated inventory model that employs customized neural networks to tackle the challenge of non-stationary demand for newsvendor-type products, such as vegetables and fashion items. In this newsvendor context, demand is intertemporal-dependent and influenced by external factors such as prices, promotions, and holidays. Contrary to traditional machine-learning-based inventory models that assume stationary and independent demand, our method accounts for the temporal dynamics and the impact of external factors on demand. Our customized neural network model integrates demand estimation with inventory optimization, circumventing the potential suboptimality of sequential estimation and optimization methods. We conduct a case study on optimizing the vegetable ordering decisions for a supermarket. The findings indicate the proposed model’s effectiveness in enhancing ordering decisions, thereby reducing inventory costs by up to 21.14%. By customizing an integrated neural network, this paper presents a precise and cost-effective inventory management solution to address real-world complexities of demand, like seasonality and external factor dependency. The proposed approach is particularly beneficial for retailers in industries dealing with perishable items and market volatility, enabling them to mitigate waste (e.g., unsold vegetables) and stockouts (e.g., seasonal fashion items). This directly confronts challenges related to sustainability and profitability. Furthermore, the integrated framework provides a methodological inspiration for adapting neural networks to other time-sensitive supply chain problems. Full article

Salmonella is one of the most significant zoonotic and foodborne pathogens, and it is the leading cause of bacterial diarrhea. In this study, 156 retail meat samples were collected from three supermarkets and one local wet market in Sichuan, China, including 96 chicken samples and 60 pork samples. The prevalence of Salmonella in these samples was analyzed, and 91 samples (58.33%) tested positive, with 60 (62.5%) positive chicken samples and 31 (51.67%) positive pork samples. From these positive samples, 190 Salmonella isolates were confirmed by double PCR. Subsequent serotyping identified nine serovars, with the predominant ones being S. London (58.94%), S. Typhimurium (12.58%), and S. Enteritidis (10.60%). Antibiotic susceptibility test revealed that 168 isolates (88.42%) were resistant to at least one antibiotic, and 150 isolates (78.95%) were resistant to three or more antibiotics. The highest resistance rates were observed for ampicillin (83.16%), followed by tetracycline (76.31%) and trimethoprim/sulfamethoxazole (67.37%). In the disinfectant susceptibility test, Salmonella isolates exhibited higher resistance rates to benzalkonium bromide (100%) and benzalkonium chloride (97.37%), while showing a lower resistance rate to potassium monopersulfate triple salt (33.6%). These findings highlight the high prevalence of Salmonella and its significant resistance to antibiotics and disinfectants, indicating that effective measures must be implemented to ensure the microbiological safety of retail meat. Full article

The rise of the experience economy, driven by disruptive technologies delivering innovative experiences, has transformed the interactions between tech-enabled shoppers and the phygital retail complex system. An important knowledge gap is addressed in our study by evaluating shoppers’ perceptions of disruptive technologies and the adaptive challenges that retailers face in securing consistency within a highly complex e-commerce landscape shaped by transformative interactions. A quantitative analysis was carried out using structural equation modeling (SEM) and survey data from an international supermarket chain integrating physical and digital retail spaces. We propose a novel framework to explore how retailers can harness data-driven insights and disruptive technologies to optimize the phygital shopping experience and adapt to the shift from multichannel and omnichannel strategies to optichanneling, as well as respond to societal shifts, including the role of digital natives and the expanding influence of the metaverse. This framework integrates key principles such as emergence, feedback, and criticality. The research reveals key findings about transformative shopper experiences across phygital retail touchpoints that influence shoppers’ perceptions and behaviors. Based on these identified key insights, as shoppers increasingly expect seamless interactions, the framework includes practical recommendations for retailers relating to several key areas, including leveraging the metaverse for refined shopper engagement. Full article