Search Results (568)

Virginia’s coastal location and abundant marine resources make its seafood industry a vital contributor to the state’s economy, supporting both local communities and tourism. This study applied input–output models and updates the economic contributions of the Virginia seafood industry using 2023 data, building on models developed for 2019 that capture both direct effects and broader economic ripple effects. In 2023, the industry generated USD 1.27 billion in total economic output and supported over 6500 jobs—including watermen, aquaculture farmers, processors, and distributors—resulting in USD 238.3 million in labor income. Contributions to state GDP totaled USD 976.7 million, and tax revenues exceeded USD 390.4 million. The study also evaluates the economic role of Virginia seafood products sold in retail stores and restaurants, based on secondary data sources. In 2023, these sectors generated USD 458 million in economic output, supported more than 3600 jobs, produced USD 136.7 million in labor income, and USD 280.8 million in value-added. Combined tax contributions surpassed USD 74 million. Importantly, the analysis results for the Virginia seafood products from retail and restaurant should not be summed to the seafood industry totals to avoid double-counting, as seafood products move as output from one sector as an input to another. These results provide evidence-based insights to guide decision-making, inform stakeholders, and support continued investment in Virginia’s seafood supply chain and related economic activities. 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

The rapid proliferation of electronic waste, driven by the short lifecycle of smartphones and planned obsolescence strategies, presents escalating global environmental challenges. To address these issues from a systems perspective, this study develops an agent-based modeling (ABM) framework that simulates consumer decisions and stakeholder interactions within the smartphone reuse and recycling ecosystem. The model incorporates key behavioral drivers—privacy concerns, moral norms, and financial incentives—to examine how social and economic factors shape consumer behavior. Four primary agent types—consumers, manufacturers, recyclers, and second-hand retailers—are modeled to capture complex feedback and market dynamics. Calibrated using empirical data from Jiangsu Province, China, the simulation reveals a dominant consumer tendency to store obsolete smartphones rather than engage in reuse or formal recycling. However, the introduction of government subsidies significantly shifts behavior, doubling participation in second-hand markets and markedly improving recycling rates. These results highlight the value of integrating behavioral insights into environmental modeling to inform circular economy strategies. By offering a flexible and behaviorally grounded simulation tool, this study supports the design of more effective policies for promoting responsible smartphone disposal and lifecycle extension. Full article

In retail logistics, one typical task is to arrange a delivery schedule that guides the intake of inventory from the distribution center to stores. It is essential to accurately predict the out-of-stock (OOS) outcome for various delivery schedules to identify the optimal patterns for minimizing the OOS ratio. This paper investigates the feasibility of utilizing a neural network to accurately predict the out-of-stock (OOS) risk under each delivery pattern. Due to the zero-inflated distribution of the target values, it is necessary to evaluate two prediction accuracies simultaneously: the accuracy on data with a positive ground truth OOS rate and the accuracy on data with a zero ground truth OOS rate. In this paper, I examine how a selection of features associated with delivery schedules and the choice of activation function at the output layer, would impact the accuracy of the model. Full article

Objective: To explore the relationship between type of grocery store used (chain vs. independent), transportation access, food insecurity, and fruit and vegetable intake in Detroit, Michigan, USA, during the COVID-19 pandemic. Design: A cross-sectional online survey was conducted from December 2021 to May 2022. Setting: Detroit, Michigan. Participants: 656 Detroit residents aged 18 and older. Results: Bivariate analyses showed that chain grocery store shoppers reported significantly greater fruit and vegetable intake (2.42 vs. 2.14 times/day for independent grocery store shoppers, p < 0.001) and lower rates of food insecurity compared to independent store shoppers (45.9% vs. 65.3% for independent grocery store shoppers, p < 0.001). Fewer independent store shoppers used their own vehicle (52.9% vs. 76.2% for chain store shoppers, p < 0.001). After adjusting for socioeconomic and demographic variables transportation access was strongly associated with increased odds of shopping at chain stores (OR = 1.89, 95% CI [1.21,2.95], p = 0.005) but food insecurity was no longer associated with grocery store type. Shopping at chain grocery stores was associated with higher fruit and vegetable intake after adjusting for covariates (1.18 times more per day, p = 0.042). Qualitative responses highlighted systemic barriers, including poor food quality, high costs, and limited transportation options, exacerbating food access inequities. Conclusions: These disparities underscore the need for targeted interventions to improve transportation options and support food security in vulnerable populations, particularly in urban areas like Detroit. Addressing these structural challenges is essential for reducing food insecurity and promoting equitable access to nutritious foods. Full article

This paper explores the optimization of the bullwhip effect (BWE) and inventory costs considering price information symmetry in an omnichannel environment, offering novel insights into managing supply chain dynamics. We examine the pick-up lead time in the “buy online and pick up in store” (BOPS) channel as a critical operational factor, analyzing how the interaction with the ordering lead time affects omnichannel supply chain performance. The research highlights the impacts of the BOPS strategy on demand and inventory information, developing a comparative examination of the BWE and inventory expenses within various supply chain contexts. We discover that the interplay between ordering lead time and pick-up lead time significantly affects both inventory costs and the BWE of omnichannel retailers, with these impacts presenting an inverse relationship. While numerous studies have validated that product returns can restrain the information distortion in supply chains, our findings reveal that this relationship holds true in omnichannel retail only within specific supply chain contexts. This comprehensive approach offers valuable insights for omnichannel supply chain managers seeking to optimize the BOPS strategy and improve overall operational efficiency. Full article

Accurate retail demand forecasting is integral to the operational efficiency of any retail business. As demand is described over time, the prediction of demand is a time-series forecasting problem which may be addressed in a univariate manner, via statistical methods and simplistic machine learning approaches, or in a multivariate fashion using generic deep learning forecasters that are well-established in other fields. This study analyzes, optimizes, trains and tests such forecasters, namely the Temporal Fusion Transformer and the Temporal Convolutional Network, alongside the recently proposed Time-Series Mixer, to accurately forecast retail demand given a dataset of historical sales in 45 stores with their accompanied features. Moreover, the present work proposes a novel extension of the Time-Series Mixer architecture, the LSTMixer, which utilizes an additional Long Short-Term Memory block to achieve better forecasts. The results indicate that the proposed LSTMixer model is the better predictor, whilst all the other aforementioned models outperform the common statistical and machine learning methods. An ablation test is also performed to ensure that the extension within the LSTMixer design is responsible for the improved results. The findings promote the use of deep learning models for retail demand forecasting problems and establish LSTMixer as a viable and efficient option. Full article

Although cold storage is commonly used to extend peach fruit shelf-life, chilling injury (CI) can develop during low-temperature storage conditions and be expressed during exposure to ambient temperature. Therefore, the objectives of this study were to characterize and compare the differences in CI occurrence as well as sugar metabolism-related genes and metabolites in ‘Red Haven’ peaches stored at 0 °C and 5 °C, followed or not by storage for 3 days (d) at 20 °C (to simulate retail shelf conditions for the evaluation of CI incidence), together with fruit stored at 20 °C, and to identify significant associations between peach CI and sugar metabolism via multivariate analysis. Fruit collected at commercial maturity was stored at 0 °C, 5 °C, and 20 °C and assessed at harvest (0 d) and at 1, 3, 5, 15, and 30 d of storage, followed or not by storage for 3 d at 20 °C. Peaches kept for 30 d at 5 °C plus 3 d at 20 °C exhibited CI, expressed as decreased expressible juice. CI susceptibility was associated with reduced sucrose and increased glucose and fructose, while sorbitol contents were also lower in fruit stored at 5 °C, compared to those stored at 0 °C. This was paralleled by decreased expression of sucrose biosynthesis-related genes and by increased expression of sucrose and sorbitol breakdown-related genes as early as after 5 d of storage at 5 °C. Sugar metabolism changes that occurred during cold storage were maintained after exposure for 3 d to a temperature of 20 °C. The correlations between the evaluated features implied that alterations in sugar metabolism can modulate changes in CI susceptibility. These findings suggest that storage at 0 °C better preserves the sucrose homeostasis of ‘Red Haven’ peaches, reducing CI risk. Full article

Mating disruption is a commercially available management tactic for pyralid moths, which are pests of stored products. However, evaluations of efficacy have had limited replication, which limits the ability to draw conclusions about its effectiveness or the impact of different variables on its efficacy. We evaluated the mating disruption of Plodia interpunctella in 33 retail pet supply stores (6415 to 17,384 m³) and the impact of factors such as insect density and application rate on efficacy. Prior to starting MD, the average capture of P. interpunctella was 40.2 ± 3.6 moths/trap/month. Immediately after starting treatment, there was a sharp drop in captures (67.8 ± 4.8%) and then a more gradual overall downward. Overall, under mating disruption, the average reduction was 85.0 ± 3.0%. Geographic location, initial moth density, and pheromone application rate did not significantly impact efficacy. Analysis of the relationships between moth captures and mating disruption dispenser density indicated that competitive mechanisms were the primary mechanisms involved. This was the largest replicated assessment of MD for the management of a post-harvest pest and provides valuable foundational and applied insights into the process. Our results show that a standardized MD program can provide pest suppression in retail stores, but it takes time to be fully effective. Finally, identifying the primary mechanism for efficacy provides important information needed for further refinement of MD programs. Full article

Background: As e-commerce expands and delivery services diversifies, understanding the factors that shape consumer preferences becomes critical to designing efficient and sustainable urban logistics. This study examines how perceived walkability influences consumers’ preferences for shopping channels (in-store or online) and delivery methods (home delivery versus pickup points). Method: The analysis is based on structural equation modeling and utilizes survey data collected from 444 residents of Belo Horizonte, Brazil. Results: The findings emphasize the importance of walkability in supporting weekday store visits, encouraging pickup for online purchases and fostering complementarity between different modes of purchase and delivery services. Perceived walkability positively affects the preference to buy in physical stores and increases the likelihood of using pickup points. Educated men, particularly those living in walkable areas, are the most likely to adopt pickup services. In contrast, affluent individuals and women are less likely to forgo home delivery in favor of pickup points. Conclusions: The results highlight the role of perceived walkability in encouraging in-person pickup as a sustainable alternative to home delivery, providing practical guidance for retailers, urban planners, and logistics firms seeking to align consumer convenience with sustainable delivery strategies. Full article

The development of retail stores is determined by many factors, including the availability of retail space. The construction of a new building requires time, resources, and permits. This article aims to examine the possibilities of implementing small modular retail facilities built on the principles of vending machines, which do not require constant service and social space, by examining important groups of factors: architectural and structural, production, environmental, and costs. A vending machine in modular construction technology provides new opportunities for the development of a retail network in previously inaccessible places. The research presented in this article was conducted based on a literature review and interviews with experts, on the basis of which, using the network thinking methodology, critical factors were isolated and analysed in detail. The research results show the benefits of using modular technology, meeting the assumptions of the circular economy in terms of reducing the carbon footprint and improving the construction stage and investment costs, while taking into account the aesthetics of the surroundings. The results can contribute to the popularisation of the use of modular facilities, which can complement the development of downtown areas, making cities more sustainable. Full article

Carsharing has long supported trip purposes typically made by private vehicles, with grocery shopping especially benefiting from the carrying capacity of a personal vehicle. BlueLA is a one-way, station-based electric vehicle (EV) carsharing service in Los Angeles aimed at improving access in low-income neighborhoods. We hypothesize that BlueLA improves grocery access for underserved households by increasing their spatial-temporal reach to diverse grocery store types. We test two hypotheses: (1) accessibility from BlueLA stations to grocery stores varies by store type, traffic conditions, and departure times; and (2) Standard (general population) and Community (low-income) members differ in perceived grocery access and station usage. Using a mixed-methods approach, we integrate walking and driving isochrones, store data (n = 5888), trip activity data (n = 59,112), and survey responses (n = 215). Grocery shopping was a key trip purpose, with 69% of Community and 61% of Standard members reporting this use. Late-night grocery access is mostly limited to convenience stores, while roundtrips to full-service stores range from 55 to 100 min and cost USD 12 to USD 20. Survey data show that 84% of Community and 71% of Standard members reported improved grocery access. The findings highlight the importance of trip timing and the potential for carsharing and retail strategies to improve food access. Full article

To enhance the accuracy of product recognition in non-store retail sales and address misidentification and missed detection caused by occlusion in densely placed goods, we propose an improved YOLOv8-based network: Dense-YOLO. We first introduce an enhanced multi-scale feature extraction module (EMFE) in the feature extraction layer and employ a lightweight feature fusion strategy (LFF) in the feature fusion layer to improve the network’s performance. Next, to enhance the performance of dense product recognition, particularly when handling small and multi-scale objects in complex settings, we propose a novel multi-scale dynamic interaction attention mechanism (MDIAM). This mechanism combines dynamic channel weight adjustment and multi-scale spatial convolution to emphasize crucial features, while avoiding overfitting and enhancing model generalization. Finally, a cross-layer feature interaction mechanism is introduced to strengthen the interaction between low- and high-level features, further improving the model’s expressive power. Using the public COCO128 dataset and over 2000 daily smart retail cabinet product images compiled in our laboratory, we created a dataset covering 50 product categories for ablation and comparison experiments. The experimental results indicate that the accuracy under MDIAM is improved by 1.6% compared to other top-performing models. The proposed algorithm achieves an mAP of 94.9%, which is a 1.0% improvement over the original model. The enhanced algorithm not only significantly improves the recognition accuracy of individual commodities but also effectively addresses the issues of misdetection and missed detection when multiple commodities are recognized simultaneously. Full article

As consumer behavior increasingly shifts toward hyperlocal, digitally mediated retail journeys, community unmanned stores have emerged as a transformative model that integrates smart technologies with community proximity services. These fully automated stores offer convenient, contactless shopping and hybrid digital–physical interactions, playing an increasingly important role within broader omnichannel digital retail ecosystems. However, there remains a lack of validated instruments to assess customer experience in such autonomous and locally embedded retail formats. This study develops and validates an ECUS-scale (an experience in community unmanned store scale), a multidimensional measurement tool grounded in qualitative research and refined through exploratory and confirmatory factor analysis. The scale identifies nine key dimensions—convenient service, smooth transaction, preferential price, good quality, safe environment, secure payment, comfortable space, comfortable interaction, and friendly image—across 36 items. These dimensions reflect the technological, spatial, and emotional–social aspects of customer experience in unmanned retail settings. The findings demonstrate that the ECUS-scale offers a robust framework for evaluating consumer experience in low-staffed, tech-enabled community stores, with strong relevance to omnichannel digital retail strategies. Theoretically, it advances the literature on smart retail experience by capturing underexplored dimensions such as emotional engagement with technology and perceptions of safety in staff-free environments. Practically, it serves as a diagnostic tool for businesses to enhance experience design and optimize customer engagement across digital and physical touchpoints. Full article

In the context of digital transformation, examining the differences in commercial site selection and the factors influencing these decisions holds significant practical value for understanding market adaptation strategies across varying business models and predicting future industry trends. This study divides the research area into 100 m × 100 m grids and employs a random forest model and related interpretability methods to conduct an empirical analysis of the site selection and influencing factors of Luckin Coffee and Starbucks stores in Shanghai. By integrating the prediction results with existing planning documents, this study achieves a coupling between urban spatial structure and location strategies. The findings indicate the following: (1) The random forest model demonstrates high accuracy in predicting new retail store locations, with an accuracy rate of 90.0% for Luckin Coffee and 92.2% for Starbucks. (2) The influence of traditional factors on the expansion of new retail coffee stores is declining, while Luckin Coffee’s layout demonstrates a stronger reliance on urban functional zones. (3) Relative suitability is derived by calculating the difference between the predicted probability values and the normalized kernel density values. In the central activity areas of the city, the relationship between site selection probability and suitability exhibits an inverse correlation, with Starbucks generally showing higher relative suitability overall. (4) Suitable areas for both brands’ site selections are spatially contiguous and integrated within the urban fabric, which suggests significant growth potential for both brands in the main urban areas. This study not only focuses on commercial optimization but also offers theoretical and methodological insights by exploring how different retail models interact with urban spatial structures, thereby contributing to the fields of retail geography and spatial governance. Full article