Search

The purpose of this paper is to help online grocery retailers determine the optimal return and refund policies for perishable grocery items. Three refund policies and two return requirements are modelled: no-refund, full-refund and partial-refund, with or without product return. We first derive the optimal refund policy under two scenarios, where either product return is or is not required for refund. We further analyse how market conditions impact the optimal policies. Lastly, we investigate when product return should be required for refund. Our results show that a more generous refund policy should be offered if the product has a higher profit margin, if an unsatisfactory product has a large negative impact on consumer utility, and if consumers have high return costs when product return is required for refund. Our results also show that it is optimal not to require the unwanted product to be returned when consumer's return cost and product return rate are above certain threshold values. This paper fills the gap in the literature on return and refund policies in an online grocery setting. The findings of this paper provide explicit guidance to online grocery businesses on how to optimally determine the return and refund policies. [ABSTRACT FROM AUTHOR]

Nowadays, retailers try to optimize the shopping experience for consumers by offering personalized services. Recommending food options, i.e. providing consumers suggestions on what products to buy, is one of such services. Food recommender systems for grocery shopping are typically preference-based, using consumers' shopping history to determine what products they would like. These systems can predict well what a consumer would potentially like to buy, however, they do not stimulate consumers to buy healthier or more sustainable food options. In response to increasing global concerns about public health and sustainability, this paper aims to integrate healthiness and sustainability levels of food options in recommender systems to encourage consumers to buy better food options. To assess the impact of integrating healthiness and sustainability information of food choices in predicting an item to buy, we employ three food recommendation models: a Baseline popularity-based model, Restricted Boltzmann Machine (RBM), and Variational Bayesian Context-Aware Representation (VBCAR) based on (1) preferences, (2) preferences and health, (3) preferences and sustainability, and (4) all combined attributes. Models were trained and tested using two different datasets: Instacart and a Dutch supermarket dataset. The experimental results indicate improved performance for VBCAR compared to Baseline and RBM. Models that emphasize healthiness and/or sustainability of food choices do not significantly alter model performance compared to preference-based models. The results of the health and sustainability-based recommender systems demonstrate the potential of recommender systems to assist people in finding healthier and more sustainable products that are also suited to their preferences.

Diverting food waste from landfills is crucial to reduce emissions and meet Paris Agreement targets. Between 2014 and 2024, nine US states banned commercial waste generators—such as grocery chains—from landfilling food waste, expecting a 10 to 15% waste reduction. However, no evaluation of these bans exists. We compile a comprehensive waste dataset covering 36 US states between 1996 and 2019 to evaluate the first five implemented state-level bans. Contrary to policy-makers’ expectations, we can reject aggregate waste reductions higher than 3.2%, and we cannot reject a zero-null aggregate effect. Moreover, we cannot reject a zero-null effect for any other state except Massachusetts, which gradually achieved a 13.2% reduction. Our findings reveal the need to reassess food waste bans using Massachusetts as a benchmark for success. [ABSTRACT FROM AUTHOR]

We analyse 69 entries and relocations by the largest Norwegian discount variety chain Europris during the period 2016–2019, and measure how its location choices affect local grocery stores' performance. We use detailed data from a major Norwegian grocery chain, which enables us to combine local grocery stores' sales and traffic with travelling distance to new or relocated Europris stores, and a two-way fixed effects strategy. Our findings suggest that entries and relocations have significant effects and that the sign of the effect depends on the distance between the stores, creating a non-linear relationship between the effect of entry and the distance between the stores. Sufficiently close entries and relocations increase local demand since more customers are attracted to the market, but as the distance increases, the competitive effect of a new discount variety store dominates, and local grocery store sales and traffic are reduced. As we move further away, the entry effect is gradually reduced to zero. [ABSTRACT FROM AUTHOR]

Iowa's bottle bill needs major reform. For years the bottle deposit system made Iowans happy while also keeping land across the state litter-free. However, the COVID-19 pandemic demonstrated deep cracks that formed in the system over years of neglect. With no signs of improvement, dealers and distributors alike began blatantly breaking the law without any repercussions, leaving citizens with few options for container returns. The Iowa Legislature passed a modest reform in the spring of 2022, but the changes it implemented only exacerbate the problem by allowing grocery stores to opt out of accepting returns, making these returns more burdensome. The changes have made returning containers for consumers more difficult while failing to offer real solutions to the problems faced. By borrowing ideas from other states' bottle bills, (such as increased deposit prices, expanded container eligibility, and requiring redemption by grocers with penalties included) Iowa can save the bottle bill and continue to promote the law's ultimate goal: to protect the environment. [ABSTRACT FROM AUTHOR]

Recently people often purchase their daily needs at retail stores. Therefore, crowds might happen due to a manual queueing system. To overcome the problem, the smart system based on object detection has been conducted using several object detection methods. This study proposed YOLOv8 combined with transformer Real-Time Detection Transformer (RT-DETR) model to enhance the method performance in detecting the detail products. The intra-Class Variation method has been used to recognize the characteristics of the products such as size, color, and variant of the product. To validate the proposed model, three different datasets have been applied that is grocery dataset that displays products one by one in the training and validation process, the RPC-dataset that has many products in one image, and the D2S dataset with products that have varying lighting and stacked products. Results showed that the proposed model outperformed compared to other models, with a mean Average Precision (mAP) of 99.5% for the grocery dataset, 99.3% RPC-dataset, and 85.5% D2S dataset, respectively. [ABSTRACT FROM AUTHOR]

AbstractScopus database between 2000 and 2023 showed that while groceries are vital in disaster/pandemic management, their roles have not been adequately addressed in the literature. In this study, we argued that privately owned groceries can complement large food storage facilities and provide effective and sustainable Public–Private Partnerships-PPP for disaster/pandemic management. We supported our argument with surveys of 254 customers and 100 grocery tenants, as well as a spatial analysis utilizing Geographic Information Systems (GIS). The results indicated that 62% of customers and 89% of grocery tenants agreed that grocery sales increased during COVID-19. Furthermore, 75% of customers agreed that groceries are vital during disaster/pandemic, and 81.8% of grocery tenants agreed that their current infrastructure allows their stores to become emergency food depots. Through GIS analysis, we found that groceries are clustered around population centers, and 69% of customers have groceries within a 5–10-minute walking distance. Our results demonstrated that groceries can be crucial in disaster management. The study aligns with the Sendai Framework for Disaster Risk Reduction 2015–2030 and UN-SDG goals 11 and 17. The findings could be utilized by organizations working in the field of disaster/pandemic management. The research fills a gap in the literature and offers new insights into the potential of using privately owned groceries in disaster/pandemic management. It highlights the need for further consideration of these important community resources.