Gender, store satisfaction and antecedents: a case study of a grocery store

2010 ◽  
Vol 27 (2) ◽  
pp. 114-126 ◽  
Author(s):  
Øyvind Helgesen ◽  
Erik Nesset
Keyword(s):  
Grocery Store

Measuring the Impact of a Full Service Grocery Store in a Food Desert

2021 ◽  
Vol 3 (2) ◽  
pp. 161-176
Author(s):  
Kellie Schneider ◽  
Diana Cuy Castellanos ◽  
Felix Fernando ◽  
Jeanne A. Holcomb
Keyword(s):  
Systems Engineering ◽  
Low Income ◽  
Social Impact ◽  
Food Deserts ◽  
Well Being ◽  
Environmental Indicators ◽  
Grocery Store ◽  
The Impact ◽  
Health And Well Being

Food deserts, areas in which it is difficult to obtain affordable, nutritious food, are especially problematic in low-income neighbourhoods. One model for addressing food hardship and unemployment issues within low-income food deserts is a cooperative grocery store. Through the cooperative model, the grocery store can serve as a cornerstone to address socio-economic marginalisation of low-income neighbourhoods and improve the health and well-being of its residents. It is important for communities and policymakers to be able to assess the effectiveness of these types of endeavours beyond traditional economic factors such as profitability. This article uses a systems engineering approach to develop a framework for measuring the holistic impact of a cooperative grocery store on community health and well-being. This framework encompasses values that characterise the relationship between food retail, economic viability and social equality. We develop a dashboard to display the key metrics for measuring the economic, social and environmental indicators that reflect a grocery store’s social impact. We demonstrate the usefulness of the framework through a case study of a full-service cooperative grocery store that is planned within the city of Dayton, OH.

Multi-Context Generation in Virtual Reality Environments Using Deep Reinforcement Learning

2020 ◽  
Author(s):  
James Cunningham ◽  
Christian Lopez ◽  
Omar Ashour ◽  
Conrad S. Tucker
Keyword(s):  
Virtual Reality ◽  
Reinforcement Learning ◽  
Virtual Environments ◽  
Probability Distributions ◽  
Automatic Generation ◽  
Grocery Store ◽  
Training Data ◽  
Learning Approaches ◽  
Common Concept

Abstract In this work, a Deep Reinforcement Learning (RL) approach is proposed for Procedural Content Generation (PCG) that seeks to automate the generation of multiple related virtual reality (VR) environments for enhanced personalized learning. This allows for the user to be exposed to multiple virtual scenarios that demonstrate a consistent theme, which is especially valuable in an educational context. RL approaches to PCG offer the advantage of not requiring training data, as opposed to other PCG approaches that employ supervised learning approaches. This work advances the state of the art in RL-based PCG by demonstrating the ability to generate a diversity of contexts in order to teach the same underlying concept. A case study is presented that demonstrates the feasibility of the proposed RL-based PCG method using examples of probability distributions in both manufacturing facility and grocery store virtual environments. The method demonstrated in this paper has the potential to enable the automatic generation of a variety of virtual environments that are connected by a common concept or theme.

The Peeing Pitcher: An Inquiry-Based, Laboratory Case Study of the Endocrine and Renal Systems

2017 ◽  
Vol 79 (5) ◽  
pp. 387-392
Author(s):  
Parker Stuart ◽  
Kelsey Stuart ◽  
Mark Milanick
Keyword(s):  
Grocery Store ◽  
Diagnostic Methods ◽  
Feedback Systems ◽  
Testing Procedures ◽  
Detection Techniques ◽  
Glucose Levels ◽  
Real World Problem ◽  
Urine Production ◽  
Patient Prognosis

In this inquiry-based lab, students are provided with a case study involving a young boy with a head injury exhibiting various symptoms, as well as simulated blood and urine samples to help diagnose the boy's disease. Throughout the course of the lab, students research, design, and conduct a series of tests culminating in a patient prognosis. All of the materials, which simulate the blood, urine, and testing compounds, are readily available at the grocery store or online. This real-world problem engages the students to think about negative feedback systems, patient symptoms, the hormones associated with blood glucose levels and urine production, as well as the detection techniques employed by physicians to diagnose patients. Diagnostic methods, testing procedures, and the disease itself make this lab extraordinarily relevant to the lives of students, as evidenced by our students’ reactions to the lab.

Predicting Consumer Retail Sales Using Neural Networks

2002 ◽  
pp. 26-40 ◽  
Author(s):  
G. Peter Zhang ◽  
Min Qi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Data Preprocessing ◽  
Grocery Store ◽  
Neural Network Modeling ◽  
Retail Sales ◽  
Planning Decisions ◽  
Forecasting Performance ◽  
Preprocessing Technique

Forecasting future retail sales is one of the most important activities that form the basis for all strategic and planning decisions in effective operations of retail businesses as well as retail supply chains. This chapter illustrates how to best model and forecast retail sales time series that contain both trend and seasonal variations. The effectiveness of data preprocessing such as detrending and deseasonalization on neural network forecasting performance is demonstrated through a case study of two different retail sales: computer store sales and grocery store sales. We show that without data preprocessing neural networks are not able to effectively model retail sales with both trend and seasonality in the data, and either detrending or deseasonalization can greatly improve neural network modeling and forecasting accuracy. A combined approach of detrending and deseasonalization is shown to be the most effective data preprocessing technique that can yield the best forecasting result.

scholarly journals The Changing Spatial Organization of Ethnic Retailing: A Case Study of Chinese and South Asian Grocery Store Retailers in the Toronto CMA From 2001 to 2016

2021 ◽  
Author(s):  
Samantha Perry
Keyword(s):  
South Asian ◽  
Metropolitan Area ◽  
Healthy Food ◽  
Grocery Store ◽  
Grocery Stores ◽  
Residential Patterns ◽  
Ethnic Business ◽  
Grocery Retailing ◽  
Census Metropolitan Area

Understanding the changing spatial structure of ethnic grocery retailing in Canadian urban regions can provide insights into ethnic business development and the well-being of residents, particularly relating to the availability of healthy food and risk of nutrition-related illnesses. This study explores this through a case study of Chinese and South Asian grocery retailing in the Toronto Census Metropolitan Area (CMA). In particular, the changing spatial relationship between ethnic grocery business distribution, ethnic residential patterns, and spatial accessibility is examined between 2001 and 2016. A combination of location quotients and global and local indicators of spatial autocorrelation were utilized to assess the relationship between ethnic groups while measures of spatial central tendency and a nearest neighbor analysis assessed the distribution of grocery retailers. An integrated marginalization-accessibility index was then developed to highlight any spatial mismatch between the level of material deprivation and grocery store access, highlighting patterns of inequality throughout the CMA. The results of the study reveal that Chinese and South Asian grocery retailers and residents have suburbanized over the study period. Index results also indicate that some census tracts (CTs) experienced limited access to both mainstream and ethnic grocery stores, particularly among the South Asian community. Finally, there is a growing number of CTs that are well-serviced to Chinese and South Asian grocery stores but are under-serviced to mainstream retailers, potentially identifying areas where ethnic grocers are filling gaps in service. Key words: ethnic grocery retailing, ethnic residential patterns, accessibility, healthy food provision, marginalized neighbourhoods, Toronto Census Metropolitan Area

scholarly journals CART NARCS AND THE ENGINEERING OF SOCIAL SHAMING AS ENTERTAINMENT

2021 ◽  
Author(s):  
CJ Reynolds ◽  
Nicholas John
Keyword(s):  
Emotional Response ◽  
Grocery Store ◽  
Media Content ◽  
Communicative Interaction ◽  
Parking Lots ◽  
Social Mission ◽  
Shopping Cart ◽  
Real People ◽  
Moral High Ground

With around 160 videos, 160,000 subscribers, and 18 million views, Cart Narcs is an “elite” YouTube channel. The typical Cart Narcs YouTube video is framed around the idea of shaming people who do not return their shopping carts. The eponymous Narc, Agent Sebastian, patrols grocery store parking lots looking for miscreants to confront and film. When he spots a target, he runs towards them making siren noises. "Cart Narcs!" he shouts. "That's not where the cart goes!" While seemingly related to established entertainment genres featuring real people in everyday situations and to prank videos, we identify significant features in Cart Narcs videos that distinguish them: the lack of a debrief; the positioning of the Cart Narc as the "nice guy" in the interaction; and the Cart Narc’s claim to the moral high ground. These features each remove a redemptive moment present in analogous types of content, resulting in a communicative interaction engineered to anger people. We ask how Agent Sebastian produces such a powerful emotional response to his seemingly innocent request that people return their shopping cart and what the logic of this form of media content might signify. Cart Narcs is a revealing case study in how the economy-driven logic of participation produces undesirable types of content when it overwhelms or wholly replaces social and aesthetic logics. Cart Narcs videos are a hybrid genre concoction that trade people's anger for monetized views, cloaked by the pretense of a social mission.

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