The effect of consumers' perceived risk from the COVID-19 pandemic on their stockpiling behaviour: An evaluation from the framework of planned behaviour theory

COVID-19 pandemic has affected the entire world and brought isolation, filiation, quarantine, lockdown, social distance, etc. concepts that are not a part of our daily lives. People have reflected this deviance in many ways, including changing the consumption behaviours that visual and written media express. One of these behaviours that arise with the pandemic is stockpiling. This research aims to study the effect of perceived risk on stockpiling behaviour for grocery products by applying the Planned Behaviour Theory frame. Data has been collected from 937 attendants. ANOVA, t-test, reliability analysis, factor analysis, and confirmatory factor analysis were applied to the collected data. It was found that effective risk has an essential effect on stockpiling behaviour. On the other hand, demographic variables seem to differentiate pandemic stockpiling behaviour for grocery products.

Going healthy: how product characteristics influence the sales impact of front-of-pack health symbols

Manufacturers increasingly adopt health symbols, which translate overall product healthiness into a single symbol, to communicate about the overall healthiness of their grocery products. This study examines how the performance implications of adding a front-of-pack health symbol to a product vary across products. We study the sales impact of a government-supported health symbol program in 29 packaged categories, using over four years of scanner data. The results indicate that health symbols are most impactful when they positively disconfirm pre-existing beliefs that a product is not among the healthiest products within the category. More specifically, we find that health symbols are more effective for (i) products with a front-of-pack taste claim, (ii) lower priced products, and (iii) private label products. Furthermore, these results are more pronounced in healthier categories than in unhealthier categories. Our findings imply that health symbols can help overcome lay beliefs among consumers regarding a product’s overall healthiness. As such, adding a health symbol provides easy-to-process information about product healthiness for the consumer and can increase product sales for the manufacturer.

Improvement of One-Shot-Learning by Integrating a Convolutional Neural Network and an Image Descriptor into a Siamese Neural Network

Over the last few years, several techniques have been developed with the aim of implementing one-shot learning, a concept that allows classifying images with only a single image per training category. Conceptually, these methods seek to reproduce certain behavior that humans have. People are able to recognize a person they have only seen once, but they are probably not able to do the same with certain animals, such as a monkey. This is because our brains have been trained for years with images of people but not so much of animals. Among the one-shot learning techniques, some of them have used data generation, such as Generative Adversarial Networks (GAN). Other techniques have been based on the matching of descriptors traditionally used for object detection. Finally, one of the most prominent techniques involves using Siamese neural networks. Siamese networks are usually implemented with two convolutional nets that share their weights. They receive two images as input and can detect whether they belong to the same category or not. In the field of grocery products, there has been a lot of research on the one-shot learning problem but not so much on the use of Siamese networks. In this paper, several classifiers are firstly evaluated to decide on a convolutional model to be used with the Siamese and to improve the baseline results obtained in the dataset used. Then, two existing techniques are integrated within the Siamese model: a convolutional net and a Local Maximal Occurrence (LOMO) descriptor. The latter was initially used for the re-identification of people although it has shown its effectiveness to improve the values of a traditional Siamese with only convolutional sisters. The whole network is trained on categories and responds to different categories, showing its strong capacity to deal with the problem of having only one image per category.

A Study on the Customer Segmentation based on Psycho Graphics towards the Eco Label Awareness among the Indian Food and Grocery Retailing Customers

This paper is emphasised to understand the psycho-graphic segmentation of online food and grocery retailing customers towards the eco label awareness. The study considered VALS (Values and life styles) model as the base for this paper. The better psychological understanding helps the marketers to served them in a better way. Hence, the data has been collected from the Indian customers who preferred online to purchase their food and grocery products. There are 117 samples drawn for this study and applied cross-tabulation analysis. The results of the study are provided elaborately in the paper

Pandemic Panic and Retail Reconfiguration: Consumer and Supply Chain Responses to COVID- 19

COVID-19 ignited a revolution in retail. Family and consumer sciences (FCS) professionals, including students of retail and consumer sciences, can be the change agents that create the post-pandemic retail future. Massive disruptions to traditional retail practices for both consumers and retailers will result in changed retail environments as the United States and the world recoil and enter a new era changed by the global pandemic. Consumers have experienced panic and product availability anxieties, especially in food and grocery products. Shelves have been devoid of toilet paper, antibacterial products, and flour. Online shopping, including delivery and store pickup, is the new reality, with online sales at full-assortment grocers up 325% for March 12 and 13 in the midst of the panic (Melton, 2020). Retailers, especially grocers, have scrambled to maintain inventory and boost employment to feed the population, while non grocery brick-and-mortar retailers closed their doors and lost revenue to pay employees, leases, and outstanding invoices. How long until familiar times return—or will they? Forging ahead and influencing the post-pandemic retail world presents an opportunity for FCS professionals and consumers.

Designed to Be Noticed: A Reconceptualization of Carbon Food Labels as Warning Labels

Carbon labels are considered a fundamental tool for reducing emissions associated with grocery products. Although the prior literature has shown that both limited motivation and understanding of carbon labels explain the effectiveness of carbon labels, knowledge regarding how to improve the label design to increase noticeability is limited. Given the limited motivation of mainstream consumers to use carbon labels, this exploratory paper proposes that the label design should trigger bottom-up (or sensory-driven) attention mechanisms. Using grounded theory for the data collection and analysis of six focus groups, this study tests six features (i.e., location, size, color, icons, a colored background or border, and textual anchors) and identifies four design criteria (i.e., vividness, incongruity, simplicity, and clarity) that may increase label noticeability. The main conclusion of this qualitative study is that carbon labels are noticed when they are perceived as a cue of hazard. Based on this finding, we propose that carbon labels could be designed as warning labels; therefore, the insights already proven in the warning label literature should be applied to carbon label design to increase its noticeability and use.

What Makes Customers Repurchase Grocery Products from Online Stores in Korea

To understand customer repurchase intention in online stores, the present study has evolved with three variables: customer trust, loyalty, and satisfaction. A theoretical model was also developed to examine customer repurchase intention towards online shopping. The observation unit of this research is online stores and the analytical unit is the customers who have been purchasing grocery products via online. A random sampling technique was used to select the respondents and who are easily accessible. The data was collected from 645 online customers in Seoul. The data was analyzed with a structural measurement equation model by using Partial Least Square 3.0. Outcomes of study showed that, customer satisfaction and trust were the significant predictors of customer repurchase intention. The effect of customer loyalty towards repurchase intention seems to be very low. Hence, online retailers have advised to concentrate more on the activities needed to increase customer loyalty and to purchase more.

Are Poor Cities Cheap for Everyone? Non‐Homotheticity and the Cost of Living Across U.S. Cities

This paper shows that the products and prices offered in markets are correlated with local income‐specific tastes. To quantify the welfare impact of this variation, I calculate local price indexes micro‐founded by a model of non‐homothetic demand over thousands of grocery products. These indexes reveal large differences in how wealthy and poor households perceive the choice sets available in wealthy and poor cities. Relative to low‐income households, high‐income households enjoy 40 percent higher utility per dollar expenditure in wealthy cities, relative to poor cities. Similar patterns are observed across stores in different neighborhoods. Most of this variation is explained by differences in the product assortment offered, rather than the relative prices charged, by chains that operate in different markets.