Grocery Products Research Articles

An Empirical Examination of BuyBox Assignments in Online Grocery Retail

ABSTRACTIn Amazon's marketplaces across the globe, multiple sellers sell the same product, leading Amazon to introduce BuyBox. This box‐like feature, located on the top‐right corner of product pages, prominently displays a single seller at a given time. Using a choice model and a panel dataset of over 290,000 unique grocery products from five countries, our study investigates the key factors that influence BuyBox assignments. We found that winning the BuyBox is primarily determined by competitive pricing—being the lowest‐price seller for a 180‐day period significantly improves the chances of a BuyBox assignment. Furthermore, maintaining low current product prices, low sales ranks, and minimal out‐of‐stock percentages are crucial in becoming a BuyBox seller. These findings affect Amazon and its extensive network of over 2 million third‐party sellers, who navigate the increasingly competitive and dynamic E‐commerce landscape.

The eye at hand: when visually impaired people distribute ‘seeing’ with sensing AI

What does it mean to see and look? Can seeing and looking be done without using the eyes? This article contributes to studies in visual communication through empirical visual research into human vision, visual impairment and computer vision technologies, using video-ethnographic methods. These topics essentially enable a respecification of the concept of vision, the role of visual and material culture, and the exploration of visual means of communication in social and cultural worlds. In this article, the authors advance the understanding of visuality and vision by showing empirically how ‘seeing’ and ‘looking’ are not uniquely human abilities, but rather informational phenomena that can be achieved through distribution with a technological, nonhuman sensing AI. This study draws on video-recorded data in which visually impaired persons (VIPs) use a smartphone with a computer vision-based app while grocery shopping in a supermarket. Based on video ethnography, ethnomethodology and conversation analysis (EMCA), they show the orderly, practical organization of four specific practices: (1) fleeting glancing; (2) searching; (3) identifying; and (4) locating. In the examples, these ordinary human practices for achieving visual information are done while using the technology as a handheld ‘eye’. This research contributes to studies in visual impairment, visuospatial organization and the use of AI consumer products in a context of cultural practices for accomplishing the act of looking at and picking up grocery products. The article contributes new knowledge on visuality by expanding the concept of distributed perception and by suggesting a praxeological respecification of achieving visuospatial perception as an action in the world.

Multimodal fine-grained grocery product recognition using image and OCR text

Automatic recognition of grocery products can be used to improve customer flow at checkouts and reduce labor costs and store losses. Product recognition is, however, a challenging task for machine learning-based solutions due to the large number of products and their variations in appearance. In this work, we tackle the challenge of fine-grained product recognition by first extracting a large dataset from a grocery store containing products that are only differentiable by subtle details. Then, we propose a multimodal product recognition approach that uses product images with extracted OCR text from packages to improve fine-grained recognition of grocery products. We evaluate several image and text models separately and then combine them using different multimodal models of varying complexities. The results show that image and textual information complement each other in multimodal models and enable a classifier with greater recognition performance than unimodal models, especially when the number of training samples is limited. Therefore, this approach is suitable for many different scenarios in which product recognition is used to further improve recognition performance. The dataset can be found at https://github.com/Tubbias/finegrainocr.

A Deep Convolutional Neural Network-Based Approach for Visual Search & Recommendation of Grocery Products

A Deep Convolutional Neural Network-Based Approach for Visual Search & Recommendation of Grocery Products

IOT- Based Human Friendly Smart Trolley

Shopping and buying is an integral part of our daily lives. Big mega markets have a wide variety of items and different stores can have different deliveries of goods. It is difficult for many consumers to stand in the long queue for the billing of goods purchased. This causes wastage of money and a poor bill for the wrong customer. Trolleys are used in supermarkets or grocery stores to make shopping simpler. However, it is difficult for customers to control the trolley while shopping. An automated customer following trolley was implemented which calculates the total sum of grocery items carried in the trolley by the customers. This reduces the customer's effort to pull the trolley and keep the line for the payment of the grocery products. The customer who has a specific tag and a web camera installed in front of the trolley will recognize the tag and move the trolley to the customer. Using the RFID tag and the Raspberry Pi receiver, the item bill inserted has been obtained in the trolley. This results in an ideal solution to all these problems.

Building nutritionally meaningful classification for grocery product groups: the LoCard Food Classification process.

Analysing customer loyalty card data is a novel method for assessing nutritional quality and changes in a population's food consumption. However, prior to its use, the thousands of grocery products available in stores must be reclassified from the retailer's original hierarchical structure into a structure that is suitable for the use of nutrition and health research. We created LoCard Food Classification (LCFC) and examined how it reflects the nutritional quality of the grocery product groups. Nutritional quality was considered the main criterion guiding the reclassification of the 3574 grocery product groups. Information on the main ingredient of the product group, purpose of use and carbon footprint was also used at the more granular levels of LCFC. The main challenge in the reclassification was a lack of detailed information on the type of products included in each group, and some of the groups included products that have opposite health effects. The final LCFC has four hierarchical levels, and it is openly available online. After reclassification, the product groups were linked with the Finnish food composition database, and the nutrient profile was assessed by calculating the Nutrient-Rich Food Index (NRFI) for each product group. sd in NRFI decreased from 0·21 of the least granular level to 0·08 of the most granular level of LCFC indicating that the most granular level of LCFC has more homogeneous nutritional quality. Studies that apply LCFC to examine loyalty card data with health and environmental outcomes are needed to further demonstrate its validity.

Factors Affecting Customer Satisfaction in Online Grocery Shopping: An Empirical Analysis

In today’s era, internet has become one of the most popular platforms to shop online including grocery products. Online grocery shopping (OGS) has been considered relatively new but a promising sector of e-commerce in India. Several scholars have investigated the factors affecting the customer intention to buy grocery products online in different research contexts, however, there is no consensus in the literature on determinants of OGS. This article aims to explore the factors, such as convenience, trust, website design and security, that influence customer satisfaction in OGS. A survey was developed, and collected responses were analysed using confirmatory factor analysis and multiple regression analysis. The findings suggest that one of the major determinants of customer satisfaction while buying groceries online is website user-friendliness and the information provided about the grocery items. Furthermore, convenience and payment security also have a very significant positive impact on customer satisfaction.

Survey on Comparative Study of Online VS. Offline Buying Behavior of Consumer for Grocery Product in Surat

Survey on Comparative Study of Online VS. Offline Buying Behavior of Consumer for Grocery Product in Surat

Survey on Comparative Study of Online vs. Offline Buying Behavior of Consumer for Grocery Product in Surat

Survey on Comparative Study of Online vs. Offline Buying Behavior of Consumer for Grocery Product in Surat

Batch Normalization Free Rigorous Feature Flow Neural Network for Grocery Product Recognition

Batch Normalization Free Rigorous Feature Flow Neural Network for Grocery Product Recognition

Ecolabels and the economic recession.

We examine the effect of the 2008 economic recession on consumers' observed expenditures for eco-labelled grocery products. Traditional price theory predicts that consumers change their spending during an economic downturn and we would expect the sales share of eco-labelled products to fall since these are relatively more expensive than non-labelled products. We use supermarket loyalty card data from the UK and show that the recession had widely different effects on the expenditure share of different eco-labelled grocery products. We confirm, empirically, that expenditure shares on organic products declined over the time period under study but the expenditures share for fair-trade products increased over the same period. We evaluate alternative models of decision making to explain our results, viz., a salience model and a model of reputation signalling. We find that both of these models give a plausible explanation of our empirical results.

The weakening pricing power of major brand over private label grocery products: evidence from a Dutch retailer

The weakening pricing power of major brand over private label grocery products: evidence from a Dutch retailer

Ergonomic Analysis of a Novel Shelf Stocking Cart

This study investigated the potential for an ergonomic intervention, specifically a prototype height-adjustable stocking cart to reduce the physical demands experienced when stocking shelves. This study compared muscle activity, kinematics, and subjective preferences when participants used the prototype cart versus a traditional, manual stocking method. Nine males and three females participated in this study. A traditional stocking method was compared to the prototype cart method for two types of dry grocery products as they were moved to three different destination shelf heights. EMG data, kinematic data, and subjective feedback generally favored the cart prototype over the traditional method. Bilaterally, anterior deltoid muscle activity was reduced for the cart condition when transferring products to the lower and higher shelves; trapezius activity was reduced for only the higher shelf; right side erector spinae activity was reduced for the lower shelf. The cart primarily benefited the anterior deltoid and trapezius descendens through reduced shoulder flexion. There were interaction effects of cart and shelf height on shoulder and spine kinematics. Questionnaire data showed that participants favored using the prototype. Study results support the continued development of the prototype cart toward a commercially available ergonomic aid for shelf stockers.

Antecedents and consequences of perceived value in mobile grocery shopping

Purpose- The study aims to examine the antecedents and consequences of consumers' perceived value within the scope of mobile grocery shopping (MGS). In the study, the effect of financial risk and performance risk on perceived value, and the effect of perceived value on repurchase intention and corporate image were examined. Methodology- Convenience sampling method has been used in the study. Data are collected from 271 users living in Ankara by survey method and analyzed by structural equation model. Normality and Cronbach Alpha values are conducted with SPSS 26 package program; Goodness of fit values, validity analysis and path modelling are carried out with the AMOS 24 package program. Findings- Financial risk (β=-0.389; p<0.001) has a slightly stronger negative impact on perceived value than performance risk (β=-0.228; p<0.001). It was concluded that perceived value positively affects repurchase intention (β=0.622; p<0.001) and corporate identity (β=0.262; p<0.001). Conclusion- As a result of the research, it is revealed that financial risk and performance risk affect the perceived value negatively. When the use of MMA is worth the money to the consumer, it will lead to the individual to consider continuing to use this mobile service to purchase products. Moreover, the fact that the use of MMA is beneficial for the consumer causes the perception that this company, which sells grocery products, has a social contribution and a positive image to the society.

Development of a Hybrid Method for Multi-Stage End-to-End Recognition of Grocery Products in Shelf Images

Product recognition on grocery shelf images is a compelling task of object detection because of the similarity between products, the presence of the different scale of product sizes, and the high number of classes, in addition to constantly renewed packaging and added new products’ difficulty in data collection. The use of conventional methods alone is not enough to solve a number of retail problems such as planogram compliance, stock tracking on shelves, and customer support. The purpose of this study is to achieve significant results using the suggested multi-stage end-to-end process, including product detection, product classification, and refinement. The comparison of different methods is provided by a traditional computer vision approach, Aggregate Channel Features (ACF) and Single-Shot Detectors (SSD) are used in the product detection stage, and Speed-up Robust Features (SURF), Binary Robust Invariant Scalable Key points (BRISK), Oriented Features from Accelerated Segment Test (FAST), Rotated Binary Robust Independent Elementary Features (BRIEF) (ORB), and hybrids of these methods are used in the product classification stage. The experimental results used the entire Grocery Products dataset and its different subsets with a different number of products and images. The best performance was achieved with the use of SSD in the product detection stage and the hybrid use of SURF, BRISK, and ORB in the product classification stage, respectively. Additionally, the proposed approach performed comparably or better than existing models.

How the Situational Factors Impact the Consumers to Buy Food and Grocery Products Impulsively: A Study Conducted in the Organized Retail Formats of Kolkata

Impulsive purchasing is quickly becoming one of the highly observable behaviour for an increasing number of Indian consumers. Situational factors as experienced by the consumers inside an organized retail format affect the consumers to purchase different food and grocery products impulsively. These situational factors are impacted by a number of socio-economic and technological issues such as increase in online transaction facilities, unavailability of time to shop leisurely, increase in disposable income, financial independence of young shoppers, peer pressure, nuclear family structure etc. Food and grocery retailers might capitalize this change of buying pattern among tech-savvy and time pressed urban consumers by offering a range of technological facilities along with a decent service quality. This research studies the impact of the various situational factors eliciting impulsive buying behaviour among the consumers in the organized retail food and grocery outlets in Kolkata, through consumer survey by a standardized questionnaire.

A Study on Impact of E-Commerce on Consumer Buying Behaviour (With Special Reference to Grocery Products, Consumer of Coimbatore District)

E-commerce not only means conducting business on the internet. In the era of internet technology, e-commerce completely changes our lifestyle. E-commerce continuously achieving success, and its become more important for businessman to follow the current trends because as technology changes, consumer behavior also changes. Consumer behavior has a tremendous impact on e-commerce. Electronic commerce is far more than just transacting business over the internet. It will have a far-reaching and more significant effect than we presently realize. This is since the information technology revolution is occurring at the same time as other trends, most notably the globalization of business. The new era of global e-commerce is spawning an entirely new economy, which will profoundly influence our lives, reshape competition in multiple sectors, and change the world economy.

CUSTOMERS OPINION ON E-GROCERY STORES

Abstract : The advent of the Internet era has created incredible new opportunities, and e-commerce has emerged as the ideal fusion of technology and marketing know-how. E-grocers have mechanisms in place to take orders over the phone, which may be a stand-alone business model. We offer the most inexpensive pricing for Indian grocery products that one may purchase online. You can get significant discounts on each purchase you make from us and save a lot of money. This study investigates how consumers see online grocery retailers. The emphasis of this study is exclusively on big basket's online food buying. Technology is utilised to promote marketing campaigns and sell goods through internet marketing and shopping. We provide internet shoppers with the most affordable prices for Indian grocery items. Online merchants' bargains and discounts, large product selection, free home delivery, website usability, and the cash on delivery payment option are all factors that influence customer product purchases. Several factors influence how consumers see grocery shopping online. It is advised to research consumer perception, which

A Review of Recent Advances and Challenges in Grocery Label Detection and Recognition

When compared with traditional local shops where the customer has a personalised service, in large retail departments, the client has to make his purchase decisions independently, mostly supported by the information available in the package. Additionally, people are becoming more aware of the importance of the food ingredients and demanding about the type of products they buy and the information provided in the package, despite it often being hard to interpret. Big shops such as supermarkets have also introduced important challenges for the retailer due to the large number of different products in the store, heterogeneous affluence and the daily needs of item repositioning. In this scenario, the automatic detection and recognition of products on the shelves or off the shelves has gained increased interest as the application of these technologies may improve the shopping experience through self-assisted shopping apps and autonomous shopping, or even benefit stock management with real-time inventory, automatic shelf monitoring and product tracking. These solutions can also have an important impact on customers with visual impairments. Despite recent developments in computer vision, automatic grocery product recognition is still very challenging, with most works focusing on the detection or recognition of a small number of products, often under controlled conditions. This paper discusses the challenges related to this problem and presents a review of proposed methods for retail product label processing, with a special focus on assisted analysis for customer support, including for the visually impaired. Moreover, it details the public datasets used in this topic and identifies their limitations, and discusses future research directions of related fields.

An empirical analysis of shopping basket similarities across consumers

ABSTRACT What makes shopping baskets similar or different across consumers and how much of shopping basket similarities is explained by similarities in observable characteristics, especially when a typical basket includes a few hundred products as a result of the consumer’s purchase decisions over tens of thousands of products? I attempt to answer these questions by expressing the entirety of households’ baskets as a vector of expenditure shares on various grocery products and computing pairwise cosine similarities. I find households’ shopping baskets vary greatly, with similarities in the demographic profiles and where they shop explaining only about 13%-16% of their similarities. The similarity in where households shop has the largest explanatory power, orders of magnitude larger than similarities in demographic profiles such as income and race. It underlines the importance of similarities in the products offered to households in explaining similarities in the products they ultimately purchase.