Smart Retail Research Articles

Effect of location-based advertising filtering mechanism in smart service

Location-based advertising (LBA) refers to the delivery of marketing messages to mobile devices based on consumer location. The literature finds that LBA improves marketing effectiveness; however, distrust threatens consumers’ acceptance of LBA. This research suggests that an LBA filtering mechanism, which allows consumers to select advertising categories according to their preferences autonomously, may overcome the distrust. Its effect was empirically investigated in a smart retailing service platform. Two field experiments were conducted in a large shopping center with a total of 360 consumers. The results reveal that filtered (vs. unfiltered) LBA leads to more favorable behavioral intention through serial mediation of reduced psychological distance and enhanced trust. Moreover, the effect of LBA filtering in reducing psychological distance is more prominent for consumers with a prevention (vs. promotion) focus and with (vs. without) a purchase plan. The findings have implications for the system design and marketing of LBA.

Marketing Strategies at Retail Be Smart in Increasing Sales

For a Be Smart Retail, a suitable marketing concept is a social marketing concept because current market conditions are a buyer's market, so customers are everything. Customer needs, desires and satisfaction must really be considered. Objective it was founded Sunday mart This is look for profit, The same with companies other Which Also look for profit. This research uses qualitative methods. Qualitative methods are research procedures that produce descriptive data: speech or writing and observable behavior of the people (subjects) themselves. It was concluded that Retail Be Smart has succeeded in implementing various effective marketing strategies to increase sales volume. It was concluded that Retail Be Smart has succeeded in implementing various effective marketing strategies to increase sales volume. By understanding the concept of market segmentation, determining the right target market, and determining a unique market position, Be Smart is able to attract consumer attention and maintain its advantage in the market. In addition, by using the right marketing mix, SWOT Analysis also helps a company to understand its internal strengths, identify external opportunities and threats.

Examining Human Interactions with Smart Retail Technology: A Hybrid SEM- fsQCA Investigation

As technologies evolve, businesses have focused on enhancing human interactions beyond simple satisfaction. This new research builds on recent exploratory work, responding to calls for testing antecedents and consequences of Smart Retail Technology (SRT) interactions. An online survey was administered to 338 respondents. In addition to structural equation modelling (SEM), fuzzy set Qualitative Comparative Analysis (fsQCA) was employed to identify value outcomes. Results demonstrate that the more individuals perceive a business’s technologies to be distinctive, the more likely they are to interact with those technologies. Respondents’ innovativeness was positively associated with their intentions to interact with SRT. The fsQCA outcomes offer an understanding of the types of individuals who perceive value through SRT interaction. Findings offer managerial implications and extend current knowledge about adopting interactive technologies. This is the first study to contribute a comprehensive SRT model that examines the inter-relationships between antecedents and consequences of interacting with SRT.

Discussion on the New Mode of Rural Smart Retail

Discussion on the New Mode of Rural Smart Retail

Revenue forecasting in smart retail based on customer clustering analysis

Understanding your customers is among one of the most important strategies to boost retail profit. In this research, we propose a WiFi-based sensing method to analyze customer behaviors. The monitoring of customer behaviors may lead to revenue growth. Specifically, the strategy is focused on understanding and grouping customers’ behaviors in which we track customers who share similar visiting patterns through WiFi sensing. Accordingly, we can have group-based prediction done for customers who own similar behaviors. We extract customers’ visiting patterns including the customers’ Service Set Identifier list and related information. After all, the proposed system is realized in a cafeteria place where we have the deployed WiFi access points continuously collect data over a time horizon of three months to serve as the inputs for data analysis. The data samples include the number of customers’ devices, number of products and revenue amounts. The dataset also integrates group information and weather conditions. We adopt several machine learning methods including Support Vector Regression and Random Forest for model induction. We conduct these models in terms of three main prediction tasks consisting of coffee shop’s revenue, the number of products, and the number of customers’ devices for evaluation. Furthermore, considering these predictions, we separate between the staying-in and to-go parts. Based on the experiment result, customers’ group information helps, as well as weather conditions. Overall, we can achieve the best prediction result when both the group information and weather conditions are included where we can enjoy as good as 6% to 10% in MAPE.

Are young adult consumers ready to be intelligent shoppers? The importance of perceived trust and the usefulness of AI-powered retail platforms in shaping purchase intention

Purpose The advancement of artificial intelligence (AI) has brought intelligent online shopping experiences to customers. AI-powered retail platforms deliver personalized shopping experiences through tailored recommendations, promotions and assistance. Given the increasing preference for online shopping, it is crucial to explore methods to optimize the adoption of AI-powered retail platforms. To address this, this study aims to examine the impact of technology readiness motivators (optimism and innovativeness) and inhibitors (discomfort and insecurity) on perceived trust, perceived usefulness and purchase intention toward AI-powered retail platforms. Design/methodology/approach Data were collected from 276 customers in Egypt, primarily from the millennial and Gen Z demographic segments. The collected data were then analyzed using the statistical package for social sciences (SPSS) and partial least squares structural equation modeling (PLS-SEM). Findings The findings revealed that optimism, innovativeness and discomfort significantly influence perceived trust, while optimism, insecurity and perceived trust significantly influence perceived usefulness. Both perceived trust and usefulness are significant predictors of purchase intention. Perceived trust mediates the effects of technology readiness motivators on perceived usefulness and purchase intention. Moreover, perceived usefulness mediates the effects of technology readiness motivators, insecurity and perceived trust on purchase intention. Originality/value To date, there are few investigations regarding the acceptance and adoption of AI-powered retail platforms in developing countries. Thus, this study offers valuable theoretical and practical implications in the context of smart retail technology adoption.

Perceived retailer innovativeness and its impact on customer engagement behavior in smart retailing

Perceived retailer innovativeness and its impact on customer engagement behavior in smart retailing

Analysis of Economic Benefits of Scenario-based Transformation of Business Models: Taking Suning.com as an Example

With the rapid development of science and technology and economy, the performance of traditional retail enterprises has been declining, and in order to change this unfavorable situation, they have to develop in the field of e-commerce. The O2O model that passes through the online and offline channels is no longer competitive in the market, and now the market is more focused on personalized service that puts the customer in the first place, and Suning.com, as a representative retailer, first put forward the smart retail model. Through the transformation of business model scenario to improve the quality of service, so that users have a better service experience. The transformation of business model scenario makes changes in the economic benefits of the enterprise, and the changes in the economic benefits of the enterprise also reflect the effect of the transformation of business model scenario. In this paper, Suning eBay Group Co. Ltd. is selected as the research object, and the development process of Suning eBay's business model scenario transformation is studied through the economic benefit analysis of Suning eBay's annual reports from 2017 to 2022, so as to analyze the impact of the business model scenario transformation on the enterprise's economic benefits. Through the in-depth analysis of the case, the research conclusions are summarized to provide reference for other retail enterprises.

Exploring consumers’ adoption and recommendation in smart retailing: a cognitive absorption perspective

Exploring consumers’ adoption and recommendation in smart retailing: a cognitive absorption perspective

Collaborative Multi-task Learning for Multi-Object Tracking and Segmentation

The advancement of computer vision has pushed visual analysis tasks from still images to the video domain. In recent years, video instance segmentation, which aims at tracking and segment multiple objects in video frames, has drawn much attention for its potential applications in various emerging areas such as autonomous driving, intelligent transportation, and smart retail. In this article, we propose an effective framework for instance-level visual analysis on video frames, which can simultaneously conduct object detection, instance segmentation, and multi-object tracking. The core idea of our method is collaborative multi-task learning which is achieved by a novel structure, named associative connections among detection, segmentation, and tracking task heads in an end-to-end learnable CNN. These additional connections allow information propagation across multiple related tasks, so as to benefit these tasks simultaneously. We evaluate the proposed method extensively on KITTI MOTS and MOTS Challenge datasets and obtain quite encouraging results.

Augmented reality in smart retail for inventory management

The ever-growing complexity of supply chains and the dynamic nature of modern business environments demand innovative solutions for efficient and cost-effective inventory management. This paper introduces a novel approach to address these challenges through the integration of augmented reality (AR) technology. Our proposed system leverages AR to enhance the accuracy, speed and adaptability of inventory management processes while maintaining a focus on cost-effectiveness. By overlaying digital information onto the physical world, AR transforms the way inventory is monitored, tracked and updated. The system utilises wearable AR devices, such as smart glasses, to provide real-time insights into inventory levels, product locations and order fulfilment status. This not only reduces manual errors but also significantly improves the overall efficiency of inventory-related tasks. To validate the effectiveness of our approach, we conducted a series of simulations and case studies across diverse industries. The results demonstrate notable improvements in inventory accuracy, order fulfilment speed and overall operational efficiency. The proposed AR-based inventory management system emerges as a viable and cost-effective solution, poised to revolutionise traditional inventory management practices in the contemporary business landscape.

Simultaneous consideration of consumer preferences and seller revenue as a smart retail sales and management strategy

PurposeThe authors propose a dairy bundle, integrating strategies to jointly maximise producer revenue and consumer utility according to the latter's preferences.Design/methodology/approachAn algorithm based on a nested logit model identifies the bundle maximising producer revenue based on factors affecting consumer purchase behaviour. The data are drawn from a mall-intercept survey administered in Iran, with consumers stating a hypothetical choice among a comprehensive set of dairy products.FindingsDemographic characteristics and marketing mix elements significantly affect consumers' preferences. An algorithm based on the estimated dissimilarity parameter determines the best bundle of dairy products, simultaneously obtaining the highest utility and the highest expected revenue.Originality/valueConsumer preference and maximum producer or retail seller income are considered simultaneously. The bundling promotion strategy is widely used for food offerings and fresh foods and can be extended to other products.

Smart Retail: The Convergence of IoT and AI in the Future of Commerce

Smart Retail: The Convergence of IoT and AI in the Future of Commerce

Parameters of Online and Offline Shopping Experience of Gen Z Customers with Reference to Fashion Industry in India

Sellers are attempting to resurrect offline businesses by implementing retail technologies in response to the decline of physical fashion stores. The need for this study was to ascertain how perceived risk and quality were impacted by aspects of the off the internet mobile connected smart selling familiarity in terms of fulfilment. A study including 345 female customers in their 20s and 30s was carried out using an online poll. The analysis, which employed a structural equation model, made sure that perceived advantage, perceived enjoyment, and interactivity affected quality among the five smart retailing experience-related factors, and that perceived advantage and connectivity significantly affected seeming risk. However, perceived personalisation or control had no effect on perceived threat or quality. Perceived risk had no effect on mobile satisfaction, whereas perceived quality had a significant impact on offline, overall, and mobile fulfilment. This study affirmed that smart retailing experiences' perceived benefits and interactivity are crucial for raising customer happiness.

Customer behavior when experiencing smart retail technology: a conceptual framework

Due to the rapid growth of Smart Retail Technology (SRT) in the digital age, the retail industry has transformed how it engages with customers and manages daily operations. This research utilizes the Value-Based Adoption Model to investigate the mechanism that may affect to customer loyalty to retail stores and electronic Word-of-Mouth (eWOM) behavior following their experience with SRT, focusing on customers' value-oriented perspectives that may not have received adequate attention in previous studies. Based on the literature review, this study identifies five factors affecting the perceived value of a customer and, consequently, his/her loyalty behavior. It also proposes the research framework to analyze customer behavior when experiencing smart retail technology.

PLASTIC PACKAGING WASTE MANAGEMENT THROUGH FILLING MACHINE WITH AGILE REENGINEERING PROJECT MANAGEMENT APPROACH

In Indonesia, the number of plastic wastes has reached 66 million tons per year in which 50% of them come from plastic packaging waste. Meanwhile, the recycling of plastic packaging waste only reaches 14%. Hence, it is highly urgent to be aware of plastic packaging waste management in order to tackle this issue. The research discussed the capability and potential of applying digitalization and automation to Smart Retail Technology which focuses on filling machines to manage plastic packaging waste management and increase the competitive advantage. This research uses two main approaches, namely Business Process Reengineering and Agile Project Management to recommend a new business process on ongoing project. The result of the study suggests the urgency to take advantage of the QR Code and Human Machine Interface to maximize the effectiveness and results of implementing the business process. By using simulation, this effort is expected to reach up to 334 consumers each day with up to 42.14% of machine utilization.

Smart retail SKUs checkout using improved residual network

Intelligent signal processing in unmanned stores enhances operational efficiency, notably through automated SKUs (Stock Keeping Units) recognition, which expedites customer checkout. Distinguishing itself from generic detection algorithms, the retail product detection algorithm addresses challenges like densely arranged items, varying scales, large quantities, and product similarities. To mitigate these challenges, firstly we propose a novel boundary regression neural network architecture, which enhances the detection of bounding box in dense arrangement, minimizing computational costs and parameter sizes. Secondly, we propose a novel loss function for hierarchical detection, addressing imbalances in positive and negative samples. Thirdly, we enhance the conventional non-maximum suppression (NMS) with weighted non-maximum suppression (WNMS), tying NMS ranking scores to candidate box accuracy. Experimental results on SKU-110K and RPC datasets, two public available databases, show that the proposed SKUs recognition algorithm provides improved reliablity and efficiency over existing methods.

Smart retail: How AI and IoT are revolutionising the retail industry

Technological advancements mean the retail industry is expected to spend US$7.3bn per year on artificial intelligence (AI) from 2022. Retailers that are looking to stay one step ahead are increasingly turning to AI and the Internet of Things (IoT) to help them understand the millions of data points in-store and turn them into insightful information that they can use to improve the speed and effectiveness of their business decisions. This paper demonstrates how AI+IoT (AIoT) can combine to provide a powerful digital transformation experience in retail stores. It also describes some examples of current AIoT implementations in retail stores and explains some of the latest ways retailers can adopt AIoT to drive operational excellence. We will also deep dive into one such example, electronic shelf labels (ESL), showing how these are revolutionising pricing in stores and offering some real-world examples where ESLs are driving efficiency.

Systematic Review of Cashierless Stores (Just Walk Out Stores) Revolutionizing The Retail

Abstract The paper aims to examine the evolving retail sector in recent years, specifically how digitalisation and technological innovations have transformed it. All actors have had to adapt to remain competitive. Notably, a new innovation in the retail sector, namely the checkout-free or cashierless store, emerged in 2018. Systematic literature is relied upon to achieve the study's objectives. The significance of this study lies in the use of multiple IT tools such as AI, cameras, sensors, and self-organising shelves to replace human intervention in the retail sector. Globally, several startup companies have developed this new unmanned solution, and Amazon Go stands out as one of the most well-known among them. The primary objective of this pioneering concept is to enhance efficiency by saving time and reducing queues. The aim is to enable customers to enter and exit the store with minimal human contact as quickly as possible. This paper presents the recent trend of the cashierless concept, its evolution, and proliferation. A systematic literature review and data analysis from the Crunchbase Database were conducted. The findings demonstrate that this recent concept is altering both consumers’ purchasing behaviours and companies’ business models. This paper provides novel perspectives and insights into the wider literature on cashierless concepts and smart retail in the context of digital business.

Entrepreneurship and self-service technologies as a driver of customer loyalty to the retailer during the COVID-19 pandemic

Purpose This research aims (i) to assess the level of customer satisfaction and loyalty to self-service technologies and Auchan Retail Portugal, (ii) to identify the determinants of customer satisfaction and loyalty to these technologies and also, (iii) to identify their influence on customer loyalty to this modern distribution retailer operating in Portugal.Design/methodology/approach A conceptual model was defined to meet the research objectives and to carry out the quantitative analysis applied to the random sample (n = 483) of customers. The data used where gathered via an online questionnaire survey, which covered all dimensions of the conceptual model, applied in 2021 in Portugal. To validate the hypotheses, Cronbach’s alpha and multiple linear regression models were used.Findings The results reveal that customer satisfaction with self-service technologies has a direct and positive effect on customer loyalty to Auchan Retail Portugal. Furthermore, results reveal that the technology utility factors significantly influence the customer technology experience which has an impact on perceived service quality and perceived risk. The findings of this research provide data on how to improve customer adoption and satisfaction with self-service technology and highlight that these technologies should be part of firm’s competitive strategy.Originality/value This study presents itself as a novelty for science, while granting important contributions to the retailer. It presents an innovative conceptual model that delivers to Auchan the basis for it to move toward smart retail technologies, aiming at the market trend of personalization. For future research, this study can be used as an instrument to evaluate the customer experience with self-service technology and to examine the determinants and effects of self-service technology separately.