Search Results (18)

We explored the adoption of virtual try-on (VTO) technology in Taiwan’s fashion retail sector, which has gained prominence as consumer behavior online has changed since the COVID-19 pandemic. Using an extended unified theory of acceptance and use of technology 2 (UTAUT2) model, we examined the behavioral intentions and actual usage of VTO. The original framework of the UTAUT2 model was modified by excluding experience and incorporating personality traits as moderating variables. Based on 257 valid survey responses analyzed using SmartPLS 4.1, influencing factors were identified, revealing that gender was a significant moderator in VTO adoption. Full article

To enhance the accuracy of product recognition in non-store retail sales and address misidentification and missed detection caused by occlusion in densely placed goods, we propose an improved YOLOv8-based network: Dense-YOLO. We first introduce an enhanced multi-scale feature extraction module (EMFE) in the feature extraction layer and employ a lightweight feature fusion strategy (LFF) in the feature fusion layer to improve the network’s performance. Next, to enhance the performance of dense product recognition, particularly when handling small and multi-scale objects in complex settings, we propose a novel multi-scale dynamic interaction attention mechanism (MDIAM). This mechanism combines dynamic channel weight adjustment and multi-scale spatial convolution to emphasize crucial features, while avoiding overfitting and enhancing model generalization. Finally, a cross-layer feature interaction mechanism is introduced to strengthen the interaction between low- and high-level features, further improving the model’s expressive power. Using the public COCO128 dataset and over 2000 daily smart retail cabinet product images compiled in our laboratory, we created a dataset covering 50 product categories for ablation and comparison experiments. The experimental results indicate that the accuracy under MDIAM is improved by 1.6% compared to other top-performing models. The proposed algorithm achieves an mAP of 94.9%, which is a 1.0% improvement over the original model. The enhanced algorithm not only significantly improves the recognition accuracy of individual commodities but also effectively addresses the issues of misdetection and missed detection when multiple commodities are recognized simultaneously. Full article

As consumer behavior increasingly shifts toward hyperlocal, digitally mediated retail journeys, community unmanned stores have emerged as a transformative model that integrates smart technologies with community proximity services. These fully automated stores offer convenient, contactless shopping and hybrid digital–physical interactions, playing an increasingly important role within broader omnichannel digital retail ecosystems. However, there remains a lack of validated instruments to assess customer experience in such autonomous and locally embedded retail formats. This study develops and validates an ECUS-scale (an experience in community unmanned store scale), a multidimensional measurement tool grounded in qualitative research and refined through exploratory and confirmatory factor analysis. The scale identifies nine key dimensions—convenient service, smooth transaction, preferential price, good quality, safe environment, secure payment, comfortable space, comfortable interaction, and friendly image—across 36 items. These dimensions reflect the technological, spatial, and emotional–social aspects of customer experience in unmanned retail settings. The findings demonstrate that the ECUS-scale offers a robust framework for evaluating consumer experience in low-staffed, tech-enabled community stores, with strong relevance to omnichannel digital retail strategies. Theoretically, it advances the literature on smart retail experience by capturing underexplored dimensions such as emotional engagement with technology and perceptions of safety in staff-free environments. Practically, it serves as a diagnostic tool for businesses to enhance experience design and optimize customer engagement across digital and physical touchpoints. Full article

The retail industry faces increasing challenges in matching supply with demand due to evolving consumer behaviors, market volatility, and supply chain disruptions. While existing approaches employ statistical and machine learning methods for demand forecasting, they often fail to capture complex temporal dependencies and lack the ability to simultaneously optimize inventory decisions. This paper proposes a novel multi-agent deep reinforcement learning framework that jointly optimizes demand forecasting and inventory management in retail supply chains, leveraging data from IoT sensors, RFID tracking systems, and smart shelf monitoring devices. Our approach combines transformer-based sequence modeling for demand patterns with hierarchical reinforcement learning agents that coordinate inventory decisions across distribution networks. The framework integrates both historical sales data and real-time sensor measurements, employing attention mechanisms to capture seasonal patterns, promotional effects, and environmental conditions detected through temperature and humidity sensors. Through extensive experiments on large-scale retail datasets incorporating sensor network data, we demonstrate that our method achieves 18.2% lower forecast error and 23.5% reduced stockout rates compared with state-of-the-art baselines. The results show particular improvements in handling promotional events and seasonal transitions, where traditional methods often struggle. Our work provides new insights into leveraging deep reinforcement learning for integrated retail operations optimization and offers a scalable solution for modern sensor-enabled supply chain challenges. Full article

This study explores how visual merchandising and store layout influence customer shopping decisions. This research aims to identify the key factors that shape consumer behavior and provide useful insights for retailers by using interactive marketing factors. Data were collected from 488 respondents in the South Muntenia region of Romania, a key economic hub with significant retail activity. Given its economic diversity and proximity to Bucharest, this region serves as a representative microcosm of consumer behavior trends in Romania. The survey was distributed physically and virtually through social media platforms (Facebook and WhatsApp groups, and email networks) to ensure a diverse and representative sample. To analyze the data, this study utilized SmartPLS, a powerful tool for structural equation modeling (SEM). This approach allowed for an in-depth examination of the relationships between visual merchandising, store layout, and customer shopping behavior, providing a more comprehensive view of the conceptual model. This study’s theoretical contributions stem from its holistic approach, which combines different aspects of visual merchandising and store layout into a unified conceptual model. By analyzing how these elements work together, this study offers a deeper understanding of customer shopping behavior. Retailers are encouraged to prioritize product arrangement, make effective use of exterior lighting, and maintain engaging window displays. These strategies can attract more customers, encourage impulse purchases, and improve the overall store profitability. This study underscores the critical role that visual merchandising and store layout play in shaping customer shopping decisions. By optimizing product layout, enhancing exterior lighting, and creating captivating window displays, retailers can design a more engaging shopping experience that boosts customer satisfaction, increases sales, and builds customer loyalty. Full article

This article introduces a novel smart deployable scissor lift brace system designed to mitigate earthquake risks in buildings prone to the soft-story effect. The system addresses the limitations of traditional retrofitting methods, providing an efficient solution for enhancing the structural integrity of buildings while preserving the functionality of open lower floors, commonly used for car parking or retail spaces. The soft-story effect, characterized by a sudden reduction in lateral stiffness in one or more levels of a building, often leads to catastrophic collapses during large earthquakes, resulting in significant structural damage and loss of life. The proposed system is triggered by signals from the Earthquake Early Warning (EEW) system, advanced technologies capable of detecting and broadcasting earthquake alerts within seconds which are currently implemented in countries and regions such as Japan, parts of the USA, and parts of Europe. The smart deployable system functions by instantly activating upon receiving EEW signals. Unlike traditional retrofitting approaches, such as adding braces or infill walls, which compromise the open layout of lower floors, this innovative device deploys dynamically during seismic events to enhance the building’s stiffness and lateral stability. The article demonstrates the system’s functionality through a conceptual framework supported by proof-of-concept experiments. Historical earthquake time histories are simulated to test its effectiveness. The results reveal that the system significantly improves the stiffness of the structure, reducing displacement responses during events of seismic activity. If properly proportioned and optimized, this system has the potential for widespread commercialization as a seismic risk mitigation solution for buildings vulnerable to the soft-story effect. Full article

Here, we examine the extension of smart retailing from the indoor confines of stores, outward to high streets. We explore how several technologies at the union of retail intelligence and smart city monitoring could coalesce into retail high streets that are both smart and sentient. We examine the new vantages that smart and sentient retail high streets provide on the customer journey, and how they could transform retailers’ sway over customer experience with new reach to the public spaces around shops. In doing so, we pursue a three-way consideration of these issues, examining the technology that underpins smart retailing, new advances in artificial intelligence and machine learning that beget a level of street-side sentience, and opportunities for retailers to map the knowledge that those technologies provide to individual customer journeys in outdoor settings. Our exploration of these issues takes form as a review of the literature and the introduction of our own research to prototype smart and sentient retail systems for high streets. The topic of enhancing retailers’ acuity on high streets has significant currency, as many high street stores have recently been struggling to sustain custom. However, the production and application of smart and sentient technologies at hyper-local resolution of the streetscape conjures some sobering considerations about shoppers’ and pedestrians’ rights to privacy in public. Full article

To provide analytic materials for business management for smart retail solutions, it is essential to recognize various customer behaviors (CB) from video footage acquired by in-store cameras. Along with frequent changes in needs and environments, such as promotion plans, product categories, in-store layouts, etc., the targets of customer behavior recognition (CBR) also change frequently. Therefore, one of the requirements of the CBR method is the flexibility to adapt to changes in recognition targets. However, existing approaches, mostly based on machine learning, usually take a great deal of time to re-collect training data and train new models when faced with changing target CBs, reflecting their lack of flexibility. In this paper, we propose a CBR method to achieve flexibility by considering CB in combination with primitives. A primitive is a unit that describes an object’s motion or multiple objects’ relationships. The combination of different primitives can characterize a particular CB. Since primitives can be reused to define a wide range of different CBs, our proposed method is capable of flexibly adapting to target CB changes in retail stores. In experiments undertaken, we utilized both our collected laboratory dataset and the public MERL dataset. We changed the combination of primitives to cope with the changes in target CBs between different datasets. As a result, our proposed method achieved good flexibility with acceptable recognition accuracy. Full article

Cryptocurrency is gaining popularity worldwide, with some countries already starting to regulate and accept cryptocurrency in their financial services. Malaysia’s Securities Commission (SC) announced in October 2021 that over MYR 16 billion (USD 3.85 billion) involving digital assets and cryptocurrencies were traded between August 2020 and September 2021. Since cryptocurrencies are issued by private corporations and are technically beyond the federal government’s control, criminals may use them for illegal reasons such as money laundering and terrorist funding. Consequently, it is vital to examine why investors are engaged in cryptocurrency in the first place. This study aims to provide insight into Malaysian investors’ perceptions by evaluating the influence of perceived risk and perceived value on their cryptocurrency adoption decision. The retail investors’ demographic characteristics (gender, age, education, income, and investment experience) were analyzed as control variables. Data were gathered using purposive sampling, and responses from 211 respondents from various cities in Malaysia were used in the final analysis. Data were examined using Smart PLS Structural Equation Modelling (PLS-SEM). Based on the finding’s, perceived value was found to have a significant influence on cryptocurrency adoption. Meanwhile, perceived risk had no significant influence on the adoption of cryptocurrency among the Malaysian investors. Full article

Human–computer interactions (HCIs) use computer technology to manage the interfaces between users and computers. Object detection systems that use convolutional neural networks (CNNs) have been repeatedly improved. Computer vision is also widely applied to multiple specialties. However, self-checkouts operating with a faster region-based convolutional neural network (faster R-CNN) image detection system still feature overlapping and cannot distinguish between the color of objects, so detection is inhibited. This study uses a mask R-CNN with data augmentation (DA) and a discrete wavelet transform (DWT) in lieu of a faster R-CNN to prevent trivial details in images from hindering feature extraction and detection for deep learning (DL). The experiment results show that the proposed algorithm allows more accurate and efficient detection of overlapping and similarly colored objects than a faster R-CNN with ResNet 101, but allows excellent resolution and real-time processing for smart retail stores. Full article

Transactive energy systems (TS) use automated device bidding to access (residential) demand flexibility and coordinate supply and demand on the distribution system level through market processes. In this work, we present TESS, a modularized platform for the implementation of TS, which enables the deployment of adjusted market mechanisms, economic bidding, and the potential entry of third parties. TESS thereby opens up current integrated closed-system TS, allows for the better adaptation of TS to power systems with high shares of renewable energies, and lays the foundations for a smart grid with a variety of stakeholders. Furthermore, despite positive experiences in various pilot projects, one hurdle in introducing TS is their integration with existing tariff structures and (legal) requirements. In this paper, we therefore describe TESS as we have modified it for a field implementation within the service territory of Holy Cross Energy in Colorado. Importantly, our specification addresses challenges of implementing TS in existing electric retail systems, for instance, the design of bidding strategies when a (non-transactive) tariff system is already in place. We conclude with a general discussion of the challenges associated with “brownfield” implementation of TS, such as incentive problems of baseline approaches or long-term efficiency. Full article

Retail enterprises are embracing new technologies to provide innovative services to customers and engage them. Unmanned retail stores offer a completely new seamless shopping experience for customers. Moreover, artificial intelligence (AI) technology and sophisticated customer behavior should be explored in depth to develop a smart system to serve customers. This study focused on achieving a sustainable user experience with a smart system installed in an autonomous store. The development of core functions and a rule-based knowledge set are regarded as the most important tasks in designing autonomous services. In the case study, the core functions were developed on the basis of the design science concept and the rule-based knowledge set was constructed using the action research approach. The developed smart system was optimized to understand in-store customer behavior by continuously observing and refining the user experience. The practical experience of this study provides insights into AI technology use in retailing and can guide enterprises in developing smart systems. Full article

In recent years, blockchains systems have seen massive adoption in retail and enterprise environments. Cryptocurrencies become more widely adopted, and many online businesses have decided to add the most popular ones, like Bitcoin or Ethereum, next to Visa or Mastercard payments. Due to the decentralized nature of blockchain-based systems, there is no possible way to revert confirmed transactions. It may result in losses caused by human error or poor design of the user interface. We created a cryptocurrency wallet with a full on-chain solution for aliasing accounts and tokens to improve user experience and avoid unnecessary errors. The aliasing system consists of a number of smart contracts deployed on top of the blockchain network that give the ability to register aliases to accounts and tokens and use them instead of opaque addresses. Our solution shows how performant modern blockchains are and presents a way of building fully decentralized applications that can compete with centralized ones in terms of performance. Full article

With the reduction in offline fashion stores, retailers are trying to revitalize offline stores by applying smart retail technologies. This study aimed to determine how factors related to the offline–mobile connected smart retailing experience affected satisfaction through perceived quality and perceived risk. An online survey was conducted on female consumers in their 20s and 30s, and 302 questionnaires were distributed. The analysis, which utilized a structural equation model, confirmed that, from among five smart retailing experience-related factors, perceived advantage, perceived enjoyment, and interactivity affected perceived quality and that perceived advantage and interactivity significantly affected perceived risk. However, perceived control and personalization did not affect perceived quality and perceived risk. Furthermore, perceived quality significantly affected overall satisfaction, offline satisfaction, and mobile satisfaction, while perceived risk did not affect mobile satisfaction. This study confirmed that the perceived advantage and interactivity of smart retailing experiences play an important role in enhancing customer satisfaction. Full article

The problem concerning facilities delocation in the retail sector is addressed in this paper by proposing a novel mixed 0-1 linear optimization model. For this purpose, the aim of the problem is to decide whether to close existing stores or consider an alternative type of store management policy aimed at optimizing the profit of the entire retail network. Each management policy has a different repercussion on the final profit of the stores due to the different margins obtained from the customers. Furthermore, closing stores can cause customers to leave the whole retail network according to their behavior. This behavior is brought about through their tendency to abandon this network. There are capacity constraints imposed depending on the number of stores that should stay open and cease operation costs, customer behavior and final prices. These constraints depend on the type of management policy implemented by the store. Due to the commercial requirements concerning customer behavior, a set of non-linear constraints appears in the definition of the model. Classical Fortet inequalities are used in order to linearize the constraints and, therefore, obtain a mixed 0-1 linear optimization model. As a result of the size of the network, border constraints have been imposed to obtain results in a reasonable computing time. The model implementation is done by introducing smart sets of indices to reduce the number of constraints and variables. Finally, the computational results are presented using data from a real-world case study and, additionally, a set of computational experiments using data randomly generated as shown. Full article