Search Results (132)

The aim of this study is to analyze the factors that influence consumer referents or reference points and their interaction during the decision-making process, along with the principles of prospect theory in the metaverse with market and retail examples. We conducted an integrative literature review. Consumers’ preference for reference points is determined and structured during the buying process, which can be affected by potential signals and biased decisions. To guide consumers’ shopping experiences and purchasing behavior in the most effective way, marketers and organizations must investigate the factors that influence consumer reference points beyond physical or tangible attributes. Businesses must be adaptable and adapt their strategies to changing consumer preferences based on reference points. Our findings can advance discussions about how reference points are being used in the market by using consumer decision-making claims in the discursive construction of the metaverse. By comprehending this, developers can create better experiences and assist users in navigating virtual risks. Our research aids us in better comprehending the influence of referents on consumer purchasing decisions in the marketing communications field. Numerous opportunities for academic research into consumer reference points have arisen, in which individuals as digital consumers are influenced by the same biases and heuristics that guide their behavior in reality. Full article

As smart voice assistants (SVAs) become increasingly integrated into digital commerce, understanding the psychological factors driving their adoption or resistance is essential. While prior research has addressed the impact of privacy concerns, few studies have explored the competing forces that shape user decisions. This study investigates the dual role of privacy cynicism as a context-specific belief influencing both trust (reason-for) and perceived creepiness (reason-against)—which in turn affect attitudes, behavioral intentions, and resistance toward SVA usage, based on the Behavioral Reasoning Theory (BRT). The study used a convenience sampling method, gathering data from 250 Turkish consumers aged 18–35 through an online survey technique. The research model was analyzed using PLS-SEM. The findings revealed that perceived creepiness increases resistance intention but does not significantly affect attitudes toward using SVAs. Perceived cynicism was found to positively influence perceived trust, and perceived trust, in turn, increased both behavioral intentions and attitudes toward using SVAs. Furthermore, attitudes toward SVA usage decreased resistance intention but increased behavioral intention. The results emphasize consumer trust and skepticism in AI-driven marketing. The study offers both theoretical contributions by extending BRT with a novel dual-path conceptualization of privacy cynicism, and practical implications for developers aiming to boost SVA adoption through trust-building and privacy assurance strategies. Full article

It is great challenge for visually impaired (VI) people to shop in narrow and crowded farmers’ markets. However, there is no research related to guiding them in farmers’ markets worldwide. This paper proposes the Radio-Frequency–Visual Tag Positioning and Automatic Detection (RFTPAD) algorithm to quickly build a high-precision navigation map. It combines the advantages of visual beacons and radio-frequency signal beacons to accurately calculate the guide robot’s coordinates to correct its positioning error and simultaneously perform the task of mapping and detecting information. Furthermore, this paper proposes the A*-Fixed-Route Navigation (A*-FRN) algorithm, which controls the robot to navigate along fixed routes and prevents it from making frequent detours in crowded aisles. Finally, this study equips the guide robot with a flexible robotic arm and proposes the Intelligent-Robotic-Arm-Guided Shopping (IRAGS) algorithm to guide VI people to quickly select fresh products or guide merchants to pack and weigh products. Multiple experiments conducted in a 1600 m² market demonstrate that compared with the classic mapping method, the accuracy of RFTPAD is improved by 23.9%. What is more, compared with the general navigation method, the driving trajectory length of A*-FRN is 23.3% less. Furthermore, the efficiency of guiding VI people to select products by a robotic arm is 100% higher than that through a finger to search and touch. Full article

This study proposes a shopping assistant robot, called CartBot, to facilitate the grocery shopping experience for customers/shoppers. A grocery store environment can be complex and confusing to customers. Therefore, the main aim is to assist customers in navigating this environment efficiently while carrying their purchased items, and hence improve the overall shopping experience and reduce shopping time. To achieve this, a unified framework for implementing path planning and collision avoidance in a supermarket environment is proposed. Here, with a shopping list as the input, an efficient (or near-optimal) global path is generated to complete the shopping. Then, a real-time local planner is proposed to navigate this path while avoiding any obstacles that are encountered. Various features/strategies to facilitate navigation and obstacle interactions are also addressed in this work. Simulation studies of CartBot are carried out in a grocery store environment along with other CartBots, employees, and human shoppers to validate the performance of the proposed approach. Full article

Online shopping has become popular due to its convenience and potential cost savings. However, clothing size cannot be accurately estimated, particularly when buying shirts. Many shoppers provide size choices but with inaccurate fits. To assist users in selecting the correct size when purchasing t-shirts online, we estimated shirt size using calculated upper body dimensions. Computer vision algorithms, including YOLO, PoseNet, body contour detection, and a trained convolutional neural network (CNN) model were employed to estimate shirt sizes from 2D images. The model was tested using images of 30 participants taken at a distance of 180–185 cm away from a Raspberry Pi camera. The estimation accuracy was 70%. Inaccurate predictions were attributed to the precision of body measurements from computer vision and image quality, which needs to be solved in further studies. Full article

Zero-defect manufacturing is one of the most promising strategies to mitigate failures within manufacturing processes, allowing industries to increase product quality efficiently and effectively. One of the challenges faced in the practical adoption of zero-defect manufacturing is that the most important aspect of manufacturing, people, is often neglected. Aiming to support shop floor operators, this work introduces a human-centric approach assisting them to become aware of defects in the production line and imminently reconfigure it. Our system comprises an Augmented Reality application that encompasses interfaces that dynamically adapt to different contexts of use and enable operators to interact naturally and effectively and reconfigure the manufacturing process. The system leverages the efficiency of the shop floor operators in monitoring and controling the production line they are working on, according to the task they are performing, and their level of expertise, to produce appropriate visual components. To demonstrate the versatility and generality of the proposed system we evaluated it in three different production lines, conducting cognitive walkthroughs with experts and user-based evaluations with thirty shop floor operators. The results demonstrate that the system is intuitive and user-friendly, facilitating operator engagement and situational awareness, enhancing operator attentiveness, and achieving improved operational outcomes. Full article

The job shop scheduling problem (JSSP) is a classical NP-hard combinatorial optimization challenge that plays a crucial role in manufacturing systems. Deep reinforcement learning has shown great potential in solving this problem. However, it still has challenges in reward function design and state feature representation, which makes it suffer from slow policy convergence and low learning efficiency in complex production environments. Therefore, a human feedback-based large language model-assisted deep reinforcement learning (HFLLMDRL) framework is proposed to solve this problem, in which few-shot prompt engineering by human feedback is utilized to assist in designing instructive reward functions and guiding policy convergence. Additionally, a self-adaptation symbolic visualization Kolmogorov–Arnold Network (KAN) is integrated as the policy network in DRL to enhance state feature representation, thereby improving learning efficiency. Experimental results demonstrate that the proposed framework significantly boosts both learning performance and policy convergence, presenting a novel approach to the JSSP. Full article

This study proposes a design method for the evaluation and redesign of product–service systems (PSSs) from the perspective of social sustainability, one that applies Max-Neef’s framework of fundamental human needs. The proposed method systematically connects PSS functions and requirements—identified through service blueprints and value graphs—to “satisfiers” and “barriers” extracted via needs-based workshops. This connection enables the identification of functions that either contribute to or hinder the fulfillment of fundamental human needs and guide the generation of redesign proposals aimed at sufficiency-oriented outcomes. A case study involving a smart-cart system in Osaka, Japan, was conducted to demonstrate the applicability of the method. Through an online workshop, satisfiers and barriers related to both physical and online shopping experiences were identified. The analysis revealed that existing functions such as promotional information and automated checkout processes negatively impacted needs such as understanding and affection due to information overload and reduced human interaction. In response, redesign concepts were developed, including filtering options for information, product background storytelling, and optional slower checkout lanes with human assistants. The redesigned functions contribute to the fulfillment of fundamental human needs, indicating that the proposed method can enhance social sustainability in PSS design. This study offers a novel framework that extends beyond traditional customer requirement-based approaches by explicitly incorporating human needs into function-level redesign. Full article

In response to increasing numbers of people switching from offline to online shopping after the COVID-19 pandemic, this study sought to obtain an in-depth understanding of apparel website design and digital accessibility for all people, including people with visual impairments (PVI). Given the Convention on the Rights of Persons with Disabilities Article 9, which mandates that all 191 international parties take measures to ensure people with disabilities have equal access to information and communication technology, this study explored the challenges PVI face while accessing informational content about apparel products online. To achieve this goal, Culnan’s dimensions of perceived accessibility to information, a lens for understanding how consumers experience and evaluate the accessibility of information systems, were used as the theoretical framework. We applied phenomenological methods to explore the daily “lived experience” in depth through observations and semi-structured interviews with eight female participants in their 20 s to 60 s, each lasting more than 45 min. Based on thematic analysis, the findings highlighted the unmet website meta descriptions for product information and navigation functionality for assistive technology, which, as a result, negatively impacts digital accessibility for PVI to shop online for apparel. The study concludes with contributions that extend the theoretical framework to the digital landscape, addresses the gap of inclusive digital apparel retailing practices, and emphasizes the opportunities for apparel educators to incorporate an inclusive design curriculum. Full article

The reclamation of illegal landfills poses a significant threat to the environment. An example of such a case is Łomianki near Warsaw, where an illegal landfill contained alarming levels of arsenic and chromium, posing a potential risk to the health of local residents due to the possibility of these metals contaminating a nearby drinking water source. Initial geochemical tests revealed high concentrations of these metals, with chromium reaching up to 24,660 mg/kg and arsenic up to 10,350 mg/kg, well above international environmental standards. This study presents effective reclamation strategies that can be used in similar situations worldwide. The reclamation allowed this land to be used for the construction of the M1 shopping center while minimizing environmental hazards. The study is based on a case study of the reclamation of this illegal landfill. The methods used in this project included the relocation of approximately 130,000 m³ of hazardous waste to a nearby site previously used for sand mining. Bentonite mats and geotextiles were used to prevent the migration of contaminants into the groundwater. The waste was layered with sand to assist in the structural stabilization of the site. In addition, proper waste segregation and drainage systems were implemented to manage water and prevent contamination. Eight years after the reclamation, post-remediation soil surveys showed significant improvements in soil quality and structural stability. Specifically, the Proctor Compaction Index (I_S) increased from an estimated 0.5–0.7 (for uncontrolled slope) to 0.98, indicating a high degree of compaction and soil stability, while arsenic and chromium levels were reduced by 98.4% and 98.1%, respectively. Reclamation also significantly reduced permeability and settlement rates, further improving the site’s suitability for construction. The cost-benefit analysis showed a cost saving of 37.7% through local waste relocation compared to off-site disposal, highlighting the economic efficiency and environmental benefits. The main conclusions of this study are that land reclamation effectively reduced environmental hazards; innovative solutions, such as bentonite mats, advanced waste sorting, geotextiles, and drainage systems, improved environmental quality; and the Łomianki case serves as a model for sustainable waste management practices. Full article

The empowerment of lean intelligent manufacturing technologies has provided a solid foundation for enterprises to achieve a balance between economic benefits and sustainable development. In production workshops, various disruptive factors, especially in multi-variety small-batch production environments, often lead to deviations from the planned schedule. This creates an urgent need to enhance the workshop’s dynamic responsiveness and self-regulation capabilities. Existing single-trigger mechanisms in job shops focus on changes in overall performance or deviations from production goals but lack a representation of the varying degrees of impact on different equipment under multiple disturbances. This results in either over-scheduling or under-scheduling in terms of scope, thereby impacting the optimization of production efficiency and resource utilization. To address this, this paper proposes a method for coordinated decision-making on rescheduling timing and location in intelligent job shops under disturbance environments. First, by analyzing the relationship between disturbance impact and the scope of rescheduling implementation, a mapping relationship is established between disturbance impact and disturbance response hierarchy. A trigger is set up on each piece of equipment to characterize the differences in the degree of impact on different equipment, which not only reduces the complexity of disturbance information processing but also provides support for specific location decisions for disturbance response. Second, a decision module for the triggers is constructed using a multilayer perceptron, establishing a mapping relationship between process and workpiece data attributes and response categories. Based on the basic processing units of the manufacturing process and the relevant quantitative indicators of the processed objects, disturbance response strategies are generated. Finally, through a case study, the proposed method is evaluated and validated in an intelligent factory setting. The new rescheduling decision support method can effectively make timing and location decisions for disturbance events. Full article

In recent years, the global economy has experienced significant shifts, leading to a trend of consumption downgrading. Amid economic pressures and uncertainties, consumers are increasingly turning to cost-effective shopping methods. The COVID-19 lockdowns further accelerated the growth of e-commerce platforms, presenting both opportunities and challenges for sales. Electronic commerce has played a crucial role in enhancing the sales of agricultural products with regional characteristics in China, thereby opening new channels for farmers. This article utilizes tangerines, particularly popular in Zhejiang Province, as a case study to explore e-commerce reviews and assist merchants in delivering more satisfactory products. The analysis of tangerine reviews revealed that customers primarily focused on the taste, service, quality, and price. By applying the latent Dirichlet allocation (LDA) topic model, comments were categorized into four themes: ‘quality’, ‘service’, ‘price’, and ‘flavor’, with key terms identified for each theme. Through sentiment analysis using SnowNLP and bidirectional encoder representations from transformers (BERT), it was found that online shoppers generally expressed positive sentiment toward tangerines. However, there was also some negative feedback. These findings are of paramount importance for businesses aiming to meet consumer demands. The study acknowledges certain limitations including the reliability of data mining and the accuracy of Chinese corpus analysis. Future research could benefit from employing more precise language models to enhance the analysis, ultimately improving the consumer shopping experience and aiding businesses in service improvement. Full article

Customer context analysis (CCA) in brick-and-mortar shopping malls can support decision makers’ marketing decisions by providing them with information about customer interest and purchases from merchants. It makes offline CCA an important topic in marketing. In order to analyze customer context, it is necessary to analyze customer behavior, as well as to obtain the customer’s location, and we propose an analysis system for customer context based on these two aspects. For customer behavior, we use a modeling approach based on the time-frequency domain, while separately identifying movement-related behaviors (MB) and semantic-related behaviors (SB), where MB are used to assist in localization and the positioning result are used to assist semantic-related behavior recognition, further realizing CCA generation. For customer locations, we use a deep-learning-based pedestrian dead reckoning (DPDR) method combined with a node map to achieve store-level pedestrian autonomous positioning, where the DPDR is assisted by simple behaviors. Full article

In the current era of advanced IoT technology, human occupancy monitoring and positioning technology is widely used in various scenarios. For example, it can optimize passenger flow in public transportation systems, enhance safety in large shopping malls, and adjust smart home devices based on the location and number of occupants for energy savings. Additionally, in homes requiring special care, it can provide timely assistance. However, this technology faces limitations such as privacy concerns, environmental factors, and costs. Traditional cameras may not effectively address these issues, but infrared thermal sensors can offer similar applications while overcoming these challenges. Infrared thermal sensors detect the infrared heat emitted by the human body, protecting privacy and functioning effectively day and night with low power consumption, making them ideal for continuous monitoring scenarios like security systems or elderly care. In this study, we propose a system using the AMG8833, an 8 × 8 Infrared Thermal Array Sensor. The sensor data are processed through interpolation, adaptive thresholding, and blob detection, and the merged human heat signatures are separated. To enhance stability in human position estimation, a dynamic sliding window adjusts its size based on movement speed, effectively handling environmental changes and uncertainties. Full article

Background/Objectives: Maintaining functional independence and minimizing disability among older adults living in the community is paramount for mitigating rising care demands. Our study focused on shopping as a critical instrumental activity of daily living (ADL) to explore the association between shopping assistance and functional decline among older individuals receiving support through long-term care insurance (LTCI). Methods: This retrospective, cross-sectional study included 6202 participants aged >65 years living in a Japanese regional town receiving LTCI support, suggesting that they required assistance with local community life. Logistic regression analysis identified several factors associated with shopping assistance among the participants, including physical and cognitive functions, functional ADL, and psychobehavioral symptoms. Results: In male participants, walking dysfunction, short-term memory decline, decreased frequency of going outdoors, and decreased engagement in personal grooming were significantly associated with requiring shopping assistance. Conversely, in female participants, reduced physical function and walking performance were significantly associated with requiring shopping assistance, whereas dependence on personal grooming was less pronounced than in male participants. Conclusions: These findings suggest that, in addition to direct shopping assistance, tailored interventions targeting physical, cognitive, and ADL functions—while considering gender-specific needs—may help older adults maintain independence in shopping activities as part of their daily community life. Full article