Search Results (116)

Although digital hoarding behavior does not directly affect physical space, with the popularization of cloud storage services, its impact on energy consumption has become increasingly significant, posing a challenge to environmental sustainability. This study focuses on the factors influencing consumer digital hoarding behavior on e-commerce platforms, aiming to provide management decision-making references for e-commerce enterprises to deal with consumer digital hoarding phenomena and improve transaction effectiveness. Based on the Motivation–Opportunity–Ability (MOA) Theory and through the Adversarial Interpretive Structure Modeling Method (AISM), this study systematically identifies and analyzes the influencing factors. The findings reveal that emotional attachment, burnout, and fear of missing out are the main motivational factors directly affecting consumer digital hoarding behavior, with strong interconnections between these factors. Perceived usefulness and platform interaction design are significant opportunity factors, indirectly affecting digital hoarding behavior by improving user experience and satisfaction. E-commerce platform convenience, anticipated ownership, perceived economic value, emotional regulation ability, auxiliary shopping decision-making, perceived behavioral control, and information organization ability are the foundational and intermediate factors. The research results emphasize the importance of understanding consumer digital hoarding behavior in the context of sustainable development. This is not only conducive to optimizing the shopping cart function and data management strategy of e-commerce platforms and improving transaction conversion rates but also provides a reference for policymakers to formulate data management and privacy protection policies. Full article

Pick-up points (PUPs) have become a very attractive alternative for conventional home deliveries due to the growth of e-commerce. This paper investigates the level of satisfaction of the students (Gen Z) as well as the research, teaching, and administrative staff of the Aristotle University of Thessaloniki (AUTH), Greece, and the University of Enna “Kore”, Italy, implemented in November 2023. Optimizing the PUP users’ satisfaction is contingent upon various aspects, including but not limited to location accessibility, expedient pick-up procedures, unambiguous communication, and ensured item availability. The research recorded information about the users’ knowledge about the specific service, their level of satisfaction, their preferences on when and how they use the service, and information about the types of goods they order using the PUPs as their point of collection. The analysis of the collected data revealed very interesting findings that could be useful to the providers of this service, especially when taking into consideration that the majority of the poll’s participants are familiar with the existence of the PUPs in the Municipality of Thessaloniki, that they use this service mainly occasionally, and that the majority are quite pleased with the level of the provided services (accessibility, availability, safety, and security). For the case of Enna in Sicily, similar trends are shown: a high percentage of respondents are familiar with PUPs, and they use pick-up points occasionally and are pleased with the provided level of service. The comparative statistical analysis makes it possible to compare two contexts located in areas of the Mediterranean, i.e., two urban areas with different population sizes but with similar habits on the part of the university student cluster. Full article

The integration of the Internet of Things (IoT) and Distributed Computing Systems (DCS) is transforming business models across industries. IoT devices allow immediate monitoring of equipment and processes, mitigating lost time and enhancing efficiency. In this case, manufacturing companies use IoT sensors to monitor machinery, predict failures, and schedule maintenance. Also, automation via IoT reduces manual intervention, resulting in boosted productivity in smart factories and automated supply chains. IoT devices generate this vast amount of data, which businesses analyze to gain insights into customer behavior, operational inefficiencies, and market trends. In turn, Distributed Computing Systems process this data, providing actionable insights and enabling advanced analytics and machine learning for future trend predictions. While, IoT facilitates personalized products and services by collecting data on customer preferences and usage patterns, enhancing satisfaction and loyalty, IoT devices support new customer interactions, like wearable health devices, and enable subscription-based and pay-per-use models in transportation and utilities. Conversely, real-time monitoring enhances security, as distributed systems quickly respond to threats, ensuring operational safety. It also aids regulatory compliance by providing accurate operational data. In this way, this study, through a Bibliometric Literature Review (LRSB) of 91 screened pieces of literature, aims at ascertaining to what extent the aforementioned capacities, overall, enhance business models, in terms of efficiency and effectiveness. The study concludes that those systems altogether leverage businesses, promoting competitive edge, continuous innovation, and adaptability to market dynamics. In particular, overall, the integration of both IoT and Distributed Systems in business models augments its numerous advantages: it develops smart infrastructures e.g., smart grids; edge computing that allows data processing closer to the data source e.g., autonomous vehicles; predictive analytics, by helping businesses anticipate issues e.g., to foresee equipment failures; personalized services e.g., through e-commerce platforms of personalized recommendations to users; enhanced security, while reducing the risk of centralized attacks e.g., blockchain technology, in how IoT and Distributed Computing Systems altogether impact business models. Future research avenues are suggested. Full article

The growth in per capita consumption levels and the e-commerce industry have shifted the shopping mall model from a product consumption to a time-based consumption focus. This paper evaluates customer satisfaction with shopping malls using an importance–performance questionnaire, which identified eight key areas for improvement. Through field surveys and virtual reality (VR) simulation experiments, the impact of the components of public spaces within shopping malls on user satisfaction was assessed using both quantifiable and unquantifiable elements. Our findings inform the formulation of optimization design strategies in the areas of multipurpose functionality of facilities, intensification of boundaries, complexity of functions, and integration of resources, and have implications for the future design of internal public spaces in shopping malls. Full article

This study examines the relationships between socio-demographic factors, purchase frequency, internet expertise, and unethical return behavior in apparel e-commerce, with a particular focus on the act of wardrobing—wearing and then returning used apparel. The research involved a survey of 1026 online apparel consumers from Portugal and the Czech Republic. The results show that frequent buyers, internet-savvy users, women and younger e-consumers report more satisfactory return experiences. However, several e-consumers engage in wardrobe shopping, with higher rates observed among males, internet-savvy users and youth. There are differences between the countries studied: in the Czech sample, men and advanced internet users are more likely to engage in wardrobing, while in the Portuguese sample, it is more prevalent among younger e-consumers. The results also document that, overall, men are seven times more likely to practice unethical return, while increasing age decreases the likelihood. The originality of the study lies in its approach and findings, which contribute to the understanding of post-purchase behavior and moral hazards in e-commerce and highlight the need for retailers to balance return policies that prevent abuse while maintaining customer satisfaction. Recommendations are made for improving loyalty programs and personalizing the e-shopping experience to minimize returns and promote ethical consumer behavior. Further research is suggested to develop these findings and improve return management in apparel e-commerce. Full article

Logistics service quality (LSQ) plays a vital role in providing excellent customer experience, particularly in e-commerce. Using mobile devices for food and fresh product orders is very common, but delivering these products is very challenging. In this study, we aim to evaluate the factors influencing logistics service quality (LSQ) in the context of fresh product e-commerce. The relevant literature was reviewed and a preliminary survey among field experts was conducted to establish the proposed LSQ scale. A qualitative study was carried out on fresh product e-commerce customers. A survey involving 222 participants was analyzed, and an LSQ evaluation scale was formed and evaluated scientifically and empirically. Research results showed that reliability, convenience, freshness, and personnel contact quality are the four key dimensions of the LSQ scale in the e-commerce platform for fresh and perishable items. The results of the study can help the managers of e-commerce companies to understand the LSQ criteria that determine customer satisfaction and consequently make the appropriate LSQ improvements. Full article

This study deeply integrates multimodal data analysis and big data technology, proposing a multimodal learning framework that consolidates various information sources, such as user geographic location, behavior data, and product attributes, to achieve a more comprehensive understanding and prediction of consumer behavior. By comparing the performance of unimodal and multimodal approaches in handling complex cross-border e-commerce data, it was found that multimodal learning models using the Adam optimizer significantly outperformed traditional unimodal learning models in terms of prediction accuracy and loss rate. The improvements were particularly notable in training loss and testing accuracy. This demonstrates the efficiency and superiority of multimodal methods in capturing and analyzing heterogeneous data. Furthermore, the study explores and validates the potential of big data and multimodal learning methods to enhance customer satisfaction in the cross-border e-commerce environment. Based on the core findings, specific applications of big data technology in cross-border e-commerce operations were further explored. A series of innovative strategies aimed at improving operational efficiency, enhancing consumer satisfaction, and increasing global market competitiveness were proposed. Full article

Serendipity-oriented recommendation systems have been widely applied in major e-commerce and social platforms. Platform managers aim to enhance user satisfaction and increase platform sales by creating serendipitous encounters with information. Previous research has shown that the unexpectedness of encountering product information in serendipity-oriented recommendation systems can effectively stimulate positive emotions in customers, resulting in unplanned purchases, such as impulse buying. However, little research has focused on another critical aspect of encountering product information: perceived value. Our study suggests that encountering product information can positively affect the intention to purchase planned products (focal products) based on their perceived value. To explore this, we conducted three experiments and found that: (1) encountering product information positively influences planned product purchase intention (e.g., reduced decision-making time, improved focal product purchase intention), compared to the absence of encountering product information (precision-oriented recommendation systems); (2) this effect is mediated by customer inspiration; and (3) the characteristics of recommendation system strategies can moderate this effect. Specifically, when the strategy features exhibit a low level of explainability, the impact of encountering product information on customer inspiration and purchase intention is more significant than when a high level of explainability is presented. Full article

The e-commerce sector is in a constant state of growth and evolution, particularly within its subdomain of online food delivery. As such, ensuring customer satisfaction is critical for companies working in this field. One way to achieve this is by providing an accurate delivery time estimation. While companies can track couriers via GPS, they often lack real-time data on traffic and road conditions, complicating delivery time predictions. To address this, a range of statistical and machine learning techniques are employed, including neural networks and specialized expert systems, with different degrees of success. One issue with neural networks and machine learning models is their heavy dependence on vast, high-quality data. To mitigate this issue, we propose two Bayesian generalized linear models to predict the time of delivery. Utilizing a linear combination of predictor variables, we generate a practical range of outputs with the Hamiltonian Monte Carlo sampling method. These models offer a balance of generality and adaptability, allowing for tuning with expert knowledge. They were compared with the PSIS-LOO criteria and WAIC. The results show that both models accurately estimated delivery times from the dataset while maintaining numerical stability. A model with more predictor variables proved to be more accurate. Full article

E-commerce marketplace platforms have evolved into integral digital intermediaries that shape online transactions in competitive environments. Companies continuously endeavor to improve e-service quality, customer satisfaction, and e-trust to gain a competitive advantage. This study aimed to identify the relationships between e-service quality, customer satisfaction, e-trust, and continuous usage intention in e-commerce marketplace platforms. Moreover, this study examined the roles of customer satisfaction and e-trust as mediators. We estimated nine hypothesized relationships using a structural equation modeling technique. Data from 311 users were used in the data analysis. The results are as follows: First, e-service quality significantly and positively affects customer satisfaction, e-trust, and continuous usage intention. Second, customer satisfaction has a significant and positive impact on e-trust and continuous usage intention. Third, e-trust has a significant and positive impact on continuous usage intention. Finally, both customer satisfaction and e-trust serve as significant mediating factors in the relationship between e-service quality and continuous usage intention. These insights hold strategic importance for e-commerce marketplace platform operators, allowing them to formulate service strategies and policies tailored to enhance user experience, foster trust, and drive continued usage, thereby strengthening their market position and ensuring sustained success. Full article

Live-streaming commerce (LSC) is a new shopping method that combines the characteristics of social commerce and e-commerce. Since the global coronavirus disease 2019 (COVID-19) outbreak, the number of branded platforms is growing rapidly, and their competition is fiercer than ever. Understanding consumer needs and improving service quality have become the key issues for survival. This study aims to develop and empirically validate a multidimensional hierarchical model for measuring service quality on LSC platforms. A hierarchical reflective construct was proposed to capture dimensions based on the literature on e-retail and social commerce service quality. The proposed model was rigorously tested using two waves of survey data through the partial least squares method. Results showed that the service quality of LSC is a third-order, reflective construct and includes five primary dimensions (the streamer’s interaction quality, physical environment, website quality, outcome quality, and ordering process) and twelve sub-dimensions (trustworthiness, expertise, responsiveness, telepresence, consumption scenarios, information quality, system operation quality, fulfillment and refund/compensation, privacy/security, contact, and ease of use). Findings also supported the hypothesis that service quality has a significant impact on customers’ satisfaction and their behavioral intentions. Furthermore, we tested an alternative model, and the results showed that the relationship between dimensions and overall assessment is reflective rather than formative. We offered directions for further research on LSC service quality and discussed managerial implications stemming from the empirical findings. Full article

Background: The systematic literature review with additional descriptive analysis at hand focuses on analysing returns management in e-commerce, which is an increasingly critical issue as the volume of online shopping is rising. Methods: Drawing from a comprehensive search of academic databases and a manual review of Google Scholar, 54 articles dating from 2007 onwards were collected and fully read. Results: The review reveals a main research effort emerging mainly from Germany and other countries, with a notable focus on fashion retail. The bulk of these studies aim to understand and reduce the frequency of customer returns, addressing a substantial operational challenge for online retailers. The findings provide multiple research streams extracted from the collected literature and combined to an overview. Conclusions: Through this, there are tendencies which can be interpreted to derive the evolution of the research field. The illustrated results in this review paint a detailed picture of the existing research landscape. This highlights the importance of ongoing research, which, e.g., holds potential benefits for customer satisfaction and environmental sustainability. The review also lists future research directions, recommending the continued investigation of areas such as predictive analytics and customer behaviour to further refine returns management practices. Full article

This study aimed to identify the constructs related to customer experience that underpin e-commerce, as well as their interconnections, to develop a comprehensive conceptual model based on theories-in-use. A quantitative approach was employed through a survey of 441 respondents. Data analysis was conducted using partial least squares structural equation modeling. The research findings revealed that there are a total of 11 constructs: customer experience, customer satisfaction, customer loyalty, word-of-mouth, trust, perceived risk, security and privacy, web content, perceived price, perceived value, and service quality. Furthermore, twelve relationships were established between these constructs, which led to the development of a holistic conceptual model. The identified constructs and the relationships between them are hierarchized, which has practical implications for businesses. It allows them to concentrate on operational activities and formulate and implement strategies that are valued by consumers and supported by empirical evidence. The originality and value of this research lie in the conception and development of a comprehensive e-commerce model, which includes eleven constructs and twelve relationships. It also highlights the pivotal role of the customer experience. Full article

Brand reputation directly influences customer trust and decision-making. A good reputation can lead to greater customer loyalty, commitment, and advocacy. This study aims to understand the effects of brand reputation on customer trust and loyalty and to determine how brands can protect their reputation. This study, which was conducted on the iPhone 11 sample by obtaining statistical data from customer reviews, can be adapted and used by researchers and companies that want to measure brand reputation. In this study, customer reviews for the iPhone 11 phone on the Trendyol e-commerce site, the largest e-commerce platform in Turkey, are analyzed using sentiment analysis and machine learning methods. While 85 percent of customers are satisfied with the iPhone 11, 13 percent are dissatisfied with it. The neutral comment rate of 2 percent indicates that some customers do not express a clear positive or negative opinion about the product. In the comments of customers who bought the iPhone 11, there are those who express satisfaction with the quality, technical features, performance, and price/performance ratio of the product, as well as those who express significant complaints about delivery, quality, price, and customer service. Neutral comments generally focus on the product itself, price, quality, shipping, and packaging, and make informative evaluations. A sustainable reputation is based on the extent to which an organization embraces ethical principles, social responsibility, and sustainable practices throughout its operations and business relationships. Brands can improve, protect, and increase their brand reputation by considering and analyzing the thoughts and feelings of their customers. For this, they should develop policies and strategies to reinforce their strong features and improve their faulty and deficient features. Full article

India’s retail industry has grown and seen significant progress over the past few years. However, after the COVID-19 pandemic, there has been a move towards e-commerce convenience, which has changed how people shop, and retailers still need to adapt. The current practices of traditional retail are more retailer-centric and limited to in-store shopping, causing dissatisfaction among customers. Current issues include the absence of personalized suggestions, difficulties related to in-store buying such as product trials, overwhelming product options, limited community involvement, geographical limitations, and other issues. This study addresses retail customers’ challenges and highlights the need for a design intervention to enhance the overall retail shopping experience. This study aims to propose an innovative omnichannel retail solution that can be tailored to specific locations and users’ needs, leading to overall customer satisfaction. Modern technologies like artificial intelligence (AI) and augmented reality (AR) have a huge role in the proposed solution. The proposed AR Retail solution can potentially affect millions of people, including customers, retailers, influencers, and industry experts. As a result, this will help businesses make more revenue and serve customers by enhancing their retail shopping experiences. Full article