Search Results (110)

The article aims to identify consumer types based on their attitudes and behaviors toward phygital tools and solutions. The analysis was based on the authors’ empirical research. The research was conducted on a sample of 2160 Polish consumers. The study employed an online survey technique. To determine the types of consumers, a 20-item scale was used, allowing the respondents to express their attitudes toward solutions and tools that improve shopping in the phygital space. The extraction of types was carried out in two steps. The first was cluster analysis, conducted using the hierarchical Ward method with the square of the Euclidean distance, and the second was non-hierarchical cluster analysis using the k-means method. As a result of the analyses, three relatively homogeneous types of consumers were distinguished: phygital integrators, digital frequenters, and physical reality anchors. The behaviours of consumers from each type were examined in the context of their impact on sustainable consumption and the sustainable development of the planet. The proposed typology contributes to developing consumer behavior theory in sustainable consumption environments. It provides practical implications for designing customer experiences that are more inclusive, resource-efficient, and aligned with responsible consumption patterns. Understanding how different consumer groups engage with phygital tools allows businesses and policymakers to tailor strategies that support equitable access to digital services and foster more sustainable, adaptive consumption journeys in an increasingly digitized marketplace. Full article

In an increasingly digitalized marketplace, understanding Generation Z’s (Gen Z) online consumer behavior has become a critical priority, particularly in relation to newly launched technological products. Although online consumer behavior has been widely studied, a gap remains in understanding how the location of the e-shop (domestic vs. international) moderates this behavior. Addressing this gap, the present study adopts a quantitative, cross-sectional design with data from 302 Gen Z participants, using a hybrid sampling method that combines convenience and systematic techniques. A structured questionnaire, grounded in 19 well-established behavioral theories, was employed to examine the influence of six key factors, behavioral and attitudinal traits, social and peer influences, marketing impact, online experience, brand perceptions, and Gen Z characteristics, across various stages of the consumer journey. Moderation analysis revealed that e-shop location significantly affects the strength of relationships between these factors and both purchase intention and post-purchase behavior. Notably, Gen Z’s values and marketing responsiveness were found to be more predictive in the context of international e-shops. These findings highlight the importance of marketing strategies that are both locally relevant and globally informed. For businesses, this research offers actionable insights into how digital engagement and brand messaging can be tailored to meet the unique expectations of Gen Z consumers across diverse e-commerce contexts, thereby enhancing consumer satisfaction, loyalty, and brand advocacy. Full article

We extended real estate property listings on the online prop-tech platform. On the platform, the images were classified into the specified classes according to quality criteria. The necessary interventions were made by measuring the platform’s appropriateness level and increasing the advertisements’ visual appeal. A dataset of 3000 labeled images was utilized to compare different image classification models, including convolutional neural networks (CNNs), VGG16, residual networks (ResNets), and the LLaVA large language model (LLM). Each model’s performance and benchmark results were measured to identify the most effective method. In addition, the classification pipeline was expanded using image enhancement with contrastive unsupervised representation learning (CURL). This method assessed the impact of improved image quality on classification accuracy and the overall attractiveness of property listings. For each classification model, the performance was evaluated in binary conditions, with and without the application of CURL. The results showed that applying image enhancement with CURL enhances image quality and improves classification performance, particularly in models such as CNN and ResNet. The study results enable a better visual representation of real estate properties, resulting in higher-quality and engaging user listings. They also underscore the importance of combining advanced image processing techniques with classification models to optimize image presentation and categorization in the real estate industry. The extended platform offers information on the role of machine learning models and image enhancement methods in technology for the real estate industry. Also, an alternative solution that can be integrated into intelligent listing systems is proposed in this study to improve user experience and information accuracy. The platform proves that artificial intelligence and machine learning can be integrated for cloud-distributed services, paving the way for future innovations in the real estate sector and intelligent marketplace platforms. Full article

(1) Live streaming commerce has refined online consumer engagement by fostering a real-time, socially enriched shopping environment. Despite its growing prominence, the role of social scene characteristics in consumer purchase decisions in live streaming remains insufficiently examined. (2) This study uses the Cognition-Affection-Conation (C-A-C) framework to examine how these social characteristics influence purchase intention through the mediating roles of emotional and cognitive trust, with privacy concerns as a moderator factor. The research employs Structural Equation Modeling (SEM) to test the hypothesis using data from 504 valid responses. (3) The results demonstrate that the characteristics of social scenes proposed in this study enhance consumer trust and positively impact purchase intention. Moreover, privacy concerns weaken the effect of interactivity atmosphere and scene immersion on emotion trust, though they do not weaken the effect of social identity on emotion trust. (4) These findings contribute to the theoretical understanding of live commerce by identifying the psychological mechanisms linking the social service scene to purchasing behavior. They also offer practical implications for platforms and merchants seeking to improve consumer engagement and trust in competitive digital marketplaces. This research highlights the importance of integrating social scenes and privacy management into the strategic design of live streaming commerce services. Full article

Despite free access to complete information regarding hotel quality and reference prices, consumers perceive significant price differences across different online platforms. We explore how perceived price dispersion on online travel agency platforms influences consumer purchase intention through mental account theory and propose a psychological mechanism explaining why consumers may tolerate and even embrace price discrepancies. Study 1 employs a scenario-based experiment that manipulates differing levels of price dispersion for the same hotel booking, demonstrating that higher PPD significantly amplifies perceived transaction utility and, in turn, acquisition utility. Study 2 corroborates these findings through an online survey with judgment sampling, highlighting that consumers—despite access to comprehensive OTA information—are often motivated, rather than deterred, by price discrepancies; multiple variable combinations were tested to ensure robust findings. This study challenges traditional marketing theories suggesting that price dispersion signals market unfairness and reduces consumers’ purchasing intention; instead, it mentally stimulates consumers. This perception enhances transaction and acquisition utility, positively impacting purchase intention. We also offer a robust model for mechanism study and provide insights for leveraging price dispersion as a cost-less promotional strategy, potentially increasing consumer engagement without additional marketing expenditure. We contribute to the literature by integrating the mental account theory into the context of online marketplaces and developing a price dispersion model with psychological utility in the consumer decision-making process. Full article

Parcel delivery networks have grown rapidly during the last few years due to the intensive evolution of online marketplaces. We address the issue of managing the operation of a network’s pick-up point, including the selection of the warehouse’s capacity and the policy for accepting orders for delivery. The existence of the time lag between order placing and delivery to the pick-up point is accounted for via modeling the order’s processing as the service in the dual tandem queueing system. Distinguishing features of this tandem queue are the account of possible irregularity in order generation via consideration of the versatile Markov arrival process and the possibilities of batch transfer of the orders to the pick-up point, group withdrawal of orders there, and client no-show. To reduce the probability of an order rejection at the pick-up point due to the overflow of the warehouse, a threshold strategy of order admission at the first stage on a tandem is proposed. Under the fixed value of the threshold, tandem operation is described by the continuous-time multidimensional Markov chain with a block lower Hessenberg structure for the generator. Stationary performance measures of the tandem system are calculated. Numerical results highlight the dependence of these measures on the capacity of the warehouse and the admission threshold. The possibility of the use of the results for managerial goals is demonstrated. In particular, the results can be used for the optimal selection of the capacity of a warehouse and the policy of suspending order admission. Full article

This study aims to understand how InsurTech has developed in Korea. To achieve this, we collected InsurTech-related news articles published in the Korean media over the past eight years. Using a relatedness analysis based on the TopicRank algorithm, a text-mining technique, we extracted the top keywords associated with InsurTech by year. The extracted keywords were analyzed and discussed in terms of development trends: which technologies gained prominence over time, who the key players were, and what solutions were introduced. The analysis revealed several key trends in InsurTech’s development in Korea. First, regarding changes in InsurTech technology, blockchain and the Internet of Things initially garnered significant attention, but artificial intelligence and big data later emerged as more critical technologies. Second, in terms of market players, government agencies and research institutes initially created forums for discussion, such as seminars to draw social attention to InsurTech. Over time, innovative startups entered the market, general agencies specializing in insurance brokerage gained prominence in the online marketplace, and the entry of Big Tech platforms further diversified the market. Finally, in terms of InsurTech-related insurance solutions, early attention was focused on developing new products. However, the trend gradually shifted toward improving the accessibility and convenience of existing insurance services. Additionally, asset management and payment settlement services—linked to financial services beyond traditional insurance—emerged, along with new concepts such as healthcare, which reshaped the approach to insurance services. This study contributes to understanding how InsurTech has evolved by identifying key trends in emerging technologies, leading market players, and innovations in the insurance value chain. The Korean case provides insights that may help explore similar patterns in other countries. Full article

Nowadays, the increase in sustainable investments, especially when it comes to energy efficiency in buildings, has been recognised as an important pillar towards reductions in energy consumption. In this context, there is a need for efficient and user-friendly digital tools that can support decision-making procedures for all involved parties in the energy efficiency value chain. The scope of this paper is to present a high-level architecture and system design of the energy efficiency marketplace developed within the framework of the ENERGATE project, an EU-funded initiative aiming to assist Building Owners, Project Implementors, and Financial Institutions to collaborate and execute energy-efficient building renovations. To this end, structured data through predefined information entries will be collected. This will facilitate the interactions between heterogenous stakeholders and contribute to the standardisation of processes. The paper focuses on the functional description of the system design of the ENERGATE platform by defining the architecture, components, modules, interfaces, and structured data, as well as highlighting the requirements of potential platform users and design principles to meet the necessary requirements while ensuring security, reliability, and effectiveness. Full article

In response to the widespread issue of fake comments on e-commerce platforms, this study aims to analyze and propose a blockchain-based solution to incentivize authentic user feedback and reduce the prevalence of fraudulent reviews. Specifically, this paper constructs a tripartite evolutionary game model between sellers, buyers, and e-commerce platforms to study the real comment mechanism of blockchain. The strategy evolution under different incentive factors is simulated using replication dynamic equation analysis and Matlab software simulation. The study found that introducing smart contracts and “tokens” for incentives not only increased incentives for real comments but also reduced the negative experiences caused by “speculative” sellers, thereby influencing buyers to opt for authentic reviews. By structuring interactions through blockchain, the mechanism helped lower informational entropy thus reducing disorder and unpredictability in buyer and seller behavior and contributing to system stability. Further, by increasing penalties for dishonest behavior under the “credit on the chain” system, the platform lowered entropy in the system by promoting trust and reducing fraudulent activities. The real comment mechanism based on blockchain proposed in this paper can effectively enhance the order and transparency within the comment ecosystem. These findings contribute to theory and practice by providing strategic insights for e-commerce platforms to encourage genuine feedback, reduce informational entropy, and mitigate fake comments, ultimately fostering a more reliable online marketplace. Full article

In the online shopping context, brands aim to achieve a high level of profit by providing better customer satisfaction by using various artificial intelligence tools. They try creating a satisfactory customer experience by creating a system that provides never-ending customer support by using dialog-based chatbots, especially in the field of customer service. However, there is a lack of research investigating the impact of business and customer-based chatbot activities together on online purchase intention and the intention to reuse chatbots. This research considers the use of chatbots as a marketing tool from both customer and business perspectives and aims to determine the factors that affect the customers’ intention to purchase online and reuse chatbots. Accordingly, the impact on customer satisfaction with chatbot usage, which is based on chatbots’ communication quality and customers’ motivations to use chatbots, on online purchase intention and intention to reuse chatbots was examined. Through an online questionnaire with two hundred and ten participants, employing structural equation modeling, we revealed that customer satisfaction with chatbot usage has a greater impact on the intention to reuse chatbots than on online purchase intentions. In addition, chatbot communication quality has a greater impact on customer satisfaction with chatbot usage than customers’ motivation to use chatbots. To solidify these findings, confirmatory factor analysis, along with reliability and validity assessments, were implemented within the analytical framework to provide robust support for the study’s hypotheses. These findings not only provide empirical evidence and implications for companies in online shopping but also extend the understanding of AI tools in marketing, highlighting their subtle impact on consumer decision-making in the dynamic digital marketplace. Full article

(1) Background: 3D printable materials with accurately defined iodine content enable the development and production of radiological phantoms that simulate human tissues, including lesions after contrast administration in medical imaging with X-rays. These phantoms provide accurate, stable and reproducible models with defined iodine concentrations, and 3D printing allows maximum flexibility and minimal development and production time, allowing the simulation of anatomically correct anthropomorphic replication of lesions and the production of calibration and QA standards in a typical medical research facility. (2) Methods: Standard printing resins were doped with an iodine contrast agent and printed using a consumer 3D printer, both (resins and printer) available from major online marketplaces, to produce printed specimens with iodine contents ranging from 0 to 3.0% by weight, equivalent to 0 to 3.85% elemental iodine per volume, covering the typical levels found in patients. The printed samples were scanned in a micro-CT scanner to measure the properties of the materials in the range of the iodine concentrations used. (3) Results: Both mass density and attenuation show a linear dependence on iodine concentration (R² = 1.00), allowing highly accurate, stable, and predictable results. (4) Conclusions: Standard 3D printing resins can be doped with liquids, avoiding the problem of sedimentation, resulting in perfectly homogeneous prints with accurate dopant content. Iodine contrast agents are perfectly suited to dope resins with appropriate iodine concentrations to radiologically mimic tissues after iodine uptake. In combination with computer-aided design, this can be used to produce printed objects with precisely defined iodine concentrations in the range of up to a few percent of elemental iodine, with high precision and anthropomorphic shapes. Applications include radiographic phantoms for detectability studies and calibration standards in projective X-ray imaging modalities, such as contrast-enhanced dual energy mammography (abbreviated CEDEM, CEDM, TICEM, or CESM depending on the equipment manufacturer), and 3-dimensional modalities like CT, including spectral and dual energy CT (DECT), and breast tomosynthesis. Full article

This paper examines the impact of stringent regulations on personal data protection on customer perception of data security and online shopping behavior. In the context of the rapidly expanding e-commerce landscape, ensuring the security of personal data is a complex and crucial task. The study of several legal frameworks, including Malaysia’s compliance with EU regulations and Indonesia’s Personal Data Protection Law, provides valuable insights into consumer data protection. The challenges of balancing data safeguarding and unrestricted movement and tackling misuse by external entities are significant and require careful consideration. This research elucidates the pivotal role of trust in e-commerce environments and the deployment of innovative e-commerce models designed to minimize personal data sharing. By integrating advanced privacy-enhancing technologies and adhering to stringent regulatory standards such as the GDPR, this study demonstrates effective strategies for robust data protection. The paper contributes to the academic discourse by providing a comprehensive framework that synergizes legal, technological, and procedural elements to fortify data security and enhance consumer trust in digital marketplaces. This approach aligns with international data protection standards and offers a pragmatic blueprint for achieving sustainable data security in e-commerce. Full article

Background. In high-income countries, shopping for non-prescription multivitamin–multimineral supplements has tremendously increased. Objective and Methods. The purpose of this labeling analysis is to inform on the daily elemental iron (with or without vitamin C) supply provided by multivitamin–multimineral supplements sold online by Amazon in Western and Southern Europe (amazon.es^®, amazon.de^®, amazon.it^®, and amazon.fr^®). Results. We identified 298 iron-containing multivitamin–multimineral preparations sold by Amazon marketplaces: 153 preparations sourced from amazon.de^®, 68 from amazon.fr^®, 54 from amazon.it^®, and 23 from amazon.es^®. The daily iron dose provided by these preparations was 14 [5–14] mg (median and interquartile range), with no differences among the marketplaces. Approximately 90% (n = 265) of the preparations contained ferrous iron. Moreover, 85% (n = 253) of the preparations were fortified with vitamin C in a dose of 80 [40–100] mg daily. Conclusions. The median supply of iron (about 14 mg) and vitamin C (80 mg) in iron-containing multivitamin–multimineral preparations offered on Amazon platforms in Western and Southern Europe falls below that currently recommended for iron deficiency in review articles, namely 100 mg of iron and 500 mg of vitamin C per day. The iron supply of iron-containing multivitamin–multimineral preparations falls also below the dose of 30–60 mg advocated to prevent iron deficiency in menstruating women. 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

In the rapidly evolving online marketplace, accurately predicting the demand for pre-owned items presents a significant challenge for sellers, impacting pricing strategies, product presentation, and marketing investments. Traditional demand prediction methods, while foundational, often fall short in addressing the dynamic and heterogeneous nature of e-commerce data, which encompasses textual descriptions, visual elements, geographic contexts, and temporal dynamics. This paper introduces a novel approach utilizing the Graph Neural Network (GNN) to enhance demand prediction accuracy by leveraging the spatial relationships inherent in online sales data, named SGNN. Drawing from the rich dataset provided in the fourth Kaggle competition, we construct a spatially aware graph representation of the marketplace, integrating advanced attention mechanisms to refine predictive accuracy. Our methodology defines the product demand prediction problem as a regression task on an attributed graph, capturing both local and global spatial dependencies that are fundamental to accurate predicting. Through attention-aware message propagation and node-level demand prediction, our model effectively addresses the multifaceted challenges of e-commerce demand prediction, demonstrating superior performance over traditional statistical methods, machine learning techniques, and even deep learning models. The experimental findings validate the effectiveness of our GNN-based approach, offering actionable insights for sellers navigating the complexities of the online marketplace. This research not only contributes to the academic discourse on e-commerce demand prediction but also provides a scalable and adaptable framework for future applications, paving the way for more informed and effective online sales strategies. Full article