Search Results (539)

The rapid growth of e-commerce has transformed customer behaviors, demanding deeper insights into how demographic factors shape online user preferences. This study performed a threefold analysis to understand the impact of these changes. Firstly, this study investigated how demographic factors (e.g., age, gender, education) influence e-customer preferences in Serbia. From a sample of n = 906 respondents, conditional dependencies between demographics and user preferences were tested. From a hypothetical framework of 24 tested hypotheses, this study successfully rejected 8/24 (with p < 0.05), suggesting a high association between demographics with purchase frequency and reasons for quitting the purchase. However, although the reported test statistics suggested an association, understanding how interactions between categories shape e-customer profiles was still required. Therefore, the second part of this study considers an MCA-HCPC (Multiple Correspondence Analysis with Hierarchical Clustering on Principal Components) to identify user profiles. The analysis revealed three main clusters: (1) young, female, unemployed e-customers driven mainly by customer reviews; (2) retirees and older adults with infrequent purchases, hesitant to buy without experiencing the product in person; and (3) employed, highly educated, male, middle-aged adults who prioritize fast and accurate delivery over price. In the third stage, the clusters are used as labels for Machine Learning (ML) classification tasks. Particularly, Gradient Boosting Machine (GBM), Decision Tree (DT), k-Nearest Neighbors (kNN), Gaussian Naïve Bayes (GNB), Random Forest (RF), and Support Vector Machine (SVM) were used. The results suggested that GBM, RF, and SVM had high classification performance in identifying user profiles. Lastly, after performing Permutation Feature Importance (PFI), the findings suggested that age, work status, education, and income are the main determinants of shaping e-customer profiles and developing marketing strategies. Full article

With the rapid development of e-commerce, malicious accounts have become a threat, especially in the business field. Therefore, how to efficiently detect malicious accounts has become one an important issue requiring resolution. Present research on malicious detection mainly uses the basic statistics of the original data to build models, and ignores malicious activities in the business field. To address the context-independent nature of business activities and their lack of social structure, this study constructs an online model for detecting malicious accounts in the business field based on a stacking ensemble strategy with BiGRU-Conv1D-Capsule, XGBoost, and LightGBM as individual learners and AdaBoost as a meta-learner. Experimental results show that the stacking ensemble model constructed in this study has better predictive power than the typical shallow learning and baseline deep learning models. In addition to the basic feature, the behavior sequence of users is also applied in the proposed model. Overall, our experiments based on a real dataset from a financial platform show that the TPR, AUC, and F1 of the stacking ensemble model can reach 0.8258, 0.9860, and 0.8922 respectively, which outperforms all baseline models. Full article

When users interact with mobile platforms in an Augmented Reality (AR) environment, cognitive and emotional engagements change through different stimuli cues that respond to users’ behavioral intentions. Although AR engages more interactions in mobile platforms, there is a significant gap in assessing UX, considering the physical distance between users and virtual products in a mobile platform. Considering the effect of user generation, the impacts of perceived engagements toward continuance intention through user-platform interactions are unexplored. This study investigated a nuanced understanding of how stimuli cues in augmented reality affect sense of immersion and sense of presence, followed by an Interaction-Engagement-Intention (I-E-I) model. A quantitative method was used to validate the proposed model. Based on an online survey with 886 responses, product fit, network quality, and Artificial Intelligence-driven Recommendation (AIR) influences were assessed for cognitive engagements. This study examined the importance of engaging satisfaction and trust as emotional engagements, influencing users’ continuance intention. The findings showed that sense of presence has a more significant influence on building trust and satisfaction. Also, trust has a more significant impact on the continuance intention to use AR mobile platforms. This study also explored the positive effects of user generation on continuance intention. This could enhance the capabilities of information system designers, researchers, marketing professionals, and solution providers to attain sustainable user retention. 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

The sustainable development of urban transportation is facing various challenges, including traffic congestion, increasing carbon emissions, and diversified travel demands. New concepts of mobility and business models are emerging to address these challenging conditions, such as mobility as a service (MaaS). As a new paradigm of travel services, users’ recognition, acceptance, and continuous use of MaaS are prerequisites for its survival and development. Hence, to ensure the successful implementation of MaaS, it is crucial to precisely identify the key factors influencing individuals’ willingness to use MaaS. In order to analyze the mechanisms that influence individuals’ willingness to use MaaS, this study first conceptualized a behavioral model by drawing on the modified Technology Acceptance Model (TAM) and the fundamental characteristics of MaaS. Based on the behavioral model, a structured questionnaire consisting of eight sections and thirty-three questions was designed and conducted online in Beijing, China. A total of 1260 valid questionnaire data were collected, and a descriptive analysis was conducted on the collected data, including the frequency distribution and intention to use MaaS based on the socioeconomic and mobility characteristics. Then, reliability and validity analyses were conducted on the questionnaire data using Cronbach’s alpha coefficient method and the Confirmatory Factor Analysis (CFA) method, respectively. Finally, the behavioral model was analyzed quantitatively using the Structural Equation Model (SEM). The results show that 77.62% of the respondents are willing to use MaaS after it is implemented, and 44.29% of them strongly agree to using it, while 2.06% of them strongly disagree to using it. Travel philosophy, travel preference, and perceived usefulness have positive impacts on individuals’ behavioral intention to use MaaS, while perceived usefulness exerts the greatest influence, with a coefficient of 0.364. Meanwhile, the latent variable of perceived risk has a significantly negative impact on behavioral intention, with a coefficient of −0.141. From the perspective of observed variables, convenience and efficiency are the most important factors affecting intention to use MaaS, while environment protection is the least influential factor. The results of this study can provide a decision-making basis for transportation planners, MaaS service providers, and policymakers, enhancing the level of sustainable development of urban transportation. Full article

Social networks, functioning as core platforms for modern information dissemination, manifest distinctive user clustering behaviors and state transition mechanisms, thereby presenting new challenges to traditional information propagation models. Based on hypergraph theory, this paper augments the traditional SEIR model by introducing a novel hypernetwork information dissemination SSEIR model specifically designed for online social networks. This model accurately represents complex, multi-user, high-order interactions. It transforms the traditional single susceptible state ($S$) into active ($S_a$) and inactive ($S_i$) states. Additionally, it enhances traditional information dissemination mechanisms through reaction process strategies (RP strategies) and formulates refined differential dynamical equations, effectively simulating the dissemination and diffusion processes in online social networks. Employing mean field theory, this paper conducts a comprehensive theoretical derivation of the dissemination mechanisms within the SSEIR model. The effectiveness of the model in various network structures was verified through simulation experiments, and its practicality was further validated by its application on real network datasets. The results show that the SSEIR model excels in data fitting and illustrating the internal mechanisms of information dissemination within hypernetwork structures, further clarifying the dynamic evolutionary patterns of information dissemination in online social hypernetworks. This study not only enriches the theoretical framework of information dissemination but also provides a scientific theoretical foundation for practical applications such as news dissemination, public opinion management, and rumor monitoring in online social networks. Full article

Online reviews play a crucial role in influencing seller–customer dynamics. This research evaluates the credibility and consistency of reviews based on volume, length, and content to understand the impacts of incentives on customer review behaviors, how to improve review quality, and decision-making in purchases. The data analysis reveals major factors such as costs, support, usability, and product features that may influence the impact. The analysis also highlights the indirect impact of company size, the direct impact of user experience, and the varying impacts of changing conditions over the years on the volume of incentive reviews. This study uses methodologies such as Sentence-BERT (SBERT), TF-IDF, spectral clustering, t-SNE, A/B testing, hypothesis testing, and bootstrap distribution to investigate how semantic variances in reviews could be used for personalized shopping experiences. It reveals that incentive reviews have minimal to no impact on purchasing decisions, which is consistent with the credibility and consistency analysis in terms of volume, length, and content. The negligible impact of incentive reviews on purchase decisions underscores the importance of authentic online feedback. This research clarifies how review characteristics sway consumer choices and provides strategic insights for businesses to enhance their review mechanisms and customer engagement. Full article

The current research sought a comprehensive understanding about the consequences of information-sharing behavior on social media, given public concerns about privacy violations. We used a mixed-methods approach to investigate the influence of the self on “revealing” and emotional “healing” experiences online. Respondents completed a survey measuring sense of self and motivations for using social media, as well as revealing and healing attitudes and behavior. We conducted a principal component factor analysis on separate parts of the survey and ran Pearson correlations of the emerging factors. Qualitative data describing experiences of online self-disclosure were used to illustrate the correlational findings. The “revealing” factors contrasted adaptive with maladaptive and naïve posting. The sense of self, as well as motivations for social media use, influenced whether users engaged in destructive posting behaviors. The “healing” factors were associated with positive motivations for self-disclosure, seeking a supportive online community, and building resilience. Correlational data revealed that respondents with an insecure or asocial sense of self felt the greater need for online self-disclosure. Motivations to self-disclose online and experiences of “healing”, with the help of a supportive online community, depended on whether the sense of self was secure, insecure, or asocial. Full article

Online social media (OSM) has revolutionized the manner in which communication unfolds, enabling users to spontaneously generate, disseminate, share, and aggregate multimedia data across the internet. Nevertheless, in this exchange of information, OSM platforms assume a dominant, central role, wielding excessive power. Blockchain online social media (BOSM) seeks to mitigate the drawbacks of traditional centralized OSM by leveraging the decentralized nature of blockchain technology, migrating the functionalities of social media into a decentralized realm, and positioning the users at the core of the OSM ecosystem. However, current BOSM models often rely on tokens for incentives and are hampered by the centralized, inefficient blockchain consensus mechanisms, alongside vulnerabilities such as collusion attacks. This paper introduces a novel blockchain system, NexoNet, tailored for decentralized social media, exploring the application of blockchain technology in the realm of online social media from both technical and economic perspectives. The NexoNet quantifies and evaluates user participation within the system, employing a multiple incentive mechanism to equitably distribute value created by users without the need for tokens. Furthermore, we propose the Proof-of-Active-Participation (PoAP) blockchain consensus mechanism, enabling all users to partake in the maintenance of the blockchain system, thus ensuring its security and efficiency. Theoretical analysis and simulations across various scenarios demonstrate that the NexoNet, with extensive user engagement, achieves equitable value distribution through its multiple incentive mechanism. It successfully safeguards against a spectrum of malicious attacks and attains high transaction processing efficiency. The simulation results show that NexoNet achieves an average transaction throughput of 2000 transactions per second (TPS) and a consensus delay of 2.385 s with 100 maintainers in the network. Furthermore, our tests demonstrated that even collusion with users comprising 75% of the total would only allow an additional 30 chances to propose a block. By deeply integrating user behavior with the underlying mechanisms of the blockchain system, the NexoNet fosters a user-centric blockchain social media ecosystem. Full article

This article takes representative uncultivated riverfront internet-famous sites (uncultivated RIFSs) in Changsha city, China, as an example to explore the internal mechanism of their formation and finds that they are closely related to the “urban subculture” and the “informality of urban public space”. In terms of methodology, through questionnaire surveys and in-depth interviews, this study investigates the behavioral characteristics of onsite visitors, the overall perceptions and satisfaction of public spaces, and the perceptions of spatial and humanistic elements of visitors. The main findings are as follows: ① Onsite visitors are mainly male, with local tourists and nearby residents accounting for over 80%. Furthermore, over half of the visitors have limited understanding of the uncultivated RIFSs. ② People’s overall attitudes towards the uncultivated RIFSs are positive. And the ability to carry out meaningful activities and find comfort and safety are of the greatest concern to onsite tourists. ③ Among the visiting reasons, leisure stays accounted for the highest proportion, followed by sightseeing, sports stays and social stays. ④ The onsite visitors’ main focus of spatial elements and humanistic elements is different according to the different sites. However, visitors’ dissatisfaction is mainly reflected in poor site safety and sanitation conditions, inadequate facilities and poor surrounding environments. This paper also compares the online–offline differences in the spatial perceptions of the uncultivated RIFSs between this study and previous research; instead of focusing on the urban physical spaces, online social media users pay more attention to their self-presentation. Meanwhile, the visitors place greater emphasis on the functionality, practicality and experiential activities of the urban physical spaces. Finally, this article proposes optimization strategies for uncultivated RIFSs from planning and governance and public space design aspects to protect and strengthen the composite utilization of space, therefore enhancing diverse vitality. Full article

Understanding customer journeys is key to e-commerce success. Many studies have been conducted to obtain journey maps of e-commerce visitors. To our knowledge, a complete, end-to-end and structured map of e-commerce journeys is still missing. In this research, we proposed a four-step methodology to extract and understand e-commerce visitor journeys using process mining. In order to obtain more structured process diagrams, we used techniques such as activity type enrichment, start and end node identification, and Levenshtein distance-based clustering in this methodology. For the evaluation of the resulting diagrams, we developed a model utilizing expert knowledge. As a result of this empirical study, we identified the most significant factors for process structuredness and their relationships. Using a real-life big dataset which has over 20 million rows, we defined activity-, behavior-, and process-level e-commerce visitor journeys. Exploitation and exploration were the most common journeys, and it was revealed that journeys with exploration behavior had significantly lower conversion rates. At the process level, we mapped the backbones of eight journeys and tested their qualities with the empirical structuredness measure. By using cart statuses at the beginning and end of these journeys, we obtained a high-level end-to-end e-commerce journey that can be used to improve recommendation performance. Additionally, we proposed new metrics to evaluate online user journeys and to benchmark e-commerce journey design success. Full article

Group chat socialization is increasingly central to online activities, yet design strategies to enhance this experience remain underexplored. This study builds on the Stimuli–Organism–Response (SOR) framework to examine how usability, chat rhythm, and user behavior influence emotions and participation in group chats. Using data from 546 users in China, a relevant demographic given the dominance of platforms like WeChat in both social and professional settings, we uncover insights that are particularly applicable to highly connected digital environments. Our analysis shows significant relationships between usability (γ = 0.236, p < 0.001), chat rhythm (γ = 0.172, p < 0.001), user behavior (γ = 0.214, p < 0.001), and emotions, which directly impact participation. Positive emotions (γ = 0.128, p < 0.05) boost participation, while negative emotions (γ = −0.144, p < 0.01), particularly when linked to user behaviors, reduce it. Additionally, we discussed the mediating effects, notably that usability significantly impacts participation through positive emotions, while user behavior exerts a significant influence on participation through negative emotions. This research offers actionable design strategies, such as tailoring sensory inputs to reduce cognitive load and implementing reward systems to motivate participation. Positive feedback mechanisms enhance engagement by leveraging the brain’s reward systems, while optimized error messages can minimize frustration. These insights, which are particularly relevant for China’s active group chat culture, provide a framework to improve platform design and contribute valuable findings to the broader HCI field. Full article

The COVID-19 pandemic profoundly affected people’s travel behavior and travel desires, particularly regarding trips to prominent destinations. This study explores the pandemic’s impact on travel behavior and online search patterns for 12 landmarks across six continents, utilizing data from three online platforms, i.e., Google Trends, X, and Tripadvisor. By comparing visitation and search behavior before (2019) and during (2020/2021) the pandemic, the study uncovers varying effects on the spatial separation between user location and landmarks. Google Trends data indicated a decline in online searches for nearby landmarks during the pandemic, while data from X showed an increased interest in more distant sites. Conversely, Tripadvisor reviews reflected a decrease in the distance between users’ typical review areas and visited landmarks, underscoring the effects of international travel restrictions on long distance travel. Although the primary focus of this study concerns the years most affected by COVID-19, it will also analyze Tripadvisor data from 2022 to provide valuable insights into the travel recovery beyond the pandemic. Full article

Traceability is a cornerstone of sustainable honey production and consumption. Honey fraud and a lack of traceability have been recently highlighted by the European Commission. Innovative systems aimed at guaranteeing food safety ’from farm to fork’ and improved controls are highly recommended. Within the framework of the BPRACTICES project, part of the European Union’s Horizon 2020 research and innovation program, and the ERA-Net SusAn initiative—focused on Sustainable Animal Production Systems—an advanced traceability system has been developed. This system utilizes QR code and radio-frequency identification (RFID) technology, along with a user-friendly web application, to facilitate direct interactions between producers and consumers. Despite existing research, studies on the information needs of Italian consumers regarding honey and its traceability remain limited. Understanding these needs is vital for creating effective communication strategies that enhance consumer satisfaction and trust. This study aims to identify the needs of Italian consumers’ honey during the purchasing and consumption decisions. To explore consumer perceptions, behaviors, expectations, and needs regarding honey, we employed diverse social research methodologies, including a quantitative online survey, paper-and-pencil interviews, and focus groups. The results of this study indicate a robust demand for more information on honey’s origin, production processes, and beekeeping practices, aligning with the recent EU Directive 2024/1438, which mandates clear labeling. Italian consumers would be willing to pay a premium for honey that offers detailed information about production practices and transparency. The positive reception of QR code technology by consumers suggests a growing openness to digital tools that enhance transparency and access to information. Ultimately, this research emphasizes the need for the beekeeping sector to adopt sustainable practices, improve traceability systems, and actively engage with consumers to foster trust and ensure long-term viability in the honey market. By addressing these information needs, the sector can align itself with increasing consumer demand for quality, sustainability, and transparency. Full article

This exploratory study examines the socio-technical dynamics of Artificial Intelligence Companions (AICs), focusing on user interactions with AI platforms like Replika 9.35.1. Through qualitative analysis, including user interviews and digital ethnography, we explored the nuanced roles played by these AIs in social interactions. Findings revealed that users often form emotional attachments to their AICs, viewing them as empathetic and supportive, thus enhancing emotional well-being. This study highlights how AI companions provide a safe space for self-expression and identity exploration, often without fear of judgment, offering a backstage setting in Goffmanian terms. This research contributes to the discourse on AI’s societal integration, emphasizing how, in interactions with AICs, users often craft and experiment with their identities by acting in ways they would avoid in face-to-face or human-human online interactions due to fear of judgment. This reflects front-stage behavior, in which users manage audience perceptions. Conversely, the backstage, typically hidden, is somewhat disclosed to AICs, revealing deeper aspects of the self. Full article