Search Results (216)

Shopping mobility accounts for a significant share of total travel, while the growth of e-commerce is reshaping consumer purchasing behaviour and retail dynamics. Comprehending how territorial and sociodemographic factors shape the choice between physical and digital retail channels is therefore a key issue for transport planning and sustainable urban mobility. In this context, it is important to understand how accessibility to different classes of retailers is configured and how it can impact purchasing choices. Through a discrete choice analysis, this study examines the sociodemographic and territorial determinants of purchasing behaviour, focusing on the clothing market. Four purchase alternatives are considered: medium-sized and small urban retail stores, shopping malls, online purchasing, and no purchase. This multi-alternative framework enables the direct estimation of substitution patterns not only between physical and digital retail, but also between distinct forms of physical retail. Data were collected through a survey conducted in Southern Italy, providing empirical evidence from a territorial setting that is structurally underrepresented in the existing literature. A multinomial logit model and a two-level hierarchical logit model incorporating pedestrian accessibility—measured as walking time from residence to the nearest clothing store—alongside sociodemographic and territorial attributes were calibrated to analyse alternative choice behaviour. The calibrated models show interesting results, highlighting the role of pedestrian accessibility in the choice of clothing stores in city centres. Age, income, and territorial variables further differentiate channel preferences across population segments. The findings offer relevant implications for policymakers, governance managers, urban planners, and researchers concerned with retail location, sustainable accessibility, and consumer behaviour. These insights are highly valuable for developing planning that addresses the United Nations 2030 Agenda, particularly Sustainable Development Goal 11. Full article

This article examines how the Ecuadorian national digital press has represented the relationship between criminal violence, declining mobility, tourism contraction, and the erosion of intangible cultural access in Machala, Puerto Bolívar, and the route to Jambelí during 2025. This study aims to explain how mediated representations of insecurity can contribute to the symbolic narrowing of culturally meaningful urban–coastal spaces, even when those spaces remain materially present and formally open. The article responds to a gap in the literature at the intersection of critical heritage studies, media framing, urban fear, and Latin American security studies. The existing research has examined heritage as social practice, media representation of crime, and urban securitization, but has rarely connected these fields to explain how criminal violence erodes lived access to intangible cultural environments in secondary port cities of the Global South. Methodologically, this study applies qualitative content analysis to a purposive corpus of eight focal journalistic texts published in Ecuadorian digital outlets, such as El Universo, El Comercio, Expreso, El Mercurio, Extra, Primicias, GK, and La Hora. Deductive–inductive coding was complemented by descriptive article-level indicators of themes, keyword clusters, and temporal distribution. The findings show that the press did not merely report violent events; it progressively reorganized the symbolic meaning of Machala by re-signifying Puerto Bolívar, the marine environment, the cabotage pier, and the maritime route to Jambelí as spaces of risk, interruption, and conditional access. This study contributes conceptually by defining intangible cultural access and symbolic enclosure, empirically by documenting the mediated erosion of coastal public–cultural life, and practically by proposing integrated policy actions for security governance, cultural reactivation, local commerce, maritime mobility, and responsible public communication. Full article

Digital commerce ecosystems increasingly depend on the alignment between social media advertising formats and digital payment systems, yet existing research has examined these mechanisms in isolation, overlooking their combined influence on consumer behaviour, conversion, and long-term value creation. This study addresses that gap by developing an integrative conceptual framework that examines how advertising formats and payment infrastructures jointly shape sustainable digital monetisation within an Environmental, Social, and Governance (ESG) framework. Methodologically, the study adopts a structured narrative literature review of interdisciplinary peer-reviewed studies and selected high-quality institutional reports, drawn from Scopus, Web of Science Core Collection, and Google Scholar, covering publications from 2015 to April 2026. A four-stage PRISMA-adapted selection protocol was applied to ensure transparency, replicability, and analytical rigour across the review process. The findings demonstrate that advertising formats including native advertising, influencer marketing, user-generated content, short-form video, live streaming, and augmented reality drive consumer attention and purchase intention, while payment systems encompassing digital wallets, BNPL services, and in-platform checkout shape transactional trust and friction. Conversion and customer lifetime value emerge as joint outcomes of this interaction, mediated by consumer trust and transaction friction. The study further identifies key sustainability tensions related to digital carbon footprints from data-intensive formats, financial vulnerability associated with frictionless credit tools, and governance concerns surrounding transparency, privacy, and platform power concentration. The study contributes an integrative conceptual model linking advertising formats, payment systems, consumer behaviour, and ESG dimensions within a unified framework, supported by six theoretically grounded hypotheses (H1–H6) to guide future empirical research in sustainable digital commerce. Full article

The rapid growth of e-commerce has significantly increased direct-to-consumer deliveries, putting competitive and environmental pressure on urban last-mile logistics. Out-of-home (OOH) delivery options, particularly parcel lockers, are increasingly integrated into city mobility strategies to reduce congestion and emissions. However, the role of mobile applications front-ending these networks remains under-researched. This study aims to evaluate the user experience (UX) and functional adequacy across three major parcel-locker apps in Poland: InPost Mobile, DPD Mobile, and ORLEN Paczka. A cross-sectional, mixed-methods approach combining in situ corridor testing and structured post-task questionnaires was employed with 30 users at real locker locations in Katowice. The results indicate that interface simplicity, predictable information flow, and technical stability are the dimensions most consistently associated with higher user ratings. InPost Mobile consistently achieved the highest ratings due to its focus on core workflows, whereas applications emphasizing broader functional coverage (ORLEN Paczka) exhibited usability trade-offs, and DPD Mobile underperformed in speed and stability. Because the study relied on a small convenience sample (n = 30) in a single city and was skewed toward younger adults (18–24), the findings should be interpreted as exploratory and primarily reflective of a digitally proficient demographic rather than the broader user population. Full article

Internet advertising, while enabling unprecedented commercial reach, has become a pervasive vehicle for deceptive practices that inflict measurable harm on consumers. This study empirically investigates the structural relationships between internet advertising falsity and consumer harm by integrating analyses of the mediating role of consumer cognitive processes and the moderating role of consumer vulnerability within a unified structural framework. Survey data were collected from 600 adult consumers with online purchase experience in the Republic of Korea—an advanced digital economy characterized by exceptionally high mobile-commerce penetration, mature e-commerce infrastructure, and evolving digital consumer protection regulation—and analyzed using structural equation modeling (SEM) with AMOS 24.0, supplemented by Hayes’ PROCESS macro Model 59 for conditional process analysis. All 13 hypotheses were supported, although path magnitudes varied substantially across falsity dimensions and mediator pathways—with direct effects ranging from β = 0.156 (false scarcity) to β = 0.224 (performance exaggeration), and indirect effects dominated by the risk assessment distortion pathway. Among the four sub-dimensions of advertising falsity—factual misrepresentation, performance exaggeration, price deception, and false scarcity—performance exaggeration exerted the strongest direct effect on consumer harm. The three cognitive mediators—perceived advertising credibility, risk assessment distortion, and purchase decision pressure—all demonstrated significant partial mediation, with risk assessment distortion emerging as the most powerful indirect pathway. All four consumer vulnerability dimensions—digital literacy level, demographic vulnerability, prior victimization experience, and impulsive buying tendency—significantly moderated the falsity–harm relationship, with low-digital-literacy consumers experiencing approximately 1.7 times the adverse effect of high-literacy counterparts. Moderated mediation analysis revealed that the conditional indirect effect for the high-vulnerability group was approximately 2.3 times that of the low-vulnerability group, confirming that the cognitive harm mechanism intensifies systematically for vulnerable consumers. These findings advance consumer vulnerability theory in the digital context and offer evidence-based implications for consumer protection policy, platform governance, and digital literacy education. Full article

The accelerated growth of e-commerce has intensified the dual challenges of weak infrastructure and carbon emission pressures in last-mile delivery for rural and mountainous regions. As the World Bank calls for integrating carbon market development into national strategies, Truck-Drone Collaborative Delivery (TDCD) has emerged as a critical sustainable solution. However, existing research often overlooks the strict airspace regulations in sensitive border areas. Therefore, this paper proposes a Vehicle Routing Problem with Drones and Mobile Base Stations (VRPDBS) model that explicitly incorporates airspace constraints and mobile hub deployment. We introduce a quantified “Regional Flyability Factor” ($f_k$) to measure the impact of airspace restrictions on routing decisions and solve the problem using a hybrid metaheuristic algorithm. A case study based on real-world data from the Yanbian Korean Autonomous Prefecture reveals that strict airspace compliance imposes an absolute delivery delay of 4–5 h and an operational cost premium of up to 15%, an impact that can be effectively mitigated through a mobile base station mediation strategy. More importantly, multi-scenario sensitivity analysis under carbon price uncertainty indicates that although truck-dominant modes are cost-effective at current low carbon prices, drone-intensive configurations demonstrate superior economic robustness and environmental performance under high carbon price scenarios. This study not only provides a technical framework for green logistics planning in complex airspace but also offers strategic decision support for logistics enterprises to navigate long-term climate policy risks. Full article

This study examines the impact of explainable artificial intelligence (XAI) features on user satisfaction and purchase intention in Saudi mobile shopping applications, utilising the stimulus–organism–response (S–O–R) framework. With the increasing reliance on AI-driven decision support in e-commerce, enhancing transparency, fairness, trustworthiness, and interpretability has become crucial for shaping consumer perceptions and behavioural responses. The research employed a quantitative methodology using partial least squares structural equation modelling (PLS-SEM) to examine the relationships among stimulus factors, cognitive and affective states, consumer satisfaction, and purchase intention. In a survey of 597 respondents from Jeddah and Makkah, Saudi Arabia, the findings highlight that fairness and bias detection, trustworthiness, and transparency significantly influence consumers’ cognitive and affective states, which in turn enhance satisfaction and intention to purchase. Consumer satisfaction emerged as a critical mediator, reinforcing the role of positive emotional and cognitive experiences in driving purchase behaviours. However, interpretability showed limited impact, suggesting that consumers may prioritise fairness and trustworthiness over technical clarity of explanations. Theoretically, this study contributes to advancing knowledge on the role of XAI in consumer behaviour by integrating fairness, transparency, and affective responses into the S–O–R paradigm. From a managerial perspective, the results underscore the importance for mobile shopping platforms to design AI systems that foster trust, reduce perceived bias, and ensure transparency, thereby improving consumer engagement and purchase outcomes. By addressing gaps in interpretability and transparency, businesses can strengthen user trust and loyalty, ultimately enhancing competitive advantage in Saudi Arabia’s rapidly growing e-commerce sector. Full article

The rapid expansion of mobile healthcare (mHealth) applications has transformed health-related e-commerce, creating new challenges for understanding and responding to user needs. This study proposes a data-driven framework to systematically identify and prioritize unmet user requirements from negative reviews of Chinese mHealth applications. Using a dataset of 31,124 user reviews collected between 2019 and 2025, the framework integrates sentiment analysis, topic modeling, and machine learning regression to uncover six key areas of user concern and examine their temporal evolution. Among several predictive models linking user concerns to app ratings, the k-nearest neighbors (KNN) model demonstrated superior performance. Subsequent SHAP-based interpretability analysis reveals that account authentication, system accessibility, and application stability have the most significant impact on user ratings, highlighting the critical roles of trust and technical reliability in health e-commerce. This research not only provides actionable insights for platform governance but also contributes a generalizable methodology for leveraging user-generated content to inform evidence-based management and policy decisions in mobile digital services. Full article

Autonomous mobile robots are transforming industries from e-commerce logistics to field exploration, but their effectiveness depends on onboard energy storage. This study addresses the challenge of selecting optimal battery technologies for autonomous mobile robots, balancing performance, energy efficiency, thermal stability, and cost across diverse applications. We focus on lithium-ion, lithium-polymer, and nickel-metal hydride batteries, the most common power solutions, each with distinct advantages and disadvantages in energy density, form factor, thermal stability, and cost. A dynamic modeling and simulation framework in MapleSim evaluated these chemistries under defined and representative operating conditions, tracking state of charge and temperature during charging and discharging. A Particle Swarm Optimization algorithm evaluated 37 battery configurations by thermal stability, energy efficiency, and cost across five use cases. Key results indicate that for logistics and warehousing, lithium nickel manganese cobalt oxide with graphite is optimal; for healthcare, lithium nickel manganese cobalt oxide with lithium titanate oxide excels; for manufacturing, lithium nickel cobalt aluminum oxide with graphite leads; for agricultural robots, lithium manganese oxide with graphite is best; and for exploration and mining, lithium iron phosphate with graphite is most reliable. These results provide a structured basis for battery selection, showing how simulation-driven, multi-criteria decision-making enhances energy management and operational reliability. Full article

Circular economy (CE) claims in fashion aim to mobilize consumer participation in reuse and recycling, yet the interpretative flexibility of “circular” language can also enable vague messaging and skepticism. This study investigates how consumers assess CE fashion claims in terms of (a) claim substantiation quality (CSQ) and (b) claim support credibility (CSC), and how these assessments influence perceived green authenticity (PGA), green trust (GTR), and circular purchase intention (CPI) in Greece and the United Kingdom. A cross-national online stimulus-based survey utilizing standardized e-commerce product-card claims for a fictitious circular fashion brand gathered data from Greece (n = 640) and the UK (n = 572). PLS-SEM and multi-group analysis evaluated a model distinguishing CSQ and CSC as complementary message properties. In the overall sample, both CSQ and CSC exhibited a positive correlation with CPI, whereas PGA and GTR emerged as the most significant proximal predictors, with authenticity demonstrating the most substantial impact. Indirect-effect tests showed that CSQ affected CPI through both authenticity and trust. On the other hand, CSC was only effective through authenticity, and there was no clear pathway for CSC trust intention. The multi-group results also showed context sensitivity: Greece exhibited a stronger trust-based path to intention, while the UK had a stronger authenticity-based path to intention. Overall, the results support a dual-route theory of CE claim persuasion. Additionally, they suggest that effective CE fashion communication should combine clear, specific content with credible, externally checkable support cues. Full article

To examine how digitally empowered rural e-commerce affects the market-oriented allocation of urban–rural factors at the county level and the underlying mechanism, this study treats the National E-commerce into Rural Counties Demonstration Program as a quasi-natural experiment. Using a panel of 1898 Chinese counties from 2000 to 2022, we conduct multi-period DID with staggered adoption and mediation analyses. The results show that rural e-commerce significantly raises the marketization level of factor allocation; the effect grows stronger over time and is most pronounced during the rapid-expansion phase, in agriculture-oriented e-commerce counties, in poverty-stricken counties, and in the Central and Western regions. The impact operates mainly through three channels: enlarging market size, upgrading industrial structure, and deepening digital financial usage. Notably, the digital finance channel exhibits a suppression effect, suggesting a complex role of financial digitalization in the early stages of rural development. To further ensure the robustness of our findings, we also conduct rigorous checks using the CSDID method and alternative proxy variables, consistently reaffirming the policy’s significant positive impact. These findings offer actionable evidence for deepening county-level factor-market reforms and advancing common prosperity, leading to policy recommendations on strengthening county digital infrastructure, tailoring e-commerce support systems, and improving the institutional environment for factor mobility. Full article

The rapid growth of social networks and online platforms has heightened the importance of trust evaluation in various applications, including e-commerce, social networking, online collaboration, and mobile crowdsourcing. Traditional trust evaluation methods often rely on handcrafted features and simple models, which fail to fully capture the implicit patterns within the complex, heterogeneous structures of social networks. To address this issue, we propose TrustGTN, a novel method based on Heterogeneous Graph Neural Networks (HGNNs). It incorporates a soft selection mechanism that dynamically adjusts the training matrix weights. This enables it to capture the evolving structural and semantic patterns of the graph. The model can automatically learn important trust chains without the need to manually set their lengths. Experimental results show that TrustGTN outperforms existing trust evaluation methods on public datasets, demonstrating its advantages in handling heterogeneous graph data. Full article

Sentiment classification plays a crucial role in analyzing customer feedback to identify market trends, enhance product recommendations, and improve customer satisfaction. This study focuses on sentiment analysis of Amazon reviews using two major datasets—Fine Food Reviews and Unlocked Mobile Reviews—which exhibit label imbalance. To address this challenge, both oversampling and undersampling techniques were applied to balance the datasets. Various machine learning (ML) algorithms, including Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Naïve Bayes (NB), and Gradient Boosting Machine (GBM), as well as deep learning (DL) models such as Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and transformer-based models like RoBERTa, were implemented. After data cleaning and preprocessing, models were trained, and performance was evaluated. The results indicate that oversampling significantly enhances classification accuracy, particularly for the Fine Food dataset. Among ML models, Random Forest achieved the highest accuracy due to its ensemble approach and robustness in handling high-dimensional data. DL models, particularly RoBERTa, also demonstrated superior performance owing to their capacity to capture contextual dependencies. The findings emphasize the importance of data balancing for optimal sentiment analysis and contribute valuable insights toward advancing automated opinion classification in e-commerce applications. Full article

The emergence of the Smart Cities paradigm and the rapid expansion and integration of Internet of Things (IoT) technologies within this context have created unprecedented opportunities for high-resolution behavioral analytics, urban optimization, and context-aware services. However, this same proliferation intensifies privacy risks, particularly those arising from cross-modal data linkage across heterogeneous sensing platforms. To address these challenges, this paper introduces a comprehensive, statistically grounded framework for generating synthetic, multimodal IoT datasets tailored to Smart City research. The framework produces behaviorally plausible synthetic data suitable for preliminary privacy risk assessment and as a benchmark for future re-identification studies, as well as for evaluating algorithms in mobility modeling, urban informatics, and privacy-enhancing technologies. As part of our approach, we formalize probabilistic methods for synthesizing three heterogeneous and operationally relevant data streams—cellular mobility traces, payment terminal transaction logs, and Smart Retail nutrition records—capturing the behaviors of a large number of synthetically generated urban residents over a 12-week period. The framework integrates spatially explicit merchant selection using K-Dimensional (KD)-tree nearest-neighbor algorithms, temporally correlated anchor-based mobility simulation reflective of daily urban rhythms, and dietary-constraint filtering to preserve ecological validity in consumption patterns. In total, the system generates approximately 116 million mobility pings, 5.4 million transactions, and 1.9 million itemized purchases, yielding a reproducible benchmark for evaluating multimodal analytics, privacy-preserving computation, and secure IoT data-sharing protocols. To show the validity of this dataset, the underlying distributions of these residents were successfully validated against reported distributions in published research. We present preliminary uniqueness and cross-modal linkage indicators; comprehensive re-identification benchmarking against specific attack algorithms is planned as future work. This framework can be easily adapted to various scenarios of interest in Smart Cities and other IoT applications. By aligning methodological rigor with the operational needs of Smart City ecosystems, this work fills critical gaps in synthetic data generation for privacy-sensitive domains, including intelligent transportation systems, urban health informatics, and next-generation digital commerce infrastructures. Full article

Digitalization is increasingly central to economic growth strategies, yet robust macro-level evidence on the role of SME-led e-commerce remains limited. Drawing on the Resource-Based View, this study examines how SME digitalization, internet finance, and platform-based activities influence regional economic growth in China, and how these effects depend on digital infrastructure readiness (DIR). We construct an annual panel of 30 provincial-level regions in China over 2015–2024 and estimate dynamic relationships using two-step system GMM to address endogeneity and growth persistence. The results show that SME digitalization, supply-chain efficiency, mobile payment penetration, tech-driven employment growth, platform-economy contribution, and DIR all exert statistically significant positive effects on GDP growth. Quantitatively, a 10-percentage-point increase in SME digitalization is associated with approximately 0.3-percentage-point higher regional GDP growth, while a 10-point increase in DIR corresponds to about 0.4-percentage-point higher growth. Moderation analyses reveal that DIR significantly amplifies the growth effects of e-commerce expansion, mobile payments, and digital marketing, whereas its moderating role is weaker or insignificant for cross-border payments and supply-chain efficiency. These findings reconceptualize digitalization as a coordinated bundle of complementary resources and position DIR as a critical enabling capability for translating SME digital transformation into macroeconomic growth. The study offers policy-relevant evidence for targeting infrastructure investment and digital-economy strategies in emerging platform economies. Full article