Search Results (860)

The rise of e-commerce platforms has completely revolutionized the way in which consumers interact with the market. In our digital world, due to the evolution of technology, people can purchase with ease the desired products, regardless of time and place, directly from their personal devices. This has led to a considerable improvement in users’ experiences, saving both time and money and avoiding stores’ congestions. At the same time, the emerging technologies, such as machine learning, artificial intelligence, augmented reality, and blockchain, registered a substantial contribution to optimizing e-commerce platforms by enhancing the efficiency of the processes, better understanding users’ needs, and offering personalized solutions. Therefore, the present bibliometric investigation aims to provide a comprehensive overview of the research domain-electronic commerce exploration using emerging technologies. Based on a dataset collected from the Web of Science database, the study reveals key details of the field, research characteristics, main themes, and current trends. Within the analysis, the R-tool—Biblioshiny 4.2.1—has been used for the creation of tables, graphs, and visual representations. The high importance of the domain, together with the significant interest within academics in publishing papers around this area, is validated by the value obtained for the annual growth rate, more specifically 44.65%, as well as by the cross-validation analyses performed in VOSviewer 1.6.20 and CiteSpace 6.3.R1, along with topic analysis performed through Latent Dirichlet Allocation and BERTopic. The results of this research represent precious information for the scientific community, authorities, and even companies that are oriented to e-commerce platforms, since crucial details about the market trends, domain’s impact, and key contributions are exposed. Full article

The rapid rise of fashion resale platforms has created new pathways for sustainable consumption, yet little research has compared how different governance models, company-controlled versus seller-controlled, shape consumer trust and purchasing behavior. This study addresses that gap by applying the Stimulus–Organism–Response (SOR) framework to examine how information precision, authenticity, and risk aversion influence consumer trust and purchase intention within circular fashion markets. Drawing on an experimental design with 524 U.S. consumers randomly assigned to each platform type, multi-group structural equation modeling reveals that the three stimuli significantly enhance trust, which in turn drives purchase intention. Risk aversion exerted stronger effects in company-controlled contexts, whereas trust translated more directly into purchase intention on seller-controlled platforms. Theoretically, the research extends SOR applications to sustainability by identifying trust as the psychological bridge linking platform design to circular consumption. Practically, it offers actionable guidance for brands and peer-to-peer platforms on authentication, information transparency, and risk-reduction strategies that strengthen consumer confidence and promote environmentally responsible resale participation. The findings advance understanding of how governance structures can accelerate sustainable fashion retailing and contribute to the circular economy. Full article

The kernel of intelligent manufacturing is data utilization, where data can be trained to build AI models to generate optimized control parameter values for production processes. The data includes dynamic (or state) data from sensors, process (control) data from devices/equipment, and quality data from the Manufacturing Execution System (MES). Since the data sampling frequency is high, which causes a large amount of collected data, and the production devices are diverse, which leads to varying communication protocols, it is a challenge to store and display this data. This paper attempts to develop a metamodel-based topology representation of a production scene to store and display the data. The idea is to design metamodels for Ends (for devices), Edges (for production lines), and the Cloud (the central control platform), respectively. Then the model of each specific manufacturing scene is an instantiation of the metamodel. Our work has the following advantages: (1) It can store data at cloud, edge and end, such that it provides fast data querying and supports local computing such as local optimization, and automatically displays their physical topology; (2) it provides a template to describe the topological representation of all kinds of production scenes, such that the description of a specific production scene is an instantiation of the metamodel configured by the collected data; and (3) it provides a way to automatically generate code to connect devices and a database by configuring the meta-models without training or tuning a modern large model, which is almost impossible for most companies. This work serves as the bottom layer of a generated operating system for intelligent manufacturing that provides data service for the upper control layer. Full article

The transition to a circular economy (CE) in plastic packaging faces persistent barriers, including regulatory fragmentation, technological limitations, and supply chain disconnection. This study examines how multinational companies address these challenges by leveraging enablers such as advanced policies, technological innovation, and cross-sectoral collaboration. Based on a PRISMA-guided systematic review and a descriptive–explanatory case study, semi-structured interviews with senior managers were analyzed through thematic coding and data triangulation. Findings reveal that regulatory measures like virgin plastic taxation and post-consumer recycled material (PCR) incentives are effective only when synchronized with technical capacities. Investments in recycling infrastructure and circular design, such as resin standardization, enhance the quality of secondary materials, while local supply contracts and digital traceability platforms reduce volatility. Nevertheless, negative consumer perceptions and inconsistent PCR quality remain major obstacles. Unlike prior studies that examine barriers and enablers separately, this research develops an integrative framework where their interaction is conceptualized as a systemic and non-linear process. The study contributes to CE theory by reframing barriers as potential drivers of innovation and provides practical strategies, combining policy instruments, Industry 4.0 technologies, and collaborative governance to guide multinational firms in accelerating circular transitions across diverse regulatory contexts. Full article

The Blockchain-Based Medical Insurance Transaction System (BMIT) developed in this study addresses key issues in traditional medical insurance—information silos, data tampering, and privacy breaches—through innovative blockchain architectural design and technical infrastructure reconstruction. Built on a consortium blockchain architecture with FISCO BCOS (Financial Blockchain Shenzhen Consortium Blockchain Open Source Platform) as the underlying platform, the system leverages FISCO BCOS’s distributed ledger, granular access control, and efficient consensus algorithms to enable multi-stakeholder on-chain collaboration. Four node roles and data protocols are defined: hospitals (on-chain data providers) generate 3D coordinate hashes of medical data via an algorithmically enhanced Bloom Filter for on-chain certification; patients control data access via blockchain private keys and unique parameters; insurance companies verify eligibility/claims using on-chain Bloom filters; the blockchain network stores encrypted key data (public keys, Bloom filter coordinates, and timestamps) to ensure immutability and traceability. A 3D-enhanced Bloom filter—tailored for on-chain use with user-specific hash functions and key control—stores only 3D coordinates (not raw data), cutting storage costs for 100 records to 1.27 KB and reducing the error rate to near zero ($1.77\%$ lower than traditional schemes for 10,000 entries). Three core smart contracts (identity registration, medical information certification, and automated verification) enable the automation of on-chain processes. Performance tests conducted on a 4-node consortium chain indicate a transaction throughput of 736 TPS (Transactions Per Second) and a per-operation latency of 181.7 ms, which meets the requirements of large-scale commercial applications. BMIT’s three-layer design (“underlying blockchain + enhanced Bloom filter + smart contracts”) delivers a balanced, efficient blockchain medical insurance prototype, offering a reusable technical framework for industry digital transformation. Full article

The landscape of marketing is rapidly evolving as companies adapt to new societal dynamics and technological advancements. Guerrilla marketing, characterized by its unconventional, creative, and cost-effective tactics, has proven effective in capturing consumer attention. When integrated with social media platforms, these tactics gain amplified reach, immediacy, and interactive potential, fostering viral spread and deeper consumer engagement. This research seeks to answer the pivotal question: “How does guerrilla marketing on social media influence consumer behavior and brand awareness?” To address this, a comprehensive literature review was conducted to provide an in-depth analysis of relevant studies in the field. Our findings reveal that this integration significantly enhances consumer engagement, boosts brand awareness, and positively influences purchasing behavior. Moreover, it provides firms with competitive advantages in dynamic, resource-constrained markets. The results highlight the importance of leveraging creative marketing approaches alongside digital platforms to shape consumer choices and drive successful marketing outcomes in the digital age. Full article

Blockchain technology has become a game changer in sectors like accounting and auditing. Its usage is still restricted due to a lack of insight into what drives people to adopt it for financial services like accounting and auditing. This research delves into the factors that influence the adoption of blockchain systems in accounting and auditing services by utilizing an enhanced edition of the Theory of Planned Behavior. In this study, alongside the previously established elements like Attitude, subjective norm, and Perceived Behavioral Control, self-perception and personal moral values are included to reflect how identity and ethics impact decision-making processes. Data were gathered via an online survey (N = 751) conducted on the Prolific platform, and the hypotheses were tested using Structural Equation Modeling. The hypotheses were examined through the Structural Equation Modeling method. The findings indicate that each of the five predictors plays a significant role in influencing Behavioral Intention, with personal moral values being the influential factor followed by subjective norm and Perceived Behavioral Control. Attitude plays an important role in shaping adoption choices and showcases the complexity involved in such decisions. As such, it is crucial to take into account ethical factors when encouraging the use of blockchain technology. This study adds to the existing knowledge of the Theory of Planned Behavior framework, offering insights for companies aiming to boost the implementation of blockchain systems in professional settings. Future research avenues and real-world implications are explored with an emphasis placed on developing targeted strategies that align technological adoption with personal values and organizational objectives. Full article

Sales and marketing personnel are among the most knowledgeable individuals regarding the safety of the medications they promote. No previous work has assessed pharmaceutical sales and marketing personnel’s knowledge, attitude, practice (KAP), and barriers toward pharmacovigilance (PV) in Saudi Arabia; therefore, the present study aimed to assess these aspects and to scrutinize their associations with the subjects’ baseline characteristics. A validated questionnaire comprising five sections (baseline characteristics, knowledge, attitude, practice, and barriers) was disseminated via email networks and social media platforms between 18 March and 31 May 2025. All employees working in the sales and marketing departments of pharmaceutical companies in Saudi Arabia were eligible to participate. Participants’ responses were categorized as good or poor knowledge, positive or negative attitude, good or poor practice, and challenging or non-challenging work environment, based on the cumulative score in each respective section, using a 60% cutoff. A total of 400 participants completed the survey. Of these, about one-third (37.3%) had 2–4 years of professional experience and two-thirds (63%) were employed by multinational companies. Overall, 57% and 83.5% had good knowledge and positive attitude, respectively. The work environment was considered non-challenging by 92.8% of participants, and 61% reported good practice. We noted that holding a non-pharmacy degree was a significant predictor of poor knowledge and a challenging work environment. Additionally, employment in a local company was significantly associated with poor knowledge and practice. Pharmaceutical sales and marketing personnel in Saudi Arabia demonstrated acceptable levels of KAP and reported few barriers toward PV, with an opportunity for improvement. Full article

Using systems with machine learning technologies for process automation is a global trend in agriculture. However, implementing this technology comes with challenges, such as the need for a large amount of computing resources under conditions of limited energy consumption and the high cost of hardware for intelligent systems. This article presents the possibility of applying a modern ESP32 microcontroller platform in the agro-industrial sector to create intelligent devices based on the Internet of Things. CNN models are implemented based on the TensorFlow architecture in hardware and software solutions based on the ESP32 microcontroller from Espressif company to classify objects in crop fields. The purpose of this work is to create a hardware–software complex for local energy-efficient classification of images with support for IoT protocols. The results of this research allow for the automatic classification of field surfaces with the presence of “high attention” and optimal growth zones. This article shows that classification accuracy exceeding 87% can be achieved in small, energy-efficient systems, even for low-resolution images, depending on the CNN architecture and its quantization algorithm. The application of such technologies and methods of their optimization for energy-efficient devices, such as ESP32, will allow us to create an Intelligent Internet of Things network. Full article

Nowadays, Flutter with the Dart programming language has become widely popular in mobile developments, allowing developers to build multi-platform applications using one codebase. An increasing number of companies are adopting these technologies to create scalable and maintainable mobile applications. Despite this increasing relevance, university curricula often lack structured resources for Flutter/Dart, limiting opportunities for students to learn it in academic environments. To address this gap, we previously developed the Flutter Programming Learning Assistance System (FPLAS), which supports self-learning through interactive problems focused on code comprehension through code-based exercises and visual interfaces. However, it was observed that many students completed the exercises without fully understanding even basic concepts, if they already had some knowledge of object-oriented programming (OOP). As a result, they may not be able to design and implement Flutter/Dart codes independently, highlighting a mismatch between the system’s outcomes and intended learning goals. In this paper, we propose a guided self-study approach of integrating documentation, code, visual output, and exercise in FPLAS. Two existing problem types, namely, Grammar Understanding Problems (GUP) and Element Fill-in-Blank Problems (EFP), are combined together with documentation, code, and output into a new format called Integrated Introductory Problems (INTs). For evaluations, we generated 16 INT instances and conducted two rounds of evaluations. The first round with 23 master students in Okayama University, Japan, showed high correct answer rates but low usability ratings. After revising the documentation and the system design, the second round with 25 fourth-year undergraduate students in the same university demonstrated high usability and consistent performances, which confirms the effectiveness of the proposal. Full article

Environmental, Social, and Governance (ESG) performance has become a vital instrument for corporations to integrate sustainable development principles into business operations. Against the dual backdrop of disruptions in the international order and economic instability, investigating the impact of corporate ESG performance on total factor productivity (TFP) under environmental uncertainty is of significant importance. Utilizing data from Chinese A-share listed companies spanning the period 2011 to 2022, this study employs a baseline regression model, a mediation effect model, a moderation effect model, and a moderated mediation model to examine the impact of corporate ESG performance on TFP under conditions of environmental uncertainty. The results indicate that (1) corporate ESG performance exerts a positive influence on TFP, particularly in tertiary industry firms, state-owned enterprises (SOEs), and enterprises with lower environmental risks; (2) improving ESG performance helps alleviate financing constraints, enhance human capital, and boost innovation capability, thereby strengthening TFP; and (3) environmental uncertainty moderates the pathway through which ESG performance affects TFP, amplifying its positive effect. Based on these findings, it is recommended that countries collaborate to establish a global, cross-industry platform for sharing ESG practices, develop a stable ESG policy framework and incentive mechanisms, and encourage enterprises to enhance their ESG management and resilient governance capabilities to promote sustainable economic development. Full article

For organizations, protecting computer networks has always been a very tough and demanding task. In the current technological era digital resources can now be protected without the need for outdated traditional perimeter-based security techniques. Organizations can use the Zero Trust Network Access (ZTNA) approach to safeguard and filter their vital digital assets for the company’s benefit. This platform uses sophisticated logical authentication to test the system’s ability to authenticate users, and network monitoring is used to look for possible security flaws and system vulnerabilities. By evaluating users’ interactions with the system and their handling of assigned digital resources, multi-factor authentication filters out unwanted access attempts. Three fundamental access control styles are provided by network segmentation, giving administrators the option to manage access in a democratic, strict, or flexible manner (least privilege approach). Full article

With the deepening integration of cultural heritage preservation and tourism, the application of intelligent and digital technologies in industrial heritage tourism has increasingly become a research focus for promoting cultural sustainability. This study takes the Wuhan Iron and Steel Company industrial heritage in Wuhan, Hubei Province, China, as the research object. Based on the UTAUT2 model, a structural equation model was constructed to examine the factors influencing users’ behavioral intention, with technology anxiety introduced as an additional variable to explore the acceptance mechanism of intelligent and digital tourism platforms for industrial heritage. Through path analysis and mediation effect testing, the study proposes a platform design strategy of “technological adaptation–emotional resonance–behavioral guidance,” develops the platform interface design, and conducts a multidimensional evaluation using the fuzzy comprehensive evaluation method to verify its feasibility. The findings not only extend the applicability of the UTAUT2 model in the field of cultural heritage tourism at the theoretical level, but also provide practical pathways for optimizing user experience and guiding functional iteration of industrial heritage tourism platforms. Moreover, the study offers practical support for the revitalization, digital inheritance, and sustainable preservation of industrial cultural heritage, thereby fostering the integrated development of cultural value, social value, and the tourism industry. Full article

Augmented reality (AR) technologies—such as Instagram filters—bridge the digital and physical worlds by allowing users to virtually try on clothing, thereby reducing the risk of virus transmission. In the T-shirt retail industry, AR enables product personalization, decreases the need for physical production, minimizes textile waste, and lowers carbon emissions. It also benefits individuals with limited mobility or those who prefer shopping online. This study tested several hypotheses on 105 active Instagram filter users using filters from the ’Apprecio’ account on mobile devices. Data analyzed using the partial least squares method revealed that interactivity significantly influences both purchase intention and continued use of digital platforms. While hedonic and vivid features enhance the user experience, they have a limited impact on driving purchases or long-term engagement. Customers’ engagement and buying intent are more strongly shaped by practical and interactive elements. The study recommends that companies invest in developing interactive AR features to boost customer satisfaction and foster trust. Future research should involve larger participant samples and investigate specific interactive elements—such as virtual try-on tools—to better understand their impact on consumer behavior. This study highlights the critical role of interactivity in AR for delivering meaningful and engaging shopping experiences. Full article

Ensuring traffic safety within urban public transport systems is essential for achieving sustainable urban development, particularly in densely populated metropolitan areas. This study investigates the integration of artificial intelligence (AI) technologies to enhance safety performance in closed public transport environments, with a focus on the city of Belgrade as a representative case. The research aims to evaluate how AI-enabled systems can contribute to the early detection and reduction of traffic incidents, thereby supporting broader goals of sustainable mobility, infrastructure resilience, and urban livability. A hybrid methodological framework was developed, combining computer vision, supervised machine learning, and time series analytics to construct a real-time risk detection platform. The system leverages multi-source data—including video surveillance, onboard vehicle sensors, and historical accident logs—to identify and predict high-risk behaviors such as harsh braking, speeding, and route adherences across various public transport modes (buses, trams, trolleybuses). The AI models were empirically assessed in partnership with the Public Transport Company of Belgrade (JKP GSP Beograd), revealing that the most accurate models improved incident detection speed by over 20% and offered enhanced spatial identification of network-level safety vulnerabilities. Additionally, routes with optimized AI-driven driving behavior demonstrated fuel savings of up to 12% and a potential reduction in emissions by approximately 8%, suggesting promising environmental co-benefits. The study’s findings align with multiple United Nations Sustainable Development Goals, particularly SDG 11 (Sustainable Cities and Communities) and SDG 9 (Industry, Innovation, and Infrastructure). Moreover, the research addresses ethical, legal, and governance implications surrounding the use of AI in public infrastructure, emphasizing the importance of privacy, transparency, and inclusivity. The paper concludes with strategic policy recommendations for cities seeking to deploy intelligent safety solutions as part of their digital and green transitions in urban mobility planning. Full article