Authors - Anis A, Kasi B Anand, Adithyan B, Navaneeth A Nayan, Durgalashmi CV Abstract - The advent of the prospects of using startups especially tech-based having unprecedented significance and continuous severity with regard to Sustainable Development Goals (SDGs) has increased the understanding of how important it is to understand students' knowledge and perceptions in this field. This study is designed for survey to understand and measure students' depths of knowledge regarding startup companies, the SDGs, as well as potentially precautionary attitudes towards startups in achieving sustainable development by 2030. The robust quantitative research design, were a well-designed and systematically developed questionnaire was employed to identify a systematic collection of questionnaires from students who are studying in the different higher educational institutions by incorporating cross-sectional survey methodology data collection technique. In general, the results show that students agree that startups are good for economy, society as a whole and even environment. However, research also mentions that there is low perception about startup doing Sustainable development work along-with moderate awareness and moderate belief in strong government support. These parts together suggest deep necessary work by the policymakers, educators and other stakeholders to raise the level of awareness and support. Furthermore, the study demonstrates the need for systematic integration of sustainability and entrepreneurship education to enhance students' knowledge on sustainability issues as well as their involvement in sustainable development-oriented tasks. The findings of this study offer new and practical lessons to policymakers, educators and researchers facing the continuing challenge of building and reshaping startup ecosystems that reflect or foster their successful fulfilment of sustainable development goals.
Authors - Chethana R.M. and Dr S.P. Manikandan Abstract - The rapid evolution of cyber threats has intensified risks to organisational security, necessitating intelligent, data-driven approaches to threat assessment and mitigation. This study presents a comprehensive analysis of the evolving cyber threat landscape and its impact on organizational security using a dataset of 1,200 cybersecurity incidents reported across major sectors in India from 2019 to 2024. The dataset includes diverse incident categories such as phishing, ransomware, data breaches, online fraud, identity theft, and hacking, along with associated financial losses, geographic distribution, and affected organizational domains. To investigate threat patterns and predict incident behavior, three machine learning models, Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR) were employed for classification and regression tasks. Experimental results reveal significant challenges posed by class imbalance and feature complexity, leading to relatively low classification accuracies, with Random Forest marginally outperforming other models. Regression analysis for predicting financial losses also demonstrated limited explanatory power, indicating the influence of latent factors beyond the available attributes. Despite these constraints, the study identifies important sector-specific vulnerability patterns, highlighting significant financial impacts across healthcare, financial services, and government. The findings emphasize that conventional machine learning models alone may be insufficient for capturing the highly dynamic and nonlinear nature of cyber threats, underscoring the need for advanced threat intelligence frameworks, richer datasets, and adaptive security analytics. This research contributes empirical insights into cyber risk modeling and offers practical implications for policymakers and organizations seeking evidence-based cybersecurity strategies.
Authors - Xamdamov Utkir Raxmatillaevich, Elov Botir Boltayevich, Alavutdinova Nadira Ganiyevna, Malika Suyunova Odil qizi, Sharipov Soxib Salimovich , Narimova Gulnora Abdumanonovna Abstract - In this article, the architecture of an information system for sentiment analysis of Uzbek-language texts and its key components are examined from both scientific and practical perspectives. The system is based on a multi-layered and microservice architecture, consisting of a user interface (front-end) and a server (back-end) that provides services through a REST API. The back-end components, implemented via a Flask-based RESTful API server, carry out the business logic and sentiment classification. Deep learning models, especially transformer-based architectures (BERT, XLM-RoBERTa), were utilized for analyzing Uzbek texts and demonstrated effective results. The system ensures security, provides integration capabilities, and offers a user-friendly interface to enhance user experience. The modular architecture of the system allows broad scalability and integration with various platforms. As a result of scientific and practical experiments, the system achieved high accuracy (90%) and proved effective for real-time sentiment analysis tasks.
Authors - Sunandita Adhikary, Dipanwita Chakrabarty, Arunangshu Giri, Shamba Chatterjee, Dibyendu Rath, Soumya Kanti Dhara, Solanki Pattanayak, Samik Bagchi Abstract - The study evaluates how shopping engagement gets influenced by mobile apps in digital retail platform. In e-commerce platform it is important to understand user preference in the context of customization, quality of information, usability and interactivity. The present study investigates how these contextual parameters play a pivotal role in shaping users’ emotional and cognitive reactions. These reactions subsequently influence user engagement and purchase-related decisions. The study has proposed a structured framework to identify the antecedents’ influence on shopping engagement and how it shapes user satisfaction. The findings of the study shows that mobile app plays a crucial role in engaging user in digital retail platform and consequently users’ shopping engagement influence their choice satisfaction. The study has notable contribution for marketers and mobile app developers so that they can enhance users’ satisfaction and can achieve competitive advantage. The study has enriched existing literature as well by extending expectation confirmation theory in the context of shopping engagement through mobile application in digital retail platform.
Authors - Maria Genoveva Moreira Santos, Eric Geovanny Cedeno Zambrano Abstract - Document management in public institutions constitutes a strategic element for improving administrative efficiency and strengthening decision making. In this context, the present study analyzes the implementation of a centralized repository in Ecuador’s Water Secretariat, aimed at the use of business intelligence tools and platforms to optimize access to, control of, and utilization of institutional information. The study was conducted using a quantitative methodology, supported by interviews applied across different departments of the institution, in order to identify needs, limitations, and practices related to records and process management. The results revealed a low adoption of specialized technological solutions and a limited appreciation of their strategic potential within the public sector. A total of 83.7% of the results supported the need to establish clear regulations for document management. It is concluded that the integration of document management systems with business intelligence platforms promotes the generation of timely information, institutional monitoring, and evidence based decision making.
Authors - Eka Dewi Utari, Darma Rika Swaramarinda, Maulana Amirul Adha, Triesninda Pahlevi, Yuliansyah, Dewi Nurmalasari, Agung Kresnamurti Rivai P, Ferry Setyadi Atmadja, Fauzan Fadlullah, Alifah Kusumaningrum, Sabo Hermawan, Renata Rachel Abstract - Academic websites have emerged as critical intelligent digital infrastructures for delivering institutional information and services in higher education. However, existing evaluation frameworks often capture either technical quality dimensions or subjective user experience in isolation. This study proposes and empirically validates an integrated evaluation model combining WebQual 4.0 with the Ease of Use construct from the End User Computing Satisfaction (EUCS) model, applied to the official website of the Faculty of Economics and Business, Universitas Negeri Jakarta (www.feb.unj.ac.id). The integration is motivated by the growing imperative to align academic web services with intelligent service design principles encompassing data-driven content governance, responsive interaction channels, and user centred personalization as foundations for future AI augmented academic portals. A quantitative descriptive design collected data from 124 respondents (93.5% students; 6.5% lecturers) via a 17-item validated questionnaire across four dimensions: Usability, Information Quality, Interaction Quality, and Ease of Use. Multiple linear regression (IBM SPSS 23) revealed that Information Quality (β = 0.419, p < 0.001) and Interaction Quality (β = 0.260, p = 0.002) exerted statistically significant partial effects on user satisfaction, whereas Usability and Content did not reach partial significance. Collectively, the four dimensions explained 70.8% of satisfaction variance (R² = 0.708; F = 72.074; p < 0.001). Bibliometric keyword-network analysis contextualises the study within the broader digital-services literature. The integrated WebQual–EUCS model offers a replicable diagnostic tool for higher education institutions seeking to align web services with intelligent user expectations.
Authors - Anggita Sharon Simanjuntak, Eva Nurhazizah Abstract - This study investigates the impact of cognitive perception on destina tion trust and intention to visit, while examining the moderating role of social media influencers at Taman Impian Jaya Ancol, an urban tourism destination in Indonesia. Utilizing a quantitative approach, data were collected from 385 re spondents via purposive sampling and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS. The results reveal that cognitive perception significantly enhances both destination trust and intention to visit. Similarly, destination trust and social media influencers exhibit a signif icant positive effect on visit intention and destination trust, respectively. How ever, social media influencers do not significantly moderate the cognitive per ception-destination trust relationship. Ultimately, these findings highlight the ne cessity of cultivating positive perceptions and trust, offering strategic insights for destination managers to optimize social media marketing.
