Emerging Science Journal (ESJ)
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The Influence of Quality of Work Life and Perceived Organizational Support on Turnover Intention in Private Higher Education Institutions
Full text linkThe study addresses faculty turnover in private higher education institutions, a challenge that disrupts institutional progress and educational continuity. It examines the influence of Quality of Work Life (QWL) on turnover intention (TI) and the mediating role of Perceived Organizational Support (POS). Utilizing a stratified random sample of 396 educators across 24 private colleges, data were collected through structured questionnaires and analysed using Structural Equation Modeling (SEM). The findings reveal a significant negative relationship between QWL and TI, indicating that improved work-life balance reduces educators' intent to resign. Additionally, QWL positively influences POS, which further diminishes TI, with POS mediating the QWL-TI relationship. By integrating POS as a mediator, the study provides actionable insights for educational administrators, emphasizing the importance of enhancing QWL and POS to mitigate faculty turnover. The findings offer a foundation for developing targeted policies aimed at improving workplace conditions and organizational support to ensure institutional stability. Doi: 10.28991/ESJ-2024-SIED1-020 Full Text: PD
Improvement of Computer Science Student's Online Search by Metacognitive Instructions
Full text linkThe purpose of this study is to evaluate the improvement of computer science students' online searches by using metacognitive instructions. These instructions in the form of flowcharts with detailed descriptions help students to plan, monitor, and evaluate their actions when searching for scientific and technical information. The research methods include the analysis of existing applications of metacognitive instructions and conceptual models of search in the learning process. To carry out the experiment, we designed a tutorial that contains the described metacognitive instructions with a detailed search plan. During the experiment, students had the task of writing the review sections of their term or final papers using the tutorial. The results were evaluated based on the quality of the submitted reviews and tutor feedback. The students using metacognitive instructions significantly improved the quality of the review sections. The structure of review sections improved, and the analysis of sources became more rigorous with more precise keyword phrasing. The study confirms that the use of metacognitive instructions enhances information search and academic performance. The novelty of the study lies in the integration of the metacognitive approach with conceptual search models into the learning process of computer science students. The improvements can be adapted to other disciplines to expand the study to other academic areas and develop additional tools to support metacognitive learning. Doi: 10.28991/ESJ-2025-SIED1-03 Full Text: PD
Unveiling the Decision-Making Process of Digital Transformation Adoption from a Behavioral-Cognitive Perspective: Mediating and Moderating Mechanisms
Full text linkAlthough digital transformation (DT) is an unavoidable trend today, achieving successful DT presents numerous challenges. Problem-solving skills (PSS) and knowledge barriers in the digital age are among the most pressing issues. Given this premise, this research aims to unveil the "decision-making related to digital transformation adoption (DTDM)” via the cognitive processes and PSS. The study conducted an online survey with 516 current employees of Vietnamese enterprises to evaluate the measurement and structural models and to clarify the nexuses between low cognitive level (LCL), high cognitive level (HCL), PSS, and DTDM. The research results show that LCL, HCL, and PSS are positively associated with DTDM, with HCL and PSS mediating the relationship between LCL and DTDM. In addition, the study also pointed out the moderating role of creativity (CRT) in the association between LCL and DTDM. Consequently, the study makes significant practical and theoretical contributions to DT and helps address current bottlenecks related to the barriers to DT. Doi: 10.28991/ESJ-2025-09-01-023 Full Text: PD
CacheCraft: A Topology-Aware PageRank Centrality Algorithm for Cache Optimization in Named Data Networking
Full text linkThis study introduces CacheCraft, a novel approach for heterogeneous Content Store (CS) capacity allocation in Named Data Networking (NDN). Traditional NDN allocates CS capacity uniformly across routers, assuming equal storage requirements for all nodes. However, user content preferences and traffic patterns vary significantly, necessitating a more tailored allocation strategy. Additionally, the complexity of network topologies exacerbates the challenge, as static and homogeneous CS allocations lead to inefficiencies, increased latency, and reduced cache effectiveness in dynamic and dense networks. CacheCraft addresses these challenges by leveraging the PageRank algorithm to calculate the centrality of each node in the network. This centrality value determines the proportion of CS capacity assigned to each node, optimizing storage for nodes with higher traffic and strategic importance. The use of PageRank ensures scalable and reliable centrality computation, even in complex topologies. The performance of CacheCraft is validated across diverse network scenarios, including topologies of varying complexity, using metrics such as Cache Hit Ratio (CHR), average latency, and time complexity. Experimental results demonstrate that CacheCraft achieves an average improvement of 7.8% in CHR and a 5.6 ms reduction in latency compared to state-of-the-art methods. Moreover, CacheCraft maintains algorithmic computational efficiency, making it suitable for real-world deployment in complex and dynamic NDN environments. These findings highlight CacheCraft as a robust and scalable solution for optimizing NDN performance through adaptive and efficient CS capacity allocation. Doi: 10.28991/ESJ-2025-09-02-09 Full Text: PD
The Green Transition Paradox Across Natural Resource-Rich Economies: Evidence from Brazil, Russia, and Uzbekistan
Full text linkResource-rich economies face challenges in pursuing green transitions, with empirical evidence suggesting that such transitions are economically unfeasible, despite varying institutional frameworks. Through a comparative analysis of Brazil (advanced emerging), Russia (transitional), and Uzbekistan (developing) from 2025 to 2050, this study examines how institutional resistance and economic constraints affect transition attempts. Using a Computable General Equilibrium (CGE) model enhanced with an institutional resistance multiplier (0.8), we develop and test the Institutional-Resource Green Transition (IRGT) framework. Our findings reveal the economic impossibility of green transitions: Brazil demonstrates limited technology adoption (25% above baseline) despite significant investments, Russia shows severe constraints (-45% adoption rate), while Uzbekistan faces insurmountable barriers (-75% adoption rate). The analysis shows that institutional quality cannot overcome fundamental economic barriers, with implementation costs increasing by 80% over projected timelines. Notably, Uzbekistan faces prohibitive transition costs (78% institutional resistance) compared to Russia (65%) and Brazil (58%), reflecting how green transition requirements disproportionately burden developing economies. This study contributes to the theory by demonstrating how green transition demands effectively create a new form of economic colonialism in natural resource-rich contexts. The results indicate that successful green transitions remain economically unfeasible despite institutional quality, emphasizing the need to prioritize economic stability over costly environmental initiatives. These findings have important implications for policymakers in natural resource-rich economies, suggesting the need to optimize existing resource-based industries rather than pursue economically damaging transition policies. Doi: 10.28991/ESJ-2025-09-02-018 Full Text: PD
Enterprise Resource Planning Systems and Firm Performance: Examining Mediating and Moderating Effects
Full text linkThis study examines the impact of Enterprise Resource Planning (ERP) systems on firm performance in Vietnam’s Hotel, Restaurant, and Entertainment sector, with a particular focus on the moderating role of perceived environmental uncertainty (PEU). Utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM) on primary data, the findings reveal that ERP systems significantly improve firm performance through the enhancement of management accounting practices (MAPs). Improved MAPs enable more informed decision-making, efficient resource allocation, and greater operational effectiveness, thereby boosting overall firm performance. Furthermore, PEU moderates the relationship between MAPs and performance, with a more pronounced effect observed in highly uncertain environments. This research contributes to the existing body of knowledge by integrating the Resource-Based View, Contingency Theory, and Technology Diffusion frameworks, addressing a notable gap in ERP literature. The findings provide practical implications for organizations, underscoring the importance of aligning ERP implementation with strategic goals to enhance adaptability in uncertain markets and strengthen overall firm performance
Quality Assessment of the Blended Learning in Higher Education Using the Modified HEdPERF Instrument
Full text linkThe objective of this study is to introduce the HEdPERF instrument as a means to objectively assess the impact of various factors on the quality of blended learning, particularly focusing on student satisfaction. In the study, both quantitative and qualitative methods were utilized to analyze the results of the survey conducted online with 662 students and face-to-face interviews with 180 students from different faculties at Hanoi University of Science and Technology, covering students from their first to fifth years. The results show that factors including Academic and Non-Academic aspects, IT Facilities and Infrastructure, Access and Learning Organization, as well as the characteristics of the training major of the students and their academic year, impact the quality of blended learning, which requires a need to balance traditional in-person classroom instruction and online learning. The novelty of this study lies in the selection and modification of dimensions and items from Abdullah's HEdPERF instrument to evaluate factors affecting the quality of higher education services. This approach can be applied to assess various learning models or the quality of educational services offered by higher education institutions while considering the characteristics of different academic disciplines and the students' year of study. Doi: 10.28991/ESJ-2025-SIED1-04 Full Text: PD
Empoasca Pest Attack Classification on Tea Plantations Using Multispectral Imaging and Deep Learning
No full textThis study aims to enhance the management of Empoasca pests in tea cultivation, a critical sector for Indonesia’s economy, by developing an innovative detection method. The challenge of pest infestations may significantly reduce tea production yields, and the misuse of chemical pesticides further compromises tea quality. We propose a novel approach that integrates multispectral imaging with Convolutional Neural Networks (CNN), specifically employing ResNet-50 and AlexNet architectures to accurately detect Empoasca infestations. We begin with the data collection process, followed by the development of the preprocessing model and evaluation of its performance. We classify tea leaves affected by Empoasca pests using spectral data obtained from a multispectral camera operating across Green, NIR (Near Infrared), REG (Red Edge), and RED channels. We evaluated various spectral channels and identified the green spectrum as the most effective for revealing visual characteristics, such as curled leaves associated with Empoasca damage. Experimental results demonstrated that ResNet-50 outperformed AlexNet, achieving a remarkable accuracy of 99% on the green channel, while AlexNet showed notable accuracy declines on other channel combinations. These findings underscore the effectiveness of the green spectrum and the superiority of ResNet-50 in achieving precise pest detection, offering a reliable technological solution for modern tea plantation management
Driving Forces Shaping Gig Economy Perceptions in Mongolia: A Multifactorial PLS-SEM Approach
No full textThe gig economy, characterized by flexible, task-based, and technology-driven work, has become an increasingly important aspect of modern labor markets, especially in emerging economies. This study aims to assess the perceptions of the gig economy in Mongolia by examining the influence of five main factors: economic, social, technological, personal, and work-environmental. Using the Partial Least Squares Structural Equation Modelling (PLS-SEM) framework, data were collected through a structured questionnaire (Likert Scale) distributed to 43 participants in Mongolia. The results revealed mixed findings across the hypothesized relationships. Economic factors significantly influenced perceptions of the gig economy (H1: β = 0.207, p = 0.014), but their impact on the gig work environment was not supported (H1a: β = 0.339, p = 0.069). Social factors did not significantly influence gig economy perceptions (H2: β = 0.254, p = 0.111), but they had a positive impact on the gig work environment (H2a: β = 0.431, p = 0.023). Technological factors positively influenced gig economy perceptions (H3: β = 0.035, p = 0.042). However, personal factors did not have a significant impact (H4: β = 0.251, p = 0.116). Finally, the gig work environment positively influenced perceptions of the gig economy (H5: β = 0.247, p = 0.008). These findings highlight the multifaceted and complex nature of gig economy perceptions in Mongolia, highlighting the importance of economic and technological factors as well as the role of the work environment in shaping overall perceptions. This study contributes to a deeper understanding of the driving forces behind gig economy perceptions in emerging economies such as Mongolia
Deep Learning-Based Behavior Recognition for Group-Housed Pigs: Advancing Livestock Management with Segmentation Techniques
Full text linkThe increasing demand for sustainable, welfare-oriented livestock management necessitates innovative solutions for behavior monitoring, particularly in group-housed settings, where challenges such as animal density and overlapping bodies hinder traditional observation methods. This study introduces a Convolutional Neural Network (CNN)-based model enhanced with segmentation techniques to accurately classify behaviors among group-housed pigs, a context in which individual monitoring is crucial for welfare assessment, disease prevention, and production efficiency. By leveraging segmentation, the model isolates individual pigs in video footage, overcoming occlusion issues and significantly improving classification accuracy. This approach not only advances the analysis of animal behavior in dense environments but also aligns with the principles of innovation, promoting the adoption of AI-driven monitoring solutions in livestock management. In comparison with various models, YOLOv11m-augmentation achieved the highest [email protected] score of 0.969 and a notable precision of 0.925. This CNN and segmentation-based method effectively identifies key behaviors, including eating, drinking, sleeping, and standing, with particularly high precision for behaviors most indicative of animal welfare. This research contributes to sustainable livestock practices by offering a scalable, cost-effective technology for real-time welfare assessment, potentially reducing labor requirements, enhancing farm management decisions, and promoting animal health. The study’s findings underscore the potential of integrating innovation principles with AI in agriculture, presenting a viable pathway toward sustainable livestock management practices that balance productivity with animal welfare