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INDONESIA
Emerging Science Journal
Published by Ital Publication
ISSN : 26109182     EISSN : -     DOI : -
Core Subject : Social,
Environmental Science
Emerging Science Journal is not limited to a specific aspect of science and engineering but is instead devoted to a wide range of subfields in the engineering and sciences. While it encourages a broad spectrum of contribution in the engineering and sciences. Articles of interdisciplinary nature are particularly welcome.
Arjuna Subject : -
Articles 1,058 Documents
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Strategies for Pedagogical Interventions to Develop Emotional Intelligence (EI) of Employees in a Hybrid Work Schedule Matulčíková, Marta; Brevení­ková, Daniela; Vaľko, Michal; Gawrych, Roman; Procházka, David A.
Emerging Science Journal Vol. 8 No. 5 (2024): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-05-023

Abstract

The purpose of the research article is to identify the educational methods suitable for developing employee emotional intelligence. The focus was on the number of hours that small and medium sized enterprises are willing to invest in training their employees in emotional intelligence and on the benefits, i.e., changes in work outcomes as evaluated by respondents. The questionnaire method and interviews were used to obtain data from respondents, line managers, and education managers. Based on correlation coefficient calculations, brainstorming was identified as a frequently used method of active learning, which is related to the physical presence in the learning premises. The analysis of the responses of the respondents and their calculation using the correlation coefficient surprisingly showed that the lecture method gained great support and was considered by the respondents, i.e., managers and education managers, as very important to achieve the cognitive, affective, and psychomotor goals of education. Moreover, it was assessed as a method suitable for remote learning, i.e., for virtual educational spaces. Doi: 10.28991/ESJ-2024-08-05-023 Full Text: PDF
The Assessment of the Green Development of the Tobacco Industry Using a Multicriteria Method Lapinskiene, Giedrė; Blazaitis, Martynas; Gedvilaite, Dainora; Slavinskaite, Neringa
Emerging Science Journal Vol. 9 No. 1 (2025): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-01-018

Abstract

The tobacco industry is heavily regulated due to the significant health implications associated with tobacco use. The industry also involves numerous stakeholders, including farmers, manufacturers, distributors, retailers, regulators, and consumers. The aim of this research is to select the most relevant environmental criteria for the green development of the tobacco industry. This article uses Analytical Hierarchy Process (AHP) methods to create a hierarchical structure of the criteria and subcriteria necessary for green business development, establishing the relative weights of these subcriteria to find the areas in which attention and resources are most urgently required. The assessment of the concordance of expert opinions shows a satisfactory level of agreement. The article advances a more comprehensive view towards the evaluation of green criteria that are significant for the whole industry, seeking to highlight the need to think holistically. According to the views of experts, the most significant sub-criteria for the green development of the tobacco industry are increasing energy efficiency; safeguarding against hazardous wastewater in the environment; reducing the content of hazardous materials used in products; improving air, land, and water quality where economic activity takes place; sustainable forest management; eco-design, especially for efficient material use, biodegradability, and recyclability; and collaboration with suppliers. The entire industry should collaborate in seeking global green development by gradually investing in the improvement of green criteria. Doi: 10.28991/ESJ-2025-09-01-018 Full Text: PDF
The Effects of Digital Game-Based Learning on Arithmetic and Selective Attention in Students with Dyscalculia Aboud, Yusra Zaki; Helali, Mamdouh Mosaad; AlAli, Rommel Mahmoud; Al-Qahtani, Mohammed Saeed; Mashal, Anees Abdullatif
Emerging Science Journal Vol. 9 (2025): Special Issue "Emerging Trends, Challenges, and Innovative Practices in Education"
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-SIED1-027

Abstract

The study aimed to investigate the effect of Digital Game-Based Learning (DGBL) on numeracy skills and selective attention among primary school students with dyscalculia in Saudi Arabia. The researchers used a mixed quasi-experimental approach. Ninety randomly selected female fifth-grade students were divided into two groups. The experimental group (n= 45) used the Lumosity cognitive training platform (Raindrops and Memory Match games). The control group (n=45) received traditional mathematics instruction. The students were assessed before and after the intervention using the arithmetic learning difficulties test and the selective attention test developed by researchers. The results showed statistically significant improvements in the skills of students in the experimental group in arithmetic and selective attention. Quantitative analysis revealed a very large effect size (η² = 0.97) for the DGBL intervention on both scales. The perceptions of 32 teachers, gathered through semi-structured interviews, confirmed these findings, with the majority reporting significant improvements in the math skills and auditory and visual attention of students with dyscalculia. Qualitative data demonstrated the effectiveness of the DGBL program in increasing student engagement and motivation, reducing anxiety, and providing more enjoyable and engaging learning than traditional teaching. These findings advocate for the broader integration of DGBL into special education curricula in Saudi Arabia to create more inclusive, engaging, and effective learning.
Teacher Attitudes and Barriers to GeoGebra Adoption in Secondary Mathematics Education Vlachoni, Georgia; Antonopoulou, Hera; Karanikola, Zoe; Halkiopoulos, Constantinos
Emerging Science Journal Vol. 9 (2025): Special Issue "Emerging Trends, Challenges, and Innovative Practices in Education"
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-SIED1-028

Abstract

Objectives: This study investigates GeoGebra implementation patterns and identifies facilitators and barriers to adoption among Greek secondary mathematics teachers using the Technology Acceptance Model (TAM) framework. Methods: A cross-sectional survey of 82 secondary mathematics teachers in Western Greece was conducted during June-July 2023 (87.7% response rate), using a validated questionnaire to assess actual usage, perceived usefulness, ease of use, and attitudes toward implementation across four mathematical domains. Findings: Results revealed an implementation paradox: low actual usage (M = 2.21) despite positive attitudes (M = 3.53) and recognized usefulness (M = 3.42). Domain-specific variation showed moderate Geometry implementation (M = 2.77) versus very low Statistics adoption (M = 1.77). Regression analysis explained 40.1% of the variance in usage through Ease of Use (β = 0.360) and Attitude (β = 0.281). B2-level ICT certification emerged as a critical threshold for implementation success. Novelty: This study introduces the Contextual Implementation Cascade Framework (CICF), which explains multi-level barriers to educational technology adoption and demonstrates that positive attitudes cannot overcome systemic infrastructural and institutional barriers without comprehensive professional development.
Motivation Profiles, Personal Values, and Personality Traits: The Interplay in Research Management and Administration José M. R. C. A. Santos; Melinda Fischer; Simon Kerridge
Emerging Science Journal Vol. 10 No. 2 (2026): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-02-029

Abstract

This study addresses a significant gap in the literature by examining the motivation profiles of Research Managers and Administrators (RMAs) and their correlation with personal values and personality traits. Drawing on Self-Determination Theory (SDT), the research sought to propose and validate a conceptual framework specifically for RMAs, introducing the distinct profile of outcome-driven motivation. Empirical data were collected using a quantitative, cross-sectional survey (N=1,095 valid responses) distributed via snowball sampling. The methodological rigor was demonstrated through Exploratory Factor Analysis (EFA), with highly suitable data (KMO=0.915, p<0.001) and high reliability (Cronbach’s alpha ranging from 0.757 to 0.880). The EFA validated the construct of three distinct motivation profiles. RMAs were found to exhibit a predominantly autonomous (intrinsic) drive, confirmed by the highest mean score among the profiles, with statistically significant differences between all three types of motivation. This intrinsic motivation aligns with personal values that emphasize benevolence and universalism while downplaying power and tradition, and personality traits showing high conscientiousness, openness, and agreeableness. This work extends the use of SDT in Science and Technology Studies by validating a specific measurement scale for RMA motivation profiles. The results offer practical guidance, supporting the need for flexible, tailored motivational strategies and policies that enhance intrinsic factors such as autonomy and competency to boost RMA performance.
Castor-Based Ester Oil Production Using SnCl₂/HZSM-5 Catalyst for Sustainable Transformer Istiqomah; Widayat Widayat; John Philia; Sriyono; Kevin Gausultan Hadith Mangunkusumo; Aji Suryo Alam; Hadiyanto
Emerging Science Journal Vol. 10 No. 2 (2026): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-02-06

Abstract

Mineral oil remains the most common insulating fluid in power transformers; nevertheless, its non-biodegradable nature and carbon-based emissions have prompted the quest for environmentally safer alternatives. Castor oil, a sustainable and non-edible derivative, provides a promising source for high-performance ester-based insulating lubricants. This study investigates the synthesis and process optimization of castor-based polyol esters via esterification with trimethylolpropane using a heterogeneous SnCl₂/HZSM-5 catalyst. The alcohol-to-oil molar ratio, temperature, and catalyst loading were among the critical reaction parameters that were modeled and optimized using response surface methods with a central composite design. Under optimized conditions of 130°C and 2.207 wt% catalyst, an ester yield of 81.77% was obtained. The resulting ester oil demonstrated advantageous characteristics, such as acceptable color and dielectric performance, while viscosity and acidity were improved by a two-step process to comply with the IEC 62975:2021 standards for distribution transformer insulating oils. The statistical study validated the model's reliability, with ANOVA indicating a substantial quadratic regression (R² = 0.952). The key novelty of this work lies in demonstrating the potential of SnCl₂/HZSM-5 to catalyze the synthesis of castor-derived polyol esters with tailored physicochemical properties, supporting their future scalability and use as sustainable insulating oils.
BIoT-DApp: A Prototype for Real Time Traceability in Agricultural Supply Chains Sajid Safeer; Victoria Lemieux; Chang Lu; Cataldo Pulvento
Emerging Science Journal Vol. 10 No. 2 (2026): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-02-02

Abstract

Agricultural supply chains frequently experience inefficiencies, including a lack of transparency, post-harvest losses, and inequitable compensation for stakeholders. This study aims to develop and evaluate a Blockchain-IoT decentralized application (BIoT-DApp) that enhances traceability, efficiency, and resilience in agri-food supply chains. Utilizing a lab-based prototyping methodology, the system integrates Ethereum smart contracts with IoT sensors to automate workflows from cultivation to retail, employing a hybrid architecture that stores raw sensor data off-chain while anchoring cryptographic hashes on-chain. The methods involve designing role-specific smart contracts, managing batch life cycles across six stages, and conducting real-time environmental monitoring through IoT data processed by Raspberry Pi, with deployment and testing performed on the Sepolia testnet. The findings demonstrate automated quality control, reduced storage costs through optimized on-chain practices, and seamless product ownership transfers validated by four role-based MetaMask accounts representing a farmer, wholesaler, retailer, and end-user. Core functions were executed successfully, with gas costs ranging from 30,000 (data logging) to 112,300 (batch initialization), confirming both cost efficiency and scalability. The novelty of this work lies in bridging blockchain theory and practice by providing a modular, adaptable prototype capable of supporting perishable agricultural supply chains globally. This offers policymakers and agri-tech developers actionable insights for decentralized solutions in resource-constrained environments.
Strategic Dividend Policy Adaptation and Stock Market Reactions in State-Owned Enterprises Across Crises Georgina Maria Tinungki; Powell Gian Hartono; Nurhafifah Amalina; Dewie Tri Wijayati Wardoyo; Reniati Karnasi; Gatri Lunarindiah; Marieta Ariani; Lidia Wahyuni
Emerging Science Journal Vol. 10 No. 2 (2026): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-02-012

Abstract

This study investigates the strategic adaptation of dividend policy in Indonesian state-owned enterprises across the pre-crisis, crisis, and recovery phases. Adaptation is operationally defined as firm-level, measurable changes in cash dividend indicators during the crisis and post-crisis phases relative to the pre-crisis average. Empirically, dividend behavior is estimated using a dynamic panel framework with system GMM, and an event-study approach evaluates abnormal returns and cumulative abnormal returns around dividend announcement dates in each phase. The results indicate that SOEs increased dividends during the crisis relative to pre- and post-crisis periods, and that the market exhibited stronger positive reactions in the crisis and recovery phases than in the pre-crisis phase. These patterns suggest adaptive choices consistent with managing uncertainty and reinforcing policy credibility within Indonesia’s state-ownership setting. The findings highlight the strategic role of dividend signals in shaping investor perceptions during economic shocks, while theoretically challenging the core cash-conservation premise of the pecking order and reinforcing the relevance of signaling theory for state-controlled firms with complex fiscal and political mandates.
Thermoelectric Generator Efficiency Enhancement Through Copper Electrical Contact Optimization N. Jagadesh Babu; Rajesh Kumar Burra
Emerging Science Journal Vol. 10 No. 2 (2026): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-02-028

Abstract

Thermoelectric generators (TEGs) can transform heat into electricity and have considerable potential for diverse applications. Nonetheless, their widespread use is limited by their low efficiency, mainly owing to their high internal electrical resistance. This study aims to improve TEG performance by reducing the electrical contact resistance through the application of copper. This study addresses the critical challenge of improving the performance of thermoelectric generators (TEG) by reducing the electrical contact resistance, which directly affects the output power and conversion efficiency, particularly at low temperatures. The main objective of this study was to investigate how the contact resistance influences the electrical conductance and overall energy conversion efficiency of TEGs and to optimize the contact geometries to enhance the performance. Copper contacts with different shapes (flat and circular) were designed and fabricated to evaluate their impact on electrical resistance. Experimental investigations using a commercial TEG module were conducted to measure the contact resistances and analyze their effects on electrical parameters, including the output voltage, power, and conversion efficiency. A comprehensive theoretical model was developed to assess the contact area, energy loss, and thermal factors. Equations were applied to quantify the contact resistance and its influence on the power output and efficiency. Notably, small circular copper contacts exhibited a significant reduction in contact resistance compared to flat contacts, leading to an 18.6% improvement in efficiency at low temperatures. This study demonstrates that optimizing the geometry and size of copper contacts can substantially reduce energy losses at the interfaces, thereby enhancing the current flow and boosting the TEG conversion efficiency. These findings provide a novel approach for addressing the prevalent issue of high internal resistance in thermoelectric devices, paving the way for more effective energy harvesting and waste-heat recovery. This study underscores the critical role of contact engineering in TEG technology and offers promising strategies for improving device efficiency and output power for future applications.
Macroeconomic Uncertainty and Banking Stability in ASEAN Emerging Markets: A Causal Machine Learning Approach Truong Nguyen Tuong Vy; Dao Le Kieu Oanh; Pham Anh Thuy
Emerging Science Journal Vol. 10 No. 2 (2026): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-02-04

Abstract

This study aims to examine the causal impact of macroeconomic uncertainty on banking stability across six ASEAN emerging markets from 2010 to 2023, with particular attention to structural regime shifts triggered by the COVID-19 pandemic. To achieve this objective, a novel country-specific uncertainty index is constructed using Principal Component Analysis (PCA) based on three indicators World Uncertainty Index (WUI), World Pandemic Uncertainty Index (WPUI), and World Sentiment Index (WSI). Employing advanced causal inference methods, including Double Machine Learning (DML) and Causal Forests, the study estimates both Average Treatment Effects (ATEs) and Conditional Average Treatment Effects (CATEs). The results reveal that a one-unit rise in macroeconomic uncertainty reduces the Z-Score by 10.7% on average, signaling increased financial instability. The adverse effect is most pronounced for small banks (21.9% decline), reflecting limited capital buffers and structural vulnerability, and becomes more severe after the COVID-19 outbreak. CATEs results highlight significant cross-country heterogeneity, with Singapore and Thailand showing resilience, while Indonesia and the Philippines exhibit greater fragility. This study contributes to the literature by integrating SHAP-based model interpretability into causal machine learning for banking stability analysis, offering novel, policy-relevant insights for uncertainty management in emerging ASEAN economies.

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