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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 30 Documents
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Search results for , issue "Vol. 10 No. 1 (2026): February" : 30 Documents clear
Accent Classification Across Continents: A Deep Learning Approach Hossain, Md. Fahad; Khan, Anzir Rahman; Rahman, Md. Sadekur; Ohidujjaman
Emerging Science Journal Vol. 10 No. 1 (2026): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-01-030

Abstract

This study focuses on a deep learning based accent classification across continents and greatly enhances speech recognition systems by identifying the accents of Asia, Europe, North America, Africa, and Oceania. The Convolutional Neural Network (CNN) was trained on the Mozilla Common Voice dataset, which comprises the features extracted - Mel-Frequency Cepstral Coefficients, Delta, Delta-Delta, Chroma Frequency, and spectral features- and trained to classify accents. Multiple convolutional and dense layers for accent classification were combined with dropout and batch normalization layers to avoid overfitting during training. Out of the total validation data, 82% accuracy has been achieved. The Asian and European accents were classified with greater accuracy since their datasets were larger, whereas African and Oceanian accents were more misclassified due to limited representation and the greater diversity of languages. In contrast to the past research, which focused only on country-based accent classification, this work introduced a feature based deep learning approach of continent-based accent classification along the way. The recognition of this accent variation, in turn, helps integrate and improve various aspects of speech recognition systems and makes their application more inclusive for voice assistants and language learning tools with diverse linguistic patterns. The future work will concentrate on extending the dataset to the seven continents while enhancing classification accuracy via better feature engineering and model tuning.
Brand Image and Customer Loyalty: Exploring the Power of Word of Mouth Tran, Nhinh Thi; Nguyen, Le Hang Thi
Emerging Science Journal Vol. 10 No. 1 (2026): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-01-021

Abstract

This study aims to examine the moderating role of word of mouth (WOM) in the relationship between brand image and customer loyalty within the Vietnamese market context. While previous research has primarily explored WOM as a direct influencing factor, limited attention has been given to its moderating effect on this specific relationship. Employing a quantitative research design, the study collected data through a structured survey, distributing 950 questionnaires and obtaining 829 valid responses. The data were analyzed using PLS-SEM via SmartPLS 4.0 software. The findings indicate that WOM significantly moderates the relationship between brand image and customer loyalty, such that positive WOM reinforces consumer loyalty when brand perceptions are favorable, particularly when the message comes from influential sources such as celebrities or opinion leaders. Conversely, negative WOM can attenuate the effects of brand image and result in decreased loyalty. The study contributes to the existing literature by clarifying the contextual influence of WOM in a collectivist culture and offering practical implications for marketers in developing communication strategies that leverage WOM to enhance brand equity and customer retention. This research provides novel empirical evidence relevant to both theory development and managerial practice in emerging markets. JEL Classification: M30, M31, O35.
Assessing the Impact of Ghost Car Attacks on Traffic Flow in Vehicular Ad Hoc Networks Drahman, Isyraf Nazmi; Yogarayan, Sumendra; Abdul Razak, Siti Fatimah; Sayeed, Md. Shohel; Abdullah, Mohd. Fikri Azli; Kannan, Subarmaniam; Azman, Afizan
Emerging Science Journal Vol. 10 No. 1 (2026): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-01-024

Abstract

Vehicular Ad Hoc Networks (VANETs) play a crucial role in enhancing road safety, traffic management, and driving efficiency through real-time communication between vehicles and infrastructure. However, VANETs are vulnerable to various security threats, one of which is the “ghost car” attack. In this attack, a malicious entity injects false information into the network, simulating the presence of a non-existent or “ghost” vehicle. This can lead to severe consequences such as traffic disruptions, accidents, and a compromised trust in the system’s reliability. This study aims to simulate and analyze the impacts of ghost car attacks on Vehicular Ad Hoc Networks (VANETs), focusing specifically on intersection waiting times and overall traffic flow. We used Simulation of Urban Mobility (SUMO) integrated with ns-3 for realistic VANET simulations, introducing varying numbers of ghost vehicles. Results indicate significant increases in waiting times and vehicle counts at intersections due to ghost cars, leading to traffic disruptions. This study evaluates ghost car attacks within realistic urban scenarios and proposes targeted detection and mitigation strategies, leveraging authentication, machine learning, and blockchain technologies.
Job Characteristics, Affective Commitment, and Turnover Intentions: A Dual-Theory Examination of Generation Z Mac, Yen Thi Hai; Tran, Cuong Thi
Emerging Science Journal Vol. 10 No. 1 (2026): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-01-016

Abstract

This study investigates the extent to which core job characteristics—autonomy, task identity, task significance, and skill variety—influence affective commitment and turnover intentions among Generation Z employees in Vietnam. Based on the Job Demands–Resources (JD-R) and Conservation of Resources (COR) theoretical frameworks, the study seeks to elucidate how early-career employees interpret job resources within a collectivist, hierarchical, and resource-constrained environment. A structured quantitative design was employed, drawing on survey data from 312 Gen Z respondents across diverse organizational settings. Measurement validity and reliability were established via Confirmatory Factor Analysis and Cronbach’s Alpha, while hypotheses were tested using Structural Equation Modeling (SEM). Empirical findings reveal that task identity exerts a negative influence on affective commitment, whereas autonomy, task identity, and skill variety are positively associated with turnover intentions. Task significance demonstrates no significant effect on either outcome. Conversely, affective commitment emerges as a strong inverse predictor of turnover intentions, underscoring its role as a stabilizing psychological resource. This research contributes to the existing literature by integrating JD-R and COR theories to challenge the presumed universality of job resource effects. It underscores the contextual sensitivity of job design, particularly for younger cohorts in emerging markets. The findings offer practical implications for designing culturally and generationally responsive retention strategies.
Time-Varying Impacts of Robust Determinants on Greenhouse Gas Emissions: Panel Data Evidence Nguyen, Tho M.; Pankwaen, Kansuda; Pastpipatkul, Pathairat; Saijai, Worrawat
Emerging Science Journal Vol. 10 No. 1 (2026): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-01-010

Abstract

Understanding the key drivers of greenhouse gas (GHG) emissions is crucial for designing effective and adaptable climate policies, particularly given the complex interplay among structural, institutional, and energy-related factors. This study examines the time-varying impacts of key determinants of GHG emissions across 29 countries from 1993 to 2018, with an emphasis on the shadow economy, energy security risks, and geopolitical volatility. The analysis follows a four-step framework: countries are classified using principal component analysis (PCA) and K-means clustering, robust covariates are selected via Bayesian Model Averaging (BMA), and their impacts are estimated with time-varying coefficient panel models. Model robustness is evaluated through grouped cross-validation, confirming the superior performance of the time-varying random effects (tvRE) specification. The results reveal that the shadow economy and energy security risk exert more dynamic and substantial impacts in the Higher-income group, while their effects are comparatively muted in the Lower-income group. Geopolitical risk, however, shows limited explanatory power for emissions in both contexts. This study provides a novel empirical framework for capturing the dynamic influences of emissions drivers and contributes actionable insights toward achieving sustainable development goals.
Unveiling the Power of Intellectual Capital in Driving Financial Performance: A Deep Dive into the IT Sector Dsouza, Suzan; Kallach, Layal; Demiraj, Rezart; Zaidi, Syed Faizan Hussain
Emerging Science Journal Vol. 10 No. 1 (2026): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-01-08

Abstract

This study aims to theoretically and empirically investigate the relationship between intellectual capital (IC) and the financial performance of firms in the U.S. information technology (IT) sector, with a particular focus on Return on Assets (ROA) as a key performance indicator. Data were collected from 345 publicly listed IT companies over the period 2011–2022, yielding 1,792 firm-year observations. The research employed descriptive statistics, correlation matrices, box plot analyses, and multiple regression models to examine the effects of IC and its components, human capital efficiency, structural capital efficiency, and capital employed efficiency on financial outcomes. The analysis revealed that, contrary to conventional expectations and prior literature, IC exhibited a negative and statistically significant association with financial performance, highlighting potential inefficiencies in the utilization of intangible assets within the IT industry. These findings underscore the complexity of translating investments in IC into measurable financial gains, suggesting that firms may be overinvesting or misallocating resources in areas that do not yield immediate profitability. The novelty of this research lies in uncovering an unexpected inverse IC-performance link in a knowledge-intensive sector, thereby offering executives and policymakers new insights into how IC strategies should be re-evaluated and aligned with long-term value creation.
The Leadership Attributes and Commercial Bank Financial Performance: The Mediating Role of Capital Adequacy Ratio Nguyen, Hien Thu Thi
Emerging Science Journal Vol. 10 No. 1 (2026): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-01-011

Abstract

This paper aims to explore how factors such as age, gender, professional qualifications, and experience of the Chairman of the Board of Directors (COB) and Chief Executive Officer (CEO) influence the financial performance of Vietnamese commercial banks. Data are collected from 28 listed banks in Vietnam from 2013 to 2023. The study employs regression methods including OLS, FEM, REM, and FGLS. To ensure robustness and address potential model limitations, SGMM regression is also utilized. The results indicate that banks led by female COBs and CEOs tend to achieve better financial performance. Additionally, older COBs are associated with lower bank performance. The findings also highlight the significant impact of bank leaders' expertise and educational background on financial performance. To the author's knowledge, this is the first study to analyze the combined effect of bank leadership attributes on the profitability of Vietnamese commercial banks within an emerging economy. It is also among the first to examine the mediating role of the capital adequacy ratio (CAR). The results provide a solid basis for stakeholders to make informed decisions related to the recruitment, appointment, training, and development of bank leaders to enhance financial performance.
Structure-Aware Chunking for Complex Tables in Retrieval-Augmented Generation Systems Koay, Xin-Kuang; Ong, Lee-Yeng; Goh, Pey-Yun
Emerging Science Journal Vol. 10 No. 1 (2026): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-01-09

Abstract

Retrieval-Augmented Generation (RAG) is a hybrid method that combines information retrieval with large language models to generate context-aware, factually grounded responses. However, the RAG system relies heavily on well-structured input data to generate accurate and contextually relevant responses. Documents with complex table layouts pose significant challenges, as most chunking strategies are text-centric and often overlook table-rich documents containing multi-column and multi-row structures. Hence, this study proposes a customized structure-aware chunking framework specifically designed for university course documents containing multi-column, multi-row tables with nested headers. The framework employs Camelot for high-fidelity table extraction, followed by customized logic that constructs semantically coherent chunks by preserving academic term, subject name, credit hour, and category. This prevents semantic fragmentation during retrieval. The proposed method is evaluated using the RAGAS framework and compared against several baselines using standard parsing and chunking techniques. Results show that the proposed approach achieves the highest answer accuracy of 0.73 and substantially improves retrieval relevance and contextual precision. These findings demonstrate the framework’s effectiveness in handling structure-dependent academic queries. This study highlights that ensuring both parsing quality and chunking strategy is essential to retain semantic relationships in table-rich documents, offering a practical improvement for RAG systems in structurally complex scenarios.
Debt by Design: Exploring Market Forces Behind Leverage in Two Economies Habibniya, Houshang; Dsouza, Suzan; Tripathy, Naliniprava
Emerging Science Journal Vol. 10 No. 1 (2026): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-01-013

Abstract

Abstract This study investigates the determinants of capital structure by comparing firms listed on two prominent global stock indices: the S&P 500 (United States) and the NSE CNX 500 (India). Specifically, it examines how firm-specific factors—such as liquidity, asset tangibility, and sustainability practices—influence leverage decisions within differing economic and institutional contexts. Drawing on a comprehensive dataset of 3,575 firm-year observations from 406 S&P 500 companies and 4,180 observations from 419 NSE CNX 500 firms between 2011 and 2021, the analysis employs Two-Stage Least Squares (2SLS) regression, the Generalized Method of Moments (GMM), and a series of diagnostic tests addressing heteroskedasticity and model robustness. The empirical results indicate that liquidity, tangibility, and sustainability performance significantly affect firms’ capital structure decisions. Moreover, growth opportunities and profitability also play key roles. Cross-country differences highlight the influence of macroeconomic conditions and financial system structures on leverage behavior. This research enriches the capital structure literature by offering a comparative, cross-national perspective and provides actionable insights for corporate managers, investors, and policymakers seeking to optimize capital structure in diverse financial environments
Does Board Diversity Influence Green Revenue and Firm Value? Evidence From an Emerging Market Alshawadfy Aladwey, Laila Mohamed
Emerging Science Journal Vol. 10 No. 1 (2026): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-01-025

Abstract

This study investigates the effect of board diversity on green revenue among Saudi-listed firms. It places particular emphasis on the moderating role of shareholder value. To achieve this objective, the study constructs a composite board diversity index using principal component analysis (PCA). It employs random-effects panel regression models on firm-level data covering the period 2020–2024. Robustness is ensured through alternative model specifications and Generalized Method of Moments (GMM) estimations. The findings reveal that board diversity is positively associated with green revenue. In contrast, higher shareholder value, as measured by market valuation, is negatively associated with green revenue. Importantly, board diversity significantly mitigates this negative relationship. This indicates that diverse boards encourage stronger engagement in sustainable activities, even in highly valued firms. The study contributes to the literature by integrating board diversity, green revenue, and shareholder value within a single empirical framework in an emerging market context. The results offer novel evidence that board diversity serves as an effective governance mechanism for aligning sustainability objectives with value-driven corporate strategies. From both theoretical and policy perspectives, the findings support agency and resource-dependence theories. They highlight the importance of inclusive board structures in embedding sustainability into corporate decision-making.

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