cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
,
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 803 Documents
 74   75   76   77   78 
The Cultural and Financial Dynamics of Female Entrepreneurs as Well as Their Empowering Ventures Andalib, Tarnima W.; Hossain, Syed Arman; Ramayah, T.; Azizan, Noor A.; Hassan, Dauwood I.; Khawar, Sara
Emerging Science Journal Vol. 9 No. 4 (2025): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-04-018

Abstract

This research examines how female entrepreneurs in Bangladesh navigate the cultural, social, and financial challenges of F-commerce, where informal digital platforms like Facebook and Instagram have become vital spaces for women-led businesses. In addition, despite the growth of digital entrepreneurship, existing models such as the gendered growth framework and ‘TOCOM contingency model’ often overlook how localized cultural dynamics shape women’s entrepreneurial experiences. To bridge this gap, this research explores how these dynamics influence not only the constraints female entrepreneurs face, but also the motivations and resilience strategies that drive their success. However, grounded in ‘Consumer Culture Theory’ and enriched by anthropological perspectives, this research uses a qualitative approach, featuring instrumental case studies and in-depth interviews. The analysis, conducted through NVivo coding, captures both the lived realities and the strategic digital engagements of these women. Although the outcome is a proposed conceptual framework that links culture, motivation, and F-commerce participation offering insight into how female entrepreneurs adapt, persist, and redefine their roles in the digital economy. Therefore, this research also outlines practical recommendations to enhance digital inclusion and gender equity through skills training, mentorship, as well as policy support.
Ultrasound-Assisted Extraction of Bioactive Compounds from Tanacetum vulgare L.: Antibacterial and Cytotoxic Evaluation Burdelnaya, Yelena; Solyanov, Dmitry; Akhmetova, Saule; Pozdnyakova, Yelena
Emerging Science Journal Vol. 9 No. 4 (2025): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-04-024

Abstract

This study investigates ultrasound-assisted extraction (UAE) of bioactive compounds from Tanacetum vulgare L. collected in Central Kazakhstan’s Akmola region, focusing on optimizing extraction parameters, analyzing chemical composition, and evaluating biological activity. The novelty lies in the first comprehensive analysis of T. vulgare populations under the region’s extreme continental climate, known to affect metabolite accumulation. Using 70% ethanol, UAE at 20 minutes provided the highest extraction efficiency, as evidenced by a substantial recovery of phenolic compounds. High-performance liquid chromatography (HPLC) identified key bioactive components – luteolin (6.9 µg/mL), quercetin (5.0 µg/mL), apigenin (1.45 µg/mL), cynaroside (2.7 µg/mL), rutin (1.28 µg/mL), chlorogenic acid (1.1–1.14 µg/mL), and ferulic acid (2.46–2.69 µg/mL) – with extraction time significantly influencing their yield. The antibacterial assessment revealed strong inhibition against Staphylococcus aureus, with a 30-minute flower extract producing an inhibition zone of 34±1.1 mm, surpassing benzylpenicillin (30±1.1 mm). By contrast, weak or no activity was observed against Escherichia coli, Bacillus subtilis, Pseudomonas aeruginosa, and Candida albicans. In cytotoxicity tests using Artemia salina, all extracts – regardless of concentration or duration – resulted in 100% lethality, suggesting potential toxic effects. These findings underscore the impact of Kazakhstan’s harsh ecological conditions on the phytochemical profile of T. vulgare and point to both the plant’s promising pharmacological applications and the need for caution in its use.
CFD Analysis of Heat Exchanger Effectiveness and LMTD with Varying Pipe Length Al-Rawashdeh, Shahed A.
Emerging Science Journal Vol. 9 No. 4 (2025): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-04-020

Abstract

This paper presents a new numerical analysis for 2D heat exchanger (HE) model by employing computational fluid dynamics (CFD) simulations to analyze the impact of pipe length on the efficiency and the Log Mean Temperature Difference (LMTD) of parallel and counterflow double-pipe heat exchangers while maintaining constant flow rates, inlet temperatures, and fluid properties. The findings demonstrate that heat exchanger efficiency and LMTD in both the parallel and counter-flow HEs are significantly influenced by pipe length, with longer heat exchangers improving heat transfer effectiveness by allowing more time for thermal exchange, larger heat exchange surface area, and achieving a more uniform temperature distribution. Counterflow heat exchangers also showed higher efficiencies at all lengths than parallel flow heat exchangers due to the larger temperature difference between the fluids. These insights are particularly valuable for engineers and designers seeking to optimize heat exchanger configurations for industrial applications, where enhancing heat transfer efficiency and minimizing energy losses are critical for cost-effective and sustainable thermal management systems.
Leveraging External Networks and Internal Capabilities: A Pathway to Innovation in an Export Economy Boonchoo, Pattana; Thoumrungroje, Amonrat; Racela, Olimpia C.
Emerging Science Journal Vol. 9 No. 4 (2025): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-04-014

Abstract

Objectives: This study examines the role of business network ties, financial resource accessibility, and export market-oriented capability in influencing product innovation intensity in Myanmar’s transitional economy. Grounded in social network theory and resource-advantage theory, it explores how firms leverage external networks and internal capabilities to foster innovation despite economic and institutional constraints. Methods/Analysis: Survey data were collected from 161 Myanmar exporters representing various industries. Structural equation modeling (SEM) was employed to test the hypothesized relationships. Findings: The results confirm that business network ties significantly enhance financial resource accessibility, with quantity, proximity, and frequency all playing critical roles. Financial resource accessibility exerts a greater influence on innovation than market-oriented capability, highlighting their instrumental role. Exporters should prioritize strategic network development to enhance financial resource access. Policymakers should facilitate business networking, financial accessibility, and export support programs to promote sustainable innovation-driven growth. Novelty/Improvement: This study fills a critical literature gap by empirically linking business network ties, financial resource acquisition, market-orientation, and innovation in a transitional economy, offering rare insights for exporters striving in resource-constrained environments. Future research should explore network dynamics, resource access, market orientation, and innovation in various transitional economies to improve generalizability.
Machinery Usage and Productivity in Manufacturing: Firm-Level Matter in Developing Countries Nguyen, Manh Hung; Vu, Hoang Dat; Thanh, Tran Thi Mai; Pham, Sy An
Emerging Science Journal Vol. 9 No. 4 (2025): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-04-015

Abstract

This study examines the determinants of machinery usage and its relationship with productivity outcomes among Vietnamese manufacturing firms, using nationally representative panel data from 2010 to 2019. A multinomial logit model and panel regressions with first- and second-differences reveal substantial heterogeneity in machinery choices, reflecting differences in firm size, ownership, and sectoral contexts. Medium and large enterprises tend to use computer-controlled machinery and are more likely to exhibit positive associations with labor productivity, although these effects often diminish over time. In contrast, micro and small firms remain reliant on handheld tools and show mixed or short-lived productivity gains. Foreign-invested enterprises demonstrate more consistent productivity benefits from advanced machinery than state-owned firms. These findings suggest that sustained productivity improvements require more than technological upgrades alone. The study highlights the potential importance of complementary investments – such as workforce development, managerial capacity, and institutional support – for fostering inclusive and effective machinery usage. These insights may inform targeted policy efforts aimed at narrowing technology gaps across heterogeneous firms in developing economies.
Obstacles to Finding the Ideal Workplace: A Gender-Based Analysis Across the V4 Countries Poór, József; Módosné Szalai, Szilvia; Jenei, Szonja; Gyurián, Norbert; Singh, Dhruv Pratap; Kálmán, Botond Géza; Dávid, Lóránt Dénes
Emerging Science Journal Vol. 9 No. 4 (2025): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-04-029

Abstract

This study explores gender-specific barriers to finding an ideal workplace in the Visegrád countries (Czech Republic, Hungary, Poland, and Slovakia), where similar historical and socioeconomic contexts shape labor market inequalities. Based on the relevant literature, women are disproportionately affected by challenges related to language proficiency, professional networks, and mobility. The research applied a quantitative methodology, including chi-square tests, multiple logistic regression, and cluster analysis, using SPSS Statistics software to analyze the survey data. Findings revealed significant gender disparities. Women report greater difficulties with language and mobility, particularly in Hungary and Slovakia, whereas men benefit more from strong professional connections. The cluster analysis identified three respondent groups: those hindered by language barriers, those with weak networks, and those facing limited mobility. International experience mitigates language challenges, and robust networks ease job search difficulties. In line with the ideals of a circular society, this study also explores how circularity, inclusiveness, and collaboration can help break down gender-based barriers in the labor market. The study’s novelty lies in its comparative regional focus and the integration of statistical methods to segment job-seeker profiles. These insights highlight the need for targeted policies that enhance language skills and foster professional networking opportunities, especially for women. By addressing these barriers, policymakers can better support gender equality in labor market access across Central Europe.
Promoting Pro-Environmental Behaviors via Green HRM: The Roles of Green Empowerment and Leadership Moleiro Martins, José; Umair Ahmed, Muhammad; Samreen, Farah; Farrukh Shahzad, Muhammad
Emerging Science Journal Vol. 9 No. 4 (2025): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-04-027

Abstract

With growing environmental concerns, organizations worldwide are increasingly integrating green practices into their operational frameworks. The interplay between GHRM practices, green empowerment, and green leadership creates a conducive environment for promoting task-related and broader proactive pro-environmental performance among employees. Effective GHRM practices lay the foundation, while green empowerment acts as the driving force, and green leadership enhances the overall impact, ensuring a sustainable organizational culture. Therefore, this study explores the influence of GHRM practices on task-related proactive pro-environmental performance (T-PEP) and proactive pro-environmental performance (P-PEP) through green empowerment. This study examines the moderating role of green leadership in GHRM and green empowerment. Using partial least squares structural equation modeling (PLS-SEM), we analyze data collected from 312 Pakistan food industry employees. The results indicate that GHRM significantly influences the P-PEP and T-PEP through green empowerment of employees’ food industries of Pakistan. Additionally, green leadership is identified as a significant moderator in the relationship between GHRM and green empowerment. These findings underscore the importance of aligning HRM practices with leadership initiatives to cultivate an organizational culture supportive of environmental sustainability. Based on affective events theory principles, this study offers theoretical insights and practical guidance, presenting valuable recommendations for industry managers and academic researchers in the food manufacturing sector.
Optimizing Consensus in Blockchain with Deep and Reinforcement Learning Villegas-Ch, William; Govea, Jaime; Gutierrez, Rommel
Emerging Science Journal Vol. 9 No. 4 (2025): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-04-08

Abstract

This study aims to optimize blockchain consensus mechanisms by integrating artificial intelligence techniques to address critical limitations in latency, scalability, computational efficiency, and security inherent in traditional protocols, such as PoW, PoS, and PBFT. The proposed model combines deep neural networks (DNNs) for feature extraction with deep reinforcement learning (DRL), specifically Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO), to enable dynamic validator selection and real-time adjustment of consensus difficulty. The training process utilizes a hybrid dataset of historical blockchain records from Ethereum and Hyperledger networks and synthetic data from simulated attack scenarios involving Sybil, 51%, and DoS threats. Experimental evaluations were conducted in private and permitted environments under varying transactional loads. Results show a 60% reduction in confirmation latency compared to PoW, achieving 320 ms, and a 20% improvement over PBFT. Transaction throughput increased to 22,000 transactions per second (TPS), and computational resource consumption was reduced by 30%. The model achieved an attack tolerance of up to 92%, significantly enhancing network resilience. The novelty of this work lies in its autonomous consensus optimization strategy, which enables adaptive and secure protocol behaviour without manual intervention, representing a scalable and efficient solution for future blockchain infrastructures.
A Multi-Dimensional Framework for Assessing the Societal Benefits of Collaborative R&I Projects Over Time Brandão, Ana Sofia; Santos, José M. R. C. A.
Emerging Science Journal Vol. 9 No. 4 (2025): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-04-022

Abstract

This paper contributes to the ongoing discussion on assessing the actual societal benefits of collaborative research and innovation (R&I) projects, focusing specifically on Circular Bioeconomy (CBE) initiatives funded under European Interreg programs. Utilizing an abductive method aligned with a grounded theory approach, the study conducted a multiple case study of five cross-border CBE projects. Data from project leaders and secondary sources underwent inductive content analysis and were classified using the Triple Bottom Line (TBL) framework. Seven cross-cutting benefit categories emerged: capacity building, collaborative learning, community empowerment, networking, knowledge sharing, policy development, and sustainable business practices, identified as influencing results across TBL dimensions temporally. Findings reveal projects excel at generating short/medium-term outputs and outcomes strongly aligned with the social dimension, particularly through capacity building, collaborative learning, and knowledge sharing. Over time, long-term impacts demonstrate a more balanced distribution across all three TBL dimensions (social, environmental, and economic), indicating a trajectory towards broader benefits. Policy development and networking are emphasized as key drivers for achieving significant long-term, multi-dimensional impacts. This study introduces a novel, empirically grounded, multi-dimensional theoretical model. By inductively categorizing benefits and analyzing their temporal manifestation across TBL, it provides a practical framework for assessing comprehensive societal impact beyond conventional output metrics.
Driving Forces Shaping Gig Economy Perceptions in Mongolia: A Multifactorial PLS-SEM Approach Batmunkh, Altanshagai; Lakner, Zoltan
Emerging Science Journal Vol. 9 No. 4 (2025): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-04-06

Abstract

The 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.

Page 76 of 81 | Total Record : 803

 74   75   76   77   78 

Filter by Year

2017 2025


Reset
Filter By Issues
All Issue Vol. 9 No. 5 (2025): October Vol. 9 No. 4 (2025): August Vol. 9 No. 3 (2025): June Vol 9, No 1 (2025): February Vol. 9 (2025): Special Issue "Emerging Trends, Challenges, and Innovative Practices in Education" Vol 8, No 6 (2024): December Vol 8, No 5 (2024): October Vol. 8 No. 5 (2024): October Vol 8, No 4 (2024): August Vol 8, No 3 (2024): June Vol 8, No 2 (2024): April Vol 8, No 1 (2024): February Vol 8 (2024): Special Issue "Current Issues, Trends, and New Ideas in Education" Vol 7 (2023): Special Issue "COVID-19: Emerging Research" Vol 7, No 6 (2023): December Vol 7, No 5 (2023): October Vol 7, No 4 (2023): August Vol 7, No 3 (2023): June Vol 7, No 2 (2023): April Vol 7, No 1 (2023): February Vol 7 (2023): Special Issue "Current Issues, Trends, and New Ideas in Education" Vol 6 (2022): Special Issue "COVID-19: Emerging Research" Vol 6, No 6 (2022): December Vol 6, No 5 (2022): October Vol 6, No 4 (2022): August Vol 6, No 3 (2022): June Vol 6, No 2 (2022): April Vol 6, No 1 (2022): February Vol 6 (2022): Special Issue "Current Issues, Trends, and New Ideas in Education" Vol 5 (2021): Special Issue "COVID-19: Emerging Research" Vol 5, No 6 (2021): December Vol 5, No 5 (2021): October Vol 5, No 4 (2021): August Vol 5, No 3 (2021): June Vol 5, No 2 (2021): April Vol 5, No 1 (2021): February Vol 4 (2020): Special Issue "IoT, IoV, and Blockchain" (2020-2021) Vol 4, No 6 (2020): December Vol 4, No 5 (2020): October Vol 4, No 4 (2020): August Vol 4, No 3 (2020): June Vol 4, No 2 (2020): April Vol 4, No 1 (2020): February Vol 3, No 6 (2019): December Vol 3, No 5 (2019): October Vol 3, No 4 (2019): August Vol 3, No 3 (2019): June Vol 3, No 2 (2019): April Vol 3, No 1 (2019): February Vol 2, No 6 (2018): December Vol 2, No 5 (2018): October Vol 2, No 4 (2018): August Vol 2, No 3 (2018): June Vol 2, No 2 (2018): April Vol 2, No 1 (2018): February Vol 1, No 4 (2017): December Vol 1, No 3 (2017): October Vol 1, No 2 (2017): August Vol 1, No 1 (2017): June More Issue