About Emerging Science Journal (ESJ) Emerging Science Journal is a multidisciplinary, an open-access, internationally double-blind peer-reviewed journal which 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. ESJ covers the following main subjects: Engineering and Technical Sciences Natural Sciences Social and Management Sciences Formal Sciences Physical Sciences For more information about ESJ Focus and Scope, you can click here. Current areas of interest, along with related associate editors, are listed below: Engineering and Technical Sciences Prof. Francesco Tornabene Department of Innovation Engineering, University of Salento Lecce, Italy Associate Editor Natural Sciences Prof. Nour Shafik Emam El-Gendy Egyptian Petroleum Research Institute (EPRI) Cairo, Egypt Associate Editor Social and Management Sciences Prof. Ferda Halicioglu University of Lincoln Lincoln, United Kingdom Associate Editor Formal Sciences Prof. Sunil Kumar National Institute of Technology Jamshedpur, India Associate Editor Physical Sciences Prof. Shen Xiu-Min Beijing Normal University Beijing, China Associate Editor Editorial Team View More International Emerging Science Journal Open Special Issues Special Issue "Emerging Trends, Challenges, and Innovative Practices in Education" Aim and Scope Current issues in education involve differing views on standardized tests, socioeconomic factors, educational equity, and adaptive learning. It is important to understand that these issues are distinct from current trends in education, such as changes in the higher education system, the rise of online education, and the adoption of standards-based learning. To address and resolve these challenges, innovative ideas must be conceived and implemented. This Special Issue prioritizes studies that integrate insights from diverse disciplines, contextualize research findings within a broader framework, advance methodological and theoretical understanding, and possess the potential to influence education policy and practice. It welcomes quantitative, qualitative, and mixed-method research papers, as well as comprehensive reviews of significant research studies and technical reports previously published. Guest Editor: Dr. Stamatis Papadakis Affiliations: Department of Preschool Education, School of Education, University of Crete, Crete, Greece. Interests: Distance Learning; Mobile Learning; Learning Analytics; Game-based Learning; Preschool Education; Primary Education; Secondary Education; Educational Robotics. 0000-0003-3184-1147 Co-Guest Editor: Prof. Paula Escudeiro Affiliations: Instituto Superior de Engenharia do Porto, Porto, Portugal. Interests: Educational Technology; Learning Technology; Information Technology in Education; Serious Games. 0000-0003-2528-572X Manuscripts should be submitted online at www.ijournalse.org by registering and logging in to this website. For more information Click Here. This Issue is now open for submissions. Deadline for manuscript submissions: 31 December 2025. Closed Special Issues Special Issue "Internet of Things, Internet of Vehicles, and Blockchain"-2020-2021 - Published Papers Special Issue "COVID-19: Emerging Research"-2021 - Published Papers Special Issue "COVID-19: Emerging Research"-2022 - Published Papers Special Issue "Current Issues, Trends, and New Ideas in Education"-2022 - Published Papers Most Cited & Viewed Articles The most cited articles from Emerging Science Journal published since 2019, extracted from Scopus. Citation Article Big Data, Knowledge Mapping for Sustainable Development: A Water Quality Index Case Study by: Lei Zhang Effect of Salinity on Evaporation and the Water Cycle by: Jude Iloabuchi Obianyo Optical and Structural Characterization of Bi2FexNbO7 Nanoparticles for Environmental Applications by: Allan Ramone Araujo Scharnberg, Adrison Carvalho de Loreto, Annelise Kopp Alves Revisiting Agroforestry for Building Climate Resilient Communities: A Case of Package-Based Integrated Agroforestry Practices in Nepal by: Kishor Aryal, Prakash Singh Thapa, Dhananjaya Lamichhane Impact of Transportation on Carbon Dioxide Emissions from Locally vs. Non-locally Sourced Food by: Bradley Striebig, Eric Smitts, Samuel Morton Preliminary Studies on the Use of Sawdust and Peanut Shell Powder as Adsorbents for Phosphorus Removal from Water by: Marian Asantewah Nkansah, Moses Donkoh, Osei Akoto, James Hawkins Ephraim
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Defining the Determinants of Corporate Financial Performance: A Machine Learning Approach
This study investigates the determinants of corporate financial performance (CFP) among Russian enterprises (2012–2023) through the lens of geopolitical disruptions, employing ensemble machine learning (ML) to address methodological gaps in modeling non-linear institutional interactions. Using data from 25 large non-financial firms, we analyze sectoral, organizational, and strategic drivers, integrating train-test splits (75%/25%) and 10-fold cross-validation to mitigate overfitting. Results reveal that industry affiliation, initially dominant (28% explanatory power pre-2022), declined sharply post-sanctions (15%), reflecting vulnerabilities in globally integrated sectors like manufacturing and extractives. Organizational size exhibited a nonlinear relationship with CFP, favoring comparatively smaller firms’ agility over larger enterprises’ rigidity, consistent with transaction cost economics. Strategic investments in corporate social responsibility (CSR) and research and development (R&D) diminished post-2022 as firms prioritized liquidity and operational stability, aligning with resource-based view principles. Methodologically, Shapley Additive Explanations (SHAP) clarified threshold effects in CSR returns and innovation’s reduced role under sanctions. The study innovates by applying ensemble machine learning to sanction-affected emerging markets, challenging linear econometric assumptions and advancing institutional theory through a crisis-contextualized framework of resource dependence and stakeholder salience. Findings underscore the fragility of intangible assets under systemic shocks and advocate adaptive resource allocation frameworks to balance short-term survival with long-term resilience. This work provides policymakers and managers actionable insights for fostering operational agility and strategic foresight in volatile institutional environments.
European Green Deal Objective for Sustainable Agriculture: Opportunities and Challenges to Reduce Pesticide Use
Agriculture in the 21st century faces complex global challenges, including rising food demand, resource depletion, and climate change. These pressures, however, also create opportunities to foster sustainability, enhance resource efficiency, and reduce reliance on synthetic pesticides. In response, the European Union (EU) adopted the European Green Deal in 2019, aiming for climate neutrality by 2050. The Farm to Fork (F2F) strategy sets a specific target: reducing chemical pesticide usage and its related risks by half by 2030. This study aims to assess the overall situation in the EU and the Member States' contributions to achieving the F2F objective of reducing pesticide use as well as risks at the policy level. A novel methodological approach was developed to assess Member State performance using a set of EU-defined indicators – such as Harmonised Risk Indicators (HRI 1 and HRI 2), pesticide sales data from Eurostat and FAOSTAT – and to classify countries into contribution-based groups. Findings reveal progress at the EU level: pesticide sales have declined and HRI 1 has dropped, but HRI 2 has increased. Significant variation among Member States was observed, highlighting the need for tailored policy actions. The study provides an innovative framework and practical insights for policymakers and stakeholders working toward sustainable agricultural transitions in the EU.
The Impact of Innovation-Driven Digital Transformation on Export Performance of SMEs
This study uses quantitative research methods to analyze the impact of innovation-driven digital transformation on the export performance of small and medium-sized enterprises in Vietnam. Data was collected from 403 valid responses from exporting enterprises and analyzed using the partial least squares structural equation model (PLS-SEM). The factors considered include management capacity, corporate culture, competitive pressure, enterprise resources, and government support. Digital transformation was identified as an intermediary variable between these factors and export performance. The research results indicate that digital transformation directly and significantly impacts export performance (β = 0.523, p < 0.01). At the same time, management capacity (β = 0.296, p < 0.01) and competitive pressure (β = 0.295, p <0.01) are important factors promoting the digital transformation process, while enterprise resources (β = 0.274, p < 0.01) and enterprise culture (β = 0.145, p < 0.01) also positively support it. Despite having a lower impact, government support (β = 0.118, p < 0.01) still encourages enterprises to apply digital technology. The study provides practical insights for policymakers and businesses to optimize digital transformation strategies, emphasizing the need for more substantial managerial capabilities and competitive adaptability. Unlike previous research, this study highlights corporate culture and competitive pressure as crucial factors shaping digital transformation in Vietnam’s export sector. Based on the results, the study proposes solutions to enhance competitiveness and export efficiency for small and medium-sized enterprises in Vietnam.
Empoasca Pest Attack Classification on Tea Plantations Using Multispectral Imaging and Deep Learning
This 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.
Gradient Descent Decision Tree Algorithm and Nonlinear Programming for Credit Risk Assessment and Credit Strategy
This research aimed to develop a scientific and accurate credit risk assessment model for small and medium-sized enterprises (SMEs) to support banks in credit decision-making. An improved decision tree model is proposed by integrating regularization to control complexity and employing an ensemble learning approach to enhance prediction accuracy. Multiple weak classifiers are iteratively refined using gradient descent optimization to form a robust, strong classifier. The model is trained through supervised learning, with the default probability of SMEs as the objective function, enabling a quantitative assessment of credit risk. The findings show that the proposed gradient descent decision tree algorithm achieves an AUC of 0.99 under 70% and 80% training set ratios, outperforming Adaptive Boosting (AUC = 0.97), Random Forest (AUC = 0.91), and traditional decision trees (AUC = 0.82). To further optimize bank loan strategies, this paper constructs a nonlinear multi-objective programming model that maximizes expected loan returns while considering risk constraints. The proposed approach not only improves credit risk prediction but also assists banks in formulating optimal lending strategies. This study advances credit risk modelling by integrating regularization and ensemble learning, offering a novel and practical solution for SME credit assessment.
Driving Forces Shaping Gig Economy Perceptions in Mongolia: A Multifactorial PLS-SEM Approach
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.
Impact of Technologically Vigilant Leadership on Smart Sustainable Circular Supply Chain Management
The main aim of this study is to explore how technologically vigilant leadership (TVL) impacts on smart sustainable circular supply chain management (SSCSCM) in the context of Industry 6.0. This is accomplished by analyzing the mediating effect of the digital environment management accounting system (DEMA) in the relationship between TVL and SSCSCM. This research evaluated the proposed model using two distinct statistical methods within the framework of structural equation modelling, which facilitated the assessment of both linear and non-linear correlations among the constructs. The data was obtained from a sample of respondents employed in small and medium enterprises. Linear relationships were analyzed using SmartPLS, whereas non-linear relationships were examined with WarpPLS. In both instances, the influence of TVL on SSCSCM was significant and positive, as demonstrated by CB-SEM in SmartPLS 4.1.0.9 and PLS-SEM in WarpPLS 7.0. Furthermore, DEMA partially mediated the relationship between TVL and SSCSCM. The current investigation is one of the few that provides conclusive evidence on the impact of TVL on SSCSCM. Also, it offers new insights by highlighting that DEMA partially mediates the relationship between TVL and SSCSCM. These profound insights would provide guidance for practitioners and policymakers in developing improved TVL mechanisms to leverage the advantages of DEMA and thereby strengthen SSCSCM.
Optimizing Consensus in Blockchain with Deep and Reinforcement Learning
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.
Sustainability Transformation: Leadership, Innovation, and Strategic Flexibility Improve Sustainability Performance
This study investigates how sustainability leadership impacts sustainability performance through strategic flexibility and sustainability innovation, which function as the mediators. This study collected data from 200 respondents using purposive sampling and 6-point Likert scale questionnaires. The data were subsequently analyzed using PLS-SEM. The findings confirm that sustainability leadership significantly and positively impacts sustainability performance through strategic flexibility and innovation. Additionally, data analysis revealed that sustainability leadership has a positive and significant impact on strategic flexibility and sustainability innovation. This study highlights the unique aspect of the Indonesian mining sector. It has not received extensive examination regarding sustainability and sustainability leadership as systems thinking that requires the perspective of interdependent structures and adaptability to changes. Strategic flexibility, as an organization's capability to adapt to changes reactively and proactively, is crucial to the organization's effort to create sustainability innovation to ensure sustainability performance.
The Impact of Land Transfer on Farmers' Happiness: The Mediating Effect of Social Aspects
This study investigated the mechanism through which land transfer impacts farmers' happiness in China, focusing on the mediating roles of household income and social equity, and the moderating effect of social capital. Utilizing convenience sampling through WJX platform, 431 farmers in Guangxi (2024) were selected as samples, and conducted structural equation modeling with Smart-PLS 4.0. Key findings reveal: (1) Land transfer exerts a significant positive effect on farmers' happiness; (2) Household income and social equity mediate 69.63% of this effect, with social equity demonstrating stronger mediation; (3) Social capital amplifies the equity pathway while showing nonsignificant moderation on income effects. Methodologically, this study applied multi-mediation moderated SEM in farmers' happiness studies, integrating both economic and psychosocial dimensions. Theoretically, these results challenge conventional income-centric paradigms by establishing social equity as the dominant mechanism, revealing that policy effectiveness in land reforms depends more on equity perceptions than absolute income gains. They provide empirical support for the application of social capital theory and social equity theory in rural land issue studies, highlighting critical factors that should be considered in policy formulation, and provide valuable empirical evidence for the government and policymakers, aiding in the optimization of land transfer policies to enhance farmers' happiness.
Nonlinear and Heterogeneous Effect of Digitalization on Foreign Direct Investment: Evidence from Developing Countries
This study aims to clarify whether digitalization has a nonlinear and heterogeneous effect on foreign direct investment (FDI) inflows in developing countries. We employ data from the period 2002–2023 and apply various econometric methods, including System Generalized Method of Moments (S-GMM), Dynamic Panel Threshold Regression (DTPR), and Method of Moment Quantile Regression (MMQR), to address the research question. The findings report an inverted U-shaped relationship between digitalization and FDI. Specifically, the effect of digitalization on FDI changes across different levels of digitalization. Initially, digitalization positively affects FDI, but beyond a certain threshold, its impact turns negative. This indicates that the benefits of digitalization for FDI are not unlimited but may be constrained by the risks and costs associated with excessive digital infrastructure expansion. Additionally, the MMQR analysis shows a heterogeneous effect of digitalization on FDI. Digitalization has a stronger impact on FDI at lower quantiles. However, as a country's development level increases, the effectiveness of digitalization in attracting FDI gradually diminishes. Policymakers need to identify and maintain an optimal level of digitalization to promote FDI. This requires not only investment in digital infrastructure but also in supporting factors such as improved governance quality and the development of legal frameworks to ensure a stable and conducive economic environment for FDI. Moreover, flexible policies tailored to different country or regional groups are necessary to maximize the benefits of digitalization without triggering negative impacts on long-term economic development.
Unleashing Effective Identification of ALS Based on Vowel Phonation: A Deep Learning Approach
ALS (Amyotrophic Lateral Sclerosis) is one of the fatal diseases across the world. Therefore, early detection can save patients suffering from ALS from life-threatening consequences. Typically, ALS can be identified based on different factors, and one such factor is voice analysis. Detection of ALS using sound signals is convenient and simpler than other methods, as it is a non-invasive approach, which makes the process faster and more efficient for detection. However, detection of ALS using traditional approaches is challenging, as it is a time-consuming process and heavy reliance on medical experts is needed. Therefore, AI-based models can be used for effective classification of ALS and non-ALS patients, as AI-based models possess the immense ability to examine vast amounts of data, including audio files, effectively. Owing to these factors, the proposed model focuses on employing an AI-based model for ALS classification based on vowel phonation /a/ and /i/. The process is carried out using the Minsk2020 dataset, where important features needed for the proposed model are extracted using MFCC (Mel-frequency cepstral coefficients) by removing the shakiness and jitteriness of the voice. The MFCC feature extraction technique extracts features based on the mel scale, as this reflects human auditory perception, thereby extracting features that are useful for classification. These extracted features are fed to CNN-LSTM (Convolutional Neural Network – Long Short Term Memory) with rapid dilatenet for classifying ALS and non-ALS patients accurately by identifying even the subtle changes in audio signals using maximizing the expansion/dilation rate and aid the context information for interpreting and analyzing the sound of vowels accurately and correctly without any loss of information. Finally, the efficacy of the proposed model is assessed using evaluation metrics. The proposed research work can assist medical professionals in detecting patients with ALS based on vowel phonation.
Extending C-TAM-TPB: Dual-level Moderation of Perceived Web Security and Age in Digital Banking
This study examines the intention to adopt digital banking in Saudi Arabia by integrating the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB). It includes Perceived Web Security (PWS) and Age as moderators, addressing trust and security perceptions across user segments in emerging markets. Data were collected from 353 digital banking users in Saudi Arabia using a cross-sectional quantitative design. The model was tested via Partial Least Squares Structural Equation Modeling (PLS-SEM), assessing measurement validity, path significance, moderation, and mediation effects. Predictive accuracy was evaluated with in-sample (R²) and out-of-sample (Q², PLS-Predict) indicators. The results confirmed the significance of core TAM variables—Perceived Ease of Use (PEU), Perceived Usefulness (PU), and Attitude Toward Use (ATU)—on Behavioral Intention (BI). Attitude was a strong predictor of BI, but Subjective Norms (SN) and Perceived Behavioral Control (PBC) were not significant. PWS moderated the ATU–BI relationship, enhancing intention under high security perception, but Age's dual-moderation effect was unsupported. Sequential mediation analysis validated that PEU and PU influence BI indirectly via ATU. This study enhances digital adoption research through a validated dual-level moderation model combining security perception and age. It refines TAM-TPB integration and offers practical insights for creating secure, user-centered digital banking systems tailored to specific cultures.
Leveraging External Networks and Internal Capabilities: A Pathway to Innovation in an Export Economy
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
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.
Alternative Method for Calculating Service Level Agreements for Internet Access Services in Rural Areas
Indonesia faces significant challenges in providing equitable internet access, particularly in remote areas. To address these challenges, the government collaborates with private Internet Service Providers (ISPs) under Service Level Agreements (SLAs) that define key service metrics, such as uptime and response time. Network Management System A (NMS A), managed by the government, handles telecommunications infrastructure, while NMS B, operated by the private ISP, delivers the internet services. A critical challenge in assessing SLA performance is distinguishing between service disruptions caused by network issues and those caused by power outages. This study proposes an SLA calculation method that prioritizes uptime data from NMS A, reflecting the government's perspective. If NMS A’s data is unavailable, UPS data is used as an alternative. NMS B’s data is considered only if its correlation with NMS A is strong (≥0.6). Additionally, the study classifies downtime: link failures exceeding 300 seconds require compensation (restitution). Downtime less than or equal to 300 seconds is considered non-restitution, and no compensation is needed. Power outages are considered non-restitution events, as they stem from external factors beyond the service provider's control. This method has been tested in over 1,000 locations, proving its reliability and adaptability for fair, transparent SLA evaluations.
Reindustrialization Plan Using the DPSIR and TOPSIS Methods
(De)industrialization is a phenomenon that affects the economic development of developed and developing countries. However, there is a lack of studies that evaluate strategies to promote the industrialization of a country through the information of industry experts. Objective: this article aims to develop a reindustrialization plan based on strategies to increase the relative participation of Brazilian industry in the Gross Domestic Product (GDP) in order to mitigate the Brazilian deindustrialization process. Method: the method used was DPSIR (Driving Forces, Pressures, State, Impacts, and Responses) in order to map information from specialists directly involved in the theme of industrial development in the areas of economics, public law, scientific research, public management, and private management. In addition, the TOPSIS method was used to prioritize the specialists' responses in order of urgency of implementation. Findings: the main result of this work revealed the strategies that should be prioritized to promote the country's industrialization. Novelty: this research served as a basis for the elaboration of the Brazilian Reindustrialization Plan, presented at the end of the article.
The Cultural and Financial Dynamics of Female Entrepreneurs as Well as Their Empowering Ventures
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.
Exploring the Relationship Between Stress, Burnout, and Occupational Accidents in Civil Construction
This study investigates the factors influencing work-related stress, burnout, and occupational accidents in the civil construction sector. Using a mixed-methods approach, the research integrates both qualitative and quantitative methodologies. Measurement scales for stress, fatigue, and occupational accidents were reviewed, translated, and adapted from prior studies to suit the context of construction workers. A total of 275 questionnaires were distributed, with 269 valid responses analyzed using SmartPLS (ver 3.2.8). Data analysis encompassed evaluating the validity and reliability of scales and exploring the relationships among key concepts in the research model. The findings highlight the significant influence of job attributes, co-worker relationships, and work environment on work-related stress. These factors trigger psychological changes that exacerbate worker fatigue and contribute to increased occupational accident rates. Novel insights emphasize the interdependence of these elements in shaping worker behavior and safety outcomes. The study provides a foundation for developing targeted strategies to mitigate stress and fatigue, reduce occupational hazards, and promote well-being among construction workers. By offering an integrated analysis, this research contributes to advancing the understanding of safety management practices in the construction industry.
CFD Analysis of Heat Exchanger Effectiveness and LMTD with Varying Pipe Length
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.
Influencer Credibility, Parasocial Relationships, and Product Involvement in Purchase Intentions
The increasing influence of social media influencers (SMIs) on consumer purchase intentions has become a crucial topic in marketing research, particularly in understanding the mechanisms that drive this effect. This study examines how SMI credibility—defined by trustworthiness, expertise, and attractiveness—affects consumer purchase intentions through the mediating role of parasocial relationships (PSRs) and the moderating role of product involvement. A survey of 205 Thai social media users was conducted, and data were analyzed with Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that SMI credibility positively impacts purchase intentions and strengthens PSRs, which partially mediate this relationship. Moreover, the moderating role of product involvement uncovers a conditional mediation effect: PSRs have a stronger influence on purchase intentions for low-involvement products, where emotional appeals are more effective than rational evaluations. In contrast, for high-involvement products, consumers prioritize cognitive processing and influencer expertise, weakening the impact of PSRs. This research enhances influencer marketing literature by incorporating emotional and cognitive pathways within a mediated-moderated framework. Practically, it highlights the importance of aligning influencer strategies with product involvement, recommending emotionally engaging influencers for low-involvement products and credibility-driven influencers for high-involvement products to maximize marketing effectiveness.
A Multi-Dimensional Framework for Assessing the Societal Benefits of Collaborative R&I Projects Over Time
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.
Structural Equation Modeling of Factors Affecting Accountability-Based Accounting in Public Sector Entities
Objectives: This study aims to identify factors influencing accountability-based accounting and propose a conceptual framework for public-sector accounting to enhance decision-making and accountability. Methods/Analysis: Conducted in Vietnam in 2023, the research employs quantitative analysis of survey data collected from 492 civil servants across 282 public entities, using SmartPLS for structural equation modeling. Findings: There are significant positive correlations between accountability-based accounting and four key factors: accrual-based accounting, felt accountability, information disclosure, and financial report quality. Among these, accrual-based accounting exerts the most substantial direct influence on the use of accounting information for accountability purposes. Felt accountability demonstrates direct and indirect effects, mediating relationships between other factors. The study highlights that effective accountability requires account givers to align with account holders’ needs, legitimacy, and expertise, while accrual-based accounting must prioritize improving user comprehension and usability of information. Novelty/Improvement: Transparency and publicity are critical for ensuring public-sector accounting information is reliable, relevant, and actionable. The proposed framework advances public-sector accounting theory by integrating accountability as a foundational principle, offering practical guidance for policymakers to strengthen accountability mechanisms. This research contributes a novel perspective by empirically validating the interplay of accounting components within an accountability-centric model, providing a basis for future conceptual and regulatory developments in public sector.
Ultrasound-Assisted Extraction of Bioactive Compounds from Tanacetum vulgare L.: Antibacterial and Cytotoxic Evaluation
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.
Optimization of nSiO2-Filled RTV Silicone Rubber Coatings for Enhanced High Voltage Outdoor Insulator Performance
This paper presents the experimental results of leakage current testing on nanosilica (nSiO₂)-filled room-temperature vulcanized (RTV) silicone rubber (SiR) coatings for outdoor insulators. Nanosilica composition is prepared based on the optimization results of surface hydrophobicity, surface resistivity, and relative permittivity of the RTV SiR matrix with varying nSiO2 contents. The study found that the insulator with a 4 wt.% nSiO₂-filled RTV SiR coating had the highest surface resistivity, better hydrophobicity, and higher permittivity compared to the unfilled RTV SiR coating. Leakage current tests are performed under several conditions (dry, clean fog, and salt fog) to evaluate the insulation characteristics of the modified RTV SiR coating applied on an actual high-voltage insulator. The results indicate that the 4 wt.% nSiO2-filled RTV SiR-coated insulator significantly reduces the leakage current magnitude and Total Harmonic Distortion (THD) when compared to that of the uncoated as well as to that of unfilled RTV SiR-coated insulators. Under all fog conditions, the 4 wt.% nSiO2-filled RTV SiR-coated insulator with a polluted surface showed the smallest leakage current THD percentage of all insulator samples. Additionally, the cross product of the leakage current magnitude and THD is also calculated to determine the condition of the insulator. The cross-product results show that the 4 wt.% nSiO₂-filled RTV SiR-coated insulator is more effective at reducing it under dry conditions, with a reduction range of 68%–81% compared to the uncoated isolator and 70%–77% compared to the unfilled RTV SiR-coated isolator. This study shows the effectiveness of RTV silicone rubber coating material with nSiO2 filler particles in reducing the magnitude of leakage current and harmonics in polluted environments.
Factors Affecting Green Performance of Food Supply Chain Firms: A Parallel Mediation Model
Objectives: The objective of this study was to examine the impact of organizational green culture (OGC) on green innovation (GI) and sustainable entrepreneurship practices (SEP), which collectively enhance green performance (GP) in Pakistani food chain sector small and medium enterprises (SMEs). This research investigates how green innovation and sustainable entrepreneurship practices mediate each other towards achieving better green performance. Method: The authors chose deductive quantitative research along with Google Forms-based online surveys to gather data from 239 SMEs using convenience sampling. Structural equation modeling through SmartPLS detected all relationship effects between constructs within the research model. Findings: The study confirms that organizational green culture leads to increased GI and SEP, which in turn contributes to enhanced GP, while SEP operates as the essential mediator between OGC and GP in establishing how cultural values become sustainable practices and environmental improvements. The research merges OGC and innovation aspects with sustainability practices and demonstrates their effects on SMEs through empirical research. Novelty: The research uncovers SEP as a key connection between green culture and performance, which provides business solutions for SMEs that want to merge cultural elements with innovative approaches for sustainability. The research explores green entrepreneurship within emerging markets by demonstrating that developing an organizational green culture leads to creative processes that create sustainable outcomes that enhance environmental results. The paper makes an exceptional contribution by examining two distinct mediators: green innovation and sustainable entrepreneurship practices.
Promoting Pro-Environmental Behaviors via Green HRM: The Roles of Green Empowerment and Leadership
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.
Impact of Green Marketing Elements on Consumers: A Behavioral Approach
This research examines the factors influencing green purchasing behavior in the Vietnamese market, utilizing the Theory of Planned Behavior (TPB) as its foundation. Utilizing an online questionnaire survey of 596 Vietnamese consumers from December 2023 to February 2024, the research examines the influence of key factors, including green advertising, awareness of green products, environmental awareness, subjective norms, attitude toward green products, and green pricing on consumer purchase intentions and behaviors. The research utilized the technical of Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine and confirm the proposed hypotheses. The findings reveal that the most influential factor on consumer purchase intentions and behaviors is the attitude toward green products, highlighting the crucial role of consumer perceptions and values regarding environmentally friendly products. Furthermore, green advertising and awareness of green products also play a vital role in shaping consumer intentions. While subjective norms and green pricing also show an influence, they have a lesser impact. This study contributes both theoretically and practically by offering valuable insights for marketers and policymakers designing effective green marketing strategies that enhance consumer engagement and encourage the adoption of environmentally friendly products in Vietnam.
Obstacles to Finding the Ideal Workplace: A Gender-Based Analysis Across the V4 Countries
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.
A Study of the Effects of Knowledge Management on Enterprise Innovation Performance
This study aims to explore how knowledge management capability and enterprise innovation behavior jointly affect the innovation performance of manufacturing SMEs. Based on the background of the knowledge economy, we selected manufacturing SMEs with knowledge as their core competitiveness as the research object and constructed and optimized the theoretical model of “Knowledge Management Capability-Innovation Behavior-Innovation Performance”. Based on the research data from 400 manufacturing SMEs in China, the study adopts the empirical analysis method and examines the relationship between the variables through structural equation modeling. The results show that knowledge management capability has a significant positive impact on firms' innovation performance, while firms' innovation behavior mediates the relationship between knowledge management capability and innovation performance. The findings of this study not only validate the key role of knowledge management in enhancing the innovation capability of enterprises but also reveal the path mechanism for enterprises to realize knowledge transformation and innovation results by stimulating innovative behaviors. Compared with previous studies, this study systematically optimizes the construction of theoretical models and the analysis of mediating effects, enriches the research content in the field of knowledge management and innovation performance, and provides new theoretical support and empirical evidence for the knowledge management practice and innovation strategy formulation of manufacturing SMEs.