Journal of Asian Scientific Research

Beyond growth: Macroeconomic drivers of poverty in the U.S. and Canada

Ihsen Abid — 2025-09-23

This study explores the macroeconomic and social determinants of poverty in the United States and Canada from 1980 to 2023, using the poverty headcount ratio at $4.20/day. It employs the Autoregressive Distributed Lag (ARDL) model and Error Correction Model (ECM) to examine both short-run and long-run dynamics between poverty and six key variables: GDP per capita growth, income share of the bottom 20%, school enrollment, inflation, labor force participation, and government consumption expenditure. The results for the United States indicate strong long-run relationships, with income distribution, education, inflation, and labor force participation showing significant impacts on poverty. The ARDL model explains 95% of the variation in poverty, and the ECM confirms a stable adjustment toward long-run equilibrium. In contrast, the Canadian model explains 58% of the variation, with inflation, income share, and labor market variables showing notable effects, while education and government spending play more modest roles. These differences reflect how national welfare systems and institutional responses to macroeconomic pressures shape poverty outcomes. The comparative analysis highlights how differing institutional settings, Canada’s universal welfare state versus the United States' liberal model, mediate macroeconomic impacts on poverty. The study provides actionable insights for regional policy design, suggesting that enhancing income redistribution, improving educational access, and stabilizing inflation can significantly reduce poverty in liberal welfare regimes such as the U.S., while reaffirming the effectiveness of universalist policies in the Canadian context. These findings underscore the importance of redistributive mechanisms and investment in human capital in mitigating poverty.

The impact of informational capital on marketing performance: A study of Chinese enterprises and the moderating role of industry type

Haiyue Cheng — 2025-11-21

This study investigates the impact of informational capital on the marketing performance of Chinese small and medium-sized enterprises (SMEs), with industry type as a moderating factor. Drawing on the Resource-Based View, it examines how informational capital contributes to marketing effectiveness in emerging economies. Data from 293 SMEs across diverse sectors were analyzed using partial least squares structural equation modeling and multi-group analysis to test the proposed hypotheses. The results indicate that informational capital significantly enhances marketing performance, with stronger effects observed in service industries compared to manufacturing. By considering industry characteristics and organizational conditions, SME marketing managers can optimize their informational strategies to achieve sustainable competitiveness in fragmented and dynamic economic contexts.

The effect of social capital and government support on women's entrepreneurship in Bangladesh: Mediation and multigroup analysis

Mohammed Aminur Rahaman — 2025-11-21

Women's entrepreneurship has emerged as a pivotal and innovative pathway for addressing socioeconomic challenges, particularly in developing economies. While much of the existing literature on entrepreneurship highlights the role of formal institutions within entrepreneurial ecosystems, sociocultural dimensions have received comparatively limited attention. This study examines the influence of family support, social capital, and government support on women's entrepreneurial activities in Bangladesh, with self-efficacy serving as a mediating variable. Employing a quantitative design, survey data were collected from 297 Bangladeshi women entrepreneurs and analyzed using the PLS-SEM approach. The findings demonstrate that social capital plays a critical role as both a direct and indirect driver of women's entrepreneurship. Conversely, family support and government support reveal significant indirect effects, with variations observed across marital status groups. Multigroup analysis indicates that unmarried women entrepreneurs experience a stronger influence of social capital compared to their married counterparts. Grounded in the resource-based view (RBV) theory, the findings underscore that social resources, particularly social capital, serve as key enablers of entrepreneurial engagement, while family and government support contribute indirectly. These insights highlight the need for policymakers to develop targeted initiatives such as awareness programs, capacity-building workshops, and women-focused entrepreneurial institutions that can foster confidence and strengthen the participation of women in entrepreneurial ventures.

The impact of personality traits on enhanced academic self-efficacy and teaching innovation behavior among vocational teachers in China

Miao Zhang — 2025-11-21

This study aims to investigate how personality trait dimensions influence teachers’ academic self-efficacy and teaching innovation behavior. A total of 635 teachers from vocational schools in China participated by filling out a comprehensive questionnaire designed to assess various personality traits and their impact on teaching practices. Structural equation modeling (SEM) was employed to examine the research hypotheses. The findings revealed that teachers’ personality trait dimensions, such as agreeableness, extraversion, neuroticism, and openness, have a positive and significant effect on teachers’ academic self-efficacy. In contrast, conscientiousness did not demonstrate a significant role in influencing teachers’ self-efficacy, suggesting that not all personality traits contribute equally to teachers' confidence in enhancing academic abilities. The findings reveal that academic self-efficacy partially mediates the relationship between teachers' personality traits and their teaching innovation behavior. This indicates that while personality traits can directly influence teaching practices, the level of self-efficacy also plays a crucial role in this dynamic. Education stakeholders should foster attractive collaborations between teachers and students, particularly by addressing teachers’ personal psychological needs during the learning process. However, this study was limited to vocational teachers in China, suggesting the need for future research to explore cross-regional cultures, various educational departments, and different religious contexts to gain a more comprehensive understanding of these relationships.

HMLR analysis on the predictions of work-force management best practices on corporate performance measures: A case study of a millennial workforce in a large-scale it company

Shouvonik Basu — 2025-11-21

The objective of the present article is to investigate the impact of Workforce Management (WFM) best practices on the overall effectiveness of Corporate Performance Measures (CPM) using a case study of a large-scale IT company employing predominantly a millennial workforce. This analysis effectively establishes the correlation between these two variables by presenting substantiated evidence regarding the impact of these practices on overall corporate performance. The facets of WFM were grouped into Business Planning and Control; Talent Acquisition and Competency; and Employee Performance and Attitude, whereas CPM facets were classified into Organizational Culture and Efficiency; and Employee Satisfaction and Motivation. A closed-ended questionnaire survey with 115 questions was designed to investigate the complex interaction between the above variables. Statistical analyses, including bivariate, normality tests (skewness and kurtosis), correlation, and Hierarchical Multiple Linear Regression (HMLR) analyses, were conducted to examine the relationships and test the hypotheses. The findings reveal that bundles focused on business planning and talent acquisition were significant predictors of organizational culture and efficiency. In contrast, talent acquisition and employee performance were found to be strong determinants of satisfaction and motivation. The research provides empirical evidence that strategic WFM implementation positively affects overall corporate outcomes by fostering a collaborative and innovative work environment. These results also offer practical guidance for HR professionals seeking to design workforce strategies that align with the expectations of millennial, digitally savvy employees.

AI-driven capital structure forecasting for automotive giants: Enhancing stability, liquidity, and solvency

Mikayel Rafayel Gyulasaryan — 2025-11-21

From 2015 to 2025, leading automotive companies listed on global stock exchanges have operated in a highly capital-intensive environment that requires effective management of financial structures. This study applies Artificial Intelligence (AI)–based forecasting models to analyze the capital structures of major automotive manufacturers, focusing on Toyota as a representative Asian firm and comparing it with Ford and BMW. The empirical results demonstrate that AI models improve the accuracy of debt-to-equity ratio forecasts by 12–15% compared to traditional statistical methods. For Toyota, AI-based forecasts indicate a stable capital structure with liquidity buffers consistently above industry averages, supporting stronger solvency and financial resilience. In contrast, Ford and BMW exhibit higher leverage sensitivity, with solvency ratios projected to decline under rising interest rate scenarios. These results underscore the comparative advantage of Toyota’s financial management practices within the Asian automotive sector while contextualizing its performance in the broader global landscape. The study further highlights the strategic value of AI-based financial forecasting tools, suggesting that their integration can optimize capital structures and support informed decision-making in industries characterized by significant capital requirements. Overall, the findings advocate for the adoption of AI methodologies as a means to strengthen financial planning and enhance corporate sustainability in the automotive sector.

Interpersonal conflict and physicians’ entrepreneurial intention: Education and motivation as mediating roles

Andri Sulaksono — 2025-11-21

The study examines how interpersonal conflict (IC) influences physicians' entrepreneurial intention (EI), with entrepreneurial education (EE) and learning motivation (LM) serving as mediators. A survey of 150 Indonesian physicians was analyzed using PLS-SEM and FIMIX-PLS. The measurement model demonstrated acceptable fit indices, with SRMR = 0.083 and GoF = 0.365. Structural results indicate that EE (β = –0.461, p < 0.01) and LM (β = –0.330, p < 0.05) significantly decrease EI, whereas IC shows no significant direct or indirect effects; the IC–EI path is marginally significant p = 0.052. The mediation analysis confirms that EE and LM do not mediate the relationship between IC and EI. The model explains 57% of the variance in EI, although its predictive relevance is weak. These findings are robust, and the marginal significance of the IC–EI path (p = 0.052) is duly reported. FIMIX-PLS identifies three distinct physician segments: (i) cautious physicians, where both EE and LM reduce EI; (ii) motivation-oriented physicians, where LM increases EI despite EE’s negative effect; and (iii) education-oriented physicians, where EE increases EI but LM suppresses it. The results suggest that physicians’ entrepreneurial orientation is heterogeneous and influenced by paradoxical effects of education and motivation. This study extends EI models to the medical field, challenges the assumption of a positive role of education, and provides practical insights for designing targeted entrepreneurship programs and health policies in Southeast Asia, particularly Indonesia.

An empirical study on the impact of organizational justice on employee job satisfaction: Evidence from the Manaseer industrial complex in the mining sector

Hareth Madadha — 2025-11-25

Employee job satisfaction is a key dependent variable in organizational behavior research as it is associated with job performance, retention, and workplace culture. It is widely accepted that employees who are satisfied with their job experience are more productive and likely to be retained by an organization. Organizational justice (i.e., distributive justice, procedural justice, and interactional justice) is linked to job satisfaction, as perceived fairness is integral to job satisfaction, working life, and employee engagement. However, there is minimal research concerning employee job satisfaction relative to organizational justice in the industrial and mining sectors of Jordan. This study aimed to determine whether organizational justice impacts employee job satisfaction within the Manaseer Industrial Complex in Jordan. A quantitative, cross-sectional study was conducted utilizing a structured questionnaire delivered to 247 employees in a variety of job levels. Data was analyzed using reliability tests, correlation, and multi-model regression analyses. Results showed a statistically significant positive relationship for distributive and interactional justice on job satisfaction, whereas procedural justice did not demonstrate a significant relationship on job satisfaction. In conclusion, organizations within the mining sector should consider the value of a transparent reward system to advance job satisfaction for employees and to develop an organizational culture that supports the well-being of employees.

Self-efficacy as a mediator of the link between learning motivation and subjective well-being among older adults in Beijing

Yuting Ding — 2025-11-25

This study explored the relationship between subjective well-being (SWB) and learning motivation (LM) among older Chinese adults, focusing on the mediating role of self-efficacy (SE) and differences by age and education level. A total of 481 Beijing residents aged 60 and above participated in a questionnaire survey, which included LM, SWB, and SE scales. Confirmatory factor analysis and reliability tests verified the instruments' validity and reliability (Cronbach's α > 0.80). Structural equation modeling showed LM had a significant positive effect on SWB (β = 0.40, p < 0.001), while SE partially mediated this relationship (indirect effect β = 0.38, p < 0.01). Significant age differences existed in LM and SE (F = 14.36 and F = 7.75, p < 0.01), with participants aged 60–69 scoring higher than those aged 70+, though no significant SWB difference was found. Education level correlated significantly with LM, SWB, and SE (F values: 6.85–18.33, p < 0.01), with higher academic qualifications linked to consistently higher scores. The findings suggest that elderly education institutions should adopt targeted strategies considering age and educational background to enhance LM and thereby promote older adults’ SWB.

AHCL: AI-based real-time hidden video stream identification platform

Shashidhar R — 2025-11-25

The objective of the research is to address increasing privacy and safety issues related to hidden cameras in personal spaces such as bedrooms, bathrooms, or dressing areas, where such cameras can wirelessly transmit video signals clandestinely. The aim is to develop and evaluate an Artificial Intelligence (AI)-Enabled Hidden Camera Localization (AHCL) platform capable of identifying and locating hidden video streams through analysis of real-time network traffic. The methodology involves packet capturing, statistical analysis, and deep learning-based classifiers to detect anomalous streaming traffic in captured packets. The research generated a dataset comprising 60,412 packets, labeled as either 'normal' or video streaming, which was used to train and evaluate several models, including Support Vector Machines (SVM), Denoising Autoencoders, and ensemble deep learning models. The experimental results indicate that the ensemble model achieved the highest performance, with a detection accuracy of up to 98.27%, demonstrating good generalization and robustness across different network environments and over multiple days. The findings show that the AHCL platform is highly reliable in detecting hidden camera traffic from benign traffic. The practical contribution of this research is significant, providing users with an intelligent and affordable system for real-time privacy protection that can be deployed in residential or commercial settings, thereby enhancing trust and safety in a connected environment.

Trajectory of reflective practices of pre-service teachers: A qualitative case study

Manisha Yadav — 2025-11-25

This study examines the reflective practices of five pre-service teachers from the 2024-25 cohort enrolled in the Bachelor of Elementary Education (B.El.Ed) programme. The purpose of this study is to gain an in-depth understanding of how reflective practices are developed and enacted within the elementary teacher education programme. This is a qualitative case study that utilizes semi-structured interviews and analyses 200 reflective notes from journals maintained by five pre-service teachers, each contributing 40 entries. The reflective notes were analysed inductively using a framework to evaluate the breadth and depth of reflection. Semi-structured interviews reveal rich insights into the participants' perspectives and conceptual understanding of reflective practices. Through thematic analysis, this study identifies trajectories and transformative aspects of reflective practices in the teacher preparation process, providing a foundation for understanding the evolution of pre-service teachers as reflective practitioners. This evolution ranges from descriptive to dialogic and critical reflection. The findings highlight the progressive development of reflective thinking, influenced by the participants’ contextual background, classroom experiences, and active involvement with pedagogical practices. To foster pre-service teachers’ reflective capacities, the study emphasizes the need for structured support and training in teacher education programmes. Consequently, it will improve the quality and effectiveness of teacher education programmes.

The impact of virtual simulation technology and blended learning methods on nursing students' performance, acceptance and transition to clinical practice in higher vocational education

Dan Liu — 2025-11-26

In the context of China's national vocational education digitalization framework, nursing education virtual simulation (VS) is still not fully utilized as a resource to link theory and practice. The objective of this study is to compare the effects of VS-based blended learning (VS-BL) and traditional blended learning (T-BL) on nursing students’ knowledge, skills, acceptance, and clinical readiness. A quasi-experimental design was employed with a sample of 79 vocational nursing students. The VS-BL group (n = 39) was exposed to a formal simulation unit prior to practical skills performance, while the T-BL group (n = 40) received traditional training. Theoretical scores (M = 86.33 ± 8.18) and OSCE performance (M = 84.38 ± 2.68) were significantly higher in the VS-BL participants than in the T-BL group (p < 0.001). Additionally, the percentage of VS-BL students who felt prepared for clinical experience, self-directed learning, or professional identity development increased by 84.6%. The findings indicate that incorporating virtual simulation within blended learning significantly enhances students’ competencies and preparedness for clinical practice, highlighting its potential as an effective educational resource in nursing training.

Machine learning-based classification of macadamia nut quality using physical features

Sirikunya Nilpanich — 2025-11-26

Macadamia nuts in shell must be classified into excellent and faulty categories to maintain market value, optimize yield efficiency, and ensure consistent product quality. Although traditional methods such as wet floating and dry specific gravity (SG) remain widely used due to their simplicity and low cost, their accuracy and consistency are often limited in large-scale or automated operations. Machine learning offers a more advanced and efficient alternative, enabling higher levels of automation, precision, and reliability. This study evaluates and compares the performance of wet floating, dry SG at different threshold levels, and machine learning models using a dataset of 1,260 macadamia nuts collected during the peak harvest period from multiple orchards in Loei Province, Thailand. The wet floating method achieved an accuracy of 78.33% with high precision (90.00%) but relatively low recall (72.97%), indicating its tendency to misclassify a considerable portion of high-quality nuts. The dry SG method demonstrated the most balanced performance at the 0.9 threshold, with 89.50% accuracy, 90.00% precision, 89.11% recall, and an F1-score of 89.55%, while threshold variation revealed clear trade-offs between precision and recall. Machine learning outperformed the traditional methods, with the Random Forest model yielding the highest performance (accuracy 92.06%, precision 94.44%, recall 91.07%, and F1-score 91.07%). These findings highlight the potential of integrating machine learning–based classification to enhance accuracy, increase operational efficiency, strengthen product quality assurance, and support more sustainable and competitive agricultural value chains.

The effect of digital infrastructure on export technological complexity in manufacturing: Empirical evidence from China 2011-2023

Xu Dandan — 2025-11-26

Existing research has mainly focused on export volume or performance, with relatively limited discussion of the impact of technological complexity on manufacturing products. This study utilizes panel data from 30 provincial regions in China from 2011 to 2023 and systematically assesses the impact of digital infrastructure on the technological complexity of manufacturing exports using a fixed-effects panel model and mediation analysis. It further conducts a comprehensive investigation from the perspectives of transmission mechanisms and regional heterogeneity. Empirical results show that digital infrastructure significantly enhances the technological sophistication of manufacturing exports, with a 1% increase in digital infrastructure associated with an average 0.394% rise in export sophistication. The results are statistically significant and robust across various specifications. Besides, heterogeneity analysis highlights that the beneficial influence is particularly evident in both eastern and western regions, accompanied by greater marginal effects in areas characterized by limited digital infrastructure development. Furthermore, the mediation analysis demonstrates that digital infrastructure indirectly enhances export technological complexity by promoting technological progress and human capital accumulation. This study offers empirical evidence for understanding the evolution of manufacturing export competitiveness under the digital economy and provides policy implications for optimizing regional digital infrastructure allocation and promoting high-quality export development.

A mixed‑methods empirical investigation of Chinese international students’ willingness to communicate in Thai within Sino‑Thai educational cooperation

Lingfen Mo — 2025-11-26

Sino-Thai collaboration in higher education has diversified in recent years, making Chinese students a pivotal group in Thailand’s international student population. This study investigates both the current state of their willingness to communicate (WTC) in Thai and how educational cooperation between the two nations shapes it. Employing a mixed-methods design, the research integrates a quantitative survey of 487 Chinese international students in Thailand with semi-structured interviews involving a purposive subsample of 13 participants. Drawing upon cross-cultural adaptation theory and the L2 Willingness to Communicate framework, the study explored the impact of contextual and personal factors on communicative behaviour. Analysis through SPSS, employing means and standard deviations, indicated that Chinese international students generally displayed moderate-to-low WTC in Thai. Additionally, coding analysis revealed that Sino-Thai educational cooperation principally influences students’ WTC through three domains: academic adaptation support, cultural participation, and career-development facilitation. Collectively, these supports alleviate pressures associated with linguistic, cultural, and environmental adjustment, while simultaneously furnishing extrinsic motivation for professional advancement, thereby modestly enhancing WTC. Nonetheless, substantive progress remains contingent upon further optimisation of immersive language milieus and the systematic design of communicative practice opportunities. The study offers empirical insights and evidence-based language policies and bilingual talent cultivation recommendations within Sino-Thai cooperation.

Low back pain in Pakistan: A scoping review of epidemiology and treatment approaches

Muhammad Naseeb Ullah Khan — 2025-11-28

Pakistan, the world’s fifth most populous country, faces a growing burden of low back pain (LBP) driven by population aging, occupational risks, and constrained health resources. This scoping review mapped clinical and epidemiological LBP research in Pakistan, summarizing prevalence, risk factors, outcome measures, and treatments, and identifying research gaps. Following JBI guidance and PRISMA-ScR, we searched PubMed, Embase, Scopus, CINAHL, Google Scholar, and PakMediNet for studies on Pakistani populations published up to 10 October 2023. Two reviewers independently screened and charted data. Of 1,176 records identified, 219 studies were included. Rehabilitation-focused research predominated (n=89, 40.6%), followed by correlational/risk-factor work (n=45, 20.5%), prevalence studies (n=41, 18.7%), medical management (n=17, 7.7%), diagnostic testing (n=14, 6.4%), surgical interventions (n=7, 3.2%), and outcome measurement (n=6, 2.7%). Reported LBP prevalence ranged from 36.7% to 87% across settings and definitions. Among intervention studies, manual therapy was most frequently investigated (n=39, 34.5%). Chronic LBP dominated the literature (n=126, 57.5%). Publication volume surged during 2021–2023 (54.3%). Several Urdu-language instruments exist (e.g., disability, fear-avoidance, catastrophising, self-efficacy), but psychological and social dimensions remain sparsely examined. LBP research in Pakistan is expanding yet remains skewed toward biomedical and rehabilitation models with limited attention to psychosocial factors and guideline-concordant, activity-based care. Future work should prioritize biopsychosocial frameworks, nationally representative epidemiology with standardized outcomes, and evaluations of education- and exercise-based interventions. Policymakers and health systems can catalyze progress by supporting balanced research agendas, workforce training in evidence-based practice, and culturally adapted patient education to enable comprehensive, guideline-informed LBP care.

Unpacking academic professionalism and teacher well-being: The roles of leadership support, psychological capital, and digital literacy in higher education

Zhang Huiqing — 2025-11-28

Drawing on the Job Demands–Resources Model and Conservation of Resources theory, this study investigates how leadership support and psychological capital influence academic professionalism and, in turn, affect university teachers’ well-being. Moreover, the mediating effect of academic professionalism and the moderating effect of digital literacy are also discussed. A mixed-methods design was adopted, combining survey data from 382 Chinese university teachers and semi-structured interviews from 8 participants. Results from partial least squares structural equation modeling supported all six hypotheses. Leadership support and psychological capital significantly positively affected academic professionalism, which in turn predicted well-being. Mediation and moderation effects were also confirmed. Qualitative findings further revealed that intrinsic motivation, peer benchmarking, emotional support, and digital competence contributed to teachers’ professional engagement and psychological satisfaction. These findings enrich the understanding of academic professionalism as a dynamic resource and highlight the importance of institutional, personal, and digital enablers of teacher well-being. Implications for policy, faculty development, and future research are discussed.

Impact of artificial intelligence on special needs education

Katherine Belén Quinaluisa-Narváez — 2025-11-28

This study examines the role of Artificial Intelligence (AI) in the education of students with special educational needs (SEN) through a systematic literature review (SLR) and bibliometric analysis of 120 studies published between 2015 and 2025, following the PRISMA protocol. It explores technological advancements, pedagogical applications, thematic trends, and ethical challenges. AI applications, including machine learning, natural language processing, and adaptive systems, demonstrate significant potential for personalizing learning, enhancing accessibility, and supporting the emotional development of students with SEN. However, implementation faces limitations related to teacher training, digital divides, and inadequate regulatory frameworks. Bibliometric analysis revealed sustained growth in publications since 2021, with influential journals such as Educational Technology and Society and the Journal of Special Education Technology standing out. Key research lines include digital accessibility, emotional recognition, personalized learning, and the use of chatbots or smart sensors. Despite progress, a disconnect persists between technological development and effective classroom application, alongside limited representation of studies from the Global South. The study concludes that AI can be a powerful ally for educational inclusion, provided its implementation is guided by ethical principles, inclusive frameworks, and contextual sensitivity. It recommends promoting longitudinal and intersectional research to assess the real impact of these technologies and foster truly equitable education grounded in educational justice.