Bangladesh Journal of Multidisciplinary Scientific Research
Centre for Research on Islamic Banking & Finance and Business
AN AI BENCHMARK SELECTION FRAMEWORK FOR SUSTAINABLE CYBERSECURITY: COMPARATIVE CHARACTERIZATION OF CIC-IDS2017, UNSW-NB15, AND IOT-23
AI is not just a tool for digital transformation, but a vital component in safeguarding the critical digital infrastructure and facilitating sustainable digital transformation. But the choice of inappropriate benchmark datasets can make AI-driven IDS less reliable, transparent, and reproducible, ultimately undermining their role in supporting resilient cybersecurity ecosystems aligned with the SDGs, especially SDG 9 (Industry, Innovation, and Infrastructure) and SDG 16 (Peace, Justice and Strong Institutions). This work proposed four complementary analytical dimensions that were comparatively analysed for three widely used benchmark datasets: dataset characterisation, attack diversity, Mutual Information (MI)-based feature importance, and feature correlation analysis. The findings show significant inter-dataset differences. There are 71,984,818 network records in the IoT-23, much larger than those of CIC-IDS2017 (2,830,743) and UNSW-NB15 (2,540,047), which makes it more suitable for large-scale deep learning research. In addition, CIC-IDS2017 offers the highest feature representation (80 features) and attack diversity (15 attack categories). In comparison, UNSW-NB15 is a good benchmark for feature representation (50 features) and attack diversity (9 attack categories) after class harmonization. The results of the feature importance analysis also reveal significant differences across datasets: statistical flow features are the most important in CIC-IDS2017, communication endpoint features are the most informative in IoT-23, and protocol-related features are the most important in UNSW-NB15. Based on these results, this study proposes a Benchmark Selection Matrix and a Benchmark Selection Framework to help translate the comparative analysis of datasets into an evidence-based decision-making process for specific AI research scenarios. JEL Classification Codes: C6, C8, C9.
Research Paper/Theoretical Paper/Review Paper/Short Communication Paper
Artificial Intelligence, Intrusion Detection Systems, Benchmark Selection Framework, Explainable Artificial Intelligence, Sustainable Cybersecurity, Resilient Digital Infrastructure, SDG 9, SDG 16, Oman Vision 2040
Copyright (c) 2026 Boumedyen Shannaq (Author) http://creativecommons.org/licenses/by/4.0
ENHANCING ENTREPRENEURIAL PERFORMANCE THROUGH DIGITAL CAPABILITY AND SUSTAINABILITY PRACTICES: THE MEDIATING ROLE OF INNOVATION ORIENTATION AND THE MODERATING INFLUENCE OF FINANCIAL INCLUSION IN BANGLADESH
This paper examines the impact of digital capability and sustainability practices on the performance of entrepreneurs in Bangladesh, and the moderating influence of innovation orientation and financial inclusion. The use of digital technologies and sustainable practices poses a challenge for entrepreneurs in emerging economies, and it is necessary to understand how these resources and capabilities affect a firm's performance. The sample consists of registered SMEs in Bangladesh, with owners or managers sampled using stratified random sampling by industry and firm size. A sample size of 400 is used. Structural equation modelling (SEM) is used to analyze data, whereas reliability and validity are evaluated using SPSS 25 and SmartPLS 4. The SEM-PLS analysis demonstrated robust measurement model properties. Indicator loadings ranged from 0.836 to 0.946, confirming strong item reliability. Internal consistency was supported by Cronbach's alpha (0.927–0.947) and composite reliability (0.945–0.959). Convergent validity was established, with AVE values ranging from 0.774 to 0.825. Model fit was acceptable (SRMR = 0.088; NFI = 0.919–0.928). Discriminant validity was confirmed via HTMT and Fornell-Larcker criteria. VIF values (1.0–4.026) indicated no multicollinearity concerns. Structural model results showed that Digital Capability (β = 0.312, p < 0.001) and Sustainable Practices (β = 0.412, p < 0.001) positively influenced Innovation Orientation. However, direct effects on Entrepreneurial Performance were insignificant for Digital Capability (β = 0.041), Sustainable Practices (β = -0.077), and Innovation Orientation (β = -0.009). Mediation effects were not supported. Importantly, Financial Inclusion significantly moderated the Sustainable Practices–Entrepreneurial Performance relationship (β = 0.105, p = 0.046), while no significant moderation was found for Digital Capability. The research has theoretical value, as it provides a comprehensive framework linking digital capabilities, sustainability practices, innovation orientation, and financial inclusion to entrepreneurial performance. In practical terms, it offers entrepreneurs, managers, and policymakers considerations for promoting innovation, embracing sustainable practices, and enhancing financial accessibility, thereby fostering entrepreneurial growth and competitiveness in developing economies such as Bangladesh. JEL Classification Codes: L26, Q56, M14.
Digital Capability, Sustainable Practices, Innovation Orientation, Financial Inclusion, Entrepreneurial Performance, Bangladesh
Copyright (c) 2026 Ireen Sultana (Author) http://creativecommons.org/licenses/by/4.0
MENTAL HEALTH AND SOCIAL NETWORKING ADDICTION AMONG HIGHER SECONDARY SCHOOL STUDENTS IN NAGALAND: A DESCRIPTIVE STUDY
The greater use of social networking sites by students leads to unhealthy addiction that may have an impact on their mental health. The study examines the relationship between the mental health of higher secondary school students and their addiction to social networking. In the study, a descriptive survey method was used, and 500 students (198 boys and 302 girls) from private and government higher secondary schools in Nagaland participated. Two research instruments, the Mental Health Scale (Jagdish & Srivastava, 2005) and the Social Networking Addiction Scale (Shahnawaz & Rehman, 2020), were adopted and revalidated in the context of Nagaland. The t-test, correlation, and percentage were employed to analyze the data. The study found that 55.8% students have below average to average levels of mental health, and 95% students have an average level of social networking addiction. No significant difference was found in the scores for social networking addiction between boys and girls (t=1.766, p > .001), government and private secondary school students (t=1.128, p > .001). There is a substantial difference in the mental health scores of boys and girls (t=6.667, p < .001), while no significant differences were found between government and private secondary school students (t=1.029, p > .001). The study also found a significant correlation between social networking addiction and the mental health of secondary school students. The findings of the study suggest that increased social networking addiction affects the mental health of students, highlighting the need for awareness, guidance, proper counselling, and effective and restricted use of social media among secondary school students. JEL Classification Codes: I300, I120, I12, I120.
Well-being, Addiction, Health Behavior, Adolescence
Copyright (c) 2026 Murathoti Rajendra Nath Babu , Ankita Lahon , Radhakrishnan Vasanthan , Raj Balamurugan , Ravi Vanguri (Author) http://creativecommons.org/licenses/by/4.0
FINANCIAL PERFORMANCE THROUGH MANAGEMENT CONTROL SYSTEMS IN RESILIENT INFRASTRUCTURE: THE MODERATING ROLE OF ACTIVE USE AND PROJECT COMPLEXITY
In project-driven businesses, financial results depend more on an organization's ability to manage activities and information and adapt to operational nuances. Even though management control systems are commonly used to achieve these goals, their impact on financial performance remains a topic of empirical debate, especially when viewed as formal systems rather than actively used managerial tools. This study examines the effects of management control systems on financial performance and whether such effects are increased when management control systems are used proactively and when projects are complex. The study uses a quantitative, hypothetical, deductive method based on a survey conducted with 170 major construction companies operating in Morocco. Data analysis was conducted using descriptive statistics, reliability analysis, exploratory factor analysis, correlation analysis, and hierarchical multiple regression models. The results suggest a positive, statistically significant relationship between management control systems and financial performance (β = 0.41, p < 0.001). The model's explanatory power improves by adding the variable "active use" (R² = 0.21-0.29); the interaction between "management control systems" and "active use" is also significant (β = 0.19, p < 0.01). This connection is further strengthened by the project's complexity, which also demonstrates a significant interaction effect (β = 0.21, p < 0.01). The final model accounts for 39% of the variability in financial performance. Further results point to the role of management control systems in fostering sustainable growth, economic productivity, and resilient infrastructure development from the perspectives of managerial decision-making and financial performance in a complex project-based environment. JEL Classification Codes: M10, M41, L74.
Management Control Systems, Financial Performance, Active Use, Project Complexity, Construction Sector, Sustainable Growth
Copyright (c) 2026 El Malki Said , Younes Azekkar, Fatima-Ezzahra Belfatmi , Jiddi Oumaima , Alaoui Mamoun (Author) http://creativecommons.org/licenses/by/4.0
THE IMPACT OF INFLATION ON ECONOMIC GROWTH: A REVIEW OF EMPIRICAL STUDIES
Inflation remains one of the most disputed macroeconomic variables in growth research because modest price increases can coexist with expansion, while persistent or volatile inflation may distort saving, investment, and productivity decisions. This study examines how the empirical literature explains the inflation–growth nexus and whether a consistent adverse threshold can be identified across countries and methods. The review employs a systematic secondary research design based on 34 empirical studies published between 1993 and 2025, including 15 studies from 2020–2025. Studies were screened from major journal platforms and policy repositories, coded by sample coverage, empirical design, and finding pattern, and synthesized through descriptive statistics and structured comparative analysis. The results show that 17 studies report a nonlinear threshold relationship, 8 identify a directly negative association, 6 report mixed or context-dependent effects, and 3 emphasize inflation uncertainty as a separate transmission channel. Overall, 28 of 34 studies (82.4%) support the view that inflation is growth-reducing either directly, beyond a threshold, or through heightened uncertainty. Among the 16 studies that report explicit threshold values, the estimated inflation threshold ranges from 4.0% to 16.46%, with a median of 8.0% and an interquartile range of 5.3%–10.9%. The major finding is that the literature converges on a conditional result rather than a universal linear rule: low and stable inflation may be compatible with growth, but growth performance deteriorates once inflation exceeds economy-specific tolerance bands or becomes more volatile, and the most credible threshold estimates cluster around single-digit to low double-digit inflation. JEL Classification Codes: E31, O40, C23, C52, E52.
Price Stability, Threshold Effects, Inflation Uncertainty, Inflation Targeting, Panel Data, Macroeconomic Stability, Developing Economies, Growth Regressions.
Copyright (c) 2026 K. M. Anwarul Islam , Nurjahan Akter Monira , Nurul Mohammad Zayed , Ranjit Singh , Werner Krings (Author) http://creativecommons.org/licenses/by/4.0
STOCK MARKET DEVELOPMENT AND ECONOMIC GROWTH: A META-ANALYSIS
Stock markets have become central to debates on long-run development because they can mobilize savings, improve liquidity, diversify risk, and discipline corporate investment, yet empirical findings on their macroeconomic contribution remain fragmented across countries, indicators, and estimation techniques. This study investigates whether stock market development systematically promotes economic growth and whether that relationship depends on the indicator used and on institutional conditions. The paper employs a secondary research design and meta-analytic direction-of-effect synthesis based on 30 peer-reviewed empirical studies published between 1998 and 2025. Each study is coded by region, period, methodology, stock market proxy, growth measure, institutional conditioning, and overall result direction, and the evidence is summarized with supportive shares, a sign test, and a weighted evidence index. The results show that 17 studies report a clearly positive relationship, 6 report a conditional positive relationship, 6 find mixed or insignificant results, and only 1 reports a net negative result. This yields a supportive share of 76.7% across all included studies and 95.8% among the 24 studies with an identifiable net direction, with the directional sign test rejecting symmetry (p < 0.001). The weighted evidence index equals 0.625, indicating moderately strong overall support. Indicator-level coding shows that liquidity-based proxies are more consistently favorable than size-based proxies, while recent threshold and interaction studies reveal that institutional quality shapes the nexus. Overall, the meta-analysis finds that stock market development is usually growth-enhancing, but the effect is heterogeneous rather than automatic, becoming strongest when markets are liquid, regulated, and embedded in supportive institutional environments.
Capital Markets, Liquidity, Institutional Quality, Emerging Economies, Financial Intermediation, Market Capitalization, Turnover Ratio
Copyright (c) 2026 K. M. Anwarul Islam , Nurjahan Akter Monira , Guneratne Wickremasinghe , Ranjit Singh , Werner Krings (Author) http://creativecommons.org/licenses/by/4.0
QUANTIFYING THE VULNERABILITY OF CREDIT CARD FRAUD DETECTION SYSTEMS TO GRADIENT BASED ADVERSARIAL EVASION
In financial cybersecurity, fraud detection is at the forefront of the battle against cybercriminals and deep learning models are the state-of-the-art method for real-time detection; yet their reliance on fixed feature distributions renders them inherently susceptible to adversarial attacks. With the rise of generative artificial intelligence (AI) in obfuscating fraudulent transactions, conventional performance indicators (accuracy and F1-score) do not reflect the cybersecurity resilience of these models. This research explores the Credit Card Fraud Detection dataset, comprising more than 550,000 anonymized records. The study uses a functional deep learning model as a benchmark and introduces the Fast Gradient Method (FGM) to simulate white box attacks on the latent features . The experiment results demonstrate a high detection accuracy of 99.85% under normal operating conditions, which dramatically reduces to 92.75% when card transactions are slightly modified by adversarial disturbances at an epsilon value of 0.1. Numerical evidence reveals a clear 7.10% "Security Gap," depicting a substantial degree of vulnerability, where transactions are mathematically reclassified from fraudulent to legitimate. Notably, our research suggests a non-linear "safety cliff" in the decrease of detection accuracy, where the model's reliability completely fails as the adversarial strength reaches 0.15 or higher. In addition, Principal Component Analysis demonstrates that gradient attacks have successfully distorted the latent features of fraudulent transactions, resulting in the movement of highly suspicious transactions towards the concentrated area of legitimate transactions, hence evading automated security screening while preserving the overall statistical distribution of the data. JEL Classification Codes: G21, K42, O33, C45.
AI Security, Adversarial ML, Financial Cyber Resilience, Stealth Fraud Patterns, Feature Perturbation Analysis, Fast Gradient Method (FGM), Electronic Payment Security
Copyright (c) 2026 Nabil El Kadhi , Marwan Alshar’e , Rifat O. Shannak , Nour Eldin Elshaiekh Osman , Basel Bani-Ismail (Author) http://creativecommons.org/licenses/by/4.0
A BUSINESS INTELLIGENCE SIMULATOR FOR SALES OPTIMIZATION USING A MACHINE LEARNING DDS FRAMEWORK
Existing business intelligence (BI) systems are predominantly descriptive and offer limited support for actionable decision-making in sales optimization, leading to a disconnect between data analytics and operational management. This study examines the development of a unified BI simulator that integrates machine learning, Pareto-based diagnostics, and what-if simulation to support data-driven sales optimization. The research employs an empirical pizza sales dataset from Kaggle, consisting of transactional, temporal, and operational variables. It applies multiple machine learning algorithms for comparative evaluation, along with Pareto analysis to identify key product contributors and a simulation engine to assess the sensitivity of low-performing products to operational changes. The results reveal that the Random Forest model outperforms other models, achieving 97.5% accuracy, an F1-score of 0.941, and an AUC of 0.996. Pareto analysis shows that approximately 30% of product categories account for nearly 80% of total sales, while a small proportion of products consistently exhibit low demand. Additionally, simulation analysis indicates that variations in operational factors, such as delivery efficiency, delivery distance, and product complexity, result in sales variability of up to 12.8%. The findings of this study suggest that the integration of predictive, diagnostic, and simulation-based analytics within a unified BI framework enables precise identification of key sales drivers and quantitatively measures the responsiveness of low-performing products to operational changes, thereby offering a comprehensive and data-driven evaluation of sales performance. JEL Classification Codes: C81, C80, C890.
Smart Business Intelligence, Sales Optimisation, and Explainable Machine Learning, Decision Support Systems, Random Forest, Retail Analytics, Predictive Analytics, Diagnostic Analytics
Copyright (c) 2026 Nabil El Kadhi , Maruf Fatima Sadriwala , Kaneez Fatima Sadriwala , Rifat O. Shannak , Marwan Alshar’e (Author) http://creativecommons.org/licenses/by/4.0
KNOWLEDGE STRUCTURE OF LINCOMYCIN STUDIES: A BIBLIOMETRIC STUDY OF SHIFTING RESEARCH PARADIGMS OVER SIX DECADES
Lincomycin is an important clinical and veterinary antibiotic targeting Gram-positive organisms with high tissue affinity. It is used to treat tissue infections in both humans and veterinary applications. Despite its long history of use and wide applications, no bibliometric study has assessed the full potential of lincomycin. Therefore, we aim to detail and highlight the emerging trends, areas, and gaps in lincomycin research. The Scopus database was used to retrieve relevant lincomycin records over the past six decades. We used both the VOSviewer software and the Bibliometrix R package to identify key indicators, such as institutional affiliations, keyword co-occurrence, country productivity, annual scientific production, and research themes and trending topics. The bibliometric analysis included 963 lincomycin-related articles across 452 journals, authored by 2,777 researchers, with an average of 16.05 citations per article. Scientific output showed an early surge in the late 1960s, followed by a long period of moderate activity and a renewed rise after 2012, peaking in 2023. The highly cited papers in lincomycin research reflect the multidisciplinary impact of this antibiotic, spanning plant photoinhibition, ribosomal binding, biosynthesis, veterinary therapy, environmental degradation, and analytical detection. International collaboration in lincomycin research was relatively limited, with only 6.75% of publications involving international co-authorship. Thematic analyses revealed a shift from earlier pharmacology topics to resistance-oriented topics and advanced analytics (LC–MS/MS), with aptasensor and electrochemiluminescence methods emerging as recent focal areas. Lincomycin research has gained focus since 2012, driven by increasing antimicrobial resistance and a shift towards biosynthetic optimization and advanced detection technologies. JEL Classification Codes: C00, C88, I19, Q01.
Lincomycin, Bibliometric Analysis, Lincosamides, Research Trends, Emerging Themes
Copyright (c) 2026 Mohammed Alquhaidan, Nawal Elkhair , Yukio Kitade , Mahmoud Kandeel (Author) http://creativecommons.org/licenses/by/4.0
RISK MANAGEMENT AND RESISTANCE TO CHANGE IN HIGHER EDUCATION INSTITUTIONS OF OMAN
Digitalisation, smart service systems, and changing managerial practices are transforming the organisational structures of institutions of higher learning. Nonetheless, these changes often provoke employee resistance, which may slow the institutional Change. The knowledge of the determinants of organizational Change and the causes of resistance to change has thus emerged as a critical concern for higher education institutions, especially for those still developing, such as those in Oman. This paper reviews the connections among Leadership, intelligent quality services, organizational practices, organizational change, and resistance to change in higher education institutions in Oman. The research design used in this study is quantitative, relying on survey data from 218 valid responses from academic and administrative employees of higher education institutions in Oman. Partial Least Squares Structural Equation Modelling (PLS-SEM) was employed to analyze the proposed research framework and to test the direct relationships among the constructs. The outcome illustrates significant effects on the Change of organizations by the Leadership (β = 0.146, t = 3.644, p < 0.001), smart quality services ( β= 0.279, t = 3.937, p < 0.001), and organizational practices ( β= 0.429, t = 6.010, p < 0.001) and attribute 56.1% of the Change to the three variables (R2 = 0.561 Smart quality services (β= 0.300, t = 3.907, p < 0.001) and organizational Change (β= 0.367, t = 6.092, p < 0.001) make significant contributions to the resistance to change (R2 = 0.525). JEL Classification Codes: I21, I28, O17.
Risk Management, Higher Education, Organisational practices, Smart Services, Organisational Change, Leadership
Copyright (c) 2026 Imran Saleem , Ambreen Saleem , Ermal Bino , Umar Safdar (Author) http://creativecommons.org/licenses/by/4.0
EDUCATIONAL AI FOR ATTENTION-ENHANCED FACIAL EMOTION RECOGNITION FOR EMOTION-AWARE LEARNING SYSTEMS USING FACE-CROPPED DEEP NETWORKS
Emotion-aware learning systems depend on reliable recognition of students' affective states. However, facial emotion recognition in child-centred settings remains difficult due to background clutter, class imbalance, limited annotated data, and subtle variations in facial expressions. This study investigates whether a face-centric, attention-enhanced deep learning framework can improve recognition performance and convergence efficiency for educational artificial intelligence applications. The study employs the Multimodal Child Emotion for Learning dataset and implements a pipeline that uses Multi-Task Cascaded Convolutional Networks to detect and crop faces, an EfficientNet-B0 backbone to learn facial features, an Efficient Channel Attention module to strengthen discriminative channel representations, and a staged training procedure involving classifier-head training followed by full-network fine-tuning; experiments are conducted in PyTorch with ImageNet-pretrained weights, Adam optimization, cross-entropy loss, and augmentation through horizontal flipping and colour jittering. The results show that face detection successfully localised 826 of 833 images, baseline validation accuracy improved from approximately 14% to 68% after integrating MTCNN-based face cropping and ECA, and the final proposed configuration reached 77.78% validation accuracy after excluding the severely underrepresented fear class. Staged training achieved the 0.60 validation-accuracy threshold in 6 epochs rather than 10 and reduced total training time from 22.79 to 20.02 minutes, equivalent to a 12.15% reduction. Ablation analysis showed that validation accuracy declined by 9.8% without face cropping, 6.2% without channel attention, and 4.1% without staged training. The findings quantitatively demonstrate that the combined framework improves recognition reliability, accelerates convergence, and achieves the strongest performance across all tested configurations in the evaluated educational dataset. JEL Classification Codes: A2, O31, O32, H52.
Recognition of facial emotions, emotion-conscious learning, Channel attention, face cropping, deep learning, and educational AI
Copyright (c) 2026 Boumedyen Shannaq , Marwan Alshar'e , Azizi Abdullah , Haitham Alsharu , Oualid Ali (Author) http://creativecommons.org/licenses/by/4.0
PROSPECTS FOR INCREASING RELIABILITY IN VERBAL INVESTIGATIVE ACTIONS USING ARTIFICIAL INTELLIGENCE TOOLS
Kazakhstan has prioritised digitalising its criminal justice processes, implementing AI-driven tools such as automated forensic systems and "digital autopsy" platforms to increase efficiency and transparency. However, challenges persist: interrogation recordings can still be falsified or incomplete due to inadequate technical equipment and procedural gaps, undermining evidence integrity. This study investigates a combined AI-blockchain framework to enhance the reliability of verbal investigative actions. It examines an integrated system in which automatic speech recognition (ASR) transcribes interrogation audio, and a private blockchain ledger immutably timestamps each transcript. The study employs a mixed-methods approach: data include recorded criminal interrogation sessions (audio transcripts) and relevant legal documents. A neural ASR model was trained on these transcripts (approximately 50 sessions) to produce digital text, and standard analytical methods were applied to evaluate system performance. Simultaneously, each transcript was recorded on a blockchain registry, and metrics such as transcription accuracy and error rate were measured. Results show the proposed system attains high transcription accuracy (94% word recognition) and significantly reduces manual review time (by about 40%). In experiments, word error rates dropped from roughly 10% (baseline) to 5% with the ASR model, and the blockchain ledger recorded 100% of transcripts with negligible overhead. The findings suggest that integrating AI-driven transcription with blockchain anchoring markedly enhances the fidelity of investigative recordings. Quantitatively, the AI-blockchain platform halved transcription errors and enabled reliable reconstruction of interrogation timelines. These results indicate the combined approach substantially improves the reliability and integrity of verbal evidence, reinforcing the objective completeness of recorded testimony. JEL Classification Codes: K10, K24, K40, K14.
Artificial Intelligence, Blockchain, Evidence Reliability, Verbal Investigative Actions, Digitalization of Justice
Copyright (c) 2026 Yergali Mabiyev , Yevgeniy Shulgin , Larissa Kussainova , Ainura Omarova , Serguei Cheloukhine (Author) http://creativecommons.org/licenses/by/4.0
DIGITALIZATION AND ENTREPRENEURSHIP AWARENESS AMONG THE HIGHER EDUCATION STUDENTS IN SAUDI ARABIA
Digitalization and the expansion of information and communication technology (ICT) are reshaping marketing and commerce, but the extent to which Saudi higher-education students understand digital entrepreneurship, digital marketing, and e-commerce remains underdocumented. This study examines digitalization and entrepreneurship awareness among students in Saudi Arabia. A mixed-method design was used, combining an online questionnaire survey of 122 undergraduate and postgraduate students from disciplines with focus-group discussions (focus-group size, procedures, and timing unspecified); the survey sample was 53.3% male and 46.7% female, and ages were <20 (47.5%), 20–22 (33.6%), 23–25 (6.6%), and >25 (12.3%). Quantitative analyses included summaries, independent-samples t tests, one-way ANOVA, correlations, and hierarchical regression (study period unspecified). The results show that 64% of students reported moderate awareness of digital marketing tools and e-commerce platforms, 23% high awareness, and 13% low awareness. General awareness scores were similar by gender (male M=16.68, SD=7.20; female M=15.61, SD=6.61; t(120)=0.845, p=0.400) and age (F(3,118)=1.311, p=0.274) but differed by field of study (F(4,117)=3.340, p=0.013), with higher means in management (M=18.86, SD=6.57) and science (M=17.81, SD=7.08) than in arts (M=13.20, SD=8.50). Field differences were also observed for channel awareness (F(4,117)=2.452, p=0.050) and operational awareness (F(4,117)=2.620, p=0.038). Awareness dimensions were strongly interrelated (r=0.825–0.851, all p<0.01). In regression, demographic/academic variables explained R²=0.097 (F=1.750, p=0.104), with a management effect in Model 1 (B=5.088, p=0.012) that became nonsignificant in Model 2 (p=0.462), while adding general awareness (β=0.453, p<0.001) and channel awareness (β=0.471, p<0.001) increased explained variance to R²=0.796 (F=48.441, p<0.001); confidence intervals and standardized effect sizes beyond β were unspecified. JEL Classification Codes: L26, M13, M15, M31, O33, L81.
Digitalization, Entrepreneurship, Digital Entrepreneurship, E-commerce, Students, Digital Marketing, Saudi Arabia
Copyright (c) 2026 Madina Rakhimberdinova , Yousef Falah E Albalawi (Author) http://creativecommons.org/licenses/by/4.0
REGIONAL ECONOMIC RESILIENCE: THE IMPACT OF DIGITALIZATION ON LABOR PRODUCTIVITY DYNAMICS
Digital transformation raises uncertainty about whether digitalization in healthcare yields measurable labour productivity gains, especially when healthcare financing is volatile and regional evidence is sparse. Evidence on how digitalization of healthcare and financial sustainability jointly shape regional productivity in Kazakhstan remains limited. This study examines the associations among healthcare digitalization, healthcare system financial sustainability, and labour productivity dynamics in Kazakhstan, with a focus on the Karaganda region. This study employs regional time-series and cross-regional data for 2018–2024 from the Bureau of National Statistics of the Republic of Kazakhstan and applies Ward hierarchical clustering, factor analysis, and multiple linear regression of labour productivity growth on ICT spending growth and fixed-asset investment growth (2019–2024, n=6). ICT costs increased from 10,164 to 19,840 million tenge, and GVA per employee increased from 6,238.4 to 15,681.2 thousand tenge (2018–2024). A two-factor solution explained 77.41% of variance; Factor 1 ("digital production factor") explained 56.37% and, in a factor regression on healthcare costs, had a coefficient of 26,933,232.93 (p=0.004542), while Factor 2 had a coefficient of 2,339,550.60 (p=0.775537). In the productivity-growth regression, ICT spending growth rate was positive (β=0.0926, SE=0.0365, t=2.5408, p=0.0846), fixed-asset investment growth was negative and not significant (β=−0.0611, SE=0.0299, t=−2.0407, p=0.1340), with R²=0.738 and Prob(F)=0.134. Inline table: ICT β=0.0926 (p=0.0846) | Investment β=−0.0611 (p=0.1340) | R²=0.738 | n=6 | Prob(F)=0.134. The findings of this study suggest that higher ICT spending growth is associated with higher labour productivity growth at the 10% level. In contrast, fixed-asset investment growth and the second factor's effect are not statistically distinguishable from zero in this sample. JEL Classification Codes: J20, J21, M15, M54, I15.
Economic Sustainability, Digitalization, Healthcare, Labour Productivity, Information And Communications Technology
Copyright (c) 2026 Lyussiya Togaibayeva , Ainura Omarova , Madina Rakhimberdinova , Zhanar Zeinullina (Author) http://creativecommons.org/licenses/by/4.0
MECHANISMS OF CREATION AND FUNCTIONING OF CONTEXTUAL SYNONYMS IN NEWSPAPER TEXTS
Newspaper discourse is characterized by high lexical variability aimed at ensuring semantic precision, stylistic diversity, and expressive evaluation of events. However, the mechanisms through which contextual synonyms are formed and function across different topical domains remain insufficiently systematized, particularly in quantitative and discourse-oriented terms. The study investigates the dominant mechanisms of creating contextual synonyms in English-language newspaper texts and examines their functional differentiation in medical, sports, and political discourse. The empirical basis of the research consists of 304 newspaper articles, including 197 medical, 66 sports, and 41 political texts, selected from British mass-market and quality newspapers published between 2021 and 2024. The study employs contextual analysis to establish synonymy within specific micro-contexts, lexico-stylistic analysis to classify the principal lexical and stylistic mechanisms involved in the creation of contextual synonymy, and statistical analysis to quantify their distribution, using semantic, functional, and referential criteria for identification. The results reveal clear domain-specific patterns in the distribution of mechanisms of contextual synonymy. In medical media texts, nominalization is the most frequent mechanism (18%), followed by metaphorisation (15%) and metaphtonymisation (7%), while pronominalisation (10%) and numeralisation (8%) serve to generalize and structure statistical information. In sports discourse, nominalization is likewise dominant (20.8%), accompanied by metaphorisation (8.3%) and periphrastic expressions (8.3%), reflecting the need for precise yet expressive event representation. Political texts demonstrate a predominance of periphrastic nominations for political figures (18%), with negative or neutral political action nominations (15%), geographical terms (15%), and military-situation nominations (13%) forming a substantial share of contextual synonym usage. The findings indicate that contextual synonymy in newspaper discourse is organized through stable semantic–functional–referential overlap and realized through quantitatively differentiated lexical-stylistic mechanisms, with nominalization prevailing in medical and sports discourse and periphrastic nomination predominating in political discourse. JEL Classification Codes: D83, L82, Z13, Z11.
Contextual Synonymy, Media Texts, Nominalisation, Metaphorisation, Periphrastic Nominations, Lexical Flexibility, Functional Commonality, Political Discourse
Copyright (c) 2026 Iryna Kazymir , Nadia Yesypenko , Tetiana Mitrousova , Yuliya Davydyuk , Olga Beshlei (Author) http://creativecommons.org/licenses/by/4.0
DISCOVERING LEARNER PERSONAS IN AI-ASSISTED ENGLISH LANGUAGE LEARNING USING COSINE-BASED CLUSTERING: IMPLICATIONS FOR PERSONALIZED SUPPORT IN GCC CONTEXTS
The rapid expansion of artificial intelligence (AI)-assisted English language learning tools has introduced substantial variability in learner outcomes due to differences in behavioural patterns, task engagement, and usage strategies among non-native learners, particularly within Omani educational contexts. This heterogeneity creates a methodological challenge in identifying consistent learner profiles without relying on predefined or subjective labels. This study investigates the effectiveness of unsupervised cosine-based clustering in identifying distinct learner personas in AI-assisted English learning environments. The study utilizes a dataset of 15,000 learner interaction records obtained from Kaggle, incorporating demographic attributes, behavioural features, task modalities, and learning outcome indicators. A structured experimental methodology is employed, beginning with baseline Euclidean K-Means clustering, followed by dimensionality reduction using Singular Value Decomposition (SVD), and subsequent clustering using cosine similarity across multiple algorithms, including K-Means, Gaussian Mixture Models, Agglomerative Clustering, and BIRCH. The results reveal that cosine-based K-Means clustering (k = 6) achieves a Silhouette Score of 0.678 compared to 0.10 for baseline Euclidean clustering, representing an absolute improvement of 0.578 and approximately a sixfold increase in clustering performance. Compared to SVD-based Euclidean clustering (Silhouette = 0.41), cosine similarity improves clustering effectiveness by approximately 65%, while the Davies–Bouldin Index decreases to 0.56 and the Calinski–Harabasz Index increases to 33,074. The findings indicate that cosine-based unsupervised modelling effectively identifies distinct learner personas, demonstrating that learning-gain variations are driven by behavioural interaction patterns rather than usage intensity alone. JEL Classification Codes: G32, F65, L66, L25, M41.
AI-Assisted Language Learning; English as a Second Language (ESL); Learner Profiling; Unsupervised Learning; Clustering Analysis; Cosine Similarity; Educational Data Mining; Oman Education
Copyright (c) 2026 Marwan Alshar‘e , Shaher Elayyan , Abdallah Abualkishik , Khaled Abuhmaidan , Wasin Al Kishri (Author) http://creativecommons.org/licenses/by/4.0
EARLY FAULT SEVERITY PREDICTION IN DIGITAL-TWIN–ENABLED INDUSTRIAL SYSTEMS USING IMBALANCE-AWARE MACHINE LEARNING AND TEMPORAL FEATURE ENGINEERING
Continuous industrial telemetry in high dimensions generated by digital twin (DT) platforms offers opportunities for large-scale fault and predictive maintenance (PdM). However, the real-life applications of PdM remain very challenging. In this paper, we investigate the potential of early fault severity prediction using a five-year, hourly dataset of DT-derived data from January 2019 to January 2024. The data includes 38 variables, including measurements such as vibration, temperature, pressure, acoustic signals, operational load, maintenance and repair history, anomaly scores, fault probability estimates, and a multiclass fault diagnosis label of No Fault or Critical Fault. The research presented in this paper follows an end-to-end analytical approach, using a set of machine-learning classifiers, then optimizing them using class-weighted classifiers, probability calibration, and a cost-sensitive decision policy, all aligned with the strategic goals of PdM. The results of the experiments conducted in this research show that the baseline and temporally enhanced models achieve ≈0.69 accuracy. When class-weighted learning is introduced, it increases fault recall to 0.059 and macro-F1 to 0.451, while threshold optimization achieves perfect fault recall (1.00) with a corresponding fault F1-score of 0.468. The main findings of this work show that the joint effects of temporal modelling, imbalance-aware learning, and calibrated decision thresholds govern early severity prediction performance. Our research is expected to yield a reproducible, deployment-oriented framework that improves minority-class detection and enables effective maintenance decisions for DT-enabling Industry 4.0/5.0 applications. JEL Classification Codes: O14, O32, L42.
Digital Twin, Predictive Maintenance, Fault Severity Classification, Imbalanced Learning, Temporal Feature Engineering
Copyright (c) 2026 Oualid Ali (Author) http://creativecommons.org/licenses/by/4.0
HARNESSING ARTIFICIAL INTELLIGENCE FOR REPURCHASE INTENTION: THE MEDIATING ROLE OF SOCIAL PLATFORMS AND CONSUMER EXPERIENCE
Rapid advancements in artificial intelligence (AI) technologies have substantially transformed online service processes in the digital travel and flight booking industry, where customer interaction, conversion efficiency, and experience are critical drivers of repurchase intention. This study examines the direct and indirect effects of AI technology on repurchase intention by incorporating customer interaction in service processes (CISP), conversion rate optimization (CRO), and customer experience satisfaction (SCE) as mediating variables. Adopting descriptive and causal-comparative research designs, data were collected from 480 respondents in the Kathmandu Valley who had booked flights online within the preceding six months, using a convenience sampling technique. Partial Least Squares Structural Equation Modelling (PLS-SEM) was employed for data analysis. The results indicate that AI technology has a significant positive effect on customer interaction (β = 0.349, t = 2.784, p < 0.001) and conversion rate optimization (β = 0.516, t = 1.995, p < 0.001). Customer interaction significantly influences customer experience (β = 0.567, t = 3.706, p = 0.004), while conversion rate optimization also has a strong positive effect on customer experience (β = 0.458, t = 5.856, p < 0.001). Satisfying customer experience significantly predicts repurchase intention (β = 0.419, t = 1.991, p < 0.001). However, the direct effect of AI technology on repurchase intention is not statistically significant (β = −0.063, t = 0.801, p = 0.451). Mediation analysis confirms full mediation, as AI technology indirectly influences repurchase intention through CRO and SCE (β = 0.402, t = 2.743, p = 0.048) and through CISP and SCE (β = 0.302, t = 3.006, p = 0.005). Overall, the model explains 47.3% of the variance in repurchase intention, demonstrating that AI-driven service features enhance repeat purchase behaviour primarily through improved interactions, optimized conversion processes, and satisfying customer experiences, rather than through a direct effect. JEL Classification Codes: M31, M15, O33.
Artificial Intelligence, Customer, Experience, Repurchase, Social
Copyright (c) 2026 Sajeeb Kumar Shrestha , Krishna Prasad Pandey , Sanket Shrestha, Sabita Karki , Dipak Mahat (Author) http://creativecommons.org/licenses/by/4.0
FROM RESOURCES TO RESULTS: THE ROLE OF SUSTAINABILITY PRACTICES IN TRANSLATING RESOURCE CAPACITY AND ETHICAL LEADERSHIP INTO ENTREPRENEURIAL PERFORMANCE
In sustainability-oriented markets, entrepreneurs must transform internal resources and ethics-based leadership into performance, yet the mediating role of sustainability practices is left quantitatively unclear. This study investigates, under the resource-based view and ethical leadership framework, whether sustainability practices mediate the effects of resource capacity and ethical leadership on entrepreneurial performance. Cross-sectional survey data were collected from 400 entrepreneurs and small business owners in Bangladesh (industry and collection period unspecified) using a Likert-scale questionnaire; analysis used structural equation modeling in SmartPLS 4 and SPSS 25, with reliability and validity testing, bootstrapped path estimation, and mediation analysis; confidence intervals were unspecified. Measurement quality met thresholds (indicator loadings 0.864-0.940; Cronbach's alpha 0.908-0.939; composite reliability 0.935-0.956; AVE 0.783-0.845; SRMR 0.041; NFI 0.918; chi-square 432.343). Discriminant validity was acceptable (HTMT 0.045-0.312), and collinearity was within thresholds (VIF 2.367-4.729). The model explained 12.6% of the variance in entrepreneurial performance (R2=0.126; adjusted R2=0.119) and 3.1% of the variance in sustainability practices (R2=0.031; adjusted R2=0.026). Resource capacity had a positive direct association with entrepreneurial performance (beta=0.176, t=3.532, p<0.001, f2=0.034) and with sustainability practices (beta=0.157, t=3.138, p=0.002, f2=0.025). Ethical leadership was positively related to performance (beta=0.106, t=2.179, p=0.029, f2=0.012) but not to sustainability practices (beta=0.078, t=1.391, p=0.164, f2=0.006). Sustainability practices showed the largest direct association with performance (β = 0.256, t = 5.395, p < 0.001, f2 = 0.072). The indirect effect of resource capacity on sustainability practices was significant (β = 0.040, t = 2.637, p = 0.008). In contrast, the analogous indirect effect from ethical leadership was not significant (β = 0.020, t = 1.299, p = 0.194), yielding a quantitative pattern in which mediation was limited to the resource-capacity pathway overall. JEL Classification Codes: L26, Q56, M14.
Resource Capacity, Ethical Leadership, Sustainability Practices, Entrepreneurial Performance, Resource-Based View, Sustainable Entrepreneurship
CONTRIBUTING FACTORS FOR IMPROVING ENGLISH READING COMPREHENSION AMONG CHINESE STUDENTS CATION
Reading comprehension remains a central challenge for Chinese students learning English as a Foreign Language, particularly when limited analytical ability, weak self-management, and ineffective learning strategies restrict them. These issues highlight the need to examine the cognitive and behavioral factors that shape reading comprehension. This study investigates the role of critical reading skills in predicting reading comprehension. It explores how teachers' perceived role, student self-management, and student learning strategies contribute to this relationship. Survey data were collected from 395 Chinese EFL students enrolled in colleges in Shanghai and Beijing using validated instruments. Data analysis was conducted using SPSS 29 and Smart PLS 4 to assess measurement reliability, structural relationships, and mediating effects within a PLS-SEM framework. The results show that critical reading skills significantly influence student self-management (β = 0.319, t = 6.624), student learning strategies (β = 0.322, t = 7.101), and reading comprehension (β = 0.218, t = 4.721), but do not significantly affect the perceived role of teachers (β = 0.097, t = 1.723). Reading comprehension is significantly influenced by the perceived role of teachers (β = 0.165, t = 3.588), student self-management (β = 0.183, t = 3.760), and student learning strategies (β = 0.249, t = 5.007). Mediation analysis indicates significant indirect effects of critical reading skills on reading comprehension through student self-management (β = 0.058, t = 3.434) and student learning strategies (β = 0.080, t = 4.323). In contrast, the mediating effect of teachers' perceived role is not significant. JEL Classification Codes: C12, C20, C31.
Critical Reading Skills, Role of Teacher, Reading Comprehension
Copyright (c) 2025 Guangyun Zhang , Rafizah Binti Mohd Rawian (Author) http://creativecommons.org/licenses/by/4.0
FORMATION OF DIGITAL COMPETENCES OF STUDENTS IN THE SYSTEM OF HIGHER PEDAGOGICAL EDUCATION
Given the ongoing digitalization in modern education worldwide, it is important to determine the features of the development of digital competence among future teachers. The research problem lies in the insufficient understanding of how innovative digital interventions affect the development of these competences in teacher education. The purpose of this study is to find out the impact of innovative (digital) intervention on the development of digital competence of future teachers. A mixed-method design was used, combining qualitative and quantitative approaches. A total of 186 students of pedagogical specializations participated in the study. Participants were divided into two groups: experimental (n = 93), who underwent an intervention program, and control (n = 93), who studied according to the traditional program. The primary data were collected using a digital competence questionnaire (28 items), performance tasks ("Micro-lesson" and "Mini-module in LMS"), LMS analytics, and semi-structured interviews. Moreover, descriptive statistics, paired t-tests, ANCOVA controlling for pre-test indicators, and effect size calculations were used for data analysis. The results confirmed that the experimental group recorded a significant increase in digital competences in all domains (Δ = +2.1–+3.6; p < 0.001; d = 0.63–0.88). In addition, the intervention was more effective among students with a lower initial level of competencies (F(1,182) = 8.47; p < 0.01), as well as among students in pedagogical and humanitarian specialties. The findings suggest that targeted digital interventions can substantially improve the development of digital competence in future teachers. JEL Classification Codes: I21, I23, I25, O33, M15.
Digital Competences, Higher Pedagogical Education, Digital Literacy, Professional Training, Innovative Teaching
Copyright (c) 2025 Zabiyeva Kamshat Kazbekovna , Zhiyembayev Zhomart Tumarbek, Seitova Sabyrkul , Kalzhanova Gulmira Kenesovna , Yesseikyzy Aiym (Author) http://creativecommons.org/licenses/by/4.0
INFLUENCE OF PERSONAL ATTRIBUTES ON DIGITAL ENTREPRENEURIAL INTENTION: THE ADDED IMPACT OF ENTREPRENEURIAL PASSION AND CREATIVITY
Digital entrepreneurship is gaining global relevance as digital technologies transform the way ventures are conceived and launched. However, the individual-level traits influencing digital entrepreneurial intention (DEI) remain under-examined. This study extends prior frameworks by integrating two novel predictors, entrepreneurial passion and creativity, alongside five established traits: empathy, prior Experience, locus of control, self-efficacy, and risk-taking Tendency. Using a structured questionnaire, data were collected from 589 Bangladeshi university students, a population well-positioned as future digital entrepreneurs. Multiple regression analysis revealed that all seven personal attributes significantly and positively influence DEI (p < 0.05). The extended model accounted for 59.4% of the variance in DEI (Adjusted R² = 0.594, F(7, 581) = 122.4, p < 0.001). Quantitatively, risk-taking Tendency (β = 0.301, p < 0.001) and entrepreneurial passion (β = 0.244, p < 0.001) emerged as the strongest predictors. Creativity (β = 0.127, p = 0.002), locus of control (β = 0.119, p = 0.001), and self-efficacy (β = 0.102, p = 0.004) followed in magnitude. Empathy (β = 0.079, p = 0.024) and prior Experience (β = 0.058, p = 0.047) also showed statistically significant but comparatively smaller effects. These results highlight that both affective (passion, empathy) and cognitive (creativity, self-efficacy, locus of control) traits play a pivotal role in fostering digital entrepreneurship. The findings underscore the importance of cultivating emotional motivation and innovative capacity in digital entrepreneurship education. Overall, this study presents a robust empirical model that explains DEI, offering practical implications for entrepreneurship educators and policymakers in developing contexts. JEL Classification Codes: L26, M13, O33.
Digital Entrepreneurial Intention, Personal Attributes, Digital Entrepreneurship, Entrepreneurial Passion, Creativity, Innovation
Copyright (c) 2025 Nurjahan Akter Monira , Ireen Sultana , Werner J. Krings , Fariha Tazin , Sayma Sharmen (Author) http://creativecommons.org/licenses/by/4.0
CONSUMERS’ ATTITUDE TOWARD AI-DRIVEN E-COMMERCE ADOPTION IN BANGLADESH: AN EXTENSION OF PLANNED BEHAVIOR
In recent years, the online shopping sector in Bangladesh has witnessed a tremendous transition driven by technological advancements and changing consumer habits. Artificial intelligence (AI) technologies, including chatbots, AI-enhanced Personalization, and intelligent recommendations, have further developed this sector. However, research indicates that many consumers in Bangladesh still favor offline shopping. This situation highlights the necessity of identifying the factors affecting consumers' AI-driven online shopping behavior. Therefore, this study employs an extended Theory of Planned Behavior (TPB) framework to investigate the factors determining consumer preferences for AI-enabled e-commerce platforms in Bangladesh. Data was gathered using a stratified random sampling method from 384 online shoppers in Rajshahi City Corporation. Structural Equation Modeling (SEM) assessed the affinities between the key variables. The findings reveal that consumers' perceptions of promotional discounts and perceived behavioral control significantly influenced their attitudes toward AI-driven online shopping. Factors such as promotional discounts, perceived benefits, and AI-based Personalization notably influence consumers' purchase intentions. These results underscore the importance of competitive pricing strategies, value-added services, and personalized experiences in encouraging consumer adoption of AI-powered e-commerce platforms, providing valuable insights for improving the online shopping environment nationwide. This research offers actionable strategies for online retailers, suggesting that prioritizing AI integration, promotional offers, and tailored customer experiences can better position e-commerce businesses to meet evolving consumer expectations and sustain long-term market growth in Bangladesh's competitive retail landscape. JEL Classification Codes: O33, L81, M31, M37, D12, D91.
Technological adoption, E-commerce, AI-based Personalization, Marketing strategy, Consumer behavior, Theory of planned behavior
Copyright (c) 2025 Sanjida Nourin, Md. Ashiqur Rahman, Md. Elias Hossain, Md. Hafiz Iqbal Iqbal http://creativecommons.org/licenses/by/4.0
USAGE OF ADDITIVE MANUFACTURING IN THE AUTOMOTIVE INDUSTRY: A REVIEW
Additive manufacturing (AM), commonly known as 3D printing, is an emerging technology with immense potential across various industries, particularly in the automotive sector. It involves the layer-by-layer fabrication of parts by gradually depositing material, distinguishing itself from traditional manufacturing through various ways, including design flexibility, reduced material wastage, shorter lead time, and the absence of tooling requirements. It is still in its nascent phase, and several drawbacks, such as a lack of mechanical strength, limited material availability, and poor surface finish, still hinder its integration into the mainstream production workflow. As a result, further exploration is required to harness its potential along with traditional methods. The automotive sector is long known for its adoption of cutting-edge technologies. Due to the vast potential of AM, the automotive industry is actively trying to incorporate additive manufacturing into mainstream production processes. Consequently, the automotive industry continuously explores ways to enhance this process for seamless integration into product development. However, research elucidating different additive manufacturing processes used within the automotive industry, such as stereolithography (SLA), fused deposition modeling (FDM), and selective laser sintering (SLS), is limited. This paper aims to illuminate various additive manufacturing processes used by automotive companies worldwide. In addition to exploring different processes, the study delves into the advantages and limitations of additive manufacturing, contributing to a comprehensive understanding of its application in the automotive manufacturing landscape. JEL Classification Codes: Y90.
Additive Manufacturing, Automotive Industry, 3D Printing, Metal Additive Manufacturing, Automobile.
Copyright (c) 2023 Abusaleh Md Nayeem, Mir Md Nasim Hossain
Application of Single-Server Queue System in Performance Analysis of Shuttle Bus Operation: A Case Study of Federal University of Technology Akure
This study has examined the performance of University transport bus shuttle based on utilization using a Single-server queue system which occur if arrival and service rate is Poisson distributed (single queue) (M/M/1) queue. In the methodology, Single-server queue system was modelled based on Poisson Process with the introduction of Laplace Transform. Also, PASTA was introduced in queuing systems with Poisson arrivals. It is concluded that the performance of University transport bus shuttle is 93 percent which indicates a very good performance such that the supply of shuttle bus in FUTA is capable of meeting the demand. This study can be improved upon by examining the peak and off-peak period of traffic in the two major corridors (North gate and South gate) of FUTA, the economic cost of operating bus shuttle services can also be examined.
Single-Server Queue System; Transit Shuttle Buses; FUTA
Copyright (c) 2019 Kanyio Olufunto Adedotun
THE EFFECTIVENESS OF THE FISCAL MECHANISM OF ENSURING SUSTAINABLE DEVELOPMENT IN UKRAINE
Fiscal mechanisms, principally taxation and public expenditure, are central to aligning economic growth with social equity and environmental objectives in transitioning economies. In Ukraine, persistent challenges in revenue mobilization and spending efficiency necessitate quantification of how fiscal variables co-move with GDP growth. This study examines the association between budgetary revenues and GDP growth in Ukraine. Specifically, it investigates whether total tax revenue and the composition of taxation (direct versus indirect) are differentially related to growth across business cycle phases. The analysis uses annual time-series data for 2000–2022 from the Ministry of Finance of Ukraine, the World Bank, and the International Monetary Fund. Variables include GDP growth (dependent), tax revenue (% of GDP), direct and indirect taxes (% of GDP), government expenditure (% of GDP), inflation, foreign direct investment (FDI, % of GDP), and trade openness (% of GDP). Quantile regression is estimated at q = 0.25, 0.50, and 0.75 to capture heterogeneity across low-, median-, and high-growth conditions. Results indicate positive and statistically significant associations between tax revenue and GDP growth at all quantiles: 0.28 (q25), 0.35 (q50), and 0.42 (q75). Direct taxes display larger coefficients (0.31, 0.44, 0.51) than indirect taxes (0.12, 0.18, 0.24). Government expenditure is positively related to growth (0.15, 0.19, 0.22), as are FDI (0.20, 0.25, 0.30) and trade openness (0.18, 0.22, 0.28). Inflation is negative and not statistically significant (−0.08, −0.10, −0.12). The principal finding is that association magnitudes increase toward upper quantiles, with direct taxes exhibiting the largest coefficients relative to indirect taxes. JEL Classification Codes: H20, H21, O40, Q01.
Sustainable Development, Community-Based Tourism, Environmental Impact, Cultural Preservation, Tourism Management
Copyright (c) 2025 Vadym Hrebenozhko , Nataliia Kriuchkova , Viktor Melnyk (Author) http://creativecommons.org/licenses/by/4.0
IMPACT OF CULTURAL AUTHENTICITY AND AUDIENCE ENGAGEMENT ON GLOBAL APPRECIATION OF YIYANG TUNE: THE MEDIATING ROLE OF EMOTIONAL RESONANCE AND THE MODERATING ROLE OF INTERCULTURAL SENSITIVITY
Traditional Chinese performing arts, such as the Yiyang tune, face increasing difficulty maintaining global relevance as cultural experiences move into digital and intercultural environments. Although authenticity and online participation are widely discussed, empirical research explaining how these factors shape emotional and appreciative responses among international audiences remains limited. This study addresses this gap by examining how the cultural authenticity of Yiyang tune presentations and digital audience engagement influence emotional resonance and international appreciation of the Yiyang tune. The study uses primary survey data collected from 290 international students in China via convenience sampling, and the data were analyzed with Smart PLS 4 to evaluate reliability, validity, direct relationships, mediating effects, and moderating effects using a structural equation modeling approach. Results indicate that cultural authenticity significantly predicts emotional resonance (p = 0.001) and international appreciation (p = 0.009). Digital audience engagement also shows significant positive effects on emotional resonance (p = 0.000) and international appreciation (p = 0.004). Emotional resonance further predicts appreciation (p = 0.006). Mediation analysis shows that emotional resonance mediates the effects of authenticity (p = 0.038) and digital engagement (p = 0.016) on appreciation. Intercultural sensitivity significantly moderates the relationships between authenticity and emotional resonance (p = 0.009), digital engagement and emotional resonance (p = 0.019), and emotional resonance and appreciation (p = 0.000). These findings show that emotional resonance acts as a central mechanism linking cultural authenticity and digital engagement to international appreciation, while intercultural sensitivity strengthens these relational pathways. JEL Classification Codes: C12, C20, C31.
Yiyang Tune, Cultural Authenticity, Audience Engagement, Emotional Resonance
Copyright (c) 2025 Chen Yang , Mohamad Fitri Bin Mohamad Haris (Author) http://creativecommons.org/licenses/by/4.0
THE ROLE OF COMMUNITY-BASED TOURISM IN ENHANCING SUSTAINABLE TOURISM DEVELOPMENT IN KAZAKHSTAN
Community-based tourism (CBT) plays a crucial role in sustainable tourism development in Kazakhstan, contributing to economic growth, cultural preservation, and environmental sustainability. In a study, the barriers to CBT implementation, which play a key role in advancing United Nations Sustainable Development Goals (SDGs), notably SDG 8 (Decent Work and Economic Growth), SDG 11 (Sustainable Cities and Communities), and SDG 12 (Responsible Consumption and Production), were analyzed. This study aims to fill gaps by examining how managers of Kazakhstan-based travel companies understand CBT's economic, environmental, and cultural aspects. This study employs a mixed-methods approach, integrating qualitative interviews with travel company managers, participant observations, and quantitative surveys. Thematic analysis and descriptive statistics were used to analyze the data. The data samples included All Managers, Experienced Managers, and Novice Managers. The regression analysis was performed using two models: baseline, which included Awareness only, and expanded, which included Engagement and Infrastructure Challenge as control variables. For statistical significance, the standard errors (0.5-0.8) and mean values (3.5-4.5) were calculated, with a significant coefficient at p < 0.05. The findings of this study highlighted the importance of training programs and mentorship opportunities in helping less experienced professionals acquire new skills and bridge the knowledge gap. The findings of this study indicated that CBT aligns with SDG 8, SDG 11, and SDG 12, while also highlighting challenges, including infrastructure deficits and community engagement issues that hinder its implementation. JEL Classification Codes: Z32, Q56, O13, R11, O18.
Copyright (c) 2025 Zhassulan Sadykov , Nina Petrichsheva , Assylkhan Yessilov , Zubaira Temiraliyeva , Inna Rey (Author) http://creativecommons.org/licenses/by/4.0
LEGAL MECHANISMS FOR THE PROTECTION OF PROPERTY EXPECTATIONS IN THE LIGHT OF MODERN LEGAL REFORMS: A SYSTEMATIC REVIEW
The study of legal mechanisms for protecting property expectations is an urgent task for modern legal science, as in the context of reforms and legal uncertainty, guaranteeing expected property rights becomes particularly important. Existing theoretical approaches and law enforcement practice reveal contradictions in the interpretation of the concept of "property expectations" and its relationship to the "legitimate expectations" category used by the European Court of Human Rights. In this regard, there is a need for a comprehensive analysis of legal instruments that ensure an appropriate level of protection of expected property rights. The purpose of the study is to clarify the possibilities of modern legal development in providing adequate protection for property expectations. The work uses the PRISMA methodology to form a sample of scientific sources. The study included 45 sources of various types (monographs, empirical and review articles, conference proceedings), selected according to precise criteria. The chronological framework was 2016–2025. The results showed that regulatory approaches to protecting property expectations are based on the implementation of a set of principles and individual mechanisms. The quantitative ratio of protection instruments was established: 42% of the studied sources emphasize civil law mechanisms, 31% - administrative law, and 27% - judicial mechanisms. The decisions of the ECHR play a significant role, using the Convention for the Protection of Human Rights as an instrument to guarantee property expectations. JEL Classification Codes: K11, K12, K15, K20.
Property Expectations, Property Rights, Legal Protection Mechanisms, Modern Legal Reforms; Rule of Law, Systematic Review
Copyright (c) 2025 Dmytro Lutsenko , Krystyna Rezvorovych , Maksym Melnyk , Mariia Vovk , Yuliia Sushytska (Author) http://creativecommons.org/licenses/by/4.0
OMANI STUDENTS' PERCEPTIONS OF THE IMPACT OF PRIVATE TUTORING ON ACADEMIC PERFORMANCE
Private tutoring is becoming increasingly common worldwide as a way to supplement formal education, especially in developing countries. This growing trend has attracted significant attention from researchers who are using various methods to understand the reasons behind the demand for private tutoring and its Impact on students, focusing on key variables such as gender, grade level, and the relationship between tutoring participation and test scores. However, few research studies examine how Omani students perceive the Impact of private tutoring on their academic performance. Therefore, this study aims to investigate Omani students' perceptions of private tutoring and its Impact on their academic performance. The study employed a quantitative research method and collected data from 1,111 Omani students studying in grades 9 to 12 (high and higher secondary schools) by distributing a self-prepared questionnaire. The data were analyzed in SPSS using descriptive and regression analysis to determine the factors influencing the test scores. The multiple regression analyses indicate a positive correlation between private tutoring and Omani students' academic achievement (F = 5.210, p = 0.006). However, receiving private tutoring (p = 0.543) is not statistically significant, whereas the number of days attended is statistically significant (p = 0.001). Overall, the study's findings reveal that mere participation in private tutoring does not have a positive effect on test scores, but regularly attending private tutoring is positively associated with test scores. The study recommends consistent engagement with private tutoring to improve students' academic performance. The study also provides significant implications for teachers, parents, and policymakers. JEL Classification Codes: I21, D12, O15.
Private Tutoring, Academic Performance, Omani Students, Quantitative Study, Regression Analysis
Copyright (c) 2025 Hamood Ahmed Al Hunaini , Rashid Mohammed Al Hajri , Kodhandaraman Chinnathambi (Author) http://creativecommons.org/licenses/by/4.0