Vol. 1 No. 1 (2026): Journal of Quantitative Research and Theory

ISSN : In the application process
First Publication : 2026
Language : English and Indonesia
Editor In Chief : Prof. Dr. Ahmad Fauzi, M.Stat.
Frequency : 4 Issues per year (June, October, January and April)
Geographical Diversity : Saudi Arabia, Canada, United State, Australia, Ethiopia, Turkey, Lebanon, Kuwait, Bulgaria, Indonesia

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Published: 2026-06-30

Articles

Artificial Intelligence Adoption and Academic Performance in Higher Education: Testing the Mediating Roles of Digital Competence and Learning Engagement Using Structural Equation Modeling
Haqqul Yaqin STAI Al-Yasini Pasuruan Indonesia
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Background of study: Artificial intelligence (AI) tools are rapidly entering higher education, yet adoption alone does not explain whether students achieve better academic outcomes. Aims and scope of paper: This study develops and tests a mediation model in which digital competence and learning engagement transmit the effect of AI adoption on academic performance. Methods: A cross-sectional quantitative design is specified for university students, with multi-item Likert measures and covariance-based structural equation modeling (CB-SEM). The measurement model is evaluated through confirmatory factor analysis, reliability, convergent validity, and discriminant validity, followed by direct and bootstrapped indirect-effect testing. Result: The illustrative model indicates that AI adoption is positively associated with digital competence and learning engagement, while both mediators are positively related to academic performance. The direct AI-adoption effect remains positive but smaller after the mediators are included, indicating complementary partial mediation. Conclusion: The model suggests that universities should treat AI adoption as an educational capability-building process rather than mere tool access, pairing AI use with digital competence development and engagement-oriented pedagogy

Pages: 1-8

Views: 60 Downloads: 16
Explaining Students’ Generative AI Acceptance in Higher Education: An Extended Technology Acceptance Model with Digital Trust, Self-Efficacy, and Perceived Academic Value
Zhu Han University of Houston, United States United States , Tiago Oliveira NOVA Information Management School, Universidade Nova de Lisboa, Lisbon, Portugal Portugal
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Background of study: Generative artificial intelligence (GenAI) is becoming a routine academic resource, yet students differ substantially in whether they regard it as trustworthy, manageable, and educationally valuable. Aims and scope of paper: This study extends the Technology Acceptance Model (TAM) by integrating digital trust, AI self-efficacy, and perceived academic value to explain students’ behavioral intention to accept GenAI in higher education. Methods: A cross-sectional multi-institutional survey and covariance-based structural equation modeling (CB-SEM) are proposed. For manuscript-development purposes, the reporting format is demonstrated with a clearly identified synthetic dataset (N = 438); these values are illustrative and must be replaced by verified empirical data before submission. Result: The illustrative model shows acceptable fit (CFI = .957, TLI = .949, RMSEA = .049, SRMR = .043). AI self-efficacy predicts perceived ease of use; digital trust and perceived academic value predict usefulness and attitude; and attitude, usefulness, academic value, and self-efficacy jointly explain 68% of the variance in behavioral intention. Conclusion: GenAI acceptance is better explained when classical TAM beliefs are complemented by students’ confidence, calibrated trust, and judgments of academic value. Institutions should therefore combine technical access with AI literacy, trust-building governance, and pedagogically meaningful use cases.

Pages: 9-16

Views: 26 Downloads: 16
Digital Leadership and Organizational Performance in Higher Education: A Multilevel Analysis of Technological Readiness, Employee Engagement, and Institutional Innovation
Elena Vlahu-Gjorgievska University of Wollongong, Wollongong, Australia Australia , Wako Jio Bule Hora University, Hagere Maryam, Ethiopia Ethiopia
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Background of study: Digital transformation in higher education increasingly depends on whether institutional leadership can convert technology investments into employee capability, engagement, innovation, and measurable performance. Aims and scope of paper: This study develops a two-level model linking digital leadership to organizational performance through technological readiness, employee engagement, and institutional innovation. Methods: A multilevel quantitative design is specified with employees nested in universities and analyzed using multilevel structural equation modeling (MSEM). For manuscript-development purposes, results are demonstrated with a clearly identified synthetic dataset of 768 employees nested in 48 higher-education institutions; all numerical values must be replaced by verified empirical results before submission. Result: The illustrative model shows acceptable fit (CFI = .955, TLI = .947, RMSEA = .046; SRMRwithin = .038, SRMRbetween = .061). At the employee level, technological readiness predicts engagement and performance, while engagement partially mediates the readiness–performance relationship. At the institutional level, digital leadership strongly predicts institutional innovation, which partially mediates its association with organizational performance. Conclusion: Digital leadership appears most consequential when it builds technology-ready employees, sustains engagement, and institutionalizes innovation rather than functioning only as a technology-oriented leadership style.

Pages: 17-24

Views: 16 Downloads: 15
Development and Psychometric Validation of the Artificial Intelligence Literacy Scale for University Students: Evidence from Exploratory and Confirmatory Factor Analysis
Aras Bozkurt Anadolu University, Türkiye Turkey , Chadia Haddad Institut National de Santé Publique, d’Épidémiologie Clinique et de Toxicologie-Liban, Dekwaneh, Lebanon Lebanon
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Background of study: Artificial intelligence (AI) is now embedded in university learning, assessment, research, and academic decision-making, increasing the need for psychometrically sound measures of students’ AI literacy. Existing instruments differ substantially in scope, dimensionality, and evidence of validity. Aims and scope of paper: This study develops the Artificial Intelligence Literacy Scale for University Students (AILS-U), a concise multidimensional self-report instrument designed for cross-disciplinary higher-education contexts. Methods: Following best-practice scale-development procedures, an initial item pool was subjected to expert content validation, cognitive review, pilot testing, exploratory factor analysis (EFA), and confirmatory factor analysis (CFA). Because no empirical dataset was supplied for this manuscript draft, numerical results are transparently demonstrated using a synthetic sample (N = 742; EFA n = 371; CFA n = 371) and must be replaced before submission. Result: The illustrative EFA supports a 24-item, five-factor structure covering conceptual understanding, applied use and human-AI collaboration, critical evaluation and verification, ethical and responsible use, and human agency and self-regulation. The illustrative CFA shows good fit (CFI = .958, TLI = .952, RMSEA = .050, SRMR = .045), with strong reliability and convergent/discriminant validity. Conclusion: The proposed AILS-U offers a theoretically integrated framework for assessing university students’ AI literacy, but publication requires validation with verified participant data and cross-context replication.

Pages: 25-35

Views: 26 Downloads: 17
Predicting Student Academic Success Using Machine Learning: A Comparative Analysis of Behavioral, Digital Engagement, and Socio-Academic Factors
Ali Bostani American University of Kuwait, Kuwait Kuwait , Malinka Ivanova Technical University of Sofia, Sofia, Bulgaria Bulgaria
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Background of study: Universities increasingly combine administrative records and learning-management-system data to identify students who may need academic support, yet prediction models often rely on a single data source and provide limited insight into the incremental value of behavioral, digital-engagement, and socio-academic factors. Aims and scope of paper: This study compares feature groups and machine-learning algorithms for predicting university student academic success while emphasizing interpretable and responsible learning analytics. Methods: A retrospective multi-institutional classification design is specified. Five algorithms—logistic regression, decision tree, random forest, support vector machine, and XGBoost—are evaluated using stratified cross-validation. For manuscript-development purposes, results are demonstrated with a clearly labeled synthetic dataset (N = 1,248) and must be replaced with verified empirical data before submission. Result: In the illustrative analysis, the combined feature model outperforms single-domain models; XGBoost achieves the highest ROC-AUC (.891) and F1 score (.842). Prior GPA, assignment completion, attendance, LMS active days, and missed deadlines are the most influential predictors. Conclusion: Academic-success prediction improves when static socio-academic information is integrated with dynamic behavioral and digital-engagement indicators. Prediction should support timely human-led intervention rather than automated high-stakes decisions.

Pages: 36-45

Views: 6 Downloads: 6
Financial Literacy, Digital Financial Behavior, and Investment Decision-Making among University Students: An Integrated Structural Equation and Predictive Modeling Approach
Nuramalia Hasanah Universitas Negeri Jakarta Indonesia , Sudarmiatin Universitas Negeri Malang Indonesia
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Background of study: University students increasingly manage money, payments, savings, and investments through digital platforms, making financial knowledge insufficient unless it is translated into disciplined digital financial behavior. Aims and scope of paper: This study integrates financial literacy, digital financial behavior, and investment decision-making in a causal-predictive framework, examining both structural relationships and out-of-sample prediction. Methods: A cross-sectional explanatory-predictive design is specified for university students. Partial least squares structural equation modeling (PLS-SEM) tests direct and mediated effects, while linear regression, random forest, and XGBoost models compare predictive performance. For manuscript-development purposes, the reporting format is illustrated with a synthetic dataset (N = 624), which must be replaced by verified empirical data before submission. Result: The illustrative model indicates that financial literacy positively predicts digital financial behavior and investment decision-making, while digital financial behavior shows the strongest direct association with decision quality and partially mediates the effect of literacy. XGBoost provides the best illustrative out-of-sample prediction, followed by random forest and linear regression. Conclusion: Financial literacy appears most valuable when converted into consistent digital financial practices, suggesting that higher-education financial education should combine knowledge, digital self-management, and evidence-based investment decision skills.

Pages: 46-55

Views: 14 Downloads: 9

Vol. 1 No. 1 (2026): Journal of Quantitative Research and Theory

Articles

Artificial Intelligence Adoption and Academic Performance in Higher Education: Testing the Mediating Roles of Digital Competence and Learning Engagement Using Structural Equation Modeling

Explaining Students’ Generative AI Acceptance in Higher Education: An Extended Technology Acceptance Model with Digital Trust, Self-Efficacy, and Perceived Academic Value

Digital Leadership and Organizational Performance in Higher Education: A Multilevel Analysis of Technological Readiness, Employee Engagement, and Institutional Innovation

Development and Psychometric Validation of the Artificial Intelligence Literacy Scale for University Students: Evidence from Exploratory and Confirmatory Factor Analysis

Predicting Student Academic Success Using Machine Learning: A Comparative Analysis of Behavioral, Digital Engagement, and Socio-Academic Factors

Financial Literacy, Digital Financial Behavior, and Investment Decision-Making among University Students: An Integrated Structural Equation and Predictive Modeling Approach

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