AI chatbots are becoming essential within contemporary education, significantly influencing student engagement and learning processes. They provide personalized support by delivering instant resources designed for individual learning styles and needs, enabling students to study subjects at their own pace. Therefore, this study seeks to investigate the impact of AI chatbots on self-reliance, their role in enhancing educational outcomes, and the factors influencing their adoption. A random sample of 384 questionnaires was collected from students enrolled in Jordanian universities. Data were collected through an online survey conducted from June to September 2025. The study's hypotheses were tested using SPSS for regression analyses of sub-hypotheses and PLS-SEM for the primary hypotheses. The results reveal that various dimensions of AI chatbots positively impact both self-reliance and the adoption of these tools, both individually and collectively, suggesting that their effective implementation can enhance learning outcomes and student engagement in educational settings. These results emphasize the significance of AI chatbots in fostering self-reliance and their adoption. The study advises integrating AI tools into the educational process while maintaining a balance with traditional teaching methods, as this approach can enhance learning outcomes and ensure that students benefit from both innovative and established educational strategies.
Despite the growing relevance of sustainable niche tourism, there is a lack of empirical studies on tourist consumer behavior in the context of coffee tourism—particularly regarding the factors that stimulate the urge to travel. The aim of the present study is to examine how digital literacy, habit, destination trust, and destination preference influence tourists’ urge to travel to coffee plantations. A conceptual framework was formulated to determine the ways in which habituated interaction between tourists and AI-based tourism infrastructure shapes tourists’ cognitions and affections to trust and preference, and these further drives travel enthusiasm. Data were gathered through an online survey on 225 tourists using convenient sampling. The measurement model was examined using Exploratory Factor Analysis (EFA), and the structural model and hypotheses were evaluated using Structural Equation Modelling (SEM) with AMOS version 29. The findings indicated that digital literacy had a significant and positive impact on the construction of habitual use of technology. Habit further emerged as an essential antecedent for destination trust and destination preference. Moreover, trust and preference were significant antecedents of tourists’ urge to travel. The model had superb fit indices (χ²/DF = 1.124, CFI = 0.995, RMSEA = 0.024), validating its theoretical construct and empirics. The findings provide deeper insights into consumer behavior, digital tourism consumption, and destination marketing practices, particularly, in response to the growing sustainability demands within the emerging context of coffee tourism.
This study investigated the role of artificial intelligence (AI) in improving knowledge management and organizational learning within Saudi Arabian educational institutions, with particular emphasis on the empirical statistical validation of the relationships between AI implementation and organizational outcomes. A descriptive-analytical approach was employed utilizing a stratified random sample of 213 faculty members from five Saudi government universities: Taiba University, King Faisal University, King Khalid University, Al-Jouf University, and Qassim University. Data were collected through a validated 20-item questionnaire (α = 0.978) measuring four dimensions: AI implementation in knowledge management, AI's role in organizational learning, implementation challenges, and effectiveness. Statistical analyses included descriptive statistics, Pearson correlations, multiple regression analysis, one-way ANOVA, independent samples t-tests, and structural equation modeling to test the hypothesized relationships. The results revealed high levels of AI implementation in knowledge management (M = 3.89, SD = 0.67) and organizational learning (M = 3.79, SD = 0.75). Multiple regression analysis demonstrated that AI implementation significantly predicted organizational learning outcomes (β = 0.624, p ≤ 0.001, R² = 0.389). Significant differences in AI implementation effectiveness were found based on institutional affiliation (F(4,208) = 4.87, p = 0.001, η² = 0.086) and academic rank (F(3,209) = 3.42, p = 0.018, η² = 0.047). The primary challenges identified, lack of AI expertise (M = 3.64), staff resistance (M = 3.58), and infrastructure limitations (M = 3.55), were found to significantly moderate the relationship between AI and knowledge management (interaction effect: β = -0.187, p = 0.008). This study contributes robust empirical evidence to the limited body of quantitative research on AI integration in Saudi higher education, providing statistically validated insights for policymakers and institutional leaders working to advance Vision 2030 objectives.
The main objective of this research was to determine the impact of Information and Communication Technologies (ICTs) on human talent management within a public university in Ica, Peru. Using a quantitative, basic, and correlational-causal approach, a sample of 474 participants, comprised of administrative and teaching staff, was analyzed. The participants were characterized by a professional profile with high academic qualifications and intermediate work experience. Data collection was carried out through a survey based on the Technology Acceptance and Use (UTAUT) model, and the information was processed using partial least squares structural equation modeling (PLS -SEM). The results of the structural model confirmed that all the hypotheses were significant with a p-value of 0.000, highlighting that Expectations of Effort (β = 0.635) and Enabling Conditions (β = 0.632) were the most influential predictors of the dependent variable. The model achieved a coefficient of determination R² of 0.325, explaining 32.5% of the variability in human talent management. In conclusion, the modernization of talent management in the public university environment depends critically on reducing the technical complexity perceived by the user and strengthening the institutional technological infrastructure, factors that are more important than social influence or net performance expectations.
This paper examines the complex interdependencies between the big data analytics capabilities, competitive intelligence, organizational agility, and organizational outcomes in the telecommunication industry in Jordan. Using a quantitative design and data gathered through 350 participants and processed by SmartPLS. The results show that big data analytics capabilities influence organizational outcomes in a positive manner both directly and indirectly via competitive intelligence. Although competitive intelligence does not influence organizational outcomes directly, it mediates the relationship between the big data analytics capabilities and organizational outcomes significantly. Moreover, organizational agility is identified to moderate positively between big data analytics capabilities and organizational outcomes, which underscores its significant contribution to boosting the success of analytics. These findings present important theoretical insights by clarifying the processes by which big data analytics competencies are transformed to generate better organizational performance, as well as have practical implications to telecommunication companies seeking to better exploit their data assets.
