Synchronous video conferencing (VC) has become an established mode of higher education, but sustaining student engagement remains challenging, particularly in practice-oriented disciplines such as graphic design. This study examined the effects of three gamification mechanisms, including animated badges, experience points, and level upgrades, on behavioural, emotional, and cognitive engagement in synchronous VC learning. Guided by Self-Determination Theory (SDT), a quasi-experimental non-equivalent control group design was implemented with 124 first-year graphic design students from three public universities in China. Participants were distributed equally across a non-gamified control condition and three gamified conditions (n = 31 per group). Engagement was assessed before and after a four-week instructional intervention. Significant improvements were observed within the engagement components across all three gamified conditions, with no statistically significant change observed within the control condition. Participant-level total scores for each dimension of engagement were then used to estimate baseline-adjusted regression models. The models accounted for 97.4% of the variance in post-intervention behavioural engagement, 96.6% in emotional engagement, and 98.8% in cognitive engagement. Relative to the control condition and after controlling for baseline engagement, animated badges, experience points, and level upgrades were associated with increases of 40.35, 46.90, and 49.36 points in behavioural engagement; 48.82, 51.76, and 52.48 points in emotional engagement; and 95.86, 105.46, and 107.36 points in cognitive engagement, respectively. The results suggest that gamification mechanisms vary in the strength and direction of their relationship with engagement, with the strongest adjusted relationships being found between progression-based gamification and the cognitive dimension of engagement. The study extends the analysis beyond significance testing by introducing a quantitative, baseline-adjusted model to estimate post-intervention engagement given gamification condition and baseline engagement and offers a more precise basis for the design of gamified VC interfaces.
This research establishes a definitive closed-form equation for the average run length (ARL) of the double modified exponentially weighted moving average (DMEWMA) control chart employed for the ARMA(p,q) process. The proposed formula is derived analytically and validated through comparison with the numerical integral-equation (NIE) method. To assess the agreement between the two methods, we examine the percentage of accuracy. To evaluate the monitoring capabilities of the DMEWMA chart, additional performance metrics—SDRL and MRL—are computed across various chart design configurations. We use RMI, AEQL, and PCI to examine how well the DMEWMA chart detects mean-process shifts when applied to the modified EWMA (MEWMA) and the EWMA charts. A real-world example of monthly unemployment-rate production-index data from Bangkok and southern Thailand demonstrates the effectiveness of the explicit ARL-based DMEWMA chart in detecting changes in process means. The findings demonstrate that the DMEWMA chart outperforms the MEWMA and EWMA charts in identifying process shifts.
This research examines the impact of motivation and networking on students’ entrepreneurial intentions in Small and Medium Enterprises (SMEs). Based on relevant theoretical frameworks such as the Theory of Planned Behavior (TPB) and Social Capital theory, this research aims to contribute to a better understanding of the factors that promote entrepreneurial intentions. This study used a quantitative research design; the data were collected using a simple random sampling from 120 students in the Enculturation of Entrepreneurship course at Universiti Pendidikan Sultan Idris (UPSI). The results show that motivation has a positive and significant effect on entrepreneurial intention (β = 0.62, p < 0.001), meaning motivated students tend to be more engaged in an entrepreneurial career path. Networking was positively related to entrepreneurial intention (β = 0.28, p < 0.01), which indicates the importance of social networks, exposure to entrepreneurship, and access to a range of support systems in aspirations towards entrepreneurship. In addition, networking positively moderated the relationship between motivation and entrepreneurial intention (β = 0.19; p < 0.01), suggesting that students with wider and more supportive networks increase the strength of the effect between motivation and entrepreneurial intention. In general, this indicates that the interaction between individual motivation and social capital is influencing the entrepreneurial intention.
Structural Equation Modeling (SEM) is widely used to analyze complex relationships among latent variables across scientific fields. However, its application is often affected by methodological and practical issues that may compromise result validity. This study provides a critical review of challenges in SEM, including model specification, construct validity, sampling, model evaluation, and reporting practices. A Systematic Literature Review (SLR) of 28 peer-reviewed articles (2019–2025) from the Scopus database was conducted using thematic synthesis and frequency analysis. The findings identify five dominant issue clusters and demonstrate that these challenges are associated with observable estimation consequences, such as non-convergence, parameter instability, inflated standard errors, and estimator sensitivity. These effects reflect underlying bias–variance trade-offs between CB-SEM and PLS-SEM. The study contributes to improving SEM practice by linking methodological shortcomings with their numerical implications.
- This study forecasts CO2 emissions from industrial processes in Vietnam to support climate change mitigation and sustainable development. Accurate forecasting provides valuable insights for policymakers and businesses in designing emission reduction strategies and achieving the Net Zero target by 2050. Three Grey forecasting models, namely the Gompertz Grey Model (GGM), Auto-Regressive Grey Model (ARGM (1,1)), and Nonlinear Grey Model (NGM (1,1,k,c)), were evaluated using World Bank data from 2000–2023. Forecasting performance was assessed using Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). The results indicate that all models achieved acceptable accuracy (MAPE < 10%), with GGM outperforming the others (MAPE = 5.51%, RMSE = 2.1363). Therefore, GGM was selected to forecast CO2 emissions for 2025–2030, projecting a continued upward trend and highlighting the need for cleaner technologies and effective emission reduction policies.
