In a rapidly evolving digital economy, cyberpreneurship has emerged as a pivotal force driving innovation and economic growth. The study applies the Theory of Planned Behaviour in predicting entrepreneurial intention in the context of Malaysia, where the government has actively championed digital entrepreneurship. Drawing from a sample of 473 final-year university students in the Klang Valley region of Malaysia, the study investigates the impact of Individual Entrepreneurial Orientation (IEO) dimensions, namely innovativeness, risk-taking, and proactiveness, on the intention to engage in cyberpreneurship within the context of Digital Free Trade Zones (DFTZ). The study further examines the moderation effect of psychological characteristics incorporating visionary thinking, self-efficacy, opportunism, and creativity to provide a comprehensive understanding of the factors influencing cyberpreneurial intentions. With the moderating variable, the paper presents a comprehensive model to investigate the IEO and psychological characteristics contributing to cyberpreneurship intentions and its impact on engagement in DFTZ. An empirical examination of data and hypotheses found that risk-taking (RISK) and proactiveness (PRO) are significantly related to cyberpreneurial intention. Psychological characteristics significantly proved its moderating role in its interaction with innovatiness (INNO), risk-taking (RISK), and proactivness (PRO) in influencing cyberpreneurial intentions (CYBER_PI). Innovativeness (INNO) without the influence of the moderating variable is not significantly related to cyberpreneurial intentions. Engagement with the Digital Free Trade Zone (DFTZ) through the mediating role of cyberpreneurial intentions (CYBER_PI), the innovativeness (INNO) did not succeed. On the other hand, risk-taking (RISK) and proactiveness (PRO) are found to be significant. The paper contributes to the landscape of e-commerce and digital trade literature by advancing our understanding of the factors driving individuals’ intentions to participate in cyberpreneurship and engage in DFTZ. The findings of this study provide valuable insights for policymakers, educators, and entrepreneurs alike.

In the dynamic landscape of modern education, it is essential to understand and recognize the psychological habits that underpin students’ learning processes. These habits play a crucial role in shaping students’ learning outcomes, motivation, and overall educational experiences. This paper shifts the focus towards a more nuanced exploration of these psychological habits in learning, particularly among secondary school students. We propose an innovative assessment model that integrates multimodal data analysis with the quality function deployment theory and the subjective-objective assignment method. This model employs the G-1-entropy value method for an objective evaluation of students’ psychological learning habits. The G-1-entropy method stands out for its comprehensive, objective, and practical approach, offering valuable insights into students’ learning behaviors. By applying this method to assess the psychological aspects of learning, this study contributes to educational research and informs educational reforms. It provides a robust framework for understanding students’ learning habits, thereby aiding in the development of targeted educational strategies. The findings of this study offer strategic directions for educational management, teacher training, and curriculum development. This research not only advances theoretical knowledge in the field of educational psychology but also has practical implications for enhancing the quality of education. It serves as a scientific foundation for educators, administrators, and policymakers in shaping effective educational practices.

With the rapid increase in electric bicycle (e-bikes) use, the rate of associated traffic accidents has also escalated. Prior studies have extensively examined e-bike riders’ injury risks, yet there is a limited understanding of how their behavior contributes to these accidents. This study aims to explore the relationship between e-bike riders’ risk-taking behaviors and the incidence of traffic accidents, and to propose targeted safety measures based on these insights. Utilizing a mixed-methods approach, this research integrates quantitative data from traffic accident reports and qualitative observations from naturalistic studies. The study employs a binary logistic regression model to analyze risk factors and uses observational data to substantiate the model findings. The analysis reveals that assertive driving behaviors among e-bike riders, such as running red lights and speeding, significantly contribute to the high rate of accidents. Moreover, the lack of protective gear and inadequate safety training are identified as critical factors increasing the risk of severe injuries. The study concludes that comprehensive policy interventions, including stricter enforcement of traffic laws and mandatory safety training for e-bike riders, are essential to mitigate the risks associated with e-bike use. The findings advocate for an integrated approach to urban traffic management that enhances the safety of all road users, particularly vulnerable e-bike riders.

This research explores the interactions within supply chains in the manufacturing sector, with a special emphasis on the distinctive obstacles encountered by the mosquito coil industry. The study is motivated by the need to comprehensively understand and address the multifaceted challenges encountered by manufacturers in their supply chain processes. The mosquito coil industry holds significant importance in Malaysia, primarily due to the country’s tropical climate, which is conducive to mosquito proliferation and the transmission of mosquito-borne diseases. Nowadays, there are growing complexities and disruptions experienced by the mosquito coil sector’s supply chain, prompting an in-depth investigation. The main objective is to identify the challenges and resilience strategies employed by manufacturers in this sector, providing an understanding that contributes to the broader discourse on supply chain dynamics. Employing a qualitative case study methodology, this research engages in extensive data collection through interviews, document analysis, and direct observations within the selected mosquito coil manufacturing entity. This methodology allows for an immersive exploration of the challenges faced, revealing insights into the factors influencing the supply chain dynamics. The study reveals a wide array of challenges, from obtaining raw materials to managing distribution logistics, underscoring the unique complexities specific to the sector. As a result, the research identifies and analyzes resilience strategies implemented by the mosquito coil manufacturer to mitigate challenges, such as procurement challenges faced in financial related issues, logistical complexities occurred from recent years’ worldwide pandemic, production disruptions from company’s human resource-related issues, global factors from the company’s competitors and market challenges, and technology integration from rapid technological advancements. Thus, implications of this study extend beyond the mosquito coil sector, contributing valuable knowledge to the academic community, practitioners, and policymakers involved in supply chain management. The research not only addresses the identified challenges but also serves as a foundation for enhancing the overall understanding of manufacturing supply chain dynamics, thereby fostering informed decision-making for improved industry resilience.

In the wake of the COVID-19 pandemic, the prevalence of online education in primary education has exhibited an upward trajectory. Relative to traditional learning environments, online instruction has evolved into a pivotal pedagogical modality for contemporary students. Thus, to comprehensively comprehend the repercussions of environmental changes on students’ psychological well-being in the backdrop of prolonged online education, this study employs an innovative methodology. Founded upon three elemental feature sequences—images, acoustics, and text extracted from online learning data—the model ingeniously amalgamates these facets. The fusion methodology aims to synergistically harness information from diverse perceptual channels to capture the students’ psychological states more comprehensively and accurately. To discern emotional features, the model leverages support vector machines (SVM), exhibiting commendable proficiency in handling emotional information. Moreover, to enhance the efficacy of psychological well-being prediction, this study incorporates an attention mechanism into the traditional Convolutional Neural Network (CNN) architecture. By innovatively introducing this attention mechanism in CNN, the study observes a significant improvement in accuracy in identifying six psychological features, demonstrating the effectiveness of attention mechanisms in deep learning models. Finally, beyond model performance validation, this study delves into a profound analysis of the impact of environmental changes on students’ psychological well-being. This analysis furnishes valuable insights for formulating pertinent instructional strategies in the protracted context of online education, aiding educational institutions in better addressing the challenges posed to students’ psychological well-being in novel learning environments.