In 2018, the World Health Organization warned of a potential reemergence of Ebola Virus Disease in regions previously affected. By May 2026, outbreaks resurfaced in the DRC and Uganda, demonstrating the risk of resurgence in other regions such as the Mano River Union basin. Looking toward future epidemics or novel pathogens, this article contends that effective epidemic response depends on a co-produced system in which institutional readiness and community health knowledge are mutually dependent and jointly influence outcomes. The article examines Sierra Leone’s management of the 2014–2015 Ebola crisis, utilizing peer-reviewed research, policy documents, epidemiological data, and reports from affected districts. It tracks: (1) the interaction of institutional weaknesses with community mistrust, rumor-driven perceptions, and culturally rooted burial customs that contributed to faster transmission; and (2) how the response improved as collaboration between institutions and communities grew stronger. Using thematic synthesis and process tracing, the article demonstrates how institutional readiness and community knowledge impacted various stages of the outbreak. The results reveal that epidemic control depends not only on technical readiness but also on institutions’ ability to build trust, incorporate local insights, and adapt strategies to community contexts. The conclusion emphasizes that strengthening epidemic preparedness for potential Ebola resurgence or novel pathogens requires institutionalizing co-produced approaches. This article advances conceptual understanding by framing epidemic response not merely as a unilateral deployment of expertise but as a relational process. This perspective challenges deficit-based narratives that portray communities as obstacles and provides empirical evidence for planning in areas at risk of resurgence.
The growing access to artificial intelligence (AI) speaking tools provides Malaysian tertiary ESL students with opportunities to practise spoken English beyond traditional classroom settings. Despite the growing interest in AI-assisted language learning, limited research has examined how students use socio-affective strategies during AI-assisted speaking practice. This study investigated students’ use of socio-affective strategies, the perceived benefits associated with these strategies, and their reflections on the role of AI speaking tools in supporting speaking development. A mixed methods design was employed involving 165 Malaysian tertiary ESL students with prior experience in using AI speaking tools. Quantitative and qualitative data were collected through an adapted questionnaire based on Oxford’s (1990) Strategy Inventory for Language Learning (SILL). Descriptive statistics, Pearson correlation, multiple regression, and thematic analyses were conducted. The findings revealed that students used both social and affective strategies during AI-assisted speaking practice with affective strategies being used slightly more frequently. Students perceived socio-affective strategies as beneficial for enhancing enjoyment, confidence, fluency, and error management. Affective strategies emerged as the only significant predictor while both strategy dimensions were positively associated with perceived benefits. This finding suggests that the affective dimension may play an important role in shaping students’ perceived benefits within AI-assisted speaking environments. Qualitative findings further indicated that AI features such as feedback, role-play, progress tracking, and avatars supported the use of socio-affective strategies although challenges related to pronunciation, technical issues, and limited human interaction were also reported. The study extends socio-affective strategy research into AI-assisted speaking contexts and offers pedagogical insights for integrating AI-supported speaking activities in Malaysian higher education. More broadly, the findings suggest that AI-assisted speaking environments may represent a distinguished context in which established language learning strategies are implemented through both technological affordances and learner self-regulation.
Reading comprehension is a key skill in second language learning, especially for English as a Second Language (ESL) learners. It involves several cognitive processes that enable a reader to extract meaning from the words on the page, comprehend information presented, and make connections between different parts of the text. Pupils with a strong reading comprehension ability can extract information, analyse content critically, and apply their understanding in various contexts. Studies show that the use of customised visual aids can improve reading comprehension and vocabulary among second language learners. This study investigates the effectiveness of the Circle Book as a multimodal instructional tool in improving reading comprehension among Year 4 pupils in a rural Malaysian primary school. A quasi-experimental pre-test and post-test design was employed, complemented by qualitative data from classroom observations and semi-structured interviews. A total of 32 pupils participated in the six-week intervention. The findings suggest that multimodal instructional tools such as the Circle Book can effectively enhance reading comprehension among ESL learners, particularly in resource-constrained rural contexts. The study highlights the potential of low-technology, culturally responsive teaching strategies in improving literacy outcomes.
In today’s dynamic business environment, organizational innovation is critical for sustaining competitiveness. This study examines the mediating role of perceived organizational support (POS) in the relationship between organizational culture, based on the Competing Values Framework (CVS), and organizational innovation. A quantitative approach was employed using data collected from 377 employees in the UAE energy sector. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings indicate that cuff culture significantly influences both organizational innovation and perceived organizational support. Furthermore, POS has a significant positive effect on innovation and partially mediates the relationship between organizational culture and innovation. This study contributes to the literature by providing empirical evidence from the UAE context and highlights the importance of aligning organizational culture with supportive practices to enhance innovation outcomes.
This study examines employee perception as a mediator between safety and security management practices and investigation efficiency, with organisational culture as a moderator in the UAE energy sector. The study addresses the need for efficient, fair, and learning-oriented investigation processes in high-risk energy organisations where safety incidents, security threats, regulatory obligations, and operational disruptions require timely and reliable responses. A quantitative research design was adopted, and data were collected using a structured questionnaire distributed to employees who were directly involved in, affected by, or formally expected to cooperate with internal investigation processes. From 520 distributed questionnaires, 426 valid responses were retained after data screening and outlier removal. The proposed framework was analysed using Partial Least Squares Structural Equation Modelling. The measurement model confirmed satisfactory reliability, convergent validity, and discriminant validity for all constructs. The structural model showed strong explanatory and predictive power, with Safety Management Practices and Security Management Practices explaining 60.0% of the variance in Employee Perception. Employee Perception, Organisational Culture, and their interaction explained 62.7% of the variance in Investigation Efficiency. The findings revealed that both safety and security management practices significantly influence Employee Perception, with Safety Management Practices showing the stronger effect. Employee Perception had a significant positive effect on Investigation Efficiency. The mediation analysis confirmed that Employee Perception significantly mediates the relationships between both management practices and Investigation Efficiency. The moderation analysis showed that Organisational Culture significantly moderates the relationship between Employee Perception and Investigation Efficiency. The study concludes that investigation efficiency is shaped by formal management systems, employee perceptions, and cultural conditions within the organisation.
This study examines the effect of Technology Adoption on Job Satisfaction in the Sharjah Government, with HR Service Quality and Strategic Alignment tested as mediating variables. Drawing on the literature on digital transformation, e-HRM, service quality, strategic alignment, and public-sector job satisfaction, the study developed and tested a conceptual framework linking Technology Adoption, HR Service Quality, Strategic Alignment, and Job Satisfaction. Data were collected through a structured questionnaire from employees in Sharjah public-sector organisations who had experience with HR-related digital systems. After data screening, 519 usable responses were retained for analysis. The measurement model confirmed satisfactory reliability, convergent validity, and discriminant validity. The structural model results showed that Technology Adoption had a positive and significant effect on Job Satisfaction, HR Service Quality, and Strategic Alignment. HR Service Quality and Strategic Alignment also had positive and significant effects on Job Satisfaction. In addition, both HR Service Quality and Strategic Alignment significantly mediated the relationship between Technology Adoption and Job Satisfaction, with HR Service Quality showing the stronger mediating effect. The model explained 29.1% of the variance in Job Satisfaction. The findings indicate that HR digital transformation can improve employee satisfaction when digital systems enhance HR service delivery and are aligned with organisational goals. The study contributes to public-sector digital transformation and e-HRM literature by showing how technology adoption affects job satisfaction through service-oriented and strategic organisational mechanisms.
Effective strategic management and leadership are critical for improving organizational performance, particularly in public sector organizations. In the UAE, public institutions continue to face challenges in achieving desired performance outcomes. While prior research has examined leadership and strategy independently, there is a limited understanding of the mediating role of transformational leadership in the relationship between strategy evaluation and organizational performance. This study investigates the direct effect of strategy evaluation on organizational performance and determines whether transformational leadership mediates this relationship within the UAE Ministry of Energy and Infrastructure. A quantitative research design was adopted, with survey questionnaires administered to managers and administrative supervisors. A total of 351 valid responses were collected, achieving a response rate of 99.4%. Data was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings indicate that strategy evaluation alone does not have a significant direct effect on organizational performance. However, transformational leadership fully mediates the relationship, demonstrating that effective leadership is essential for translating strategic evaluation practices into improved organizational outcomes. This study contributes to the literature by providing empirical evidence of the mediating role of transformational leadership in the UAE public sector. Practically, the findings offer guidance for policymakers and public sector managers on aligning leadership development with strategic evaluation processes to enhance performance. The results underscore the importance of fostering transformational leadership to achieve higher efficiency, accountability, and service delivery in public organizations.
This study aimed to explore the role of the National Education course in developing the dimensions of citizenship among Jordanian university students, taking Al-Isra University as a model. A questionnaire was designed and validated for reliability and validity to achieve the study’s objectives. It was administered to 334 undergraduate students enrolled in the National Education course during the first semester of the 2024/2025 academic year at Al-Isra University in Jordan. The results revealed that the role of the National Education course in developing the dimensions of citizenship among Jordanian university students was high. The “political dimension” ranked first, while the “economic dimension” ranked fifth and last. The study also found statistically significant differences in the role of the course attributed to gender, with males scoring higher and no statistically significant differences attributed to the type of faculty. Based on these findings, the researcher proposed several recommendations and suggestions.
This study examines the role of self-leadership in enhancing lecturer productivity in Malaysian higher education, with innovative work behaviour, knowledge sharing, and self-efficacy as key supporting mechanisms. In the context of increasing demands for teaching excellence, high-impact research, and meaningful community engagement, academic staff are required to demonstrate not only technical competence but also strong self-regulation, intrinsic motivation, and innovative capability. Drawing on self-leadership theory and social cognitive theory, this paper proposes a comprehensive conceptual framework that explains how self-leadership influences academic productivity through psychological and social processes. Using a quantitative survey approach, the study outlines the relationships among the key constructs and provides a narrative synthesis of expected findings based on existing empirical evidence. The analysis suggests that lecturers who exhibit high levels of self-leadership are more likely to engage in innovative work behaviour, actively share knowledge with colleagues, and possess stronger self-efficacy beliefs, which in turn contribute to higher levels of teaching effectiveness, research output, and overall academic performance. The study contributes theoretically by integrating leadership, innovation, and social cognitive perspectives in a higher education context, and practically by offering insights for the design of academic leadership development programmes, knowledge management initiatives, and human resource strategies aimed at strengthening sustainable productivity in universities.
English pronunciation plays a critical role in second language communication; however, learners’ subjective perceptions of their own pronunciation ability remain under-theorized. While pronunciation research has traditionally emphasized intelligibility, accuracy, and instructional effectiveness, comparatively little attention has been given to how learners perceive, evaluate, and emotionally experience their pronunciation performance. Addressing this gap, this paper proposes a theoretical framework for understanding English pronunciation self-concept among Chinese university engineering students. The framework integrates multidimensional self-concept theory, symbolic interactionism, and motivational self-theory to conceptualize pronunciation self-concept as a micro-level learner construct that is socially constructed, motivationally influenced, and contextually embedded. By synthesizing research on language self-concept, pronunciation learning, and sociocultural influences in Chinese higher education, the paper advances a context-sensitive conceptual model. The proposed framework offers a foundation for future empirical research examining pronunciation-related affect and engagement, and it provides pedagogical insight for designing feedback and instructional practices that support positive pronunciation self-concept development alongside technical accuracy.
This study examined the relationship between Technology Readiness Index (TRI) constructs and Technology Acceptance Model (TAM) constructs in the context of UAE e-government services. Specifically, the study investigated how optimism, innovativeness, insecurity, and discomfort were associated with perceived ease of use and perceived usefulness. A quantitative research approach was adopted, and data were collected from adult users of UAE e-government services, including Emirati citizens and expatriate residents. After data screening, 419 valid responses were used for analysis. The data were analysed using SPSS through ranking analysis based on mean and standard deviation values, followed by Pearson correlation analysis. The ranking results showed that innovativeness recorded the highest mean score among the TRI constructs, while perceived ease of use ranked slightly higher than perceived usefulness among the TAM constructs. The correlation results indicated that all TRI constructs were significantly associated with both TAM constructs at the 0.01 significance level. Optimism and innovativeness were positively related to perceived ease of use and perceived usefulness, indicating that users with stronger readiness toward technology were more likely to perceive UAE e-government services as easy to use and useful. Insecurity and discomfort also showed significant relationships with TAM constructs, although their interpretation required consideration of item wording and coding. Overall, the findings supported the integration of TRI and TAM in explaining user acceptance of UAE e-government services. The study concluded that users’ technology readiness played an important role in shaping acceptance beliefs toward digital government platforms in the UAE.
This study examines the mediating role of employee perception in the relationship between safety and security management practices and investigation efficiency in the United Arab Emirates energy sector. The study addresses the need for efficient, fair, and learning-oriented investigations in high-risk energy organisations where safety failures, security threats, and operational disruptions can have serious legal, operational, and reputational consequences. A quantitative research design was adopted, and data were collected using a structured questionnaire distributed to employees who were directly involved in, affected by, or formally expected to cooperate with internal investigation processes. From 520 distributed questionnaires, 426 valid responses were retained after data screening and outlier removal. The proposed conceptual framework was analysed using Partial Least Squares Structural Equation Modelling. The measurement model confirmed satisfactory reliability, convergent validity, and discriminant validity for the four main constructs: Safety Management Practices, Security Management Practices, Employee Perception, and Investigation Efficiency. The structural model demonstrated strong explanatory and predictive power. Safety and security management practices explained 60.0% of the variance in employee perception, while employee perception explained 58.1% of the variance in investigation efficiency. The findings revealed that both safety and security management practices significantly influence employee perception, with safety management practices showing the stronger effect. Employee perception also had a strong positive effect on investigation efficiency. Furthermore, mediation analysis confirmed that employee perception significantly mediates the relationships between both management practices and investigation efficiency. The study concludes that formal safety and security systems improve investigation efficiency when employees perceive them as fair, reliable, transparent, supportive, and trustworthy.
This study explores the ranking and interrelationships of artificial intelligence (AI) capabilities, training, and project management performance within the United Arab Emirates (UAE) public sector. Four AI capability dimensions which are predictive analytics, intelligent scheduling and automation, decision support systems, and risk management and resource allocation that were assessed using data from a structured questionnaire distributed to government and public-sector employees engaged in project-related activities. Of the 600 questionnaires distributed, 515 valid responses were retained for analysis. Findings reveal that all AI capability dimensions, training, and project management performance were perceived at moderate-to-high levels. Ranking analysis identified project management performance as the most influential factor, followed by risk management and resource allocation, training, decision support systems, intelligent scheduling and automation and predictive analytics. Correlation analysis further confirmed positive and significant associations among all study dimensions, with training showing the strongest relationship to project management performance. These results underscore the pivotal role of human capability development in maximizing AI-supported project outcomes. The study contributes to the literature on AI-enabled project management by highlighting the combined importance of technological capability and workforce training in enhancing project performance in the UAE public-sector context.
This study examines the effect of Green Human Resource Management (GHRM) on Sustainability Performance and investigates the mediating role of Green Innovation in UAE manufacturing firms. Drawing on the Resource-Based View and Stakeholder Theory, the study proposes that green HRM practices develop internal environmental capabilities that support green product and process innovation, which in turn enhances sustainability outcomes. Data were collected through a structured survey of managers and professionals from UAE manufacturing firms, resulting in 306 valid responses. The data were analysed using partial least squares structural equation modelling. The findings show that GHRM has a positive and significant effect on Green Innovation and Sustainability Performance. Green Innovation also has a positive and significant effect on Sustainability Performance. In addition, Green Innovation partially mediates the relationship between GHRM and Sustainability Performance, indicating that green HRM improves sustainability outcomes both directly and indirectly through innovation capability. The model explains 59.9% of the variance in Green Innovation and 73.3% of the variance in Sustainability Performance, demonstrating substantial explanatory power. The study contributes to the green HRM and sustainability literature by providing empirical evidence from the UAE manufacturing sector, an underexplored context in Middle Eastern sustainability research. Practically, the findings suggest that manufacturing firms should align HR practices with innovation strategies to strengthen environmental, economic, and social performance.
This paper proposes an instructional framework that integrates Peeragogy and Higher-Order Thinking Skills (HOTS) in the design of data analysis courses in Chinese higher vocational education. Grounded in social constructivist theory and Marzano’s higher-order thinking theory, the framework emphasises collaborative learning and authentic data analysis tasks as key mechanisms for fostering advanced cognitive processes. The framework development is guided by three research questions: (1) What peer learning tendency patterns exist among students in Chinese higher vocational data analysis courses? (2) What is the level of students’ HOTS in these courses? and (3) Does the integration of Peeragogy and HOTS significantly improve students’ post-test HOTS performance? The framework contributes theoretically by showing how social and cognitive dimensions of learning integrate to enhance vocational pedagogy. Practically, it provides a model that aligns vocational education with the analytical and collaborative demands of data-driven workplaces. By embedding peer-supported learning and higher-order reasoning into curricula, it transforms teacher-centred practices into learner-centred approaches that develop analytical reasoning, collaborative problem-solving and transferable cognitive skills. For further application, the framework can adopt a mixed-methods research design. This design observes peer learning behaviours, evaluates HOTS through pre- and post-tests, and applies an experimental approach to measure instructional impact.
This study presents the development of a framework that identifies and evaluates the key adoption factors for successfully deploying Internet of Things (IoT) technologies in the monitoring of greenhouse gas emissions within the United Arab Emirates. A unique aspect of this research lies in its focus on a specific sector, involving 384 employees from the UAE’s Department of Hazard Forecasting, Monitoring, and Control (HFMC), who are directly engaged with IoT-based emissions monitoring. The study employs a robust methodological approach, using Partial Least Squares (PLS) and Structural Equation Modelling (SEM) with SmartPLS software to analyze both the measurement and structural components of the model. The findings reveal that Interoperability and Compatibility (IC) is the most influential factor in greenhouse gas monitoring and utilization (GMAU), followed by Data Analytics and Processing (DAP) and Data Security and Privacy (DSP). Interestingly, Sensor Accuracy and Calibration (SAAC) and Connectivity and Network Infrastructure (CNI) were found to have negligible impacts. This study underscores the crucial importance of advanced data analytics capabilities and stringent data security measures in ensuring the effectiveness of IoT in emissions monitoring. Furthermore, it highlights that enhancing IC significantly boosts monitoring efficiency, providing novel insights into the factors that drive IoT adoption for environmental monitoring. The study’s findings also demonstrate that the proposed framework has strong predictive relevance, as evidenced by Q² values exceeding 0.35, which further reinforces its practical applicability. This research contributes novel insights into the deployment of IoT for environmental monitoring, offering a comprehensive guide for improving emissions tracking in the UAE.
This study examined the impact of artificial intelligence in customer relationship management on customer satisfaction, with customer engagement as a mediating variable, to propose an AI-CRM framework for the Ministry of Interior, United Arab Emirates. The study focused on five AI-enabled CRM factors: predictive analytics, churn prediction and retention tools, chatbots and virtual assistants, personalization, and sentiment analysis. A quantitative approach was adopted, and data were collected from service users through an online questionnaire. Out of 500 questionnaires distributed, 351 valid responses were used for analysis after data screening. The findings revealed that predictive analytics, chatbots and virtual assistants, personalization, and sentiment analysis significantly influenced customer satisfaction. However, churn prediction and retention tools did not have a significant direct effect on customer satisfaction. The results also showed that customer engagement had a significant positive effect on customer satisfaction and significantly mediated the relationship between AI-enabled CRM factors and customer satisfaction. The study concludes that AI in CRM can enhance customer satisfaction when it strengthens customer engagement. Therefore, the proposed AI-CRM framework for the Ministry of Interior, UAE, should place customer engagement at the centre of AI implementation to support responsive, customer-centred, and technology-driven public service delivery.
Healthcare service quality is a multidimensional construct that continues to challenge policymakers and practitioners, particularly in rapidly developing contexts such as the United Arab Emirates (UAE). Despite substantial investment in advanced infrastructure, medical technologies, and workforce development, patient satisfaction remains below expectations. This underscores the need for a comprehensive framework that captures both technical and institutional dimensions of healthcare delivery. This study proposes a Theoretical framework that integrates the SERVQUAL model, emphasizing Reliability, Assurance, Responsiveness, Tangibility, and Empathy, with the PubHosQual model, which incorporates Social Responsibility as a critical institutional dimension. Empathy is positioned as a moderating variable, reflecting its role in humanizing healthcare interactions and strengthening the relationship between service quality and patient satisfaction. Drawing on recent regional and international evidence, the framework hypothesizes both direct effects of service quality dimensions on patient satisfaction and moderation effects of empathy. The proposed model provides theoretical and practical contributions. Theoretically, it extends existing service quality models by elevating empathy from a single dimension to a relational amplifier. Practically, it offers healthcare providers and policymakers a diagnostic tool to evaluate strengths and weaknesses in service delivery, guiding interventions that enhance both efficiency and human-centered care. By embedding empathy into service quality reforms, UAE healthcare organizations can foster trust, loyalty, and satisfaction, advancing the nation’s vision of patient-centered, world-class healthcare.
This study investigates demographic patterns of peeragogy learning tendencies among art students in Chinese higher education, addressing the limited empirical evidence on how peer-oriented learning approaches are perceived across diverse learner backgrounds. A quantitative survey design was employed, and 350 questionnaires were distributed to students at Yunnan Arts University and the College of Arts, Dali University. A total of 339 valid responses were analysed using descriptive statistics. Peeragogy tendencies were measured across five dimensions: Student-centred, Self-learning, Agreement, Sharing, and Goal-oriented learning. The findings indicate consistently high endorsement of peeragogy across all dimensions, with agreement levels exceeding 95 percent. Minimal variation was observed across gender, academic year, and socioeconomic status, suggesting that demographic characteristics do not substantially influence peeragogy tendencies in the sampled context. Slight differences across year levels may reflect developmental shifts in autonomy and collaborative maturity. Overall, the results suggest that peeragogy is broadly accepted among art students and appears compatible with disciplinary learning cultures in art education. The study contributes empirical evidence to the growing literature on peer-supported learning in digitally transforming higher education environments.
This study examines the correlation between working conditions and employee performance among construction workers in the United Arab Emirates (UAE). The study focuses on working conditions as a multidimensional construct consisting of job aid, supervisor support, physical work environment, work incentives, and performance feedback. Employee performance is also treated as a multidimensional construct comprising adaptive performance, task performance, and contextual performance. A quantitative research approach was adopted, and data were collected from construction workers using a structured questionnaire. The final usable sample consisted of 435 respondents. Descriptive analysis was used to rank the dimensions, while Pearson correlation analysis was conducted to examine the relationships between the dimensions of working conditions and employee performance. The results showed that the employee performance dimensions recorded slightly higher mean scores than the working conditions dimensions. Adaptive performance ranked highest, followed by task performance and contextual performance. Among the working conditions dimensions, job aid recorded the highest mean score, while performance feedback recorded the lowest. The correlation results revealed that all relationships among the dimensions were positive and statistically significant at the 0.01 level. The findings indicate that better working conditions are associated with stronger employee performance among construction workers. In particular, supervisor support and performance feedback appeared to be important dimensions linked with task, contextual, and adaptive performance. The study concludes that improving workplace support, feedback systems, incentives, and physical work conditions may help enhance employee performance in the UAE construction sector.
