This study examined the links between intrinsic and extrinsic academic motivation and academic achievement, as well as the mediating effects of learning strategies and academic burnout in these links. A sample of 700 undergraduate students participated in this study (M = 20.15, SD = 1.71), including 679 female participants. The results showed that intrinsic and extrinsic motivation were related to higher academic achievement. Serial mediation analyses results showed that intrinsic and extrinsic motivation were related to higher academic achievement through deep and surface learning strategies as first level mediators, and the constructs of academic burnout, that is exhaustion, cynicism, and professional efficacy, as second level mediators. Specifically, intrinsic and extrinsic motivation were related to academic achievement through deep learning strategy, and in turn with all three academic burnout constructs. Furthermore, intrinsic and extrinsic motivation were related to academic achievement through surface learning strategy, and in turn with exhaustion but not professional efficacy. Gaining insights into how various types of academic motivation interact with learning strategies and academic burnout provides a more thorough understanding of the motivational processes underlying academic achievement. The implications of this study for the students of education are also discussed.
Objectives This study aims to assess the impact of higher education on graduates’ perceived employability of young people through dimensions such as study programs in higher education (SPH), collaboration between the universities and businesses (CUB), teaching quality (TQ), flexibility of academic programs (FAP), and higher education infrastructure and resources (HEIR) by identifying factors that influence the interplay between the higher education provision and the demands of the labor market. In addition, differences in employability depending on the ownership of the higher education institution and the characteristics of the graduate profile and field of study are also analyzed. Methods To achieve the research objectives, a quantitative method was used through a structured questionnaire, where 300 persons who have graduated in academic years 2019/20 – 2023/24 were surveyed. Findings The results show a significant positive relationship between higher education and employability (R 2 = 0.367, p < 0.001), with labor market demands and program flexibility as the strongest predictors. Private institutions performed better in all dimensions, with the biggest difference displayed in infrastructure. The field of study also had a significant impact, with graduates in engineering and information technology fields showing the highest levels of employability. Novelty/improvement This research aims to inform social partners about the ways to fill the gap between the supply of higher education and the needs of the labor market both at home and abroad through sustainable mechanisms, which would guide the provision of study programs.
This systematic review evaluates the effectiveness of organisational-level mental health promotion interventions in healthcare, construction, and Telework and ICT-based Mobile (TICTM) work, focusing on burnout, depression, anxiety, stress, and overall mental well-being. Following PRISMA 2020 guidelines and registered with PROSPERO (CRD42024541652), we searched PubMed Medline, Scopus, Web of Science, and EBSCOHost Medline. Studies were included if they assessed organisational-level interventions targeting mental health outcomes, used quantitative methods with control groups, and applied validated measurement tools. Eligible publications were those appearing between 10 July 2021 and 30 June 2024 for healthcare and 17 May 2022 and 30 June 2024 for construction, reflecting the cut-off dates of our previous reviews, and between 1 January 2014 and 30 June 2024 for TICTM. Included studies were appraised for quality using the Quality Assessment Tool for Quantitative Studies (QATQS), and findings were synthesised using a narrative synthesis approach. Six controlled trials (four randomised, two non-randomised) met the inclusion criteria. The four healthcare-sector studies each reported significant improvements in at least one primary mental health outcome-burnout, depression, or stress. The construction-sector study found a short-term reduction in stress at 12 months, but this was not sustained at 24 months. The single TICTM study reported improvements in positive affect, a component of psychological well-being. This update confirmed our previous findings, showing that evidence on organisational-level interventions remains strongest in healthcare, where most studies reported improvements in at least one mental health outcome, while studies in construction and TICTM settings remain scarce. Only one study each was found for the construction sector and the TICTM setting, confirming the lack of organisational-level interventions for better mental health in sectors and settings other than healthcare. Due to this limited evidence base, no general trends can be identified. Nevertheless, the few studies suggest that organisational-level mental health promotion interventions have the potential to improve mental health outcomes in diverse settings when appropriately adapted to sector-specific conditions.
A WHO report states that retinal diseases are among the most serious conditions affecting people worldwide, mostly as a result of excessive use of smart devices. Our study tackles this by presenting a hybrid convolutional neural network (CNN) model for the identification of multiple retinal conditions, such as cataracts, glaucoma, and diabetic retinopathy. 6,281 high-resolution photos of conditions like cataract, glaucoma, diabetic retinopathy, retinopathy of prematurity, corneal opacity, retinal vein occlusion, and normal retinas are included in the Kaggle dataset. Two hybrid transfer learning models were put into practice: VGG16 in conjunction with ResNet50 and VGG16 in conjunction with EfficientNetB3. To improve model resilience and lessen overfitting during training, dataset normalization within the [0,1] range was used. Because these pretrained transfer learning models have already been exposed to comparable large-scale datasets, their analysis is better. 90
Purpose Artificial intelligence (AI) is increasingly embedded in organizational processes, challenging established assumptions about decision-making in project environments. Prior research has predominantly conceptualized AI either as a decision-support tool or as a substitute for human judgment, thereby overlooking the socio-technical complexity through which decisions are constituted. Thus, existing literature offers limited insight into decision-making in AI-enabled public sector contexts. This study aims to address this limitation by problematizing the theoretical foundations of project decision-making in AI-enabled public sector contexts. Design/methodology/approach Drawing on a mixed-methods design, the study analyses 80 public-sector projects and 240 decision episodes across Kosovo, Albania, North Macedonia and Montenegro, complemented by 48 semi-structured interviews conducted across the four transition-economy countries. Findings The findings suggest that AI does not function as a deterministic decision-maker but is enacted through ongoing interactions between human actors, institutional structures and algorithmic systems. Three interrelated mechanisms are identified: (i) hybrid rationality, reflecting the interplay between computational inference and contextual judgment; (ii) institutionally embedded distributed agency, capturing the asymmetric distribution of analytical influence, interpretive authority, decision authority and formal accountability across human and algorithmic actors; and (iii) algorithmic mediation, through which AI structures information flows and temporal dynamics of decision processes. Originality/value This study contributes to project management and organizational decision-making research by developing augmented project decision-making (APDM) as an empirically grounded, mechanism-based integration of previously fragmented perspectives on AI-enabled decision-making. It explains the asymmetric distribution of analytical influence, authority and accountability across human and algorithmic actors, moving beyond human-centric, tool-based and technologically deterministic accounts of AI-enabled decision-making.
