Higher education institutions (HEIs) are crucial in transitioning to sustainable, carbon-neutral societies. However, achieving sustainability in HEIs requires systemic transformation across governance, operations, teaching, and research. This paper presents a simulation model designed to enhance awareness and strategic decision-making regarding sustainability in HEIs. The model was developed within a European transnational project focusing on a Whole Institution Approach (WIA) to sustainability through systems thinking. By integrating feedback loops and dynamic interdependencies, the model allows HEIs to simulate and evaluate policies, assess long-term sustainability impacts, and identify leverage points for transformation. A hypothetical case study illustrates the model’s application, providing an interactive learning environment for stakeholders. The model highlights the importance of aligning policies, resource allocation, and cultural shifts within HEIs to address the green transition and stimulates reflection on mental models and the development of sustainability strategies.
While there is a growing body of research on using Artificial Intelligence technology and generative AI in education, the comprehensive examination of its impact on student’s learning outcomes and satisfaction remains largely uncharted. We briefly introduce artificial intelligence and its subfields, including machine learning, deep learning, and generative AI. We then discuss the roles of AI in e-learning inputs, processes, and output through the lens of the System’s view of the e-learning success model. We examined, through the lens of the System’s view of e-learning, the impact of AI on each of the isolated subentities, such as input (student motivation and instructor course facilitation), processes (self-regulated learning behavior, student-student dialogue, and student-instructor dialogue) and output (learning outcomes and satisfaction). We conclude that the systems approach helps us realize that the current literature on the role of AI and generative AI in e-learning systems failed to achieve the goal of e-learning systems as a dynamic set of interdependent subentities interacting to optimize learning outcomes and student satisfaction. We recommend conducting AI and generative AI research to contribute positively to learning outcomes and student satisfaction. To make that happen, we recommend going one step further by integrating AI/generative AI research results with structural equation modeling-based empirical research to optimize e-learning processes and learning outcomes.
To support the decision-making process and enhance user interaction, Decision Support Systems (DSSs) are being developed and continuously improved. The aim of this study has been to investigate the interface of the FITradeoff DSS in order to investigate decision makers’ preferences in holistic evaluation and decomposition process. This study analyzed the differences between four interfaces in a DSS, as well as eye movement data from 37 participants. In the experiment (total average duration of approximately 5 min), fixation and pupil data were captured using Area of Interest (AOI) drawings, as well as heatmaps and scan paths. The results revealed that the decision-makers seemed to spend more time, attention and cognitive effort on the screen declared as preferred. In addition, it is suggested that, in one of the interfaces, the change in the arrangement of the data on the graph resulted in an increase in the demand for the task, suggesting an increase in task time, duration of fixation and pupil diameter. Hence, based on the results, the study aims to improve the FITradeoff DSS providing modulations in the FITradeoff method and it DSS.
Emerging technologies (ETs) are being applied on farms to promote efficiency, productivity and automation. However, their low maturity, small-scale application and high investment costs impede further implementation in more farming areas. The total factor productivity (TFP) associated with China’s agricultural mechanization rate has grown exponentially, reaching 71.25
Blockchain technology is evolving steadily and rapidly in this modern day, finding application in multiple sectors. While its primary application is in the financial sector, there is also a growing interest in supply chain management. Although research efforts recognize the need for further study into the correlation between these two sectors, the present research presents an important step towards understanding and exploiting this relationship. This paper analyzes the application of Blockchain in the supply chain, by first identifying the basic definitions and functions of the two concepts. Then, it examines their dynamics by analyzing the advantages and challenges accompanying Blockchain integration into supply chain processes. Through the presentation of practical examples and the evaluation of their impacts, the study highlights the importance of improving efficiency, reducing costs, and increasing transparency. The conclusion of the work highlights the prospects and challenges arising from integrating Blockchain into the supply chain (by solving the Tactical Berth Allocation problem in Maritime Logistics), while emphasizing the importance of further research and development in this area. As a result, the work provides a significant foundation for understanding how Blockchain technology can influence and transform the supply chain sector.
