Hybrid learning (HL) has become a significant part of the learning style for the higher education sector in the Sri Lankan context amidst the COVID-19 pandemic and the subsequent economic crisis. This research study aims to discover the effectiveness of hybrid learning (EHL) practices in enhancing undergraduates’ outcomes in Sri Lankan Higher Educational Institutions (HEIs) management faculties. The data for the study were gathered through an online questionnaire survey, which received 379 responses. The questionnaire contained 38 questions under four sections covering independent variables, excluding demographic questions. The results indicate that hybrid learner attitude, interaction, and benefits of hybrid learning positively impact the effectiveness of hybrid learning. The results remain consistent even after controlling for socio-demographic factors and focusing only on students employed during their higher education. The study concluded that employed students have a higher preference for the effectiveness of hybrid learning concepts, and the benefits of hybrid learning play a crucial role in enhancing the effectiveness among undergraduates. The study analyzes COVID-19’s impact on higher education, proposing hybrid learning and regulatory frameworks based on pandemic experiences while stressing the benefits of remote teaching and research.
With the rapid development of digital technology, the digital infrastructure enables the rapid formation, modification and refactoring of digital products through continuous experimentation and implementation, reduces the cost of innovation, and facilitates the implementation of digital innovation. To solve the problem that the technical scope of digital innovation is relatively concentrated and the knowledge flow between the achievements of digital innovation is insufficient, this study investigates the impact of digital infrastructure on organizational digital innovation in China. The cross-sectional study was conducted from November 2023 to March 2024 among 384 employees and managers in the core industries of the digital economy, as well as enterprises in traditional industries in China. Data were collected using closed-ended questionnaires adapted from previous literature. Structural equation modelling (SEM) was employed to analyze the data using SPSS 28 and AMOS 28. The results reveal that both the information infrastructure and the innovation infrastructure have a positive and direct effect on organizational digital innovation in China, as well as an indirect effect through data flows. Converged infrastructure has only an indirect impact on organizational digital innovation through the flow of data.
This paper assesses South Africa’s massive infrastructure drive to revive growth and increase employment. After years of stagnant growth, this is now facing a deep economic crisis, exacerbated by the COVID-19 pandemic. This drive also comes after years of weak infrastructure investment, widening the infrastructure deficit. The plan outlines a R1 trillion investment drive, primarily from the private sector through the Infrastructure Fund over the next 10 years (Government of South Africa, 2020). This paper argues that while infrastructure development in South Africa is much-needed, the emphasis on de-risking for private sector buy-in overshadows the key role the state must play in leading on structurally transforming the economy.
To achieve the electrification of private vehicles, it is urgent to develop public charging infrastructure. However, choosing the most beneficial type of public charging infrastructure for the development of a country or region remains challenging. The municipal decision’s implementation requires considering various perspectives. An important aspect of energy development involves effectively integrating and evaluating public charging infrastructure. While car charging facilities have been thoroughly studied, motorcycle charging facilities have been neglected despite motorcycles being a vital mode of transportation in many countries. The study created a hybrid decision-making model to evaluate electric motorcycle charging infrastructure. Firstly, a framework for evaluating electric motorcycle charging infrastructure was effectively constructed through a literature survey and expert experience. Secondly, decision-makers’ opinions were gathered and integrated using Bayesian BWM to reach a group consensus. Thirdly, the performance of the alternative solutions was evaluated by exploring the gaps between them and the aspiration level through modified VIKOR. An empirical analysis was conducted using examples of regions/countries with very high rates of motorcycle ownership worldwide. Finally, comparative and sensitivity analyses were conducted to demonstrate the practicality of the proposed model. The study’s findings will aid in addressing municipal issues and achieving low-carbon development objectives in the area.
This study aims to identify the risk factors causing the delay in the completion schedule and to determine an optimization strategy for more accurate completion schedule prediction. A validated questionnaire has been used to calculate a risk rating using the analytical hierarchy process (AHP) method, and a Monte Carlo simulation on @RISK 8.2 software was employed to obtain a more accurate prediction of project completion schedules. The study revealed that the dominant risk factors causing project delays are coordination with stakeholders and changes in the scope of work/design review. In addition, the project completion date was determined with a confidence level of 95%. All data used in this study were obtained directly from the case study of the Double-Double Track Development Project (Package A). The key result of this study is the optimization of a risk-based schedule forecast with a 95% confidence level, applicable directly to the scheduling of the Double-Double Track Development Project (Package A). This paper demonstrates the application of Monte Carlo Simulation using @RISK 8.2 software as a project management tool for predicting risk-based-project completion schedules.
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