ELearning has been the medium of delivery of medical educational institutions to address the scarcity of medical professionals during the COVID-19 pandemic. In this study, the Unified Theory of Acceptance and Use of Technology (UTAUT2) was extended to determine the factors affecting the acceptance of eLearning platforms to medical education in the Philippines during the COVID-19 pandemic. A total of 360 medical students voluntary participated and answered an online questionnaire that consisted of 40 questions. Structural Equation Modeling (SEM) indicated that performance expectancy was found to have the highest effect on behavioral intention, which was followed by learning value and instructor characteristics. A high behavioral intention was found to affect the actual use of eLearning platforms. Interestingly, social influence and habit were found not to be significant to behavioral intentions. This study is the first study that has explored the acceptance of eLearning platforms among medical students in the Philippines during the COVID-19 pandemic. The findings can be a theoretical guideline of the Commission on Higher Education of the Philippines for eLearning platforms. Finally, the framework would be very valuable for enhancing the open innovation in eLearning platforms in medical fields worldwide.

The restrictions of the ongoing COVID-19 pandemic resulted in the downturn of various industries and in contrast a massive growth of the information technology industry. Consequently, more Filipinos are considering career changes to earn a living. However, more people still need to be upskilled. This study combines the extended Technology Acceptance Model and Task Technology Fit framework to determine factors affecting a career shifter’s use of software testing tools and its impact on perceived performance impact amidst the COVID-19 pandemic in the Philippines. A total of 150 software testers voluntarily participated and accomplished an online questionnaire consisting of 39 questions. The Structural Equation Modeling and Deep Learning Neural Network indicated that Task Technology Fit had a higher effect on Perceived Performance Impact. Moreover, Task Technology Fit positively influenced Perceived Usefulness. Computer Self-Efficacy was a strong predictor of Perceived Ease of Use. Perceived Ease of Use confirmed the Technology Acceptance Model framework as a strong predictor of Actual System Use. Intention to Use, Perceived Usefulness, Actual Use, and Subjective Norm were also significant factors affecting Perceived Performance Impact. This study is the first to explore the career shifter’s use of software testing tools in the Philippines. The framework would be very valuable in enhancing government policies for workforce upskilling, improving the private sector’s training and development practices, and developing a more competitive software testing tool that would hasten users’ adaptability. Lastly, the methodology, findings, and framework could be applied and extended to evaluate other technology adoption worldwide.

Software testing effort estimation is a critical phase in software project management. Software testing is an integral part of the software development lifecycle (SDLC). It ensures that requirement specifications are met before a product is deployed. Accurate test estimation is crucial to the on-time delivery of a project and ensures that it meets resource constraints. In this study fuzzy logic is used to estimate the manual test script authoring effort in the test process. MATLAB software is used to design the fuzzy logic system that would calculate the duration of the scripting effort. The variables considered were test complexity, test script reusability, and number of test scenarios. The crisp output is the effort estimation in hours. The Delphi method was used to collate the ratings of each expert for the parameters of each membership function in each variable. The linguistic rules were defined after. Crisp inputs for each project were applied to the model and the output was compared with the actual effort duration of previous projects and expertbased estimation. The results showed that the mean absolutive relative error (MARE) of the fuzzy logic model has performed better than the expert-based estimation technique. Thus, the fuzzy logic framework may be used as a decision support tool in manual scripting effort estimation. This study is the first to explore the effort estimation in manual test script authoring. This model could serve as a baseline to test engineers, test leads, project managers, and business analysts.