Determining the Factors Affecting a Career Shifter’s Use of Software Testing Tools amidst the COVID-19 Crisis in the Philippines: TTF-TAM Approach

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

Predicting Factors Affecting the Intention to Prepare for Mitigation of Man-Made Fire Disasters in Chonburi Province, Thailand: An Integration of Structural Equation Modeling and Artificial Neural Network Hybrid Approach

News regarding different man-made fire disasters has been increasing for the past few years, especially in Thailand. Despite the prominent fire in Chonburi Province, Thailand, the intention to prepare has been widely underexplored. This study aimed to predict factors affecting the intention to prepare for the mitigation of man-made fire disasters in Chonburi Province, Thailand. A total of 366 valid responses through convenience sampling were utilized in this study that produced 20,496 datasets. With the 20,496 datasets, structural equation modeling and artificial neural network hybrid were utilized to analyze several factors under the extended and integrated protection motivation theory and the theory of planned behavior. Factors such as geographic perspective, fire perspective, government response, perceived severity, response cost, perceived vulnerability, perceived behavioral control, subjective norm, and attitude were evaluated simultaneously to measure the intention to prepare for a fire disaster. The results showed that geographic perspective, subjective norm, and fire experience were the most important factors affecting the intention to prepare. Other factors were significant with perceived behavioral control as the least important. In addition, the results showed how the region is prone to man-made fire disasters and that the government should consider mitigation plans to highlight the safety of the people in Chonburi Province, Thailand. This study is considered the first complete study that analyzed behavioral intention to prepare for the mitigation of man-made fire disasters in the Chonburi Province region of Thailand. The results of this study could be utilized by the government as a foundation to create mitigation plans for the citizens of Thailand. Finally, the findings of this study may be applied and extended to measure the intention to prepare for other man-made fire disasters worldwide

Utilization of Random Forest and Deep Learning Neural Network for Predicting Factors Affecting Perceived Usability of a COVID-19 Contact Tracing Mobile Application in Thailand “ThaiChana”

The continuous rise of the COVID-19 Omicron cases despite the vaccination program available has been progressing worldwide. To mitigate the COVID-19 contraction, different contact tracing applications have been utilized such as Thai Chana from Thailand. This study aimed to predict factors affecting the perceived usability of Thai Chana by integrating the Protection Motivation Theory and Technology Acceptance Theory considering the System Usability Scale, utilizing deep learning neural network and random forest classifier. A total of 800 respondents were collected through convenience sampling to measure different factors such as understanding COVID-19, perceived severity, perceived vulnerability, perceived ease of use, perceived usefulness, attitude towards using, intention to use, actual system use, and perceived usability. In total, 97.32% of the deep learning neural network showed that understanding COVID-19 presented the most significant factor affecting perceived usability. In addition, random forest classifier produced a 92% accuracy with a 0.00 standard deviation indicating that understanding COVID-19 and perceived vulnerability led to a very high perceived usability while perceived severity and perceived ease of use also led to a high perceived usability. The findings of this study could be considered by the government to promote the usage of contact tracing applications even in other countries. Finally, deep learning neural network and random forest classifier as machine learning algorithms may be utilized for predicting factors affecting human behavior in technology or system acceptance worldwide

Determination of Factors Influencing the Behavioral Intention to Play “Mobile Legends: Bang-Bang” during the COVID-19 Pandemic: Integrating UTAUT2 and System Usability Scale for a Sustainable E-Sport Business

The rise of mobile games during the COVID-19 pandemic era was evident, especially in Asia. One of the most popular online mobile games that has been evident across the world due to its live worldwide competition is “Mobile Legends: Bang Bang” (MLBB). This study aimed to determine factors influencing the behavioral intention to play MLBB by utilizing the integrated model of UTAUT2 and System Usability Scale (SUS). A total of 507 MLBB players voluntarily answered an online questionnaire that consisted of 69 items. Through convenience sampling, the online survey was collected from November 2021–January 2022 from different social media platforms. Several factors such as hedonic motivation, effort expectancy, performance expectancy, perceived usefulness, security, perceived usability, facilitating conditions, social influence, habit, behavioral intention, and SUS were considered in this study. Using Structural Equation Modeling (SEM), results showed that habit was the most significant factor in behavioral intention, followed by perceived usability, facilitating conditions, social influence, and hedonic motivation. In addition, it was evident from the results that when the mobile application is free and resources are available, then continuous patronage of the mobile application will be considered. In-game resources may be capitalized on by developers after gaining these habits and hedonic motivations among users. This is the first study that evaluated MLBB by utilizing the integrated models of UTAUT2 and SUS during the COVID-19 pandemic. The results of this study could be beneficial for developers to entice users for team play and entertainment-based mobile applications. Finally, the model considered may be extended and applied to other mobile applications worldwide