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

Abstract

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.

Unraveling Knowledge-Based Chatbot Adoption Intention in Enhancing Species Literacy

“…Although surprising and not aligned with most studies, the finding is in line with Sanni et al (2013) and Wu and Wu (2005). Furthermore, this result also aligns with several TAM and UTAUT studies where the perceived usefulness and performance expectancy have not always affected the intention to use (Ong et al, 2022;Sharif et al, 2019). One plausible reason could be the measurement timing.…”

“…TTF has been integrated with the abovementioned theories, such as TAM and UTAUT. The findings have shown that TTF indirectly influenced the successful adoption and utilization of technologies via perceived ease of use, such as for wearable devices (Chang et al, 2016) and knowledge management systems (Kuo & Lee, 2011), as well as through perceived usefulness (Ong et al, 2022). On the other hand, TTF indirectly influenced the usage intention of smart home healthcare services positively via performance expectancy (Kang et al, 2022) and affected effort expectancy in a learning management system (Sharif et al, 2019).…”

Exploring the Determinants and Consequences of Task-Technology Fit: A Meta-Analytic Structural Equation Modeling Perspective

“…This study has theoretically proved the soundness and robustness of the research framework built on the combination of TTF, TAM, and UTAUT designed to analyze the BI and PI of users. Furthermore, besides providing a robust model, this study provides a novel and testable methodology related to applying MASEM [73]. Consequently, this study theoretically contributes to the literature on MASEM methodology and proposes a new testable methodology for other scholars to use in various fields.…”

“…However, there are some limitations regarding capturing cultural and individual characteristics [80]. Hence, future researchers are advised to include TPB and system usability scales to study the cultural context [73].…”

An empirical investigation of college students’ acceptance of translation technologies

“…For example, Teo et al [ 34 ] found that teachers with high computer self-efficacy tend to perceive technology as easy to use. Similarly, a study on software testers identified computer self-efficacy as a robust predictor of perceived ease of use of software testing tools [ 35 ]. The correlation has also been found in the context of e-learning.…”