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

Ardvin Kester S. Ong, Yogi Tri Prasetyo*, Ralph Andre C. Roque, Jan Gabriel I. Garbo, Kirstien Paola E. Robas, Satria Fadil Persada, Reny Nadlifatin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

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.

Original languageEnglish
Article number11084
JournalSustainability (Switzerland)
Volume14
Issue number17
DOIs
Publication statusPublished - Sept 2022

Keywords

  • career shifter
  • deep neural network
  • software testing tools
  • structural equation modeling
  • task technology fit

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