Factors Influencing the Perceived Effectiveness of COVID-19 Risk Assessment Mobile Application “MorChana” in Thailand: UTAUT2 Approach

Abstract

COVID-19 contact-tracing mobile applications have been some of the most important tools during the COVID-19 pandemic. One preventive measure that has been incorporated to help reduce the virus spread is the strict implementation of utilizing a COVID-19 tracing application, such as the MorChana mobile application of Thailand. This study aimed to evaluate the factors affecting the actual usage of the MorChana mobile application. Through the integration of Protection Motivation Theory (PMT) and Unified Theory of Acceptance and Use of Technology (UTAUT2), latent variables such as performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), hedonic motivation (HM), habit (HB), perceived risk (PCR), self-efficacy (SEF), privacy (PR), trust (TR), and understanding COVID-19 (U) were considered to measure the intention to use MorChana (IU) and the actual usage (AU) of the mobile application. This study considered 907 anonymous participants who voluntarily answered an online self-administered survey collected via convenience sampling. The results show that IU presented the highest significant effect on AU, followed by HB, HM, PR, FC, U, SEF, PE, EE, TR, and SI. This is evident due to the strict implementation of using mobile applications upon entering any area of the vicinity. Moreover, PCR was not seen to be a significant latent factor affecting AU. This study is the first to have evaluated mobile contact tracing in Thailand. The integrated framework can be applied and extended to determine factors affecting COVID-19 tracing applications in other countries. Moreover, the findings of this study could be applied to other health-related mobile applications worldwide.

Utilization of Random Forest Classifier and Artificial Neural Network for Predicting Factors Influencing the Perceived Usability of COVID-19 Contact Tracing “MorChana” in Thailand

“…Comapred to the results obtained from Yuduang et al [10], this study highlighted how other factors that were affected from the mediating effect from structural equation modeling was highly significant. In addition, a comparison to other studies, usability of contact tracing, and application has been discussed.…”

“…This may come from the presence of mediators are supported by studies that discussed the limitations of SEM [19,20]. They also highlighted that SEM was a limitation and suggested using a machine learning ensemble to validate the findings of the study [10]. Machine learning algorithms are not being utilized to evaluate human behavior and human factors [43].…”

“…With different COVID-19 contact tracing available and different methods in different countries, the evaluation may be deemed special per type since it is affected by language, culture, and perception of the general users. Yuduang et al [10] considered the MorChana COVID-19 contact tracing mobile application. However, the analysis using SEM was seen to evidently affect the results due to the presence of a full mediating effect.…”

“…Under PMT, several factors such as understanding COVID-19 (U), Perceived Risk (PCR), and Self-Efficacy (SEF) were considered as extended factors for UTAUT2. These factors were considered as representations of threat and coping appraisal behaviors, respectively [10,14,15]. Under UTAUT2, several factors such as Social Influence (SI), Habit (HB), Hedonic Motivation (HM), Privacy (PR), and Trust (TR) were considered alongside the PMT variables to measure intention to use (IU) the MorChana COVID-19 contact tracing mobile application leading to Actual Use (AU) of the mobile application.…”

“…To this aim, this study considered the integration of PMT latent variables to holistically measure the actual usage of a new health-related mobile technology. Ong et al [10] suggested that PMT is a framework used to measure the coping and threat appraisal of health-related behaviors. The exploration to integrate PMT with another framework has been suggested to holistically measure health-related behavior and the integration of another theory would completely measure human behavior [10].…”