Factors Affecting the Perceived Usability of the COVID-19 Contact-Tracing Application "Thai Chana" during the Early COVID-19 Omicron Period

Immersive Virtual Reality Simulation for Learning Fundamental Nursing Skills: A Mixed-Methods Study

BACKGROUND

Literature showed that learners' perceived usability and perspective toward a technology application affected their learning experience. Fewer studies have investigated immersive virtual reality (IVR) simulation learning of fundamental nursing skills learning (FNSL).

PURPOSE

The aim of the study was to explore the perceived usability of IVR simulations for FNSL among first-year nursing students and their perspectives toward this learning modality.

METHODS

This study used a mixed-methods design with an educational intervention. Sixty-five first-year nursing students participated in 2 IVR simulation procedures in complementary mode. Surveys and focus groups were conducted in the postintervention period.

RESULTS

The findings demonstrated students' positive inclinations toward IVR simulation learning. Two areas emerged: using IVR simulation as a complementary modality for FNSL and barriers affecting students' perceived usability toward this technology.

CONCLUSIONS

With addressing the concerns from students' perceived usability, immersive virtual reality simulation could be a potential complementary modality for FNSL.

Epidemiological investigation support application and user evaluation based on infectious disease self-management model in the endemic era

Objectives: Rapid epidemiological investigations are fundamental to prevent the spread of infectious diseases such as coronavirus disease 2019. An epidemiological investigation presents significant challenges for both epidemiologists and infected individuals. It requires creating an environment that enables people to independently manage infectious diseases and voluntarily participate in epidemiological investigations. Methods: We developed the KODARI application, an epidemiological investigation support system that users can voluntarily use. We developed the questionnaires based on literature reviews. We evaluated the application through an online survey from December 2 to 14, 2022. Results: The application automatically or manually collect epidemiological investigation information. The application improved data accuracy through accurate information collection. It voluntarily can transmit self-management information to epidemiologist terminals or users in real time. We collected 248 users from an online survey. Most users had high ratings and willingness to use. They have willingness to manage infectious patients was substantial. The application was evaluated as helpful for epidemiological investigations and could shorten the time required for epidemiological investigations by more than 30 min. Conclusion: The application proposes a model based on people's voluntary participation. We demonstrated that the application could enhance epidemiological investigations and diminish the duration of existing epidemiological investigation processes.

Determining the factors affecting implementation of health and safety app towards improvement of safety culture: A study in school of engineering building

The improvement of safety culture in the academic sector is often overlooked, as Occupational Safety and Health (OSH) practices are typically associated with industries. Consequently, research on health and safety in educational centers building is limited. This study aims to bridge this gap by investigating the factors influencing the implementation of a health and safety application in an engineering school building in the Philippines, using the dimensions of safety culture and the UTAUT framework. The research involved designing and executing the health & safety app using the affiliated MS 365 - Microsoft Power Apps, which was tested by 232 engineering students in Metro Manila. The obtained data were then analyzed using the Partial Least Squares-Structural Equation Modeling (PLS-SEM) approach. The results indicated that the users' intention to use and perceived usability were crucial factors influencing their behavior towards the app. Additionally, performance expectancy and social influence affected their behavioral intentions towards the application. Moreover, the app's perceived usability and usage behavior significantly influenced users' overall participation in health and safety activities. This study stands as a unique contribution to the literature on safety culture in the academic sector. The study emphasizes the importance of incorporating technology in health and safety culture on campus, benefiting researchers, academic institutions, industries, and students alike. By integrating technology such as safety training apps, institutions can improve safety performance, empower students with relevant skills, and enhance overall safety culture on campus and in workplaces.

Hits and Misses: Digital Contact Tracing in a Pandemic

Traditional contact tracing is one of the most powerful weapons people have in the battle against a pandemic, especially when vaccines do not yet exist or do not afford complete protection from infection. But the effectiveness of contact tracing hinges on its ability to find infected people quickly and obtain accurate information from them. Therefore, contact tracing inherits the challenges associated with the fallibilities of memory. Against this backdrop, digital contact tracing is the "dream scenario"-an unobtrusive, vigilant, and accurate recorder of danger that should outperform manual contact tracing on every dimension. There is reason to celebrate the success of digital contact tracing. Indeed, epidemiologists report that digital contact tracing probably reduced the incidence of COVID-19 cases by at least 25% in many countries, a feat that would have been hard to match with its manual counterpart. Yet there is also reason to speculate that digital contact tracing delivered on only a fraction of its potential because it almost completely ignored the relevant psychological science. We discuss the strengths and weaknesses of digital contact tracing, its hits and misses in the COVID-19 pandemic, and its need to be integrated with the science of human behavior.

Antecedents predicting digital contact tracing acceptance: a systematic review and meta-analysis

An awareness of antecedents of acceptance of digital contact tracing (DCT) can enable healthcare authorities to design appropriate strategies for fighting COVID-19 or other infectious diseases that may emerge in the future. However, mixed results about these antecedents are frequently reported. Most prior DCT acceptance review studies lack statistical synthesis of their results. This study aims to undertake a systematic review and meta-analysis of antecedents of DCT acceptance and investigate potential moderators of these antecedents. By searching multiple databases and filtering studies by using both inclusion and exclusion criteria, 76 and 25 studies were included for systematic review and meta-analysis, respectively. Random-effects models were chosen to estimate meta-analysis results since Q, I 2, and H index signified some degree of heterogeneity. Fail-safe N was used to assess publication bias. Most DCT acceptance studies have focused on DCT related factors. Included antecedents are all significant predictors of DCT acceptance except for privacy concerns and fear of COVID-19. Subgroup analysis showed that individualism/collectivism moderate the relationships between norms/privacy concerns and intention to use DCT. Based on the results, the mean effect size of antecedents of DCT acceptance and the potential moderators may be more clearly identified. Appropriate strategies for boosting the DCT acceptance rate can be proposed accordingly.

A machine learning ensemble approach to predicting factors affecting the intention and usage behavior towards online groceries applications in the Philippines

The emergence of e-commerce platforms, especially online grocery shopping, is heightened by the COVID-19 pandemic. Filipino consumers started to adapt online due to the strict quarantine implementations in the country. This study intended to predict and evaluate factors influencing the intention and usage behavior towards online groceries incorporating the integrated Protection Motivation Theory and an extended Unified Theory of Acceptance and Use of Technology applying machine learning ensemble. A total of 373 Filipino consumers of online groceries responded to the survey and evaluated factors under the integrated framework. Artificial Neural Network that is 96.63 % accurate with aligned with the result of the Random Forest Classifier (96 % accuracy with 0.00 standard deviation) having Perceived Benefits as the most significant factor followed by Perceived Vulnerability, Behavioral Intention, Performance Expectancy, and Perceived. These factors will lead to very high usage of online grocery applications. It was established that machine learning algorithms can be used in predicting consumer behavior. These findings may be applied and extended to serve as a framework for government agencies and grocers to market convenient and safe grocery shopping globally.

Modeling the dynamics of COVID-19 with real data from Thailand

In recent years, COVID-19 has evolved into many variants, posing new challenges for disease control and prevention. The Omicron variant, in particular, has been found to be highly contagious. In this study, we constructed and analyzed a mathematical model of COVID-19 transmission that incorporates vaccination and three different compartments of the infected population: asymptomatic [Formula: see text], symptomatic [Formula: see text], and Omicron [Formula: see text]. The model is formulated in the Caputo sense, which allows for fractional derivatives that capture the memory effects of the disease dynamics. We proved the existence and uniqueness of the solution of the model, obtained the effective reproduction number, showed that the model exhibits both endemic and disease-free equilibrium points, and showed that backward bifurcation can occur. Furthermore, we documented the effects of asymptomatic infected individuals on the disease transmission. We validated the model using real data from Thailand and found that vaccination alone is insufficient to completely eradicate the disease. We also found that Thailand must monitor asymptomatic individuals through stringent testing to halt and subsequently eradicate the disease. Our study provides novel insights into the behavior and impact of the Omicron variant and suggests possible strategies to mitigate its spread.

Determination of factors influencing young adults' intention to have COVID-19 vaccine in the Philippines: An integration of Health Belief Model and the Theory of Planned Behavior

Objectives

The COVID-19 pandemic continues to increase around the world and businesses and markets across the world significantly decreased. The purpose of this study was to determine the factors that affect the intention to be vaccinated for the COVID-19 vaccine among young Filipino adults by integrating the Health Belief Model and Extended Theory of Planned Behavior.

Study design

A cross-sectional study design was utilized.

Methods

Factors such as understanding of the COVID-19 vaccine, self-efficacy, cues to action, perceived barriers, perceived benefits, perceived side effects, perceived behavioral control, attitude, subjective norm, and intention to be vaccinated were analyzed by utilizing Structural Equation Modeling (SEM).

Results

With 865 young Filipino adults who answered a self-administered survey, it was seen that Understanding of the COVID-19 vaccine has the highest direct significant effect on cues to action, followed by perceived barriers, and perceived benefits. Interestingly, the primary factor was Understanding COVID-19 vaccines which had an indirect significant effect on the intention to get vaccinated. This is because knowing what the vaccine is for, its effects, and the application would lead to the acceptance of the COVID-19 vaccine. Moreover, the impact of being known to have the COVID-19 vaccine would lead to the intention to get vaccinated.

Conclusions

The findings of this study can be utilized especially by the government in developing strategies for encouraging people to take the COVID-19 vaccine. Finally, the model construct of the study can be applied to explore more factors that can affect the intention to be vaccinated with the COVID-19 vaccine and other vaccines people worldwide.

Modeling Consumer Acceptance and Usage Behaviors of m-Health: An Integrated Model of Self-Determination Theory, Task-Technology Fit, and the Technology Acceptance Model

Although mobile health (m-health) has great potential to reduce the cost of medical care and improve its quality and efficiency, it is not widely accepted by consumers. In addition, there is still a lack of comprehensive insight into m-health acceptance, especially among consumers with different demographic characteristics. This study aimed to explore the factors affecting consumers' acceptance and usage behaviors of m-health and to examine whether their roles differ by demographic characteristics. A comprehensive m-health acceptance model was proposed by integrating factors from the Self-Determination Theory, Task-Technology Fit, and Technology Acceptance Model. Survey data were collected from 623 Chinese adults with at least 6 months of m-health usage experience and analyzed using structural equation modeling techniques. Multi-group analyses were performed to assess whether the model relationships were different across gender, age, and usage experience. The results indicated that relatedness and competence were significant motivational antecedents of perceived ease of use. Task-technology fit and the perceived ease of use significantly affected the perceived usefulness. The perceived ease of use and perceived usefulness were significant determinants of consumer usage behaviors of m-health and together explained 81% of its variance. Moreover, the relationships among autonomy, perceived usefulness, and usage behaviors of m-health were moderated by gender. Consumer usage behaviors of m-health were affected by factors such as self-motivation (i.e., relatedness and competence), technology perceptions (i.e., perceived ease of use and perceived usefulness), and task-technology fit. These findings provide a theoretical underpinning for future research on m-health acceptance and provide empirical evidence for practitioners to promote the better design and use of m-health for healthcare activities.

Classification modeling of intention to donate for victims of Typhoon Odette using deep learning neural network

The need for stability in the economy for world development has been a challenge due to the COVID-19 pandemic. In addition, the increase of natural disasters and their aftermath have been increasing causing damages to infrastructure, the economy, livelihood, and lives in general. This study aimed to determine factors affecting the intention to donate for victims of Typhoon Odette, a recent super typhoon that hit the Philippines leading to affect 38 out of 81 provinces of the most natural disaster-prone countries. Determining the most significant factor affecting the intention to donate may help in increasing the engagement of donations among other people to help establish a more stable economy to heighten world development. With the use of deep learning neural network, a 97.12% accuracy was obtained for the classification model. It could be deduced that when donors understand and perceive both severity and vulnerability to be massive and highly damaging, then a more positive intention to donate to victims of typhoons will be observed. In addition, the influence of other people, the holiday season when the typhoon happened, and the media as a platform have greatly contributed to heightening the intention to donate and control over the donor's behavior. The findings of this study could be applied and utilized by government agencies and donation platforms to help engage and promote communication among donors. Moreover, the framework and methodology considered in this study may be extended to evaluate intention, natural disasters, and behavioral studies worldwide.

Factors Affecting Preventive Behaviors for Safety and Health at Work during the COVID-19 Pandemic among Thai Construction Workers

Occupational health and safety risks are of major concerns in construction industry. The COVID-19 outbreak provides an additional risk that could drastically affect the safety risks and health of construction workers. Understanding the factors that affect the health and safety of construction workers is significant in reducing risky behaviors and enhancing worker preventive behaviors. Via integrating the Protection Motivation Theory (PMT) and the extended Theory of Planned Behavior (TPB), this study investigates the factors that affect preventive behaviors among construction workers during the COVID-19 pandemic in Bangkok, Thailand. A total of 610 Thai construction workers participated in an online questionnaire survey, which consisted of nine factors with 43 questions. Structure equation modeling (SEM) was adopted to analyze the causal relationships among the latent variable. The SEM results indicated that organizational support and knowledge about COVID-19 had significant (p < 0.0001) direct influences on perceived vulnerability and perceived severity. In addition, perceived vulnerability and perceived severity had significant direct influences on perceived behavioral control. Perceived severity had significant (p < 0.0001) direct influence on attitude towards behavior. Moreover, perceived behavioral control and attitude towards behavior had significant (p < 0.0001) direct influence on intention to follow the preventive measure. Furthermore, the intention to follow the preventive measure had significant (p < 0.0001) direct influences on the COVID-19 preventive behavior. Of note, organizational support and knowledge about COVID-19 had significant (p < 0.0001) indirect influence on COVID-19 preventive behavior. The findings of this study may assist project managers/supervisors and authorities in the construction industry in understanding the challenge during COVID-19 and possible similar epidemics in the future. In addition, conducting effective strategies would improve construction industry safety and promote preventive behaviors among construction workers.

Understanding the Antecedents and Effects of mHealth App Use in Pandemics: A Sequential Mixed-Method Investigation

Pandemics such as COVID-19 pose serious threats to public health and disrupt the established systems for obtaining healthcare services. Mobile health (mHealth) apps serve the general public as a potential method for coping with these exogenous challenges. However, prior research has rarely discussed the antecedents and effects of mHealth apps and their use as a coping method during pandemics. Based on the technology acceptance model, empowerment theory, and event theory, we developed a research model to examine the antecedents (technology characteristics and event strength) and effects (psychological empowerment) of mHealth apps and their use. We tested this research model through a sequential mixed-method investigation. First, a quantitative study based on 402 Chinese mHealth users who used the apps during the COVID-19 pandemic was conducted to validate the theoretical model. A follow-up qualitative study of 191 online articles and reviews on mHealth during the COVID-19 pandemic was conducted to cross-validate the results and explain the unsupported findings of the quantitative study. The results show that (1) the mHealth app characteristics (perceived usefulness and perceived ease of use) positively affect mHealth app use; (2) mHealth app use positively affects the psychological empowerment of mHealth users; and (3) the characteristics of pandemic events (event criticality and event disruption) have positive moderating effects on the relationship between mHealth app characteristics and mHealth app use. This study explains the role of mHealth apps in the COVID-19 pandemic on the micro-level, which has implications for the ways in which mHealth apps are used in response to public pandemics.

Understanding the perceived behavior of public utility bus passengers during the era of COVID-19 pandemic in the Philippines: Application of social exchange theory and theory of planned behavior

Over the years, passengers in the Philippines have relied on public utility bus (PUB) to reach a destination. However, PUB has been greatly affected by the adverse effects of the coronavirus disease (COVID-19) outbreak. Therefore, the study analyzed passengers' behavior using PUB during the COVID-19 pandemic by integrating social exchange theory (SET) and the theory of planned behavior (TPB). A total of 505 PUB users completed the online questionnaire containing sixty-seven (67) measures. Through confirmatory factor analysis - structural equation modeling (CFA-SEM), all the twelve (12) hypotheses were found significant. Accessibility, safety, economic benefit, and crisis management had a significant and positive influence on passengers' trust. Moreover, passengers' trust produced a significant positive influence on attitude, subjective norm, and perceived behavioral control. Additionally, attitude, subjective norm, and perceived behavioral control generated a significant positive influence on the intention to use PUB. Furthermore, perceived behavioral control had a significant positive influence on perceived passenger behavior. Lastly, the intention to use PUB developed a substantial and positive influence on perceived passenger behavior. PUB companies and policymakers can utilize these findings to develop additional PUB reforms for the benefit of passengers, drivers, operators, companies, and the government during the COVID-19 pandemic.

COVID-19 public health and social measures: a comprehensive picture of six Asian countries

The COVID-19 pandemic will not be the last of its kind. As the world charts a way towards an equitable and resilient recovery, Public Health and Social Measures (PHSMs) that were implemented since the beginning of the pandemic need to be made a permanent feature of health systems that can be activated and readily deployed to tackle sudden surges in infections going forward. Although PHSMs aim to blunt the spread of the virus, and in turn protect lives and preserve health system capacity, there are also unintended consequences attributed to them. Importantly, the interactions between PHSMs and their accompanying key indicators that influence the strength and duration of PHSMs are elements that require in-depth exploration. This research employs case studies from six Asian countries, namely Indonesia, Singapore, South Korea, Thailand, the Philippines and Vietnam, to paint a comprehensive picture of PHSMs that protect the lives and livelihoods of populations. Nine typologies of PHSMs that emerged are as follows: (1) physical distancing, (2) border controls, (3) personal protective equipment requirements, (4) transmission monitoring, (5) surge health infrastructure capacity, (6) surge medical supplies, (7) surge human resources, (8) vaccine availability and roll-out and (9) social and economic support measures. The key indicators that influence the strength and duration of PHSMs are as follows: (1) size of community transmission, (2) number of severe cases and mortality, (3) health system capacity, (4) vaccine coverage, (5) fiscal space and (6) technology. Interactions between PHSMs can be synergistic or inhibiting, depending on various contextual factors. Fundamentally, PHSMs do not operate in silos, and a suite of PHSMs that are complementary is required to ensure that lives and livelihoods are safeguarded with an equity lens. For that to be achieved, strong governance structures and community engagement are also required at all levels of the health system.

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

With the constant mutation of COVID-19 variants, the need to reduce the spread should be explored. MorChana is a mobile application utilized in Thailand to help mitigate the spread of the virus. This study aimed to explore factors affecting the actual use (AU) of the application through the use of machine learning algorithms (MLA) such as Random Forest Classifier (RFC) and Artificial Neural Network (ANN). An integrated Protection Motivation Theory (PMT) and the Unified Theory of Acceptance and Use of Technology (UTAUT) were considered. Using convenience sampling, a total of 907 valid responses from those who answered the online survey were voluntarily gathered. With 93.00% and 98.12% accuracy from RFC and ANN, it was seen that hedonic motivation and facilitating conditions were seen to be factors affecting very high AU; while habit and understanding led to high AU. It was seen that when people understand the impact and causes of the COVID-19 pandemic’s aftermath, its severity, and also see a way to reduce it, it would lead to the actual usage of a system. The findings of this study could be used by developers, the government, and stakeholders to capitalize on using the health-related applications with the intention of increasing actual usage. The framework and methodology used presented a way to evaluate health-related technologies. Moreover, the developing trends of using MLA for evaluating human behavior-related studies were further justified in this study. It is suggested that MLA could be utilized to assess factors affecting human behavior and technology used worldwide.

Utilizing Structural Equation Modeling-Artificial Neural Network Hybrid Approach in Determining Factors Affecting Perceived Usability of Mobile Mental Health Application in the Philippines

Mental health problems have emerged as one of the biggest problems in the world and one of the countries that has been seen to be highly impacted is the Philippines. Despite the increasing number of mentally ill Filipinos, it is one of the most neglected problems in the country. The purpose of this study was to determine the factors affecting the perceived usability of mobile mental health applications. A total of 251 respondents voluntarily participated in the online survey we conducted. A structural equation modeling and artificial neural network hybrid was applied to determine the perceived usability (PRU) such as the social influence (SI), service awareness (SA), technology self-efficacy (SE), perceived usefulness (PU), perceived ease of use (PEOU), convenience (CO), voluntariness (VO), user resistance (UR), intention to use (IU), and actual use (AU). Results indicate that VO had the highest score of importance, followed by CO, PEOU, SA, SE, SI, IU, PU, and ASU. Having the mobile application available and accessible made the users perceive it as highly beneficial and advantageous. This would lead to the continuous usage and patronage of the application. This result highlights the insignificance of UR. This study was the first study that considered the evaluation of mobile mental health applications. This study can be beneficial to people who have mental health disorders and symptoms, even to health government agencies. Finally, the results of this study could be applied and extended among other health-related mobile applications 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.

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

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.

Analyzing the Service Quality of E-Trike Operations: A New Sustainable Transportation Infrastructure in Metro Manila, Philippines

The electric tricycle, often known as an e-trike, is a three-wheeled electric vehicle designed to transport a small group of people over short distances on side streets. This study aims to develop a service quality model of sustainable e-trike operations in the city of Manila, Philippines using stepwise regression analysis. A total of 230 participants from three districts in the City of Manila: Binondo, Recto, and Intramuros, were selected using the stratified sampling method. The main contribution of this study emerges from the quantification of the influence of sustainability indicators on the perceived service quality of e-trike passengers. The study identified 10 indicators: PWD accessibility (β = 0.2128), smoothness of the ride (β = 0.1001), noise level (β = 0.0886), discount rate (β = 0.0886), land use (β = 0.0835), comfort load (β = 0.0723), fare acceptability (β = 0.0577), e-trike intensity (β = 0.0420), fare affordability (β = 0.0339), and ease of availability (β = 0.0317) have significant importance in the service quality of e-trike operations. These indicators revealed the areas where improvements are needed to ensure the long-term viability of e-trike operations. Therefore, it is concluded that these factors should be the focus and priority for the improvement of e-trike operators, drivers, and transport groups to attain sustainability of e-trike operation in the country. Moreover, this study can also be used for other public transportations to improve their current service quality and operations.