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Ong AKS, Prasetyo YT, Tapiceria RPKM, Nadlifatin R, Gumasing MJJ. Factors affecting the intention to use COVID-19 contact tracing application "StaySafe PH": Integrating protection motivation theory, UTAUT2, and system usability theory. PLoS One 2024; 19:e0306701. [PMID: 39088508 PMCID: PMC11293755 DOI: 10.1371/journal.pone.0306701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Accepted: 06/21/2024] [Indexed: 08/03/2024] Open
Abstract
PURPOSE StaySafe PH is the Philippines' official contact tracing software for controlling the propagation of COVID-19 and promoting a uniform contact tracing strategy. The StaySafe PH has various features such as a social distancing system, LGU heat map and response system, real-time monitoring, graphs, infographics, and the primary purpose, which is a contact tracing system. This application is mandatory in establishments such as fast-food restaurants, banks, and malls. OBJECTIVE AND METHODOLOGY The purpose of this research was to determine the country's willingness to utilize StaySafe PH. Specifically, this study utilized 12 latent variables from the integrated Protection Motivation Theory (PMT), Unified Theory of Acceptance and Use of Technology (UTAUT2), and System Usability Scale (SUS). Data from 646 respondents in the Philippines were employed through Structural Equation Modelling (SEM), Deep Learning Neural Network (DLNN), and SUS. RESULTS Utilizing the SEM, it is found that understanding the COVID-19 vaccine, understanding the COVID-19 Delta variant, perceived vulnerability, perceived severity, performance expectancy, social influence, hedonic motivation, behavioral intention, actual use, and the system usability scale are major determinants of intent to utilize the application. Understanding of the COVID-19 Delta Variant was found to be the most important factor by DLNN, which is congruent with the results of SEM. The SUS score of the application is "D", which implies that the application has poor usability. IMPLICATIONS It could be implicated that large concerns stem from the trust issues on privacy, data security, and overall consent in the information needed. This is one area that should be promoted. That is, how the data is stored and kept, utilized, and covered by the system, how the assurance could be provided among consumers, and how the government would manage the information obtained. Building the trust is crucial on the development and deployment of these types of technology. The results in this study can also suggest that individuals in the Philippines expected and were certain that vaccination would help them not contract the virus and thus not be vulnerable, leading to a positive actual use of the application. NOVELTY The current study considered encompassing health-related behaviors using the PMT, integrating with the technology acceptance model, UTAUT2; as well as usability perspective using the SUS. This study was the first one to evaluate and assess a contact tracing application in the Philippines, as well as integrate the frameworks to provide a holistic measurement.
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Affiliation(s)
- Ardvin Kester S. Ong
- School of Industrial Engineering and Engineering Management, Mapúa University, Manila, Philippines
- E.T. Yuchengo School of Businseeess, Mapúa University, Makati, Philippines
| | - Yogi Tri Prasetyo
- International Bachelor Program in Engineering, Yuan Ze University, Chung-Li, Taiwan
- Department of Industrial Engineering and Management, Yuan Ze University, Chung-Li, Taiwan
| | | | - Reny Nadlifatin
- Department of Information Systems, Institut Teknologi Sepuluh Nopember, Kampus ITS Sukolilo, Surabaya, Indonesia
| | - Ma. Janice J. Gumasing
- Department of Industrial and Systems Engineering, Gokongwei College of Engineering, De La Salle University, Manila, Philippines
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Kuo KM. Antecedents predicting digital contact tracing acceptance: a systematic review and meta-analysis. BMC Med Inform Decis Mak 2023; 23:212. [PMID: 37821864 PMCID: PMC10568897 DOI: 10.1186/s12911-023-02313-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 09/28/2023] [Indexed: 10/13/2023] Open
Abstract
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.
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Affiliation(s)
- Kuang-Ming Kuo
- Department of Business Management, National United University, No.1, 360301, Lienda, Miaoli, Taiwan, Republic of China.
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Pienwisetkaew T, Wongsaichia S, Pinyosap B, Prasertsil S, Poonsakpaisarn K, Ketkaew C. The Behavioral Intention to Adopt Circular Economy-Based Digital Technology for Agricultural Waste Valorization. Foods 2023; 12:2341. [PMID: 37372552 DOI: 10.3390/foods12122341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 06/05/2023] [Accepted: 06/08/2023] [Indexed: 06/29/2023] Open
Abstract
Thailand generates considerable amounts of agricultural food waste. This research focuses on the manufacturing and retail agricultural food system in the northeastern region of Thailand. Our study aimed to investigate the user segments and factors that influence users' behavioral intentions to utilize mobile technology for agricultural waste valorization. This study is based on the Unified Theory of the Adoption and Utilization of Technology (UTAUT2). In order to classify these segments, we performed a cluster analysis using demographic variables: gender, age, and income. In addition, the researchers employed a method known as multigroup structural equation modeling to determine and contrast the users' behavioral intentions. The results showed two types of users: (1) older users with various income ranges, and (2) younger users with a low-income range. Explicitly, age and income were the significant variables for the demographic segmentation, but gender was not. The results also revealed that social influence, price value, and trust highly affected the behavioral intentions of older and various-income users, but did not influence younger and low-income users. However, privacy strongly affected the behavioral intentions in the younger segment, but not those in the older one. Lastly, habit or regularity influenced the behavioral intentions of users in both segments. This study highlights implications for how developers and practitioners might adapt their platform strategies using a circular agricultural platform and user behaviors.
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Affiliation(s)
- Teerapong Pienwisetkaew
- International College, Khon Kaen University, Khon Kaen 40002, Thailand
- Center for Sustainable Innovation and Society, Khon Kaen University, Khon Kaen 40002, Thailand
| | - Sasichakorn Wongsaichia
- International College, Khon Kaen University, Khon Kaen 40002, Thailand
- Center for Sustainable Innovation and Society, Khon Kaen University, Khon Kaen 40002, Thailand
| | - Benyapa Pinyosap
- International College, Khon Kaen University, Khon Kaen 40002, Thailand
| | | | | | - Chavis Ketkaew
- International College, Khon Kaen University, Khon Kaen 40002, Thailand
- Center for Sustainable Innovation and Society, Khon Kaen University, Khon Kaen 40002, Thailand
- Faculty of Business and Economics, University of Antwerp, 2000 Antwerpen, Belgium
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German JD, Ong AKS, Redi AANP, Prasetyo YT, Robas KPE, Nadlifatin R, Chuenyindee T. Classification modeling of intention to donate for victims of Typhoon Odette using deep learning neural network. ENVIRONMENTAL DEVELOPMENT 2023; 45:100823. [PMID: 36844910 PMCID: PMC9939386 DOI: 10.1016/j.envdev.2023.100823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 02/13/2023] [Accepted: 02/14/2023] [Indexed: 06/18/2023]
Abstract
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.
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Affiliation(s)
- Josephine D German
- School of Industrial Engineering and Engineering Management, Mapúa University, Manila, Philippines. 658 Muralla St., Intramuros, Manila, 1002, Philippines
| | - Ardvin Kester S Ong
- School of Industrial Engineering and Engineering Management, Mapúa University, Manila, Philippines. 658 Muralla St., Intramuros, Manila, 1002, Philippines
| | | | - Yogi Tri Prasetyo
- Department of Industrial Engineering and Management, Yuan Ze University, 135 Yuan-Tung Road, Chung-Li, 32003, Taiwan
- International Bachelor Program in Engineering, Yuan Ze University, 135 Yuan-Tung Road, Chung-Li, 32003, Taiwan
| | - Kirstien Paola E Robas
- School of Industrial Engineering and Engineering Management, Mapúa University, Manila, Philippines. 658 Muralla St., Intramuros, Manila, 1002, Philippines
| | - Reny Nadlifatin
- Department of Information Systems, Institut Teknologi Sepuluh Nopember, Kampus ITS Sukolilo, Surabaya, 60111, Indonesia
| | - Thanatorn Chuenyindee
- Department of Industrial Engineering and Aviation Management, Navaminda Kasatriyadhiraj Royal Air Force Academy, Bangkok, 10220, Thailand
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Trkman M, Popovič A, Trkman P. The roles of privacy concerns and trust in voluntary use of governmental proximity tracing applications. GOVERNMENT INFORMATION QUARTERLY 2022. [DOI: 10.1016/j.giq.2022.101787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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Ong AKS, Dejucos MJR, Rivera MAF, Muñoz JVD, Obed MS, Robas KPE. Utilizing SEM-RFC to predict factors affecting online shopping cart abandonment during the COVID-19 pandemic. Heliyon 2022; 8:e11293. [DOI: 10.1016/j.heliyon.2022.e11293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 09/22/2022] [Accepted: 10/24/2022] [Indexed: 11/06/2022] Open
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Ong AKS, Prasetyo YT, Yuduang N, Nadlifatin R, Persada SF, Robas KPE, Chuenyindee T, Buaphiban T. Utilization of Random Forest Classifier and Artificial Neural Network for Predicting Factors Influencing the Perceived Usability of COVID-19 Contact Tracing “MorChana” in Thailand. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19137979. [PMID: 35805634 PMCID: PMC9265314 DOI: 10.3390/ijerph19137979] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Revised: 06/25/2022] [Accepted: 06/27/2022] [Indexed: 02/08/2023]
Abstract
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.
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Affiliation(s)
- Ardvin Kester S. Ong
- School of Industrial Engineering and Engineering Management, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines; (A.K.S.O.); (N.Y.); (K.P.E.R.)
| | - Yogi Tri Prasetyo
- School of Industrial Engineering and Engineering Management, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines; (A.K.S.O.); (N.Y.); (K.P.E.R.)
- Department of Industrial Engineering and Management, Yuan Ze University, 135 Yuan-Tung Road, Chung-Li 32003, Taiwan
- Correspondence: ; Tel.: +63(2)-8247-5000 (ext. 6202)
| | - Nattakit Yuduang
- School of Industrial Engineering and Engineering Management, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines; (A.K.S.O.); (N.Y.); (K.P.E.R.)
- School of Graduate Studies, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines
| | - Reny Nadlifatin
- Department of Information Systems, Institut Teknologi Sepuluh Nopember, Kampus ITS Sukolilo, Surabaya 60111, Indonesia;
| | - Satria Fadil Persada
- Entrepreneurship Department, BINUS Business School Undergraduate Program, Bina Nusantara University, Jakarta 11480, Indonesia;
| | - Kirstien Paola E. Robas
- School of Industrial Engineering and Engineering Management, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines; (A.K.S.O.); (N.Y.); (K.P.E.R.)
| | - Thanatorn Chuenyindee
- Department of Industrial Engineering and Aviation Management, Navaminda Kasatriyadhiraj Royal Air Force Academy, Bangkok 10220, Thailand; (T.C.); (T.B.)
| | - Thapanat Buaphiban
- Department of Industrial Engineering and Aviation Management, Navaminda Kasatriyadhiraj Royal Air Force Academy, Bangkok 10220, Thailand; (T.C.); (T.B.)
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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". INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19106111. [PMID: 35627647 PMCID: PMC9141929 DOI: 10.3390/ijerph19106111] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Revised: 05/08/2022] [Accepted: 05/10/2022] [Indexed: 02/01/2023]
Abstract
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.
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