A modified UTAUT model for the acceptance and use of digital technology for tackling COVID-19

Abstract

COVID-19 pandemic expedites the development of digital technologies to tackle the spread of the virus. Several digital interventions have been deployed to reduce the catastrophic impact of the pandemic and observe preventive measures. However, the adoption and utilisation of these technologies by the affected populace has been a daunting task. Therefore, this study carried out exploratory investigation of the factors influencing the behavioural intention (BI) of people to accept COVID-19 digital tackling technologies (CDTT) using the UTAUT (Unified Theory of Acceptance and Use of Technology) framework. The study applied principal components analysis and multiple regression analysis for hypotheses testing. The study revealed that performance expectancy (PE), facilitating conditions (FC) and social influence (SI) are the best predictors of people's BI to accept CDTT. Also, organizational influence and benefit (OIB) and government expectancy and benefits (GEB) influence the people's BI. However, variables such as age, gender and voluntariness to use CDTT have no significance to influence BI because the CDTT is still nascent and not easily accessible. The results show that the decision-makers and regulators should consider inciting variables such as PE, FC, SI, OIB and GEB, that motivate the acceptance and use of CDTT. Furthermore, the populace must be sensitized to the availability and use of CDTT in all communities. Also, the path diagram and hypothesis testing results for CDTT acceptance and use, will help government and private organizations in planning and responding to the digitalization of COVID-19 protective measures and hence revise the COVID-19 health protection regulation.

Exploration on the Influencing Factors of Overseas Students' Online Chinese Learning Intention During the Epidemic Period

“…Thirdly, community influence in UTAUT is the users' perception surrounding groups' attitudes toward a technology (Jadil et al, 2021 ). Relevant studies have uncovered that community influence directly or indirectly impacts online learners' Willingness To Use (WTU) (Akinnuwesi et al, 2021 ). This study contends that external factors, such as school fellows, teachers, and parents, significantly impact online learners' WTU.…”

Understanding Users’ Continuance Usage Behavior Towards Digital Health Information System Driven by the Digital Revolution Under COVID-19 Context: An Extended UTAUT Model

“… 24 Boluwaji et al adopted the UTAUT model to study the factors influencing users to accept COVID-19 digital tackling technologies. 25 …”

“…24 Boluwaji et al adopted the UTAUT model to study the factors influencing users to accept COVID-19 digital tackling technologies. 25 Prior studies have analyzed the user's willingness of accepting digital health information system or online medical by using TAM and UTAUT and revealed that several factors, such as self-efficacy, effort expectancy and performance expectancy, influence users' behavior and intention. These findings can be regarded as contributions to understanding the user's willingness of accepting digital health information system or online medical.…”

Adoption of electronic medical records in developing countries—A multi-state study of the Nigerian healthcare system

“…Hence, we posit that: H8: Alignment of EMR technology with business defined critical success factors will positively influence user decision to adopt EMR Previous studies have highlighted key innovation diffusion factors which include relative advantage and emphasised the impact of relative advantage on user perception of usefulness of new technology (30). Therefore, we posit that: H9: Relative advantage positively mediates user decision to adopt and use EMR Research has shown that age, gender and years of experience of users can have an impact on their decision to adopt and use new technology (31). Moreover, users that are already comfortable with a particular system or way of working for years might be reluctant to change and adopt a new system (30).…”

“…Research has shown that age, gender and years of experience of users can have an impact on their decision to adopt and use new technology ( 31 ). Moreover, users that are already comfortable with a particular system or way of working for years might be reluctant to change and adopt a new system ( 30 ).…”