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

被引：0
作者：
Akinnuwesi B.A. [1 ]
Uzoka F.-M.E. [2 ]
Fashoto S.G. [1 ]
Mbunge E. [1 ]
Odumabo A. [3 ]
Amusa O.O. [4 ]
Okpeku M. [5 ]
Owolabi O. [6 ]
机构：
[1] Department of Computer Science, Faculty of Science and Engineering, University of Eswatini, Kwaluseni
[2] Department of Mathematics and Computing, Mount Royal University, Calgary, T3E6K6, AB
[3] Department of Computer Science, Faculty of Science, Lagos State University, Lagos, Ojo
[4] Department of English Studies, Faculty of Arts, Adekunle Ajasin University, Akungba-Akoko, Ondo State
[5] Department of Genetics, University of KwaZulu-Natal
[6] Department of Computer Science, University of Abuja, Abuja
来源：
Sustainable Operations and Computers | 2022年 / 3卷
关键词：
Acceptance and use; Behavioural intention; COVID-19 digital tackling technology; Nigeria; People's; UTAUT;
D O I：
10.1016/j.susoc.2021.12.001
中图分类号：
学科分类号：
摘要：
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 utilization 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. © 2021
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页码：118 / 135
页数：17
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