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Due to the eruption of the COVID-19 pandemic, many universities were forced to shift from the traditional learning practices to digital learning. Hence, the purpose of…
Due to the eruption of the COVID-19 pandemic, many universities were forced to shift from the traditional learning practices to digital learning. Hence, the purpose of this study is to evaluate the factors that affect the university student's adoption of mobile technologies for mobile learning (m-learning) in their learning process.
Technology acceptance model (TAM) is incorporated to study the adoption of mobile learning by university students. Quantitative research technique is used as core research approach in this study. Structural equation modelling (SEM), which is a part of quantitative research method, was employed on the congregated data via a set of questionnaire from 268 University students. SEM is used to explore the relationships among the hypothesized constructs. SPSS and AMOS software were used for the analysis of data.
This study validated the updated TAM model and assessed the students' adoption of mobile technologies for m-learning during COVID-19. All the constructs of proposed model were found to be significant with more than 50% average variance extracted. It was found that two external constructs mobile system efficacy and mobile service efficacy appended in technology acceptance model show the direct positive effect on perceived usefulness and perceived ease of use constructs. However, hypothesized relationships were found to be unsupported among perceived usefulness and perceived ease of use. Furthermore, perceived usefulness and ease of use during m-learning impact the students' usage attitude which consequently impact the students' adoption behaviour towards adoption of mobile technology.
Six constructs were considered for this study; however, mobile information quality for mobile learning was not included which could affect students' adoption criteria. Additionally, this study is limited to a country where future study needs validation of propose constructs in different demographic settings.
No study allied to the students' adoption of mobile technology for m-learning has accomplished in the context of India during COVID-19. Furthermore, TAM model has been updated with regard to the students' adoption of mobile learning during COVID-19 in Indian higher education setting.
The significance of cloud services in information technology (IT) is increasing as a means of achieving enhanced productivity, efficiency and cost reduction. Through…
The significance of cloud services in information technology (IT) is increasing as a means of achieving enhanced productivity, efficiency and cost reduction. Through cloud-based service, the reliability and scalability of an organization’s systems can be enhanced since organizations such as local governments are able to concentrate on their main business strategies. This research seeks to identify critical factors that may have an impact on the acceptance of cloud-based services, where the organizational context is based on local governments in Australia.
To formulate a more comprehensive IT innovation adoption model for cloud technology, factors from the technology-organizational-environment framework, desires framework and diffusion of innovation model were integrated. Data was obtained from 480 IT staff working in 47 local government organizations.
The research results show that the factors which had a statistically significant and positive impact on the adoption of cloud-based services in local governments were compatibility, complexity, cost, security concerns, expected benefits and organization size. It is likely that the outcomes from this research will provide insights to any organization seeking to make investment decisions on the adoption of cloud-based services.
Limitations include generalizability of the findings since the data is restricted to local government areas in Queensland, Australia. Further, the sample mostly included individuals with managerial positions and may not completely capture the cloud adoption factors relevant for front line IT employees. Another limitation is the possible omission of factors that may be relevant but not considered due to the selected theories. Lastly, this research did not differentiate between different types of cloud adoption such as private, public, community and hybrid models that are possible in this context.
The paper provides a combination framework of cloud-based service adoption based on a literature review on cloud adoption from an IS perspective. It adapts integrated model to establish a more comprehensive innovation adoption framework for cloud technology.