Predicting the Drivers of Cloud Computing Adoption by using Structural Equation Modeling and Artificial Neural Network
- Title
- Predicting the Drivers of Cloud Computing Adoption by using Structural Equation Modeling and Artificial Neural Network
- Authors
- 송치훈
- Date Issued
- 2020
- Publisher
- 포항공과대학교
- Abstract
- This study explores the predictors of individual-level adoption of cloud computing (CC) by using both an artificial neural network (ANN) and the structural equation modeling (SEM), which has been the standard tool to evaluate this process. This study uses data collected by an online survey in South Korea. Application of the extended Unified Theory of Acceptance and Use of Technology shows that the ANN is a better option than the SEM to identify determinants of CC adoption and can capture nonlinear relationships, which SEM cannot. The SEM results indicate that only performance expectancy, effort expectancy and habit are significant predictors of CC adoption. However, when the importance is normalized by using a multilayer perceptron, the ANN analysis shows that all variables (performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, and habit) are significant, although to different degrees. This study provides researchers and practitioners with a differentiated and extended perspective on understanding adoption of cloud-enabled technology.
- URI
- http://postech.dcollection.net/common/orgView/200000335406
https://oasis.postech.ac.kr/handle/2014.oak/111489
- Article Type
- Thesis
- Files in This Item:
- There are no files associated with this item.
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