AI Article Synopsis
- The paper explores the factors influencing companies' intentions to adopt Big Data Applications, noting the scarcity of research in this area.
- It utilizes an updated UTAUT model, incorporating elements like resistance to use and perceived risk, and employs a neural network analysis to predict adoption.
- The findings highlight the effectiveness of the multilayer perceptron model for predicting Big Data usage in companies, marking this study as a novel approach with significant theoretical and practical implications.
Article Abstract
The purpose of this paper is to identify the factors that affect the intention to use Big Data Applications in companies. Research into Big Data usage intention and adoption is scarce and much less from the perspective of the use of these techniques in companies. That is why this research focuses on analyzing the adoption of Big Data Applications by companies. Further to a review of the literature, it is proposed to use a UTAUT model as a starting model with the update and incorporation of other variables such as resistance to use and perceived risk, and then to perform a neural network to predict this adoption. With respect to this non-parametric technique, we found that the multilayer perceptron model (MLP) for the use of Big Data Applications in companies obtains higher AUC values, and a better confusion matrix. This paper is a pioneering study using this hybrid methodology on the intention to use Big Data Applications. The result of this research has important implications for the theory and practice of adopting Big Data Applications.
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Source |
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8046906 | PMC |
| http://dx.doi.org/10.3389/fpsyg.2021.651398 | DOI Listing |
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