Predicting Big Data Adoption in Companies With an Explanatory and Predictive Model

Area of Science:

• Information Systems
• Business Analytics
• Artificial Intelligence

Background:

• Limited research exists on Big Data adoption intention from a corporate perspective.
• Understanding factors driving Big Data Application usage in businesses is crucial for competitive advantage.

Purpose of the Study:

• To identify factors influencing the intention to use Big Data Applications within companies.
• To analyze the adoption of Big Data Applications by businesses.
• To explore a novel hybrid methodology for predicting Big Data adoption.

Main Methods:

• Literature review to establish a baseline UTAUT (Unified Theory of Acceptance and Use of Technology) model.
• Incorporation of additional variables: resistance to use and perceived risk.
• Application of a neural network, specifically a multilayer perceptron (MLP), to predict adoption intention.

Main Results:

• The multilayer perceptron (MLP) model demonstrated superior performance in predicting Big Data Application adoption.
• Higher AUC values and improved confusion matrix results were achieved using the MLP model.
• The hybrid methodology proved effective in analyzing the intention to use Big Data Applications.

Conclusions:

• The study provides a pioneering hybrid methodological approach for analyzing Big Data adoption intention.
• Findings offer valuable theoretical and practical implications for companies seeking to adopt Big Data Applications.
• The research highlights the predictive power of neural networks in understanding technology adoption in a business context.