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Identifying Key Drivers and Predicting Technology Adoption Using Classification Algorithms
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Orientador(es)
Resumo(s)
This thesis explores the key factors influencing the adoption of new technologies within
organizational environments, with a particular focus on Business Intelligence (BI) systems.
Grounded in the Technology Acceptance Model (TAM) and extended with constructs from the
IS Success Model and recent literature, the study combines theoretical insights with a datadriven approach. A structured survey was conducted to capture user perceptions,
organizational readiness, and behavioral intentions. Using classification algorithms such as
AdaBoost and Logistic Regression, the research identifies the most impactful variables driving
technology adoption and evaluates model performance through precision, recall, F1 score,
and ROC AUC. The findings highlight the importance of managerial support, perceived
usefulness, and system quality in predicting adoption behavior. This work contributes to both
academic understanding and practical strategies for enhancing technology uptake in
organizations.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Business Intelligence
Palavras-chave
Technology Adoption Business Intelligence Classification Algorithms Technology Acceptance Model Predictive Modeling Information Systems