Document Type
Abstract
Abstract
Bank profitability is strongly related to e-banking. Convenience, familiarity and perceived ease of use, and security and privacy have been found to be major concerns customers have towards using e-banking product and services[1]. This study tried to classify PC banking users and non PC banking users by using a binary response variable (PC banking users vs. non PC banking users), as well as seven predictor variables (income, net worth, age, education, race, gender, financial sophistication, and years with bank) with CHAID TREE and CART. Data were divided into a training data set (60% of the data), validation data set (20% of the data), and test data set (20% of the data). A predictive classification model was estimated and validated for CHAID and CART. Results from CHAID and CART were compared.
Recommended Citation
Yin, Wen, "Classifying PC Banking Users vs. Non PC Banking Users" (2008). ICEB 2008 Proceedings (Hawaii, Waikoloa, Big Island). 21.
https://aisel.aisnet.org/iceb2008/21
Abstract Only