User Satisfaction Prediction in ERP using KNN Classifier for high Prediction Accuracy
Abstract
widely used in organizations; because, ERP provides a single
platform to manage all the processes and functions of
organizations. This single platform improves their productivity,
business performance, decision making capabilities and
efficiency. However, to achieve a proper level of ERP success
depends on various factors e.g. organization, technology,
environment and User Satisfaction etc. ‘User Satisfaction’ (US) is
most important factor to make ERP successful. US refer the
user’s comfort and acceptability of ERP system during the use
and interaction with the ERP system. This paper deploys the
conceptual model for US prediction by considering Human,
Technological and Organizational factors as predictors. In this
report, we proposed K-Nearest Neighbor (KNN) Classification
method first time in literature to predict the US and we compare
it with ANFIS and ANN. We calculated average error for all test
cases and demonstrate that KNN gives high predication accuracy
in most of the cases and low average error (0.25) in comparison
ANFIS (0.3378) and ANN (0.6053) methods. So our approach is
novel and using KNN, prediction accuracy can be further
improved for US to make successful ERP.
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