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Using machine learning-based binary classifiers for predicting organizational members' user satisfaction with

Yituo Feng¹, Jungryeol Park²

¹Management Information System, Chungbuk National University, Cheongju, South Korea.

Peerj. Computer Science

|August 7, 2023

Summary

Predicting employee satisfaction with collaboration software is crucial for digital transformation. This study uses machine learning to forecast user satisfaction before implementation, identifying key influencing factors.

Keywords:

Binary classifier Collaboration software Feature importance Machine learning Prediction model User satisfaction

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Area of Science:

Information Systems
Human-Computer Interaction
Data Science

Background:

Enterprises adopt collaboration software for digital transformation, but low user satisfaction can impede benefits.
Existing research focuses on post-implementation satisfaction, leaving a gap in predictive methods.
This study addresses the need for pre-implementation user satisfaction forecasting.

Purpose of the Study:

To develop and validate a machine learning-based forecasting method for employee satisfaction with collaboration software.
To identify key factors influencing user satisfaction prior to software implementation.

Main Methods:

Utilized national public data from South Korea's national information society agency.
Applied machine learning, specifically a binary classifier, after discretizing predictor variables.
Validated the prediction model using feature importance scores and prediction accuracy metrics.

Main Results:

Identified 10 key factors predicting user satisfaction across institutional guidance, ICT environment, company culture, and demographics.
Naive Bayes (NB) classifier achieved the highest accuracy (0.780), followed by Logistic Regression (LR) (0.767).
Other models evaluated included XGBoost, SVM, KNN, and Decision Tree, with varying accuracy rates.

Conclusions:

The study provides essential indicators for predicting collaboration software user satisfaction.
Enterprises can leverage these findings to assess current collaboration status and strategize software adoption.
A novel, validated machine learning approach for predicting user satisfaction was presented.