Using machine learning-based binary classifiers for predicting organizational members’ user satisfaction with collaboration software

Author:

Feng Yituo1ORCID,Park Jungryeol2

Affiliation:

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

2. Technology Policy Research Division, Electronics and Telecommunications Research Institute, Daejeon, South Korea

Abstract

Background In today’s digital economy, enterprises are adopting collaboration software to facilitate digital transformation. However, if employees are not satisfied with the collaboration software, it can hinder enterprises from achieving the expected benefits. Although existing literature has contributed to user satisfaction after the introduction of collaboration software, there are gaps in predicting user satisfaction before its implementation. To address this gap, this study offers a machine learning-based forecasting method. Methods We utilized national public data provided by the national information society agency of South Korea. To enable the data to be used in a machine learning-based binary classifier, we discretized the predictor variable. We then validated the effectiveness of our prediction model by calculating feature importance scores and prediction accuracy. Results We identified 10 key factors that can predict user satisfaction. Furthermore, our analysis indicated that the naive Bayes (NB) classifier achieved the highest prediction accuracy rate of 0.780, followed by logistic regression (LR) at 0.767, extreme gradient boosting (XGBoost) at 0.744, support vector machine (SVM) at 0.744, K-nearest neighbor (KNN) at 0.707, and decision tree (DT) at 0.637. Conclusions This research identifies essential indicators that can predict user satisfaction with collaboration software across four levels: institutional guidance, information and communication technology (ICT) environment, company culture, and demographics. Enterprises can use this information to evaluate their current collaboration status and develop strategies for introducing collaboration software. Furthermore, this study presents a novel approach to predicting user satisfaction and confirm the effectiveness of the machine learning-based prediction method proposed in this study, adding to the existing knowledge on the subject.

Publisher

PeerJ

Subject

General Computer Science

Reference51 articles.

1. Early prediction of employee turnover using machine learning algorithms;Atef;International Journal of Electrical and Computer Engineering Systems,2022

2. Understanding the influence of environmental production practices on firm performance: a proactive versus reactive approach;Baah;Journal of Manufacturing Technology Management,2020

3. Integrating digital technologies in education: a model for negotiating change and resistance to change;Berger,2011

4. Some future software engineering opportunities and challenges;Boehm,2011

5. Classification based on decision tree algorithm for machine learning;Charbuty;Journal of Applied Science and Technology Trends,2021

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