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

Yituo Feng · Jungryeol Park

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Technology Adoption and User Behaviour Data Mining and Machine Learning Applications AI and HR Technologies

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.

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2020An affordance perspective of team collaboration and enforced working from home during COVID-19 2010Consumer credit-risk models via machine-learning algorithms 2020Analysis of User Satisfaction with Online Education Platforms in China during the COVID-19 Pandemic 2020COVID-19 Pandemic: Shifting Digital Transformation to a High-Speed Gear 2020Statistical and machine learning models in credit scoring: A systematic literature survey 2012Identification of Variables Affecting Employee Satisfaction and Their Impact on the Organization 2016Enterprise social networking: A knowledge management perspective 2021Predicting student satisfaction of emergency remote learning in higher education during COVID-19 using machine learning techniques

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