Activity-based flexible office

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

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.

To work at home or in the office? Well-being, information flow and relationships between office workers before and during the COVID-19 pandemic

Purpose

This paper aims to compare employee well-being, information flow and relationships with co-workers and supervisors for people working at home and working in different office types before and during the COVID-19 pandemic.

Design/methodology/approach

A nationwide study of 2,845 Estonian office workers in autumn 2019 and 2,972 in spring 2020 was carried out.

Findings

It was discovered that in normal circumstances, people at home had similar results to those in a cell office or activity-based office. Open-plan offices were found to be the worst in respect to the facets of work studied. However, in the context of the pandemic, the playing field became more level in some respects and worse in the case of activity-based offices.

Practical implications

When telework is well arranged both in terms of facilities and organising the necessary communication and information flow, then it is a viable alternative to working in an office. What is more, employers need to pay more attention to the physical and social work conditions in open-plan offices and also activity-based offices in the context of a pandemic.

Originality/value

Previous studies have only compared telework with working in an office in general. Comparing working at home with different kinds of offices gives valuable insights.