Uncovering the Impact of Working from Home on Employees’ Collaboration, Happiness, and Promotion Chances Using Machine Learning

Main Article Content

Mehdi Imani

Abstract

This research explores the impact of working from home on employees' happiness, collaboration, and promotion prospects using machine learning techniques. The study is guided by three research questions aiming to investigate the correlation between working from home and employees' collaboration and promotion prospects. Moreover, the research aims to find a relationship between the number of households and employees' happiness levels while working from home. The data is collected from ICT engineers working at a technology company in Sweden through a questionnaire-based survey. Probability sampling was selected for data collection to reduce bias and enhance the generalizability of the findings. The data is pre-processed and then analysed in Jupyter Notebook using the Python programming language. Various libraries and models, including Pandas, NumPy, Matplotlib, Seaborn, and Scikit-learn, were employed for data analysis. Both Pearson correlation and p-values in Pearson correlation were used in this study to analyse the relationships between different variables. However, based on the results, this study did not find any significant relationship between working from home and employees' promotion prospects or collaboration issues. Additionally, the results did not provide evidence of a significant relationship between the number of households of employees and their happiness levels while working from home.

Article Details

How to Cite
[1]
Mehdi Imani, “Uncovering the Impact of Working from Home on Employees’ Collaboration, Happiness, and Promotion Chances Using Machine Learning”, Int. J. Comput. Eng. Res. Trends, vol. 11, no. 1, pp. 9–23, Jan. 2024.
Section
Research Articles

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