Version 1
: Received: 30 September 2023 / Approved: 1 October 2023 / Online: 1 October 2023 (09:48:40 CEST)
How to cite:
Imani, M. Uncovering the Impact of Working from Home on Employees’ Collaboration, Happiness, and Promotion Chances Using Machine Learning. Preprints2023, 2023100016. https://doi.org/10.20944/preprints202310.0016.v1
Imani, M. Uncovering the Impact of Working from Home on Employees’ Collaboration, Happiness, and Promotion Chances Using Machine Learning. Preprints 2023, 2023100016. https://doi.org/10.20944/preprints202310.0016.v1
Imani, M. Uncovering the Impact of Working from Home on Employees’ Collaboration, Happiness, and Promotion Chances Using Machine Learning. Preprints2023, 2023100016. https://doi.org/10.20944/preprints202310.0016.v1
APA Style
Imani, M. (2023). Uncovering the Impact of Working from Home on Employees’ Collaboration, Happiness, and Promotion Chances Using Machine Learning. Preprints. https://doi.org/10.20944/preprints202310.0016.v1
Chicago/Turabian Style
Imani, M. 2023 "Uncovering the Impact of Working from Home on Employees’ Collaboration, Happiness, and Promotion Chances Using Machine Learning" Preprints. https://doi.org/10.20944/preprints202310.0016.v1
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.
Keywords
Machine learning; COVID-19; Working from home; Employees’ collaboration; Employees’ happiness; Employees’ promotion chances
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
