Evaluating the Impact of remote work on Employee Productivity and Satisfaction using Machine Learning Approaches

Zende, Rajas Abhijit (2025) Evaluating the Impact of remote work on Employee Productivity and Satisfaction using Machine Learning Approaches. Masters thesis, Dublin, National College of Ireland.

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Abstract

Remote working model is being implemented by almost every organization after Covid 19 pandemic. The implementation of remote work model offers flexibility and a better work life balance to employees with some studies indicating that employees who work remote have high productivity and satisfaction scores. On the contrary some studies discuss the negative effects of working remote such as stress, lack of social contact which harms the mental health of employees further affecting the productivity and satisfaction of employees. In this study we investigated whether remote work has a direct impact on employee efficiency. By using a dataset consisting of 10000 employees records we conducted feature engineering and developed 2 machine learning models Random Forest model and LGBM model. Also, in this study we conducted feature engineering and developed complex features and a target variable by combing performance score and satisfaction score along with 2 remote work interaction terms that were successful in capturing the indirect impact of remote work on employee efficiency. The LGBM model that was trained and received an exceptional R2 score of 0.8099. in our study a key finding revealed that remote work didn’t have a direct impact on the efficiency of employees but had an indirect impact through remote work interaction features which when combined contributed to (30.10%) towards the model’s performance. This study will be useful for organizations who view the impact of remote work of productivity and satisfaction is similar for all the employees. Further enabling them to create personalized remote work policies.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Jameel Syed, Muslim UNSPECIFIED
Subjects:	Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management > Human Resource Management > Performance Management > Employee Engagement H Social Sciences > HD Industries. Land use. Labor > Issues of Labour and Work > Hours of Labour > Flexible work arrangements Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning H Social Sciences > HD Industries. Land use. Labor > Issues of Labour and Work > Quality of Work Life / Job Satisfaction
Divisions:	School of Computing > Master of Science in Artificial Intelligence for Business
Depositing User:	Ciara O'Brien
Date Deposited:	24 Jun 2026 11:58
Last Modified:	24 Jun 2026 11:58
URI:	https://norma.ncirl.ie/id/eprint/9407

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