Employee Productivity Modelling on a Work From Home Scenario During the Covid-19 Pandemic: A Case Study Using Classification Trees

Dilhari Attygalle¹ and Geethanadee Abhayawardana^2,

¹Department of Statistics, University of Colombo, Sri Lanka

²The Asia Foundation, Colombo, Sri Lanka

Cite this paper

Dilhari Attygalle and Geethanadee Abhayawardana. Employee Productivity Modelling on a Work From Home Scenario During the Covid-19 Pandemic: A Case Study Using Classification Trees. Journal of Business and Management Sciences. 2021; 9(3):92-100. doi: 10.12691/JBMS-9-3-1

Abstract

Employee productivity is recognized as a key factor for the development of any organization. Through many research in the past, “work environment” has emerged as one of the most significant aspects that greatly contributes towards employee productivity. When employees are reverted to a work from home scenario, the work environment can change substantially due to varied reasons such as social, economic and cultural scenarios, different than usual. This research investigates employee productivity in relation to a new work environment that has emerged due to the Covid-19 pandemic. The study aims to find factors impacting on employee productivity under this new work environment and views employees, as subgroups or segments, within the new setup. A survey based on 60 employees of a non-government organization in Sri Lanka, is used to exemplify the approach to meet the study aims. Employee Productivity is considered as a binary variable, the two categories being positive productivity and non-positive productivity, compared to the situation prior to Covid. The classification tree, with an accuracy of over 88%, revealed that, four features, namely, complying with daily hours of work, overall experience of working from home, ease of focusing on work and clear communication regarding work, significantly impacted on productivity. The analysis also showed that among the five different employee subgroups that emerged from the analysis, 36% of the employees forming the largest positive group seemed to be able to comply with the required hours easily, had a good overall experience and were able to focus well at home. On the other hand, 42% belonging to the largest non-positive group stated that their work expectations were communicated but they were not able to comply with the due hours or work per day. The proposed evidence-based quantitative approach has shown promising results in studying employee productivity during a work from home scenario.

Keywords

classification trees, employee productivity, Covid-19 Pandemic, work-from-home

Copyright

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References

[1]	Rohan Singh, Madhumita Mohanty, “Impact of Training Practices on Employee Productivity: A Comparative Study,” Inter science management review, 3 (1), Jan 2010.
[2]	Komal Khalid Bhatti, Tahir Masood Qureshi, “Impact Of Employee Participation On Job Satisfaction, Employee Commitment And Employee Productivity,” International Review of Business Research Papers, 3 (2), 54-68, June 2007.
[3]	Quratul-Ain Manzoor, “Impact of Employees Motivation on Organizational Effectiveness,” European Journal of Business and Management (Online), 3 (3), 36, 2011.
[4]	Oko Anambra, Obiefuna Okafor, “Effect of Motivation on Employee Productivity: A Study of Manufacturing Companies in Nnewi Chukwuma,” Journal of Managerial Studies and Research (IJMSR), 2 (7), 137-147, Aug.2014.
[5]	Jalal Hanaysha, “Testing the Effects of Employee Empowerment, Teamwork, and Employee Training on Employee Productivity in Higher Education Sector,” International Journal of Learning & Development, 6, 164, 2016.
[6]	Ebenezer Duru, Dominic Shimawua, “The effect of work environment on employee productivity,” European Journal of Business and Innovation Research, 5, 23-39, Oct.2017.
[7]	Mohamad Hammoud, Schrita Osborne, “Effective Employee Engagement in the Workplace,” International Journal of Applied Management and Technology, 16 (1), 50-67, 2017.
[8]	Lan Zongjun, “Research on Factors Affecting Employee Productivity in Shanghai,” International Journal of Management (IJM), 10 (6), 147-160, 2019.
[9]	Mahesh V.J, Sanjay Kumar, “Work from home experiences during COVID -19 pandemic among IT employees,” Journal of Contemporary Issues in Business and Government, 26, 2020.
[10]	Russo, D., Hanel, P.H.P., Altnickel, S. et al, “Predictors of well-being and productivity among software professionals during the COVID-19 pandemic – a longitudinal study,” Empirical Software Engineering, 62, 2021.
[11]	Himani Sharma, Sunil Kumar, “A Survey on Decision Tree Algorithms of Classification in Data Mining,” International Journal of Science and Research (IJSR) (Online), 5 (4), 2015.
[12]	Harsh H. Patel, Purvi Prajapati, “Study and Analysis of Decision Tree Based Classification Algorithms,” International Journal of Computer Sciences and Engineering Open Access Research Paper, 6 (10), 2018.
[13]	Anand Bahety, “Extension and Evaluation of ID3 – Decision Tree Algorithm, 2009.