The impact of work from home on productivity among manufacturing industry workers during MCO
الموضوعات :Mohd Amran Mohd Daril 1 , Nur Syairah Dazreena Nor ‘Azman 2 , Mohamad Ikbar Abdul Wahab 3 , Khairanum Subari 4 , Nohman Khan 5 , Sobia Irum 6
1 - Quality Engineering Research Cluster, Quality Engineering Section,Universiti Kuala Lumpur, Malaysian Institute of Industrial Technology, Johor Bahru, Johor, Malaysia
2 - Quality Engineering Section,Universiti Kuala Lumpur, Malaysian Institute of Industrial Technology, Johor Bahru, Johor, Malaysia.
3 - Quality Engineering Research Cluster, Quality Engineering Section,Universiti Kuala Lumpur, Malaysian Institute of Industrial Technology, Johor Bahru, Johor, Malaysia.
4 - Quality Engineering Research Cluster, Quality Engineering Section,Universiti Kuala Lumpur, Malaysian Institute of Industrial Technology, Johor Bahru, Johor, Malaysia.
5 - UniKL Business School Universiti Kuala Lumpur
6 - Department of Management and Marketing, College of Business Administration, University of Bahrain, Sakhir, Kingdom of Bahrain.
الکلمات المفتاحية: Productivity, Pandemic, WFH, EFA,
ملخص المقالة :
Thousands of people had significant health problems as the COVID-19 virus swept over the country which is increasing the fatality rate. The only way to stop the epidemic from spreading was for affected countries to halt all civil and eco-nomic operations for an extended period. As a result, some governments have imposed a global lockdown, which is still in effect. In this scenario, all business activity in all industries is halted. For numerous industries, the shutdown has a variety of ramifications. The aviation, hospitality, restaurant, and manufacturing industries, for example, have all shut down and will take years to recover. To tackle the current scenario, businesses are attempting to manage offices and administrative jobs using the "Work from Home" (WFH) paradigm. Businesses are attempting to withstand the storm. Furthermore, the factors that affects the productivity of work from home had been identified. Productivity has been largely determined by occupant self-reports. These are more subjective in nature and more prone to bias than satisfaction ratings, as respondents are asked to make an estimate based on their own emotions
Airtasker (2019). Benefits of working from home: Comparing the productivity, spending and health of remote vs. in-office employees, Airtasker Blog.
Bloom, N., Liang, J., Roberts, J., & Ying, Z. J. (2015). Does working from home work? Evidence from a Chinese experiment. The Quarterly journal of economics, 130(1), 165-218.
Chuan, C. L., & Penyelidikan, J. (2006). Sample size estimation using Krejcie and Morgan and Cohen statistical power analysis: A comparison. Jurnal Penyelidikan IPBL, 7(1), 78-86.
Dutcher, E. G. (2012). The effects of telecommuting on productivity: An experimental examination. The role of dull and creative tasks. Journal of Economic Behavior & Organization, 84(1), 355-363.
Frary, R. B. (2003). A brief guide to questionnaire development. Virginia Polytechnic Institute & State University. Retrieved October, 9, 2003.
Ghasemi, A., & Zahediasl, S. (2012). Normality tests for statistical analysis: a guide for non-statisticians. International journal of endocrinology and metabolism, 10(2), 486.
Gibbs, M., Mengel, F., & Siemroth, C. (2023). Work from Home and Productivity: Evidence from Personnel and Analytics Data on Information Technology Professionals. Journal of Political Economy Microeconomics, 1(1), 7-41.
Giedre Raišiene, A., Rapuano, V., Varkuleviciute, K., & Stachová, K. (2020). Working from Home—Who Is Happy. A Survey of Lithuania’s Employees during the COVID-19 Quarantine Period. Sustainability, 12, 5332.
Gunawan, I., Kusnawan, A., & Hernawan, E. (2021). Impact of Work from Home Policy Implementation on Work Effectiveness and Productivity in Tangerang City. Primanomics: Jurnal Ekonomi & Bisnis, 19(1), 99-107.
Harker Martin, B., & MacDonnell, R. (2012). Is telework effective for organizations? A meta‐analysis of empirical research on perceptions of telework and organizational outcomes. Management Research Review, 35(7), 602-616.
Howell, D.C. (2008) The analysis of missing data. In Outhwaite, W. & Turner, S. Handbook of Social Science Methodology. London: Sage.
Kang, H. (2013). The prevention and handling of the missing data. Korean journal of anesthesiology, 64(5), 402-406.
Kaushik, M., & Guleria, N. (2020). The impact of pandemic COVID-19 in workplace. European Journal of Business and Management, 12(15), 1-10.
Krejcie, R. V., & Morgan, D. W. (1970). Determining sample size for research activities. Educational and psychological measurement, 30(3), 607-610.
Kumar, S., & Chong, I. (2018). Correlation analysis to identify the effective data in machine learning: Prediction of depressive disorder and emotion states. International journal of environmental research and public health, 15(12), 2907.
Moens, E., Lippens, L., Sterkens, P., Weytjens, J., & Baert, S. (2022). The COVID-19 crisis and telework: a research survey on experiences, expectations and hopes. The European Journal of Health Economics, 23(4), 729-753.
Morikawa, M. (2021). Productivity of working from home during the COVID-19 pandemic: Evidence from a firm survey. Research Institute of Economy, Trade and Industry (RIETI).
Mukaka, M. M. (2012). A guide to appropriate use of correlation coefficient in medical research. Malawi medical journal, 24(3), 69-71.
Shareena, P., & Shahid, M. (2020). Work From Home During Covid-19: Employees Perception And Experiences. Gjra-Global Journal For Research Analysis, 95, 7–10.
Wong, A. H. K., Cheung, J. O., & Chen, Z. (2020). Promoting effectiveness of “working from home”: findings from Hong Kong working population under COVID-19. Asian Education and Development Studies, 10(2), 210-228.
