During the COVID-19 pandemic, it affected most of the businesses and all the employees are forced to work from home. The purpose of this study is to see how working from home affects workers' work-life balance during COVID-19.
RESEARCH OVERVIEW
- Introduction
- Research Background
- Problem Statement
- Research Questions
- Research Objectives
- Research Context
- Research Significance
- Contribution of Study
- Summary
What are the factors affecting work from home and employee productivity during COVID-19 in Malaysia. To study the factors affecting working from home and employee productivity during COVID-19 in Malaysia.
LITERATURE REVIEW
Introduction
Underlying Theory
Theory and Models
- Organizational Adaptation Theory
- Human Relations Theory
According to the research of Sart et al. 2021), explained that organizational sociologists show how maintaining social ties or taking measures of internationalization in specific institutional contexts can help people develop their skills within this subject. The theory of human relations was created as a response to the scientific approach to management, which moves towards the human side of management, as we have already noted.
Review of Variables
- Employee Productivity
- Work from Home (WFH)
- Work-Life Balance (WLB)
- Work Stress
- Job Satisfaction
- Technical Perspective
Work from home (WFH), often known as telecommuting, is a term synonymous with telecommuting or telecommuting (Garrett & Danziger, 2007). According to a study by Jyothi Sree and Jyothi Sree, work-life balance is defined as "establishing a balance between the work and personal lives of workers".
Research Domain
- Work from Home
- Platform: Microsoft SharePoint
- COVID-19
On the other hand, this form of work arrangement is seen only in employees or regular workers. Working from home, on the other hand, provides a more comfortable environment to focus on. The mobile app enables anyone to stay up-to-date with news on the go.
Between September and October 2020, the most common changes implemented by companies were reduced working hours and lower wages, as shown in Figure 2.6 (Kuriakose et al., 2021). But there are a huge number of companies making big changes at the edge, with a lot of churn, meaning 36 percent of companies have laid people off and 43 percent have hired staff. By October, 60% of companies were late with payments or were on the verge of becoming late in the next six months.
From other perspectives, digital transformation occurred in Malaysia due to COVID-19, such as working from home, remote health and distance learning (Lucy, 2021).
Conceptual Framework
Hypotheses Development
- Relationship between Work from Home and Work-Life Balance during
- Relationship between Work from Home and Work Stress during COVID-
- Relationship between Work from Home and Employee Productivity
- Relationship between Work from Home and Job Satisfaction during
- Relationship between Job Satisfaction and Employee Productivity during
- Relationship between Work from Home and Technical Perspective
- Relationship between Technical perspective and Employee Productivity
Working from home improves work-life balance in general, and working from home was thought to be beneficial (Tanjung et al., 2021). According to the study by Irawanto et al. 2021), has clarified that working from home has an important link towards work-life balance. Working from home can increase stress (Gajendran & Harrison, 2007), however if one has a flexible schedule, it can also reduce stress (Kim et al, 2020).
According to a research by Irawanto et al., working from home is linked to increased workplace stress (2021). There is a significant relationship between working from home and job satisfaction, according to empirical studies (Irawanto et al., 2021; Vega, Anderson & Kaplan, 2015). This shows that working from home and technical perspective have a positive relationship between each other.
It thus shows that there is a significant relationship between working from home and the technical perspective.
Summary
According to the research of Suresh & Gopakumar (2021), it stated that organizations that provide computer equipment to their workers so that they can work from home will benefit more in terms of productivity and profit. If a corporation does not provide appropriate technological tools for the function to be performed in a virtual office, it can have a negative impact on employee productivity. Based on the study of Attygalle & Abhayawardana (2021), the technical perspectives such as internet connection and technical problems occurred when working from home.
The reviews of other research from H1 to H9 are evaluated for the hypothesis development section, and each hypothesis is given an acceptable study objective.
METHODOLOGY
- Introduction
- Research Design
- Sampling Design
- Population
- Sampling Frame
- Sampling Technique
- Determination of Sample Size
- Data Collection Method
- Primary Data
- Questionnaire Development
- Pilot Study Analysis
- Data Analysis Technique
- Descriptive Analysis
- Inferential Analysis
- Scale Measurement
- Nominal Scale
- Ordinal Scale
- Likert Scale
- Summary
According to the study by Bryman & Bell (2011), it was stated that the population includes all elements from which the sample will be selected. Probability and non-probability sampling are the two main approaches to determining a sample of the population. This study found that convenience sampling is preferable because of the accessibility and purpose of the study.
An online survey is appropriate for the type of data collected by the researcher, as a large part of the study relates to the respondent's experience working from home and how working from home affects employee productivity. In addition, the second part of the questionnaire, including the adoption from previous studies, was listed in Table 3.1. Based on the results of the pilot study, the questionnaire runs efficiently and contains no errors.
Descriptive analysis is a type of data analysis that helps in the constructive explanation, demonstration or summary of data points, allowing patterns to emerge that meet all the data's needs.
DATA ANALYSIS
Introduction
Demographic of Respondents
- Gender
- Age
- Marital Status
- Education
- Tenure (years)
- Current Employment
According to the results of the survey, the majority of respondents are in the age group of 18 to 27 years. This is followed by 10 respondents from the age group of 38 to 47 years, which is 4.3% of the respondents. It is said that 80.9% of respondents are single, which also means that 186 out of 230 are single.
However, the rest of the respondents are married, making up only 19.1% of the survey results, meaning only 44 of the 230 respondents are married. Based on the results of the survey, most respondents have a tenure of 1 to 5 years. Based on the survey results, most of the respondents are employees of private companies.
It accounts for 92.6% of respondents, which also means that 213 out of 230 are employees of private companies.
General Questions
- Working Hours Per Day
- Platforms Used During Work from Home
- Work Arrangements
- Frequency of Work from Home in a Week
Based on the results of the survey, most of the respondents use Zoom as a platform when working from home. This includes reports for full-time office/site work, part-time work from home and full-time work from home. Based on the results of the survey, most of the respondents work from home 5-6 times a week.
This applies to 34.8% of the respondents, which also means that 80 of the 230 respondents work from home 5-6 times a week. Next are the respondents who work from home 3-4 times a week, which is 33.0%, which amounts to 76 respondents. In addition, 15.2% of the respondents work at home twice a week, i.e. 35 respondents.
Then there are 29 respondents who less than often or never work from home in a week, which is 12.6%.
Descriptive Analysis of Variables
- Mean and Standard Deviation of Productivity
- Mean and Standard Deviation of Work-Life Balance
- Mean and Standard Deviation of Work Stress
- Mean and Standard Deviation of Job Satisfaction
- Mean and Standard Deviation of Work from Home
- Mean and Standard Deviation of Technical Perspective
Referring to Table 4.11, item 3 would have the highest rank, as it has the highest mean value of 5.40 and the standard deviation of 1.169. Referring to Table 4.12, item 2 would have the highest rank, as it has the highest mean value of 5.24 and the standard deviation of 1.474. Referring to Table 4.13, item 1 would have the highest rank, as it has the highest mean value of 5.24 and the standard deviation of 1.332.
Referring to Table 4.14, item 3 would have the highest rank, as it has the highest mean value of 5.45 and the standard deviation of 1.139. Referring to Table 4.15, item 2 would have the highest rank, as it has the highest mean value of 5.48 and the standard deviation of 1.242. The table below shows the mean and standard deviation of each question asked in the questionnaire (Technical Perspective section) contributed by the 230 participants.
Referring to Table 4.16, item 3 would have the highest ranking as it has the highest mean value of 5.50 and the standard deviation of 1.270.
Reliability Test
Pearson Correlation
The correlation coefficient values for H1, which is WFH →WLB is 0.337**, show a low positive correlation; H2 is WLB → EP has a correlation coefficient value of 0.288** shows negligible correlation; H3 is WFH → WS has a correlation coefficient value of 0.261** shows negligible correlation. In addition, H4 shows the path WS → EP has a correlation coefficient value of 0.223** shows a negligible correlation; H5 is WFH → EP has a correlation coefficient value of 0.530** shows moderate positive correlation; H6 is WFH → JS has a correlation coefficient value of 0.563** shows moderate positive correlation. Then H7 shows the path JS → EP, which has a correlation coefficient value of 0.599** shows a moderate positive correlation; H8 is WFH → TP has a correlation coefficient value of 0.505** shows moderate positive correlation; H9 is TP → EP has a correlation coefficient value of 0.525** shows moderate positive correlation.
Multiple Regression
- Relationship between Work from Home towards Work-Life Balance,
- Relationship between Work-Life Balance, Work Stress, Job Satisfaction
- Relationship between Work from Home towards Employee Productivity
Based on the results in Table 4.28, it appears that H1, namely working from home, has a significant impact on work-life balance during COVID-19. The significant value is 0.001, p<0.05. Second, H2, i.e. work-life balance, which has a significant impact on employee productivity during COVID-19, was accepted as the significant value is 0.045, p<0.05. In addition, H5, or working from home, has a significant impact on employee productivity during COVID-19, and is accepted in the results as the significant value is 0.000, p<0.05.
Furthermore, H6 is working from home has a significant impact on job satisfaction during COVID-19 has shown an accepted result as the significant value is 0.000, p<0.05. H7 is Job satisfaction has a significant impact on employee productivity during COVID-19 has shown an accepted result as the significant value is 0.000, p<0.05. However, H8 is work from home has a significant impact on the technical perspective during COVID-19 which has accepted results as the significant value is 0.001, p<0.05.
Finally, H9 is the technical perspective has a significant impact on employee productivity during COVID-19, which was accepted in the results as the significant value is 0.003, p<0.05.
Summary
DISCUSSION, CONCLUSION AND IMPLICATIONS
- Introduction
- Summary of Statistical Analysis
- Descriptive Analysis
- Discussion of Major Findings
- Objective 1
- Objective 2
- Objective 3
- Implications of the Study
- Theoretical Implications
- Practical Implications
- Limitations of the Study
- Recommendations for Future Research
- Conclusion
Studying the relationships between working from home and work-life balance, job stress, job satisfaction and technical perspectives. First, Hypothesis 1 shows that working from home has a significant impact on work-life balance during COVID-19. In addition, Hypothesis 3 shows that working from home has a significant impact on work stress during COVID-19, but this hypothesis was rejected.
Furthermore, hypothesis 8 shows that working from home has a significant impact on the technical perspective during COVID-19. In conclusion, this study investigated the factors influencing telecommuting and employee productivity during the COVID-19 pandemic in Malaysia. Employee productivity modeling following a work-from-home scenario during the Covid-19 pandemic: A case study using classification trees.
Working from home: Measuring work-life satisfaction and workplace stress during the COVID-19 pandemic in Indonesia.
