Determinants of Work-Life Balance in Malaysia Setting.

Data analysis

Work-life balance in Malaysia is a significant topic that helps identify critical HR practices that can help streamline organizational performance. Accordingly, the respondents from the survey had diverse views on the topic. Accordingly, responses were subjected to SPSS Software, and statistical tests were conducted. Significant variables were interested in flexible hours, monetary rewards, training and development, technology impact, supportive management promotion, and satisfaction at the workplace. Specifically, the level of satisfaction, which illustrated work-life balance, was considered an independent variable. Three significant tests were carried out to analyze the relationship and impact of each variable on work-life balance: descriptive statistics, coefficient correlation, multi-linear regression, and paired sample T-test. SPSS output tables have been attached to the Appendixes.

Correlation analysis

Correlation analysis is a statistical test used to identify the relationship between variables. The relationship is measured by Pearson’s correlation value, which ranges from negative to positive. Accordingly, the significance of the relationship is proven by the p-value, estimated to be less than 0.05. Based on the study, five factors were selected on their relationship to work-life balance. The first is how training and development enhance individual satisfaction with work. From the output, the correlation coefficient is 0.231 with a p-value of 0.000 (Appendix 2). This illustrates that about 23% of employee work-life balance is caused by access to training and development, which enhance competency and motivation. The second factor is monetary rewards for performance. The output correlation coefficient is 0.151 and a p-value of 0.005(Appendix). This illustrates a weak relationship; approximately 15% of work-life balance is associated with rewards such as bonuses, incentives, and other financial compensation.

Technology is another significant factor in work-life balance. The correlation coefficient is 0.242, and the p-value is 0.000. This illustrates that 24.2% of work-life balance is associated with technological advancements. Technology helps to lower the amount of work done and increase efficiency. Accordingly, having supportive management shows a correlation coefficient of 0.177 and a p-value of 0.001. overall, these factors illustrate a weak relationship with work-life balance. However, more can be derived to demonstrate that an imperative approach of combining these factors can enhance work-life balance—notably, correlation analysis causality and examples of two variables at a time. Therefore, regression analysis addresses issues of multivariate relationships, lower limitations, and the assumption of linear relationships.

Regression

Regression is more effective and provides a comprehensive understanding of the relationship between the variables. According to Skiera, Reiner, and Albers, 2021, regression helps to predict and forecast future outcomes based on the changes in one variable. For instance, how will the level of work-life balance change if specific changes are made on independent variables? The regression output illustrates how work-life balance is affected by training and development, promotion, flexible working hours, and monetary compensation. A model summary is presented below, while detailed regression Output from SPPS is shown in Appendix 2.

Based on the mode summary above, a Coefficient correlation of 0.273 illustrates that the proportion of variance, approximately 27.3 percent, is affected by changes in the predictor variable. These variables include flexibility, training, promotion, and monetary compensation. More importantly, a p-value of 0.000 shows that one of the variables must be having an impact. The interpretation of the R square change of 0.075 shows that the new predictor contributes to 7.5% of the variance.

Further analysis of the ANOVA table and coefficient in Appendix Three reveals that the unique contribution of the factors does not support work-life balance. Each factor contributes a small portion of the job satisfaction. The coefficient of flexible hours and time off is positive at 0.085, indicating that increased time off promotes employee satisfaction. Accordingly, this aligns with Roberson and Perry’s (2022) explanation of organizational performance, noting that employees need to relieve burnout and pressure from busty days (Kalogiannidis, 2021).

Accordingly, the coefficient for monetary rewards attracts a negative coefficient value of -0.017, showing that it has a lower impact on improving work-life balance. The literature looks counter-intuitive, but deeper analysis proves and supports the value; in a Journal of Business and Change, Kalogiannidis discusses that compensation for extra time rewards employees with extra effort and contribution (Kalogiannidis, 2021). However, the argument for work-life balance supports employee wellness to accommodate personal and world responsibilities. Similarly, promotion has a low Pearson coefficient attached to improving work-life balance. The findings highlight employees’ importance in continuous growth and career progression.

Based on the findings, the regression equation supports that the result of work-life balance equals a constant 2.533 plus coefficient for each factor: flexible hours 0.085, monetary compensation -0.21, promotion 0.125, and training 0.16 (Appendix 3). However, it is essential to note that regression analysis is limited to model specification. That is, the specification and inclusion of specific variables may impact the effectiveness of the findings. Based on such limitation, the Kruskal Wallis test examines whether monthly income or job position differences will influence work-life balance.

Kruskal Wallis test

Correlation and regression analysis may fail to provide correct findings based on the complexity of factors influencing work-life balance. Accordingly, the assumptions of linear relationships may fail, and demographic characteristics influence the findings. As a result, the Kruskal Wallis test is the best approach to evaluate if other factors, such as monthly income or age group, would have influenced the findings. Therefore, a Kruskal Wallis test on job satisfaction and monthly income was conducted, and the results are presented in the following table.

Based on these results, a chi-square Value of 46. 841 illustrates a more significant difference between monthly income and job satisfaction. That is, individuals’ income does not affect their work-life balance. More importantly, a p-value of 0.000 illustrates that the difference between the groups was statistically significant. Accordingly, the test can be duplicated to test responses from individuals of different ages. If findings result in no differences, assumptions previously made in correlation and regression tests would not work effectively (Skiera et al., 2021). In essence, determinants of work-life balance are a significant topic that requires comprehensive analysis. Correlation and regression analysis identify the relationship between promotion, time off, and compensation and drives to job satisfaction. Accordingly, the Kruskal-Wallis test illustrates the difference in other factors that enhance the reliability and validity of statistical tests. However, there is a need to employ a qualitative approach to seek the meaning and perspectives of the responses.

Recommendations

The analysis of work-life balance has led to the discovery of crucial factors that can be established to enhance employee performance and work-life balance. First is an arrangement of flexible workers. This refers to giving time off to employees, which allows open communication and lower burnout from work pressure. The coefficient from correlation illustrates a weak positive influence of 0.17. this aligns with a study by Kuswati (2020), which urges that some employees work best during certain times of the week. Consequently, this would increase productivity and employee performance. The second recommendation is the establishment of training programs and the implementation of promotion. This is referred to as an opportunity for career growth and development. Through frequent training, employees have the opportunity to engage in more practice and career-building activities that break the monotony (Roberson & Perry, 2022). More importantly, training should be established to address stress management, time management, and the essentials of work-life building (Kuswati, Y., 2020).

Employee compensation is another significant factor that needs to be implemented. This encompasses rewarding employees with bonuses, incentives, and overtime allowance. Based on the findings, the coefficient correlation for monetary and performance-based compensation is 0.151, which means a positive relationship. When compensation is done, employees receive funds to recognize their efforts and have opportunities to hire house help. 8 It reflects a positive organizational culture built on wellness and employee support. In addition, supportive management staff significantly impact employee job satisfaction. The findings illustrated a positive correlation of 0.177.

Similarly, Roberson AND Perry (2022) highlight that management promotes open communication and establishes programs to support employees, build their careers, and allow work-life balance. A good example is team-building activities that engage employees and their families in socializing and networking events. Most important is establishing a comprehensive and inclusive approach to work-life balance (Kuswati, 2020). The results of the study recognized that each factor impacts job satisfaction. However, a comprehensive approach built by multiple factors is more supportive of enhancing work-life balance.

Conclusion

In essence, the complexity of work-life balance requires a strategic approach to promoting employee wellness and enhancing work-life balance. Analysis of the responses has identified promotion, supportive management, training, and monetary compensation as significant factors determining work-life balance in Malaysia. The findings of this report relied on primary data collected from a survey, and each factor was ranked between one and five, where five represented a high score while one meant a low score. Accordingly, correlation analysts produced a weak positive relationship, and regression brought a more profound understanding of the relationship. The implication has been the establishment of actionable recommendations encompassing multiple factors to enhance work-life balance. More importantly, it addresses the wellness of employees and increases organizational performance.

Referencing

Kalogiannidis, S., 2021. Impact of employee motivation on organizational performance. A scoping review paper for the public sector. The Strategic Journal of Business & Change Management, 8 (3), 984, 996(3).

Kuswati, Y., (2020). The effect of motivation on employee performance. Budapest International Research and Critics Institute-Journal (BIRCI-Journal), 3(2), 995–1002.

Roberson, Q. and Perry, J.L., (2022). Inclusive leadership in thought and action: A thematic analysis. Group & Organization Management, 47(4), 755–778.

Skiera, B., Reiner, J. and Albers, S., 2021. Regression Analysis. In Handbook of Market Research (pp. 299-327). Cham: Springer International Publishing.

Appendices

Appendix One: Descriptive Statistics

Appendix 2: Pearson correlation coefficient

Appendix 3: Regression

Appendix 4: Kruskal Wallis Test