Multivariate Modeling of Employee Retention Using Reward, Work-Life Balance, and Work Environment Factors in Malaysia’s Industrial Equipment Sector

Employee retention has become a strategic necessity in human resource management, specifically in the engineering and industrial distribution sectors. Specialized skills are rare and essential for operational continuity, customer service reliability, and profitability. The purpose of this study is to explore the
empirical factors that affect employees’ exit from Sime Darby Industrial (SDI) Sdn Bhd, a franchisee distributor of Caterpillar heavy machinery in Malaysia, with a high rate of employees’ voluntary turnover that has been observed to be over 40% for the
past decade. Based on the three theories, a quantitative deductive investigation was performed by administering a 35-item survey to N = 60 mid-level technical executives, engineers, diagnostic specialists, and managers (with a 50% return rate) from a target population of N = 120 that represents the extent of the sample. The extent of the sample to be used in the investigation was N = 120, but a 50% return rate of N = 60 mid-level technical executives, engineers, diagnostic specialists, and managers was achieved for a quantitative deductive investigation, founded on the three theories. Construct reliability (α), normality diagnostics (skewness/kurtosis z-scores), Pearson correlation, and Ordinary Least Squares (OLS) multiple linear regression were used for the analysis of the empirical data in IBM SPSS 23. The results show that reward and recognition have a statistically significant positive direct effect on employee retention (r = 0.323, p = 0.012; regression β = 0.425, t = 2.620, p = 0.011). By contrast, both Work-Life Balance (r = 0.173, p = 0.186) and Work Environment (r = 0.042, p = 0.750) have weak direct bivariate impacts but
strong, statistically significant bivariate correlations with the composite intervening psychological construct (Job Satisfaction, Organizational Commitment, and Employee Loyalty) (r = 0.546 and r = 0.658, respectively; all p ¡ 0.001). The intervening construct, in turn, significantly contributes to retention (r = 0.311,
p = 0.016). The overall regression model is statistically significant (F(3, 56) = 3.109, p = 0.034, R² = 0.143). Proposed to create a strategic retention architecture in 5 pillars for the industrial machinery businesses.