Joint Effect of Employee Age, HRM Practices and Employee Competence on
Employee Performance.
The fourth specific objective sought to establish whether the joint effect of employee age, human
resource management practices and employee competence on employee performance was greater
than the effect of the individual predictor variables on employee performance in Kenyan state
corporations. The joint effect of the study variables is compared with the effect of the
mean/average aggregate individual predictor variables on employee performance. To test this
effect, the following hypothesis was formulated.
Hypothesis IV:
The joint effect of employee age, human resource management practices and employee competence
is greater than the effect of individual predictor variables on employee performance.
Hypothesis IV was tested using multiple linear regression model. Table 4.35 a, b and c shows the
regression results. The following results were obtained. The overall model shown in Table 4.35a,
generated R2 = 0.438, F (4, 283) = 31.025, (p ˂ 0.05). Employee age, HRM Practices and
Employee competence explained 43.8 % of the variance in employee performance (R2 = 0.438),
which was significant at (p ˂ 0.05). Table 4.35 b presents Regression outcomes (ANOVA results)
of the Joint effect of employee age, HRM Practices and employee competence on employee
performance. The F values were F (4, 283) = 31.025. P ˂ 0.05. which was statistically significant.
Table 4.35 c shows the Regression coefficients for the joint effect of Employee Age, HRM
Practices and Employee Competence on Employee Performance. The regression coefficient B for
Employee age in the presence of HRMP and EC is 0.060, with a t value of 0.787 and a
significance level (p value ˂ 0.05). The regression coefficient B value for the moderating variable
HRM Practices in the presence of EA and EC was 0.120, with a t value of -2.194 and a
significance level (p value ˂ 0.05). This means that HRM Practices had a significant impact on
employee performance in the presence of EC and EA. The regression coefficient B value for the
moderating variable employee competence (EC) in the presence of EA and HRM Practices was
0.441, with a t value of 9.205 and a significance level (p value ˂ 0.05). This also means that
employee competence had a significant impact on employee performance in the presence of HRM
practices and E A. The joint effect of E A, HRM P, and EC on EP indicated B = 0. 290, t = 3.567
(p ˂ 0.05). EA + HRMP + EC explained 29.0 % of the variance in employee performance.
,,,, ,,,,
The above results can be compared with results of individual predictor variables shown in Table
4.32 and 4.34 as follows; For individual predictor variables, the research findings indicated that
the mean Employee Age, Human Resource Management Practices and Employee Competence
explained 20.7% of the variance in employee performance (B=0.207). The overall model reveals a
statistically significant relationship between EP and the mean of EA, HRM P, and E (p < 0 .05)
The results confirm hypothesis iv, that the joint effect of employee age, HRM practices and EC on
EP which indicated R2 = 0.4389 (43.9%), B= 0.290, t = 3.567 (p < .05) is greater than the effect
of the mean individual predictor variables R2= 0.273, B = 0.207, t = (p < 0.05) as shown above.
This comparison indicates that the effect of individual predictor variables was less than the joint
effect of the predictor variables.
Regression Results of the joint effect of employee age, HRMP and employee competence on employee
performance
a) MODEL SUMMARY
Mode
l
R R Square Adjusted R
Square
Std. Error
of the
Estimate
Change Statistics
R
Square
Change
F Change df1 df2 Sig F
Chang
e
1 0.665a0.438 0.227 0.4489 0.438 1.935 4 283 0.000
b) ANOVAa
Model Sum of Squares Df Mean Square
F
Sig.
Regression 24.236 4 8.079 31.025 .000b
1 Residual 73.951 283 .260
Total 98.187 287
c) REGRESSION COEFFICIENTS
Model Unstandardized
Coefficients
Standardize
d
Coefficients
t Sig.
B Std. Error Beta
(Constant)
1. Age
2. HRM management
practices
3. Employee Competence
EA + HRMP + EC
2.356
0.060
0.212 3481 0.000
0.070 0.041 0.787 0.432
0.120 0.050 -0.192 -2.194 0.030
0.441
0.290
0.048
0.081
0.478
0.263
9.205
3.567
0 .000
0.000
a. Dependent Variable: Employee Performance
b. Predictors: (Constant), Employee Age, Employee Competence, HRM management practices
The overall model showed that there was a statistically significant influence of the joint effect of
employee age, HRM practices and employee competence on employee performance. R2 = 0.438,
F (4, 283) = 31.025, (p ˂ .05). Meaning that employee age, HRM Practices and employee
competence explained 43.8% of the variance in employee performance (R2 = 0.438), which was
statistically significant at (p ˂ .05). The multiple regression model with the mean of all the three
predictors (Employee Age, Human Resource Management Practices, Employee competence)
produced R² = 0.273, F (3, 285) = 42.5, p < .05. The research findings indicated that the mean
Employee Age, Human Resource Management Practices and Employee Competence explained
27.3 % of the variance in employee performance (R2=0.273), B = 0.290, t = 3.567 (p < .05) The
overall model reveals a statistically significant relationship between EP and the mean of EA,
HRM P, and E (p < .05)
The results confirm the fourth hypothesis, that the joint effect of employee age, HRM practices
and employee competence on employee performance (R2 = 0.438), B= 0.290, t = 3.567 (p < .05)
is greater than the effect of the mean individual predictor variables R2 = 0.273, B = 0.207, t = (p <
.05) as shown above.
Discussion
The specific objective IV sought to determine the difference between the joint effect of Employee
Age, HRM Practices and Employee Competence on Employee Performance and the effect of
individual predictor variables on employee performance. The study predicted that jointly
Employee Age, HRM Practices and Employee Competence had a stronger effect on employee
performance than the mean effect of the three individual predictor variables.
The research findings indicated that overall, the joint effect of Employee Age, HRM Practices and
Employee Competence on employee performance was significant. The joint effect recording 43.8
% score (R2 = 0.438), F (4, 283) = 31.025, (p ˂ .05). The contribution of individual predictor
variables is shown in the table 4.35. This follows that the joint effect of the predictor variables
was greater than the average aggregate effect and hence the hypothesis was accepted. These
findings support a related study carried out by Muindi (2014) in the Kenyan public universities
which also found that the joint effect of the study variables was stronger than the effect of the
individual predictor variables. The final objective of Muindi’s study aimed at determining
differences between the joint effect of QWL, personality, JS and competence on employee
performance and the average aggregate of the effect of all predictor variables on performance.
The joint effect of the predictor variables was greater than the individual effect and hence the
hypothesis was accepted. Despite the fact that public universities are State Corporations, their
management, facilities and core functions could differ with other State Corporations and hence
comparing the regression results without putting other factors into consideration may not help
much. In addition, the current study focused on employee age and employee performance as
Muindi (2014) focused on Quality Work life (QWL) and performance.
,
,,
The results of Omari (2012) study indicated that the joint effect of the cognitive and contextual
factors on the relationship between employee characteristics and employee outcomes was greater
than of the effects of the individual predictor variables. Findings indicate that the effect of the
individual independent and moderating variables is not greater than their joint effect. Thus, the
hypothesis which stated that the joint effect of the independent and moderating variables on
employee outcomes (trust, job satisfaction, organization commitment and citizenship behaviors) is
greater than the individual effect of the independent and moderating factors on employee
outcomes was supported.
