The Effect of Employee Competence on the Relationship between Employee Age and
Employee Performance
The third specific objective sought to determine the moderating effect of employee competence on
the relationship between employee age and employee performance. The hypothesis formulated for
this objective was as follows:
Hypothesis III:
The effect of employee age on employee performance is moderated by employee competence
in Kenyan state corporations
The theoretical basis of this hypothesis is that people have different dispositions about their input
to the organization in terms of their individual performance which can be influenced by other
variables. The techniques and procedures used to test hypothesis three are similar to those used in
testing hypothesis two. Stepwise regression analysis proposed by Baron and Kenny (1986) was
used.
The results are shown in the Table 4.35. As shown in the table, the overall regression model was
significant R2 = 0.242, F (3, 284) = 30.269, p ˂ 0.05) implying goodness of fit. Thus, the use of
the regression technique for the test of hypothesis three was appropriate. In step one the single
relationship between employee age and employee competence produced the result R2 = 0.001, F
(1, 286) = 0.345, (p ˃.05). The relationship was not statistically significant.
In step two, both employee age and employee competence were entered into the regression
equation simultaneously as presented in model 2. This model produced R2 = 0.230, F(2, 285) =
42.584, p ˂ 0.05. At 23 %, the model reveals a statistically significant relationship between
employee age (independent variable) and employee performance (depended variable). This
implies that, a unit change of employee competence is associated with 23 % variation in employee
performance. Beta coefficient was β = 0.441, (t = 9.205, p ˂.05) which was statistically
significant. The results confirmed the second step in testing for moderation.
In step 3, employee age (independent variable) is multiplied with employee competence
(moderating variable) to create an interaction term, EA*EC which when entered in the regression
model, brings about a change in employee performance (ΔR2) accounting for R2 = 0.242, F
(3,284) = 30.269, p ˂ 0.05.). This is statistically significant. A unit change in EC explains 0.659
variation in EP (β = 0.659, t = 5.860, p ˂ 0.05) which is statistically significant. This confirms
Hypothesis three that EC moderates the relationship between EA and EP
0.000d
Regression Results for the effect of employee competence on the Relationship between Employee
Age and Employee Performance
MODEL SUMMARY
Model R R
Square
Adjusted
R Square
Std. Error
of the
Estimate
Change Statistics
R
Square
Change
F Change df1 df Sig F
Chang
e
1 0.035a0.001 -0.002 0.58557 0.001 0.345 1 286 0.557
2 0.480b0.230 0.225 0.51502 0.229 84.723 2 285 0.000
3 0.492c0.242 0.234 0.51183 0.012 4.571 3 284 0.033
ANOVAa
Model Sum of Squares df Mean Square
F
Sig.
Regression 0.118 1 0.118 0.345 0.557b
1 Residual 98.069 286 0.343
Total 98.187 287
Regression 22.591 2 11.295 42.584 0.000c
2 Residual 75.596 285 .265
Total 98.187 287
Regression 23.788 37.929 30.269
3 Residual 74.399 284 .262
Total 98.187 287
COEFFICIENTS
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
B Std. Error Beta
(Constant)
1
Age
4.279 0.081 53.070 0.000
0.026 0.045 0.035 0.587 0.557
(Constant)
Age
2
Employee
Competence
2.529
0.022
0.441
1.665
0.203
0.039
0.048
0.452
0.029
0.478
12.463
0.552
9.205
3.686
0.000
0.582
0 .000
0.000
(Constant)
Age
Employee
Competenc
e EA*EC
0.537 0.244 0.712 2.199 0 .029
3 0.659 0.113 0.715 5.860 0.000
0.172 0.061 -.734 -2.138 0 .033
a. Dependent Variable: Employee Performance
b. Predictors: (Constant), Employee Age
c. Predictors: (Constant), Employee Age, Employee competence
d. Predictors: (Constant), Employee Age, Employee competence, Employee Age *
Employee competence.
, Substituting for each coefficient at each step of the simple and Multiple Regression Analysis
Below are regression equations generated by substituting actual beta coefficients
STEP 1. EP = β0 +β1EA +ε
EP =4.279 +0.026EA +
0.081
STEP 2. EP = β0 +β1EA+EC +ε
EP = 2.529+ 0.022 EA+ 0.441EC + 0.203
.
STEP 3 EP = β0 +β1EA+β2EC + β2 EA* EC +ε
EP = 1.665+ 0.537EA + 0.659 EC + 0.5374EA *0.659EC + 0.452
Discussion
The third study objective addressed the influence of EC on the relationship between EA and EP.
The corresponding hypothesis stated that the effect of employee age on employee performance is
moderated by employee competence.
This hypothesis was tested using a stepwise, simple and multiple linear regression analysis. The
findings in Table 4.34 shows that the overall model was significant. The hierarchical multiple
regression model results revealed a statistically non-significant relationship between EA and EP in
step 1. R2 = 0.001, F (1, 286) = 0.345, (p ˃ 0.05). A unit change in EC is associated with a beta
coefficient of β 2.6 % change in EP (β = 0.026, t = 0.587, p ˂ 0.05).
In step 2 the composite index of employee competence was entered in the regression model. The
results indicated, R2 = 0.230, F (2, 285) = 42.584 (p ˂ 0.05). This was a positive relationship that
was statistically significant. When the interaction between the variables employee age and
employee competence (EA * EC) was created and introduced into the regression equation. In step
3, there was a 24.2% variance in employee performance which was statistically significant. R2 =
0.242, F (3,284) = 30.269, (p ˂.05). A unit change in employee competence is associated with
65.9% variance in EP (β = 0.659, t = 5.860), p ˂ 0.05. Hypothesis three was confirmed. This
study supports findings of other studies and also contradicts the findings of others as stated below.
A related study by Schmdt et al. (2014) which tested the influence of competence on age and
performance, established that employee performance and competence are closely related in terms
of age, individual abilities, experience and motivation. In a study to establish the moderating role
of personality between quality work life (QWL) and job satisfaction, Muindi (2014) found that
personality had a moderating effect on QWL and JS of employees in public universities and the
third study hypothesis was supported. The above results are in line with those of the current study
in terms of the moderating effects of the interaction term between study variables. On the contrary
the fourth objective of Muindi (2014)’s study, which stated that competence moderates the
relationship between job satisfaction and performance of academic staff in Kenya public
universities. It found that competence has no moderating effect on this relationship. The study
background was in the Kenyan public universities which is a good comparison point with Kenyan
State Corporations as public universities are part of the Kenyan State Corporations.
The current study findings contradict the findings of other studies such as those of Cassar (2001),
Chen and Silva (2008) and Tumley et al. (2003) who found statistically significant relationships
between psychological contract and employee performance without involving any moderator. The
findings also contradict the study of Ayan and Kocacik (2010) who concluded that personality
characteristics such as employee age have an impact on J S and hence employee performance. No
moderator was used in this study. Scheider and Dachler (1978) on their study, called stability of
Job Descriptive Index, indicated that, with time, job satisfaction remains usually stable, and that
people’s personality causes job satisfaction more than other variables and Judge et al. (2002)
found that conscientiousness was a significant predictor of J S. Employee characteristics such as
age, employee outcomes such as J S and employee competence are key determinants of employee
performance.
However, most of the results that contradicted or agreed with these outcomes were from studies
conducted in Europe and the USA. The studies were conducted in developed countries whose
context is different from which the current study was conducted. The current study results are a
significant advancement in knowledge particularly coming from a study done in a developing
country, Kenya. The study concludes that the regression weights of employee age and employee
competence changed upon the inclusion of the interaction term and their effect on employee
performance and age was no longer statistically insignificant. This implies that employee
competence moderated the relationship between employee age and employee performance,
confirming the third hypothesis. There is need for organizations to enhance competency skills
among their employees through age management practices to cultivate maximum productivity
from them. For instance, competence through education enables employees to be more responsive
in receiving instructions and performing new tasks and easily adopting new technology, which
enhances their creativity and innovation to improve their performance. This finding is in line with
Human capital theory which states that widespread investment in Human capital in terms of
education and training of groups or individuals, creates a skill based labor force that is
indispensable for economic growth. These results confirm and therefore strengthens the
expectancy theory.
