Relationship between Employee Age and sub variables of employee Performance
Table below presents the correlation between employee age and
the sub variables of employee performance. From the correlation results above, Employee Age did
not predict employee performance and it is important to note how employee age relates with the
sub variables of employee performance. This will help understand better the contribution of each
sub variable of employee performance to the relationship between employee age and employee
performance.
The correlation between employee age and employee performance sub variables
Scale
Employee
Performa nce
1.
Attendan
ce at your
place of
work
2. Attendanc
e at
department al
meetings
3.Competi ng
tasks within
the set time
4.Achievin g
individual
targets
5.Achievin
g group
targets
6. Ability to
meet
deadlines
set
7.Your ranking
in target
achievemen
ts
Employee
Age 0.000 -.045* .07* -.016* -.058* -.020* -.054* -.007*
* * Correlation is significant at the 0.01 level (2-tailed). p = ˂ 0.01 (2- tailed)
*Correlation is significant at the 0.05 level (2-tailed). p = ˂ 0.05 (2- tailed
Generally, most sub variables of employee performance were negatively and not significantly
statistical to employee age. Attendance of departmental meetings (r = 0 .07, p ˂ 0.05), had a
positive but weak significant relationship with employee age. Achieving individual targets (r = -
.058, p ˂ 0.05) and ability to meet deadlines as set by the supervisor (r = -.054, p ˂ 0.05),
indicated a negative but weak significant relationship with employee age. Completing tasks
within the set time (r = -.016, p ˃ 0.05), attendance of place of work (r = -.045, p ˃ 0.05),
achieving group targets (r = -.020, p ˃ 0.05) and ranking in target achievements (r = -.007, p ˃
0.05), all recorded a negative and statistically insignificant relationship with employee age.
It should be noted that the overall correlation between employee age and employee performance
was not statistically significant. The purpose of this correlation was to assess the individual
contribution of the sub variables of employee performance on the relationship between employee
age and employee performance. This will partly help to understand why employee age could not
predict employee performance.
,
Test of Hypotheses
Introduction
The aim of this section was to test the hypotheses of the study. The listed hypotheses explain the
linkages among the various study variables outlined in the conceptual framework that laid the
foundation for this study. The study was based on the premise that a relationship exists between
Employee age and employee performance. The study predicted that Human Resource
Management Practices and Employee Competence will have a moderating effect on this
relationship. Four hypotheses were developed from the four research objectives. The variables
included employee age, human resource management practices, employee competence and
employee performance (Task performance factors and contextual performance).
Data was collected using the interval Likert-type scale. To test the hypotheses, simple and
multiple regression analysis techniques were conducted at 95% confidence level. Simple and
Multiple regression analysis techniques were utilized to assess the predictive ability of a given
independent variable on one given dependent variable and a number of independent variables on
one given dependent variable respectively. Multiple regression in this study is justified by the fact
that Multiple determinants were believed to have a predictive ability on one dependent variable,
namely employee performance. To indicate how well the predictor variables account for variance
in the dependent variable, the goodness for fit test was applied. To establish the predicted
significance of the predictor variables on dependent variable, the significance was determined.
The results of the tests of the hypotheses are presented in sections 4.11.1 to 4.11. 5 and in Tables
4.32 to 4. 35. All the hypotheses were derived from the research objectives, the literature reviewed
in chapter two and the conceptual framework shown in Figure 2.1.
The broad objective of the study was to establish the effect of employee age on employee
performance and the effect of human resource management practices and employee competence
on this relationship. Simple and Multiple Stepwise regression was applied using unstandardized
regression coefficients to assess the moderating effect on the relationship between employee age
and employee performance. To test the hypotheses, simple and multiple regression techniques
analysis were conducted at 95% confidence level. For each hypothesis, coefficient of
determination (R2) was used to establish the amount or magnitude of variation between the study
variables. Beta () was used to establish the contribution of individual predictor variable to the
significance of the model. F test was used to assess the overall significance of the model. P-value
0.05 was used to check the statistical significance of the model.
Relationship between Employee Age and Employee Performance
The study’s specific objective one was to establish the nature of the relationship between
employee age and employee Performance in Kenyan State Corporations. Simple linear regression
technique was used to explore the predictive magnitude and extent of employee age on employee
performance of State Corporations in Kenya. Age comprised the following sub variables, young
employees (1977 – 1994), middle age employees (1966 -1976) and older employees (1955 –
1965). Respondents had been asked to state their age, data that was used for this purpose.
Components of employee performance comprised of task performance (specific and non-specific)
and contextual performance (effort, personal discipline and teamwork). The following hypothesis
was used to establish the relationship between the variables employee performance and employee
age in State Corporations of Kenya.
Hypothesis 1: There is a relationship between employee age and employee performance in
Kenyan State Corporations
Simple linear regression analysis was carried out to test the effect of employee age on
employee performance in Kenyan State Corporations. The simple regression model used is
shown below:
Employee performance [EP] = f (Employee age [EA]).
EP =0 + 1 EA +
Where
EP = Employee Performance
0 = Constant, 1 = Regression coefficient for employee age, EA = Composite index of Employee age,
= Error term
4.32 Results of Regression for the effect of employee age on employee performance
Table a MODEL SUMMARY
Model
R
R Square Adjusted R Square Std. Error of the Estimate
1 0.035a0.001 -.002 0 .58557
b ANOVA
Model Sum of Squares Df Mean Square F P – value
1 Regression 0.118 1 0.118 0.345 0.557b
Residual 98.069 286 0.657
Total 98.187 287
c COEFFICIENTS
Model Unstandardized Coefficients
Standardized
coefficients
t
P – value
Β Standard
Error Beta
1 (Constant) 4.279 0.081 53.070 0.000
Age 0.026 0.045 0.035 0.587 0.557
Predictors: (Constant), Age.
Dependent Variable; Employee performance.
The regression model produced R2 = .001, F (1, 286) = .345, p ˃ .05. Table 4.33a indicates the
regression results. It reveals that age indicated 0.1 % of the variance in employee performance (R2
= 0.001). R2 assessed how much of the independent variable, employee age, varied in its
relationship with the dependent variable employee performance. Results in the table also show
that the overall model reveals anon significant relationship between employee age and employee
performance since the p - value (0.557) ˃ 0.05 as shown in Table 4.33 b. (ANOVA results).
(1, 286) = .345, p ˃ .05. since the p - value (0.557) ˃ 0.05.
Table 4.32 c. (COEFFICIENTS results). The influence of employee age on employee
performance was also insignificant (β = 0.026, t = 0.587, p ˃ 0.05) in Kenyan State Corporations
as shown in table 4.32 c. The hypothesis is therefore not supported.
Discussion
The first specific objective sought to establish the effect of employee age on employee
performance in Kenyan State Corporations. The hypothesis formulated to test this relationship
stated that: “There is a relationship between employee age and employee performance.” The
study predicted that employee age would significantly predict performance of employees in
Kenyan State Corporations. The regression results presented earlier revealed a statistically non-
significant effect of employee age on employee performance (R2 = 0.001, F (1, 286) = 0.345, p ˃
0.05). This finding was reinforced by the value of beta coefficient and t value. (β = 0.026, t =
0.587, p ˃ 0.05). This was an indication that the influence of employee age on employee
performance was insignificant. Pearson product moment correlation analysis had been done earlier
to explore the relationship between employee age and employee performance. The correlation
coefficient indicated that the relationship between employee age and employee performance was
statistically non-significant (r =-.002, p>0.01).
Generally, most indicators of employee performance did not contribute significantly to this
relationship as seen in Table 4.31. The combined effect of these indicators could not meet the
threshold to create a significant relationship between employee age and employee performance.
Hypothesis one was not confirmed. This finding strengthens the expectancy theory of Vroom
(1964) which states that performance depends not only on the magnitude of efforts but also on
other factors such as individual abilities, traits and role perception. This implies that age alone
may not necessarily predict employee performance until other factors such as competence and
motivators come into play.
A related study conducted in the Kenyan State Corporations by Omari (2012) found a statistically
non-significant relationship between employee age and employee outcomes. Employee outcomes
influence employee performance. The study, assessed this relationship using the Pearson Product
Moment correlation. The results indicated that the relationship between age and employee
outcomes (commitment, job satisfaction, employee trust and organizational citizenship behavior)
is not statistically significant at p ˃ 0.05. This result is also similar to results obtained by Tu et al.
(2005) who found no statistical relationship between age and job satisfaction of employees of
higher education in China and Taiwan. Schmidt (2014) established a more positive relationship
than age.
Contrasting results can be seen in the studies of Scott and Cook (1981) who established a
significant relationship between age and performance. Grant (2005) and Karpinen (2011) found a
statistically significant relationship between employee age and employee performance. A study
by Hickson and Oshagbemi (1999) also indicated a positive relationship between age and
satisfaction of employees in teaching faculty of higher education.
The inability of employee age to significantly predict employee performance was an indicator that
other factors could moderate age to significantly predict this relationship. The results relate to
employee behavior theories such as Vroom (1964) expectancy theory. According to Vroom
(1964), expectancy theory, reward expectations increase job satisfaction and hence performance.
Vroom (1964) states that performance depends not only on the magnitude of efforts but also on
other factors such as individual abilities, traits and role perception. Employee age does not
statistically predict employee performance. Therefore, the implication is, that age alone is not
enough to significantly predict employee performance, other factors, come in as moderators on
this relationship. The results are also in line with continuity theory which states that individuals
who are successfully, continue positive habits, improve their preferences and lifestyles and
relations through middle life and later which maintains or improves productivity at the work
place. The implication is that state corporations should provide suitable work environment,
motivation and maintain good relationship with their workers. This will enhance successful
ageing, continued positive habits, preferences and lifestyles that maintain or improve their
productivity.
Other previous studies like Omari (2012), concentrated on the relationship between employee age
and aspects of employee outcomes such job satisfaction and/or organizational commitment. This
study in Kenyan State Corporations, focused on organization performance which may not come
out clearly as compared to employee performance focused in the current study. Thus, by exploring
this relationship, the current study goes further in enhancing the body of knowledge on the
relationship between employee age and employee performance. Employee performance is likely
to bring out more specific and clearer results as compared to organization performance which is
likely to generalize results in most organizations.
Age may not have been a significant predictor of employee performance because on itself alone, it
may be weak. This implies that there are other factors that can influence its relationship with
performance. Another possible reason for age failing to significantly predict performance is that,
some intervening factors such as nature of the job tasks and differences in individual abilities were
not put into consideration. Another possible explanation for the research findings being different
from those that found a significant relationship could be attributed to country settings. The studies
that found significant relationships were conducted in countries where age discrimination issues
were explicit, prompting more attention to issues of age. The fact that these results are obtained in
a different context - Kenya, means that the present research has made a significant contribution to
the existing body of knowledge by bringing out the scenario on the relationship between employee
age and employee performance in the Kenyan State Corporations, a developing country.
