big can be said to be the largest among all the values
of other coefficients, which means that among the
independent variables motivation has a rather large
contribution on individual inputs, but theoretically
also for motivation it should be the same logic as for
training, but in fact it is not like that. The explanation
that can be given in this case is that motivation
directly affects the internal motivation of the
individual at work, which causes motivation to have
a high coefficient. Safety at work is represented by
β4=0.0457699 but this coefficient is not statistically
significant since pvalue > 0.1, however this
coefficient has a positive value which represents a
positive relationship with the inputs.
The performance evaluation is represented by
β5=0.1288074, this coefficient is statistically
significant since P-value < 0.05, the coefficient value
of this variable is positive which testifies to a positive
relationship of job security with individual inputs.
Career direction is represented by β6= - 0.0673226,
as can be seen this variable has a negative value
which means that career direction has a negative
effect on individual inputs, however this coefficient
is statistically significant as its P-value < 0.05.
Talent management is also a variable that has a
negative relationship with individual inputs, this
independent variable is represented by β7= -
0.0724287, however this variable is statistically
significant as p-value < 0.05. Compensation and
reward is represented in this model by the coefficient
β8=0.0915289, its value is positive which testifies to
a positive relationship with individual inputs, this
coefficient is statistically significant since p-value <
0.01, in fact it is not expected that the compensation
of this any major impact on inputs or on any
dependent variable since, as has been repeated, the
structure of compensations and rewards is well-
defined in the public administration of Kosovo, so
they cannot be used as a genuine instrument in the
management of human resources. Male gender, status
married civil, central and local Research and
Scientific Institutes have presented negative values of
their coefficients, so they have a negative relationship
with individual inputs. The number of dependents has
a statistically significant coefficient since p - value <
0.1, the value of this coefficient is β19=0.0300472.
R2=0.5447 which means that 54.47% of the
dependent variable, i.e. individual inputs, is
explained by the independent variable. To realize
this, the number of observations was 500, the level of
significance of this model is significant, in this way
we can say that the hypothesis does not fall and the
alternative hypothesis is proven, that is, human
resource management practices have an impact on
the individual inputs of
Equation 1 in this case would have the form:
'Individual inputs' = 0.4038916 + 0.19471
'recruitment' + 0867411 'training' + 0457499
'motivation' +0.1288074 'job security' - 0.0673226
'career management' - 0.0724287 'talent management'
+ 0.915289 'compensation'
We now test the relationship of human resource
management practices on outputs and individual job
performance. After we do After the regression
analysis, we notice that recruitment again presents a
strong and positive relationship with individual
outputs, the recruitment coefficient has the value
β1=0.2631697, this coefficient, in addition to
presenting a positive relationship with the dependent
variable, is also statistically significant as p-value <
0.01 , so recruitment has a relatively large
contribution to individual outputs. Training and
development also have a positive coefficient
β2=0.0850378, this variable is statistically significant
after P-value < 0.05, however the contribution of this
variable to the independent variable is smaller than
we would expect since it is also smaller than the
coefficient that had the same variable in the input
equation, as it is expected that in individual outputs
training is perceived to have a greater impact.
Motivation in this case also represents the variable
that has the largest coefficient, its value in this case is
β3=0.3400562, this variable is statistically significant
since p-value < 0.01, the value of this variable is
positive, which means that there is a positive
relationship with the dependent variable, i.e.
individual output.
The next variable to be discussed is job security, the
coefficient of this variable is positive which
determines a positive relationship with the dependent
variable individual output, the value of this variable
is β4=0.1288074 and is statistically significant since
p-value < 0.05, but the same variable related to
individual inputs was statistically insignificant.
Meanwhile, the performance evaluation results in this
positive value, so there is a positive relationship with
the dependent variable individual output and its value
is β5=0.577134, but since p - value > 0.1 this variable
is statistically insignificant.
WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT
DOI: 10.37394/232015.2022.18.110
Bislim Lekiqi, Afrim Loku, Emin Neziraj