EFFECT OF
E-PERFORMANCE AND COMPETENCE
INFLUENCE ON CIVIL SERVANT PERFORMANCE THROUGH
MOTIVATION AS AN INTERVENING VARIABLE
Imroatul Chasanah*, Eka Askafi, Nisa Mutiara
Faculty of Economics, Universitas
Islam Kadiri, East Java, Indonesia
Email:
[email protected]*
|
Article
Information |
|
ABSTRACT |
|
Received:
December 19, 2022 Revised:
December 28, 2022 Approved: January 11, 2023 Online: January 27, 2023 |
|
This research is motivated by the launch of a new innovation from BKN,
namely E-Kinerja which is used as a monitoring of the performance of ASN
employees, in accordance with Government Circular number
16/SE/VII/20205/SE/V/ 2020. Apart from the E-Kinerja system, other things
that can be done are: influencing performance improvement is competence, both
of which become important variables with the addition of an intervening
variable, namely motivation which is still interesting to study. The purpose
of this study is to analyze the effect of E-Kinerja and Competence on
Performance. In addition, to analyze the effect of implementing E-Kinerja and
Competence on Performance through Motivation. This study uses quantitative
research methods with 31 employees as respondents. The data analysis method
uses validity and reliability tests, while the classical assumption test uses
heteroscedasticity test, multicollinearity test and normality test and is
equipped with path analysis using trimming theory. The results of this study
are E-Kinerja has no significant effect on motivation, competence has a
significant effect on motivation, E-Kinerja has no significant effect on
performance, competence has no effect on performance, motivation has a
positive effect on performance. E-Kinerja through motivation has an effect on
performance, competence through motivation has an effect on performance. |
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Keywords |
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E-Kinerja; competence; motivation; employee performance |
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INTRODUCTION
This research is motivated by the launch
of a new innovation from the State Civil Service Agency, namely E-Kinerja which
is used as a monitoring of the performance of ASN employees which can be
monitored anywhere and can be done anywhere and anytime (Badan Kepegawaian Negara, n.d.). With this innovation, it is hoped that
employees will be able to improve their performance, but in reality many ASNs
complain about the complicated E-Kinerja workflow and lack of understanding of
the workflow and preparation of performance targets in the E-Kinerja system (Tahir, 2021; Veri et al., 2022) Improving the performance of ASN
employees is not only seen from the E-Kinerja system, but another thing that
can affect improving employee performance is competence (Nurfadilah, 2020; Rizki, 2021). These two things become important
variables along with the addition of an intervening variable, namely motivation
which is still interesting to study.
Previous research related to this
research is concerning the effect of intellectual abilities and work motivation
on the performance of sales productive training subject teachers (Yuliana, 2006). Another study which is also a
description of the researchers is that of Nugroho et al, entitled The Effect of
Intellectual Ability and Emotional Ability on Auditor Performance Through Job
Satisfaction as an Intervening Variable (Setiyoningsih, 2011).
Performance according to Robbins (2003)
is a function of the interaction between ability and motivation. According to
Robbins, performance indicators are as follows (1) quality of work (2) quantity
(3) timeliness (4) effectiveness (5) independence (Robbins & Judge, 2015).
According to Circular Letter Number
16/SE/VII/20205/SE/V/2020 concerning Procedures for Implementing Guidelines for
Electronic Civil Servant Performance Reporting (E-Lapkin) E-Kinerja is an
information technology-based system application in the form of a website. The
e-Kinerja will be used as a tool or way to monitor the State Civil Apparatus
(ASN) within government agencies (Badan Kepegawaian Negara, n.d.).
The notion of competence as a basic
characteristic of an individual which is causally related to the criterion
referenced effective and/ or very high performance in a job or situation (Spencer
& Spencer, 2008).
Motivation is a movement from within a
person's heart to do or achieve a goal (Robbins, 2003). According to Robbin motivation is the
need of every human being. The following is a hierarchy of needs, this theory
of needs was put forward by Abraham Maslow. Indicators of needs according to
Maslow are as follows (1) physiological needs (2) security needs (3) social
needs (4) needs for self-esteem (5) self-actualization needs (Maslow & Iman, 1993).
The hypothesis put forward in this study
is that (1) there is an effect of the application of the E-Performance
assessment system on employee motivation in the Korwil Education Office of the
Sukomoro District. (2) There is an influence of employee competence on employee
motivation in the Regional Coordinator of the Sukomoro District Education
Office. (3) There is an influence of the implementation of E-Kinerja on the
performance of employees in the Regional Coordinator of the Sukomoro District
Education Office. (4) There is an influence of employee competence on employee
performance in the Regional Coordinator of the Sukomoro District Education
Office. (5) There is an effect of employee motivation on employee performance
in the Regional Coordinator of the Sukomoro District Education Office, (6)
There is an effect of implementing E-Kinerja on employee performance through
motivation as an intervening variable.
METHODS
This type of research used is
quantitative research. according to Sugiyono quantitative research is a
research method based on positive philosophy, used to examine certain
populations or samples [9]. The subject of this study was ASN in the Korwil
area of the Education Office in Sukomoro District. The sample in this study
is 31 ASN which includes Civil Servants (PNS) and Government Employees with
Employment Agreements (PPPK) using the formula Issac and Michael (2016) as
follows:
Information:
S
= Number of Samples
ƛ2
= Chi Square whose value depends on the degree of freedom and error rate. For
1% Degrees of Freedom and 5% error the price of Chi Square = 3.841.
N
= Total population
P
= Correct probability (0.5)
Q
= Probability of being wrong (0.5)
d
= Difference between the sample mean and the population mean (The difference
can be 0.01, 0.05, and 0.10)
Based on the formula above, we can
calculate the number of samples to be used for this study as follows:
= 3.841; N = 147; P = 0.5; Q = 0.5; d =
0.05
S
=
S
= =
=
= 30.6
S
= 31 samples
So the number of samples is 31 samples.
The calculation of the number of samples refers to the opinion of Isaac and
Michael (Sugiyono, 2017). Data collection was carried out by
distributing questionnaires.
The data analysis technique used is
normalization test, simple linear regression analysis and regression
prerequisite test (Hartanto & Yuliani, 2019). The normality test aims to determine
whether the sample taken comes from a normally distributed population or not,
using a significance level of more than 0.05. The multicollinearity test was
used to test whether the regression model found a correlation between the
independent variables. The heteroscedastic test was carried out to test whether
there is an inequality of variance from the residuals of one observation to
another in the regression model.
Test the hypothesis using path analysis
(path analysis). Path analysis is part of the regression analysis which is used
to determine whether there is influence exerted by the independent variables
through the intervening variables on the dependent variable (Hamid et al., 2019). The regression test model in this study
is as follows:
A. Equation
1 Regression Test Model
Figure
1. Equation 1 regression test model
Source:
Primary data processed, 2022
The
regression model above is used to determine whether or not there is an effect
of X1 on M and X2 on M.
B. Regression
Test Model Equation 2
Figure
2. Equation
2 regression test model
Source:
Primary data processed, 2022
The
regression model above is used to test the effect of X1 on Y, X2 on Y and M on
Y. Then to determine the direct effect and indirect effect by comparing the
beta coefficient using the formula (Hartanto
& Yuliani, 2019):
multiplying
the beta value of X1 to M with the beta value of M to Y
RESULTS
Respondents in this study
were ASN, totaling 31 people, the following is the list:
Table 1
List of Respondents
|
NO |
Name |
The
Schools |
|
1 |
Dian
Tri Larasati |
SDN
2 Blitaran |
|
2 |
Aprilia
W |
SDN
2 Blitaran |
|
3 |
Sri
Kurniati K |
SDN
2 Ngrengket |
|
4 |
Endang
Nurhayati |
SDN
2 Ngrengket |
|
5 |
Sumardi
Santoso |
SDN
2 Nglundo |
|
6 |
Haryadi |
SDN
2 Blitaran |
|
7 |
Zulva
Tunnisak D |
SDN
1 Sukomoro |
|
8 |
Pudji
Rahayu |
SDN
2 Pehserut |
|
9 |
Siti
Fatimah |
SDN
2 Putren |
|
10 |
Lilik
Hariyati |
SDN
2 Kedungsoko |
|
11 |
Muhajir |
SDN
3 Bungur |
|
12 |
Wijiatin |
SDN
4 Kapas |
|
13 |
Toto
Priyono |
SDN
2 Kedungsoko |
|
14 |
Dukut |
SDN
3 Kapas |
|
15 |
Eko
Purwanto |
SDN
1 Sumengko |
|
16 |
Citra
Boedi Hartatik |
SDN
3 Putren |
|
17 |
Dwi
Cahyono |
SDN
1 Bagorwetan |
|
18 |
Panji
Kristiawan |
SDN
3 Putren |
|
19 |
Alfina
Bintang S |
SDN
3 Sumengko |
|
20 |
Fiviana |
SDN
2 Putren |
|
21 |
Ririn
Agustina S |
SDN
3 Sukomoro |
|
22 |
Hanifah
Hidayatul L |
SDN
2 Bagorwetan |
|
23 |
Siti
Mudi'atul I. |
SDN
2 Bagorwetan |
|
24 |
Lilis
Dwi Wulandari |
SDN
2 Bagorwetan |
|
25 |
Sukartini |
SDN
2 Ngrengket |
|
26 |
Siti
Asiah |
SDN
2 Bungur |
|
27 |
Uliyah
Tiyas Wati |
SDN
3 Kapas |
|
28 |
Pipit
Rahmadhany |
SDN
2 Blitaran |
|
29 |
Wasis |
SDN
1 Kapas |
|
30 |
Siswoyo |
SDN
2 Kedungsoko |
|
31 |
Nyamiati |
SDN
1 Nglundo |
Source: Primary data processed in 2022
Description characteristics respondents
are presented as follows:
A. Age
The characteristics of respondents based on age are presented in
the following table:
Table 2
Characteristics of
Respondents Based on Age
|
Age |
Frequency |
Percentage (%) |
|
≤25 years |
1 |
3,23 |
|
26 – 30 years |
2 |
6,45 |
|
31 – 35 years |
5 |
16,13 |
|
36 – 40 years |
11 |
35,48 |
|
41 – 45 years |
2 |
6,45 |
|
≥46 years |
10 |
32,26 |
|
Amount |
31 |
100 |
Data sourceprimary dexercise
2022
Based on the data above, it
can be concluded that the majority of respondents in this study were aged 36 to
40 years, as many as 11 people with a percentage of 35.48%. While the
respondents with the least number were respondents aged less than or equal to
25 years with a percentage of 3.23%.
B. Gender
Description of the
characteristics of respondents based on gender is presented in the following
table:
Table 3
Characteristics of Respondents Based on Gender
|
Gender |
Frequency |
Percentage (%) |
|
Man |
10 |
32,26 |
|
Woman |
21 |
67,74 |
|
Amount |
31 |
100.00 |
Data source primary exercise 2022
Based on the table above, it
can be seen the data about the gender of the research respondents. The research
respondents consisted of 10 men with a percentage of 32.26% and 21 women withpercentage67.74%.
Based on the table data above, the majority respondent are women with a percentage of 67.74%.
C. Position Status
Table 4
Characteristics of Respondents Based on Position Status
|
Position Status |
Frequency |
Percentage (%) |
|
Classroom teacher |
17 |
54,84 |
|
PJOK teacher |
4 |
12.90 |
|
PAI teacher |
1 |
3,23 |
|
Headmaster |
8 |
25,81 |
|
Plt. KS |
1 |
3,23 |
|
Amount |
31 |
100.00 |
Data source primary exercise 2022
Based on the data above, it
can be seen that the majority of respondents have positions as class teachers,
namely as many as 17 people with a percentage of 54.84%.
D. Length of working
Description
of the characteristics of respondents based on length of work is presented in
the following table:
Table 5
Characteristics of Respondents Based on Length of
Work
|
Length of work |
Frequency |
Percentage (%) |
|
≤ 1 year |
4 |
12.90 |
|
16 years |
4 |
12.90 |
|
6 – 11 years |
3 |
9,68 |
|
11 – 16 years |
7 |
22.58 |
|
16 – 21 years |
4 |
12.90 |
|
21 – 26 years |
3 |
9,68 |
|
26 – 31 years |
5 |
16,13 |
|
31 – 36 years |
1 |
3,23 |
|
Amount |
31 |
100 |
Data source primary exercise 2022
Based on the data above, it
can be concluded that the majority of respondents in this study worked for 11
years to 16 years as many as 7 people with a percentage of 22.58%. While the
respondent with the least number of respondents who worked for 31 years to 36
years as many as 1 person with a percentage of 3.23%.
E. Validity test
This validity test is carried out to
measure whether the data that has been obtained after the research is valid
data or not, by using the measuring instrument used (questionnaire) to test the
validity of each item, namely by correlating the score of each item with the
total score which is the sum each item score. If the correlation coefficient is
equal to or above 0.05 then the item is declared valid, but if the correlation
value is less than 0.05 then the item is declared invalid (Hartanto & Yuliani, 2019).
Table 6
Validation
Test
|
Variable |
Items |
Sig. Value |
Information |
|
E-PERFORMANCE |
X1.1 |
0.00 |
Valid |
|
X1.2 |
0.00 |
Valid |
|
|
X1.3 |
0.00 |
Valid |
|
|
X1.4 |
0.00 |
Valid |
|
|
X1.5 |
0.00 |
Valid |
|
|
X1.6 |
0.00 |
Valid |
|
|
X1.7 |
0.00 |
Valid |
|
|
X1.8 |
0.00 |
Valid |
|
|
X1.9 |
0.00 |
Valid |
|
|
X1.10 |
0.00 |
Valid |
|
|
COMPETENCE |
X2.1 |
0.00 |
Valid |
|
X2.2 |
0.00 |
Valid |
|
|
X2.3 |
0.00 |
Valid |
|
|
X2.4 |
0.00 |
Valid |
|
|
X2.5 |
0.00 |
Valid |
|
|
X2.6 |
0.00 |
Valid |
|
|
X2.7 |
0.00 |
Valid |
|
|
X2.8 |
0.00 |
Valid |
|
|
X2.9 |
0.00 |
Valid |
|
|
X2.10 |
0.00 |
Valid |
|
|
X2.11 |
0.00 |
Valid |
|
|
X2.12 |
0.00 |
Valid |
|
|
X2.13 |
0.00 |
Valid |
|
|
X2.14 |
0.00 |
Valid |
|
|
X2.15 |
0.00 |
Valid |
|
|
MOTIVATION |
X2.16 |
0.00 |
Valid |
|
X2.17 |
0.00 |
Valid |
|
|
X2.18 |
0.00 |
Valid |
|
|
X2.19 |
0.00 |
Valid |
|
|
X2.20 |
0.00 |
Valid |
|
|
X2.21 |
0.00 |
Valid |
|
|
M. 1 |
0.00 |
Valid |
|
|
M. 2 |
0.00 |
Valid |
|
|
M. 3 |
0.00 |
Valid |
|
|
M. 4 |
0.00 |
Valid |
|
|
M. 5 |
0.00 |
Valid |
|
|
M. 6 |
0.00 |
Valid |
|
|
M. 7 |
0.00 |
Valid |
|
|
M. 8 |
0.00 |
Valid |
|
|
M. 9 |
0.00 |
Valid |
|
|
M. 10 |
0.00 |
Valid |
|
|
M. 11 |
0.00 |
Valid |
|
|
M. 12 |
0.00 |
Valid |
|
|
M. 13 |
0.00 |
Valid |
|
|
M. 14 |
0.01 |
Valid |
|
|
M. 15 |
0.00 |
Valid |
|
|
M. 16 |
0.00 |
Valid |
|
|
M. 17 |
0.00 |
Valid |
|
|
M. 18 |
0.00 |
Valid |
|
|
M. 19 |
0.00 |
Valid |
|
|
M. 20 |
0.00 |
Valid |
|
|
M. 21 |
0.00 |
Valid |
|
|
M. 22 |
0.00 |
Valid |
|
|
M. 23 |
0.00 |
Valid |
|
|
M. 24 |
0.00 |
Valid |
|
|
M. 25 |
0.00 |
Valid |
|
|
M. 26 |
0.00 |
Valid |
|
|
M. 27 |
0.00 |
Valid |
|
|
M. 28 |
0.00 |
Valid |
|
|
PERFORMANCE |
Y. 1 |
0.00 |
Valid |
|
Y.2 |
0.00 |
Valid |
|
|
Y.3 |
0.00 |
Valid |
|
|
Y.4 |
0.00 |
Valid |
|
|
Y.5 |
0.00 |
Valid |
|
|
Y.6 |
0.00 |
Valid |
|
|
Y.7 |
0.00 |
Valid |
|
|
Y. 8 |
0.00 |
Valid |
|
|
Y.9 |
0.00 |
Valid |
|
|
Y.10 |
0.00 |
Valid |
|
|
Y.11 |
0.00 |
Valid |
|
|
Y. 12 |
0.00 |
Valid |
|
|
Y. 13 |
0.00 |
Valid |
|
|
Y.14 |
0.00 |
Valid |
|
|
Y.15 |
0.00 |
Valid |
|
|
Y.16 |
0.00 |
Valid |
|
|
Y.17 |
0.00 |
Valid |
|
|
Y. 18 |
0.00 |
Valid |
|
|
Y.19 |
0.00 |
Valid |
|
|
Y.20 |
0.00 |
Valid |
|
|
Y. 21 |
0.00 |
Valid |
|
|
Y. 22 |
0.00 |
Valid |
|
|
Y. 23 |
0.00 |
Valid |
|
|
Y. 24 |
0.00 |
Valid |
|
|
Y.25 |
0.00 |
Valid |
|
|
Y. 26 |
0.00 |
Valid |
|
|
Y. 27 |
0.00 |
Valid |
|
|
Y. 28 |
0.00 |
Valid |
|
|
Y. 29 |
0.00 |
Valid |
Source: Primary data processed in 2022
F. Classic assumption test
1.
Multicollinearity
Test
Multicolinearity test was conducted to test whether the
regression model found a correlation between the independent variables.
Commonly used values to indicate multicollinearity are tolerance values ≤
0.1 or VIF values ≥ 10 (Ghozali, 2016).
Based on the multicollinearity test conducted on equation
1 using SPSS 25.0, the VIF value was obtained for the E-Performance variable of
1.013 and for the competency variable of 1.013. The variable of implementing
E-Performance (X1) and employee competence (X2) on employee motivation (M)
obtained from the results of calculating the Variance Inflation Factor (VIF)
value also shows that there are no independent variables that have a VIF value
of more than 10. So, it can be concluded that there is no multicollinearity
between independent variables in the regression model used in this study.
Based on the multicollinearity test performed on equation
2, the VIF E-Performance value was 1.098, Competency was 2.953 and Motivation
was 3.117. The variables of implementing E-Performance (X1), employee
competence (X2), and employee motivation (M) on employee performance (Y)
obtained from the results of calculating the Variance Inflation Factor (VIF)
value also show that there are no independent variables that have a VIF value
of more than 10. So, it can be concluded that there is no multicollinearity
between the independent variables in the regression model used in this study.
2. Heteroscedasticity
Test
The heteroscedasticity test was carried out to test
whether there is an unequal variance from the residuals of one observation to
another in the regression model. If the significance value generated for each
variable is less than 0.05, it indicates heteroscedasticity occurs (Hartanto & Yuliani, 2019). Based on the results of the heteroscedasticity test
using the Glejser test, the significance value for the E-Performance variable
was 0.217 and the significance value for the competency variable was 0.610. In
addition to the scatter plot chart, it shows that it does not form a certain
pattern or spreads. so it can be concluded that the two variables do not occur
heteroscedasticity.
3. Normality test
The normality test is used to test whether in the
regression model, there is a normal distribution between the dependent variable
and the independent variable. If the results show a significant probability
value above 0.05 then the variable is normally distributed (Hartanto & Yuliani, 2019). Based on the results of the normality test using one
sample Kolmogorov Smirnov, the Asymp value was obtained. Sig. (2-tailed) of
0.200 is greater than 0.05. In addition, the probability plot graph shows the
data spread around the diagonal line and follows the direction of the
transverse diagonal slash. so that the data can be said to be normally
distributed.
G. Hypothesis test
The hypothesis is a temporary answer to the problems that
are formulated and will be examined in research. Testing the hypothesis in this
study basically uses two basic techniques, namely simple regression analysis
techniques and path analysis techniques which are the elaboration of multiple
regression analysis. To make it easier to do hypothesis testing calculations,
the following terms are used:
1) E-PERFORMANCE
(Exogenous Latent 1)
2) COMPETENCE
(Exogenous Latent 2)
3) MOTIVATION
(Mediator)
4) PERFORMANCE (Endogen
Latent)
a) Hypothesis Test 1
Based on the results of the regression test, it can be
seen the significance of the effect of implementing E-Performance on employee
motivation. If the value of Sig. smaller than 0.05, the effect that occurs is
significant, otherwise if the value of Sig. more than 0.05 then it is not
significant. Based on the data processing output, it can be seen that the value
of Sig. The E-Performance variable on Performance is 0.05 <0.137. That is,
the effect of implementing E-Kinerja on employee motivation is not significant.
Thus, the first hypothesis which states that the implementation of E-Kinerja
has a significant effect on employee motivation is not proven and cannot be
accepted.
b) Hypothesis Test 2
Testing the second hypothesis is done by
simple regression analysis. If the value of Sig. smaller than 0.05, the effect
that occurs is significant, otherwise if the value of Sig. more than 0.05 then
it is not significant. Based on the data processing output, it can be seen that
the value of Sig. of 0.000 <0.05. That is, the effect of employee competence
on employee motivation is significant. Thus, the second hypothesis which states
that employee competence has a positive and significant influence on employee
motivation can be proven and can be accepted.
c) Hypothesis Test 3
Testing the third hypothesis is done by
simple regression analysis to know the significance of the effect of
implementing E-Performance on employee performance. If the value of Sig.
smaller than 0.05, the effect that occurs is significant, otherwise if the
value of Sig. more than 0.05 then it is not significant. Based on the data
processing output, it can be seen that the value of Sig. of 0.05 <0.246.
Thus, the third hypothesis which states that the implementation of E-Kinerja
has a positive and significant effect on employee performance is not proven and
cannot be accepted.
d) Hypothesis Test 4
Testing the fourth hypothesis is done by
simple regression analysis
. Based
on the test results obtained from data processing with the SPSS program. If the
value of Sig. smaller than 0.05, the effect that occurs is significant,
otherwise if the value of Sig. more than 0.05 then it is not significant. Based
on the data processing output, it can be seen that the value of Sig. of 0.05
<0.943. That is, the effect of employee competence on employee performance
is not significant. Thus, the fourth hypothesis which states that employee
competence has a positive and significant influence on employee performance is
not proven and cannot be accepted.
e) Hypothesis Test 5
Testing the fifth hypothesis
is done by simple regression analysis to obtain the value of the effect of
employee motivation on employee performance. If the value of Sig. smaller than
0.05, the effect that occurs is significant, otherwise if the value of Sig.
more than 0.05 then it is not significant. Based on the data processing output,
it can be seen that the value of Sig. of 0.000 <0.05. That is, the effect of
employee motivation on employee performance is significant. Thus, the fifth
hypothesis which states that employee motivation has a positive and significant
influence on employee performance is proven and acceptable.
H. Path Analysis
Testing the sixth and seventh hypotheses is done by path
analysis. Path analysis (path analysis) is an analytical model used to
determine patterns of relationships between variables with the aim of knowing
the direct and indirect effects of the independent variables on the dependent
variable (Marwan, 2019) [17].
1.
Output coefficient and Model
Summary Equation 1
Table 7
Graph of the Coefficient of Equation 1
|
Coefficientsa |
||||||
|
Model |
|
Unstardized B |
Coefficients Std. Error |
Standardized Coefficients Beta |
t |
Sig. |
|
1 |
(Constant) |
-90.480 |
27.678 |
|
-3.269 |
.003 |
|
E KINERJA |
.378 |
.247 |
.165 |
1.532 |
.137 |
|
|
COMPETENCE |
2.108 |
.288 |
.789 |
7.323 |
.000 |
|
|
a.
Dependent Variable: Motivation |
||||||
Source: Primary data
processed in 2022
Based on the picture above,
it can be seen that the Standardized Coefficient Beta value of the
E-Performance variable (X1) is 0.165 and the competency variable (X2) is 0.789.
Table 8
Model Summary together 1
|
Model Summary |
|
||||
|
Model |
R |
R Square |
Adjusted R Square |
Std. Error of the Estimate |
|
|
1 |
.824a |
.679 |
.656 |
4.62903 |
|
|
a.
Predictors: (Constant), COMPETENCE, E KINERJA |
|||||
Source: Primary data
processed in 2022
The R Square value is 0.679.
This shows that the contribution of the influence of the application of
E-Performance (X1) and competency (X2) to motivation (M) is 67.9% while the
remaining 32.1% is the contribution of other variables not included in the
study.
I.
2. Output coefficient
and Model Summary Equation 2
II.
Table 9
Chart Coefficient together 2
|
Coefficientsa |
||||||
|
Model |
|
Unstardized B |
Coefficients Std. Error |
Standardized Coefficients Beta |
t |
Sig. |
|
1 |
(Constant) |
6.650 |
7.130 |
|
.933 |
.359 |
|
E KINERJA |
.067 |
.056 |
.030 |
1.186 |
.246 |
|
|
COMPETENCE |
-.008 |
.108 |
-.003 |
-.072 |
.943 |
|
|
|
MOTIVATION |
.946 |
.041 |
.986 |
22.830 |
.000 |
|
a.
Dependent Variable: E KINERJA |
||||||
Source: Primary data processed in 2022
It can be seen that the value
of the Standardized Coefficient Beta of the E-Performance variable (X1) is
0.030; competency variable (X2) of -0.003; and motivational variable (M) of
0.986
Table 10
Chartcoefficient equation 2
|
Model Summary |
|
||||
|
Model |
R |
R Square |
Adjusted R Square |
Std. Error of the Estimate |
|
|
1 |
.992a |
.984 |
.982 |
4.01442 |
|
|
a.
Predictors: (Constant), MOTIVATION, E KINERJA, COMPETENCE |
|||||
Source: Data Processed 2022
The R Square value is 0.984.
This shows that the contribution of the influence of the implementation of
E-Performance (X1), competence (X2), and motivation (M) on performance (Y) is
98.4% while the remaining 1.6% is a contribution from other variables that do
not included in the study.
Figure 3. Path diagrams
Source:
primary data processed in 2022
f) Hypothesis test 6
Based on the test results using SPSS 25.0, it
can be seen that the Standardized Coefficient Beta value of the E-Performance
variable (X1) is 0.165 and the competency variable (X2) is 0.789.
Based on the path diagram above, it can be seen that the
direct effect X1 (E-Performance) has on Y (performance) is 0.030. Meanwhile,
the indirect effect of X1 (E-Performance) through M (motivation) on Y
(performance) is the multiplication of the beta value of X1 on M and the value
of beta M on Y, which is as follows:
(X1→M)×(M→Y)=0.165
×0.986 =0.163
Then the
total effect that X1 has on Y is as follows:
Total
influence = direct influence + indirect influence
=0.030+0.163
=0.193
Based on
the calculation results above, it is known that the direct effect value is
0.030 and the indirect effect is 0.163, which means that the effect value of
implementing E-Performance on performance through motivation is greater than
the value of the effect of implementing E-Performance on performance. Thus
hypothesis 6 which states that there is an effect of implementing E-Performance
(X1) on employee performance (Y) through motivation (M) as an intervening
variable is proven and acceptable.
g) Hypothesis Test 7
Testing the seventh hypothesis was also carried out using
path analysis such as testing on hypothesis 7. Calculation of
path analysis model 2, namely the competency variable (X2) through motivation
(M) on performance (Y).
Based on the path diagram above, it can be seen that the
direct effect given by X2 (competence) on Y (performance) is -0.003. Meanwhile,
the indirect effect of X2 (competence) through M (motivation) on Y
(performance) is the multiplication of the beta value of X2 on M and the value
of beta M on Y, which is as follows:
(X2→M)×(M→Y)=0.789 ×0.986 =0.778
Then the total effect that X2 has on Y is as follows:
Total Impact = Direct influence + Indirect influence
=-0.003+0.778 =0.775
Based on the calculation results above, it is known that
the value of competence on performance is -0.003 and the value of the influence
of competence on performance through motivation as an intervening variable is
0.778, which means that the value of the effect of competence on employee
performance through motivation is greater than the value of the influence of
competence on employee performance. Thus hypothesis 7 which states that there
is an influence of competence on performance through motivation as an
intervening variable is proven and acceptable.
CONCLUSION
Based
on the research above, it can be concluded that the implementation of
E-Performance has no effect on employee motivation. So that employees who
understand the flow and arrangement of E-Kinerja do not necessarily have high
motivation.
Competence has a significant
effect on employee motivation, this shows that employees who have high
competence also have high motivation as well. Employees who have more skills in
their work are more enthusiastic in working on work targets so that it affects
employee motivation to do their work according to performance targets.
E-Kinerja has no effect on
employee performance, this is because E-Kinerja is only an application to
monitor employees. So that employees who already understand how to work on the
E-Kinerja application do not necessarily experience an increase in performance.
Employee competence has no
effect on employee performance. The high competence of each individual can lead
to unhealthy competition against employees inside and outside the institution
which causes the institution's goals to not be achieved optimally.
Motivation has a significant
effect on employee performance. Giving awards, motivation from superiors and
rewards from superiors to employees who excel can affect employee performance
improvement.
Motivation is able to be a
mediator of the effect of E-Performance on employee performance. The effect of
E-Performance through motivation increases employee performance compared to
without motivation, that is, the indirect effect has a beta value of 0.163 while
the direct effect has a beta value of -0.030.
Motivation can be a mediator
of the influence of competence on employee performance. The influence of
competence through motivation through motivation increases employee performance
compared to without motivation, namely the indirect effect has a beta value of
0.778 while the direct effect has a beta value of -0.030.
A review is needed by
conducting a re-examination using motivation as a mediator variable and the
relationship with E-Kinerja. Researchers also need to know more about other
factors that can affect employee performance. Researchers are advised to
develop this research by adding other variables in the context of development
and assessment, researchers can use other analytical methods, namely SEM (Structural
Equation Modeling) analysis.
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