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ANALYSIS OF THE EFFECT OF MOTIVATION FOLLOWING TRAINING
AND LEADERSHIP ROLES ON EMPLOYEE PERFORMANCE THROUGH
LEARNING ORIENTATION
Muhammad Hanif Abdillah STIE Sekolah Tinggi Manajemen Indonesia Tangerang.
Indonesia
Article Information IMWI STUDENT RESEARCH
JOURNAL
Volume 5.
Nomor 2 Agustus 2024 Halaman.
: 111-124
Institut Manajemen Wiyata Indonesia.
Jl.
Gudang No.
7-9,
Kota Sukabumi.
Jawa Barat.
Keywords:
Training Motivation.
Leadership Role.
Learning OrIentation, and Employee Performance Corresponding Author:
vebrinahania@students.
Abstract This research analyzes the influence of innovation training motivation and leadership role to employee performance trought learning The problem of the research is fully refers to research problem: there is decreasing inclination at 2017-2019 icoming tax This mean tax incoming achievement in Kantor Pelayanan Pajak Pemalang not yet A literature review is arranged based on theory and practice of motivation to clarify relationship between management practice and it influence to training motivation and employee This reserch also refers to control theory at leader orientation wich impres that leadership role gives feedback to arrange some one behaviour to company goal.
There is a model that have been developed and five hypothese to answer this research problem.
This research use census methods.
Respondent are 114 person, wich are the employee of Kantor Pelayanan Pajak Pemalang.
Data analyze tools wich used Structural Equation Modeling (SEM) under AMOS 4.
01 program.
Model and the result from research can be accepted, and prove that the influence of training motivation and leaderhip role to learning orientation are positive and significant, training motivation and leadership role are have positive and significant influence to improve employee performance.
Company mangerial implication have to be based on supervisor role at step as follow.
Leadership role must be some one totally credible .
pen minded, intelegen.
Leader have to be able to give solution to employee complains WINTER JOURNAL VOL.
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INTRODUCTION
Intensive global competition, rapid technological change, and dynamic and competitive economic conditions require companies to be adaptive and change.
Businesses now understand that rapid change is needed for sustainable competitive performance (Walker, 1.
(Sutaguna et al.
, 2.
(Arif & Anggraeni, 2.
The period of renewal of recent years has demanded that many management functions seek to reinvent themselves through new visions, strategies, structures, processes, and systems.
Management professionals must develop and demonstrate a new set of competencies to fulfill roles and responsibilities for the continuity of the transformation of management functions and to maintain the continuity of the transformation of management functions (Yeung et al.
, 1.
(M.
Saputra, 2.
In the opinion of Frederick Taylor, the first problem faced by HRM is the problem of labor (Nurochim & Nurochim, 2.
The organization should be able to put the workforce on the right task, include the right training, the right working methods and tools, and have legitimate support for work (Setiawan, 2.
Confidence continues for the existence of components of effective workforce management.
The success of the organization in the placement of workers can attract and master the ability of a trained workforce .
earning proces.
The organization must be willing to match what the workforce wants with what the organization has given and done.
A reliable workforce will become a reality if the organization treats its employees well, and will take care of the company as well.
Many companies innovate and make a lot of profits (Arifuddin & SE, 2.
(Rahardjo, 2.
Pemalang Tax Service Office which is a government-owned institution has a role as a source of state revenue for national development purposes.
The role of the Pemalang Tax Service Office is taken in fulfilling the revenue plan.
This can be seen in Table 1.
Table 1.
Target Data and Realization of Tax Revenue of the Pekalongan Tax Service Office for 2017-2019 .
n Million Rupia.
Year Tax Revenue Plan Realization of Tax Achievement Revenue (%) Source: Pemalang Tax Service Office .
The table illustrates, namely, that there is a tendency to decrease in tax revenue realization from 2017-2019.
This means that the achievement of tax revenue from the Pemalang Tax Service Office .
has not been optimal.
On the other hand, it is a real form of the problem .
faced by the Pemalang Tax Service Office.
Measuring employee performance construction for the Pemalang Tax Service Office is one of the WINTER JOURNAL
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steps that can be taken by the Pemalang Tax Service Office, to get out of the problem.
Previous studies have stated that learning orientation is influenced by several factors, for example in research (Glorianto, 2.
Where the six factors that determine the orientation of learning include training and education, rewards, organizational vision and strategy, information flow, individuals and development teams, and gender.
This opinion is in line with Shani's 2000 research that employee knowledge is expected to only be built through the construction of technology support, relationships with the team, decision-making by the leadership, the performance of each meeting, the quality of the leader's attention, innovation and creativity (Shani et al.
, 2.
The research is in line with the findings of the 1996 Oldham and Cummings study that employee creativity can only develop with support for employee characteristics, leadership characteristics, and organizational characteristics (Oldham & Cummings, 1.
Research gap research refers to the framework presented in Olian and Durham's research, covering structural characteristics, policies, and practices that together build a sustainable learning climate.
No single factor is responsible for a strategically appropriate training system that supports continuous learning.
An effective training system results from the shared influence of elements of the training framework, and the extent to which employees are integrated into organizational structures, policies, and practices.
Referring to the study, a conclusion can be drawn that learning variables are worthy of being referred to as study material in future research.
Olian and Durham's 1996 study identified that today's executives reject uncertainty without signaling, given the rate of technological change and market transformation (Oldham & Cummings, 1.
No organization can have the vision to predict the exact talent that will take 5 or even 10 years from now.
How can organizations respond to and address the risks associated with market uncertainty? That is by developing internal discipline that creates readiness for several organizational strategic The close relationship between organizational planning and training systems creates leadership and skill readiness (Daryanto et al.
, 2.
As organizations show in research, training can be at a strong strategic level.
Furthermore, training became the main study material for research.
The relationship between leadership, training programs, and competency improvement in the study has not provided a clear picture of the relationship between these constructs.
Furthermore, a deeper study of the relationship between these constructs is needed as a direction for future research.
The emphasis on ability designation .
erformance orientatio.
among employees who mm confidence in their ability, may freeze employee achievement behavior, and leadership continues to do so (Tanjung et al.
, 2.
The researchers continue to counsel motivated employees through performance orientation, regardless of employee The mmnya motivation is then incorrectly labeled "mmnya ability".
The findings of Sujan 1994 explain that changing the focus on learning objectives is a better choice than taking an assessment of ability (Sujan et al.
, 1.
Instead of evaluating employees on just ability and performance, it's important to evaluate employees on motivation to learn.
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Working smart and hard developed through a learning orientation, may be more important for employees who typically face repetitive and routine tasks, such as inventory picking and order writing for replacement stock (Mahendrawathi, 2.
(D.
Saputra et , 2.
On the other hand, working smart may be more important for employees who usually face highly creative and complex tasks.
The importance of future research is to determine employee performance based on activities and learning orientations carried out, through understanding contingencies that influence the importance of choosing motivational training programs that are more appropriate to employee performance.
This study aims to analyze the influence of motivation to attend training and leadership roles on learning orientation, as well as investigate the impact of motivation to attend training and leadership roles on employee performance.
In addition, this study will also analyze the relationship between learning orientation and employee performance.
Thus, the objectives of this study include key aspects involving antecedent variables such as motivation to attend training and leadership roles, as well as consequence variables such as learning orientation and employee performance.
METHOD
Research is included in the type of causal research design that is to identify cause and effect relationships between variables and researchers look for the true type of facts to help understand and predict relationships.
The problems presented in the study are problems advocated by previous researchers, who need support for the latest facts.
The types and sources of data used in research are primary data.
The primary data needed in the study is from the answers of employees at the Pemalang Tax Service Office, to questions about the motivation to attend training and leadership roles on employee performance through learning orientation.
The sampling technique used in the study was the Census Method.
Researchers use the census method, which uses all members of the population as research objects.
The total population in the study amounted to 114 people.
Data collection is carried out using one type of questionnaire method, which consists of a series of questions used to obtain data on several factors that form the motivation to attend training, and the role of leadership on employee performance through learning The model used in research is the model of causality or influence relationship.
test the hypothesis proposed in the study, the analysis technique used is SEM or Structural Equation Modeling which is operated through the AMOS program.
Research modeling through SEM allows a researcher to answer dimensional research questions .
measuring what the indicators of a concept ar.
and regressive .
easuring the influence or degree of relationship between factors that have been identified dimension.
RESULT AND DISCUSSION
Test Results Structural Equation Model
Motiva
Mengik
Kinerj
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Source: Processed primary data .
Table 2.
Results of Research Model Feasibility Testing for Structural Equation Model Analysis Criterion Cut of Value Small.
2 with df : 50 = 67,504 Probability > 0,05 GFI
> 0,90
AGFI
> 0,90
TAG
> 0,95
CFI
> 0,95
CMIN/DF
< 2,00
RMSEA
< 0,08
Source: Processed primary data .
Chi-Square Result
Evaluation 100,238 Good
0,418
0,901
0,863
0,997
0,997
1,023
0,014
Good Good Marjinal Good Good Good Good Table 3.
Regression Weights Test Results for Structural Equation Model Analysis Regression Weights Learning Orientation <-- Motivation_To Follow_Training Learning_Orientation <-- Leadership Roles Employee <-- Learning_Orientation Performance Employee <-- Leadership Roles Performance Employee <-- Motivation_To Performance Follow_Training <-- Motivation_To Follow_Training <-- Motivation_To Follow_Training <-- Motivation_To Estimate Std.
Est S.
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Follow_Training <-- Motivation_To Follow_Training <-- Leadership Roles <-- Leadership Roles <-- Leadership Roles <-- Learning_Orientation <-- Learning_Orientation <-- Learning_Orientation <-- Learning_Orientation <-- Employee Performance <-- Employee Performance <-- Employee Performance <-- Employee Performance <-- Employee Performance Source: Processed primary data .
As in confirmatory factor analysis, structural equation model testing is also carried out with two types of tests, namely model suitability tests and causality significance tests through regression coefficient tests.
Model Conformity Test .
oodness of fit tes.
Model suitability is evaluated through a review of various goodness-fit criteria.
The first action taken is to evaluate whether the data used can meet the assumptions of SEM assumptions.
When the assumptions have been met, then the model can be tested.
The model compatibility indices used are the same as those performed in confirmatory factor analysis.
The proposed conformity hypothesis is as follows.
H0: There is no difference between the sample covariance matrix and the estimated population covariance matrix.
Ha: There is a difference between the sample covariance matrix and the estimated population covariance matrix.
The results of data processing as shown in Figure 4.
3 and Table 4.
7 show the significance level for the difference hypothesis test .
hi-squar.
238 with a probability of 0.
The null hypothesis that "there is no difference between the sample covariance matrix and the estimated population covariance matrix" cannot be rejected.
That is, the null hypothesis is accepted.
Tests of the model hypothesis show the model fit, with available data as seen from the significance level to the chi-square of 0.
Other indices such as GFI .
TLI
CFI .
CMIN/DF .
, and RMSEA .
are within the expected value range and therefore models are acceptable, although AGFI .
, lacks Good.
Evaluation of Regression Weight for Causality Test To test the hypothesis regarding causality developed in the model, it is necessary to test the null hypothesis (H.
which states that the regression coefficient between relationships is equal to zero.
The test is used by looking at CR values that are identical to the t-test in regression.
From Table 4.
8 above, it is known that all CR values are above the value of 1.
This means that the null hypothesis which states that the regression WINTER JOURNAL VOL.
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coefficient between relationships is equal to zero can be rejected.
Tests on the research model conducted did not show any symptoms of identification problems as stated in Chapter i about the possibility of identification Evaluation Criteria Goodness of Fit The suitability of the research model is evaluated for the level of goodness of fit, but what needs to be done before is to evaluate the data used to meet the criteria required by SEM.
Uji Outliers Outliers are observational data that appear in the form of extreme values.
Good for a single variable or combination of variables (Wahyuni & Pardamean, 2.
The outliers can be evaluated in two ways, namely analysis of univariate outliers and analysis of multivariate outliers (Udjang & Subarjo, 2.
Evaluasi Univariate Outliers Detecting univariate outliers can be done by determining the threshold value categorized as outliers by converting the value of research data into a standard score commonly called z score, which has an average value of zero with a standard deviation Testing of univariate outliers is carried out by analyzing the Zscore value whether there is a value greater than A 3.
Test results as in Table 4.
9 below show no outliers Table 4.
Z Score.
Descriptive Statistics Zscore(X.
Zscore(X.
Zscore(X.
Zscore(X.
Zscore(X.
Zscore(X.
Zscore(X.
Zscore(X.
Zscore(X.
Zscore(X.
Zscore(X.
Zscore(X.
Zscore(X.
Zscore(X.
Zscore(X.
Zscore(X.
Valid N .
Minimu Maximu Mean
11E-15
3E-16
33E-17
66E-15
33E-16
48E-16
52E-16
32E-15
9E-16
0E-15
53E-16
Std.
Deviation 8E-16
8E-16
5E-15
0E-15
37E-16
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Source: Processed primary data .
Evaluation Multivariate Outlier Evaluation of Multivariate Outliers is necessary because although the data analyzed show no outliers at the Univariate level, observations can become outliers when combined.
The mahalonobis distance based on the chi-square at DF of 16 .
he number of independent variable.
with p = 0.
05 is 67.
ased on the distribution table While the SEM results show the largest mahalonobis distance is 29.
098 The mahalanobis distance results show a value below 67.
This means that there are no multivariate outliers in the study.
Data Normality Test The distribution of data should be analyzed to see if the normality assumptions are met so that the data can be further processed for SEM modeling.
Normality testing is carried out by observing the skewness value of the data used and whether there is a CR value that exceeds 1.
96 at a significance level of 0.
From the results of the study, it can be seen that there is no CR value outside 1.
96 so it can be concluded that there is no evidence that the data used has an abnormal distribution.
Evaluation of Multicollinearity and Singularity To see if there is multicollinearity or singularity in a combination of variables, it is necessary to look at the determinants of the covariance matrix.
A really small determinant indicates the presence of multicollinearity or singularity.
Indications of multicollinearity and singularity are characterized by determinant values of the covariance matrix of the sample that are completely small or close to zero (Hardana et al.
, 2.
(Ritonga, 2.
The result of the determinant of sample covariance matrix analysis in the study was 7.
9854e 001 The results showed that the determinant value of the sample covariance matrix was far from zero.
Thus it can be said that there is no multicollinearity and singularity.
Test Compatibility and Test Statistics Testing the suitability of the research model is used to test how good the level of goodness of fit of the research model is.
Based on the test results presented in Table 7 above, it can be seen that of the eight required criteria, there are seven of them that are in Good condition and one .
AGFI) is still in marginal condition.
Overall it can be said that the research model has a good level of goodness of fit.
Model Interpretation and Modification After the model estimation is done, researchers can still make modifications to the model, if it turns out that the estimate has a prediction level not as expected, namely if there is a large residual.
To provide an interpretation of whether the tested model is acceptable or needs further development, the researcher must direct his attention to the predictive power of the model, namely by observing the magnitude of the residual produced.
The standardized residual matrix will be observed to test whether there is a residual value greater than 2.
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Interpretation of the residuals produced by the model is carried out by observing indicators that have a standard residual value greater than 2.
From research, it is known that no indicator has a residual value greater than 2.
Therefore the model is acceptable and there is no need to make modifications to the model being tested.
Reliability Test and Variance Extract Reliability Test The reliability test performed shows the extent to which a measuring instrument can give relatively the same results when measured again on the same object.
If a measuring instrument is used repeatedly and the measurement results obtained are relatively consistent, the measuring instrument is considered reliable .
The minimum reliability value of the acceptable latent variable-forming dimension is Ou 0.
Table 5.
Standard data loading results Motivation to Join Training Leadership Roles Learning Orientation Employee Performance = 0,77 0,72 0,71 0,74 = 2,94 = 0,73 0,78 0,72 = 0,78 0,72 0,74 0,78 = 0,75 0,78 0,75 0,77 0,74 = 2,23 = 3,02 = 3,79 Table 6.
Result measurement error data Motivation to Join Training Leadership roles Learning Orientation Employee performance = .
= 1,84 = .
= .
= 1,34 = 1,71 = .
= 2,12 Data reliability calculation:
Motivation to Join Training = -------------------------------------------- = 0,82 .
2 1,84 .
2 Leadership Roles = -----------------------------= 0,78 .
1,34 .
2 Learning Orientation = -----------------------------= 0,84 .
1,71 .
2 Employee Performance = -----------------------------= 0,87 .
2 2,12 Based on the results of the data reliability measurement, the data reliability value in the study has a value of 0.
Thus research is acceptable.
Variance Extract The next test is the variance extract test.
The variance extract test shows the WINTER JOURNAL VOL.
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amount of variance of the indicator extracted by the developed latent construct/variable.
The acceptable variance extra value is a minimum of 0.
The equation to get the variance extract value is:
Standard Loading2 Variance Extract = Standard Loading2 Table 7.
Standard data loading results Motivation to Join Training Leadership Roles Learning Orientation Employee Performance = 0,77A 0,72A 0,71A 0,742 = 2,14 = 0,73A 0,78A 0,72A = 0,78A 0,72A 0,74A 0,782 = 0,75A 0,78A 0,75A 0,772 0,742 = 1,64 = 2,25 = 2,85 Variance Extract Calculation :
2,14
Motivation for Training = ----------------------------------- = 0,53 2,16 1,84 Leadership Leaders 1,64 = ---------------------------- = 0,55 1,64 1,34 Learning Orientation 2,25 = ------------------------------ = 0,56 2,25 1,71 Employee Performance 2,85 = -------------------------------= 0,57 2,85 2,12 Based on the results of variance extract data measurements, a value of > 0.
5 was Thus research is acceptable.
From the above observations, it appears that there is no reliability value smaller than 0.
Similarly, the variance extract test did not find a value below 0.
Thus, it can be said that the research data used has a good level of consistency .
Hypothesis Testing Hypothesis testing is intended to test the hypotheses proposed in Chapter II.
Hypothesis testing is carried out by analyzing the C.
R value and P value of the data processing results as in Table 4.
8, then compared with the required statistical limits.
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which are above 1.
96 for CR values and below 0.
05 for P values.
Furthermore, the discussion on hypothesis testing will be carried out gradually by the sequence of hypotheses that have been proposed.
The Tree of Life I Hypothesis I in the study is "Motivation to attend training has a positive effect on learning orientation".
From data processing, it is known that the CR value in the relationship between the training motivation variable and the learning orientation variable, as shown in Table 4.
8 is a 3.
062 P value of 0.
Both values show qualified results, which are above 1.
96 for CR and below 0.
05 for P.
Thus it can be said that hypothesis I in research is acceptable.
The Birth of the Devil II Hypothesis II in the study is "Motivation to attend training has a positive effect on employee performance".
From data processing, it is known that the CR value of the relationship between the training motivation variable and the employee performance variable as shown in Table 4.
8 is a CR of 2.
073 with a P value of 0.
Both values show qualified results, which are above 1.
96 for CR and below 0.
05 for P.
Thus it can be said that hypothesis II in research is acceptable The Birth of the Devil i Hypothesis i in the study is "The role of leadership has a positive effect on learning orientation".
From data processing, it is known that the CR value of the relationship between the leadership role variable and the learning orientation variable as shown by Table 4.
8 is 4.
579 with a P value of 0.
Both values show qualified results, which are above 1.
96 for CR and below 0.
05 for P.
Thus it can be said that hypothesis i in research is acceptable Uji Hypoplant IV Hypothesis IV in the study is "The role of leadership has a positive effect on employee performance".
From data processing, it is known that the CR value in the relationship between leadership role variables and employee performance, as shown in Table 4.
8 is a 1.
985 P value of 0.
Both values show qualified results, which are above 96 for CR and below 0.
05 for P.
Thus it can be said that hypothesis IV in the study is Uji hypothesis in Hypothesis V in the study is "Learning orientation has a positive effect on employee performance".
From data processing, it is known that the CR value of the relationship between the learning orientation variable and the employee performance variable as shown in Table 4.
8 is a CR of 3.
095 with a P value of 0.
Both values show qualified results, which are above 1.
96 for CR and below 0.
05 for P.
Thus it can be said that hypothesis V in research is acceptable Furthermore, the test results of each hypothesis that has been carried out above will be presented briefly in Table 8 about the conclusion of the hypothesis below.
Table 8 Conclusion of the Hypothesis WINTER JOURNAL VOL.
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Hipotesis Hypothesis Motivation to attend training has a positive effect 1: on learning orientation Hypothesis Motivation to attend training has a positive effect 2: on employee performance Hypothesis The role of leadership has a positive effect on 3: learning orientation.
Hypothesis Leadership roles have a positive effect on employee 4: performance Hypothesis Learning orientation has a positive effect on 5: employee performance.
Source: Processed primary data .
Test Results Accepted Accepted Accepted Accepted Accepted CONCLUSION AND RECOMMENDATION In this study, it was concluded that there is a positive influence between the motivation to attend training and leadership roles on employee performance through learning orientation.
The results showed that motivation to attend training and leadership roles significantly positively influenced, improved learning orientation, and in turn, contributed to improved employee performance.
To achieve the revenue plan of the Pemalang Tax Service Office, steps need to be taken, such as increasing employee motivation to attend training, maximizing leadership roles in providing feedback, and implementing effective supervision.
With high motivation and good leadership roles, employee performance can be improved according to work plans, achieve tax revenue targets, and produce positive results.
Therefore, learning orientation also has an important role in improving employee performance, because motivated employees will always strive continuously to learn and develop their potential, resulting in increasingly good
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REFERENCES