The Influence of Career Development, Competence, Work Motivation, Job Satisfaction, Against the Performance of Health Service Employees in Bintan Regency

This study uses a causal model survey method using path analysis techniques. This study aims to confirm the theoretical model with empirical data, the study population is employees in Investment and Integrated Service Office Bintan Regency, with sample of 39 employees. Data collecting technical using variable measurements by a questionnaire instrument, this instrument was developed based on theoretical studies. Data analysis uses descriptive statistics and statistical analysis. Statistical tests are used to test the significance of path coefficients using Partial Least Square (PLS) which is a Multivariate Analysis in the second generation using structural equation modeling (SEM). PLS can be used for a small number of samples and does not require the assumption that data distribution must be normal or not. The results of the analysis found that the relationships between variables formulated in the formulation of the problem as many as 6 pieces obtained significant results. Keywords​: Emotional Intelligence, Job Promotion, Work Environment, Work Discipline, Loyalty Copyright © 2021 IAIC All rights reserved.


Introduction
The organization in carrying out its duties and functions is largely determined by the quality of its human resources and supporting infrastructure. Human resources and equipment are elements in driving the organization's wheels, as well as internal factors that influence the progress of an organization. To achieve these objectives there are many factors that support, one of which is organizational culture. Organizational culture is a set of basic assumptions about how to think, how to feel, and how to behave in carrying out work. In every organization, work culture is always expected to be good because the good work culture will be related to the success or failure of organizational goals achieved. With a good organizational culture, usually the organization will easily overcome the problems faced and can achieve organizational goals by relying on the strengths in the organization. One of the factors that support the organization's progress is work discipline [1].
Every employee must have awareness and obey all rules and be aware of their duties and responsibilities. With high discipline from employees, it is possible for company goals to be achieved effectively and efficiently. For this reason, every employee at the Investment and Integrated Service Office in Bintan Regency is demanded to have a work discipline, so that the goals of the organization will be achieved. The work environment is an important aspect that must be considered by the organization because most of the employee's activities are in the surrounding work environment. The work environment is the overall tools and materials faced, the environment around where a person does his work, his work methods, and his work arrangements both as individuals and as group. With a comfortable work environment can lead to employee morale and employees feel satisfied with the facilities provided so that it causes satisfaction in work [ The theoretical framework of this research was developed from the synthesis of theories based on facts, observations and literature review, therefore this theoretical framework contains the relationship or influence between the variables involved in research based on supporting theories, and clearly explains the interrelationships between the intertwined variables, in addition to that can be used as a basis for answering problems and the logic flow of relationships between variables that are intertwined so that it will be very relevant to the problem studied as follows. Organizational culture according to Kreitner and Kinicki (2009: 18) is assumption that are owned, implicitly accepted by groups and determine how groups feel, think, and react to their diverse environments [3].
According to Davis (2010: 112) discipline is a management action to encourage the implementation of organizational standards, this is training that leads to efforts to justify and involve knowledge of employees' attitudes and behaviors so that there is a willingness in employees to lead to better cooperation and achievement.2 According to Nitisemito in Restuhadi & Sembiring (2017: 2534) the work environment is everything that exists around the workers who can influence themselves in carrying out the tasks that are charged. According to Nitisemito (2010: 160) enthusiasm for work is to work harder so that it can be expected to be faster and better.3 According to Rivai (2012: 549) performance is a real behavior that is displayed by everyone as a work achievement produced by employees in accordance with their role in the company or organization [4].

Research Method
This research method uses a causal model survey method using path analysis techniques. The population of this study is employees in the Investment and Integrated Service Office environment, which consists of 27 civil servants and 12 honorary employees, the sample is determined by the number of sample members 39, because of the limited population of all members of the sample population so this study uses a saturated sample, while the data collection technique uses variable measurement by using a questionnaire instrument where each employee respondent is given five questionnaire instruments to be a source of measurement of the studied variable and to test the significance of the path coefficient using Partial Least Square (PLS) which is a Multivariate Analysis in the second generation using structural equation modeling (Structural Equation Model / SEM) [5].

Results and Analysis 3.1. Internal Consistency Analysis
Internal consistency analysis is a form of reliability used to assess the consistency of results across items on the same test. Internal consistency testing uses composite reliability values with the criteria of a variable said to be reliable if the composite reliability value > 0.600 [6]. Based on internal consistency analysis data in the above table, the results show that the variables X1, X2, X3, X4, Y have a composite reliability > 0.600, so all questions developed on the 5 variables are reliable meaning cross-item questions developed on the questionnaire of all variables in the test the same has consistency [7].

Convergent Validity
Convergent validity is used to see the extent to which a measurement is positively correlated with alternative measurements of the same construct. To see an indicator of a construct variable is valid or not, it is seen from the outer loading value. If the outer loading value is greater than (0.4) then an indicator is valid [8]. Based on the above table, it can be seen that the outer loading value for variables X1, X2, X3, X4, Y where the value of all item items in the 5 variables tested is greater than 0.4, then all items developed for all variables are declared valid, meaning that the measurement is positively correlated with alternative measurements of the same construct thus the indicators of all construct variables are valid [9,10].

Validity Of Diskriminan
Discriminant validity aims to assess an indicator of a construct variable is valid or not, namely by looking at the Heterotrait -Monotrait Ratio Of Correlation (HTMT) <0.90, then the variable has a good discriminant validity (valid) [ 11,12].  [13]. Analysis of structural models or (inner models) aims to test the research hypothesis. The part that needs to be analyzed in the structural model is the coefficient of determination (R Square) by testing the hypothesis [14].
Collinearity testing is to prove the correlation between latent / construct variables is strong or not. If there is a strong correlation it means that the model contains problems if viewed from a methodological point of view, because it has an impact on the estimation of statistical significance [15]. This problem is called colinearity. The value used to analyze it is by looking at the value of Variance Inflation Factor (VIF). ( From the above data it can be described as follows : The VIF value for the correlation of X1 with Y, X2 with Y, X3 with Y, X4 with Y is < 5.00 (there is no colinearity problem). Therefore, from the data above and the development of structural models in this case there is no problem [18]. In this test there are two stages, namely testing the direct influence hypothesis and testing the indirect effect hypothesis. The coefficients of the hypothesis testing path are in the figure below : Test the significance of the structural coefficient of the path model (Structural Model Path Coeffisient). This test is to determine the path coefficient of the structural model, the aim is to test the significance of all relationships or hypothesis testing [19].

Figure 2. Hypothesis Testing
Direct influence hypothesis testing aims to prove the hypotheses of the influence of a variable on other variables directly (without intermediaries). If the value of the path coefficient is positive indicates that an increase in the value of a variable is followed by an increase in the value of another variable [20]. If the value of the path coefficient is negative indicates that an increase in a variable is followed by a decrease in the value of other variables. If the probability value (P-Value) < Alpha (0.05) then Ho is rejected (the effect of a variable with other variables is significant). If the value of probability (P-Value) > Alpha (0.05) then Ho is rejected (the effect of a variable with other variables is not significant) [21,22].  (2020) 1. The direct effect of variable X1 on variable X4 has a path coefficient of 1.228 (positive), then an increase in the value of variable X1 will be followed by an increase in variable X4. The effect of the variable X1 on X4 has a P-Values value of 0.023 < 0.05, so it can be stated that the influence between X1 on X4 is significant. 2. The direct effect of variable X1 on variable Y has a path coefficient of 0.878 (positive), then an increase in the value of variable X1 will be followed by an increase in variable Y. The effect of variable X1 on Y has a P-Values value of 0.039 < 0.05, so it can be stated that the influence between X1 on Y is significant. 3. The direct effect of variable X2 on variable X4 has a path coefficient of 23.459 (positive), then an increase in the value of variable X2 will be followed by an increase in variable X4. The effect of variable X2 on X4 has a P-Values value of 0.000 < 0.05, so it can be stated that the influence between X2 on X4 is significant. 4. The direct effect of variable X2 on variable Y has a path coefficient of 3.356 (positive), then an increase in the value of variable X2 will be followed by an increase in variable Y. The influence of variable X2 to Y has a P-Values value of 0.002 < 0.05, so it can be stated that the influence between X2 to Y is significant. 5. The direct effect of variable X3 on variable X4 has a path coefficient of 2.274 (positive), then an increase in the value of variable X3 will be followed by an increase in variable X4. The effect of variable X3 on X4 has a P-Values value of 0.029 < 0.05, so it can be stated that the influence between X3 to X4 is significant. 6. The direct effect of variable X3 on variable Y has a path coefficient of 39.442 (positive), then an increase in the value of variable X3 will be followed by an increase in variable Y. The effect of variable X3 on Y has a P-Values value of 0.000 < 0.05, so it can be stated that the influence between X3 on Y is significant. , then an increase in the value of variable X1 will be followed by an increase in variable X4. The effect of the variable X1 on X4 has a P-Values value of 0.023 < 0.05, so it can be stated that the influence between X1 on X4 is significant. 2. The direct effect of variable X1 on variable Y has a path coefficient of 0.878 (positive), then an increase in the value of variable X2 will be followed by an increase in variable Y. The effect of variable X1 on Y has a P-Values value of 0.039 < 0.05, so it can be stated that the influence between X1 on Y is significant. 3. The direct effect of variable X2 on variable X4 has a path coefficient of 23.459 (positive), then an increase in the value of variable X2 will be followed by an increase in variable X4. The effect of variable X2 on X4 has a P-Values value of 0.000 < 0.05, so it can be stated that the influence between X2 on X4 is significant. 4. The direct effect of variable X2 on variable Y has a path coefficient of 3.356 (positive), then an increase in the value of variable X2 will be followed by an increase in variable Y. The influence of variable X2 to Y has a P-Values value of 0.002 < 0.05, so it can be stated that the influence between X2 to Y is significant. 5. The direct effect of variable X3 on variable X4 has a path coefficient of 2.274 (positive), then an increase in the value of variable X3 will be followed by an increase in variable X4. The effect of variable X3 on X4 has a P-Values value of 0.029 < 0.05, so it can be stated that the influence between X3 to X4 is significant. 6. The direct effect of variable X3 on variable Y has a path coefficient of 39.442 (positive), then an increase in the value of variable X3 will be followed by an increase in variable Y. The effect of variable X3 on Y has a P-Values value of 0.000 < 0.05, so it can be stated that the influence between X3 on Y is significant.