The Influence of Transformational Leadership, Compensation, Organizational Climate, Work Satisfaction on the Performance of Puskesmas Kijang Employees in Bintan Regency

This study uses a causal model survey method using path analysis techniques. The aims of this study is to confirm the theoretical model with empirical data, the study population is employees at Kijang Health Center in Bintan Regency, which consists of civil servants and temporary employees with samples of 44 employees. Data collecting technical using variable measurements by questionnaire instruments, this instrument was developed based on theoretical studies. Data analysis uses descriptive statistics and analysis statistics. 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 relationships between variables formulated in the formulation of the problem as many as 6 pieces obtained significant results. Keywords​: Transformational Leadership, Compensation, Organizational Climate, Work Satisfaction, the performance 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 transformational leadership. Transformational leadership is a situation where the followers of a transformational leader feel the existence of trust, admiration, loyalty, and respect for the leader, and they are motivated to do more than they initially expected. Transformational leaders must also have the ability to equate the vision of the future with their subordinates, and increase the needs of subordinates at a higher level than what they need. Interaction arising between leaders and subordinates is characterized by the influence of leaders to change the behavior of subordinates into someone who feels capable and highly motivated and strives to achieve high and quality work performance [1].
The leader influences followers so that organizational goals will be achieved. Compensation is also an important factor in producing good employee performance. Compensation is all compensation received by an employee for his services or work for an organization or company where the compensation can be in the form of money or goods, either directly or indirectly. Kijang Health Center in Bintan Regency must have a good allowance to improve employee performance. Organizational climate is an environment where employees of an organization do their work. Every employee would want to have a comfortable work environment and be able to cooperate with other employees. For this reason, the role of agencies in realizing comfort and compactness at work is necessary to produce employee job satisfaction. Most employees tend to get a little satisfaction if they are faced with a boring and repetitive work. Employees are far more satisfied with work that convinces them to succeed by freely exercising control over how they do things. If the employee feels satisfied with what he wants automatically the employee will produce good performance [ 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. According to Bass in Robbins and Judge (2009: 387), transformational leadership is a leader who gives consideration and intellectual stimulation that is individualized and has charisma.1 According to Soekidjo (2009: 142), compensation is anything that is received by employees as compensation for their work or service.2 According to Higgins (2010: 204), organizational climate is defined as a totality of the perceptions of employees including managers of their work and social environment. According to Robbins (2010: 170), job satisfaction is a general attitude towards one's work as a difference between the number of rewards received by workers and the amount of rewards believed to be received.3 According to Boxall et al (in Mokaya and Gitari, 2012), employee performance is a function of ability, motivation and opportunity [3].

Research Method
This research method uses a causal model survey method using path analysis techniques. The population of this study were employees in the Kijang Community Health Center consisting of 37 civil servants and 7 honorary employees, the sample was determined by the number of sample members 44, because of the limited population of all members of the sample population so this study used all the population as sample, while the data collection technique uses variable measurement by using a questionnaire instrument where each employee is given five questionnaire instruments to be a source of measurement of the variables studied and to test the significance of the path coefficient using Partial Least Square (PLS) which is a Multivariate Analysis in the second generation using equation modeling structural (Structural Equation Model / SEM) [4].

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 [5]. 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 [6].

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 [7]. 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 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 [8].

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) [9]. 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. This problem is called collinearity. The value used to analyze it is by looking at the value of Variance Inflation Factor (VIF). (Hair, Hult, Ringle, & Sarstedt, 2014; Garson, 2016). 6 If the VIF value is greater than 5.00 then it means there is a collinearity problem, and in contrast there is no collinearity problem if the VIF value < 5.00. Table 4. Collinearity. Source Data Processing (2020) 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 collinearity problem). Therefore, from the data above and the development of structural models in this case there is no problem. 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 Coefficient). 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. 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. 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) Table 5. Hypothesis of Direct Effect. Source Data Processing (2020) 1. The direct effect of variable X1 on variable X3 has a path coefficient of 4.084 (positive), then an increase in the value of variable X1 will be followed by an increase in variable X3. The effect of the variable X1 on X3 has a P-Values value of 0.000 < 0.05, so it can be stated that the influence between X1 on X3 is significant.

Figure 2. Hypothesis Testing
2. The direct effect of variable X2 on variable X4 has a path coefficient of 2.548 (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 0.014 < 0.05, so it can be stated that the influence between X2 on X4 is significant.
3. The direct effect of variable X2 on variable Y has a path coefficient of 3.415 (positive), then an increase in the value of variable X2 will be followed by an increase in variable Y. The effect of variable X2 on Y has a P-Values value of 0.001 < 0.05, so it can be stated that the influence between X2 on X4 is significant.
4. The direct effect of variable X3 on variable X4 has a path coefficient of 5.933 (positive), then an increase in the value of variable X3 will be followed by an increase in variable X4. The influence of variable X3 to X4 has a P-Values value of 0.000 < 0.05, so it can be stated that the influence between X3 to X4 is significant.
5. The direct effect of variable X3 on variable Y has a path coefficient of 1.781 (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 0.008 < 0.05, so it can be stated that the influence between X3 to Y is significant.
6. The direct effect of variable X4 on variable Y has a path coefficient of 0.850 (positive), then an increase in the value of variable X4 will be followed by an increase in variable Y. 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 0.014 < 0.05, so it can be stated that the influence between X2 on X4 is significant. 3. The direct effect of variable X2 on variable Y has a path coefficient of 3.415 (positive), then an increase in the value of variable X2 will be followed by an increase in variable Y. The effect of variable X2 on Y has a P-Values value of 0.001 < 0.05, so it can be stated that the influence between X2 on X4 is significant. 4. The direct effect of variable X3 on variable X4 has a path coefficient of 5.933 (positive), then an increase in the value of variable X3 will be followed by an increase in variable X4. The influence of variable X3 to X4 has a P-Values value of 0.000 < 0.05, so it can be stated that the influence between X3 to X4 is significant. 5. The direct effect of variable X3 on variable Y has a path coefficient of 1.781 (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 0.008 < 0.05, so it can be stated that the influence between X3 to Y is significant. 6. The direct effect of variable X4 on variable Y has a path coefficient of 0.850 (positive), then an increase in the value of variable X4 will be followed by an increase in variable Y. The effect of variable X4 on Y has a P-Values value of 0.040 < 0.05, so it can be stated that the influence between X4 on Y is significant.