Rizki Mayandi Hasibuan

INTRODUCTION A performance management system is the key factor used in determining whether an organization can manage its human resources and talent effectively. Performance management provides information on who should be trained and in what areas, which employees should be rewarded, and what type of skills are lacking a t the organization or unit level. Therefore, performance management also provides information on the type of employees that should be hired. When implemented well, performance management systems provide critical information that allows organizations to make sound decisions regarding their people resources. The paper endeavors to emphasize the Performance Management practices in ten organizations across sectors in the Indian scenario. It inspects concerns in the performance management procedure and offers suggestions. In this project, we want to investigate the relationship between the two factors affected by PMS called “responsiveness” and “distinctiveness”. Research Question Is “responsiveness” has an effect on “distinctiveness” in the performance management system? In particular, we argue that the degree of coupling in a PMS emerges from both its fundamental determinants, namely responsiveness and distinctiveness. While responsiveness addresses organizational control and efficiency, distinctiveness enables autonomy and fosters innovation. A PMS shows loose coupling between its mechanisms when they are both responsive and distinctive. According to loose coupling theory, a loose coupling structure shows organizational elements that can be linked to each other in such a way that they are “responsive, but retain evidence of separateness and identity”. Thus, a loose coupling PMS can exhibit internal consistency with regard to some relationships in some locations of the system. There is a total theory about PMS: If there is neither responsiveness nor distinctiveness, the system is not really a system, and it can be defined as a noncoupled system. If there is responsiveness without distinctiveness, the system is tightly coupled. If there is distinctiveness without responsiveness, the system is decoupled. If there is both distinctiveness and responsiveness, the system is loosely coupled. Based on this theory, the hypothesis can be stated as follows: Responsiveness has a significant effect on distinctiveness in the performance management system. METHODOLOGY Confirmatory Factor Analysis Factor analysis is a family of statistical strategies used to model unmeasured sources of variability in a set of scores. Confirmatory factor analysis (CFA), otherwise referred to as restricted factor analysis, structural factor analysis, or the measurement model, typically is used in a deductive mode to test hypotheses regarding unmeasured sources of variability responsible for the commonality among a set of scores. It can be contrasted with exploratory factor analysis (EFA), which addresses the same basic question but in an inductive, or discovery-oriented, mode. Although CFA can be used as the sole statistical strategy for testing hypotheses about the relations among a set of variables, it is best understood as an instance of the general structural equation modeling (SEM). In that model, a useful distinction is made between the measurement model and the structural model. The measurement model (i.e., CFA) concerns the relations between measures of constructs, indicators, and the constructs they were designed to measure (i.e., factors). The structural model concerns the directional relations between constructs. In a full application of SEM, the measurement model is used to model constructs, between which directional relations are modeled and tested in the structural model. In this part, we have two factors. Now, we can make a structural model equation and consider our hypothesis to investigate the relationship between them. The model is shown as follows: Responsiveness Distinctiveness Partial Least Squares regression (PLS) is a quick, efficient and optimal regression method based on covariance. It is recommended in cases of regression where the number of explanatory variables is high, and where it is likely that there is multicollinearity among the variables, i.e. that the explanatory variables are correlated. In this research, the purpose is to measure the research model by PLS. The structural model shows the relationship among the variables and indicators. These indicators make the latent variables to build the structural equations model. By this model, the hypothesis is examined. Also, the validity and reliability of the model are checked. The research model is shown as follows here: Measurement model analysis Validity and reliability of the model is so important to examine the goodness of fitness. Actually, these two correspond to making an evaluation of the measurement model. Before this, it is essential to check the existing of the missing values in the data. After checking the indictors, it was observed that there are two missing values in the data. Because it is few, the mean value of the corresponding indictor is substituted. To evaluate the measurement model, we have to distinguish among constructs measured formatively and reflectively; reflective measurement models are assessed on their internal consistency, reliability and validity. Since the constructs of this study are all measured reflectively, we will use Cronbach’s Alpha and Composite Reliability values to assess their reliability. Constructs’ reliability Cronbach’s alpha coefficient measures the internal consistency, or reliability, of a set of survey items. Use this statistic to help determine whether a collection of items consistently measures the same characteristic. Cronbach’s alpha quantifies the level of agreement on a standardized 0 to 1 scale. Higher values indicate higher agreement between items. High Cronbach’s alpha values indicate that response values for each participant across a set of questions are consistent. For example, when participants give a high response for one of the items, they are also likely to provide high responses for the other items. This consistency indicates the measurements are reliable and the items might measure the same characteristic. The most common measurement used for internal consistency is Cronbach alpha and composite reliability, in which it measures the reliability based on the interrelationship of the observed items variables. In PLS-SEM, the values are organized according to their indicator’s individual reliability. The values range from 0 to 1, where a higher value indicates higher reliability level. In exploratory research, values of composite reliability/Cronbach alpha between 0.60 to 0.70 are acceptable, while in more advanced stage the value have to be higher than 0.70. However, the value that is more than 0.90 is not desirable and the value that is 0.95 or above is definitely undesirable. Table 1 show these values for the model. In our model, all the indexes are in the acceptable range. Variable Cronbach’s alpha Composite reliability (CR) Average variance extracted (AVE) Distinctiveness 0.883 0.922 0.750 Responsiveness 0.894 0.934 0.826 Constructs’ validity Convergent validity is another important aspect to take into consideration when analyzing the measurement model. It indicates the extent to which a measure is positively correlated to all the other measures of the same construct. Convergent validity is the assessment to measure the level of correlation of multiple indicators of the same construct that are in agreement. To establish convergent validity, the factor loading of the indicator, composite reliability (CR) and the average variance extracted (AVE) have to be considered. The value ranges from 0 to 1. AVE value should exceed 0.50 so that it is adequate for convergent validity. This index is also acceptable for the model. Coefficient of determination, the R2 value The coefficient of determination is a statistical measurement that examines how differences in one variable can be explained by the difference in a second variable when predicting the outcome of a given event. In other words, this coefficient, more commonly known as r-squared (or R2), assesses how strong the linear relationship is between two variables and is heavily relied on by investors when conducting trend analysis. The coefficient of determination is a measurement used to explain how much the variability of one factor is caused by its relationship to another factor. This correlation is represented as a value between 0.0 and 1.0 (0% to 100%). This value is 0.464 for the model. It means that the 46% of distinctiveness can be explained by responsiveness. Structural model path coefficients Path coefficients are standardized versions of linear regression weights which can be used in examining the possible causal linkage between statistical variables in the structural equation modeling approach. The standardization involves multiplying the ordinary regression coefficient by the standard deviations of the corresponding explanatory variable: these can then be compared to assess the relative effects of the variables within the fitted regression model. The idea of standardization can be extended to apply to partial regression coefficients. The significance of these path coefficients is explained by t test and p-value. If the t value is more than 1.96, the significance of the coefficient is confirmed. T test is done by the bootstrapping. The hypothesis result is shown in table: Direction Responsiveness -> Distinctiveness + Original Sample (O) 0.681 Sample Mean (M) 0.682 STD EV 0.05 6 T Statistics (|O/STDEV|) 12.092 P Values 0.001 Results In the above table, the results are shown. The p-value for the hypothesis is less than 0.05 and the t statistics is more than 1.96. Therefore, it can be said that responsiveness has a significant effect on distinctiveness in the performance management system. This relationship is positive and its value is 0.681.