CHAPTER 5 RESULTS AND ANALYSIS

Size: px
Start display at page:

Download "CHAPTER 5 RESULTS AND ANALYSIS"

Transcription

1 CHAPTER 5 RESULTS AND ANALYSIS This chapter exhibits an extensive data analysis and the results of the statistical testing. Data analysis is done using factor analysis, regression analysis, reliability analysis and validity analysis of statistical software SPSS. This chapter focuses on the results and discussion, based on the tables generated by SPSS. In the beginning, the proposed framework is discussed and then data analysis is done for the required competence level followed by data analysis of the existing competence level. The hypothesis testing results are then discussed and summarized. Subsequently the summary of the results are presented. This chapter also explores the competency gaps of HR professionals working in the Indian IT industry and to use the results for identifying the training needs and give suggestions in order to bridge the gaps. This chapter presents the t-test and radar chart to highlight the competency gaps in all the competency groups. The training needs assessment is then revealed. In the end, results are discussed and conclusions are drawn. 5.1 Proposed Framework Figure 5.1 depicts the proposed research model with hypothesis indications. The figure states seven hypotheses. One hypothesis is attached with each competency group. The proposed framework consists of two parts. The first part measures the HR professionals required competence level (RCL) on six competency groups and the second part measures the existing competence level (ECL) on these six competency groups. Competency mapping is the process of identifying the key competencies needed for the job. On these identified competencies, the desired/expected level of competence for the job, i.e., the required competence level (RCL) is measured. Further, against this required competence level (RCL) the jobholder s actual/current/demonstrated level of expertise is measured, which is the existing competence level (ECL). 127

2 Leadership competencies H1b H1a H3a Performance Interpersonal competencies H2a H2b H4a improvement Business competencies H7 Technical competencies H4b Analytical competencies H5a H6a H5b H3b Effectiveness Technological competencies H6b Figure 5.1: Proposed Framework Following hypotheses have been framed from the literature survey. The literature support for these hypotheses has been discussed in chapter four (section 4.4). H1a: Perceived possession of leadership competencies will have a positive effect on perceived performance improvement. H2a: Perceived possession of interpersonal competencies will have a positive effect on perceived performance improvement. H3a: Perceived possession of business competencies will have a positive effect on perceived performance improvement. H4a: Perceived possession of technical competencies will have a positive effect on perceived performance improvement. 128

3 H5a: Perceived possession of analytical competencies will have a positive effect on perceived performance improvement. H6a: Perceived possession of technological competencies will have a positive effect on perceived performance improvement. H7: Effectiveness will be positively related to performance improvement. 5.2 Required Competence Level (RCL) - Data Analysis and Results The data for the research was collected through a questionnaire and was analyzed using statistical software Statistical Package for Social Sciences (SPSS) version 21.0 for windows. Measured Variables The variable we want to predict is called the dependent variable (or sometimes, the outcome, target or criterion variable). The variables we are using to predict the value of the dependent variable are called the independent variables (or sometimes, the predictor, explanatory or regressor variables). In this study, the variables that have been treated as dependent or criterion variables are: performance improvement and effectiveness. In this study, independent or predictor variables are six competency groups: leadership, interpersonal, business, technical, analytical, and technological Validity Analysis Validity is the ability of an instrument to measure what it is designed to measure [Straub (1989)]. To ensure the content validity of the scales, the selected items for the constructs were mainly adapted from prior studies as discussed in preceding sections. Eight items for the interpersonal competency group construct, four items for business competency group construct, seven items for the technical competency group construct, four items for analytical competency group construct, eight items for leadership competency group construct, two items for the technological competency group constructs, two items for effectiveness and four items for performance improvement constructs were made. Likert scales (1~5), with anchors ranging from strongly disagree to strongly agree were used for all questions. 129

4 Confirmatory factor analysis (CFA) was conducted to assess the properties of measures in terms of testing convergent and discriminate validity of the measures to identify adequate fit of scale items [Kline (1998)] Measurement Assessment of Confirmatory Factor Analysis Kaiser-Meyer-Olkin (KMO) and Bartlett test of sphericity is carried out to ensure whether the correlation matrix has a significant correlation among at least some of the variables. It also quantifies the degree of inter correlation among the variables and the appropriateness of factor analysis to test the adequacy of the sample. Table 5.1 presents the SPSS output for KMO and Bartlett s test. Table 5.1: KMO and Bartlett s Test Kaiser-Meyer-Olkin Measure of Sampling.801 Adequacy. Bartlett s Test of Sphericity Approx. Chi-Square df 741 Sig..000 Kaiser-Meyer-Olkin Measure of sampling adequacy is 0.801, which is well above the screening limit of 0.5, so we are confident that factor analysis is appropriate for these data. Bartlett s Test of sphericity measure tests the null hypothesis that the original correlation matrix is an identity matrix. For factor analysis to work we need some relationships between variables and if the R-matrix were an identity matrix then all correlation coefficients would be zero. Therefore, we want this test to be significant (i.e., to have a significance value p less than 0.05). For our data, Bartlett s test of sphericity (p= 0.000) is highly significant and therefore it is appropriate to conduct factor analysis for the data. Table 5.2 depicts the total variance explained in the factor analysis. 130

5 Table 5.2: Total Variance Explained Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings % of Cumulative % of Cumulative % of Cumulative Component Total Variance % Total Variance % Total Variance % Extraction Method: Principal Component Analysis. 131

6 Factor Analysis Factor analysis determines the correlations among the variables. It is analyzed by computing the correlations between the variables when the effects of other variables are taken into account. Construct validity determines the extent to which a scale measures a variable of interest. In this study, Straub s (1989) process of validating instruments is followed in terms of convergent validity and discriminant validity. SPSS offers three options for orthogonal rotation: Varimax, Quartimax, Equamax. Orthogonal rotation results in a rotated component / factor matrix that presents the post-rotation loadings of the original variables on the extracted factors, and a transformation matrix that gives information about the angle of rotation. Thus, a principal components factor analysis with Varimax rotation was conducted to investigate the distinctions among leadership competency group (LC), interpersonal competency group (IC), business competency group (BC), technical competency group (TC), analytical competency group (AC), technological competency group (TGC), effectiveness (EFF) and performance improvement (Performance) in the RCL data obtained from the questionnaire. Table 5.3 explains rotated component matrix of the factor analysis. As shown in table 5.3, eight factors emerged with no cross-construct loadings, indicating good discriminant validity. The instrument also demonstrated convergent validity with factor loadings exceeding 0.5 for each construct. The magnitude of the factor loading should be equal to or greater than 0.5 for adequate individual item reliability, providing support for convergent validity [Bagozzi and Yi (1989)]. Consequently, these results confirm that each of the eight constructs is unidimensional and factorially distinct and that all items used to operationalize a particular construct is loaded onto a single factor (table 5.3). 132

7 Table 5.3: Rotated Component Matrix Components Sub-elements LC LC LC LC LC LC LC LC IC IC IC IC IC IC IC IC BC BC BC BC TC TC TC TC TC TC TC AC AC AC AC TGC TGC EFF EFF Performance Performance Performance Performance Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 6 iterations. 133

8 5.2.2 Reliability Analysis The purpose of the reliability testing was to examine the properties of measurement scales and the items in order to obtain the overall index of internal consistency of the scales [Hair et al. (2006)]. Cronbach s alpha is the most common measure of internal consistency ( reliability ). It is most commonly used when multiple Likert questions are used in the survey questionnaire that form a scale, and to determine if the scale is reliable. Table 5.4 depicts the reliability analysis of the dataset used for the factor analysis. It shows the Cronbach s alpha values of the dataset. Table 5.4: Reliability Statistics Cronbach s Alpha Cronbach s Alpha Based on N of Items Standardized Items The reliability test (alpha) of the entire data set used for factor analysis is 0.767, which exceeds the common threshold value recommended by Nunnally (1978). All variables were subjected to reliability analysis to assess the dimensionality of the measurement scale. The test results show that all constructs exhibited high reliabilities, as Cronbach s alpha exceeded the acceptable level of 0.70 [Hair et al. (2006)]. These results are given in table 5.5, depicting the reliability of the dataset. Table 5.5: Reliability of the Constructs Code Constructs Cronbach s Alpha LC Leadership competencies.892 IC Interpersonal competencies.973 BC Business competencies.793 TC Technical competencies.958 AC Analytical competencies.883 TGC Technological competencies.947 EFF Effectiveness.763 Performance Performance improvement

9 The value of Cronbach s alpha between ±.41 and ±.70 denotes moderate reliable, while alpha greater than ±.71 denotes high reliability [Sekaran (2006)]. All measures exhibited high reliabilities with coefficient alphas ranging from 0.7 up to 0.9, exceeding the acceptable level of 0.70 [Tabachnick and Fidell (2007)]. It reveals an acceptable level of the reliability of the five-point scale and thereby allows further analysis. Therefore, the measurement model in this research shows satisfactory reliability, convergent validity and discriminant validity Measurement Assessment of Regression Analysis The regression analysis has been done using statistical software SPSS. The results are discussed below. This section discusses the outputs of the measurement assessment of multiple regression Descriptive Statistics Table 5.6 depicts the descriptive statistics of the RCL data used for the regression analysis. The table shows the mean and standard deviation value. Table 5.6: Descriptive Results Mean Std. Deviation N LC IC BC TC AC TGC EFF Performance Correlations Correlation measures the degree to which the change in one variable follows the pattern of change in the other variable. It cannot be said that one variable caused the change in the other; 135

10 in the sense that it can be guaranteed that a change in one thing will invariably produce another result. Pearson product-moment correlation coefficients were computed to assess the relationship between performance improvement and all the competency groups. Overall, there was a positive correlation between performance improvement and competency groups. Table 5.7 depicts the values of Pearson s correlation along with the associated significance value between the independent variables. It shows the Pearsonian r s, the significance of each r, and the sample size (N) for each r. Table 5.7: Correlations Performance LC IC BC TC AC TGC Pearson Correlation Sig. (1- tailed) N Performance LC IC BC TC AC TGC Performance LC IC BC TC AC TGC Performance LC IC BC TC AC TGC

11 Here the research objective is to find whether there is a significant correlation between competencies and performance improvement. It can be assumed that the value of the one variable is a linear function of the value of the other variable. If this relationship is perfect, then it can be described by the slope-intercept equation for a straight line, Y = a + bx. Even if the relationship is not perfect, one may be able to describe it as non-perfect linear. Correlation coefficients describe how well a straight line fits the data. If plot shows that the line that relates X and Y is linear, Pearson correlation can be used. If the plot shows that the relationship is monotonic (not a straight line, but a line whose slope is always positive or always negative), Spearman correlation coefficient can be used. In Pearson correlation, a standardized index is obtained of the degree of linear association by dividing covariance by the two standard deviations, removing the effect of the two-univariate standard deviations. This index is called the Pearson product moment correlation Z xz coefficient, r for short, and is defined as a mean, r N y, where the Z-scores are computed using population standard deviations, Pearson r will vary from 1 to 0 to +1. If r = +1 the relationship is perfect positive, and every pair of X,Y scores has Z x = Z y. If r = 0, there is no linear relationship. If r = 1, the relationship is perfect negative and every pair of X, Y scores has Z x = Z y. The correlation coefficient or r provides a numerical measure of the strength of the relationship between two numeric variables. The magnitude of the correlation coefficient can vary from 0 (indicating no relationship), to 1 (indicating a perfect relationship). Moreover, it can have either a positive sign or a negative sign. If the sign is positive, it means that the variables change in the same direction, i.e., when one goes up, the other goes up; when one goes down, the other goes up. If the sign of the correlation coefficient is negative, it means that the variables change in opposite directions, when one goes up, the other goes down and vice versa. When two variables change in the same direction, this characterizes the relationship among the variables as a direct relationship. When two variables change in opposite directions, this refers to the relationship as an inverse relationship [Cohen (2003)]. 137

12 The rule of thumb for interpreting correlation coefficient is to divide the range of possible scores in five intervals: 0 to 0.20 corresponds to a very weak relationship; 0.21 to 0.40 corresponds to a weak relationship, 0.41 to 0.60 corresponds to a moderate relationship, 0.61 to 0.80 corresponds to a strong relationship, and 0.81 to 1.00 corresponds to a very strong relationship. These rules apply whether the sign of the correlation coefficient is positive or negative. In the social sciences, obtaining correlations in the range of 0.40 to 0.60 is often considered to be about the best can be hoped for. Due to the complexity of social and psychological phenomena, it is unlikely that any two variables will have a more distinctive relationship. If the correlation coefficient is squared (multiplied by itself), it is called the coefficient of determination, or the r 2 statistic. r 2 is usually interpreted as the proportion of variance (differences among scores) on the dependent variable explained by differences among scores on the independent variable. While r 2 has a potential range from 0 to 1.0, high values are less probable. A very strong correlation coefficient of 0.8 corresponds to an r 2 of Since the coefficient of determination is the square of the correlation coefficient, it has only positive values Multiple Regression of Performance Improvement on Competency Groups First, the dependent variable performance improvement is regressed on the independent variables, i.e., six competency groups Variables Entered/Removed Table 5.8 depicts the variable entered and removed in the regression analysis. The dependent variable in this analysis is the performance improvement. Table 5.8: Variables Entered/Removed a Model Variables Entered Variables Removed Method 1 TGC, BC, TC, IC, AC, LC b. Enter a: Dependent Variable: Performance b: All requested variables entered. 138

13 The Model column indicates the number of the model being reported. SPSS allows us to specify multiple models in a single regression command. The Variables Entered column shows the list all of the independent variables specified. The column Variables Removed lists the variables that were removed from the current regression. None variable was removed (table 5.8). The Method column shows the method that SPSS used to run the regression. Enter means that each independent variable was entered in the usual fashion. Because Enter regression was requested, SPSS first tested a model with variables: technological competencies, analytical competencies, technical competencies, leadership competencies, business competencies, and interpersonal competencies Model Summary The multiple regression analysis was performed on the RCL data, which shows the value of R 2, adjusted R 2 and the standard error of estimate. It also shows the change statistics, which include F, change, its associated significance value and the Durbin-Watson values. Table 5.9 depicts the comprehensive result of the model summary of the multiple regression. Model R R Square Table 5.9: Model Summary of the Multiple Regression Model Summary b Adjusted R Square Std. Error of the Estimate R Square Change Change Statistics F Change df 1 df2 Sig. F Chang e Durbin- Watson a a. Predictors: (Constant), TGC, BC, TC, IC, AC, LC b. Dependent Variable: Performance R is the square root of R 2 and is the correlation between the observed and predicted values of the dependent variable. R 2 is the proportion of the variance in the values of the dependent variable (Y) which can be explained by all the independent variables (Xs) in the equation together. This is an overall measure of the strength of association and does not reflect the extent to which any particular independent variable is associated with the dependent variable. 139

14 R 2 is a measure of how much of the variability in the outcome (in this case performance improvement) is accounted for by the predictors (i.e., six competency groups: TGC, AC, TC, LC, BC, and IC). As shown in table 5.9, R 2 value is 0.658, which means that six competency groups account for 65.8 % of the variation in performance improvement. This suggests that the model is quite significant in explaining the variances. The significance result at p < provides support for the relationship. Adjusted R 2 is an adjustment of the R-squared that penalizes the addition of extraneous predictors to the model. Adjusted R 2 gives us some idea of how well the proposed model generalizes and ideally we would like its value to be the same, or very close to, the value of R 2. The adjusted R 2 is a standard, arbitrary downward adjustment to penalize for the possibility that, with many independents, some of the variance may be due to chance. The more the number of independents, the more the adjustment penalty. Here the adjusted R- square is (table 5.9). In this case the difference for the final model is ( ) or 1.8%. This shrinkage means that if the model were derived from the population rather than a sample it would account for approximately 1.8% less variance in the outcome. Std. Error of the Estimate is also referred to as the root mean squared error. It is the standard deviation of the error term and the square root of the Mean Square for the Residuals in the ANOVA table (table 5.10). Standard error of the estimate is 0.41 units (table 5.9). In general, here the standard error is low, and any predictions using this model will be good ones. F value shows whether the equation as a whole is statistically significant in explaining Y. F value for the Change Statistics shows the significance level associated with adding the variable. The significance level for F value change statistics is Durbin-Watson statistic tests for serial correlation of error terms for adjacent cases. Durbin Watson statistic informs us about whether the assumption of independent errors is tenable. The test statistic can vary between 0 and 4 with a value of 2 meaning that the residuals are uncorrelated. A value greater than 2 indicates a negative correlation between adjacent residuals whereas a value below 2 indicates a positive correlation. As a conservative rule of thumb, Field (2000) suggests that the value less than 1 and greater than 3 are definitely 140

15 cause for concern. The closer to 2 that the value is, the better, and for these data the value is 1.781, which is close to 2 that the assumption has almost certainly been met (table 5.9) Analysis of Variance Table 5.10 reports the analysis of the variance (ANOVA), which assesses the overall significance of our model. The table shows the value of the sum of squares, degree of freedom, mean square value, F value and its associated significance value. The dependent value is performance improvement. Table 5.10: Analysis of Variance (ANOVA) a Model Sum of Squares df Mean Square F Sig. 1 Regression b Residual Total a. Dependent Variable: Performance b. Predictors: (Constant), TGC, BC, TC, IC, AC, LC Looking at the breakdown of the variance in the outcome variable, there are three categories to be examined: Regression, Residual, and Total. The total variance is partitioned into the variance which can be explained by the independent variables (Regression) and the variance which is not explained by the independent variables (Residual). Sum of Squares is associated with the three sources of variance: Regression, Residual, and Total. df is the degree of freedom associated with the sources of variance. The total variance has N- 1 degrees of freedom. The regression degrees of freedom correspond to the number of coefficients estimated minus 1. Including the intercept, there are 7 coefficients, so the regression has 7-1= 6 degrees of freedom. The residual degrees of freedom are the df total minus the df regression, =113. Mean Square is calculated by dividing the sum of squares by their respective df. F-statistic is the mean square (regression) divided by the mean square (residual). The p-value is compared to some alpha level in testing the null hypothesis that all of the model coefficients are 0. ANOVA was conducted to compare the effect of six competency groups on performance improvement. There was a significant effect of six competency groups on performance improvement at the p <.001 level [F (6, 119) = , p <.001]. 141

16 Table 5.10 tests whether the model is significantly better at predicting the outcome than using the mean as a best guess. Specifically, the F-ratio represents the ratio of the improvement in prediction that results from fitting the model (labeled Regression in the table), relative to the inaccuracy that still exists in the model (labeled Residual in the table). If the improvement due to fitting the regression model is much greater than the inaccuracy within the model, then the value of F will be greater than 1 and SPSS calculates the exact probability of obtaining the value of F by chance. For the initial model the F-ratio is (table 5.10), which is very unlikely to have happened by chance (p <0.000). This result shows that the final model significantly improves our ability to predict the outcome variable. The ANOVA table (table 5.10) tests the overall significance of the model (that is, of the regression equation), the significance of the F value is below 0.000, so the model is significant Coefficients Table 5.11 depicts the coefficients of multiple regression. It shows in detail the beta (standardized and unstandarized) value of various independent variables and its associated significance value. The table also shows the collinearity statistics with two parameters, namely the tolerance value and the VIF value. Table 5.11: Coefficients of Multiple Regression Unstandardized Coefficients Standardized Coefficients Coefficients a % Confidence Interval for B Lower Upper Bound Bound Model t Sig. Std. B Beta Error 1 (Constant) Zeroorder Correlations Collinearity Statistics Partial Part Tolerance VIF LC IC BC TC AC TGC a. Dependent Variable: Performance

17 The following points discuss the interpretation of the table Model column shows the predictor variables (LC, IC, BC, TC, AC, and TGC). The first variable (Constant) represents the constant, also referred to as the Y intercept, the height of the regression line when it crosses the Y axis. In other words, this is the predicted value of performance improvement when all other variables are 0. B column shows the values for the regression for predicting the dependent variable from the independent variable. Std. Error column shows the standard errors associated with the coefficients. Beta (standardized coefficients) is a measure of how strongly each predictor variable influences the criterion variable. These are the coefficients obtained if all of the variables in the regression are standardized, including the dependent and all of the independent variables, and then running the regression. By standardizing the variables before running the regression, all of the variables are put on the same scale, and the magnitude of the coefficients can be compared to see which one has more of an effect. t and Sig. (p) values give a rough indication of the impact of each predictor variable a big absolute t value and small p value suggest that a predictor variable is having a large impact on the criterion variable. The bigger betas are associated with the larger t- values and lower p-values. 95% Confidence Interval for B shows the 95% confidence intervals for the coefficients. The confidence intervals are related to the p-values such that the coefficient will not be statistically significant if the confidence interval includes 0. These confidence intervals can help us put the estimate from the coefficient into perspective by seeing how much the value could vary. The confidence intervals on coefficient are the Beta s, which could be placed in the prediction equation to get the high and low estimates, though this is rarely done. Recall that is the number of units the dependent changes when the independent changes one unit. For example, when a case increases reliability by one, leadership competencies are increased by.242 units on the average, but at the 95% level this might be as low as.115 units or as high as.370 units. The zero-order correlation is simply the raw correlation from the correlation matrix given at the top of this output. The partial correlation is the correlation of the given variable with perceived value, controlling for other independent variables in the equation. Partial correlation removes the effect of the control variable(s) on both 143

18 the dependent and the independent variables. Part correlation, in contrast, removes the effect of the control variable(s) on the independent variable alone. Part correlation is used when one hypothesizes that the control variable affects the independent variable but not the dependent variable and when one wants to assess the unique effect of the independent variable on the dependent variable. The collinearity statistics need scrutiny when the independent variables are highly interrelated. The tolerance for a variable is 1 - R-squared for the regression of that variable on all the other independents, ignoring the dependent. When tolerance is close to 0 there is high multicollinearity of that variable with other independents and the b and beta coefficients will be unstable. VIF (Variance Inflation Factor) is simply the reciprocal of tolerance. Therefore, when VIF is high there is high multicollinearity and instability of the b and beta coefficients. Multicollinearity refers to the correlation between three or more independent variables and evidenced when one is regressed against the others [Hair et al. (2009)]. As collinearity increases, the unique variance explained by each independent variable decreases. The presence of multicollinearity in our analyses was checked for by estimating variance inflation factors (VIF) for each predictor. VIF indicators range from 1 to and signal the extent of non-orthogonality among the predictors; i.e., the higher the VIF score for a predictor, the more it is correlated with other predictors. All VIF values were in the range of 1.05 to 1.35, well below the cut-off value of 10 suggested by Neter et al. (1989). Hence, multicollinearity is not a threat to the substantive conclusions of this study. The coefficients are the standardized regression coefficients. Their relative absolute magnitudes for a given step reflect their relative importance in predicting perceived value. Betas are only compared within a model, not between. Moreover, Betas are highly influenced by misspecification of the model. Adding or subtracting variables in the equation will affect the size of the Betas. Beta is measured in units of standard deviation. For example, a beta value of.238 indicates that a change of one standard deviation in the predictor variable (in this case leadership competencies) will result in a change of.238 standard deviations in the dependent variable (performance improvement). Thus, the higher beta value indicates that a unit change in this predictor variable has a large impact on the criterion variable. 144

19 Here the Beta ( ) values tell us about the relationship between performance improvement and each predictor. If the value is positive, then there is a positive relationship between the predictor and the outcome, whereas a negative coefficient represents a negative relationship. In this case all the predictors have positive ( ) values indicating a positive relationship (table 5.11). The value also tells us to what degree each predictor affects the outcome if the effects of all other predictors are held constant. Each of these values has an associated standard error indicating to what extent these values would vary across different samples, and these standard errors are used to determine whether or not the value differs significantly from zero (using the t-statistics). Therefore, if the t-test associated with a value is significant (i.e., p <.001) then that predictor is making a significant contribution to the model. From table 5.11, it is evident that all the predictors are making a significant contribution to the model. The contribution of the predictor will be greater if the value of significance is smaller and the value of t is larger. For this model, interpersonal competencies and technological competencies are significant predictors of performance improvement. The coefficients and the constant are used to create the prediction (regression) equation, predicted performance improvement = 0.238* leadership competencies * interpersonal competencies * business competencies *technical competencies *analytical competencies *technological competencies. The t-test tests the significance of each coefficient. It is possible to have a regression model, which is significant overall by the F test, but where a particular coefficient is not significant Multiple Regression of Performance Improvement on Effectiveness The dependent variable performance improvement is regressed on the independent variable, i.e., effectiveness Variables Entered/Removed Table 5.12 depicts the variable entered and removed in the regression analysis. The method enter is used in the analysis. The dependent variable in this analysis is performance improvement. 145

20 Table 5.12: Variables Entered/Removed a Model Variables Entered Variables Removed Method 1 EFF b. Enter a: Dependent Variable: Performance b: All requested variables entered. Because enter regression was requested, SPSS first tested a model with variable effectiveness. None variable was removed (table 5.12) Model Summary Table 5.13 depicts the comprehensive result of the model summary of the multiple regression. The table shows the value of R 2, adjusted R 2 and the standard error of estimate. Along with it also shows the change statistics, which include F, change, its associated significance value and the Durbin-Watson values. Model R R Square Table 5.13: Model Summary of the Multiple Regression Model Summary b Adjusted R Square Std. Error of the Estimate R Square Change Change Statistics F Change df1 df2 Sig. F Change Durbin- Watson a a. Predictors: (Constant), EFF b. Dependent Variable: Performance R-square is the percent of the dependent variable explained by the independent variable. In this case, the model (predictor effectiveness) explains 86.2% of the variation in performance improvement. This suggests the model is quite significant in explaining the variances. Here the adjusted R-square is In this case the difference for the final model is ( ) or 0.1%. This shrinkage means that if the model were derived from the population rather than a sample it would account for approximately 0.1% less variance in the outcome (table 5.13). Standard error of the estimate is about 0.25 units (table 5.13). In general, here the standard error is low, and any predictions using this model will be good ones. 146

21 The F value for the Change Statistics shows the significance level associated with adding the variable. The significance level for F value change statistics is The Durbin-Watson statistic tests for serial correlation of error terms for adjacent cases. The test statistic can vary between 0 and 4 with a value of 2 meaning that the residuals are uncorrelated. A value greater than 2 indicates a negative correlation between adjacent residuals whereas a value below 2 indicates a positive correlation. The closer to 2 that the value is, the better, and for these data the value is 2.208, which is close to 2 that the assumption has almost certainly been met (table 5.13) Analysis of Variance Table 5.14 depicts the analysis of the variance (ANOVA). The table shows the value of the sum of squares, degree of freedom, mean square value, F value and its associated significance value. The dependent variable is performance improvement. Table 5.14: Analysis of Variance (ANOVA) a Model Sum of Squares df Mean Square F Sig. 1 Regression b Residual Total a. Dependent Variable: Performance b. Predictors: (Constant), EFF When only effectiveness was used to predict performance improvement, a significant model emerged with [F (1, 119) = , p <.001], showing that effectiveness accounts for 86.2% of the variation in performance improvement. There was a significant effect of effectiveness on performance improvement at the p <.001 level. The ANOVA table (table 5.14) tests the overall significance of the model (i.e., of the regression equation), the significance of the F value is below 0.000, so the model is significant Coefficients Table 5.15 depicts the coefficients of multiple regression. It shows in detail the beta (standardized and unstandarized) value of various independent variables and its associated 147

22 significance value. The table also shows the collinearity statistics with two parameters, namely the tolerance value and the VIF value. Unstandardized Coefficients Table 5.15: Coefficients of Multiple Regression Standardized Coefficients Coefficients a 95.0% Confidence Interval for B Lower Upper Bound Bound Model t Sig. Std. B Beta Error 1 (Constant) Correlations Collinearity Statistics Zeroorder Partial Part Tolerance VIF EFF a. Dependent Variable: Performance The value tells us about the relationship between performance improvement and predictor effectiveness. If the value is positive there is a positive relationship between the predictor and the outcome, whereas a negative coefficient represents a negative relationship. For this case the predictor has a positive value indicating positive relationship (table 5.15). If the t-test associated with a value is significant (i.e., p <.001) then that predictor is making a significant contribution to the model. From the table 5.15, it is evident that the predictor effectiveness is making a significant contribution to the model. The contribution of the predictor will be greater if the value of significance is smaller and the value of t is larger. For this model, effectiveness is the significant predictor of performance improvement. The following points discuss the interpretation of the table The coefficients and the constant are used to create the prediction (regression) equation, predicted performance improvement = 0.928* effectiveness. VIF (variance inflation factor) is simply the reciprocal of tolerance. Therefore, when VIF is high there is high multicollinearity and instability of the b and beta coefficients. The VIF value was 0.100, well below the cutoff value of 10 suggested by Neter et al. (1989). Hence, multicollinearity is not a threat to the substantive conclusions of this study Multiple Regression of Effectiveness on Competency Groups Now the dependent variable effectiveness is regressed on all the six competency groups. 148

23 Variables Entered/Removed Table 5.16 depicts the variable entered and removed in the regression analysis. The method enter is used in the analysis. The dependent variable in this analysis is effectiveness. Table 5.16: Variables Entered/Removed a Model Variables Entered Variables Removed Method 1 TGC, BC, TC, IC, AC, LC b. Enter a: Dependent Variable: EFF b: All requested variables entered. Because enter regression was requested, SPSS first tested a model with variable technological competencies, analytical competencies, technical competencies, leadership competencies, business competencies, and interpersonal competencies. None variable was removed (table 5.16) Model Summary Table 5.17 depicts the comprehensive result of the model summary of the multiple regression. The table shows the value of R 2, adjusted R 2 and the standard error of estimate. Along with it also shows the change statistics, which include F, change, its associated significance value and the Durbin-Watson values. Model R R Square Table 5.17: Model Summary of the Multiple Regression Model Summary b Adjusted R Square Std. Error of the Estimate R Square Change Change Statistics F Change df1 df2 Sig. F Change Durbin- Watson a a. Predictors: (Constant), TGC, BC, TC, IC, AC, LC b. Dependent Variable: EFF As shown in table 5.17, value of R 2 is 0.652, which means that predictors (TGC, LC, BC, TC, AC, IC) account for 65.2 % of the variation in effectiveness. This suggests the model is quite significant in explaining the variances. Here the adjusted R 2 is In this case the difference for the final model is ( ) or 1.9%. This shrinkage means that if the model were derived from the population rather than a sample it would account for approximately 1.9% less variance in the outcome (table 5.17). Standard error of estimate is about 0.36 units (table 5.17). 149

24 The F value for the Change Statistics shows the significance level associated with adding the variable. The significance level for F value change statistics is The value for Durbin-Watson test is A value greater than 2 indicates a negative correlation between adjacent residuals, whereas a value below 2 indicates a positive correlation. As a conservative rule of thumb, Field (2000) suggests that the value less than 1 and greater than 3 are definitely cause for concern. The closer to 2 the Durbin-Watson value is, the better Analysis of Variance Table 5.18 depicts the analysis of variance (ANOVA). The table shows the value of the sum of squares, degree of freedom, mean square value, F value and its associated significance value. The dependent variable is effectiveness. Table 5.18: Analysis of Variance (ANOVA) a Model Sum of Squares df Mean Square F Sig. 1 Regression b Residual Total a. Dependent Variable: EFF b. Predictors: (Constant), TGC, BC, TC, IC, AC, LC If the improvement due to fitting the regression model is much greater than the inaccuracy within the model, then the value of F will be greater than 1 and SPSS calculates the exact probability of obtaining the value of F by chance. For the initial model the F-ratio is (table 5.18), which is very unlikely to have happened by chance (p < 0.000). This result shows that the final model significantly improves our ability to predict the outcome variable. The ANOVA table above (table 5.18) tests the overall significance of the model (i.e., of the regression equation), the significance of the F value is below 0.000, so the model is significant Coefficients Table 5.19 depicts the coefficients of multiple regression. It shows in detail the beta (standardized and unstandarized) value of various independent variables and its associated 150

25 significance value. The table also shows the collinearity statistics with two parameters, namely the tolerance value and the VIF value. Model Unstandardize d Coefficients B Std. Error Table 5.19: Coefficients of Multiple Regression Standardize d Coefficients Beta Coefficients a t Sig. 95.0% Confidence Interval for B Lower Bound Upper Bound Zeroorder Correlations Partial Part Toler ance Collinearity Statistics 1 (Constant ) LC IC BC TC AC TGC a. Dependent Variable: EFF VIF For the data, all the predictors have positive values indicating a positive relationship (table 5.19). The value also tells us to what degree each predictor affects the outcome if the effects of all other predictors are held constant. If the t-test associated with a value is significant (i.e., p<0.001) then that predictor is making a significant contribution to the model. From the table 5.19, it is evident that all the predictors are making a significant contribution to the model. The contribution of the predictor will be greater if the value of significance is smaller and the value of t is larger. For this model, technical competencies and interpersonal competencies are significant predictors of effectiveness. The following points discuss the interpretation of the table The coefficients and the constant are used to create the prediction (regression) equation, predicted effectiveness = 0.198* leadership competencies * interpersonal competencies * business competencies *technical competencies *analytical competencies *technological competencies. 151

26 All VIF values were in the range of 1.05 to 1.35, well below the cutoff value of 10 suggested by Neter et al. (1989). Hence, multicollinearity is not a threat to the substantive conclusions of this study. 5.3 Regression Output-RCL Fig. 5.2 displays the regression output in the form of the properties of the causal paths, including standardized path coefficients, path significances, and variance explained (R 2 ) by each path in the hypothesized model. R 2 was used to assess the model s overall predictive fit. Leadership competencies.198*.238* ns Performance Interpersonal competencies.264*.367*.388* improvement (R 2 =65.8%) Business competencies.928* Technical competencies.393* Analytical competencies.243*.320*.159*.112 ns Effectiveness (R 2 =65.2%) Technological competencies.293* * p <.01, ns = not significant. Figure 5.2: Regression Output (n = 120). 152

27 5.4 Hypothesis Testing-RCL The hypothesized relationships were tested using the multiple regression analysis of SPSS. The average scores of the items representing each construct were used in the data analysis. In hypotheses H1a, H2a, H3a, H4a, H5a, and H6a, the impact of six competency groups TGC, LC, BC, TC, AC, and IC on performance improvement was investigated. As shown in table 5.20, technical competencies (β= 0.388, t-value= 6.059, p<0.001) exhibited the strongest direct effect on performance improvement. Technological competencies (β= 0.320, t-value= 5.507, p<0.001) and interpersonal competencies (β = 0.264, t-value= 4.666, p<0.001) had a strong positive influence on performance improvement. Also, leadership competencies (β= 0.238, t- value= 3.759, p<0.001) and analytical competencies (β= 0.243, t-value= 4.118, p<0.001) had a significant positive effect on the performance improvement. Therefore, hypotheses H1a, H2a, H4a, H5a, and H6a were supported. However, business competencies (β= 0.023, t-value= 0.404, p=0.687) had no significant influence on performance improvement at the 0.05 significance level. Thus, hypothesis H3a was rejected. The proposed model explained a significant percentage of variance in performance improvement (R 2 = 65.8%). Table 5.20: Hypothesis Testing Results for Performance Improvement Hypothesis Competencies β t-value p Description H1a Leadership Competencies (LC) Accepted H2a Interpersonal Competencies (IC) Accepted H3a Business Competencies (BC) Rejected H4a Technical Competencies (TC) Accepted H5a Analytical Competencies (AC) Accepted H6a Technological Competencies (TGC) Accepted Hypotheses H1b, H2b, H3b, H4b, H5b, and H6b examine the paths from TGC, LC, BC, TC, AC, and IC to effectiveness. Table 5.21 shows testing results for these hypotheses. About 65% of the variance in effectiveness accounted for by TGC, LC, BC, TC, AC, and IC, thus providing a considerable degree of confidence in interpreting the results. 153

28 All the competencies had a significant positive effect on effectiveness, except H3b. Therefore, hypotheses H1b, H2b, H4b, H5b, and H6b were supported. However, since business competencies (β= 0.112, t-value= 1.928, p=0.056) was found to be non-significant for effectiveness, hypotheses H3b was rejected. Table 5.21: Hypothesis Testing Results for Effectiveness Hypothesis Competencies β t-value p Description H1b Leadership Competencies (LC) Accepted H2b Interpersonal Competencies (IC) Accepted H3b Business Competencies (BC) Rejected H4b Technical Competencies (TC) Accepted H5b Analytical Competencies (AC) Accepted H6b Technological Competencies (TGC) Accepted Effectiveness (β= 0.928, t-value= , p<0.001) had a significant positive influence on performance improvement (table 5.22). The significance relationship at p < level provides support for the hypothesis that the perceived effectiveness will be positively related to the perceived performance improvement. Hence hypothesis H7 was supported. Table 5.22: Hypothesis Testing Results for H7 Hypothesis Variable β t-value p Description H7 Effectiveness Accepted When explaining the model, it is necessary to compare standardized direct, indirect, and total effects of the model. As summarized in table 5.23, technical competencies exhibited the strongest total effect on performance improvement. The impact of each competency on performance improvement is different; few competencies like technical competencies and interpersonal competencies have greater impact on the performance improvement, whereas leadership competencies and analytical competencies have moderate to low impact on performance improvement (table 5.23). 154

29 Table 5.23: Summary of Direct, Indirect and Total Effect on Performance Improvement Competencies Direct effect Indirect effect Total effect Leadership Competencies (LC) * Interpersonal Competencies (IC) * Business Competencies (BC) * Technical Competencies (TC) * Analytical Competencies (AC) * Technological Competencies (TGC) * Charts Normally and independently distributed residuals indicated independence of error terms. Linearity was assessed based on residual plots from the regression analyses. The following sub-sections depict various plots of the multiple regressions. (a) Histogram The zresid histogram (figure 5.3) provides a visual way of assessing if the assumption of normally distributed residual error is met. Regression is robust in the face of some deviation from this assumption. The following plot is about the histogram of the regression standardized residual. Figure 5.3: Histogram 155

30 (b) Normal P-P Plot of Regression Standardized Residual The normal probability plot (zresid normal p-p plot) is another test of normally distributed residual error. Under perfect normality, the plot will be a 45-degree line. In this case (figure 5.4), it is close. Figure 5.4: Normal p-p Plot The following chart (figure 5.5) is a plot of ZRESID (Y-axis) against ZPRED (X- axis), this plot is useful to determine whether the assumption of random errors and homoscedasticity have been met [Field (2000)]. Ideally the values should fall between +2 and 2.Most of the values in this case are within the range of +2 and 2. Figure 5.5: Plot of ZRESID against ZPRED 156

31 The following chart (figure 5.6) is a plot of SRESID (Y-axis) against ZPRED (X-axis) and helpful in finding out any heteroscedasticity [Field (2000)]. Ideally the values should fall between +2 and 2. Most of the values in this case are within the range of +2 and 2. Figure 5.6: Plot between SRESID and ZPRED The following chart (figure 5.7) is a plot of SDRSEID (Y-axis) and ZPRED (X-axis). This plot should show that 95% of the residuals fall between -2 and +2, and only 1 in 1000 should fall outside plus or minus 3. The case below comes close to meeting this test. This plot also reveals any nonconstant variance. Ideally, the points should plot in a constant horizontal band. Most of the values in this case are within the range of +2 and 2. Figure 5.7 Plot between SDRSEID and ZPRED 157

32 The assumption of multivariate normal distribution must be satisfied. Therefore, it should be tested by looking at the actual departure from normality of the measured items [Norušis (1993)]. It is assumed that if all the individual items appear to be normally distributed, the overall sample distribution is multivariate normal [Noronha (1999)]. For checking the extent of the actual departure from normality of each measured variable, normal P-P plots have been drawn and found to be normal (figure 5.8 to figure 5.15). A more reliable approach to test for normality is the normal probability plot [Hair et al. (2006)]. Besides using the normal probability plot, statistical tests can also be used to assess normality. A rule of thumb to assess normality is based on the skewness values. Skewness values should be below 2.0 [Tabachnick and Fidell (2007)]. In this study, statistical test based on the skewness values was used to assess normality and this is shown in table Figure 5.8: Normal p-p Plot for Leadership Competencies 158

33 Figure 5.9: Normal p-p Plot for Interpersonal Competencies Figure 5.10: Normal p-p Plot for Business Competencies 159

34 Figure 5.11: Normal p-p Plot for Technical Competencies Figure 5.12: Normal p-p Plot for Analytical Competencies 160

35 Figure 5.13: Normal p-p Plot for Technological Competencies Figure 5.14: Normal p-p plot for Effectiveness 161

Chapter Six- Selecting the Best Innovation Model by Using Multiple Regression

Chapter Six- Selecting the Best Innovation Model by Using Multiple Regression Chapter Six- Selecting the Best Innovation Model by Using Multiple Regression 6.1 Introduction In the previous chapter, the detailed results of FA were presented and discussed. As a result, fourteen factors

More information

5 CHAPTER: DATA COLLECTION AND ANALYSIS

5 CHAPTER: DATA COLLECTION AND ANALYSIS 5 CHAPTER: DATA COLLECTION AND ANALYSIS 5.1 INTRODUCTION This chapter will have a discussion on the data collection for this study and detail analysis of the collected data from the sample out of target

More information

Application of Leadership and Personal Competencies for Augmented Managerial Performance: Empirical Evidence from Indian Manufacturing Units

Application of Leadership and Personal Competencies for Augmented Managerial Performance: Empirical Evidence from Indian Manufacturing Units Application of Leadership and Personal Competencies for Augmented Managerial Performance: Empirical Evidence from Indian Manufacturing Units Sambedna Jena * and Chandan Kumar Sahoo ** Numerous studies

More information

Nguyen Thi Duc Loan Ba Ria - Vung Tau University (BVU)

Nguyen Thi Duc Loan Ba Ria - Vung Tau University (BVU) RESEARCH FACTORS AFFECTING THE ORGANIZATION OF COST MANAGEMENT ACCOUNTING: A CASE OF THE ENTERPRISES OF MINING, PROCESSING AND TRADING CONSTRUCTIONS IN THE SOUTHERN REGION Nguyen Thi Duc Loan Ba Ria -

More information

CHAPTER 4 RESEARCH FINDINGS. This chapter outlines the results of the data analysis conducted. Research

CHAPTER 4 RESEARCH FINDINGS. This chapter outlines the results of the data analysis conducted. Research CHAPTER 4 RESEARCH FINDINGS This chapter outlines the results of the data analysis conducted. Research findings are organized into four parts. The first part provides a summary of respondents demographic

More information

Author please check for any updations

Author please check for any updations The Relationship Between Service Quality and Customer Satisfaction: An Empirical Study of the Indian Banking Industry Sunayna Khurana* In today s intense competitive business world, the customer is educated

More information

Segmentation, Targeting and Positioning in the Diaper Market

Segmentation, Targeting and Positioning in the Diaper Market Segmentation, Targeting and Positioning in the Diaper Market mothers of infants were surveyed. Each was given a randomly selected brand of diaper (,, ou Huggies) and asked to rate the diaper on 9 attributes

More information

ASSESSMENT APPROACH TO

ASSESSMENT APPROACH TO DECISION-MAKING COMPETENCES: ASSESSMENT APPROACH TO A NEW MODEL IV Doctoral Conference on Technology Assessment 26 June 2014 Maria João Maia Supervisors: Prof. António Brandão Moniz Prof. Michel Decker

More information

How to Get More Value from Your Survey Data

How to Get More Value from Your Survey Data Technical report How to Get More Value from Your Survey Data Discover four advanced analysis techniques that make survey research more effective Table of contents Introduction..............................................................3

More information

CONTRIBUTORY AND INFLUENCING FACTORS ON THE LABOUR WELFARE PRACTICES IN SELECT COMPANIES IN TIRUNELVELI DISTRICT AN ANALYSIS

CONTRIBUTORY AND INFLUENCING FACTORS ON THE LABOUR WELFARE PRACTICES IN SELECT COMPANIES IN TIRUNELVELI DISTRICT AN ANALYSIS CONTRIBUTORY AND INFLUENCING FACTORS ON THE LABOUR WELFARE PRACTICES IN SELECT COMPANIES IN TIRUNELVELI DISTRICT AN ANALYSIS DR.J.TAMILSELVI Assistant Professor, Department of Business Administration Annamalai

More information

Studying the Employee Satisfaction Using Factor Analysis

Studying the Employee Satisfaction Using Factor Analysis CASES IN MANAGEMENT 259 Studying the Employee Satisfaction Using Factor Analysis Situation Mr LN seems to be excited as he is going to learn a new technique in the statistical methods class today. His

More information

SPSS Guide Page 1 of 13

SPSS Guide Page 1 of 13 SPSS Guide Page 1 of 13 A Guide to SPSS for Public Affairs Students This is intended as a handy how-to guide for most of what you might want to do in SPSS. First, here is what a typical data set might

More information

REASONS BEHIND CONSUMERS SWITCHING BEHAVIOR TOWARDS MOBILE NETWORK OPERATORS: A STUDY CONDUCTED IN WESTERN PART OF RURAL WEST BENGAL

REASONS BEHIND CONSUMERS SWITCHING BEHAVIOR TOWARDS MOBILE NETWORK OPERATORS: A STUDY CONDUCTED IN WESTERN PART OF RURAL WEST BENGAL REASONS BEHIND CONSUMERS SWITCHING BEHAVIOR TOWARDS MOBILE NETWORK OPERATORS: A STUDY CONDUCTED IN WESTERN PART OF RURAL WEST BENGAL Debarun Chakraborty, Assistant Professor, Department of Management &

More information

STUDY REGARDING THE IMPACT OF THE AUDIT COMMITTEE CHARACTERISTICS ON COMPANY PERFORMANCE

STUDY REGARDING THE IMPACT OF THE AUDIT COMMITTEE CHARACTERISTICS ON COMPANY PERFORMANCE STUDY REGARDING THE IMPACT OF THE AUDIT COMMITTEE CHARACTERISTICS ON COMPANY PERFORMANCE ANGHEL Ioana Valahia University of Târgoviște, Romania MAN Mariana University of Petroșani, Romania Abstract: Regardless

More information

CHAPTER 8 T Tests. A number of t tests are available, including: The One-Sample T Test The Paired-Samples Test The Independent-Samples T Test

CHAPTER 8 T Tests. A number of t tests are available, including: The One-Sample T Test The Paired-Samples Test The Independent-Samples T Test CHAPTER 8 T Tests A number of t tests are available, including: The One-Sample T Test The Paired-Samples Test The Independent-Samples T Test 8.1. One-Sample T Test The One-Sample T Test procedure: Tests

More information

CHAPTER 5 DATA ANALYSIS AND RESULTS

CHAPTER 5 DATA ANALYSIS AND RESULTS 5.1 INTRODUCTION CHAPTER 5 DATA ANALYSIS AND RESULTS The purpose of this chapter is to present and discuss the results of data analysis. The study was conducted on 518 information technology professionals

More information

ANALYSING QUANTITATIVE DATA

ANALYSING QUANTITATIVE DATA 9 ANALYSING QUANTITATIVE DATA Although, of course, there are other software packages that can be used for quantitative data analysis, including Microsoft Excel, SPSS is perhaps the one most commonly subscribed

More information

AIS Contribution in Navigation Operation- Using AIS User Satisfaction Model

AIS Contribution in Navigation Operation- Using AIS User Satisfaction Model International Journal on Marine Navigation and Safety of Sea Transportation Volume 1 Number 3 September 2007 AIS Contribution in Navigation Operation- Using AIS User Satisfaction Model A. Harati-Mokhtari

More information

Model Building Process Part 2: Factor Assumptions

Model Building Process Part 2: Factor Assumptions Model Building Process Part 2: Factor Assumptions Authored by: Sarah Burke, PhD 17 July 2018 Revised 6 September 2018 The goal of the STAT COE is to assist in developing rigorous, defensible test strategies

More information

Chapter 3. Basic Statistical Concepts: II. Data Preparation and Screening. Overview. Data preparation. Data screening. Score reliability and validity

Chapter 3. Basic Statistical Concepts: II. Data Preparation and Screening. Overview. Data preparation. Data screening. Score reliability and validity Chapter 3 Basic Statistical Concepts: II. Data Preparation and Screening To repeat what others have said, requires education; to challenge it, requires brains. Overview Mary Pettibone Poole Data preparation

More information

Using Factor Analysis to Generate Clusters of Agile Practices

Using Factor Analysis to Generate Clusters of Agile Practices Using Factor Analysis to Generate Clusters of Agile Practices (A Guide for Agile Process Improvement) Noura Abbas University of Southampton School of Electronics and Computer Science Southampton, UK, SO17

More information

Chapter 3. Database and Research Methodology

Chapter 3. Database and Research Methodology Chapter 3 Database and Research Methodology In research, the research plan needs to be cautiously designed to yield results that are as objective as realistic. It is the main part of a grant application

More information

Multiple Regression. Dr. Tom Pierce Department of Psychology Radford University

Multiple Regression. Dr. Tom Pierce Department of Psychology Radford University Multiple Regression Dr. Tom Pierce Department of Psychology Radford University In the previous chapter we talked about regression as a technique for using a person s score on one variable to make a best

More information

ijcrb.webs.com INTERDISCIPLINARY JOURNAL OF CONTEMPORARY RESEARCH IN BUSINESS An Empirical study on talent retention strategy by BPO s in India

ijcrb.webs.com INTERDISCIPLINARY JOURNAL OF CONTEMPORARY RESEARCH IN BUSINESS An Empirical study on talent retention strategy by BPO s in India An Empirical study on talent retention strategy by BPO s in India Saravana Praveen Kumar. P MA, MBA, M Phil, EGMP (IIMB), Ph D Manager, Human Resources, Siemens Ltd Author is a Research Scholar pursuing

More information

Motivation as a Determinant of Stress and Its Effect on Employee Performance in Public Universities in Kenya

Motivation as a Determinant of Stress and Its Effect on Employee Performance in Public Universities in Kenya Motivation as a Determinant of Stress and Its Effect on Employee Performance in Public Universities in Kenya Rev. John Karihe 1, Prof. Namusonge 2, DR. Mike Iravo 3 1 JKUAT, Department of Entrepreneurship

More information

Chapter 5 RESULTS AND DISCUSSION

Chapter 5 RESULTS AND DISCUSSION Chapter 5 RESULTS AND DISCUSSION 5.0 Introduction This chapter outlines the results of the data analysis and discussion from the questionnaire survey. The detailed results are described in the following

More information

Comparative Assessment of Triveni Supermarket, Margin free Markets and Private..

Comparative Assessment of Triveni Supermarket, Margin free Markets and Private.. COMPARITIVE ASSESSMENT OF TRIVENI SUPERMARKET, MARGIN- FREE MARKETS AND PRIVATE SUPERMARKETS USING SELECTED RETAIL VARIABLES 7 C o n t e n t s 7.1 Measures 7.2 Validity and Reliability of Data 7.3 Retail

More information

FACTORS AFFECTING JOB STRESS AMONG IT PROFESSIONALS IN APPAREL INDUSTRY: A CASE STUDY IN SRI LANKA

FACTORS AFFECTING JOB STRESS AMONG IT PROFESSIONALS IN APPAREL INDUSTRY: A CASE STUDY IN SRI LANKA FACTORS AFFECTING JOB STRESS AMONG IT PROFESSIONALS IN APPAREL INDUSTRY: A CASE STUDY IN SRI LANKA W.N. Arsakularathna and S.S.N. Perera Research & Development Centre for Mathematical Modeling, Faculty

More information

IMPACT OF SELF HELP GROUP IN ECONOMIC DEVELOPMENT OF RURAL WOMEN WITH REFERENCE TO DURG DISTRICT OF CHHATTISGARH

IMPACT OF SELF HELP GROUP IN ECONOMIC DEVELOPMENT OF RURAL WOMEN WITH REFERENCE TO DURG DISTRICT OF CHHATTISGARH IMPACT: International Journal of Research in Business Management (IMPACT: IJRBM) ISSN(E): 2321-886X; ISSN(P): 2347-4572 Vol. 3, Issue 9, Sep 2015, 81-90 Impact Journals IMPACT OF SELF HELP GROUP IN ECONOMIC

More information

CHAPTER 5 ANALYSIS OF WORK LIFE BALANCE WITH RESPECT TO OTHER VARIABLES

CHAPTER 5 ANALYSIS OF WORK LIFE BALANCE WITH RESPECT TO OTHER VARIABLES CHAPTER 5 ANALYSIS OF WORK LIFE BALANCE WITH RESPECT TO OTHER VARIABLES 201 CHAPTER- 5 ANALYSIS OF WORK LIFE BALANCE WITH RESPECT TO OTHER VARIABLES The second level of analysis was carried out to examine

More information

The impacts of intellectual capital of China s public pharmaceutical company on company s performance

The impacts of intellectual capital of China s public pharmaceutical company on company s performance Available online www.jocpr.com Journal of Chemical and Pharmaceutical Research, 2014, 6(4):999-1004 Research Article ISSN : 0975-7384 CODEN(USA) : JCPRC5 The impacts of intellectual capital of China s

More information

CHAPTER 4 EMPIRICAL RESULTS

CHAPTER 4 EMPIRICAL RESULTS CHAPTER 4 EMPIRICAL RESULTS 4.1 Descriptive Statistics of Data There 249 survey questionnaires were recovered, of which 245 were valid, four invalid. The sample background information includes sex, age,

More information

USING EXPLORATORY FACTOR ANALYSIS IN INFORMATION SYSTEM (IS) RESEARCH

USING EXPLORATORY FACTOR ANALYSIS IN INFORMATION SYSTEM (IS) RESEARCH USING EXPLORATORY FACTOR ANALYSIS IN INFORMATION SYSTEM (IS) RESEARCH Maria Argyropoulou, Brunel Business School Brunel University UK Maria.Argyropoulou@brunel.ac.uk Dimitrios N.Koufopoulos Brunel Business

More information

Published by European Centre for Research Training and Development UK (www.eajournals.org)

Published by European Centre for Research Training and Development UK (www.eajournals.org) MODERATING EFFECT OF INFORMATION TECHNOLOGY UTILIZATION ON THE RELATIONSHIP BETWEEN COMMUNICATION AND CUSTOMER SATISFACTION Charles Bosire Nyameino Dr.Ronald Bonuke Prof.Thomas Kimeli Cheruiyot Moi University

More information

Supplier Perceptions of Dependencies in Supplier Manufacturer Relationship

Supplier Perceptions of Dependencies in Supplier Manufacturer Relationship International Conference on Information, Business and Education Technology (ICIBIT 2013) Supplier Perceptions of Dependencies in Supplier Manufacturer Relationship Mohamad Ghozali Hassan1 Mohd Rizal Razalli2

More information

CHAPTER 4 RESEARCH OBJECTIVES AND METHODOLOGY

CHAPTER 4 RESEARCH OBJECTIVES AND METHODOLOGY CHAPTER 4 RESEARCH OBJECTIVES AND METHODOLOGY 4.1 PROBLEM STATEMENT Various studies in the area of service quality management highlight the fact that as customer behavior is dynamic in nature, it is hard

More information

INTERNATIONAL JOURNAL OF MANAGEMENT (IJM)

INTERNATIONAL JOURNAL OF MANAGEMENT (IJM) INTERNATIONAL JOURNAL OF MANAGEMENT (IJM) International Journal of Management (IJM), ISSN 0976 6502(Print), ISSN 0976 ISSN 0976 6502(Print) ISSN 0976 6510(Online) Volume 3, Issue 3, September- December

More information

Content Appendicises

Content Appendicises Abstract In this paper a communication survey within a company is evaluated using exploratory as well as confirmatory factor analysis. Data sets from two surveys, one performed in 2008 and one in 2009,

More information

CHAPTER IV DATA ANALYSIS

CHAPTER IV DATA ANALYSIS CHAPTER IV DATA ANALYSIS 4.1 Descriptive statistical analysis 4.1.1 The basic characteristics of the sample 145 effective questionnaires are recycled. The sample distribution of each is rational. The specific

More information

The Effect of Transformational Leadership on Employees Self-efficacy

The Effect of Transformational Leadership on Employees Self-efficacy International Research Journal of Applied and Basic Sciences 2015 Available online at www.irjabs.com ISSN 2251-838X / Vol, 9 (8): 1328-1339 Science Explorer Publications The Effect of Transformational

More information

CHAPTER SIX DATA ANALYSIS AND INTERPRETATION

CHAPTER SIX DATA ANALYSIS AND INTERPRETATION CHAPTER SIX DATA ANALYSIS AND INTERPRETATION In this chapter various factors influencing preference of using Delhi Metro were checked and the impact of demographic characteristics (age, gender) of consumers

More information

The Effect of Transformational Leadership on Employees Self-efficacy

The Effect of Transformational Leadership on Employees Self-efficacy International Research Journal of Applied and Basic Sciences 2015 Available online at www.irjabs.com ISSN 2251-838X / Vol, 9 (8): 1328-1339 Science Explorer Publications The Effect of Transformational

More information

demographic of respondent include gender, age group, position and level of education.

demographic of respondent include gender, age group, position and level of education. CHAPTER 4 - RESEARCH RESULTS 4.0 Chapter Overview This chapter presents the results of the research and comprises few sections such as and data analysis technique, analysis of measures, testing of hypotheses,

More information

CHAPTER 4 DATA ANALYSIS, PRESENTATION AND INTERPRETATION

CHAPTER 4 DATA ANALYSIS, PRESENTATION AND INTERPRETATION CHAPTER 4 DATA ANALYSIS, PRESENTATION AND INTERPRETATION 4.1 OVERVIEW The responses given by 385 faculty members and 30 directors working in NBA accredited institution business schools in Northern India

More information

The Effect of Trust and Information Sharing on Relationship Commitment in Supply Chain Management

The Effect of Trust and Information Sharing on Relationship Commitment in Supply Chain Management Available online at www.sciencedirect.com ScienceDirect Procedia - Social and Behavioral Scien ce s 130 ( 2014 ) 266 272 INCOMaR 2013 The Effect of Trust and Information Sharing on Relationship Commitment

More information

GREEN PRODUCTS PURCHASE BEHAVIOUR- AN IMPACT STUDY

GREEN PRODUCTS PURCHASE BEHAVIOUR- AN IMPACT STUDY ORIGINAL RESEARCH PAPER Commerce GREEN PRODUCTS PURCHASE BEHAVIOUR- AN IMPACT STUDY KEY WORDS: Green Product, Green Awareness, Environment concern and Purchase Decision Sasikala.N Dr. R. Parameswaran*

More information

PRELIMINARY DATA ANALYSIS

PRELIMINARY DATA ANALYSIS PRELIMINARY DATA ANALYSIS CHAPTER 6 6.1 Introduction This chapter presents the results of the psychometric evaluation of the constructs in this study. First of all, the reliability and the validity of

More information

= = Name: Lab Session: CID Number: The database can be found on our class website: Donald s used car data

= = Name: Lab Session: CID Number: The database can be found on our class website: Donald s used car data Intro to Statistics for the Social Sciences Fall, 2017, Dr. Suzanne Delaney Extra Credit Assignment Instructions: You have been hired as a statistical consultant by Donald who is a used car dealer to help

More information

CHAPTER - 4 RESEARCH METHODOLOGY

CHAPTER - 4 RESEARCH METHODOLOGY CHAPTER - 4 RESEARCH METHODOLOGY 4.1 Introduction 4.1.1 Consumer attitude 4.1.2 Purchase decision 4.1.3 Decision machining process 4.2 Statement of Research Problem 4.3 Rationale of the Study 4.4 Objectives

More information

Chapter Five- Driving Innovation Factors by Using Factor Analysis

Chapter Five- Driving Innovation Factors by Using Factor Analysis Chapter Five- Driving Innovation Factors by Using Factor Analysis 5.1 Introduction In the previous chapter, the results of preliminary stages of analysis including normality, reliability, and demographic

More information

KNOWLEDGE MANAGEMENT INITIATIVES IN EDUCATION

KNOWLEDGE MANAGEMENT INITIATIVES IN EDUCATION KNOWLEDGE MANAGEMENT INITIATIVES IN EDUCATION Principal, College of Computer Sciences, Wakad Pune 57 (MS) INDIA Information practices and learning strategies known as Knowledge management are gaining importance

More information

CHAPTER V RESULT AND ANALYSIS

CHAPTER V RESULT AND ANALYSIS 39 CHAPTER V RESULT AND ANALYSIS In this chapter author will explain the research findings of the measuring customer loyalty through the role of customer satisfaction and the role of loyalty program quality

More information

PRINCIPAL COMPONENT ANALYSIS IN TOURISM MARKETING

PRINCIPAL COMPONENT ANALYSIS IN TOURISM MARKETING PRINCIPAL COMPONENT ANALYSIS IN TOURISM MARKETING Abstract. The analysis methods of the interdependences are meant to give a meaning to a set of variables or to group variables in a certain way. This work

More information

SUCCESSFUL ENTREPRENEUR: A DISCRIMINANT ANALYSIS

SUCCESSFUL ENTREPRENEUR: A DISCRIMINANT ANALYSIS SUCCESSFUL ENTREPRENEUR: A DISCRIMINANT ANALYSIS M. B. M. Ismail Department of Management, Faculty of Management and Commerce, South Eastern University of Sri Lanka, Oluvil mbmismail@seu.ac.lk ABSTRACT:

More information

Using SPSS for Linear Regression

Using SPSS for Linear Regression Using SPSS for Linear Regression This tutorial will show you how to use SPSS version 12.0 to perform linear regression. You will use SPSS to determine the linear regression equation. This tutorial assumes

More information

CHAPTER 4 RESEARCH METHODOLOGY

CHAPTER 4 RESEARCH METHODOLOGY 91 CHAPTER 4 RESEARCH METHODOLOGY INTRODUCTION This chapter presents how the study had been designed and orchestrated and provides a clear and complete description of the specific steps that were taken

More information

Correlation between Carbon Steel Corrosion and Atmospheric Factors in Taiwan

Correlation between Carbon Steel Corrosion and Atmospheric Factors in Taiwan CORROSION SCIENCE AND TECHNOLOGY, Vol.17, No.2(2018), pp.37~44 pissn: 1598-6462 / eissn: 2288-6524 [Research Paper] DOI: https://doi.org/10.14773/cst.2018.17.2.37 Correlation between Carbon Steel Corrosion

More information

5.1 Marketing details of SHGs

5.1 Marketing details of SHGs Marketing Problems of Self-Help Groups in Kerala Chapter 5 MARKETING PROBLEMS OF SELF-HELP GROUPS IN KERALA Contents 5.1 Marketing details of SHGs 5.2 Marketing problems of SHGs 5.3 Evaluation of agencies

More information

Hierarchical Linear Modeling: A Primer 1 (Measures Within People) R. C. Gardner Department of Psychology

Hierarchical Linear Modeling: A Primer 1 (Measures Within People) R. C. Gardner Department of Psychology Hierarchical Linear Modeling: A Primer 1 (Measures Within People) R. C. Gardner Department of Psychology As noted previously, Hierarchical Linear Modeling (HLM) can be considered a particular instance

More information

Employability Skills: A Study on Perception on Engineering Employees in Chennai District

Employability Skills: A Study on Perception on Engineering Employees in Chennai District Volume 118 No. 20 2018, 879-888 ISSN: 1314-3395 (on-line version) url: http://www.ijpam.eu ijpam.eu Employability Skills: A Study on Perception on Engineering Employees in Chennai District P. Carolin Golda

More information

CSR organisational taxonomy and job characteristics on performance: SME case studies

CSR organisational taxonomy and job characteristics on performance: SME case studies See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/316869842 CSR organisational taxonomy and job characteristics on performance: SME case studies

More information

Job and Work Attitude Determinants: An Application of Multivariate Analysis

Job and Work Attitude Determinants: An Application of Multivariate Analysis Job and Work Attitude Determinants: An Application of Multivariate Analysis Peter Josephat (Corresponding author) Dept. of Statistics, University of Dodoma P. O. Box 338, Dodoma, Tanzania E-mail: mtakwimu@yahoo.com

More information

12 Exploratory Factor Analysis (EFA): Brief Overview with Illustrations

12 Exploratory Factor Analysis (EFA): Brief Overview with Illustrations 1 12 Exploratory Factor Analysis (EFA): Brief Overview with Illustrations Topics 1. Logic of EFA 2. Formative vs Reflective Models, and Principal Component Analysis (PCA) vs Exploratory Factor Analysis

More information

Regression analysis of profit per 1 kg milk produced in selected dairy cattle farms

Regression analysis of profit per 1 kg milk produced in selected dairy cattle farms ISSN: 2319-7706 Volume 4 Number 2 (2015) pp. 713-719 http://www.ijcmas.com Original Research Article Regression analysis of profit per 1 kg milk produced in selected dairy cattle farms K. Stankov¹*, St.

More information

Chapter 5 DATA ANALYSIS & INTERPRETATION

Chapter 5 DATA ANALYSIS & INTERPRETATION Chapter 5 DATA ANALYSIS & INTERPRETATION 205 CHAPTER 5 : DATA ANALYSIS AND INTERPRETATION 5.1: ANALYSIS OF OCCUPATION WISE COMPOSITION OF SUBSCRIBERS. 5.2: ANALYSIS OF GENDER WISE COMPOSITION OF SUBSCRIBERS.

More information

= = Intro to Statistics for the Social Sciences. Name: Lab Session: Spring, 2015, Dr. Suzanne Delaney

= = Intro to Statistics for the Social Sciences. Name: Lab Session: Spring, 2015, Dr. Suzanne Delaney Name: Intro to Statistics for the Social Sciences Lab Session: Spring, 2015, Dr. Suzanne Delaney CID Number: _ Homework #22 You have been hired as a statistical consultant by Donald who is a used car dealer

More information

Impact of ERP Implementation on Supply Chain Performance of Transport and Logistics Companies in Sri Lanka

Impact of ERP Implementation on Supply Chain Performance of Transport and Logistics Companies in Sri Lanka ISSN: 2513-2520 R4TLI Conference Proceedings 2017 Impact of ERP Implementation on Supply Chain Performance of Transport and Logistics Companies in Sri Lanka 1. Introduction Dilini Yapa University of Moratuwa,

More information

THE STUDY OF THE RELATION BETWEEN ORGANIZATIONAL ATMOSPHERE AND ORGANIZATIONAL COMMITMENT AMONG FACULTY MEMBERS IN COLLEGES IN DEHRADUN

THE STUDY OF THE RELATION BETWEEN ORGANIZATIONAL ATMOSPHERE AND ORGANIZATIONAL COMMITMENT AMONG FACULTY MEMBERS IN COLLEGES IN DEHRADUN THE STUDY OF THE RELATION BETWEEN ORGANIZATIONAL ATMOSPHERE AND ORGANIZATIONAL COMMITMENT AMONG FACULTY MEMBERS IN COLLEGES IN DEHRADUN Dr. Amar Kumar Mishra Assistant Professor, IMS Unison University,

More information

Opening SPSS 6/18/2013. Lesson: Quantitative Data Analysis part -I. The Four Windows: Data Editor. The Four Windows: Output Viewer

Opening SPSS 6/18/2013. Lesson: Quantitative Data Analysis part -I. The Four Windows: Data Editor. The Four Windows: Output Viewer Lesson: Quantitative Data Analysis part -I Research Methodology - COMC/CMOE/ COMT 41543 The Four Windows: Data Editor Data Editor Spreadsheet-like system for defining, entering, editing, and displaying

More information

MdAIR Spring Institute. April 28, 2006

MdAIR Spring Institute. April 28, 2006 Introduction to Factor Analysis MdAIR Spring Institute Denise Nadasen April 28, 2006 Today s Objective To understand the general application To learn some of the language To review various decision points

More information

STATISTICS PART Instructor: Dr. Samir Safi Name:

STATISTICS PART Instructor: Dr. Samir Safi Name: STATISTICS PART Instructor: Dr. Samir Safi Name: ID Number: Question #1: (20 Points) For each of the situations described below, state the sample(s) type the statistical technique that you believe is the

More information

AN ANALYSIS OF CUSTOMERS SATISFACTION AND FACTORS INFLUENCING THE INTERNET BANKING

AN ANALYSIS OF CUSTOMERS SATISFACTION AND FACTORS INFLUENCING THE INTERNET BANKING CHAPTER V AN ANALYSIS OF CUSTOMERS SATISFACTION AND FACTORS INFLUENCING THE INTERNET BANKING 5.1 INTRODUCTION Banking industry is also one of the predominant industries adopting technologies which are

More information

Reliability and Validity Testing of Research Instruments

Reliability and Validity Testing of Research Instruments Reliability and Validity Testing of Research Instruments Mr. Sajijul Islam Research Scholar, Vidyasagar University Abstract: The aims of this paper are to determine the validity and reliability of research

More information

Phd Program in Transportation. Transport Demand Modeling. Session 4

Phd Program in Transportation. Transport Demand Modeling. Session 4 Phd Program in Transportation Transport Demand Modeling João de Abreu e Silva Session 4 Factor Analysis Phd in Transportation / Transport Demand Modelling 1/38 Factor Analysis Definition and Purpose Exploratory

More information

AN EMPIRICAL STUDY ON ORGANIZATIONAL CITIZENSHIP BEHAVIOR IN PRIVATE SECTOR BANKS IN TAMILNADU

AN EMPIRICAL STUDY ON ORGANIZATIONAL CITIZENSHIP BEHAVIOR IN PRIVATE SECTOR BANKS IN TAMILNADU AN EMPIRICAL STUDY ON ORGANIZATIONAL CITIZENSHIP BEHAVIOR IN PRIVATE SECTOR BANKS IN TAMILNADU B.THIAGARAJAN MBA.,M.Phil., MLM.,M.Phil., M.Com., Associate Professor & Head, Department of Management Studies

More information

Data Analysis and Results

Data Analysis and Results Chapter 5 Data Analysis and Results 5.1 Introduction In this chapter, data analysis and results required to answer the research questions are presented. A total of 200 MSMEs of different firm sizes were

More information

FACTORS AFFECTING THE BUILDING OF ACCOUNTING WORK IN PUBLIC SECTORS IN DONG NAI PROVINCE

FACTORS AFFECTING THE BUILDING OF ACCOUNTING WORK IN PUBLIC SECTORS IN DONG NAI PROVINCE FACTORS AFFECTING THE BUILDING OF ACCOUNTING WORK IN PUBLIC SECTORS IN DONG NAI PROVINCE Lam Ngoc Nhan Lecturer of the Faculty of Finance - Accounting at Lac Hong University (LHU) ABSTRACT: Accounting

More information

IMPACT OF BILLBOARDS ADVERTISEMENTS ON CONSUMER S BELIEFS: A STUDY

IMPACT OF BILLBOARDS ADVERTISEMENTS ON CONSUMER S BELIEFS: A STUDY IMPACT OF BILLBOARDS ADVERTISEMENTS ON CONSUMER S BELIEFS: A STUDY Mr. Anil Kumar Asst. Prof. CBS Group of Institutions Fetehpuri, Jhajjar (Haryana) ABSTRACT The purpose of this study was to determine

More information

EFFECTIVENESS OF PERFORMANCE APPRAISAL: ITS MEASUREMENT IN PAKISTANI ORGANIZATIONS

EFFECTIVENESS OF PERFORMANCE APPRAISAL: ITS MEASUREMENT IN PAKISTANI ORGANIZATIONS 685 EFFECTIVENESS OF PERFORMANCE APPRAISAL: ITS MEASUREMENT IN PAKISTANI ORGANIZATIONS Muhammad Zahid Iqbal * Hafiz Muhammad Ishaq ** Arshad Zaheer *** INTRODUCTION Effectiveness of performance appraisal

More information

Business Strategies for Rural Women Entrepreneurs in India

Business Strategies for Rural Women Entrepreneurs in India Research Article Business Strategies for Rural Women Entrepreneurs in India V. Arunkumar*, C. Gnanaprakasam 1 1 HoD-Agni School of Business Excellence, Trichy Road, Morepatti, Vadamadurai, Dindigul, India.

More information

Global Journal of Engineering Science and Research Management

Global Journal of Engineering Science and Research Management AN INVESTIGATION INTO THE RELATIONSHIP BETWEEN FIRM SIZE AND PERFORMANCE AND THE USE OF ELECTRONIC COMMERCE (EC) BY THE FIRMS AT VUNG TAU CITY Lê Sĩ Trí *1 Dr, Ba RIA Vung Tau University. KEYWORDS: Firm

More information

MEASURING COMPETITIVENESS OF ISLAMIC BANKING INDUSTRY IN MALAYSIA FROM THE PERSPECTIVE OF KNOWLEDGE MANAGEMENT

MEASURING COMPETITIVENESS OF ISLAMIC BANKING INDUSTRY IN MALAYSIA FROM THE PERSPECTIVE OF KNOWLEDGE MANAGEMENT International Journal of Industrial Management (IJIM) ISSN (Print): 2289-9286; e-issn: xxxx; Volume xx, pp. xx-xx, June 2015 Universiti Malaysia Pahang, Malaysia MEASURING COMPETITIVENESS OF ISLAMIC BANKING

More information

The Effect of Managerial Competencies on Employee Engagement in Multinational IT Industries

The Effect of Managerial Competencies on Employee Engagement in Multinational IT Industries International Journal of Latest Trends in Engineering and Technology Vol.(7)Issue(3), pp. 028 034 DOI: http://dx.doi.org/10.21172/1.73.504 e ISSN:2278 621X The Effect of Managerial Competencies on Employee

More information

Tran Trung Tuan. National Economics University (NEU), Ha Noi, Viet Nam

Tran Trung Tuan. National Economics University (NEU), Ha Noi, Viet Nam Economics World, Nov.-Dec. 2017, Vol. 5, No. 6, 573-583 doi: 10.17265/2328-7144/2017.06.009 D DAVID PUBLISHING Application Responsibility Accounting to Sustainable Development in Vietnam Manufacturers:

More information

Assessing Social and Intellectual Competencies as Predictors of Managerial Performance: In Context to Manufacturing Units

Assessing Social and Intellectual Competencies as Predictors of Managerial Performance: In Context to Manufacturing Units Assessing Social and Intellectual Competencies as Predictors of Managerial Performance: In Context to Manufacturing Units Sambedna Jena and Chandan Kumar Sahoo Abstract Organizations worldwide are focused

More information

Evaluating the differences between Managerial and Executive level Personal Competencies -A critical analysis of select IT companies

Evaluating the differences between Managerial and Executive level Personal Competencies -A critical analysis of select IT companies BHAVAN S INTERNATIONAL JOURNAL OF BUSINESS Vol:4, 2 (2010) 71-76 ISSN 0974-0082 Evaluating the differences between Managerial and Executive level Personal Competencies -A critical analysis of select IT

More information

INVESTIGATION OF SOME FACTORS AFFECTING MANUFACTURING WORKERS PERFORMANCE IN INDUSTRIES IN ANAMBRA STATE OF NIGERIA

INVESTIGATION OF SOME FACTORS AFFECTING MANUFACTURING WORKERS PERFORMANCE IN INDUSTRIES IN ANAMBRA STATE OF NIGERIA INVESTIGATION OF SOME FACTORS AFFECTING MANUFACTURING WORKERS PERFORMANCE IN INDUSTRIES IN ANAMBRA STATE OF NIGERIA Nwosu M. C., Ikwu G. O. R. and Uzorh A.C Department of Industrial and Production Engineering,

More information

2nd INTERNATIONAL CONFERENCE ON BUILT ENVIRONMENT IN DEVELOPING COUNTRIES (ICBEDC 2008)

2nd INTERNATIONAL CONFERENCE ON BUILT ENVIRONMENT IN DEVELOPING COUNTRIES (ICBEDC 2008) USING FACTOR ANALYSIS TO ASSESS THE KEY FACTORS INFLUENCING THE SUCCESS COMPLETION OF A PUBLIC SCHOOL PROJECT IN MALAYSIA Siti Rashidah Mohd Nasir 1 and Muhd Zaimi Abd.Majid 2 Faculty of Civil Engineering,

More information

The impact of human resource competencies of front line employees on tourist arrivals of unclassified hotels in Western province, Sri Lanka

The impact of human resource competencies of front line employees on tourist arrivals of unclassified hotels in Western province, Sri Lanka Journal of Advanced Research in Social Sciences and Humanities Volume 2, Issue 1 (09-16) DOI: https://dx.doi.org/10.26500/jarssh-02-2017-0102 The impact of human resource competencies of front line employees

More information

Principal Component Analysis of Influence of Organizational Justice on Employee Engagement

Principal Component Analysis of Influence of Organizational Justice on Employee Engagement Principal Component Analysis of Influence of Organizational Justice on Employee Engagement Ravichandran. D Department of Business and Management Studies, Trincomalee Campus, Eastern University Sri Lanka.

More information

CHAPTER 5 DATA ANALYSIS

CHAPTER 5 DATA ANALYSIS 142 CHAPTER 5 DATA ANALYSIS The first and foremost procedure in the data analysis stage was to verify the quality of collected data for finalizing the tools required for further analysis. 5.1 ANALYZING

More information

STUDY BACKGROUND AND MOTIVATION

STUDY BACKGROUND AND MOTIVATION Relationships among Psychological Contract, Organizational Justice, and Organizational Commitment: Taking the Accommodation and Maintenance Institutions for the Disabled as Example Hsi-kong Chin Wang,

More information

Relationship of Leadership Styles and Employee Creativity: A Mediating Role of Creative Self-efficacy and Moderating Role of Organizational Climate

Relationship of Leadership Styles and Employee Creativity: A Mediating Role of Creative Self-efficacy and Moderating Role of Organizational Climate Pakistan Journal of Commerce and Social Sciences 2017, Vol. 11 (2), 698-719 Pak J Commer Soc Sci Relationship of Leadership Styles and Employee Creativity: A Mediating Role of Creative Self-efficacy and

More information

The Effects of Job Rotation Practices on Employee Development: An Empirical Study on Nurses in the Hospitals of Vellore District

The Effects of Job Rotation Practices on Employee Development: An Empirical Study on Nurses in the Hospitals of Vellore District The Effects of Job Rotation Practices on Employee Development: An Empirical Study on Nurses in the Hospitals of Vellore District Kokila Mohan Research Associate, VIT Business school, VIT University Email:

More information

Correlations. Regression. Page 1. Correlations SQUAREFO BEDROOMS BATHS ASKINGPR

Correlations. Regression. Page 1. Correlations SQUAREFO BEDROOMS BATHS ASKINGPR multreg.sav squarefo bedrooms baths askingpr 3632 4 2.5 49 2 4889 6 5.0 399 3 3000 5 3.5 395 4 3669 4 3.5 379 5 2800 4 3.0 359 6 3600 5 3.5 349 7 2800 5 2.5 320 8 2257 3 3.0 299 9 2000 3 3.0 295 0 2455

More information

CULTURAL INFLUENCES ON PRE-PAY MOBILE TELECOMMUNICATIONS SERVICES USERS

CULTURAL INFLUENCES ON PRE-PAY MOBILE TELECOMMUNICATIONS SERVICES USERS CULTURAL INFLUENCES ON PRE-PAY MOBILE TELECOMMUNICATIONS SERVICES USERS Lecturer PhD Georgeta-Madalina MEGHISAN University of Craiova, Romania E-mail: madalina_meghisan@yahoo.com Abstract: Purpose: The

More information

CHAPTER 4 EMPIRICAL ANALYSIS AND DISCUSSION

CHAPTER 4 EMPIRICAL ANALYSIS AND DISCUSSION 79 CHAPTER 4 EMPIRICAL ANALYSIS AND DISCUSSION 4.1 SAMPLE SELECTION AND DESCRIPTIVE STATISTICS The study begins by identifying industrial sectors heavily reliant on intellectual capital. The data covers

More information

A Study on Financial Performance Evaluation of High-Tech Experprises in Liaoning Province using Factor Analysis

A Study on Financial Performance Evaluation of High-Tech Experprises in Liaoning Province using Factor Analysis A Study on inancial Performance Evaluation of High-Tech Experprises in Liaoning Province using actor Analysis Hua Zhang,*, Qiang Liu, Chunmei Cheng, Li Gen School of Economics Liaoning University of Technology

More information

Problem Points Score USE YOUR TIME WISELY SHOW YOUR WORK TO RECEIVE PARTIAL CREDIT

Problem Points Score USE YOUR TIME WISELY SHOW YOUR WORK TO RECEIVE PARTIAL CREDIT STAT 512 EXAM I STAT 512 Name (7 pts) Problem Points Score 1 40 2 25 3 28 USE YOUR TIME WISELY SHOW YOUR WORK TO RECEIVE PARTIAL CREDIT WRITE LEGIBLY. ANYTHING UNREADABLE WILL NOT BE GRADED GOOD LUCK!!!!

More information