DATA ANALYSIS AND INTERPRETATION OF EMPLOYEES PERSPECTIVES ON HIGH ATTRITION

DATA ANALYSIS AND INTERPRETATION OF EMPLOYEES PERSPECTIVES ON HIGH ATTRITION Analysis is the key element of any research as it is the reliable way to test the hypotheses framed by the investigator. This chapter deals with the analysis of the primary data collected through the administration of the questionnaire. The collected data has been codified, tabulated and analysis has been conducted using the different statistical tools such as Reliability Analysis, Factor Analysis, Multiple Regression analysis, and Testing of the Hypotheses focusing on Analysis of Variance (One-way ANOVA), Chi-Square test, t-test, pie-charts, averages, percentages graphs and bar diagrams. The five major analyses conducted in the study focusing on the employee s perspective are listed as: 4.1 Reliability Analysis 4.2 Factor Analysis 4.3 Analysis of personal and other factors 4.4 Data Analysis based on Objectives 4.5 Multiple Regression Analysis The above five analyses are conducted and the results of the different statistical procedures are discussed below: 4.1 RELIABILITY ANALYSIS A pilot study has been conducted for a sample of 50 respondents and reliability analysis (scale- split) is done. Three measures of reliability are given. The scale consists of 40 items, which measures the attitude of the respondents on a Likert type five point scale. 50 respondents were selected for reliability analysis. Chapter-IV 133

Table: No.4.1 Analysis of Factor Variables Statistics for of items Part 1 59.9400 9.9476 20 Part 2 59.5200 11.9953 20 Scale 119.4600 20.1982 40 The scale items were divided into two parts (forms) each part containing 20 items selected randomly. The correlation between two forms was found to be 0.6919, indicating that the items between the two parts correlates well. Spearman-Brown and Guttman split-half reliabilities are used to find reliability coefficients of the scale by dividing the scale items into two halves in some random manner. Table: No.4.2 Reliability coefficients No. of Cases 50 No. of Items 40 20 Items in part 1 20 items in part 2 Correlation between forms 0.6919 Equal-length Spearman-Brown 0.8179 Guttman Split-half 0.8095 Unequal-length Spearman-Brown 0.8179 Alpha for part 1 0.7513 Alpha for part 2 0.8676 The correlation between forms is used to find the Spearman Brown reliability and the variances of sum scale and forms are used to find Guttman reliability. Cronbach's coefficient alpha (α) uses variances for the k individual items (40) and the variance for the sum of all items. If there is no true score but only error in the items then the variance of the sum will be the same as the sum of variances of the individual items. Therefore, coefficient alpha will be equal to zero. If all items are perfectly reliable and measure the same thing (true score), then coefficient alpha is equal to 1. In all, the reliability of the three statistics namely, Spearman-Brown, Guttman and Cronbach s alpha show that the reliability of scale constructed for the General Assessment is between 0.70 and 0.87, which makes the constructed scale fairly reliable. Therefore the scale reliability is good. Since it was found that the reliability of the scale was good, factor analysis was performed on all the 400 valid responses. Chapter-IV 134

4.2 FACTOR ANALYSIS The set of 40 items included in the Employee Attrition Scale was used to find the underlying factors in it. The Factor analysis conducted in this study proceeds in four steps: Step 1 Correlation matrix for the variables, item 1 to item 40, was analyzed initially for possible inclusion in Factor Analysis. (The results of the correlation between item1 to item40 are given in Appendix). Since one of the goals of the factor analysis is to obtain 'factors' that help explain these correlations, the variables must be related to each other for the factor model to be appropriate. A closer examination of the correlation matrix may reveal what are the variables which do not have any relationship. Usually a correlation value of 0.3 (absolute value) is taken as sufficient to explain the relation between variables. All the variables from 1 to 40 have been retained for further analysis. Further, two tests are applied to the resultant correlation matrix to test whether the relationship among the variables is significant or not. Table: No 4.3 KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy.823 Approx. Chi-Square 4629.369 Bartlett's Test of Sphericity df 780 Sig. ** One test is Bartlett's test of sphericity. This is used to test whether the correlation matrix is an identity matrix. i.e., all the diagonal terms in the matrix are 1 and the off-diagonal terms in the matrix are 0. In short, it is used to test whether the correlations between all the variables is 0. The test value (4629.369) and the significance level (P<.01) are given above. Chapter-IV 135

With the value of test statistic and the associated significance level is so small, it appears that the correlation matrix is not an identity matrix, i.e., there exists correlations between the variables. Another test is Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy. This test is based on the correlations and partial correlations of the variables. If the test value, or KMO measure is closer to 1, then it is good to use factor analysis. If KMO is closer to 0, then the factor analysis is not a good idea for the variables and data. The value of test statistic is given above as 0.823 which means the factor analysis for the selected variables is found to be appropriate to the data. Step 2 Next step is to determine the method of factor extraction, number of initial factors and the estimates of factors. Here Principal Components Analysis (PCA) is used to extract factors. PCA is a method used to transform a set of correlated variables into a set of uncorrelated variables (here factors) so that the factors are unrelated and the variables selected for each factor are related. Next PCA is used to extract the number of factors required to represent the data. The results from principal components analysis are given below. To start with, in the correlation matrix, where the variances of all variables are equal to 1.0. Therefore, the total variance in that matrix is equal to the number of variables. In this study, there are 40 variables (items) each with a variance of 1 then the total variability that can potentially be extracted is equal to 40 times 1. Chapter-IV 136

The variance accounted for by successive factors would be summarized as follows: Table: No.4.4 Total Variance Explained Initial Eigen values Extraction Sums of Squared Loadings Component % of % of Cumulative Variance Cumulative % Variance Variance Variance % 1 6.863 17.158 17.158 6.863 17.158 17.158 2 3.835 9.587 26.745 3.835 9.587 26.745 3 2.230 5.576 32.320 2.230 5.576 32.320 4 1.875 4.686 37.007 1.875 4.686 37.007 5 1.612 4.031 41.038 1.612 4.031 41.038 6 1.368 3.419 44.457 1.368 3.419 44.457 7 1.247 3.117 47.574 1.247 3.117 47.574 8 1.109 2.772 50.346 1.109 2.772 50.346 9 1.089 2.722 53.068 1.089 2.722 53.068 10 1.061 2.652 55.720 1.061 2.652 55.720 11 1.027 2.567 58.287 1.027 2.567 58.287 12 1.013 2.533 60.820 1.013 2.533 60.820 13 1.000 2.501 63.320 1.000 2.501 63.320 14.895 2.238 65.558 15.850 2.125 67.684 16.813 2.032 69.716 17.790 1.975 71.691 18.745 1.862 73.553 19.702 1.754 75.307 20.680 1.701 77.007 21.678 1.695 78.702 22.646 1.616 80.318 23.607 1.517 81.835 24.589 1.472 83.307 25.564 1.410 84.717 26.559 1.398 86.115 27.528 1.319 87.435 28.503 1.257 88.691 29.493 1.232 89.924 30.471 1.176 91.100 31.445 1.112 92.212 32.418 1.044 93.256 33.403 1.006 94.263 34.401 1.002 95.265 35.363.908 96.173 36.352.881 97.054 37.345.863 97.917 38.311.778 98.695 39.273.682 99.377 40.249.623 100.000 Chapter-IV 137

In the second column (Initial Eigen values) the column titled Variance, we find the variance on the new factors that were successively extracted. In the third column, these values are expressed as a percent of the total variance. As we can see, factor 1 account for about 17.16 percent of the total variance, factor 2 about 9.6 percent, and so on. As expected, the sum of the eigen values is equal to the number of variables. The third column contains the cumulative variance extracted. The variances extracted by the factors are called the eigen values. From the measure of how much variance each successive factor extracts we can decide about the number of factors to retain. Retain only factors with eigen values greater than 1. In essence, this is like saying that, unless a factor extracts at least as much as the equivalent of one original variable, we drop it. This criterion is probably the one most widely used and is followed in this study also. In this study, using the above criterion, 13 factors (principal components) have been retained. The tableno.4.5 shown below gives the Component Matrix or Factor Matrix where PCA extracted 13 factors. These are all coefficients used to express a standardized variable in terms of the factors. These coefficients are called factor loadings, since they indicate how much weight is assigned to each factor. Factors with large coefficients (in absolute value) for a variable are closely related to that variable. For example, Factor 1 is the factor with largest loading (0.639) for the variable, Statement 33. These are all the correlations between the factors and the variables, Hence the correlation between Statement 33 and Factor 1 is 0.639. Thus the factor matrix is obtained. These are the initially obtained estimates of factors. Chapter-IV 138

Table No.4.5 Component Matrix Statements Components 1 2 3 4 5 6 7 8 9 10 11 12 13 33 0.639-0.100-0.324 0.008-0.003 0.095-0.216 0.019-0.075 0.001-0.158-0.017 0.295 31 0.638 0.113-0.212-0.041-0.055-0.217 0.043-0.178 0.027 0.052 0.023 0.003 0.082 27 0.614 0.034-0.156 0.024-0.293 0.141-0.007-0.077-0.104 0.013 0.189-0.006-0.173 29 0.611 0.107-0.319 0.145-0.016-0.115 0.015-0.165-0.056 0.037-0.062-0.091 0.285 26 0.581-0.008-0.271 0.061-0.267 0.094 0.107 0.105 0.153 0.178 0.133-0.143-0.201 24 0.579 0.032-0.370-0.163-0.022 0.172 0.094 0.068 0.199 0.055-0.149-0.094-0.122 25 0.570-0.032-0.321-0.126-0.296 0.049-0.001-0.004 0.168 0.100 0.072-0.045-0.359 19 0.569-0.109-0.039 0.187-0.024 0.258-0.263-0.041-0.094-0.020-0.287 0.028 0.112 38 0.561-0.185 0.253 0.018-0.001-0.141 0.023 0.087 0.416-0.071 0.098 0.226 0.187 32 0.543-0.099 0.080 0.070-0.138 0.161 0.118 0.181 0.053-0.261 0.139 0.087 0.362 36 0.493 0.099 0.317-0.156 0.024-0.256 0.166-0.057 0.339-0.028 0.074 0.073 0.064 37 0.492-0.207 0.241 0.108 0.086-0.047-0.070-0.158 0.049 0.270-0.186 0.379-0.205 12 0.476-0.061-0.065-0.110-0.061-0.158-0.118 0.274-0.338 0.078-0.123 0.060 0.159 22 0.476-0.133-0.271 0.120-0.157 0.062 0.058-0.119-0.255-0.031 0.225 0.294 0.043 35 0.474-0.133 0.370-0.076 0.149 0.289-0.183-0.079 0.127-0.078 0.053-0.231-0.195 1 0.465 0.099 0.360 0.130-0.118-0.121-0.108 0.357-0.249 0.225 0.026-0.129-0.024 14 0.453-0.140 0.389 0.096-0.067 0.344-0.010-0.181 0.120 0.157-0.142-0.085 0.055 23 0.421-0.076 0.323-0.144-0.001 0.053 0.135-0.094-0.247-0.256 0.268-0.256-0.142 20 0.419 0.246-0.095-0.190 0.099-0.315-0.143 0.105-0.034-0.285-0.133-0.179-0.026 17 0.412 0.005-0.036-0.246 0.354 0.296 0.367 0.029-0.120 0.083 0.103 0.057 0.016 34-0.337 0.285-0.180-0.324-0.257-0.128 0.208 0.103 0.086 0.303-0.106 0.252-0.090 9 0.098 0.690 0.124-0.017 0.103 0.176-0.161 0.114-0.039-0.013 0.010 0.242-0.197 4 0.020 0.664 0.002-0.048 0.001-0.156 0.029-0.207 0.060-0.059 0.056-0.207 0.000 7-0.026 0.622 0.035-0.127-0.304 0.125-0.124 0.022-0.026-0.084 0.226-0.034 0.101 21 0.021 0.599 0.155 0.004 0.237 0.238-0.071 0.132 0.027-0.040-0.062 0.198-0.034 28 0.081 0.585 0.101-0.100-0.129 0.122-0.260 0.068 0.246-0.139-0.132 0.094 0.037 3 0.062 0.580 0.051 0.206 0.084 0.059 0.213-0.125-0.006 0.011-0.191 0.080 0.172 8 0.007 0.544 0.195 0.056 0.164-0.082 0.047-0.171 0.072 0.355 0.117-0.244 0.163 40 0.003-0.511 0.098-0.352 0.065-0.182 0.095 0.155 0.173-0.209 0.089 0.103 0.079 5 0.393 0.445-0.264-0.034 0.002-0.125 0.239-0.120 0.098-0.335-0.253-0.043-0.080 16 0.474-0.106 0.541 0.219-0.166-0.007 0.161-0.006 0.035 0.085 0.028 0.028 0.075 13-0.087 0.133-0.176 0.704 0.185 0.013-0.091 0.092 0.058-0.114 0.048-0.014-0.139 15-0.039 0.168-0.326 0.501 0.181 0.023 0.013 0.387 0.253 0.045 0.406 0.030 0.056 10 0.352 0.171-0.065-0.397 0.304 0.046 0.021 0.166-0.152 0.030 0.061 0.233-0.042 6 0.474 0.106-0.004-0.186 0.570 0.028-0.010 0.119-0.108-0.033 0.074-0.061-0.107 30 0.289-0.286-0.153 0.333 0.449-0.113 0.332-0.022 0.041 0.037-0.200-0.067-0.126 39 0.321 0.157-0.068-0.090 0.259-0.366-0.289-0.260-0.051 0.301 0.300 0.037 0.093 18-0.324-0.005-0.176-0.303 0.073 0.366 0.182 0.199 0.121 0.291-0.058-0.285 0.300 11-0.014 0.400 0.204 0.206-0.216-0.004 0.539 0.004-0.291-0.053 0.005 0.095-0.001 2 0.307 0.112 0.252 0.093-0.141-0.349 0.034 0.448-0.024 0.114-0.238-0.220-0.112 Chapter-IV 139

Table No.4.6 Communalities Items Initial Extraction Items Initial Extraction Statement-2 1.000.653 Statement-27 1.000.592 Statement-1 1.000.670 Statement-31 1.000.560 Statement-6 1.000.644 Statement-29 1.000.647 Statement-11 1.000.679 Statement-33 1.000.698 Statement-3 1.000.529 Statement-19 1.000.615 Statement-39 1.000.686 Statement-32 1.000.637 Statement-40 1.000.563 Statement-22 1.000.583 Statement-13 1.000.642 Statement-17 1.000.616 Statement-15 1.000.805 Statement-28 1.000.580 Statement-9 1.000.682 Statement-23 1.000.621 Statement-20 1.000.555 Statement-34 1.000.650 Statement-21 1.000.566 Statement-35 1.000.646 Statement-12 1.000.529 Statement-30 1.000.688 Statement-8 1.000.633 Statement-14 1.000.611 Statement-4 1.000.564 Statement-16 1.000.645 Statement-10 1.000.521 Statement-37 1.000.690 Statement-5 1.000.704 Statement-38 1.000.716 Statement-24 1.000.632 Statement-36 1.000.605 Statement-25 1.000.710 Statement-18 1.000.713 Statement-26 1.000.651 Statement-7 1.000.599 The Table No.4.6 titled Communalities is given above. This provides communalities for each variable calculated from the factor matrix described above. The proportion of variance explained by the common factors is called Communality of the variable. For example the proportion of variance explained by the 13 factors in the variable, that is the statement-1 is 0.653. That is 65.3% of the variance in Statement 3 is explained by all the 13 factors. So the communality of the variable Item 1 is 0.653. Further, the table titled Total Variance Explained gives the proportion of total variance explained by all the factors. The column '% of Variance' explains how much variance is attributed to each factor and the next column is the cumulative percent of variance. So, Factor 1 is the one which accounts for maximum proportion of total variance. These eigen values are calculated from the factor matrix described above. Thus for any factor, its corresponding Chapter-IV 140

highest factor loading will contribute much to that factor. By looking at the last column it is understood that the 13 factor model explains 63.32% of the variance in the selected variables. Step 3 Although the factor matrix (Table No.4.5 titled Component Matrix) obtained in the extraction phase indicates the relationship between the factors and the individual variables, it is usually, difficult to identify meaningful factors based on this matrix. Since the idea of factor analysis is to identify the factors that meaningfully summarize the sets of closely related variables, the Rotation phase of the factor analysis attempts to transfer initial matrix into one that is easier to interpret. It is called the rotation of the factor matrix. The Rotated Factor Matrix (Table titled Rotated Component Matrix) using Oblique rotation is given in Table: No 4.7 where each factor identifies itself with a few set of variables. The variables which identify with each of the factors were sorted in the decreasing order and are highlighted against each column and row. Chapter-IV 141

Table No.4.7 Rotated Component Matrix 1 2 3 4 5 6 7 8 9 10 11 12 13 25 0.838 0.028-0.035-0.026-0.003-0.075-0.125 0.027-0.002-0.035-0.072 0.043-0.064 26 0.765-0.053 0.014 0.154-0.031 0.087 0.018 0.131 0.047 0.019 0.020 0.000 0.012 27 0.605 0.086 0.152-0.024 0.025-0.178 0.124-0.001-0.035 0.022 0.080-0.046 0.141 24 0.578-0.012-0.031 0.002 0.181 0.201-0.110 0.000 0.036-0.087-0.260 0.102 0.157 28 0.004 0.707-0.031 0.007-0.070 0.036-0.084 0.021 0.117-0.011-0.211 0.072 0.050 9 0.014 0.647-0.025 0.134 0.305-0.139 0.096 0.063-0.125 0.039-0.039 0.156-0.110 30 0.008-0.641 0.064 0.271 0.206-0.002 0.080 0.044 0.015-0.025-0.281 0.285 0.010 7 0.114 0.617 0.016-0.015-0.080 0.044 0.157-0.021-0.030 0.150 0.030-0.293 0.010 21-0.171 0.542 0.012 0.166 0.328 0.035 0.108 0.010-0.030-0.003-0.079 0.177-0.039 34 0.184 0.148-0.675-0.190 0.027 0.127 0.116 0.026-0.040 0.005 0.027 0.049-0.252 35 0.175 0.125 0.642-0.093 0.115 0.040-0.206 0.043 0.092-0.008 0.019 0.179-0.093 23 0.126-0.078 0.577-0.211 0.181-0.145 0.198 0.080 0.038 0.014-0.010-0.256-0.141 15 0.137 0.039-0.110 0.895 0.055 0.082-0.045 0.013 0.146 0.057 0.148-0.181-0.052 13-0.032-0.033 0.110 0.690-0.142-0.185 0.030 0.010-0.171-0.044-0.091 0.098 0.009 10 0.027 0.151-0.110-0.099 0.659-0.043-0.047 0.034 0.047 0.024 0.005-0.020 0.045 6-0.020-0.055 0.246 0.090 0.652-0.002-0.143 0.100-0.012 0.137-0.144 0.005 0.003 17 0.129-0.146 0.122-0.051 0.647 0.209 0.224-0.126 0.074 0.008 0.033 0.048 0.031 18 0.007-0.043-0.108-0.050 0.123 0.790-0.057-0.050-0.093-0.012 0.127-0.112 0.068 11-0.045 0.006-0.083-0.018 0.018-0.070 0.802 0.123-0.035-0.059-0.053-0.051-0.114 3-0.182 0.193-0.086 0.087 0.029 0.088 0.421-0.042 0.005 0.169-0.265 0.185 0.144 2 0.061-0.055-0.071 0.036-0.093 0.041 0.032 0.792 0.056-0.050-0.140 0.018-0.077 1 0.060 0.070 0.131 0.035 0.055-0.037 0.101 0.691-0.006 0.099 0.243 0.011 0.090 38 0.034 0.046 0.016 0.100-0.007-0.082-0.092 0.045 0.780 0.012 0.016 0.124 0.064 36 0.060 0.039 0.027-0.099 0.047-0.047 0.048 0.081 0.626 0.213-0.172 0.080-0.161 32 0.031 0.110 0.171 0.113 0.054 0.043 0.196 0.012 0.500-0.195 0.053-0.190 0.389 40-0.126-0.214-0.051-0.160 0.129-0.022-0.233-0.038 0.462-0.241 0.018-0.194-0.140 16 0.013-0.046 0.244-0.058-0.131-0.060 0.311 0.266 0.393 0.041 0.180 0.219 0.014 39-0.006-0.055-0.054 0.000 0.162-0.220-0.251-0.023 0.059 0.736 0.118-0.015 0.095 8-0.099 0.096 0.062 0.057-0.017 0.274 0.149 0.094-0.017 0.649-0.006 0.067-0.099 4 0.027 0.258 0.051-0.017-0.072 0.024 0.147-0.023-0.069 0.402-0.356-0.137-0.129 31 0.304-0.100-0.069-0.070 0.036-0.126 0.041 0.006 0.181 0.304-0.206-0.009 0.263 5 0.201 0.078-0.049-0.025 0.063-0.088 0.214-0.063 0.052-0.037-0.707-0.031 0.086 20 0.010 0.092 0.074-0.076 0.127-0.141-0.179 0.265 0.036 0.065-0.487-0.221 0.131 37 0.109-0.083-0.058-0.122 0.116-0.312-0.028 0.074 0.178 0.060 0.143 0.624 0.036 14 0.122 0.046 0.393-0.178-0.104 0.171 0.087 0.026 0.169 0.024 0.133 0.411 0.160 33 0.152-0.010 0.027-0.013 0.075 0.052-0.162 0.037 0.045 0.030-0.081 0.013 0.709 19 0.085 0.096 0.219-0.033-0.019-0.043-0.077 0.071-0.054-0.117-0.027 0.286 0.588 29 0.173-0.159-0.014 0.044-0.037-0.002 0.091-0.001 0.078 0.295-0.222-0.032 0.550 12-0.004-0.040-0.141-0.134 0.207-0.089-0.015 0.420-0.022-0.024 0.071-0.096 0.443 22 0.296-0.082-0.058 0.045 0.138-0.337 0.222-0.188 0.045-0.007 0.192-0.067 0.348 Chapter-IV 142

Step 4 Normally, from the factor results arrived above, factor score coefficients can be calculated for all variables (since each factor is a linear combination of all variables) which are then used to calculate the factor scores for each individual. Since PCA was used in extraction of initial factors, all methods will result in estimating same factor score coefficients. However, for the study, original values of the variables were retained for further analysis and factor scores were thus obtained by adding the values (ratings given by the respondents) of the respective variables for that particular factor, for each respondent. Thus the 40 variables considered in the primary data were reduced to 13 factors model and each factor was given a name which associated with the corresponding variables. The factor names and descriptions of the factors are given in the following Table No.4.8. Table No.4.8. 13 Factors Model Item No. Statement 25 Unclear performance goals causes high attrition 26 Missing of personal touch in the organization leads to high attrition 27 Lack of scientific goal setting process causes high attrition 24 Lack of integration of people in the company leads to high attrition 28 This company s location is good and it makes my work easier 09 The culture of this company is such that it creates a very positive work environment 30 Salary hike in every six months can be a better option to reduce high attrition 07 I feel that I get self-respect and dignity in this organization 21 This company s infrastructure is good and makes my work easier 34 Introduction of family benefit plans will reduce high attrition 35 Social isolation is a major cause for high attrition 23 Family issues and influence of family members leads to high attrition Factor Name 1. Lack of integration and goal setting 2. Work atmosphere 3. Work and family conflict Chapter-IV 143

15 My organization provide hygiene and timely food to the employees 13 This organization conduct stress reduction programs like yoga, meditation etc. 4. Food and relaxation This company is not very open to ideas and suggestions 10 given by employees 5. Motivation and This organization does not conduct effective 06 appreciation motivational programs 17 Internal job rotation will lead to high attrition 18 Work from home option will reduce high employee attrition 6. Work from home This organization provides sufficient holidays for 11 7. Dissatisfaction with employees salary and perks 03 I am paid enough for the work I do in this company 02 Odd working hours causes high employee attrition 8. Maximum hours worked/ 01 Lengthy working hours leads to high attrition abnormal working hours 38 Absence of counseling and medical health checkups causes high attrition 9. Occupational health 36 Lack of spiritual sessions organized in the company problems leads to high attrition 32 Eye fatigueness and vision deterioration leads to high attrition 40 This organization has a logical, bias free promotion policy 16 Sleeping disorders causes high employee attrition 39 This organization do not provide labour welfare measures like housing schemes, health club etc. 08 This company has high standards of corporate governance 10. Labour welfare and 04 I believe that the company s leadership is doing what is corporate governance required for its growth 31 Low perceived equity of rewards system leads to high attrition 05 I am not satisfied with the kind of salary hikes I get 11. Dissatisfaction with 20 Reward systems in this organization are not transparent rewards and hikes 37 Lack of talent management in the organization leads to high attrition 12. Miscellaneous-lack of 14 Lack of safe and good transportation facility leads to transportation and talent high attrition 33 Lack of communication around total value causes high attrition 19 Lack of work value and ethics causes high attrition 29 Absence of performance-based bonus causes high attrition 13. Lack of work ethics 12 Constant pull of higher salaries leads to high attrition 22 Mismatching of job expectations creates the problem of attrition Chapter-IV 144

4.3 ANALYSIS OF THE PERSONAL AND OTHER FACTORS The personal factors included in the study are gender, location, age, designation, qualification, area of work, salary, and global position. An analysis of the respondents based on gender, location, age, designation, qualification, area of work, salary, and global position have been conducted and the findings are discussed as follows: 4.3.1 Gender-wise distribution of the sample: Analysis of the respondents based on the gender is conducted and the results are given as follows: Table No.4.9 Gender-wise distribution of the sample Gender No. of respondents Percentage of the total sample Male 236 59 Female 164 41 Total 400 100 Source: Survey Data Table No.4.9 indicates the classification of data according to their gender as male and female. There are 236 male respondents and 164 female respondents included in the sample. Chart No.4.1 Percentage distribution of the gender Gender 59% 41% Male Female 0% 50% 100% Percentage Chapter-IV 145

The Chart No.4.1 reveals that there are 59% male respondents and 41% female respondents in the selected sample. Inference: From the above chart, it is inferred that approximately 60% of the respondents selected for the study are male and nearly 40% of the respondents are female. It shows that the selected number of male and female respondents in the study is moderate. 4.3.2 Location-wise distribution of the sample: Analysis of the respondents based on location is conducted and the results are given as follows: Table: No.4.10 Location wise distribution of the respondents Location No. Percentage Karnataka 285 71.3 Kerala 115 28.7 Total 400 100 Source: Survey Data Table 4.10 shows the location-wise distribution of the sample. There are 285 respondents from Karnataka state and 115 respondents from Kerala state in the sample. Also the percentages of the two groups are tabulated along with their numbers. Chart No.4.2 Percentage-wise distribution of the location Percentage 80% 70% 60% 50% 40% 30% 20% 10% 0% 71.3% Karnataka 28.7% Kerala Karnataka Kerala Location Chapter-IV 146

The chart No.4.2 shows that 71.3% of the respondents in the sample belong to Karnataka State and 28.7% of the respondents are from Kerala. Inference: From the percentage distribution, it is inferred that approximately 70% of the respondents are from Karnataka and approximately 30% of the respondents are from Kerala which shows that the selection of respondents from Karnataka and Kerala are moderate for the present study. 4.3.3 Global position-wise distribution of the sample: Analysis of the respondents based on global position is conducted and the results are given as follows: Table No.4.11 Distribution of the sample based on global position (national/ multinational) Global Position Number of respondents Percentage National 212 53 Multinational 188 47 Total 400 100 Source: Survey Data Table No.4.11 shows the grouping of the respondents under national and multinational BPO employees. 212 respondents belong to national BPO s and 188 respondents belong to multinational BPO companies. Chart No. 4.3: Percentage wise distribution of Global position Percentage 60% 50% 40% 30% 20% 10% 0% 53% 47% National Multinational Global Position National Multinational Chapter-IV 147

Chart No.4.3 indicates the grouping of the respondents in the sample under national and multinational BPO employees. It shows that 53% of the sample belongs to national BPO employees and 47% of the sample belongs to multinational BPO employees. Inference: From the chart No.4.3, it is inferred that 53% of the respondents are selected from national BPO s and 47% of the respondents are selected from multinational BPO s which shows that there is almost equal representation from national and multinational BPO s. 4.3.4 Age-wise distribution of the sample: Analysis of the respondents based on age is conducted and the results are given as follows: Table: No.4.12 Age-wise distribution of the sample Age No. of respondents Percentage of the total sample < 18 yrs. 04 01.0 18 20 yrs. 27 06.8 21 25 yrs. 260 65.0 Above 25 yrs. 109 27.3 Total 400 100 Source: Survey data Table No. 4.12 shows the grouping of the respondents under different age groups as less than 18 years group, 18 20 years, 21 25 years and Above 25 years group.260 respondents belong to 21-25 years group, 109 respondents belong to above 25 years group, 27 respondents under 18-20 years group and 4 respondents belong to less than 18 years group. Chart No 4.4 Percentage distributions of the age groups 27.30% 1% 6.80% 65% < 18 yrs. 18 20 yrs. 21 25 yrs. Above 25 yrs. Chapter-IV 148

From the chart No. 4. 4, it is found that 65% of the respondents fall in the age group of 21-25 years and 27.3% of the respondents fall in above 25 years category. Also, 06.8% of the respondents belong to 18-20 years group and 01% of the respondents belong to less than 18 years category. Inference: From the chart it is observed that among the respondents 65% of them are in the age group of 21-25 years and 27.3% of the respondents are above 25 years. Also, 06.8% of the respondents are in 18-20 years group and only 01% of the respondents are less than 18 years. It is found that majority of the respondents (65%) are in the entry level age group of 21-25 years which accounted for the highest employee attrition in BPO sector. 4.3.5 Experience-wise distribution of the sample: Analysis of the respondents based on experience groups is done and the results are given as follows: Table: No. 4.13 Distribution of Experience groups in the organization Experience Groups No. of Respondents Percentage of the total sample < 6 months 39 9.8 6 months 1 year 80 20.0 1 2 years 191 47.8 3 5 years 78 19.5 > 5 years 12 3.0 Total 400 100 Source: Survey Data Table No. 4.13 gives the classification of the respondents as per their experience in the present organization. Five groups have been formed to include the experience groups ranging from less than 6 months to above 5 years groups. Chart: No.4.5 Percentage distribution of Experience groups Percentage 50% 45% 40% 35% 30% 25% 20% 15% 10% 9.8% 20.0% 47.8% 19.5% 5% 0% < 6 months 6 months 1 year 1 2 years 3 5 years > 5 years Experience 3.0% Chapter-IV 149

Chart No. 4.5 indicates that 47.8% of the respondents belong to 1-2 years group and 20% of the respondents belong to 6 months 1 year group. Also 19.5% of the sample belong to 3-5 years category, 09.8% of the sample belong to less than 6 months group and 03% of the sample fall in above 5 years group. Inference: It is observed that among the respondents, approximately half of them are in the experience group of 1-2 years and an equal number of them are either in 6 months 1 year group, or 3-5 years experience group. 4.3.6 Salary-wise distribution of the sample: Analysis of the respondents based on salary groups is done and the results are given as follows: Table No. 4.14 Salary-wise distribution of the respondents Salary groups (per month) No. of respondents Percentage <Rs. 5,000 07 1.8 Rs. 5,000 10,000 82 20.5 Rs. 10,000 15,000 160 40.0 Rs. 15,000 20,000 94 23.5 Above Rs. 20,000 57 14.2 Total 400 100 Source: Survey Data Table No. 4.14 exhibits classification of respondents as per their salary per month. There are five salary groups included in the sample as less than Rs. 5000, Rs.5000-10000 group, Rs.10000-15000 group, Rs. 15,000 20,000 and above Rs. 20,000 group. Chart No. 4.6: Percentage-wise distribution of salary groups 14.2% 23.5% Salary 20.5% 40% 1.8% 0% 5% 10% 15% 20% 25% 30% 35% 40% Percentage < Rs. 5,000 Rs. 5,000 10,000 Rs. 10,000 15,000 Rs. 15,000 20,000 Above Rs. 20,000 Chapter-IV 150

Inference: From the chart, it is observed that among the respondents 40% of them are in the salary group of Rs.10, 000 15,000 and 23.5% of the respondents are in Rs.15, 000 20,000 salary group. Also 20.5% of the samples are in Rs. 5,000 10,000 salary group, 14.2% of the samples are in above Rs. 20,000 group and 1.8% of the samples fall in less than Rs. 5,000 category. Thus analysis shows that majority of the respondents salary is above Rs. 10000 which accounts to higher salary drawers group. 4.3.7 Designation-wise distribution of the sample: Analysis of the respondents based on designation groups is done and the results are given as follows: Table No.4.15 Designation-wise distribution of the sample Designation No. Percentage Process Analyst 246 61.5 Senior Process Analyst 95 23.8 Team Leader 34 8.5 Supervisor 15 3.8 Manager 10 2.5 Total 400 100 Source: Survey Data Table: No. 4.15 gives an account of the designation groups and their numbers and percentages in the sample. The groups included are process analyst, senior process analyst, team leader, supervisor and manager. The process analysts group has 246 respondents, senior process analyst group has 95 respondents, team leader has 34 respondents, supervisor has 15 numbers and manager has 10 respondents. Chart: No.4.7 Percentage distribution of Designation groups 61.50% Percentage 80% 60% 40% 20% 23.80% 8.50% 3.80% 2.50% Process Analyst Senior Process Analyst Team Leader Supervisor Manager 0% Chapter-IV 151

Inference: From the chart, it is observed that among the respondents 61.5% of them are in the designation group of process analyst (entry level), and from the total sample 23.8% of the sample are in senior-process analyst group. Also, 08.5% of the sample belongs to team leader category, 03.8% of the sample belong to supervisor group and 02.5% of the sample belong to manager category. Thus it is concluded that from the total sample, majority (61.5%) of them are from process analyst (entry level) group, where employee attrition is highest which further justifies the sample selection. 4.3.8 Qualification-wise distribution of the sample: Analysis of the respondents based on qualification groups is done and the results are given as follows: Table: No.4.16 Qualification wise distribution of the sample Qualification No. of respondents Percentage ITI/Diploma 15 3.8 Undergraduate 21 5.3 Graduate 217 54.3 Postgraduate 147 36.8 Total 400 100 Source: Survey Data Table No. 4.16 gives an account of the qualification groups, the number of respondents in each group and their percentage distribution. The four qualification groups included are ITI/Diploma, undergraduate, graduate and postgraduate. The graduate group has 217 respondents, post graduate group has 147 respondents, and under graduate group have 21 respondents and ITI/Diploma group have 15 respondents. Chart: No. 4.8 Percentage distribution of Qualification groups Postgraduate 36.80% Qualification Graduate Undergraduate 5.30% 54.30% ITI/Diploma 3.80% 0% 10% 20% 30% 40% 50% 60% Percentage Chapter-IV 152

Inference: From chart No. 4.8 it is observed that among the respondents 54.3 % of them are in graduate group and from the total sample, 36.8 % of the sample is in postgraduate group. Also, 05.3% of the sample belongs to under graduate group and 03.8% of the sample belong to ITI/Diploma group. Thus it is concluded that from the total sample more than half of them (54.3%) are from graduate group. Also 36.8 %of the respondents are in postgraduate group. Therefore minimum qualification of majority of the respondents is graduation which implies BPO jobs seekers must start their search after graduation. 4.3.9 Distribution of the sample as per area of work groups: Analysis of the respondents based on area of work groups is done and the results are given as follows: Table: No. 4.17 Distribution of the sample as per Area of work groups Area of work Number of respondents Percentage Financial Accounting 126 31.5 Customer Services 125 31.3 Procurement 14 3.5 Human Resources 41 10.3 Application Process 67 16.8 Others 27 6.8 Total 400 100 Source: Survey Data Table No. 4.17 gives the grouping of the respondents as per their area of work groups. The six areas of work groups included in the sample are Financial Accounting, Customer Services, and Procurement, Human Resources, Application Process and Others. The number of respondents in each category is tabulated with their percentages. Chart: No. 4.9 Percentage distribution of Area of work groups Others 6.8% Application Process 16.8% Area of work Human Resource Procurement Customer Services 3.5% 10.3% 31.3% Financial Accounting 31.5% 0% 5% 10% 15% 20% 25% 30% 35% Percentage Chapter-IV 153

Inference: From chart No.4.9, it is observed that among the respondents31.5 % of them are in financial accounting group and from the total sample 31.3% of the sample is in customer services group. Also 16.8 % of the sample is from application processes group, 10.3% of the sample is in human resource group, 06.8% belong to Others group and 03.5% of the respondents is in procurement category. Thus it is concluded that from the total sample, majority (63%) of the respondents are chosen from the two important areas namely financial accounting and customer services groups. 4.3.10 Ranking of reasons for stress in the sample: Analysis of the reasons based on the ranks they scored in the survey is done and the results are given as follows: Table No.4.18 Ranking of reasons for stress Reasons Rank Long working hours 3.25 Working timings 3.75 Repetitive nature of work 4.06 Pressure to perform on metrics 4.80 Social isolation 6.24 Lack of quality of sleep 4.57 Lack of transportation 5.54 Stress due to verbal abuse 6.12 Travel time 6.67 Source: Survey Data Table No.4.18 gives the list of the reasons for stress to employees in BPO sector with their corresponding ranking. Chart No.10: Ranking of reasons for stress to BPO employees Rank 7 6 5 4 3 2 1 0 3.25 3.75 4.06 4.8 6.24 Reasons 5.54 4.57 6.12 6.67 Long w orking hours Work timings Repetitive nature of w ork Pressure to perform on metrics Social isolation Lack of quality of sleep Lack of transportation Stress due to verbal abuse Travel time Chart No.4.10 shows that the reason long working hours holds the rank 1, work timings is given rank 2 followed by the repetitive nature of work in the 3 rd rank position. The other ranks given by the respondents are 4 th rank pressure to perform on Chapter-IV 154

metrics, 5 th rank social isolation, 6 th rank lack of quality sleep, 7 th rank lack of safe and good transportation facility, 8 th rank stress due to verbal abuse and 9 th rank is travel time of respondents. Inference: It is inferred that long working hours is the primary reason for stress to BPO employees. Work timing is the second reason identified for stress to BPO employees. The third reason for stress is repetitive nature of work.the fourth reason identified for stress is pressure to perform on metrics. Social isolation stands as the fifth reason. The sixth reason identified is lack of quality sleep. Lack of safe and good transportation occupies the seventh position, eighth reason has been found as stress due to verbal abuse and finally travel time of respondents is identified as the ninth reason for stress to BPO employee. 4.3.11 Distribution of the respondents undergone training: Analysis of the respondents based on number of respondents undergone training and the results are given as follows: Table No.4.19 Distribution of respondents undergone training Opinion Number of respondents Percentage Yes 366 91.5 No 34 8.5 Total 400 100.0 Source: Survey Data Table No.4.19 gives an account of number of respondents undergone training and their percentage. It also gives the number of respondents who have not undergone training. Chart No: 4.11 Percentage distribution of Respondents undergone training Percentage 91.50% 8.50% Yes No 85% 90% 95% 100% Opinion Chapter-IV 155

Chart No. 4.11 indicates that 91.5% of the respondents have undergone training and 08.5% of the respondents have not undergone training. Inference: It is found that majority of the respondents have undergone training programs. 4.3.12 Distribution of number of training programs undergone: Analysis of the respondents based on number of trainings undergone and the results are given as follows: Table: No.4.20 Distribution of number of training programs undergone Training groups No. of Respondents Percentage 1-2 143 35.8 3 4 196 48.9 5 & Above 61 15.3 Total 400 100.0 Source: Survey Data Table No.4.20 shows the distribution of number of training programs undergone by respondents in the sample. It gives three numbers of training groups, the number of respondents in each category and their percentage. Chart: No.4.12 Percentage of training program undergone by respondents 15.30% 48.90% 35.80% 1-2 3 4 5 & Above Inference: From the chart, it is found that15.3% of the respondents have undergone more than five training programs and 48.9% of the respondents have undergone 3-4 training programs. Also 35.8% of the respondents have undergone 1-2 training programs. Thus it is observed that nearly 84% of the respondents have undergone at least one training program, which Chapter-IV 156

shows that the BPO companies have made training programs mandatory for their employees. 4.3.13 Rating of the training programs undergone: Rating of the training programs effectiveness has been done and the results are given as follows: Table: No.4.21 Rating of training program effectiveness Grade/Opinion Number of respondents Percentage Poor 10 2.7 Average 99 27.0 Good 232 63.4 Excellent 25 6.8 Total 400 100.0 Source: Survey Data Table No.4.21 gives an account of the rating of training programs effectiveness using the four grades namely excellent, good, average and poor. The number of respondents in each group has been tabulated with the percentage values. Chart No.4.13 Percentage distribution of rating of training program effectiveness 63.40% Percentage 70% 60% 50% 40% 30% 20% 10% 0% 27.0% 6.80% 2.70% Poor Average Good Excellent Opinion Inference: From the survey it is found that 63.4% of the respondents have rated the training program effectiveness as Good. 27% of the respondents have rated the training program effectiveness as Average and 02.7% of the respondents rated it as Poor. Chapter-IV 157

Since only 63 % of the respondents are happy with the effectiveness of the training programs, the quality of training programs is to be improved. 4.3.14 Distribution of maximum number of worked: Analysis of the respondents based on maximum number of hours worked is done and the results are given as follows: Table No.4.22 Distribution of maximum number of hours worked Number of hours groups Number of respondents Percentage 0 8 hrs. 80 20.0 8 12 hrs. 243 60.8 Above 12 hrs. 77 19.3 Total 400 100.0 Source: Survey Data Table No.4.22 shows the distribution of number of hours worked by employees (respondents) and their percentage. There are 3 number of hours worked groups as 0 8 hours, 8-12 hours and above 12 hours group. Chart: No.4.14 Percentage distribution of the maximum number of hours worked 70% 60.80% 60% Percentage 50% 40% 30% 20% 10% 20.0% 19.30% 0 8 hrs. 8 12 hrs. Above 12 hrs. 0% 0 8 hrs. 8 12 hrs. Above 12 hrs. No. of hours Inference: From the chart, it is found that19.3% of the respondents have worked more than 12 hours and 60.8% of the respondents have worked for a period of 8-12 hours. Also 20% of the respondents have worked for a period of 0 8 hours. Chapter-IV 158

Therefore it can be found that maximum number of respondents has worked for a period of 8-12 hours. Therefore on an average the maximum number of hours worked by a BPO employee is more. 4.3.15 Distribution of opinion on level of satisfaction for strength factors: Analysis of the respondents based on opinion on level of satisfaction for the strength factors: high standards of corporate governance, exciting growth opportunities and company s work value and ethics is done and the results are given as follows: Table No.4.23 Distribution of opinion on level of satisfaction for strength factors Strength factors High standards of corporate governance Exciting growth opportunities Company s work value and ethics Source: Survey Data Very strongly agree Agree Opinion Partially agree Disagree TOTAL Number of respondents 101 218 71 10 400 % 25.3 54.5 17.8 2.4 100.0 Number of respondents 63 153 152 32 400 % 15.8 38.3 38.0 7.9 100.0 Number of respondents 108 197 84 11 400 % 27.0 49.3 21.0 2.7 100.0 Table No. 4.23 shows the distribution of opinion on level of satisfaction for the strength factors: high standards of corporate governance, exciting growth opportunities and company s work value and ethics. Chart No.4.15: Percentage distribution of level of satisfaction for strength factors Percentage 60% 50% 40% 30% 20% 10% 54.5% 25.3% 17.8% 2.5% 15.8% 38.3% 38.0% 8.0% 49.3% 27.0% 21.0% 2.8% 0% High standards of corporate governance Exciting growth opportunities Strength Factors Company s work value and ethics Chapter-IV 159

Chart No.4.15 indicates the percentage distribution of opinion on the level of satisfaction for each strength factor. For the factor high standards of corporate governance, it indicates that 25.3% of the respondents very strongly agree that there are high standards of corporate governance and 54.5% of the respondents agree that there are high standards of corporate governance For the factor, exciting growth opportunities, the chart indicates that 15.8% of the respondents very strongly agree that there are exciting growth opportunities and 38.3% of the respondents agree that there are exciting growth opportunities. Also, 38% of the respondents partially agree that there are exciting growth opportunities. For the factor, company s work value and ethics, the chart shows that 27% of the respondents very strongly agree that there is respect for company s work value and ethics and 49.3% of the respondents agree that there is respect for company s work value and ethics. Inference: From the opinion on high standards of corporate governance, it is found that overall 97.6% of the respondents agree that there are high standards of corporate governance in the organizations where they were working. Again for the same factor only 2.4% of the respondents have disagreed on there is high standards of corporate governance in the organization for which they are working. From the opinion on exciting growth opportunities, it is observed that overall 92.1% of the respondents agree that there are exciting growth opportunities in the organizations where they are working. Again for the same factor only 7.9% of the respondents have disagreed on there are exciting growth opportunities in the organization for which they are working. From the opinion on company s work value and ethics, it is observed that overall 97.3% of the respondents agree that there is work value and ethics in the organizations where they are working. Again for the same factor only 2.7% of the respondents have disagreed on there is work value and ethics in the organization for which they are working. Chapter-IV 160

4.3.16 Rating of Human Resource Management Practices: Rating of the Human Resource Management Practices has been conducted and the results are given as follows: Table No.4.24 Rating of Human Resource Management Practices Grade/Opinion Number of respondents Percentage of the total sample Excellent 32 8.0 Good 180 45.0 Average 129 32.2 Satisfactory 43 10.8 Poor 16 4.0 Total 400 100.0 Source: Survey Data Table No. 4.24 gives an account of the human resource management practices rating using the grades: Excellent, Good, Average, Satisfactory and Poor. The group Excellent has 32 respondents, Good has 180 respondents, Average has 129 respondents, Satisfactory has 43 respondents and the Poor group has 16 respondents. Chart No. 4.16 Percentage distribution of human resource management practices rating 50% 45.0% 40% 32.30% Percentage 30% 20% 10% 8.0% 10.80% 4.0% Excellent Good Average Satisfactory Poor 0% Excellent Good Average Satisfactory Poor Opinion Chart No. 4.16 shows that 45% of the respondents belong to the grade Good, 32.3% of the respondents belong to Average group, 10.8% of the respondents belong to the satisfactory group, 08% of the sample belong to the Excellent group and 04% of the sample belong to the Poor group. Chapter-IV 161

Inference: From the HRM practices rating, it is found that 08% of the respondents have rated HRM practices of their organizations as Excellent and 45% of the respondents has rated HRM practices of their organizations as Good. Therefore the HRM practices of the organizations have to improve for reducing high employee attrition. 4.4 DATA ANALYSIS BASED ON OBJECTIVES This section includes the testing of the hypotheses that were framed based on the set objectives and the results obtained. The analysis of the primary data obtained from questionnaire is conducted based on the set objectives and framed hypotheses and the results are summarized as follows: 4.4.1 Objective: Variation in factors among different BPO areas The following hypotheses were framed to study the association between different BPO areas and proposed attrition factors : lack of integration and goal setting, motivation and appreciation, work atmosphere, labor welfare and corporate governance, maximum number of hours worked, dissatisfaction with rewards and hikes, human resource management practices, dissatisfaction with salary and perks, food and relaxation, lack of transportation and talent, work and family conflict, work from home and lack of work ethics. In each combination of BPO area and attrition factor, suitable hypotheses were framed and testing (ANOVA) of the hypotheses were done and the results are discussed as given below: Chapter-IV 162

Hypothesis 1.1: Lack of Integration and Goal Setting Vs BPO areas H0:1.1. There is no significant difference among the BPO areas in the average scores of lack of integration and goal setting. H1:1.1. There is significant difference among the BPO areas in the average scores of lack of integration and goal setting. Table: No. 4.25 Lack of Integration and Goal Setting Vs BPO areas Lack of integration and goal setting Financial Accounting 12.52 3.22 126 Customer Services 11.75 3.03 125 Area of work Procurement 12.43 2.59 14 Human Resource 12.24 3.01 41 Application Process 12.70 3.18 67 Others 10.56 4.10 27 Total 12.15 3.21 400 Table: No.4.26 ANOVA for Lack of integration and goal setting Sum of Squares df Mean Square Between Groups 127.871 5 25.574 2.526 * Within Groups 3988.427 394 10.123 Total 4116.298 399 F Sig. : One way ANOVA was applied to find whether there is significant difference among the area of work groups in the average lack of integration and goal setting scores. Since the calculated F-ratio value 2.526 is higher than the table value 2.237 at 5% level of significance, we reject the null hypothesis. Hence, it is inferred that there is significant difference among the area of work groups in the average lack of integration and goal setting scores. Chapter-IV 163

Hypothesis 1.2: Motivation and Appreciation Vs BPO areas H 0 :1.2. There is no significant difference among the BPO areas in the average scores of motivation and appreciation. H1:1.2. There is significant difference among the BPO areas in the average scores of motivation and appreciation. Table No. 4.27 Motivation and Appreciation Vs BPO areas Motivation and appreciation Financial Accounting 7.76 2.52 126 Customer Services 7.98 2.23 125 Area of work Procurement 8.64 1.69 14 Human Resource 7.32 2.64 41 Application Process 8.46 2.96 67 Others 8.00 3.35 27 Total 7.95 2.57 400 Table No. 4.28 ANOVA for Motivation and appreciation Sum of Squares df Mean Square F Sig. Between Groups 45.426 5 9.085 1.381 NS Within Groups 2591.574 394 6.578 Total 2637.000 399 One way ANOVA was applied to find whether there is significant difference among the area of work groups in the average motivation and appreciation scores. Since the calculated F-ratio value 1.381 is lower than the table value 2.237 at 5% level of significance, we accept the null hypothesis. Hence, it is inferred that there is no significant difference among the area of work groups in the average motivation and appreciation scores. Chapter-IV 164

Hypothesis 1.3: Work Atmosphere Vs BPO areas H0:1.3. There is no significant difference among the BPO areas in the average scores of work atmosphere. H1:1.3. There is significant difference among the BPO areas in the average scores of work atmosphere. Table No.4.29 Work Atmosphere Vs BPO areas Work atmosphere Financial Accounting 14.65 2.57 126 Customer Services 14.76 2.76 125 Area of work Procurement 14.71 3.17 14 Human Resource 13.27 3.66 41 Application Process 15.04 3.32 67 Others 14.00 3.67 27 Total 14.57 3.01 400 Table No. 4.30 ANOVA for Work atmosphere Sum of Squares df Mean Square Between Groups 99.971 5 19.994 2.246 * Within Groups 3508.207 394 8.904 Total 3608.178 399 F Sig. One way ANOVA was applied to find whether there is significant difference among the area of work groups in the average work atmosphere scores. Since the calculated F-ratio value 2.246 is higher than the table value 2.237 at 5% level of significance, we reject the null hypothesis. Hence, it is inferred that there is significant difference among the area of work groups in the average work atmosphere scores. Chapter-IV 165

Hypothesis 1.4: Labor welfare and corporate governance Vs BPO areas H 0 :1.4. There is no significant difference among the BPO areas in the average scores of labor welfare and corporate governance. H1:1.4. There is significant difference among the BPO areas in the average scores of labor welfare and corporate governance. Table No. 4.31 Labor welfare and corporate governance Vs BPO areas Labour welfare and corporate governance Financial Accounting 11.52 2.55 126 Customer Services 11.73 2.27 125 Area of work Procurement 12.21 2.67 14 Human Resource 10.76 3.06 41 Application Process 12.99 2.97 67 Others 11.22 3.03 27 Total 11.76 2.70 400 Table No. 4.32 ANOVA for Labor welfare and corporate governance Sum of Squares df Mean Square Between Groups 159.727 5 31.945 4.591 ** Within Groups 2741.750 394 6.959 Total 2901.477 399 F Sig. One way ANOVA was applied to find whether there is significant difference among the area of work groups in the average labour welfare and corporate governance scores. Since the calculated F-ratio value 4.591 is higher than the table value 3.064 at 1% level of significance, we reject the null hypothesis. Hence, it is inferred that there is significant difference among the area of work groups in the average labour welfare and corporate governance scores. Chapter-IV 166

Hypothesis 1.5: Maximum number of hours worked Vs BPO areas H0:1.5. There is no significant difference among the BPO areas in the average scores of maximum number of hours worked. H1:1.5. There is significant difference among the BPO areas in the average scores of maximum number of hours worked. Table No. 4.33 Maximum number of hours worked Vs BPO areas Maximum hours worked Financial Accounting 6.45 1.89 126 Customer Services 6.98 1.69 125 Area of work Procurement 5.71 1.20 14 Human Resource 6.83 2.05 41 Application Process 6.93 2.13 67 Others 6.19 2.13 27 Total 6.69 1.91 400 Table No.4.34 ANOVA for Maximum hours worked Sum of Squares df Mean Square F Sig. Between Groups 42.632 5 8.526 2.388 * Within Groups 1406.545 394 3.570 Total 1449.178 399 One way ANOVA was applied to find whether there is significant difference among the area of work groups in the average maximum hours worked scores. Since the calculated F-ratio value 2.388 is higher than the table value 2.237 at 5% level of significance, we reject the null hypothesis. Hence, it is inferred that there is significant difference among the area of work groups in the average maximum hours worked scores. Chapter-IV 167

Hypothesis 1.6: Dissatisfaction with rewards and hikes Vs BPO areas H0:1.6. There is no significant difference among the BPO areas in the average scores of dissatisfaction with rewards and hikes. H1:1.6. There is significant difference among the BPO areas in the average scores of dissatisfaction with rewards and hikes. Table No. 4.35 Dissatisfaction with rewards and hikes Vs BPO areas Dissatisfaction with rewards and hikes Financial Accounting 6.27 1.80 126 Customer Services 6.05 1.81 125 Area of work Procurement 5.86 1.70 14 Human Resource 5.37 2.27 41 Application Process 6.60 2.25 67 Others 4.78 1.42 27 Total 6.05 1.96 400 Table No. 4.36ANOVA for Dissatisfaction with rewards and hikes Sum of Squares df Mean Square F Sig. Between Groups 89.548 5 17.910 4.885 ** Within Groups 1444.550 394 3.666 Total 1534.097 399 One way ANOVA was applied to find whether there is significant difference among the area of work groups in the average dissatisfaction with rewards and hikes scores. Since the calculated F-ratio value 4.885 is higher than the table value 3.064 at 1% level of significance, we reject the null hypothesis. Hence, it is inferred that there is significant difference among the area of work groups in the average dissatisfaction with rewards and hikes scores. Chapter-IV 168

Hypothesis 1.7: Human Resource Management Practices Vs BPO areas Ho: 1.7. There is no significant difference among the BPO areas in the average scores of human resource management practices. H1: 1.7. There is significant difference among the BPO areas in the average scores of human resource management practices. Table No. 4.37 Human Resource Management Practices Vs BPO areas Human Resource Management practice Financial Accounting 2.45.81 126 Customer Services 2.52.90 125 Procurement 2.79.80 14 Area of work Human Resource 2.24.92 41 Application Process 2.99 1.11 67 Others 2.81.83 27 Total 2.58.93 400 Table No.4.38 ANOVA for Human Resource Management practices Sum of Squares df Mean Square F Sig. Between Groups 20.206 5 4.041 4.924 ** Within Groups 323.392 394.821 Total 343.597 399 One way ANOVA was applied to find whether there is significant difference among the area of work groups in the average human resource management practices scores. Since the calculated F-ratio value, 4.924 is higher than the table value 03.064 at 1% level of significance, we reject the null hypothesis. Hence, it is inferred that there is significant difference among the area of workgroups in the average human resource management practices scores. Chapter-IV 169

Hypothesis 1.8: Dissatisfaction with salary and perks Vs BPO areas H 0 : 1.8 There is no significant difference among the BPO areas in the average scores of dissatisfaction with salary and perks. H1:1.8 There is significant difference among the BPO areas in the average scores of dissatisfaction with salary and perks. Table No.4.39 Dissatisfaction with salary and perks Vs BPO areas Dissatisfaction with Salary and perks Financial Accounting 6.21 1.84 126 Customer Services 5.85 1.79 125 Area of work Procurement 6.50 1.61 14 Human Resource 5.88 2.05 41 Application Process 5.99 1.75 67 Others 5.63 1.90 27 Total 6.00 1.83 400 Table No.4.40 ANOVA for Dissatisfaction with Salary and perks Sum of Squares df Mean Square F Sig. Between Groups 16.071 5 3.214.961 Ns Within Groups 1317.919 394 3.345 Total 1333.990 399 One way ANOVA was applied to find whether there is significant difference among the area of work groups in the average dissatisfaction with salary and perks scores. Since the calculated F-ratio value 0.961is lower than the table value 2.237 at 5% level of significance, we accept the null hypothesis. Hence, it is inferred that there is no significant difference among the area of work groups in the average dissatisfaction with salary and perks scores. Chapter-IV 170

Hypothesis 1.9: Food and relaxation Vs BPO areas H 0 : 1.9 There is no significant difference among the BPO sectors in the average scores of food and relaxation. H1: 1.9 There is significant difference among the BPO sectors in the average scores of food and relaxation. Table No.4.41 Food and relaxation Vs BPO areas Food and relaxation Financial Accounting 6.42 1.84 126 Customer Services 6.62 2.06 125 Area of work Procurement 6.57 1.65 14 Human Resource 7.07 2.47 41 Application Process 7.43 2.27 67 Others 7.78 2.34 27 Total 6.82 2.12 400 Table No. 4.42 ANOVA for Food and relaxation Sum of Squares df Mean Square F Sig. Between Groups 78.712 5 15.742 3.620 ** Within Groups 1713.598 394 4.349 Total 1792.310 399 One way ANOVA was applied to find whether there is significant difference among the area of work groups in the average food and relaxation scores. Since the calculated F-ratio value 3.620 is higher than the table value 3.064 at 1% level of significance, we reject the null hypothesis. Hence, it is inferred that there is significant difference among the area of work groups in the average food and relaxation scores. Chapter-IV 171

Hypothesis 1.10: Lack of transportation and talent Vs BPO areas H 0 : 1.10 There is no significant difference among the area of work groups in the average lack of transportation and talent scores. H1: 1.10 There is significant difference among the area of work groups in the average lack of transportation and talent scores. Table No. 4.43 Lack of transportation and talent Vs BPO areas Area of work Miscellaneous-lack of transportation and talent Financial Accounting 6.52 1.67 126 Customer Services 6.45 1.81 125 Procurement 6.29 1.49 14 Human Resource 6.44 1.82 41 Application Process 6.60 2.03 67 Others 5.81 2.24 27 Total 6.45 1.83 400 Table No. 4.44 ANOVA for Miscellaneous-lack of transportation and talent Sum of Squares df Mean Square F Sig. Between Groups 13.262 5 2.652.792 Ns Within Groups 1319.528 394 3.349 Total 1332.790 399 One way ANOVA was applied to find whether there is significant difference among the area of work groups in the average lack of transportation and talent scores. Since the calculated F-ratio value 0.792 is lower than the table value 2.237 at 5% level of significance, we accept the null hypothesis. Hence, it is inferred that there is no significant difference among the area of work groups in the average lack of transportation and talent scores. Chapter-IV 172

Hypothesis 1.11: Work and family conflict Vs BPO areas H 0 :1.11 There is no significant difference among the area of work groups in the average work and family conflict scores. H1:1.11 There is significant difference among the area of work groups in the average work and family conflict scores. Table No. 4.45 Work and family conflict Vs BPO areas Work and family conflict Financial Accounting 8.88 1.51 126 Customer Services 8.79 1.47 125 Area of work Procurement 8.43 1.16 14 Human Resource 8.24 1.84 41 Application Process 8.57 1.88 67 Others 8.30 2.13 27 Total 8.68 1.64 400 Table No.4.46. ANOVA for Work and family conflict Sum of Squares df Mean Square F Sig. Between Groups 20.167 5 4.033 1.504 Ns Within Groups 1056.873 394 2.682 Total 1077.040 399 One way ANOVA was applied to find whether there is significant difference among the area of work groups in the average work and family conflict scores. Since the calculated F-ratio value 1.504 is lower than the table value 2.237 at 5% level of significance, we accept the null hypothesis. Hence, it is inferred that there is no significant difference among the area of work groups in the average work and family conflict scores. Chapter-IV 173

Hypothesis 1.12: Work from home Vs BPO areas H 0 :1.12 There is no significant difference among the area of work groups in the average work from home scores. H1:1.12 There is significant difference among the area of work groups in the average work from home scores. Table No.4.47 Work from home Vs BPO areas Area of work Work from home Financial Accounting 2.48 1.04 126 Customer Services 2.48 1.10 125 Procurement 2.64 1.22 14 Human Resource 2.78 1.01 41 Application Process 2.64 1.30 67 Others 2.93 1.30 27 Total 2.57 1.13 400 Table No 4.48 ANOVA for Work from home Sum of Squares df Mean Square F Sig. Between Groups 7.775 5 1.555 1.225 Ns Within Groups 500.122 394 1.269 Total 507.898 399 One way ANOVA was applied to find whether there is significant difference among the area of work groups in the average work from home scores. Since the calculated F-ratio value 1.225 is lower than the table value 2.237 at 5% level of significance, we accept the null hypothesis. Hence, it is inferred that there is no significant difference among the area of work groups in the average work from home scores. Chapter-IV 174

Hypothesis 1.13: Lack of work ethics Vs BPO areas H 0 : 1.13 There is no significant difference among the area of work groups in the average lack of work ethics scores. H1:1.13 There is significant difference among the area of work groups in the average lack of work ethics scores. Table No.4.49 Lack of work ethics Vs BPO areas Lack of Work ethics Financial Accounting 15.70 3.16 126 Customer Services 15.26 3.37 125 Area of work Procurement 14.43 3.25 14 Human Resource 15.68 3.64 41 Application Process 16.25 3.98 67 Others 15.22 5.37 27 Total 15.58 3.60 400 Table No 4.50 ANOVA for Lack of Work ethics Sum of Squares df Mean Square F Sig. Between Groups 67.742 5 13.548 1.043 Ns Within Groups 5116.008 394 12.985 Total 5183.750 399 One way ANOVA was applied to find whether there is significant difference among the area of work groups in the average lack of work ethics scores. Since the calculated F-ratio value 1.043 is lower than the table value 2.237 at 5% level of significance, we accept the null hypothesis. Hence, it is inferred that there is no significant difference among the area of work groups in the average lack of work ethics scores. Chapter-IV 175

4.4.2 Variation in factors between the states of Karnataka and Kerala The following hypotheses were set to study the relationship between different BPO locations and proposed attrition factors : lack of integration and goal setting, motivation and appreciation, work atmosphere, labor welfare and corporate governance, maximum number of hours worked, dissatisfaction with rewards and hikes, human resource management practices, dissatisfaction with salary and perks, food and relaxation, lack of transportation and talent, work and family conflict, work from home and lack of work ethics. In each combination of location and attrition factor, suitable hypotheses were framed and testing (t-test) of the hypotheses were done and the results are discussed as given below: Hypothesis 2.1: Dissatisfaction with salary and perks Vs location H0: 2.1. There is no significant difference between the employees of Karnataka and Kerala in the average scores of dissatisfaction with salary and perks. H1: 2.1. There is significant difference between the employees of Karnataka and Kerala in the average scores of dissatisfaction with salary and perks. Table No.4.51 Dissatisfaction with salary and perks Vs location Dissatisfaction with Salary and perks Karnataka 6.25 1.79 285 Location Kerala 5.37 1.77 115 Total 6.00 1.83 400 Table No.4.52 t-test for Equality of Means t df Sig. 4.414 398 ** Chapter-IV 176

The t-test was applied to find whether there is significant difference between Karnataka and Kerala employees in the average dissatisfaction with salary and perks scores. The calculated value is 4.44 which is higher than the table value of 2.588 at 1% level of significance, we reject the null hypothesis. Hence, it is inferred that there is significant difference between Karnataka and Kerala employees in the average dissatisfaction with salary and perks scores. Hypothesis 2.2: Lack of integration and goal setting Vs employee s location H0:2.2. There is no significant difference between the employees of Karnataka and Kerala in the average scores of lack of integration and goal setting. H1:2.2. There is significant difference between the employees of Karnataka and Kerala in the average scores of lack of integration and goal setting. Table No. 4.53 Lack of integration and goal setting Vs employee s location Lack of integration and goal setting Karnataka 12.40 3.07 285 Location Kerala 11.53 3.48 115 Total 12.15 3.21 400 Table No.4.54 t-test for Equality of Means t df Sig. 2.456 398 * The t-test was applied to find whether there is significant difference between Karnataka and Kerala employees in the average lack of integration and goal setting scores. Since the calculated t-test value 2.456 is higher than the table value 1.966 at 5% level of significance, we reject the null hypothesis. Chapter-IV 177

Hence, it is inferred that there is significant difference between Karnataka and Kerala employees in the average lack of integration and goal setting scores. Hypothesis 2.3: Work atmosphere Vs Employee s location H0:2.3. There is no significant difference between the employees of Karnataka and Kerala in the average scores of work atmosphere. H1:2.3. There is significant difference between the employees of Karnataka and Kerala in the average scores of work atmosphere. Table No. 4.55 Work atmosphere Vs Employee s location. Work atmosphere Location Karnataka 14.84 2.74 285 Kerala 13.90 3.52 115 Total 14.57 3.01 400 Table No. 4.56 t-test for Equality of Means t df Sig. 2.864 398 ** The t-test was applied to find whether there is significant difference between Karnataka and Kerala employees in the average work atmosphere scores. Since the calculated t-test value 2.864 is higher than the table value 2.588 at 1% level of significance, we reject the null hypothesis. Hence, it is inferred that there is significant difference between Karnataka and Kerala employees in the average work atmosphere scores. Chapter-IV 178

Hypothesis 2.4: Food and relaxations Vs employee s location H0:2.4. There is no significant difference between the employees of Karnataka and Kerala in the average scores of food and relaxation. H1:2.4. There is significant difference between the employees of Karnataka and Kerala in the average scores of food and relaxation. Table: No. 4.57 Food and relaxations Vs employee s location Food and relaxation Karnataka 6.58 2.03 285 Location Kerala 7.40 2.22 115 Total 6.82 2.12 400 Table: No. 4.58 t-test for Equality of Means t df Sig. 3.557 398 ** The t-test was applied to find whether there is significant difference between Karnataka and Kerala employees in the average food and relaxation scores. Since the calculated t-test value 3.557 is higher than the table value of 2.588 at 1% level of significance, we reject the null hypothesis. Hence, it is inferred that there is significant difference between Karnataka and Kerala employees in the average food and relaxation scores. Chapter-IV 179

Hypothesis 2.5: Dissatisfaction with rewards and hikes Vs employee s location H0:2.5. There is no significant difference between the employees of Karnataka and Kerala in the average scores of dissatisfaction with rewards and hikes. H1:2.5. There is significant difference between the employees of Karnataka and Kerala in the average scores of dissatisfaction with rewards and hikes. Table: No. 4.59 Dissatisfaction with rewards and hikes Vs employee s location Dissatisfaction with rewards and hikes Karnataka 6.18 1.93 285 Location Kerala 5.73 2.01 115 Total 6.05 1.96 400 Table: No. 4.60 t-test for Equality of Means t df Sig. 2.063 398 * The t-test was applied to find whether there is significant difference between Karnataka and Kerala employees in the average dissatisfaction with rewards and hikes scores. Since the calculated value is 2.063, which is higher than the table value of 1.966 at 5% level of significance, we reject the null hypothesis. Hence, it is inferred that there is significant difference between Karnataka and Kerala employees in the average dissatisfaction with rewards and hikes scores. Chapter-IV 180

Hypothesis 2.6: Lack of work ethics Vs employee s location H 0 :2.6. There is no significant difference between the employees of Karnataka and Kerala in the average scores of lack of work ethics. H1:2.6. There is significant difference between the employees of Karnataka and Kerala in the average scores of lack of work ethics. Table: No. 4.61 Lack of work ethics Vs employee s location Lack of Work ethics Karnataka 15.67 3.23 285 Location Kerala 15.35 4.41 115 Total 15.58 3.60 400 Table: No. 4.62 t-test for Equality of Means t Df Sig. 0.800 398 Ns The t-test was applied to find whether there is significant difference between Karnataka and Kerala employees in the average lack of work ethics scores. Since the calculated value is 0.800, which is less than the table value of 1.966 at 5% level of significance, we accept the null hypothesis. Hence, it is inferred that there is no significant difference between Karnataka and Kerala employees in the average lack of work ethics scores. Chapter-IV 181

Hypothesis 2.7: Motivation and appreciation Vs employee s location H 0 : 2.7 There is no significant difference between the employees of Karnataka and Kerala in the average motivation and appreciation scores. H 1 : 2.7 There is significant difference between the employees of Karnataka and Kerala in the average motivation and appreciation scores. Table: No. 4.63 Motivation and appreciation Vs employee s location Motivation and appreciation Karnataka 7.82 2.62 285 Location Kerala 8.28 2.42 115 Total 7.95 2.57 400 Table: No. 4.64 t-test for Equality of Means t df Sig. 1.626 398 Ns The t-test was applied to find whether there is significant difference between Karnataka and Kerala employees in the average motivation and appreciation scores. Since the calculated value 1.626 is lower than the table value of 1.966 at 5% level of significance, we accept the null hypothesis. Hence, it is inferred that there is no significant difference between Karnataka and Kerala employees in the average motivation and appreciation scores. Chapter-IV 182

Hypothesis 2.8: Work from home Vs employee s location H 0 : 2.8 H1:2.8 There is no significant difference between the employees of Karnataka and Kerala in the average work from home scores. There is significant difference between the employees of Karnataka and Kerala in the average work from home scores. Table: No. 4.65 Work from home Vs employee s location Work from home Karnataka 2.58 1.08 285 Location Kerala 2.55 1.24 115 Total 2.57 1.13 400 Table: No. 4.66 t-test for Equality of Means t df Sig. 0.278 398 Ns The t-test was applied to find whether there is significant difference between Karnataka and Kerala employees in the average work from home scores. Since the calculated value is 0.278, which is lower than the table value of 1.966 at 5% level of significance, we accept the null hypothesis. Hence, it is inferred that there is no significant difference between Karnataka and Kerala employees in the average work from home scores. Chapter-IV 183

Hypothesis 2.9: Work and family conflict Vs employee s location H 0 : 2.9 H 1 : 2.9 There is no significant difference between the employees of Karnataka and Kerala in the average work and family conflict scores. There is significant difference between the employees of Karnataka and Kerala in the average work and family conflict scores. Table: No. 4.67 Work and family conflict Vs employee s location Work and family conflict Karnataka 8.80 1.51 285 Location Kerala 8.39 1.91 115 Total 8.68 1.64 400 Table: No. 4.68 t-test for Equality of Means t df Sig..244 398 * The t-test was applied to find whether there is significant difference between Karnataka and Kerala employees in the average work and family conflict scores. Since the calculated value 2.244 is higher than the table value of 1.966 at 5% level of significance, we reject the null hypothesis. Hence, it is inferred that there is significant difference between Karnataka and Kerala employees in the average work and family conflict scores. Chapter-IV 184

Hypothesis 2.10: Labour welfare and corporate governance Vs employee s location H 0 : 2.10 There is no significant difference between the employees of Karnataka and Kerala in the average labor welfare and corporate governance scores. H 1 : 2.10 There is significant difference between the employees of Karnataka and Kerala in the average labor welfare and corporate governance scores. Table No. 4.69 Labour welfare and corporate governance Vs employee s location. Labour welfare and corporate governance Karnataka 11.92 2.58 285 Location Kerala 11.35 2.93 115 Total 11.76 2.70 400 Table: No. 4.70 t-test for Equality of Means t df Sig. 1.937 398 Ns The t-test was applied to find whether there is significant difference between Karnataka and Kerala employees in the average labour welfare and corporate governance scores. Since the calculated value is 1.937, which is less than the table value of 1.966 at 5% level of significance, we accept the null hypothesis. Hence, it is inferred that there is no significant difference between Karnataka and Kerala employees in the average labour welfare and corporate governance scores. Chapter-IV 185

Hypothesis 2.11: Occupational health problems Vs employee s location H 0 :2.11 H 1: 2.11 There is no significant difference between the employees of Karnataka and Kerala in the average occupational health problems scores. There is significant difference between the employees of Karnataka and Kerala in the average occupational health problems scores. Table: No. 4.71 Occupational health problems Vs employee s location Occupational health problems Karnataka 14.56 3.18 285 Location Kerala 14.98 3.84 115 Total 14.69 3.38 400 Table: No. 4.72 t-test for Equality of Means t df Sig. 1.119 398 Ns The t-test was applied to find whether there is significant difference between Karnataka and Kerala employees in the average occupational health problems scores. Since the calculated value is 1.119, which is less than the table value of 1.966 at 5% level of significance, we accept the null hypothesis. Hence, it is inferred that there is no significant difference between Karnataka and Kerala employees in the average occupational health problems scores. Chapter-IV 186

Hypothesis 2.12: Human resource management Practices Vs employee s location H 0 : 2.12 H1:2.12 There is no significant difference between Karnataka and Kerala employees in the average scores of human resource management practices. There is significant difference between Karnataka and Kerala employees in the average scores of human resource management practices. Table No. 4.73 Human resource management Practices Vs employee s location Human Resource Management practice Karnataka 2.55.92 285 Location Kerala 2.63.96 115 Total 2.58.93 400 Table: No. 4.74 t-test for Equality of Means t df Sig. 0.784 398 Ns The t-test was applied to find whether there is significant difference between Karnataka and Kerala employees in the average human resource management practices scores. Since the calculated t-test value 0.784 is lower than the table value 1.966 at 5% level of significance, we accept the null hypothesis. Hence, it is inferred that there is no significant difference between Karnataka and Kerala employees in the average human resource management practices scores. Chapter-IV 187

Hypothesis 2.13: Average strength factor scores Vs employee s location H 0 : 2.13 There is no significant difference between Karnataka and Kerala employees in the average strength factors scores. H1: 2.13 There is significant difference between Karnataka and Kerala employees in the average strength factors scores. Table: No. 4.75 Average strength factor scores Vs employee s location Strength Factor Score Karnataka 8.56 1.87 285 Location Kerala 8.86 1.96 115 Total 8.65 1.90 400 Table: No. 4.76 t-test for Equality of Means t df Sig. 1.432 398 Ns The t-test was applied to find whether there is significant difference between Karnataka and Kerala employees in the average strength factor scores. Since the calculated t-test value 01.432 is less than the table value 1.966 at 5% level of significance, we accept the null hypothesis. Hence, it is inferred that there is no significant difference between Karnataka and Kerala employees in the average strength factor scores. Chapter-IV 188

4.4.3 Variation between national and multinational BPOs The following hypotheses were set to study the relationship between two global positions (national/ multinational) and the attrition factors: lack of integration and goal setting, motivation and appreciation, work atmosphere, labor welfare and corporate governance, maximum number of hours worked, dissatisfaction with rewards and hikes, human resource management practices, dissatisfaction with salary and perks, food and relaxation, work and family conflict, and work from home. In each combination of global position (national/multinational) and attrition factor suitable hypotheses were framed and testing (t-test) of the hypotheses were done and the results are discussed as given below: Hypothesis 3.1: Global position Vs Lack of integration and goal setting H0: 3.1. There is no significant difference between national BPO employees and multinational BPO employees in the average scores of lack of integration and goal setting. H1: 3.1. There is significant difference between national BPO employees and multinational BPO employees in the average scores of lack of integration and goal setting. Table: No. 4.77 Lack of integration and Goal setting Vs Global position Lack of integration and goal setting Global position National 11.81 3.04 212 Multinational 12.53 3.36 188 Total 12.15 3.21 400 Table: No. 4.78 t-test for Equality of Means t df Sig. 2.234 398 * Chapter-IV 189

The t-test was applied to find whether there is significant difference between national and multinational BPO employees in the average lack of integration and goal setting scores. Since the calculated value 2.234 is higher than the table value 1.966 at 5% level of significance, we reject the null hypothesis. Hence, it is inferred that there is significant difference between national and multinational BPO employees in the average lack of integration and goal setting scores. Hypothesis 3.2 Global position Vs Dissatisfaction with salary and perks H0:3.2. There is no significant difference between national BPO employees and multinational BPO employees in the average scores of dissatisfaction with salary and perks. H1:3.2. There is significant difference between national BPO employees and multinational BPO employees in the average scores of dissatisfaction with salary and perks. Table: No. 4.79 Dissatisfaction with salary and perks Vs Global position Dissatisfaction with Salary and perks Global position National 5.73 1.78 212 Multinational 6.30 1.84 188 Total 6.00 1.83 400 Table: No. 4.80 t-test for Equality of Means t Df Sig. 3.154 398 ** The t-test was applied to find whether there is significant difference between National and Multinational BPO employees in the average dissatisfaction with salary and perks scores. Chapter-IV 190

Since the calculated value 3.154 is higher than the table value 2.588 at 1% level of significance, we reject the null hypothesis. Hence, it is inferred that there is significant difference between national and multinational BPO employees in the average dissatisfaction with salary and perks scores. Hypothesis 3.3: Global position Vs Dissatisfaction with rewards and hikes H0:3.3. There is no significant difference between national BPO employees and multinational BPO employees in the average scores of dissatisfaction with rewards and hikes. H1:3.3. There is significant difference between national BPO employees and multinational BPO employees in the average scores of dissatisfaction with rewards and hikes. Table: No. 4.81 Dissatisfaction with rewards and hikes Vs Global position Dissatisfaction with rewards and hikes Global position National 5.85 1.83 212 Multinational 6.27 2.08 188 Total 6.05 1.96 400 Table: No. 4.82 t-test for Equality of Means t df Sig. 2.159 398 * The t-test was applied to find whether there is significant difference between national and multinational BPO employees in the average dissatisfaction with rewards and hikes scores. Since the calculated value 2.159 is higher than the table value 1.966 at 5% level of significance, we reject the null hypothesis. Chapter-IV 191

Hence, it is inferred that there is significant difference between national and multinational BPO employees in the average dissatisfaction with rewards and hikes scores. Hypothesis 3.4: Global position Vs Human resource management practices H0: 3.4. There is no significant difference between national and multinational BPO employees in the average scores of human resource management practices. H1: 3.4. There is significant difference between national and multinational BPO employees in the average scores of human resource management practices. Table: No. 4.83 Human Resource Management Practices Vs Global position Human Resource Management practice Global position National 2.57.92 212 Multinational 2.59.94 188 Total 2.58.93 400 Table: No. 4.84 t-test for Equality of Means t df Sig. 0.154 398 Ns The t-test was applied to find whether there is significant difference between national and multinational employees in the average human resource management practices scores. Since the calculated t-test value 0.154 is less than the table value 1.966 at 5% level of significance, we accept the null hypothesis. Hence, it is inferred that there is no significant difference between national and multinational employees in the average human resource management practices scores. Chapter-IV 192

Hypothesis 3.5: Global position Vs Work atmosphere H0: 3.5. There is no significant difference between national BPO employees and multinational BPO employees in the average scores of work atmosphere. H1:3.5. There is significant difference between national BPO employees and multinational BPO employees in the average scores of work atmosphere. Table: No. 4.85 Work atmosphere vs global position. Work atmosphere Global position National 14.45 3.19 212 Multinational 14.70 2.79 188 Total 14.57 3.01 400 Table: No. 4.86 t-test for Equality of Means t df Sig. 0.810 398 Ns The t-test was applied to find whether there is significant difference between National and Multinational BPO employees in the average work atmosphere scores. Since the calculated value 0.810 is lower than the table value 1.966 at 5% level of significance, we accept the null hypothesis. Hence, it is inferred that there is no significant difference between National and Multinational BPO employees in the average work atmosphere scores. Chapter-IV 193

Hypothesis 3.6: Global position Vs Work and family conflict H 0 :3.6 H1:3.6 There is no significant difference between national BPO employees and multinational BPO employees in the average scores of work and family conflict. There is significant difference between national BPO employees and multinational BPO employees in the average scores of work and family conflict. Table: No. 4.87 Work and Family Conflict Vs Global position Work and family conflict Global position National 8.58 1.75 212 Multinational 8.80 1.51 188 Total 8.68 1.64 400 Table: No. 4.88 t-test for Equality of Means t df Sig. 1.353 398 Ns The t-test was applied to find whether there is significant difference between National and Multinational BPO employees in the average work and family conflict scores. Since the calculated value 1.353 is lower than the table value 1.966 at 5% level of significance, we accept the null hypothesis. Hence, it is inferred that there is no significant difference between National and Multinational BPO employees in the average work and family conflict scores. Chapter-IV 194

Hypothesis: 3.7 Global position Vs Food and relaxation H 0: 3.7 H 1 : 3.7 There is no significant difference between national BPO employees and multinational BPO employees in the average scores of food and relaxation. There is significant difference between national BPO employees and multinational BPO employees in the average scores of food and relaxation. Table: No. 4.89 Food and relaxation Vs Global position Food and relaxation Global position National 6.94 2.12 212 Multinational 6.68 2.12 188 Total 6.82 2.12 400 Table: No. 4.90 t-test for Equality of Means t df Sig. 1.240 398 Ns The t-test was applied to find whether there is significant difference between National and Multinational BPO employees in the average food and relaxation scores. Since the calculated value 1.240 is lower than the table value 1.966 at 5% level of significance, we accept the null hypothesis. Hence, it is inferred that there is no significant difference between National and Multinational BPO employees in the average food and relaxation scores. Chapter-IV 195

Hypothesis 3.8: Global position Vs Motivation and appreciation H 0 :3.8 H 1 : 3.8 There is no significant difference between national BPO employees and multinational BPO employees in the average scores of motivation and appreciation. There is significant difference between national BPO employees and multinational BPO employees in the average scores of motivation and appreciation. Table: No. 4.91 Motivation and appreciation Vs Global position Motivation and appreciation Global position National 8.04 2.48 212 Multinational 7.85 2.67 188 Total 7.95 2.57 400 Table: No. 4.92 t-test for Equality of Means t df Sig. 0.763 398 Ns The t-test was applied to find whether there is significant difference between national and multinational BPO employees in the average motivation and appreciation scores. Since the calculated value 0.763 is lower than the table value 1.966 at 5% level of significance, we accept the null hypothesis. Hence, it is inferred that there is no significant difference between national and multinational BPO employees in the average motivation and appreciation scores. Chapter-IV 196

Hypothesis 3.9: Global position Vs Labor welfare and corporate governance H 0 : 3.9 H1:3.9 There is no significant difference between national BPO employees and multinational BPO employees in the average scores of labor welfare and corporate governance. There is significant difference between national BPO employees and multinational BPO employees in the average scores of labor welfare and corporate governance. Table: No. 4.93 Labour welfare and corporate governance Vs Global position Labour welfare and corporate governance Global position National 11.68 2.47 212 Multinational 11.84 2.93 188 Total 11.76 2.70 400 Table: No. 4.94 t-test for Equality of Means t Df Sig. 0.579 398 Ns The t-test was applied to find whether there is significant difference between national and multinational BPO employees in the average labour welfare and corporate governance scores. Since the calculated value 0.579 is lower than the table value 1.966 at 5% level of significance, we accept the null hypothesis. Hence, it is inferred that there is no significant difference between national and multinational BPO employees in the average labour welfare and corporate governance scores. Chapter-IV 197

Hypothesis 3.10: Global position Vs Maximum number of hours worked Ho: 3.10 There is no significant relationship between maximum number of hours worked and the global position of the company. H1:3.10 There is significant relationship between maximum number of hours worked and the global position of the company. Table: No. 4.95 Maximum number of hours worked Vs Global position Maximum no. of hours worked National Global position Multinational No. % No. % TOTAL No. % 0-8 hrs 63 29.7 17 9.0 80 20.0 8-12 hrs 120 56.6 123 65.4 243 60.8 Above 12 hrs 29 13.7 48 25.5 77 19.3 Total 212 100.0 188 100.0 400 100.0 Table: No. 4.96 Chi-Square Test Value df Sig. Chi-Square 29.843 2 ** Chi-square test was applied to find whether there is significant relationship between maximum number of hours worked and the global position of the company. Since the calculated chi-square value, 29.843 is higher than the table value of 09.210 at 1% level of significance, we reject the null hypothesis. Hence, it is inferred that there is significant relationship between maximum number of hours worked and the global position of the company. Chapter-IV 198

Hypothesis 3.11: Global position Vs Work from home H 0 :3.11 H 1 :3.11 There is no significant difference between national BPO employees and multinational BPO employees in the average work from home scores. There is significant difference between national BPO employees and multinational BPO employees in the average work from home scores. Table: No. 4.97 Work from Home Vs Global position Work from home Global position National 2.66 1.16 212 Multinational 2.47 1.09 188 Total 2.57 1.13 400 Table: No. 4.98 t-test for Equality of Means t df Sig. 1.658 398 Ns The t-test was applied to find whether there is significant difference between national and multinational BPO employees in the average work from home scores. Since the calculated value 1.658 is lower than the table value 1.966 at 5% level of significance, we accept the null hypothesis. Hence, it is inferred that there is no significant difference between national and multinational BPO employees in the average work from home scores. Chapter-IV 199

4.4.4 Relationship between Age groups and Attrition factors Following hypotheses were set to study the relationship between age groups and proposed attrition factors: lack of integration and goal setting, motivation and appreciation, work atmosphere, labor welfare and corporate governance, maximum number of hours worked, dissatisfaction with rewards and hikes, human resource management practices, dissatisfaction with salary and perks, food and relaxation, work and family conflict, strength factor and work from home. In each combination of age groups and attrition factors, suitable hypotheses were framed and testing (ANOVA) of the hypothesis were done and the results are given below: Hypothesis4.1: Lack of integration and goal setting Vs Age H0:4.1. There is no significant difference among the respondent s age groups in the average scores of lack of integration and goal setting. H1: 4.1. There is significant difference among the respondents age groups in the average scores of lack of integration and goal setting Table: No. 4.99 Lack of integration and goal setting Vs Age Lack of integration and goal setting < 18 yrs 12.25 3.10 4 Age of the respondent 18-20 yrs 14.04 3.29 27 21-25 yrs 12.13 3.16 260 Above 25 yrs 11.72 3.19 109 Total 12.15 3.21 400 Table: No. 4.100 ANOVA for Lack of integration and goal setting Sum of Squares df Mean Square F Sig. Between Groups 116.847 3 38.949 3.856 ** Within Groups 3999.450 396 10.100 Total 4116.297 399 Chapter-IV 200

One way ANOVA was applied to find whether there is significant difference among the age of the respondent groups in the average lack of integration and goal setting scores. Since the calculated F-ratio value 3.856 is higher than the table value 3.831 at 1% level of significance, we reject the null hypothesis. Hence, it is inferred that there is significant difference among the age of the respondent groups in the average lack of integration and goal setting scores. Hypothesis 4.2: Work and family conflict Vs Age H 0 : 4.2. There is no significant difference among the respondent s age groups in the average scores of work and family conflict. H1:4.2. There is significant difference among the respondent s age groups in the average scores of work and family conflict. Table: No. 4.101 Work and family conflict Vs Age Work and family conflict < 18 yrs 9.00.82 4 18-20 yrs 8.74 1.16 27 Age of the respondent 21-25 yrs 8.85 1.60 260 Above 25 yrs 8.25 1.80 109 Total 8.68 1.64 400 Table: No. 4.102 ANOVA for Work and family conflict Sum of Squares df Mean Square F Sig. Between Groups 28.393 3 9.464 3.574 * Within Groups 1048.647 396 2.648 Total 1077.040 399 One way ANOVA was applied to find whether there is significant difference among the age of the respondent groups in the average work and family conflict scores. Chapter-IV 201

Since the calculated F-ratio value 3.574 is higher than the table 2.628 at 5% level of significance, we reject the null hypothesis. Hence, it is inferred that there is significant difference among the age of the respondent groups in the average work and family conflict scores. Hypothesis 4.3: Strength factor Vs Age H0: 4.3. There is no significant difference among the respondent s age groups in the average scores of strength factor. H1: 4.3. There is significant difference among the respondent s age groups in the average scores of strength factor. Table: No. 4.103 Strength factor Vs Age Strength Factor Score < 18 yrs 11.00 1.41 4 18-20 yrs 8.30 2.30 27 Age of the respondent 21-25 yrs 8.53 1.78 260 Above 25 yrs 8.93 2.00 109 Total 8.65 1.90 400 Table: No. 4.104 ANOVA for Strength Factor Score Sum of Squares df Mean Square F Sig. Between Groups 37.501 3 12.500 3.546 * Within Groups 1395.796 396 3.525 Total 1433.298 399 One way ANOVA was applied to find whether there is significant difference among the age of the respondent groups in the average strength factor scores. Since the calculated F-ratio value, 03.546 is higher than the table value 02.628 at 5% level of significance, we reject the null hypothesis. Chapter-IV 202

Hence, it is inferred that there is significant difference among the age of the respondent groups in the average strength factor scores. Hypothesis 4.4: Maximum number of hours worked Vs Age H 0 : 4.4 There is no significant relationship between maximum number of hours worked and the age group of the respondents. H1: 4.4 There is significant relationship between maximum number of hours worked and the age group of the respondents. Table: No. 4.105 Maximum number of hours worked Vs Age Maximum hours worked < 18 yrs 5.75.96 4 Age of the respondent 18-20 yrs 6.63 2.00 27 21-25 yrs 6.63 1.84 260 Above 25 yrs 6.88 2.06 109 Total 6.69 1.91 400 Table: No. 4.106 ANOVA for Maximum number of hours worked. Sum of Squares df Mean Square Between Groups 8.393 3 2.798.769 Ns Within Groups 1440.784 396 3.638 Total 1449.178 399 F Sig. One way ANOVA was applied to find whether there is significant difference among the age of the respondent groups in the average scores of maximum hours worked. Since the calculated F-ratio value 0.769 is lower than the table 2.628 at 5% level of significance, we accept the null hypothesis. Hence, it is inferred that there is no significant difference among the age of the respondent groups in the average scores of maximum hours worked. Chapter-IV 203

Hypothesis 4.5: Human Resource Management Practices Vs Age H 0 : 4.5 There is no significant difference among the respondents age groups in the average human resource management practices scores. H1: 4.5 There is significant difference among the respondent s age groups in the average human resource management practices scores. Table: No. 4.107 Human Resource Management Practices Vs Age Human Resource Management practice < 18 yrs 2.75 1.26 4 18-20 yrs 2.59.80 27 Age of the respondent 21-25 yrs 2.62.90 260 Above 25 yrs 2.48 1.02 109 Total 2.58.93 400 Table: No. 4.108 ANOVA for Human Resource Management practices Sum of Squares df Mean Square Between Groups 1.598 3.533.617 Ns Within Groups 342.000 396.864 Total 343.598 399 One way ANOVA was applied to find whether there is significant difference among the age of the respondent groups in the average human resource management practices scores. Since the calculated F-ratio value, 0.617 is lower than the table value 2.628 at 5% level of significance, we accept the null hypothesis. Hence, it is inferred that there is no significant difference among the age of the respondent groups in the average human resource management practices scores. F Sig. Chapter-IV 204

Hypothesis 4.6: Lack of work ethics Vs Age H 0 : 4.6 There is no significant difference among the respondents age groups in the average lack of work ethics scores. H1: 4.6 There is significant difference among the respondents age groups in the average lack of work ethics scores. Table: No. 4.109 Lack of work ethics Vs Age Lack of Work ethics < 18 yrs 15.00.00 4 Age of the respondent 18-20 yrs 16.19 4.39 27 21-25 yrs 15.43 3.61 260 Above 25 yrs 15.79 3.45 109 Total 15.58 3.60 400 Table: No. 4.110 ANOVA for Lack of Work ethics Sum of Squares df Mean Square F Sig. Between Groups 21.775 3 7.258.557 Ns Within Groups 5161.975 396 13.035 Total 5183.750 399 One way ANOVA was applied to find whether there is significant difference among the age of the respondent groups in the average lack of work ethics scores. Since the calculated F-ratio value 0.557 is lower than the table 2.628 at 5% level of significance, we accept the null hypothesis. Hence, it is inferred that there is no significant difference among the age of the respondent groups in the average lack of work ethics scores. Chapter-IV 205

Hypothesis 4.7: Dissatisfaction with rewards and hikes Vs Age H 0 : 4.7 There is no significant difference among the age of the respondent groups in the average dissatisfaction with rewards and hikes scores. H1: 4.7 There is significant difference among the age of the respondent groups in the average dissatisfaction with rewards and hikes scores. Table: No. 4.111 Dissatisfaction with rewards and hikes Vs Age Dissatisfaction with rewards and hikes < 18 yrs 7.00 1.83 4 18-20 yrs 6.85 1.68 27 Age of the respondent 21-25 yrs 6.05 1.96 260 Above 25 yrs 5.82 2.00 109 Total 6.05 1.96 400 Table: No. 4.112 ANOVA for Dissatisfaction with rewards and hikes Sum of Squares df Mean Square F Sig. Between Groups 26.914 3 8.971 2.357 Ns Within Groups 1507.184 396 3.806 Total 1534.098 399 One way ANOVA was applied to find whether there is significant difference among the age of the respondent groups in the average dissatisfaction with rewards and hikes scores. Since the calculated F-ratio value 2.357 is lower than the table 2.628 at 5% level of significance, we accept the null hypothesis. Hence, it is inferred that there is no significant difference among the age of the respondent groups in the average dissatisfaction with rewards and hikes scores. Chapter-IV 206

Hypothesis 4.8: Labour welfare and corporate governance Vs Age H 0 :4.8 H1:4.8 There is no significant difference among the age of the respondent groups in the average labour welfare and corporate governance scores. There is significant difference among the age of the respondent groups in the average labour welfare and corporate governance scores. Table: No. 4.113 Labour welfare and corporate governance Vs Age Labour welfare and corporate governance < 18 yrs 11.75.96 4 18-20 yrs 12.59 2.29 27 Age of the respondent 21-25 yrs 11.65 2.62 260 Above 25 yrs 11.82 2.99 109 Total 11.76 2.70 400 Table: No. 4.114 ANOVA for Labour welfare and corporate governance Sum of Squares df Mean Square F Sig. Between Groups 22.433 3 7.478 1.028 Ns Within Groups 2879.045 396 7.270 Total 2901.477 399 One way ANOVA was applied to find whether there is significant difference among the age of the respondent groups in the average labor welfare and corporate governance scores. Since the calculated F-ratio value 1.028 is lower than the table 2.628 at 5% level of significance, we accept the null hypothesis. Hence, it is inferred that there is no significant difference among the age of the respondent groups in the average labor welfare and corporate governance scores. Chapter-IV 207

Hypothesis 4.9: Dissatisfaction with salary and perks Vs Age H 0 :4.9 There is no significant difference among the age of the respondent groups in the average dissatisfaction with salary and perks scores. H1:4.9 There is significant difference among the age of the respondent groups in the average dissatisfaction with salary and perks scores. Table: No. 4.115 Dissatisfaction with salary and perks Vs Age Dissatisfaction with Salary and perks < 18 yrs 5.75.50 4 18-20 yrs 5.85 1.43 27 Age of the respondent 21-25 yrs 6.10 1.94 260 Above 25 yrs 5.80 1.66 109 Total 6.00 1.83 400 Table: No. 4.116 ANOVA for Dissatisfaction with Salary and perks Sum of Squares df Mean Square F Sig. Between Groups 7.677 3 2.559.764 Ns Within Groups 1326.313 396 3.349 Total 1333.990 399 One way ANOVA was applied to find whether there is significant difference among the age of the respondent groups in the average dissatisfaction with salary and perks scores. Since the calculated F-ratio value 0.764 is lower than the table 2.628 at 5% level of significance, we accept the null hypothesis. Hence, it is inferred that there is no significant difference among the age of the respondent groups in the average dissatisfaction with salary and perks scores. Chapter-IV 208

Hypothesis 4.10: Work from Home Vs Age H 0 :4.10 H1:4.10 There is no significant difference among the respondents age groups in the average work from home scores. There is significant difference among the respondents age groups in the average work from home scores. Table: No. 4.117 Work from Home Vs Age Work from home < 18 yrs 3.75.96 4 18-20 yrs 2.30.91 27 Age of the respondent 21-25 yrs 2.56 1.12 260 Above 25 yrs 2.62 1.18 109 Total 2.57 1.13 400 Table: No. 4.118 ANOVA for Work from home Sum of Squares df Mean Square F Sig. Between Groups 7.925 3 2.642 2.092 Ns Within Groups 499.973 396 1.263 Total 507.897 399 One way ANOVA was applied to find whether there is significant difference among the age of the respondent groups in the average work from home scores. Since the calculated F-ratio value 2.092 is lower than the table 2.628 at 5% level of significance, we accept the null hypothesis. Hence, it is inferred that there is no significant difference among the age of the respondent groups in the average work from home scores. Chapter-IV 209

Hypothesis 4.11: Motivation and Appreciation Vs Age H 0 :4.11 H1:4.11 There is no significant difference among the respondents age groups in the average motivation and appreciation scores. There is significant difference among the respondents age groups in the average motivation and appreciation scores. Table: No. 4.119 Motivation and Appreciation Vs Age Motivation and appreciation < 18 yrs 8.75 2.22 4 18-20 yrs 8.74 2.58 27 Age of the respondent 21-25 yrs 7.94 2.62 260 Above 25 yrs 7.74 2.44 109 Total 7.95 2.57 400 Table: No. 4.120 ANOVA for Motivation and appreciation Sum of Squares df Mean Square F Sig. Between Groups 24.123 3 8.041 1.219 Ns Within Groups 2612.877 396 6.598 Total 2637.000 399 One way ANOVA was applied to find whether there is significant difference among the age of the respondent groups in the average motivation and appreciation scores. Since the calculated F-ratio value 1.219 is lower than the table 2.628 at 5% level of significance, we accept the null hypothesis. Hence, it is inferred that there is no significant difference among the age of the respondent groups in the average motivation and appreciation scores. Chapter-IV 210

Hypothesis 4.12: Work Atmosphere Vs Age H 0 : 4.12 There is no significant difference among the age of the respondent groups in the average work atmosphere scores. H1: 4.12 There is significant difference among the age of the respondent groups in the average work atmosphere scores. Table: No. 4.121 Work Atmosphere Vs Age Work atmosphere < 18 yrs 16.00 1.41 4 18-20 yrs 15.07 2.32 27 Age of the respondent 21-25 yrs 14.59 3.11 260 Above 25 yrs 14.34 2.95 109 Total 14.57 3.01 400 Table: No. 4.122 ANOVA for Work atmosphere Sum of Squares df Mean Square F Sig. Between Groups 20.920 3 6.973.770 Ns Within Groups 3587.258 396 9.059 Total 3608.178 399 One way ANOVA was applied to find whether there is significant difference among the age of the respondent groups in the average work atmosphere scores. Since the calculated F-ratio value 0.770 is lower than the table 2.628 at 5% level of significance, we accept the null hypothesis. Hence, it is inferred that there is no significant difference among the age of the respondent groups in the average work atmosphere scores. Chapter-IV 211

4.4.5 Relationship between maximum number of hours worked and Attrition The following hypotheses were set to study the relationship between maximum number of hours worked groups and proposed attrition factors namely area of work, gender, location and monthly salary. In each combination of maximum number of hours worked groups and attrition factors, suitable hypotheses were framed and testing (Chi-Square Test) of the hypothesis were done and the results are given below: Hypothesis 5.1: Maximum number of hours worked Vs Gender Ho: 5.1. The maximum number of hours worked is independent of the employee s gender. H1: 5.1. The maximum number of hours worked is dependent on the employee s gender. Table No. 4.123 Maximum number of hours worked Vs Gender Gender Male Female No. % No. % TOTAL No. % Maximum no. of hours worked 0-8 hrs 43 18.2 37 22.6 80 20.0 8-12 hrs 138 58.5 105 64.0 243 60.8 Above 12 hrs 55 23.3 22 13.4 77 19.3 Total 236 100.0 164 100.0 400 100.0 Table: No. 4.124 Chi-Square Test Value df Sig. Chi-Square 6.319 2 * Chi-square test was applied to find whether the maximum number of hours worked is dependent on the gender. Since the calculated chi-square value, 6.319 is higher than the table value of 5.991 at 5% level of significance, we reject the null hypothesis. Chapter-IV 212

Hence, it is inferred that the maximum number of hours worked is dependent on the gender. Hypothesis 5.2: Maximum number of hours worked Vs Location Ho: 5.2. H1:5.2. The maximum number of hours worked is independent of the employee s location. The maximum number of hours worked is dependent on the employee s location. Table: No. 4.125 Maximum number of hours worked Vs Location Location TOTAL Karnataka Kerala No. % No. % No. % 0-8 hrs 36 12.6 44 38.3 80 20.0 Maximum no. of hours worked 8-12 hrs 189 66.3 54 47.0 243 60.8 Above 12 hrs 60 21.1 17 14.8 77 19.3 Total 285 100.0 115 100.0 400 100.0 Table: No. 4.126 Chi-Square Test Value df Sig. Chi-Square 33.639 2 ** Chi-square test was applied to find whether the maximum number of hours worked is dependent on the employees location. Since the calculated chi-square value, 33.639 is higher than the table value of 09.210 at 1% level of significance, we reject the null hypothesis. Hence, it is inferred that the maximum number of hours worked is dependent on the location. Chapter-IV 213

Hypothesis 5.3: Maximum number of hours worked Vs salary per month Ho: 5.3. H1:5.3. The maximum number of hours worked is independent of the employee s salary per month. The maximum number of hours worked is dependent on the employee s salary per month. Table: No. 4.127 Maximum number of hours worked Vs salary per month Salary per month TOTAL < Rs.5000 Rs.5000-10000 Rs.10000-15000 Rs.15000-20000 Above Rs.20000 No. % No. % No. % No. % No. % No. % Maximum no. of hours worked 0-8 hrs 4 57.1 25 30.5 33 20.6 11 11.7 7 12.3 80 20.0 8-12 hrs 3 42.9 50 61.0 98 61.3 57 60.6 35 61.4 243 60.8 Above 12 hrs 7 8.5 29 18.1 26 27.7 15 26.3 77 19.3 Total 7 100.0 82 100.0 160 100.0 94 100.0 57 100.0 400 100.0 Table: No. 4.128 Chi-Square Test Value df Sig. Chi-Square 25.958 8 ** Chi-square test was applied to find whether there is significant relationship between maximum number of hours worked and salary per month. Since the calculated chi-square value, 25.958 is higher than the table value of 20.090 at 1% level of significance, we reject the null hypothesis. Hence, it is inferred that there is significant relationship between maximum number of hours worked and salary per month. Chapter-IV 214

Hypothesis 5.4: Maximum number of hours worked Vs area of work Ho: 5.4 The maximum number of hours worked is independent of the employee s area of work. H1:5.4 The maximum number of hours worked is dependent on the employee s area of work. Table: No. 4.129 Maximum number of hours worked Vs area of work Financial Accounting Customer Services Area of work Procurement Human Resource Application Process Others No. % No. % No. % No. % No. % No. % TOTAL No. % Maximum no. of hours worked 0-8 hrs 8-12 hrs Above 12 hrs 21 16.7 29 23.2 2 14.3 10 24.4 13 19.4 5 18.5 80 20.0 83 65.9 78 62.4 8 57.1 23 56.1 38 56.7 13 48.1 243 60.8 22 17.5 18 14.4 4 28.6 8 19.5 16 23.9 9 33.3 77 19.3 Total 126 100.0 125 100.0 14 100.0 41 100.0 67 100.0 27 100.0 400 100.0 Table: No. 4.130 Chi-Square Test Value df Sig. Chi-Square 9.566 10 Ns Chi-square test was applied to find whether the maximum number of hours worked is dependent on the area of work. Since the calculated chi-square value, 09.566 is lower than the table value of 18.307 at 5% level of significance, we accept the null hypothesis. Hence, it is inferred that the maximum number of hours worked is independent on the area of work. Chapter-IV 215

4.4.6 Difference among the designation groups towards Attrition factors The following hypotheses were set to study the relationship between designation groups and proposed attrition factors namely: lack of integration and goal setting, motivation and appreciation, work atmosphere, dissatisfaction with rewards and hikes, human resource management practices, dissatisfaction with salary and perks, work and family conflict, and strength factor. In each combination, suitable hypothesis were framed and testing (ANOVA) of the hypothesis were done and the results are discussed as below: Hypothesis 6.1: Designation groups Vs strength factor scores H0: 6.1. There is no significant difference among the designation groups in the average strength factor scores. H1:6.1. There is significant difference among the designation groups in the average strength factor scores Table: No. 4.131 Designation groups Vs strength factor scores Strength Factor Score Process Analyst 8.41 1.90 246 Senior Process Analyst 8.91 1.80 95 Designation Team Leader 9.15 1.73 34 Supervisor 9.27 2.09 15 Manager 9.50 2.17 10 Total 8.65 1.90 400 Table: No. 4.132 ANOVA for Strength Factor Score Sum of Squares df Mean Square F Sig. Between Groups 42.102 4 10.526 2.989 * Within Groups 1391.195 395 3.522 Total 1433.298 399 One way ANOVA was applied to find whether there is significant difference among the designation groups in the average strength factor scores. Chapter-IV 216

Since the calculated F-ratio value, 02.989 is higher than the table value 02.395 at 5% level of significance, we reject the null hypothesis. Hence, it is inferred that there is significant difference among the designation groups in the average strength factor scores. Hypothesis 6.2: Work and Family Conflict Vs designation H0:6.2. There is no significant difference among the designation groups in the average scores of work and family conflict. H1:6.2. There is significant difference among the designation groups in the average scores of work and family conflict. Table: No. 4.133 Work and Family Conflict Vs designation Designation Table: No. 4.134 Work and family conflict Process Analyst 8.80 1.57 246 Senior Process Analyst 8.74 1.46 95 Team Leader 8.59 1.67 34 Supervisor 7.27 2.15 15 Manager 7.60 2.72 10 TOTAL 8.68 1.64 400 ANOVA for Work and family conflict Sum of Squares df Mean Square F Sig. Between Groups 45.810 4 11.453 4.387 ** Within Groups 1031.230 395 2.611 Total 1077.040 399 One way ANOVA was applied to find whether there is significant difference among the designation groups in the average work and family conflict scores. Since the calculated F-ratio value 4.387 is higher than the table value 3.367 at 1% level of significance, we reject the null hypothesis. Chapter-IV 217

Hence, it is inferred that there is significant difference among the designation groups in the average work and family conflict scores. Hypothesis 6.3: Motivation and Appreciation Vs designation H0:6.3 There is no significant difference among the designation groups in the average scores of motivation and appreciation. H1:6.3 There is significant difference among the designation groups in the average scores of motivation and appreciation. Table: No. 4.135 Motivation and Appreciation Vs designation Motivation and appreciation Process Analyst 8.17 2.57 246 Senior Process Analyst 7.67 2.56 95 Designation Team Leader 8.12 2.21 34 Supervisor 6.87 2.77 15 Manager 6.30 2.79 10 Total 7.95 2.57 400 Table: No. 4.136 ANOVA for Motivation and appreciation Sum of Squares df Mean Square F Sig. Between Groups 64.586 4 16.147 2.479 * Within Groups 2572.414 395 6.512 Total 2637.000 399 One way ANOVA was applied to find whether there is significant difference among the designation groups in the average motivation and appreciation scores. Since the calculated F-ratio value 2.479 is higher than the table value 2.395 at 5% level of significance, we reject the null hypothesis. Hence, it is inferred that there is significant difference among the designation groups in the average motivation and appreciation scores. Chapter-IV 218

Hypothesis 6.4: Lack of integration and goal setting Vs designation H 0 :6.4 There is no significant difference among the designation groups in the average scores of lack of integration and goal setting. H1: 6.4 There is significant difference among the designation groups in the average scores of lack of integration and goal setting. Table: No. 4.137 Lack of integration and goal setting Vs designation Lack of integration and goal setting Process Analyst 12.33 3.34 246 Senior Process Analyst 12.03 2.77 95 Designation Team Leader 11.79 2.93 34 Supervisor 10.20 3.67 15 Manager 12.90 3.54 10 Total 12.15 3.21 400 Table: No. 4.138 ANOVA for Lack of integration and goal setting Sum of Squares df Mean Square F Sig. Between Groups 76.204 4 19.051 1.863 Ns Within Groups 4040.093 395 10.228 Total 4116.297 399 One way ANOVA was applied to find whether there is significant difference among the designation groups in the average lack of integration and goal setting scores. Since the calculated F-ratio value 1.863 is less than the table value 2.395 at 5% level of significance, we accept the null hypothesis. Hence, it is inferred that there is no significant difference among the designation groups in the average lack of integration and goal setting scores Chapter-IV 219

Hypothesis 6.5: Work Atmosphere Vs designation H 0 : 6.5 There is no significant difference among the designation groups in the average scores of work atmosphere. H1: 6.5 There is significant difference among the designation groups in the average scores of work atmosphere. Table: No. 4.139 Work Atmosphere Vs designation Work atmosphere Process Analyst 14.63 3.13 246 Senior Process Analyst 14.71 2.69 95 Designation Team Leader 14.62 2.93 34 Supervisor 13.40 2.92 15 Manager 13.20 3.16 10 Total 14.57 3.01 400 Table: No. 4.140 ANOVA for Work atmosphere Sum of Squares df Mean Square F Sig. Between Groups 42.128 4 10.532 1.167 Ns Within Groups 3566.050 395 9.028 Total 3608.178 399 One way ANOVA was applied to find whether there is significant difference among the designation groups in the average work atmosphere scores. Since the calculated F-ratio value 1.167 is less than the table value 2.395 at 5% level of significance, we accept the null hypothesis. Hence, it is inferred that there is no significant difference among the designation groups in the average work atmosphere scores. Chapter-IV 220

Hypothesis 6.6: Dissatisfaction with salary and perks Vs designation H 0 : 6.6 There is no significant difference among the designation groups in the average scores of dissatisfaction with salary and perks. H1:6.6 There is significant difference among the designation groups in the average scores of dissatisfaction with salary and perks. Table: No. 4.141 Dissatisfaction with salary and perks Vs designation Dissatisfaction with Salary and perks Process Analyst 6.11 1.87 246 Senior Process Analyst 5.82 1.62 95 Designation Team Leader 6.09 1.90 34 Supervisor 5.67 1.99 15 Manager 5.00 2.00 10 Total 6.00 1.83 400 Table: No. 4.142 ANOVA for Dissatisfaction with Salary and perks Sum of Squares df Mean Square F Sig. Between Groups 17.927 4 4.482 1.345 Ns Within Groups 1316.063 395 3.332 Total 1333.990 399 One way ANOVA was applied to find whether there is significant difference among the designation groups in the average dissatisfaction with salary and perks scores. Since the calculated F-ratio value 1.345 is lower than the table value 2.395 at 5% level of significance, we accept the null hypothesis. Hence, it is inferred that there is no significant difference among the designation groups in the average dissatisfaction with salary and perks scores. Chapter-IV 221

Hypothesis 6.7: Dissatisfaction with rewards and hikes Vs designation H 0 : 6.7 There is no significant difference among the designation groups in the average scores of dissatisfaction with rewards and hikes. H1: 6.7 There is significant difference among the designation groups in the average scores of dissatisfaction with rewards and hikes. Table: No. 4.143 Dissatisfaction with rewards and hikes Vs designation Dissatisfaction with rewards and hikes Process Analyst 6.13 1.99 246 Senior Process Analyst 5.96 1.83 95 Designation Team Leader 6.09 1.90 34 Supervisor 5.73 2.09 15 Manager 5.10 2.42 10 Total 6.05 1.96 400 Table: No. 4.144 ANOVA for Dissatisfaction with rewards and hikes Sum of Squares df Mean Square F Sig. Between Groups 13.124 4 3.281.852 Ns Within Groups 1520.973 395 3.851 Total 1534.097 399 One way ANOVA was applied to find whether there is significant difference among the designation groups in the average dissatisfaction with rewards and hikes scores. Since the calculated F-ratio value 0.852 is lower than the table value 2.395 at 5% level of significance, we accept the null hypothesis. Hence, it is inferred that there is no significant difference among the designation groups in the average dissatisfaction with rewards and hikes scores. Chapter-IV 222

Hypothesis 6.8: Designation groups Vs Human Resource Management Practices H 0 :6.8 There is no significant difference among the designation groups in the average scores of human resource management practices. H1: 6.8 There is significant difference among the designation groups in the average scores of human resource management practices. Table: No. 4.145 Designation groups Vs Human Resource Management Practices Human Resource Management practice Process Analyst 2.64.94 246 Senior Process Analyst 2.49.85 95 Designation Team Leader 2.38.78 34 Supervisor 2.67 1.18 15 Manager 2.30 1.34 10 Total 2.58.93 400 Table: No. 4.146 ANOVA for Human Resource Management practice Sum of Squares df Mean Square F Sig. Between Groups 3.867 4.967 1.124 Ns Within Groups 339.730 395.860 Total 343.597 399 One way ANOVA was applied to find whether there is significant difference among the designation groups in the average human resource management practices scores. Since the calculated F-ratio value, 01.124 is less than the table value 02.395 at 5% level of significance, we accept the null hypothesis. Hence, it is inferred that there is no significant difference among the designation groups in the average human resource management practices scores. Chapter-IV 223

4.5 MULTIPLE REGRESSION ANALYSIS Regression Analysis was applied to find the critical factors and non-critical factors or variables which might affect the attrition of the employees. For this, overall attrition score was considered as dependent variable. The other variables namely Gender (coded as 1-M, 0-F), Location (coded as 1-Karnataka, 0-Kerala), Global Position (1-National, 0- Multinational), Age of the respondent, Experience in the present organization, Salary per month, Number of training programs attended, Maximum number of hours worked, Strength factors and HRM Practices were selected as independent variables which might affect the dependent variable namely Overall Attrition Score. The result of the regression analysis is given below: Table No. 4.147 Dependent Variable: Overall Attrition Score Regression Coefficients (B) (Constant) 131.540 7.074 Std. Error t Sig. Gender.264 1.436.184 Ns Location.413 1.773.233 Ns Global position -.147 1.594 -.092 Ns Age of the respondent -2.766 1.343-2.060 * Experience in the present organization -.450.865 -.520 Ns Salary per month 1.761.861 2.045 * No. of training programs attended -1.318.892-1.478 Ns Maximum no. of hours worked 1.506 1.197 1.259 Ns Strength Factor Score -1.942.419-4.633 ** Human Resource Management practice -3.217.854-3.765 ** Table: No.4.148 R and F-Ratio values R R Square F Sig..423.179 8.500 ** Chapter-IV 224

The multiple correlation co-efficient (R) value was found to be 0.423 which shows that there is moderate level of correlation between the dependent variable and the set of independent variables taken together. The F ratio value (8.500) shows that there is significant relationship between the overall attrition score and the set of independent variables. The R square value indicates that 17.9% of variation in the overall attrition score is explained by the set of independent variables included in the model. Individually, looking at the regression co-efficient, it is seen that age, experience, number of training programs attended, strength factor, HRM management practice have affected the overall attrition score negatively. That is the employee s attitude towards attrition decreases when these variables are on the higher side. For example, respondents in the older age group have lesser attitude towards attrition. Also, those who have given higher scores or ratings for strength factor or HRM management practice, the attrition scores are lesser. Salary and number of hours worked affect the attrition score positively. That is, those who draw higher salary and those who work longer hours have higher level of attrition than those who draw lesser salary or work lesser hours. Gender-wise, males have more attrition tendency than females. Location-wise, Karnataka respondents are found to have more attrition scores than Kerala employees. Global position-wise, multinational employees are having higher level of attrition scores than national employees. Among all these regression co-efficients, it is found that age, salary, strength factor, HRM practice significantly affect the attrition scores either at 1% or 5% level. Chapter-IV 225