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

Transcription

1 CHAPTER 5 ANALYSIS OF WORK LIFE BALANCE WITH RESPECT TO OTHER VARIABLES 201

2 CHAPTER- 5 ANALYSIS OF WORK LIFE BALANCE WITH RESPECT TO OTHER VARIABLES The second level of analysis was carried out to examine the quality of work life and quality of family/social life as experienced by the respondents of the study. One way analysis of variance (ANOVA) was conducted as a confirmatory test to establish the relationship between the respondent variables as gender, age, marital status etc. with work life balance of the respondents. This followed a two stage factor analysis to identify the factors and dimensions affecting the work life balance. In order to understand the direction and strength of factors and dimensions influencing work-life balance, multiple regression was conducted. This helped in predicting the positive/negative association of the factors to the overall work life balance of the respondents. 5.1 ANALYSIS OF RELATIONSHIP BETWEEN RESPONDENTS VARIABLES WITH WORK LIFE BALANCE USING ONE WAY ANOVA. One way ANOVA was carried out for the various respondent variables and work-life balance. This was used as a confirmatory to check the significance of the various respondent based variables on work life balance. 202

3 By Gender: The table 5.1 shows the impact of Gender on work life balance. TABLE: 5.1 F RATIO OF IMPACT OF GENDER ON WORK LIFE BALANCE The F statistic 6.39 is significant, at 0.05 level of significance. This implies that Gender has a significant impact on work life balance. 203

4 The mean plot of Gender and Work Life Balance has been depicted in figure 5.1. FIGURE: 5.1 MEAN PLOT WORK LIFE BALANCE BY GENDER The mean plot and mean value indicates thatt males have higher work life balance than females. 204

5 By Age: The table 5.2 shows the impact of Age on Work Life Balance. TABLE: 5.2 F RATIO OF IMPACT OF AGE ON WORK LIFE BALANCE The F statistic is significant at 0.05 level of significance. This implies that age has a significant impact on work life balance. 205

6 The mean plot of Age and Work Life Balance has been depicted in figure 5.2 FIGURE: 5.2 MEAN PLOT WORK LIFE BALANCE BY AGE The mean plot and mean value indicates that individuals in the age group of years have least work life balance and individuals in the age group of 41 years and above have highest work life balance. 206

7 By Marital Status: The table 5.3 shows the impact of Marital Status on Work Life Balance. TABLE: 5.3 F RATIO OF IMPACT OF MARITAL STATUS ON WORK LIFE BALANCE The F statistic, 7.02, is significant at 0.05 level of significance. This implies that marital status has a significant impact on work life balance. 207

8 The mean plot of Marital Status and Work Life Balance has been depicted in figure 5.3 FIGURE: 5.3 MEAN PLOT WORK LIFE BALANCE BY MARTIAL STATUS The mean plot and mean value indicates that unmarried individuals have higher work life balance than married individuals. 208

9 By Type of Family: The table 5.4 shows the impact of Type of Family on Work Life Balance. TABLE: 5.4 F RATIO OF IMPACT OF TYPE OF FAMILY ON WORK LIFE BALANCE The F statistic, , is significant at 0.05 level of significance. This implies that type of family has a significant impact on work life balance 209

10 The mean plot of type of Family and Work Life Balance has been figure 5.4. depicted in FIGURE: 5.4 MEAN PLOT WORK LIFE BALANCE BY TYPE OF FAMILY. The mean plot and mean value indicates that individuals living in joint families have higher work life balance than individuals living in nuclear families. 210

11 By Experience in IT Sector: The table 5.5 shows the impact of Experience in IT sector on Work Life Balance. TABLE: 5.5 F RATIO OF IMPACT EXPERIENCE IN IT SECTOR ON WORK LIFE BALANCE The F statistic, , is significant at 0.05 level of significance. This implies that experience in IT sector has a significant impact on work life balance. 211

12 The mean plot of Experience in IT sector and Work Life Balance has been depictedd in figure 5.5. FIGURE: 5.5 MEAN PLOT WORK LIFE BALANCE BY EXPERIENCE IN IT SECTOR The mean plot and mean value indicates that individuals have experience in IT sector of 3 to 5 years have highest work life balance as compared to individuals having experience e less than 3 years who have least work life balance. 212

13 By Association with Present Organization: The table 5.6 showss the impact of Association with Present Organization onwork Life Balance. TABLE: 5.6 F RATIO OF ASSOCIAT TION WITH PRESENT ORGANIZATION ON WORK LIFE BALANCE The F statistic, , is significant at 0.05 level of significance. This implies that association with present organization has a significant impact on work life balance. 213

14 The mean plot of association with present organization and work life balance has been depicted in figure 5.6. FIGURE: 5.6 MEAN PLOT WORK LIFE BALANCE BY ASSOCIATION WITH PRESENT ORGANIZATION The mean plot and mean value indicates that individuals have association with present organization of 3 to 5 years have highest work life balance as compared to individuals having association with present organizationn of less than 3 years who have least work life balance. 214

15 By Working and Non-working Spouse:Thee table 5.7 shows the impact of Working and Non-Working Spouse on Work Life Balance. TABLE: 5.7 F RATIO OF SPOUSE WORKING AND NON-WORKING ON WORK LIFE BALANCE The F statistic , is significant at 0.05 level of significance. This implies that whether spouse is working or not has a significant impact on work life balance. 215

16 The mean plot of working and non-working spouse and work life balance has been depicted in figure 5.7. FIGURE: 5.7 MEAN PLOT WORK LIFE BALANCE BY SPOUSE- WORKING AND NON- WORKING The mean plot and mean value indicates that individuals having not working spousess have higher work life balance than individuals having working spouses. 216

17 The table5.8 below depicts work life balance. the summary of impact of respondent variables on TABLE: 5.8 SUMMARY OF IMPACT OF RESPONDENT BASED VARIABLES ON WORK LIFE BALANCE The table above shows all the F-ratios to be significant at 0.05 level of significance indicating that all the chosen respondent based variables have a 217

18 significant impact on Work Life Balance of the employees of IT sector. Further, from the mean values it can be predicted that males, individuals in the age group of 41 years and above, unmarried individuals, individuals belonging to joint family, individuals having experience in IT sector of 3-5 years, individuals having association with the present organization of 3-5 years and individuals having non-working spouses have a greater work life balance. 218

19 5.2 FACTOR ANALYSIS- 1 st Orderr A factor analysis carried out resulted in clustering of variables.the reliability and validity of the scale was established by calculating Cronbach s alpha coefficient. TABLE: 5.9 CRONBACH S ALPHAA VALUE The cronbach alpha value derived was which indicates a high level of reliability. This was followed by Barlett s testt of sphericity and Kaiser-Meyer Olkin (KMO) measure of sampling adequacy was used to examine the appropriateness of factor analysis. TABLE: 5.10 KMO AND BARTLETT S TEST-IST ORDER FACTOR ANALYSIS The approximate Chi-Square statistic is with 1275 degrees of freedom, which is significant at level of significance. The KMO statistic (0.679) is also greater than (0.5). Hence, factor analysis is considered as appropriate technique for analysis of data. 219

20 The factor loading of variables with respective factors is depicted in Table: 5.11 TABLE: 5.11 FACTOR LOADINGS OF VARIABLES WITH RESPECTIVE FACTORS 220

21 A principal component factor analysis (PCA) using Rotation method of Varimax with Kaiser Normalization was conducted. A total of eleven factors were extracted as a result of PCA. All factors had Eigen values greater than one. None of the variables had loading of below and none of the variables were cross loaded. Thus, all the variables were included in the final rotation matrix.these factors explain around 70% variance of the total data The first factor named Work Overload (F1) comprises of three variables. This is the most important factor contributing towards Work Life Balance and explains % of variance in the data. The second factor named Compliant Work Culture (F2) is composed of eight variables and explains % of variance in the data. The third factor is named Paucity of Personal Time (F3) and consists of six variables. A heavy load of nine variables is clung to the fourth factor named Negative Affectivity (F4) which explains 8.346% of variance in data. The fifth factor is named Implied Organizational Culture (F5) and consists of only two variables and explains 5.436% of the total variance in the data. The sixth factor explains 5.269% of variance comprises of five variables and is named Sleep Snags (F6). Explaining 4.057% variance in data, the seventh factor is named Support from Supervisors (F7) and comprises of four variables. The eighth factor Explicit Work Policies (F8) is composed of four variables and explains 4.020% variance in the data. The ninth factor again has four variables and is named Progressive Work Culture (F9);it explains 221

22 3.821% of the variance in the data. The tenth factor named Support from Co- workers (F10) consists of three variables and explains % variance in the data. The eleventh factor named Perfect Work Place Ambience (F11), consists of threee variables and explains 3.101% of the variance in the data. Thus, in total all factors put together explain % of variance in data. In an attempt to 5.3 FACTOR ANALYSIS- 2 ND SECOND ORDER further reduce the number of factors, second level factor analysiss was conducted. Once again Barlett s test of sphericity and Kaiser-Meyer Olkin (KMO) measure of sampling adequacy was used to examine the appropriateness of second order factor analysis. TABLE: 5.12 KMO AND BARTLET TT S TEST-2 ND ORDER FACTOR ANALYSIS The approximate Chi-Square statistic is with 555 degrees of freedom, which is significant at level of significance. The KMO statistic (0.694) is also greater than (0.5). Hence, factor analysis is considered as appropriate technique for analysis of data

23 The dimension loading of factor variables with respective Table: 5.13 factors is depicted in TABLE:5.13 DIMENSION LOADINGS OF FACTORS WITH RESPECTIVE DIMENSIONS A principal component factor analysis (PCA) using Rotation method of Varimax with Kaiser Normalization was conducted. A total of three factors were extracted as a result of PCA. All factors had Eigen values greater than one. None of the variables had loading of below and none of the factor variables were cross loaded. Thus, all the variables weree included in the final rotationmatrix. These factors explain around % variance of the total data. 223

24 Since the factors had been named earlier, these were converged and named as Dimensions. The first and the most important dimension named as Professional Sphere D1, constitutes seven factors and explaining 33.9% of variance in the data. The second dimension, Social Sphere D2, constitutes of two factors and explains 22.4% variance in the data. The last dimension, named Physical Sphere D3, also comprises of two factors and explains 10.2% variance in data. Thus, after two levels of factor analysis, it can be concluded that Work Life Balance is composed of three dimensions the professional, social and the personal spheres which in turn summarizes the eleven factors deduced earlier. 224

25 5.4 MULTIPLE REGRESSION In this part of the analysis, the researcher attempted to establish the understanding of those areas which contribute to work life balance and segregate those that work against the work life balance. To evaluate this, multiple regression analysis was run at two levels of: Using Work Life balance as the dependent variable and the various extracted factors of factor analysis as the independent variables Using Work Life balance as the dependent variable and the various dimensions as extracted from the second order of factor analysis as the independent variables The following hypotheses were formulated using the 11 factors for this 1st stage of multiple regression: H 0 (1): There is no significant impact of work overload for an employee and his work life balance. H 0 (2): There is no significant impact of compliant work culture of the organization and the work life balance of the individual employee. H 0 (3): There is no significant impact of paucity of personal time of an employee and his work life balance. H 0 (4): There is no significant impact of negative affectivity for an employee and his work life balance. 225

26 H 0 (5): There is no significant impact of implied organizational culture of the organization and work life balance of an employee. H 0 (6): There is no significant impact of sleep snags for an employee and his work life balance. H 0 (7): There is no significant impact of support from supervisors in the organization and work life balance of an employee. H 0 (8): There is no significant impact of explicit work policies at work and work life balance of an employee. H 0 (9): There is no significant impact of progressive work culture of the organization and work life balance of an employee. H 0 (10): There is no significant impact of support from co-workers at work and work life balance of an employee. H 0 (11): There is no significant impact of perfect work place ambience and work life balance of an employee. 226

27 In order to arrive the values of the various factors, the factor equation were computed as follows: F i = W i1 X 1 + W i2 X 2 + W i3 X W ik X k where, F i = estimate of ith factor W i = weight or factor score coefficient k = number of variables The factor value thus received was used in multiple regression model. Factor scores calculated by multiplying factor loadings from rotated component matrix by mean of actual values each variable. The regression equation computed for the analysis is as follows: Y = α + β1x1 + β2x2 + β3x3 + β4x4 + β5x5 + β6x6 + β7x7 + β8x8 + β9x9 + β10x10 + β11x11 Where, Y = Work Life Balance α = Constant X1 =Work Overload X2 = Compliant Work Culture X3 = Paucity of Personal Time X4 = Negativity Affectivity X5 = Implied Organizational Culture X6 = Sleep Snags 227

28 X7 = Support from Supervisors X8 = Explicit Work Policies X9 = Progressive Work Culture X10 = Support from Coworkers X11 = Perfect Work Place Ambience TABLE: 5.14 REGRESSION COEFFICIENT FROM REGRESSION RESULTS FOR WORK LIFE BALANCE BY FACTORS 228

29 TABLE:5.15 BETA COEFFICIENTS OF REGRESSION RESULTS S FOR WORK LIFE BALANCE BY FACTORS In the multiple regression model, least square method was used. Since, the independent variables were in the form of factors, multi-collinearity was not an influencing factor. The result as detailed in table 5.14 above indicate that the adjustedd coefficient of determination R 2 (considered because of different sample sizes) is This indicates that the proportion of variance of the dependent variable about its mean thatt is explained by independent variables is 81%. The ANOVAA value, F = , is significant at 0.05 level of significance, indicating the overall model fit. Further, as per table 5.15 all the beta coefficients indicating the strength and direction of their relationship with the 229

30 dependent variable are significant since the t values for all beta coefficients is significant at 0.05 level of significance. Thus, from the model it can be predicted that certain independent variables (factors) have a positive relationship to Work- Life Balance whereas others have a negative relationship to Work Life Balance. The factors having a positive relationship with Work Life Balance (WLB), arranged in their order of impact on WLB, are Support from Supervisors, Explicit Work Policies, Progressive Work Culture, Support from Coworkers and Perfect Work Place Ambience. This suggests that when these factors increase Work Life Balance also increases. The independent variables (factors) having a negative relationship with Work Life Balance, arranged in their order of impact on WLB, are Work Overload, Compliant Work Culture, Personal Time, Negative Affectivity, Implied Work Culture and Sleep Snags. These factors when increase, create a pressure on Work Life Balance. A second stage Multiple Regression was done using the Dimensionsas evolved in the 2 nd order factor analysis. The following hypothesis were formulated for this multiple regression: H 0 (12) = There is no significant impact of an employee s professional sphere and his work life balance in the IT sector. 230

31 H 0 (13) = There is no significant impact of an employee s personal sphere and his work life balance in the IT sector. H 0 (14) = There is no significant impact of an employee s social sphere and his work life balance in the IT sector. On the pattern same as above, to confirm the fit of the model, multiple regression was conducted for Dimensions also. The equation used for the model was as follows: Y = α + β1x1 + β2x2 + β3x3 Where, Y = Work Life Balance α = Constant X1 =Professional Sphere X2 = Social Sphere X3 = Physical Sphere 231

32 TABLE:5.16 REGRESSION COEFFICIENT FROM REGRESSION RESULTS FOR WORK LIFE BALANCE BY DIMENSIONS TABLE: 5.17 BETA COEFFICIENTS OF REGRESSION RESULTS FOR OVERALLL WORK LIFE BALANCE BY DIMENSIONS The results indicate thatt the adjusted coefficient of determination R (considered because of different sample sizes) is This indicates that the proportion of variance of the dependent variable about its mean that is explained R 2 by independent variables is 81%. The ANOVA value F = , is significant at 0.05 level of significance, indicating the overall model fit. Further, as per table all the beta coefficients indicating the strength and direction of their 232

33 relationship with the dependent variable are significant since, the t values for all beta coefficients is significant at 0.05 level of significance. Thus, from the model it can be predicted that the sole independent variable (dimension) having a positive relationship with Work Life Balance, is Professional Sphere This suggests that when there is a positive growth in Professional Sphere, Work Life Balance also increases. Similarly, it can be predicted that independent variables (dimensions) having a negative relationship with Work Life Balance, when arranged in their order of impact on WLB, are Social Sphere and Personal Sphere. When these create a negative pressure on the individual, Work Life Balance tends to reduce. 233

34 5.5 RANKING The mean scores of the statements under the section IX of the questionnaire weree calculated. This was done to take a cursory view of acceptability of possible solution among the respondents. The statements were ranked by the respondents in order of their perception of prioritity given to a possible solution whichh was contributing to their work life balance. The table 5.18 below reflects the ranking as per the respondent s perception. TABLE:5.18 MEAN VALUES OF STATEMENTS ALONG WITH THEIR RANKING IMPORTANCE TO RESPONDENTS. IN DESCENDING ORDERR ACCORDING TO THEIR The ranking mean scores as tabulated in the table: 5.18 clearly depict that the respondentsgive utmost importance to leaving work on time at the end of the 234

35 work day. Job sharing arrangement, compressed work week, working from home, voluntary shorter working time follow the next few ranks respectively. The rank mean scores are further depicted by a bar graph in the figure 5.8 FIGURE: 5.8 MEAN VALUES OF WORK LIFE BENEFIT POLICY STATEMENTS ACCORDING TO THEIR IMPORTANCE TO RESPONDENTS- BAR GRAPH 235