CONTRIBUTORY AND INFLUENCING FACTORS ON THE LABOUR WELFARE PRACTICES IN SELECT COMPANIES IN TIRUNELVELI DISTRICT AN ANALYSIS DR.J.TAMILSELVI Assistant Professor, Department of Business Administration Annamalai University, Annamalai Nagar-608 002 And J.LEFTY JOYSON Ph.D. Research Scholar of Bharathiar University Coimbatore- 641 046 ABSTRACT The present paper presents the contributions and impact of factors influencing on labour welfare practices (LWP) in select companies in Tirunelveli district. Using Principal Component Analysis and Multiple Regression, researcher has found the variables impacts and contributions to the LWP. The findings and suggestions will help the organizations. Key words: Labour Welfare Practice (LWP), Impact of LWP www.apjor.com Page 50
Introduction People are stepping into a new social economic order, aiming at high productivity, low cost, maximum utilization of resources of the dynamic economy. Being a developing country, it is not offered to lose so much by money or man power. An analytical study in this field is of paramount importance. By discovering attitudes on factors to the welfare can correct certain bad situation and thereby improve the welfare of employees. An industrial organization can benefit materially if it knows what individual attitudes constitute to welfare. For one thing, applying this knowledge will result in better selection procedures. This is the broad implication as far as welfare provided is concerned. Objectives 1. To find out the contributory influencing factors on the labour welfare practices. (Principal Component Analysis) 2. To analyze the impact of independent variables on the dependent variable of labour welfare practices. (Multiple Regression) MULTIPLE REGRESSION Multiple regression is a statistical tool used to derive the value of a criterion from several other independent, or predictor, variables. It is the simultaneous combination of multiple factors to assess how and to what extent they affect a certain outcome. This technique breaks down when the nature of the factors themselves is of an unmeasurable or pure-chance nature. In the present study, researcher has analyzed the data by enter wise method in a multiple regression analysis. In this context, a multiple regression was performed; the overall model fit for regression equation was determined by F statistics. The model indicates positive and statistically significant relationship. To test how well the model fit the data and findings, R, R2 (Coefficient of determination), variance, analysis of variance (ANOVA) and the t statistic were used. In order to prove the impact of each independent variable on dependent variable and to check the hypothesis developed linear regression analysis was applied. Results of linear regression analysis are presented for LWP. Null hypothesis (In General) There is no impact of independent variables of labour welfare practice dependent variable. Other Facilities, Rest Room Facilities, Monetary, Non - Monetary, Safety Practices, Training, Promotion, Present Working Conditions, Educational Facilities, Retirement No impact on Benefits, Entertainment Facilities, Transfer LWP Table No. 1 Model Summary for labour welfare practice Model R R Square Adjusted R Square Std. Error of the Estimate 1.769 a.591.571.39349 a. Predictors: (Constant), Other Facilities, Rest Room Facilities, Monetary, Non - Monetary, Safety Practices, Training, Promotion, Present Working Conditions, Educational Facilities, Retirement Benefits, Entertainment Facilities, Transfer R-square value =0.0591. This means 59.1 % of the variation in LWP can be explained by (or accounted by) the variation in independent variables of LWP. In order to test the study hypotheses, multiple regressions analysis was used. As mentioned earlier, the other facilities, rest room facilities, monetary, non - monetary, safety practices, training, promotion, present working conditions, educational facilities, retirement benefits, entertainment facilities, and transfer are treated as an independent variable and LWP as a dependent variable. Table No. 2 ANOVA Model Sum of Squares df Mean Square F Sig. 1 Regression 53.101 12 4.425 28.580.000 b Residual 36.695 237.155 Total 89.796 249 a. Dependent Variable: Labour Welfare Practices b. Predictors: (Constant), Other Facilities, Rest Room Facilities, Monetary, Non - Monetary, Safety Practices, Training, Promotion, Present Working Conditions, Educational Facilities, Retirement Benefits, Entertainment Facilities, Transfer The above table shows that the F value was 28.58 and the p value was.000. Because the p-value is smaller than the level of significance (0.001), the research model is accepted at the p 0.001 significance level. www.apjor.com Page 51
Table No. 3 Coefficients for LWP Unstandardized Coefficients Standardized Coefficients Model B Std. Error Beta t Sig. 1 (Constant) -1.524.249-6.116.000 Monetary.185.124.063 1.493.137 Non - monetary.085.036.104 2.350.020 Present working conditions.187.047.186 3.966.000 Safety practices.117.035.143 3.313.001 Training.119.029.174 4.055.000 Promotion.179.032.257 5.664.000 Transfer.116.053.115 2.178.030 Entertainment facilities.244.050.259 4.894.000 Rest room facilities.144.045.178 3.207.002 Educational facilities.136.038.172 3.592.000 Retirement benefits.101.036.136 2.786.006 Other facilities.119.038.147 3.088.002 a. Dependent Variable: Labour Welfare Practices From the above table infers that the Relating to the results of testing the 13 hypotheses, the t and sig. values, as shown in Table 3, the absolute t value and all p value suggest that an independent variable has a large impact on the dependent variable. The results show that promotion, entertainment facilities and training practices have a significant impact on LWP. Also, the standardized beta coefficient is a measure of the contribution of each predictor or a measure of how strongly each predictor variable influences the criterion variable. The strongest predictors are promotion (β = 0.179) Entertainment facilities (β =.244) and Training practices (β =.119) FACTOR ANALYSIS Factor analysis is a method of data reduction. It does this by seeking underlying unobservable (latent) variables that are reflected in the observed variables (manifest variables). There are many different methods that can be used to conduct a factor analysis (such as principal axis factor, maximum likelihood, generalized least squares, unweighted least squares). There are also many different types of rotations that can be done after the initial extraction of factors, including orthogonal rotations, such as varimax and equimax, which impose the restriction that the factors cannot be correlated, and oblique rotations, such as promax, which allow the factors to be correlated with one another. Researcher also needs to determine the number of factors that want to extract the LWP variables. Given the number of factor analytic techniques and options, it is not surprising that different analysts could reach very different results analyzing the same data set. However, all analysts are looking for simple structure. Simple structure is pattern of results such that each variable loads highly one to one and only one factor. Table No. 4 Descriptive Statistics for LWP LWP Mean Std. Deviation Analysis N Monetary 2.73 0.46 250 Non - monetary 3.57 0.37 250 Present working conditions 4.26 0.50 250 Safety practices 4.02 0.45 250 Training 3.66 0.52 250 Promotion 3.43 0.71 250 Transfer 4.28 0.53 250 Entertainment facilities 4.28 0.60 250 Rest room facilities 4.20 0.53 250 Educational facilities 4.03 0.49 250 Retirement benefits 4.08 0.61 250 Other facilities 4.16 0.54 250 General opinion on LWP 4.23 0.46 250 www.apjor.com Page 52
From the above table infers the output of the univariate option on the selected variables of the factor. The number of cases used in the analysis is 250 respondents. The factor analysis is being conducted on the correlations (as opposed to the covariance), it is not much of a concern that the variables have very different means and/or standard deviations (which is often the case when variables are measured on different scales). Table No. 5 Total Variance Explained for LWP Initial Eigenvalues Extraction Sums of Squared Loadings Component Total % of Variance Cumulative % Total % of Variance Cumulative % 1 2.652 20.398 20.398 2.652 20.398 20.398 2 2.106 16.199 36.596 2.106 16.199 36.596 3 1.612 12.396 48.993 1.612 12.396 48.993 4 1.011 7.774 56.767 1.011 7.774 56.767 5.894 6.877 63.644 6.861 6.623 70.267 7.800 6.152 76.419 8.740 5.690 82.108 9.609 4.681 86.789 10.554 4.258 91.047 11.458 3.524 94.571 12.391 3.004 97.575 13.315 2.425 100.000 Extraction Method: Principal Component Analysis The initial number of factors is the same as the number of variables used in the factor analysis. However, all 13 components have not been retained. In this analysis, only the first four components have been retained. Eigen values are the variances of the factors. Because the factor analysis on the correlation matrix, the variables are standardized, which means that the each variable has a variance of 1, and the total variance is equal to the number of variables used in the analysis, in this case, 13. The first factor has always account for the most variance (20.398% have the highest Eigen value), and the next factor has account for as much of the left over variance of 16.199% and the third set of components had 12.396% and rest of them have the Eigen value respectively on the above table is concerned. Hence, each successive factor has account for less and less variance. The variance column contains the per cent of total variance accounted for by each factor. Thus, the high amount of variations has been obtained by the variables like from the above tables variables values. Table No. 6 Component Matrix for LWP LWP 1 2 3 4 Monetary.214 -.097 -.414.553 Non - monetary -.227.223.640.396 Present working conditions -.036.612 -.141.458 Safety practices -.055.392 -.446 -.277 Training -.287.080.551 -.172 Promotion -.429.442.439.082 Transfer.752.201.278 -.175 Entertainment facilities.734.204.289 -.122 Rest room facilities.808.226.217.088 Educational facilities.689.105 -.149.143 Retirement benefits -.131.622 -.211 -.053 Other facilities -.183.669 -.127.074 General opinion towards labour welfare -.042.582 -.158 -.379 Extraction Method: Principal Component Analysis. a. 4 components extracted. From the above table infers that the influencing variables of the LWP in the select companies in Tirunelveli district is have highly contributed through these aspects in the first set of components as Rest room facilities, Transfer, Entertainment facilities, and Educational facilities. The second set of component highly contributed components is other facilities and retirement benefits. The third set of component is influenced on non-monetary benefits and Trainings. This rest of the components are not influenced more than the first set of components calculated in this analysis. And also the negative loading first set of components is non-monetary benefits, training and promotions are induced to lower the contributions of LWP prevailed in the select companies in Tirunelveli district. www.apjor.com Page 53
Conclusion From the above analyses researcher concluded that the strongest predictors are promotion (β = 0.179) Entertainment facilities (β =.244) and Training practices (β =.119) from the multiple regression analysis. The contributory factor of the LWP is transfer, entertainment facilities, rest room facilities, and educational facilities contributed more in the perception of LWP prevailed in the select companies in Tirunelveli district. Therefore, the companies should concentrate on the non-monetary benefits, training and promotions components of LWP. References 1. Bhooshan B. Agalgatti, Labour Welfare and Industrial Hygiene, Nirali, Prakashan, 2008 2. Vaid K.N., Labour Welfare in India, Sri Ram Centre for Industrial Relations, 1970 3. Sivarathinamohan R., Industrial Relations and Labour Welfare Text and Cases PHI Learning Private Limited, Delhi, Third Edition, 2016 4. Jayaprakash Reddy, Labour Welfare and Personnel Service, APH Publishing Corporation, New Delhi, 2004 www.apjor.com Page 54