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

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1 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 And J.LEFTY JOYSON Ph.D. Research Scholar of Bharathiar University Coimbatore 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 Page 50

2 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 a 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 = 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 b Residual Total 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 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 significance level. Page 51

3 Table No. 3 Coefficients for LWP Unstandardized Coefficients Standardized Coefficients Model B Std. Error Beta t Sig. 1 (Constant) Monetary Non - monetary Present working conditions Safety practices Training Promotion Transfer Entertainment facilities Rest room facilities Educational facilities Retirement benefits Other facilities 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 Non - monetary Present working conditions Safety practices Training Promotion Transfer Entertainment facilities Rest room facilities Educational facilities Retirement benefits Other facilities General opinion on LWP Page 52

4 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 % 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 % and the third set of components had % 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 Monetary Non - monetary Present working conditions Safety practices Training Promotion Transfer Entertainment facilities Rest room facilities Educational facilities Retirement benefits Other facilities General opinion towards labour welfare 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. Page 53

5 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, Vaid K.N., Labour Welfare in India, Sri Ram Centre for Industrial Relations, Sivarathinamohan R., Industrial Relations and Labour Welfare Text and Cases PHI Learning Private Limited, Delhi, Third Edition, Jayaprakash Reddy, Labour Welfare and Personnel Service, APH Publishing Corporation, New Delhi, Page 54