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Paper Title :
Work Life Balance Among Non-Teaching Staff In Self-Financing Colleges In Tamilnadu
An Empirical Investigation

Abstract
The demand for work-life-balance solutions by non-teaching staff and managers is expanding at an unprecedented rate. As a result, work-life balance is an increasingly hot topic in boardrooms and government halls today. Over the coming decade it will be one of the most important issues that executives and human resource professionals will be expected to manage. This study provides the methods for you to accelerate the implementation of a very successful work-life strategy within your organization. The study is to examine the factors influencing non teaching staff towards work-life balance at various Self Financing Engineering Colleges in Madurai, Tamilnadu. The researcher carried out the study with descriptive research. Simple random sampling using lottery method was adopted to choose the respondents with the sample size of 150 for the study. Top three self financing engineering colleges in Madurai City – Tamilnadu were taken for the study and due to various reasons the name of the colleges are not disclosed in the research. 50 respondents were taken from each college. Using Statistical Package for Social Sciences (SPSS) some of the relevant research tools were administered 1) Factor Analysis 2) Multiple Regression and 3) Reliability Test. Based on the test results some of the relevant findings are derived that will be significant to present Indian scenario.

Authors:

S.P.Karuppasamy Pandian
Business school, Islington College
London Metropolitan University, Kathmandu, Nepal

Navulur Krishna Surarchith, Assistant Lecturer
Komar University of Science and Technology
Kurdistan Region, Iraq.

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Paper Transcript of Paper Titled :
Work Life Balance Among Non-Teaching Staff In Self-Financing Colleges In Tamilnadu
An Empirical Investigation


Work Life Balance Among Non-Teaching Staff In Self-Financing Colleges In Tamilnadu
An Empirical Investigation


S.P.Karuppasamy Pandian, Lecturer,                                                                    
Business school, Islington College,
London Metropolitan University, Kathmandu, Nepal. 
Navulur Krishna Surarchith, Assistant Lecturer,                                                      
Komar University of Science and Technology,
Kurdistan Region, Iraq.


 


Abstract—The demand for work-life-balance solutions by non-teaching staff and managers is expanding at an unprecedented rate. As a result, work-life balance is an increasingly hot topic in boardrooms and government halls today. Over the coming decade it will be one of the most important issues that executives and human resource professionals will be expected to manage.  This study provides the methods for you to accelerate the implementation of a very successful work-life strategy within your organization. The study is to examine the factors influencing non teaching staff towards work-life balance at various Self Financing Engineering Colleges in Madurai, Tamilnadu. The researcher carried out the study with descriptive research. Simple random sampling using lottery method was adopted to choose the respondents with the sample size of 150 for the study. Top three self financing engineering colleges in Madurai City – Tamilnadu were taken for the study and due to various reasons the name of the colleges are not disclosed in the research. 50 respondents were taken from each college. Using Statistical Package for Social Sciences (SPSS) some of the relevant research tools were administered 1) Factor Analysis 2) Multiple Regression and 3) Reliability Test. Based on the test results some of the relevant findings are derived that will be significant to present Indian scenario.
Keywords- Work Life Balance, College, Non-Teaching etc.

 Introduction

The demand for work-life-balance solutions by non-teaching staff and managers is expanding at an unprecedented rate. As a result, work-life balance is an increasingly hot topic in boardrooms and government halls today. Over the coming decade it will be one of the most important issues that executives and human resource professionals will be expected to manage.  This study provides the methods for you to accelerate the implementation of a very successful work-life strategy within your organization. A well-implemented work-life strategy greatly reduces both the real and perceived overwork and out-of-balance pressures that hamper productivity, producing a dramatic positive return on investment. 
Non-teaching staff expect their employers to recognize that in addition to having a job, they have a life. This is reflected by the fact that work-life balance issues with the boss or the organization are the number-one reason that individuals quit their jobs.  The rising organizational cost of health care is driving senior management to become more proactive about non-teaching staffs health. Senior managers view engaging non-teaching staff in improving their own lifestyle behaviors and creating a healthier workplace as key components to the solution.
Because of the business and personal links between these critical issues, executives now recognize that organizational objectives and individual work-life objectives are not “either/or” choices. Positive new trends in the field, however, show that results can live up to expectations. In planning your own work-life efforts, be careful to avoid the pitfalls others have stumbled into and focus on implementing the successes.

Review of Literature

Mark Wickham & Simon Fishwick (2008) in their study examined the following. The effective management of non-teaching staff’ work-life balance (WLB) requires organizations to recognize and account for the array of work and non-work roles that impact their non-teaching staff’ Working-lives.  Despite the literary attention given to the ‘work life balance’ in recent years, however, contemporary researchers still note the concept’s inadequacy both in terms of its definition and administration. Research suggests that in order to manage the WLB effectively, human resource managers should consider better representing the non-teaching staff’ perspective in their management of the WLB. 
Brennan (2007) provides an important insight in to the development of these issues since the conceptualization and implementation of WLB policies in the early 1980s.  Fundamentally, she argues, the WLB system represents an ongoing series of tactical responses to major productivity issues facing the government and business sector leaders of the day.  Claus (2007: 9) supports this assessment, asserting that “successful managers recognize and support their non-teaching staff as ‘whole people’, and they continually [need to] experiment with the way work is done”. Guest (1997; 2002) suggests that to integrate the WLB literature with the holistic SHRM approach, their design and implementation needs to understand and accommodate more explicitly the needs and perspectives of the employee. Lewinson (2006) goes further to state that non-teaching staff themselves must take responsibility for their career development, raise issues/concerns, and be willing to set work and life goals with their employer in such ways that their progress towards them can be measured as objectively as possible.                                                       

Research Questions

      To study the factors influencing non teaching staff towards work-life balance at various Self Financing Engineering colleges in Madurai, Tamilnadu.
      To study the impact of work-life balance of the non teaching staff in their workplace.
      Support from the family and work-life balance in organization should be customized to individual needs.

Hypotheses

      There is a relationship among the factors that influence staff towards work-life balance.
      Support from family and work-life balance in self financing engineering colleges should be customized to individual needs.

Methodology

The researcher carried out the study with descriptive research. Simple random sampling using lottery method was adopted to choose the respondents with the sample size of 150 for the study. Top three self financing engineering colleges in Madurai City – Tamilnadu were taken for the study and due to various reasons the name of the colleges are not disclosed in the research. 50 respondents were taken from each college. Five point likert-type scale was used and that varied from                 1 = strongly disagree to 5 = strongly agree. Primary data has collected through questionnaire administered to non teaching staff and the secondary data collected from reputed journals, magazine and websites. The researcher analyzed the data using the SPSS – Statistical Package for Social science. The following tests were administered,
      Factor analysis
      Multiple regression

Reliability Test

Cronbach’s (alpha) is a statistical used in this study. It is commonly used as a measure of the internal consistency or reliability of a psychometrics test score for a sample of examinees.


TABLE 1 Reliability Statistics

Cronbach's Alpha

N of Items

.762

24

Inference

The alpha values were calculated to assess the internal consistency reliabilities of the construct. For this construct, the value of .762 indicates adequate reliability (Nunnally, 1978).
Cronbach’s (alpha) is a statistics used in this study. It is commonly used as a measure of the internal consistency or reliability of a psychometric test score for a sample of examinees. Cronbach’s alpha will generally increase as the inter correlations among test items increase, and is thus known as an internal consistency estimate of reliability of test scores, because intercorrelations among test items are maximized when all items measure the same construct, Cronbach’s alpha is widely believed to indirectly indicate the degree to which a set of items measures a single unidimensional latent construct.

Factor Analysis

Hypothesis 1 is being tested using factor analysis.
Hypothesis 1 - There is a relationship among the factors that influences non-teaching staff towards work-life balance.
Factor analysis (FA) and principal component analysis (PCA) are techniques used when the researcher is interested in identifying a smaller number of factors underlying a large number of observed variables. Variables that have a high correlation between them, and are largely independent of other subsets of variable, are combined into factors.

KMO and BARTLETT”S TEST

TABLE 2 KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy.

.765

Bartlett's Test of Sphericity

Approx. Chi-Square

270.728

Df

55

Sig.

.000

Inference

From the above table, Barlett’s test of sphericity indicates whether the correlation matrix is an identity matrix, which indicates that the variables are unrelated and the significance level (0.000) indicate that there is a significant relationship among the variables. The KMO value of 0.765 states that, the factor analysis is valid and can be performed in this study.

Rotated Component Matrix

This table (called the pattern matrix for oblique rotation) reports the factor loading for each variable on the component or factor after rotation. Each number represents the partial correlation between the item and the rotated factor. These correlations can help us to formulate an interpretation of the factor or components. From the below table, each factor loading values represent the partial correlation between the variables the rotated  factor by inferring a common thread among the variables that have large loading above 0.5 values for a particular factor.

 

TABLE 3 Rotated Component Matrixa

 

Component

1

2

3

4

worry about work

 

.727

 

 

missed family or friend due to work pressure

 

.689

 

 

Depressed because of work

 

.795

 

 

flexible starting hours of work

 

 

.928

 

flexible finishing time

 

 

.913

 

job sharing

.820

 

 

 

time off for family engagement

 

 

 

.594

support from colleagues

.851

 

 

 

support from family

 

 

 

.632

Technology

.474

 

 

 

being able to bring children to work on occasion

 

 

 

.778

Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.

a. Rotation converged in 6 iterations.

Inference

From the above factor analysis it is inferred that a)Less support from colleagues,
b) Depression because of work,
c) Lack of flexible starting hour and
d) Inability to bring children to work on certain occasions is the major factors influencing the respondents.

Multiple Regression

Hypothesis 2 is being tested using multiple regressions.
      Hypothesis 2 - Support from family and work-life balance in organization should be customized to individual needs
Regression analysis is used to assess the relationship between on dependent variable (DV) and several independent variables (IVs). This is the most commonly used techniques in much of the social science research.

Regression Analysis

Enter method in multiple regression analysis used for this study. Work-life variables under various dimensions were regressed with the overall satisfaction of the non-teaching staff.


Table 4: Model Summary

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

.755a

.645

.656

.693

a. Predictors: (Constant), health, long working hours, negative attitude of colleagues and supervisors, technology, shift work, frequently traveling away from house, meeting/training after office hours, negative attitude of family members, compulsory overtime.

Inference

The above model summary table shows R-Square for this model is .645. This means that 64.5 percent of the variation in overall work-life balance policy in my organization is (dependent variable) can be explained from the 31 independent variables. This table also shows the adjusted R-square for the model as .656


Table-5: Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

T

Sig.

B

Std. Error

Beta

 

(Constant)

2.343

.514

 

4.559

.000

long working hours

-.559

.127

-.482

-4.398

.000

compulsory overtime

.255

.141

.257

1.810

.075

shift work

-.501

.116

-.505

-4.308

.000

meeting/training after office hours

-.065

.105

-.067

-.621

.537

negative attitude of colleagues and supervisors

.195

.073

.218

2.655

.002

negative attitude of family members

.284

.111

.305

2.568

.013

frequently traveling away from house

.098

.081

.127

1.215

.229

Technology

-.346

.084

-.347

-4.142

.000

Health

.551

.107

.481

5.161

.000

a. Dependent Variable: overall work life balance policy in my organization


Inference

To determine if one or more of the independent variables are significant predictors of overall work life balance, we examine the information provided in the coefficient table. From the above ten independent variable only four independent variables are having larger beta value and statistically significant. Negative attitude of colleagues, supervisors and family members plus health are the variables which are significant.


Table-6: ANOVAa

Model

Sum of Squares

Df

Mean
Square

F

Sig.

1

Regression

51.860

9

5.762

11.987

.000b

Residual

29.323

61

  .481

 

 

Total

81.183

70

 

 

 

a. Dependent Variable: overall work life balance policy in my organization.

b. Predictors: (Constant), health, long working hours, negative attitude of colleagues and supervisors, technology, shift work, frequently traveling away from house, meeting/training after office hours, negative attitude of family members, compulsory overtime

Inference

The ANOVA table, as displayed in the above table shows the F ratio for the regression model that indicates the statistical of the overall regression model. The larger the F ratio there will be more variance in the dependent variable that is associated with the independent variable. The F ratio = 11.987. The statistical significance is .000- “sig”. There is relationship between independent and dependent variables.

Conclusion

In this most spirited, custom-made and added aggressive atmosphere, it is more crucial for management to sort out the reasons, why the non teaching staffs are not happy in their work place. Negative attitude of colleagues and supervisors, family members and health are the major factors affecting respondent’s behavior in the research. Talented and balancing life is becoming the prime ingredient of organization victory. Work-life balance is a beneficial process for non teaching staff, managers and the organization as a whole. It has been shown that work-life balance results in increased non teaching staff satisfaction, involvement in their work.  Nowadays organizations have major problem in retaining staff, for which they are developing emotionally engaged workforce by various work-life balance activities. The management should not consider these engagement programs as costly. Staffs are to be considered as an asset of the organization and this cost involved should be treated as an investment to mould the staff and also to drive the organization in a smooth and profitable comportment. Hence the organization will become profitable with the dedication and hard work of the staff.

 
References

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