A STRUCTURAL EQUATION MODEL-II FOR WORK-LIFE BALANCE OF IT PROFESSIONALS IN CHENNAI

The study developed and tested a model of work life balance of IT professionals employing structural equation modeling (SEM) to analyze the relationship between work place support (WPS) and work interference with personal life (WIPL), personal life interference with work (PLIW), satisfaction with work-life balance (SWLB) and improved effectiveness at work (IEW). The model fit the data well and hypotheses are generally supported. WPS and SWLB are negatively related to WIPL and PLIW. However, there is a positive relationship between SWLB and IEW.


INTRODUCTION
In the recent years, it is being realized that life involves multiple domains and not restricted to the domains of work and family only. Kirchmeyer (2000) defined a balanced life as achieving satisfying experiences in all life domains. Kirchmeyer went on to be more prescriptive, stating that to achieve satisfying experiences in all life domains requires personal resources like energy, time and commitment to be well distributed across domains.
Although definitions and explanations vary, work/life balance is generally associated with equilibrium, or maintaining an overall sense of harmony in life (Clarke et al., 2004). On the basis of various theories of work life balance and conceptual models, multiple factors related to individual, work and family affect the work life balance of an individual and researchers too have employed broader terms and terminologies. Jang (2008) employed structural equation modeling (SEM) to analyze the associations among the perceptions of workplace support, supervisory support, work-schedule flexibility, work-life balance, and personal well-being. Perceived workplace support and perceived supervisory support were reported as latent exogenous constructs and "boundary-spanning resources" (Jang, 2008). Work social support construct included the measures of coworker social support, emotional / informational / instrumental support from work, group cohesiveness, lack of personal support at work/non-support, leader/managerial support, level of work group support, mentor supportiveness, perceived organizational support, psychosocial support, supervisor support (Michel et al., 2009). Social support is conceptualized as the structure of relationships as well as the flow of resources provided by relationships. Literature suggests that support available in the work domain ranges from the organization itself, supervisors and peers (Chan, 2009). Seiger and Wiese (2009) reported social support as an antecedent of work-family conflicts. Different sources of social support were supervisors, co-workers, partners and other family members. Yildirim and Aycan (2008) examined social support both as a moderator and a main effect in the relationship among work demands, work-to-family conflict, and satisfaction with job and life. According to the authors, supervisory support was an important source of social support in coping with problems associated with work-tofamily conflict.
In terms of work-life balance, researchers have also noted that the adoption of a supportive workplace culture in companies may be an important variable (Appelbaum et al., 2005). Organizational and support from co-workers and supervisors have always been an influential variable for the study of work life balance issues. Previous studies demonstrated that, in order for employees to have better work life balance it is equally important that they get supportive work environment (Thompson et al., 1999;Allen, 2001;O'Driscoll et al., 2003). The objective of this paper is to analyze the associations among Work Place Support (WPS) (as resource) and consequences such as Work Interference with Personal Life (WIPL), Personal Life Interference with Work (PLIW), Satisfaction with Work-Life Balance (SWLB) and Improved Effectiveness at Work (IEW) and evolve a model to that effect.

RESULTS AND DISCUSSION
WPS refers to actions and work practices that are designed to facilitate workers' effectiveness and wellbeing. Work Place Support has two key functions. 1. Ensure workers' wellbeing (social/emotional support) and 2. Ensure workers are capable of effective performance (instrumental support). Social/emotional support refers to support that is focused on meeting workers' needs to feel valued, cared for, respected and liked. Instrumental support refers to support that provides workers with practical assistance in terms of their roles, responsibilities and tasks (Skinner, 2005). Resources are needed to deal with increasing demands and stress. All resources are finite and can be consumed. We have therefore included work environment support, organizational support, manager support, coworker support to define WPS (Banu & Duraipandian, 2014) in our study.
This study employed the methodology reported elsewhere (Banu, 2015). Table 1 presents the hypotheses in five causal paths to determine the relationships between the variables under consideration, namely, exogenous variables (WPS) and endogenous variables, (WIPL, PLIW, SWLB and IEW).
Beta values presented in Table 2 are path coefficients or standardized regression coefficients that indicate the direct effect of independent variable to direct variable. Standardized beta values are used to estimate the path coefficients (Noor, 2003). The single-headed arrows represent linear dependencies indicating the extent to which one variable (construct) is dependent on another (causal paths or relationships). The results presented in Table 2 indicate that the hypotheses H1 to H5 are statistically significant and in the hypothesized direction.
Thus these hypotheses are supported. Since there is significant relationship established in the hypothesized direction, the null hypotheses H01 to H05 in Table 1 are rejected.
To evaluate the structural model, goodness-of-fit indices are examined to assess if the hypothesized structural model fits the data. Table 3 presents the model fit indices.
Of primary interest in SEM is the extent to which a hypothesized data "fits", or in

PLIW SWLB
H4=Personal Life Interference with Work will negatively affect Satisfaction with Work-Life Balance H05=There is no significant relationship between Satisfaction with Work-Life Balance and Improved Effectiveness at Work

SWLB IEW
H5=Satisfaction with Work-Life Balance will positively affect (Improved)Effectiveness at Work other words, adequately describes the sample data. The model fitting process involves determining the goodness-of fit between the hypothesized model and the sample data (Jang, 2008). Goodness of fit indicates how well the specified model reproduces the observed covariance matrix among the indicator items (i.e. the similarity of the observed and estimated covariance matrices). There are many indices provided by SEM, although there is no agreement among scholars as to which fit indices should be reported (Shammout, 2007). A range of acceptable values for the Χ 2 ⁄df ratio have been suggested, ranging from less than 2.0 (Bollen, 1989;Hair et al., 1995;Tabachnick & Fidell, 2001), through less than 3.0 (Carmines & McIver, 1981), to more liberal limits of less than 5.0 (Wheaton et al., 1977). RMSEA assists in correcting the tendency of chi-square to reject specified models. The general rule of thumb is that when RMSEA ≤ 0.10, the fit is acceptable (Kline, 2005). While Holmes-Smith et al. (2006) recommend that RMSEA should be less than 0.05, MacCallum and Browne (1993) suggest a value of up to 1.0 as reasonable. However, it has been found that a value ranging from 0.05 to 0.08 is commonly acceptable (Hair et al., 1995).
Acceptable model fit is indicated by a CFI value of 0.90 or greater (Hair et al., 1995;Hu & Bentler, 1999). Similarly, alternative measures of fit, such as the NFI, the GFI are considered acceptable if above 0.90 (Hu & Bentler, 1999;Kaplan, 2000). Similar to GFI, AGFI measure range from 0 (indicating a poor fit) to 1 (indicating a perfect fit), where a recommended level of acceptance is 0.90 (Hair et al.,1995). TLI is known as a non normed fit index (NNFI) (Marsh et al., 1988;Hair et al., 1995). TLI combines a measure of parsimonious into a comparative index between the proposed or hypothesized and null models, resulting in values ranging from 0 (not fit at all) to 1 (perfect fit).

CONCLUSIONS
It is identified through this work that those who had satisfactory work-life balance with the assistance of the policies implemented by the employing organization, tend to be more effective at work. When employees are not satisfied with their job and are not able to balance between the two domains of work and family, they tend to withdraw from their work related activities. Hence managers must not only have a theoretical understanding of work-life balance, conflict and enrichment, but also an understanding of the measurement tools to assess them. Only then can managers propose interventions to promote organizational and individual development, change initiatives and performance improvement. The solution lies with sound planning at individual level and initiatives from the organizations to aid productivity of the new age employees. It is believed that this study will contribute to assess the factors impacting the work life balance of IT professionals in Chennai and likely to aid more workplace interventions.