Employee engagement & industrial relations climate in a large public sector organization.
| Author | Talukdar, Asim |
Introduction
One of the large public sector organizations in power generation and distribution in India from West Bengal recently carried out an organization climate survey (OCS). The organization has been running in loss for the last few years. The management of the company decided to undertake the survey and initiate strategic action following the findings of a diagnostic study for improving organization performance. The survey was conducted during November-December 2011. One of the objectives was to identify the key reasons for low level of apparent employee engagement and have a special strategic focus on the same to improve organizational performance. This paper highlights the approach used to identify the key areas for organizational intervention for improving employee-engagement.
Research Objectives
The conventional approach to OCS is to identify the satisfaction level of each area of organization diagnostic indices by calculating the mean value and standard deviation and compare the same with those from a study of previous years, if available (Dickson, Resick & Hanges, 2006). This conventional approach normally is taken to draw the action plan to improve the organizational performance and employee motivation based on the identified areas of low satisfaction and or drop in satisfaction level in particular dimensions compared to those in previous studies. In the present study, the basic research question is: "What are the critical constructs explained by the manifested variables of the OCS in this organization which have major bearing on the employee engagement?" The conventional approach of comparing the satisfaction level of each diagnostic dimension with the corresponding level from a previous period is simply incapable of addressing this question and any such approach if resorted to will be misguiding the organization to meet the core objective of improving the organization performance and employee motivation.
For the present study, based on literature (Hartler, Schimdt & Hays, 2002; Macey & Schneider, 2008)'intrinsic motivation', 'job satisfaction', 'organization values' and 'leadership style' have been conceptualized as drivers of employee engagement. Addressing the basic research question calls for using appropriate advanced statistical methods to establish the causal relations of various constructs, derived from measured variables, with the employee intrinsic motivation, job satisfaction, and leadership behavior, and also in area of industrial relations.
Organization Climate Survey
According to Hay Group (2010) climate affects organizational performance by influencing employee motivation. In most jobs, especially in the complex ones, there is a gulf between what employees need do to 'get by' and what they can do if they perform at their full potential. A positive climate will encourage this discretionary effort and commitment. Organization climate, as represented by the aggregation of perceptions of individual employees within the organization can be traced back to the work of Lewin, Lipitt& White (1939). Although organization climate has been defined in many different ways (Litwin & Stringer, 1968), there seems to be consensus that it includes three behavioral levels, namely the individual, the interpersonal and the organizational. Organization climate dimensions on the formal level are structure, policy, objectives, management practice, task specialization, decision making, standard and reward (Cilliers & Kossuth, 2002). At the informal level, it refers to identity, employee needs, responsibility, interactive communication, information sharing, support, warmth and conflict handling (Kline & Boyd, 1991). Sampane, Ringer & Roodt (2002) and Hutcheson (1996) viewed organization climate as the description of the organization's "objective" variables like structure, size, policies and leadership style, by the employees.
Extensive research proved that job satisfaction does not happen in isolation, as it is dependent on organizational variables such as structure, size, pay, working conditions and leadership, which constitute organization climate (Sampane, Ringer & Roodt, 2002). Kline & Boyd (1991) conducted a study to determine the relationship between organization structure, context and climate with job satisfaction amongst the three levels of management. Their study revealed that employees at different levels of the organizations are affected by different work factors. Based on the outcome of this study, they recommended that different aspects of the work environment be looked into when addressing the issues of job satisfaction amongst different positions in the same organization
Organization climate is an inherently multilevel construct involving distinct perceptions and beliefs about an organization's physical and social environment (Dickson et al, 2006). At the individual level, psychological climate refers to individuals' perceptions of and the meanings they assign to their environment. As a higher level construct, organization climate reflects beliefs about the organization's environment that are shared among members and to which members attach psychological meaning to help them make sense of their environment (Schneider & Reichers, 1983).
Statistical Methods
Causal inference is an important goal of social science, the meaning of something being "causal" is often not made explicit in applied studies, for example, what does it exactly mean that x causes y? Is it different from x having a positive causal effect on y? Indeed, there are numerous types of causal questions one can ask about a given set of variables, and one must choose appropriate methods to analyze different types of causal hypotheses (Teppei, 2012)
First-generation techniques, such as regression-based approaches (e.g., multiple regression analysis, discriminant analysis, logistic regression, analysis of variance) and factor or cluster analysis, belong to the core set of statistical instruments which can be used to either identify or confirm theoretical hypothesis based on the analysis of empirical data. (Haenlein & Kaplan, 2004).
Viewed as a coupling of two traditions--an econometric perspective focusing on prediction and a psychometric emphasis that model concepts as latent (unobserved) variables that are indirectly inferred from multiple observed measures (alternately termed as indicators or manifest variables)--Structural Equation Model (SEM) has allowed social scientists to perform path analytic modeling with latent variables (LVs), which in turn has led some to describe this approach as an example of "a second generation of multivariate analysis"(Fornell, 1987).When applied correctly, SEM-based procedures have substantial advantages over first-generation techniques such as principal components analysis, factor analysis, discriminant analysis, or multiple regressions because of the greater flexibility that a researcher has for the interplay between theory and data (Chin, 1998). Specifically, SEM provides the researcher with the flexibility to: (a) model relationships among multiple predictor and criterion variables, (b) construct unobservable LVs, (c) model errors in measurements for observed variables, and (d) statistically test a priori substantive/theoretical and measurement assumptions against empirical data (i.e., confirmatory analysis) (Chin, 1998)
Structural Equation Models (SEM) include a number of statistical methodologies meant to estimate a network of causal relationships, defined according to a theoretical model, linking two or more latent complex concepts, each measured through a number of observable indicators; the basic idea is that complexity inside a system can be studied taking into account a causality network among latent concepts (LVs), each measured by several observed indicators usually defined as Manifest Variables (MV) (Vinczi, Tinchera & Amato, 2010).In general, there are two approaches to estimating the parameters of an SEM, namely, the covariance-based approach and the variance-based (or components-based) approach (Haenlein & Kaplan, 2004). Covariance-based SEM, in particular, has received high prominence during the last few decades and, "to many social science researchers, the covariance-based procedure is tautologically synonymous with the term SEM"(Chin, 1998).
Unlike covariance-based SEM, partial least squares method (PLS) first introduced by H. Wold (1975) under the name NIPALS (nonlinear iterative partial least squares), focuses...
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