Employee Engagement: An Empirical...

Employee Engagement: An Empirical Analysis

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CHAPTER-4 EMPLOYEE ENGAGEMENT: AN EMPIRICAL ANALYSIS

The two main objectives and consequences of this study included an engagement survey fully validated, and an EE model which elucidated the EE dynamics at the organizations. The EE model will also explain the dynamics of EE outcomes and how organizations can ensure engagement of employees.

This chapter gives all the intrinsic details to these outcomes by specifying and elucidating the compilation and examination of qualitative data from which survey items were generated (driver identification) and accordingly analysed. The ensuing chapter mentions the collection of survey data by administering survey questionnaires. The findings provide an overall picture of the main EE dynamics at the organizations and sectors covered according to the responses given by the employees. Emphasis was placed on clarifying what generated and maintained EE at varied organizations surveyed, as the identification of EE predictors (drivers) was deemed indispensable in the development of an EE scale which measured engagement and its antecedents. 4.1 Developing the Engagement Survey

The investigation progress and corroboration progression was divided into five steps, as recommended by Hinkin (1998). The first step is item generation, the second step in the process includes survey administration, the third step requires initial item reduction through Exploratory Factor Analysis (EFA), the fourth step is confirming the factor structure through Confirmatory Factor Analysis (CFA), and then fifthly to assess the convergent /discriminant validity of the factor so deciphered. Hinkin (1998) also advocates the sixth step which is to confirm the factor analysis of study results by taking into perspective some other population through which the survey reliability would be confirmed. Since the sixth step would require replicating the entire exercise all over again on a different population, therefore, it was not undertaken considering the scope of the study.


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After having established the construct validity through the CFA, the next steps conceptualized were to check for the structural relations of the drivers identified with the latent EE construct. The EE then would be assessed to check the consequences it has on the organization. For this purpose, five possible consequences as identified through the literature review viz., In Role Performance (IRP), Organizational Citizenship Behaviour (OCB), Job Involvement (JI), Job Satisfaction (JS), and Intention to Stay (ITS) would be assessed in the model itself. 4.2 Step One: Item Generation

The generation of survey items was carried out by utilising the deductive approach (Hinkin, 1998). The preliminary premises were zeroed in from the literature available on EE and thus integrated into the intended EE model as engagement drivers, as explained in detail in Chapter 3 of this thesis. It was very important to justify the drivers so identified. Due diligence was employed in analysing each item in order to avoid any gratuitous unconstructive questions, confusing questions, miss-placed modifiers, vague or ambiguous question purpose, leading or loaded questions, and restricted language (Groves, Fowler, Couper, Lepkowski, Singer, & Tourangeau, 2004; Page & Meyer, 2000; Whitley, 2002). On preliminary investigation, some of the questions were reworded, some were modified, and some of the words which were not commonly used in the Indian context were explained in the questionnaire itself.

One question of Emotional Engagement “I often feel emotionally detached from my job” was rephrased as “I often feel emotionally attached to my job”. This edition was carried out in order to make the questionnaire more employee friendly, and to align the same with Indian cultural context. The Indian respondents believe that detachment from the worldly pleasures are eternal and offers bliss, serenity and oneness with the God as given in Bhagwad Gita. Therefore, the question was rephrased. Another item which measured Co-workers Norm Adherence viz., “I don’t rock the boat with my co-workers” was somewhat vague. In order to provide clarity to the phrase ‘rock the boat’ which is used as slang in the western world was provided meaning for in the questionnaire text itself. Similarly, in the Job involvement Questionnaire (JIQ), an item read “Usually I feel detached from my job” and was moulded as “Usually I feel


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attached to my job”. The said modification was again done in order to align the questionnaire in terms of cultural relativism of the organizations of India.

Pilot test and Survey revision

The pilot test was administered to 30 faculty members of the University of Delhi (Education Sector) and 65 employees of IT sector. Pilot testing was carried out in order to ensure that the survey items were germane and effortlessly implicit by the intended population (Page & Meyer, 2000; Whitley, 2002). The survey was stratified and random and it intended to record the responses of employees from all the levels of management. In the IT sector, the top level had 5 members, the middle level had 10, and the junior level management consisted of 15 employees. In the Education sector, the researcher administered questionnaires to 25 Assistant Professors, and 5 Associate Professors. It was imperative to take account of both sets of employees from both sectors, as the nature of their employment was diverse and investigation items required being pertinent to IT and Education sectors.

Surveys were distributed to IT sector employees via HR personnel who were approached directly. The questionnaire had 111 questions and the participants were asked to provide their comments regarding succinctness, precision, duplication, and relevance of the item set. All the survey questionnaires which were administered to the employees were returned, therefore, a total count of 95 was adhered to, which exceeded the proposed minimum of 12 participants for a pilot test (Page & Meyer, 2000). Out of the 95 questionnaire responses three were discarded as the respondents did not answer about 30% of the questions. All the items were then checked for perceived repetitions, and the researcher found that there was no such question, and that each question had been framed according to the task entrusted. Internal reliability tests employing correlations, split half tests, and Cronbach’s Alpha were conducted. Preferably the model sample size for internal reliability check and pilot factor analysis should have been proportioned towards a larger sample size; Tabachnick and Fidell (2001) propose at least 300 cases, 150 cases are advocated by Pallant (2005), however some type of fine-tuning was required initially to move on further. One item in the Job Satisfaction questionnaire viz., “Each day of work seems like it will never end”


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which was showing negative contribution to the Cronbach’s Alpha, was then not included in the final survey (Oreg, 2003; Whitley, 2002). As an additional enhancement, a preliminary exploratory factor analysis was performed. Due care was taken while analysing some items during factor analysis in order to ensure that they did not account for discrepancy in responses (Hinkin, 1998). One of the item “I feel overwhelmed by the things going on at work” showed negative loading in the Rotated Component Matrix, the possible cause of the same was investigated. The researcher found that the item had been construed by the respondents as a positively worded statement; however, the researcher intended to keep the item in a negative stead, as originally overwhelmed means snowed under or besieged or weighed down. Overwhelmed was construed by the respondents as a positive word, therefore, in order to match the cultural edifices of organizations of India, and the respondents subsequently, the aforesaid item was deleted. Therefore, out of a total questionnaire set of 111 items the researcher deleted two questions which were ambiguous as far as the cultural context was taken into consideration.

The questionnaire had seven demographic variables viz., Age, Gender, Structure of the Family, Levels in the Organization, Educational Qualification, Experience in the Organization, and Marital Status. It was always a conscious effort to have scale instrument parsimony so that the employees don’t feel fatigue while answering the questionnaire. Further, it was also ensured that each item of the construct be answered without any bias, and, therefore, the researcher purposely did not name the construct in the questionnaire set. 4.3 Step Two: Questionnaire Administration

The most challenging aspect of any research work is to get one’s questionnaire administered properly, and that the respondents respond in a manner envisaged. Many researchers like Whitley (2002), Hinkin (1985) advocate that the Likert study arrangement is the most appropriate for theoretical constructs viz., engagement, Job Involvement (JI), Job Satisfaction (JS), Organizational Citizenship Behaviour (OCB) etc. Consequently, a five point Likert scale was employed which had a continuum ranging from Strongly Disagree (1) to Strongly Agree (5). The five point Likert scale


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is also considered to be the most favourable range for carrying out exploratory factor analysis (EFA) (Hinkin, 1985). As this study is basically exploratory in nature and the researcher intended to carry out EFA to arrive at a factor structure, therefore, five-point Likert scale was considered the most appropriate. Further, the coefficient alpha reliability with Likert scales has been reported to increase from two up to a five point scale but after that it levels off i.e., it shows no further gains beyond this fifth point (Lissitz & Green, 1975). A better equipped and inclusive seven point likert scale was also assessed, because it augments the variability (DeVellis, 2003), however the idea was discarded as the survey fill up time would considerably increase and this would lead to survey exhaustion among the respondents. The use of five point scales ensured an odd numbered instrument. The advantage of an odd numbered instrument is that for those respondents who feel indifferent, neutral, undecided, or ambiguous regarding the question could always go for the middle option (Groves et al., 2004), further, in order that a detection of a centre faction could be deciphered (answers of 3/5), as the literature on EE advocate that the centre faction responded most to the engagement stimulating tactics (Coffman & Gonzalez-Molina, 2002). The researcher considered the use of negatively framed statements; however, most of the questions were kept in a positive frame in order to gauge the cultural acceptability in the Indian context. As mentioned before some of the questions which were not easy to answer were reframed, or some of the words were explained in the questionnaire text itself in order to understand the context of the question.

Variables which related to definite themes like ‘Psychological Meaningfulness’ or ‘Workplace Spirituality’ were grouped together in the questionnaire set. The particulars of the respondent viz., the name of the organization, name of the employee, and contact details which were deliberately made ‘not mandatory’ questions because the respondents generally do not want to delve such details on the pretext of confidentiality. Further, the opening line of the questionnaire declared that the questions asked were purely for research work and the particulars of the candidates would be kept confidential at all the stages of the research work. Further, the seven demographic variables were placed at the beginning of the questionnaire.


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The methodology of questionnaire administration was very precise. The questionnaire was formatted and sent through Google Drive, wherein an online link is created. This link could be posted in social networking forums and also e-mailed separately. On clicking on this link, the respondent comes to the questionnaire page, specially designed for this exercise. When the respondent has finished filling the response questions, the page automatically asks for submission. If the respondent clicks on the submit button, the responses are automatically generated in the form of Document Sheet in the Google Drive page of the researcher. From the Document Sheet the researcher can easily download the responses in MS- Excel. This ensured that the researcher had direct access over survey administration and also the downloading of information. This also ensured secrecy for respondents, as filled questionnaires went straight to the researcher’s database (Whitley, 2002). There are many researchers (Dillman, 2000; Groves et al., 2004; Whitley, 2002) who support the perspective that an online questionnaire response was well thought-out and most appropriate, as most of the employees are computer savvy these days, having a familiarity with operating the numerous online surveys that are directed to them. Further, they hold a viewpoint which is practically feasible is that online surveys save time and cost to the researcher, as the researcher personally experienced it throughout the whole activity of data collection. It was felt by the researcher that the data administration became convenient, once there was the utilisation of online Google Drive link.

For the respondents of the Education Sector (University of Delhi), the Google online link was pasted on the Delhi University Facebook group page. However, most of the responses were registered from those participants who had been individually administered the questionnaire in hard copy via the ‘Head of Department’ of particular colleges. This ensured that the personal bias of the researcher towards a certain respondent was minimized and randomness was assured. Similarly, the respondents from IT and Banking Sector were administered questionnaire through the HR Manager and the Branch Manager respectively. The questionnaire link was also posted on the Intranet website of particular organizations so that the question could be easily answered via the online mode.


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Analysis of Survey Results

The first and foremost work after collecting all the data required was to check for any errors which could have crept in the data set and then formatting the data set as to be utilised for analysis purpose. It was imperative to check for data discrepancy arising out of individual cases and variable cases. While checking for individual cases, Case Screening was resorted to, in this the researcher can go for:

1. Checking missing values 2. Checking unengaged responses

Out of the 360 survey responses collected according to the research design, there were 21 incomplete responses which had missing data issue. Among the 21 incomplete responses, 18 of the respondents were those who did not answer more than 10% of the questions in the questionnaire set. Those candidates were candidates for elimination (Hair, Black, Babin, and Anderson, 2010). The rest of the missing values were taken care of by using median imputation. On analysing the data set then for variability in responses or unengaged responses, a further 10 respondents were eliminated. The methodology which was followed all the while was to calculate the standard deviation of the responses of each candidate. 10 respondents reported variability of less than 0.30, and hence were eliminated. This was done in order to remove the unengaged responses from the data set to have a filtered data set for analysis.

The next step was to check for variable discrepancies by following steps in Variable Screening:

1. Variable Missing Data 2. Assessing the Normality - Skewness and Kurtosis

In order to check the variable missing data, frequency of the entire data set is calculated and any missing value is automatically listed in terms of valid and missing data value. In this case there were no missing values.


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The second step was to check whether the data set is normal. The assessment of data normality is, however, an ambiguous work, wherein one simplified way to check normality is not provided. The parametric procedures entail that the data taken from the population is normally distributed; however, it is very unlikely for the data set to be absolutely normally distributed because the sample data generally deviates from the population. Micceri (1989) advocates that to find an absolute normal data set in psychological data is really rare. Moreover, for assessment of normality in the ordinal kind of data set becomes all the more difficult because interval between scale points cannot be said to be equal, and so it’s not strictly possible to regard an ordinal data set to be normal. In order to check the data normality in statistical packages, the rules are: The MK1 eyeball test: observing the histogram of the variable and superimposing the normal curve upon it to check any deviation from normality. Secondly, to check for the P-P plots this says that the data points should lie in the diagonal to assess the normality. Checking for Skewness and Kurtosis can be the third option. Generally, in a perfectly normal distribution the Skewness and Kurtosis should be zero. However, in behavioural sciences, the skewness and kurtosis can vary up to 3. The best way is to divide the skewness and kurtosis values by their standard errors to obtain the z scores and then subsequently assessing normality. Thirdly, the researcher can go for K-S Lilliefors Test, the Shapiro-Wilk test. In this research study, the researcher employed all the measures to assess normality, immediate tests such as K-S Lilliefors Test, the Shapiro-Wilk test were utilised, because in particular, when the sample is large, available statistical tests for normality can be sensitive to very small (i.e., negligible) deviations in normality. Therefore, if the sample is very large (n>300), a statistical test may reject the assumption of normality when the data set, using graphical methods, is essentially normal and the deviation from normality is too small to be of practical significance. The researchers have commonly advised those research studies having data set of number of respondents greater than 300, to check for skewness and kurtosis value greater/lesser than ±2. Any value which is greater/lesser than ±2 indicates non-normality, the kurtosis values greater than 2 signifies that the most of the respondents have answered the questions in a similar manner, and a value less than 2 denotes that respondents differ entirely in answering to a particular question (Gaskin, 2013a). On assessment of the data set, the researcher found that


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some of the variables were having values greater than 2, and those variables were kept under observation while conducting EFA so that any problem in case of assessing communality could be tackled by deleting these data variables (Gaskin, 2013a).

Table 4.1: Sample Composition Sector Male Female Total Junior

Level Middle Level

Top Level

Total

IT 83 30 113 55 38 20 113 Banking 79 20 99 43 38 18 99 Education 27 93 120 60 40 20 120 Total 189 143 332 158 116 58 332

The sample appeared to be representative of the target population with female employees being 143 out of the total of 332 employees (43.07%), male being 189 out of 332 (56.93%). 4.4 Step Three: Initial Item Reduction – Exploratory Factor Analysis

Exploratory factor analysis (EFA) was undertaken in order to abridge the study composition, eliminate gratuitous variables, and recognize principal engagement drivers (Pallant, 2005). The first point of contention which needs to be addressed is the number of sample respondents required to carry out EFA. General guides include, Tabachnick and Fidell (2007) rule of thumb that suggests having at least 300 cases for factor analysis. Hair et al. (1995) have envisaged that sample sizes should be 100 or greater. However, these rules of thumb are generally refuted by the different school of authors, (MacCallum, Widaman, Zhang, and Hong, 1999), they disapprove such claims by substantiating that when the communalities are higher (greater than 0.6), and each factor is explained by several variables, then relatively small sample size could also suffice. The caveats also point towards inspection of the correlation matrix; Tabachnick and Fidell (2007) have recommended the inspection of the correlation matrix (also called Factorability of R) for correlation coefficients over the cut-off point of 0.30. Hair et al. (1995) categorised these loadings using another rule of


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thumb as ±0.30=minimal, ±0.40=important, and ±0.50=practically significant. To put it in a simpler manner, if the factor correlation is greater than 0.3, then the factors account for 30% of the relationship, within the data.

Before checking for the extraction of factors, prior tests should be carried out in order to assess the appropriateness of the respondent data for EFA. These tests comprise of the Kaiser – Meyer – Olkin (KMO) test (Kaiser et al., 1974) and Bartlett’s test of Sphericity (Bartlett, 1954). A preliminary examination of the correlation matrix depicted that several of the items were correlated (above 0.3) with a KMO value of 0.897, being more than the suggested value of 0.6 (Tabachnick & Fidell, 2001), and for Bartlett’s test of Sphericity (Bartlett, 1954), the value should be significant (p<0.05) for factor analysis to be suitable.

Table 4.2: KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. 0.897 Bartlett's Test of Sphericity

Approx. Chi-Square 13185.309Df 2346 Sig. 0.000

Df- Degrees of Freedom, Sig.- Significance

This test also provides for that the correlation matrix has significant correlations among at least some of the variables. In this case the Bartlett’s test of Sphericity reached statistical significance, which supported the factorability of the correlation matrix.

Exploratory Factor Analysis: Key Driver Identification

Choice of the Extraction Method: According to Hair et al. (2010), the researchers can choose from the two basic methods of factor extraction based on the objective of the factor analysis. In the same vein it has to be discussed that variance of the variable actually determines the type of extraction. In common parlance there are three types of variance:


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1. Common Variance: is the variance which is shared by all the variables which are there in the analysis.

2. Specific Variance: which is also referred to as unique variance is associated with only a specific variable.

3. Error Variance: is the variance which cannot be explained by correlations with other variables.

Therefore, the total variance of any variable comprises of the aforesaid variance. In addition to this, if one variable is correlated strongly with one or more variables, then there is an increase in common variance i.e., communality. As far as choosing the method of extraction is concerned, Principal Component Analysis (PCA) takes into account the total variance and obtains factors that hold little percentage of unique variance and, in some occurrences error variance. However, common factor analysis considers only the common or shared variance presuming that both the unique variance and error variance are not of significance. Now the question again was which extraction form to choose, the caveats in this regard point that if data reduction is the primary concern, then PCA is the best bet. However, if the primary objective is to identify the latent dimensions or constructs represented in the original variables, then one should go for common factor analysis (Hair et al., 2010). According to this theory, the present study calls for common factor analysis, and thus the researcher went in for Principal Axis Factoring (PAF).

The second question was the type of rotation to be employed. When the research goal is primarily concerned with data reduction researchers usually go for orthogonal rotation. Oblique rotation is used when the objective is to obtain varied theoretically meaningful factors or constructs (Hair et al., 2010). The researcher employed Direct Oblimin rotation which is a kind of Oblique rotation.

Exploratory factor analysis of all 69 items was used to determine the underlying structure of engagement (Tabachnick & Fidell, 2001). The researcher employed Principal Axis Factoring and Direct Oblimin rotation first to check the conjectural associations involving the recognized themes earlier and probable factors (Fabrigar, Wegner, MacCallum, & Strahan, 1999). In an initial rotation, before further analyses, 16 factors were extracted with Eigen values above 1.0. Though the factor structure


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was established, there were some loadings which could not be justified on the pretext of face validity. Therefore, it was thought imperative to go for Component Extraction Method and use orthogonal rotation for the same set. The researcher then employed Principal Component Analysis (PCA) and utilised Varimax for rotation purpose. Since, in most of the applications, both the component analysis and common factor analysis reach almost the same conclusion and demonstrate similar results if the number of variables exceed 30 (Gorsuch, 1983) or the communalities value for most of the variables exceed 0.60. In this particular case we can safely state that the number of variables is 69 exceeding the recommended value of 30, and on closely scrutinising the communality values the researcher found that most of the values were above 0.60. Therefore, the researcher also intended to check this assumption by extracting the data set by employing both the ways of extraction viz., PCA and PAF. Since researchers such as Pett, Lackey, and Sullivan (2003) and Kieffer (1999), advocate that both the extraction method viz., PAF and PCA should be employed for comparison and assessment of best fit. They further add that if either of the rotated solution constructs the best fit and factor suitability, both perceptively and theoretically should be employed. On extracting the 69 items based on extraction method PCA then, and rotation method Varimax, the initial rotation based on Eigen values greater than 1 extracted 16 factors. However, in this case, the factor structure was more pronounced, and most of the variables loaded as desired. Though, the loading based on PAF extraction was more or less producing the same structure, the researcher deemed the PCA extraction to be more exact according to the theory of engagement and its predictors.

The Drivers Scale

After the initial inspection of the data set, careful scrutiny of each database was required. Since, the face validity of the EFA was coming out stronger in case of extraction based on PCA; the researcher extended the research based on PCA.

Extraction based on number of factors

Out of the total of 16 factors extracted based on the Kaiser Criterion (Kaiser, 1960), i.e., Eigen Values greater than 1, only 8 factors were showing face validity. The rest of the factors were either having variables less than 3, and thus scattered. It has been


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advocated by varied academicians, that if logic cannot be derived out of Kaiser Criterion, then multiple criteria should be used in order to extract factors based on fixed number of factors (Costello & Osborne, 2005). Thompson and Daniel (1996, p. 200) also advocated that the “simultaneous use of multiple decision rules is appropriate and often desirable”. Hair et al. (1995) envisage that the mainstream of factor analysts normally employ multiple criteria. In the same context, the researcher also deemed appropriate to conduct factor analysis based on fixed number of factors. According to Kaiser Criterion 16 factors were extracted. Thereafter, the close perusal of the Scree Plot suggested that 12 factors extraction could normally be the best option (Chatterjee, Jamieson, & Wiseman, 1991). According to the cumulative percentage of variance criterion, which envisages that for social sciences 50-60% of variance explained is sufficing (Hair et al. 1995; Pett, Lackey, & Sullivan, 2003), 12 factors emerged to be true. Therefore, the next step was to run the EFA with the aid of extraction technique PCA and rotation method Varimax, based on fixed number of factors 12.

The next step was to check for the anti-image matrix. It becomes imperative to check the diagonal of anti-image matrix, which depicts the KMO value of each item. Preferably each of the value should be greater than 0.6 (Tabachnick & Fidell, 2001). In the diagonal of the anti-image matrix, each value was greater than 0.6, substantiating the claim that the data is adequate for each of the item. Secondly, it is also advised that the correlation matrix is thoroughly checked before drawing any conclusions. Multicollinearity issue necessitates this step, since if the correlation value between two variable is greater than 0.9, then it shows that there can well be a problem of singularity. Singularity refers to a statistical problem where two or more variables are measuring the same dimension. Singularity then may cause problem in determining the unique contribution of statements towards a factor. In such cases the researchers delete one of the two statements. In this particular case, the researcher could not find any correlation or ‘r’ value greater or equal to 0.9, which depicts that there is no multicollinearity issue in the factor structure. Further, since the researcher has employed PCA, chances of multicollinearity are greatly reduced. The next table to be assessed is Communality table. Hair et al. (2010) advice the researchers to examine


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each variable’s communality, which represents the amount of variance accounted for by the factor solution for each variable. The communalities must be assessed in order to check whether the variables meet the acceptable level of explanations. Hair et al. (2010) recommend the cut off value of 0.5 for each variable when assessing the communality. In this case, there were 11 items (variables) out of 69 items, having communality value of less than 0.5, 10 of those items were having communality value greater than 0.4, and one item had a communality value of 0.395. The remedy for this situation as suggested by Hair et al. (2010) could be:

1. Ignoring those problematic variables, when the aim is data reduction, 2. Deleting variables based on careful evaluation that the variable is of minor

importance to the study, 3. Checking for Oblique rotation, if not employed earlier, 4. Modifying the extraction method.

Those items could not be deleted straightaway as they were significant part and of major importance. Changing the extraction as well as rotation method had little or no effect on the identified items. Therefore, in this context the researcher’s viewpoint and subsequent decision was to ignore the problems at the EFA stage, and employ other techniques like CFA at a later part to gauge the construct’s reliability and validity. The next step was to check the cross-loading in order to assess the unidimensionality of the factors, an optimal structure is established only when all the variables have high loadings on one single factor (Hair et al. 2010). If cross-loading exist for a variable on multiple factors, that variable is a candidate for deletion, further the test of unidimensionality is that each summated scale should consist of item loadings highly on one factor (McDonald 1981; Nunnally 1979; Hattie 1985). Significant cross loading can be checked by following the caveat that the difference between the highest loading of a variable into one factor and that of second highest loading into some other factor should be greater than 0.2. On closer perusal of the Rotated Component Matrix, it was established that there were no significant cases of cross-loadings as provided in Appendix 4.


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Interpretation

After having all the caveats checked and re-checked for the successful employment of EFA, the next step was to interpret the Rotated Component Matrix. The driver identification process had been undertaken earlier in a precise manner in order to reach definite factorability. While interpreting the results strong conceptual foundation of the anticipated structure was already specified, and the rationale behind the anticipated structure was also strong. This anticipation was based in the theoretical paradigms, and to some extent the prior research done in this field of study. As has been specified earlier a robust study of EFA was done in order to underpin the structure of the predictors of EE. The researcher repeatedly made subjective judgements related to the number of factors to be extracted, extraction techniques and so on. As Hair et al. (2010) have clarified that almost anything can be uncovered if robust trial based on factor modelling, extraction based on different number of factors, and various forms of rotation is carried out. The researcher employed all the techniques to unearth the most logical set of factors. It was assessed that face validity was established, when extraction is done on PCA based on fixed number of factors to be extracted at 12, and rotation technique to be used as Varimax. The form and appropriateness of the factor solution was established applying the researcher’s discretion and arbitration. As the final process, the factor loadings for each variable were processed, in order to determine the variable’s role and contribution in determining the factor structure. As the factor loading is the correlation of the variable and the factor, the squared loadings is the amount of the variable’s total variance accounted. Therefore, a 0.5 loading denotes that 25% of the variance is accounted for by the factor. Since the loadings of ±0.5 or greater are considered practically significant, the coefficients below 0.5 were suppressed, to show strong correlation of the variable with the factors. The next question is related to the statistical significance; Cliff and Hamburger (1967) envisage that factor loadings having typically larger standard errors than typical correlations, therefore, factor loadings should be evaluated on a stricter level. Hair et al. (2010) specify that in order to suppress the


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coefficients below 0.5, the sample size should be at least 120, in this case the sample size is 332, therefore, factor extraction by suppressing coefficients below 0.3 will also suffice. When a satisfactory factor solution was obtained, the labelling of the factors was carried out. This process is about assigning the meaning to the factor structure. After examining all the significant variables for a particular factor, the factors were named by following the caveat of accurate reflection of the variables on the factor. Labelling in this particular case was an easy exercise, as the variables loaded on specific factors as originally construed. The underlying factor structure has been appended here.

Table 4.3: Supportive Supervisory Relations (SSR)

Sl. No.

Items (Variables) Factor Loadings

1. My supervisor encourages employees to speak up when they disagree with a decision.

0.809

2. My supervisor praises good work. 0.794 3. My supervisor is committed to protecting my interests. 0.790 4. My supervisor encourages me to develop new skills. 0.786 5. Employees are treated fairly by my supervisor. 0.785 6. My supervisor encourages employees to participate in important

decisions. 0.779

7. My supervisor helps me solve work-related problems. 0.748 8. My supervisor keeps informed about how employees think and

feel about things. 0.736

9. I trust my supervisor. 0.722 10. My supervisor does what he/she says he/she will do. 0.656


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Table 4.4: Rewarding Co-worker Relations (RCWR)

Sl. No. Items (Variables) Factor Loadings

1. My co-workers value my input. 0.797 2. I believe that my co-workers appreciate who I am. 0.763 3. I sense a real connection with my co-workers. 0.759 4. My co-workers listen to what I have to say. 0.745 5. My co-workers really know who I am. 0.714 6. I feel worthwhile when I am around my co-workers. 0.709 7. I trust my co-workers. 0.680 8. My co-workers and I have mutual respect for one another. 0.670 9. My interactions with my co-workers are rewarding. 0.659 10. I feel a real ‘kinship’ with my co-workers; (kinship- a close

relationship). 0.643

11. My co-workers value my input. 0.797

Table 4.5: Spirituality and Alignment (SAA)


1. My work provides me with inner source of inspiration as if the work I am doing is my ‘calling’.

0.751

2. My job ‘fits’ how I see myself in the future. 0.724 3. The work I do on this job helps me satisfy who I am. 0.698 4. My job allows me to unleash my full potential. 0.680 5. My work adds a lot to the general purpose of my life. 0.625 6. My job ‘fits’ how I see myself. 0.620 7. I feel a sense of connectedness while performing my job. 0.609 8. I like the identity my job gives me. 0.574 9. My personal goals are aligned with the organizational goals 0.512


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Table 4.6: Psychological Meaningfulness (PM)


1. The work I do on this job is meaningful to me. 0.772 2. My job activities are significant to me. 0.677 3. My job activities are personally meaningful to me. 0.677 4. The work I do on this job is worthwhile. 0.636 5. I feel that the work I do on my job is valuable. 0.634 6. The work I do on this job is very important to me. 0.619

Table 4.7: Employee’s Job Resources (ER)

Sl. No.


1. I feel emotionally drained from my work. 0.766 2. I feel like I’m at the end of my rope emotionally; (end of my rope-

no option left). 0.749

3. I can’t think straight by the end of my workday. 0.634 4. I feel physically used up at the end of the workday. 0.625 5. I feel tired before my workday is over. 0.600

Table 4.8: Physical and Emotional Engagement (PEE)


1. I exert a lot of energy while performing my job. 0.703 2. My own feelings are affected by how well I perform my job. 0.656 3. I get excited when I perform well on my job. 0.567 4. I often feel emotionally attached to my job. 0.562 5. I stay until the job is done. 0.523


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Table 4.9: Psychological Availability (PA)


1. I am confident in my ability to deal with problems that come up at work.

0.782

2. I am confident in my ability to think clearly at work. 0.765 3. I am confident in my ability to handle competing demands at

work. 0.739

4. I am confident in my ability to display the appropriate emotions at work.

0.588

Table 4.10: Employee’s Self-Consciousness (ESC)


1. I don’t worry about being judged by others at work. 0.660 2. I worry about how others perceive me at work. 0.592 3. I am afraid my failings will be noticed by others. 0.588

Table 4.11: Co-worker Norm Adherence (CWNA)


1. I do what is expected of me by my co-workers. 0.709 2. I don’t ‘rock the boat’ with my co-workers; (rock the boat-

creating disturbance). 0.504

Table 4.12: Employee Cognition (EC)

Sl. No.


1. I am rarely distracted when performing my job. 0.735


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Table 4.13: Psychological Safety (PS)


1. I am afraid to express my opinions at work. 0.614 2. I avoid working overtime whenever possible. 0.572

Table 4.14: Flow


1. Performing my job is so absorbing that I forget about everything else.

0.542

Reliability of the Antecedents

To assess the scale reliability, internal consistency of each driver (antecedents) was analysed. To assess the internal reliability, Cronbach’s alpha was employed. The strong item co-variance is indicated when the Cronbach’s alpha score exceeds the minimum recommended value of 0.70 (Whitely, 2002). The Cronbach’s alpha score for each antecedent identified is mentioned, those factors which had Cronbach’s alpha score of less than 0.7 were candidates for elimination and hence not considered for Split Half Tests.

Table 4.15: Cronbach’s Alpha Score of Antecedents

Antecedents Items scale summated

Cronbach’s Alpha

Supportive Supervisory Relations (SSR) 10 0.935 Rewarding Co-worker Relations (RCWR) 10 0.917 Spirituality and Alignment (SAA) 7 0.912 Psychological Meaningfulness (PM) 5 0.876 Employee’s Job Resource (ER) 3 0.796 Physical and Emotional Engagement (PEE) 5 0.742 Psychological Availability (PA) 4 0.817


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Split Half Test for Antecedents

Under this method the researchers divide the scale/test into two halves, such that the halves form parts of the entire scale/test. Both the halves should be of equal lengths preferably. The determination of reliability is after correlating the results of the two halves of the same scale/test. The two halves should be the parallel forms of one another, and it is suggested that after giving due consideration to the item difficulty and fatigue factor, odd-even reliability should be considered (DeVellis, 2003).

Therefore, the mechanism followed for split half test was splitting the total scale/test into two equal halves following odd-even distribution. SPSS 19 was used to test the split half consistencies.

Table 4.16: Supportive Supervisory Relations (SSR)

Reliability Statistics Cronbach's Alpha Part 1 Value 0.863

N of Items 5a Part 2 Value 0.885

N of Items 5b Total N of Items 10

Correlation Between Forms 0.894 Spearman-Brown Coefficient

Equal Length 0.944 Unequal Length 0.944

Guttman Split-Half Coefficient 0.943 a. The items are: ESSR1_1, ESSR3_1, ESSR5_1, ESSR7_1, ESSR9_1. b. The items are: ESSR2_1, ESSR4_1, ESSR6_1, ESSR8_1, ESSR10_1. On conducting split half test on SSR, it was ascertained through the correlation values among the two forms that the two forms display correlation to the tune of 0.894 and hence reliable. Spearman-Brown test also provides a better estimate of the reliability of the full test conducted. The Guttman split half is also showing good reliability for SSR.


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Table 4.17 Rewarding Co-worker Relations (RCWR)






Guttman Split-Half Coefficient 0.935 a. The items are: ERCWR1_1, ERCWR3_1, ERCWR5_1, ERCWR7_1, ERCWR9_1. b. The items are: ERCWR2_1, ERCWR4_1, ERCWR6_1, ERCWR8_1, ERCWR10_1.

The test results show a correlation value of 0.835, Spearman Brown Split Half and Guttman Split Half coefficients show good reliability to the extent of 0.937 and 0.935 respectively for the factor RCWR.

Table 4.18 Spirituality and Alignment (SAA)






Guttman Split-Half Coefficient 0.897 a. The items are: EWRF1_1, ES1_1, EWRF2_1, ES2_1, EWRF3_1. b. The items are: ES3_1, EWRF4_1, ES4_1, ES5_1.


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The test results show a correlation value of 0.823, Spearman Brown Split Half and Guttman Split Half coefficients show good reliability to the extent of 0.903 and 0.897 respectively for the factor SAA.

Table 4.19 Employee’s Job resources (ER)






Guttman Split-Half Coefficient 0.820 a. The items are: ER2_1, ER5_1, ER7_1. b. The items are: ER4_1, ER6_1. The test results show a correlation value of 0.728, Spearman Brown Split Half and Guttman Split Half coefficients show good reliability to the extent of 0.843 and 0.820 respectively for the factor ER.


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Table 4.20 Psychological Meaningfulness (PM)

Reliability Statistics Cronbach's Alpha Part

1 Value 0.755 N of Items 3a

Part 2

Value 0.805 N of Items 3b

Total N of Items 6 Correlation Between Forms 0.780 Spearman-Brown Coefficient


Guttman Split-Half Coefficient 0.876 a. The items are: EM1_1, EM3_1, EM5_1. b. The items are: EM2_1, EM4_1, EM6_1. The test results show a correlation value of 0.780, Spearman Brown Split Half and Guttman Split Half coefficients show good reliability to the extent of 0.876 and 0.876 respectively for the factor PM.

Table 4.21 Physical and Emotional Engagement (PEE)






Guttman Split-Half Coefficient 0.743 a. The items are: Ee2_1, EP1_1, Ee3_1. b. The items are: EP2_1, Ee4_1.


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The test results show a correlation value of 0.628, Spearman Brown Split Half and Guttman Split Half coefficients show good reliability to the extent of 0.772 and 0.743 respectively for the factor PEE.

Table 4.22 Psychological Availability (PA)



Part 2




Guttman Split-Half Coefficient 0.828 a. The items are: EPA1_1, EPA3_1. b. The items are: EPA2_1, EPA4_1.

The test results show a correlation value of 0.708, Spearman Brown Split Half and Guttman Split Half coefficients show good reliability to the extent of 0.829 and 0.828 respectively for the factor PA.

Validation of Factor Analysis

The next step is to assess the validity of the EFA so conducted. As it has been reported in the earlier sections of this chapter, EFA is generally data driven which is dependent on a number of subjective decisions to be taken by the researcher. By employing confirmatory factor analysis (CFA), the researchers can cross validate the factor structure in an appropriate way (Byrne, 1989; Jöreskog & Sörbom, 1989; Pedhazur & Schmelkin, 1991). However, there are other methods also to validate the factor structure. Validity assessment is done mainly to make the results more robust. Further, it is also recommended to make the results factor stable. Hair et al. (2010),


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recommend that the researchers can then go for the summation of the variables of different factor to bring model parsimony and also to reduce the measurement errors. However, the summation produces effective results only when it has a theoretical justification, test of unidimensionality has been established through EFA or CFA, and the test of reliability of the construct has been established through Cronbach’s Alpha whose value should be essentially more than 0.7 (Whitley, 2002). After establishing the reliability of the said construct the next step is to identify and prove the validity in terms of:

1. Convergent validity - scales correlates with other scales 2. Discriminant validity - scale is sufficiently different from other related scales 3. Nomological validity - scale predicts as theoretically predicted.

The researcher however deemed it appropriate to further exercise the test of CFA in order to establish the factor structure in a more pronounced manner. In the same vein it is safely assumed that CFA is an appropriate statistical method to establish the scale validity. So summation of the aforesaid variables into factors could be undertaken only when the assessment of CFA is completed. 4.5 Confirmatory Factor Analysis (CFA)

CFA is a technique which is used to assess that how well the measured variables represent a construct. The quality of the measured variables is tested when the employing of CFA is coupled with the construct validity test. No valid conclusions exist for the want of valid measurement. So both the things are inseparable and go hand in hand. In CFA the researchers have to specify beforehand the number of factors that exist for a set of variables. CFA is applied in order to test the extent to which the priori theoretical pattern of loadings on pre-specified constructs represent the data. It is a way to either confirm or reject the priori theory. The six steps as provided by Hair et al. (2010) have been taken into account for CFA and SEM analysis. The first stage is the identification of and defining of individual constructs. In this study the researcher has already specified, identified, and defined each construct after EFA.


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Second stage requires the development of the overall model of measurement. In this latent constructs should be indicated by at least three measured variables, in preference there should be four or more. This is done in order to bring the latent factors in the domain of statistical identification. It is an established theory now that less than 3 variables per construct are probably under identified. In CFA, one parameter can be estimated for each unique variance and covariance in the observed covariance matrix. Covariance matrix provides the degrees of freedom (DOF) which is used to estimate the parameters. If there are ‘v’ measured items, then the calculation of the number of unique variances/covariances is done as ½[v(v+1)]. Therefore, if the measured variables are 2 for example, then ½[2(2+1)] = 3 is the number of unique variance/covariance. The total numbers of measured parameters then are two loading estimates and two error variance i.e., four. The difference of the number of variance/covariance and the total numbers of measured parameters give the degrees of freedom which should be positive or zero to be identified. In this case it is 3-4 = -1, which shows that the DOF is negative. The model should be over-identified i.e., having more unique covariance or variance terms than the parameters to be estimated, thus having positive degrees of freedom (DOF).

Just-identified or under-identified models do not test theories as the unique variance/covariance is either exceeded or equal to the number of estimated parameters. In the present study, therefore, the researcher did not test further those factors which were under-identified i.e., factors having variables less than 3. Further, Kline, (2004) also advocates that CFA may not produce good results when the number of variables per factor is less than 3. Therefore, CFA was run on eight factors which were having three or more than 3 variables namely: SSR, RCWR, SAA, PM, ER, PEE, PA, and ESC. It is a matter of consideration that whenever unidimensionality is assumed, then each measured variable is hypothesized to relate to only one construct and all cross-loadings are hypothesized to be zero.


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Figure 4.1: Confirmatory Factor Analysis (CFA) of EE Antecedents

SSR – Supportive Supervisory Relations, RCWR – Rewarding Co-workers Relations, SAA – Spirituality and Alignment, PM – Psychological Meaningfulness, ER – Employee’s Job Resources, PEE – Physical and Emotional Engagement, PA – Psychological Availability, ESC – Employee’s Self-Consciousness

The third stage is designing the study to produce empirical results. The standard rules and procedures should be applied here to produce a valid descriptive research (Hair, Babin, Money, & Samouel, 2003). In CFA the researcher specifies the indicators associated with each construct and the correlations between constructs. One essential procedure here is to set the scale of the latent factor. As the latent factor is unobserved, hence no metric scale usage, therefore, the researcher needs to set values for exogenous and endogenous constructs. Identification of the model is one of the crucial aspects of CFA and Structural Equation Model (SEM) (Bollen & Joreskog 1985). Hair et al. (2010) suggest that Order and Rank conditions should be met in


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order to identify the model statistically. Order condition which is based on DOF has already been assessed by the researcher. However, the Rank condition is a tedious job to fulfil and verify, and hence researchers have come up with alternative solutions. The first alternative is to adhere to the three indicator rule per factor; the second alternative includes recognizing identification problem. In the present study, there was no identification as well as specification problem. Further, no issue of any large standard error for one or more coefficient was identified. In addition to this the research studies having large sample sizes i.e., sample greater than 300, commonly do not face problems of Heywood cases (Hair et al., 2010). This study having a sample size of 332 wards of the general problems of Heywood cases, in which communality value (squared correlations) exceeds 1, and SEM solution produces error variance estimate of less than 0 (negative error variance) (Hair et al., 2010).

The fourth stage is to assess the measurement model validity. In this the researcher should basically check for the Fit Indices, Path Estimates, Size of Path Estimate, and Statistical Significance. Each of the drivers so identified and subsequent analysis conducted certify that the path estimates in this present study was strong as well as significant. Since, a significant loading greater than 0.5 is not sufficient to establish an item performance, therefore, the researcher should analyse the loadings which can be assessed at an impressive significance level of p < 0.01. Since in the present study the researcher has already established the dimensionality, the next thing for scale building is construct validity assessment. For construct validity there are most commonly four caveats:

1. Standardized loading estimates should be 0.5 or higher, and ideally 0.7 and higher. 2. Average Variance Extracted (AVE) should be 0.5 or greater to suggest adequate convergent validity. The AVE is calculated as the mean variance extracted for the items loading on a construct and is a summary indicator of convergence. This value can be calculated by employing the standardized loadings – Li, where i is the number of items. For n items AVE is calculated as the total of all squared multiple correlations (r2) divided by the number of items n.


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3. AVE estimates for two factors should always be greater than the square of the correlation between the two factors to provide evidence of discriminant validity viz.; AVE should be greater than the Maximum Shared Variance (MSV) and Average Shared Variance (ASV). Discriminant validity is the degree to which a construct is strictly distinctive from other constructs. High discriminant validity presumes that the construct is unique and captures those aspects which the other constructs are not doing. 4. Construct reliability (CR) should be preferably greater than 0.7 or higher to indicate adequate convergence or internal reliability. CR is calculated from the squared sum of factor loadings (Li) for each construct and the sum of the error variance terms for a construct (ei). The relative calculations were carried out in the Smart Tool Package (Gaskin, 2012).

Validity issues are highlighted in bold in the table no. 4.23:

Table 4.23: Testing Validity Scores for Antecedents

Some of the variables which were having the standardized loading estimates less than 0.5 were then the candidates for immediate deletion. One item in the factor ESC, which had a standardized loading estimate of 0.309, was deleted first. But the problem of under-identification i.e., factors having less than 3 variables (Hair et al. 2010; Kline, 2004) surfaced. Therefore, ESC as a factor was also dropped from further analysis. After subsequent deliberations, PEE also showed some discriminant validity

CR AVE MSV ASV PA SSR RCWR SAA PM ER PEE ESC

PA 0.838 0.569 0.204 0.114 0.754

SSR 0.935 0.591 0.216 0.112 0.301 0.769

RCWR 0.918 0.530 0.247 0.129 0.302 0.436 0.728

SAA 0.914 0.545 0.561 0.230 0.359 0.465 0.497 0.738

PM 0.881 0.553 0.561 0.196 0.386 0.268 0.455 0.749 0.744

ER 0.810 0.520 0.277 0.109 0.261 0.335 0.278 0.318 0.286 0.721

PEE 0.742 0.368 0.338 0.146 0.452 0.301 0.231 0.581 0.534 0.218 0.607

ESC 0.635 0.393 0.277 0.061 0.260 0.111 0.188 0.138 0.121 0.526 0.002 0.627


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issues and, therefore, was dropped from the final structure. Two items of the factor ‘SAA’ ‘My personal goals are aligned with the organizational goals’ and ‘I like the identity my job gives me’ had to be deleted in order to meet the discriminant validity issue. Two items of ‘ER’ ‘I feel like I’m at the end of my rope emotionally’ and ‘I can’t think straight by the end of my workday’ were deleted to tackle the discriminant validity issue.

Figure 4.2: CFA Measurement Model of the Drivers Identified


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Table 4.24: Validity Assessment (Revised)

CR AVE MSV ASV ER SSR RCWR SAA PM PA

ER 0.810 0.519 0.112 0.088 0.721

SSR 0.935 0.591 0.214 0.135 0.335 0.769

RCWR 0.918 0.531 0.244 0.158 0.277 0.436 0.728

SAA 0.916 0.577 0.561 0.250 0.319 0.463 0.494 0.760

PM 0.870 0.574 0.561 0.211 0.288 0.265 0.435 0.749 0.758

PA 0.837 0.569 0.150 0.105 0.258 0.300 0.302 0.359 0.387 0.754

As it can be seen in the table no. 4.24, there is absolutely no validity issue in CFA.

Model Fit Summary

The overall model χ2 is 1400.33 with 673 DOF. The p-value associated with the result is significant. Thus, the χ2 goodness of fit statistic does not indicate that the observed covariance matrix matches the estimated covariance matrix within sampling variances. However, χ2 goodness of fit index does not always give good results when applied in isolation; χ2 is hailed to be quite responsive to the sample size (Gerbing & Anderson, (1985). In order to overcome this problem, it has been recommended, that a model exhibits a reasonable fit if the χ2 / DOF (i.e., chi-square divided by degrees of freedom) does not exceed 3.0 (Kline, (2004). In this case the χ2 / DOF is 2.081 which exhibits that the model has a good fit. Further, the rule of thumb as suggested by Hair et al. (2010) elaborates that the researcher should focus on at least one absolute fit index and one incremental fit index, in addition to the χ2 results. The RMSEA (root mean square error of approximation), which is an absolute fit index comes out to be 0. 057. This value reportedly was very low and way below the guideline of 0.08 and hence acceptable. Next, the researcher employed SRMR (Standardized root mean square residual) to check the model fit. The value of SRMR was 0.053 which was a wee bit above the conservative cut-off limit of 0.05 (Byrne, 1998; Diamantopoulos & Siguaw, 2000); generally any value of less than 0.08 is acceptable for SRMR (Hu & Bentler, 1999). Moving towards the incremental fit


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indices, the researcher assessed CFI value. CFI is the most widely used index. In the present CFA model of EE predictors, the CFI has a value of 0.910, exceeding the CFI guideline of greater than 0.90 for a model of this complexity i.e., having more than three constructs assessed and a sample size greater than 300 (Hair et al., 2010). The CFA results show that the EE predictor model provides a reasonably good fit, and thus it is suitable to proceed to further examination of the model results.

Ultimately, the aim of CFA is to establish that a given measurement model is valid in all respects. The best way to establish validity is to diagnose the model. Diagnosing the model involves assessment of the standardised residuals covariances. “Residuals are the individual differences between observed covariance terms and the fitted or estimated covariance terms” (Hair et al. 2010). In case of better fitting models, the residuals are smaller. In this case the researcher analysed the standardised residuals covariance, where only one item pair belonging to Psychological Availability (PA) viz., ‘I am confident in my ability to display the appropriate emotions at work’, and Rewarding Co-worker Relations (RCWR) ‘My interactions with my co-workers are rewarding’ was having a standardised residual value of 4.054 which greater than 4.0. Hair et al. (2010) suggests that one or two large residuals could be accepted for further analysis, so the item variables were not deleted. The next assessment is for the Modification Indices (MI), MI value of 4.0 or greater indicate that the fit could be improved significantly. However, the researchers caution the use of MI in a haphazard manner. MI should be sought only when there could be a theoretical justification to the items co-varied. The regression weights with the level of significance and the squared multiple correlations of the antecedents are depicted in table no. 4.25 and 4.26 respectively.


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Table 4.25 Regression Weights

Estimate S.E. C.R. P ESSR1_1 <--- SSR 1.000 ESSR2_1 <--- SSR 1.079 0.064 16.929 *** ESSR3_1 <--- SSR 1.065 0.082 12.936 *** ESSR4_1 <--- SSR 1.140 0.082 13.853 *** ESSR5_1 <--- SSR 1.210 0.080 15.174 *** ESSR6_1 <--- SSR 1.216 0.079 15.335 *** ESSR7_1 <--- SSR 1.236 0.084 14.791 *** ESSR8_1 <--- SSR 1.185 0.082 14.503 *** ESSR9_1 <--- SSR 0.882 0.076 11.638 *** ESSR10_1 <--- SSR 1.101 0.080 13.699 *** ERCWR1_1 <--- RCWR 1.000 ERCWR2_1 <--- RCWR 1.137 0.082 13.903 *** ERCWR3_1 <--- RCWR 1.049 0.086 12.177 *** ERCWR4_1 <--- RCWR 1.108 0.105 10.555 *** ERCWR5_1 <--- RCWR 1.256 0.103 12.192 *** ERCWR6_1 <--- RCWR 1.425 0.106 13.428 *** ERCWR7_1 <--- RCWR 1.136 0.093 12.198 *** ERCWR8_1 <--- RCWR 1.133 0.104 10.886 *** ERCWR9_1 <--- RCWR 1.128 0.094 11.992 *** ERCWR10_1 <--- RCWR 1.136 0.098 11.615 *** EWRF1_1 <--- SAA 1.000 EWRF3_1 <--- SAA 1.106 0.080 13.882 *** EWRF4_1 <--- SAA 1.104 0.081 13.578 *** ES2_1 <--- SAA 0.867 0.078 11.050 *** ES3_1 <--- SAA 0.950 0.079 12.080 *** ES4_1 <--- SAA 1.042 0.074 14.072 *** ES5_1 <--- SAA 0.881 0.068 12.893 ***


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EM2_1 <--- PM 1.000 EM3_1 <--- PM 0.925 0.077 11.944 *** EM4_1 <--- PM 0.950 0.067 14.211 *** EM5_1 <--- PM 0.963 0.066 14.534 *** EM6_1 <--- PM 0.919 0.067 13.780 *** ER5_1 <--- ER 1.000 ER6_1 <--- ER 0.944 0.158 5.971 *** ER7_1 <--- ER 1.007 0.167 6.028 *** EPA1_1 <--- PA 1.000 EPA2_1 <--- PA 1.249 0.092 13.511 *** EPA3_1 <--- PA 1.156 0.089 13.009 *** EPA4_1 <--- PA 1.004 0.105 9.583 ***

S.E. – Standard Error; C.R. – Estimate/S.E.

Table 4.26 Squared Multiple Correlations

Particulars Estimate EPA4_1 0.336 EPA3_1 0.671 EPA2_1 0.801 EPA1_1 0.468 ER7_1 0.418 ER6_1 0.402 ER5_1 0.505 EM6_1 0.588 EM5_1 0.650 EM4_1 0.623 EM3_1 0.450 EM2_1 0.564 ES5_1 0.547


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Particulars Estimate ES4_1 0.638 ES3_1 0.476 ES2_1 0.399

EWRF4_1 0.608 EWRF3_1 0.629 EWRF1_1 0.545

ERCWR10_1 0.492 ERCWR9_1 0.530 ERCWR8_1 0.429 ERCWR7_1 0.550 ERCWR6_1 0.691 ERCWR5_1 0.550 ERCWR4_1 0.401 ERCWR3_1 0.551 ERCWR2_1 0.581 ERCWR1_1 0.462 ESSR10_1 0.562 ESSR9_1 0.412 ESSR8_1 0.620 ESSR7_1 0.645 ESSR6_1 0.687 ESSR5_1 0.673 ESSR4_1 0.571 ESSR3_1 0.504 ESSR2_1 0.606 ESSR1_1 0.550


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The next step was to check the robustness of the model, for this Gaskin (2013b) suggests four steps:

1. To get a decent model with good model fit, this has already been catered to. 2. Invariance Test - to assess the CFA based on different groups (demographic

groups), in order to establish robustness of the model. 3. Validity and reliability assessment, this has been already been done. 4. Common Method Bias test in order to gauge any bias which may have crept in.

Therefore, this research study is left with Invariance testing, and Common Method Bias testing before we establish and underpin the results emphatically.

Invariance testing or Multi-sample Confirmatory Factor Analysis (MCFA) – in order to carry out this test, the researchers need to divide the whole sample into specific groups based on demographics. Therefore, two groups were created:

1. Based on Experience (Less than 5 years experience; and 5 years and more) 2. Based on Level (Junior Level; and middle level and senior level were clubbed into one)

Regression weights for each group was analysed with pair-wise parameter comparison with the aid of AMOS 20 and Stats Tool Package (Gaskin, 2012). In this for every item, a z-score will be generated. If z-score is significant then it signifies that within groups, there is significant difference between different groups formed for a particular item. However, very few cases of significant z-score could be traced, in both the groups. Hence, the invariance test establishes that the CFA model is robust as testing the same model with different groups, shows no significant difference.


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Common Method Bias test

Figure 4.3: Testing of Common Method Bias through Common Latent factor (CLF)

Common Method Bias tests refers to a bias which is generated in the dataset due to an externality, meaning thereby that any other external factor which is not related to the variable or item itself was able to influence or bias the dataset (Gaskin, 2013b). There are different methods to check for common method bias; however, the researcher employed common latent factor (CLF) method in AMOS 20 to gauge the bias if inherited in the model.


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The rule is then to check the standardised regression weights, where there should not be any low loadings i.e., loadings less than 0.4, which would indicate that there is no common method bias. On careful perusal of the standardised regression weights table, the researcher found no case of low loadings i.e., less than 0.4 as can be seen in the table no 4.27:

Table 4.27: Standardized Regression Weights

Particulars Estimate ESSR1_1 <--- SSR 0.750 ESSR2_1 <--- SSR 0.767 ESSR3_1 <--- SSR 0.735 ESSR4_1 <--- SSR 0.767 ESSR5_1 <--- SSR 0.805 ESSR6_1 <--- SSR 0.822 ESSR7_1 <--- SSR 0.796 ESSR8_1 <--- SSR 0.775 ESSR9_1 <--- SSR 0.670 ESSR10_1 <--- SSR 0.731 ERCWR1_1 <--- RCWR 0.663 ERCWR2_1 <--- RCWR 0.765 ERCWR3_1 <--- RCWR 0.727 ERCWR4_1 <--- RCWR 0.669 ERCWR5_1 <--- RCWR 0.774 ERCWR6_1 <--- RCWR 0.823 ERCWR7_1 <--- RCWR 0.728 ERCWR8_1 <--- RCWR 0.647 ERCWR9_1 <--- RCWR 0.725 ERCWR10_1 <--- RCWR 0.703 EWRF1_1 <--- SAA 0.705


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EWRF3_1 <--- SAA 0.751 EWRF4_1 <--- SAA 0.756 ES2_1 <--- SAA 0.624 ES3_1 <--- SAA 0.725 ES4_1 <--- SAA 0.825 ES5_1 <--- SAA 0.759 EM2_1 <--- PM 0.695 EM3_1 <--- PM 0.636 EM4_1 <--- PM 0.783 EM5_1 <--- PM 0.736 EM6_1 <--- PM 0.798 ER6_1 <--- ER 0.622 ER7_1 <--- ER 0.698 EPA1_1 <--- PA 0.689 EPA2_1 <--- PA 0.893 EPA3_1 <--- PA 0.820 EPA4_1 <--- PA 0.574

The researcher can, therefore, conclude that there is no case of common method bias, therefore, the CFA model reached is far from any kind of anomalies and robust in nature.

Table 4.28 Reliability of the Consequences Identified

Consequences Items scale summated

Cronbach’s Alpha

In-Role Performance (IRP) 7 0.773 Organizational Citizenship Behaviour (OCB) 14 0.710 Job Involvement (JI) 10 0.880 Job Satisfaction (JS) 4 0.757 Intention to Stay (ITS) 5 0.826


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It can be seen from the table no. 4.28 that the reliability of all the consequence construct identified viz., IRP, OCB, JI, JS, and ITS, were showing good reliability, with a Cronbach’s Alpha score of 0.773, 0.710, 0.880, 0.757, and 0.826 respectively. The researcher then conducted the split half tests for the entire five consequence construct identified which has been enlisted from table no. 4.29 to 4.33.

Split Half Test of Consequences Table 4.29: In-Role Performance (IRP)






Guttman Split-Half Coefficient 0.845 a. The items are: IRP1_1, IRP3_1, IRP5_1, IRP7_1. b. The items are: IRP2_1, IRP4_1, IRP6_1 Table 4.30: Organizational Citizenship Behaviour (OCB)



Part 2




Guttman Split-Half Coefficient 0.787 a. The items are: OCBI1_1, OCBI3_1, OCBI5_1, OCBI7_1, OCBO2_1, OCBO4_1, OCBO6_1. b. The items are: OCBI2_1, OCBI4_1, OCBI6_1, OCBO1_1, OCBO3_1, OCBO5_1, OCBO7_1.


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Table 4.31: Job Involvement (JI)






Guttman Split-Half Coefficient 0.889 a. The items are: JI1_1, JI3_1, JI5_1, JI7_1, JI9_1. b. The items are: JI2_1, JI4_1, JI6_1, JI8_1, JI10_1.

Table 4.32: Job Satisfaction (JS)






Guttman Split-Half Coefficient 0.770 a. The items are: JS1_1, JS2_1. b. The items are: JS3_1, JS4_1.


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Table 4.33: Intention to Stay (ITS)






Guttman Split-Half Coefficient 0.852 a. The items are: IQ1_1, IQ3_1, IQ5_1. b. The items are: IQ2_1, IQ4_1.

4.6 Structural Equation Modelling (SEM)

SEM as a tool can be used to assess both the measurement model and structural model simultaneously. However, the researcher’s advice initial analysis based on CFA i.e., the measurement of the model, followed by the structural part (Hair et al., 2010; Anderson & Gerbing, 1992). If there is any discrepancy in the measurement model i.e., it cannot be tested for validity, then the researchers are advised to go for data modification or data collection as deemed appropriate. In this research study, since the four steps in the CFA model was duly considered and met well with all specifications, it was deemed fit to progress towards the structural part of the analysis wherein the measurement model is converted into structural model by emphasising on the dependence relationship rather than correlation relationship. Theoretically, measurement model provide the base or ground for assessing the validity of the structural model (Anderson & Gerbing, 1992).

This takes the study to the fifth stage which is specification of the structural model. In this first of all, the researcher has specified the unit of analysis as individual analysis, and not group level or organizational level i.e., every response is basically related to


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an individual’s perception. Secondly, specification as to the relationship parameters has been done prior to the analysis. The endogenous and exogenous variables have been defined clearly. Endogenous constructs are easily recognized in the path estimation diagram as one or more arrows showing structural relationships are drawn pointed towards them. On the contrary, the exogenous constructs are those which are traditional independent variables in which the arrows are always pointed out of them and never towards them. Thirdly, for every endogenous variable, the structure quantifies that they are not fully explained; therefore, error term (E) is introduced. Fourthly, the model has been depicted as recursive model, in which there predictors i.e., antecedents have a causal effect on the outcomes or consequences. The aforesaid antecedents are not shown in any way as to be the consequence of some other relationship, i.e., there is no feedback loop otherwise it would have been depicted as non-recursive model. Fifthly, the researchers need to specify whether the construct is reflective or formative construct. Reflective measurement are based on the premise that latent constructs cause the measured variables and the error variance if any is because of the inability of the construct to explain the indicators. Thus, the direction of the arrows points from the latent construct to the indicators i.e., the measured variables. In the present study, the first order constructs which have been assimilated through the measurement model i.e., (CFA), are reflective in nature. For reflective constructs, the classical conditions of reliability and validity (psychometric properties) works well (Nunnally, 1978). For reflective constructs, individual items could be interchangeable and any single item can be removed for the want of validity assurance when the following two criteria are met:

1.) Construct must have sufficient reliability,

2.) At least 3 items per construct must be specified in order to avoid identification issues (Bollen & Lenox, 1991). In this case each of the six predictors so identified were having sufficient reliability measured through composite reliability, and also each construct was having at least 3 effect indicators. However, the researcher specified the second order construct i.e., EE to be a formative construct. This assertion was made on the basis that the drivers or predictors or the antecedents of EE form into EE, and EE does not reflect on the latent factors identified through the CFA.


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Another key assumption for a formative measure in this case is that, EE may not be considered as latent, therefore the effect indicators may not have a reliable innate meaning (Jarvis, MacKenzie, & Podsakoff, 2003). The next issue is that we cannot take a formative measure in remoteness, the formative indicator measurement model is statistically under identified (Bollen & Lennox, 1991); the model can be investigated only if it is positioned within a generously proportioned model that integrates consequences (i.e., effects) of the latent variable in question (Bollen, 1989a). Further, researchers, point out that a formative measure requires at least two reflective or other endogenous constructs to act as consequences to be assessed and measured (Heise, 1972; MacCallum & Browne, 1993). In this model the formative construct is EE, therefore, the consequences identified are IRP, OCB, JI, JS, and ITS. Diamantopoulos and Siguaw, (2006) advocate that the outcome variables are as important as the construct indicators in the formative model measurement. The researcher, therefore, formulated the multiple indicator multiple cause (MIMIC) model (Landis, Beal, & Tesluk, 2000). Further, SEM using AMOS 20 was used; SEM was deemed an appropriate method for this analysis, as it could determine causal links between variables allowing for the confirmation of engagement drivers (antecedents) and outcomes (consequences) (Joreskog & Sorbom, 1989). Maximum likelihood estimation was used, as it is robust and is reasonably tolerant of normality violations (Chou & Bentler, 1995). Formative constructs are nowadays more accepted in the academic literature, and has been receiving extensive attention (Diamantopoulos, Riefler, & Roth, 2008). Further, the researchers have been aware of the misspecification problems which may arise by incorrectly employing reflective rather than formative measures (Hair et al. 2010; Jarvis et al. 2003). However, it should be specified in the same vein that the use of formative constructs is relatively new, and the innate lack of internal validity assessment in formative constructs is that of a concern. As far as specification of the construct as reflective or formative is concerned, it is more likely the prerogative of the researcher. Bollen and Ting (2000) substantiate that same set of indicator variables can be used as reflective and formative in two different formulations. The present research study is basically a higher order factor structure, wherein the first order is set to be reflective, and the second order as formative construct. When the research compares measurement models of higher orders, it may lead to greater nomological validity than the first order (Hair et al. 2010).


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Figure 4.4: Formative Assessment of EE Model

SSR – Supportive Supervisory Relations, RCWR – Rewarding Co-workers Relations, SAA – Spirituality and Alignment, PM – Psychological Meaningfulness, ER – Employee’s Job Resources, PA – Psychological Availability, EE – Employee Engagement, IRP – In-Role Performance, OCB – Organizational Citizenship Behaviour, JI – Job Involvement, JS – Job Satisfaction, ITS – Intention to Stay


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The point now to be raised and the opportune time for it is the validity check in a formative type of measurement. It has already been mentioned before that in formative measurements classical tests of validity do not work. The reason behind this is that the cause indicators are exogenous, their variances, and covariances are, therefore, not explained by the formative measurement and hence difficulty in assessment of validity (Bollen, 1989a). Further, the measures are not conjectured to be established by the compound latent variable; the model itself does not assume or require the measures to be correlated (Bollen & Lennox, 1991). The indicator measures could be entirely uncorrelated and even then it would be perfectly consistent with the formative models (MacKenzie, Podsakoff, & Jarvis, 2005). On that pretext internal consistency reliability may not be an appropriate yardstick for evaluating the sufficiency of the measures in formative models. As advocated by Bollen and Lennox (1991, p. 312), “causal [formative] indicators are not invalidated by low internal consistency so to assess validity we need to examine other variables that are effects of the latent construct”. Therefore, in order to gauge the validity of formative constructs, researchers should take into consideration the nomological and/or criterion-related validity (MacKenzie et al., 2005). In order to assess the nomological validity, Freeze and Raschke, (2007) have given three step formulations, first is to assess the correlations of the formative construct’s indicators, high correlation between constructs indicate that the constructs are measuring the same thing, therefore, leading to multicollinearity issue. On assessing the correlation between the formative factors, the researcher finds that SAA was having positive high correlations with all the other factors, but not high enough (r > 0.8, 0.9) to show multicollinearity.

Table 4.34 Correlation Between Antecedents

Particular

Correlation

SSR <--> RCWR 0.441 SSR <--> SAA 0.465 SSR <--> PM 0.262 SSR <--> ER 0.377


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SSR <--> PA 0.302 RCWR <--> SAA 0.511 RCWR <--> PM 0.435 RCWR <--> ER 0.330 RCWR <--> PA 0.297 SAA <--> PM 0.758 SAA <--> ER 0.401 SAA <--> PA 0.361 PM <--> ER 0.381 PM <--> PA 0.389 ER <--> PA 0.314

Secondly, to assess the construct in isolation using MIMIC, and thirdly to place the formative construct in the context of a structural model. As the researcher has followed all the three steps, the nomological validity stands confirmed.

Assuming that the overall measure is a valid criterion, the relationship between a formative indicator and the overall measure, indicates indicator validity (Eggert & Fassot, 2003; MacKenzie et al., 2005). These authors envisage that those indicators (factors) correlating highly with the external variable (latent variable) are retained whereas those showing less or non-significant relationships are candidates for elimination in a formative measure model. The subsequent relationship has been depicted in the table no. 4.35. According to the strength and significance test as envisaged by MacKenzie et al. (2005), only SAA passed the test, rest of the drivers (predictors) so identified were then candidates for deletion. However, PM as a predictor did have the standardised regression weight to be better than the others; it could not pass the significance test.


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Table 4.35: Result of Formative Assessment of EE Model

Relationship Depicted Estimate S.E. C.R. P S.R.W SSR --- > EE 0.269 0.189 1.424 0.155 0.077

RCWR --- > EE -0.311 0.233 -1.336 0.182 - 0.070 SAA --- > EE 2.525 0.330 7.649 *** 0.787 ER --- > EE 0.058 0.180 0.322 0.747 0.019 PA --- > EE 0.092 0.219 0.419 0.676 0.020 PM --- > EE 0.434 0.316 1.371 0.170 0.112

S.E. – Standard Error; C.R. – Estimate/S.E., S.R.W. – Standardized Regression Weight

The model then would have only one predictor variable i.e., SAA. Such a model was earlier estimated and tested by the researcher, in which only one factor i.e., Spirituality, Meaningfulness, and Alignment (SMAA) was established to be forming into the latent construct EE (Singh & Kumar, 2012). The sample size in the aforesaid study was 120.

The relationship was then assessed between SAA and the other identified antecedent’s viz., SSR, RCWR, PM, ER, and PA. It was found that ER and PA had insignificant relationship with SAA and hence removed from the final analysis. SSR, RCWR, and PM depicted strength and significance, therefore, were retained for further analysis. The researcher identified that further model respecification should be carried out; therefore, model respecification was considered to be the next viable step. The researcher then decided to check for the moderation test of SAA on other predictors in the formative setting. A moderating effect is caused when a third construct is able to change the relationship between two related variables or constructs (Baron & Kenny, 1986). However, analysis of moderators becomes easy when the moderator has no significant linear relationship with either of the constructs (Cohen & Cohen, 1983; Hiese, 1972; Baron & Kenny, 1986). On the contrary, on closer perusal the researcher found that SAA cannot be taken as a moderator variable as it has linear relationship with the antecedent constructs as well as consequences, in this context the assessment


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of SAA as mediating variable was undertaken. A mediator is one which needs to be related to both the constructs in the relationship being mediated. On having considered SAA as the mediating variable mediating the relationship of the three driver’s viz., SSR, RCWR, PM with EE, the researcher found mixed results.

Figure 4.5: Direct Effect without SAA


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Figure 4.6: Mediating effect of SAA depicted in the figure

Baron and Kenny (1986) Approach: According to this approach, the direct effect without the mediator variable i.e., SAA is checked first on the dependent variable EE.


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The effects were captured and noted down in the second column named ‘Direct without Mediator’, as shown in the table no. 4.36, in the same column the significance of the relationship is also mentioned in parenthesis. Subsequently, the mediator variable SAA is added back in the figure and the analysis results are observed in the third column i.e., ‘Direct with Mediator’. There can be three results based on Baron and Kenny (1986) approach, viz., no mediation, partial mediation, and full mediation.

1. If there is drop in strength in the second case, and still significant then it indicates ‘partial mediation’. This can be seen in case number 1 and 3.

2. Not significant in first case and still not significant in the second case indicating ‘no mediation’.

3. Drop in strength when compared to the first case, significant in first case and insignificant in second case depicting ‘Full Mediation’.

Bootstrapping:

“An increasingly popular method of testing the indirect effect is bootstrapping (Bollen & Stine, 1990; Shrout & Bolger, 2002). Bootstrapping is a non-parametric method based on re-sampling with replacement which is done many times, e.g., 5000 times. From each of these samples the indirect effect is computed and a sampling distribution can be empirically generated” (Kenny, 2013).

No Mediation

• If Indirect effect is not significant • Also if direct effect of Independent Variable on Mediator is insignificant.

Indirect Effects:

• Both direct effects are not significant, but Indirect effect is significant

Full Mediation

• Given the direct effects were significant prior to adding the mediator • If Indirect is significant and Direct (with mediator) is not significant


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Partial Mediation

• If Direct (with mediator) & Indirect are significant

To test the mediation effect three hypotheses were developed:

H01: SAA does not mediate the relationship between SSR and EE. HA1: SAA mediates the relationship between SSR and EE. H02: SAA does not mediate the relationship between PM and EE. HA2: SAA mediates the relationship between PM and EE. H03: SAA does not mediate the relationship between RCWR and EE. HA3: SAA mediates the relationship between RCWR and EE.

Results were then compared according to Baron and Kenny (1986) approach. It was deemed important to check for indirect effect using the Bootstrapping mechanism in AMOS 20. The results are depicted in the table 4.36 below:

Table 4.36: Testing Mediation

HyP. Relationship Direct Without Mediator

Direct with Mediator

Decision based on Baron and Kenny (1986) Approach

Indirect Decision on Bootstrapping

HA1 SSR - SAA - EE 0.272 (0.000)

0.089 (0.085)

Full Mediation 0.089 (0.000)

Full Mediation

HA2 RCWR – SAA – EE 0.020 (0.731)

-0.081 (0.124)

No mediation -0.001 (0.053)

No Mediation

HA3 PM - SAA - EE 0.626 (0.000)

0.101 (0.207)

Full Mediation 0.390 (0.000)

Full Mediation

As we can see from the analysis that SAA fully mediates in two cases viz., SSR – EE and PM – EE, lending support to our hypothesis that SAA will mediate between SSR and EE; and PM and EE. No mediation effect of SAA was reported between RCWR and EE. Numerous model respecifications were conducted in order to check the best fitting model and having an impact in terms of squared multiple correlations (r2) and standardized regression weights (Hair et al. 2010). Since, SAA was the only factor which was able to show strength and significance in terms of validity assessment of


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formative model (MacKenzie et al., 2005); therefore, it was necessary to assess the relationship of SAA with the other drivers (predictors). Further, in the mediation test, the relationship of three drivers namely SSR, RCWR, and PM could be established. Therefore, the final model which was conceptualized contained the three drivers identified viz., SSR, RCWR, and PM along with the highest loading driver SAA. The total effect of SSR and PM on EE through SAA was assessed, however, there was no mediation effect of SAA on RCWR-EE, and therefore, a direct relationship was assessed in the model by joining a regression line to that effect. The direct relationship between RCWR and EE came out to be negative with negative standardised loading of -0.029 and an insignificant p value of 0.536. So, after having assessed the role of RCWR in the model it was deemed logical to delete RCWR as the antecedent and test it as a consequence in the final model. After conceptualising RCWR to be the consequence, subsequently the model was run again, this time the model fit, the standardised regression weights, the squared multiple correlations (r2) reported to be stronger for every relationship, and the p value reported significant at 0.000 levels (p < .001). Further, EE so formed was able to explain considerable variances in the outcome (consequences) variables. Therefore, the EE model which was conceptualized from the very beginning of this research project and which was the most important objective of the study was underpinned after carrying out a number of respecification. In order to run the SEM model on AMOS 20 the researcher employed Maximum likelihood estimation. Chou and Bentler, (1995) envisage the use of Maximum likelihood while carrying out estimation which is robust and is logically tolerant of normality violations which are very common in psycho-behavioural studies (Micceri, 1989). Normality All the observed variables (EE predictors viz., SAA, SSR, RCWR, and PM, and outcomes viz., IRP, OCB, JI, JS, ITS) depicted to some extent non-normality. The four drivers underpinned were negatively skewed; the values were considered within acceptable limits and, therefore, were not transformed (Tabachnick & Fidell, 2001).

Outliers The presence of multivariate outlier was assessed by employing the test of normality and outliers in AMOS 20. The observations farthest from the centroid i.e., the


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Mahalanobis distance was calculated, when analysing the P1 value significance, each value should be more than 0.05, if the value is less than 0.05 for each observation, then that observation is said to be the multivariate outlier. On removing those observations and running the model again, the researcher could not find any significant difference or a very little difference could be traced with respect to loadings and model fit; therefore, subsequent individual observations were left in the data set.

Figure 4.7: EE Model


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SSR – Supportive Supervisory Relations, PM – Psychological Meaningfulness, SAA – Spirituality and Alignment, EE – Employee Engagement, IRP – In-Role Performance, OCB – Organizational Citizenship Behaviour, JI – Job Involvement, JS – Job Satisfaction, ITS – Intention to Stay, RCWR – Rewarding Co-workers Relations

Figure 4.8: EE Model Simplified

Modification Indices

Model respecification could be carried out in order to increase the model fit of the model. One such respecification method is to modify the model based on the modification indices. Modification indices must be used to indicate a possible relationship in the model, which was not indicated before. The caution which should be exercised by the researcher is that any possible relationship must have a theoretical background to it (Hair et al., 2010). Further, covariance lines must be added in the error terms of specific variables in a given model based on the larger (modification indices) MI values depicted in the AMOS 20 statistics. Each of the covariance added in the EE model was theoretically justified and not solely to improve the model fit (Hair et al. 2010).


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Assessing the Structural Model Validity

As recommended by Hair et al. (2010), a selection of fit indices was used to assess model adequacy, at least one absolute fit, and one incremental fit should be shown in addition to the χ2 statistic. The use of χ2 statistic is recommended by researchers (Aron, Aron, & Coups, 2006; Child, 1990; Klem, 2000) in order to compare whether the measured data fitted the expected data by assessing the difference between the proposed EE model and the actual data. However, the χ2 statistic is a statistical significance test which is very sensitive to sample size which implies that the χ2 statistic almost always rejects the model when large samples are utilized for investigation (Bentler & Bonnet, 1980; Jöreskog & Sörbom, 1993). The χ2 is 667.991 with 332 degrees of freedom (DOF) (p < 0.05), and the normed chi-square χ2/DOF (Wheaton, Muthen, Alwin, & Summers, 1977) is 2.012. The second absolute fit measure the root mean square error of approximation (RMSEA) (Browne & Cudeck, 1993) was employed. Three comparative fit indices the incremental fit index IFI: (Bollen, 1989b), the non-normed fit index: NNFI (Bentler & Bonett, 1980) – also known as the Tucker – Lewis Index: TLI, the comparative fit index: CFI (Bentler, 1990), were utilized. The model suitability was considered while assessing the aforesaid model fit indices.

In order to assess the lack of model fit compared to a perfect model, RMSEA is employed (Tabachnick & Fidell, 2001, p. 699). The RMSEA is sensitive to model misspecification, but is not as sensitive to distribution and sample size as the χ2 (Hu & Bentler, 1998). The RMSEA signifies how well the model, with unknown but optimally chosen parameter estimates would fit the population’s covariance matrix (Byrne, 1998). In recent years it has become regarded as ‘one of the most informative fit indices’ (Diamantopoulos & Siguaw, 2000, p. 85) due to its sensitivity to the number of estimated parameters in the model. For this model the RMSEA was reported to be 0.055 with the P CLOSE of 0.075, therefore, considered acceptable (Hair et al., 2010). P CLOSE tests the null hypothesis that the population RMSEA is no greater than 0.05 (Brown & Cudeck, 1993). The researcher employed SRMR (Standardized root mean square residual) to check the model fit. The value of SRMR was 0.0529, which was a little more than the conservative cut-off limit of 0.05 (Byrne,


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1998; Diamantopoulos & Siguaw, 2000); generally any value of less than 0.08 is acceptable for SRMR (Hu & Bentler, 1999). The Comparative Fit Index (CFI) (Bentler, 1990) is a revised form of the Normed Fit Index (NFI) which takes into consideration the sample size (Byrne, 1998), the performance of CFI is good even when the sample under study is small (Tabachnick & Fidell, 2007). This measure is recommended for assessing model fit for small samples (Bentler, 1990). The cut-off criterion of CFI ≥ 0.90 was earlier propagated. On the other hand, more recent studies have illustrated that a CFI value greater than 0.90 is required so as to assert the claim that any mis-specified models are not acknowledged (Hu & Bentler, 1999). The CFI value in the present EE model comes out to be 0.942 and hence shows the tremendous fit of the EE model.

The Tucker-Lewis coefficient TLI (Bollen, 1989b) was discussed by Bentler and Bonett (1980) in the context of analysis of moment structures, and is also known as the Bentler-Bonett non-normed fit index (NNFI). The reported value of TLI was 0.934 more than the recommended value of 0.9 (Hair, 1998). The Incremental Fit Index (IFI) reports less sampling variability than the TLI (Tabachnick & Fidell, 2001). The recommended value of IFI should be a value higher than 0.9 (Hair, 1998). The EE model depicts IFI value to be 0.942 showcasing the remarkable fit of the model. 4.7 Specifying the Structural Model

After numerous respecifications the construct measures were then placed promptly, according to the fit indices the model credibility has also been proved. The structural relationship so identified will now be analysed in terms of assessment of the hypotheses:

H01: The predictors of EE are not predicting EE in the model specified HA1: The predictors of EE are positively predicting EE in the model specified H02: EE will have no causal effect on IRP HA2: EE will have a causal effect on IRP H03: EE will have no causal effect on OCB


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HA3: EE will have a causal effect on OCB H04: EE will have no causal effect on JI HA4: EE will have a causal effect on JI H05: EE will have no causal effect on JS HA5: EE will have a causal effect on JS H06: EE will have no causal effect on ITS HA6: EE will have a causal effect on ITS H07: EE will have no causal effect on RCWR HA7: EE will have a causal effect on RCWR

The above seven null and alternative hypotheses will be dealt with by observing the path coefficients and the loading estimates. Validation of the model remains incomplete if individual parameter estimates are not examined.

Table 4.37: EE Model Result Summarised

RELATIONSHIP Standardized Regression Weights Significance (p)

SAA --- > EE 0.925 *** SSR --- > SAA 0.298 *** PM --- > SAA 0.694 *** EE --- > IRP 0.490 *** EE --- > OCB 0.419 *** EE --- > JI 0.749 *** EE --- > JS 0.768 *** EE --- > ITS 0.744 *** EE --- > RCWR 0.472 ***

Note: ***p < 0.001

4.8 Addressing the Hypotheses

HA1: The predictors of EE viz., SSR, PM, and SAA predict EE in a significant manner. As we see in the table no. 4.37 that the standardised regression weight of


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0.925 with a p value indicating significance at 0.001 levels (p < 0.001), establish that a positive, strong, and, significant relationship is depicted between its predictors and EE. The researcher has already assessed the mediating effect of SAA on the relationship between SSR-EE; and PM-EE. Therefore, we reject the null hypothesis H01 in the favour of alternate hypothesis HA1.

HA2: The second hypothesis which says that EE will have causal effect on IRP is substantiated by looking in the table 4.37. The standardised regression weight of 0.490 and a p value indicating significance at 0.001 levels (p < 0.001); establish that EE has a positive causal effect on IRP. Thus, the null hypothesis H02 is rejected in favour of HA2.

HA3: The third hypothesis which says that EE will have causal effect on OCB is substantiated by looking in the table 4.37. The standardised regression weight of 0.419 and a p value indicating significance at 0.001 levels (p < 0.001); establish that EE has a positive causal effect on OCB. Thus, the null hypothesis H03 is rejected in favour of HA3.

HA4: The fourth hypothesis which says that EE will have causal effect on JI is substantiated by looking in the table 4.37. The standardised regression weight of 0.749 and a p value indicating significance at 0.001 levels (p < 0.001); establish that EE has a positive causal effect on JI. Thus, the null hypothesis H04 is rejected in favour of HA4.

HA5: The fifth hypothesis which says that EE will have causal effect on JS is substantiated by looking in the table 4.37. The standardised regression weight of 0.768 and a p value indicating significance at 0.001 levels (p < 0.001); establish that EE has a positive causal effect on JS. Thus, the null hypothesis H05 is rejected in favour of HA5.

HA6: The sixth hypothesis which says that EE will have causal effect on ITS is substantiated by looking in the table 4.37. The standardised regression weight of 0.744 and a p value indicating significance at 0.001 levels (p < 0.001); establish that


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EE has a positive causal effect on ITS. Thus, the null hypothesis H06 is rejected in favour of HA6.

HA7: The seventh hypothesis which says that EE will have causal effect on RCWR is substantiated by looking in the table 4.37. The standardised regression weight of 0.472 and a p value indicating significance at 0.001 levels (p < 0.001); establish that EE has a positive causal effect on RCWR. It is fascinating to note that, earlier the researcher was considering RCWR as an antecedent factor. However, owing to the negative loading score which was evident through earlier respecification, it was deemed imperative to check RCWR as an outcome construct. The point that EE was able to explain 22% of RCWR, with a good standardised regression score, was sufficient to justify the causal link but in an opposite direction. Thus, the null hypothesis H07 is rejected in favour of HA7.

After having established the EE model, the next valid step was to make an inter-sector comparison and find whether or not there is significant difference in groups when comparing the antecedents of EE viz., SAA, SSR, and PM. This was also a way of assessing the relationship further, since different sectors may produce different results as far as consequences are concerned. The first step was to check for normality assumptions for the three aforesaid antecedents. Based on the Kolmogorov Smirnov (K-S) and Shapiro-Wilk test, the constructs were showing some kind of non-normality, however based on the kurtosis whose value should lie between -2 and +2, all the constructs had kurtosis value lying between -2 and +2 (Gaskin, 2013a), thereby signifying that the constructs were not deviating much from normality, and parametric tests could be conducted. However, non-parametric tests were conducted in order to check any significant difference within groups. Kruskal Wallis Test was conducted to find out whether there is a significant difference among the sectors when comparing with SAA, SSR, and PM. Kruskal Wallis test is equivalent to One-way ANOVA test of parametric test. It is an extension of Mann-Whitney U test, where more than two independent variables can be compared. In the table no. 4.38, results show that there is a significant difference among the sectors with regard to PM and SAA. In terms of SSR, no significant difference was found among the three sectors.


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Table 4.38 Kruskal Wallis Test

Test Statisticsa,b PM SAA SSR

Chi-Square 45.837 54.807 0.766 Df 2 2 2 Asymp. Sig. 0.000 .000 0.682

a. Kruskal Wallis Test b. Grouping Variable: Name of the Organization

Though, significant difference was found between the sectors in terms of PM and SAA, but Kruskal Wallis test does not specify which sectors were different from each other. For this, post-hoc analysis was done with the help of Mann-Whitney U test (Field, 2009). For comparing more than two variables in Mann-Whitney U test, number of comparisons done was calculated by using the formula of n (n-1)/2, where “n” refers to number of groups in a variable. Here, in the sectors, three groups were there i.e., IT, Education, and Banking. Hence, number of comparisons comes out to be 3 by using the formula. Comparison was done as follows:

• IT and Education • IT and Banking • Education and Banking

These comparisons were done in Mann-Whitney U test, but while doing this Bonferroni correction was the main adjustment which cannot be neglected. As per Bonferroni correction, alpha/critical value changes with the number of comparisons made in the Mann-Whitney U test (Field, 2009). The alpha value i.e., 0.05 is divided by the number of comparisons, to give the adjusted alpha value. Since, there were 3 comparisons in case of sectors; the alpha value was then divided by the number of comparisons that were conducted. The adjusted alpha value came out to be 0.017 (i.e., 0.05/3), which was further used to test the significance of the results. But, when the number of comparisons to be made is more, the alpha value reduces accordingly which also invites Type I error (Field, 2009).


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Table 4.39 Post hoc Analysis using Mann-Whitney U test PM

Note: ***p < 0.001 Sig: Significant, Insig.: Insignificant

Table 4.40 Post hoc Analysis using Mann-Whitney U test SAA


Dependent Variable : PM (I) Name of

the Organization

(J) Name of the

Organization

Z score Sig. Critical value

Remark

Mann Whitney U test

IT EDU -6.658 *** 0.017 Sig. BANKING -1.424 0.154 0.017 Insig.

EDU IT -6.658 *** 0.017 Sig. BANKING -4.510 *** 0.017 Sig.

BANKING IT -1.424 0.154 0.017 Insig. EDU -4.510 .000 0.017 Sig.

Dependent Variable : SAA (I) Name of

the Organization

(J) Name of the

Organization


Remark

Mann Whitney U test

IT EDU -7.304 *** 0.017 Sig. BANKING -1.963 .050 0.017 Insig.

EDU IT -7.304 *** 0.017 Sig. BANKING -4.746 *** 0.017 Sig.

BANKING IT -1.963 .050 0.017 Insig. EDU -4.746 *** 0.017 Sig.


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In the table no. 4.39 and 4.40, it was found that there is a significant difference between Education and IT sector, and also between Education and Banking sectors with regard to both PM and SAA, however, IT and Banking sector showed no significant difference within. Therefore, IT and Banking sectors were grouped into one sector, and Education was assessed as a different group.

IT and Banking Sector EE model

As we see in the figure no. 4.9, the squared multiple correlations (r2) of EE, decreases from 86% as shown in the overall model to 83%. This shows that the level of engagement assessed through SSR, PM, and SAA is a wee bit lower than all the sectors combined. The possible reasons for this through closer perusal showed that the relationship of PM-SAA is weaker in IT-Banking Sector when compared to the overall model. The standardised regression weight lowers down to 0.60 from 0.69. However, at the same time the SSR-SAA relationship does become stronger from 0.30 to 0.42, signifying a more pronounced supervisory role displayed in case of IT-Banking sector. The path SAA-EE which depicted a standardised regression weight of 0.93 reduced to 0.91, further signifying the lesser level of spirituality and alignment in the employees of IT-Banking sector. The outcome variables explained by EE in IT-Banking sector showed mixed results. The squared multiple correlations (r2) value declined for IRP, OCB, JI, JS, and ITS, but improved for RCWR. This further explains the assertions that peer and supervisory relationship are more pronounced in the IT-Banking sector. The model fit displayed moderate to good fit, with CMIN/DOF reporting 1.843, CFI = 0.914, IFI = 0.915, RMSEA = 0.063, SRMR = 0.0602.


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Figure 4.9: IT-Banking Sector – EE Model

On the assessment of the EE model in the Education sector in figure no. 4.10, the researcher deciphered that the engagement level as predicted by SSR, PM, and SAA significantly increases from the overall EE model. The PM-SAA relationship shows better standardised regression weight of 0.63 when compared to the IT-Banking sector. The point to be further noted is that the SSR-SAA relationship shows a comparative lower standardised regression weight of 0.31 when compared with IT-Banking sector. However, the SAA-EE relationship which shows a 0.97 standardised regression weight depicts why EE is explained to the tune of 94%. This shows the spirituality and alignment quotient is higher in the Education sector than in the IT-Banking sector. The outcome variables in Education sector also showed mixed results. On one hand, when the squared multiple correlations (r2) of IRP, and JS improved, the other outcome variables either showed a lower or equal squared multiple correlations (r2) when compared to the EE model for all the sectors. The model fit showed moderate fit, with CMIN/DOF = 1.771, CFI = 0.890, IFI = 0.892, RMSEA = 0.08, SRMR = 0.0761.


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Figure 4.10: Education Sector - EE Model

The IT-Banking sector, showed less engagement as compared to the Education sector, there could be possible reasons for this, which would be discussed in the summary findings section. However, one thing which looks quite clear is that the level of EE in the Education sector by far outweighs the IT-Banking sector. 4.9 Analysis based on Demographic Variables

A number of analyses were done based on the demographic variables Gender, Structure of the Family, Age, Level in the Organization, Marital Status, Experience in the organization, and Educational Level. It has to be noted that those demographic variables having more than two groups should be tested through Kruskal Wallis test, however if the number of groups are restricted to 2, the researchers can employ


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Mann-Whitney U test for making comparisons within the groups. Secondly, the groups should depict consistency in terms of having equivalent cases i.e., each group to be compared should have approximately equivalent number of cases to produce correct results. In this wake, some of the groups were modified to have equivalent cases. The comparison with respect to gender was not modified, because approximately equivalent number of male female ratio was evident. In terms of Structure of the family again the number of valid respondents for both the groups depicted equivalence. Thirdly, in case of age, there was wide disparity. The number of employees in the first age bracket 20-30 reported 165 respondents out of a total of 132 respondents; therefore, two valid groups were formed viz., Age 30 years and less was placed in first group and age more than 30 years was placed in the second group. Fourthly, in case of the level in the organization, the stratified random sampling which was designed was as such that from each organization there would be 30 employees, 15 in junior level, 10 in middle level, and 5 in top level. Therefore, group comparisons based on Kruskal Wallis and Mann-Whitney U test for post-hoc analysis did not give conclusive results. Therefore, two groups were formed for level in the organization. First group was named Junior Level which had originally the employees of junior level, however, the second group which had employees from middle and top level were merged into one and named Senior Level. Fifthly, for the demographic variable Marital Status the earlier division was Married, Unmarried, and Divorcee/Widowed. There were only two respondents in the third group, hence, modifications were conducted. Two groups were made ‘Married’ and ‘Single’, in which divorcee/widowed were kept in the single bracket. Sixthly, for experience in the organization, earlier the study had five groups viz., experience of 0-1 years, 2-5 years, 6-10 years, 11-15 years, 15 years and above. However, disparity was reported as far as the number of employees in groups was concerned. 128 out of a total of 332 employees were having experience of 2-5 years. However, rest of the groups showed similar pattern of having approximately 50 employees. Therefore, two groups were created, in the first group which was named ‘Experience Low’, the first two original groups i.e., 0-1 years and 2-5 years were merged. For the second group which was named ‘Experience High’, three other original groups i.e., 6-10 years, 11-15 years, and Above 15 years were merged. ‘Experience Low’ then had a total of 183


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employees and ‘Experience High’ had 149 employees. Lastly, the demographic variable based on the Educational Qualification was assessed. On closer perusal the researcher noted that total numbers of graduates were only 103 and 2 of the respondents were from Pre-University level. The numbers of the post-graduates were 186 out of 332 respondents, further the number of employees having the doctorate degree was 41. The grouping which was done here was based on Low and High level of Education. Though, graduates could not be assessed as having low qualification, however, when the comparison is with the post-graduates and doctorates it was thought imperative to have such a comparison. Disparity in groups could not, however, be minimized, as ‘Education Low’ i.e., the first group had 105 employees, in ‘Education High’ there were 227 employees.

Gender

The number of male respondents was 189 out of 332 valid respondents, and the number of female respondents was 143 out of a total of 332 respondents. Since there were two groups, Mann Whitney U test was conducted to gauge any significant difference between groups in context of the antecedent variables.

Table 4.41 PM, SAA, & SSR w.r.t. GENDER

Mann Whitney U test Variable

(I) Gender (J) Gender Z score Sig. Critical value

Remark

PM MALE FEMALE -2.550 0.011 0.05 Insig. SAA MALE FEMALE -3.184 0.001 0.05 Sig. SSR MALE FEMALE -0.667 0.504 0.05 Insig. Sig.: Significant, Insig.: Insignificant

Based on table no. 4.41, it was found that there is a significant difference between male and female employees while comparing the dimension SAA. Based on the mean scores of both the groups female score was 27.87 and male score was 26.31, therefore, it can be said that female employees are slightly ahead with their male counterparts


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when the level of spirituality and alignment is assessed. However, in terms of PM and SSR, no significant difference was found between the two groups.

Structure of the Family

The number of employees belonging to nuclear family was 194; however, the number of employees belonging to joint family was 138. It was assessed that the two groups are comparable, and non-parametric Mann Whitney U test could be utilized to assess if there is any significant difference between groups in terms of the antecedents of EE viz., SAA, SSR, and PM. As it is evident by the Mann Whitney U test as depicted in the table no. 4.42, that significant difference could be traced as far as SAA was concerned between the two groups. On assessing the mean scores it was found that those employees who belonged to joint family structure demonstrated higher scores in the SAA dimension.

Table 4.42 PM, SAA, & SSR w.r.t. STRUCTURE

Sig.: Significant, Insig.: Insignificant

Age

In the age group ‘30 and Less’ there were 165 employees, subsequently in the age group ‘More than 30’ there were 167 employees.

On assessing the table no. 4.43, the two age groups, i.e., ‘30 and Less’ and ‘More than 30’ it was found that there exist a significant difference between the two age groups with respect to PM and SAA. On assessing the mean values of both the groups it was found that employees whose age is 30 or less reported less scores on the dimensions


(I) Structure of

family

(J) Structure of family


Remark

PM Joint Nuclear -1.916 0.055 0.05 Insig. SAA Joint Nuclear -2.418 0.016 0.05 Sig. SSR Joint Nuclear -1.055 0.292 0.05 Insig.


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SAA and PM. Though, no significant difference was found between the age groups on comparing it with SSR.

Table 4.43 PM, SAA, & SSR w.r.t. AGE


(I) Age (J) Age Z score Sig. Critical value

Remark

PM 30 and Less More than 30 -4.925 *** 0.05 Sig. SAA 30 and Less More than 30 -4.687 *** 0.05 Sig. SSR 30 and Less More than 30 -0.702 0.483 0.05 Insig. Note: ***p < 0.001 Sig.: Significant, Insig.: Insignificant

Level in the Organization

As mentioned before two levels were created in order to bring down the inequality in both the groups. On forming the new group ‘Junior’ and ‘Senior’ level, the ‘Junior’ level had 158 employees and the ‘Senior’ level had 174 employees. After taking the group inequality, non-parametric Mann Whitney U test was employed.

Table 4.44 PM, SAA, & SSR w.r.t. LEVEL IN THE ORGANIZATION


(I) Level in the

Organization

(J) Level in the

Organization


Remark

PM Junior Senior -2.134 0.033 0.05 Sig. SAA Junior Senior -1.737 0.082 0.05 Insig. SSR Junior Senior -0.032 0.974 0.05 Insig. Note: ***p < 0.001, Sig.: Significant, Insig.: Insignificant

In terms of PM, it was found that there is a significant difference between the two age groups. On checking the mean values of the two groups, it was found that the senior


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level employees displayed more psychological meaningfulness at work. However, in terms of other antecedents the two groups showed no significant difference.

Marital Status

Table 4.45 PM, SAA, & SSR w.r.t. MARITAL STATUS


(I) Marital Status

(J) Marital Status


Remark

PM Married Single -5.149 *** 0.05 Sig. SAA Married Single -5.146 *** 0.05 Sig. SSR Married Single -1.988 0.047 0.05 Sig. Note: ***p < 0.001 Sig.: Significant, Insig.: Insignificant

Among the total 332 respondents, 184 employees were in the ‘married’ group, 148 employees were in the ‘Single’ group. When the two groups of marital status were compared in terms of the antecedents of EE viz., PM, SAA, and SSR, the result showed that there exists a significant difference between the two groups with respect to all the three antecedents, i. e., PM, SAA, and SSR. On further perusal of the mean scores it was found that the married employees showed greater mean values in terms of all the antecedents investigated here.

Experience in the Organization

On the basis of experience two groups were created as already mentioned before viz., ‘Experience Low’ and ‘Experience High’.


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Table 4.46 PM, SAA, & SSR w.r.t EXPERIENCE

Mann Whitney U test

Variable

(I) Experience in the

Organization

(J) Experience in

the Organization


Remark

PM Experience Low

Experience High

-4.292 *** 0.05 Sig.

SAA Experience Low

Experience High

-4.232 *** 0.05 Sig.

SSR Experience Low

Experience High

-1.100 0.271 0.05 Insig.


As shown in the table no. 4.46, it was found that there exists a significant difference between the two groups when taking PM and SAA into consideration. On assessing further through mean scores it was found that employees having experience of more than 5 years displayed higher level of psychological meaningfulness (PM), and spirituality and alignment (SAA). However, no significant difference was found between the experience groups on comparing it with SSR.

Educational Qualification

Though equitable groups in terms of educational qualification could not be created, however, it was found that extreme case of inequality was not there. Hence, Mann Whitney U test was employed to check the difference between the two groups ‘Education Low’ and ‘Education High’.

As it can be seen in the table no. 4.47, there exists significant difference between the two groups in terms of assessment of PM and SAA. On closer perusal of the results and the mean scores it was found that those with higher educational qualifications i.e., post graduation and beyond displayed high levels of spirituality and alignment as well


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as high psychological meaningfulness. However, on comparing the groups with respect to SSR, no significant difference was established.

Table 4.47 PM, SAA, & SSR w.r.t. EDUCATION


(I) Education

(J) Education Z score Sig. Critical value

Remark

PM Education Low

Education High

-3.658 *** 0.05 Sig.

SAA Education Low

Education High

-5.016 *** 0.05 Sig.

SSR Education Low

Education High

-1.364 0.173 0.05 Insig.

Note: ***p < 0.001 Sig.: Significant, Insig.: Insignificant

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