CHAPTER 5 DATA ANALYSIS AND RESULTS

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1 5.1 INTRODUCTION CHAPTER 5 DATA ANALYSIS AND RESULTS The purpose of this chapter is to present and discuss the results of data analysis. The study was conducted on 518 information technology professionals employed in software companies situated in national capital region of India. Data analysis includes validation of EWP scale developed in phase one of this study along with hypotheses testing. Analysis starts with the presentation of demographic information about the sample. The next sections of this chapter discuss in detail the procedure and results of confirmatory factor analysis and construct validation of EWP scale. Further, reliability and validity of other research instruments used in this study was also calculated and results of the same are presented in this chapter. Lastly, hypotheses testing were done with the help of correlation and structural equation modelling technique and results are discussed in detail. 5.2 SAMPLE DESCRIPTION AND DEMOGRAPHICS The sample consisted of 518 Information Technology professionals working in Northern Capital Region of India. Descriptive analysis showed that 61.8 of the respondents were male and 38.2% were female respondents. Out of total 67.9% of respondents were married, 30.6% unmarried along with 1.5% of belonging to other category. Gender and marital status details of respondents are shown in Table 5.1 and Table 5.2 respectively. Table 5.1 Gender Frequency Percent Female Male Total

2 Table 5.2 Marital Status Frequency Percent Unmarried Married Others Total Out of total respondents 32% were below 30 years of age, 45.6% were between 31-40years of age and remaining 22.4% belonged to higher age groups. 52.2% of respondents were graduates while 46.1% were having post graduate degrees along with 1.7% belonging to other category. Table 5.3 and 5.4 depicts the details of age and education level of respondents. Table 5.3 Age Years Frequency Percent Below Above Total Table 5.4 Education Level Frequency Percent Graduates PG Others Total

3 Out of all 49.2% respondents were from entry level, 28.2 from middle and rest 22.6 were from senior level. Table 5.5 contains information regarding the level of respondents in their current organization. Table 5.5 Level in the organization Frequency Percent Entry Middle Senior Total Although demographic variables were not taken as a part of study but before proceeding with hypotheses testing, data was analysed using gender and employee s level in the organization. Analysis was done to find out whether there exists a difference between levels of work passion among men and women or does passion differ on the basis of seniority. T-test and F-test was used to perform this analysis. Result of t-test found no difference between men and women in terms of experiencing passion. Result of f test suggested that employees working at senior levels experience more passion as compared to middle and entry level. Reason behind this could be the employee working at senior post enjoys more power and autonomy at workplace. Moreover, not much difference was found between middle and senior level. As employees working at entry level are new to the environment and it takes time for them to adjust so their passion doses not get boost in the initial years of their employment. For results of analysis refer Appendix G. 5.3 CONFIRMATORY FACTOR ANALYSIS OF EMPLOYEE WORK PASSION SCALE CFA was run using AMOS software package on a final data of 518 respondents to confirm the exploratory factor model of work passion. CFA is a method which is used to confirm the results 89

4 obtained from EFA. CFA is a structural modelling technique used to determine the goodness-offit between hypothesized model and the sample data. In other words, it is used for determining how well our sample data fits the theoretical model [177]. In this study, two measurement models of EWP were tested and compared: the one-factor model and the four-factor model obtained from EFA (phase1). As the result of EFA does not confirm the originally hypothesized five factor model of EWP, therefore, to better understand the factor structure of EWP confirmatory factor analysis was conducted on EFA resulted four-factor model and was compared with its single factor model. The CFA for the four factor model of work passion was estimated with AMOS. As shown in figure 5.1, result of CFA reveals that each of the items loaded strongly on the appropriate factor with loadings ranging from.76 to.88. The details regarding factor loadings of each item are given in Table 5.6. The four factors were correlated with each other as expected, ranging from.56 to.66. Table 5.7 contains information related to correlation between four factors of EWP. Findings suggest that, however, the four factors of employee work passion are correlated with each other but are distinct. The preliminary investigation criteria suggests an acceptable fit for CFA model such that; no error variance was negative, no correlations were greater than one no parameter estimates were extremely large. Based on acceptable results of initial investigation, an evaluation of more formal criteria was made in the form goodness of fit measures. 90

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7 Figure 5.1 Employee work passion measurement model Table 5.6 Standardized factor loadings of each item Factor Loadings WE5 <--- Work Enjoyment.803 WE4 <--- Work Enjoyment.827 WE3 <--- Work Enjoyment.787 WE2 <--- Work Enjoyment.868 WE1 <--- Work Enjoyment.878 SM4 <--- Self-Motivation.802 SM3 <--- Self-Motivation.826 SM2 <--- Self-Motivation.839 SM1 <--- Self-Motivation.760 SI4 <--- Self-Identity.859 SI3 <--- Self-Identity.865 SI2 <--- Self-Identity.781 SI1 <--- Self-Identity.804 SoL4 <--- Sense of learning.768 SoL3 <--- Sense of learning.821 SoL2 <--- Sense of learning.876 SoL1 <--- Sense of learning.818 Table 5.7 Inter-factor correlations Estimate Work Enjoyment <--> Self-Motivation.650 Work Enjoyment <--> Self-Identity.643 Work Enjoyment <--> Sense of learning.577 Self-Motivation <--> Self-Identity.647 Self-Motivation <--> Sense of learning.563 Self-Identity <--> Sense of learning.585 Goodness of fit (GOF) measures 93

8 Goodness of fit indicates how well the specified model reproduces the observed covariance matrix among the indicator items [177]. In other words it is used to evaluate the level of fit between theoretical model and sample data. However, an output generated by CFA provides several types of fit indices that can be used to assess the extent to which the sample data fits the hypothesized model. The most commonly and widely used two types of fit indices are absolute and incremental. Moreover, following fit indices are considered for analysis in this study: Tucker Lewis Index (TLI), the comparative fit index (CFI), and Root Mean Square Error of Approximation (RMSEA) and chi-square/df ratio. Absolute Fit indices For this study following absolute fit indices have been evaluated to assess the model fit: Chisquare (χ2), Root mean square error of approximation (RMSEA). Chi-square (χ2) measures the amount of discrepancy between the sample and fitted covariance matrices [197]. Chi-square value alone does not provide sufficient information on model fit as its value changes with the change in sample size rather is it advisable to assess value of CMIN/DF with it. Although, there is no agreement on; what should be the standard value of CMIN/DF but opinion varies from 5.0 [198]to 2.0 [199]. The value of CMIN/DF for this model was 1.544, which suggests an acceptable fit. Root mean square error of approximation (RMSEA) is one of the most widely used measures of evaluating model fit. The RMSEA tells us how well the model, with unknown but optimally chosen parameter estimates would fit the population covariance matrix [200]. For RMSEA, value that does not exceed 0.08 is considered good while value less than 0.05 is considered excellent [193]. The RMSEA value for this model was.043, which suggests a good fit. Incremental fit indices These are also known as comparative or relative fit indices. This group of indices does not use the chi-square in its raw form rather it compares the chi-square value to a baseline model. The 94

9 two different types of incremental fit indices used in this study are: Trucker Lewis Index (TLI; Tucker & Lewis [191]), Comparative fit index (CFI; Bentler, [192].). Values for TLI statistic range between 0 and 1 where values greater than 0.90 indicating a good fit. As with the TLI, values for CFI statistic range between 0.0 and 1.0 with values closer to 1.0 indicating good fit [192]. The value of TLI and CFI for this model were.979 and.982 respectively. In summary, this four factor model of work passion that resulted from EFA has shown an acceptable fit on all the aforementioned fit indices. Table 5.8 Model fit indices of EWP CMIN/df P value RMSEA CFI TLI Four-factor Model One -factor Model To further explore the adequacy of this model, AMOS was again employed to compare this EFA emerged four-factor model with one factor model of work passion. CFA of one factor model resulted in a poor fit with CMIN/df = 17.8, p<0.001; CFI=.68; TLI=.67; RMSEA=.198. On the other hand, the four factor model emerged from EFA resulted in a good fit on all indices: CMIN/df =1.544, p < 0.001; CFI =.982; TLI=.979; RMSEA=.043. Table 5.8 provides the comparison between two measurement models. Results suggests that the four factor model out performs the one factor model of work passion on all indices. To confirm the results further, a separate CFA was conducted on each of the four dimensions of work passion. Figure 5.2 to 5.5 shows the measurement model of four different dimensions of EWP. Result of each CFA was found to have an excellent fit on various fit indices. Table 5.9 to 5.12 provides information regarding model fit indices of each dimension of EWP separately. Thus, these results indicate that work passion is a multi-dimensional construct comprising of four theoretically and empirically different dimensions, namely, work enjoyment, self-motivation, self-identity and sense of learning. Hence, for the purpose of this study, this 17-item measure of EWP will be used in subsequent analysis. 95

10 Figure 5.2 Measurement model of work enjoyment Table 5.9 Fit indices of work enjoyment construct CMIN/df P value RMSEA CFI TLI Figure 5.3 Measurement model of self-identity Table 5.10 Fit indices of self-identity construct CMIN/df P value RMSEA CFI TLI

11 Figure 5.4 Measurement model of self-motivation Table 5.11 Fit indices of self-motivation construct CMIN/df P value RMSEA CFI TLI Figure 5.5 Measurement model of sense of learning Table 5.12 Fit indices of sense of learning construct CMIN/df P value RMSEA CFI TLI 97

12 SECOND ORDER CONFIRMATORY ANALYSIS OF EWP CONSTRUCT Based on review of literature and qualitative analysis of interviews, it was hypothesized that five dimensions of employee work passion reflects a higher order construct of EWP. To model such a higher construct a second order CFA must be employed. To test second order CFA, the factor structure is further specified to describe the relationships among first order factors. The four factors obtained from EFA (and confirmed in CFA) instead of five factors (resulted in phase one) were taken as first order factors to conduct second order CFA. It was hypothesized in chapter 4 that the four dimensions (work enjoyment, self-motivation, self-identity and sense of learning) of passion encompass of three broader aspects: emotional, cognitive and behavioural. As shown in Figure 5.6 second order CFA was conducted using 17-item EWP scale. The second order model resulted in an adequate fit (CMIN/df =2.792, p < 0.001; CFI =.966; TLI=.960; RMSEA=.059). Table 5.13 depicts model fit indices of second order CFA. Path estimates between first order dimensions of EWP and their respective second order components were found to be significant and positive. Path estimate between emotion (second order) to work enjoyment (first order) was found to be β=.88 (p<.001), cognition to self-identity β=.79 (p<.001) and to self-motivation β=.80 (p<.001) and lastly between behaviour and sense of learning is β=.90 (p<.001). Although the second order CFA model resulted in an acceptable fit on various fit indices and all the paths estimates were positive and significant but all the three second order factors (emotion, cognition and behaviour) were found highly correlated with each other. Results suggest that this model needs further exploration. Moreover, it is also recommended by researchers that in order to test second order CFA, each second order factor needs to have at least three first order factors but this condition was not satisfied in this model. However, the result of second order CFA provides 98

13 initial support to the assumption that the four dimensions of EWP encompass three broader aspects but more research is needed in future to explore this model of EWP. Figure 5.6 Second Order CFA of EWP Table 5.13 Model fit indices of second order CFA of EWP CMIN/df P value RMSEA CFI TLI 99

14 CONSTRUCT VALIDITY OF EWP SCALE Construct validity is the degree to which a test measures what it claims, or purports, to be measuring. In other words it refers to the extent to which any measuring instrument measures what it is intended to measure [201] [202]. Generally, most of the researchers conducts construct validity test before moving on with the main research. The two subtypes of validity that make up construct validity are convergent and discriminant validity CONVERGENT VALIDITY Convergent Validity means that the items that are indicator of specific construct should converge or share a high proportion of variance in common [177]. Hair et al [146] suggested following measures for establishing convergent validity: Factor loadings, Average Variance Extracted (AVE) and Composite Reliability (CR). The suggested thresholds for these values are: CR > 0.7 (reliability); AVE > 0.5 and standardized factor loadings > 0.5. High factor loadings indicate that they converge on a common point which is the latent construct. All factor loadings should be statistically significant with standardized loading estimates of.5 or higher and a value more than.7 is considered ideal. Result of CFA provides evidence for this criterion where each of the items loaded strongly on the appropriate factor with loadings ranging from.76 to.88 (refer Table 5.7). Average variance extracted (AVE) is a summary measure of convergence among a set of items representing a construct. It is the average percent of variation explained among the items [146]. To indicate adequate convergent validity, value of AVE should be greater than.50. The value of AVE less than.50 indicates that variance is more due to measurement error rather than due to 100

15 construct.in addition, the AVE estimate should not be greater than composite reliability. AVE is calculated as total of all squared standardized factors loadings (squared multiple correlation) divided by number of items. Average variance extracted (AVE) = Ʃƛ²/ n Composite reliability (CR) also known as Jöreskog s rho is a measure of reliability and internal consistency based on the square of the total factor loadings for a construct. CR value of 0.7 or higher indicates adequate convergence or internal consistency. Formula for calculating CR is: CR = ( Ʃƛ) ² / ( Ʃƛ) ² + (Ʃe) DISCRIMINANT VALIDITY Discriminant validity is extent to which a construct is truly discriminant from other constructs [146]. To determine sufficient discriminant validity the square root of AVE should be greater than the corresponding inter-factor correlation. This suggests that the indicator variables have more in common with the construct they are associated with than they do with other constructs [146]. To test for convergent validity, AVE was calculated for each of the dimensions of work passion. The value of AVE for all the four dimensions of work passion was more than the suggested threshold of.50. Further CR was calculated for all the four dimensions of work passion, which were.89,.92,.88 and.89 respectively, well above the threshold of.70. Table 5.14 contains information related to AVE, CR and squared AVE. As all the conditions of convergent validity were met, thus, signifying good convergent validity. Furthermore, square root of AVE of each dimension of work passion is shown on the diagonal in Table All the squared AVE s are greater than the correlation between any two constructs, thus, meeting the requirement for discriminant validity. 101

16 Table 5.14 Convergent and discriminant validity table of EWP construct CR AVE Self-Identity Enjoyment Self-Motivation SoL Self-Identity * Enjoyment * Self-Motivation * SoL * Note SoL- sense of learning dimension of EWP, *squared AVE In summary, the construct validation of work passion construct is generally confirmed. Results of analysis provide support to the convergent and discriminant validity of four factor model of employee work passion measure through both exploratory as well as confirmatory factor analysis. After establishing the preliminary construct validity of work passion, the next step of this study was to test the hypotheses of study one by one. 5.5 HYPOTHESES TESTING Research hypotheses of the study were developed on the basis of theoretical framework of this study already discussed in chapter 3. The purpose of framework was to study the relationship between antecedents of EWP and its outcome. Structural equation modelling was used to examine the research hypotheses. Before testing the hypotheses, reliability and validity of other research instruments used in this study were calculated. As the previous section of this chapter provided detailed discussion of EWP construct validation, this section emphasizes on the validation of other research instruments of the study. First of reliability of all the research instruments used was tested using cronbach alpha then confirmatory factor analysis was run to test the factor structure of other research instruments. After validating the research instruments, we will test the research hypotheses one by one. 102

17 5.5.1 RELIABILITY OF RESEARCH INSSTRUMENTS It is mandatory for researchers to establish reliability of research instruments prior to hypotheses testing as without reliability results will not be replicable. Replicability is a fundamental condition of scientific method hence it becomes essential to demonstrate reliability of research instruments. Although, there exist variety of methods to estimate reliability but the most frequently used method in field studies is internal consistency reliability using Cronbach s alpha [203]. The internal consistency reliabilities for each of the scales used in this study is calculated. The four instruments used in the study have shown high internal reliability with all alpha values of more than 0.70 [180]. Table 5.15 depicts the alpha values of all the measures used for this study. Table 5.15 Reliability values of research instruments Measurement scale Alpha value Employee Work Passion Scale (EWPS).93 Survey of POS Scale (SPOS).94 New General Self-efficacy (NGSE) Scale.87 Career satisfaction Scale CONFIRMATORY FACTOR ANALYSIS: SELF-EFFICAY Confirmatory factor analysis (CFA) tests whether the known factor model can predict a set of observed data [204]. Here, CFA is used to establish the validity of the factor model. CFA was run using AMOS software. For measuring self-efficacy new general self-efficacy scale (NGSE) of Chen et al is used. Scale comprised of 8 items and was considered unidimensional in nature. To test the one factor structure of self-efficacy scale CFA was run. Result of analysis support the 103

18 one-factor model structure of self-efficacy construct as values of all the fit indices were in the suggested thresholds. Measurement model of self-efficacy is shown in Figure 5.7. Table 5.16 depicts the fit indices of measurement model. Figure 5.7 Measurement model of self-efficacy construct Table 5.16 Model fit indices of self-efficacy construct CMIN/df P value RMSEA CFI TLI CONFIRMATORY FACTOR ANALYSIS: PERCEIVED ORGANIZATIONAL SUPPORT Eisenberger s 8 item scale is used to measure the POS comprising of one dimension. To test the one factor structure of POS CFA was run. Results provide support to unidimensional nature of POS. Model fit indices of POS construct are shown in the Table Measurement model of POS is shown in figure

19 Figure 5.8 Measurement model of POS construct Table 5.17 Model fit indices of POS construct CMIN/df P value RMSEA CFI TLI CONFIRMATORY FACTOR ANALYSIS: CAREER SATISFACTION For measuring career satisfaction, scale developed by Greenhaus [116] is used. According to author scale is one-dimensional in nature. To test the single factor structure of career satisfaction construct, CFA was run and result supported the unidimesionality of this scale. Model fit indices of career satisfaction construct are shown in the Table Measurement model of career satisfaction is shown in Figure

20 Figure 5.9 Measurement model of career satisfaction Table 5.18 Model fit indices of career satisfaction construct CMIN/df P value RMSEA CFI TLI HYPOTHESIS 1 (H1) It predicted a relationship between self-efficacy and employee work passion. It was hypothesized that self-efficacy is positively related to EWP. Result of analysis provide support to this hypothesis where self-efficacy was found significantly positively related to all the four dimensions of EWP, namely, work enjoyment, self-identity, self-motivation and sense of learning. 106

21 First of all, the correlation analysis was done between self-efficacy and all the four dimensions of work passion to identify the pattern of relationship among them. Table 5.19 depicts the correlation results among self-efficacy and four factors of EWP. The results of correlation revealed a significant positive correlation between self-efficacy, work enjoyment, self-identity, self-motivation and sense of learning. Further, SEM was used to examine this relationship in depth. SEM was applied with the help of AMOS. In order to test our hypothesis two models were tested simultaneously: measurement model and structural model of self-efficacy and employee work passion. As shown in Figure 5.10, measurement model of H1 resulted in a good fit on various model fit indices (CMIN/df= 1.300, p =.001, RMSEA =.032, CFI=.983 and TLI=.981) presented in Table Table 5.19 Correlations among self-efficacy and four dimensions of EWP Enjoyment Self-motivation Self-identity SoL Self-efficacy Enjoyment **.573 **.513 **.468 ** Self-motivation.589 ** **.504 **.448 ** Self-identity.573 **.560 ** **.454 ** SoL.513 **.504 **.515 ** ** Self-efficacy.468 **.448 **.454 **.465 ** 1 **. Correlation is significant at the 0.01 level (2-tailed) To study the effect of self-efficacy on all the four dimensions of work passion structural model was tested. Table 5.20 displays the overall fit indices of structural model. Results reveal that this model fit the sample data reasonably well as indicated by the selected overall goodness-of-fit statistics: CMIN/df= 2.003, p =.000, RMSEA =.058, CFI=.943 and TLI=.936. As shown in Figure 5.11, a positive and significant path was found between self-efficacy and all the four dimensions of work passion: work enjoyment c, self-identity (β=.61, p<.001), self-motivation (β=.60, p<.001) and sense of learning (β=.60, p<.001). 107

22 Figure 5.10 Structural model of self-efficacy and employee work passion Table 5.20 Fit indices of structural model of self-efficacy and work passion CMIN/df P value RMSEA CFI TLI

23 Figure 5.11 Structural model of self-efficacy and employee work passion Table 5.21 Model fit indices of structural model of self-efficacy and EWP CMIN/df P value RMSEA CFI TLI Table 5.22 displays the summary of findings for H1 suggesting that our first hypothesis is accepted as self-efficacy was found significantly positively related to all the four dimensions of work passion. The results of hypothesis one is in line with past researches. Researchers suggest that efficacy beliefs positively affects the intrinsic or self-motivation of employees in terms of achieving their goals and challenges [139] [89]. Moreover, self-efficacy has also been seen associated with experience of positive emotions at work. Results suggest that highly efficacious 109

24 employees are more likely to enjoy their work, feel inner motivation to their work and they feel engrossed in their work [82]. Table 5.22 Path estimates between self-efficacy and dimensions of EWP Estimate S.E. C.R. P Work Enjoyment <--- Self-Efficacy Self-Motivation <--- Self-Efficacy Self-Identity <--- Self-Efficacy Sense Of Learning <--- Self-Efficacy HYPOTHESIS 2 (H2) It predicted a relationship between POS and work passion. It was hypothesized that POS is positively related to work passion. Results of the analysis provide support to this hypothesis where self-efficacy was found significantly positively related to all the four dimensions of work passion, namely, work enjoyment, self-identity, self-motivation and sense of learning. First of all correlation between POS and all the four dimensions of EWP was tested. Table 5.23 provides correlations between POS and EWP. As expected these correlations suggests positive and significant relationship between POS and all the four dimensions of work passion. Although, these correlations provide some initial support for the hypothesis but in order to understand the true relationship between POS and work passion, SEM was used. Table 5.23 Correlation between POS and four dimensions of EWP Enjoyment Self-motivation Self-identity SoL POS Enjoyment **.573 **.513 **.357 ** Self-motivation.589 ** **.504 **.310 ** Self-identity.573 **.560 ** **.282 ** SoL.513 **.504 **.515 ** ** POS.357 **.310 **.282 **.181 ** 1 **. Correlation is significant at the 0.01 level (2-tailed) 110

25 SEM was applied with the help of AMOS. In order to test our second hypothesis two models were tested simultaneously- measurement as well structural model of POS and EWP. As shown in Figure 5.12 measurement model resulted in a good fit as all the fit indices were in the recommended range (CMIN/df= 1.347, p =.000, RMSEA =.034, CFI=.984 and TLI=.981) presented in Table Figure 5.12 Measurement model of POS and EWP Table 5.24 Model fit indices of measurement model of POS and EWP CMIN/df P value RMSEA CFI TLI

26 Results of structural model of POS and EWP reveal that this model fit the sample data reasonably well as suggested by the selected overall goodness-of-fit statistics: CMIN/df= 2.602, p =.000, RMSEA =.034, CFI=.923 and TLI=.921. Table 5.25 displays the overall fit indices of structural model. As shown in Figure 5.13, a positive and significant path was found between POS and all the four dimensions of work passion individually: work enjoyment (β=.45, p<.001), self-identity (β=.36, p<.001), self-motivation (β=.37, p<.001) and sense of learning (β=.26, p<.001). Figure 5.13 Structural model of POS and EWP Table 5.25 Model fit indices of structural model of POS and EWP CMIN/df P value RMSEA CFI TLI

27 Table 5.26 displays the summary of findings for H2 suggesting that our second hypothesis is accepted as POS has positive and significant effect on all the four dimensions of employee work passion. Findings suggest that employees who feel that their organization cares about them reciprocate the same in terms of higher passion. These results are in line with past researches where POS has been found positively and significantly related to EWP [10]. Table 5.26 Path estimates between POS and four dimensions of EWP Estimate S.E. C.R. P Work Enjoyment <--- POS Self-Motivation <--- POS Self-Identity <--- POS SoL <--- POS HYPOTHESIS 3 (H3) It predicted a relationship between work passion and career satisfaction. It was hypothesized that work passion is positively related to employee career satisfaction. Results of the analysis provide support to this hypothesis where significantly positive relationship was found between all the dimensions of work passion and career satisfaction. Result of correlation analysis between POS and EWP suggests positive and significant relationship between the four dimensions of work passion and career satisfaction. Table 5.27 provides correlations between four dimensions of work passion and career satisfaction. 113

28 Table 5.27 Correlation between four dimensions of EWP and career satisfaction Enjoyment Self-motivation Self-identity SoL CS Enjoyment **.573 **.513 **.562 ** Self-motivation.589 ** **.504 **.534 ** Self-identity.573 **.560 ** **.610 ** SoL.513 **.504 **.515 ** ** CS.562 **.534 **.610 **.452 ** 1 **. Correlation is significant at the 0.01 level (2-tailed); CS- career satisfaction Though these correlations provide moderate support for the hypothesis, but, to understand the true relationship between dimensions of work passion and career satisfaction, SEM was used. SEM was applied with the help of AMOS. In order to test this hypothesis; measurement and structural model of employee work passion and career satisfaction were tested. Measurement model resulted in a good fit as all the fit indices were in the recommended range (CMIN/df= 1.372, p =.000, RMSEA =.035, CFI=.983 and TLI=.981) presented in table 5.28 and shown in Figure Results of the path analysis revealed a satisfactory fit of the model to the sample data, CMIN/df= 3.420, p =.000, RMSEA =.090 CFI=.886 and TLI=.874. Table 5.29 displays the overall fit indices of structural model. While some of the fit indices were not in the recommended range but were approaching the recommendable values yet a positive and significant paths were found between all the four dimensions of work passion and career satisfaction. As shown in the figure 5.14, work enjoyment (β=.32, p<.001), self-motivation (β=.28, p<.001), self-identity (β=.46, p<.001) and sense of learning (β=.13, p<.005) dimensions of employee work passion have positive effect on career satisfaction. 41% variance in career satisfaction is explained by employee work passion. 114

29 Figure 5.14 Measurement model of EWP and career satisfaction Table 5.28 Model fit indices of measurement model of EWP and career satisfaction CMIN/df P value RMSEA CFI TLI

30 Figure 5.15 Structural model of EWP and career satisfaction Table 5.29 Model fit indices of structural model of EWP and career satisfaction CMIN/df P value RMSEA CFI TLI

31 Table 5.30 displays the summary of findings for H3 suggesting that third hypothesis of the study is accepted as all the four dimensions of employee work passion were found positively and significantly related to career satisfaction. Results of the study are in line with past researches where work drive, intrinsic motivation and work enjoyment has been found positively related to career and job satisfaction [136] [137] [132]. Moreover no empirical study has been done to test the direct relationship between EWP and career satisfaction. Table 5.30 Path estimates between four dimensions of EWP and career satisfaction Estimate S.E. C.R. P Career Satisfaction <--- Work Enjoyment Career Satisfaction <--- Self-Motivation Career Satisfaction <--- Self-Identity Career Satisfaction <--- Sense Of Learning HYPOTHESIS 4 (H4) It predicted that employee work passion will mediate the relationship between antecedents (selfefficacy & POS) and outcome (career satisfaction) of EWP. For testing the mediating role of EWP, Baron and Kenny [138] approach for testing mediation was used. Authors suggested the four step procedure for testing mediation: Step 1: Show that the causal variable (self-efficacy and POS) is correlated with the outcome (career satisfaction), Step 2: Show that the causal variable (self-efficacy and POS) is correlated with the mediator (employee work passion), Step 3: Show that the mediator (employee work passion) affects the outcome variable (career satisfaction), Step 4: To establish that mediator (employee work passion) completely mediates the relationship 117

32 between antecedents and outcome, the effect of antecedents on outcome controlling for mediator (employee work passion) should be zero. Figure 5.16 Measurement model of self-efficacy, POS and career satisfaction Table 5.31 Model fit indices of measurement model of self-efficacy, POS and career satisfaction CMIN/df P value RMSEA CFI TLI

33 Further, they suggested that if all four of these steps are met, then complete mediation is indicated and if the first three steps are met but the Step 4 is not then partial mediation is indicated. Conditions specified in step 2 and 3 were already met in the form of hypotheses 1, 2 & 3. In order to test the conditions specified in step 1 and 4, two additional measurement and structural model were tested. For step 1, structural model showing direct relationship between antecedents and outcome variable was tested. Figure 5.16 depicts the measurement model and Figure 5.17 shows the structural model for step 1 (to study the relationship between self-efficacy, POS and career satisfaction). Measurement model resulted in a good fit as all the fit indices were in the recommended range (CMIN/df= 2.507, p =.000, RMSEA =.054, CFI=.961 and TLI=.955) presented in Table Results of the path analysis revealed a satisfactory fit of the model to the sample data, CMIN/df= 1.471, p =.000, RMSEA =.040 CFI=.978 and TLI=.976. Table 5.32 displays the overall fit indices of structural model. A positive and significant paths were found between all the two antecedents of EWP and career satisfaction. As shown in the figure 5.17, self-efficacy (β=.48, p<.001) and POS (β=.46, p<.005) have positive effect on career satisfaction. 45% variance in career satisfaction is explained by both the antecedents. Table 5.33 displays the summary of findings for step 1 suggesting that conditions specified in step1 are met as self-efficacy and POS have positive and significant effect on career satisfaction. These findings provide support to H4a and H4c. To test the condition specified in step 4, structural model depicting indirect relationship between antecedents (self-efficacy and POS) and outcome variable (career satisfaction) controlling for mediator (EWP) was tested. Figure 5.18 depicts the measurement model and Figure 5.19 shows the structural model for step 4. Measurement model resulted in a good fit as all the fit indices were in the recommended range (CMIN/df= 1.232, p =.000, RMSEA =.028, CFI=.981 and TLI=.979) presented in Table Results of the path analysis revealed a satisfactory fit of the model to the sample data, CMIN/df= 1.430, p =.000, RMSEA =.038 CFI=.977 and TLI=.974. Table 5.35 displays the overall fit indices of structural model. 119

34 Figure 5.17 Structural model of self-efficacy, POS and career satisfaction Table 5.32 Model fit indices of structural model of self-efficacy, POS and career satisfaction CMIN/df P value RMSEA CFI TLI

35 Table 5.33 Direct path estimates between self-efficacy, POS and career satisfaction Estimate Career Satisfaction <--- Self-Efficacy.483 Career Satisfaction <--- POS.463 As seen in Table 5.36, an effect of predicator variables (self-efficacy and POS) on outcome variable (career satisfaction) reduced when the mediator (EWP) is added. Results suggests partial mediation in case of POS as only first three conditions for mediation [138] were met. Where as in the case of self-efficacy full mediation was found as the indirect effect of self-efficacy on careers satisfaction became insignificant. Table 3.37 depicts the comparison of direct and indirect effect of antecedents on outcome variable. Results provide support to H4b and H4d. Overall, result of analysis provided partial support to H4 suggesting that EWP mediates the relationship between its antecedents and outcome. No attempts have been made in past to study employee work passion as a mediator. This study is first of its kind where employee s passion for work has been studied as a mediator between perceived organizational support and feeling of satisfaction with one s career. Similarly, passion has been studied as mediator between employee s self-efficacy and their career satisfaction in this study. The role of work passion as a mediator needs to be explored by future researchers as this study provides only initial support to such relationship. 121

36 Figure 5.18 Measurement model of self-efficacy, POS, EWP and career satisfaction Table 5.34 Model fit indices of measurement model of self-efficacy, POS, EWP and career satisfaction CMIN/df P value RMSEA CFI TLI

37 Figure 5.19 Structural model of self-efficacy, POS, EWP and career satisfaction Table 5.35 Model fit indices of structural model of self-efficacy, POS, EWP and career satisfaction CMIN/df P value RMSEA CFI TLI Table 5.36 Indirect effect of self-efficacy and POS on career satisfaction Estimate S.E. C.R. P Career Satisfaction <--- Self-Efficacy Career Satisfaction <--- POS

38 Table 5.37 Comparison between direct effect and indirect effect Direct effect Indirect effect Career Satisfaction <--- Self-Efficacy.483 (p<.001).056 (.356) NS Career Satisfaction <--- POS.463(p<.001).271(.000) Significant Note- NS- non significant 5.6 CONCLUSION The chapter provided detailed discussion of the results of construct validation of work passion and tests of hypotheses framed in theoretical framework. Result of the analysis provide strong support for the construct validation of the four dimensional measure of work passion emerged from EFA and supported by theory also. In terms of hypotheses regarding antecedents and work passion, strong support was found. Self-efficacy and POS were found to have significant effect on work passion. In the same vein, strong support was found between work passion and its outcome career satisfaction. Moreover, EWP was found mediating the relationship between antecedents and outcome of EWP in this study. Overall, these findings provide moderate support for the initial nomological linkage of work passion, its antecedents and outcome. The following chapter presents the findings and conclusion of the study along with its limitations and future scope.. 124