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CHAPTER 5
DATA ANALYSIS AND RESULTS
5.1 INTRODUCTION
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 198 38.2
Male 320 61.8
Total 518 100.0
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Table 5.2 Marital Status
Frequency Percent
Unmarried 159 30.6
Married 352 67.9
Others 7 1.5
Total 518 100.0
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 30 166 32.0
31-45 236 45.6
46-55 65 12.5
Above 56 51 9.8
Total 518 100.0
Table 5.4 Education Level
Frequency Percent
Graduates 270 52.2
PG 239 46.1
Others 9 1.7
Total 518 100.0
89
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 255 49.2
Middle 146 28.2
Senior 117 22.6
Total 518 100.0
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
90
obtained from EFA. CFA is a structural modelling technique used to determine the goodness-of-
fit 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.
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92
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Figure 5.1 Employee work passion measurement model
Table 5.6 Standardized factor loadings of each item
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
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
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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: Chi-
square (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
95
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 1.544 .000 .043 .982 .979
One -factor Model 17.8 .000 .198 .68 .67
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.
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Figure 5.2 Measurement model of work enjoyment
Table 5.9 Fit indices of work enjoyment construct
CMIN/df P value RMSEA CFI TLI
2.089 .064 .061 .995 .989
Figure 5.3 Measurement model of self-identity
Table 5.10 Fit indices of self-identity construct
CMIN/df P value RMSEA CFI TLI
.569 .566 .000 1.000 1.000
97
Figure 5.4 Measurement model of self-motivation
Table 5.11 Fit indices of self-motivation construct
CMIN/df P value RMSEA CFI TLI
3.288 .037 .088 .993 .978
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
98
2.483 .084 .071 .999 .987
5.3.1 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
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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
100
2.792 .000 .059 .966 .960
5.4 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.
5.4.1 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
101
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)
5.4.2 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 5.14. All the squared AVE‟s are
greater than the correlation between any two constructs, thus, meeting the requirement for
discriminant validity.
102
Table 5.14 Convergent and discriminant validity table of EWP construct
CR AVE Self-Identity Enjoyment Self-Motivation SoL
Self-Identity 0.896 0.683 0.826*
Enjoyment 0.921 0.699 0.626 0.836*
Self-Motivation 0.884 0.655 0.660 0.648 0.809*
SoL 0.896 0.683 0.599 0.596 0.610 0.827*
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.
103
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 .88
5.5.2 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
104
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
1.488 .074 .041 .991 .988
5.5.3 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 5.17. Measurement model of
POS is shown in figure5.8.
105
Figure 5.8 Measurement model of POS construct
Table 5.17 Model fit indices of POS construct
CMIN/df P value RMSEA CFI TLI
1.581 .047 .044 .994 .992
5.5.4 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 5.18. Measurement model of career
satisfaction is shown in Figure 5.9.
106
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
2.399 .035 .069 .990 .980
5.5.5 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.
107
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 5.20.
Table 5.19 Correlations among self-efficacy and four dimensions of EWP
Enjoyment Self-motivation Self-identity SoL Self-efficacy
Enjoyment 1 .589**
.573**
.513**
.468**
Self-motivation .589**
1 .560**
.504**
.448**
Self-identity .573**
.560**
1 .515**
.454**
SoL .513**
.504**
.515**
1 .465**
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).
108
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
1.300 .001 .032 .983 .981
109
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
2.003 .000 .058 .943 .936
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
110
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 .604 .156 8.986 .000
Self-Motivation <--- Self-Efficacy .606 .126 8.362 .000
Self-Identity <--- Self-Efficacy .603 .139 8.594 .000
Sense Of Learning <--- Self-Efficacy .596 .133 8.539 .000
5.5.6 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 1 .589**
.573**
.513**
.357**
Self-motivation .589**
1 .560**
.504**
.310**
Self-identity .573**
.560**
1 .515**
.282**
SoL .513**
.504**
.515**
1 .181**
POS .357**
.310**
.282**
.181**
1 **. Correlation is significant at the 0.01 level (2-tailed)
111
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 5.24.
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
1.347 .000 .034 .984 .981
112
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
2.602 .000 .034 .923 .921
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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 .448 .053 7.534 .000
Self-Motivation <--- POS .371 .042 5.886 .000
Self-Identity <--- POS .356 .047 5.751 .000
SoL <--- POS .263 .045 4.239 .000
5.5.7 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.
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Table 5.27 Correlation between four dimensions of EWP and career satisfaction
Enjoyment Self-motivation Self-identity SoL CS
Enjoyment 1 .589**
.573**
.513**
.562**
Self-motivation .589**
1 .560**
.504**
.534**
Self-identity .573**
.560**
1 .515**
.610**
SoL .513**
.504**
.515**
1 .452**
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 5.14.
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.
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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
1.372 .000 .035 .983 .981
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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
3.420 .000 .090 .886 .874
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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 .319 .034 5.386 .000
Career Satisfaction <--- Self-Motivation .282 .036 4.788 .000
Career Satisfaction <--- Self-Identity .465 .034 7.501 .000
Career Satisfaction <--- Sense Of Learning .129 .036 2.284 .022
5.5.8 HYPOTHESIS 4 (H4)
It predicted that employee work passion will mediate the relationship between antecedents (self-
efficacy & 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
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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
2.507 .000 .054 .961 .955
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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 5.31.
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 5.34. 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.
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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
1.471 .000 .040 .978 .976
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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.
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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
1.232 .000 .028 .981 .979
123
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
1.430 .000 .038 .977 .974
Table 5.36 Indirect effect of self-efficacy and POS on career satisfaction
Estimate S.E. C.R. P
Career Satisfaction <--- Self-Efficacy .056 .063 .935 .350
Career Satisfaction <--- POS .271 .024 5.660 .000
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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..