CHAPTER 5 DATA ANALYSIS AND RESULTS - Shodhgangashodhganga.inflibnet.ac.in/bitstream/10603/74190/13/13_chapter5.pdf · separate CFA was conducted on each of the four dimensions of

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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

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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

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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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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





SM4 <--- Self-Motivation .802




SI4 <--- Self-Identity .859




SoL4 <--- Sense of learning .768




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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

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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


2.089 .064 .061 .995 .989

Figure 5.3 Measurement model of self-identity

Table 5.10 Fit indices of self-identity construct


.569 .566 .000 1.000 1.000

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Figure 5.4 Measurement model of self-motivation

Table 5.11 Fit indices of self-motivation construct


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


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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


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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

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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.

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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.

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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

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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


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.

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Figure 5.8 Measurement model of POS construct

Table 5.17 Model fit indices of POS construct


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.

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Figure 5.9 Measurement model of career satisfaction

Table 5.18 Model fit indices of career satisfaction construct


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.

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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).

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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


1.300 .001 .032 .983 .981

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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


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

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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


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**


1 .560**

.504**

.310**


.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)

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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)


Figure 5.12 Measurement model of POS and EWP

Table 5.24 Model fit indices of measurement model of POS and EWP


1.347 .000 .034 .984 .981

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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


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


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


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**


1 .560**

.504**

.534**


.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


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


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


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


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


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)


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


1.471 .000 .040 .978 .976

121

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


1.232 .000 .028 .981 .979

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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


1.430 .000 .038 .977 .974

Table 5.36 Indirect effect of self-efficacy and POS on career satisfaction


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..

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CHAPTER 5 DATA ANALYSIS AND RESULTS - Shodhgangashodhganga.inflibnet.ac.in/bitstream/10603/74190/13/13_chapter5.pdf · separate CFA was conducted on each of the four dimensions of