CHAPTER 5 DIMENSIONAL ANALYSIS OF OCB &...
Transcript of CHAPTER 5 DIMENSIONAL ANALYSIS OF OCB &...
Jiwan Jyoti Maini, PhD Thesis Chapter 5
149
CHAPTER 5
DIMENSIONAL ANALYSIS OF OCB & EI
5.1 CHAPTER OVERVIEW
This chapter focuses on the relationship among the five dimensions of
organizational citizenship behaviour and four dimensions of emotional intelligence.
The origins and development of organizational citizenship behaviour and emotional
intelligence has been discussed in the chapter 2. In the present study, five dimensions
of organizational citizenship behaviour i.e. altruism, sportsmanship,
conscientiousness, courtesy and civic virtue have been included as criterion variables.
While four dimensions of EI, based on the work of Salovey & Mayer (1990); Schutte
et al. (1998); Ciarrochi et al., (2001); Ciarrochi et al., (2002) i.e. perception of
emotions, managing own emotions, managing others’ emotions and utilization of
emotions have been included as predictor variables.
5.2 INTRODUCTION
Dimensions of OCB and EI have been considered for the purpose of analysis.
Apart from analyzing these constructs as a collective scale, dimensional analysis of
these constructs will throw further light on the underlying relationship between these
variables. Dimensional analysis can reveal the relative importance of these
dimensions included in the study and thus help to understand the organizational
citizenship behaviour of the respondents in a better way. Some of the constructs have
to be studied as an overall scale in order to make decisions relating to the recruitment
Jiwan Jyoti Maini, PhD Thesis Chapter 5
150
and selection; while other constructs have to be broken down into sub-dimensions to
understand their core structure and association with other variables. It will thus be
instrumental in creating better understanding about the nitty-gritty of employee
behaviour at the workplace.
5.3 DATA ANALYSIS
This chapter focuses on dimensional analysis of organizational citizenship
behaviour and emotional intelligence. As there are multiple criterion variables (OCB)
and multiple predictor variables (EI), canonical correlation analysis has been applied
by typing the syntax for MANOVA and Cancorr in SPSS v.18. In order to validate
the results obtained through canonical correlation analysis, sensitivity analysis has
also been carried out by removing one predictor variable at a time and the same
process is further repeated by removing one criterion variable at a time. To further
Perception of
Emotions
Managing Own
Emotions
Managing Others’
Emotions
Utilization of
Emotions
Altruism
Sportsmanship
Civic Virtue
Courtesy
Conscientious
-ness Emotional
Intelligence
OCB
Variables
FIGURE 5.1:
Proposed Research Model
Jiwan Jyoti Maini, PhD Thesis Chapter 5
151
support the results, hierarchical regression is carried out by controlling the effect of
demographic variables. The proposed research model has been included in Figure 5.1
and this model is to be tested and examined by the stated statistical tools.
5.4 RESULTS
The objective of the study is to assess the relationship between two variable
sets as per Figure 5.1. Prior to conducting the canonical correlation analysis, the
assumptions related to its testing are satisfied. The basic conditions that the data
should be free from multi-collinearity and assumption of homoscedasticity have been
ensured before conducting the analysis. Though several variables are related, no
severe problems of multi-collinearity are indicated as none of the related variables
exceeded the value of 0.60 (Table 4.4) for the correlation coefficients among the sets
of predictor and criterion variables.
It lays down a preliminary foundation for the testing of relationships between
dimensions of EI and OCB. Canonical correlation analysis is a multivariate statistical
model that facilitates the study of inter-relationships among multiple criterion
variables and multiple predictor variables. Initially, the proposed research model in
Figure 5.1 is examined through four predictor variables and five criterion variables.
A canonical correlation analysis has been conducted using four EI dimensions (POE,
MOE, MOtE, UOE) as predictors of the five outcome variables (altruism,
sportsmanship, conscientiousness, courtesy and civic virtue) to evaluate the
multivariate shared relationship among the two sets of variables. The analysis yielded
four functions, of which only first has been found to be statistically significant.
Jiwan Jyoti Maini, PhD Thesis Chapter 5
152
Table 5.1: Analysis of Canonical Correlation Results
Results with all
variables
Sensitivity Analysis
Results after removal/deletion of
POE MOE MOtE UOE
Canonical
Correlation (RC)
.566 .555 .539 .539 .518
Explained
Varaince (Rc)2
.320 .308 .290 .290 .268
Eigen Value .470 .444 .409 .409 .366
Wilks .657 .669 .695 .698 .720
F statistic 5.394 6.978 6.282 6.203 5.642
P value .000 .000 .000 .000 .000
Criterion
variables
Canonical
Loadings
Canonical
Cross
Loadings
Canonical Loadings
Altruism .491 .278 .490 .507 .540 .405
Sportsmanship .697 .394 .699 .697 .676 .713
Conscientiousness .517 .292 .517 .475 .570 .506
Courtesy .582 .329 .579 .583 .596 .557
Civic Virtue .494 .283 .494 .480 .503 .500
Shared Variance .315 .315 .308 .336 .298
Redundancy
Index
.101 .097 .089 .097 .080
Predictor
variables
Canonical
Loadings
Canonical
Cross
Loadings
Canonical Loadings
POE .487 .275 NA .507 .509 .534
MOE .764 .432 .779 NA .804 .838
MOtE .690 .390 .704 .727 NA .759
UOE .786 .445 .801 .829 .828 NA
Shared Variance .478 .582 .491 .530 .521
Redundancy
Index
.153 .179 .143 .154 .139
Note: N= 250, POE = Perception of Emotions, MOE= Managing Own Emotions,
MOtE = Managing Others’ Emotions, UOE = Utilization of Emotions
Collectively the full model in Table 5.1, is statistically significant for
canonical function, using the Wilks λ =0.66, F (20, 800.26) = 5.39, p < 0.001. As
Wilks λ represents the variance unexplained by the model, 1- λ yields the full model
effect size in r2 metric. Thus, for the first canonical function, the r
2 type effect size is
0.34, which indicates that the full model explained a substantial portion of about 34 %
Jiwan Jyoti Maini, PhD Thesis Chapter 5
153
of the variance shared between the canonical variates, which are sets of criterion and
predictor variables.
Canonical loadings also known as canonical structure correlations, and cross
loadings have been used in this analysis as they are superior to canonical weights,
which are criticized because of their instability (Hair, Anderson, Tatham, & Black,
1998). Canonical loadings reflect the variance that the observed variable shares with
the canonical variate (latent variable). Thus, it computes within-set-variable-to-variate
correlation. The larger the coefficient, the more it contributes in deriving the
canonical variate. The values of multivariate tests (Hotelling’s trace = .504 with
F(20,958) = 6.04 (p < .001), Pillai’s criterion = .35 with F (20,976) = 4.73 (p < .001),
and Roys gcr = .320) show that canonical correlation analysis is significant in the
present analysis.
Table 5.1 lists significant positive canonical correlation with RC = 0.57, p <
0.001.Therefore, hypotheses 17 and 18 are supported that there is a positive
relationship between the predictor and criterion variables. Sportsmanship and
courtesy have the highest canonical loadings of 0.70 and 0.58 respectively among the
criterion variables. UOE and MOE have the largest canonical loadings of 0.79 and
0.76 respectively for the predictor canonical variate. Both altruism and civic virtue
have the lowest & same canonical loadings (0.49 and 0.49), as well as same cross
loadings (0.28 and 0.28) among criterion variable set. POE has the lowest canonical
loading of 0.49 and cross loading of 0.28 among predictor variable set. Moreover,
canonical cross loadings have also been considered to interpret the results which
correlate each of the original observed criterion variables directly with the predictor
Jiwan Jyoti Maini, PhD Thesis Chapter 5
154
canonical variate (latent variable), and vice-versa. The cross loadings of criterion
variable from this analysis indicates that sportsmanship and courtesy have the largest
canonical cross loadings of 0.40 and 0.33 respectively. While the cross loadings of
predictor set revealed the highest loadings of 0.45 and 0.43 for UOE and MOE
respectively. Thus, the cross loadings reveal that the results are in parity with the
canonical loadings, for both the canonical variates as listed in Table 5.1.
Shared variance has been calculated separately for two canonical variates,
which shows the amount of variation in each of the criterion variables explained by
the criterion canonical variate and vice-versa. Shared variance for the criterion
canonical variate and predictor canonical variate are 0.32 and 0.48 respectively. The
redundancy index listed in the Table 5.1 is 0.10 and 0.15 for the criterion and
predictor canonical variates respectively, which indicates the amount of mean
variance of the variable of one set that is explained by the other set.
Furthermore, to assess the stability of the canonical loadings and for the
validation of the results, sensitivity analysis is conducted by deleting one individual
predictor variable at a time from the analysis. As per the sensitivity analysis in Table
5.1, the canonical loadings in the present study are remarkably stable and consistent
in each of the four cases, whenever a predictor variable is deleted. The overall
canonical correlation also remained stable ranging from 0.52 to 0.56 depicting a fair
degree of stability.
Jiwan Jyoti Maini, PhD Thesis Chapter 5
155
Table 5.2: Analysis of Canonical Correlation Results
Results with all
variables
Sensitivity Analysis
Results after removal/deletion of
ALT SPS CONS COUR CV
Canonical
Correlation (RC)
.566 .564 .396 .565 .523 .541
Explained
Varaince (Rc)2
.320 .318 .156 .320 .273 .292
Eigen Value .470 .467 .186 .469 .376 .413
Wilks .657 .669 .821 .668 .706 .686
F statistic 5.394 6.499 3.084 6.534 5.592 6.068
P value .000 .000 .000 .000 .000 .000
Criterion
variables
Canonical
Loadings
Canonical
Cross
Loadings
Canonical Loadings
Altruism .491 .278 NA .735 .492 .534 .520
Sportsmanship .697 .394 .699 NA .697 .751 .727
Conscientiousness .517 .292 .518 .762 NA .569 .544
Courtesy .582 .329 .582 .833 .582 NA .610
Civic Virtue .494 .283 .496 .701 .495 .537 NA
Shared Variance .315 .336 .576 .328 .365 .367
Redundancy
Index
.101 .107 .090 .105 .099 .107
Predictor
variables
Canonical
Loadings
Canonical
Cross
Loadings
Canonical Loadings
POE .487 .275 .490 .480 .488 .490 .482
MOE .764 .432 .769 .770 .767 .792 .761
MOtE .690 .390 .695 .570 .687 .659 .671
UOE .786 .445 .777 .843 .786 .782 .803
Shared Variance .478 .479 .465 .479 .478 .477
Redundancy
Index
.153 .152 .073 .155 .131 .140
Note: N= 250, POE = Perception of Emotions, MOE= Managing Own Emotions, MOtE =
Managing Others’ Emotions, UOE = Utilization of Emotions, ALT=Altruism,
SPS=Sportsmanship, CONS = Conscientiousness, COUR=Courtesy, CV= Civic Virtue
The sensitivity analysis has also been conducted by removal of one individual
criterion variable (Table 5.2) at a time, to validate the results obtained through
canonical correlation analysis. The results of canonical correlation (RC) by removal of
altruism, sportsmanship, conscientiousness, courtesy and civic virtue are 0.56, 0.40,
Jiwan Jyoti Maini, PhD Thesis Chapter 5
156
0.57, 0.52 and 0.54 respectively. These values are also remarkably stable, except for
the Rc when sportsmanship variable has been dropped from the analysis, canonical
correlation plummeted to 0.40, a drop of nearly 0.17. It implies that in the present
analysis, sportsmanship is a key outcome variable.
The relationship between these variables is also explored through hierarchical
regression model. In the first step, control variables are entered and in the second
step, predictor variable is entered. As per the demographic analysis of these variables
explained in chapter 3, control variables included in step one are designation,
education level, age, annual income and experience. Other variables like gender,
marital status, number of dependents are excluded as their impact is found to be non-
significant.
5.4.1 Altruism & Perception of Emotions
Hypothesis 18a: Perception of emotions has a significant influence on altruism after
controlling for demographic variables.
Table 5.3 displays the result of the hierarchical regression analysis used to test
hypothesis 18a. In this analysis, the control variables designation, education level,
experience, age and income are entered in step 1. Among the demographic variables,
in the 1st step, income has the highest positive significant beta (β =.32, p ˂ .01) while
experience has a significant negative beta (β = -.40, p ˂ .01).
In Step 2, perception of emotions is regressed on altruism. According to the
results, perception of emotions is found to be a positive and significant predictor for
the altruism (β =.10, p ˂ .10), which explains incremental variance of 1 % beyond
Jiwan Jyoti Maini, PhD Thesis Chapter 5
157
that is explained by the control variables (∆R2 = .01, p ˂ .10). Therefore, hypothesis
18a is supported, as perception of emotions is associated with an increase in altruism.
Table 5.3: Hierarchical Regression Results for the Association of Perception of
Emotions with Altruism
Predictor Altruism
Step 1 Step 2
β se β se
Step 1: Control Variables
Designation -.147 .051
Education Level -.045 .049
Experience -.398* .014
Age .193 .015
Income .318*** .083
Step 2:
Model F
Overall R2
R2 Change
Perception of Emotions .102* .102
3.125*** 3.074***
.060*** .071*
.010*
Notes: N = 250. Standardized beta coefficients are for the full model.
*p ˂ .10, ** p ˂ .05, ***p ˂ .01
5.4.2 Altruism & Managing Own Emotions
Hypothesis 18b: Managing own emotions have a significant influence on altruism
after controlling for demographic variables.
Table 5.4 displays the result of the hierarchical regression analysis used to test
hypothesis 18b. In this analysis, the control variables designation, education level,
experience, age and income were entered in step 1.In step 2, managing own emotion
is regressed on altruism. According to the results, managing own emotions are found
Jiwan Jyoti Maini, PhD Thesis Chapter 5
158
to be a positive and significant predictor for the altruism (β =.19, p ˂ .01), and
explains significant incremental variance of 3% beyond that is explained by the
control variables (∆R2 = .03, p ˂ .01).
Table 5.4: Hierarchical Regression Results for the Association of Managing Own
Emotions with Altruism
Predictor Altruism
Step 1 Step 2
β se β se
Step 1: Control Variables
Designation -.147 .051
Education Level -.045 .049
Experience -.398* .014
Age .193 .015
Income .318*** .083
Step 2:
Model F
Overall R2
R2
Change
Managing Own Emotions .186*** .108
3.125*** 4.182***
.060*** .094***
.033***
Notes: N = 250. Standardized beta coefficients are for the full model.
*p ˂ .10, * p ˂ .05, ***p ˂ .01
Therefore, hypothesis 18b is supported, as managing own emotion is
associated with an increase in altruism. Even though the contribution of 3% is very
less, but it is statistically significant.
5.4.3 Altruism & Managing Others’ Emotions
Hypothesis 18c: Managing others’ emotions have a significant influence on altruism
after controlling for demographic variables.
Jiwan Jyoti Maini, PhD Thesis Chapter 5
159
Table 5.5: Hierarchical Regression Results for the Association of Managing
Others’ Emotions with Altruism
Predictor Altruism
Step 1 Step 2
β se β se
Step 1: Control Variables
Designation -.147 .051
Education Level -.045 .049
Experience -.398* .014
Age .193 .015
Income .318*** .083
Step 2:
Model F
Overall R2
R2 Change
Managing Others’ Emotions .118*** .114
3.125*** 3.231***
.060*** .074**
.014***
Notes: N = 250. Standardized beta coefficients are for the full model.
*p ˂ .10, ** p ˂ .05, ***p ˂ .01
Table 5.5 displays the result of the hierarchical regression analysis applied to
test hypothesis 18c. In this analysis, the control variables designation, education level,
experience, age and income are entered in step 1.In step 2, managing others’ emotions
have been regressed on altruism.
According to the results, managing others’ emotion is found to be a positive
and significant predictor for the altruism (β =.12, p ˂ .01), which explains incremental
variance, beyond that is explained by the control variables (∆R2= .01, p ˂ .01).
Therefore, hypothesis 18c is supported, as managing others’ emotions are associated
with an increase in altruism.
Jiwan Jyoti Maini, PhD Thesis Chapter 5
160
5.4.4 Altruism & Utilization of Emotions
Hypothesis 18d: Utilization of emotions has a significant influence on altruism after
controlling for demographic variables.
Table 5.6 displays the result of the hierarchical regression analysis used to
examine hypothesis 18d. In this analysis, the control variables designation, education
level, experience, age and income are entered in step 1. In step 2, utilization of
emotions has been regressed on altruism.
Table 5.6: Hierarchical Regression Results for the Association of Utilization of
Emotions with Altruism
Predictor Altruism
Step 1 Step 2
β se β se
Step 1: Control Variables
Designation -.147 .051
Education Level -.045 .049
Experience -.398* .014
Age .193 .015
Income .318*** .083
Step 2:
Model F
Overall R2
R2 Change
Utilization of Emotions .286*** .083
3.125*** 6.471***
.060*** .138**
.078***
Notes: N = 250. Standardized beta coefficients are for the full model.
*p ˂ .10, ** p ˂ .05, ***p ˂ .01
According to the results, utilization of emotions is found to be a positive and
significant predictor for the altruism (β =.29, p ˂ .01), and explains incremental
Jiwan Jyoti Maini, PhD Thesis Chapter 5
161
variance beyond that is explained by the control variables (∆R2= .08, p ˂ .01). Hence,
hypothesis 18d is supported, as utilization of emotions is associated with an increase
in altruism. Utilization of emotions (β =.29, p <. 01) emerges as the important
dimension, out of the four dimensions of emotional intelligence, which have been
regressed to predict altruism. So, it implies that it contributes maximum among its
various dimensions.
5.4.5 Sportsmanship & Perception of Emotions
Hypothesis 18e: Perception of emotions has a significant influence on sportsmanship
after controlling for demographic variables.
Table 5.7: Hierarchical Regression Results for the Association of Perception of
Emotions with Sportsmanship
Predictor Sportsmanship
Step 1 Step 2
β se β se
Step 1: Control Variables
Designation .005 .070
Education Level .047 .067
Experience -.332 .020
Age .300 .020
Income -.076 .115
Step 2:
Model F
Overall R2
R2 Change
Perception of Emotions .190*** .138
.735 2.153**
.015 .050***
.036***
Notes: N = 250. Standardized beta coefficients are for the full model.
*p ˂ .10, ** p ˂ .05, ***p ˂ .01
Jiwan Jyoti Maini, PhD Thesis Chapter 5
162
Table 5.7 displays the result of the hierarchical regression analysis used to
investigate hypothesis 18e. In this analysis, the control variables designation,
education level, experience, age and income are entered in step 1. In step 2,
perception of emotion is regressed on sportsmanship. Demographic variables entered
in step 1 have not made any worthwhile contribution towards the prediction of
sportsmanship. The overall R2 value of model 1 is statistically insignificant.
According to the results, perception of emotion is found to be a positive and
significant predictor for the sportsmanship (β = .19, p ˂ .01), and explain incremental
variance beyond that is explained by the control variables (∆R2
= .04, p ˂ .01).
Therefore, hypothesis 18e is supported, as perception of emotion is associated with an
increase in sportsmanship. In this model, control variables have a very minute
contribution of 1.5% that is statistically non-significant.
5.4.6 Sportsmanship & Managing Own Emotions
Hypothesis 18f: Managing own emotions have a significant influence on
sportsmanship after controlling for demographic variables.
Table 5.8 displays the result of the hierarchical regression analysis used to test
hypothesis 18f. In this analysis, the control variables designation, education level,
experience, age and income are entered in step 1. In step 2, managing own emotion is
regressed on sportsmanship. According to the results, managing own emotion is
found to be a positive and significant predictor for the sportsmanship (β = .30, p ˂
.01), which explains incremental variance of 9% beyond that is explained by the
control variables (∆R2
= .09, p ˂ .01). Therefore, hypothesis 18f is supported, as
managing own emotion is associated with an increase in sportsmanship.
Jiwan Jyoti Maini, PhD Thesis Chapter 5
163
Table 5.8: Hierarchical Regression Results for the Association of Managing Own
Emotions with Sportsmanship
Predictor Sportsmanship
Step 1 Step 2
β se β se
Step 1: Control Variables
Designation .005 .070
Education Level .047 .067
Experience -.332 .020
Age .300 .020
Income -.076 .115
Step 2:
Model F
Overall R2
R2 Change
Managing Own Emotions .303*** .145
.735 4.673***
.015 .103***
.089***
Notes: N = 250. Standardized beta coefficients are for the full model.
*p ˂ .10, ** p ˂ .05, ***p ˂ .01
5.4.7 Sportsmanship & Managing Others’ Emotions
Hypothesis 18g: Managing others’ emotions have a significant influence on
sportsmanship after controlling for demographic variables.
Table 5.9 displays the result of the hierarchical regression analysis used to test
hypothesis 18g. In this analysis, the control variables designation, education level,
experience, age and income are entered in step 1. In step 2, managing others’
emotions have been regressed on sportsmanship. According to the results, managing
others’ emotion have been found to be a positive and significant predictor for the
sportsmanship (β =.29, p ˂ .01), and explains unique incremental variance of 8.3%
beyond that is explained by the control variables (∆R2= .08, p ˂ .01). Managing
Jiwan Jyoti Maini, PhD Thesis Chapter 5
164
others’ emotions appears out to be a significant dimension for prediction of
sportsmanship.
Table 5.9: Hierarchical Regression Results for the Association of Managing
Others’ Emotions with Sportsmanship
Predictor Sportsmanship
Step 1 Step 2
β se β se
Step 1: Control Variables
Designation .005 .070
Education Level .047 .067
Experience -.332 .020
Age .300 .020
Income -.076 .115
Step 2:
Model F
Overall R2
R2 Change
Managing Others’ Emotions .290*** .151
.735 4.394***
.015 .098***
.083***
Notes: N = 250, standardized beta coefficients are for the full model.
*p ˂ .10, ** p ˂ .05, ***p ˂ .01
Therefore, hypothesis 18g is supported, as managing others’ emotions are associated
with an increase in sportsmanship.
5.4.8 Sportsmanship & Utilization of Emotions
Hypothesis 18h: Utilization of emotions has a significant influence on sportsmanship
after controlling for demographic variables.
Table 5.10 displays the results of the hierarchical regression analysis used to
test hypothesis 18h. In this analysis, the control variables designation, education
Jiwan Jyoti Maini, PhD Thesis Chapter 5
165
level, experience, age and income are entered in step 1. In step 2, utilization of
emotions is regressed on sportsmanship. According to the results, utilization of
emotion is found to be a positive and significant predictor for the sportsmanship (β =
.30, p ˂ .01), which explains incremental variance of 8.4% beyond that is explained
by the control variables (∆R2
= .08, p ˂ .01). Therefore, hypothesis 18h is supported,
as utilization of emotions is associated with a raise in sportsmanship.
Table 5.10: Hierarchical Regression Results for the Association of Utilization of
Emotions with Sportsmanship
Predictor Sportsmanship
Step 1 Step 2
β se β se
Step 1: Control Variables
Designation .005 .070
Education Level .047 .067
Experience -.332 .020
Age .300 .020
Income -.076 .115
Step 2:
Model F
Overall R2
R2 Change
Utilization of Emotions .298*** .113
.735 4.458***
.015 .099***
.084***
Notes: N = 250. Standardized beta coefficients are for the full model.
*p ˂ .10, ** p ˂ .05, ***p ˂ .01
Utilization of emotions, managing own emotions and managing others’
emotions have been found to be important predictor variables in predicting
sportsmanship; explaining 8%, 9% and 8% of the variance respectively. However,
Jiwan Jyoti Maini, PhD Thesis Chapter 5
166
POE is found to be least significant in prediction of sportsmanship with a variance of
3% only.
5.4.9 Conscientiousness & Perception of Emotions
Hypothesis 18i: Perception of emotions has a significant influence on
conscientiousness after controlling for demographic variables.
Table 5.11: Hierarchical Regression Results for the Association of Perception of
Emotions with Conscientiousness
Predictor Conscientiousness
Step 1 Step 2
β se β se
Step 1: Control Variables
Designation -.094 .045
Education Level .153** .043
Experience .564*** .012
Age -.595*** .013
Income -.259** .073
Step 2:
Model F
Overall R2
R2 Change
Perception of Emotions .136* .089
6.058*** 5.989***
.110*** .129**
.019**
Notes: N = 250. Standardized beta coefficients are for the full model.
*p ˂ .10, ** p ˂ .05, ***p ˂ .01
Table 5.11 displays the result of the hierarchical regression analysis used to
verify hypothesis 18i. In this analysis, the control variables designation, education
level, experience, age and income are entered in step 1. In step 2, perception of
emotions is regressed on conscientiousness. Demographic variables which are entered
Jiwan Jyoti Maini, PhD Thesis Chapter 5
167
in the 1st step show significant beta for age, experience, and income and education
level. Together it explains 11% of significant variance in the model 1.
According to the results, perception of emotion is found to be a positive and
significant predictor for the conscientiousness (β = .14, p ˂ .05), and explain a
minute incremental variance (∆R2
= .02, p ˂ .05) beyond that is explained by the
control variables. Therefore, hypothesis 18i is supported, as perception of emotion is
associated with an increase in conscientiousness. In this model, apparently control
variables have made a worthwhile contribution of 11% (p ˂ .01). Henceforth,
including these control variables in the model helped to explain the variables in a
better way.
5.4.10 Conscientiousness & Managing Own Emotions
Hypothesis 18j: Managing own emotions have a significant influence on
conscientiousness after controlling for demographic variables.
Table 5.12 displays the result of the hierarchical regression analysis to test
hypothesis 18j. In this analysis, the control variables designation, education level,
experience, age and income are entered in step 1. In step 2, managing own emotions
is regressed on conscientiousness.
According to the results, managing own emotion is found to be a positive and
significant predictor for the conscientiousness (β =.26, p ˂ .05), and explains
incremental variance of 7% beyond that is explained by the control variables (∆R2 =
.07, p ˂ .01). Therefore, hypothesis 18j is supported, as managing own emotion is
associated with a raise in conscientiousness.
Jiwan Jyoti Maini, PhD Thesis Chapter 5
168
Table 5.12: Hierarchical Regression Results for the Association of Managing
Own Emotions with Conscientiousness
Predictor Conscientiousness
Step 1 Step 2
β se β se
Step 1: Control Variables
Designation -.094 .045
Education Level .153** .043
Experience .564*** .012
Age -.595*** .013
Income -.259** .073
Step 2:
Model F
Overall R2
R2 Change
Managing Own Emotions .258** .093
6.058*** 8.574***
.110*** .175***
.065***
Notes: N = 250. Standardized beta coefficients are for the full model.
*p ˂ .10, ** p ˂ .05, ***p ˂ .01
5.4.11 Conscientiousness & Managing Others’ Emotions
Hypothesis 18k: Managing others’ emotions have a significant influence on
conscientiousness after controlling for demographic variables.
Table 5.13 displays the result of the hierarchical regression analysis used to
test hypothesis 18k. In this analysis, the control variables designation, education
level, experience, age and income are entered in step 1. In step 2, managing others’
emotions are regressed on conscientiousness.
According to the results, managing others’ emotions are found to be a positive
and significant predictor for the conscientiousness (β =.14, p ˂ .01), which explains a
Jiwan Jyoti Maini, PhD Thesis Chapter 5
169
quite minute incremental variance of 2% beyond that is explained by the control
variables (∆R2= .02, p˂ .01). Therefore, hypothesis 18k is supported, as managing
others’ emotions are associated with an increase in conscientiousness.
Table 5.13: Hierarchical Regression Results for the Association of Managing
Others’ Emotions with Conscientiousness
Predictor Conscientiousness
Step 1 Step 2
β se β se
Step 1: Control Variables
Designation -.094 .045
Education Level .153** .043
Experience .564*** .012
Age -.595*** .013
Income -.259** .073
Step 2:
Model F
Overall R2
R2 Change
Managing Others’ Emotions .144*** .099
6.058*** 6.108***
.110*** .131***
.021***
Notes: N = 250. Standardized beta coefficients are for the full model.
*p ˂ .10, ** p ˂ .05, ***p ˂ .01
5.4.12 Conscientiousness & Utilization of Emotions
Hypothesis 18l: Utilization of emotions has a significant influence on
conscientiousness after controlling for demographic variables.
Table 5.14 displays the results of the hierarchical regression analysis used to
test hypothesis 18l. In this analysis, the control variables designation, education level,
experience, age and income are entered in step 1. In step 2, utilization of emotions is
Jiwan Jyoti Maini, PhD Thesis Chapter 5
170
regressed on conscientiousness. According to the results, utilization of emotion is
found to be a positive and significant predictor for the conscientiousness (β =.28, p ˂
.05), and explains incremental variance beyond that is explained by the control
variables (∆R2 = .08, p ˂ .01).
Table 5.14: Hierarchical Regression Results for the Association of Utilization of
Emotions with Conscientiousness
Predictor Conscientiousness
Step 1 Step 2
β se β se
Step 1: Control Variables
Designation -.094 .045
Education Level .153* .043
Experience .564*** .012
Age -.595*** .013
Income -.259** .073
Step 2:
Model F
Overall R2
R2 Change
Utilization of Emotions .284** .072
6.058*** 9.320***
.110*** .187***
.077***
Notes: N = 250. Standardized beta coefficients are for the full model.
*p ˂ .10, **p ˂ .05, ***p ˂ .01
Therefore, hypothesis 18l is supported, as utilization of emotion is associated
with an increase in conscientiousness. Managing own emotions and utilization of
emotions dimension explain 6.5% and 8% of the unique variance respectively for the
prediction of conscientiousness. While the contribution of perception of emotions and
Jiwan Jyoti Maini, PhD Thesis Chapter 5
171
managing others’ emotions have been quite minuscule (∆R2
= .02, p ˂ .01) in the
prediction of conscientiousness.
5.4.13 Courtesy & Perception of Emotions
Hypothesis 18m: Perception of emotions has a significant influence on courtesy after
controlling for demographic variables.
Table 5.15: Hierarchical Regression Results for the Association of Perception of
Emotions with Courtesy
Predictor Courtesy
Step 1 Step 2
β se β se
Step 1: Control Variables
Designation .074 .046
Education Level .092 .044
Experience .107 .013
Age - .189 .013
Income .119 .075
Step 2:
Model F
Overall R2
R2 Change
Perception of Emotions .142** .091
3.495*** 3.846***
.067*** .087**
.020**
Notes: N = 250. Standardized beta coefficients are for the full model.
*p ˂ .10, ** p ˂ .05, ***p ˂ .01
Table 5.15 displays the result of the hierarchical regression analysis used to
test hypothesis18m. In this analysis, the control variables designation, education
level, experience, age and income are entered in step 1. In step 2, perception of
emotions is regressed on courtesy. According to the results, perception of emotions is
Jiwan Jyoti Maini, PhD Thesis Chapter 5
172
found to be a positive and significant predictor for the courtesy (β =.14, p ˂ .05),
which explains quite a modest incremental variance beyond that is explained by the
control variables (∆R2= .02, p ˂ .05). Therefore, hypothesis 18m is supported, as
perception of emotions is associated with an improvement in courtesy. In the model
1, demographic variables explain 7% (p ˂ .01) of the variance, whereas there is a
minute improvement of 2% has been there in model 2 by inclusion of perception of
emotions dimension.
5.4.14 Courtesy & Managing Own Emotions
Hypothesis 18n: Managing own emotions have a significant influence on courtesy
after controlling for demographic variables.
Table 5.16: Hierarchical Regression Results for the Association of Managing
Own Emotions with Courtesy
Predictor Courtesy
Step 1 Step 2
β se β se
Step 1: Control Variables
Designation .074 .046
Education Level .092 .044
Experience .107 .013
Age - .189 .013
Income .119 .075
Step 2:
Model F
Overall R2
R2 Change
Managing Own Emotions .242*** .096
3.495*** 5.693***
.067*** .123***
.056***
Notes: N = 250. Standardized beta coefficients are for the full model.
*p ˂ .10, ** p ˂ .05, ***p ˂ .01
Jiwan Jyoti Maini, PhD Thesis Chapter 5
173
Table 5.16 displays the results of the hierarchical regression analysis used to
test hypothesis 18n. In this analysis, the control variables designation, education
level, experience, age and income are entered in step 1. In step 2, managing own
emotions are regressed on courtesy.
According to the results, managing own emotions are found to be a positive
and significant predictor for the courtesy (β =.24, p ˂ .01), which explained
incremental variance of 6% beyond that is explained by the control variables (∆R2=
.06, p ˂ .01). Therefore, hypothesis 18n is supported, as managing own emotions are
associated with an increase in courtesy.
5.4.15 Courtesy & Managing Others’ Emotions
Hypothesis 18o: Managing others’ emotions have a significant influence on courtesy
after controlling for demographic variables.
Table 5.17 displays the results of the hierarchical regression analysis used to
test hypothesis 18o. In this analysis, the control variables designation, education
level, experience, age and income are entered in step 1. In step 2, managing others’
emotions have been regressed on courtesy. Demographic variables collectively
explain 7% (p < .01) of the variance.
According to the results, managing others’ emotions are found to be a positive
and significant predictor for the courtesy (β =.21, p ˂ .01), which explains unique
incremental variance beyond that is explained by the control variables (∆R2= .04, p ˂
.01). Therefore, hypothesis 18o is supported, as managing others’ emotions are
associated with an increase in courtesy.
Jiwan Jyoti Maini, PhD Thesis Chapter 5
174
Table 5.17: Hierarchical Regression Results for the Association of Managing
Others’ Emotions with Courtesy
Predictor Courtesy
Step 1 Step 2
β se β se
Step 1: Control Variables
Designation .074 .046
Education Level .092 .044
Experience .107 .013
Age - .189 .013
Income .119 .075
Step 2:
Model F
Overall R2
R2 Change
Managing Others’ Emotions .205*** .101
3.495*** 4.923***
.067*** .108***
.042***
Notes: N = 250. Standardized beta coefficients are for the full model.
*p ˂ .10, ** p ˂ .05, ***p ˂ .01
5.4.16 Courtesy & Utilization of Emotions
Hypothesis 18p: Utilization of emotions has a significant influence on courtesy after
controlling for demographic variables.
Table 5.18 displays the result of the hierarchical regression analysis used to
test hypothesis 18p. In this analysis, the control variables designation, education
level, experience, age and income are entered in step 1. In step 2, utilization of
emotions is regressed on courtesy.
According to the results, utilization of emotions is found to be a positive and
significant predictor for the courtesy (β =.32, p ˂ .01), which explains unique
Jiwan Jyoti Maini, PhD Thesis Chapter 5
175
incremental variance beyond that is explained by the control variables (∆R2
= .10, p ˂
.01). Therefore, hypothesis 18p is supported, as utilization of emotions is associated
with an increase in courtesy. So far, utilization of emotions has explained the
maximum variance of 10% over and above the variance explained by control
variables.
Table 5.18: Hierarchical Regression Results for the Association of Utilization of
Emotions with Courtesy
Predictor Courtesy
Step 1 Step 2
β se β se
Step 1: Control Variables
Designation .074 .046
Education Level .092 .044
Experience .107 .013
Age - .189 .013
Income .119 .075
Step 2:
Model F
Overall R2
R2 Change
Utilization of Emotions .320*** .073
3.495*** 7.937***
.067*** .164***
.097***
Notes: N = 250. Standardized beta coefficients are for the full model.
*p ˂ .10, ** p ˂ .05, ***p ˂ .01
Exploration for prediction of courtesy reveals that utilization of emotions is
quite an important dimension, explaining nearly 10% of variance in model 2. Next
important dimension for the prediction of courtesy, emerges out to be managing own
emotions, making an improvement of 6% in the previous model.
Jiwan Jyoti Maini, PhD Thesis Chapter 5
176
5.4.17 Civic Virtue & Perception of Emotions
Hypothesis 18q: Perception of emotions has a significant influence on civic virtue
after controlling for demographic variables.
Table 5.19 displays the result of the hierarchical regression analysis used to
test hypothesis 18q. In this analysis, the control variables designation, education
level, experience, age and income are entered in step 1. In step 2, perception of
emotions is regressed on civic virtue.
Table 5.19: Hierarchical Regression Results for the Association of Perception of
Emotions with Civic Virtue
Predictor Civic Virtue
Step 1 Step 2
β se β se
Step 1: Control Variables
Designation .087 .050
Education Level .029 .047
Experience .298 .014
Age -.340 .014
Income .023 .081
Step 2:
Model F
Overall R2
R2 Change
Perception of Emotions .139** .099
1.293 1.902*
.026 .045**
.019**
Notes: N = 250. Standardized beta coefficients are for the full model.
*p ˂ .10, ** p ˂ .05, ***p ˂ .01
According to the results, perception of emotions is found to be a positive and
significant predictor for the civic virtue (β =.14, p ˂ .05), which explains minute
Jiwan Jyoti Maini, PhD Thesis Chapter 5
177
incremental variance beyond that explained by the control variables (∆R2 = .02, p ˂
.05). Therefore, hypothesis 18q is supported, as perception of emotions is associated
with an increase in civic virtue. Demographic variables don’t contribute much in this
model.
5.4.18 Civic Virtue & Managing Own Emotions
Hypothesis 18r: Managing own emotions has significant influence on civic virtue
after controlling for demographic variables.
Table 5.20: Hierarchical Regression Results for the Association of Managing
Own Emotions with Civic Virtue
Predictor Civic Virtue
Step 1 Step 2
β se β se
Step 1: Control Variables
Designation .087 .050
Education Level .029 .047
Experience .298 .014
Age -.340 .014
Income .023 .081
Step 2:
Model F
Overall R2
R2
Change
Managing Own Emotions .239*** .1.043
1.293 3.576***
.026 .081***
.055***
Notes: N = 250. Standardized beta coefficients are for the full model.
*p ˂ .10, ** p ˂ .05, ***p ˂ .01
Table 5.20 displays the result of the hierarchical regression analysis used to
test hypothesis 18r. In this analysis, the control variables designation, education level,
Jiwan Jyoti Maini, PhD Thesis Chapter 5
178
experience, age and income were entered in step 1. In step 2, managing own emotions
are regressed on civic virtue. According to the results, managing own emotions are
found to be a positive and significant predictor for the civic virtue (β = .24, p ˂ .01),
which explains unique incremental variance beyond that is explained by the control
variables (∆R2
= .06, p ˂ .01). Therefore, hypothesis 18r is supported, as managing
own emotions are associated with an increase in civic virtue.
5.4.19 Civic Virtue & Managing Others’ Emotions
Hypothesis 18s: Managing others’ emotions has significant influence on civic virtue
after controlling for demographic variables.
Table 5.21: Hierarchical Regression Results for the Association of Managing
Others’ Emotions with Civic Virtue
Predictor Civic Virtue
Step 1 Step 2
β se β se
Step 1: Control Variables
Designation .087 .050
Education Level .029 .047
Experience .298 .014
Age -.340 .014
Income .023 .081
Step 2:
Model F
Overall R2
R2 Change
Managing Others’ Emotions .184*** .1.110
1.293 2.546**
.026 .059***
.033***
Notes: N = 250. Standardized beta coefficients are for the full model.
*p ˂ .10, ** p ˂ .05, ***p ˂ .01
Jiwan Jyoti Maini, PhD Thesis Chapter 5
179
Table 5.21 displays the result of the hierarchical regression analysis used to
test hypothesis 18s. In this analysis, the control variables designation, education level,
experience, age and income were entered in step 1. In step 2, managing others’
emotions have been regressed on civic virtue.
According to the results, managing others’ emotions are found to be a positive
and significant predictor for the civic virtue (β = .18, p ˂ .01), which explained
unique incremental variance beyond that is explained by the control variables (∆R2
=
.03, p ˂ .01). Therefore, hypothesis 18s is supported, as managing others’ emotions
are associated with an increase in civic virtue.
5.4.20 Civic Virtue & Utilization of Emotions
Hypothesis 18t: Utilization of emotions has significant influence on civic virtue after
controlling for demographic variables.
Table 5.22 displays the results of the hierarchical regression analysis to
examine hypothesis 18t. In this analysis, the control variables designation, education
level, experience, age and income are entered in step 1. In step 2, utilization of
emotions has been regressed on civic virtue.
According to the results, utilization of emotions have been found to be a
positive and significant predictor for the civic virtue (β =.25, p ˂ .01), which explains
unique incremental variance beyond that is explained by the control variables (∆R2=
.06, p ˂ .01). Therefore, hypothesis 18t is supported, as utilization of emotions is
associated with an increase in civic virtue.
Jiwan Jyoti Maini, PhD Thesis Chapter 5
180
Table 5.22: Hierarchical Regression Results for the Association of Utilization of
Emotions with Civic Virtue
Predictor Civic Virtue
Step 1 Step 2
β se β se
Step 1: Control Variables
Designation .087 .050
Education Level .029 .047
Experience .298 .014
Age -.340 .014
Income .023 .081
Step 2:
Model F
Overall R2
R2 Change
Utilization of Emotions .246*** .1.081
1.293 3.673**
.026 .083***
.057***
Notes: N = 250. Standardized beta coefficients are for the full model.
*p ˂ .10, ** p ˂ .05, ***p ˂ .01
Exploration for prediction of civic virtue revealed that EI dimensions have a
modest variance relating to it, whereby utilization of emotions is explaining 6% of the
variance followed by managing own emotions (5%), managing others’ emotions (3%)
and perception of emotions (2%). Overall this contribution is quite minuscule for
predicting civic virtue; it infers that there are many other variables contributing to it
which are not part of the current research initiative.
Summary of the hypotheses tested has been included in Table 5.23, which
briefs up about the results of the regression and canonical correlation analysis.
Jiwan Jyoti Maini, PhD Thesis Chapter 5
181
Table 5.23: Summary of Hypothesis Testing Hypothesis Results Outcome
17: There is a positive relationship among four
dimensions of EI (POE, MOE, MOtE, UOE) and five OCB Dimensions (Altruism, Sportsmanship,
Conscientiousness, Courtesy and Civic Virtue).
r = .505*** & RC = .57 Supported
18: POE, MOE, MOtE and UOE (dimensions of EI)
are the predictor variables for five dimensions of OCB.
RC = .57 Supported
18a: Perception of emotions has a significant
influence on altruism. β= .10*, ∆R
2 = .01* Supported
18b: Managing own emotions have a significant
influence on altruism. β= .19***
, ∆R
2 = .03*** Supported
18c: Managing others’ emotions have a significant influence on altruism.
β= .12***, ∆R2 = .01*** Supported
18d: Utilization of emotions has a significant
influence on altruism. β= .29***, ∆R
2 = .08*** Supported
18e: Perception of emotions has a significant influence on sportsmanship.
β= .19***, ∆R2 = .04*** Supported
18f: Managing own emotions have a significant
influence on sportsmanship. β= .30***, ∆R
2 = .09*** Supported
18g: Managing others’ emotions have significant influence on sportsmanship.
β= .29***, ∆R2 = .08*** Supported
18h: Utilization of emotions has a significant
influence on sportsmanship. β= .30***, ∆R
2 = .08*** Supported
18i: Perception of emotions has a significant influence on conscientiousness.
β= .14*, ∆R2 = .02** Supported
18j: Managing own emotions have a significant
influence on conscientiousness. β= .26**
, ∆R
2 = .07*** Supported
18k: Managing others’ emotions have a significant influence on conscientiousness.
β= .14***, ∆R2 = .02*** Supported
18l: Utilization of emotions has a significant
influence on conscientiousness. β= .28**, ∆R
2 = .08*** Supported
18m: Perception of emotions has a significant influence on courtesy.
β= .14**, ∆R2 = .02** Supported
18n: Managing own emotions have a significant
influence on courtesy. β= .24***, ∆R
2 = .06*** Supported
18o: Managing others’ emotions have a significant influence on courtesy.
β= .21***, ∆R2 = .04*** Supported
18p: Utilization of emotions has a significant
influence on courtesy. β= .32***
, ∆R
2 = .10*** Supported
18q: Perception of emotions has a significant influence on civic virtue.
β=.14***, ∆R2 =
.02** Supported
18r: Managing own emotions have a significant
influence on civic virtue. β=.24**, ∆R
2 = .05*** Supported
18s: Managing others’ emotions have a significant influence on civic virtue.
β=.18***, ∆R2 =
.03** Supported
18t: Utilization of emotions has a significant
influence on civic virtue. β=.25***, ∆R
2 =.06*** Supported
Jiwan Jyoti Maini, PhD Thesis Chapter 5
182
5.5 DISCUSSION
The major goal of this study is to examine the extent to which EI of the
participants augment their OCB. The results of canonical correlation analysis (Table
5.1 & 5.2) and hierarchical regression analysis support both the hypotheses 17 and 18
formulated for this research. The entire variables in the criterion set and the predictor
set are found to be positively related through preliminary support provided by
correlations in Table 4.4 and further by canonical correlation analysis alongwith
hierarchical regression. The canonical correlation obtained in the present analysis is
similar to what has been obtained by Cichy et al. (2009). Further studies can be taken
up to validate this relationship.
An important finding of this study is that dimensions of EI are really powerful
as predictors for OCB dimensions. Another finding to be seen from hierarchical
regression analysis is that UOE is found to have significant impact for prediction of
all OCB dimensions. This study advances the literature concerning to EI by
empirically demonstrating the impact of dimensions of EI on OCB of the participants.
This study countered the criticism of self-report measurements, by employing
evaluator ratings of OCB. That is, unlike the previous studies which relied on self-
report evaluation of these measures, this study employed the dyadic design to counter
the problem of common method variance.
It is found that in Indian scenario, sportsmanship emerges as the most
important dimension during the canonical correlation analysis, and this outcome is
further sustained by the results of the hierarchical regression analysis. This implies
that emotionally intelligent employees are not just the fair weather friends, but they
Jiwan Jyoti Maini, PhD Thesis Chapter 5
183
are ready to stand by the organization through the thick and thin. However, it entails
that emotionally intelligent employees in this sample is taking minor hardships or
inconveniences in their stride. The reason underlying this could be the impact of
Indian culture, in which Indian employees prefer more directive, task oriented style of
leadership (Ilangovan, Scroggins, & Rozell, 2007). All the items in the sportsmanship
scale are reverse coded; it might have caused the superiors to take a strong stand
against negatively worded statements. Hence, future studies can include some
positive statements to verify and augment the present results.
Directive leadership style in India breeds on the premise that the leader is like
a father figure and one has to obey him, not only without complaints but with respect
and dignity. In Indian culture, karma theory i.e., all actions that are done have the
power to ordain for their doers joy or sorrow in the future depending if the action is
good or bad (Mulla & Krishna, 2006). Moreover, the karma (action) theory says that
your good and bad karmas are going to rebound sooner or later. So, the participants in
this study is engaged in their karmas (actions) required for their jobs. Participants in
the present study showed second largest canonical loading for courtesy after
sportsmanship.
There has been quite appealing finding, that it is quite difficult to achieve
conscientious behaviour in self by managing others’ emotions (β = .14, ∆R2
= .02 p ˂
.01). It can better be achieved by managing own emotions (β = .26, ∆R2
= .07, p ˂
.05) which are under self-control. The results of regression analysis also supported
this notion. Furthermore, MOE dimension has contributed more than the MOtE in the
Jiwan Jyoti Maini, PhD Thesis Chapter 5
184
prediction of OCB dimensions, which is again supporting the view that managing
own emotions are under personal control than managing others’ emotions.
The dimension of UOE emerges as the most significant one having the largest
canonical loading and cross loading. It infers that it has the largest relation as an
individual variable with the canonical variate of the same set (EI dimensions) and
with that of the opposite canonical variate (OCB dimensions). Among the predictor
variables, the second vital dimension that surfaces, is MOE (as per the results of
Table 5.1, 5.2 and regression results). The dimension having least impact in the
present study is POE. The results reveal that UOE and MOE are powerful predictors
for the OCB dimensions.
Moreover, the status of the power plants has undergone change as earlier these
were under the control of the state government, and later on its status was changed to
that of a corporation. But the employees, who staged dharnas and opposed this
decision of the government, have later taken a soft stand for their organization which
is giving them more than the bread and butter. When the study was started, the
thermal power plants were under the control of the state government and data have
been collected from the employees after a year of such status conversion. From this
study, it seems that ultimately the employees have accepted the change without any
bickering.
The role of demographic variables for prediction of the intended variables has
been mixed. Designation is not at all significant as a control variable. For the
prediction of altruism, income is positively related (β = .32, p <. 01), while
experience has an inverse relationship ((β = -.40, p <. 01) contributing together 6% of
Jiwan Jyoti Maini, PhD Thesis Chapter 5
185
the variance. For the prediction of conscientiousness, education and experience are
positively associated and statistically significant. Age (β = -.60, p < .01) and income
(β = -.26, p < .05) are also found to be statistically significant for the prediction of
conscientiousness but has an inverse relationship. It entails that with the progression
in education level (β = .15, p < .05), and experience (β = .56, p < .01), there is
corresponding rise in conscientiousness i.e., a person is becoming more compliant
while following rules, punctual, not wasting resources or time. But on the other hand,
there is fall in conscientiousness with the progression in age hinting that this attitude
is more applicable to younger employees. However, these demographic variables are
almost insignificant for the prediction of sportsmanship, courtesy and civic virtue.
Overall, EI dimensions have a statistically significant relationship with OCB
dimensions. Not only a positive relationship is shared between the predictor and
criterion dimensions, it shares a substantial 32 % (RC2) of the variance between the
canonical variates. Henceforth, two sets of variables of EI dimensions and OCB
dimensions are positively and significantly related.
5.6 CHAPTER SUMMARY
In this chapter dimensional analysis of organizational citizenship behaviour
and emotional intelligence has been carried out. Canonical correlation analysis, a
multivariate statistical technique has been applied along with hierarchical regression
analysis to validate the results obtained through canonical correlation. The proposed
model in Figure 5.1 depicting canonical correlation (Rc=.57), is highly significant and
shows that four dimensions of EI and five dimensions of OCB are positively related.
Role of demographic variables have also been explored as control variables in the
Jiwan Jyoti Maini, PhD Thesis Chapter 5
186
hierarchical regression analysis. Age and experience are found to be most important
followed by income and education. UOE and sportsmanship emerged as vital
dimensions among predictor and criterion variable set in canonical correlation
analysis.