CHAPTER 5 DIMENSIONAL ANALYSIS OF OCB & EIshodhganga.inflibnet.ac.in/bitstream/10603/13559/14/14_chapter 5.pdf · CHAPTER 5 DIMENSIONAL ANALYSIS OF OCB & EI ... Dimensions of OCB

Jiwan Jyoti Maini, PhD Thesis Chapter 5

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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


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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


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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.


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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 %


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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


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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.


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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,


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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


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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.


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


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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


Predictor Altruism

Step 1 Step 2

β se β se





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***


*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



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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





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***


*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.


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5.4.4 Altruism & Utilization of Emotions

Hypothesis 18d: Utilization of emotions has a significant influence on altruism after


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


Predictor Altruism

Step 1 Step 2

β se β se





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***


*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


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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



Emotions with Sportsmanship

Predictor Sportsmanship

Step 1 Step 2

β se β se


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***


*p ˂ .10, ** p ˂ .05, ***p ˂ .01


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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.


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.


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Table 5.8: Hierarchical Regression Results for the Association of Managing Own



Step 1 Step 2

β se β se





Age .300 .020

Income -.076 .115

Step 2:

Model F

Overall R2

R2 Change


.735 4.673***

.015 .103***

.089***


*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.


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


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others’ emotions appears out to be a significant dimension for prediction of

sportsmanship.


Others’ Emotions with Sportsmanship


Step 1 Step 2

β se β se





Age .300 .020

Income -.076 .115

Step 2:

Model F

Overall R2

R2 Change


.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


Table 5.10 displays the results of the hierarchical regression analysis used to

test hypothesis 18h. In this analysis, the control variables designation, education


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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.




Step 1 Step 2

β se β se





Age .300 .020

Income -.076 .115

Step 2:

Model F

Overall R2

R2 Change


.735 4.458***

.015 .099***

.084***


*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,


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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.


Emotions with Conscientiousness

Predictor Conscientiousness

Step 1 Step 2

β se β se



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**


*p ˂ .10, ** p ˂ .05, ***p ˂ .01


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


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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


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.


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Own Emotions with Conscientiousness


Step 1 Step 2

β se β se





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***


*p ˂ .10, ** p ˂ .05, ***p ˂ .01

5.4.11 Conscientiousness & Managing Others’ Emotions

Hypothesis 18k: Managing others’ emotions have a significant influence on



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


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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.


Others’ Emotions with Conscientiousness


Step 1 Step 2

β se β se





Age -.595*** .013

Income -.259** .073

Step 2:

Model F

Overall R2

R2 Change


6.058*** 6.108***

.110*** .131***

.021***


*p ˂ .10, ** p ˂ .05, ***p ˂ .01

5.4.12 Conscientiousness & Utilization of Emotions

Hypothesis 18l: Utilization of emotions has a significant influence on



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


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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).


Emotions with Conscientiousness


Step 1 Step 2

β se β se



Education Level .153* .043


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***


*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


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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



Emotions with Courtesy

Predictor Courtesy

Step 1 Step 2

β se β se




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**


*p ˂ .10, ** p ˂ .05, ***p ˂ .01


test hypothesis18m. In this analysis, the control variables designation, education


emotions is regressed on courtesy. According to the results, perception of emotions is


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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



Own Emotions with Courtesy

Predictor Courtesy

Step 1 Step 2

β se β se





Age - .189 .013

Income .119 .075

Step 2:

Model F

Overall R2

R2 Change


3.495*** 5.693***

.067*** .123***

.056***


*p ˂ .10, ** p ˂ .05, ***p ˂ .01


173


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



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.


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.


174


Others’ Emotions with Courtesy

Predictor Courtesy

Step 1 Step 2

β se β se





Age - .189 .013

Income .119 .075

Step 2:

Model F

Overall R2

R2 Change


3.495*** 4.923***

.067*** .108***

.042***


*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



test hypothesis 18p. In this analysis, the control variables designation, education


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


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.


Emotions with Courtesy

Predictor Courtesy

Step 1 Step 2

β se β se





Age - .189 .013

Income .119 .075

Step 2:

Model F

Overall R2

R2 Change


3.495*** 7.937***

.067*** .164***

.097***


*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.


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5.4.17 Civic Virtue & Perception of Emotions

Hypothesis 18q: Perception of emotions has a significant influence on civic virtue



test hypothesis 18q. In this analysis, the control variables designation, education


emotions is regressed on civic virtue.


Emotions with Civic Virtue

Predictor Civic Virtue

Step 1 Step 2

β se β se





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**


*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


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



Own Emotions with Civic Virtue


Step 1 Step 2

β se β se





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***


*p ˂ .10, ** p ˂ .05, ***p ˂ .01


test hypothesis 18r. In this analysis, the control variables designation, education level,


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



Others’ Emotions with Civic Virtue


Step 1 Step 2

β se β se





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***


*p ˂ .10, ** p ˂ .05, ***p ˂ .01


179


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.


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


Table 5.22 displays the results of the hierarchical regression analysis to

examine hypothesis 18t. In this analysis, the control variables designation, education


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.


180


Emotions with Civic Virtue


Step 1 Step 2

β se β se





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***


*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.


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


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


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


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


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


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


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


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


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


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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


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


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


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


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.

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