The Role of Emotional Intelligence on Productivity Among the Software Professionals

CONTENT

LIST OF TABLES

LIST OF FIGURES

I INTRODUCTION AND DESIGN OF THE RESEARCH

II REVIEW OF EXISTING LITERATURE

III EMOTIONAL INTELLIGENCE OVERVIEW

IV PROFILE OF SOFTWARE PROFESSIONALS WORKING IN TRIVANDRUM TECHNOPARK CAMPUS, KERALA

V EMOTIONAL INTELLIGENCE AND PRODUCTIVITY OF SOFTWARE PROFESSIONALS

VI IMPACT OF EMOTIONAL INTELLIGENCE ON PRODUCTIVITY

VII SUMMARY OF FINDINGS AND CONCLUSIONS

BIBLIOGRAPHY

APPENDIX

ACKNOWLEDGEMENT

I acknowledge my sincere gratitude to the Director and Staff of Research and Development Department of Bharathiar University.

I express my profound gratitude to my Guide and mentor Prof (Dr.) R JUBI, Director, Mar Thoma Institute of Information Technology, Chadayamangalam, Kollam, Kerala, for his continued support and guidance on all aspects of the research.

I am grateful to the Trivandrum Technopark authorities for granting permission to collect the data relevant for the thesis work and other support extended for this cause.

I am thankful to UST Global, a software company in Trivandrum Technopark and the PMI (Project Management Institute) Kerala Chapter friends who have immensely supported me for the completion of this thesis.

I am personally indebted to Mr. Anand Scotlin, Consultant, Trivandrum, Kerala, for the help and support to complete this thesis in time.

I am grateful to Dr. Bindu B R, Lecturer, Department of Organon, Shreevidyadhiraja Homoeopathic Medical College, Nemom, Trivandrum, Kerala, for the constant inspiration and guidance for this thesis, specially related to the Medical field.

I take this opportunity to record my love, affection and sincere gratitude to my Wife Ms. Bindu Sanjay, my Children Mr. Ashwin Sanjay and Mr. Abhimanue Sanjay, my Father Mr. Bala Bhaskaran Nair, my Brother Mr. Rajesh Bhaskaran and Family, my in-laws Mr. Radhakrishna Pillai and Ms. Radhamony, for their immense support and help throughout.

Lastly, I thank the Almighty for giving me the strength and guidance required throughout for successfully completing the research work.

LIST OF ABBREVIATIONS

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LIST OF TABLES

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LIST OF FIGURES

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CHAPTER - I INTRODUCTION AND DESIGN OF THE RESEARCH
1.1 INTRODUCTION
1.2 EMOTIONAL INTELLIGENCE
1.3 ROLE OF EMOTIONAL INTELIGENCE ON PRODUCTIVITY
1.4 SCOPE OF RESEARCH
1.5 PROBLEM DEFINITION
1.6 BACKGROUND
1.7 RESEARCH GAP
1.8 OBJECTIVES OF THE RESEARCH
1.9 HYPOTHESES
1.10 SAMPLING FRAMEWORK
1.11 GEOGRAPHICAL AREA
1.12 METHODOLOGY
1.13 DATA COLLECTION AND PROCESSING
1.14 STATISTICAL TOOLS APPLIED
1.15 PERIOD OF STUDY
1.16 LIMITATIONS
1.17 CHAPTERISATION

CHAPTER - I

INTRODUCTION AND DESIGN OF THE RESEARCH

1.1 INTRODUCTION

The most valuable asset of a 21st century institution (whether business or non-business) will be its knowledge workers and their productivity (Peter F Drucker, 1999). Since knowledge workers have a greater impact in the economy’s performance, they are perceived as an important area of opportunity and are starting to be included in organizational strategic plans to improve productivity (Gordon, 1997; Berglind and Scales, 1987). Increasing the productivity of knowledge workers provides a prospect for increasing profits by improving the overall process or product instead of simply eliminating costs. In the process of improving productivity, a factor called Emotional Intelligence (EI), a multi-dimensional construct, which includes a precise understanding of the emotion in self and emotional state of others, found to play a critical role.

Emotional intelligence, a relatively new offshoot of social intelligence and emotions is a growing behavioural research area and is being highly researched and critically reviewed among the general public and the scientific community. Emotional intelligence also connects with several cutting-edge areas of psychological science, including the neuroscience of emotion, self-regulation theory, studies of meta-cognition and the search for human cognitive abilities beyond ‘traditional ‘academic intelligence. (Zeidner, 2004)

Even though social intelligence (Thorndike; 1921) characterised the importance of emotions, EI was proposed as a concept by Mayer and Salovey in 1990. (Mayer, DiPalolo & Salovey; 1990, Salovey & Mayer; 1990). Currently, Mayer and Salovey propose that Emotional Intelligence involves the ability to perceive accurately, appraise and express emotion; the ability to access and/or generate feelings when they facilitate thought; the ability to understand emotion and emotional knowledge; and the ability to regulate emotions to promote emotional and intellectual growth.

For a concept that up until recently received less attention, EI as an important area of contemporary psychology is difficult to dispute. Thus, EI has been touted as a panacea for modern business and the essential for understanding productivity in knowledge workers. Also developing emotional intelligence within organisation creates an environment that enables the opportunity for social capital (knowledge workers) to develop, thus enhancing organisational performance.

1.2 EMOTIONAL INTELLIGENCE

Emotional intelligence, as originally conceptualised by Salovey and Mayer (1990), involves the ability to perceive accurately, appraise, and express emotion; the ability to access and/or generate feelings when they facilitate thought; the ability to understand emotion and emotional knowledge; and the ability to regulate emotions to promote emotional and intellectual growth. Mayer and Salovey (1993) suggested that there are individual differences in emotional intelligence relating to differences in our ability to appraise our own emotions and those of others. They further suggested that individuals higher in emotional intelligence might be more open to internal experience and better able to label and communicate those experiences. Later Mayer and Salovey (1997) revised their definition of EI into their four-branch (specific skills) model of EI which include (i) perceiving emotions to facilitate thought, (ii) using emotions, (iii) understanding emotions and (iv) managing emotions.

1.3 ROLE OF EMOTIONAL INTELLIGENCE ON PRODUCTIVITY

Emotional intelligence predicts various components of job performances which are used as dimensions to assess knowledge worker productivity. The major dimensions that measure productivity include quantity, cost and/or profitability, autonomy, efficiency, quality, effectiveness, customer satisfaction, innovation/creativity, project success, responsibility/importance of work, perception, absenteeism. Thus individuals with the ability to recognize emotions in one’s self and in others contributes to effective social interaction, as does the ability to regulate one’s own emotions do better in the productivity dimensions. Even in highly cognitive context, as in the case of knowledge workers (software professionals), EI had contributed to higher performance in productivity dimensions. Recent research highlights the importance of EI as a predictor in important domain such as academic performance, job performance, negotiation, leadership, emotional labour, trust, work-family conflict, and stress (Ashkanasy & Daus, 2002; Fulmer & Barry, 2004; Humphrey, 2002; 2006, Humphery, Pollack & Hawver, 2008; Jordan, Ashkanasy & Hartel, 2002). Thus it will be of interest to explore the role of emotional intelligence on the productivity of Software Professionals working in Technopark Campus, Thiruvananthapuram, Kerala.

1.4 SCOPE OF RESEARCH

The research exploring the Emotional intelligence, a derivative of emotions and intelligence where both emotion and intelligence well defined and having widely agreed definition in literature, on the productivity of Software Professionals working in Technopark Campus, Thiruvananthapuram, Kerala provides a new focus for knowledge economy. The research will delve upon the contours of emotional intelligence understanding and the usage in enhancing productivity. The environmental setting, the culture, the psychological need will also be analysed to understand the impact. The research will attempt to understand the level of influence the emotional intelligence variables on the determinants of productivity of software professionals working in Technopark campus, Trivandrum, Kerala.

The research also will explore the life orientation of software professionals, concentration, health and other associated well-being parameters which will guide in understanding the role of emotional intelligence on productivity. The well-being, self­control, sociability and emotional control of software professionals will be captured to correlate will other demographic and related variables in the research.

1.5 PROBLEM DEFINITION

Emotional intelligence research (under various streams like ability, self-report, mixed) have explored the association with interpersonal effectiveness, job satisfaction, organisation citizen behaviour, leadership, financial positions, creativity etc. The research diversity is immense and the emotional intelligence sampling domain had produced eye opening results. The Emotional intelligence scales developed based on theory have provided knowledge framework which are being implemented in various organisational settings. Technical skills can make a company competitive but it is the relationships of the employees, customers, suppliers that give the true ability to perform better than the competition. (Goleman, 1998, p. 301) The research on emotional intelligence has narrowed down to ability and trait EI models and the convergence has enabled better understanding and application.

1.6 BACKGROUND

Emotional intelligence predicts various components of job performances which are used as dimensions to assess knowledge worker productivity. The major dimensions that measure productivity include quantity, cost and/or profitability, autonomy, efficiency, quality, effectiveness, customer satisfaction, innovation/creativity, project success, responsibility/importance of work, perception, absenteeism. Thus individuals with the ability to recognize emotions in one’s self and in others contributes to effective social interaction, as does the ability to regulate one’s own emotions do better in the productivity dimensions. Even in highly cognitive context, as in the case of knowledge workers (software professionals), EI had contributed to higher performance in productivity dimensions. Thus it will be of interest to explore the role of emotional intelligence on the productivity of Software Professionals working in Technopark Campus, Thiruvananthapuram, Kerala.

1.7 RESEARCH GAP

Emotional Intelligence plays an important role in domains such as academic performance, job performance, leadership, trust, work-family conflict and stress. Even though many studies including cross-cultural differences are conducted among German (individualistic culture) versus Indian (collectivistic culture) and managerial perspectives on EI differences between India and the United States, no productivity related studies are attempted which covered the knowledge workers. Thus it became pertinent to find out the relationship between emotional intelligence and productivity of Software Professionals of Trivandrum Technopark, Kerala, India.

1.8 OBJECTIVES OF THE RESEARCH

The following objectives were set based on the research question for carrying out the research on the role of emotional intelligence on productivity among the software professionals working at the Thiruvananthapuram Techno park campus, Kerala.

1. To collate the demographic and psychographic variables of the software professionals working at the Thiruvananthapuram Techno park campus, Kerala and the associated characteristics.
2. To measure the emotional intelligence of the software professionals through a standard measuring tool.
3. To measure the productivity of the software professionals through a self-reported measure/standardised scales.
4. To identify the relationship between emotional intelligence and productivity of software professionals.
5. To construct a model incorporating the relationship among the variables which can establish the role of emotional intelligence on the productivity of software professionals.

1.9 HYPOTHESIS

The researcher has set the following hypotheses on the basis of objectives of this research

1.9.1 No difference or change in physical and mental health of software professionals working in Technopark campus, Trivandrum
1.9.2 Work nature of the software professionals working in Technopark campus, Trivandrum is typical
1.9.3 No association between the productivity incentives and profile variables of software professionals working in Technopark campus, Trivandrum
1.9.4 Life orientation of the software professionals working in Technopark campus, Trivandrum is optimistic.
1.9.5 No association between the life orientation and profile variables of software professionals working in Technopark campus, Trivandrum.
1.9.6 No significant difference among profile variable groups and emotional intelligence of the software professionals working in Trivandrum Technopark, Kerala.
1.9.7 No association between productivity and the profile variables of software professionals working in Trivandrum Technopark, Kerala.
1.9.8 No association between emotional intelligence and productivity of the software professionals working in Trivandrum Technopark Campus, Kerala.
1.9.9 No significant difference among profile variable groups and trait emotional intelligence of the software professionals working in Trivandrum Technopark, Kerala.
1.9.10 The mean of higher and lower productive software professionals working in Trivandrum Technopark, Kerala are equal for the emotional intelligence factors.

1.10 SAMPLING FRAMEWORK

1.10.1 Target Population

The population in the design is 9670 software professionals working in the Thiruvananthapuram techno park campus, Kerala and the sample frame comprised of 282 companies¹ operating in techno park campus as on 01/04/2015.

1.10.2 Sampling Frame

The sampling frame, a set of elements from which Software professionals working in 282 companies operating in the Thiruvananthapuram Techno Park Campus, Kerala was made available from Technopark Office. The details available are used to define the sample size and the method of sampling.

1.10.3 Sampling design

The proportionate probability sampling (Stratified) method was used to achieve objective evaluation of the precision of the sample results. This method allows making inference about the target population from which the sample was drawn. The stratified sampling process involves a two-step in which the population is partitioned into subpopulation. This allowed in selecting the sample on a mutually exclusive and collectively exhaustive manner. The classified strata were 282 companies in Technopark Campus and a simple random sampling was executed in each company to determine the sample size of the target population. The proportionate stratified sampling by position and years of experience of software professionals allowed the research to quantitatively analyse the variables.

1.10.4 Sampling Size

A sample is the subset of the target population which is used in the research studies and a sample is a portion, piece, or segment that is representative of a whole. Thus, a sample is selected from the population, which is less in number (size) but adequately represents the population from which it is drawn so that true inferences about the population can be made from the results obtained.²

The sample size is determined statistically with a confidence level of 95 percent. For a total population of 9670 software professionals working in the Thiruvananthapuram techno park campus, Kerala, the sample size is based on the sample size formula for the known population. Thus a sample size of 384 software professionals working in Technopark campus is surveyed using proportionate stratified sampling method.

The sample size selected for the research based on the sample size formula is given below:

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1.11 GEOGRAPHICAL AREA

India's population, as per census 2011 stood at 12105.7 lakhs (6231.2 lakhs males and 5874.5 lakhs females). The country has a low sex ratio of 943 females per thousand males, which has shown slight improvement during the last decade. The population density of India in 2011 was 382 per sq. km while urban population was 31.15 % of the total population.

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Figure 1.1

District Map of Thiruvananthapuram, Kerala

Thiruvananthapuram District is the southernmost district of the coastal state of Kerala. It is the largest city in Kerala. It came into existence in the year 1957. The headquarters is the city of Thiruvananthapuram (Trivandrum) which is also the capital city of Kerala. The district has an area of 2,192 square kilometres (846 sq miles) and a population of 3,307,284 (as per the 2011 census), the second-most populous district in Kerala after Malappuram district. It is the densest district in Kerala with 1,509 inhabitants per square kilometre (3,910/sq miles). It is divided into six taluks: Thiruvananthapuram, Chirayinkeezhu, Neyyattinkara, Nedumangadu, Varkala and Kattakada. The urban bodies in the district are the Thiruvananthapuram Corporation, Varkala, Neyyattinkara, Attingal and Nedumangad municipalities.³

1.12 METHODOLOGY

1.12.1 Qualitative Research

A depth interview was conducted to understand the dependent variables that play a major role on the productivity of the software professionals in Thiruvananthapuram Techno park campus, Kerala. The identified variables were drafted in to a qualitative questionnaire and administered to a small group of software professionals and the final survey variables finalised. Those were tabulated in to a draft questionnaire and administered to various members with n=30 and analysed using various test for coherence and structure.

1.12.2 Measurement

The emotional intelligence of the software professionals working in Trivandrum Technopark Campus, Kerala is measured using the Schutte Self Report Emotional Intelligence Test (SSEIT) and Trait Emotional Intelligence Questionnaire - Short Form (TEIQue-SF). The productivity of the software professionals is measured using self­report scales of Health & Productivity Questionnaire (HPQ).

The Rank order scaling and multiple item scaling are used which captures the demographic, psychographic and emotional intelligence variables. The reliability of the scale is derived from the SSEIT, TEIQue-SF and HPQ. The content validity of the scale is derived from the SSEIT, TEIQue-SF and HPQ⁴ measures. The TEIQue-SF showed consistent incremental effects beyond the Big Five Model and coping strategies.⁵ Also the factor structure for the SSEIT was tested in Indian context and found to be having suitable fit.⁶

1.13 DATA COLLECTION AND PROCESSING

A sample of 425 software professionals working in Technopark Campus, Thiruvananthapuram are drawn using simple random sampling and the structured questionnaire is administered manually and with option for electronic scoring for the selected sample. Out of the 425 administered/mailed, only 396 returned the questionnaire or took the online version of the questionnaire. After evaluating the questionnaire received, a total of n=384 respondents are finalized for analysis.

The collected data is codified and tabulated in excel for checking the anomaly in the data and consistency and the same imported to the SPSS package. The variables were created in variable view and data from each questionnaire was entered in data view.

1.14 STATISTICAL TOOLS APPLIED

The following tools are used to infer the data of software professionals collected through the survey questionnaire.

1.14.1 Chi-square statistic ( χ )

Chi-square statistic ( χ ) is used to test the statistical significance of the observed association in a cross tabulation. It assists in determining whether a systematic association exists between two variables. The null hypothesis H0, is that there is no association between the variables. The test is conducted by computing the cell frequencies that would be expected if no association were present between the variables, given the existing row and column totals. These expected cell frequencies, denoted fe are then compared to the actual observed frequencies, fo, found in the cross-tabulation to calculate the chi-square statistic. The greater the discrepancies between the expected and actual frequencies, the larger the value of the statistic. Assuming that a cross tabulation has r rows and c columns and a random sample of n observations. Then the expected frequency for each cell can be calculated by using a simple formula:

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1.14.2 t-Test (One Sample)

The t test is based on the Student’s t statistic. The t statistic assumes that the variable is normally distributed and the mean is known (or assumed to be known), and the population variance is estimated from the sample. Assume that the random variable X is normally distributed, with mean μ and unknown population variance σ , which is estimated by the sample variance s . The standard deviation of the sample mean X, is estimated as sx = s/Vñ. Then t = (X - μ)/ s is t distributed with n-1 degrees of freedom.

1.14.3 Garrett’s ranking

This method devised by Henry E Garrett which is used especially in the calculation of coefficients of correlation, to be able to transmute measures arranged in order of merit into measures in units of amount or " scores " on some linear scale. The order of merit was accomplished by means of tables, provided the data assume "normality" in the trait for which the ranking has been made. The order of merit is calculated as follow

Formula⁷

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1.14.4 Analysis of Variance (ANOVA)

Analysis of variance is used as a test of means of two or more populations. The null hypothesis, typically, is that all means are equal. ANOVA include the following steps of identifying the dependent and independent variable, decomposing the total variation, measure of effects, test of significance and interpretation of results. The total variation in Y denoted by SSy, can be decomposed into two components:

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where the subscripts between and within refer to the categories of X. SSbetween is the variation in Y related to the variation in the means of the categories of X. It represents variation between the categories of X.

The effects of X on Y are measured by SSx because SSx is related to the variation in the means of the categories of X, the relative magnitude of SSx increases as the differences among the means of Y in the categories of X increase. The relative magnitude of SSx also increases as the variations in Y within the categories of x decrease. The strength of the effects of X on Y are measured as follows:

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In one-way analysis of variance, the interest lies in testing the null hypothesis that the category means are equal in the population. The null hypothesis may be tested by the F statistic based on the ration between the two estimates and that statistic follows the F distribution. The F distribution is a probability distribution of the ratios of sample variances. It is characterized by degrees of freedom for the numerator and degrees of freedom for the denominator.

1.14.5 Multiple Analysis of Variance (MANOVA)

Multivariate analysis of variance (MANOVA) is an extension of the univariate analysis of variance (ANOVA). In an ANOVA, the statistical differences on one continuous dependent variable by an independent grouping variable are examined. The MANOVA extends this analysis by taking into account multiple continuous dependent variables, and bundles them together into a weighted linear combination or composite variable. The MANOVA compares whether or not the newly created combination differs by the different groups, or levels, of the independent variable. In this way, the MANOVA essentially tests whether or not the independent grouping variable simultaneously explains a statistically significant amount of variance in the dependent variable.

Levene’s Test of Equality of Variance is used to examine whether or not the variance between independent variable groups are equal; also known as homogeneity of variance. Non-significant values of Levene’s test indicate equal variance between groups. Box’s M Test results are used to know the equality of covariance between the groups. This is the equivalent of a multivariate homogeneity of variance. Usually, significance for this test is determined at α = .001 because this test is considered highly sensitive. Partial eta square (η ) shows how much variance is explained by the independent variable. It is used as the effect size for the MANOVA model.

Subsequently, post hoc test are conducted to test if there is a significant difference between groups. Post hoc tests determine where the significant differences lie (i.e., which specific independent variable level significantly differs from another).

Finally, the F- statistic is derived by essentially dividing the means sum of the square (SS) for the source variable by the source variable mean error.⁸

1.14.6 Product Moment Correlation

Product Moment Correlation summarise the strength of association between two metric variables. It is an index used to determine whether a linear, or straight line relationship exists between X and Y. It indicated the degree to which the variation in one variable, X is related to the variation in another variable, Y. From a sample of n observations, X and Y, the product moment correlation, r, can be calculated

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In these equations, X and Y denote the sample means, and sx and sy the standard deviation. COVxy, the covariance between X and Y, measures the extent to which X and Y are related. The covariance may be either positive or negative. Division by sxsy achieves standardization, so that r varies between -1.0 and 1.0.

1.14.7 Multiple Regression

In statistics, regression analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modelling and analysing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. More specifically, regression analysis helps one understand how the typical value of the dependent variable (or 'Criterion Variable') changes when any one of the independent variables is varied, while the other independent variables are held fixed.

Most commonly, regression analysis estimates the conditional expectation of the dependent variable given the independent variables - that is, the average value of the dependent variable when the independent variables are fixed. Less commonly, the focus is on a quantile, or other location parameter of the conditional distribution of the dependent variable given the independent variables. In all cases, the estimation target is a function of the independent variables called the regression function. In regression analysis, it is also of interest to characterize the variation of the dependent variable around the regression function, which can be described by a probability distribution.

Regression analysis is widely used for prediction and forecasting, where its use has substantial overlap with the field of machine learning. Regression analysis is also used to understand which among the independent variables are related to the dependent variable, and to explore the forms of these relationships. In restricted circumstances, regression analysis can be used to infer causal relationship between the independent and dependent variables. However this can lead to illusions or false relationships, so caution is advisable; for example, correlation does not imply causation.⁹

Many techniques for carrying out regression analysis have been developed. Familiar methods such as linear regression and ordinary regression are parametric, in that the regression function is defined in terms of a finite number of unknown parameters that are estimated from the data. Nonparametric regression refers to techniques that allow the regression function to lie in a specified set of functions, which may be infinite- dimensional.¹⁰

Multiple regression attempts to predict a normal dependent variable from a combination of several scale and /or dichotomous independent/predictor variables. The general form of the multiple regression model is as follows:

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1.15 PERIOD OF THE RESEARCH

The research was conducted during the period from January 2015 to December 2016. During the period questionnaire were administered and data collected from the sample of software professionals working in Technopark Campus, Thiruvananthapuram, Kerala.

1.16. LIMITATIONS

Every research is confronted with challenges and issues that have a profound influence on the broader framework of the research. Even though every step has been taken to make the research free from limitations, a few limitations were experienced during data collection.

(i) Even though performance measures would provide more information on Emotional intelligence, self-report measures was used as the performance measure across domain verticals and standardisation is not possible due to constraint of time.
(ii) The software professionals were having difficulty in sharing many of the information sought for during the research.
(iii) Time is a major limiting factor as the software professionals members were hard pressed for time and sparing time off for the taking the questionnaire was cumbersome during office hours.
(iv) In many cases the accuracy of the scoring is vague and the software professionals did not spent enough time in reading the questionnaire.
(v) Software professionals are working in tight schedule and questionnaire scoring was not done continuously, in some cases days and months.
(vi) The scoring on emotional items by certain individuals could have been the assumed self than the real self.

1.17 CHAPTERISATION

The study on role of emotional intelligence on productivity among the software professionals working in Trivandrum Technopark campus Kerala has the depicted chapter scheme.

Chapter I provide an introduction to emotional intelligence, role of emotional intelligence in productivity and cover the fundamentals of the topic which include the problem definition, background, research gap and the methodology of the research. .
Chapter II depicts the literature reviewed which provides the insight into the already existing knowledge on the research area.
Chapter III covers overview of emotional intelligence, models, use and applications.
Chapter IV narrate the profile variable like demographic, socio-economic, psychographic aspects, work environment, life orientation, and productivity incentives of software professionals.
Chapter V analyse the emotional intelligence, productivity and association among the variables of software professionals.
Chapter VI investigates the impact of emotional intelligence on productivity using multivariate analysis.
Chapter VII finalises the entire research by summarising the major findings, suggests important interventions and recommends areas for further research and concludes the research

The ‘Bibliography’ section lists the Books, Journals, Articles, Reports and Websites that were referred during the research process.

The ‘Appendix’ contains the population frames (list of software companies) which were approached for distributing questionnaire and Research Questionnaire which was used during the data collection.

CHAPTER - II
REVIEW OF EXISTING LITERATURE
2.1 INTRODUCTION
2.2 LITERATURE RELATED TO EMOTIONAL INTELLIGENCE
2.3 LITERATURE RELATED TO EMOTIONAL INTELLIGENCE MODELS
2.4 LITERATURE RELATED TO EMOTIONAL INTELLIGENCE SCALES
2.5 LITERATURE RELATED TO PRODUCTIVITY
2.6 LITERATURE RELATED TO PRODUCTIVITY SELF MEASUREMENT SCALES
2.7 LITERATURE RELATED TO ROLE OF EMOTIONAL INTELLIGENCE ON DEPENDENT VARIABLES
2.8 CONCLUSION

CHAPTER - II

REVIEW OF EXISTING LITERATURE

2.1 INTRODUCTION

In order to understand the existing research on emotional intelligence, productivity and comprehend both the dependent and independent variables, which help in providing the summary and synthesis of the knowledge repositories is undertaken. The review also help in tracking the intellectual contribution in the field and also the interpretation of those existing resources in new ways which can provide insight for the current research. This process will substantiate the most pertinent and relevant information which can be a guide in establishing the hypothesis on which the research can be framed and undertaken.

For the purpose of the research, a thorough study of all possible academic and non-academic work in the field is done and this can be classified as - (a) Doctoral Theses (b) Text and Reference books (c) Peer Reviewed Journals (d) Industry Reports (e) Articles and (f) Web based Knowledge Repositories.

The reviewed literature for the present research is organized under the following headings:

I. Literature related to Emotional Intelligence.
II. Literature related to Emotional Intelligence Models
III. Literature on Emotional Intelligence Scales
IV. Literature related to Productivity
V. Literature on Productivity Self-Measurement Scales
VI. Literature on role of Emotional Intelligence on dependent variables

The details of the research papers, journals and thesis analysed are detailed below according the year of publication in ascending order and as per the organised sections.

2.2 LITERATURE RELATED TO EMOTIONAL INTELLIGENCE

The literature reviewed under this category include emotional intelligence framework, abilities and mechanism that underlie emotional intelligence, inclusion of emotional intelligence in traditional cognitive abilities framework, competency models, moods and emotions, alternative views of emotional intelligence, cooperative combination of intelligence and emotion, invalidity of emotional intelligence, emotional and social competence models, metal analysis studies and examination of both ability and trait EI constructs.

The details of the research papers, journals etc. analysed are detailed below according the year of publication in ascending order.

Salovey, P and Mayer, J. D. (1990) presented a framework for emotional intelligence, a set of skills hypothesised to contribute to the accurate appraisal and expression of emotions in oneself and in others, the effective regulation of emotion in self and others, and the use of feeling to motivate, plan and achieve in one’s life. The authors defined emotional intelligence as the subset of social intelligence that involves the ability to monitor one’s own and others’ feelings and emotions, to discriminate among them and to use this information to guide one’s thinking and actions. The authors conceptualised the emotional intelligence under three sub categories viz., appraisal and expression of emotions, regulation of emotions and utilisation of emotions. The authors concluded that appraising and expressing emotions accurately is a part of emotional intelligence because those who are more accurate can more quickly perceive and respond to their own emotions better, express those emotions to others.¹¹

Salovey, P and Mayer, J. D. (1993) in their paper discussed whether intelligence is an appropriate metaphor for the construct and the abilities and mechanism that may underlie emotional intelligence. The authors argued that emotional intelligence have better discriminant validity from general intelligence than social intelligence. The authors submitted that the emotional intelligence, as compared with social intelligence, may be more clearly distinguished from general intelligence as involving the manipulation of emotions and emotional context. The authors concluded that emotionally intelligent individuals may be more aware of their own feelings and those of others. They may be more open to positive and negative aspects of internal experience, better able to label them, and when appropriate, communicate them. Such awareness will often lead to the effective regulation of affect within themselves and others, and so contribute to well-being.¹²

Davies, Michaela, Lazar Stankov, and Richard D. Roberts (1998) argued that emotional intelligence be included within the traditional cognitive abilities framework where the authors explored 3 studies (total N = 530) by investigating the relations among measures of emotional intelligence, traditional human cognitive abilities, and personality. The studies suggested that the status of the emotional intelligence construct was limited by measurement properties of its tests. Measures based on consensual scoring exhibited low reliability. Self-report measures had salient loadings on well- established personality factors, indicating a lack of divergent validity. These data provided controvertible evidence for the existence of a separate Emotion Perception factor that (perhaps) represents the ability to monitor another individual's emotions. The authors concluded that perception factor was narrower than that postulated within the models of emotional intelligence¹³

Dulewicz, Victor, and Malcolm Higgs (2000) reviewed the literature on emotional intelligence and defined the construct using competency based and personality factor scales. The authors also explored the reliability and construct/predictive validity of three scales. The authors found that EQ scale based on 16 relevant competencies showed high reliability and validity and the other two competency scales predicted organisational advancement. The authors also found that when these scales are taken together, they have higher validity. The authors concluded that EQ constructs can be measured more effectively by performance analysis than classic paper and pencil tests.¹⁴

Mayer, John D., Peter Salovey, and David R. Caruso. (2000) discussed three uses of the term emotional intelligence (EI), first as a zeitgeist, or cultural movement of the times, next as a synonym or near synonym for personality, and finally, as an actual intelligence or ability within personality that was concerned with processing emotions.

As a Zeitgeist, it was unclear whether EI was simply a passing fad or could conceivably qualify as some sort of historical movement. Turning to the realm of personality, using EI to refer to broad areas of personality beyond the emotional and cognitive seems unnecessarily vague and problematic when such usage was meant to refer to the entirety of personality or character. The authors suggested a change in terminology to describe an emotionally intelligent constellation of personality traits. Then, a person's EI could be compared with a variety of other personality types with admirable qualities. Finally, the ability definition of EI had its own set of competing constructs and concepts. Matching most closely are such concepts as emotional competence and emotional creativity, as well as intrapersonal intelligence, which includes motivational and other relations to the self.¹⁵

George, Jennifer M. (2000) in emotions and leadership: the role of EI suggested that feelings (moods and emotions) played a central role in the leadership process. The authors proposed that emotional intelligence, the ability to understand and manage moods and emotions in the self and others, contributed to effective leadership in organisations. The four major aspects of emotional intelligence, the appraisal and expression of emotions, the use of emotions to enhance cognitive processes and decision making, knowledge about emotions, and management of emotions were corroborated with the five essential elements of leader effectiveness: development of collective goals and objectives, instilling in others an appreciation of the importance of work activities, generating and maintaining enthusiasm, confidence, optimism, cooperation and trust, encouraging flexibility in decision making and change and establishing and maintain a meaningful identify for organisation. The author concluded that, at a minimum, emotions and emotional intelligence are worthy of consideration in the leadership domain.¹⁶

Mayer, John D., et al. (2001) briefly restated view of intelligence, emotion, and EI. They then presented arguments for the reasonableness of measuring EI as an ability, indicated that correct answers existed and summarised the recent data suggesting that such measure are indeed reliable. The authors suggested that emotional intelligence was probably related to general intelligence in being ability, but it may well also have its differences in terms of mechanisms and manifestations. Underlying mechanisms included emotionality, emotion management, and neurological substrates. Its manifestations included greater verbal fluency in emotional domains, as well as greater overall information transmission under emotional threat.¹⁷

Mayer, D. J., P. Salovey, and R. D. Caruso (2004) emphasising their previous view on emotional intelligence clarified that the theoretical perspective referred specifically to the cooperative combination of intelligence and emotion. The authors described the nature of El, as well as the four-branch model of El they developed, and the measurement instruments used to study EI. The authors also examined the growing evidence that El existed, that it satisfied many of the criteria that identifed intelligence, and that it predicted matters of consequence.¹⁸

Locke, E. A. (2005) argued that the concept of emotional intelligence (EI) was invalid both because it was not a form of intelligence and because it was defined so broadly and inclusively that it had no intelligible meaning. The author distinguished the so-called concept of EI from actual intelligence and from rationality. The author identified the actual relation between reason and emotion and revealed the fundamental inadequacy of the concept of EI when applied to leadership. Finally the author suggested some alternatives to the EI concept.¹⁹

Antonakis, John, and Joerg Dietz (2010) reviewed Chemiss’s vision for the future of “Emotional Intelligence”(EI). However, the authors proposed that clarifying the concept by distinguishing definitions from models and support for “Emotional and Social Competence” (ESC) models will not make the field advance. To be upfront, the authors agreed that emotions are important for effective decision-making, leadership, performance and the like; however, at that time, EI and ESC had not demonstrated incremental validity over and above IQ and personality tests in meta-analyses. The authors anticipated that ability model of Mayer, Salovey and associates (e.g, Mayer, Caruso, & Salovey, 2000), could holds the most promise for Emotional Intelligence.²⁰

O'Boyle, Ernest H., et al. (2011) using meta-analysis classified EI studies into three streams: (i) ability-based models that use objective test items; (2) self-report or peer-report measures based on the four branch model of EI; and (iii) ‘‘mixed models’’ of emotional competencies. The three streams have corrected correlations ranging from 0.24 to 0.30 with job performance. The three streams correlated differently with cognitive ability and with neuroticism, extraversion, openness, agreeableness, and conscientiousness. Streams (ii) and (iii) had the largest incremental validity beyond cognitive ability and the Five Factor Model (FFM). The dominance analysis conducted by the authors demonstrated that all three streams of EI exhibited substantial relative importance in the presence of FFM and intelligence when predicting job performance. The authors noted that publication bias had negligible influence on observed effect sizes and the results support the overall validity of EI.²¹

Austin, Elizabeth J., and Donald H. Saklofske (2014) observed that current EI research are very much focused on examining both the ability and trait EI constructs. The authors explained that ability EI was assessed by problem-solving tests which were similar to those used in intelligence tests and trait EI was measured using self-report tests and has been found to be an emotion-related dispositional trait which forms part of the personality domain. The authors further emphasised that certain ability EI sub-scales were found to be correlated with intelligence test scores, but ability EI had not convincingly established as an intelligence component. But the authors agreed that, trait EI largely accounted for in contemporary personality models and scales. However, research suggested that there was sufficient variance accounted for by trait EI to continue its examination and contribution to individual differences psychology. The authors presented the studies of both trait and ability EI in their special issue compilation.²²

2.3 LITERATURE RELATED TO EMOTIONAL INTELLIGENCE MODELS

The literature reviewed under this category include models of emotion regulation, competency models, trait emotional intelligence model, ability emotional intelligence model and a integrative ability emotional intelligence model.

The details of the research papers, journals and thesis analysed are detailed below according the year of publication in ascending order.

Mayer, J. D. and Salovey, P. (1995) identified and compared several models of emotion regulation; for example, one internally consistent model includes tenets such as "happiness should be optimized over the lifetime applied that internally consistent model to the way a person can intervene in mood construction and regulation at non-, low-, and high-conscious levels of experience. Research related to the construction and regulation of emotion at each of these levels were also reviewed. The proposed intelligent and adaptive model of the authors’ stated that: (a) happiness required an optimization of positive feelings over the lifespan, (b) such positive feelings must be both pro-individual in the sense of benefiting the individual's long-term welfare and also pro-social in the sense of assisting those people around the individual, and (c) emotional construction and regulation must be open and flexible. Finally, the authors connected the concept of emotionally intelligent regulation to its potential applications to personality and clinical psychology.²³

Boyatzis, Richard E., Daniel Goleman, and Kenneth Rhee (2000) described a model of emotional intelligence based on the competencies that enabled a person to demonstrate intelligent use of their emotions in managing themselves and working with others to be effective at work. The authors based on the EI model with twenty five competencies arrayed in five clusters viz. the self-awareness cluster, self-regulation cluster, motivation cluster, empathy cluster and social skill cluster developed the Emotional Competence Inventory (ECI) which was validated against performance for a variety of job families. Reliability and construct validation had been established against other questionnaire measures as well as behavioural measures coded from videotapes and audiotapes, and numerous longitudinal studies of competency development.²⁴

Petrides, K. V., and Adrian Furnham (2000) scrutinized the psychometric properties of the self-report emotional intelligence (EI) measured by Schutte et al.(1998) and the development and validation of a measure of emotional intelligence, and found several weaknesses in that model. The authors argued that by virtue of the construction strategy adopted by Schutte et al. (1998) the test could not measure general EI factor and it was not successfully mapped onto Salovey and Mayer's (1990) [Salovey, P., & Mayer, J. D. (1990) framework. It is also shown via confirmatory factor analysis that the test was not uni-factorial. A theoretical distinction between trait and information-processing EI was proposed. The authors proposed that Trait EI appertained to the greater personality realm whereas information-processing EI was an attempt to chart new territory in the field of human mental ability.²⁵

Boyatzis, Richard E., Elizabeth C. Stubbs, and Scott N. Taylor (2002) analysed the entering and graduating data from six full-time and three part-time cohorts taking an MBA programme designed to develop the cognitive and emotional intelligence competencies by comparing two full-time and two part-time cohorts baseline data. The authors found using a self-report Learning Skills Profile, that cognitive and emotional intelligence competencies could be developed in MBA students, but not with a typical MBA curriculum. The authors concluded that the leadership course and a wide range of learning activities integrated into the MBA programme caused the improvement in results.²⁶

Mayer, John D., Peter Salovey, and David R. Caruso. (2008) analysed the greater capacity of some individuals as compared to others who carry out sophisticated information processing about emotions and emotion-relevant stimuli and to use this information as a guide to thinking and behaviour have termed those set of capabilities as ability emotional intelligence (EI). The authors argued that EI— conceptualized as ability—is an important variable both conceptually and empirically, and it showed incremental validity for predicting socially relevant outcomes. The authors provided few recommendations and separated ability EI from other constructs that may be important in their own right but are ill-labelled as emotional intelligence.²⁷

Mayer, John D., Richard D. Roberts, and Sigal G. Barsade (2008) discussed the origins of the EI concept, defined EI, and described the scope of the field as on that date. The authors reviewed three approaches taken to date from both a theoretical and methodological perspective. The authors found that Specific-Ability and Integrative- Model approaches adequately conceptualized and measured EI. The authors based on various review narrated the general effect of EI on listed the following (i) better social relations for children, (ii) better social relation for adult, (iii) high EI individuals are perceived more positively by others, (iv) better family and intimate relationship, (v) better academic achievement, (vi) better social relations during work performance and its negotiations, and (vii) better psychological well-being. The authors believe that the concept had proven a valuable addition to contemporary science and practice. Consideration of EI theory and assessment had proven beneficial to the study of emotions and the study of intelligence, and raised awareness of the importance of emotional components in diverse domains of human abilities and their application in people’s lives.²⁸

Singh, S. K. (2009) attempted to meta-analyse available research findings and develop a framework to be used by the industry practitioners. The conceptual model based on research literature was assumed to fill-in the gap and also to address the organizational concerns. The authors after examining and providing a framework concluded that those who have well developed emotional competencies may have advantage over others to better manage people, relationships roles, etc. for their own psychological wellbeing as well as organizational productivity. Based on the meta­analysis the author argued that the emotional competencies are not innate but acquired abilities which can be nurtured in the human resources over a period of time. At the same time, the humanity in organization with developed emotional competencies may not remain same for long until and unless it is maintained and sustained through specifically designed training programs at a regular interval. It is contended that one cannot make organization free from ‘stress’ but leveraging the benefits of emotional intelligence competencies can make both personal and professional lives psychologically healthy.²⁹

Brackett, M. A., Rivers, S. E. and Salovey, P. (2011) presented and overview of the ability model of emotional intelligence and included a discussion about how and why the concept became useful in both educational and workplace settings. The authors reviewed four underlying emotional abilities comprising emotional intelligence and the assessment tools that were developed to measure the construct and provided the review of the research describing the correlates of emotional intelligence. The authors argued that scientific findings on emotional intelligence support the notion that emotions were functional when the information they provided are attended to, interpreted accurately, integrated into thinking and behaviour, and managed effectively. Other research conducted by the authors also showed that the emotion knowledge and skills that comprise emotional intelligence could be taught and developed. The authors concluded that the emotional intelligence as a construct was operationalized best as a set of mental abilities involving emotion-based problem solving measured with performance tests, as opposed to a set of traits and perceived abilities measured with self-report batteries.³⁰

2.4 LITERATURE RELATED TO EMOTIONAL INTELLIGENCE SCALES

The literature reviewed under this category include various emotional intelligence scales/tests like Trait Meta-Mood Scale (TMMS), Schutte Self Report Emotional Intelligence Test (SSEIT), Multifactor Emotional Intelligence Scale (MEIS), Mayer Salovey Caruso Emotional Intelligence Test (MSCEIT), Emotional Competence Inventory (ECI), Multi-factor Emotional Intelligence Scale (MEIS), and the Bar-On, Emotional Quotient Inventory (EQi).

The details of the research papers, journals and thesis analysed are detailed below according the year of publication in ascending order.

Salovey, Peter, et al. (1995) presented a study on the factor structure and reliability of a scale that measures the more enduring qualities of the reflective experience of mood called the Trait Meta-Mood Scale (TMMS) which was designed to assess relatively stable individual differences in people’s tendency to attend to their moods and emotions, discriminate clearly among them and regulate them. The authors described the development of the Trait Meta-Mood Scale and the extraction and confirmation of its three factors: Attention to feelings, clarity in discrimination of feelings and mood repair. The authors concluded that TMMS is a reasonable operationalization of aspects of emotional intelligence with attention to, clarity and repair of feelings seem fundamental to the self-regulatory domain of emotional intelligence.³¹

Schutte, Nicola S., et al (1998) had series of studies which described the development of a measure of emotional intelligence based on the model of emotional intelligence developed by Salovey and Mayer [Salovey, P. & Mayer, J. D. (1990)]. The authors developed a pool of 62 items which represented the different dimensions of the model. The factor analysis of the responses of 346 participated suggested the creation of a 33-item scale. Additional studies indicated of the authors showed that the measure had a good internal consistency and test-retest reliability. Validation studies of authors indicate that scores on the 33-item scale measure (a) correlated with eight of nine theoretically related constructs, including alexithymia, attention to feelings, clarity of feelings, mood repair, optimism and impulse control, (b) predicted first year college grades, (c) were significantly higher for therapists than for therapy clients of for prisoners (d) were significantly higher for females than males, consistent with prior findings in studies of emotional skills, (e) were not related to cognitive ability and (f) were associated with the openness to experience trait of the big five personality dimensions.³²

Mayer, John D., David R. Caruso, and Peter Salovey. (1999) argued that intelligence must meet several standard criteria before it can be considered scientifically legitimate with the first being capable of operationalized as a set of abilities. Second it should meet certain correlation criteria: the abilities defined by the intelligence should form a related set and be related to pre-existing intelligences while showing some unique variance. Third, the abilities of the intelligence should develop with age and experience.

The authors conducted two studies, adults (N=503) and adolescents (N=299) who took a new 12 subscale ability test of emotional intelligence: the Multifactor Emotional Intelligence Scale (MEIS). The authors indicated that emotional intelligence as measured by MEIS meets the above three classical criteria of a standard intelligence. The authors concluded that a general intelligence that includes emotional intelligence will be a more predictor of important life outcomes than one that does not predict the objective.³³

Mayer, John D., David R. Caruso, and Peter Salovey (2000) compared the tests developed by the authors with others tests which also claim to be emotional intelligence tests and analysed the test design. They also gave new information on the correlates to emotional intelligence as it is measured by their tests, the MEIS, and the new version, the MSCEIT.³⁴

Watkin, C. (2000) discussed the Hay 360 Emotional Competence Inventory (ECI) that provided the needed assessment and development tool for building EI competencies in workplace. The ECI contained two measures of data validity, Rater Familarity and Rater Agreement designed to assess the quality of raters data and to aid participants in interpreting their feedback which had 110 items with at least 3 items to assess each competency. The author argued that EI has a direct impact on bottom line performance with compelling facts like using EI for selection decision gave an advantage, EI had direct impact on sales, EI lead to more value added for highly differential technical jobs such as IT, EI was the key to superior leadership and group EI meant better use of intellectual capital and brain power. The author concluded that EI and ECI were means of developing employees in the characteristics that led to outstanding performance.³⁵

Ciarrochi, Joseph V., Amy YC Chan, and Peter Caputi. (2000) critically evaluated the Emotional Intelligence construct as measured by the Multi-factor Emotional Intelligence Scale (MEIS) The authors administered MEIS to Australian undergraduates along with a battery of IQ, personality and other theoretically relevant criterion measures including life satisfaction and relationship quality. The authors also induced moods in the students and examined whether people high in EI were better than others at managing their moods and preventing their moods from biasing their social judgements. The analyses revealed that EI was not related to IQ but was related, to specific personality measures and to other criterion measures even after controlling for IQ and personality traits. The authors found that EI was also related to people’s ability to manage their moods, but not to their ability to prevent moods from biasing their judgements. IQ was surprisingly related to both these mood processes. The results suggested that the EI construct was distinctive and useful but that traditional IQ might also be important in understanding emotional processes.³⁶

Bachman, John, et al. (2000) conducted and reported two studies which compared more or less successful account officers (debt collectors) in terms of their emotional intelligence, measured using Bar-On Emotional Quotient Inventory (Bar-On EQi). In the first study, the participants were divided into best practices group and less successful group and administered the EQ-i to both the groups. The best practices officers as a group were found to possess a level of emotional intelligence that is significantly higher than of the North American population at large. In the second study participants were analysed for best practices in their job and the comparison were made between the high and low cash collectors and their emotional intelligence. The results indicated that higher level of emotional intelligence in account officers led to increased cash goal attainment. The findings supported the view that higher level of emotional intelligence lead to enhanced job performance.³⁷

Wong, Chi-Sum, and Kenneth S. Law. (2002) developed a psychometrically sound and practically short EI measure that was for use in leadership and management studies. The authors provided exploratory evidence for the effects of the EI of both leaders and followers on job outcomes. The authors applied the Gross emotion regulation model and argued that the EI of leaders and followers should have positive effects on job performance and attitudes. The authors proposed that the emotional labour of the job moderated the EI-job outcome relationship. The result of the study indicated that EI of followers affected job performance and job satisfaction, while the EI of leaders affected their satisfaction and extra-role behaviour.³⁸

Mayer, John D., et al. (2003) examined the reliability, possible factor structure and validity of Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT; Mayer, Salovey and Caruso, 2002) and found that expert convergence was better in areas where more emotions research had been conducted. Reliabilities for branch, area and total test scores were reasonably high. The factor analysis indicated that one, two and four factor models provided viable representations of the EI domain, as assessed by the MSCEIT V2.0. The authors concluded that based on the findings the MSCEIT was a quality tool to measure the EI.³⁹

Brackett, Marc A., and John D. Mayer (2003) investigated the convergent, discriminant and incremental validity of one ability test of emotional intelligence (EI) - the Mayer-Salovey-Caruso-Emotional intelligence Test (MSCEIT) and two self report measures of EI - the Emotional Quotient Inventory (EQ-i) and the self-report EI test (SREIT) of 207 predominantly Caucasian American (97%) college students (130 women, 77 men). The MSCEIT showed minimal relations to the EQ-i and SREIT, whereas the latter two measures were moderately interrelated. Among EI measures, the MSCEIT was discriminable from well-studied personality and well-being measures, whereas the EQ-I and SREIT shared considerable variance with these measures. After personality and verbal intelligence were held constant, the MSCEIT was predictive of social deviance, the EQ-i was predictive of alcohol use, and the SREIT was inversely related to academic achievement. In general, results showed that ability EI and self­report EI are weakly related and yield different measurements of the same person⁴⁰

Boyatzis, R. E and Sala, F. (2004) discussed the development of a measure of emotional intelligence competencies, called the Emotional Competence Inventory (ECI) and its later revisions, the ECI-2 and ECI-U (ECI University version), are described and documented with an emphasis on the ECI and ECI-2.

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