lawrence technological universitys), the 12-item soar profile, and the nine-item team collaboration...

Lawrence Technological University

College of Management

An Evaluation of the Relationship among Emotional Intelligence,

SOAR, and Collaboration: Implications for Teams

Presented in partial fulfillment of the requirements

for the degree of

Doctor of Business Administration

John D. Cox

2014

ii

© COPYRIGHT BY

John D. Cox

2014

All Rights Reserved

iv

LAWRENCE TECHNOLOGICAL UNIVERSITY

AN EVALUATION OF THE RELATIONSHIP AMONG EMOTIONAL

INTELLIGENCE, SOAR, AND COLLABORATION: IMPLICATIONS

FOR TEAMS

by

John D. Cox

Master of Business Administration, Lawrence Technological University, 1993

Bachelor of Science Electrical Engineering, Lawrence Technological University, 1986

Dissertation Submitted to the

Graduate Faculty of the College of Management

in Partial Fulfillment of the Requirements for the Degree of

DOCTOR OF BUSINESS ADMINISTRATION

DISSERTATION COMMITTEE CHAIR: Matthew Cole, Ph.D.

COMMITTEE MEMBERS: Jacqueline Stavros, D.M., and Patricia Castelli, Ph.D.

v

Abstract

There is a need to develop a climate of collaboration in today’s business culture

due to the rapid expansion of a global environment of open communication, cooperative

work, and an expanded consideration of consumer markets. In order to improve

collaboration at all levels of the organization, it is essential that professionals acquire

emotional intelligence (EI)—the ability to develop awareness and management of

emotions in themselves and others. Research suggests that EI has the ability to impact

performance outcomes in organizations, in particular those in which successful

negotiation, cohesion, and collaboration is desired. Furthermore, it is important that

teams collaborate from a strengths, opportunities, aspirations, and results (SOAR)-based

perspective that maximizes collaborative strategies that are inclusive.

The purpose of this dissertation was to evaluate the relationships between EI,

SOAR, and collaboration among a sample of professionals either actively working in

teams or who have had recent experience working in teams. A sample of 308 participants

completed the 16-item Work Group Emotional Intelligence Profile-Short Form (WEIP-

S), the 12-item SOAR Profile, and the nine-item Team Collaboration Questionnaire.

This study used a quantitative cross-sectional design with moderating and mediating

variables to test the prediction of collaboration by EI, the moderation of the EI-

collaboration relationship by team role, team type, and time in teams, and the mediation

of the EI-collaboration relationship by SOAR. Data analysis using multiple linear

regression and structural equation modeling (SEM) with bootstrapped confidence

intervals found EI was a significant predictor of collaboration, the impact of EI on

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collaboration was moderated by team role, team type, and time in teams, and SOAR

mediates the effect that EI has on collaboration.

This study has implications for teams and team members working collaboratively.

First, creation of the Team Collaboration Questionnaire, an original rapid assessment tool

developed in this study, has implications for the reliable and valid measurement of

collaboration. Second, by showing that EI growth improves elements of collaboration

related to integrating, compromising, and communication, this study recommends

methods to improve EI abilities in team members that may ultimately improve team

effectiveness, such as improving ability to be more effective at integrating ideas, seeking

compromise, and encouraging open and effective communication. Third, by testing

moderating variables, this study found that the impact of EI on collaboration is

maximized when teams are comprised of leaders, when teams are virtual, and when

experience with teams is greater than one year. Lastly, this study found SOAR

functioned as a partial mediating variable, suggesting that a framework for strategy based

on the strengths and aspirations of team members may explain how EI impacts team-

based collaboration.

Recommendations for future research include studying the relationship between

trait and ability-based measures of EI via a longitudinal study that focuses on EI growth

and its effect on collaboration. Future research should also study the impact of EI on

team-based collaboration using objective rather than self-report measures of

collaboration, such as observation of collaboration by independent observers. Finally,

future research should seek to clarify the distinction between partial and full mediation

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that were found in this study in order to determine other variables that may explain how

EI impacts team-based collaboration.

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Dedication

This dissertation is dedicated to Grant and Elaine.

May you continue to cultivate a passion for accomplishment, life-long learning, and

personal growth in yourselves and others.

With pride in you, Dad.

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Acknowledgements

Don and Sandy Cox

In gratitude for your unwavering support and inspiration throughout my academic career.

For facilitating the means to reach these academic achievements, the encouragement

when the future wasn’t so bright, and never losing faith in what I could become.

Thank you.

Deborah Cox

For all the days when we would have preferred doing something else. For the late dinners

after class, the early breakfast before class, and your patience with the other frequent and

unanticipated dissertation priorities. For sharing your pride in me, and the motivation to

live life after the dissertation.

Thank you.

Dissertation Committee

Thank you also to my dissertation chair, Dr. Matthew Cole and my committee members,

Dr. Jacqueline Stavros and Dr. Patricia Castelli for your support and guidance throughout

the dissertation journey.

Lawrence Technological University

An institution founded and dedicated to the practical success of its students, LTU has

consistently provided exemplary faculty in the Colleges of Management and Engineering.

I am proud to say I received a BSEE, MBA, and DBA from LTU.

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Table of Contents

ABSTRACT ........................................................................................................................ V

DEDICATION ................................................................................................................ VIII

ACKNOWLEDGEMENTS .............................................................................................. IX

TABLE OF CONTENTS .................................................................................................... X

LIST OF TABLES ......................................................................................................... XIV

LIST OF FIGURES ...................................................................................................... XVII

CHAPTER 1 INTRODUCTION ......................................................................................1

Background to the Study .................................................................................................3

Problem Statement ..........................................................................................................4

Purpose of the Study .......................................................................................................5

Research Variables ..........................................................................................................6

Research Questions and Hypotheses ...............................................................................8

Significance of the Study ................................................................................................9

Overview of the Research Methodology.......................................................................10

Limitations of the Research ..........................................................................................11

Definitions of Key Terms..............................................................................................13

Organization of Discussion ...........................................................................................15

CHAPTER 2 LITERATURE REVIEW .........................................................................18

Introduction ...................................................................................................................18

Emotional Intelligence ..................................................................................................18

Theory. .....................................................................................................................19

Relevance to hypothetical model. ............................................................................21

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Framework. ..............................................................................................................24

The emotionally intelligent individual. ....................................................................33

The emotionally intelligent leader. ...........................................................................35

Emotional intelligence and teamwork. .....................................................................41

Emotional intelligence and collaboration. ................................................................41

Measures of emotional intelligence. .........................................................................42

Collaboration .................................................................................................................48

Theory. .....................................................................................................................48


Collaboration and leaders. ........................................................................................51

Collaboration and integration. ..................................................................................53

Collaboration and compromise. ...............................................................................57

Collaboration and communication. ..........................................................................58

Improving collaboration through the development of emotional intelligence. ........59

Measures of collaboration. .......................................................................................62

SOAR ............................................................................................................................67

Theory. .....................................................................................................................67


Measures of SOAR ...................................................................................................71

Summary .......................................................................................................................73

CHAPTER 3 RESEARCH METHODOLOGY ..............................................................77

Introduction ...................................................................................................................77

Research Design ............................................................................................................77

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Research Questions and Hypotheses .............................................................................78

Research Variables ........................................................................................................79

Population and Sample ..................................................................................................81

Measures .......................................................................................................................82

Pilot Study .....................................................................................................................83

Data Collection Procedure ............................................................................................85

Data Analysis ................................................................................................................86

Descriptive statistics. ................................................................................................87

Psychometric properties ...........................................................................................87

Inferential statistics. .................................................................................................88

CHAPTER 4 RESULTS .................................................................................................89

Introduction ...................................................................................................................89

Demographic Characteristics of the Sample .................................................................90

Reliability and Validity .................................................................................................96

Intercorrelations Between Study Variables .................................................................105

Descriptive Statistics ...................................................................................................107

Emotional Intelligence (EI). ...................................................................................107

Collaboration. .........................................................................................................113

SOAR. ....................................................................................................................118

Hypotheses Testing Results for H1 .............................................................................125



CHAPTER 5 DISCUSSION .........................................................................................144

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Introduction .................................................................................................................144

Summary of Results and Discussion ...........................................................................144

Implications for Practice and Recommendations ........................................................154

Recommendations for Future Research ......................................................................166

Study Limitations ........................................................................................................169

Summary .....................................................................................................................170

REFERENCES ................................................................................................................172

APPENDIX A ..................................................................................................................187

Certificate of Training .................................................................................................187

APPENDIX B ..................................................................................................................188

IRB Letter of Approval ...............................................................................................188

APPENDIX C ..................................................................................................................189

Informed Consent ........................................................................................................189

APPENDIX D ..................................................................................................................190

Survey Instrument .......................................................................................................190

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List of Tables

Table 2.1 Summary of Common Measures of Emotional Intelligence (EI) ..................... 44

Table 2.2 Summary of Common Measures of Collaboration in Business Journals ......... 63

Table 2.3 Strategic Inquiry—Appreciative Intent: Inspiration to SOAR ......................... 70

Table 4.1 Characteristics of Sample by Gender, Age, Ethnicity, and Education ............. 93

Table 4.2 Characteristics of Sample by Industry, and Position ........................................ 94

Table 4.3 Characteristics of Sample by Team Size, Team Membership, Team Type, Team

Role, and Time Working in This Particular Team ............................................................ 95

Table 4.4 Characteristics of Sample by Team Role, and Time Involved in Teams (When

Working in Team-Based Activities in General) ............................................................... 96

Table 4.5 Reliability and Validity of the WEIP-S (Workgroup Emotional Intelligence

Profile – Short Version, 16-items) .................................................................................... 98

Table 4.6 Reliability and Validity of the Team Collaboration Questionnaire (15-items,

and 14-items) .................................................................................................................... 99

Table 4.7 Reliability and Validity of the Team Collaboration Questionairre (9-items) . 102

Table 4.8 Reliability and Validity of the SOAR Profile (20-items) ............................... 104

Table 4.9 Reliability and Validity of the SOAR Profile (12-items) ............................... 105

Table 4.10 Intercorrelations Between Study Variables .................................................. 106

Table 4.11 Mean and SD of Emotional Intelligence and its Four Constitutive Factors

across Gender, Age, Ethnicity, and Education ............................................................... 108


across Industry, and Position .......................................................................................... 109

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across Team Size, Team Membership, Team Type, Team Role, and Time Working in

This Particular Team ....................................................................................................... 111


across Team Role, and Time Involved in Teams (When Working in Team-Based Activities

in General) ...................................................................................................................... 113

Table 4.15 Mean and SD of Collaboration and its Three Constitutive Factors across

Gender, Age, Ethnicity, and Education .......................................................................... 114


Industry, and Position ..................................................................................................... 115

Table 4.17 Mean and SD of Collaboration and its Three Constitutive Factors across Team

Size, Team Membership, Team Type, Team Role, and Time Working in This Particular

Team ............................................................................................................................... 117


Role, and Time Involved in Teams (When Working in Team-Based Activities in General)

......................................................................................................................................... 118

Table 4.19 Mean and SD of SOAR and its Four Constitutive Factors across Gender, Age,

Ethnicity, and Education ................................................................................................. 120

Table 4.20 Mean and SD of SOAR and its Four Constitutive Factors across Industry, and

Position ........................................................................................................................... 121

Table 4.21 Mean and SD of SOAR and its Four Constitutive Factors across Team Size,

Team Membership, Team Type, Team Role, and Time Working in This Particular Team

......................................................................................................................................... 123

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Table 4.22 Mean and SD of SOAR and its Four Constitutive Factors across Team Role,

Time Involved in Teams (When Working in Team-Based Activities in General) ......... 124

Table 4.23 Collaboration Regressed on EI Alone (EI Predicting Collaboration) ........... 125

Table 4.24 Collaboration Regressed on EI (EI predicting collaboration controlling for

age, ethnicity, and education) ......................................................................................... 127

Table 4.25 Collaboration Regressed on SA (SA predicting collaboration) .................... 128

Table 4.26 Collaboration Regressed on SM (SM predicting collaboration) .................. 129

Table 4.27 Collaboration Regressed on AO (AO predicting collaboration) .................. 130

Table 4.28 Collaboration Regressed on MO (MO predicting collaboration) ................. 131

Table 4.29 Collaboration Regressed on SA, SM, AO and MO ...................................... 132

Table 4.30 Integrating Regressed on EI (EI predicting integrating) ............................... 133

Table 4.31 Compromising Regressed on EI (EI predicting compromising) .................. 134

Table 4.32 Communication Regressed on EI (EI predicting communication) ............... 135

Table 4.33 Hieararchical Regression of Collaboration on EI and Team Role ................ 137

Table 4.34 Hieararchical Regression of Collaboration on EI and Team Type ............... 138

Table 4.35 Hieararchical Regression of Collaboration on EI and Time in Teams ......... 138

Table 4.36 Mediation of the Effect of Emotional Intelligence on Collaboration through

Strengths, Opportunities, Aspirations, and Results (Full Construct) Background Factors

......................................................................................................................................... 141


Strengths, Opportunities, Aspirations, and Results Background Factors ....................... 142

Table 5.1 Summary of Practical Recommendations ....................................................... 162

Table 5.2 Personalized Summary Assessment Example: EI, Collaboration and SOAR 166

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List of Figures

Figure 1.1 Hypothetical Model of the Study: SOAR Mediating the Impact of Emotional

Intelligence on Collaboration .............................................................................................. 7

Figure 2.1 The SOAR Framework .................................................................................... 68


Intelligence on Collaboration ............................................................................................ 81

Figure 4.1 SOAR Mediating the Effect of EI on Collaboration ..................................... 141

Figure 4.2 SOAR and its Constitutive Factors Mediating Collaboration ....................... 143

Figure 5.1 Model of the Study: SOAR Mediating the Impact of Emotional Intelligence on

Collaboration with Demographic Moderating Variables ................................................ 147

Figure 5.2 Team Role as a Moderator of the Relationship between EI and Collaboration

......................................................................................................................................... 151

Figure 5.3 Team Type as a Moderator of the Relationship between EI and Collaboration

......................................................................................................................................... 152

Figure 5.4 Time in Teams as a Moderator of the Relationship between EI and

Collaboration................................................................................................................... 153

EMOTIONAL INTELLIGENCE, SOAR, AND COLLABORATION 1

Chapter 1 Introduction

Successful teamwork requires more than the intelligence of team leaders and team

members—it requires consideration of all participants engaged in the team process. The

proposition is that leaders of the future with emotional intelligence (EI) will be more

adept in promoting collaboration among teams and team members. EI provides active

support of the collaborative process, and as EI is developed throughout the collaborative

team, support of a common goal grows and team effectiveness increases (Xavier, 2005).

Emotional intelligence is defined as the capacity to perceive, reason about and

recognize the meaning of emotions, to effectively regulate and manage emotions, and to

problem-solve and act on them so as to promote emotional and intellectual growth

(Mayer, Salovey, & Caruso, 2004). EI is also defined as a set of emotion processing

abilities that lead to improving social interactions. These emotion processing abilities are

awareness of own and others’ emotions, emotional facilitation, emotional understanding,

and management of own and others’ emotions (Mayer & Salovey, 1997). While other

models of emotional intelligence vary from this (e.g., Goleman, 1995), the common point

they share is a focus on emotional awareness and emotional management as core abilities.

In understanding how emotional intelligence works in teams, the focus is on abilities

related to one’s own emotions and dealing with other people’s emotions (Jordan &

Lawrence, 2009).

Emotional intelligence is composed of several factors, or abilities such as self-

awareness (awareness of own emotions), self-management (management of own

emotions), social-awareness (awareness of others’ emotions), and relationship-

management (management of others’ emotions) (Mayer & Salovey, 1997). However, it


is not clear which of the EI abilities are viewed as most important for collaboration in

teams. An individual’s strengths-based strategic thinking capacity from the SOAR

framework (Strengths, Opportunities, Aspirations, and Results) may also be one of the

mechanisms by which EI has a significantly positive impact on collaboration. If EI

involves awareness of one’s own and others’ emotions, SOAR may help to characterize

the mechanism of how this awareness of emotions impacts collaboration. Awareness and

management of own and others’ emotions has closeness to Appreciative Inquiry and

inclusion which are the central philosophical tenets of SOAR.

Wolff and Koman (2008) emphasize the impact that emotionally competent

leaders have on organization development and positive performance outcomes: “Leaders

who are emotionally intelligent are essential to developing a climate where employees are

encouraged to perform to the best of their ability” (p. 59). In addressing the potential

impact that EI has on leadership, this dissertation evaluated the relationship between

emotional intelligence (EI) and collaboration, and determined which components of EI

promote collaboration in teams. It also considered SOAR as a mediating variable to help

characterize the mechanism of how EI (awareness and management of own and others’

emotions) impacts collaboration in teams.

Methodologically, this dissertation conducted an empirical investigation of EI and

its contribution to collaborative outcomes utilizing a cross-sectional design. A sample of

professionals who were either actively engaging in teamwork or who have engaged in

teamwork in the past within their organization completed a self-report survey comprised

of an existing measure of EI, and a measure of collaboration uniquely developed for this

study. Additionally, the survey measured an individual’s strengths-based strategic


thinking capacity from the SOAR framework (Strengths, Opportunities, Aspirations, and

Results) in order to determine the role that SOAR plays in mediating the relationship

between EI and collaboration. This dissertation is intended to provide scholars and

practitioners recommendations for improving collaboration among individuals working in

teams.

Background to the Study

The framework of emotional intelligence (EI) has been defined and refined over the

last ten years, and a majority of interest in the literature centers on the assessment and

development of EI abilities and their application to positive outcomes in leadership and

teamwork (Goleman, 2006). There is a growing body of literature seeking to advance the

measurement of EI via rapid self-report assessment instruments for individuals and teams

in order to investigate the link between EI and outcome variables that have implications for

collaboration. For example, studies by Moore and Mamiseishvili (2012), and Troth,

Jordan, and Lawrence (2012), have investigated the relationship between EI and group

cohesion, and EI and social cohesion, respectively. In both of these studies, team

cohesiveness is positively related to collaboration and team effectiveness (Dailey, 1978).

Therefore, there has been interest among researchers in business and management to

investigate the potential relationship between EI and collaboration.

One aspect of collaboration that is important for increasing team effectiveness is

supporting collaborative strategies that are inclusive and draw upon the potentials and

expertise of team members and their commitment to sharing and exchanging knowledge

(Shaw & Lindsay, 2008). In light of the need for collaborative strategies that are inclusive,

the SOAR framework was included in this study due to its ability to promote an inclusive


approach that builds the capacity for strategic thinking and planning (Cole & Stavros,

2013). At the heart of the SOAR framework is an inclusive approach that promotes team

members to frame strategy from a strengths-based perspective utilizing the team’s unique

strengths, assets, networks, resources, and capabilities (Cooperrider, Whitney, & Stavros,

2008).

This study reviewed the current state of research on EI, some of the widely used

self-report assessment measures of EI and collaboration, and conducted empirical research

to characterize the relationship between EI and collaboration within teams. SOAR was

evaluated as a mediating variable to characterize what may be one of the mechanisms by

which EI has a significantly positive effect on collaboration. An essential component of

the dissertation was to explain the theory and development of EI for the purposes of

extending its measurement, assessment, and analysis within the context of collaborative

teams.

Problem Statement

In multi-national organizations, the rapid, and essentially real-time

communication afforded by the world-wide-web has increased consumers’ access to

international markets and manufacturers of all kinds who are capable of engaging in an

endless array of expansive thought and ideas (Gereffi, 2001). Through virtual

communication and virtual collaboration, the Internet has played a prominent role in

location becoming largely irrelevant in business, and the for-profit, non-profit, and

academia circles of today must address the consequences of communication occurring

without boundaries. As globalization increases, the business landscape is shifting to a

culture in which efficient communication and collaboration are key factors in


organizational success. In fact, “collaboration is widely recognized as a mechanism for

leveraging competitiveness and thus increasing survivability in turbulent market

conditions” (Romero, Galeano, & Molina, 2009, p. 4691). Thus, modern business

cultures are finding that there is a need to develop a climate of collaboration due to the

rapid expansion of a global environment of open communication, cooperative work, and

an expanded consideration of consumer markets (Jassawalla & Sashittal, 1998). In order

to maintain a competitive pace in this environment, it is essential that professionals

acquire EI in order to develop awareness and management of emotions in themselves and

others to improve collaboration at all levels of the organization (Gohm, 2004).

Furthermore, it is important that teams collaborate from a perspective that maximizes

their strengths-based approach to strategic thinking and planning to facilitate team

effectiveness (Bright, Cooperrider, & Galloway, 2006).

Purpose of the Study

Individuals have an opportunity to recognize that interactive, codependent, and

collaborative relationships are manifested in communication intended to produce a

positive result. With an influential and cooperative intent in collaboration, leaders with

EI skills and abilities have the capacity to establish an emotional connection with

followers who may overcome resistance to produce meaningful change (Groves, 2006).

Emotional intelligence (EI) and the application of EI abilities to recognize, understand,

and use emotional information about oneself and others has led to positive outcomes in

leadership (Anand & Udayasuriyan, 2010; Blattner & Bacigalupo, 2007; Boyatzis, 2007),

and are seen to be increasingly important to an individual's ability to be socially effective

and engaging in team collaboration (Kerr, Garvin, Heaton, & Boyle, 2006). Within this


context, the purpose of this dissertation was to evaluate the link between EI and

collaboration outcomes in teams, to characterize the EI abilities that contribute to

collaboration, and to investigate the mediating role that SOAR (i.e., strengths-based

strategic thinking and planning capacity) has on the relationship between EI and

collaboration among team members.

Research Variables

Emotional intelligence (EI) exists within a framework of multiple abilities and

competencies (cf. Goleman, 2006), and knowledge and practice of these competencies

can be a life-long journey. Similar to Intelligence Quotient (IQ), emotional

intelligence—often referred to as Emotional Quotient (EQ)—involves personal growth

and a commitment to practice. For example, EI involves self-awareness and self-

management of one’s own feelings, and social awareness and management of what others

are feeling (Dulewicz & Higgs, 2000). Leaders and team members learning the EI

competencies typically practice self-awareness and self-management; self-awareness,

social awareness, and relationship management are common threads explored by

researchers and individuals intending to expand their understanding of the EI

competencies.

Collaboration in the scholarly literature is most commonly discussed in terms of

team building and team integration, cooperative work relationships that foster negotiation

and comprise, group cohesiveness, and effective communication (Aram & Morgan, 1976;

Quoidbach, & Hansenne, 2009; Rahim, 1983a, 1983b; Thomson, Perry, & Miller, 2009;

Whitaker, 2009). Collaboration is also discussed in the context of strategy and strategic

thinking (Gray, 1985), and strengths-based strategic thinking may have an impact on


collaboration in teams due to the relationship between group cohesiveness and positive

attitudes (Lott & Lott, 1965). The relationship between EI and collaboration, and SOAR

as a mediating variable was evaluated in this study with a cross-sectional research design

and quantitative data for the purpose of helping leaders and team members understand the

EI factors that promote positive collaboration outcomes.

Figure 1.1 presents the hypothetical model for this study. According to the model,

EI, which has been derived from the Mayer and Salovey (1997) model of EI in a research

study by Jordan and Lawrence (2009), EI in teams is comprised of four factors—

awareness and management of own emotions, and awareness and management of others’

emotions. EI is an independent variable (IV) that impacts the dependent variable (DV),

collaboration, which is comprised of three factors—integrating, compromsing, and

communication.


Intelligence on Collaboration


To explore the impact of variables that could moderate the impact of EI on

collaboration, demographic characteristics (such as gender, age, position, and previous or

current participation in teams) were included in the model as moderators (MOD). In

consideration of variables that could mediate the indirect effects of EI on collaboration, a

construct used for framing a strengths-based approach to strategic thinking, SOAR, was

included in the model as a mediator (MED). According to Baron and Kenny (1986),

“Moderator variables specify when certain effects will hold, mediators speak to how or

why such effects occur” (p. 1176). Therefore, this study proposed that various

demographic characteristics will serve as moderators, and SOAR will serve as a mediator

of the impact that EI has on collaboration.

Research Questions and Hypotheses

People engaged in cooperative work seek to advance their mutual interests

(Whitaker, 2009); however, would individuals working in teams be in a better position to

advance their cooperative work if they adopted EI in both theory and practice? Are EI

competencies differentially related to collaboration? For example, are there differences

in the impact that emotional self-awareness and self-management, and awareness and

management of other’ emotions may have on collaboration? Are there any variables that

influence EI and its impact on collaboration and may help to explain the mechanism by

which EI affects collaboration? For example, are there certain demographic

characteristics that moderate the potential impact of EI on collaboration, and are there

certain mediators, such as (i.e., SOAR), that help to explain the impact that EI has on

collaboration?


The following four research questions were posed in this empirical study of

emotional intelligence and its relationship to collaboration:

Q1. Is there a relationship between emotional intelligence and collaboration?

Q2. Are there differences in the contribution of the emotional intelligence

abilities awareness of own emotions, management of own emotions, awareness of others’

emotions, and management of others’ emotions to collaboration?

Q3. Are there any demographic characteristics that moderate the impact

emotional intelligence may have on improved collaboration outcomes?

Q4. To help understand a potential mechanism for why EI may have an impact

on collaboration, does the SOAR framework for strengths-based strategic thinking,

planning, and leading mediate the impact that EI may have on collaboration?

The following three hypotheses were tested to answer the research questions:

H1. Emotional intelligence is related to collaboration such that EI has a positive

impact on collaboration.

H2. The impact of emotional intelligence on collaboration is moderated by

participants’ demographic characteristics.

H3. The SOAR framework mediates the relationship between emotional

intelligence and collaboration.

Significance of the Study

In traditional EI research, theoretical development of the construct and its

assessment have been primarily organized around individual EI abilities and/or the

individual’s personal level of EI competency within distinct EI domains. Currently, there

is a growing focus on the application of EI to such real-world issues as team-based


collaboration and collaborative outcomes (e.g., Blattner & Bacigalupo, 2007; Farh, Seo,

& Tesluk, 2012; Troth et al., 2012). This dissertation intends to contribute to that

interest, and advance the scholarly literature to understand EI and its impact on

collaboration.

The significance of this study is in four areas. First is the contribution to the

theoretical development of EI by utilizing an existing and widely acceptable individual EI

assessment tool to incorporate the benefits of EI to team-based applications. Second is

conducting new scholarly research on the link between EI and collaboration. Third is

investigating the moderating and mediating effects in EI research (i.e. SOAR and

demographic variables). Fourth is offering practical recommendations suitable for

practitioner adoption.

Overview of the Research Methodology

The research methodology for this study was a quantitative cross-sectional design

with moderating and mediating variables. The independent variable, EI, was tested as a

predictor of the dependent variable, collaboration, using linear regression inferential

statistics. Demographic characteristics, such as industry type, leadership experience, and

team experience were tested as moderators via significant interactions between EI and the

demographic characteristics in predicting collaboration in a linear regression. Finally, the

variable SOAR was tested as a mediator by looking for an indirect effect between EI and

collaboration in a mediation path model using structural equation modeling (SEM).

The population for this study were professionals actively working in teams or

those who have had recent experience working in teams. A sample of these individuals

were invited to participate in an electronic survey designed to assess their demographic


characteristics, EI, collaboration, and SOAR. EI was measured by the 16-item WEIP-S

(Work Group Emotional Intelligence Profile-Short Form; Jordan & Lawrence, 2009) to

establish areas of respondent competency in four EI abilities helpful for understanding

how EI works in teams (Mayer & Salovey, 1997): awareness of own emotions,

management of own emotions, awareness of others’ emotions, and management of

others’ emotions. The measure for assessing collaboration was the nine-item Team

Collaboration Questionnaire, an original measure of collaboration adapted from Aram

and Morgan (1976), and Rahim (1983a, 1983b), which measures three factors of self-

reported collaborative activity among members of a team: integrating, compromising, and

communication. Finally, strengths-based strategic thinking was measured by the 12-item

SOAR Profile (Cole & Stavros, 2013), a self-report measure of strategic capacity from

the SOAR framework. The measures were selected for their ability to rapidly identify EI

competency in teams, collaboration, and SOAR most critical to achieving positive

outcomes in collaboration.

Limitations of the Research

The limitations of this research concern the main construct of the study, emotional

intelligence (EI), and its relationship to the outcome and mediator variables in the study,

collaboration and SOAR, respectively. Specifically, while EI is an appealing, yet

occasionally controversial construct, the idea that an individual’s intelligence and

capability, normally quantified via the IQ, may have an equivalent emotional component

quantified by the EQ, is controversial. Being a fairly recent theory in the relevant

literature, the idea of EI does face some scrutiny and potential limitations. Although

studies have shown that EI can be a useful predictor and enabler of improved


performance (e.g., Sy, Tram, S., & O’hara, 2006), it has been argued that there is little if

any incremental validity to justify its relative importance over cognitive abilities which

are routinely measured via the IQ (Van Rooy, Viswesvaran, & Pluta, 2005).

There are also disjointed conclusions over what actually constitutes the EI

construct. One viewpoint claims that EI is nothing more than a renaming of existing

constructs of intelligence and emotional stability, while others dismiss its importance

because of inconsistency in its definition and lack of reliable and agreed upon assessment

models (Van Rooy et al., 2005). The basis for this argument rests in the definition of

intelligence narrowly scoped to cognitive intelligence—or inherently personal traits and

characteristics. Accordingly, one of the limitations of this study acknowledge that while

EI may be investigated from a variety of perspectives and possibilities, the construct was

studied from a framework of effective teamwork and positive outcomes in collaboration

mediated by SOAR.

Collaboration is also not without its challenges—it requires more than merely the

establishment of a group of individuals who are directed to engage in achievement of a

common goal. Factors that may have a negative impact on collaboration and team

success include size of the group, ability to meet and work together in person (as opposed

to working in a virtual team), whether any of the team members know each other, and the

disparity in expertise. These challenges may be overcome through EI and the application

of EI abilities that have been found to be important for collaboration in teams (e.g.,

Borges, Kirkham, Deardroff, & Moore, 2012; Troth et al., 2012). Additionally, these

challenges may be overcome through the SOAR framework and its application to

strengths-based strategic thinking and planning. This dissertation acknowledges that


while collaboration is investigated from a variety of perspectives, this study focused on

factors that represent characteristics of collaboration (i,e., integrating, compromising, and

communication).

This study has limitations related to the use of self-report data in general, as self-

report methodology has inherent limitations of validity of the data. Another limitation

may include the potential for common-method bias. Common-method bias may occur

when data for the independent variable comes from the same source as the dependent

variable. Ideally, team based collaboration would be measured by independent observers.

Finally, since research participants estimated the collaborative outcomes of their

teamwork, as well as their capacity for strengths-based strategic thinking and planning

(i.e., SOAR) through the use of two novel assessment tools—the Team Collaboration

Questionnaire and the SOAR Profile—the psychometric properties of these assessment

instruments were evaluated using tests of reliability and validity prior to data analysis.

Definitions of Key Terms

The following key terms are used throughout this dissertation.

Appreciative Inquiry (AI). The search for the best in people, their

organizations, and the relevant world around them (Cooperrider, Whitney, & Stavros,

2008).

Collaboration. The degree to which study participants rate integrating,

compromising, and communication among co-workers in a team (Aram & Morgan, 1976;

Rahim, 1983a, 1983b).

Emotional Intelligence (EI). “The capacity to reason about emotions, and of

emotions to enhance thinking. It includes the abilities to accurately perceive emotions, to


access and generate emotions so as to assist thought, to understand emotions and

emotional knowledge, and to reflectively regulate emotions so as to promote emotional

and intellectual growth. Emotional intelligence from this theoretical perspective refers

specifically to the cooperative combination of intelligence and emotion” (Mayer,

Salovey, & Caruso, 2004, p. 197).

Emotional Intelligence Abilities. EI abilities are interchangeably referred to in

the literature as domains, clusters, dimensions, skills, and competencies (Bar-On, 1997;

Dulewicz & Higgs, 2000, 2004; Goleman, 1998, 2006; Mayer et al., 2004; Salovey &

Mayer, 1990). An individual’s awareness, management, and application of EI abilities

can enhance his or her effectiveness with tasks that involve interpersonal relationships.

Fundamentally, the framework of EI followed in this dissertation is the four abilities

helpful for understanding how EI works in teams (Mayer & Salovey, 1997): awareness of

own emotions, management of own emotions, awareness of others’ emotions and

management of others’ emotions.

Emotional Quotient (EQ). Considered the level of one’s emotional intelligence,

EQ is often used interchangeably with EI.

Intelligence Quotient (IQ). A quantitative index of one’s verbal and nonverbal

intelligence.

Mediating Variable (MED). “A mediator is defined as a variable that explains

the relationship between a predictor and an outcome. In other words, a mediator is the

mechanism through which a predictor influences an outcome variable” (Frazier, Tix, &

Barron, 2004, p. 116). The MED in this study was SOAR.


Moderating Variable (MOD). “A moderator is a variable that alters the

direction or strength of the relation between a predictor and an outcome” (Frazier et al.,

2004, p. 116). The MODs in this study were three demographic characteristics, team

role, team type, and time in teams.

SOAR (Strengths, Opportunities, Aspirations and Results). “SOAR enables

individuals, organizations, business units and teams to create strategic plans in new ways

by addressing the following key concern of most organizations: ‘How do we sustain the

value, momentum, energy and commitment to see the plan implemented and achieve the

desired results of the planning effort?’” (Stavros, 2013, p. 12).

Organization of Discussion

This dissertation is organized into five chapters. The first chapter provided an

introduction and background to the research study investigating the relationship between

emotional intelligence and collaboration. Chapter One defined the problem statement,

purpose, and significance of the study. An introduction to the research methodology was

presented, and the chapter concluded with the study research questions and the

hypotheses tested to answer them.

Chapter Two provides a comprehensive investigation into the existing literature

on emotional intelligence, EI’s relationship to collaboration and team-based activities,

and some of the more widely used measures of EI. Similarly, the theoretical foundations

of SOAR and relevance to the hypothetical model are discussed. The SOAR Profile

(Cole & Stavros, 2013), a unique measure of SOAR, is introduced as the measurement

instrument used for investigating the mediating effects of SOAR on the relationship


between EI and collaboration. Finally, the extant literature on collaboration is presented,

followed by relevance to the hypothetical model, and existing measures.

In this literature review, applications of the study constructs are considered in the

presence of team-based activities. Beyond the theoretical framework of each construct

and relevance to the hypothetical model, their relationships with team-based activities are

investigated. These include the emotionally intelligent individual, the emotionally

intelligent leader, EI and teamwork and EI and collaboration. For each study construct,

measurement instruments are reviewed. SOAR, being a relatively new construct, is

specific in its theoretical framework, and measures of SOAR are limited to the SOAR

Profile (Cole and Stavros, 2013). To consider if SOAR is one mechanism of action by

which positive collaboration outcomes can be achieved in the presence of EI, SOAR is

specifically investigated as a potential mediator of the relationship between EI and

collaboration. Finally, collaboration is similarly described in theory, relevance and

measurement. Extensions to leadership and teamwork are studied, with a specific

emphasis on the collaboration factors of integration, compromise, and communication.

Chapter Three provides the research methodology used in this study. The study

sample, organization of the survey instruments, procedures used, and analysis methods

are definitively described. The research variables are identified and the subsequent

analysis methods planned for Chapter Four are introduced. These include the descriptive

statistics of the study variables, the psychometric properties of the survey instruments,

and the inferential statistics used for hypothesis testing.

Chapter Four presents the research study results, and begins with a review of the

research questions and the hypotheses tested to answer them. Descriptive statistics are


used to summarize the detailed participant responses to various demographic

characteristics and each of the study variables EI, SOAR, and collaboration. Each of the

study constructs also included survey questions related to their constitutive factors. For

EI, this included self-awareness, self-management, awareness of others’ emotions, and

management of others’ emotions. SOAR included Strengths, Opportunities, Aspirations

and Results. Finally, collaboration included the factors integrating, compromising, and

communication.

Chapter Five focuses on the interpretation of results, followed by a discussion of

study implications and recommendations. Results of hypothesis testing, moderation and

mediation analyses are interpreted, and discussed in terms of implications for

practitioners. The chapter closes with a brief summary, study limitations, and suggestions

for future research.


Chapter 2 Literature Review

Introduction

The literature review is strategically organized around the practical definitions

and research of the study variables, emotional intelligence (EI), collaboration, and

SOAR. For each variable, the underlying theory and relevance to the study model are

presented, including a review of EI and its potential relationship to collaboration.

Concepts describing collaboration and relevant extensions are explored to review the

scope of research on collaboration. In consideration of a strengths-based strategic

thinking and planning style that may mediate the relationship between EI and

collaboration, the SOAR framework is evaluated to the extent it may help to explain the

mechanism by which EI impacts collaboration. The chapter also includes a review of

conflicts and controversies in recent literature related to the theoretical development of

EI.

In this study, the measurement of the study variables required careful

consideration in accurately selecting measures well correlated with the study variables.

The prominent measurement tools in the literature for EI, SOAR, and collaboration were

reviewed and evaluated for their applicability to the hypothetical model. Each following

section on EI, SOAR, and collaboration concludes with the measurement tool selected,

and the reasons why it was chosen.

Emotional Intelligence

Emotional intelligence (EI), the first of three study constructs used in this study,

functions as the independent variable (IV) in the hypothetical model (see Figure 1.1).

The following sections present the theoretical foundations of EI, relevance to the


hypothetical model, and the framework used in this study (i.e., an ability-based model

consisting of four factors, self-awareness (SA), self-management (SM), awareness of

others’ emotions (AO), and management of others’ emotions (MO). The literature on EI

is subsequently explored in areas applicable to EI’s relationship with team based

collaboration: EI in individuals and leaders, teams, and collaborative teamwork. The

section concludes with a review of the mainstream measures used in assessing levels of

EI in individuals.

Theory. Emotional Intelligence (EI) as a fairly recent theory first labeled and

explained in the mid-1990s by the pioneering research of two main teams of theorists,

Daniel Goleman and Richard Boyatzis, and Peter Salovey and John Mayer (Boyatzis,

Goleman, & Rhee, 2000; Goleman, 1995; Goleman, Boyatzis, & McKee, 2002; Salovey

& Mayer, 1990), EI is broadly defined as a construct representing a set of competencies

for identifying, processing, and managing emotions (Zeidner, Roberts, & Matthews,

2008). EI is an evolving extension of the quantitative measures of intelligence, e.g.,

intelligence quotient (IQ). Research on EI began at the end of the 20th

century and gained

popularity with the public through Goleman’s (1995) book, Emotional Intelligence: Why

It Can Matter More than IQ. A widely accepted definition of EI is rooted in the

pioneering efforts of Goleman, Mayer, Salovey and Caruso:

[Emotional intelligence is] the capacity to reason about emotions, and of emotions

to enhance thinking. It includes the abilities to accurately perceive emotions, to

access and generate emotions so as to assist thought, to understand emotions and

emotional knowledge, and to reflectively regulate emotions so as to promote

emotional and intellectual growth. Emotional intelligence from this theoretical


perspective refers specifically to the cooperative combination of intelligence and

emotion. (Mayer et al., 2004, p. 197)

The primary theory of EI utilized in this dissertation is based on the four EI

abilities of Mayer and Salovey (1997): awareness of emotions (own and others),

management of emotions (own and others), emotional understanding, and emotional

facilitation (generation of emotions). These abilities are further refined for understanding

how EI works in teams by focusing on self- and other-awareness and management of

emotions; in the context of teams, EI is generally considered to be a value added

competency to various aspects of individual and group performance, namely

collaboration (Jordan & Troth, 2004).

Research in the field of intelligence proposes that intelligence extends beyond the

traditional quantitative index of IQ to incorporate knowledge of one’s emotions, and

knowledge of the emotions of self and others contributes to higher levels of individual

intelligence and subsequent performance outcomes in leadership, organization

development, negotiation, collaboration, and positive communication (Caruso, Mayer, &

Salovey, 2004). The essence of EI for teams and leadership is self-knowledge of

emotions, e.g., EI involves self-awareness and self-management of one’s own feelings,

and social awareness and management of what others are feeling (Dulewicz & Higgs,

2000; Mayer & Salovey, 1997). Keys to a leader’s success are improved self- and social-

awareness, relationship management, and team building. Thus, leadership requires more

than cognitive intelligence, it requires consideration and awareness of both self and

others. The importance that emotional awareness of self and others has on positive

relationship outcomes is one of the benefits of EI for understanding emotions in


organizations (Gabriel & Griffiths, 2002) and for building team process effectiveness and

collaboration (Jordan, Ashkanasy, Härtel, & Hooper, 2002).

Unlike IQ which focuses on verbal and nonverbal cognitive abilities, EI focuses

on awareness, management, and understanding of self-emotions, and the emotions of

others. EI also involves the ability of an individual to self-regulate emotions and to use

emotions to make good decisions, and to act and interact effectively and empathetically.

EI is the basis for personal self-confidence, integrity, knowledge of strengths and

weaknesses, resilience in times of adversity, self-motivation, perseverance, and the ability

to get along well with others. EI is the primary source of personal energy, authenticity,

and aspiration, and activates innermost values in life, transforming them from thoughts to

actions. EI helps people to recognize, readily acknowledge, respond appropriately, and

value core feelings in themselves and others. EI spurs creative genius and intuition,

shapes trusting relationships, clarifies important decision making, and guides people to

consider creative possibilities and breakthrough solutions (Nwokah & Ahiauzu, 2010).

Relevance to hypothetical model. Today’s business climate is characterized by

limited face-to-face interactions. Each personal interaction that occurs must be as

successful as possible. Increasing the value of personal interactions requires more than

intelligence, it requires understanding of emotions in leaders and teams, i.e.,

understanding of EI. Research suggests that EI has the ability to impact performance

outcomes in organizations, in particular those in which successful negotiation, cohesion,

and collaboration is desired (Kerr et al., 2006). Collaboration is a process of social

interaction, where one’s ability to influence the emotional climate and behavior of others

can strongly influence performance outcomes. As an emerging leadership attribute, EI


competency is seen to be increasingly important to an individual's ability to be socially

effective and therefore more adept at enabling successful collaborative outcomes.

Within a collaborative team, individuals who are emotionally self-aware may

have a positive attitude that contributes to effective conflict management and resolution

of disagreements, i.e., emotional self-awareness improves one’s ability to negotiate,

compromise, and seek the best alternatives that yield positive results (Xavier, 2005). Just

as the personal development of EI improves an individual’s ability to manage change, the

development of EI among team members improves the team’s ability to manage

change—as EI competencies are developed throughout the collaborative team, the more

effective the team can become. This is especially important because it allows teams to

dispel norms and develop new and more prosperous cultures supporting a common goal

(Xavier, 2005). Furthermore, in helping a team to attain desired goals, effective leaders

have substantial levels of EI as well as cognitive intelligence. As Nwokah and Ahiauzu

(2010) note:

Under the guidance of an emotionally intelligent leader, people feel a mutual

comfort level. They share ideas, learn from one another, make decisions

collaboratively, and get things done as they form an emotional bond that helps

them stay focused even amid profound change and uncertainty. (p. 159)

Researchers have been studying aspects of EI for some time; however, there is

still much that is unclear about the nature, assessment, and application of EI. Numerous

case studies on the application of EI and its impact on individuals and organizations have

been reviewed (Dulewicz, & Higgs, 2000). Case studies that describe EI competency as

a benefit to the scenario under study have critics who debate over whether EI is a valid


construct “because it is not a form of intelligence and because it is defined so broadly and

inconclusively that it has no intelligible meaning” (Locke, 2005, p. 425).

Criticism is also presented on the measurement and predictive power of EI in

influencing leadership and group effectiveness (Singh, 2008).

By asserting that leadership is an emotional process, Goleman [as one of the

primary EI proponents] denigrates the very critical role played by rational

thinking and actual intelligence in the leadership process. Given all the add-ons

to the concept proposed by Goleman, any associations between leadership

effectiveness and an emotional intelligence scale that included these add-ons

would be meaningless. (Locke, 2005, p. 430)

The basis for these critical views begins with an assertion that it is arbitrary to attach the

word ‘intelligence’ to what are simply habits or skills. For example, the ability to

monitor one’s emotions refers to the EI competency of self-awareness rather than an

intelligence per-say. One of the conclusions reached by these critics is that “there is no

such thing as actual emotional intelligence, although intelligence can be applied to

emotions” (Locke, 2005, p. 430).

Some researchers say that EI is not viable as a scientific construct, is inadequately

defined, defined inconsistently between researchers, and represents a continuation of a

long line of discredited research into social intelligence that lacks consistency in

application with no appropriate measure (Ashkanasy & Daus, 2005). In response to this

harsh criticism, proponents see EI neither as some new form of social intelligence, nor a

substitute for intellectual intelligence, but rather as a valid and viable construct for

organizational behavior researchers and practitioners to use in their efforts to understand


and predict behavior in order to improve individual and organizational performance

outcomes. Indeed, there is growing evidence that research on EI comprises an exciting

and developing area of research in organizational behavior and leadership competency

(e.g., Anand & Udayasuriyan, 2010; Blattner & Bacigalupo, 2007; Rosete & Ciarrochi,

2005).

Framework. The framework of EI used in this dissertation follows the four

abilities of EI useful for understanding how EI works in teams (Mayer & Salovey, 1997):

self-awareness (awareness of own emotions), self-management (management of own

emotions), social-awareness (awareness of others’ emotions), and relationship-

management (management of others’ emotions). While this framework is centered on the

theoretical work of Mayer and Salovey (1997), it overlaps with the theoretical

perspectives put forth by the primary researchers in the field of EI (Bar-On, 1997;

Dulewicz & Higgs, 2000, 2004; Goleman, 1998, 2006; Mayer et al., 2004; Salovey &

Mayer, 1990). Collectively, these EI researchers conceptualized EI from a framework of

four, five, or seven abilities that share a common focus on the core abilities of emotional

awareness and emotional management. An individual’s awareness and management of

these core EI abilities can enhance his or her effectiveness with tasks that involve

interpersonal relationships. Since motivation is required in managing emotions, the

domain of self-motivation has been addressed in the EI literature, and it will also be

reviewed in this overview of the framework of EI.

Self-Awareness. Leaders competent in self-awareness are aware of their

weaknesses and are comfortable in admitting them, view constructive criticism

positively, and recognize their emotions and the effect they have in the collaborative


environment (Xavier, 2005). As the keystone of EI, self-awareness refers to recognizing

one’s emotions and feelings as they occur. Self-awareness involves monitoring feelings

from moment to moment, and offers crucial insight to self-understanding. People with

greater certainty about their feelings are better pilots of their lives and have a definite

sense of how they feel about personal decisions (Goleman, 2006). Self-awareness is

comprised of three components: emotional self-awareness, accurate self-assessment, and

self-confidence.

Emotional self-awareness refers to recognizing one’s own emotions and their

effects on us and others. Emotions drive behaviors, and a leader’s ability to consider the

potential overwhelming importance his or her own emotions may have on decision

making is important. This awareness does not mean that the emotionally aware leader

has to detach emotions from leadership, but rather that they be understood, and in control

(Xavier, 2005). People with this competence know which emotions they are feeling and

why, realize the link between their feelings and what they think, do and say, recognize

how their feelings affect their performance, and have a guiding awareness of their values

and goals (“Emotional competence framework,” 1998).

Accurate self-assessment refers to knowing one’s strengths, limits, and

weaknesses. A leader can be slightly aware of the emotions of others if he does not have

an accurate view of his own (Xavier, 2005). People with an accurate self-assessment are

aware of their strengths and weaknesses, learn from experience through reflection, are

open to feedback, new perspectives, continuous learning, and are able to show a sense of

humor and perspective about themselves (“Emotional competence framework,” 1998).


Self-confidence refers to having a strong sense of one’s self-worth and abilities.

Leaders lacking in self-worth can appear weak and lacking in inspiration. The ability to

inspire and motivate followers is a desirable leadership attribute; self-confidence, if

genuine, can lead to improved leadership effectiveness (Xavier, 2005). People with this

competence present themselves with self-assurance, can voice unpopular views, and are

decisive in the presence of uncertainty and pressure (“Emotional competence

framework,” 1998).

Self-Management. Emotionally intelligent leaders recognize the importance of

creating a collaborative environment regulated with trust and equality, and are careful to

self-manage their emotions and resultant behavior accordingly. Followers quickly adopt

the optimism, enthusiasm, and inspiration of a leader demonstrating a genuine interest in

the shared success of the team. Self-management is comprised of seven components:

adaptability, emotional self-control, initiative, achievement orientation,

conscientiousness, innovativeness, and trustworthiness.

Adaptability refers to flexibility in handling change and dealing with changing

situations, emotional self-control refers to inhibiting emotions that are in contrast to

organizational norms and managing disruptive emotions and impulses (“Emotional

competence framework,” 1998; Xavier, 2005). People with adaptability can smoothly

handle multiple demands, can rapidly change and shift priorities, can adapt their

responses and tactics to fit changing circumstances, and can behave with flexibility in

how they see events ("Emotional competence framework," 1998). People with emotional

self-control manage their impulsive feelings well, stay composed, positive and

unflappable even in distressing times, and think clearly and stay focused under pressure.


Having initiative in self-management refers to being proactive and operating with

a bias toward action that can be contagious. With achievement orientation, one’s ability

to self-manage their emotions involves striving to do better and coaching others to reach

their highest potentials (Xavier, 2005). Self-management also involves

conscientiousness, i.e., taking responsibility for personal performance. A person with

this competence keeps promises, meets commitments, holds themselves accountable for

meeting objectives, and is organized and careful in their work (“Emotional competence


Innovativeness is a component of self-management that involves individuals

being comfortable with, and open to, new ideas and new information. People with this

competence seek out fresh ideas from a variety of sources, entertain original solutions to

problems, generate new ideas, and take fresh perspectives and risks in their thinking

(“Emotional competence framework," 1998). With trustworthiness, the ability to

demonstrate integrity and consistency with emotions and behavior is an important

component of self-managing emotions (Xavier, 2005). People with this competence act

ethically, build trust through their reliability, admit their own mistakes and confront

unethical situations in others, and take principled stands even if they are unpopular

("Emotional competence framework," 1998).

Awareness of others’ emotions. Effective leaders and individuals are not only

aware of their own emotions but those of their team. Empathy, trust, and integrity are

critical competencies in collaborative effectiveness, particularly in consideration of the

diverse backgrounds and cultures that may be present in collaborative teams. People with

high social awareness can assess situations from other points of view which contribute to


improved comradery, trust, and confidence in the capabilities of the collaborative team.

This also leads to improved employee satisfaction, decreased turnover, and confidence in

leadership (Xavier, 2005). Individuals who exhibit empathy are more attuned to the

subtle social signals that indicate what others need (Goleman, 2006). This makes them

better at meeting those needs, and exhibiting support for others’ abilities and reaching of

their highest potentials. Social awareness is comprised of five components: empathy,

service orientation, organizational and political awareness, developing others, and

leveraging diversity.

Empathy refers to understanding others and taking an active interest in their

concerns. Effective leaders value the ideas and futures of their followers (Xavier, 2005).

People with this competence are attentive to emotional queues and listen well, show

sensitivity, understand others’ perspectives, and are willing to help others based on an

understanding of their needs and feelings ("Emotional competence framework,” 1998).

Service orientation refers to anticipating, recognizing, and meeting customers’

needs. Leaders who consider themselves as a resource to their followers, and offering of

themselves to help meet objectives gain the respect and camaraderie of their followers

(Xavier, 2005). People with this competence understand customers’ needs and match

them to services, seek ways to increase customer satisfaction and loyalty, gladly offer

assistance, and grasp a customer’s perspective (“Emotional competence framework,”

1998).

Organizational and political awareness refers to establishing meaningful

relationships with customers, within work teams, and the organization (Xavier, 2005).

People with this competence read a group’s emotional currents and power relationships,


detect crucial social networks, understand the forces that shape views and actions of

others, and accurately read situations and organizational and external realities

(“Emotional competence framework,” 1998).

Developing others involves sensing what others need in order to develop and

improve their abilities. People with this competence acknowledge and reward others’

strengths, accomplishments, development needs, offer useful feedback, mentor, give

timely coaching, and offer assignments that challenge and grow a person’s skill


Leveraging diversity refers to the cultivation of opportunities through diverse

workgroups. People with this competence respect and relate well to others from different

backgrounds, are sensitive to group differences, see diversity as an opportunity, and

challenge bias and intolerance ("Emotional competence framework,” 1998).

Management of others’ emotions. Effective leaders work constructively with

others and understand the importance of moving their collaborative teams toward desired

outcomes (Xavier, 2005). Handling relationships is in large part the skill of managing

emotions in others, and the EI competency of relationship management encompasses

abilities that mediate popularity, leadership, and interpersonal effectiveness. Leaders and

individuals who are effective at relationship management do well at tasks that rely on

interacting smoothly with others—they are social stars (Goleman, 2006). Relationship

management is comprised of eight components: inspirational leadership, being a change

catalyst, conflict management, influence, communication, teamwork and collaboration,

building bonds, and collaboration and cooperation.


Inspirational leadership is the component of relationship management that refers

to inspiring and guiding behavior of others intellectually and emotionally (Xavier, 2005).

People with the competence of managing others’ emotions articulate and arouse

enthusiasm for a shared vision and mission, step forward to lead where needed regardless

of position, guide the performance of others, and lead by example ("Emotional

competence framework,” 1998).

The relationship management component refers to the ability to initiate and

manage change, and of having a positive attitude inclusive of the impact change has on

others (Xavier, 2005). People with this competence recognize the need for change and

remove barriers, challenge the status quo acknowledging the need for change, champion

positive change and involve others in its pursuit, and model the change expected of others


Conflict management refers to the resolving of disagreements, and being able to

negotiate compromise and seek the best alternatives for the team (Xavier, 2005). The

competence of managing others’ emotions has application to essential leadership

characteristics that support successful teams, such as conflict management, influence,

open communication, and collaboration. The ability to manage conflict with diplomacy

and tact brings disagreements into the open, encourages debate and open discussion, and

orchestrates win-win solutions ("Emotional competence framework," 1998).

Influence refers to the ability of gaining the agreement of others. Leaders avoid

autocratic dictation, yet remain influential in decision making that yields positive results

(Xavier, 2005). People with the EI competence of managing others’ emotions are skilled

at influence and persuasion, arrange presentations that appeal to the listener, use indirect


influence to build consensus and support, and orchestrate dramatic events to effectively

make a point ("Emotional competence framework,” 1998).

Communication is another component of managing others’ emotions, and

effective leaders use communication to send clear and convincing messages. Leaders

with strong communication skills are effective in compromise, deal with difficult issues

straightforwardly, listen well, seek mutual understanding and welcome information

exchange whether the news is good or bad ("Emotional competence framework," 1998).

Teamwork and collaboration are components of managing others’ emotions based

on building relationships with a shared vision and synergy in pursuing collective goals

(Xavier, 2005). When teams and team members engage in teamwork and collaboration

they work with others toward shared goals. By managing the emotions of others, leaders

have the ability to promote teamwork and collaboration by modeling team attributes such

as respect, helpfulness, and cooperation, drawing all team members into active and

enthusiastic participation, building team identity and cooperation, protecting the

reputation of the team and team members, and sharing credits and success. They also

balance task focus with attention to relationships, and share plans, information and

resources while promoting a friendly and cooperative climate ("Emotional competence


Another important component involved in effectively managing the emotions of

others is building bonds. Building bonds refers to the nurturing of instrumental

relationships, i.e., relationships that are instrumental in collaboration. Leaders and

individuals who build bonds are essentially cultivating and maintaining informal

networks, seeking out relationships that are mutually beneficial, building rapport, and


maintaining open communication to keep others in the loop ("Emotional competence


Finally, collaboration and cooperation are components of relationship

management that refers to working with others toward shared goals. Leaders who are

effective at promoting collaboration and cooperation among teams and team members

maximize the EI competence of managing others’ emotions. Additionally, they balance

task focus with attention to relationships, collaboration, sharing of plans, information and

resources while promoting a friendly cooperative climate within the team or organization

("Emotional competence framework,” 1998).

The foregoing framework of EI, based on an ability-based model useful for

understanding how EI works in teams, is the theoretical foundation for which this

dissertation is based. As put forth by Mayer and Salovey, (1997), self-awareness, self-

management, awareness of others’ emotions, and management of others’ emotions are

competencies necessary for team effectiveness. These four competencies are what Jordan

and Lawrence, (2009) set out to measure with their Workgroup Emotional Intelligence

Profile. Together, this dissertation seeks to determine if aptitude and betterment of these

abilities may lead to improved collaboration and ultimately team effectiveness.

Self-Motivation. Managing emotions in support of a goal is essential for self-

motivation, attentiveness, and creativity. Delaying gratification and stifling

impulsiveness underlies accomplishment of every sort. People with this skill tend to be

more productive and effective in whatever they undertake (Goleman, 2006). Self-

motivation is comprised of four components: achievement drive, commitment, initiative,

and optimism.


Achievement drive refers to the motivation to improve or meet a standard of

excellence. People with this competence are results-oriented, exhibit a high personal

drive to meet their objectives and standards, set challenging goals and take careful risks,

pursue information to reduce uncertainty, and find ways to do better and improve their

performance. While achievement drive motivates leaders toward goal attainment,

commitment seeks to align the goals of the group, team or organization. People with this

competence readily make personal or group sacrifices to meet organizational goals, find a

sense of purpose in their larger objectives, use the group’s core values in guiding decision

making, and actively seek out opportunities to fulfill the group’s mission ("Emotional

competence framework,” 1998).

Initiative refers to a readiness to act on opportunities. People with this

competence are ready to seize opportunities, pursue goals beyond expectations, cut

through red tape when necessary to get jobs done, and mobilize others through unusual,

enterprising efforts. While motivation, achievement drive, and initiative seek the ideals

of accomplishment, optimism guides individuals toward a positive view of life and the

future. Optimistic leaders also demonstrate persistence in pursuing goals despite

obstacles and setbacks, operate from hope of success rather than fear of failure, and see

setbacks as due to manageable circumstance rather than a personal flaw (“Emotional

competence framework”, 1998). Without the element of optimism in a leader’s self-

motivation, followers are unlikely to embrace the pessimistic views therein (Xavier,

2005).

The emotionally intelligent individual. EI improves one’s ability to initiate and

manage change. Within a collaborative team, having a positive attitude inclusive of the


impact change has on others contributes to effective conflict management and resolving

of disagreements. Thus, EI affords an individual the ability to negotiate compromise and

seek the best alternatives that yield positive results (Xavier, 2005).

“Emotional intelligence refers to an ability to recognize the meaning of emotion

and their relationships, and to reason and problem solve on the basis of them” (Marques,

2007, p. 645). Without EI, an individual might lack the crucial quality of reading

between the lines and listening to the unspoken. Awareness and application of EI

competencies have the potential to facilitate problem solving, ease conflict resolution,

and bring collaborative teams to a higher state of being. EI involves the capacity to

perceive emotions, interpret the information expressed in emotions, and manage emotions

effectively (Marques, 2007). When emotions are positive, individuals experience a

broadening of their momentary thought-action reflex. This theory suggests that an

individual expressing positive emotion will have a wider array of response considerations

for a particular scenario. In terms of enabling improved collaboration outcomes,

“positive emotions broaden habitual modes of thinking or acting” (Cameron, Dutton, &

Quinn, 2003, p. 166).

EI, particularly positive EI, has the potential to equip an individual with cognitive

abilities to effectively process difficult decision making and conflict. A growing body of

research suggests that conflict can be beneficial, and experiencing conflict helps an

individual to be emotionally activated (Bodtker, Jameson, 2001). Maintaining self-

awareness and self-control of these activated emotions relies on the cognitive abilities of

EI. Experiencing positive emotions has several benefits, such as helping to down-play

negative emotional arousal, improving one’s ability to cope with adversity, and


transforming individuals into more resilient, socially integrated, and capable versions of

themselves. Positive emotions also help to curb organizational conflict by promoting

constructive interpersonal engagement. “Positive emotions can transform organizations

because they broaden people’s habitual modes of thinking, and in doing so make

organization members more flexible, empathic, creative, and so on” (Cameron et al.,

2003, p. 174). “The bottom-line message is that people should cultivate positive

emotions in themselves and those around them, not just as an end-state in themselves, but

also as a means to achieving psychological growth and improved psychological and

physical well-being over time” (Fredrickson, 2004, p. 1367).

Individuals who embrace an environment that cultivates and exploits their

positive emotions will grow to levels not ordinarily achieved. This optimal level of

functioning will provide them with a means for sustaining optimal performance in

themselves and their organization. When an environment taps an individual’s ideas and

promotes empowerment and teamwork, sustainable change action is possible throughout

the collaborative organization (Bramson & Buss, 2002). As individuals develop their

capacity, or ability to evaluate and manage emotions in themselves and others, team

effectiveness will improve. This dissertation identified those particular emotion

processing abilities most important to achieving improved collaboration and team

effectiveness.

The emotionally intelligent leader. Leadership is a process of social interaction

where the leader's ability to influence the emotional climate and behavior of followers

can strongly influence performance outcomes. Leadership consists of such dimensions as

supervision, delegation, discipline, and power utilization, and refers to the ability to


influence, motivate and enable others to contribute to the effectiveness and success of

their organization (Kilburg, 2000). As an emerging leadership attribute, EI competency

is seen to be increasingly important to an individual's ability to be socially effective (Kerr

et al., 2006). The application of the EI competencies to recognize, understand, manage,

and use emotional information about oneself and others contributes to and impacts

effective performance by leaders and managers (Boyatzis, 2007). In order to maximize

leadership effectiveness and the ability to influence others, leaders and managers should

possess the knowledge and skills of EI (Palmer, Walls, Burgess, & Stough, 2001).

Furthermore, “leaders who are emotionally intelligent are essential to developing a

climate where employees are encouraged to perform to the best of their ability” (Wolff &

Koman, 2008, p. 59).

Regardless of the leadership model, leadership effectiveness is enhanced by

leaders possessing EI (Higgs, 2003). EI competency at the individual, team, and

organization levels are key to leaders of the future possessing leadership effectiveness.

Executives who are motivated to understand and adapt to change, and who are motivated

to assess themselves and their employees, EI fosters an emotional and intellectually

healthy environment necessary for successful leadership (Xavier, 2005).

Leaders without EI may be missing a valuable skill that effective leaders of the

future will possess. Emotionally intelligent leaders are likely to have followers who are

motivated to do their best because they feel enthusiastic, passionate, and believe in the

values of both the leader and themselves. EI can be a subtle, yet powerful competitive

advantage in the collaborative team’s ability to succeed as leaders with EI seek to

increase team competency in the EI abilities of self-awareness, self-management, social


awareness, and relationship management. “People high in emotional intelligence will

build real social fabric within an organization, and between an organization and those it

serves, whereas those low in emotional intelligence may tend to create problems for the

organization through their individual behaviors” (Mayer, Salovey, & Caruso, 2002, p. 4).

Since EI and leadership is seen to be increasingly important to team effectiveness,

determining the specific EI abilities uniquely important for leaders was an objective of

this dissertation.

A fundamental component of leadership is the development of sound decision

making, and emotions are essential for rational thought and reasoning leading to sound

decision making. EI competency plays a key role in determining leadership success, and

while often subtle, the influence of emotional competency on effective decision making

and positive interaction with others remains important in determining leadership

effectiveness. Macaleer and Shannon (2002) describe the important relationship between

leadership development and emotional awareness:

Anyone who has any role in working with organizations and their long-term

effectiveness should begin to understand how emotional intelligence can affect

leadership development. The idea is not to suppress emotions (because every

feeling has its value and significance), but to strike a balance between rational

thought and emotions. One of the keys to sound decision making is a greater

awareness of our emotions and those of others. (p. 10)

Application of EI can also be seen as a leader’s ability to effectively deal with

their interpersonal relationships. This connection is based on the idea that one explores

their emotions looking at such attributes as empathy, self-image, social skills, feelings,


flexibility, happiness, stress tolerance, optimism, assertiveness, and impulse control.

Followers need a sense of security, and the behaviors of the leader play a role in their

ability to feel secure. If the leader is not in control of his or her emotions, followers may

lose confidence and support for the leader. Conversely, if leaders have no understanding

or consideration of the feelings of followers, they will be less effective in maintaining

cohesion and team effectiveness. The objective is to strengthen and exploit the emotions

of the situation in order to accomplish a desired goal in a collaborative environment. In

essence one needs to know oneself along with specific tendencies as well as come to

know as quickly as possible the emotions of followers in order to have a quality

exchange. The ability to size up a given situation and act positively on it leads to a

desired advantage (Chrusciel, 2006). Ideally, EI is used such that emotional issues do not

detract from the leader’s effectiveness, or team's progress. In terms of performance

management, it is important for a leader to be able not only to deliver outputs, but also to

deal effectively with themselves and staff. Leaders higher on EI are more likely to

achieve business outcomes, and be considered as effective leaders by their subordinates

and direct manager (Rosete & Ciarrochi, 2005).

Typically emotions are viewed as too personal to be discussed at the workplace,

yet leaders who appreciate the impact emotions have on the workplace environment have

an advantage over those who ignore them (Xavier, 2005). Gardner and Stough (2002)

address the emotionally intelligent leader:

The ability to successfully manage emotions allows the leader to handle the stress

of the job, the frustrations, disappointments, and joys. Leaders who are able to

understand and manage their emotions and display self-control act as role models


for followers, enhancing the followers trust and respect for the leader. This

contagion is carried over to the collaborative team. The ability to manage

emotions and relationships permits the emotionally intelligent leader to

understand followers’ needs and to react accordingly. (p. 70)

Becoming an emotionally intelligent leader or team member who also has the

ability to be collaborative involves learning and trusting one’s own emotions, and

improving the personal qualities and skills in the awareness of others’ emotions. As

leaders of the future look to improve their competitive advantage, speed to market,

quality, and team effectiveness, application of EI competencies will enhance one’s ability

to recognize and control feelings, and to recognize those of other people and respond to

them constructively and skillfully (Mackenzie & Welch, 2005).

“Emotional expressivity skills allow visionary leaders to establish an emotional

connection with followers that may overcome resistance to produce meaningful

organizational change” (Groves, 2006, p. 578). Maintaining a competitive advantage

remains a torturous struggle of change and adaptation to shifting market, economic, and

regulatory conditions. Leaders want honesty, commitment, and trust from their

followers, but they also must exemplify these ideals. Leadership does not flourish in a

climate of targets, testing and suspicion - it requires trust. Trust that people will seek to

achieve within themselves a passion for their work (Mackenzie & Welch, 2005).

Emotionally intelligent leaders are thought to be happier and more committed to

their organization, achieve greater success, perform better in the workplace, take

advantage of and use positive emotions to envision major improvements in organizational

functioning, and use emotions to improve their decision-making. This instills a sense of


enthusiasm, trust, and cooperation in their employees through interpersonal relationships.

This is addressed by Gardner and Stough (2002):

Emotional intelligence enhances a leader’s ability to solve problems and to

address issues and opportunities facing them and the organization. A leader high

in emotional intelligence is able to accurately appraise how their followers feel

and use this information to influence their subordinate’s emotions, so that they are

receptive and supportive of the goals and objectives of the organization. Leaders

who are able to use emotions to guide decision-making are able to motivate

subordinates by engaging in activities facilitated by emotions, and are able to

encourage open-minded idea generation, decision-making and planning, because

they can consider multiple points of view. (p. 70)

Successful leaders who are able to manage positive and negative emotions within

themselves and within others are able to articulate a vision for the future, talk

optimistically, provide encouragement, stimulate thinking, encourage the expression of

new ideas, and intervene in problems before they become serious. Emotional

management may underlie the ability of the leader to be inspirationally motivating and

intellectually stimulating. The ability to identify and understand the emotions of others in

the workplace is important for leaders so that they can influence the feeling of

subordinates to maintain enthusiasm, productivity, and organizational effectiveness.

Given the extensively positive relationship between EI and leadership, and the

importance of leadership to team effectiveness, this study aimed to identify the specific

EI competencies and abilities critical for leaders to develop within themselves and their

collaborative teams.


Emotional intelligence and teamwork. A team is a cohesive group of people

who collaborate in support of a common vision and aspirations (Katzenbach, 1998). EI

has gained popularity as an essential personal factor for effective teamwork since leaders

with high EI are successful in negotiating and resolving conflict (Anand & Udayasuriyan,

2010; Blattner & Bacigalupo, 2007). Modern business cultures reflect accelerated

changes in work force, impact of technology, industrialization, and globalization. People

currently need to function in a world vastly different from that of previous generations.

To function effectively in what are now inherently natural collaborative environments,

individuals and leaders working collaboratively require EI aptitude.

Research suggests that managers with high EI obtain results from employees that

are beyond expectations, while developing and using talents crucial for organizational

effectiveness (Chen, Jacobs, & Spencer, 1998). Effective managers steer their own

feelings, acknowledge the feelings of subordinates concerning their work situation, and

intervene effectively to enhance morale. Moreover, close to 90 percent of success in

leadership positions can be attributed to EI (Anand & Udayasuriyan, 2010). Therefore,

an environment for collaborative success is created when emotionally intelligent

leadership is combined with an emotionally intelligent team. Optimizing this relationship

for team effectiveness and collaboration necessitates the development of EI skills within

the collaborative team.

Emotional intelligence and collaboration. Collaboration involves sharing risks,

resources, and responsibilities in order to achieve a common goal that would not be

possible if attempted individually. Collaborative team members integrate themselves into

a collaborative culture which comprises an awareness of self and others, seeks a


willingness to adapt for the benefit of all, and demonstrates supportive and positive

behaviors to enhance the capabilities of others (Romero, et al., 2009). The importance of

teams developing EI and operating from an awareness of the EI competencies is

demonstrated by the team’s ability to dispel norms, and allows their respective

organizations to develop common collaborative goals that may contribute to an

organizational culture that is prosperous and successful (Xavier, 2005).

The link between EI and effective collaboration is demonstrated when the same

EI competencies that are displayed in strong leadership are displayed by teams, i.e., to

recognize, understand, manage, and use emotional information about oneself and others

(Boyatzis, 2007). Teams that develop and practice EI are likely to be effective with

collaboration because positive emotions reverberate through individuals as they

interact—positive emotions are contagious. When team members are aware of self and

others’ positive emotions, performance increases, when emotions are negative,

performance decreases and there is dissonance within the collaborative team (Xavier,

2005).

Measures of emotional intelligence. Several measures of EI are available to

researchers, and in general, the EI measures that are reviewed in the literature vary

widely in both content and method of assessment (Conte, 2005; McEnrue & Groves,

2006; Zeidner et al., 2008). Measures of EI provide information about an individual’s

level of EI using two approaches: measures based on abilities (i.e., EI competencies), and

measures based on traits. While EI abilities refer to those abilities related to emotions

(Salovey and Mayer, 1990), trait EI refers to emotion-related behavioral dispositions and


attributes (Petrides, 2011). Table 2.1 presents a comparison of some of the most common

measures of EI abilities and EI traits used in the literature.

The Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT; Mayer,

Salovey, & Caruso, 2002) is the prototypical ability-based self-report measure of EI in

which emotional awareness and emotional management are core abilities referenced

against expert and consensus opinion. The Emotional Competence Inventory (ECI;

Boyatzis et al., 2000) and the Bar-On Emotional Quotient Inventory (EQ-i; Bar-On,

1997) are self-report trait-based measures. The MSCEIT and EQ-i have a large number

of items, with 141 and 133-items respectively; the ECI has 72-items.


Table 2.1 Summary of Common Measures of Emotional Intelligence (EI)

EI Measure Type Theoretical

Model

EI Dimensions and

Scales

Length

Mayer-Salovey-

Caruso Emotional

Intelligence Test

(MSCEIT)

Self-report

questionnaire

referenced

against

expert and

consensus

opinion

(Ability-based)

Salovey &

Mayer

(1990,

1997)

Perception, appraisal,

and expression of

emotion

Emotional facilitation

of thinking

Understanding and

analyzing emotional

information

Regulation and

management of

emotion

141

Items

Emotional

Competence

Inventory,

Version 2

(ECI-2)

Self-report

questionnaire

(Trait based)

Goleman

(1995,1998)

Boyatzis,

Goleman, &

Rhee

(1999)

Self-awareness

Self-management

Social awareness

Social skills

72

Items

Emotional

Quotient

Inventory

(EQ-i)

Self-report

questionnaire

(Trait based)

Bar-On

(1997)

Intrapersonal

Interpersonal

Adaptation

Stress management

General mood

133

Items

Trait Emotional

Intelligence

Questionnaire—

Short Form

(TEIQue-SF)

Self-report

questionnaire

(Trait based)

Petrides

(2009)

Well-being

Emotionality

Sociability

Self-control

30

Items

Work Group

Emotional

Intelligence

Profile-Short

Version (WEIP-S)

Self-report

questionnaire

(Ability-based)

Jordan &

Lawrence

(2009)

Awareness of own

emotions

Management of own

emotions

Awareness of others’

emotions

Management of

others’ emotions

16

Items

Note. Adapted from McEnrue and Groves (2006).


EI measures with considerably fewer numbers of items have been developed for

rapid assessment of EI within larger assessment batteries. For example, the Trait

Emotional Intelligence Questionnaire—Short Form (TEIQue—SF; Cooper & Petrides,

2010) is a 30-item self-report trait-based measure of EI, and the Workgroup Emotional

Intelligence Profile-Short Version (WEIP-S) scale (Jordan & Lawrence, 2009) is a 16-

item self-report questionnaire of EI abilities. Of the EI measures used in empirical

research, ability-based measures of EI, such as the MSCEIT and the WEIP-S have been

researched in team settings. The WEIP-S was selected for this dissertation in order to

measure team-based EI abilities.

MSCEIT (Mayer-Salovey-Caruso emotional intelligence test). The MSCEIT

(Mayer et al., 2002) is an ability-based self-report instrument designed to assess the four

EI abilities of Mayer and Salovey (1997) useful for understanding how EI works in

teams: self-awareness (awareness of own emotions), self-management (management of

own emotions), social-awareness (awareness of others’ emotions), and relationship-

management (management of others’ emotions). The MSCEIT has been used in team-

based studies; however, its considerable length of 141-items did not support practical use

in this study.

ECI (emotional competency inventory). The ECI (Boyatzis et al., 2000) is a self-

report trait-based measure of EI based on Goleman’s (1995, 1998) model of four EI

clusters: self-awareness, self-management, social awareness, and social skills (Cherniss,

2000). While useful in certain research settings as a peer-review instrument, the ECI is

not well suited for this study which seeks a self-assessment of EI abilities best suited for

teams.


Bar-On’s EQ-i (emotional quotient inventory). The Bar-On EQ-i is often

referred to as one of the most widely used instruments in assessing EI, perhaps only for

the reason that it was one of the first attempts at creating an EI assessment tool and has

familiarity in the field due to its age (Macaleer & Shannon, 2002; Anand &

Udayasuriyan, 2010). The EQ-i self-report trait-based measure of EI is long (133-items),

takes about 45 minutes to complete, and doesn’t lend itself well to comparison - one

person’s EI assessment doesn’t necessarily imply higher EI compared to another.

Discriminating among the most intelligent people seems to be desirable by many, and

self-report measures like the Bar-On EQ-i are not generally accepted as an accurate way

of doing so. In particular with EI, how one feels on a particular day could influence how

questions are answered versus another day (Van Rooy et al., 2005). This remains a clear

limitation in demonstration, acceptance, and quantitative validity of the EI construct,

particularly in discussion relative to the Bar-On EQ-i. Bar-On however, defends the

ability to accurately provide an EI assessment and indicates that the term intelligence was

used to describe the collection of skills, competencies and abilities of an individual, and

emotional was added as a prefix to discriminate this intelligence from cognitive ability

(Van Rooy et al., 2005).

The EQ-i provides an assessment of five general categories of EI: interpersonal

EQ, intrapersonal EQ, stress management, adaptability, and general mood. These five

categories of EI consist of fifteen subscales which measure areas such as empathy,

independence and optimism which are considered to be certain facets of personality (Van

Rooy et al., 2005). This is the basis for the most common argument raised by critics of

EI, that it simply draws from different aspects of personality. The EQ-i was primarily


designed to assess those personal traits that enable improved emotional well-being for an

individual, not necessarily how the traits may affect or contribute to improved work-place

situations, or collaborative activities. Hence, the EQ-i was not selected for this research.

TEIQue—SF (trait emotional intelligence questionnaire—short form). Trait EI

refers to a set of emotional self-perceptions related to personality and emotional

experience, and the TEIQue—SF is a self-report questionnaire developed to

comprehensively evaluate trait EI domains rather than EI abilities (Cooper & Petrides,

2010). The TEIQue—SF, which was developed from the 144-item TEIQue (Petrides &

Furnham, 2006; Petrides, Pérez, & Furnham, 2003), covers 15 EI traits grouped in four

factors: well-being, emotionality, sociability, and self-control. Two items from each of

the 15 EI traits comprise the 30-item self-report questionnaire. Since the tool measures

EI traits rather EI abilities, it was not selected for use in the current study.

WEIP-S (workgroup emotional intelligence profile-short version). The

Workgroup Emotional Intelligence Profile-Short Version (WEIP-S) is based on abilities

vital during the interaction of team members (Jordan & Lawrence, 2009). The WEIP-S is

a 16-item self-report questionnaire aligned with the Mayer and Salovey (1997)

framework for EI which measures four EI abilities helpful for understanding how EI

works in teams: awareness of own emotions, management of own emotions, awareness of

others’ emotions, and management of others’ emotions. Each ability is measured by its

own subscale, and each subscale demonstrates high reliability (Cronbach’s alpha = 0.805

– 0.903). The WEIP-S is a short version of the Workgroup Emotional Intelligence

Profile (WEIP), a 27-item self-report measure of EI within a team context consisting of

items that were selected from an original item pool of 52-items (Jordan et al., 2002). The


WEIP was revised over six versions, and the WEIP-S is comprised of a selection of the

ability-based items listed in the WEIP-S (Jordan & Troth, 2004). The WEIP-S was

selected for this study based on its high reliability statistics for each of the four subscales

that measure EI abilities of individuals to work effectively with others in a team (Borges

et al., 2012; Moore & Mamiseishvili, 2012; Troth et al., 2012).

Collaboration

Collaboration, the second of three study constructs used in this study, functions as

the dependent variable (DV) in the hypothetical model (see Figure 1.1). The following

sections present the theoretical foundations of collaboration, relevance to the hypothetical

model, and the framework used in this study (i.e., a team-based model consisting of three

factors, integrating, compromise, and communication). The literature on collaboration is

explored relative to leadership, and introduces the objective of increasing collaboration

through EI growth. The section concludes with a review of the mainstream measures

used in assessing levels of collaboration in teams.

Theory. Collaborative relationships offer a unique opportunity to innovate in

uncertain conditions, and collaboration may prove most beneficial to organizations

undergoing change (Shaw & Lindsay, 2008). Precursors to successful collaboration

include trust, shared goals, and open communication (Hattori & Lapidus, 2004).

Fundamental in collaborative efforts is trust, and in business relationships trust indicates

the highest dynamic of relationships. While trust is essential for collaborative innovation

and collaborative success, the absence of trust creates significant barriers to

collaboration. As trust wanes, so do relationships, and consequently, any potential for

successful collaboration is diminished. Because collaboration involves taking


responsibility for the organization or team as a whole, the concerns of the team must

support the collective interests of the organization and must be aligned with the

individual (i.e., the individual refers to individual members of a team or organization

versus the team or organization as a whole). Trust is an extension of normal team

dynamics, and as the foundation of collaboration, trust facilitates group success and

achievement of a common goal.

In collaboration, the concept of leadership is unilaterally applied. Although the

collaborative team may identify a chairperson or leader by title, each and every team

member brings a particular set of skills and expertise. Through the interaction,

development and un-biased sharing of these unique skills, collaborative efforts can

produce positive outcomes in ways not ordinarily achievable. “Collaboration between

different organization and organizational parts is often critical for the accomplishment of

the common goal and is therefore an important factor that explains organizational

outcomes and performance” (Dietrich, Eskerod, Dalcher, & Sandhawalia, 2010, p. 63).

High quality characteristics of collaboration include fluency and openness of the

participants, adaptability, and a willingness to align efforts toward a common goal.

Alignment and activation of these characteristics contribute to knowledge integration

leading to otherwise unattainable learning and innovation, project success, and future

collaboration.

Relevance to hypothetical model. Primary elements linking collaboration to EI

are inclusion (Shaw & Lindsay, 2008), integration and compromise (Rahim, 1983a,

1983b), and communication (Aram & Morgan, 1976; Romero et al., 2009). Inclusion

draws upon the potential and expertise of individuals, the need for attention in managing


the complexity of collaboration, and the need for an ongoing commitment to knowledge

sharing in the development of collaborative strategies within a team (Shaw & Lindsay,

2008). Integration and compromise involve an active intent to support collaborative

strategies through the establishment of a common ground, unified strategies, and

integration of ideas (Rahim, 1983a, 1983b). Teams seeking compromise develop

supportive and positive behaviors to enhance the capabilities of others and to adapt for

the benefit of all (Romero et al., 2009). Communication involves information exchange

for mutual benefit among individuals and teams, aligning of efforts so that more efficient

results can be achieved, and sharing of resources to reach compatible goals (Aram &

Morgan, 1976).

The EI factors of self-awareness and self-management of emotions correlate with

compromise and integration in collaboration. Awareness and management of others’

emotions is a necessary influence in resolving conflict for the purpose of achieving

compromise in a collaborative environment (Mita & Debasis, 2008). Concern for self

and others promotes integration of ideas, sharing of resources, cooperation and inclusion

of all team members focused on shared goals (Romero et al., 2009). Further, additional

elements revealed in the literature essential to having a direct effect on the quality of

collaboration are communication, coordination, mutual support, aligned efforts, and

cohesion (Dietrich et al, 2010). While communication refers to open and efficient

information exchange and coordination refers to shared and mutual goals, mutual support

refers to willingness to help others and exhibiting the flexibility to do so. Aligned efforts

refer to alignment of contributions with expectations and priorities. Finally, cohesion,

which is the most important source of success for groups (Carron & Brawley, 2000), is


the extent to which members of a group like and trust one another, are committed to

accomplishing goals, and share group pride (Beal, Cohen, Burke, & McLendon, 2003).

The essence of this study is that the more cohesive a group, team, or organization is, the

greater the likelihood that the group, team, or organization will experience positive

collaboration.

A discussion on collaboration in the Harvard Business Review identifies eight

factors that contribute to the creation of collaborative teams that are productive and

innovative (Gratton & Erickson, 2007). Among these factors are leadership behaviors

among top executives indicative of EI and EI competencies, including building successful

relationships, modeling collaborative behavior, and enabling a strong sense of community

within the team of collaborative participants. The practices of the team leader support a

model where the elements of EI have an impact on collaboration. Specifically, success in

collaboration is strongly influenced by the extent that top leaders endorse EI, practice EI

competencies, invest in supportive social relationships, and demonstrate collaborative

behavior themselves.

Collaboration and leaders. Where leaders were once seen to control, plan, and

inspect the overall running of an organization, leadership roles are now seen to also

motivate and inspire others, to foster positive attitudes at work, and to create a sense of

contribution, importance, and collaboration with team members. During the last decade,

interpersonal skills have become integral to effective leadership and positive

collaboration outcomes (Palmer et al., 2001). The importance of EI lies in the obvious

but often ignored fact that the mood of the leader and how it impacts others on the team

are interrelated. EI is more than being happy or sad, it's the ability to effectively express


and manage one's emotions and relationships with others leading to a positive result

(Xavier, 2005).

The connection between leadership and collaboration suggests that improved

leadership outcomes are possible through a combination of facilitative, democratic, and

collaborative skills. This style of leadership runs contrary to the experience of many

administrators, particularly where hierarchical organizational structures are deeply

engrained in a company’s cultural base. A non-hierarchical leadership style where the

central theme is a network of individuals working together collaboratively requires, or at

least is recommended to exhibit behaviors of caring, building trust, and open sharing of

ideas in collaboration (Slater, 2005).

Recognizing the relationship between leadership and collaboration, Gratton and

Erickson (2007) have identified eight leadership behaviors that can guide teams to

collaborative success. First is by investing in signature relationship practices. This

behavior refers to executive support and investment in a commitment to collaboration,

e.g., open floor plans, shared and sufficient meeting locations, and other mechanisms to

enable open communication. Second is in modeling collaborative behavior. This refers

to senior executives who model collaborative behavior themselves while also

encouraging followers to do so. Third is creating a gift culture. This behavior involves

mentoring and coaching to help employees build the networks they need to work across

corporate boundaries. Fourth is by ensuring the requisite skills, which refers to having

the training or support of human resources and organizational development leaders that

teach employees how to build relationships, improve communication and resolve

conflicts. Fifth is by supporting a strong sense of community. This behavior involves


knowledge and management of team member emotions in order to help team members

experience a sense of community. Sixth is by assigning team leaders that are both task

and relationship oriented. This behavior is critical to building productive teams and is

important for ensuring that relationship orientation is fostered once team-based tasks are

underway. Seventh is building on heritage relationships, which involves creating teams

that contain some team members who already know and have experience with each other.

Eighth is understanding role clarity and task ambiguity, which refers to the knowledge

and management of clear role definitions within a collaborative environment that

supports individuality.

Collaboration and integration. Collaboration implies sharing risks and rewards

among team members acting as a joint entity in order to achieve common goals that

would not be possible if attempted individually. Collaboration is also recognized as a

mechanism for leveraging competitiveness in turbulent market conditions (Romero et al.,

2009). Successful collaboration also requires team member competence, a nurturing

climate, commitment, and relationships calling for an extraordinary degree of trust

among the participants (Hattori & Lapidus, 2004, p. 97). This level of trust and

relationship building can be enabled by a team of emotionally competent members. EI

involves thinking intelligently about emotions in group settings and using emotions to

think intelligently about groups and group performance (Druskatt & Wolff, 1999). EI is

thus personal as well as social, leading to certain behaviors concerning group

performance. These behaviors, whether positive or negative, depend in large part on how

emotions are interpreted. Understanding the personal and social impact these behaviors

have on collaboration is where the EI framework enters, and the more leaders understand


themselves and their employees, the more likely they are to lead their teams to successful

outcomes (Xavier, 2005). In this study, collaboration is comprised of three factors,

integration, compromise and communication. Integrating ideas involves the sharing of

opinions, listening, keeping an open mind, and being respectful of each other in the

interest of idea gathering.

EI has a strong impact on improving the leadership success of individuals and

increases the likelihood of a collaborative organization achieving its strategic and

collaborative goals. That way a win-win scenario can be achieved for both the individual

and the organization (Chrusciel, 2006). “As with emotionally intelligent leaders,

members who are emotionally intelligent form strong relationships and a solid team

support system” (Prati et al., 2003, p. 30). Further, team cohesion is closely aligned to

collaboration in terms of referring to the existence of the collaborative spirit between

team members (Hoegl & Gemuenden, 2001), and cohesiveness and collaboration are

each positively related to team success and group productivity (Carron & Brawley, 2000;

Dailey, 1977).

The ability to work collaboratively is becoming a core requisite in the global

economy, and therefore a further understanding of collaborative behaviors in terms of

their emotional content is warranted. Successful collaboration drives a style of leadership

that is more supportive and participative than directive, and demands behaviors that are

concerned with healthy interpersonal relationships as collaboration is essentially

emotional work. Action that supports collaboration is behavior that encourages

empowerment and valuing the capacity of individuals. This includes leading by example,

listening, sharing in leadership responsibility, openness in relationships, and the honest


sharing and disclosure of information. Valuing the capacity of individuals means more

than just listening, but to take their input and use it for improving the collaboration

process and related decision making. Team members need to embrace and exhibit an

advocacy for collaboration that promotes the beliefs, goals, and value of the collaborative

process (Slater, 2005).

Since collaboration has emerged as an integral component of working together in

new ways, workplace skills related to collaboration should also be developed. This

doesn’t mean necessarily that some new innovative working style is required, but rather

proper application of skills already available within the domains of EI. Of primary

importance to studying collaboration and the factors that impact collaboration is in

understanding the emotions of others, and allowing each member of a team or group to be

heard—this is supported by a firm foundation of emotional competency based on

empathy and taking an active interest in others for mutual understanding (Slater, 2005).

Emotional self-awareness is another crucial skill in collaboration. Self-awareness

relies on the ability of an individual to have a strong sense of self-worth, so as to be

confident in presenting new ideas. “Having the courage to speak out is an emotional

competency based on self-confidence; a dimension of self-awareness” (Slater, 2005, p.

329). Emotional self-awareness also means being aware of strengths and weaknesses,

where in a collaborative situation individuals recognize the strengths they bring to the

group, but also acknowledge when weaknesses exist.

“Relationships are the building blocks of collaboration” (Slater, 2005, p. 330).

Individuals and leaders that are adept at building relationships are more likely to succeed

as they share time and experiences together through open communication, trust, and


rising above an initial common ground. Relationships enable the development of key

competencies required for facilitation, reaching consensus, conflict resolution, team

building and problem solving in a shared context. Non-verbal behavior has also been

acknowledged as an effective measure for reducing conflict and promoting positive

relationships. The emotional nature of collaborative work appears to be important and

essential for successful collaboration, and as Macaleer and Shannon (2002) note,

effective work teams are cohesive, innovative, and supportive of its members:

When managers understand the competencies, and especially the emotional

intelligence capabilities of their team, they will have a better understanding of the

team’s strengths and developmental areas and will be in a better position to

maximize the effectiveness of the team. Understanding an employee’s emotional

intelligence skills will enable better decisions on career development programs,

saving countless dollars that are typically spent on training that does not advance

the capabilities of the individual. Any restructuring of an organization, whether to

acquire additional capabilities or reduce staff, should start with an understanding

of the existing makeup and talents of the employees. Otherwise you might

eliminate or waste important assets. (p. 17)

Emotionally intelligent individuals lend themselves more easily to the team

qualities of collaboration, innovation, and support, hence the inference that EI is essential

for effective team interaction and productivity. Additionally, team members high in EI

are likely to contribute to the overall EI of the group, recognize their roles in the team

structure, are more prone to empathetic behavior, form strong relationships, and enable a

cohesive support system in and among themselves. This cohesiveness facilitates trust


and innovation, as well as efficient decision making and promotes the effective

functioning of the team (Prati et al., 2003).

Collaboration and compromise. Emotional intelligence provides a unique

insight in trying to enhance the chances of conflict resolution and compromise. It is

important to not only recognize the value of EI but to encourage and promote the

improvement of EI in situations of conflict (Chrusciel, 2006). If an individual does not

regulate their emotions, negotiations can sometimes degenerate so that both parties leave

the negotiation dissatisfied with the outcomes. Given the reciprocal social influence

inherent in a negotiation, the EI of both negotiators can strongly influence objective and

subjective outcomes. Each negotiator aims to create value, and claim value. Having an

understanding, and control of emotions in this interaction can improve outcomes for each

party (Foo, Elfenbein, Tan, & Aik, 2005).

EI can be seen as an influential means to enhance the chances of conflict

resolution and compromise (Chrusciel, 2006), and EI can strengthen one’s

communication skills (Xavier, 2005). Meaningful and effective negotiation is a

necessary skill for productive business professionals to produce relationships that are

manifested in communication, and negotiation improves one’s ability to initiate and

manage change. Having a positive attitude inclusive of the impact change has on others

contributes to both effective conflict management and resolution of disagreements

through negotiation, compromise, and seeking of the best alternatives that yield positive

results for both parties (Xavier, 2005). Thus, the second factor of collaboration in this

study, compromise, seeks to extend the basis of idea integration leading to a balanced

solution for all team members.


Collaboration and communication. Successful communication requires an

emphasis on self and social awareness, and awareness of the emotions of others.

Maintaining relationships with clients, customers, and colleagues requires the delivery of

messages that are accurate in meaning, importance, and emotion. For example, senders

and receivers often interpret e-mail communication differently; words have different

meanings, emotions are difficult to express (and interpret) correctly, and clarifying

feedback is not immediately available. In considering these factors and their inevitable

risks, one may not hear the emotional intensity a sender desires to achieve, and one may

not adequately reply with appropriate emotional appreciation for their intended result.

Goleman, Boyatzis, and McKee (2009) discuss the impact of communication on

successful collaboration:

The difference between ineffective and effective leaders lay in the mood and tone

with which they deliver their messages: One may drive the group toward

antagonism and hostility, the other toward optimism, even inspiration, in the face

of difficulty. While most people recognize that a leader’s mood – and how he or

she impacts the mood of others – is important, emotions are often seen as too

personal or unquantifiable to talk about. The best leaders find effective ways to

understand and improve the way they handle their own and other people’s

emotions. Understanding the powerful role of emotions sets the best leaders apart

from the rest – not just in tangibles, such as better business results and the

retention of talent, but also in intangibles, such as higher morale, motivation, and

commitment. (p. 9)


Communication therefore, the third factor of collaboration in this study, is

involved in all aspects of the collaborative process. Team members seek the exchange of

ideas that are meaningful and relevant, consider compromise in the solution, and that are

mindful of the environment, i.e., face-to-face, virtual, or both. Finally, communication is

closely related to relationship management and recognition of self and others, which are

ability-based EI competencies important in this study.

Improving collaboration through the development of emotional intelligence.

“Emotional intelligence is generally accepted to be a combination of emotional and

interpersonal competencies that influence our behavior, thinking, and interaction with

others” (Macaleer & Shannon, 2002, p. 9). Since EI is believed to influence interaction

with others, collaboration should be improved through the development of EI abilities

and competencies. Emotional competencies within the domains of EI can be acquired

over time through education, practice and emotional maturity (Macaleer & Shannon,

2002). According to Seal, Boyatzis and Bailey (2006), EI competencies should be taught

in management schools in order to improve the eventual collaborative abilities of future

leaders and managers:

In terms of management education, although schools are largely lauded for their

ability to prepare students for the technical knowledge necessary for future jobs,

they are routinely criticized for not adequately preparing the types of managers

and leaders that organizations need. Few graduate professional program

curriculums adequately address the intrapersonal and interpersonal skills that

prospective employers want most in their employees and that employees find

most useful in their work. To better prepare future managers for the changing,


team-oriented workplace of the future, management education must be willing to

accept the challenge by incorporating these various ‘people skills’ into their

curriculums. In order for future managers to deal with the rapid pace of change

and the increasing use of teams, management education must respond to the

challenge by addressing emotional and social intelligence in their curriculums. (p.

202)

Individual EI has a group analog; collaborative groups boost their performance by

developing their team’s EI. Absent of EI, a collaborative group can still continue through

the motions of participation and cooperation, but the team may not be as effective as it

could be if members do not fully engage emotionally. To be most effective, the team

needs to develop and nurture emotionally intelligent norms that support the building of

trust, group cooperation and efficacy. However,

…a team with emotionally intelligent members does not necessarily make for an

emotionally intelligent group…creating an upward, self-reinforcing spiral of trust,

group identity, and group efficacy requires more than a few members who exhibit

emotionally intelligent behavior. It requires a team atmosphere in which the

norms build emotional capacity and influence emotions in constructive ways.

(Druskatt & Wolff, 1999, p. 2)

EI is favored by aspiring individuals, teams, and leaders desiring of improvement

in self and social awareness, negotiation, and upward spirals in themselves and their

collaborative activities. “Given that the key components of the collaborative process are

inherently emotional in nature, leaders who are successful in developing collaborative

work cultures may be those who are able to manage, rather than deny, their emotional


selves” (Slater, 2005, p. 330). Since collaboration has the potential to yield positive

outcomes not otherwise attainable, leaders and managers who learn to manage the

emotional aspects of their work and add EI competency to rationale, technical, and

organizational decision making may be successful collaborators at all levels of the

organization (Slater, 2005).

Teams that are emotionally intelligent and collaborative build social capital

(Brooks & Nafukho, 2006). Specifically, EI impacts effective task processes and

individual engagement in those processes, and impacts effective information sharing,

knowledge and idea integration leading to positive collaboration outcomes. Emotion

drives behavior, and behavior affects relationships between individuals, groups and

within the collaborative environment. Emotions and subsequent behaviors can be

positive or negative, but EI allows for the positive application of these emotions

contributing to positive collaborative outcomes. Individuals and teams lacking in trust

are less likely to respond to emotional stressors in ways that build the social capital of the

group (Druskatt & Wolff, 1999). Being cognizant of trust which is founded in the EI

domain of self-management can enable pathways to success thereby avoiding pitfalls of

negative social capital, and ultimately demise of the collaborative process.

Emotions are connected to rationality and reasoning, not only behaviors. Positive

emotions act as catalysts for creativity, and contribute to motivation. Since emotions can

be intense but short-lived, the resultant behaviors can have lasting effects on the

productivity of a collaborative team. It is important to remember that since EI reflects the

ability to accurately appraise and understand emotions, the positive application of these


emotions to facilitate thinking and creativity, flexibility and trust must remain the

primary goals of the collaborative individual (Chattopadhyay & Finn, 2000).

Measures of collaboration. Successful collaboration implies that a team has

maximized the benefits of cooperative work. A review by Thomson et al. (2009) found

few instruments exist to measure collaboration. Nevertheless, researchers have used

measures which include a variety of factors that represent characteristics of collaboration,

such as integration of ideas, negotiating compromise, communication, problem solving,

sharing risk, and team cohesiveness. Table 2.2 presents a summary of the most common

measures of collaboration found in the scholarly literature on business and management.

Aram and Morgan’s (1976) Work Collaboration Questionnaire measures problem

solving, communication, and knowledge- based risk taking using an 18-item self-report,

and Mattisich and Monsey’s (1992) Collaboration Experience Questionnaire measures

factors that influence successful collaboration using a checklist of 19-items. In contrast

to these two measures of collaboration, Rahim’s (1983a, 1983b) Organizational Conflict

Inventory-II measures variables that are highly correlated with collaboration, such as

communication, mutual support (compromising), and aligned efforts (integration and

inclusion).


Table 2.2 Summary of Common Measures of Collaboration in Business Journals

Measure of

Collaboration

Type Theoretical

Model

Collaboration Factors Length

Work Collaboration

Questionnaire

Self-report

questionnaire

Aram & Morgan

(1976) Problem solving

Communication

Knowledge based risk

taking

18

Items

Collaboration

Experience

Questionnaire

Self-report

checklist of

factors that

influence

successful

collaboration

Mattisich &

Monsey (1992) Environment

Measurement

Process/Structure

Communication

Purpose

Resources

19

Items

Rahim organizational

conflict inventory-II

(ROCI-II)

Self-report

questionnaire

Rahim (1983a,

1983b) Integrating

Obliging

Compromising

Dominating

Avoiding

28

Items

Team Collaboration

Questionnaire

Self-report

questionnaire

Adapted from

Aram and

Morgan (1976)

and Rahim

(1983a, 1983b)

Integrating

Compromising

Communication

15

Items

Collaboration is an essential part of teamwork, and effective collaboration leads to

effective team outcomes (Aram & Morgan, 1976). In this study, the measurement of

collaboration required careful consideration in accurately selecting factors measured by

items correlated with the study variables applicable to the hypothetical model—

integrating, compromising, and communication. For this reason, an original

measurement instrument was uniquely developed for this study, a 15-item team

collaboration questionnaire, adapted from the measures developed by Aram and Morgan

(1976), and Rahim (1983a, 1983b).

Emotional intelligence, while rooted in self-awareness and self-management, is

inherently social in its manifestation to bring about cooperative exchange between

individuals, i.e., to invest in relationships with concern for others. In collaboration, a

similar cooperative exchange is desired to bring about positive outcomes. Collaboration


encompasses the collective effort of a group or team of diverse members or organizations

who are working together for a common purpose. Collaboration moves beyond a

working group and implies that stakeholders are working together to accomplish an

outcome that is more significant as a team than that which could be accomplished by the

individual members alone (Gray, 1985; Romero, 2009). Determining the effectiveness of

that collaboration, and having a quantitative method for collaboration assessment is the

purpose of this section of the study.

Collaboration involves mutual influence, open communication, and conflict

resolution (Aram & Morgan, 1976). An assessment of collaboration can be accomplished

by evaluating such team attributes as integration of ideas (integrating), seeking

compromise that optimizes team effectiveness (compromising), and team interaction that

fosters open and authentic dialogue (communication). The Team Collaboration

Questionnaire, a unique measure of team-based collaboration developed for this study,

includes these three factors. The integrating and compromising collaboration factors

were adapted from Rahim (1983a, 1983b), and the communication factor was adapted

from Aram and Morgan (1976). Each factor of collaboration includes five- items each

specific to collaboration and team effectiveness. The complete 15-item questionnaire is

shown in Appendix B. Note after adjusting for improved reliability and validity of the

measurement instrument, the final form of the Team Collaboration Questionnaire

included three factors with three-items each, for a nine-item questionnaire.

Work collaboration questionnaire. The work collaboration questionnaire (Aram

& Morgan, 1976) is a self-report questionnaire focused on three factors of collaboration

(problem solving, communication, and knowledge-based risk taking) with 18-items of


behavior relevant to collaboration. The questionnaire seeks to relate items of

interpersonal influence, conflict resolution, and support for innovation. The study of

Aram and Morgan encourages risk taking in an environment of collaboration and focuses

on individual performance as well as successful team outcomes. The strength of the

communication factor was adapted to this study based on its relevance to team

cohesiveness and recognition of self and other awareness, which are ability-based EI

competencies important to this study.

Collaboration experience questionnaire. The collaboration experience

questionnaire (Mattisich & Monsey, 1992) is a self-report measure focused on six-factors

of collaboration (environment, measurement, process/structure, communication, purpose

and resources). These factors and related 19-items are intended to measure individual

experiences which influence success in collaboration. The purpose of this questionnaire

is to provide a resource for people who wish to enhance their experience in collaboration,

and to understand the elements one should consider in starting or improving their

experience in a collaborative activity. While very suitable for that purpose, the

collaboration experience questionnaire is not specifically geared to team performance in a

measurable way. The questionnaire is very comprehensive in describing items to

consider in collaboration, and functions as a collaboration handbook with case examples,

but does not provide a specific measure of collaboration items closely linked to team

cohesiveness, or the EI factors of self and other awareness. For these reasons, the

collaboration experience questionnaire was not chosen for this study.

ROCI-II. The Rahim Organizational Conflict Inventory-II (ROCI-II) (Rahim,

1983a, 1983b) was originally developed as a measure of interpersonal conflict, but also


has strong ties to the EI factors of self-awareness, and awareness of others. The ROCI-II

is a five-factor self-report questionnaire with 28-items. The factors are integrating,

obliging, compromising, dominating, and avoiding. The ROCI-II factors relevant to

collaboration and team effectiveness are integrating and compromise. These factors seek

to measure the extent to which team members integrate themselves in the collaborative

process. Compromise and negotiation further build on the ability-based EI competencies

of concern for self and others, and the relationship between these factors and EI fit the

hypothetical model this study is based on.

Collaboration is a process in which team members seek compromise, jointly

create rules and structures involving their relationships, and invest in activities for the

purpose of mutually beneficial interactions (Thomson et al., 2009). With integration, the

team can envision and create possibilities that go beyond their own limited vision of what

is possible (Gray, 1989). For this reason, the ROCI-II factors of compromise and

integration were adapted for this study and included in the original measure of

collaboration for research on collaborative activity.

Team collaboration questionnaire. This study uses an original measure of

collaboration adapted from Aram and Morgan (1976), and Rahim (1983a, 1983b), and

includes three factors with 15-items. Note after adjusting for reliability and validity of

the measurement instrument, the Team Collaboration Questionnaire included three

factors with three-items each in its final form. The factors that represent characteristics

of collaboration (integrating, compromising, and communication) are common themes in

the literature on collaboration. These measurable items of collaboration were selected

because of their applicability to team cohesiveness and positive outcomes in


collaboration. The items correlate well with the ability-based EI factors of self and other

awareness, and management of self and others’ emotions. The collaboration factors

shown in the hypothetical model of the study (see Figure 1.1), will be the measures used

to investigate the hypothesized relationship between EI and collaboration.

SOAR

SOAR, the third of three constructs used in this study, functions as the mediating

variable (MED) in the hypothetical model (see Figure 1.1). The following sections

present the theoretical foundations of SOAR, relevance to the hypothetical model, and the

framework used in this study (i.e., strengths (S), opportunities (O), aspirations (A) and

results (R)). The section concludes with a review of the SOAR Profile (Stavros & Cole,

2013) used in assessing levels of SOAR in individuals, and subsequently used in this

study.

Theory. SOAR (Strengths, Opportunities, Aspirations, and Results) is an

innovative, strengths-based approach to strategic thinking and planning involving all

individuals with a stake in the strategic thinking process (Stavros & Hinrichs, 2009).

SOAR is a “strengths-based framework with a participatory approach to strategic

analysis, strategy development, and organizational change” (Stavros & Saint, 2010, p.

380). As such, SOAR integrates Appreciative Inquiry (AI) with a strategic planning

framework to create a transformational process that inspires organizations and

stakeholders of the organization to engage in results-oriented strategic planning efforts

(Stavros, Cooperrider, & Kelley, 2003).

As shown in Figure 2.1, the SOAR framework transforms alternative approaches

to strategic thinking that do not emphasize strengths, opportunities, aspirations, and


results, such as SWOT-based traditional strategic planning of strengths, weaknesses,

opportunities and threats. In contrast to SWOT-based strategic thinking that reinforces

historical focus on weaknesses and threats, SOAR-based strategic thinking engages

organizational members to frame organizational issues from a solution-oriented

perspective that is generative and focused on organizational strengths, opportunities,

aspirations, and desired results to build a positive future (Stavros & Wooten, 2012). As a

framework for strategic thinking and planning, SOAR describes the elements and

activities that team members, teams and organizations should follow in their collaborative

strategic thinking and planning if they are following a strengths-based approach (Stavros,

Cooperrider, & Kelley, 2007).

Figure 2.1 The SOAR Framework

Available from www.soar-strategy.com. Used with permission.

http://www.soar-strategy.com/


The SOAR approach is inherently a team-oriented collaborative process that

focuses on the strengths, values, and shared vision and mission of those having a critical

interest in the team and organization’s success or failure. SOAR seeks to involve all

individuals having a perspective and stake in the organization’s strategic planning

initiatives and begins with an inquiry into what works well, followed by the identification

of possible opportunities for growth. The SOAR approach enables stakeholders to

identify and build on strengths, define specific goals and strategic initiatives, and identify

enabling objectives. With the SOAR foundation, the organization is able to design

strategies and methods to meet objectives, define performance metrics aligned with goals

and objectives, and discover new and profitable opportunities. Such an approach

promotes individual and organizational freedom to imagine an innovative and creative

future in which a strengths-based strategic plan is implemented that is dynamic and

enabling of positive outcomes (Stavros & Hinrichs, 2009).

Since SOAR depends on the interaction of key stakeholders in the strategic

planning process, it may be appropriate to suggest that participants be emotionally

intelligent in their exchange of ideas. Participants are invited to share their perceptions,

ideas, goals and vision for the future, and the key abilities of EI addressed in this

dissertation necessarily include those personal skills that will benefit from SOAR-based

thinking. It is important to recognize the distinction of the SOAR approach as its focus

remains to identify and build on strengths and opportunities, rather than weaknesses and

threats. The collaborative process of dialogue and strengths-based information exchange

may lead an organization to understand what happens when they are at their best, and to

identify what and where they wish to be for the future (Stavros & Hinrichs, 2009). Table


2.3 depicts the specific activities within the SOAR framework that act as enablers for the

successful interaction of team members (Stavros et al., 2003).

Table 2.3 Strategic Inquiry—Appreciative Intent: Inspiration to SOAR

Planning

Processes

SOAR

Elements

SOAR Activities

Strategic

Inquiry

Strengths

What are we doing well?

What are our greatest assets?

Opportunities

What are the best possible market opportunities?

How do we best partner with others?

Appreciative

Intent

Aspirations

To what do we aspire?

What is our preferred future?

Results

What are the measurable results?

What do we want to be known for?

Note. Adapted from Stavros et al. (2003).

Relevance to hypothetical model. For teams and organizations, a shared vision,

purpose and respect for each other’s roles is necessary to achieve breakthrough results.

While SOAR is essentially a strategic thinking and planning framework for

organizations, it depends on the successful interactions of people. The EI domains are

closely related to the skills necessary to achieve these successful interactions. The

contribution SOAR competency has in successful collaboration outcomes has been

considered and supported, but there is no evidence to suggest that SOAR has any

influence on the link between EI and collaboration. Since this process invites all

stakeholders to participate and focus on the elements of SOAR, dynamic relationships

within the collaborative group are important. The elements of EI are likely to support

these relationships. Elements of trust, respect, empathy, and understanding must be

present in order for the collaborative team to succeed. This study organizes the EI


domains and their influence on positive collaboration outcomes, and considers the

interaction of SOAR dynamics in this relationship.

Strengths-based strategic thinking and planning within the SOAR framework is an

example of organizational collaboration uniquely suitable for EI. The SOAR framework

has the potential to build strong and dynamic relationships, and may help teams and team

members to understand the importance of working collaboratively to develop strategy,

measurable objectives, and methods to achieve a visionary future based on strengths and

opportunities, rather than weaknesses and threats. The manifestation of SOAR

relationships are exemplified in self-reflection, mutual understanding, and a consideration

for the collaborative group as a whole. As participants exchange ideas, aspirations, and

desired results, they share a vision for the future with energy, vitality, and commitment

(Stavros & Hinrichs, 2009). EI abilities are closely linked to a SOAR-based pattern of

idea exchange and are supportive of the competencies necessary to achieve desired

results from a SOAR-based perspective. The results, implications, and recommendations

of this study contribute to clarifying and enhancing the relationships necessary for

positive collaboration outcomes in the presence of the SOAR framework.

Measures of SOAR. Traditional measures of SOAR involve qualitative case

study methodology, such as interviews and grounded theory analysis, and quantitative

self-report rating scales. To date, SOAR has been explored qualitatively by one doctoral

dissertation (Malone, 2010), and quantitatively by one peer-review publication (Sprangel,

Stavros, & Cole, 2011) and two doctoral dissertations (Glovis, 2012; Sprangel, 2009).

This study is best-suited for a quantitative index of SOAR in which SOAR is rapidly

assessed among a survey-based assessment battery of EI and collaboration measures.


Malone’s (2010) qualitative study of SOAR involved 39 in-depth interviews

analyzed using grounded theory in which SOAR was examined as a method to build

strategic capacity among individuals who used or supported the use of the SOAR

framework in organizations and in books and articles in the strategy field. The study

results support SOAR as a framework for building strategic capacity. The study also

demonstrated that the SOAR framework can be utilized in a small group setting to build

strategic capacity that is expressed as the capabilities to engage in strengths-based

strategic thinking and planning.

Among quantitative measures, published measures of strategy and strategic

thinking include measures of strategic learning, such as the Motivated Strategies for

Learning Questionnaire (Pintrich, Smith, Garcia, & McKeachie, 1993), and the Learning

and Study Strategy Inventory (Weinstein, Schulte, & Palmer, 1987). Quantitative

measures of thinking style, such as the Thinking Styles Inventory (Sternberg & Wagner,

1992), and measures of decision making, such as the General Decision-Making Style

Inventory (Scott & Bruce, 1995), account for the most widely recognized measurement

instruments most closely related to the SOAR construct and its factors, S, O, A, and R.

In the three quantitative studies of SOAR, SOAR was measured using a 16-item

self-report questionnaire developed by Sprangel (2009) that assessed eight elements

involved in the SOAR framework approach to strategic thinking that are descriptive of

SOAR-based capabilities: (1) an internal capability is analyzed; (2) an external capability

assessment is conducted; (3) values, vision and mission are created; (4) innovations and

potential outcomes are developed; (5) strategies and strategic initiatives are outlined; (6)

tactical/functional plans and integrated programs are planned; (7) goals and objectives are


established; and (8) the implementation and continuous improvement initiatives are

implemented. Sprangel’s questionnaire was used to study the relationship between

SOAR, trust, environmental management systems (EMS), and supplier performance was

investigated in a sample of 71 chemical management services (CMS) program managers

and customers (Sprangel, 2009; Sprangel et al., 2011). In the Glovis (2012) study, the

Sprangel questionnaire was used to investigate SOAR as a mediator of the relationship

between flow and project success in a sample of 122 SAP professionals. Both studies

demonstrate the utility of measuring SOAR quantitatively in order to investigate the role

of strengths-based strategic thinking and SOAR-based capabilities in managerial

performance and project management contexts.

Recently, a new quantitative measure of SOAR was created, the SOAR Profile, in

which the four elements of the SOAR framework of strategic thinking are assessed using

a 16-item self-report questionnaire (Cole & Stavros, 2013). The SOAR Profile asks

respondents to rate their current level of strategic thinking along a 10-point Likert rating

scale (Never-Always) using 3-items for each of the SOAR elements and 4-items for

engaging in strategic thinking from an appreciative inquiry perspective. In pilot studies,

the SOAR Profile demonstrates good reliability and construct validity (M. Cole, personal

communication, July 10, 2013).

Summary

This chapter has presented a literature review of the study variables: emotional

intelligence, collaboration, and SOAR. A central theme in the EI literature is that EI

improves one’s ability to be socially effective and can lead to improved leadership,

performance, and collaborative outcomes. The strength of the research in emotional


intelligence has been to advance its application with the competencies that contribute to

emotional awareness and regulation at all levels of interaction, so that teams can build the

solid foundation of trust, group identity, and group efficacy they need for true

cooperation and collaboration (Druskatt & Wolff, 2001).

The literature on emotional intelligence also reveals that consistency in the

measurement and assessment of the EI competencies is an area frequently investigated by

researchers. The challenges in EI measures are almost unilaterally focused on the use of

the word “intelligence”, and the inconsistent methods of emotional intelligence

assessment. These are not necessarily problems indicative of an invalid and

inappropriate construct, but rather suggesting of one that is in development and open to

new ideas and refinement, in particular, how to measure it. The early research of EI by

Goleman (1995, 1998) and later by Mayer, Salovey, and Caruso (2002, 2004) establish

the most well-recognized and accepted models of EI today. These models are often

targeted for refinement and measurement by other researchers seeking to advance the

positive outcomes of EI and its application. For example, researchers postulate differing

numbers of competencies, whether EI should be a self-report or peer review, or in what

ways elements of EI competency should be organized (Barling et al., 2000).

Notwithstanding the conceptual origins of EI by the seminal researchers

Goleman, Salovey, Mayer and Caruso, EI measures vary in content and method of

assessment (Conte, 2005; McEnrue & Groves, 2006; Zeidner et al., 2008). Although

there is no standard metric, instrument, or assessment method for determining the

aptitude of an emotionally intelligent individual, the research of Bar-On (1997), Jordan

and Lawrence (2009), Salovey and Mayer (1990) and Goleman (1995, 1998) has


provided scholars and practitioners with EI measures that have acceptable psychometric

properties and applied significance. Although these measures of EI vary in style, their

use in a variety of research studies suggests EI is a value-added construct for leadership

and teamwork applications. The continuing investigation into EI measures represents an

energetic and logical process of new construct development, and is an inherent part of the

process of theory development and scientific discovery in any field (Emmerling &

Goleman, 2003).

This literature review provided a theoretical foundation of research on EI,

collaboration, and SOAR. The literature reviewed suggests that knowledge and

application of EI abilities have the potential to positively impact collaboration in team

settings. The potential of EI as a viable construct remains evident in the research

initiatives of those seeking to further the EI framework and assessment models that are

currently under development today. Investigators also remain in support of EI as a means

to equip individuals and leaders with skills to better inform, perform, and interact with

colleagues within and throughout their organizations to further their collaborative

performance and competitive advantage.

This dissertation seeks to advance the development of an ability-based model of

EI aimed at having a distinct relationship with positive collaboration outcomes. Further,

this study aims to evaluate the link between EI and collaboration, to characterize the EI

abilities that contribute to collaboration, and to investigate the moderating role that

demographic characteristics and the mediating role that strengths-based strategic thinking

and planning (i.e., SOAR) has on the relationship between EI and collaboration among

team members. The methodology for this empirical study was a cross-sectional design,


drawing from the experiences of a sample of individuals with team experience. EI

abilities were assessed using the Workgroup Emotional Intelligence Profile-Short Form

(WEIP-S; Jordan & Lawrence, 2009). Collaboration was assessed using an original

measure of collaboration developed for this study, the Team Collaboration Questionnaire,

adapted from Aram and Morgan (1976) and Rahim (1983a, 1983b), and SOAR was

measured by the SOAR Profile (Cole & Stavros, 2013).


Chapter 3 Research Methodology

Introduction

The purpose of this dissertation was to evaluate the relationship between

emotional intelligence (EI) and collaboration. The dissertation aimed to identify the EI

competencies that are most critical for achieving collaboration among teams and team

members. The relationship between EI and collaboration was considered in the presence

of potential moderating demographic factors and the mediating effects of SOAR, a

framework for strengths-based strategic thinking and planning.

EI was evaluated for its impact on collaboration among a sample of professionals

either actively working in teams or who have had recent experience working in teams.

Existing measures were used to measure EI and SOAR, and an original measure of team-

based collaboration developed for this study was used for collaboration. This chapter

presents the research questions, hypotheses, and methods that were used in the study,

including description of the research design, sample, variables, data collection, and data

analysis methods.

Research Design

In this study, the research methodology was a quantitative cross-sectional design

with moderating and mediating variables. The analysis was linear regression in which

the dependent variable, collaboration, was regressed on the independent variable,

emotional intelligence (EI). An interaction term of EI x demographic characteristics was

included in the regression analysis to test if any of the sample demographic

characteristics moderate the impact of EI on collaboration. To determine if SOAR

mediates the relationship between EI and collaboration, a mediation path model was


analyzed using structural equation modeling (SEM) to test if there was an indirect effect

of SOAR on the relationship between EI and collaboration (Preacher & Hayes, 2008).

For this cross-sectional design, a sample of individuals were sought from a population of

professionals with some degree of team experience to participate in an electronic survey

that measured demographic characteristics, EI, collaboration, and SOAR.

Research Questions and Hypotheses

People engaged in cooperative work seek to advance their mutual interests

(Whitaker, 2009); however, would individuals working in teams be in a better position to

advance their cooperative work if they adopted emotional intelligence (EI) in both theory

and practice? Are EI abilities differentially related to collaboration? For example, are

there differences in the impact that emotional self-awareness and self-management, and

awareness and management of other’ emotions may have on collaboration? Are there

any variables that influence EI and its impact on collaboration and may help to explain

the mechanism by which EI affects collaboration? For example, are there certain

demographic characteristics that moderate the potential impact of EI on collaboration,

and are there certain mediators, such as strengths-based strategic thinking (i.e., SOAR),

that help to explain the impact that EI has on collaboration?

The four research questions posed in this study are:











The following three hypotheses were tested to answer the research questions:




participant demographic characteristics.



Research Variables

Three research variables were studied in this dissertation: emotional intelligence

(EI), collaboration, and SOAR (i.e., strengths-based strategic thinking). EI involves self-

awareness and self-management of one’s own feelings, and social awareness and

management of what others are feeling (Dulewicz & Higgs, 2000); collaboration involves

integration, compromise and communication (Aram & Morgan, 1976; Rahim, 1983a,

1983b). Collaboration is also discussed in the context of strategy and strategic thinking

(Gray, 1985), and SOAR and its measurement by the SOAR Profile served as the index

of a strengths-based approach to strategic thinking for consideration as a mediating

variable between EI and collaboration (Stavros & Hinrichs, 2009; Stavros et al., 2003,

2007; Cole & Stavros, 2013).


Figure 3.1 presents the hypothetical model for this study. According to the model,

EI, which has been derived from the Mayer and Salovey (1997) model of EI in a research

study by Jordan and Lawrence (2009) on EI in teams and workgroups, is comprised of

four factors—awareness and management of own emotions, and awareness and

management of others’ emotions. EI is an independent variable (IV) that impacts the

dependent variable (DV), collaboration, which is comprised of three factors—integrating,

compromsing, and communication.

To explore the impact of variables that could moderate the impact of EI on

collaboration, team-based demographic characteristics (such as team role, team type, and

time in teams) were included in the model as moderators (MOD). In consideration of

variables that could mediate the indirect effects of EI on collaboration, a construct used

for framing a strengths-based approach to strategic thinking, SOAR, was included in the

model as a mediator (MED). According to Baron and Kenny (1986), “Moderator

variables specify when certain effects will hold, mediators speak to how or why such

effects occur” (p. 1176). Therefore, this study also proposed that SOAR serves as a

mediator of the impact that EI has on collaboration. All together, the ultimate goal of the

research was to characterize the relationship between EI and collaboration.



Intelligence on Collaboration

Population and Sample

The population for this study was comprised of professionals actively working in

teams, or professionals who have had recent experience working in teams. In order to

expose participation in this study to as many individuals as possible, a concerted effort

was made to connect with individuals who routinely work in teams in general.

Invitations to participate were distributed across a wide range of professionals from

industry, academia, and the U.S. Government. Country data was not collected as no

respondents were to be excluded based on their location. However, it is known from

personal respondent feedback that approximately 2% were from outside the U.S. Those

respondents providing voluntary consent to participate in the electronic survey became

the study sample. The study survey was designed to assess their demographic

characteristics, EI, collaboration, and SOAR. The target sample size was N > 200.


Protection of participants’ rights. Research participants in this study were

protected according to the federal requirements specified by the Department of Health

and Human Services’ Code of Federal Regulations, 45 CFR 46. The author received the

National Institute of Health (NIH) Office of Extramural Research Certificate of

Completion of the NIH web-based training course “Protecting Human Research

Participants” (Appendix A). In accordance with the federal requirements, approval to

conduct research with human participants was obtained from The Lawrence

Technological University Institutional Review Board (IRB). IRB approval (Appendix B)

was obtained before any research was conducted, and stipulated that participants

voluntarily complete an informed consent prior to participating in the study. The

informed consent (Appendix C) was included as the first page of the on-line survey, and

required acceptance by the survey respondent in order to proceed.

Measures

Study variables were measured via an online survey instrument (see Appendix D).

The survey instrument consisted of 68 questions divided into five sections: (1) team

characteristics (5 questions), (2) emotional intelligence (16 questions), (3) team

collaboration (15 questions), (4) SOAR Profile (24 questions), and (5) demographics (8

questions). The questions on team characteristics asked respondents about the size of

their most current team (from 2 team members up to more than 20 team members), the

characteristics of team members in their most current team (internal vs. external), the

type of team (face-to-face vs. virtual), team role (leader vs. member), and time

involvement with their most current team (less than 1 month through greater than 20

years).


EI was measured by the 16-item “WEIP-S” (Work Group Emotional Intelligence

Profile-Short Form; Jordan & Lawrence, 2009) to establish areas of respondent

competency in four EI abilities helpful for understanding how EI works in teams (Mayer

& Salovey, 1997): awareness of own emotions, management of own emotions, awareness

of others’ emotions, and management of others’ emotions. The measure for assessing

collaboration was the 15-item Team Collaboration Questionnaire, an original measure of

collaborative activity among teams, adapted from Aram and Morgan (1976), and Rahim

(1983a, 1983b). Note after adjusting for improved reliability and validity of the

measurement instrument, the Team Collaboration Questionnaire was reduced to nine-

items, three per factor. Participants rated both the EI and the Collaboration items using a

7-point Likert scale where 1 = strongly disagree, and 7 = strongly agree.

SOAR was measured by the 24-item SOAR Profile (Cole & Stavros, 2013), a

self-report measure of strategic capacity from a SOAR framework. Note after adjusting

or improved reliability and validity of the measurement instrument, the SOAR Profile

was reduced to 12-items, three per factor. These measures were selected for their ability

to rapidly identify EI competencies, collaboration, and SOAR characteristics most critical

to achieving positive outcomes in collaboration. Finally, demographic questions asked

respondents about their age, gender, ethnicity, education, general role in teams (leader vs.

member), general involvement in teams (from less than 1 year through greater than 20

years), industry, and position in current company.

Pilot Study

A two-part pilot study was conducted on the survey instrument in order to assess

question clarity, proper function of the on-line administration method, and to evaluate


psychometric properties of the survey instrument on a sample of N = 75 participants. In

part one, prior to administration of the survey instrument, the researcher’s dissertation

committee members reviewed the questions for clarity, provided feedback on

organization of the questions, and tested the on-line function in SurveyMonkey. This

evaluation resulted in the modification of five questions for readability, and added one

response item to the team-type question (a choice of ‘both’ was added to the original two

choices of face-to-face or virtual). The factors of EI, SOAR and collaboration were

consolidated into a single list for each such that the distinct factors were not clearly

identifiable. For example, instead of having four groups of four questions each for EI

(each group representing the EI factors SA, SM, AO and MO respectively), the questions

were randomized to a final list of 16-items. This was done similarly for SOAR and

collaboration. The fidelity of the age demographic question was improved to provide a

more accurate representation of the age groups participating in the study. Finally, the

survey instrument offered participants the opportunity of providing their contact

information. When testing this function in SurveyMonkey, an issue was identified that

caused the survey to halt prior to completion. This issue was resolved and did not

reoccur in the administration of the final survey.

Part two of the pilot study required administering the survey to a large enough

population to evaluate the psychometric properties of the survey instrument. This was

accomplished by administering the final survey (see Appendix D) to the target population

with the intent of halting the survey after a sufficient sample was reached (N = 75).

Seven days into data collection, seventy-five surveys were evaluated for reliability of the

survey instrument. The psychometric properties of the WEIP-S, Team Collaboration


Questionnaire, and the SOAR Profile were evaluated via Cronbach’s coefficient alpha

test of internal consistency reliability (Cronbach, 1951), and via CFA test of construct

validity (Lu, 2006). With no negative or otherwise constructive feedback from the

sample of N = 75, and having demonstrated acceptable reliability and validity of the

survey instrument, the on-line survey was allowed to continue.

Data Collection Procedure

Data collection was comprised of the following 8-step process:

(1) IRB approval was obtained.

(2) Colleagues from academia, industry and government were invited to participate in

the study.

(3) Relevant LinkedIn groups in the dissertation areas of interest were identified, and

approval to join the LinkedIn groups was requested from group managers.

(4) After joining relevant LinkedIn groups, permission was requested from the

selected LinkedIn group managers to post an invitation to group members for

participation in the dissertation research study.

(5) LinkedIn group postings were monitored for activity, questions, and comments.

(6) After 1-2 weeks, depending on LinkedIn group activity, a second invitation to

participate in the study was posted.

(7) Surveys were administered and data were collected via SurveyMonkey from

11/9/13 through 12/10/13; the dissertation chair monitored and reported progress

only by survey respondent count (target sample size was N > 200).

Invitations to participate included a brief description of the study and a

SurveyMonkey link. Invitations to participate were also sent via email to colleagues in


industry, government, and academia. In addition, invitations to participate were posted

on relevant LinkedIn groups. Relevant LinkedIn groups were identified as suitable

forums in which to solicit potential study participants. Once appropriate permissions

were granted by each LinkedIn group manager, the survey invitations were posted. The

selected relevant LinkedIn groups were organized around the study areas of interest.

These areas included emotional intelligence, leadership, Appreciative Inquiry, team-work

and team-effectiveness, strategic planning, change management, project management,

academia, financial management, general business management, and several industrial

organizations. The LinkedIn group postings were monitored daily for participant

interactions, modification of the invitation, or were removed from the group site if there

was no activity or interest.

The survey was administered over a four week period from 11/9/13 through

12/10/13. If a sufficient quantity of respondents were not obtained in this timeframe (N <

200), the data collection period would have been extended with an action to reinvigorate

and follow-up on the invitation postings on LinkedIn. This extension was not needed as

N = 405 surveys were collected during the four week period, at which time the on-line

survey was closed. Collected data from SurveyMonkey were stored in a secure database

at Lawrence Technological University (LTU) with access only to the researcher and the

dissertation chair.

Data Analysis

Survey data were entered into Excel via SurveyMonkey. Data were transferred

from Excel to Minitab version 16.2.1 for descriptive and inferential quantitative statistical

analysis. Data were also transferred to Mplus version 7 for confirmatory factory analysis


(CFA) and mediation path models via structural equation modeling (SEM). For each

statistical procedure, all available data were used. For all inferential statistics,

significance was evaluated at the 95% confidence level.

Descriptive statistics. Descriptive statistics of categorical demographic

characteristics were comprised of frequency analysis. Descriptive statistics of continuous

variables were comprised of means and standard deviations.

Psychometric properties. The psychometric properties of the WEIP-S, the Team

Collaboration Questionnaire and The SOAR Profile were evaluated via Cronbach’s

coefficient alpha test of internal consistency reliability (Cronbach, 1951), and via CFA

test of construct validity (Lu, 2006). The essence of Cronbach’s alpha test is the

calculation of the intercorrelations among items in a scale, which can range from an alpha

of 0.0, to an alpha of 1.0. Alpha measures of 0.7 or higher serve as a reference for

acceptable reliability (Hinkin, 1998). In evaluating construct validity using CFA, the

Comparative Fit Index (CFI), Root Mean Square Error of Approximation (RMSEA), and

the ratio of chi-square to the degrees of freedom (df) was examined. CFI values of at

least 0.90, RMSEA less than 0.08, and χ2/df ratio less than 2 to 1 were indicative of

acceptable construct validity (Bentler, 1990; Bentler, 2007; Loehlin, 1998).

In evaluating construct validity using CFA, the Comparative Fit Index (CFI),

Root Mean Square Error of Approximation (RMSEA), and the ratio of chi-square to the

degrees of freedom (df) were examined. CFI values of at least 0.90, RMSEA less than

.08, and χ2/df ratio less than 2 to 1 were indicative of acceptable construct validity

(Bentler, 1990; Bentler, 2007; Loehlin, 1998).


Inferential statistics. The significance of the frequency distribution of

categorical demographic variables was tested using chi-square statistics. The relationship

between demographic characteristics and study variables was tested using univariate

analysis of variance (ANOVA) models. Post-hoc comparisons were conducted for

significant ANOVA findings using Tukey’s honestly significant difference analysis to

minimize the inflation of type I error (Shavelson, 1996). Hypothesis testing of H1 was

carried out using linear regression; testing of H2 was carried out using linear regression

with a demographic x EI interaction term included using the Baron and Kenny (1986)

causal-steps approach; in addition, a bootstrapped confidence interval for the ab indirect

effect was obtained using procedures described by Preacher and Hayes (2008).

Specifically, the dependent variable, collaboration, was regressed on the independent

variable, emotional intelligence (EI). An interaction term of EI x demographic

characteristics was included in the regression analysis to test if any of the sample

demographic characteristics moderate the impact of EI on collaboration. Finally, a

mediation path model was analyzed using SEM to test if there was an indirect effect of

SOAR on the relationship between EI and collaboration, i.e., to determine if SOAR

mediates the relationship between EI and collaboration.


Chapter 4 Results

Introduction

This study investigated the relationship between emotional intelligence (EI) and

collaboration, and determined the EI competencies that are most important for achieving

collaboration among teams and team members. An interaction term of EI x demographic

characteristics was included in the regression analysis to test if any of the sample

demographic characteristics moderate the impact of EI on collaboration. The study also

investigated the mediating effects of SOAR, a framework for strengths-based strategic

thinking, on the relationship between EI and collaboration. Data were collected via an

electronically administered survey using SurveyMonkey. The survey respondents were

those actively engaged in teams, or who have had recent experience working in teams.

The survey instrument was attempted by four hundred five participants (N = 405),

serving as the full sample size for initial frequency distribution. Three hundred ninety

nine (N = 399) provided their voluntary consent to participate in the study, while six did

not. Forty eight of the consenting participants did not complete the survey, and an

additional 40 provided a partially complete survey in which not all of the construct data

was provided. Two survey respondents did not work in teams, and although they

provided responses they were dropped from the analysis. The final sample size was N =

308. Data analysis involved analysis of all available data. Data were analyzed using

regression-based inferential statistics and SEM (Structural Equation Modeling) to test the

following three research hypotheses:








First, descriptive statistics were used to describe the demographic and team

characteristics of the sample, measured by frequency analysis and chi-square tests for

distribution (Tables 4.1 through 4.4). Next, the psychometric properties (internal

consistency reliability measured by Cronbach’s alpha, and construct validity measured by

CFA) of the WEIP-S, Team Collaboration Questionnaire, and SOAR Profile are

presented (Tables 4.5 through 4.9), followed by the intercorrelations between the study

variables (Table 4.10). Tables 4.11 through 4.22 describe the results of an analysis of the

study constructs across demographic characteristics. Specifically, mean and SD of

Emotional Intelligence (Tables 4.11 through 4.14), Collaboration (Tables 4.15 through

4.18), and SOAR (Tables 4.19 through 4.22). Tables 4.23 through 4.37 present the

results of inferential statistics and testing of the three study hypotheses: H1, a series of

linear regressions in Tables 4.23 through 4.32; H2, tests of moderation in Tables 4.33

through 4.35; and H3, mediation path analysis using structural equation modeling in

Tables 4.36 and 4.37. For all inferential statistics, significance was evaluated at the 95%

confidence level.

Demographic Characteristics of the Sample

Tables 4.1 to 4.4 report the descriptive statistics, comprised of frequency analysis

and chi-square tests for distribution, used to describe the categorical demographic and

team characteristics of the sample. Tables 4.1 and 4.2 present the results of the


demographic characteristics. As shown in Table 4.1, the sample (N = 308) was

essentially equally distributed among males and females. In contrast, age, ethnicity, and

education were significantly distributed according to chi-square test for equality of

distribution. This is shown in the age variable by the large percentage of participants in

the 45-64 years of age class (52%) compared to the 25-44 years of age class (29% of the

participants). Similarly, 77% of the participants were white, and 70% earned a Master’s

degree or higher. As shown in Table 4.2, significant distributions by industry type were

reported with more than 25% working in education, followed by consulting (13%),

healthcare (10%) and automotive (7%). However, positions held within these industries

were essentially equally distributed according to chi-square test for equality of

distribution.

Tables 4.3 and 4.4 present the results of the team characteristic variables. Table

4.3, which refers to the team the study participant is currently active with, or has most

recent experience with, shows that there were significant distributions among the team

characteristic variables: team size, team membership, team type, team role, and time with

team. For example, as shown in Table 4.3, 12% of the participants reported working in

teams with two or three members, whereas 70% reported working in teams of 4-15

members (36% in teams of 4-6 members, and 34% in teams of 7-15 members), and 17%

in teams of 16 or more. For the team membership variable, 71% of the sample reported

team membership was internal to their organizations, 8% was external, and 21% were

comprised of internal and external team members. For team type, the majority of

participants (62%) reported working in face-to-face as opposed to virtual teams (4%), and

34% reported teams comprised of face-to-face and virtual interactions. Regarding team


role, 52% reported serving as the team leader, while 47% were team members. Finally,

48% reported working with their team for less than 1 year, 33% for 1-3 years, and 18%

for more than 3 years.

In addition to asking study participants to report characteristics of the team they

are currently involved with, the study survey also asked participants to report information

concerning their team experiences in general (see Table 4.4). Results found that 57% of

participants reported functioning typically as the team leader, and most (66%) of the

participants have been involved in team-based activities for more than 15 years.


Table 4.1 Characteristics of Sample by Gender, Age, Ethnicity, and Education

Characteristic n %

Total Sample 308 100.0

Gender

Female 152 49.4

Male 131 42.5

No Response 25 8.1

Age

18-24 13***

4.2

25-34 35 11.4

35-44 54 17.5

45-54 80 26.0

55-64 80 26.0

65-74 22 7.1

75+ 0 0.0

No Response 24 7.8

Ethnicity

Asian 19***

6.2

Black/African American 15 4.9

Hispanic/Latino 6 1.9

White 236 76.6

2 or More Races 2 0.7

Decline 1 0.3

Other 5 1.6

No Response 24 7.8

Education

High school 9***

2.9

Associate 8 2.6

Bachelor 51 16.6

Master 147 47.7

Doctoral 70 22.7

No Response 23 7.5 Note. Sample frequency is expressed as % of all participants, N = 308.

*** p < .001 Chi-square test for equality of distribution.


Table 4.2 Characteristics of Sample by Industry, and Position

Characteristic n %


Industry

Automotive 22***

7.1

Consulting 41 13.3

Education 79 25.6

Engineering 24 7.8

Finance 19 6.2

Government 14 4.5

Healthcare 32 10.4

IT 17 5.5

Marketing 14 4.6

Non-profit 16 5.2

Other 2 0.7

No response 28 9.1

Position

Administrative 38 12.3

CEO 16 5.2

Consultant 8 2.6

Director 37 12.0

Educator 42 13.6

Engineer 14 4.5

Manager 87 28.2

Student 5 1.6

Supervisor 18 5.8

VP 16 5.2




Table 4.3 Characteristics of Sample by Team Size, Team Membership, Team Type, Team

Role, and Time Working in This Particular Team

Characteristic n % Characteristic n %


Team Role

Team Size Leader 159***

51.6

2 Members 13***

4.2 Member 146 47.4

3 Members 25 8.1 No Response 3 1.0

4 Members 35 11.4 Time With Team

5 Members 44 14.3 Less Than 1 Month 10***

3.2

6 Members 32 10.4 1-3 Months 37 12.0

7-10 Members 69 22.4 3-6 Months 40 13.0

11-15 Members 37 12.0 6-9 Months 28 9.1

16-20 Members 24 7.8 9-12 Months 33 10.7

21 or More Members 29 9.4 1-2 Years 57 18.5

Team Membership 2-3 Years 46 15.0

Internal 218***

70.8 3-5 Years 26 8.4

External 25 8.1 5-10 Years 22 7.1

Both 63 20.5 10-20 Years 8 2.6

No Response 2 0.6 No Response 1 0.3

Team Type

Face-to-Face 191***

62.0

Virtual 12 3.9

Both 104 33.8




Table 4.4 Characteristics of Sample by Team Role, and Time Involved in Teams (When

Working in Team-Based Activities in General)

Characteristic n %


Team Role

Leader 175***

56.8

Member 108 35.1

No Response 25 8.1

Time Involved in Teams

Less Than 1 Year 7***

2.3

1-2 Years 6 1.9

2-3 Years 9 2.9

3-4 Years 3 1.0

4-5 Years 16 5.2

5-10 Years 38 12.3

11-15 Years 36 11.7

16-20 Years 52 16.9

More Than 20 Years 116 37.7



Reliability and Validity

The psychometric properties of the WEIP-S, Team Collaboration Questionnaire

and the SOAR Profile were evaluated via Cronbach’s coefficient alpha test of internal

consistency reliability (Cronbach, 1951), and via CFA test of construct validity (Lu,

2006). Reliability of the survey instrument was determined by Cronbach’s alpha, and

construct validity was evaluated with CFA. The essence of Cronbach’s alpha test is the

calculation of the intercorrelations among items in a scale, which can range from an alpha

of 0.0, to an alpha of 1.0. Alpha measures of 0.7 or higher serve as a reference for

acceptable reliability (Hinkin, 1998). In evaluating construct validity using CFA, the

Comparative Fit Index (CFI), Root Mean Square Error of Approximation (RMSEA), and

the ratio of chi-square to the degrees of freedom (df) were examined. CFI values of at


least .90, RMSEA less than .08, and χ2/df ratio less than 2 to 1 are indicative of

acceptable construct validity (Bentler, 1990; Bentler, 2007; Loehlin, 1998). Construct

validity tests the fit of the items within a scale to the construct that the items are intending

to measure. Tables 4.5 through 4.9 present the results of reliability and validity testing

using Cronbach’s alpha and CFA for each construct full-scale and subscales.

The psychometric properties of the WEIP-S, Team Collaboration Questionnaire,

and SOAR Profile are presented in sequence. Initial results and subsequent item analysis

revealed that dropping certain items from the Team Collaboration Questionnaire and

SOAR Profile would yield improved reliability and validity of the survey instruments. In

their final form, all scales had acceptable reliability, with alpha values ranging from .853-

.893 for the three study variables, EI, Collaboration and SOAR. Cronbach’s alpha values

for the EI subscales (self-awareness, self-management, awareness of others and

management of others) were also acceptable and ranged from .805-.903. The Team

Collaboration Questionnaire sub-scales (integrating, compromising, and communication)

showed alpha values ranging from .721-.909. Alpha values for the SOAR subscales

(Strengths, Opportunities, Aspirations, and Results) ranged from .649-.850. Results of

higher-order CFA support the construct validity of the study constructs, with all three sets

of measures satisfying the goodness of fit indices used to evaluate CFA—chi-square/df

ratio less than 2, RMSEA < .08, and CFI > .900. Additionally, the factor loadings of all

indicators were significant, as was the factor loadings of all first-order latent constructs

onto the higher-order constructs.

The reliability and validity testing results of the WEIP-S are presented in Table

4.5. The mean and SD for the WEIP-S full-scale, subscales (self-awareness, self-


management, other awareness and other management) and each of the 16-items is shown.

Cronbach’s alpha for the WEIP-S full-scale (.893) and each of the four subscales is also

shown, and all are within acceptable limits for internal consistency reliability. Tests of

model fit for confirmatory factor analysis (CFA) are supportive of construct validity (CFI

= .984, χ2/df ratio = 1.44, and RMSEA = .039). With all three sets of measures satisfying

the goodness of fit indices used to evaluate CFA—chi-square/df ratio less than 2,

RMSEA < .08, and CFI > .900, acceptable validity of the survey instrumented was

demonstrated. Finally, the factor loadings were also shown to be supportive of construct

validity.

Table 4.5 Reliability and Validity of the WEIP-S (Workgroup Emotional Intelligence

Profile – Short Version, 16-items)

Emotional Intelligence (EI) Items Mean1

SD2

Alpha3

Factor4

EI Full Scale (16-items)

5.32 .79 .893

Self-Awareness (Awareness of Own Emotions) 4-items 4.76 1.36 .888 .524

I can explain the emotions I feel to team members 5.14 1.49 .840

I can discuss the emotions I feel with other team members 4.85 1.56 .920

If I feel down, I can tell team members what will make me feel better. 4.27 1.62 .642

I can talk to other members of the team about the emotions I experience. 4.76 1.58 .812

Self-Management (Management of Own Emotions) 4-items 5.96 .84 .805 .655

I respect the opinion of team members, even if I think they are wrong. 6.00 1.06 .636

I can overcome my frustration with other team members. 5.60 1.11 .624

I try to see all sides of a disagreement before I come to a conclusion. 6.07 1.01 .818

I give a fair hearing to fellow team members’ ideas. 6.18 1.00 .791

Other Awareness (Awareness of Others’ Emotions) 4-items 5.10 1.11 .886 .646

I can read fellow team members ‘true’ feelings, even if they try to hide them. 5.10 1.30 .862

I am able to describe accurately the way others in the team are feeling. 5.03 1.26 .890

I can gauge true feelings of team members from their body language. 5.20 1.22 .785

I can tell when team members don’t mean what they say. 5.12 1.22 .685

Other Management (Management of Others’ Emotions) 4-items 5.47 .99 .903 .856

My enthusiasm can be contagious for members of a team. 5.54 1.16 .847

I am able to cheer team members up when they are feeling down. 5.47 1.10 .818

I can get fellow team members to share my keenness for a project. 5.46 1.08 .754

I can provide the ‘spark’ to get fellow team members enthusiastic. 5.41 1.13 .855

Note. Psychometric properties conducted on EI data from N = 308 study participants. Tests of model fit for

confirmatory factor analysis (CFA): χ2 = 141.532, df = 98, p = .003; RMSEA (90% CI) = .039 (.023-.052);

CFI = .984. 1Mean of items within scale where each item is measured on a 7-point Likert scale; 1 =

strongly disagree, 7 = strongly agree. 2Standard deviation.

3Cronbach’s alpha reliability measure of internal

consistency. 4Factor loading scores from CFA significant at p < .05 unless otherwise noted as non-

significant (ns).


The reliability and validity testing results of the Team Collaboration Questionairre

are presented in Tables 4.6 and 4.7. First, results of the original 15-item survey

instrument (as administered in this study), and a 14-item analysis are shown in Table 4.6.

Results of the final nine-item survey instrument that satisfied the study criteria for

reliability and validity (and was therefore used in study analyses) are shown in Table 4.7.

Table 4.6 Reliability and Validity of the Team Collaboration Questionnaire (15-items,

and 14-items)

Collaboration Items

Mean2

SD Alpha

Factor4

Collaboration Full Scale (15-items) 5.72 .67 .872


Integrating (5-items) 6.23 .83 .925 .852

I work with my team for a proper understanding of a problem. 6.29 .96 .817

I investigate an issue with my team to find acceptable solution. 6.24 .92 .854

I integrate my ideas with my team to come up with a joint decision. 6.10 .96 .821

I work with my team to find solutions to problems that satisfy our needs. 6.23 .92 .942

I exchange accurate information with team to solve a problem together. 6.28 .93 .774

Compromising (5-items) 5.42 .99 .844 .528

I usually propose a middle ground for breaking deadlocks. 5.18 1.35 .661

I bring concerns in the open so issues are resolved the best possible way. 5.83 1.17 .440

I try to find a middle course to resolve an impasse. 5.16 1.39 .737

I negotiate with my team so that a compromise can be reached. 5.52 1.19 .878

I use ‘give and take’ so that a compromise can be made. 5.42 1.25 .802

Communication (5-items) 5.52 .64 .492 1.079

I value the opinions of team members. 6.35 .87 .687

My opinion matters most. 3.16 1.61 .692

I consider suggestions of team members to maximize team effectiveness. 6.16 .83 .676

I share my expertise to satisfy the needs of my team. 6.14 .93 .531

I share my ideas with the team concisely. 5.80 1.09

Communication (4-items; “My opinion matters most” dropped) 6.11 .72 .779

Note. Psychometric properties conducted on Collaboration data from N = 308 study participants.

Tests of model fit for confirmatory factor analysis (CFA): χ2 = 223.326, df = 70, p = .000; RMSEA (90%

CI) = .087 (.075-.100); CFI = .943. 1Mean of items within scale where each item is measured on a 7-point

Likert scale; 1 = strongly disagree, 7 = strongly agree. 2Standard deviation.

3Cronbach’s alpha reliability

measure of internal consistency. 4Factor loading scores from CFA significant at p < .05 unless otherwise

noted as non-significant (ns).

The 15-item Team Collaboration Questionnaire is an original measure of

collaborative activity among teams, adapted from Aram and Morgan (1976), and Rahim

(1983a, 1983b). The 15-item questionnaire consisted of three sub-scales (integrating,


compromising, and communication) with five-items in each factor. Reliability analysis

showed acceptable reliability as measured by Cronbach’s alpha for the 15-item full-scale

(.872), the integrating sub-scale (.925), and the compromising sub-scale (.844).

However, the communication sub-scale showed unacceptable reliability with Cronbach’s

alpha equal to .492. The communication item “my opinion matters most” had significant

respondent variation as indicated by the high SD = 1.61. This may have been a good

item if trying to solely determine if “my opinion matters most”, but not if measuring a

construct called communication. This item doesn’t fit well as shown by its influence on

Cronbach’s alpha; it brings down the reliability of the whole sub-scale. In dropping this

item, Cronbach’s alpha for the communication factor improved to .779, which is within

acceptable limits for construct reliability. Additionally, Cronbach’s alpha for the 15-item

full-scale (.872) improved to .904 in the 14-item scale.

Within the communication sub-scale, there were two items related to opinions in a

team setting. The first being “I value the opinions of team members”, and the latter, “my

opinion matters most”. Similarity in meaning of these items may have caused

respondents to think differently about the two items causing variability in the responses.

Eliminating one of the items doesn’t relax the attention given to considering the opinions

of self and others in a team setting. Dropping the item “my opinion matters most” from

the construct provided items that fit well together, and Cronbach’s alpha rises to .779.

Further analysis revealed that while dropping the item “my opinion matters most”

from the communication sub-scale would provide for strong reliability with Chronbach’s

alpha equal to .779, CFA showed that the model fit was not acceptable with the set of 14-

items. The CFI for the 14-item scale was .943, with the ratio of chi-square to the degrees


of freedom (df) = 3.19, and RMSEA = .087. Validity of the study constructs depends on

all three sets of measures satisfying the goodness of fit indices used to evaluate CFA—

chi-square/df ratio less than 2, RMSEA < .08, and CFI > .900. With the 14-item scale,

both the χ2/df ratio and the RMSEA were above the desired threshold for construct

validity.

Continuing the analysis described above, the contribution of each sub-scale

(integrating, compromising, and communication) to the CFA was evaluated, while

maintaining or improving scale reliability. In this case, two-items from the integrating

and compromising subscales, and one additional item from the communication subscale

were dropped (see Table 4.7). The result was a nine-item full-scale Team Collaboration

Questionairre with three-items in each factor (integrating, compromising, and

communication). The CFI for the nine-item scale = .990, χ2/df ratio = 1.62, and RMSEA

= .046. With all three sets of measures satisfying the goodness of fit indices used to

evaluate CFA—chi-square/df ratio less than 2, RMSEA < .08, and CFI > .900, this

extended analisys resulted in an optimized nine-item survey instrument with strong

reliability and validity for measuring collaboration in a team setting. All analyses were

conducted on the nine-item collaboration full-scale and the three-item collaboration

subscales.


Table 4.7 Reliability and Validity of the Team Collaboration Questionairre (9-items)

Collaboration Items Mean1

SD2

Alpha3

Factor4


Integrating (3-items) 6.19 .86 .909 .888

I investigate an issue with my team to find acceptable solution. 6.24 .92 .852

I integrate my ideas with my team to come up with a joint decision. 6.10 .96 .853

I work with my team to find solutions to problems that satisfy our needs. 6.24 .91 .951

Compromising (3-items) 5.36 1.12 .849 .467

I try to find a middle course to resolve an impasse. 5.18 1.39 .674

I negotiate with my team so that a compromise can be reached. 5.53 1.19 .968

I use ‘give and take’ so that a compromise can be made. 5.43 1.25 .732

Communication (3-items) 6.03 .77 .721 .903

I consider suggestions of team members to maximize team effectiveness. 6.17 .82 .723

I share my expertise to satisfy the needs of my team. 6.14 .93 .746

I share my ideas with the team concisely. 5.79 1.09 .668

Note. Psychometric properties conducted on Collaboration data from N = 308 study participants. Tests of

model fit for confirmatory factor analysis (CFA): χ2 = 35.695, df = 22, p = .033; RMSEA (90% CI) = .046

(.013-.073); CFI = .990. 1Mean of items within scale where each item is measured on a 7-point Likert scale;

1 = strongly disagree, 7 = strongly agree. 2Standard deviation.

3Cronbach’s alpha reliability measure of

internal consistency. 4Factor loading scores from CFA significant at p < .05 unless otherwise noted as non-

significant (ns).

The 24-item SOAR Profile (Cole & Stavros, 2013) administered in this study

included five sub-scales, five-items each for Strengths (S), Opportunities (O), Aspirations

(A), and Results (R), and 4-items related to Appreciative Inquiry (AI). The four-items

for AI were included in the survey instrument (inherent to the SOAR Profile), but were

not used in this study or the analyses. First, results of the original 24-item survey

instrument as administered in this study are shown in Table 4.8 (the four-items related to

AI are not included, providing a 20-item scale for initial analysis). Results of the final

12-item survey instrument that satisfied the study criteria for reliability and validity (and

was therefore used in study analyses) are shown in Table 4.9. The four-items related to

AI (inherent to the SOAR Profile) were discarded in both cases as they were not relevant

to this study.


Performing analysis similar to that described with the Team Collaboration

Questionnaire, the initial 20-item scale showed acceptable reliability (Cronbach’s alpha

measures of 0.7 or higher for SOAR full-scale and sub-scales), but not construct validity

as determined by CFA. The CFI for the 20 item scale was .780, with the ratio of chi-

square to the degrees of freedom (df) = 4.34, and RMSEA = .114. Validity of the study

construct depends on all three sets of measures satisfying the goodness of fit indices used

to evaluate CFA—chi-square/df ratio less than 2, RMSEA < .08, and CFI > .900. With

the 20-item scale, both the χ2/df ratio and the RMSEA were above the desired threshold

for construct validity, and the CFI was below the threshold.

Continuing the analyis to improve CFA using items with highest factor loadings,

the contribution of each sub-scale (S, O, A and R) to the CFA was evaluated while

maintaining or improving scale reliability. Subsequently, it was revealed that the SOAR

Profile would yield improved validity of the survey instrument if certain items were

dropped. In this case, 2-items from each SOAR subscale (see Table 4.9). The result was a

12 item full-scale SOAR Profile with 3-items in each factor. Cronbach’s alpha for the

SOAR Profile 12 item full-scale = .855, which is above the minimum threshold of 0.7 for

construct reliability. Alpha for the four sub-scales, Strengths (.649), Opportunities (.795),

Aspirations (.850), and Results (.790) also show acceptable reliability of the survey

instrument. The CFI for the 12-item scale = .970, χ2/df ratio = 1.8, and RMSEA = .055.

With all three sets of measures satisfying the goodness of fit indices used to evaluate

CFA—chi-square/df ratio less than 2, RMSEA < .08, and CFI > .900, this extended

analysis resulted in an optimized 12-item survey instrument with strong reliability and

validity for measuring SOAR, a strengths-based strategic thinking style in a team setting.


All analyses were conducted on the 12-item SOAR Profile, and the three-item sub-scales

of S, O, A, and R.

Table 4.8 Reliability and Validity of the SOAR Profile (20-items)

SOAR Items Mean1

SD2

Alpha3

Factor4

SOAR Full Scale (20-items)

7.82 1.03 .908

Strengths (5-items) 7.96 1.17 .791 .845

Strengths 8.53 1.42 .707

Abilities 7.95 1.46 .668

Assets 7.13 1.85 .605

Capabilities 8.26 1.42 .819

Resources 7.99 1.64 .597

Opportunities (5-items) 8.10 1.23 .788 .882

Opportunities 8.26 1.53 .733

Connections 7.15 2.11 .510

Ideas 8.62 1.37 .765

Innovations 8.19 1.64 .651

Possibilities 8.31 1.57 .731

Aspirations (5-items) 7.45 1.42 .813 .646

Aspirations 7.24 1.83 .875

Ambitions 6.72 1.90 .745

Desires 7.01 1.95 .773

Inspiration 7.98 1.82 .577

Values 8.30 1.90 .514

Results (5-items) 7.77 1.32 .823 .677

Results 8.41 1.49 .790

Achievement 6.94 1.84 .478

Completed tasks 7.57 1.88 .678

Goals obtained 7.53 1.84 .718

Outcomes 8.41 1.49 .804

Note. Psychometric properties conducted on SOAR data from N = 308 study participants. Tests of model fit

for confirmatory factor analysis (CFA): χ2 = 720.750, df = 166, p = .000; RMSEA (90% CI) = .114 (.105-

.112); CFI = .780. 1Mean of items within scale where each item is measured on a 10-point Likert scale; 1 =

never, 4 = rarely, 7 = often, 10 = always. 2Standard deviation.



significant (ns).


Table 4.9 Reliability and Validity of the SOAR Profile (12-items)

SOAR Items Mean1

SD2

Alpha3

Factor4

SOAR Full Scale (12-items)

8.00 1.05 .861

Strengths (3-items) 7.96 1.27 .722 .893

Strengths 8.53 1.42 .759

Assets 7.13 1.85 .573

Capabilities 8.26 1.42 .588

Opportunities (3-items) 8.40 1.26 .795 .887

Opportunities 8.26 1.53 .793

Ideas 8.62 1.37 .706

Possibilities 8.31 1.57 .754

Aspirations (3-items) 7.52 1.54 .739 .949

Aspirations 7.24 1.83 .821

Desires 7.01 1.95 .822

Values 8.30 1.90 .679

Results (3-items) 8.13 1.37 .790 .539

Results 8.41 1.49 .844

Completed Tasks 7.57 1.88 .584

Outcomes 8.41 1.49 .854

Note. Psychometric properties conducted on SOAR data from N = 308 study participants. Tests of model fit

for confirmatory factor analysis (CFA): χ2 = 88.345, df = 49, p = .001; RMSEA (90% CI) = .055 (.036-

.073); CFI = .970 1Mean of items within scale where each item is measured on a 10-point Likert scale; 1 =

never, 4 = rarely, 7 = often, 10 = always. 2Standard deviation.



significant (ns).

Intercorrelations Between Study Variables

As presented in the psychometric properties analysis section above, the three

study variables (EI, collaboration and SOAR) were determined by a 16-item measure of

EI, a nine-item measure of collaboration, and a 12-item measure of SOAR. The

intercorrelations of these variables (and their constitutive factors) are presented in Table

4.10, and show strong and significant correlations among the study variables. For

example, correlations between EI and its factors range from .66 to .79. Collaboration and

its factors show correlations between .78 and .82, and SOAR is correlated with its factors

from .69 to .80. In the analysis of intercorrelations, significant correlation (p < .05)

means all variables are well correlated, and that there are strong linear relationships

between them.


The intercorrelations are important in seeing how changes in one variable and its

factors affect other factors. Correlations range from +1 to –1, and can be positive or

negative. If p < .05, correlation is considered significant, and stronger with p < .01.

Correlations of zero indicate no correlation, .3 is low, .5 is medium and .7 is high. In this

study, essentially all of the study variables are correlated at the p < .01 level of

significance which indicates strong and relevant interactions between the variables and

their factors. All variables are positively correlated with each other, 98% are correlated

at p < .05, and 97% are correlated at p < .01. This provides statistical support of the

preceding CFA which measures construct validity, and taken together support the next

step in determining inferential statistics.

Table 4.10 Intercorrelations Between Study Variables

Var Mean (SD) EI SA SM AO MO SO ST OP AS RE CL IN CP

EI 5.32 (0.79) --

SA 4.76 (1.36) 0.75+ --

SM 5.96 (0.84) 0.66+ 0.31+ --

AO 5.10 (1.11) 0.74+ 0.33+ 0.33+ --

MO 5.47 (0.99) 0.79+ 0.40+ 0.46+ 0.52+ --

SO 8.00 (1.05) 0.45+ 0.28+ 0.19+ 0.41+ 0.43+ --

ST 7.96 (1.27) 0.35+ 0.21+ 0.17* 0.31+ 0.34+ 0.82+ --

OP 8.40 (1.26) 0.43+ 0.23+ 0.24+ 0.36+ 0.41+ 0.81+ 0.57+ --

AS 7.52 (1.54) 0.41+ 0.31+ 0.09 0.37+ 0.37+ 0.78+ 0.53+ 0.57+ --

RE 8.13 (1.37) 0.21+ 0.10 0.09 0.23+ 0.20+ 0.69+ 0.48+ 0.40+ 0.26+ --

CL 5.86 (0.73) 0.49+ 0.24+ 0.56+ 0.31+ 0.40+ 0.43+ 0.37+ 0.39+ 0.23+ 0.38+ --

IN 6.19 (0.86) 0.45+ 0.22+ 0.58+ 0.23+ 0.37+ 0.35+ 0.33+ 0.31+ 0.17+ 0.30+ 0.82+ --

CP 5.36 (1.12) 0.30+ 0.13* 0.30+ 0.26+ 0.23+ 0.31+ 0.24+ 0.25+ 0.18+ 0.29+ 0.78+ 0.37+ -- CO 6.03 (0.77) 0.46+ 0.26+ 0.50+ 0.24+ 0.39+ 0.40+ 0.35+ 0.39+ 0.21+ 0.32+ 0.79+ 0.68+ 0.35+

Note. EI = Emotional Intelligence; SA = Self-Awareness; SM = Self-Management; AO = Awareness of

Others; MO = Management of Others; SO = SOAR; ST = Strengths; OP = Opportunities; AS =

Aspirations; RE = Results; CL = Collaboration; IN = Integrating; CP = Compromising; CO =

Communication. *p < .05. Correlation is significant at the 0.05 level (2-tailed).

+p < .01. Correlation is significant at the 0.01 level (2-tailed).


Descriptive Statistics

This section presents the results of descriptive statistical analysis of the following

study constructs: EI, Collaboration, and SOAR. For each study construct variable, the

mean and SD are given. Additionally, the mean and SD for each study variable is given

across each demographic characteristic. For the descriptive statistics tables, if the

distribution was not evenly distributed, i.e, there was a significant distribution, and if the

mean scores within each demographic characteristic were significantly different, then the

mean score for the relevant demographic characterstic was annotated depending on the

level of significance ( *p < .05,

+p < .01). This implies the mean scores were different

from each other at either the .05 or .01 level. The analysis was done with ANOVA which

looks at mean construct scores across each demographic characteristic. Regression,

which looks at each unit change of the IV (emotional intelligence, and other covariates)

on the DV (collaboration), are presented in the next section.

Emotional Intelligence (EI). Tables 4.11 through 4.14 present the results of the

descriptive statistics of EI and its four constitutive factors, SA, SM, AO and MO across

each demographic characteristic. Results are presented as the mean and SD.

Additionally, each table presents results of ANOVA test for any difference in mean EI,

SA, SM, AO or MO score across each of the demographic characteristics.

As shown in Table 4.11, the mean scores for EI and three of its factors (SA, SM

and AO) were not significantly different across gender, age, ethnicity, and education

according to ANOVA. However, for the EI factor MO, ANOVA found differences in the

mean score for age, ethnicity, and education. Results of Tukey’s post-hoc analysis found

that the impact of age, ethnicity, and education on MO occurred in the 25-34 vs. the 55-


65 age range (mean = 5.18 vs. 5.72), Hispanics vs. White (mean = 4.38 vs. 5.57), and

High School education vs. College education (mean = 4.53 vs. 5.62), respectively.

Table 4.11 Mean and SD of Emotional Intelligence and its Four Constitutive Factors across

Gender, Age, Ethnicity, and Education

Demographic EI Full-Scale

Self-

Awareness

Self-

Management

Awareness of

Others

Management

of Others

Characteristic M SD M SD M SD M SD M SD

Total Sample 5.32 0.79 4.76 1.36 5.96 0.84 5.10 1.11 5.47 0.99

Gender

Female 5.40 0.78 4.87 1.32 5.98 0.80 5.22 1.16 5.52 1.03

Male 5.28 0.75 4.67 1.33 5.98 0.81 5.02 1.02 5.47 0.90

Age

18-24 5.18 0.75 4.77 0.84 6.08 0.58 4.92 1.45

5.00* 1.35

25-34 5.18 0.91 4.47 1.39 5.89 1.10 5.18 1.20 5.18 1.12

35-44 5.24 0.76 4.59 1.40 5.86 0.85 5.12 0.97 5.42 1.01

45-54 5.37 0.77 4.89 1.21 5.92 0.76 5.21 1.15 5.48 0.97

55-64 5.47 0.73 4.92 1.45 6.17 0.65 5.09 1.12 5.72 0.80

65-74 5.32 0.61 4.73 1.20 5.86 0.82 4.97 0.80 5.70 0.73

Ethnicity

Asian 5.18 0.93 4.33 1.62 5.80 1.00 5.37 1.27 5.24* 1.11

Black 5.21 1.11 4.45 1.78 5.90 0.99 5.20 1.15 5.30 1.11

Hispanic 4.75 1.18 4.42 1.44 5.46 1.42 4.75 1.42 4.38 1.39

White 5.39 0.70 4.84 1.26 6.03 0.72 5.12 1.07 5.57 0.91

2 or More 4.59 0.93 2.75 0.35 6.13 0.53 4.13 1.59 5.38 1.24

Other 5.25 0.64 4.80 1.98 5.35 1.23 5.35 1.35 5.60 1.13

Education

High school 5.11 0.44 4.83 0.56 5.94 0.66 5.14 1.54 4.53* 1.03

Associate 5.63 0.43 5.28 1.17 6.28 0.60 4.97 1.61 5.97 0.69

Bachelors 5.29 0.74 4.75 1.28 5.91 0.88 5.07 0.99 5.46 0.98

Masters 5.35 0.82 4.70 1.38 5.98 0.84 5.18 1.08 5.55 1.00

Doctoral 5.34 0.72 4.82 1.38 5.99 0.70 5.07 1.10 5.48 0.84

Note. *p < .05,

**p < .01 significant difference between scores within demographic characteristic according

to ANOVA. Total sample N = 308.

Table 4.12 shows that industry did not have a significant effect on EI or its four

factors. However, among self-reported position, the mean scores for EI and SA were

significantly different. Tukey’s post-hoc analysis found that for EI, CEO’s and managers

had significantly higher scores than engineers (5.64/5.51 vs. 4.83). For SA, similarly


higher scores were found for CEO’s and managers (5.53/4.99) vs. engineers (4.11). For

other self-reported positions, which may have been interpreted as titles, there was no

significant difference between positions.


across Industry, and Position


Self-

Awareness

Self-

Management

Awareness of

Others

Management

of Others


Total Sample 5.32 0.79 4.76 1.36 5.96 0.84 5.10 1.11 5.47 0.99

Industry

Automotive 5.07 0.75 4.50 1.25 5.84 0.89 4.77 1.10 5.16 1.10

Consulting 5.46 0.45 4.99 1.05 6.06 0.56 5.19 0.87 5.59 0.65

Education 5.34 0.79 4.83 1.37 5.97 0.83 5.09 1.13 5.47 1.00

Engineering 5.04 0.97 4.21 1.37 5.80 1.04 4.94 1.40 5.19 1.06

Finance 5.53 1.03 4.71 1.76 6.03 0.84 5.57 1.03 5.82 1.21

Government 5.35 0.67 4.63 1.39 5.95 0.46 5.20 0.98 5.60 0.98

Healthcare 5.34 0.61 4.94 1.45 6.03 0.67 4.94 1.12 5.49 0.77

IT 5.32 0.64 4.51 1.14 6.03 0.78 5.15 1.13 5.59 0.57

Marketing 5.67 1.01 5.07 1.27 6.00 1.33 5.84 0.91 5.79 1.15

Non-profit 5.38 0.74 4.86 1.43 6.15 0.53 5.00 1.07 5.48 1.16

Other 4.97 0.93 4.13 0.18 5.38 0.53 4.88 1.59 5.50 1.41

Position

Administrative 5.17*

0.87 4.55*

1.59 5.97 0.88 4.90 1.43 5.24 1.17

CEO 5.64 0.42 5.53 0.59 5.98 0.62 5.25 0.71 5.78 0.66

Consultant 5.51 0.89 4.72 1.46 6.16 0.60 5.41 1.41 5.75 1.17

Director 5.39 0.56 4.93 1.06 6.05 0.56 4.96 0.88 5.59 0.84

Educator 5.28 0.85 4.61 1.53 5.93 0.91 5.06 1.22 5.52 0.97

Engineer 4.83 0.88 4.11 1.18 5.64 1.11 4.44 1.10 5.14 0.78

Manager 5.51 0.58 4.99 1.13 6.12 0.57 5.31 0.94 5.63 0.77

Student 4.98 0.51 4.75 0.64 6.15 0.58 4.70 1.87 4.30 0.89

Supervisor 5.16 0.96 4.28 1.73 5.60 1.21 5.29 0.89 5.46 1.03

VP 5.22 1.13 4.34 1.66 5.76 1.08 5.40 1.03 5.40 1.47

Note. *p < .05,



Table 4.13 presents descriptive statistics for the team study participant was

currently active in when they completed the survey. As shown in Table 4.13 the mean


scores for EI and its four factors (SA, SM, AO, and MO) were not significantly different

across team size according to ANOVA. However, for EI, ANOVA found differences in

the mean score for team membership, team type, team role and time with team. Results

of Tukey’s post-hoc analysis found that the impact of team membership on EI was

significant between teams composed of strictly internal members, vs. both. The mean EI

score for teams composed of internal members was 5.24 vs. teams composed of internal

and external members (5.48). Mean EI scores for team type were higher in those who

interacted face-to-face (5.28) as opposed to virtually (4.87), and were highest in those

who interacted in both ways (5.45). Team role showed a very significant difference (p <

.0001) in mean EI scores between team leaders and team members, with leaders at 5.48

and members at 5.17. Time with team was also significant with a mean EI score of 5.06

for those active in a team for one to three months, vs. those active in a team for five to ten

years (5.78). Consistently, mean EI scores rose with increasing time with team.

The mean score for self-awareness (SA) was significantly different (p < .0001)

across team role, with leaders having higher SA (4.95) than members (4.57). As was

seen with mean EI scores, mean AO scores were similarly distributed across team type

with higher scores in those who interacted face-to-face (5.06) as opposed to virtually

(4.44), and were highest in those who interacted in both ways (5.27). For team role,

mean AO scores were significantly higher in team leaders (5.33) vs. 4.86 for members.

Similar to mean EI scores, mean MO scores were higher (5.96) in those who have been

with a team for five to ten years vs. one to three months (5.05), and MO consistently rose

with time in team.



across Team Size, Team Membership, Team Type, Team Role, and Time Working in

This Particular Team


Self-

Awareness

Self-

Management

Awareness of

Others

Management

of Others


Total Sample 5.32 0.79 4.76 1.36 5.96 0.84 5.10 1.11 5.47 0.99

Team Size

2 Members 5.53 0.88 5.14 1.26 6.08 0.86 5.13 1.14 5.77 1.03

3 Members 5.35 0.74 5.12 1.42 5.86 0.74 4.99 0.89 5.42 0.88

4 Members 5.04 0.82 4.44 1.45 5.69 0.97 4.81 1.14 5.24 1.07

5 Members 5.35 0.69 4.72 1.28 6.16 0.66 5.16 1.13 5.36 1.00

6 Members 5.50 0.65 4.68 1.19 6.22 0.61 5.53 0.70 5.63 0.96

7-10 Members 5.17 0.83 4.58 1.30 5.83 0.88 4.95 1.29 5.31 0.99

11-15 Members 5.37 0.76 4.80 1.43 6.05 0.85 5.04 1.20 5.62 0.94

16-20 Members 5.26 1.02 4.76 1.68 5.90 0.98 5.03 1.11 5.36 1.12

21+ Members 5.61 0.68 5.16 1.20 5.94 0.90 5.46 0.85 5.91 0.88

Team Membership

Internal 5.24*

0.75 4.65 1.32 5.92 0.84 5.01 1.11 5.38 0.96

External 5.52 0.88 4.83 1.54 6.14 0.69 5.37 1.08 5.72 1.23

Both 5.48 0.87 5.05 1.38 5.97 0.90 5.27 1.11 5.66 0.99

Team Type

Virtual 4.87* 0.91 4.08 1.18 5.71 0.81 4.44 1.50 5.21 1.19

Face-to-face 5.28

0.81 4.76 1.38 5.92 0.91 5.06*

1.13 5.42 1.01

Both 5.45 0.69 4.85 1.32 6.06 0.68 5.27 0.98 5.61 0.93

Team Role

Leader 5.48**

0.77 4.95*

1.26 5.98 0.80 5.33**

1.05 5.66**

0.98

Member 5.17 0.77 4.57 1.44 5.95 0.86 4.86 1.12 5.29 0.96

Time With Team

Less than 1 Month 5.08*

0.84 4.18 1.39 5.90 0.67 4.95 1.36 5.39*

1.00

1-3 Months 5.06 0.89 4.75 1.33 5.70 1.13 4.72 1.20 5.05 1.17

3-6 Months 5.27 0.73 4.66 1.22 5.84 0.65 5.00 1.03 5.57 0.84

6-9 Months 5.26 0.58 4.45 1.37 6.11 0.56 5.29 0.89 5.18 0.97

9-12 Months 5.20 0.83 4.56 1.48 5.89 0.95 4.98 1.17 5.39 0.93

1-2 Years 5.31 0.89 4.75 1.16 5.92 0.99 5.12 1.17 5.43 1.10

2-3 Years 5.37 0.78 4.90 1.52 6.17 0.68 4.99 1.15 5.44 0.95

3-5 Years 5.45 0.65 4.74 1.58 5.92 0.82 5.39 0.99 5.78 0.69

5-10 Years 5.78 0.72 5.48 1.14 6.05 0.67 5.64 0.93 5.96 1.00

10-20 Years 5.78 0.44 4.94 1.34 6.41 0.48 5.63 0.33 6.16 0.27

Note. *p < .05,




For team-based activities in general, Table 4.14 shows that the mean scores for EI

across team role and time in teams are significantly different according to ANOVA.

Team leaders are shown to have higher EI (5.43) compared to members (5.19). For time

in teams, mean EI scores are higher (5.50) for those in teams 16-20 years, vs. two to three

years (4.65). For SA, there is a significant difference in mean scores for time in teams,

with Tukey’s post-hoc analysis showing that mean SA scores are highest in those with

experience in teams 16-20 years (5.01), compared to those with two to three years of

experience (3.50). The impact of SM across team role and time in teams shows no

significant difference, but mean scores for AO are significantly different across team role

with leaders again having the higher AO score (5.28) vs. members (4.87). Time in teams

however had no impact on AO. Mean scores for MO were found to be significantly

different across team role with team leaders having the higher score (5.66) vs. members

(5.23). Additionally, mean MO scores were highest in those with extensive experience

working in teams (16+ years), compared to those with less (two to three years).


Table 4.14 Mean and SD of Emotional Intelligence and its Four Constitutive Factors across

Team Role, and Time Involved in Teams (When Working in Team-Based Activities in General)


Self-

Awareness

Self-

Management

Awareness of

Others

Management

of Others


Total Sample 5.32 0.79 4.76 1.36 5.96 0.84 5.10 1.11 5.47 0.99

Team Role

Leader 5.43* 0.71 4.78 1.29 6.00 0.73 5.28

+ 1.06 5.66

+ 0.89

Member 5.19 0.83 4.72 1.43 5.94 0.90 4.87 1.13 5.23 1.03


Less Than 1 Yr 5.31* 0.99 4.89

* 1.27 6.00 0.95 5.60 1.09 4.71

** 1.43

1-2 Years 5.71 0.47 5.00 0.63 6.25 0.63 5.75 0.69 5.83 0.82

2-3 Years 4.65 0.84 3.50 1.55 5.78 1.20 4.81 1.52 4.53 1.27

3-4 Years 5.46 0.85 5.50 1.09 5.92 1.01 4.92 0.76 5.50 0.66

4-5 Years 5.27 0.82 4.66 1.62 6.09 0.63 4.80 1.32 5.53 1.09

5-10 Years 5.03 1.01 4.28 1.54 5.75 1.15 4.98 1.24 5.14 1.18

11-15 Years 5.32 0.72 4.58 1.33 6.06 0.60 5.15 1.20 5.49 0.89

16-20 Years 5.50 0.71 5.01 1.09 6.04 0.74 5.37 0.93 5.58 0.92

20+ Years 5.41 0.65 4.91 1.28 5.99 0.72 5.07 1.03 5.67 0.80

Note. *p < .05,



Collaboration. Tables 4.15 through 4.18 present the results of the descriptive

statistics of collaboration and its three constitutive factors, integrating, compromising,

and communication across each demographic characteristic. Results are presented as the

mean and SD. Additionally, each table presents results of ANOVA test for any

difference in mean collaboration, integrating, compromising, or communication score

across each of the demographic characteristics.

As shown in Table 4.15, the mean scores for collaboration and two of its factors

(integrating and compromising) were not significantly different across gender, age,

ethnicity, and education according to ANOVA. However, for the collaboration factor

communication, ANOVA found differences in the mean score for age. Results of

Tukey’s post-hoc analysis found that the impact of age on communication occurred in the

25-34 years of age range (5.64) vs. the 55-64 years of age range (6.22).



Gender, Age, Ethnicity, and Education

Demographic

Collaboration

Full-Scale Integrating Compromising Communication

Characteristic M SD M SD M SD M SD

Total Sample 5.86 0.73 6.19 0.86 5.36 1.12 6.03 0.77

Gender

Female 5.91 0.74 6.27 0.88 5.46 1.08 6.02 0.78

Male 5.80 0.74 6.10 0.84 5.25 1.17 6.05 0.77

Age

18-24 5.81 0.56 6.18 0.70 5.41 0.61 5.85**

0.82

25-34 5.66 1.01 6.04 1.24 5.30 1.23 5.64 1.13

35-44 5.78 0.76 6.07 0.95 5.31 1.00 5.94 0.80

45-54 5.86 0.73 6.17 0.84 5.36 1.19 6.06 0.75

55-64 6.00 0.63 6.31 0.71 5.47 1.17 6.22 0.57

65-74 5.91 0.62 6.33 0.56 5.20 1.18 6.19 0.48

Ethnicity

Asian 5.96 0.80 6.21 0.85 5.54 1.37 6.12 0.88

Black 5.75 1.13 5.91 1.09 5.40 1.50 5.93 1.08

Hispanic 5.54 1.18 5.33 1.66 5.61 1.06 5.67 1.25

White 5.87 0.69 6.33 0.80 5.34 1.09 6.04 0.74

2 or more 5.72 0.24 6.67 0.47 4.33 0.94 6.17 0.71

Other 6.07 0.58 5.93 1.23 6.13 0.51 6.13 0.69

Education

High school 5.80 0.69 6.26 0.62 5.33 1.25 5.81 0.53

Associate 5.90 0.97 6.29 0.70 5.29 1.72 6.13 0.91

Bachelors 5.71 0.69 6.07 0.86 5.16 1.01 5.90 0.76

Masters 5.87 0.79 6.21 0.92 5.38 1.16 6.02 0.84

Doctoral 5.94 0.64 6.19 0.80 5.50 1.05 6.14 0.65

Note. *p < .05,




Table 4.16 shows that the mean scores for industry and self-reported position did

not have a significant effect on collaboration or its three factors, integrating,

compromising, and communication.


Industry, and Position

Demographic

Collaboration



Total Sample 5.86 0.73 6.19 0.86 5.36 1.12 6.03 0.77

Industry

Automotive 5.81 0.67 6.21 0.77 4.91 1.29 6.32 0.43

Consulting 5.85 0.59 6.19 0.65 5.18 1.11 6.17 0.68

Education 5.95 0.72 6.19 0.95 5.66 0.94 6.00 0.72

Engineering 5.66 1.07 5.96 1.22 5.39 1.17 5.64 1.18

Finance 5.90 0.89 6.26 0.88 5.47 1.04 5.96 0.99

Government 5.86 0.54 6.19 0.57 5.29 1.29 6.10 0.58

Healthcare 5.78 0.62 6.24 0.74 5.13 1.21 5.98 0.62

IT 5.94 0.67 6.20 0.66 5.37 1.36 6.25 0.43

Marketing 5.76 1.12 6.05 1.41 5.40 1.11 5.83 1.23

Non-profit 5.90 0.62 6.33 0.60 5.23 1.20 6.15 0.69

Other 5.78 0.16 6.00 0.00 5.50 0.71 5.83 0.24

Position

Administrative 5.78 0.82 6.18 0.85 5.39 1.18 5.78 0.87

CEO 5.96 0.66 6.38 0.70 5.19 1.37 6.31 0.65

Consultant 5.90 0.70 6.38 0.81 5.25 1.38 6.08 0.83

Director 5.79 0.52 6.16 0.75 5.11 0.91 6.09 0.52

Educator 5.90 0.73 6.12 1.00 5.60 0.95 6.00 0.72

Engineer 5.55 1.05 5.93 1.37 5.10 1.08 5.62 1.26

Manager 5.96 0.69 6.23 0.70 5.48 1.20 6.17 0.67

Student 5.93 0.36 6.40 0.55 5.40 0.72 6.00 0.47

Supervisor 5.65 1.03 6.02 1.33 5.11 1.25 5.83 1.09

VP 5.95 0.77 6.23 0.83 5.40 1.18 6.23 0.70

Note. *p < .05, +p < .01 significant difference between scores within demographic characteristic according



As shown in Table 4.17, the mean scores for collaboration and its three

constitutive factors were not significantly different across team size and team

membership according to ANOVA. However, there was a significant difference in the

mean collaboration score across time in team. Results of Tukey’s post-hoc analysis

found that the impact of time in team on collaboration occurred in the 9-12 month range

(5.52) vs. 10-15 years (6.37). ANOVA found differences in the mean integrating score

across team type, with both face-to-face and virtual team settings being significantly

higher than either of the two alone (6.37 vs. 6.12 and 6.10 respectively). There was no

impact on the mean compromising score across team type, team role or time with team.

However, the communication factor showed significant differences in team type and team

role. Here also, mean communication scores are highest in settings where teams engage

face-to-face as well as virtually (6.22), compared with either of the two alone (6.02 and

5.93 respectively). Finally, mean scores for communication were higher for team leaders

(6.17) vs. team members (5.90).



Size, Team Membership, Team Type, Team Role, and Time Working in This Particular

Team

Demographic

Collaboration



Total Sample 5.86 0.73 6.19 0.86 5.36 1.12 6.03 0.77

Team Size

2 Members 5.85 0.81 6.31 0.85 5.31 1.24 5.95 0.77

3 Members 5.81 0.78 6.17 0.83 5.35 1.13 5.93 0.91

4 Members 5.69 0.70 5.97 1.07 5.27 1.04 5.82 0.67

5 Members 5.95 0.58 6.12 0.62 5.53 1.05 6.21 0.57

6 Members 5.91 0.60 6.29 0.76 5.37 0.87 6.08 0.83

7-10 Members 5.84 0.88 6.20 1.05 5.34 1.15 5.97 0.94

11-15 Members 5.87 0.65 6.28 0.69 5.33 1.16 5.99 0.65

16-20 Members 5.87 0.73 6.12 0.79 5.45 1.30 6.03 0.68

21+ Members 5.94 0.76 6.29 0.85 5.27 1.37 6.27 0.60

Team Membership

Internal 5.81 0.76 6.12 0.92 5.33 1.10 5.96 0.80

External 5.95 0.66 6.38 0.58 5.40 1.21 6.08 0.72

Both 5.98 0.64 6.30 0.72 5.43 1.18 6.21 0.64

Team Type

Face-to-face 5.80 0.79 6.10*

0.95 5.36 1.14 5.93+

0.84

Virtual 5.86 0.71 6.12 0.65 5.45 0.95 6.02 0.68

Both 5.99 0.56 6.37 0.62 5.37 1.12 6.22 0.56

Team Role

Leader 5.93 0.65 6.26 0.76 5.36 1.11 6.17+

0.66

Member 5.80 0.79 6.12 0.94 5.39 1.14 5.90 0.83

Time With Team

Less than 1 Mth 5.80*

0.60 5.96 0.82 5.52 0.75 5.93 0.57

1-3 Months 5.74 0.93 6.02 1.12 5.37 1.24 5.83 0.99

3-6 Months 5.62 0.56 6.07 0.66 4.91 0.75 5.88 0.76

6-9 Months 6.04 0.56 6.47 0.55 5.46 1.26 6.19 0.60

9-12 Months 5.52 0.86 5.84 1.24 4.94 1.20 5.80 1.00

1-2 Years 5.99 0.69 6.33 0.79 5.47 1.15 6.15 0.70

2-3 Years 5.97 0.68 6.29 0.70 5.46 1.19 6.15 0.60

3-5 Years 5.92 0.68 6.14 0.83 5.63 1.02 6.00 0.60

5-10 Years 5.99 0.73 6.17 0.77 5.74 0.93 6.06 0.85

10-20 Years 6.36 0.53 6.75 0.58 5.75 1.34 6.58 0.24 Note. *p < .05,

+p < .01 significant difference between scores within demographic characteristic according



For team-based activities in general, Table 4.18 shows the mean scores for

collaboration and two of its three factors (integrating and compromising) were not

significantly different across team role and time in teams. However, for the

communication factor, ANOVA found differences in the mean score for both team role

and time in teams. Team role showed team leaders with significantly higher scores (6.11)

compared to team members (5.89). Results of Tukey’s post-hoc analysis found that the

impact of time involved in teams on communication occurred in the two to three year

range (5.52) vs. 16-20 years (6.20).


Team Role, and Time Involved in Teams (When Working in Team-Based Activities in

General)

Demographic

Collaboration



Total Sample 5.86 0.73 6.19 0.86 5.36 1.12 6.03 0.77

Team Role

Leader 5.92 0.67 6.26 0.76 5.41 1.11 6.11*

0.67

Member 5.76 0.83 6.06 1.01 5.33 1.13 5.89 0.91


Less Than 1 Yr 6.03 0.77 6.29 0.76 5.81 1.03 6.00*

0.61

1-2 Years 6.19 0.57 6.28 0.80 6.28 0.65 6.00 0.60

2-3 Years 5.51 0.63 5.96 0.81 5.04 0.73 5.52 0.93

3-4 Years 6.11 0.51 6.00 1.00 5.78 0.84 6.56 0.38

4-5 Years 5.86 0.63 6.46 0.69 5.31 1.13 5.81 0.88

5-10 Years 5.61 1.09 5.94 1.35 5.19 1.23 5.69 1.19

11-15 Years 5.84 0.73 6.16 0.95 5.39 1.08 5.98 0.83

16-20 Years 5.93 0.73 6.17 0.81 5.42 1.12 6.20 0.74

20+ Years 5.92 0.61 6.27 0.66 5.36 1.16 6.14 0.53



SOAR. Tables 4.19 through 4.22 present the results of the descriptive statistics of

SOAR and its four constitutive factors, S, O, A, and R across each demographic

characteristic. Results are presented as the mean and SD. Additionally, each table


presents results of ANOVA test for any significant difference in mean SOAR, S, O, A, or

R score across each of the demographic characteristics. As shown in Table 4.19, S and A

did show a significant difference across gender, with females having a higher mean score

of 8.03 vs. 7.68, and 7.19 vs. 6.77 respectively.

For age, mean scores for SOAR, S, and O showed a significant difference

according to ANOVA. Tukey’s post-hoc analysis showed difference between the age

range 18-24 (7.03) compared to the age range 65-74 (8.17), but also a general increase in

SOAR with age. Mean scores for S across age showed a difference in the age range 18-

24 (6.85) vs. age range 45-54 (7.94) and 65-74 (8.27). Mean scores for O across age

showed difference in the age range 18-24 (6.82) vs. all other age ranges which were

significantly higher between 8.24 and 8.81.

For education, mean scores for SOAR, S and O showed a very significant

difference between those who achieved at least an associate degree, compared to those

who’s highest level of education was high school. In all cases, recognizing some level of

university education grows mean scores for SOAR, S, O, A and R by at least 1 full point.

While mean scores for SOAR and all its constitutive factors generally increase with

education, the largest incremental increase comes with education beyond high school.


Table 4.19 Mean and SD of SOAR and its Four Constitutive Factors across Gender, Age,

Ethnicity, and Education

Demographic

SOAR Full-

Scale Strengths Opportunities Aspirations

Results


Total Sample 8.00 1.05 7.96 1.27 8.40 1.25 7.52 1.54 8.13 1.37

Gender

Female 8.10 1.07 8.12* 1.29 8.46 1.26 7.68 1.49 8.16 1.32

Male 7.89 1.03 7.79 1.25 8.32 1.26 7.35 1.56 8.10 1.44

Age

18-24 7.06* 0.91 6.92* 0.82 6.82+ 1.26 7.08 1.40 7.44 1.07

25-34 8.08 0.90 8.06 1.21 8.34 1.26 7.46 1.40 8.47 1.00

35-44 7.89 1.30 7.87 1.55 8.24 1.52 7.63 1.76 7.83 1.58

45-54 7.98 1.03 7.98 1.11 8.30 1.25 7.53 1.56 8.11 1.32

55-64 8.13 0.96 8.03 1.33 8.75 0.93 7.54 1.52 8.18 1.44

65-74 8.34 0.78 8.36 0.94 8.81 0.75 7.56 1.20 8.61 1.24

Ethnicity

Asian 8.30 0.94 7.91 1.31 8.76 1.12 7.84 1.66 8.70 0.98

Black 8.12 1.14 8.02 1.29 8.29 1.25 7.71 1.68 8.44 1.03

Hispanic 7.75 2.07 7.67 2.39 7.94 2.28 7.22 2.03 8.17 1.81

White 7.99 1.01 7.97 1.23 8.41 1.21 7.51 1.48 8.06 1.40

2 or more 6.71 1.36 6.67 0.00 6.50 2.59 5.50 1.65 8.17 1.18

Other 8.54 1.44 9.08 1.42 8.33 1.47 7.75 2.87 9.00 1.41

Education

High school 6.57+ 0.83 6.67* 0.96 6.63+ 1.43 5.96 1.62 7.04 1.12

Associate 8.17 1.67 8.29 1.58 8.33 1.57 7.46 2.66 8.58 1.64

Bachelors 7.92 0.88 7.79 1.06 8.27 1.00 7.58 1.29 8.04 1.08

Masters 8.04 1.10 8.08 1.36 8.41 1.34 7.55 1.59 8.11 1.47

Doctoral 8.13 0.89 7.96 1.16 8.66 1.00 7.58 1.36 8.32 1.29



Table 4.20 shows that the mean scores for SOAR, S and R did not have a

significant difference across industry and self-reported position according to ANOVA.

Tukey’s post-hoc analysis revealed that the mean score for A showed a significant

difference across industry, with marketing at 8.29 and engineering at 6.08. Mean scores

for O across self-reported position also were significantly different between students (6.6)

and managers (8.58), directors (8.64) and VP’s (8.67). It can also be observed that mean


EI scores for engineering and engineers are consistently lowest in SOAR and each of its

constitutive factors.

Table 4.20 Mean and SD of SOAR and its Four Constitutive Factors across Industry, and

Position

Demographic

SOAR Full-


Results


Total Sample 8.00 1.05 7.96 1.27 8.40 1.25 7.52 1.54 8.13 1.37

Industry

Automotive 8.02 0.89 8.11 1.11 8.64 0.97 7.24* 1.62 8.08 1.55

Consulting 7.99 0.86 7.62 1.20 8.54 0.97 7.74 1.21 8.08 1.26

Education 8.07 1.13 8.10 1.31 8.40 1.37 7.52 1.52 8.25 1.38

Engineering 7.54 0.97 7.56 1.23 7.99 1.36 6.67 1.41 7.96 1.24

Finance 8.31 1.53 8.35 1.55 8.65 1.65 7.89 1.84 8.37 1.63

Government 7.92 1.07 7.92 1.51 8.02 1.19 7.36 1.58 8.38 1.55

Healthcare 7.82 0.96 7.93 1.09 8.07 1.36 7.59 1.41 7.69 1.56

IT 8.00 1.19 7.98 1.33 8.37 1.25 7.06 2.10 8.61 1.09

Marketing 8.52 0.78 8.33 1.10 8.83 0.98 8.60 0.94 8.31 0.78

Non-profit 8.02 0.93 7.84 1.38 8.64 0.88 7.40 1.43 8.18 1.37

Other 7.33 0.82 8.00 0.94 7.67 1.41 6.83 1.65 6.83 0.71

Position

Administrative 7.76 1.39 7.83 1.53 7.90+ 1.48 7.28 1.75 8.01 1.61

CEO 8.05 1.05 7.73 1.35 8.58 0.94 7.77 1.46 8.10 1.36

Consultant 8.13 1.17 8.21 1.48 8.67 1.37 7.25 1.54 8.38 1.01

Director 8.01 0.87 8.05 1.16 8.64 0.99 7.51 1.28 7.83 1.20

Educator 8.02 1.14 7.91 1.36 8.41 1.35 7.37 1.48 8.40 1.44

Engineer 7.54 0.86 7.57 0.77 7.93 1.43 6.52 1.79 8.12 0.94

Manager 8.19 0.89 8.10 1.18 8.58 1.15 7.87 1.41 8.21 1.41

Student 6.85 0.97 7.07 0.83 6.60 1.59 6.47 1.83 7.27 0.92

Supervisor 7.91 1.13 7.86 1.50 8.28 1.10 7.24 1.59 8.26 1.12

VP 8.22 1.00 8.31 1.08 8.67 1.06 7.60 1.65 8.29 1.65



As shown in Table 4.21 the mean scores for SOAR were not significantly

different across team size, team membership, team type, team role and time with team

according to ANOVA. For S, ANOVA found a significant difference in mean S scores

across team size, and following Tukey’s post-hoc analysis, this significant difference


occurs with teams of two members having a mean S score of 8.23 vs. teams with 21 or

more members (8.46). It can also be observed that in general, as team size grows so does

S. For O, there was no significant difference in mean O scores across team size and time

with team according to ANOVA. However, there was a significant difference in mean O

scores across team membership, team type and team role. Across team membership,

mean O scores were lowest (8.27) for internal teams, then 8.62 for teams that engaged

both internal and external team members, and highest for external teams (8.82). Mean

scores for O across team type were significantly different with the mean O score for face-

to-face interactions (8.28) vs. teams engaging members from both internal and external

sources (8.62). Mean O scores across team role are also significantly different according

to ANOVA, with team leaders showing higher mean O scores (8.65) than team members

at 8.24. And finally, significant by ANOVA, are mean A scores across team type.

Tukey’s post-hoc analysis found that teams engaging in a strictly virtual space have the

lowest mean A scores (5.65) compared to the differentially higher mean A scores for

face-to-face teams (7.03).


Table 4.21 Mean and SD of SOAR and its Four Constitutive Factors across Team Size,

Team Membership, Team Type, Team Role, and Time Working in This Particular Team

Demographic

SOAR Full-


Results


Total Sample 8.00 1.05 7.96 1.27 8.40 1.25 7.52 1.54 8.13 1.37

Team Size

2 Members 8.07 0.78 8.46* 1.17 8.15 1.01 7.40 1.60 8.26 1.45

3 Members 7.63 0.88 7.43 1.26 8.28 1.23 6.96 1.48 7.85 1.23

4 Members 7.73 1.21 7.69 1.44 8.01 1.49 7.21 1.59 8.00 1.32

5 Members 7.92 0.98 7.88 1.16 8.39 1.30 7.30 1.54 8.11 1.33

6 Members 7.98 1.35 7.69 1.68 8.32 1.44 7.47 1.38 8.45 1.53

7-10 Members 8.06 1.01 8.06 1.13 8.40 1.16 7.61 1.44 8.17 1.36

11-15 Members 8.07 0.99 7.91 1.22 8.49 1.29 7.75 1.37 8.14 1.47

16-20 Members 8.06 1.02 8.16 1.17 8.51 1.29 7.76 1.49 7.83 1.43

21+ Members 8.53 0.96 8.62 1.09 8.96 0.85 8.17 1.93 8.36 1.35

Team Membership

Internal 7.91 1.09 7.93 1.31 8.27* 1.34 7.39 1.54 8.04 1.40

External 8.28 0.75 8.05 1.20 8.82 0.92 7.83 1.77 8.42 1.28

Both 8.18 0.95 7.98 1.19 8.62 1.00 7.83 1.39 8.31 1.28

Team Type

Face-to-face 7.95 1.11 7.96 1.30 8.28* 1.31 7.53 1.45 8.03 1.37

Virtual 7.71 1.04 7.95 1.49 8.03 1.44 6.83 1.60 8.03 1.24

Both 8.16 0.90 7.98 1.20 8.67 1.06 7.61 1.66 8.37 1.35

Team Role

Leader 8.09 0.95 8.01 1.23 8.56*

1.06 7.67 1.37 8.14 1.34

Member 7.93 1.14 7.95 1.32 8.24 1.41 7.40 1.69 8.16 1.40

Time With Team

Less than 1 Mth 7.58 0.97 7.39 1.30 7.59 1.19 7.31 1.82 8.04 1.32

1-3 Months 8.03 1.17 7.98 1.37 8.41 1.45 7.44 1.53 8.27 1.36

3-6 Months 7.90 0.80 7.88 1.08 8.18 1.16 7.44 1.24 8.11 1.00

6-9 Months 8.07 0.80 7.78 1.03 8.68 0.95 7.39 1.19 8.42 1.24

9-12 Months 7.70 1.23 7.50 1.41 8.05 1.47 7.37 1.70 7.88 1.52

1-2 Years 8.14 0.94 8.27 1.14 8.38 1.09 7.74 1.43 8.20 1.31

2-3 Years 8.01 1.05 7.95 1.25 8.53 1.30 7.45 1.77 8.10 1.51

3-5 Years 8.10 1.05 8.11 1.49 8.57 0.83 7.38 1.66 8.35 1.25

5-10 Years 7.89 1.46 8.08 1.54 8.48 1.70 7.70 1.79 7.30 1.70

10-20 Years 8.97 0.54 8.63 0.95 9.24 0.71 8.75 0.64 9.29 0.85




As shown in Table 4.22, the mean scores for SOAR, S, A, and R were not

significantly different across team role and time involved with teams according to

ANOVA. However for O, ANOVA found a significant difference in mean O scores

across team role, and time involved with teams. When working in team-based activities

in general, team leaders reported significantly higher O scores (8.51) compared to

members (8.20). Tukey’s post-hoc analysis found the significant difference with time in

teams to be from two to three years (7.44) vs. time in teams greater than five years (8.06).

From five to greater than 20 years, mean O scores rose consistently peaking at 8.72 for

those involved in teams for more than 20 years.

Table 4.22 Mean and SD of SOAR and its Four Constitutive Factors across Team Role,

Time Involved in Teams (When Working in Team-Based Activities in General)

Demographic

SOAR Full-


Results


Total Sample 8.00 1.05 7.96 1.27 8.40 1.25 7.52 1.54 8.13 1.37

Team Role

Leader 8.08 0.96 7.99 1.22 8.51* 1.06 7.61 1.43 8.20 1.30

Member 7.87 1.18 7.90 1.36 8.20 1.51 7.35 1.68 8.02 1.48


Less Than 1 Yr 7.97 1.10 7.58 2.00 8.61+ 1.18 7.33 0.94 8.33 1.23

1-2 Years 8.44 1.15 8.39 1.39 8.22 0.98 8.08 1.54 9.06 1.06

2-3 Years 7.36 1.04 7.41 1.14 7.44 1.64 6.48 1.41 8.11 1.29

3-4 Years 8.00 0.93 8.67 0.88 8.33 0.67 7.11 2.22 7.89 1.02

4-5 Years 7.67 0.88 7.56 1.25 7.77 1.36 7.19 1.39 8.15 1.20

5-10 Years 7.86 1.29 7.78 1.39 8.06 1.55 7.49 1.66 8.11 1.47

11-15 Years 7.86 1.20 7.84 1.31 8.18 1.40 7.52 1.47 7.90 1.28

16-20 Years 8.15 0.98 8.14 1.35 8.44 1.09 7.67 1.60 8.34 1.36

20+ Years 8.11 0.96 8.05 1.17 8.72 1.06 7.57 1.54 8.09 1.43




Hypotheses Testing Results for H1



This section presents the results of linear regression analyses used to test H1:

there is a significant relationship between emotional intelligence (EI) and collaboration.

Results are first presented of the regression of collaboration on the EI full scale score.

Next, results are presented of regression of collaboration on the EI factors in order to

determine the EI competencies that are most important for achieving collaboration among

teams and team members. In all regressions, demographic characteristics were included

as covariates where appropriate.

Table 4.23 shows the result of collaboration regressed on EI to determine if EI

predicts collaboration. Expressed as unstandardized regression coefficients (to assist

with real-world practical interpretation), the regression of collaboration on EI alone was β

= .470**

, and Z = 9.67, which supports H1 and confirms that EI is a significant predictor

of collaboration. β was found to be significant at p < .01 which implies that for every

unit increase in EI, there is a linear relationship with collaboration such that there is a

positive change of 0.47 units of collaboration.

Table 4.23 Collaboration Regressed on EI Alone (EI Predicting Collaboration)

Collaboration

Variable Beta SE Z

Constant 3.353**

.262 12.81

EI 0.470**

.049 9.67

R-square

24.1% Note. See text for coding of variables. *p < .05,

**p < .01 Regression coefficient


In testing hypotheses, it is important it investigate the potential impact of sample

characteristics in order to determine their effect on the outcome variable. In the analysis

of EI as a predictor of collaboration, it was necessary to include those demographic

characteristics that were not evenly distributed across the sample (non-equal proportions

in each category of the variable) in order to determine their effect on collaboration. In

this case age, ethnicity, and education were included as covariates since the sample was

significantly distributed across these three fundamental demographic characteristics (see

Table 4.1), i.e., the regression analyses controlled for age, ethnicity, and education. As

covariates, age and education were included using their initial coding scheme, whereas

ethnicity was recoded as a dichotomous variable with whites coded as a two, and all other

ethnicities coded as a one. All betas for H1 and H2 reflect this recoding.

As a first step in the investigation of age, ethnicity, and education as covariates in

the analysis of collaboration regressed on EI, Tables 4.11 and 4.15 were revisited to

further understand the potential impact of these demographic characteristics on EI and

collaboration according to ANOVA. As shown in Table 4.11, ANOVA found no

difference in mean scores of EI and three of its factors, SA, SM, and AO across age,

ethnicity, and education. However, for MO, there was a significant difference across age,

ethnicity, and education, thereby increasing the need to include these demographic

variables as covariates. As shown in Table 4.15, ANOVA found no significant difference

in mean scores for collaboration and two of its factors, integrating and compromising

across age, ethnicity, and education. However, there was a significant different in mean

scores for the collaboration factor communication across age.

Tables 4.24-4.32 present regression analyses as the following two-step process:


1) In the first step, collaboration was regressed on age, ethnicity, and education to

test the regression of demographic characteristics on collaboration.

2) In the second step, EI and its factors SA, SM, AO and MO were added to the

regression model to test the full impact of the study predictor EI on collaboration

while controlling for age, ethnicity, and education.

Table 4.24 presents the regression of collaboration on age, ethnicity, and

education alone (see Step 1). As shown, there was no significant effect for age, ethnicity,

and education, i.e., the β’s were minimal (.059 or less), p > .05, R-square was low

(1.7%), and the variation on β was negligible. Also shown in Table 4.24 is the regression

of collaboration on EI controlling for age, ethnicity, and education (see Step 2). As

shown, the β for EI was significant at .475 (p < .01), and R-square increased to 25.4%,

with EI contributing 23.7% of the positive variance in collaboration. Steps one and two

show that when including age, ethnicity, and education in the regression of collaboration

on EI, EI remains a significant predictor of positive collaboration. There is essentially no

variation in collaboration that can be attributed to the demographic characteristics.

Table 4.24 Collaboration Regressed on EI (EI predicting collaboration controlling for

age, ethnicity, and education)

Collaboration

Variable Step 1 Step 2

Beta SE Z Beta SE Z

Age .059 .035 1.67 .032 .031 1.04

Ethnicity .003

.119 .028 -.111 .104 -1.06

Education .046 .050 .918 .043 .044 .991

EI .475**

.051 9.38

R-square

1.7% 25.4%

Change in R-square 23.7%**

Note. See text for coding of variables. *p < .05,

**p < .01 Regression coefficient, Change in R-square






of collaboration on SA controlling for age, ethnicity, and education (see Step 2). As

shown, the β for SA was significant at .133 (p < .01), and R-square increased to 7.4%,

with SA contributing 5.7% of the positive variance in collaboration. Steps one and two


on SA, SA remains a significant predictor of positive collaboration. There is essentially

no variation in collaboration that can be attributed to the demographic characteristics.

Table 4.25 Collaboration Regressed on SA (SA predicting collaboration)

Collaboration


Beta SE Z Beta SE Z

Age .059 .035 1.67 .049 .034 1.409

Ethnicity .003

.119 .023 -.050 .116 -.431

Education .046 .050 .915 .051 .049 1.040

SA .133**

.032 4.112

R-square

1.7% 7.4%








of collaboration on SM controlling for age, ethnicity, and education (see Step 2). As

shown, the β for SM was significant at .522 (p < .01), and R-square increased to 32.6%,


with SM contributing 30.9% of the positive variance in collaboration. Steps one and two


on SM, SM remains a significant predictor of positive collaboration. There is essentially


Table 4.26 Collaboration Regressed on SM (SM predicting collaboration)

Collaboration


Beta SE Z Beta SE Z

Age .059 .035 1.672 .046 .029 1.577

Ethnicity .003 .119 .028 -.135 .099 -1.356

Education .046 .050 .918 .043 .042 1.038

SM .522**

.046 11.272

R-square

1.7% 32.6%








of collaboration on AO controlling for age, ethnicity, and education (see Step 2). As

shown, the β for AO was significant at .215 (p < .01), and R-square increased to 12.2%,

with EI contributing 10.5% of the positive variance in collaboration. Steps one and two


on AO, AO remains a significant predictor of positive collaboration. There is essentially



Table 4.27 Collaboration Regressed on AO (AO predicting collaboration)

Collaboration


Beta SE Z Beta SE Z

Age .059 .035 1.672 .061 .033 1.836

Ethnicity .003 .119 .028 .013 .113 .119

Education .046 .050 .918 .044 .047 .933

AO .215**

.038 5.742

R-square

1.7% 12.2%








of collaboration on MO controlling for age, ethnicity, and education (see Step 2). As

shown, the β for MO was significant at .300 (p < .01), and R-square increased to 16.3%,

with MO contributing 14.7% of the positive variance in collaboration. Steps one and two


on MO, MO remains a significant predictor of positive collaboration. There is essentially



Table 4.28 Collaboration Regressed on MO (MO predicting collaboration)

Collaboration


Beta SE Z Beta SE Z

Age .054 .036 1.515 .016 .033 .484

Ethnicity .008 .119 .070 -.092 .111 -.826

Education .048 .050 .961 .036 .046 .780

MO .300**

.043 6.957

R-square

1.6% 16.3%




Analyzing the impact of EI and its constitutive factors as predictors of

collaboration, Tables 4.24 through 4.28 have shown by the regression coefficients that EI

(β = .475), SA (β = .133), SM (β = .522), AO (β = .215) and MO (β = .300) all contribute

positively to collaboration at p < .01, with SM (β = .522) and MO (β = .300) being the

primary predictors.





of collaboration on the four EI factors (SA, SM, AO, and MO) controlling for age,

ethnicity, and education (see Step 2). As shown, the β for SM was significant at .434 (p

< .01), as was MO at .104 (p < .05). R-square increased to 36.4%, with the four EI

factors (SA, SM, AO, and MO) contributing 34.8% of the positive variance in

collaboration. Steps one and two show that when including age, ethnicity, and education

in the regression of collaboration on SA, SM, AO, and MO, the four EI factors remain a

significant predictor of positive collaboration. There is essentially no variation in


collaboration that can be attributed to the demographic characteristics, and SM (β = .434)

and MO (β = .104) are the most influential in predicting collaboration.

Table 4.29 Collaboration Regressed on SA, SM, AO and MO

Collaboration


Beta SE Z Beta SE Z

Age .054 .036 1.512 .032 .030 1.093

Ethnicity .008 .119 .066 -.147 .098 -1.496

Education .048 .050 .958 .040 .041 .990

SA .014 .030 .468

SM .434**

.051 8.586

AO .070 .039 1.787

MO .104* .049 2.149

R-square

1.6% 36.4%




In the preceding analyses, the determination of EI and its factors as predictors of

collaboration was confirmed. To further understand the relationship between EI and

collaboration, regression of the individual collaboration factors (integrating,

compromising, and communication) on EI are shown in Tables 4.30 through 4.32. These

regressions will show how EI predicts the constitutive elements of collaboration similar

to how EI and its factors predict collaboration.

In this regression two steps were conducted:

1) Integrating was regressed on age, ethnicity, and education (to show their

impact alone on integrating).

2) Integrating was regressed on EI to show its impact on integrating

(controlling for age, ethnicity, and education).


Table 4.30 presents the regression of integrating on age, ethnicity, and education

alone (see Step 1). As shown, there was no significant effect for age, ethnicity, and

education, i.e., the β’s were minimal (.190 or less), p > .05, R-square was low (1.7%),

and the variation on β was negligible. Also shown in Table 4.30 is the regression of

integrating on EI controlling for age, ethnicity, and education (see Step 2). As shown, the

β for EI was significant at .487 (p < .01), and R-square increased to 20.1%, with EI

contributing 18.4% of the positive variance in integrating. Steps one and two show that

when including age, ethnicity, and education in the regression of integrating on EI, EI

remains a significant predictor of integrating. There is essentially no variation in

integrating that can be attributed to the demographic characteristics.

Table 4.30 Integrating Regressed on EI (EI predicting integrating)

Integrating


Beta SE Z Beta SE Z

Age .059 .041 1.439 .031 .037 .843

Ethnicity .190 .138 1.375 .073 .126 .583

Education .006 .058 .094 .003 .053 .053

EI .487**

.061 7.982

R-square

1.7% 20.1%





1) Compromising was regressed on age, ethnicity, and education (to show

their impact alone on compromising).

2) Compromising was regressed on EI (to show its impact on compromising

(controlling for age, ethnicity, and education).


Table 4.31 presents the regression of compromising on age, ethnicity, and




of compromising on EI controlling for age, ethnicity, and education (see Step 2). As


with EI contributing 10.9% of the positive variance in compromising. Steps one and two

show that when including age, ethnicity, and education in the regression of compromising

on EI, EI remains a significant predictor of compromising. There is essentially no

variation in compromising that can be attributed to the demographic characteristics.

Table 4.31 Compromising Regressed on EI (EI predicting compromising)

Compromising


Beta SE Z Beta SE Z

Age -.004 .054 -.070 -.032 .052 -.618

Ethnicity -.144 .183 -.784 -.262 .174 -1.505

Education .090 .077 1.167 .088 .073 1.197

EI .496**

.085 5.850

R-square

0.7% 11.7%





1) Communication was regressed on age, ethnicity, and education (to show

their impact alone on communication).

2) Communication was regressed on EI (to show its impact on

communication (controlling for age, ethnicity, and education).


Table 4.32 presents the regression of communication on age, ethnicity, and




of communication on EI controlling for age, ethnicity, and education (see Step 2). As


with EI contributing 18.3% of the positive variance in communication. Steps one and

two show that when including age, ethnicity, and education in the regression of

communication on EI, EI remains a significant predictor of communication. There is

essentially no variation in communication that can be attributed to the demographic

characteristics.

Table 4.32 Communication Regressed on EI (EI predicting communication)

Communication


Beta SE Z Beta SE Z

Age .121**

.037 3.319 .096**

.033 2.914

Ethnicity -.037 .123 -.297 -.142 .112 -1.275

Education .044 .052 .847 .042 .047 .888

EI .441**

.054 8.135

R-square

4.9% 23.2%




In the analysis of EI as a predictor of the collaboration factors, Tables 4.30

through 4.32 have shown by the regression coefficients that EI contributes positively to

each factor, integrating (β = .487), compromising (β = .496) and communication (β =

.441) at p < .01. EI has the largest impact on compromising (β = .496), followed by

integrating (β = .487) and communication (β = .441) respectively. Each regression was


controlled for age, ethnicity, and education, and here also, the contribution of the

demographic characteristics is negligible.

Overall, the regression analyses presented in Tables 4.24-4.32 show that when

controlling for age, ethnicity, and education, EI is a significant predictor of collaboration,

and when breaking EI down into its constitutive elements (SA, SM, AO, and MO), SM

and MO are the components of EI that appear to function as the most significant

predictors of collaboration. SA and AO are also significant predictors of collaboration

according to the regression coefficients, but to a lesser extent than SM and MO. EI was

also shown to be a significant predictor of the collaboration factors.




As shown in Tables 4.33 through 4.35, an interaction term of either EI x Team

Role, EI x Team Type, or EI x Time in Teams was included in the regression analysis to

test if team role, team type, and time in teams moderate the impact of EI on collaboration.

Team role, team type, and time in teams were selected as moderators due to their

significant relationship with EI according to ANOVA (see Table 4.13). Moderation was

tested using hierarchical regression such that the effect of EI acting alone as a predictor

of collaboration was compared against a model in which EI, the moderator, and the EI x

moderator interaction term were tested.

In Table 4.33, team role was tested as a potential moderator of EI predicting

collaboration. Results of the hierarchical regression show the interaction of EI x team

role is significant (β = .255, p < .05). Furthermore, the change in R-square from 24.3%


to 26.0% is significant (1.6%, p < .05) implying that there is improvement to the model.

These results suggest that there is moderation of the EI-collaboration relationship

occurring by team role.

Table 4.33 Hieararchical Regression of Collaboration on EI and Team Role

Collaboration

Variable Step 1 Step 2 Step 3 Step 4

Age .062 .035 .036 .031

Ethnicity -.043 -.137 -.141 -.152

Education .059 .053 .052 .052

EI .458**

.464**

.334**

Team Role .037 -1.332*

EI x Team Role .255*

R-square

2.1% 24.3% 24.3% 26.0%


0.1%

1.6%*



As shown Table 4.34, team type was tested as a potential moderator of EI

predicting collaboration. Results of the hierarchical regression show that in contrast to the

interaction of EI x team role, the interaction of EI x team type is not significant (β = -

.150, p > .05). Furthermore, the change in R-square from 24.4% to 25.3% is not

significant (0.7%, p > .05) implying that there is not improvement to the model.

However, a simples slopes test of the difference in the slopes of the lines that plot EI’s

prediction of collaboration at the three different values of team type (corresponding to

virtual, face-to-face, and both) found the slopes were significantly different at p = .01

(Aiken & West, 1991; Dawson, 2014). These results suggest that team type may be

functioning as moderator of the EI-collaboration relationship.


Table 4.34 Hieararchical Regression of Collaboration on EI and Team Type

Collaboration


Age .059 .033 .032 .031

Ethnicity .012 -.102 -.109 -.101

Education .047 .044 .043 .041

EI .463**

.460**

.797**

Team Type .040 .843

EI x Team Type -.150

R-square

1.8% 24.4% 24.5% 25.3%


0.1% 0.7% Note. See text for coding of variables. *p < .05,


In Table 4.35, time in teams was tested as a potential moderator of EI predicting

collaboration. Results of the hierarchical regression show the interaction of EI x time in

teams is significant (β = -.123, p < .05). Furthermore, the change in R-square from

26.7% to 27.9% is significant (1.1%, p < .05) implying that there is improvement to the

model. These results suggest that there is moderation of the EI-collaboration relationship

occurring by time in teams.

Table 4.35 Hieararchical Regression of Collaboration on EI and Time in Teams

Collaboration


Age .059 .031 .039 .030

Ethnicity .011 -.104 -.098 -.129

Education .049 .045 .044 .045

EI .486**

.487**

.719**

Time in Teams -.020 .641

EI x Time in Teams -.123*

R-square

1.8% 26.7% 26.8% 27.9%


0.0% 1.1%*







Table 4.36 and Figure 4.1 present the results of the full SOAR construct as a

single mediator of the relationship between EI and collaboration. In support of H3, when

the mediator, SOAR, was added to the regression as a covariate, EI remained a

significant predictor of collaboration (β = .376, Z = 3.803), and SOAR was also a

significant predictor of collaboration (β = .179, Z = 3.280). Also in support of H3, Table

4.37 and Figure 4.2 present the results of the constitutive elements of SOAR included in

the mediation analysis as multiple mediators. Results of collaboration regressed on EI

and the four mediators found EI was a significant predictor of collaboration (β = .402, Z

= 4.160), and strengths (β = .089, Z = 2.169), aspirations (β = -.061, Z = 2.064) and

results (β = .112, Z = 3.742) were also significant predictors of collaboration (the slope of

opportunities was not significantly different from zero).

As shown in Table 4.36 and Figure 4.1, the unit-free index of strength of the

mediated effect of EI on collaboration through the mediating variable SOAR is given by

the total indirect effect of X on Y = βa1b1 = .110. The Sobel test (Sobel, 1982; Z =

2.449), and the use of bootstrapping (bias corrected) 95% CI (.034, .209) found the

indirect effect to differ significantly from zero. The finding of a significant mediated

path and a significant direct c' path suggests the influence of EI on collaboration is

partially mediated by SOAR. Therefore, EI may have some additional effect on

collaboration that is not mediated by SOAR.


Results shown in Table 4.37 and Figure 4.2 break down SOAR into its

constitutive elements, and test Strengths, Opportunities, Aspirations, and Results as

multiple mediators of the effect of EI on collaboration. The total indirect effect of X on Y

= .085. Although the Sobel test was not significant at the .05 level (Z = 1.874), the use of

bootstrapping (bias corrected) 95% CI (.007, .186) found the indirect effect to differ

significantly from zero. Similar to each of the c' paths, the indirect effects of EI on

collaboration through the multiple mediators strengths, aspirations and results were

significant. These results suggest that strengths, aspirations, and results were partial

mediators of the effect of EI on collaboration.



Strengths, Opportunities, Aspirations, and Results (Full Construct) Background Factors

Product of Coefficients Bootstrapping (Bias

Corrected) 95% CI

Background factors β SE Z p Lower Upper

Path c

Emotional Intelligence .470 .071 6.646 .000 .331 .606

Path a

SOAR .624 .100 6.219 .000 .431 .830

Paths b and c'

SOAR .183 .054 3.402 .001 .069 .286


Indirect effects

SOAR .114 .046 2.495 .013 .041 .224

Total .114 .046 2.495 .013 .041 .224 *significant p < .05,

**significant p < .01,

***significant p < .001; 5,000 bootstrapping

samples; CI = confidence interval

Figure 4.1 SOAR Mediating the Effect of EI on Collaboration



Strengths, Opportunities, Aspirations, and Results Background Factors

Product of Coefficients Bootstrapping (Bias

Corrected) 95% CI

Background factors β SE Z p Lower Upper

Path c


Path a

Strengths .591 .113 4.861 .000 .377 .812

Opportunities .699 .110 6.356 .000 .492 .931

Aspirations .819 .144 5.202 .000 .542 1.105

Results .385 .120 3.241 .001 .162 .635

Paths b and c'

Strengths .071 .038 1.966 .049 .008 .144

Opportunities .078 .042 1.842 .065 -.004 .158

Aspirations -.064 .037 1.739 .082 -.139 .004

Results .113 .028 4.033 .000 .056 .167


Indirect effects

Strengths .042 .025 1.966 .049 .000 .098

Opportunities .054 .031 1.968 .049 .000 .123

Aspirations -.053 .029 1.787 .074 -.117 .000

Results .044 .017 2.614 .009 .018 .087

Total .087 .044 1.985 .047 .013 .184 *significant p < .05,

**significant p < .01,

***significant p < .001; 5,000 bootstrapping

samples; CI = confidence interval


Figure 4.2 SOAR and its Constitutive Factors Mediating Collaboration


Chapter 5 Discussion

Introduction

This study investigated the relationship between emotional intelligence (EI) and

collaboration, and determined the EI abilities that are most critical for achieving

collaboration among teams and team members. The study also investigated the

moderating effects of demographic factors, and the mediating effects of SOAR (a

framework for strengths-based strategic thinking), on the relationship between EI and

collaboration. Data for the study were collected via an electronically administered survey

using SurveyMonkey. Analysis of the data was completed via regression-based

inferential statistics and Structural Equation Modeling. This chapter presents the

hypothesis testing results, implications for practice and recommendations,

recommendations for future research, and concludes with potential study limitations.

Summary of Results and Discussion

The sample for this study consisted of 308 participants, essentially equally

divided between males and females. Participants were predominantly white, age 45-64,

and educated with a Master’s degree or higher. Half reported functioning as the team

leader and working with their current team for more than one year. Participants

described the majority of their teams as including 4-15 members, were internal to the

organization, and met face-to-face (as opposed to virtually). Most of the participants

reported being involved in teams for more than 10 years, and that they typically

functioned as the team leader.

The survey instrument used in this study was administered on-line to individuals

currently working in teams or those with recent experience in doing so. The five-section


survey included questions on participant demographics and team characteristics,

emotional intelligence, collaboration and SOAR. Emotional intelligence was measured

by the WEIP-S (Jordan & Lawrence, 2009), to establish respondent competency in the

four EI abilities helpful for understanding how EI works in teams. The EI construct

factors, or abilities, were awareness of own emotions, management of own emotions,

awareness of others’ emotions, and management of others’ emotions (Mayer & Salovey,

1997). Collaboration was measured by the Team Collaboration Questionnaire, an

original measure of collaborative activity uniquely developed for this study. Adapted

from Aram and Morgan (1976), and Rahim (1983a, 1983b), the Team Collaboration

Questionnaire measured three factors of collaboration: integrating, compromising, and

communication. Finally, SOAR was measured by the SOAR Profile (Cole & Stavros,

2013), a self-report measure of strengths-based strategic thinking capacity from a SOAR

framework. The measures were selected for their ability to rapidly identify EI abilities,

collaboration, and SOAR elements most critical to achieving positive outcomes in team-

based collaboration.

The psychometric properties of the WEIP-S, Team Collaboration Questionnaire,

and the SOAR Profile were evaluated via Cronbach’s coefficient alpha test of internal

consistency reliability (Cronbach, 1951), and via CFA test of construct validity (Lu, 2006)

prior to testing the hypotheses. The WEIP-S demonstrated acceptable psychometric

properties in its original form, but the Team Collaboration Questionnaire and SOAR

Profile required the following adjustments: for collaboration, six-items of the original set

of 15-items were removed leading to a final set of nine-items; and for SOAR, eight-items

from the original set of 20-items were removed leading to a final set of 12-items. In their


final form, all scales had acceptable reliability, with alpha values ranging from .853-.893

for the three study variables, EI, Collaboration, and SOAR. Cronbach’s alpha values for

the EI subscales (self-awareness, self-management, awareness of others’ emotions, and

management of others’ emotions) were also acceptable and ranged from .805-.903. The

Team Collaboration Questionnaire sub-scales (integrating, compromising, and

communication) showed alpha values ranging from .721-.909. Alpha values for the

SOAR subscales (Strengths, Opportunities, Aspirations and Results) ranged from .722-

.795. Results of higher-order CFA supported the construct validity of the study

constructs, with all three sets of measures satisfying the goodness of fit indices used to

evaluate CFA—chi-square/df ratio less than 2, RMSEA < .08, and CFI > .900.

Additionally, the factor loadings of all indicators were significant, as was the factor

loadings of all first-order latent constructs onto the higher-order constructs.

Four research questions were posed in this study:











Three hypotheses were tested to answer the research questions and to evaluate the

relationships between the study variables (see study model shown in Figure 5.1).







Figure 5.1 Model of the Study: SOAR Mediating the Impact of Emotional Intelligence on

Collaboration with Demographic Moderating Variables

H1: Supported. Emotional intelligence is related to collaboration such that EI

has a positive impact on collaboration. H1 was tested to answer the first two research

questions: Is there a relationship between EI and collaboration? Are there differences in

the contribution of the EI factors to collaboration?


According to the results of multiple regression analyses that were used to test H1,

the answer to the first research question is that there is a robust relationship between EI

and collaboration, such that as EI grows there is a significant positive impact on

collaboration. Further regression analyses answered the second research question, that

there are differences in the contribution of each EI factor to collaboration. Self-

management (SM) and management of others (MO) were determined to be the

components of EI that function as the most significant predictors of collaboration,

followed by awareness of others (AO) and self-awareness (SA). With H1 supported,

practitioners concerned with increasing team-based collaboration are recommended to

increase EI abilities in themselves and their collaborative teams, particularly in the EI

factors of SM and MO. EI provides active support of the collaborative process, and as EI

is developed throughout the collaborative team, support of a common goal grows and

team effectiveness increases (Xavier, 2005).

EI is an important factor in predicting team performance (Jordan & Lawrence,

2009), and this study evaluated and determined the specific EI abilities that contribute to

positive collaboration in teams. The study confirmed that EI is a significant predictor of

collaboration, and that certain factors of EI contribute more than others to collaboration.

Tested by way of regression-based inferential statistics, EI was found to be a significant

predictor of collaboration, with a Beta coefficient of 0.475 when controlling for age,

ethnicity, and education (significant at the p < .01 level). This implies that for every unit

increase in EI, collaboration increases by approximately 0.48 units. When breaking

down EI into its’ constitutive factors, self-management (SM) and management of others

(MO) were determined to be the components of EI that function as the most significant


predictors of collaboration, followed by awareness of others (AO) and self-awareness

(SA). When the collective impact of the four EI abilities on collaboration were analyzed,

all had positive correlation with collaboration. SM and MO were statistically significant,

whereas SA and AO were not.

The finding that demographic characteristics of age, ethnicity, and education did

not have a significant impact on the relationship between EI and collaboration implies

that EI transcends certain participant demographic characteristics. The mean scores for

EI were not significantly different across gender, age, ethnicity, and education according

to ANOVA. Further, the regression of collaboration on age, ethnicity, and education

alone showed there was no significant effect for age, ethnicity, and education, i.e., the β’s

were minimal (.059 or less), p > .05, R-square was low (1.7%), and the variation on β

was negligible.

H2: Supported. The impact of emotional intelligence on collaboration is

moderated by participants’ demographic characteristics. H2 was tested to answer the

third research question: Are there any demographic characteristics that moderate the

impact emotional intelligence may have on improved collaboration outcomes?

According to the results of hierarchical regression analyses with demographic

interaction terms, the answer to the third research question is that team role, team type,

and time in teams moderate the impact EI has on collaboration. Furthermore, the results

of the hierarchical regression analyses suggest that EI’s impact on collaboration is

increased when team role is leader, when team type is virtual, and when time-in-teams is

greater than one year. Hierarchical regression also showed that age, gender, ethnicity and

education do not appear to have an effect on the relationship between EI and


collaboration. With H2 supported, practitioners concerned with building effective

collaborative teams are recommended to seek participants with leadership experience,

and team-based experience for greater than one year. Wolff and Koman (2008)

emphasize the impact that emotionally intelligent leaders have on organization

development and positive performance outcomes. The team should also emphasize the

impact virtual teams have on levels of EI, thereby also contributing to increased

opportunities for successful collaboration.

To facilitate the interpretation of moderating effects, three graphs were created

that plot the slope of collaboration at different levels of EI when moderated by team role

(Figure 5.2), team type (Figure 5.3), and time in teams (Figure 5.4). As shown in Figure

5.2, when individual EI is low, team role moderates the impact of EI on collaboration in

that team leaders achieve higher levels of collaboration than team members. Exhibiting

EI with a concern for self and others promotes integration of ideas, cooperation and

inclusion of all team members (Romero et al., 2009). As individual EI grows, the

distinction between leaders and members lessen, with no impact at all as EI grows from

medium to high. To have a positive impact on collaboration beyond medium levels of

EI, what matters is EI growth, not team role.


Figure 5.2 Team Role as a Moderator of the Relationship between EI and Collaboration

Figure 5.3 illustrates the moderation effect of team type on EI’s prediction of

collaboration. When individual EI is low to medium, there is minimal distinction as to

team type (face-to-face or virtual). However, as individual EI grows, the ability to be

collaborative in a virtual environment significantly grows. The difference in the slopes of

the lines that plot EI’s prediction of collaboration when team type was virtual compared

to face-to-face or both was significant at p < .01 according to simple slopes analysis

(Aiken & West, 1991; Dawson, 2014).


Figure 5.3 Team Type as a Moderator of the Relationship between EI and Collaboration

Figure 5.4 illustrates the moderation effect of time in teams on EI as a predictor of

collaboration. Similar to team role, when individual EI grows beyond the low level to the

medium and high levels, there is little moderation of time in teams on the effect that EI

has on collaboration. Further, when individual EI is low, time in teams moderates the

effect EI has on collaboration such that individuals who have team experience beyond 10

years show significantly more of an EI effect on collaboration compared to those who

have less experience.


Figure 5.4 Time in Teams as a Moderator of the Relationship between EI and

Collaboration

H3: Supported. The SOAR framework mediates the relationship between

emotional intelligence and collaboration. H3 was tested to answer the fourth research

question: Does the SOAR framework for strengths-based strategic thinking mediate the

impact that EI may have on collaboration? According to the results of structural equation

modeling (SEM) that was used to test H3, the answer to the fourth research question is

that SOAR mediates the effect EI has on collaboration.

The study supports the hypothesis that SOAR mediates the effect that EI has on

collaboration. Specifically, results of structural equation modeling (SEM) found a

significant indirect effect between EI and collaboration through the mediator, SOAR.


SEM also found that the EI-collaboration direct path remained significant along with the

EI-collaboration indirect path, suggesting that there was partial mediation (as opposed to

full mediation). Characterizing the constitutive elements of SOAR as multiple mediators

found that Strengths (S), Opportunities (O), and Results (R) were significant mediators of

the relationship between EI and collaboration. This implies that SOAR is one of the

mechanisms that may explain how the relationship between EI and collaboration occurs.

At the heart of the SOAR framework is an inclusive approach that promotes team

members to frame strategy from a strengths-based perspective utilizing the team’s unique

strengths, assets, and capabilities (Cooperrider, Whitney, & Stavros, 2008). For

practitioners, SOAR should be considered by teams and team members seeking

increasingly positive collaboration outcomes, with a particular emphasis on developing

their strategic strengths, opportunities, and results.

Implications for Practice and Recommendations

This study has implications for teams and team members such that focusing on

methods to improve EI abilities in themselves and others may be critical for increasing

collaboration in teams, and ultimately team effectiveness. EI, as measured by the WEIP-

S in this study, was concerned with the measurement of EI abilities (self-awareness, self-

management, and awareness and management of others’ emotions). The strong

psychometric properties of the WEIP-S support the implication that the WEIP-S

functions as a valid and reliable tool for determining baseline EI competency. The

implication of H1 being supported is that levels of EI have a direct effect on collaboration

outcomes in teams, i.e., the study shows that an increase in EI abilities contributes


directly to improved collaboration outcomes, particularly in the SM and MO factors of

EI.

There is more to effective teamwork than the accomplishment of tasks within

some metric of performance. Collaboration implies that team members are working

together to accomplish an outcome that is more significant as a team than that which

could be accomplished by the individual members acting alone (Gray, 1985; Romero,

2009). Achieving collaborative goals can actually be influenced by an individual’s

emotional self-awareness and awareness of others’ emotions. The significance of this

implication is important to collaborative teams seeking to gain a competitive advantage

within some framework of time, cost, and performance. Teams lacking the influence of

EI abilities in its members are immediately at a disadvantage to those that acquire EI in

order to develop awareness and management of emotions in themselves and others

(Gohm, 2004).

The finding that SM and MO were significant predictors of collaboration has

implications for organizational leadership to support teams and team members by

prioritizing emotional self-management and management of others’ emotions when

promoting the EI abilities. The finding that the collaboration factors (integrating,

compromising, and communication) have individual variation relative to changes in EI

also has implications for teams. As EI improves, so do the elements of collaboration.

This suggests that improving one’s capability for collaboration can also be achieved with

EI growth as it contributes to one’s ability to be more effective at integrating ideas,

seeking of compromise, and encouraging open and effective communication.


EI transcends diversity in age, ethnicity, and education, i.e., EI growth and

positive collaboration outcomes appear to be possible with male and female team

members of various ages, ethnicities, and education. Regarding education in particular,

the finding that there was no relationship between EI and education has important

implications to the potential makeup of a diverse work team. Unlike IQ, which has

moderate to high correlation with amount of education, such that the more time in school

generally leads to greater intelligence (Deary & Johnson, 2010), EI appears to have

minimal correlation with amount of education (Wong & Law, 2002). Results of the

current study also suggest that there is no relationship between EI and level of education.

For example, ANOVA did not find a significant relationship between the levels of

education and mean EI score. In fact, this finding overcame the significant distribution of

the sample in which there were significantly more Master’s and Doctoral level

participants in the study (greater than 70%). The implication of this finding, that EI has

no correlation with education, is that teams with diverse educational level can benefit

from targeted EI improvements. As teams organize members with certain requisite skills

and abilities, no particular distinction should be made with regard to level of education.

With H2 being supported, the implication is that certain participant demographic

characteristics affect the relationship between EI and collaboration. Specifically, higher

levels of EI increase collaboration among team members, teams that meet in a virtual

environment, and among individuals who have limited experience working in teams.

These characteristics, team role, team type, and time in teams, were found to have a

significant effect on EI’s prediction of collaboration. The further implication is that these

characteristics should be considered when building teams, and in teams seeking to


improve their potential for positive collaboration. Effective collaboration is

demonstrated when the same EI competencies that are displayed in strong leadership are

displayed by teams, i.e., to recognize, manage, and use emotional information about

oneself and others (Boyatzis, 2007).

With team role, leadership emerges as an important characteristic when EI is low.

As EI increases within the collaborative team, the distinction between leaders and

members lessens; what is important is that they have EI, or EI growth, not team role.

With team type, teams that engage virtually can actually exhibit higher potential for

collaboration when EI levels are high. When EI is high, virtual teams exhibit some

accentuation of, or emphasize their need to collaborate in recognition of the potential

difficulties with lack of face-to-face interactions. As individuals gain experience in team

settings, time-in-teams becomes less a factor in EI predicting collaboration. When EI is

low, individuals with extensive experience in teams (10+ years) have significantly higher

potential for collaboration compared to those with less experience. Finally, EI is not

affected by industry, which implies EI can be learned and applied in any setting.

Team role, team type, and time in teams are all important factors to consider when

planning, building, and participating in teams. These factors are important to teams in

meeting organizational objectives, and when supporting collaboration among team

members to maximize team effectiveness. Team leaders and team members can seek to

improve their collaborative effectiveness by focusing on the EI abilities most critical to

achieving collaborative success – emotional self-management (SM) and management of

others’ emotions (MO). By focusing on these critical EI abilities first, team members

will be in the best position to achieve positive collaboration. Becoming proficient in all


the EI abilities (SA, SM, AO, and MO) will maximize the potential impact EI may have

on collaboration.

In teams where members are low in EI, team leaders can play a strong role in

bringing the benefits of EI competency to the collaborative team, e.g., SM and MO.

Teams should consider, at least in the short-term, to identify a leader highly competent in

the EI abilities. If the technical merits necessary for team leadership preclude the

selection of a leader high in the EI competencies, a co-leader may be suggested as a team

member responsible for elevating the EI competency of the team. As EI increases in the

members of the collaborative team, team role becomes less important. Team leaders and

team members contribute essentially equally to positive collaboration as EI builds in all

team members.

In teams where interaction occurs virtually, the potential for positive collaboration

significantly improves as EI grows. Raising the level of EI competency in virtual team

members will maximize the impact EI can have on collaboration, and should therefore be

the areas targeted first for EI development. Team members with high levels of team

experience (10+ years) provide the best opportunity to bring strong EI skills to the team.

These individuals should be tasked with the guidance and development of EI in less

experienced team members.

The implication of H3 being supported is that SOAR definitively functions as a

mechanism of action between EI and collaboration, and should be considered when

seeking to improve collaboration in teams. A framework for strategy based on the

strengths and aspirations of team members helps to explain how EI leads to positive

collaboration. This implies further that EI abilities and their effect on collaboration can


be accentuated in individuals and teams competent in SOAR. Since strengths (S),

opportunities (O), and results (R) function as the main mediators of EI and collaboration,

these should be targeted first. Study results imply that when individuals are working in a

team context, especially when collaboration is the desired outcome, team members are

aware of their natural strategic capacity for strengths-based strategic thinking, are aware

of bringing their visions and aspirations into the collaboration, and are focused on

completing tasks and obtaining results to bring the team collaboration to a successful

outcome.

A framework for strategy based on the strengths and aspirations of team members

helps to explain how EI leads to positive collaboration. Enabling strategic thinking

capacity from a SOAR-based framework in team members will provide the best

opportunity to influence collaboration. Leaders can assemble teams that build on

strengths and aspirations of members to identify opportunities and achieve positive

results. Education and training in the SOAR competencies may be the best approach for

team members unfamiliar with SOAR and strengths-based strategic thinking. When

individuals are working in a team context, especially when collaboration is the desired

outcome, team members competent in SOAR will be able to maximize the impact EI has

on collaboration.

Study results found SOAR partially mediated the relationship between EI and

collaboration among participants working in teams. SOAR was measured by the SOAR

Profile, which emerged from the fields of strategy, organization development and change,

and Appreciative Inquiry (AI). AI is vital to the emergence of SOAR due to its

engagement of people at all levels of an organization in an inquiry into the organization’s


positive core, similar to strengths-based approaches to strategic thinking. The SOAR

Profile is also positioned through the lens of positive organizational scholarship (POS),

with its focus on generative dynamics of how leaders can build teams as social systems

that build on strengths and aspirations of members to achieve positive results. This

positive approach leads to changes in the organization based on images of the best

possible future as articulated and visualized by the people who make up the human

system of the organization (Cooperrider et al, 2008).

As team members seek to raise their levels of EI, they can begin by first

completing an EI self-assessment baseline such as the WEIP-S to identify areas of

strength and weakness in each EI factor, and then looking at the four-items (questions)

that make up each factor. Areas of relative strength and weakness in these items,

particularly when compared to a reference population, can provide additional

understanding as to the abilities of particular interest for EI growth. Initiating appropriate

change action to improve the EI abilities of team members should be focused on the areas

of most importance depending on their background, team role, team type, and time in

teams.

Because there is the potential for variability in the effect that EI has on the

different factors of collaboration, practitioners are also recommended to complete the

Team Collaboration Questionnaire, an original measure of collaborative activity uniquely

developed for this study. This metric of collaboration will provide an assessment of

strengths and weaknesses in the collaboration factors (integrating, compromising, and

communication) important to team effectiveness. Areas of relative strength and


weakness in these items, particularly when compared to a reference population, will

provide an indication of the factors most appropriate for improvement.

Finally, team members should complete the SOAR Profile to help understand

their natural capacity for strategic thinking, and its relevance to positive collaboration

outcomes. In this study, the SOAR Profile was used as a rapid assessment instrument

specifically designed to understand an individual’s natural tendency to reinterpret

problems as solutions, and to ultimately maximize the impact that SOAR competent team

members may have on collaboration (Cole & Stavros, 2014).

The implications of this study and resultant recommendations (summarized in

Table 5.1), offer significant opportunities for practitioners seeking improved outcomes in

their collaborative teams. Learning and practicing the EI abilities can significantly

impact team collaboration in a positive way. Self-awareness, self-management,

awareness of others’ emotions and management of others’ emotion can be acquired over

time through education, practice and emotional maturity (Macaleer & Shannon, 2002).

Other research on EI considers EI from a trait based perspective inherent to personality,

which in practice is not easily changed. EI abilities, in contrast are dynamic, and

therefore have the potential to be improved through behavior change interventions.


Table 5.1 Summary of Practical Recommendations

Emotional Intelligence

Recommendations for Practice Literature Support

Focus on ability-based models of EI and their assessment methods, e.g.,

Jordan and Lawrence (2009), and the corresponding WEIP-S. Establish

self-report baseline for mean EI and mean EI scores for each factor (SA,

SM, AO, and MO) to identify areas of strength and weakness. Compare

with a relevant reference population to further refine areas of strength

and weakness.

(Gray, 1985;

Romero, 2009;

Gohm, 2004;

Jordan and

Lawrence, 2009)

SM and MO are the primary EI factors most critical to achieving

positive outcomes in collaboration; seek to raise individual levels of SM

and MO first by studying results of the WEIP-S at the SM and MO

factor levels, with a particular emphasis on the individual questions that

make up each factor (4 questions for each factor). Seek to compare

results with a relevant reference population to determine priority for

development and training.

(“Emotional

competence

framework,” 1998;

Xavier, 2005)

Similar to SM and MO, the SA and AO factors also contribute to

positive collaboration. Seek to evaluate and improve these EI factors

with secondary priority to SM and MO. Follow the same procedure

described for SM and MO, paying particular attention to the four

questions that make up each factor. Compare to relevant reference

population.

(“Emotional

competence

framework,” 1998;

Goleman,

2006; Xavier,

2005)


Collaboration


Establish self-report baseline for collaboration and its factors,

integrating, compromise, and communication, using the Team

Collaboration Questionnaire. Compare results with other team members

or relevant reference population to identify areas of strength and

weakness. Target appropriate areas for improvement by focusing on the

individual questions that make up each factor. Repeat the collaboration

self-assessment at a later date to evaluate progress.

(Dietrich et al.,

2010; Hattori &

Lapidus, 2004;

Prati et al., 2003;;

Shaw & Lindsay,

2008)

Leaders high in EI raise collaboration in teams to a greater extent than

team members having lower EI. Improving EI in all team members

optimizes potential for positive collaboration. Identify highly EI

competent leaders or co-leaders responsible for raising levels of EI in

team members.

(Wolff & Koman,

2008; Boyatzis,

2007)

When EI is high, virtual teams exhibit significantly higher levels of

collaboration. Consider adding a virtual element to team interactions,

and/or seek to improve levels of EI in face-to-face teams.

(Romero et al.,

2009)

Team members with 10+ years of team experience exhibit highest levels

of EI. Seek to have a team member who can bring this characteristic to

the rest of the team.

(Macaleer &

Shannon, 2002).


SOAR


Complete the SOAR Profile to help understand and learn about one’s

natural capacity for strategic thinking and planning. Evaluate each

factor score by referring to the individual questions that make up each

factor. See which questions are comparatively low to determine areas of

strength and weakness. Compare with team members or relevant

reference population to refine areas of strength and weakness.

(Cole & Stavros,

2013; Stavros &

Hinrichs, 2009;

Cole & Stavros,

2014)

The elements of SOAR having the largest impact on collaboration are S,

O and R. Seek to improve these SOAR competencies first.

(Cooperrider,

Whitney &

Stavros, 2008)

Individuals working in teams should seek to advance their natural

capacity for strategic thinking and planning from a strengths-based

perspective. This and other positive approaches to problem solving can

be learned from participation in Appreciative Inquiry exercises and

SOAR-based workshops. Positive Organizational Scholarship

approaches to problem solving and team effectiveness should also be

investigated as natural strength-based competencies essential for

collaboration.

(Cooperrider,

2008; Dutton &

Quinn, 2003;

Stavros & Wooten,

2012; Dutton &

Quinn, 2003)

In this dissertation, the survey questions were derived from three independent

measurement instruments: the 16-item workgroup emotional intelligence profile (WEIP-

S), the nine-item Team Collaboration Questionnaire uniquely developed for this study,


and the 12-item SOAR Profile. The results of descriptive and inferential statistics were

reported categorically, segmented by the various demographic characteristics of the total

study population. No single respondent was used as an example to maintain

confidentiality of the participants. However, in practice, individual data would be

available for use a means to evaluate baseline competency in EI, collaboration, and

SOAR, and to provide a personalized assessment of the study categories relative to a

larger sample.

For example, if the total population (e.g., team) has a mean EI score of 5.5, and a

particular individual has a mean EI score of 5.2, this would provide the individual a

comparative assessment of EI strength or weakness relative to other team members. The

individualized data would also allow for the evaluation of the constitutive factors of EI,

namely mean scores for SA, SM, AO and MO. Results could also be compared within

various demographic segments such as gender, age, ethnicity, education, team size and

team type. Going further, the response for each individual question would be available to

pinpoint specific items which bring down, or raise the factor score being evaluated. In a

further example, if an SA score is higher or lower within a demographic group of interest,

this would be meaningful to an individual seeking to assess strength or weakness in a

particular competency, compared with a population of similar demographics, e.g., team

type, team role or time in teams.

Similarly, the data reported for both collaboration and SOAR could be analyzed

in the same way. In the end, according to the study model, the distinct relationships

between EI, collaboration, and SOAR could be understood at the individual, team, and

reference population levels. Targeted improvements through education and training can


be subsequently deployed in a strategic fashion with high confidence the effort will yield

maximum returns in achieving positive collaboration. Table 5.2 shows an example of

how individual self-report data could be summarized and used for comparison with a

reference population.

Table 5.2 Personalized Summary Assessment Example: EI, Collaboration and SOAR

Categorical Descriptive Statistic Individual self-report data Categorical Reference (Mean)

Current Team

Characteristic EI SA SM AO MO

Team size Table 4.13 6 members 5.50 4.68 6.22 5.53 5.63

Team membership Table 4.13 Internal, external 5.48 5.05 5.97 5.27 5.66

Team type Table 4.13 Face-to-face 5.28 4.76 5.92 5.06 5.42

Team role Table 4.13 Leader 5.48 4.95 5.98 5.33 5.66

Time in team Table 4.13 3-6 months 5.27 4.66 5.84 5.00 5.57

Teams in general

Team role Table 4.14 Leader 5.43 4.78 6.00 5.28 5.66

Time in teams Table 4.14 11-15 years 5.32 4.58 6.06 5.15 5.49

Demographics

Age Table 4.11 35-44 5.24 4.59 5.86 5.12 5.42

Gender Table 4.11 Female 5.40 4.87 5.98 5.22 5.52

Ethnicity Table 4.11 White 5.39 4.84 6.03 5.12 5.57

Education Table 4.11 Masters 5.35 4.70 5.98 5.18 5.55

Industry Table 4.12 Skip - - - - -

Profession/position Table 4.12 Skip - - - - -

Summary of self-report data Self-report Population

Mean EI Table 4.11 5.69 5.32

Mean SA Table 4.11 5.50 4.76

Mean SM Table 4.11 5.75 5.96

Mean AO Table 4.11 5.75 5.10

Mean MO Table 4.11 5.75 5.47

Mean Collaboration Table 4.15 5.53 5.86

Mean SOAR Table 4.19 8.25 8.00

Recommendations for Future Research

An avenue of interest for future research may include a study of both trait- and

ability-based EI competency in team members. Evaluating and comparing individual


traits and abilities relative to their impact on collaboration may yield additional

implications as to areas of EI competency that may also prove beneficial to team

effectiveness. This future research may also yield clues as to the relationship between

collaboration among team members and the team output, depending on levels of EI

within the collaborative team. This approach could also be used to evaluate EI growth

over time, as would be done with a longitudinal research methodology.

Future research should also seek to clarify the distinction between partial and full

mediation of SOAR in order to determine what other variables may explain the effect that

EI has on collaboration. The implication will be that strengths-based strategic thinking

and planning is only one mechanism by which EI impacts collaboration, and that there

may be other potential mechanisms. For practitioners, identifying additional mediating

variables may provide even greater opportunity to maximize the positive impact EI has

on collaboration.

Extending the demographic characteristics of respondents to include an

assessment of their pre-existing knowledge of EI may yield additional results in

determining the elements of EI most critical to team effectiveness. EI competency, as a

construct and its factors, could be tested as a moderator of the impact EI has on

collaboration. Introducing a demographic question such as “are you aware of, or practice

the EI abilities of self-awareness, self-management, awareness of others’ emotions and

management of others’ emotions?” This demographic variable may shed insight on

whether EI has an impact on collaboration, moderated by pre-awareness of the EI factors,

or abilities as defined by Jordan & Lawrence, 2009.


Another interesting avenue of research could be to explore whether some team

members use EI to manipulate others. Inappropriate use and application of the EI

abilities would likely come at the expense of the team, potentially failing the keys to

positive collaboration, i.e., integration, compromise, and communication. As an EI

ability, management of others’ emotions (MO) may be used ineffectively, or

inappropriately for extending one’s agenda over others. Assessing respondent views on

manipulation and its potential impact on collaboration may prove an important area to

mitigate against.

Future research may also investigate other methods of EI assessment which are

not self-report, e.g., 360 degree assessment instruments, interviews and other qualitative

approaches. Results of this future research could be compared with the results of this

study for correlation, or to exhibit unique and valuable differences in the relationship

between EI and collaboration. This could also address the potential for common-method

bias which may occur when the same source is used for the independent and dependent

variables.

As discovered in this study, survey respondents from the engineering and

technical disciplines consistently reported lower mean EI scores in almost every

demographic category. Future research could investigate and seek to understand the

apparently limited baseline EI abilities of the technical community. Implications for

modeling behavior change interventions could be developed and deployed for improving

EI and ultimately collaboration in the technical community.

In recognition of additional respondent feedback, it may be important to clarify

what “self-awareness” means in a team setting. Respondents indicated that while they


considered certain aspects of self-awareness evident in themselves, they felt constrained

by the team’s norms to effectively apply it. Further, they suggested their responses to the

WEIP-S self-awareness items would vary depending on the team being considered when

completing the survey. Since this study instructed participants to provide their self-

assessment within the context of the team they are currently involved with, or had recent

experience with, self-awareness assessments may not be representative of an individual’s

overall competency in this particular factor. In a future study, it may be interesting to

perform the WEIP-S self-assessment considering an individual’s team experiences in

general, or in comparing mean EI scores between two teams in which the respondent is

currently active.

Study Limitations

In order to satisfy construct reliability and validity of the study’s measurement

instruments, the Team Collaboration Questionnaire and the SOAR Profile were modified

in their item count. While this satisfied the psychometric properties desired in the final

study survey, it was not deployed to a new sample in order to evaluate correlation with

the initial study results.

The study survey employed a single demographic question regarding “position”.

Given the data is self-report and there is a sense of self-fulfilling prophecy, individuals

may have identified themselves with position titles that cannot be equivalently compared

across industries. With the possibility of study participants over-representing their

position relative to their level of responsibility, and apparent inconsistencies in title

definitions across industries, it was decided to draw no conclusions based on this

demographic question.


This study has limitations related to the use of self-report data in general, as self-

report methodology has inherent limitations of validity of the data. Another limitation

associated with self-report measurement instruments includes the potential for common-

method bias. Common-method bias may occur when data for the independent variable

comes from the same source as the dependent variable. Ideally, team based collaboration

would be measured by independent observers. Finally, since research participants

estimated the collaborative outcomes of their teamwork, as well as their capacity for

strengths-based strategic thinking and planning (i.e., SOAR) through the use of two novel

assessment tools—the Team Collaboration Questionnaire, and the SOAR Profile—the

psychometric properties of these assessment devices were evaluated using tests of

reliability and validity prior to data analysis.

Summary

This study considered emotional intelligence and its constitutive factors (self-

awareness, self-management, awareness of others’ emotions, and management of others’

emotions) as predictors of collaboration (integrating, compromising, and communication)

among teams and team members. Demographic characteristics (team role, team type, and

time in teams) were tested as moderators of the impact EI has on collaboration, and

capacity for strategic thinking (SOAR) was investigated as a mediator. Within this

context, the purpose of the dissertation was to evaluate the link between EI and

collaboration outcomes in teams, to characterize the EI abilities that contribute to

collaboration, and to investigate the mediating role that SOAR (i.e., strengths-based

strategic thinking and planning capacity) has on the relationship between EI and

collaboration among team members.


Results conclude as EI improves, so does the potential for improved

collaboration, most notably in the SM and MO factors of EI. EI was also found to impact

(positively) the collaboration factors, integrating, compromising, and communication.

Team role, team type, and time in teams were found to moderate the impact of EI and

collaboration. SOAR was found to be a significant mediator of EI and collaboration, with

S, A, and R functioning as partial mediators.


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Appendix A

Certificate of Training


Appendix B

IRB Letter of Approval


Appendix C

Informed Consent


Appendix D

Survey Instrument

lawrence technological universitys), the 12-item soar profile, and the nine-item team collaboration...

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