what makes a transformational leaders

181
WHAT MAKES A TRANSFORMATIONAL LEADER: AN INVESTIGATION INTO THE ANTECEDENT EXPERIENCES OF TRANSFORMATIONAL LEADERS by WILLIAM J. SCHELL IV A DISSERTATION Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in The Department of Industrial & Systems Engineering and Engineering Management to The School of Graduate Studies of The University of Alabama in Huntsville HUNTSVILLE, ALABAMA 2010

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Page 1: What Makes a Transformational Leaders

WHAT MAKES A TRANSFORMATIONAL LEADER: AN INVESTIGATION INTO THE ANTECEDENT EXPERIENCES OF

TRANSFORMATIONAL LEADERS

by

WILLIAM J. SCHELL IV

A DISSERTATION

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy

in The Department of Industrial & Systems Engineering and Engineering

Management to

The School of Graduate Studies of

The University of Alabama in Huntsville

HUNTSVILLE, ALABAMA

2010

Page 2: What Makes a Transformational Leaders

UMI Number: 3410783

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In presenting this dissertation in partial fulfillment of the requirements for a doctoral degree from The University of Alabama in Huntsville, I agree that the Library of this University shall make it freely available for inspection. I further agree that permission for extensive copying for scholarly purposes may be granted by my advisor or, in his/her absence, by the Chair of the Department or the Dean of the School of Graduate Studies. It is also understood that due recognition shall be given to me and to The University of Alabama in Huntsville in any scholarly use which may be made of any material in this dissertation. ___________________________ _______ (student signature) (date)

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DISSERTATION APPROVAL FORM

Submitted by William J. Schell IV in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Industrial and Systems Engineering, with a concentration in Engineering Management and accepted on behalf of the Faculty of the School of Graduate Studies by the dissertation committee. We, the undersigned members of the Graduate Faculty of The University of Alabama in Huntsville, certify that we have advised and/or supervised the candidate on the work described in this dissertation. We further certify that we have reviewed the dissertation manuscript and approved it in partial fulfillment of the requirements of the degree of Doctor of Philosophy in Industrial and Systems Engineering. ___________________________________ Committee Chair Dr. Dawn R Utley (Date) ___________________________________ Dr. Philip Farrington (Date) ___________________________________ Dr. Sampson Gholston (Date) ___________________________________ Dr Julie Fortune (Date) ___________________________________ Dr. Anthony Morris (Date) ___________________________________ Department Chair Dr. James Swain (Date)

___________________________________ College Dean Dr. Philip Farrington (Date) ___________________________________ Graduate Dean Dr. Debra Moriarity (Date)

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ABSTRACT

School of Graduate Studies The University of Alabama in Huntsville

Degree: Doctor of Philosophy College/Dept.: Engineering/Industrial

Systems Engineering and Engineering Management

Name of Candidate: William J Schell IV Title: What makes a transformational leader: An Investigation into the Antecedent Experiences of Transformational Leaders

The headlines of any major newspaper give evidence that American culture is

fascinated with the results of its leaders, whether they are political or sports leaders.

The scholarly work on leaders is also vast. While recent history may show an increasing

level of study, human interest in leadership is not a recent phenomenon. Discussion of

the study of leadership can be found in the classical works of the Greeks, Romans and

Chinese. Leadership has an impact on all areas of society. The empirical literature has

shown that good leadership promotes good organizational performance while bad

leadership degrades the quality of life for those associated with it. Additional research has

shown that transformational leadership is akin to good leadership. For this reason,

researchers are drawn to better understand transformational leadership and how it is

developed.

Leadership development is a vast area of literature, but there is little research that

promotes an understanding of how development experiences influence the types of

leadership behaviors displayed by leaders. This dissertation sought to address this gap in

two ways. First, a new instrument was developed, the Lifetime Leadership Inventory

(LLI), that enables researchers to understand the development experiences of the

respondent. Second, the LLI was utilized to examine the development experiences of

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leaders of entrepreneurial companies and correlate those experiences with the behaviors

measured by the Multi-factor Leadership Questionnaire (MLQ) using correlation analysis

and Structured Equation Modeling (SEM).

The study found significant (α = 0.05) correlation between many of the antecedent

areas explored and the components of transformational leadership measured by the MLQ.

These included positive correlations between transformational leadership components

and experiences with mentors, professional leadership experiences, and formal leadership

development programs. A negative correlation was found between transformational

leadership components and leadership experiences in high school and college. The

practical results of the study include implications for hiring decisions and the design of

leadership training programs.

Abstract Approval:

Committee Chair:

Dr. Dawn R Utley

Department Chair:

Dr. James J. Swain

Graduate Dean:

Dr. Debra M. Moriarity

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ACKNOWLEDGEMENTS

To Dr. Paul Schillings, thank you for encouraging a freshman engineering student

to pursue graduate work and a career in academia. If not for you, this journey would

likely never have started, someday I’ll fulfill that vision of moving into your old office.

To Geert Letens at Royal Military Academy, Belgium and Verne Harnish at

Gazelles, Inc., without your assistance to gain access to participants for this dissertation,

its completion would not have been possible.

To the staff of the UAH Salmon Library who have built a remarkable collection

of electronic reference materials. If not for the instant access and powerful search

capabilities those collections provide to distance learning students, I would still be

wading through the leadership literature.

To my dissertation committee, Dr. Dawn Utley, Dr. Phillip Farrington,

Dr. Sampson Gholston, Dr. Julie Fortune and Dr. Anthony Morris, thank you for your

support and guidance. I would like to provide special acknowledgement to my Chair,

Dr. Utley, for your encouragement and coordination and to Dr. Morris for pushing me to

take the right steps to develop the LLI and for consistently being my most vocal

supporter as the research began to take shape.

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TABLE OF CONTENTS

Page List of Figures ..................................................................................................................... x List of Tables ..................................................................................................................... xi Chapter I. INTRODUCTION .............................................................................................. 1 1.1 The Importance of Leadership................................................................ 2 1.2 The Need For Transformational Leadership........................................... 4 1.3 The Antecedents of Transformational Leadership ................................. 5 1.4 A Study into Transformational Leaders in Entrepreneurial

Organizations.......................................................................................... 7 II. REVIEW OF RELATED LITERATURE.......................................................... 9 2.1 What Is Leadership ............................................................................... 10 2.1.1 Leadership Defined for this Dissertation ................................... 11

2.1.2 Pioneering Leadership Concepts................................................ 122.1.3 Leadership or Management........................................................ 152.1.4 Why Study Leadership............................................................... 17

2.2 General Leadership Theories................................................................ 18 2.3 Transformational Leadership................................................................ 20 2.3.1 The Full Range Leadership Model ............................................ 23

2.3.2 Transformational Leadership vs. Transactional Leadership...... 24 2.4 Leadership Effectiveness ...................................................................... 25 2.4.1 Examples and Definitions of Leadership Effectiveness ............ 26

2.4.2 Effectiveness of Transformational Leadership .......................... 27 2.5 Measuring Transformational Leadership.............................................. 29 2.5.1 The Leadership Practices Inventory........................................... 29

2.5.2 The Multifactor Leadership Questionnaire................................ 31 2.6 Previous Studies into Leadership Antecedents..................................... 34 2.7 Review Summary.................................................................................. 37 III. RESEARCH STATEMENT............................................................................. 39 3.1 Research Questions, Conceptual Model and Hypotheses..................... 40 3.2 Importance of Research and Contribution............................................ 42

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IV. RESEARCH METHODOLOGY ..................................................................... 44 4.1 Study Overview .................................................................................... 44 4.2 Instrument Selection ............................................................................. 47 4.3 Development of the Lifetime Leadership Inventory (LLI)................... 49 4.3.1 The Nature of Key Relationships............................................... 50

4.3.2 Early Development Experiences................................................ 514.3.3 Exploratory Experiences............................................................ 514.3.4 Early / Previous Work Experience............................................. 524.3.5 Formal Development Experiences............................................. 524.3.6 Demographic Questions............................................................. 53

4.4 Refinement of the LLI .......................................................................... 53 4.4.1 Initial LLI Pilot Study................................................................ 54

4.4.2 Reduction of LLI Question Set into Final Form........................ 55 4.5 Description of the Survey Population................................................... 55 4.6 Deployment of the Study Instruments .................................................. 56 4.7 Data Collection and Analysis Plan ....................................................... 58 V. DATA ANALYSIS .......................................................................................... 60 5.1 Pilot Study and Refinement of the LLI................................................. 62 5.1.1 LLI Pilot Study Data Analysis................................................... 63

5.1.2 Reduction of the LLI Data Set ................................................... 67 5.2 Demographic Analysis of the Study Data Set ...................................... 68 5.3 Analysis of the Multifactor Leadership Questionnaire (MLQ) ............ 71 5.3.1 Comparing the Leadership Measures of the Study and MLQ

Population .................................................................................. 715.3.2 Examination of the MLQ Factor Structure ................................ 755.3.3 Comparing the Factor Loadings of the Study Data with the

MLQ Population ........................................................................ 79 5.4 Analysis of the Lifetime Leadership Inventory (LLI) .......................... 80 5.4.1 Descriptive Statistics for the LLI............................................... 82

5.4.2 Confirmatory Factor Analysis of the LLI .................................. 83 5.5 Exploring the Relationship Between the LLLI and the MLQ Using

Correlation ............................................................................................ 86 5.6 Structured Equation Modeling Between the LLI and MLQ................. 89 5.6.1 Further Reduction of the LLI and CFA Revisited ..................... 90

5.6.2 SEM Analysis Description and Results ..................................... 91 5.7 Correlation Analysis Between LLI Questions and the MLQ ............... 93 5.8 Analysis Summary................................................................................ 94

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VI. CONCLUSIONS AND RECOMMENDATIONS........................................... 96 6.1 Hypothesis Testing Results and Contribution to the Body of

Knowledge............................................................................................ 97 6.2 Theoretical Implications of Study ........................................................ 99 6.3 Implications for the Engineering Manager .........................................103 6.4 Limitations Of The Study ...................................................................104 6.5 Areas for Future Research ..................................................................105 APPENDIX A: Multifactor Leadership Questionnaire ................................................108APPENDIX B: Initial Lifetime Leadership Inventory Sample Questions...................111APPENDIX C: Refined Lifetime Leadership Inventory Sample Questions................113APPENDIX D: Mind Garden Permissions...................................................................115APPENDIX E: Gazelle’s Participant Invitations .........................................................120APPENDIX F: LLI Correlation Analysis ....................................................................123APPENDIX G: Factor Analysis Of Alternative LLI Model ........................................130 G.1 Exploratory Analysis of the LLI .............................................131 G.2 Correlation Analysis Between LLI Factors and the

Two Factor MLQ ....................................................................136 G.3 Correlation Analysis Between LLI Factors and the

Nine Factor MLQ....................................................................137APPENDIX H: Structured Equation Model Output.....................................................139APPENDIX I: Study Approval from IRB...................................................................152 REFERENCES ...............................................................................................................154

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LIST OF FIGURES Figure Page 2.1 Leadership Literature Review Pyramid...................................................................10

3.1 Study Conceptual Model .........................................................................................41

4.1 Overview of Theoretical LLI Model .......................................................................50

5.1 Overview of Analysis Methodology .......................................................................61

5.2 Single Linkage Dendrogram for LLI Pilot Study....................................................64

5.3 LLI Pilot Study Dendrogram Using Ward's Method ..............................................65

5.4 Scree Plot Result of Exploratory Factor Analysis of the MLQ...............................78

5.5 Factor Model for Idealized Influence (Attributed) as Measured by the MLQ........79

G.1 Dendrogram from Cluster Analysis of Full LLI Data Using Ward Linkage ........ 131

G.2 Scree Plot of Exploratory Factor Analysis on the LLI.......................................... 134

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LIST OF TABLES Table Page 4.1 Leadership Measure Selection Criteria and Winner ................................................ 47

5.1 Pilot Study Population Overview ............................................................................ 62

5.2 Pilot Study Correlation Analysis Summary............................................................. 67

5.3 Participant Ethnic Demographic Information by Gender ........................................ 69

5.4 Participant Job Level Demographics by Source ...................................................... 69

5.5 Participant Experience Level Demographic Information by Source ....................... 69

5.6 Participant Education Level Demographic Information by Source......................... 69

5.7 Descriptive Statistics for MLQ Results ................................................................... 73

5.8 P Values for Comparisons of MLQ Scores.............................................................. 74

5.9 Cronbach Alpha Reliability Score for Nine Factor MLQ Components .................. 75

5.10 Cronbach Alpha Reliability Scores for Alternate MLQ Models ............................. 76

5.11 Factor Loading Comparisons for Individual MLQ Questions within their

Expected Factor ....................................................................................................... 81

5.12 Descriptive Statistics for LLI Pillars........................................................................ 82

5.13 Loadings of Individual LLI Questions within their Hypothesized Factors ............ 85

5.14 Correlation Coefficients and Significance for LLI Pillars & Nine Factor MLQ..... 88

5.15 Correlation Values Found with SEM....................................................................... 93

5.16 Significant Correlations Between LLI Questions and MLQ Leadership Factors... 94

G.1 Varimax Factor Loadings from LLI Exploratory Factor Analysis ........................ 135

G.2 Correlation Coefficients for LLI Exploratory Factors & Two Factor MLQ ......... 137

G.3 Correlation Coefficients for LLI Exploratory Factors & Nine Factor MLQ......... 138

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DEDICATION

For Ana and Megan, thank you for being a daily source of inspiration and

fascination to your daddy. Thank you for your patience while I was locked in the office

at home, out at the library, or in Alabama - even if you didn’t understand why I was still

in school as a grown up. Someday you’ll get to tell your new little brother about all of

the fun he missed before his arrival. We’ll now have more time to spend together and

I’m looking forward to it more than you will ever know.

For Melanie, thank you for all of your support throughout the years, during this

work and on oh so many other things. Who would have guessed when this process

started, that we’d live in a different time zone, have one more kid (almost two) and see

countless other changes big and small in careers and life before it was done. Through it

all, the constant has always been us and my love for you.

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CHAPTER I

INTRODUCTION

“The gift of leadership belonged to him in supreme degree.”

- Gifford Pinchot speaking of President Theodore Roosevelt (1947)

One need look only as far as the headlines of any major newspaper on any given

day to see the culture of America is focused on the accomplishments and failings of its

leaders. Whether it is the challenges faced by a major political initiative backed by the

President, the fall from grace of leading sports figures, or the compensation packages of

executives, the public appears very interested in getting a regular feed of information on

those who hold leadership positions. This interest in leaders is not just a popular culture

phenomenon. The scholarly work on leaders is also vast, as a current Google Scholar

search on the word generates over 2.4 million results in English (Google 2010). But why

are leaders so important that interest in them borders on obsession? Why did the topic of

leadership generate such interest that no fewer than 2,800 books on the topic were

published in 2008 and 2009 (Amazon 2010)? What is this gift of leadership that

Roosevelt had and what is known about it?

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1.1 The Importance of Leadership

While recent history may show an increasing level of study, human interest in

leadership is not a recent phenomenon. Discussion of the study of leadership can be

found in the classical works of the Greeks, Romans and Chinese (Bass 1981). The study

of leadership through human history eventually found its way into two camps at the dawn

of the 20th century. The first typified by Carlyle’s (1888) belief that “The history of the

world is but the biography of great men.” The second captured by Tolstoy (1869), “In

history, so-called Great Men are but labels serving to give a name to historical events,

and like labels they have the least possible connection with the event itself.” Gergen

(2005) argues that the first half of the 20th century served to resolve this conflict of

opinion, stating that leaders do matter, a lot. The 20th century dawned with hopes for a

new golden age, as European nations had not engaged in war for over 80 years. But the

century hit its mid point having seen two of the bloodiest wars in human history while the

economy of the world suffered. Why was this the result when hopes were so high?

Keegan (2002) argues the answer to this question can be found in the biographies of

six men: Lenin, Stalin, Hitler, Mao Zedong, Churchill, and Franklin Roosevelt. The first

four acted as tyrants and could have destroyed the world, if not successfully challenged

by the other two men.

The first half of the 20th century acts as an extreme example for why Bennis

(2004, 331) states that the “quality of our lives is dependant on the quality of our

leadership.” The importance of leadership, while often most visible in politics, is not

limited to this arena. Leadership has an impact on all areas of society. In sports, the

differences in leadership can be seen by the championships amassed by coaches

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Lombardi, Auerbach and Jackson. In business, leadership drove the success of General

Electric under Jack Welch, Microsoft under Bill Gates and Apple under Steve Jobs. The

empirical literature has shown that good leadership promotes good performance while

bad leadership degrades the quality of life for those associated with it (Hogan and Kaiser

2005) and that leader differences do account for a substantial degree of an organization’s

performance variation (Thomas 1988).

It is this power of leadership that draws researchers to better understand the topic.

Subsequent to the Great Man theory, the quest to understand leadership has generally

fallen into two categories. The first, trait theories, generally hold that effective leaders

possess different traits than their less effective counterparts (Bass 1981). The second

group, behavioral theories, generally holds that the behaviors of leaders impact their

effectiveness. These behaviors are typically combined into groups similar to those of

Katz and Kahn (1952), who categorized behaviors as task oriented, relationship oriented,

and participative leadership. The relationship oriented behaviors led to the development

of charismatic leadership theories (Barbuto 2005). But these categories have often fallen

into dispute, a dispute summed up by Drucker (2001a, 269 - 270):

What then is leadership if it is not charisma and not a set of personality traits? The first thing is that it is work […] The foundation of effective leadership is thinking through the organization’s mission, defining it, and establishing it, clearly and visibly. The leader sets goals, sets the priorities and sets and maintains the standards. […] The second requirement is that the leader sees leadership as a responsibility, rather than as rank and privilege. […] [The leader] holds himself ultimately responsible for the mistakes of his associates and subordinates, he also sees the triumphs of his associates and subordinates as his triumphs […].

In this statement, Drucker captures the need for transformational leadership. This type of

leadership is defined as being able to lift a team above the day-to-day preoccupations to

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rally around a common purpose (Burns 1978). Transformational leadership differs from

transactional leadership which is more focused on a cost benefit, economic exchange

with subordinates (Bass 1985).

1.2 The Need for Transformational Leadership

“Since the 1980s, research has supported the idea that transformational leadership

is more effective than transactional leadership in generating the extra effort, commitment,

and satisfaction of those led” (Avolio and Bass 2002, 1). Transformational leadership

has been shown to have strong positive impacts on the performance of organizations from

financial firms (Walumba, et al. 2005), to school environments (Higgins 1998,

Blatt 2002), to sales forces (Jolson, et al. 1993), to the U.S. Navy (Murphy 2002), to

IBM, and the Third Army (Bass 1985). But if transformational leadership is so effective,

why has it not become part of the lexicon of the average American? Perhaps it is because

transformational leadership has often been found to be most effective in creating success

regarding organizational change (e.g., Ozaralli 2003, Zagorsek, et al. 2009) and most

people have a natural discomfort with change. However, in times of complex systems

and high technology, change is constantly on the horizon. Now seems an opportune time

to better understand transformational leadership and capture its benefits.

While it seems that most generations claim that their generation is in the most

turbulent times, Friedman (2005) has made a popularly accepted argument that the

current rate of change is the most rapid in human history. As times become more

challenging, it is held that leadership becomes more important (Goldsmith 2007, Collins

2009). How does transformational leadership fit into these challenging times?

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In times of turbulence, it has been shown that charismatic leadership, an important

subcomponent of transformational leadership, has a predictive relationship with

performance (Waldman, et al. 2001). Furthermore, one of the greatest impacts a leader

can have on their organization is to set and reinforce the values, mission and culture of an

organization (Phills 2005, Bossidy 2002, Peters and Waterman 1982). Transformational

leadership, by its very definition, is concerned with the motivation of followers through

idealized influence, creating a common purpose around which to rally (Bass 1985).

Since transformational leadership appears to hold the potential of being a powerful asset

within these turbulent times, the question arises, how is it developed?

1.3 The Antecedents of Transformational Leadership

Leadership development is a vast area of literature (Bass 1981). As mentioned

previously, most studies in this area focus on one of two paths to leadership development,

trait and behavioral. Studies of the trait theories sought to determine what innate traits

made a leader effective, the research of these theories sought to understand and identify

traits, not develop them (Bass 1981). Conversely, studies of the behavioral theories

looked to identify the behaviors that made effective leaders, so the behavior could be

taught (McCauley, et al. 1998). Both of these development theories are well understood,

with vast supporting literature. An area that is not as well understood is the effect that

experiences have on an individual’s leadership development (Bennis and Thomas 2002).

Research on how experience effects leadership development has been completed

through a variety of studies. These include investigations into leadership crucibles

(Bennis and Thomas 2002, Bennis 2004), studies into the impacts of parental

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relationships on leadership (e.g., Avolio 1994, Towler 2005), and research into the

impacts of previous leadership experiences on current leadership behavior (e.g., Howard

and Bray 1988, Atwater, et al. 1999). While some of these studies (e.g., Avolio 1994,

Atwater, et al. 1999, Towler 2005) specifically looked at the development of

transformational leadership, none investigated the breadth of development experiences

discussed in the literature. Examples of these development experiences include

relationships with mentors (e.g., Atwater, et al. 1999), activities in high school

(e.g., Avolio 1994), and exploratory experiences (e.g., Louv 2005, Evans and

Cope 2003). Clearly there is a gap in understanding, but why does this gap exist? One

reason for this gap appears to be the lack of an available instrument that explores a broad

range of potential leadership development experiences. Because of this missing

instrument, existing experience focused research has been largely completed through

structured interview techniques (e.g., Bennis and Thomas 2002, Wong 2004), which lack

the breadth of exploration and sample size generally developed through instrument based

studies. This gap in the literature points to a need to develop an instrument that could aid

in understanding the breadth of experiences that may lead to development of measurable

leadership behaviors.

The purpose of such an instrument would be to understand the development

experiences of a leader or potential leader who responds to the instrument. These

experiences could be broken into five different theoretical groups based on the different

types of development experiences examined in the literature. The first group would seek

to understand the nature of the key relationships of the participant, including their

relationships with parents and mentors (e.g., Towler 2005, Sosik, et al. 2004). The

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second group would seek to understand the early development experiences of the

participant, including high school and college activities (e.g., Muldoon, et al. 2005). The

remaining groups would investigate the exploratory experiences (e.g., Louv 2005), early

work experiences (e.g., Howard and Bray 1988), and formal development experiences

(e.g., McCauley, et al. 1998) of the participant. By utilizing this rationalized set of

experiences investigated in previous literature, the research is able to better understand

the experiences of the participant. This dissertation will then look to correlate those

experiences with the participants’ displays of transformational leadership. But the

question remains, what population of leaders should be included in the study? Since the

end of the economic crisis of the late 2000’s is expected to be driven by growth in small

entrepreneurial companies (e.g., Obama 2009, United States Small Business

Administration 2009), a study targeted to this population of leaders, that aides in the

understanding of the development of transformational leadership may be beneficial.

1.4 A Study Into Transformational Leaders in Entrepreneurial Organizations

Prior to the current global economic challenges, Drucker (2001b) argued for the

importance of an entrepreneurial society, a society in which innovation and

entrepreneurship are normal, steady, and continual. This focus on steady and continual

improvement can only be completed in a culture that is open to, even welcoming of

change. The literature has shown that a culture welcoming of change is effectively

created with transformational leadership. How can a study be structured to learn more

about leaders in these types of organizations?

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The answer came with access to the readers of the Gazelles weekly newsletter.

The newsletter serves a group of readers who are leaders of mid-market companies

focused on growth, coming from all industries (Gazelles 2009). This population was

studied to begin to learn more about these leaders, including their leadership styles and

development experiences. The study expects to have two contributions to the

Engineering Management body of knowledge. The first contribution will be the

development of a new data collection instrument that allows the researcher to understand

the experiences that may contribute to the leadership behaviors of the participant. The

second contribution will be any correlations identified between development experiences

and displays of transformational leadership in the study population.

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CHAPTER II

REVIEW OF RELATED LITERATURE

The available literature in the area of leadership, both in the popular press and

scholarly work is vast and continues to expand rapidly due to a “great interest in the

phenomenon of leadership by both academicians and practitioners” (Antonakis, et al.

2004a, vii). However, the body of knowledge presents problems to the researcher. First

not only is the literature vast, it is often disparate and inaccessible. Second, much of the

published information in the field regarding what makes a leader effective, has minimal

scientific backing, if any at all (Antonakis, et al. 2004a). In order to clarify the literature

and attempt to deal with these shortcomings, this review takes a macro to micro

approach, as illustrated in Figure 2.1. The review starts with definitions of leadership and

an investigation of foundational leadership theories, then discusses leadership and

management and the importance of leadership, and then introduces transformational

leadership, before stepping through leadership effectiveness, methods to measure

leadership and the antecedents of leadership in light of both general leadership theories

and transformational leadership. In this manner, the literature provides a multi-layered

foundation for the pinnacle of this pyramid, the proposed research investigation of the

antecedent experiences of transformational leaders.

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Leadership Antecedants

Measuring TL

Leadership Effectiveness

Transformational Leadership (TL) and the Full Range Model

Leadership or Management and the Importance of Leadership

Definitions and Foundational Theories of Leadership

Leadership Antecedants

Measuring TL

Leadership Effectiveness

Transformational Leadership (TL) and the Full Range Model

Leadership or Management and the Importance of Leadership

Definitions and Foundational Theories of Leadership

Figure 2.1 – Leadership Literature Review Pyramid

2.1 What is Leadership

What is leadership? The Merriam-Webster Online Dictionary (2007) defined

leadership simply as “the office or position of a leader.” Follett (1949) held a different

opinion of what defined leadership, noting nothing of the position, but instead stating that

it had two key tenets. First, a leader does not lead by personality, but by superior

knowledge of a situation. Second, that leadership is not only an innate quality, but is a

skill that can be learned. This concept of leadership as a born trait has its beginnings in

the Great Man Theory (Carlyle 1888), while the concept that leadership is a set of skills

that can be learned was furthered by the personality school of leadership research (Bass

1981).

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This conflict and confusion about leadership theory is not new. Almost 50 years

ago, Bennis (1959, 259) surveyed the leadership literature and concluded “it seems the

concept of leadership eludes us or turns up in another form to taunt us again with its

slipperiness and complexity. So we have invented an endless proliferation of terms to

deal with it . . . and still the concept is not sufficiently defined.” Nor has this conflict

been satisfactorily mediated in the intervening years, as Antonakis, et al. (2004b, 5) more

recently noted that “given the complex nature of leadership, a specific and widely

accepted definition of leadership does not exist and might never be found.”

2.1.1 Leadership Defined for this Dissertation

Despite the lack of a general agreement in the way that leadership is defined, in

order to continue this discussion, a broad definition is needed. For this dissertation the

general definition of leadership created by Antonakis, et al. (2004b, 5) will be utilized.

This definition is

leadership can be defined as the nature of the influencing process – and its resultant outcomes – that occurs between leader and followers and how the influencing process is explained by the leader’s dispositional characteristics and behaviors, follower perceptions and attributions of the leader, and the context in which the influencing process occurs.

This definition is consistent with those commonly used in investigations of

transformational leadership. For example, McLaurin and Bushanain Al Amri (2008, 15)

utilize a similar definition where “leadership is a dynamic relationship which is based on

mutual influence between leaders and followers which results in a higher level of

motivation and technical development as it promotes changes.”

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2.1.2 Pioneering Leadership Concepts

While the written material on leadership can often be found in studies of human

history, the professional study and research into leadership can be found in those

publications interested not in studying the past, but in how to build things with greater

efficiency. A key contribution in this area can be attributed to Taylor and his study of

scientific management (Russell 1987). In 1916, Taylor published his definitions of

scientific management principles, key among them that management could improve the

output of an organization by the scientific study of work. This study led to a better

understanding of the job and how to better fit workers to the job. Additionally, Taylor

(1916, 17) identified what he referred to as the “highest type of management” where

employers deliberately set out to make conditions for their employees better than the

conditions found at other employers. This type of action is a precursor to the

individualized attention concept included in transformational leadership (Bass 1985).

From Taylor, the research began to focus more completely on ways to understand

and motivate employees. This need to motivate employees is closely tied to the leader’s

ability to influence followers, included in this dissertation’s working definition of

leadership. This area of study began with Maslow (1950) who defined a framework for

understanding the needs of human beings in a hierarchical format. In addition to

developing the framework, Maslow contributed a deep understanding of how people

move from one level to another on the hierarchy and the ability for multiple levels to be

simultaneously partially satisfied and partially unsatisfied. In this way, Maslow provided

the foundation to understand human behavior that was applied by a number of

management philosophers in their work about how to effectively motivate employees.

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McGregor (1957) took the conventional view of management’s role to harness

and control employee actions and behavior to meet the needs of the employer and labeled

it Theory X management. He went on to challenge the view of management – that

control of employees was necessary due to employees inherently passive nature, similar

to Taylor’s soldiering (1919) – as the cause of this behavior not the result. As a solution

for this behavior, McGregor offered an alternative set of management behaviors which he

matched to different assumptions about employee behavior; these assumptions were

labeled Theory Y. Under this set of assumptions, management’s core responsibility is to

arrange the organization so that employees can once again find their motivation, and use

that motivation to determine their own path to successfully complete the goals of their

role. In this way, employees are given the autonomy to do their best work and

management is simply capturing the inherent skill in employees to deliver the results that

are needed by the organization.

The motivation thread of leadership research continued with a notable step being

taken by Herzberg et al. (1959), who outlined a two factor model for employee

motivation - hygienes and motivators. This work was further clarified to make it more

actionable almost 10 years later (Herzberg 1968). In this framework, the key was to

recognize that many of the reward approaches utilized by organizations have limited use,

since they focus on areas labeled hygienes. These hygienes possess limited opportunity

to truly engage employees and benefit from higher performance. Instead, managers

should focus on job enrichment with the intent to improve aspects that truly motivate

employees such as the opportunity for responsibility and achievement. This research

thread continues, with such researchers as Daniels (2009) and his best practices for

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eliminating practices that demotivate employees, Tompkins (2007) and his bold

leadership theories for motivations and Jacobs (2009) and his investigations into what is

wrong with employee feedback practices from the perspective of neuroscience.

In addition to the research into how to best motivate employees, a related research

stream investigated how organizational outcomes could best be achieved through

effective goal setting at both an individual and organizational level. Key concepts in this

area were developed by Drucker (1958) who presented the framework for successfully

managing the enterprise of business through the use of objectives. House and Mitchell

(1974) combined the two streams of understanding employee motivation and managing

performance toward organizational objectives with the Path Goal Theory. In their

research the authors found empirical support for higher performance against goals where

the followers were motivated by the achievement of objectives. Furthermore, that

motivation leads to greater performance against future objectives. In this environment, it

is the role of the leader to increase the motivational factors associated with goal

achievement while communicating the types of paths that might be taken to achieve the

objectives.

As Path Goal theory began to look at management as leader behaviors that

influence the resultant outcomes of an organization (House and Mitchell 1974), a number

of other investigators began to more fully focus on behaviors as the key to successful

leadership. These investigations included Hersey and Blanchard’s (1969) theory of

Situational Leadership and Tannenbaum and Schmidt’s (1973) concept of the leadership

continuum. With these studies the line between what constituted effective management

and effective leadership begins to become broader and less well defined.

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2.1.3 Leadership or Management

The review of the leadership related literature clearly points to studies that fall

into two categories, those interested with effective management and those interested in

effective leadership. But what is the difference between these two categories? Just as

there are conflicts in what the exact definition of leadership is, there exist conflicts in

how leadership and management are or are not inter-related. In his seminal work,

Sheldon (1923) developed a professional creed for managers to ensure that industry was

run with the greatest efficiency possible. Included in this creed were key tenets regarding

how management should be incorporated as a stabilizing influence on industry, one that

safeguards against disruptive change. This tenet runs in conflict with the concept of

leadership as the catalyst for managing and even promoting change in an organization to

enable further growth and success discussed by many authors including Collins (2001)

and his discussion of the Level 5 leader who quietly moves his organization forward to

greatness and Tompkins (2007) and his discussion of the bold leader who energizes the

organization to move and grow.

This division between the meaning of management and leadership is a relatively

recent split within the literature. In his extensive review of the literature in this area, Rost

(1998) found the words used interchangeably beginning in the 1930’s and continuing on

in some research areas through the 1980’s. The effort to split the meaning of the

two words began in the late 1950s and remains unresolved. Rost notes that a key gap in

these efforts to split the meaning of the two words is the tendency of researchers to

denigrate management to ennoble leadership. Or as Mintzberg (2009, 12) states simply:

“ever since the distinction was made between leadership and management – leadership

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somehow being the important stuff and management being what surgeons call the scut

work – attention focused on leadership.”

This increase in attention has seemingly driven an increase in the confusion

between the two terms, created by their being used interchangeably (Hunt 2004). To

avoid this overlap, Kotter’s (1990) distinction can be utilized. In this definition,

management, including its planning function, makes an operation run smoothly, and

leadership, including direction setting, closely related to planning, makes an organization

produce or adapt to change. In this way, management and leadership are two sides to a

coin and both are needed to successfully move an organization forward. Leadership

could be considered the key part of what Mintzberg (1971) described as the interpersonal

work of managers. A view he echoed almost four decades later when he said:

My view is that management without leadership is disheartening or discouraging. And leadership without management is disconnected, because if you lead without managing, you don’t know what’s going on. It’s management that connects you to what’s going on. (Mintzberg 2009, 12)

This understanding of the differences, both perceived and real, between

management and leadership is important because of its relationship to transformational

leadership. As will be discussed in later sections, oftentimes the break between

transformational leadership and transactional leadership is considered to be akin to the

break between leadership and management (Graham 1988).

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2.1.4 Why Study Leadership

Why is the topic of leadership so interesting and important to human kind that the

depth and breadth of research on the topic is so great? Put simply, it may be the very fact

that the “quality of our lives is dependant on the quality of our leadership” which occurs

because “leaders wield power, and so we study them with the same self-interested

intensity with which we study diabetes and other life-threatening diseases” (Bennis 2004,

331). Given the disproportionate impact that leaders can have on the population, it is of

little wonder that so much effort is dedicated to the pursuit. However, this answer does

not appear to be sufficient. For while Bennis’ words are dramatic, they best support the

interest in studying political and military leaders and do little to support the level of

interest in business and other leaders who do not hold a position with potential for life

and death impacts.

To understand the high interest in industrial and other organizational leaders,

other sources must be investigated. Beginning with early studies, the interest in

understanding leadership springs from self interest, whether it was the work of Taylor

(1916) showing how better organizational leadership lead to better working conditions or

Follett (1949) who noted that good leaders assume grave responsibilities and play a

creative part in the success of a large portion of our society. This vein of research

pioneered by Follett, where the actions of leaders were thought to impact the output of

business, has gained even more focus with the ongoing struggles of the performance of

the world economy in 2009, driven in part by ethical lapses in business leadership

(George and McLean 2007, Palmer 2009). Just as the actions of a small number of

business leaders had a large negative impact on the global economy, this highly leveraged

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impact of business leadership can create large positive impacts for humanity’s largest

problems (Maak and Pless 2009). For these reasons, even though the impact of business

leadership may not involve life and death, it can have material repercussions on society.

2.2 General Leadership Theories

It seems that leadership has been viewed as an important area of study for much

of human history. Discussions of the study of leadership can be found in the classical

works of the Greeks, Romans and Chinese (Bass 1981). Over time, the study of

leadership generally began to follow one of two paths: trait and behavior theories.

The first path, trait theories, has its origins in the Great Man theory (Carlye 1888).

This theory is focused on the traits of leaders and how those traits set the leader apart

from his followers (Bass 1981); it also sets effective leaders apart from ineffective ones

(Higgins 1994). Generally, trait are built on the idea that a leader is born, not made. As

such, the research is focused on identification of leadership traits, so the successful future

leader can be identified and hired. The research into trait theories was extremely active

in the first part of the 20th century, before falling out of favor (Bass 1981). The reason

for this change included the studies of Bird (1940), who found little agreement in a meta

study of leadership traits regarding which traits were truly important for leadership

effectiveness; Jenkins (1947) who found little agreement on important traits in a meta

study of military leadership studies; and Stogdill (1948) who, used a meta study that

found clusters of items that were more generally important than the findings of Bird or

Jenkins, identified that the importance of the cluster varied based on the situation. With

these studies pointing to a general inadequacy in the trait theories, leadership research

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faced its first crisis (Antonakis et al. 2004b). As a result of that crisis, researchers began

to focus their efforts on the identification of what was hoped to be a more universal set of

findings regarding effective leaders, which lead to the behavioral theories.

The second path, behavioral theories, looks at leadership as a series of behaviors.

This path had its origins in the studies of Lewin and Lippitt (1938) which investigated

democratic vs. autocratic leaders. The seminal works in this space were completed

through the University of Michigan (Katz, et al. 1951) and Ohio State (Stogdill and

Coons 1957) studies that identified two dimensions of leadership. The first dimension,

generally referred to as consideration, seeks to capture a leader’s employee orientation.

While the second, initiating structure, is concerned with the production of the

organization. These concepts where furthered by other researchers, notably Blake and

Mouton (1964) who developed the two-dimensional managerial grid as a guide to

understanding leader behavior in terms of a focus on people vs. a focus on production.

By breaking leadership into multiple dimensions based on the actions of the leader, the

behavioral school of research set the groundwork for the new leadership school

promoting visionary or charismatic leadership theories (Antonakis, et al. 2004b).

Included in these theories was the beginning of transformational leadership.

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2.3 Transformational Leadership

Transformational leadership has been called the new paradigm of leadership

(Bryman 1992) and is generally considered to have its foundation in the work completed

by Burns in political leadership in the late 1970’s (Barbuto 2005). At that time, Burn’s

(1978, 20 - 21) explained that transformational leadership:

. . . occurs when one or more persons engage with others in such a way that leaders and followers raise one another to higher levels of motivation and morality. Their purposes, which might have started out as separate but related, as in the case of transactional leadership, become fused. Power bases are linked not as counterweights but as mutual support for common purpose. [. . .] But transforming leadership ultimately becomes moral in that it raises the level of human conduct and ethical aspiration of both the leader and led, and thus it has a transforming effect on both.

This foundation was furthered by the work of many, predominately Bass (1985,

1990) who defined the components of the full range leadership model, including

transformational leadership, and co-researchers, notably Avolio (1994, 2005), who

completed many studies that further refined the questions and factor structure of the

Multi-factor Leadership Questionnaire (MLQ) instrument utilized to measure the full

range of participant leadership, and Kouzes and Posner (1995), who defined the concept

of exemplary leadership (Barbuto 2005). Bass (1985) was the first to publish a multi-

factor definition of transformational leadership. In his definition, which has become the

dominant definition in the research space, transformational leadership has

four dimensions:

• Charisma – The degree to which the leader behaves in admirable ways that cause

followers to identify with and trust the leader. This trait is about the leader

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providing a role model for the followers (Bass 1985). The label of charisma was

later changed to Idealized Influence when the concept of charisma was criticized

as being incompatible with transformational concepts (Barbuto 2005).

• Inspirational Motivation – The degree to which a leader articulates a vision that

appeals and inspires followers. These leaders challenge followers with high

standards, communicate optimism about future goals, and provide meaning for the

task at hand (Bass 1985).

• Intellectual Stimulation – The degree to which a leader stimulates new ideas and

creative solutions from their followers by challenging assumptions and

encouraging risk taking (Bass 1985).

• Individualized Consideration (or Individualized Attention) – The degree to which

the leader understands the individual needs of each of their followers and attends

to those needs (Bass 1985).

While each of these individual components, is itself, an important set of leadership

behaviors, it is the combination of the four areas that leads to successful transformational

leadership behavior that motivates others to do more than they thought possible (Avolio

and Bass 2002).

An alternate framework for transformational leadership is provided by Kouzes

and Posner (1995) who defined the concept of exemplary leadership, sometimes referred

to as transformational leadership (e.g., Bell-Roundtree 2004, Barbuto 2005), as

characterized by five leadership practices:

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1) Challenge the process – leaders who venture out and accept challenge.

2) Inspire a shared vision – leaders who have a clear vision for the future and can

articulate that vision in an inspiring way to followers.

3) Enable others to act – leaders who recognize that it takes a team action to

accomplish organizational objectives and empower the team to take the actions

needed to achieve success.

4) Model the way – leaders who go first, by setting the example they build

commitment and create progress and momentum.

5) Encourage the heart – leaders who show others that they can win, they

understand the needs of their followers and provide appropriate encouragement.

Although the exemplary leadership model has been utilized with the label

transformational leadership, it is both similar to and different than the complete definition

of transformational leadership created by Bass (1985). The key difference is that Bass

(1990) holds that charisma is a key component of the success of the transformational

leader, while Kouzes and Posner (1995) view charisma as a myth of leadership, stating

that leaders do not posses special powers, but instead hold strong beliefs in a purpose and

a willingness to express those convictions. Despite this difference in components, both

models hold that transformational leadership delivers results by going beyond the

individual leader and follower needs and focusing on a common purpose, vision, set of

values, commitment and intrinsic rewards (Bass 1985, Kouzes and Posner 1995).

However, the single largest difference in the two models was in their development

approach. Bass (1985) developed a theoretical model based on the work of Burns (1978)

and then built an instrument to validate that model. The approach has been the

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foundation of extensive research, resulting in evidence supporting the model (Antonakis,

Avolio and Sivasubramaniam 2003). In contrast, the exemplary leadership model was

developed by analyzing the “personal best” memoirs of a sample of leaders to identify

specific characteristics of each case and then building a long list of questions on

leadership behavior, from which factor analysis was used to extract the five key

behaviors (Kouzes and Posner 1995). This empirical case data approach provides the

Kouzes and Posner model with strength in its basis of evidence (Sashkin 2004). In this

way, Kouzes and Posner (1995) developed a model with behaviors that are much more

specific than the dimensions developed by Bass (1985). However, the exemplary

leadership model focuses almost entirely on behavior, ignoring situational context and

leadership traits and there does not appear to be any clear theory base for the model

(Sashkin 2004). For this reason and others discussed later in the measurement section,

this dissertation will focus its work on the transformational leadership framework

developed by Bass.

2.3.1 The Full Range Leadership Model

Transformational leadership is only one component of the framework Bass (1985)

developed, which he named the full range leadership model. It includes not only

transformational, but also transactional leadership and laissez-faire leadership (or the

absence of leadership). In this way, Bass utilized his definition of transactional

leadership to incorporate the behavioral leadership approaches studied earlier (Sashkin

2004). Transactional leaders use conventional reward and punishment to gain

compliance from their followers – both the leader and the follower influence each other

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to ensure that each receives something of value (Yukl 1981). The relationship becomes

one of mutual dependence where the leader must continue to be aware of changes in

follower’s expectations in order to meet them and remain successful (Kuhnert and Lewis

1987). Bass’s (1985) definition has two components:

• Contingent Reward – In this system a bargain is struck and a contract signed

between leader and subordinate. From that time forward, the employee’s efforts

(transactions) are actively monitored and when the terms of the contract are met,

positive reward in the form of praise, salary increases or promotion are provided.

When the terms of the contract are not met, penalization occurs. When utilized

consistently, contingent reward can be an effective form of leadership; however, it

is seldom maintained at the level of consistency required for sustained

performance.

• Management by Exception – This form of management is far more passive.

Since, as long as the contract is honored by the employee, there tends to be little

feedback provided to the employee. Instead there is a mode of silence when all is

well, and when something drops below standard, there is a reaction, including

negative feedback. This mode of leadership can be effective in teaching new

employees what not to do; however, it has minimal effect in teaching employees

what to do. The behavior follows directly from the role of manager as controller.

2.3.2 Transformational Leadership vs. Transactional Leadership

The differences between transformational leadership, an active leadership style,

and laissez-faire leadership, a passive style, are clear and easily identifiable (Bass 1985).

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However, since both are active styles of leadership, the differentiation between

transactional and transformational leadership, viewed as two ends of the continuum

(Burns 1978), is not as well defined and therefore more researched (Den Hartog, et al.

1997). Generally, the findings have supported that transactional leadership can be

effective when done well (e.g., Yukl 1999, Kuhnert and Lewis 1987, Tracey and Hinkin

1998). However, this effectiveness appears to be limited to environments where

organizational transformation is not an imperative (Tichy and Devanna 1990). In this

way, the split between transactional and transformational leadership can be considered to

be similar to the differences noted between management and leadership (Graham 1982).

2.4 Leadership Effectiveness

The apparent holy grail of leadership theory and research is determining what

makes a leader effective. While the numerous theories of leadership are disparate in

many ways, most share a common goal – to identify components of leadership that make

an organization effective in achieving its goals and use the identified components to

determine a methodology for creating more effective leaders.

Perhaps the simplest definition of leadership effectiveness is what Yukl (1981, 5)

notes as the most common measure, where “effectiveness is the extent to which the

leader’s group or organization performs its task successfully and attains its goals.” The

problem with this simple definition is that it misses two key components: first, it can only

utilize strictly quantifiable aspects of performance, and may miss critical subjective

measures; second, it fails to include the perceptions of the subordinates with regard to

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their leader (Yukl 1981). If these missing components are included, then the definition of

successful leadership becomes as complex as the definition of leadership itself.

2.4.1 Examples and Definitions of Leadership Effectiveness

Popular literature is quick to canonize the leader of highly successful businesses;

from the leaders of companies that were built to last (Collins and Porras 1994), to specific

leaders, such as Jack Welch of General Electric (Robinson and Robinson 2001, Slater

1998), Bill George of Medtronic (George 2003), and Bill Gates of Microsoft (Wallace

and Erickson 1992). Popular literature is equally quick to demonize those at the top of

failed enterprises such as Enron (McLean and Elkind 2003) or WorldCom (Jester 2003).

While these texts provide interesting reading, they do little to create a definition for

successful leadership. Available definitions for leadership success are present in the latest

leadership research. Is success defined as

• the ability of an individual to receive positive ratings from their leaders and peers

(e.g., Leslie and Fleenor 1998), or

• to move up the corporate ladder (e.g., Howard and Bray 1988), or

• the ability to lead an effective organization (e.g., Denison 1990), or

• the success of the organization in terms of productivity (e.g., Likert 1961, 1967),

or a composite success measure (e.g., Day 2001), or is it

• the ability to successfully bring about change (e.g., Collins 2002, Senge 1990,

Beer 1988)?

Despite the differing methods for defining successful leadership, one thing is clear, most

leadership studies now attempt to distinguish the level of success associated with the

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behaviors or other patterns being studied (McCauley 2004). This study will take the

same approach. Due to the nature of the benefits purported from transformational

leadership (e.g., Bass 1985, Avolio 2005) and the challenges associated with leadership

in times of great change (Goldsmith 2007, Collins 2009), this dissertation defines

successful transformational leadership by the performance of the organization and / or its

ability to successfully adapt and change.

2.4.2 Effectiveness of Transformational Leadership

“Since the 1980s, research has supported the idea that transformational leadership

is more effective than transactional leadership in generating the extra effort, commitment,

and satisfaction of those led” (Avolio and Bass 2002, 1). It is this claim, and those like it,

that drew this dissertation into the path of investigating what makes a transformational

leader. Is this claim of transformational leadership success supported? Yes, according to

a wide variety of research in a number of industries and environmental conditions,

transformational leadership is an effective leadership style. At the very beginning of the

transformation leadership research boom, Bass (1985) cited numerous examples of

transformational leaders who successfully changed organizations, including Thomas

Watson at IBM and George Patton with the Third Army. More empirical examples soon

followed, from studies of sales force effectiveness (Jolson, et al. 1993), to the employee

satisfaction and commitment at financial firms (Walumba, et al. 2005,) to the climate of

learning created in school environments (Blatt 2002), to the success of large corporations

(Antonakis and House 2004). In all of these cases, a positive correlation was found

between displays of transformational leadership and desired organizational outcomes.

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In a military setting, Murphy (2002) found that transformational leadership

behaviors had a significant correlation with respondent perceptions of employee

satisfaction, effort and effectiveness as well as organizational effectiveness. In this

manner, Murphy’s research supports the success measure of promoting change, but not

that of organizational success. A similar level of success regarding organizational change

is found in the research of Ozaralli (2003) and Zagorsek et al. (2009). Ozaralli (2003)

completed a study of 152 individuals in a variety of private Turkish businesses finding a

strong correlation (r = 0.619) between leader’s transformational leadership behaviors and

the team’s perceived value of their own effectiveness. Zagorsek (2009) found a

correlation of 0.79 on the organization’s behavioral and cognitive changes as measures of

organizational learning. While neither of the above studies showed a correlation to core

organizational effectiveness measures, they all showed strong influences on other areas of

organizational behavior which have been shown to positively impact organizational

outcomes. These included organizational learning (e.g., Senge 1990), team

empowerment (e.g., Katzenbach and Smith 1993), and employee satisfaction (e.g.,

Buckingham and Coffman 1999).

Transformational leadership’s impact on organizational success, is shown in the

study of education in Ohio performed by Blatt (2002). This study found a statistically

significant correlation (p < 0.001, r = 0.569) between displays of transformational

leadership by the school’s top leader and a positive school climate. In this case, school

climate was used as a key measurement of the health and effectiveness of the school in

educating their students. A further example of transformational leadership’s impact on

core organizational outcomes is found in the work of Jolson, et al. (1993), who in their

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case driven studies, noted a positive impact in sales performance with the implementation

of transformational leadership behaviors within sales management. Finally, Avolio and

Bass (2002) completed case study analyses that looked at the organizational performance

of several companies under top level leaders who display strong transformational

leadership behaviors, notably Larry Bossidy at Allied Signal and Gertrude Boyle at

Columbia Sportswear.

2.5 Measuring Transformational Leadership

In the vast literature surrounding leadership effectiveness, there are a number of

instruments developed to measure leadership practices and effectiveness. In their

overview of measuring leadership, Kroeck, et al. (2004) identify 30 unique survey

instruments that have been or are being utilized to measure leadership and leadership

effectiveness. Of these instruments, the two that appear to be most commonly utilized for

measuring transformational leadership in the literature are Kouzes and Posner’s

Leadership Practices Inventory (LPI) (e.g., Bell-Roundtree 2004, Day 2003) and Bass’

Multifactor leadership questionnaire (MLQ) (e.g., Antonakis, et al. 2003, Murphy 2002,

and Bass 1985).

2.5.1 The Leadership Practices Inventory

The LPI is a two part instrument requiring the participation of the leader and

subordinates. The first part of the instrument is a 30 question survey completed by the

leader, based on their perception of their own behavior. Each question is rated on a

10 point Likert type scale, ranging from 1 (almost never engage in this behavior) to 10

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(almost always engage in this behavior). The second instrument is a 30 question

instrument completed by the subordinates using the same scale on their perceptions of the

behaviors exhibited by the leader. The LPI was thoroughly validated by Kouzes and

Posner throughout its development and implementation (1995).

The LPI has been widely used to measure leadership behaviors including recent

dissertation work. Bell-Roundtree (2004) utilized the LPI to understand leader behaviors

as they related to knowledge worker job satisfaction within the Department of the Army

and its support contractors. The study included a total of 190 respondents, with 181

completing all three instruments. Bell-Roundtree then utilized multiple regression to

better understand the relationship between employee satisfaction and commitment and

each of the five leadership behaviors measured by the LPI. The research found each of

the five behaviors (challenge the process, inspire a shared vision, enable others to act,

model the way, encourage the heart) to significantly correlate to employee commitment

and satisfaction. No validation of the LPI was conducted, as its reliability had been

previous proven by three referenced studies with Cronbach’s alpha from 0.75 to 0.93

(Bell-Roundtree 2004). In another recent dissertation, Day (2003) utilized the LPI to

understand the leadership practices of project scientists in research and development

(R&D) at the National Aeronautics and Space Administration (NASA). Similar to the

work discussed above, Day did not perform a validation on the LPI instrument, instead

referring to the work completed by Kouzes and Posner (1999). The study obtained a self-

report sample of 59 NASA scientists and 120 project member surveys (Day 2003). The

study combined the results of the LPI with a self-reported survey of how the project

scientists spent their day. Day (2003) utilized ANOVA to find a significant relationship

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between the self reported amount of time spent on leadership duties and the exhibition of

transformational leadership behaviors. These findings were then generalized to conclude

that the more time a project scientist spent focused on leadership, the more effective they

were.

Like most of the instruments examined in this research, the LPI has its detractors.

A study completed by Carless (2001) found that the LPI had weak discriminant validity

on a single company sample of 1400 employees. The study also suggested “that while it

is possible to distinguish conceptually among separate transformational leader behaviors,

either these distinctions are not captured by the LPI or subordinates do not notice the

differences” (Carless 2001, 237).

2.5.2 The Multifactor Leadership Questionnaire

Like the LPI, the MLQ is typically utilized as a two part instrument with a self

report form for managers and a second form for raters. The instrument includes 45 items

rated on a five point Likert scale measuring how frequently the behavior fits the person

being rated, ranging from 0 (not at all) to 5 (frequently, if not always). The MLQ was

thoroughly validated by Bass (1985) during its initial design and has undergone revisions

and additional validations (e.g., Bass and Avolio 1995, Avolio, et al. 1999) over the past

22 years. Appendix A contains a sample of MLQ questions.

The literature supporting the use of the MLQ is substantial and includes

two recent dissertations as well as an application by Towler (2005) similar to the

proposed research. In the first dissertation, Murphy (2002) utilized the MLQ to study the

leadership styles within the United States Navy and correlated those styles to the

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effectiveness of Navy reengineering programs. Using a sample of 289 respondents, the

study found that transformational leadership behaviors had a significant correlation with

respondent perceptions of employee satisfaction, effort and leader effectiveness as well as

organizational effectiveness. However, no significant relationship was found between

actual goal attainment and any of the leadership styles measured. The second dissertation

was completed by Blatt (2002) and investigated the correlation between transformational

leadership, using two instruments, the MLQ and the Charles F. Kettering School Climate

Profile. The study had a sample of 201 teachers from the Ohio vocational school system.

Blatt’s findings (2002) included significant relationships between two leadership styles

and school climate. A significant positive relationship was found between directors who

utilized transformational leadership and school climate, while a significant negative

relationship was found between school climate and laissez-fare leadership.

Similar to the LPI, the MLQ’s use and reliability has been questioned by some in

the literature. Specifically, Carless (1998) using a large sample (1440) from a single

organization used factor analysis to find the MLQ to be a more suitable measure of a

single higher order model than the multi-factor model that had been validated previously.

This view is also supported by Tejeda, et al. (2001), who utilized a total sample of over

1300 participants, gathered through four distinct samples from three different

organizations. Their study found evidence of an improved model being obtained by

simplifying the transactional components of the MLQ to a three-item subscale using

Factor Analysis. In their proposed version of the MLQ, the instrument would have only

27 items. Interestingly for this dissertation, the issues found with the MLQ were isolated

to the transactional and laissez-fare components of the MLQ (Tejeda, et al. 2001), which

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are not the focus of the research presented here. These issues with non-transformational

components of the MLQ are similar to the findings of Den Hartog, et al. (1997).

However, these questions of the MLQ’s validity appear to be refuted by more recent

work by Antonakis, et al. (2003), which found the full nine factor model valid in large

homogenous samples. It was also refuted by Rowold and Heinitz (2007) who found

transformational leadership highly convergent with charismatic leadership and both to be

divergently valid from transactional leadership. These results indicated criterion validity

against subjective and objective business performance.

In addition to these two primary instruments, one additional instrument identified

by Kroeck, et al. (2004) was investigated. The Leadership Behavior Questionnaire,

Revised (LBQ) was of interest due to its self-reporting nature and basis in the managerial

grid theory (Kroeck, et al. 2004). However, further investigation yielded a very small

research space using the instrument, with the preponderance of those studies dating back

over 30 years. For this reason, that instrument was removed from consideration.

Armed with an understanding of transformational leadership and its impacts, this

literature review investigated how it might be measured. After identifying an acceptable

option for measuring transformational leadership, this literature review sought to

understand how transformational leadership is developed. In order to better understand

the potential paths for development of transformational leadership, a broad understanding

of how leadership is developed must first be established.

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2.6 Previous Studies into Leadership Antecedents

The Handbook of Leadership Development (McCauley, et al. 1998) defines

six leadership development experiences, three formal (360-degree format, feedback

intensive programs, and skill-based training) and three informal, sometimes occurring

naturally and sometimes by design (job assignments, developmental relationships, and

hardships). Bennis and Thomas (2002) utilized structured interview techniques to

understand the leadership of a small group of leaders, separated in age by two or three

generations. One of the key findings of their study was the identification of what they

termed leadership crucible experiences. These leadership crucibles appeared to be a

leading indicator of leadership success in the study group. These crucible experiences

included experiences at war or serving in the military, imprisonment, challenges in a

wilderness setting, and significant business challenges. Bennis (2004) has continued to

investigate these experiences and noted that the area remained rich for potential

additional research.

Muldoon and Miller (2005) investigated the life experiences of managers within

the context of a Manager Quad (MQ) defined by an individual success and a career

success axis. In the two quadrants of the MQ most related to transformational leadership,

excellent managers, (high, high) and achievement managers (low, high), they noted

similar behaviors to some of those included in the definition of transformational

leadership. These included effective managers displaying strong other-orientation,

notably when engaged in hardship situations. This other orientation often manifested

itself as a focus on the work unit or company over self. When investigating the

antecedent experiences of managers in these quadrants, they found leaders who often

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reported having support networks and groups as role models, as well as childhood’s rich

with experience, vividly recalling positive and negative elements.

In an investigation of army leaders in Iraq, Wong (2004) utilized interview

techniques to draw general experience antecedents that appear to be contributing to the

development of innovative leaders within the army. These elements included

successfully dealing with complexity, a behavior learned through holding complex roles;

understanding cultural differences, new war techniques, and rapid change; and being part

of a team unified in a common purpose. These innovative leaders appear to share some

similarity with transformational leaders, so their development experiences are of interest.

Atwater, et al. (1999) investigated a sample of 236 male cadets over a four year

period at a military college to identify predictors of those who would later gain leadership

roles. The study investigated the cadets on seven factors: cognitive ability,

conscientiousness, self-esteem, hardiness, moral reasoning, physical fitness, and prior

influence experiences, in addition to administering the Leader Potential Index (LPI) and

tracking changes in each dimension over time. The effectiveness of the cadets’

leadership was measured using the rank achievement of the cadets at the end of the study,

combined with peer rankings utilized by the institution. The study used regression and

found physical fitness (r = 0.22, p < 0.01) and prior influence experiences (r = 0.24,

p < 0.01) to be most strongly correlated to leadership effectiveness. The study further

hypothesized that physical fitness may be a surrogate for other personality traits such as

perseverance and self-confidence.

In perhaps the most wide ranging study, Howard and Bray (1988) studied a group

of managers over a 30 year period at Bell Labs (now AT&T). The key antecedent

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findings of that research included a negative correlation between family orientation (r

between -0.34 and -0.18) and career success with positive correlations from projected

career ambition (r between 0.28 and 0.40).

In the two most closely related studies to the research presented in this

dissertation, Towler (2005) utilized the MLQ to understand the parental attachment of

emerging college age leaders and Avolio (1994) utilized a Life History Survey to

investigate potential antecedents to transformational leadership as measured by the MLQ.

Towler’s (2005) study utilized the Parental Attachment Questionnaire and Parental

Psychological Control instruments. This study found that parental attachment style, a

measure of the level of nurturing behavior of the parents toward their child, to be

positively correlated (r = 0.32, p < 0.001) with transformational leadership. Conversely,

father’s parental control was negatively correlated to transformational leadership,

showing that the more controlling the father was, the less likely the child was to display

transformational leadership. Avolio (1994) investigated 182 community leaders’

development along seven dimensions: parental interest, parental educational background,

parent characteristics, extra curricular activities and life satisfaction. The study found life

satisfaction, school experience, and positive work experience to have a significant

relationship to self reported transformational leadership behaviors, while parental interest

and parental moral standards were significant to follower perceptions of transformational

leadership. Overall, the relationships were weaker than anticipated, which Avolio (1994)

confessed may be largely due to the marginal reliability of the life experiences

instrument.

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2.7 Review Summary

The review has followed the leadership related literature using a macro to micro

approach. It provided an understanding of some definitions of leadership, selecting the

influence and perception based definition from Antonakis, et al. (2004b, 5) for the

discussion moving forward. From there, a discussion of the published investigations into

leadership was followed, leading into an understanding of the conjectured differences

between leadership and management, where management is made up of the activities that

make an operation run smoothly, and leadership is what makes an organization produce

or adapt to change.

Once that foundation was established, a discussion of the importance of

leadership’s role in the world set the stage for a review of the development of leadership

theories in the research literature, eventually leading to transformational leadership and

the full range leadership model. This discussion and comparison provided the rationale

for why transformational leadership, as defined by Bass (1985), may be worthy of further

investigation, but it was not adequate a foundation to fully justify the need for the study.

For that justification, studies into general leadership effectiveness were

investigated. Additionally, specific findings of the benefits of transformational

leadership were discussed, which further strengthened the reason transformation

leadership should be of interest, especially in times of high change and challenge. This

led to the review of how transformation leadership is measured. This review provided

the evidence to show that transformational leadership can be reliably studied. However,

despite the depth and breadth of the review, it failed to identify how transformational

leaders develop. This development path is a gap in the available literature.

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This gap lead to a review of the studies into the antecedents of leadership. This

area of study is compelling, but has very few empirical studies into how transformational

leaders develop. Thus there is a need and justification for the proposed research. A study

to determine what the antecedents are, that when discovered and nurtured, could lead to

the development of transformational leaders. This dissertation intends to close that gap

with an investigation into the development experiences of leaders and identification of

those experiences that correlate to transformational leadership behaviors.

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CHAPTER III

RESEARCH STATEMENT

The impact of leadership on company success continues to see growing interest

and emphasis in both the popular media and the research literature. One of the primary

areas of leadership research involves examining the leaders of successful companies to

determine what makes them unique from their peers. These differences are of interest

since leadership effectiveness is believed to have a direct relationship to business

performance (e.g., Collins 2001, George 2003, Bossidy and Charan 2002). However, the

theories on what constitutes leadership effectiveness are varied, and sometimes

conflicting (Kroeck, et al. 2004).

Transformational leadership is a set of theorized leadership behaviors that has

been shown to correlate well with organizational success in a variety of environments,

examples include the United States Navy (Murphy 2002), NASA (Day 2003) and

education (Blatt 2002). In other research (Bennis and Thomas 2002), the experiences

that might make a leader effective have been examined. Examination of these studies

raises the question of whether the development experiences of a leader influence their

displays of transformational leadership.

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3.1 Research Questions, Conceptual Model and Hypotheses

What is missing from these various research streams is a study into what

experiences appear to have helped form the behaviors of transformational leaders. The

research literature contains studies into the impacts of experiences on later displays of

leadership (e.g., Howard and Bray 1988, Atwater, et al. 1999), investigations into the

personality and cognitive predictors of transformational leadership (Bass 1996,

Bass 1998), and explorations of limited developmental experiences as predictors of

transformational leadership, such as parental attachment (Towler 2005) or high school

activities (Avolio 1994). However, the literature lacks an investigation into the breadth

of leadership development experiences during a leader’s lifetime that may influence their

displays of transformational leadership. This research will explore this gap in the

literature by investigating the experiences of leaders and analyzing the correlations

between those experiences and the leader’s display of behaviors across the full range

model of leadership measured by the Multi-factor Leadership Questionnaire (MLQ). The

general research questions that will be investigated in this dissertation are

• What types of experiences may influence later displays of transformational

leadership?

• Are there types of experiences that correlate (positively or negatively) with

displays of transformational leadership?

The specific conceptual model that the study will investigate includes multiple parts.

First, the study will seek to define and explore a set of development experiences that the

literature suggests will impact leadership development. Second, these experiences will

be examined in an attempt to group them into logical development subsets, initially based

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on the findings in the research literature and subsequently supported with study data.

Finally, these experience groupings will be examined for a relationship with the

leadership elements measured by the MLQ. Figure 3.1 provides a visual representation

of the conceptual model of the study.

Figure 3.1 - Study Conceptual Model

Addressing these questions will provide insight into the development of leaders

who display the full range of leadership behaviors measured by the MLQ. The collection

and analysis of data in this study will enable answers to the following hypotheses:

1. Ho: Leadership development experiences cannot be grouped into logical

factors.

Ha: There are logical groupings of leadership development experiences

that can be grouped through Factor Analysis.

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2. Ho: No grouping of development experiences correlate to later displays

of transformational leadership.

Ha: There are groups of development experiences that can be shown to

correlate to displays of transformational leadership.

3. Ho: No individual development experiences can be shown to correlate to

displays of transformational leadership.

Ha: There are individual development experiences that can be shown to

correlate to displays of transformational leadership.

3.2 Importance of Research and Contribution

Numerous studies indicate a positive correlation between the transformational

style leadership and business results (e.g., Bass 1985, Bass and Avolio 1995, Antonakis,

et al. 2003). Because of this correlation between business results and transformational

leadership, business should be interested in hiring leaders who exhibit transformational

leadership behaviors, or at least have the potential for such.

The problem is a limited understanding of the factors and experiences that enable

a leader to develop and apply transformational behaviors. While there have been some

studies into the development of transformational leadership, notably Towler’s (2005)

investigation into the influences of parental attachment and Avolio’s (1994) study into

the influences of high school and other early experiences, the understanding is not robust.

A study to identify the roots of transformational leadership would be useful on many

levels. The primary benefit to engineering managers will be to leverage study findings

for hiring decisions. A secondary benefit could include the development of training

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programs that lead to improved exhibition of transformational leadership qualities within

the leaders of an organization. This dissertation will identify characteristics or

experiences that have a correlation to transformational leadership behaviors. Finally, this

dissertation will introduce a new instrument into the body of knowledge that will be

useful for understanding the leadership development experiences of a sample of

experienced leaders.

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CHAPTER IV

RESEARCH METHODOLOGY

The objective of this dissertation is to better understand transformational leaders.

In order to obtain this objective, an investigation into the development experiences of a

population of leaders was planned and executed. Since the population of leaders studied

was expected to contain transformation leaders, this study is expected to indentify

antecedents of displays of transformational leadership behaviors.

4.1 Study Overview

In order to complete this investigation, a study was prepared using a two part

instrument. The first instrument, the Multi-factor Leadership Questionnaire (MLQ) is

designed to measure leadership behaviors across the full range leadership spectrum

including transformational leadership (Bass 1985, Bass and Avolio 1995). In order to

understand the development experiences of transformational leaders, a second instrument

to investigate those experiences was developed and deployed. The investigation used

these instruments to look for correlation between transformational leadership behaviors

and specific antecedent experiences.

This two instrument approach is similar to other studies looking into the

development of leadership. Over a 30 year study with Bell Labs (now AT&T) Howard

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and Bray (1988) utilized a variety of predictive instruments to find correlations between

experiences and attitudes and career success. Mumford, et al. (1993) developed a robust

background data measurement instrument and then correlated that information with

leadership displays of military academy cadets using the Collegiate Activities Scale.

Atwater, et al. (1999) used a variety of instruments to examine potential correlations to

leadership effectiveness in a class of military cadets.

Several studies into the development of transformational leadership using a

similar methodology have also been conducted and while the majority have focused on

personality expectations and “the empirical support has been spotty" (Bass 1998, 122),

there are a number of studies of both personality and other factors worth noting. Avolio

(1994) linked a Life History Survey to leadership measures from the MLQ. Avolio and

Bass (1994) studied community leaders responses to the Gordon Personality Profile and

the MLQ and found several significant correlations. Popper, et al. (2000) found a

relationship between the attachment of the followers to the leader and transformational

leadership as measured by the MLQ using Bartholomew’s Relationship Questionnaire

(Griffin and Bartholomew 1994). Gershenoff (2003) examined participant’s perceptions

of self descriptive adjectives using the Adjective Checklist (Gough and Heilbrun 1983) to

investigate displays of transformational leadership in leaderless groups finding no

significant relationships between transformational leadership and components associated

with the enabling behaviors (pragmatism, nurturance, and femininity).

Bommer, et al. (2004) used cynicism about organizational change as measured by

Reichers, et al. (1997) as a predictor of transformational leadership behaviors as defined

by Podsakoff, et al. (1990). Rubin (2003), also used the transformational leadership

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measures of Podsakoff, et al. (1990) and compared them with measures of leader

personality traits (Goldberg 1999) and the leader’s emotional intelligence (EI) using

combined measures of emotion recognition (Nowicki and Duke 2001) and ability to

maintain mood (Watson, et al. 1988). Kudo (2005) examined the emergence of

transformational leadership in adolescents using the MLQ and a combination of

four other instruments investigating parenting styles. Towler (2005) found a relationship

between parental attachment and student displays of charismatic leadership as measured

by the MLQ. Brown, et al. (2006) failed to find a correlation between emotional

intelligence and transformational leadership, while Barbuto (2005) found significant

correlation between leader’s results on the Motivation Sources Inventory (Barbuto and

Scholl 1998) and the MLQ.

While many of these studies looked for, and found, antecedents to the types of

leadership being studied, the majority focused on either personality or behavioral aspects

of the leader in question, not the experiences that preceded the displays. The most

notable exception to this is the work of Howard and Bray (1988), a thirty year study of

the development of leaders within Bell Labs (now AT&T). However, this seminal study

did not investigate transformational leadership. Instead, the Howard and Bray study

investigated the development experiences of these Bell Labs leaders in order to find

predictors of career success within the company.

In the transformational arena, the most similar study to the research outlined in

this dissertation is the work of Kim (2003) who examined the ability of formal action

learning experiences (group problem solving sessions with a specific focus on learning)

to develop transformational leadership. Antecedent experiences similar to Kim’s action

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learning experiences are included in this dissertation through the fifth pillar of the LLI as

part of the formal development experiences examined. These studies provide

foundational support for the proposed methodology while further highlighting the

potential contribution this dissertation to society’s understanding of leadership and in

particular transformational leadership.

4.2 Instrument Selection

As noted in the literature review, in research on transformational leadership, there

are two main instruments utilized to understand the behavior of the leader, the Leadership

Practices Inventory (LPI, Kouzes and Posner 1995) and the Multi-factor Leadership

Questionnaire (MLQ, Bass and Avolio 1995). While both instruments have been

thoroughly validated, the MLQ holds advantages over the LLI for the proposed research.

Specifically, the MLQ has a greater depth of supporting literature and the instrument

measures the full range of leadership behaviors. For these reasons, the MLQ was selected

as the instrument for this dissertation despite the additional costs for the instrument.

Table 4.1 summarizes the comparisons made between the two instruments.

Table 4.1 - Leadership Measure Selection Criteria and Winner

Criteria MLQ LPI 1. Instrument measures desirable transformational leadership behaviors. 2. Instrument is well supported in the literature. 3. Instrument provides opportunity to compare and contrast other leadership styles. 4. Instrument cost. 5. Instrument can be deployed electronically. 6. Length of instrument is reasonable for completion when combined with the antecedent instrument.

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Once the MLQ was selected for measurement of transformational leadership, an

instrument was needed to investigate the antecedents of these leadership behaviors.

Using Tests in Print (Buros Institute 1999, Hersen 2004), a variety of tests in the area of

interest were located. Of these, four candidate instruments were investigated more

thoroughly: the Work Profile Questionnaire (Cameron unknown), the Social Insight Test

(Cassel 1959), the Measures of Psychosocial Development (Hawley 1988) and the

Experience and Background Inventory (Baehr and Froemel 1996).

The instrument that appeared, to be the best fit for the antecedent areas discussed

in the research literature was the Work Profile Questionnaire (Cameron, unknown).

Unfortunately no literature around this instrument was located and the Buros Institute

(2009) found the instrument not worthy of review. The Social Insight Test (Cassel 1959)

focused almost exclusively on personality measures, not the participants’ experiences.

The Measures of Psychosocial Development (Hawley 1988), found its areas of study also

along the lines of personality, with some expansion into relationships, one of the key

areas of antecedent interest.

The most promising instrument in terms of antecedent experiences was the

Experience and Background Inventory (Baehr and Froemel 1996). This instrument

included six subscales to represent various experiences in life including Work

Experience; Activities and Interests; Educational Experience; Financial Responsibility;

Financial Experience; and Leadership and Responsibility. Unfortunately, outside of the

studies done by the authors of the instrument, there was little literature in support of the

instrument and Ferrara (2003) found the content of the instrument confusing with poor

reliability scores. These difficulties, combined with the high cost of the instrument,

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removed from consideration. The results of this review reconfirmed the initial findings

of a gap in the literature concerning actual experiences as antecedents. Due to these

deficiencies, the decision was made to develop a new instrument. After this decision was

discussed and finalized with the author’s doctoral committee on 30 January 2009, the

development of the Lifetime Leadership Inventory (LLI) began.

4.3 Development of the Lifetime Leadership Inventory (LLI)

Using the literature review as a foundation for determining what types of

experiences might correlate to later displays of transformational leadership, a theoretical

model was created. This model includes five pillars that support the development of a

transformational leader. The pillars are used as the hypothesized factors for the question

set included in the LLI. Each question in the LLI is scored on a 5 point Likert scale.

Figure 4.1 presents a summary of these pillars. The following sections provide more

detail on the theoretical development of these pillars and the instrument developed to test

the theory.

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Figure 4.1 - Overview of Theoretical LLI Model

4.3.1 The Nature of Key Relationships

The questions included in this pillar are designed to better understand the impacts

on the respondent by those who might have a significant relationship with the respondent

and may have influenced the development of their leadership style. These relationships

include those with parents (e.g., Mumford, et al. 1993, Muldoon and Miler 2005,

Towler 2005) and those with mentors (e.g., Atwater, et al. 1999, Sosik, et al. 2004). In

order to better understand these relationships and the experiences surrounding them, a set

of twenty three questions was created to explore this area. Example questions include

• My father provided an environment that supported growth and learning.

• During my career I have developed formal relationships with a mentor(s) to

support my development.

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4.3.2 Early Development Experiences

The questions in this pillar are designed to understand the experiences the

respondent had at a young age that may have had an impact on the development of the

respondent’s leadership. This includes being active in sports and other groups as a youth

(e.g., Goertzel and Goertzel 1962) and extracurricular activities in high school and

college (e.g., Avolio 1994, Muldoon and Miller 2005). A set of 25 questions was

developed to understand these experiences, including the following examples

• In high school, I was active in student government.

• In college, I was active in club athletics.

4.3.3 Exploratory Experiences

In many biographically based studies into leadership, the strong leaders discuss a

transformational experience that defined their later leadership style (e.g., Bennis and

Thomas 2002, Strock 2003). Since these instances often involve an experience outside of

the working world, this area of exploratory experiences is of interest. The most

commonly researched exploratory development experience involves experiences in

nature (e.g., Stoltz 1992, Burnett and James 1994, Louv 2005). Additional areas include

travel and other forms of cultural immersion (e.g., Evans and Cope 2003); work with

service organizations (e.g., Carlson, et al. 1999); and moments of extreme challenges

(e.g., Bennis and Thomas 2002, Wong 2004). A set of 28 questions was developed to

understand these experiences, including the following examples

• During childhood, my family spent vacation time in natural settings.

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• During childhood or adolescence, I lived in a culture other than my current

culture.

4.3.4 Early / Previous Work Experience

The questions in the fourth pillar are designed to garner an understanding of the

specific experiences in the respondent’s work history that may have influenced leadership

development. This question set is based on the body of knowledge that generally

involved longitudinal studies of leadership development (e.g., Howard and Bray 1988,

McCauley, et al. 1998, Atwater, et al. 1999). This pillar had twelve questions, including

• I held my first regular job while still a teenager.

• In my career, it has been common for me to lead people older than myself.

4.3.5 Formal Development Experiences

The final pillar in the hypothetical model of leadership development experiences

captures those experiences designed to examine a combination of what McCauley, et al.

(1998) described as formal and informal development experiences. These experiences

are frequently purported to impact leadership behavior, but are less frequently examined

for correlation. The types of training experiences include formal training courses

(e.g., Bass and Avolio 1992, Keller and Olson 2000), management coaching using

360-degree feedback (e.g., Kirkbride 2006, van Rensburg and Prideaux 2006) and job

rotation (e.g., Howard and Bray 1988) among others. The question set developed to

explore this pillar was the smallest in the instrument, with seven questions. These

include

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• My career experience included a deliberate rotation through multiple job

assignments with a single company.

• I have received beneficial feedback through 360-degree feedback

4.3.6 Demographic Questions

In order to supplement the correlation analysis between the data elements

included in the LLI and the MLQ, the survey participants were also asked to answer the

following five demographic questions:

• years of professional experience, rated on a seven point scale;

• current job level, rated on a five point scale;

• gender;

• level of education, rated on a five point scale, plus no response;

• ethnicity, rated on a standard eight point scale.

These demographic questions were used to look for differences in the displays of

leadership and types of antecedent experiences within the subgroups.

4.4 Refinement of the LLI

As discussed above, the initial LLI was developed to examine each of the five

theoretical pillars of antecedent experiences. Using the background information

identified in the literature review and a series of interviews with current leaders who were

familiar with the MLQ survey, the initial set of questions was developed. This question

set included over 120 questions related to antecedents. Samples can be found in

Appendix B. The set was reviewed with members of the author’s doctoral committee. A

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number of questions were removed to eliminate obvious content redundancies or other

errors. This reduction left a total of 98 questions plus demographic questions. Sample

questions from this draft of the LLI is located in Appendix B. Before beginning to utilize

the LLI with the target study population a pilot study was performed to better understand

the performance of the draft instrument.

4.4.1 Initial LLI Pilot Study

An initial pilot study using the draft LLI was completed using data from

three sample groups. The first two groups were made available through the Belgian

Armed Forces. These groups included student participants in the Belgian Armed Forces

Officer’s Course (Group 1, n = 9) and students in the Senior Officer’s Course (Group 2,

n = 21). The third group included current graduate students in the Engineering

Management program at the University of Alabama in Huntsville (n = 29). While these

populations were not expected to be a representative sample of the target study

population (growth company entrepreneurs and senior leaders), it provided useful

insights into the behavior of the draft instrument. The instrument was deployed

electronically to all three groups by building the survey in the online tool

Zoomerang.com and providing a link to the survey via email to the targeted population.

The data obtained from this initial pilot study was analyzed in a number of ways,

including cluster and correlation analysis. A description of this analysis is included in

Chapter 5. The objective of this analysis was to further reduce the LLI question set in

order to develop a more user-friendly and reliable questionnaire. This was done by

identification and elimination of problematic or potentially redundant questions.

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4.4.2 Reduction of LLI Question Set into Final Form

As noted above, the goal of the pilot analysis of the LLI was to understand the

behaviors of the draft instrument and reduce the overall question set. This reduction was

completed using three primary tactics. First, redundant questions regarding the

respondent’s relationship with their mother and father were replaced with single

questions referring to parents. Second, questions that had significant (p ≤ 0.01) cross

pillar correlations were eliminated. These were questions that had significant correlation

(p ≤ 0.01) with one or more questions from a different hypothesized pillar. The third and

final strategy was the elimination of highly redundant questions within a single

hypothesized pillar. This redundancy was determined by the number of significant

(p ≤ 0.01) correlations with other questions within the same pillar. These actions reduced

question set of the LLI down too 57 questions. These included 16 questions in Pillar 1 –

Nature of Key Relationships, 13 in Pillar 2 – Early Development Experiences, 16 in Pillar

3 – Exploratory Experiences, seven questions in Pillar 4 – Early / Previous Work

Experiences and five questions in Pillar 5 – Formal Development Experiences.

Appendix C contains sample questions from the final LLI instrument. With the LLI

refinement completed, the study was ready to move into data collection from the target

population.

4.5 Description of the Survey Population

The focus of the study is to identify the antecedents of transformational

leadership. As such, a group of transformational leaders needed to be identified and

asked to participate in the study. Based on the theories of Burns (1978) and Bass (1985)

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and studies of Atwater and Atwater (1994) and Avolio and Bass (2002), it is reasonable

to assume that a population of entrepreneurs in the United States would include a number

of transformational leaders. In order to gain a broad spectrum sample from a population

of entrepreneurs, personal contacts were utilized to access members of the Gazelles

group, an executive training and development firm, with the assistance of Gazelles Inc.

CEO, Verne Harnish. Members of the Gazelles group include mid-market companies

focused on growth, coming from all industries. Gazelles members are typically the

thought leaders within their industry (Gazelles 2009). The population targeted from this

group included readers of the company’s weekly newsletter, “Verne’s Insights” with a

total readership of over 10,000.

In order to obtain a greater balance of leaders who displayed transactional

leadership behaviors as well as transformational behaviors, a second population was also

sought. This population included members of the researcher’s LinkedIn contact network,

known to the researcher to currently hold or have recently held leadership positions, with

people management responsibilities, in large companies. Ninety individuals were invited

to participate in the study through this population.

4.6 Deployment of the Study Instruments

Once agreement was received from Verne Harnish to survey his readers, final

preparations were completed to launch the data collection. These preparations started

with purchasing sufficient licenses for use of the MLQ from Mind Garden (2010). Proof

of these purchases is located in Appendix D. Once the needed permissions were

purchased, the MLQ questions were deployed using an online survey platform. When

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combined with the finalized questions from the MLQ along with the demographic

questions, this resulted in a combined instrument with 104 questions.

Once the online instruments were completed and their functionality tested, the

data collection began with an invitation to the readers of the weekly newsletter. The first

invitation to participate was sent by Harnish to his readers as part of the weekly

newsletter on 18 September 2009. The complete text of that newsletter, including the

invite is included in Appendix E. This invitation was the lead article in the newsletter

and incentivized readers to participate in the survey by offering a free 360-degree survey

with a testimony from Harnish regarding the time needed to participate. This request

generated 413 visitors of which 137 began to complete the survey instrument with 125

completing all questions. Of these 125 full completions, 120 visited the survey to

participate in the 360-degree and 63 completed the needed information to take advantage

of the incentive and obtain their feedback.

A second round of data collection with this audience was launched on

12 November 2009. This time, the 360-degree incentive was not offered, and the article

was placed in the middle of the newsletter. The full text of the newsletter and the request

is located in Appendix E. This request generated 105 visitors of which 63 began the

survey and 53 completed all requested information. Tools within the online platform

were utilized to ensure that readers of the newsletter could only complete the instruments

one time to avoid duplicate data collection.

In addition to the second round of Gazelles data collection, the instrument was

launched with the LinkedIn population defined previously. The instrument was sent to

this population on 14 September 2009. This request generated 35 visitors of which 29

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58

began the survey and 27 completed all requested information. The responses from this

population were housed in a separate collection instrument within the online platform to

ensure that the data could be easily separated for analysis. Overall the three data

collection efforts yielded a total of 229 responses to the instruments with 205 completing

all questions.

4.7 Data Collection and Analysis Plan

The data was initially collected in the online platform. Once data collection was

complete, the raw data was downloaded for manipulation in Excel, Minitab, JMP, and

LISREL. Excel tools were utilized to summarize the demographic data collected on the

response population, score the MLQ in terms of the full range leadership model (Bass

and Avolio 1995) and prepare the data for entry into Minitab, JMP, and LISREL.

Following the entry of data into Minitab, JMP, and LISREL, analysis of the data

was completed to test the research hypotheses. The analysis was broken into three parts.

The first part utilized Minitab and LISREL to perform Confirmatory Factory Analysis

(CFA) on the factors of the MLQ using the study data set. The results of this CFA were

compared with the results obtained through similar analysis completed by Avolio and

Bass (2004) on the large data set discussed in the MLQ users guide.

The second phase of the analysis examined the performance of the LLI. This

analysis was completed in two parts. The first part utilized Minitab and LISREL to

complete a CFA on the LLI using the hypothesized five pillars discussed earlier. The LLI

was then analyzed using exploratory Factor Analysis techniques and the Minitab toolset.

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59

This second level of analysis was completed to search for better models contained within

the LLI than the originally conceptualized pillar model.

The final phase of the analysis, the relationship between the MLQ and LLI was

explored. This exploration was completed using correlation analysis tools in JMP and

Minitab, regression analysis tools in JMP and Minitab and Structured Equation Modeling

tools in LISREL. The investigations were conducted seeking relationships between the

factors of the LLI and the factors of the MLQ, and between individual elements of the

LLI and the factors of the MLQ. The detailed review of results of this analysis are

presented in Chapter 5.

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CHAPTER V

DATA ANALYSIS

The objective of the study is to better understand the development experiences of

transformational leaders. This objective has been accomplished through the creation of a

new instrument to understand the leadership development experiences of participants, the

Lifetime Leadership Inventory (LLI), and the comparison of this instrument with the

Multi-factor Leadership Questionnaire (MLQ). This comparison was completed using a

sample of leaders of entrepreneurial companies. The data collected was analyzed in a

number of ways, in three distinct phases. In the first phase, a series of exploratory

analyses was completed on pilot studies of the LLI to refine the instrument and prepare

the instrument for use in the final study.

In the second phase, the complete study data was analyzed in a number of steps.

First, an exploration of the demographic data of the participants was performed and

compared to other known groups of leaders. Next, the behaviors of both the LLI and

MLQ instruments were examined for their psychometric properties using Confirmatory

Factor Analysis (CFA) techniques. The results of the CFA analysis were also compared

against results of similar analysis for other studies using the MLQ. In the final step of

this phase, correlation analysis was completed between the pillar scores of the LLI and

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61

the leadership scores of the MLQ. This analysis enabled development of the initial set of

relationships between the two instruments was developed.

In the third phase, the information found through the CFA of the LLI was utilized

to reduce the question set of the LLI even further. This reduction enabled the completion

of a Structured Equation Model (SEM) between the LLI and MLQ. This model was then

utilized to finalize the relationships discovered between the two instruments. Figure 5.1

contains a summary of the analysis methodology.

Figure 5.1 - Overview of Analysis Methodology

The analysis was completed using several different software tools. Microsoft

Excel 2003 spreadsheets were utilized to compile simple demographic information and to

format the output of other software packages into tables for this document. The

Minitab15 statistical software package was utilized to perform comparisons between

different demographic subsets in the data collected, initial CFA analysis of both the LLI

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and MLQ, and completion of the correlation analysis between the LLI and MLQ. The

JMP 8 Statistical Discovery software package was utilized to complete the correlation

analysis of elements within the LLI and for the comparison between the LLI and MLQ.

Finally, LISREL 8.8 was utilized to complete the full CFA analysis on both the LLI and

the MLQ.

5.1 Pilot Study and Refinement of the LLI

In order to initially test and refine the content of the Lifetime Leadership

Inventory (LLI) instrument a pilot study of leaders was conducted using the instrument.

This study involved the collection of data from three distinct groups as outlined in

Table 5.1. While useful for the refinement of the instrument, these groups were not

expected to be representative of the population that would be accessed in the primary

study for two key reasons. First, the native languages of over half of the respondents was

something other than English. Second, over 50% of the sample currently served in

military leadership positions which was not expected in the target population.

Table 5.1 - Pilot Study Population Overview Group Participants Description

BAF 1 9The French speaking group of the Belgian Armed forces junior officers course.

BAF 2 21The Dutch speaking group of the Belgian Armed forces junior officers course. (high degree of English fluency)

UAH 1 29A group of graduate students in Engineering Management at the University of Alabama in Huntsville

59

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5.1.1 LLI Pilot Study Data Analysis

The data set collected through the pilot study was analyzed using a variety of

techniques. Initially the data was explored using cluster analysis (Johnson and

Wichern 2002, Everitt 1993) in order to identify groupings within the data. This analysis

was completed to understand the natural groups apparent in the data set and compare

those groups with the groups created by the hypothesized pillars in the LLI. This analysis

was completed by entering the raw responses (integer values 1 – 5) to the LLI questions

into Minitab 15 and using the cluster analysis toolset to develop the initial set of clusters.

Initially the cluster analysis was completed using the single linkage method, chosen for

its simplicity (Johnson and Wichern 2002). This analysis was performed using

unconstrained parameters in Minitab 15 that allowed the software to identify as many

clusters as available in the data set, with a similarity target of 0.70. Figure 5.2 contains

an example dendrogram from this analysis. As illustrated by Figure 5.2, this method of

cluster analysis was not useful in identifying major clusters in the data set. For this

reason, additional cluster analysis techniques were explored to find a more clearly

differentiated set of clusters.

These additional rounds of cluster analysis were completed using Ward’s

method. The Ward’s method of analysis was selected for its ability to minimize the loss

of information through weighting the clusters (Johnson and Wichern 2002). Using an

unrestrained analysis setting, this method generated a set of eleven clusters as shown in

Figure 5.3. While these new clusters generally aligned with segments of the

hypothesized pillars, they also included some unexpected combinations. These

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Figure 5.2 - Single Linkage Dendrogram for LLI Pilot Study

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Figure 5.3 - LLI Pilot Study Dendrogram using Ward's Method

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unexpected combinations indicated a high degree of similarity between the seemingly

disparate questions regarding residing in multiple countries or cultures (questions 10-1

and 10-4) and holding a job while in college (question 9-1). These clusters were further

explored using correlation analysis (Lapin 1990) to better understand the questions that

were grouped together and potential reasons for the grouping. The results of this analysis

can be found in Appendix F. The cluster analysis and supporting correlation analysis of

the initial data set provided an understanding of how the data collected naturally grouped

together. This analysis was completed to ensure that the data was not grouping in an

unexpected way. If the data had grouped in an unexpected way, it would have

necessitated the need to revisit the design of the LLI. Instead, the results provided an

initial validation of the hypothetical pillars included in the LLI.

Upon reviewing the results of the analysis with the committee, a recommendation

was made to further explore the data using a broader correlation analysis than the

technique applied to the individual clusters identified using Ward’s method. This

analysis compared all available pairings of all questions included in the LLI. By

exploring the data in this manner, the intent was to identify questions that could be

eliminated from the LLI, and create a simpler instrument. This analysis proceeded with a

complete correlation analysis of all of the questions in the data set using JMP 8. The

complete matrix of all correlations is included in Appendix F and summarized below in

Table 5.2. Overall, the analysis found 152 significant (α = 0.01) pairings in the data.

Within that group, there were 52 pairings with correlation values of greater magnitude

than ±0.5. This analysis was utilized to refine the instrument by eliminating a number of

questions from the total data set.

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Table 5.2 - Pilot Study Correlation Analysis Summary Pillar 1 Pillar 2 Pillar 3 Pillar 4 Pillar 5

Total Questions 27 28 31 13 7

Significant Correlations in Pillar (0.01) 48 87 60 20 8Total Correlations in Pillar 231 300 378 66 21

Percent of Correlations Significant 20.8% 29.0% 15.9% 30.3% 38.1%

Significant Cross Pillar Correlations 17 59 60 64 30Total Cross Correlations 1584 1725 1848 984 609

Percent of Correlations Significant 1.1% 3.4% 3.2% 6.5% 4.9%

5.1.2 Reduction of the LLI Data Set

The effort to eliminate questions from the data set utilized the correlation analysis

combined with some expert opinions to drive the reduction. These eliminations were

completed using three rules:

• Combination of mother / father repetitive questions into a series of parents

questions with the addition of a mother / father differentiation question.

• Elimination of cross pillar questions – questions that correlated significantly

with a number of questions from a different pillar and whose elimination did

not appear to cause a degradation in the quality of the data collected.

• Elimination of redundant questions – questions that had a large number of

significant correlations within the pillar.

• These reductions resulted in a refined instrument with 57 questions. Samples

from this refined question set are located in Appendix C. This refined LLI

had a substantial reduction in the number of cross pillar correlations and

redundancy within a single hypothesized pillar.

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The pilot study and subsequent analysis provided a better understanding of the

behaviors of the LLI and enabled a substantial reduction in the question set. The factors

identified through exploratory Factor Analysis partially support the hypothesized pillar

models of the LLI described earlier. Based on the decision of the committee, the study

moved forward using the hypothesized pillars of the LLI. From this point, the study

moved into the full data collection and analysis using the final 57 question instrument.

5.2 Demographic Analysis of the Study Data Set

As detailed in Chapter 4, the full data collection process was completed with two

different populations – respondents generated from the Gazelles weekly newsletter

readership and the author’s online connections through LinkedIn. The Gazelles group

was expected to include a disproportionate number of transformational leaders which

created the concern that there would not be a large enough spread in leadership types to

allow for meaningful results from the study. In order to address this concern and seek a

greater diversity in the types of leaders examined, the LinkedIn group was added. These

two sources generated a total of 205 complete responses. Respondents were

predominately male and Caucasian, and included a range of experience, education and

position levels. Notably, nearly 80% of the population identified their business role as

executive level, nearly 90% of respondents had ten or more years of professional

experience, and nearly 85% held at least a Bachelor’s degree with a full 43% holding

advanced degrees. Tables 5.3 through 5.6 contain summaries of the key demographic

information of the participants.

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Table 5.3 - Participant Ethnic Demographic Information by Gender

Asian

Black, African,African

AmericanNative

American OtherPrefer not to answer

Spanish, Hispanic,

Latino

White, Caucasian,

EuropeanGrand Total Percent

Female 1 1 2 2 30 36 17.6%Male 12 1 1 8 2 143 167 81.5%Prefer Not to Answer 2 2 1.0%Grand Total 13 1 1 9 2 4 175 205

Percent 6.3% 0.5% 0.5% 4.4% 1.0% 2.0% 85.4%

Table 5.4 - Participant Job Level Demographics by Source

SourceExecutive (President, VP,

Senior Officer, etc.)Individual Contributor

(Engineer, Analyst, etc.)Manager (Director,

Department Head, etc.) Not ApplicableSupervisor (Team Lead, Assistant Manager, etc.) Grand Total

Gazelles 147 1 20 7 3 178LinkedIn 12 6 7 1 1 27Grand Total 159 7 27 8 4 205

Percent 77.6% 3.4% 13.2% 3.9% 2.0%

Table 5.5 - Participant Experience Level Demographic Information by Source Source 3-5 yrs 6-10 yrs 11-20 yrs 21-30 yrs 31+ yrs Grand TotalGazelles 1 19 57 83 18 178LinkedIn 3 10 10 4 27Grand Total 1 22 67 93 22 205

Percent 0.5% 10.7% 32.7% 45.4% 10.7%

Table 5.6 - Participant Education Level Demographic Information by Source Source

High school graduate (or equivalent) Some college

Associate degree (2 year degree)

Bachelor's degree (4 year degree)

Graduate or professional degree

Prefer Not to Answer Grand Total

Gazelles 10 16 4 76 71 1 178LinkedIn 1 8 18 27Grand Total 10 17 4 84 89 1 205

Percent 4.9% 8.3% 2.0% 41.0% 43.4% 0.5%

In order to understand how the demographics of the leaders who participated in

this study differed from other populations of leaders, a comparison was made with the

2009 listings of the Inc. 500 (Buchanan 2009) and findings of the Spencer Stuart CEO

Study (2006), which utilized publically available information to define the demographics

of the 502 CEO’s of the S&P 500 companies. This data allowed direct comparisons of

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the study data with the S&P 500 leaders in the area of education and with the Inc.

500 leaders in the areas of ethnicity and gender. Using Minitab 15 to perform

two proportion tests for all available comparisons in the data found only

four comparisons with significant differences (α = 0.05, p < 0.001):

• A higher percentage, 62.9 vs. 43.4, of S&P leaders hold advanced degrees than

found in the study population.

• A higher percentage, 97.0 vs. 86.7, of S&P leaders hold college degrees than

found in the study population.

• The study population included a higher percentage of women, 17.6 vs. 2.0, than

the Inc. 500 leadership.

• The study population included a higher percentage, 13.8 vs. 8, of non-Caucasians

that the Inc. 500 leadership.

Initially, these differences raised a concern that the data collected in this

dissertation was not a representative sample of leaders. However, due to the unique

nature of the comparison groups, which included only the top executive of large and

successful companies, versus the broader sample of leader roles included in the

dissertation data this concern was not valid. Since the CEO position represents a unique

subset of the population, it was not unexpected to find differences between the two

groups. Therefore the initial doubts raised by the comparisons were determined to be

unfounded.

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5.3 Analysis of the Multifactor Leadership Questionnaire (MLQ)

Once the demographics of the study participants were understood, the next step in

the analysis was to understand the behaviors of the MLQ within the study data set. The

analysis completed first compared the behavior of the leadership measures found in the

study against the known population of MLQ data using the nine factor model of the

MLQ. Next the factor structure of the MLQ was explored using confirmatory factor

analysis. The results of this analysis for the data collected in this dissertation were then

compared to the known population of MLQ studies.

5.3.1 Comparing the Leadership Measures of the Study and MLQ Population

In a manner similar to that completed by Avolio and Bass (2004) participant

responses were compiled, based on the various leadership measures contained within the

nine factor model of MLQ. The output of these calculations is contained in Table 5.7.

The results of the measures were then compared for each of the different groups to

determine any statistically significant differences.

These comparisons were made using a single sample Z-test (Johnson 1994). This

test was utilized because the data set compiled in the MLQ User Manual (Avolio and

Bass 2004) was considered the population of the instrument, defined here as the

comparison population. The sub groups within the study data set were compared

utilizing the two sample T-test (Johnson 1994). All comparisons against the Avolio and

Bass (2004) data set were made using their results for self-rating responses, the same

instrument utilized for this study. These tests found a number of significant differences

in the data set for this study, summarized as follows:

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• The study population displayed significantly (α = 0.05, p = 0.003) greater

tendency toward Idealized Influence (Behavior) characteristics than did the

comparison population.

• The study population displayed significantly (α = 0.05, p < 0.001) greater

tendency toward Inspirational Motivation behaviors than did the comparison

population.

• The study population displayed significantly (α = 0.05, p = 0.008) greater

tendency toward Management By Exception (Attributed) behaviors than did

the comparison population.

• The study population displayed significantly (α = 0.05, p = 0.019) greater

tendency toward Management By Exception (Passive) behaviors than did the

comparison population.

• The study population displayed significantly (α = 0.05, p = 0.002) greater

tendency toward Laissez-faire behaviors than did the comparison population.

The comparisons between the two groups included in the study data set, Gazelles

readers and the LinkedIn group, did not find the same number of significant differences.

Those found were as follows:

• The Gazelles population displayed a significantly (α = 0.05, p = 0.02) greater

tendency toward Management by Exception (Passive) behaviors than the

LinkedIn study group, which did not display a significant difference between

the Avolio and Bass population (2004).

• The Gazelles population displayed a significantly (α = 0.05, p = 0.02) greater

tendency toward Laissez-faire behaviors than did the LinkedIn population.

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73

Table 5.7 - Descriptive Statistics for MLQ Results

Scal

eM

ean

SDR

ange

Mea

nSD

Ran

geM

ean

SDR

ange

Mea

nSD

Ran

geIIA

3.00

0.72

4.00

3.00

0.75

4.00

2.93

0.56

2.23

2.95

0.53

3.50

IIB3.

110.

804.

003.

140.

814.

002.

910.

642.

502.

990.

593.

75IM

3.21

0.76

4.00

3.22

0.80

4.00

3.16

0.43

1.50

3.04

0.59

3.50

IS3.

000.

724.

002.

990.

754.

003.

150.

481.

752.

960.

523.

50IC

3.10

0.74

4.00

3.08

0.77

4.00

3.28

0.47

1.75

3.16

0.52

3.00

CR

2.93

0.76

4.00

2.92

0.76

4.00

2.96

0.76

3.50

2.99

0.53

3.50

MBE

A1.

730.

753.

501.

730.

753.

501.

690.

723.

001.

580.

794.

00M

BEP

1.17

0.70

4.00

1.21

0.72

4.00

0.93

0.54

2.00

1.07

0.62

4.00

LF0.

720.

724.

000.

750.

754.

000.

510.

431.

500.

610.

523.

50

Lege

nd:

IIA =

Idea

lized

Influ

ence

(attr

ibut

ed)

Key

of F

requ

ency

:4.

0 =

Freq

uent

ly, i

f not

alw

ays

IIB =

Idea

lized

Influ

ence

(beh

avio

r)3.

0 =

Fairl

y of

ten

IM =

Insp

iratio

nal M

otiv

atio

n2.

0 =

Som

etim

esIS

= In

telle

ctua

l Stim

ulat

ion

1.0

= O

nce

in a

whi

leIC

= In

divi

dual

ized

Con

side

ratio

n0.

0 =

Not

at a

llC

R =

Con

tinge

nt R

ewar

dM

BEA

= M

anag

emen

t by

exce

ptio

n (a

ctiv

e)M

BEP

= M

anag

emen

t by

exce

ptio

n (p

assi

ve)

LF =

Lai

ssez

-faire

MLQ

5x

2004

Sel

f (N

=3,3

75)

(Avo

lio a

nd B

ass,

200

4)To

tal S

ampl

eG

azel

les

Gro

upLi

nked

In G

roup

(n =

205

)(n

= 1

78)

(n =

27)

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74

Some of the differences in these populations, notably the increased propensity of

the study population to simultaneously display stronger tendencies toward certain

transformational and transactional components, were unexpected. Table 5.8 shows the

complete results of these comparisons, displaying first the p-values obtained when

comparing the dissertation study group and the comparison population and then the p-

values obtained comparing the two groups contained within the dissertation study.

Table 5.8 – P Values for Comparisons of MLQ Scores

LLI Study Data IIA IIB IM IS IC CR MBEA MBEP LFFull Set (n = 205) 0.223 0.003 <0.001 0.181 0.129 0.088 0.008 0.019 0.002

LLI Study IIA IIB IM IS IC CR MBEA MBEP LFLinkedIn (n = 27) 0.570 0.100 0.560 0.146 0.068 0.800 0.791 0.021 0.020

Legend: IIA = Idealized Influence (attributed) IS = Intellectual StimulationIIB = Idealized Influence (behavior) IC = Individualized ConsiderationIM = Inspirational Motivation

CR = Contingent Reward MBEP = Mgmt by exception (passive)MBEA = Mgmt by exception (active) LF = Laissez-faire

MLQ 5x 2004 Self (N=3,375) - (Avolio and Bass, 2004)

LLI Study Data - Gazelles Group (n = 178)

The primary reason for performing these comparisons made in this section was to

determine if the study participants were a representative sample of the known MLQ

respondent population. While the comparisons of the study population did find some

statistically significant differences, the practical difference in the results is limited. The

area of greatest interest in the MLQ information for this study is the measures of

transformational leadership. In these measures, the largest difference can be found in

measure of Inspirational Motivation, with a 5% difference, while the average difference

of all five measures is only slightly more than 2%. The differences in the transactional

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75

and laissez-faire components are larger, up to 18% for laissez-faire, but since these areas

of leadership are of secondary interest in this study, these differences are of little practical

consequence. For these reasons, the study data represents the known MLQ population in

practical terms. Once these behaviors of the study data set were understood, the next step

in the analysis was to move from this comparison to an examination of the behavior of

the factor structure of the MLQ using the data collected for this dissertation.

5.3.2 Examination of the MLQ Factor Structure

In order to understand how the measures within the MLQ behaved within the

study sample, the data was first examined to understand the internal reliability of the

hypothesized factors measured by the MLQ using Cronbach’s alpha (1951). Using the

nine factor model MLQ discussed in the previous section, transformational components

ranged in reliability from a low of 0.74 to a high of 0.87. All of these components

exceeded the general rule of a desired internal consistency of 0.70 or above (Cronbach

2004, Gliem and Gliem 2003, George and Mallery 2003). While the transactional

components scored consistently lower, ranging from 0.67 to 0.74. These results included

two factors that scored below the acceptable target of 0.70 and into the questionable

range (George and Mallery 2003). It is of note that this difference has little practical

impact given that the lower of the two measures is only 0.03 below the target threshold.

The results of this analysis are contained in Table 5.9.

Table 5.9 - Cronbach Alpha Reliability Score for Nine Factor MLQ Components

Reliability Scores for Study Data with Nine Factor MLQ Model

IIA IIB IM IS IC CR MBEA MBEP LF Cronbach's

Alpha 0.80 0.82 0.87 0.80 0.83 0.74 0.70 0.67 0.68

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In addition, the MLQ data was evaluated using Item Analysis for the

hypothesized three factor, two factor and single factor models (Avolio and Bass 2004).

These investigations found some support for earlier researchers (e.g., Carless 1998,

Tejeda, et al. 2001) who found the MLQ may be a better measure of a higher order model

than the nine factor model most commonly utilized in current research (Avolio and Bass

2004). The additional exploration included the three factor model - transformational,

transactional and laissez-faire, the two factor model – transformational and transactional

and a single factor model. Using George and Mallory (2003) thresholds for Cronbach’s

alpha, the study found all of these multi-factor variants had results that showed the

transformational factor had an excellent alpha value, in excess of 0.9, while the

transactional factor had a poor alpha value, below 0.6. The results of all the models are

summarized in Table 5.10.

Table 5.10 - Cronbach Alpha Reliability Scores for Alternate MLQ Models

Transformational Transactional Laissez-faireCronbach's Alpha 0.95 0.58 0.68

Reliability Scores for Study Data with 3 Factor MLQ Model

Transformational TransactionalCronbach's Alpha 0.95 0.57

Reliability Scores for Study Data with 2 Factor MLQ Model

Reliability Scores for Study Data with 1 Factor MLQ Model

Cronbach's Alpha 0.88

The single factor model presents some concerns with its good alpha score of 0.88.

Specifically, this result brings into question the validity of the multi-factor model if a

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single factor model can score so well. In addition to the alpha score for the single factor

model shown in Table 5.10, an exploratory factor analysis (Stevens 2002, Johnson and

Wichern 2002) of the data was completed. This analysis yielded a model with one factor

many times stronger than the next closest item, explaining 35.5% of the variance. These

results are displayed in the scree plot shown in Figure 5.4. However, while the plot

shows one factor with an Eigen value of nearly four times the next highest scoring factor,

it also displays eight factors that meet the threshold of significance, by holding an Eigen

value greater than 1.0 (Johnson and Wichern 2002).

While the performance of the models with fewer factors raise some questions

about the performance of the MLQ in the full nine factor model, the results are not

raising any question about the instrument beyond what had already been raised in other

research (Avolio and Bass 2004). Since there are no new finding here, the alpha scores

for each of the five transformation components of primary interest in the research were at

a good level of 0.8 or above (George and Mallery 2003), the Eigen values of the first

8 factors were all above 1.0 (Johnson and Wichern 2002) and the detailed work of Avolio

and Bass (2004) has shown the nine factor model has the greatest model fit scores; this

dissertation will simply note that alternate models of the MLQ are available, and

conclude their availability does not negate the validity of the nine factor model. For that

reason, the nine factor model was utilized throughout the remainder of the study.

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Figure 5.4 - Scree Plot Result of Exploratory Factor Analysis of the MLQ

Following the initial exploration of the data, the nine factor model was more

thoroughly examined using the confirmatory factor analysis (Albright and Park 2009,

Stevens 2002, Johnson and Wichern 2002) tools available through LISREL 8.8. This

analysis is similar to that published in the MLQ users manual (Avolio and Bass 2004).

The steps included in this analysis were defining each of the nine expected factors

contained within the MLQ, using path diagrams, and running LISREL 8.8 to determine

the factor loadings for each of the questions included in the theoretical factors.

Figure 5.5 displays an example of the path diagram created in this analysis, showing the

path for the Idealized Influence (Attributed) factor within the MLQ.

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Figure 5.5 - Factor Model for Idealized Influence (Attributed) as Measured by the MLQ

5.3.3 Comparing the Factor Loadings of the Study Data with the MLQ Population

Completing this analysis provided the factor loading values for each question

contained in the MLQ for its expected factor. Once again the results of the analysis

performed on the study data were compared with the same output calculated in the MLQ

users manual (Avolio and Bass 2004) to confirm that there were no abnormalities

contained within the data collected for this dissertation. Table 5.11 contains the full

results of this analysis with a side by side comparison of the results obtained by Avolio

and Bass (2004).

The results of these comparisons show that the factor loading scores of any given

question in its factor varied by as much as 59% between the two data sets. These

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differences averaged 21% across the 36 questions. While these differences may appear

large, using Hair, et al. (1998) guidance for significance within factor analysis based on

sample size, every question loads significantly, ±0.30, within its respective factor for the

given sample size. Additionally, 67% of the questions meet the ±0.50 threshold for

practical significant within both data sets and 97% meet the threshold within at least one

data set. Furthermore, it is notable that the factor loadings for this dissertation are

relatively stronger than those of the Avolio and Bass (2004) study when sample size is

considered. Therefore, there is no practical difference between the findings of the

participants in the dissertation study and the known population of MLQ participants.

This lack of difference supports the decision to move forward with the nine factor model

of the MLQ for the remainder of the dissertation.

5.4 Analysis of the Lifetime Leadership Inventory (LLI)

Once the behavior of the MLQ was understood, the next step in the study was to

develop a similar understanding of the behaviors of the LLI. In order to develop this

understanding, the questions of the LLI were put through a similar analysis to what was

completed with the MLQ. This analysis started by analyzing the pillar scores of the LLI

and comparing the scores obtained for each of the two data sources utilized in the

dissertation data set and then proceeded into a CFA of the LLI.

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Table 5.11 - Factor Loading Comparisons for Individual MLQ Questions within their Expected Factor

Item

LLI S

tudy

MLQ

5x

2004

Item

LLI S

tudy

MLQ

5x

2004

Item

LLI S

tudy

MLQ

5x

2004

IIA 1

00.

610.

58IIB

60.

760.

48IM

90.

680.

63IIA

18

0.59

0.39

IIB 1

40.

770.

71IM

13

0.67

0.72

IIA 2

10.

750.

57IIB

23

0.57

0.36

IM 2

60.

750.

70IIA

25

0.54

0.53

IIB 3

40.

740.

68IM

36

0.71

0.63

Item

LLI S

tudy

MLQ

5x

2004

Item

LLI S

tudy

MLQ

5x

2004

LLI S

tudy

MLQ

5x

2004

IS 2

0.67

0.42

IC 1

50.

760.

56C

R 1

0.53

0.24

IS 8

0.80

0.48

IC 1

90.

610.

37C

R 1

10.

720.

56IS

30

0.71

0.68

IC 2

90.

650.

49C

R 1

60.

730.

60IS

32

0.71

0.66

IC 3

10.

650.

74C

R 3

50.

600.

52

LLI S

tudy

MLQ

5x

2004

LLI S

tudy

MLQ

5x

2004

LLI S

tudy

MLQ

5x

2004

MBE

A 4

0.54

0.60

MBE

P30.

670.

58LF

50.

590.

53M

BEA

220.

750.

70M

BEP1

20.

640.

71LF

70.

680.

38M

BEA

240.

420.

63M

BEP1

70.

430.

33LF

28

0.70

0.57

MBE

A 27

0.77

0.69

MBE

P20

0.55

0.68

LF 3

30.

430.

54

MLQ

5x =

Sel

f rep

orte

d qu

estio

nnai

re d

ata

from

Avo

lio a

nd B

ass

(200

4), N

= 3

,375

Fact

or IC

Fact

or IM

Fact

or M

BEP

Fact

or IS

Fact

or L

F

Fact

or C

R

Fact

or II

AFa

ctor

IIB

Fact

or M

BEA

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82

5.4.1 Descriptive Statistics for the LLI

As discussed in Chapter 4, the final version of the Lifetime Leadership Inventory

(LLI) developed for the study included a total of 57 questions split between

five theoretical factors as follows:

• Pillar 1 - Nature of Key Relationships, 16 questions

• Pillar 2 – Early Development Experiences, 13 questions

• Pillar 3 – Exploratory Experiences, 16 questions

• Pillar 4 – Early / Previous Work Experiences, 7 questions

• Pillar 5 – Formal Development Experiences, 5 questions

In the first step of the analysis, the instrument was broken into its factors and the

descriptive statistics for each factor was calculated. Table 5.12 contains this data for the

entire data set as well as the separate groups involved in the study.

Table 5.12 - Descriptive Statistics for LLI Pillars

Scale Mean SD Range Mean SD Range Mean SD RangePillar 1 3.30 0.52 2.50 3.30 0.52 2.50 3.30 0.51 1.81Pillar 2 2.58 0.62 2.92 2.56 0.62 2.92 2.68 0.68 2.62Pillar 3 3.27 0.46 2.88 3.25 0.47 2.88 3.41 0.38 1.94Pillar 4 3.42 0.67 4.71 3.40 0.69 4.71 3.54 0.52 2.00Pillar 5 3.34 0.86 5.00 3.31 0.88 5.00 3.56 0.74 3.20

Legend: Pillar 1 - Nature of Key Relationships, 16 questions Frequency Key: 5.0 = Strongly AgreePillar 2 – Early Development Experiences, 13 questions 4.0 = AgreePillar 3 – Exploratory Experiences, 16 questions 3.0 = NeutralPillar 4 – Early / Previous Work Experiences, 7 questions 2.0 = DisagreePillar 5 – Formal Development Experiences, 5 questions 1.0 = Stongly Disagree

Total Sample Gazelles Group LinkedIn Group(n = 205) (n = 178) (n = 27)

The results of the pillar scores were then compared for the Gazelles and LinkedIn

groups using the nonparametric Kruskal Wallace test (Rice 1995) in Minitab 15 for each

of the pillars. This test was selected because all pillars, except Pillar 3, failed the

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Anderson Darling test for normality (Rice 1995). The tests failed to reject the null that

there was no difference between the means of the two groups (α = 0.05) for any of the

comparisons with p-values ranging from a low of 0.15 to a high of 0.77. The exception

to this high p-value was Pillar 3, with a p-value of 0.07. Due to this low score, and Pillar

3 failing to reject the Anderson Darling normality test (p = 0.36), this comparison was

run again using the 2 sample t test, which again failed to reject the null (p = 0.07). The

variances of the data were then compared using Levene’s test for Pillars 1,2,4,5 and an F-

test for Pillar 3 (Johnson 1994). The results of these tests failed to reject the null

hypothesis, indicating that the variances of the two groups were equal, with p-values

ranging from 0.20 to 0.61. Since all tests failed to find a difference between the Gazelles

and LinkedIn sources, these groups were combined into a single data set for further

analysis. Once the decision to combine the two data sources was made the analysis

moved to studying the factor structure of the LLI. This phase of the analysis follows the

same steps as were completed previously when the factor structure of the MLQ was

examined.

5.4.2 Confirmatory Factor Analysis of the LLI

The initial phase of the confirmatory factor analysis of the LLI focused on

evaluating Cronbach’s alpha (1951) for each hypothesized factor (i.e., pillar). The results

of this analysis found Pillars 1 – 3 meeting George and Mallory’s (2003) acceptable

criteria with alpha scores of 0.73, 0.72 and 0.72, respectively. Pillars 4 and 5 fall into the

questionable category, scoring 0.62 and 0.67 respectively; however, both are in a range

that is commonly deemed acceptable for behavioral data (Stevens 2002). Additional

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analysis performed on the data utilizing this tool set failed to find a better sub grouping

for the elements included in the final two pillars.

As was done with the MLQ data, LISREL 8.8 was then utilized to complete full

confirmatory factor analysis (Albright and Park 2009, Stevens 2002, Johnson and

Wichern 2002) with the LLI data. This process provided the individual factor loading

calculations shown in Table 5.13. Perhaps the most notable part of the results were the

loadings within Pillar 1. In this group, using the significance threshold of 0.30

recommended by Hair, et al. (1998) only the mentor and leader related questions loaded

at significant levels for the given sample size. All of the parent related questions loaded

below the 0.30 minimum level of significance. These results indicated the potential for

two distinct factors within Pillar 1.

Overall, these loadings were not as clean as those found for the MLQ, with only

52% of the items loading at the level of practical significance for the sample size (Hair,

et al. 1998). This result was not surprising given the difference in age and refinement of

the two instruments. However, if the factor loadings are reviewed using the ±0.30

minimum significance level recommended by Hair, et al. (1998), then a full 70% of the

individual questions load significantly within their hypothesized factor.

Utilizing this lower level as the criterion for acceptance and the Cronbach’s alpha

values found through the earlier item analysis, it seems the model is acceptable and can

be utilized for further analysis. This decision is also supported, by examining the

goodness of fit statistics provided by LISREL 8.8. This analysis utilized, the root mean

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Table 5.13 - Loadings of Individual LLI Questions within their Hypothesized Factors

Item Loading Item Loading Item Loading1A-P_Env 0.00 2B-P_Eth -0.05 4E-M_Meet 0.941B-P_Cont 0.06 3-P_LstNum 0.00 5A-L_RM 0.501C-P_Advice 0.15 4A-M_Form 1.03 5B-L_DRM 0.331D-P_Share 0.16 4B-M_LdGd 1.04 5C-L_Und 0.461E-P_MoD -0.03 4C-M_Adv 0.972A-P_ActCom 0.01 4D-M_FdBk 0.95

Item Loading Item Loading Item Loading6-Ch_CG 0.42 7E-HS_Hlp -0.85 8D-C_ResL -0.497A-HS_Aspt -0.38 7G-HS_NCSp -0.52 8E-C_Pol -0.627B-HS_ASG -0.91 8A-C_SG -0.65 8F-C_NCSp -0.577C-HS_AIG -0.83 8B-C_Grk -0.357D-HS_SCpt -0.55 8C-C_VSpt -0.28

Item Loading Item Loading Item Loading10A-Cu_Ocnt 0.33 11B-RA_VacN 0.34 12B-EE_SO 0.2810B-Cu_Ocul 0.43 11C-RA_CRA 0.37 12C-EE_ND 0.1910C-Cu_Live 0.71 11D-RA_Lfun 0.29 13A-B_Sac 0.2510D-Cu_TrvE 0.79 11E-RA_Tadv 0.49 13B-B_Lrn 0.2710E-Cu_TrvC 0.54 11F-RA_ExpE 0.3711A-RA_NatS 0.32 12A-EE_Inc 0.09

Item Loading Item Loading Item Loading14A-Wk_Teen 0.24 14D-Wk_LOld 0.74 9A-CE_Emp 0.3114B-Wk_Lmil 0.15 15E-D_Func 0.45 9B-CE_Mgmt 0.5214C-Wk_LEar 0.95

Item Loading Item Loading Item Loading15A-D_Rot 0.87 15C-D_360 0.77 15F-D_PLrn 0.3615B-D_Train 0.87 15D-D_Org 0.62

Key: Questions indicated with the number (group) the appeared withinthe instrument and a brief description (e.g. P_Env indicates Parentenvironment and M_FdBk indicates mentor feedback)

Pillar 4 – Early / Previous Work Experiences

Pillar 1 - Nature of Key Relationships

Pillar 2 – Early Development Experiences

Pillar 3 – Exploratory Experiences

Pillar 4 – Early / Previous Work Experiences

square error of approximation (Steiger 1990), a standardized measure of error

approximation, defined by

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86

⎭⎬⎫

⎩⎨⎧

⎟⎟⎠

⎞⎜⎜⎝

⎛−

Θ= 0,1)(max

ndfFRMSEA ,

where F(Θ) is the maximum likelihood fit function.

RMSEA was selected since it provides a measure that is “essentially a measure of

lack of fit per degree of freedom” (MacCallum 1995, 30). For this data, the RMSEA was

0.075. This score did not meet the threshold of 0.05 that would indicate a close

approximation (Browne and Cudeck 1993) or a good fit (Kenny 2009). However, since it

was below 0.08, it indicated a reasonable fit (Browne and Cudeck 1993) and falls

squarely in the middle of the range between a good and bad fit (Kenny 2009).

Additionally, the 90% confidence interval, (0.072 ; 0.079), had an upper threshold below

0.08, indicating a reasonable model fit (Kenny 2009).

In summary, these measures indicate a useful model. While the fit is not great, it

meets needed measures of acceptability. For this reason, the initial analysis seeking a

relationship between the LLI and MLQ utilized the LLI in this form. This relationship

analysis was completed utilizing correlation analysis techniques.

5.5 Exploring the Relationship Between the LLI and the MLQ Using Correlation

The exploration of a potential relationship between the LLI and MLQ was done

using a series of correlations that evaluated the relationships of the factors of the

two instruments. This type of analysis was widely utilized in the literature, including a

number of studies attempting to relate one or more instruments to the MLQ (e.g., Avolio

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1994, Avolio and Bass 1994, Bono and Judge 2003) and two dissertations comparing the

results of two or more instruments (Utley 1995, Bell-Roundtree 2004).

Minitab 15 and JMP 8 were utilized to generate a correlation matrix of the factors

of the two instruments. In the first comparison, the Pillars of the LLI were correlated

with the nine factors of leadership measured by the MLQ. This comparison found that all

Pillars had a significant (α = 0.05) correlation with one or more components of

transformational leadership measured within the MLQ. The strongest relationships

occurred between

• Pillar 3 (Exploratory Experiences) with Idealized Influence (Behavior) and

Inspirational Motivation, correlation at least 0.250

• Pillar 4 (Early / Previous Work Experience) with Contingent Reward, 0.265

Overall, the correlations averaged 0.195 for all 25 comparisons with a maximum value of

0.254 and a minimum value of 0.120. These scores compare favorably with those of

other studies that compared the MLQ with another instrument, including Avolio and Bass

(1994) who found correlations between the MLQ and the Gordon Personality Profile

between 0.21 and 0.25 and with the Myers-Briggs Type Indicator between -0.25 and

0.20.

The strength of the correlation between the LLI Pillars and the transformational

factors of the MLQ showed a strong contrast. These comparisons found minimal

correlation between the Pillars and the transactional components of the MLQ, as there

were no significant correlations between the Pillars and three of the four transactional

factors. These comparisons had an average correlation score of 0.009. The exception in

this category was the comparison with Contingent Reward leadership, where all

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correlations were significant (α = 0.05, p-value between <0.001 and 0.012) and the

average correlation value was 0.205. Table 5.14 provides the full output of this

comparison.

Table 5.14 – Correlation Coefficients & Significance for LLI Pillars and Nine Factor MLQ

IIA IIB IM IS IC CR MBEA MBEP LFPillar 1 0.160 0.269 0.159 0.192 0.237 0.219 0.023 -0.045 0.008Pillar 2 0.176 0.159 0.224 0.220 0.203 0.179 0.071 0.002 -0.075Pillar 3 0.120 0.251 0.254 0.216 0.245 0.187 0.048 -0.060 0.040Pillar 4 0.145 0.204 0.205 0.193 0.213 0.265 -0.004 0.021 0.021Pillar 5 0.070 0.164 0.124 0.223 0.236 0.175 0.102 -0.001 -0.009

Bold Significant, α = 0.05

Bold Significant, α = 0.01

This clear difference between the strength of the correlation between the LLI and

transformational components of the MLQ and lack of correlation with the transactional

components raised an additional question. Would the LLI correlate more clearly with the

two factor model of the MLQ than the standard nine factor model? A similar correlation

analysis was then completed using the two factor model. This analysis failed to provide a

clear distinction between the relationship the LLI held with the transformational and

transactional components. This lack of distinction is due to the strength of the

relationship the LLI holds with Contingent Reward leadership, which is included in the

transformational component. For these reasons, the comparisons between the LLI and

the nine factor model of the MLQ were retained and the comparisons with the two factor

model were discarded.

The correlation analysis found a number of significant relationships between the

elements of the LLI and the MLQ. While these relationships provided sufficient

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evidence to complete the hypothesis tests included in this dissertation, the collected data

contained additional information that could be understood with additional advanced

analysis. This analysis was completed using Structure Equation Modeling to investigate

the presumed cause and effect relationships (Johnson and Wichern 2002) between the

LLI and the MLQ.

5.6 Structured Equation Modeling Between the LLI and MLQ

The correlation analysis above yielded a number of significant relationships

between the LLI models and the MLQ. Additionally, the correlation analysis techniques

used above are well supported by the literature for applications such as this within the

social sciences (e.g., Avolio 1994, Avolio and Bass 1994, Stevens 2002). Despite this

support, it was hoped that a complete SEM would yield additional understanding of the

relationship between the two instruments. When considering utilizing a full SEM model

to compare the LLI factor structure with the MLQ factor structure, a number of problems

that raised concern about the technique for this data set were encountered. First, since

SEM is in “broad terms [the] amalgamation of multiple regression and confirmatory

factor analytic techniques” (Brewerton and Millward 2004, 165), the fact that the data did

not meet the required regression assumptions of normality could result in a failure.

However, this issue can be overcome by the modeling tools within LISREL 8.8

(Scientific Software 2009). Second, the data set is not large enough to meet the

expectation of 15 cases per predictor (Stevens 2002) and does not meet the perfectly well

behaved assumptions needed to move to five cases per parameter (Bentler and

Chou 1987). For these reasons, the initial SEM attempt in LISREL 8.8 failed.

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However, as noted earlier, a number of questions included in the LLI failed to

meet the needed thresholds of significance within the CFA of the instrument. If these

questions were eliminated from the LLI could two benefits be achieved? First, would the

five pillar model of the LLI become better fitting. Second, would the reduction in the

question set enable the existing data set to be large enough to complete the full SEM on

the LLI and MLQ. Using these questions as the objectives of the analysis, an effort was

made to further reduce the elements included in the LLI.

5.6.1 Further Reduction of the LLI and CFA Revisited

Returning to the results of the CFA on the LLI in Table 5.13 shows that there are

fifteen questions within the 57 that fail to meet the target significant loading of 0.30

(Hair, et al. 1998). However, this table also indicates that if all elements below that

threshold were eliminated than the content of Pillars 4 and 5 would no longer be

sufficient for analysis. For this reason, the reduction of the LLI was completed using a

step wise method, with the hope that elimination of very low value questions would cause

the loading in certain borderline questions to increase.

The first round of reduction was done targeting the questions that appeared to add

close to zero value to the model. This was completed using 0.1 as a threshold and

resulted in the elimination of six items and a 51 question instrument. These reductions

caused the hoped for results with some of the previously low loading questions seeing an

increase in their loadings. The next round of reduction applied the 0.30 (Hair, et al.

1998) threshold. This resulted in the reduction of eleven additional questions. From

there, two additional rounds of the CFA were run. These rounds utilized a threshold of

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0.36 based on the sample size threshold recommended by Stevens (2002), considering the

205 items included in this study. This new threshold was developed to address concerns

regarding the 0.30 standard threshold initially raised through the work of Cliff and

Hamburger (1967) and extended by Stevens (2002). These reductions ended with a 28

item instrument with all items meeting or exceeding the 0.36 threshold for significance of

factor loadings. These newly constituted pillars are defined below:

• Pillar 1 – Relationship with Mentors and Leaders: seven questions, Cronbach’s

alpha = 0.87.

• Pillar 2 – High School and College Activities: ten questions, Cronbach’s = 0.68

• Pillar 3 – Cultural Exploration: four questions, Cronbach’s = 0.64

• Pillar 4 – Early Leadership Experiences: four questions, Cronbach’s = 0.61

• Pillar 5 – Structured Leadership Development : four questions, Cronbach’s = 0.66

While these reductions did lower the reliability scores of two pillars that previously met

the 0.70 desired threshold for internal consistency (Cronbach 2004, George and Mallery

2003) below that threshold, all of the pillars remained above the 0.60 threshold

commonly indicative of a marginal model fit (George and Mallery 2003). It appears that

this degradation is more than offset by the fact that all of the questions now load

significantly within their pillar.

5.6.2 SEM Analysis Description and Results

In order to begin the Structured Equation Modeling (SEM) process for evaluating

the relationship between the LLI and MLQ, the overall path diagram indicating the

expected relationships between the questions contained in the LLI and MLQ and their

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factors was built. This diagram, along with the LISREL 8.8 outputs are located in

Appendix H. Once the model was built in LISREL 8.8, the analysis engine was run. The

results of the analysis indicated that the model was essentially a close approximation of

the data with an RMSEA value of 0.054 only 0.004 away from the 0.05 threshold

(Browne and Cudeck 1993).

Additionally, using the generally accepted |2.0| threshold of significance for the t

value generated by LISREL 8.8 (Stevens 2002), the SEM found significant relationships

between the LLI and MLQ that were more differentiable than those found with the earlier

correlation analysis. Specifically, while each pillar held a significant relationship with at

least two subcomponents of transformational leadership, only three found significant

relationships with Contingent Reward leadership (earlier analysis showed all five pillars

significantly correlated with CR); Pillar 2 (early leadership experiences) indicates an

opposite relationship than all other Pillars; Pillars 3 and 5 load on mutually exclusive

leadership components; and only Pillars 1 and 2 load on the Idealized Influence

(Attributed) component. The SEM found a similar lack of significant relationships

between the pillars and management by exception and Laissez-faire behaviors as was

found in the earlier correlation analysis. These differences in loadings indicate a more

complete contribution to the understanding of the relationship between development

antecedents and transformational leadership than was found through the more simple

correlation analysis. Table 5.15 displays the correlation values found with the SEM

analysis.

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Table 5.15 - Correlation Values Found with SEM

Pillar 1 Pillar 2 Pillar 3 Pillar 4 Pillar 5IIA 0.18 -0.17 0.10 0.14 0.03IIB 0.36 -0.23 0.21 0.21 0.14IM 0.20 -0.25 0.19 0.23 0.07IS 0.16 -0.28 0.15 0.20 0.18IC 0.26 -0.30 0.13 0.25 0.20

CR 0.26 -0.24 0.15 0.25 0.09MBEA 0.07 -0.05 -0.07 0.00 0.09MBEP -0.04 -0.02 -0.09 -0.01 0.00

LF 0.03 0.04 0.02 -0.10 0.00

indicates significant correlation

5.7 Correlation Analysis Between LLI Questions and the MLQ

The exploration of potential relationships between the individual questions contained

in the LLI and leadership measures of MLQ was completed through correlation analysis.

Minitab 15 and JMP 8 were utilized to generate a correlation matrix of these

comparisons. This analysis found a total of nineteen questions that had a significant

correlation with one or more subcomponents of the nine factor model of the MLQ at α =

0.05, while fourteen of those had a significant correlation at α = 0.01. In total, there were

72 significant (α = 0.05) correlation relationships, including 31 pairings significant at α =

0.01. These significant correlations were predominately with transformational factors of

the MLQ, a total of 59 significant relationships, 82% of all significant pairings. The

contingent reward factor of transactional leadership had twelve of the remaining thirteen

significant relationships. The full results of these pairings are shown in Table 5.16.

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Table 5.16 - Significant Correlations Between LLI Questions and MLQ Leadership Factors

LLI Question IIA IIB IM IS IC CR MBEA MBEP LFM_Form 0.189M_LDGD 0.266 0.149 0.208 0.213M_ADV 0.169 0.298 0.187 0.181 0.258 0.251M_FDBK 0.176 0.331 0.214 0.197 0.308 0.276M_MEET 0.149 0.235 0.173 0.175 0.209HS_HLP 0.149 0.158 0.182 0.206 0.22HS_NCSP 0.193 0.149C_SG 0.163 0.224 0.215 0.217C_POL 0.142 0.161 0.146 0.225C_NCSP 0.144 0.141 0.173 0.18 0.166 0.142CU_OCUL 0.141 0.202 0.138 0.145CU_LIVE 0.153 0.146CU_TRVE 0.145WK_LEAR 0.15 0.161 0.162 0.176 0.216WK_LOLD 0.139 0.15 0.158 0.2D_FUNC 0.14 0.206 0.16 0.146D_ROT 0.138D_360 0.143 0.219 0.255 0.163D_ORG 0.225 0.181 0.138 0.143

= Significant at α = 0.01

MLQ Factor

5.8 Analysis Summary

As previously discussed, the objective of the study was to better understand the

development experiences of transformational leaders. By utilizing a three phase

approach to the analysis of the data collected for the study, this objective was completed.

In the first phase, the instrument utilized to understand the development experiences of

leaders, the LLI, was explored and refined utilizing correlation analysis. In the second

phase, the behaviors of the LLI and MLQ were understood by using Confirmatory Factor

Analysis to understand their psychometric behaviors within the study group, and a

comparison population, and correlation analysis was utilized to understand any

relationship between the two instruments. This exploration included the search for

relationships between the factors contained in both instruments, as well as relationships

between the individual questions of the LLI and the leadership measures of the MLQ. In

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95

the third and final phase, the LLI was further refined using CFA tools and then the

relationship between the refined LLI and MLQ was explored utilizing SEM techniques.

The results of this analysis have enabled the study to address each of the hypothesis tests

in the dissertation, as discussed in the following chapter.

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CHAPTER VI

CONCLUSIONS AND RECOMMENDATIONS

The intent of this dissertation was to investigate the development of

transformational leadership within individual leaders and increase the body of knowledge

regarding what development experiences correlate to displays of transformational

leadership. The need for this study was indicated by a review of the literature. This

review found extensive literature on the influence of personality and other psychological

constructs on leadership (e.g., Towler 2005, Bono and Judge 2003, Avolio and Bass

1994), but few examples investigating the development experiences of leaders (e.g.,

Avolio 1994, Howard and Bray 1988). Of the literature that did investigate experiences

as a developmental precursor to leadership, none attempted to explore all of the myriad of

hypothesized development experience types in the literature (Schell, et al. 2008). The

results of this study begin to address this gap.

In order to make this contribution, a two part study was needed. One of the

reasons that no previous study had examined the impact of a breadth of potential

development experiences, was that no reliable instrument to explore these experiences

existed. Therefore, the first part of the study was the development of a new instrument,

the Lifetime Leadership Inventory (LLI). The LLI was designed to measure the

leadership development experiences of respondents. The second step distributed the LLI

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along with the Multi-factor Leadership Questionnaire (MLQ) to a population of small

business leaders and a second group of a variety of leaders known to the author, in order

to gain breadth in the study population. The responses of these leaders were examined

for correlations between the two instruments, and a number of significant correlations

were found. These findings led to the rejection of the three null hypotheses formulated in

the study.

6.1 Hypothesis Testing Results and Contribution to the Body of Knowledge

The study investigated three separate hypotheses related to the development of

transformational leadership as detailed in Chapter 3. The first hypothesis looked to the

creation of a new instrument to understand the development experiences of leaders. This

hypothesis was

Ho: Leadership development experiences cannot be grouped into logical

factors.

Ha: There are logical groupings of leadership development experiences

that can be grouped through Factor Analysis.

The analysis of the data collected to evaluate this hypothesis rejected the null. This

rejection was accomplished through Item Analysis in Minitab and Confirmatory Factor

Analysis (CFA) using LISREL. As detailed earlier, the results of the initial model using

LISREL found a number of significant loadings with an overall reasonable (Browne and

Cudeck 1993) model fit, using the RMSEA. Further refinement of the instrument to

prepare for Structured Equation Modeling (SEM) resulted in a reduced question set of

28 items. This resulted in a model where every question had a significant loading in its

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hypothetical pillar and an improved RMSEA value of 0.054, an indication of good model

fit (Browne and Cudeck 1993).

The next hypothesis explored the correlation between the groupings of

development experiences and displays of transformational leadership:

Ho: No grouping of development experiences correlate to later displays of

transformational leadership.

Ha: There are groups of development experiences that can be shown to

correlate to displays of transformational leadership.

The analysis of the data collected to evaluate this hypothesis rejected the null. Analysis

of the LLI factors against the nine factor MLQ model found a number of significant (α =

0.05 and α = 0.01) relationships between the pillars of the LLI and the leadership factors

of the MLQ. Specifically, the hypothesized pillars in the LLI correlated significantly

with all transformational components in the nine factor model of the MLQ, while

exhibiting a significant correlation with only the contingent reward (CR) component of

the transactional factors.

The final hypothesis looked in greater detail at the individual effects of specific

LLI questions on the factors of the MLQ:

Ho: No individual development experiences can be shown to correlate to

displays of transformational leadership.

Ha: There are individual development experiences that can be shown to

correlate to displays of transformational leadership.

Once again, the analysis of the data collected to evaluate this hypothesis rejected the null.

Analysis of the LLI questions against the nine factor MLQ model found a number of

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significant (α = 0.05 and α = 0.01) relationships between the pillars of the LLI and the

leadership factors of the MLQ. Specifically, a total of nineteen questions had a

significant correlation with one or more subcomponents of the nine factor model of the

MLQ at α = 0.05, while fourteen of those had a significant correlation at α = 0.01. In

total, there were 72 significant (α = 0.05) correlation relationships, including 31 pairings

significant at α = 0.01. These significant correlations were predominately with

transformational factors of the MLQ, a total of 59 relationships. The contingent reward

factor of transactional leadership had twelve of the remaining thirteen significant

relationships.

While the analysis shows proof of statistical significance for the results of the

study, what does it mean? What are the theoretical implications to future studies of

leadership development? What practical applications can be made by the engineering

manager? How do the results fit in with those of previous studies into leadership

development? The following sections address these questions, explore the limitations of

the study and suggest avenues for future research.

6.2 Theoretical Implications of Study

One of the key motivations for the study was to understand the breadth of

leadership experiences that were important in the development of leaders. This interest in

a breadth of experiences led to the development of the LLI, an instrument that addressed

the relatively narrow scope of other instruments that sought to investigate leader

experiences by exploring five distinct areas of experience, the LLI Pillars. By developing

this instrument and subjecting it to statistical validation, a new avenue for understanding

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the development experiences of leaders, through the efficient means of a survey

instrument rather than the lengthy process of interviewing of earlier studies (e.g., Howard

and Bray 1988, Bennis and Thomas 2002) has been made available to researchers.

The results of the study also have theoretical implications. By examining the

questions reduced from the LLI three separate implications are noted. First, in its final

version, the LLI contains no questions regarding the influence of parents on the leader.

These results suggest that the influence of parents is not important in the development of

transformational leadership. However, these findings conflict with the theories of

McCauley, et al. (1998) and the empirical findings of Towler (2005) and Mumford, et al.

(1993). Given the literature, these findings were unexpected and may be influenced by

the decision early in the development of the LLI to focus questions on parents, rather than

separate mother and father related inquiries. Second, in its final version, the LLI includes

no questions designed to identify the extreme experiences identified by Bennis and

Thomas (2004) as Leadership Crucibles. The elimination of these questions raises

concerns about the broad applicability of the findings from Bennis and Thomas (2004)

interview based findings from a sample of 43 leaders. Finally, the questions regarding

exploratory experiences in natural settings were eliminated from the instrument, bringing

into question the applicability of the findings of Louv (2005) regarding child social

development on later leadership development.

The next group of theoretical implications comes from the results of the

correlation analysis between the Pillars of the LLI and the leadership factors of the MLQ,

shown in Table 5.15. When reviewing these results, it is quickly noticed that all of the

Pillars of the LLI have two or more significant positive correlations with the

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transformational factors of the MLQ, except Pillar 2 (High School and College

Activities), which has a number of significant (α = 0.01) negative loadings on these same

transformational subcomponents. What makes this pillar differ from the others contained

in the LLI? Initially, it is noted that the time lag of those elements contained in Pillar 2

are greater than others in the LLI, but this explanation appeared incomplete. Instead, a

more complete theoretical answer for the difference is found in the types of experience

contained in the pillar. Specifically, each of the elements in Pillar 2 investigated either

group involvement or leadership roles held at an early age. Since these experiences are

likely to be very structured leadership roles, due to the setting of the organization and age

of the participants, it is theorized that these roles teach the young leader how to be a

transactional leader. The correlations indicate that the strength of the leader’s

transactional leadership may then fade over time, but the experience remains a negative

influence on their transformational leadership.

The second notable theoretical implication of the relationships between the pillars

of the LLI and the leadership factors of the MLQ was seen with the behavior of Pillar 5,

Structured Leadership Development. This pillar examined the participant’s experience

with 360-degree feedback, leadership training, and job rotations. It was the pillar that

had the least number of significant correlations. Those relationships were held only with

the Intellectual Stimulation and Individual Consideration aspects of transformational

leadership, most other correlations were close to 0. This tends to indicate that formal

leadership development activities can be utilized to teach and create behaviors that match

the more concrete aspects of transformational leadership while having nearly no

influence on the more abstract elements, such as Inspirational Motivation.

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The third theoretical implication examines the behavior of the Contingent

Reward component of transactional leadership. Throughout the study, a relationship

between the leadership predictors and Contingent Reward was consistently found to more

closely mimic transformational components. A potential reason for this relationship is

that the predictors of the LLI are an indication of the effectiveness of the leader, since

Contingent Reward has been theorized to be an effective leadership type when done well

(e.g., Bass 1985, Burns 1978). If not this, then it is suggested by the study that the

development Contingent Reward is a better fit with transformational leadership

components than the transactional grouping assigned by the MLQ (Avolio and Bass

2004).

The final theoretical implication of the finding of the study regards the strength of

the influence of a mentoring relationship on a leader. The role of a mentor has been

shown to have a significant influence, more than any other predictor that was examined.

In this study, this significant relationship was found for all transformational leadership

factors and Contingent Reward, with an average correlation of 0.24. In addition,

mentoring had close to zero correlation (average = 0.02) with the remaining transactional

components. This can be theorized to be driven by the fact that when the mentor takes an

active interest in the development of the leader, it causes the leader to become a more

active leader in their life. Additionally, it appears that a mentor can have a positive

influence on not only the concrete elements of transformational leadership, e.g.,

Intellectual Stimulation and Individual Consideration, but also on the more abstract

concepts, such as Inspirational Motivation.

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While these theoretical implications are interesting and lay the groundwork for

other potential areas of exploration, how can they be made actionable? What learnings

are important to share with practicing engineering managers? And what areas of business

can the learnings be applied to? These findings contribute to the Engineering

Management body of knowledge in that each significant relationship can be utilized to

better understand the influences on the development of a leader’s ability to display

certain behaviors.

6.3 Implications for the Engineering Manager

The first implication for the engineering manager comes in the area of position

hiring. When the desirable leadership behaviors for a given position are understood, the

results of the study can be utilized to seek a leader who is more likely to display those

leadership behaviors. For instance, if a highly inspirational leader is desirable for a given

opening, the selection team should look for a candidate who has held leadership roles

early in their career (Pillar 4, correlation 0.23), had meaningful influences on their

leadership from a mentor (Pillar 1, 0.20), experience with exploring other cultures

(Pillar 3, 0.19) and was not highly involved with high school and college leadership

activities (Pillar 2, -0.25).

The second implication for the engineering manager is in the design, development

and implementation of employee development programs. It is clear from the data that a

mentoring program can have a real impact on the tendency of an individual to display

transformational leadership behaviors. It appears that this common development engine

can be an effective tool, especially if meetings are held regularly and the mentor strives to

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provide constructive feedback. Additionally, formal development programs, such as

360-degree surveys, can be an effective tool to develop certain transformational

leadership characteristics, such as Intellectual Stimulation and Individualized

Consideration.

6.4 Limitations of the Study

The study has limitations that restrict the overall applicability of its findings.

First, the study was completed using a target population of leaders in small and growing

companies. Since this was a focused population, the findings cannot be generalized to

apply to leaders within other types of organizations (including large corporations,

military or academics), especially since the types of organizations included in the first

group of participants were unique (Gazelles 2009), and little is known about the

organizations of the second group of participants.

Second, individual respondents’ organizations are unknown, making it impossible

to investigate potential confounding variables (e.g., organizational culture), on the types

of leadership behaviors displayed. Other potential influences, such as peer groups or peer

behaviors cannot be investigated.

Third, the study utilized only the manager’s self reported information on their

leadership style. It is possible that the leader’s self image is vastly different than the

perception of their employees, peers and leaders. If this difference did exist, it may be

found that there are different items which correlate with the leadership behaviors reported

by others, or that the strength or direction of the correlations would change.

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Fourth, while the analysis of the LLI instrument did reject null Hypothesis I, it is

still an instrument in development. The final version of the LLI has strong psychometric

properties and produced strong results using the SEM between the LLI and MLQ,

however, its refinement caused a number of elements to be dropped from the original

hypothesized pillars. It is possible that additional study would identify a way that other

questions related to these hypothesized areas could be effectively included in the LLI

while maintaining its psychometric properties. This would improve the usefulness of the

LLI by increasing its ability to measure a broad spectrum of leadership development

experiences.

Finally, the data set utilized for the study is relatively small. While it is was

sufficient to generate meaningfully significant statistical tests, and is in line with other

similar studies (e.g., Roper 2009, Bell-Roundtree 2004, Blatt 2002), it lacks an

exhaustive investigation of multiple populations, like that included in the comparison

MLQ population (Avolio and Bass 2004). These limitations generally hamper the ability

of the findings to be interpreted broadly.

6.5 Areas for Future Research

The study has demonstrated that the development experiences can be grouped into

explainable factors and that those factors can be significantly correlated with different

types of leadership behaviors. While doing this, it has raised additional questions. The

recommended areas for future study are as follows:

• Refinement of the LLI: The MLQ, initially developed and deployed nearly

twenty five years ago, has been utilized in dozens and dozens of studies and

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106

revised no fewer than five times (Avolio and Bass 2004). The LLI appears to

have merit that could give it a robust pedigree over time, but additional

refinement is needed through exposure to greater populations and

experimentation with additional questions.

• Exploration of a Relationship Between the LLI and MLQ for Other Populations

(or a larger population): It is expected that significant relationships could be

found between the LLI and MLQ with other populations including military and

large corporate entities. It is also reasonable to believe that the value of the study

could be increased with a larger sample.

• Further Exploration of the Components of Transformational Leadership within

the Study Population: Specifically, why does Contingent Reward leadership

appear to behave more like a transformational leadership component in terms of

its antecedents than its previously demonstrated grouping (e.g., Avolio and Bass

2004) as part of the transactional leadership behaviors.

• Utilization of 360-degree Results for a Relationship between the LLI and MLQ:

As mentioned in the limitations, there is potential that the perception of others

than the leader’s regarding the leader’s leadership behaviors may yield different

results.

• Exploration of the MLQ Leadership Outcomes Factors – Form5x-Short of the

MLQ includes nine questions that purport to rate the success of the group being

led by the participant (Avolio and Bass 2004). Since these questions are not well

documented in the literature, they were not included in the correlation study but

should be explored in the future.

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APPENDICES

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APPENDIX A

MULTIFACTOR LEADERSHIP QUESTIONAIRE

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109

For use by William Schell only. Received from Mind Garden, Inc. on August 19, 2009

MLQ, © 1995 Bruce Avolio and Bernard Bass. All Rights Reserved.Published by Mind Garden, Inc., www.mindgarden.com

www.mindgarden.com

To whom it may concern,

This letter is to grant permission for the above named person to use the following copyrightmaterial;

Instrument: Multifactor Leadership Questionnaire

Authors: Bruce Avolio and Bernard Bass

Copyright: 1995 by Bruce Avolio and Bernard Bass

for his/her thesis research.

Five sample items from this instrument may be reproduced for inclusion in a proposal, thesis, ordissertation.

The entire instrument may not be included or reproduced at any time in any other publishedmaterial.

Sincerely,

Robert MostMind Garden, Inc.www.mindgarden.com

Page 123: What Makes a Transformational Leaders

For use by William Schell only. Received from Mind Garden, Inc. on August 19, 2009

MLQ, © 1995 Bruce Avolio and Bernard Bass. All Rights Reserved.Published by Mind Garden, Inc., www.mindgarden.com

MLQ Multifactor Leadership QuestionnaireLeader Form (5x-Short)

My Name: ______________________________________________________________ Date: ______________

Organization ID #: _____________________________ Leader ID #: __________________________________

This questionnaire is to describe your leadership style as you perceive it. Please answer all items on this answersheet. If an item is irrelevant, or if you are unsure or do not know the answer, leave the answer blank.

Forty-five descriptive statements are listed on the following pages. Judge how frequently each statement fits you.The word “others” may mean your peers, clients, direct reports, supervisors, and/or all of these individuals.

Use the following rating scale:

Not at all Once in a while Sometimes Fairly often Frequently,if not always

0 1 2 3 4

1. I provide others with assistance in exchange for their efforts..............................................................0 1 2 3 4

2. I re-examine critical assumptions to question whether they are appropriate .......................................0 1 2 3 4

3. I fail to interfere until problems become serious .................................................................................0 1 2 3 4

4. I focus attention on irregularities, mistakes, exceptions, and deviations from standards ....................0 1 2 3 4

5. I avoid getting involved when important issues arise ..........................................................................0 1 2 3 4

6.

7.

8.

9.

10.

11.

12.

13.

Continued =>

110

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APPENDIX B

INITIAL LIFETIME LEADERSHIP INVENTORY SAMPLE QUESTIONS

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112

Full Question PillarQuestion 1: My father provided an environment that supported growth and learning. 1

Question 1: My mother provided an environment that supported growth and learning. 1Question 1: My father tried to control many aspects of my life. 1Question 2: My father served as a role model for highly ethical behavior. 1Question 2: My mother served as a role model for highly ethical behavior. 1Question 3: I lost a parent during childhood (prior to high school / grade 9) 1Question 4: During my career I have developed formal relationships with a mentor(s) to support my development. 1Question 4: I often seek constructive feedback from a mentor. 1Question 4: At times, I have met regularly with a formal or informal mentor. 1

Question 5: Early in my career, I had a strong role model of the leader I wanted to be. 1Question 6: As a child, I was an officer in student government. 2Question 7: In high school, I was active in sports 2Question 7: In high school, I was involved in non competitive sports. 2Question 8: In college I held leadership roles in a Greek organization (fraternity or sorority). 2Question 8: In college, I was active in varsity athletics. 2Question 8: In college, I served in a leadership position for my dormitory / residence life organization. 2Question 9: In college, I typically was employed during the school year. 4Question 10: During childhood or adolescence, I lived in a country other than my current country. 3Question 10: During childhood or adolescence, I learned to speak more than one language. 3Question 10: In college I held leadership roles in a Greek organization (fraternity or sorority). 3Question 11: During childhood, I could walk to natural settings (woods, ponds, etc.). 3Question 11: During childhood, I often spent play time in natural areas (woods, ponds, etc.). 3Question 11: In college, I took trips to natural settings (National Forests, Parks, etc.) 3Question 11: I spend my leisure time engaged in activities I really look forward to. 3Question 11: My recreational activities are very active. 3Question 11: When I travel, I tend to do more than simply see the sights. 3Question 12: I have been incarcerated. 3Question 12: I served full time in a service organization (e.g. Peace Corps, AmeriCorps, etc.). 3Question 12: I've lived through a natural disaster (earthquake, hurricane, etc.) that devastated the home of me or my family 3Question 14: I held my first regular job while still a teenager. 4Question 14: I served in leadership positions in the military. 4Question 14: In my career, I have led a turn-around effort. 4Question 15: I have received substantial training through my employers on becoming an effective leader. 5Question 15: During my career I have participated in 360-degree surveys. 5Question 15: The majority of my career positions involved a great deal of learning. 5

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APPENDIX C

REFINED LIFETIME LEADERSHIP INVENTORY SAMPLE QUESTIONS

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114

Question Full Question PillarP_Env Question 1: My parents provided an environment that supported growth and learning. 1

P_Cont Question 1: My parents tried to control many aspects of my life. 1P_Advice Question 1: I often seek the advice of my parents. 1M_Form Question 4: During my career I have developed formal relationships with a mentor(s) to

support my development.1

C_SG Question 8: In college I held leadership roles in student government. 2C_Grk Question 8: In college I held leadership roles in a Greek organization (fraternity or

sorority).2

C_VSpt Question 8: In college, I was active in varsity athletics. 2C_ResL Question 8: In college, I served in a leadership position for my dormitory / residence life

organization.2

C_Pol Question 8: In college, I was active in political groups. 2Cu_Ocul Question 10: During childhood or adolescence, I lived in a culture other than my current

culture.3

RA_VacN Question 11: During childhood, my family spent vacation time in natural settings. 3RA_CRA Question 11: In college, I spent free time participating in outdoor recreation. 3Wk_LOld Question 14: In my career, it has been common for me to lead people older than myself. 4

D_Rot Question 15: My career experience included a deliberate rotation through multiple job assignments with a single company / organization.

5

D_360 Question 15: I have received beneficial feedback through 360-degree feedback. 5D_Org Question 15: I am or have been active in professional organizations focused on

leadership development.5

D_Func Question 15: I have held positions in a variety of functional areas in my career (e.g. finance, operations, etc.)

4

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APPENDIX D

MIND GARDEN PERMISSIONS

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116

MLQ, © 1995 Bruce Avolio and Bernard Bass. All Rights Reserved.Published by Mind Garden, Inc., www.mindgarden.com

www.mindgarden.com

To whom it may concern,

This letter is to grant permission for the above named person to use the following copyrightmaterial;

Instrument: Multifactor Leadership Questionnaire

Authors: Bruce Avolio and Bernard Bass

Copyright: 1995 by Bruce Avolio and Bernard Bass

for his/her thesis research.

Five sample items from this instrument may be reproduced for inclusion in a proposal, thesis, ordissertation.

The entire instrument may not be included or reproduced at any time in any other publishedmaterial.

Sincerely,

Robert MostMind Garden, Inc.www.mindgarden.com

For use by William Schell only. Received from Mind Garden, Inc. on September 28, 2009

Page 130: What Makes a Transformational Leaders

117

For use by William Schell only. Received from Mind Garden, Inc. on September 28, 2009

Permission for William Schell to reproduce 200 copies within oneyear of September 28, 2009

Multifactor Leadership QuestionnaireLeader Form, Rater Form and Scoring Key

(Form 5X-Short)

by Bruce Avolio and Bernard Bass

Distributed by Mind Garden, Inc.

[email protected]

Copyright © 1995 Bruce Avolio and Bernard Bass. All Rights Reserved. It is your legalresponsibility to compensate the copyright holder of this work for any reproduction in anymedium. The copyright holder has agreed to grant one person permission to reproduce thespecified number of copies of this work for one year from the date of purchase for non-commercial and personal use only. Non-commercial use means that you will not receive paymentfor distributing this document and personal use means that you will only reproduce this work foryour own research or for clients. This permission is granted to one person only. Each personwho administers the test must purchase permission separately. Any organization purchasingpermissions must purchase separate permissions for each individual who will be using oradministering the test. Mind Garden is a trademark of Mind Garden, Inc.

Page 131: What Makes a Transformational Leaders

118

For use by William Schell only. Received from Mind Garden, Inc. on August 19, 2009

MLQ, © 1995 Bruce Avolio and Bernard Bass. All Rights Reserved.Published by Mind Garden, Inc., www.mindgarden.com

www.mindgarden.com

To whom it may concern,

This letter is to grant permission for the above named person to use the following copyrightmaterial;

Instrument: Multifactor Leadership Questionnaire

Authors: Bruce Avolio and Bernard Bass

Copyright: 1995 by Bruce Avolio and Bernard Bass

for his/her thesis research.

Five sample items from this instrument may be reproduced for inclusion in a proposal, thesis, ordissertation.

The entire instrument may not be included or reproduced at any time in any other publishedmaterial.

Sincerely,

Robert MostMind Garden, Inc.www.mindgarden.com

Page 132: What Makes a Transformational Leaders

119

For use by William Schell only. Received from Mind Garden, Inc. on August 19, 2009

Permission for William Schell to reproduce 100 copies within oneyear of August 19, 2009

Multifactor Leadership QuestionnaireLeader Form, Rater Form and Scoring Key

(Form 5X-Short)

by Bruce Avolio and Bernard Bass

Distributed by Mind Garden, Inc.

[email protected]

Copyright © 1995 Bruce Avolio and Bernard Bass. All Rights Reserved. It is your legalresponsibility to compensate the copyright holder of this work for any reproduction in anymedium. The copyright holder has agreed to grant one person permission to reproduce thespecified number of copies of this work for one year from the date of purchase for non-commercial and personal use only. Non-commercial use means that you will not receive paymentfor distributing this document and personal use means that you will only reproduce this work foryour own research or for clients. This permission is granted to one person only. Each personwho administers the test must purchase permission separately. Any organization purchasingpermissions must purchase separate permissions for each individual who will be using oradministering the test. Mind Garden is a trademark of Mind Garden, Inc.

Page 133: What Makes a Transformational Leaders

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APPENDIX E

GAZELLE’S PARTICIPANT INVITATIONS

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Bill Schell

From: [email protected] on behalf of Verne's Insights [[email protected]]Sent: Thursday, November 12, 2009 1:58 PMTo: [email protected]: CEO of the Decade; Message Timing; Idea Paint; Three Advantages"...keeping you great" (Print-friendly Version >) HEADLINES: Steve Jobs Named CEO of the Decade -- let me take a pause from the Growth Summit highlights and pay kudos to Steve Jobs, named CEO of the Decade by Fortune magazine last week. Why? He's transformed four industries - computers in the 80s; music, movies, and mobile phones this decade. And Apple's market cap started the decade at $5 billion; today it's $170 billion, slightly bigger than Google. Take five minutes and read this awe-inspiring article and click through previously unseen photos of Steve. And you can see the list of the other 12 runners-up for CEO of the Decade along with several other fun "galleries." Steve Jobs Turnaround -- two key points that dovetail on Jim Collins' presentation at the Growth Summit. When Steve took over Apple he did three things: shore up cash by swallowing his pride and getting help from Microsoft; dramatically pruned Apple back to a handful of products so his team could focus; and replaced the top management team which has served as the nucleus of what Fortune calls "Jobs brain trust for the next ten years." Again, take five minutes and read the Steve Jobs story once again and take lessons from this business grandmaster. Message Timing -- one thing admired about Steve is not just the precision of his messaging, but the precise timing of his messaging. That's why I'm especially excited about next week's video webcast. I had my call with Dr. Robert Cialdini yesterday to discuss his presentation next Tuesday (noon -- 1:30pm ET). He left academe in the spring and is devoting full time to writing a brand new book -- not on the content of the message (the essence of his first book) but on the TIMING of the message (the essence of his next book) -- as he mentioned to me "everyone talks about how timing is everything, but no one has ever helped people figure this out." Every leader talks about getting the timing right, but are there definitive research studies and guidelines that will help a leader get it right? In fact, there are. And Dr. Cialdini will spend 15 minutes giving us a glimpse into the new research. Just trying to keep you on the bleeding edge! Three Advantages Over Competition -- Dr. Cialdini also shared with me that three of the six principles of influence, which he'll cover next Tuesday, are especially effective in a crisis when customers are putting off the buying decision. Companies that leverage these three principles to their fullest will continue to have a huge leg up on those that don't. He'll be emphasizing these three and how you apply them next Tuesday during his 90 minutes LIVE video webcast. Idea Paint -- Steve Jobs is also famous for his ideas -- and there's no better way to express those ideas than writing all over the walls, something I saw at 3M's Innovation Center in Austin. To make this easier, check out this product called Idea Paint -- you apply it to any wall and you have an instant white board -- especially fun for painting curved surfaces. Thanks to Bob Hubbard, CEO of Arizona-based Hubbard Family Swim Schools for bringing this to my attention. Are You the Next Leader of the Decade -- and to gauge the leadership effectiveness of Gazelles insight readers (anyone with people reporting to you), we're assisting Bill Schell, VP of Strategy for Printing4Less, with his PhD thesis on leadership. He's particularly looking at leadership traits of executives and managers of growth firms as a tool for helping us choose the kinds of teams that can be our "brain trust for the next decade." Those that take the survey will get to share in the results -- here's a link, takes about 10 minutes. Again, it's open to ALL insight readers if you have people reporting to you. Five Fold Growth in Five Years -- on the eve of hitting $100 million in revenue and celebrating their 2000th associate, I received this note this week from Ajay Prabhu, COO of QuEST Global, whose firm

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Bill Schell

From: [email protected] on behalf of Verne's Insights [[email protected]]Sent: Friday, September 18, 2009 10:35 AMTo: [email protected]: Living Longer; Leading Better; Giving Easier; Sept 21 Hotel Deadline"...keeping you great" HEADLINES: (Print-friendly Version >)

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APPENDIX F

LLI CORRELATION ANALYSIS

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1 - Positive View of Parents Appears to cluster with questions that put a positive view of the participants parents

• Contains 18 significant

pairings (alpha 0.01) • Contains 9 pairings where

person correlation is > 0.5

Data appears to show us that several questions could be eliminated with little loss of data. This is unexpected based on other research

Cluster 2 – Strong Leaders This cluster brings together 3 questions relating to strong life influences (controlling mother, strong role models) and an unexpected relationship with making sacrifices to achieve goals.

Of note is that the significant correlations are between:

• A controlling mother and seeking advice from the same as well as working for a leader who understood the participant

• Strong role model questions

No significant correlation exists in the unexpected relationship 4 pairings with significant correlation 0 pairings with correlation value >0.5

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3 – Mentors

• 20 significant pairings • 6 pairings > 0.5

Cluster 4 – Activities

• 28 significant pairings • 9 pairings correlation >

0.5

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Cluster 5 – Natural Settings + Challenges

• Surprise correlation with

participation in a church group and natural settings

• 9 significant pairings • 3 pairings correlation >

0.5

Cluster 6 – High School Activities

• 21 significant pairings • 12 pairings correlation >

0.5

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Cluster 7 – College Service Roles and Recreation

• 31 significant pairings • 8 pairings correlation >

0.5

Cluster 8 – Greek Activities

• Included mistaken duplicate question (10-3)

• Appears that eliminating 8-2 would cause minimal data loss

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Cluster 9 – College Employment, Cultural

Exploration and Learning “Bent”

• Most diverse grouping of all

the clusters • 7 significant pairings • 5 pairings correlation >

0.5

Cluster 10 – Military Experience

• 10 significant pairings • 8 pairings correlation >

0.5

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11 – Career Development

• 6 significant pairings • 1 pairing correlation > 0.5

9

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APPENDIX G

FACTOR ANALYSIS OF ALTERNATIVE LLI MODEL

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G.1 Exploratory Analysis of the LLI

The search for an improved explanatory model for the LLI structures began with a

cluster analysis using Ward’s linkage method (Johnson and Wichern 2002), similar to the

analysis completed during the pilot study phase on the instrument. In this instance, the

number of clusters were not specified. Instead, Minitab was utilized to specify the final

partition based on a similarity level of 0.70. This threshold was selected for its similarity

with the threshold value that indicate good reliability when utilizing Cronbach’s alpha in

an exploratory factor analysis (George and Mallery 2003). Using this threshold found a

complete model with 6 clusters. The dendrogram generated from this analysis is shown

in Figure G.1. Comparing the generated clusters against the hypothesized pillars

analyzed above found a number of differences.

Figure G.1 - Dendrogram from Cluster Analysis of Full LLI Data Using Ward Linkage

M_M

eet

M_F

dBk

M_A

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D_F

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-46.02

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Variables

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ty

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Based on the prior analysis, it was expected that the sixth cluster would represent

a split between the parent and mentor / leader related questions included in Pillar 1. This

expectation was only partially met as the tightest cluster, similarity of 75.8, was related to

mentors and consisted of questions, however, solely from Pillar 1. Parent related

questions were split between two other clusters. The first contains only parent related

questions from Pillar 1 and has a similarity of 57.0. All remaining clusters have

substantially lower similarities and are generated from questions from multiple pillars.

By descending similarity, the remaining clusters can be defined as

• Work as development, similarity 35.3 - a mix of questions from Pillars 1, 4 and 5

• A not understood collection of questions from Pillars 1, 2, and 3, similarity 27.22

• Work experience and travel, similarity of 23.5 – 50% of questions from Pillar 3

and 33% of questions from Pillar 4

• A collection of seemingly related high school and college experiences with the

addition of outdoor related experiences, similarity 8.35, 80% of questions from

Pillar 2.

While the results did indicate different groups than those hypothesized, since only

a single cluster in the analysis was created with a similarity greater than 70.0, the results

of the cluster analysis did not indicate a stronger model than the hypothesized model

explored previously. Further exploratory analysis was then completed using factor

analysis in Minitab, thereby avoiding the common pitfall of Confirmatory Factor

Analysis completed using Structured Equation Modeling where researchers interpret the

finding that when “their model fits the data it is the only model that can do so” (Jöreskog

1993, 298).

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Using the factor analysis tools in Minitab 15, the data in the LLI was examined

using a variety of techniques. Beginning with a baseline, un-rotated analysis (Johnson

and Wichern 2002) and allowing Minitab 15 to extract a large number of factors, the

scree plot in Figure G.2 was generated. Using the Kaiser criterion, where an acceptable

factor having an Eigen value greater than 1.0 (Stevens 2002), the analysis indicated that

the first 18 factors could be recommended for use. The selection of 18 factors would

keep the Q/P ratio close to being under the 0.3 target at 0.31 (Hakstian, et al. 1982).

However, since the number of variables in the study is greater than 30, a graphical

method of analysis using the scree plot is recommended (Stevens 2002). The scree plot is

used in an attempt to identify an inflection point that could limit the factors (Johnson and

Wichern 2002). Since the most obvious inflection point occurs at factor 2, where less

than 20% of the variance is explained, the next inflection point, factor 6 was selected as

the cut-off. By including six factors in the model, 38.5% of the variance is explained.

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Figure G.2 - Scree Plot of Exploratory Factor Analysis on the LLI

Subsequent analysis using a variety of rotation techniques found that both the

equamax and varimax rotations (Johnson and Wichern 2002) provided the cleanest factor

loadings. The results of these six factor models included 43 significant loadings, greater

than 0.4 (Hair, et al. 1998), with only a single question that loaded significantly on more

than one factor. Both models also had fifteen questions that failed to load significantly

on any of the six factors, 26% of the total question set. The complete table of factor

loadings from the varimax rotation is displayed in Table G.1.

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Table G.1 - Varimax Factor Loadings from LLI Exploratory Factor Analysis

Question Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6P_Env -0.03 -0.02 0.07 -0.14 -0.69 0.13P_Cont 0.05 -0.06 -0.08 0.05 -0.06 -0.17P_Advice 0.13 0.01 -0.04 0.07 -0.70 0.09P_Share 0.14 0.07 -0.01 -0.09 -0.67 0.05P_MoD -0.04 -0.11 0.08 0.07 0.12 -0.01P_ActCom -0.09 0.05 -0.10 -0.18 -0.55 -0.07P_Eth -0.11 -0.03 -0.07 -0.10 -0.67 -0.03P_LstNum -0.16 -0.16 -0.07 -0.19 0.23 0.09M_Form 0.82 -0.13 -0.08 -0.09 0.02 0.00M_LdGd 0.84 -0.13 -0.13 -0.11 -0.01 -0.04M_Adv 0.80 -0.12 -0.12 -0.09 -0.09 0.05M_FdBk 0.84 -0.13 -0.12 -0.15 -0.03 0.06M_Meet 0.83 -0.08 -0.06 -0.08 0.05 0.10L_RM 0.36 -0.55 -0.03 -0.09 -0.08 -0.11L_DRM 0.16 -0.60 0.03 0.00 -0.10 -0.29L_Und 0.29 -0.63 -0.05 0.00 -0.09 -0.24Ch_CG 0.01 0.14 0.21 0.13 0.34 0.01HS_Aspt 0.09 -0.12 -0.05 0.09 -0.07 0.71HS_ASG 0.03 -0.14 -0.50 -0.03 -0.25 0.30HS_AIG -0.09 -0.18 -0.54 -0.09 -0.31 0.11HS_SCpt 0.06 -0.11 -0.14 0.19 -0.10 0.72HS_Hlp 0.04 -0.16 -0.43 -0.11 -0.47 0.25HS_NCSp 0.04 -0.12 -0.32 -0.12 -0.09 0.29C_SG 0.07 -0.13 -0.72 0.03 0.03 -0.06C_Grk -0.03 -0.04 -0.36 0.13 -0.17 0.08C_VSpt 0.01 0.00 -0.05 0.01 0.08 0.61C_ResL 0.05 -0.18 -0.41 -0.06 -0.06 0.04C_Pol 0.08 -0.12 -0.71 -0.09 -0.04 -0.10C_NCSp 0.01 -0.08 -0.27 -0.22 -0.10 0.46CE_Emp 0.00 -0.31 0.06 -0.36 0.08 -0.18CE_Mgmt -0.07 -0.39 -0.18 -0.23 0.09 -0.12Cu_Ocnt 0.14 0.15 -0.34 -0.19 0.13 -0.06Cu_Ocul 0.03 0.16 -0.48 -0.23 0.28 0.00Cu_Live 0.02 -0.02 -0.29 -0.56 -0.12 -0.24Cu_TrvE -0.09 -0.03 -0.24 -0.58 -0.01 0.03Cu_TrvC -0.04 0.10 -0.19 -0.57 -0.09 0.00RA_NatS 0.10 -0.13 -0.04 -0.27 -0.08 0.07RA_VacN 0.13 0.03 0.12 -0.44 -0.13 0.01RA_CRA 0.13 -0.07 0.08 -0.43 -0.22 0.39RA_Lfun 0.26 0.01 0.26 -0.51 -0.09 0.27RA_Tadv 0.17 -0.09 -0.08 -0.67 0.05 0.10RA_ExpE 0.05 -0.18 -0.10 -0.58 -0.04 -0.03EE_Inc -0.03 0.09 -0.06 -0.11 0.34 0.21EE_SO 0.10 0.02 -0.47 -0.13 0.02 0.05EE_ND 0.11 -0.02 -0.45 -0.04 0.16 0.08B_Sac 0.20 -0.24 -0.13 -0.21 0.27 -0.01B_Lrn 0.15 -0.25 -0.19 -0.41 0.12 -0.01Wk_Teen 0.22 -0.26 0.14 -0.15 -0.01 -0.13Wk_Lmil 0.02 -0.12 -0.30 -0.11 0.38 0.03Wk_LEar -0.08 -0.54 -0.30 -0.14 -0.04 0.15Wk_LOld 0.09 -0.43 -0.13 -0.17 0.06 0.21D_Rot -0.04 -0.60 -0.09 -0.04 0.07 0.28D_Train 0.05 -0.64 -0.09 0.04 -0.03 0.16D_360 0.23 -0.45 -0.09 -0.09 -0.06 0.09D_Org 0.26 -0.32 -0.10 -0.14 0.03 0.25D_Func -0.01 -0.51 0.00 -0.07 0.09 0.37D_PLrn 0.14 -0.34 -0.05 -0.46 0.01 0.01

5 9 9 10 6 4Number of Significant Loadings in Factor

Factor Loading Scores

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The significant questions in each factor were then examined using Minitab’s Item

Analysis, as was completed in the confirmatory factor analysis steps above. The

six factors identified by this analysis can be defined as

• Factor 1 – Relationship with Mentors, five questions, Cronbach’s alpha = 0.92

• Factor 2 – Influence of early leaders, early leadership experience and experience

with formal development experiences, nine questions, Cronbach’s Alpha = 0.79

• Factor 3 – Participation in service organizations throughout life and exposure to

other cultures, nine questions, Cronbach’s Alpha = 0.69

• Factor 4 – subset of Pillar 3 focused on exposure to and hunger for new

experiences, 10 questions, Cronbach’s Alpha = 0.72

• Factor 5 – Respect for parents and early service experience, six questions,

Cronbach’s Alpha = 0.76

• Factor 6 – involvement in sports, four questions, Cronbach’s Alpha = 0.69

The average internal consistency within the pillars, as measured by Cronbach’s alpha, is

0.76 for the six factors versus 0.69 for the hypothetical factors, a ten percent

improvement. Because of this increase in internal consistency and the 26% reduction in

LLI questions it enables, comparisons between the LLI and MLQ will be completed

using both LLI factor sets.

G.2 Correlation Analysis Between LLI Factors and the 2 Factor MLQ

In the second comparison, the LLI factors identified through exploratory factor

analysis were compared with the factors of the MLQ. This comparison found five of the

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six factors to be significantly (ά = 0.05) correlated with the transformational factor and

two of the five significantly correlated with the transactional factor. The strongest

relationship was found between Factor 4, a subset of Pillar 3, and transformational

leadership. Table 5.16 contains the full output of this analysis. Due to the few

correlations that were not significantly related, the comparisons between the LLI and the

two factor model do not provide much differentiation between the correlated

development experiences of transformational leadership behaviors and transactional

behaviors.

Table G.2 – Correlation Coefficients for LLI Exploratory Factors & Two Factor MLQ

Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6Transform 0.247 0.167 0.227 0.254 0.115 0.157

Transact 0.132 0.178 0.198 0.040 -0.080 0.005

Bold Significant, α = 0.05

Bold Significant, α = 0.01

G.3 Correlation Analysis Between LLI Factors and the Nine Factor MLQ

In the next analysis, the LLI factors discovered through exploratory analysis are

compared with the nine factor MLQ model. While the relationships between these

factors and the transformational components of the MLQ are again strong, they are not as

strong as the Pillar factors, with ten of the thirty comparisons not having a significant

relationship and an average correlation of 0.170. There is also a difference in the

correlation these factors have with the transactional components. Once again all eighteen

of the comparisons with all transactional components, except contingent reward are not

significant; however, with these factors, only 66% of the correlations with contingent

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reward are significant. Factor 5, respect for parents, and Factor 6, involvement in sports,

are not significantly correlated. Table 5.18 contains the full results of these comparisons.

Table G.3 – Correlation Coefficients for LLI Exploratory Factors & Nine Factor MLQ

Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6

IIA 0.167 0.072 0.163 0.132 0.092 0.141IIB 0.304 0.120 0.182 0.263 0.108 0.104IM 0.189 0.114 0.235 0.225 0.075 0.157IS 0.166 0.216 0.208 0.227 0.130 0.166IC 0.249 0.215 0.206 0.262 0.098 0.125

CR 0.250 0.214 0.232 0.157 0.019 0.079MBEA 0.007 0.108 0.051 0.002 -0.090 0.015MBEP -0.038 -0.023 0.053 -0.099 -0.070 -0.092

LF 0.005 -0.046 0.005 0.046 0.010 -0.124

Bold Significant, α = 0.05

Bold Significant, α = 0.01

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APPENDIX H

STRUCTURED EQUATION MODEL OUTPUT

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DATE: 2/15/2010 TIME: 12:45 L I S R E L 8.80 BY Karl G. J”reskog & Dag S”rbom This program is published exclusively by Scientific Software International, Inc. 7383 N. Lincoln Avenue, Suite 100 Lincolnwood, IL 60712, U.S.A. Phone: (800)247-6113, (847)675-0720, Fax: (847)675-2140 Copyright by Scientific Software International, Inc., 1981-2006 Use of this program is subject to the terms specified in the Universal Copyright Convention. Website: www.ssicentral.com The following lines were read from file C:\Lisrel_Dis\Try3\sem_mlq_lli_explicit.SPJ : Raw Data from file 'C:\Lisrel_Dis\Try3\CFA_Attempt2.psf' -------------------------------- EM Algorithm for missing Data: -------------------------------- Number of different missing-value patterns= 23 Convergence of EM-algorithm in 10 iterations -2 Ln(L) = 31287.78582 Percentage missing values= 0.41 Note: The Covariances and/or Means to be analyzed are estimated by the EM procedure and are only used to obtain starting values for the FIML procedure Latent Variables Pillar1 Pillar2 Pillar3 Pillar4 Pillar5 iia iib im is ic cr mbea mbep lf Relationships M_FORM = Pillar1 M_LDGD = Pillar1

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M_ADV = Pillar1 M_FDBK = Pillar1 M_MEET = Pillar1 L_RM = Pillar1 L_UND = Pillar1 CH_CG = Pillar2 HS_ASG = Pillar2 HS_AIG = Pillar2 HS_SCPT = Pillar2 HS_HLP = Pillar2 HS_NCSP = Pillar2 C_SG = Pillar2 C_RESL = Pillar2 C_POL = Pillar2 C_NCSP = Pillar2 CU_OCUL = Pillar3 CU_LIVE = Pillar3 CU_TRVE = Pillar3 CU_TRVC = Pillar3 CE_MGMT = Pillar4 WK_LEAR = Pillar4 WK_LOLD = Pillar4 D_FUNC = Pillar4 D_ROT = Pillar5 D_TRAIN = Pillar5 D_360 = Pillar5 D_ORG = Pillar5 MLQ_10 = iia MLQ_18 = iia MLQ_21 = iia MLQ_25 = iia MLQ_6 = iib MLQ_14 = iib MLQ_23 = iib MLQ_34 = iib MLQ_9 = im MLQ_13 = im MLQ_26 = im MLQ_36 = im MLQ_2 = is MLQ_8 = is MLQ_30 = is MLQ_32 = is MLQ_15 = ic MLQ_19 = ic MLQ_29 = ic

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MLQ_31 = ic MLQ_1 = cr MLQ_11 = cr MLQ_16 = cr MLQ_35 = cr MLQ_4 = mbea MLQ_22 = mbea MLQ_24 = mbea MLQ_27 = mbea MLQ_3 = mbep MLQ_12 = mbep MLQ_17 = mbep MLQ_20 = mbep MLQ_5 = lf MLQ_7 = lf MLQ_28 = lf MLQ_33 = lf iia = Pillar1 Pillar2 Pillar3 Pillar4 Pillar5 iib = Pillar1 Pillar2 Pillar3 Pillar4 Pillar5 im = Pillar1 Pillar2 Pillar3 Pillar4 Pillar5 is = Pillar1 Pillar2 Pillar3 Pillar4 Pillar5 ic = Pillar1 Pillar2 Pillar3 Pillar4 Pillar5 cr = Pillar1 Pillar2 Pillar3 Pillar4 Pillar5 mbea = Pillar1 Pillar2 Pillar3 Pillar4 Pillar5 mbep = Pillar1 Pillar2 Pillar3 Pillar4 Pillar5 lf = Pillar1 Pillar2 Pillar3 Pillar4 Pillar5 Path Diagram Iterations = 25000 End of Problem Sample Size = 205 Number of Iterations = 50 LISREL Estimates (Maximum Likelihood) Measurement Equations M_FORM = 0.49*cr, Errorvar.= 0.78 , Rý = 0.25 (0.079) 9.90 M_LDGD = 0.54*is, Errorvar.= 0.58 , Rý = 0.34

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(0.061) 9.36 M_ADV = 0.65*mbep, Errorvar.= 0.62, Rý = 0.40 M_FDBK = 0.55*mbea, Errorvar.= 0.72 , Rý = 0.29 (0.083) 8.67 M_MEET = 0.61*lf, Errorvar.= 0.62 , Rý = 0.37 (0.076) 8.16 L_RM = 0.72*iib, Errorvar.= 0.75 , Rý = 0.41 (0.079) 9.49 L_UND = 0.71*lf, Errorvar.= 0.75 , Rý = 0.40 (0.11) (0.096) 6.47 7.89 CH_CG = 0.67*is, Errorvar.= 0.39 , Rý = 0.55 (0.080) (0.046) 8.31 8.39 HS_ASG = 0.70*im, Errorvar.= 0.45 , Rý = 0.52 (0.049) 9.35 HS_AIG = 0.60*iia, Errorvar.= 0.55 , Rý = 0.40 (0.059) 9.41 HS_SCPT = 0.65*cr, Errorvar.= 0.50 , Rý = 0.47 (0.095) (0.053) 6.86 9.47 HS_HLP = 0.65*mbep, Errorvar.= 0.27 , Rý = 0.61 (0.084) (0.054) 7.73 4.97 HS_NCSP = 0.67*im, Errorvar.= 0.21 , Rý = 0.68 (0.044) (0.025) 15.20 8.54 C_SG = 0.76*iib, Errorvar.= 0.35 , Rý = 0.63

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(0.080) (0.041) 9.51 8.48 C_RESL = 0.73*ic, Errorvar.= 0.54 , Rý = 0.50 (0.058) 9.27 C_POL = 0.64*cr, Errorvar.= 0.66 , Rý = 0.39 (0.098) (0.068) 6.54 9.68 C_NCSP = 0.43*mbep, Errorvar.= 1.06 , Rý = 0.15 (0.094) (0.11) 4.55 9.48 CE_MGMT = 0.62*iia, Errorvar.= 0.29 , Rý = 0.57 (0.068) (0.034) 9.01 8.75 CU_OCUL = 0.61*ic, Errorvar.= 0.32 , Rý = 0.54 (0.060) (0.035) 10.06 9.11 CU_LIVE = 0.56*mbep, Errorvar.= 0.57 , Rý = 0.35 (0.084) (0.071) 6.67 8.07 CU_TRVE = 0.70*iia, Errorvar.= 0.23 , Rý = 0.68 (0.073) (0.030) 9.63 7.75 CU_TRVC = 0.76*mbea, Errorvar.= 0.65 , Rý = 0.46 (0.12) (0.097) 6.12 6.66 WK_LEAR = 0.63*iib, Errorvar.= 0.42 , Rý = 0.49 (0.073) (0.045) 8.62 9.23 WK_LOLD = 0.43*mbea, Errorvar.= 0.92 , Rý = 0.16 (0.097) (0.097) 4.39 9.45 D_ROT = 0.53*iia, Errorvar.= 0.59 , Rý = 0.33 (0.074) (0.062) 7.24 9.64

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D_TRAIN = 0.76*im, Errorvar.= 0.31 , Rý = 0.65 (0.036) 8.75 D_360 = 0.79*mbea, Errorvar.= 0.33 , Rý = 0.65 (0.13) (0.084) 6.03 3.93 D_ORG = 0.67*lf, Errorvar.= 0.41 , Rý = 0.52 (0.097) (0.063) 6.88 6.55 D_FUNC = 0.63*ic, Errorvar.= 0.47 , Rý = 0.46 (0.068) (0.050) 9.27 9.42 MLQ_1 = 0.56*is, Errorvar.= 0.30 , Rý = 0.52 (0.068) (0.035) 8.17 8.57 MLQ_2 = 0.66*ic, Errorvar.= 0.21 , Rý = 0.68 (0.059) (0.026) 11.22 8.13 MLQ_3 = 0.65*is, Errorvar.= 0.26 , Rý = 0.63 (0.074) (0.033) 8.74 7.65 MLQ_4 = 0.43*lf, Errorvar.= 0.76 , Rý = 0.19 (0.086) (0.082) 4.94 9.34 MLQ_5 = 0.71*iib, Errorvar.= 0.33 , Rý = 0.60 (0.075) (0.038) 9.37 8.63 MLQ_6 = 0.66*cr, Errorvar.= 0.28 , Rý = 0.62 (0.090) (0.033) 7.32 8.57 MLQ_7 = 0.69*im, Errorvar.= 0.18 , Rý = 0.73 (0.043) (0.022) 16.27 8.05

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MLQ_8 = 1.03*Pillar1, Errorvar.= 0.40 , Rý = 0.73 (0.049) 8.05 MLQ_9 = 1.04*Pillar1, Errorvar.= 0.29 , Rý = 0.79 (0.040) 7.25 MLQ_10 = 0.97*Pillar1, Errorvar.= 0.60 , Rý = 0.61 (0.067) 8.91 MLQ_11 = 0.96*Pillar1, Errorvar.= 0.42 , Rý = 0.69 (0.052) (0.051) 18.27 8.30 MLQ_12 = 0.94*Pillar1, Errorvar.= 0.50 , Rý = 0.64 (0.057) 8.75 MLQ_13 = 0.49*Pillar1, Errorvar.= 1.31 , Rý = 0.15 (0.084) (0.13) 5.80 9.95 MLQ_14 = 0.45*Pillar1, Errorvar.= 1.21 , Rý = 0.14 (0.081) (0.12) 5.55 9.97 MLQ_15 = 0.42*Pillar2, Errorvar.= 2.04 , Rý = 0.078 (0.20) 9.96 MLQ_16 = - 0.88*Pillar2, Errorvar.= 0.86 , Rý = 0.47 (0.080) (0.10) -10.93 8.30 MLQ_17 = - 0.86*Pillar2, Errorvar.= 0.82, Rý = 0.48 MLQ_18 = - 0.41*Pillar2, Errorvar.= 2.15 , Rý = 0.071 (0.11) (0.22) -3.57 9.95 MLQ_19 = - 0.83*Pillar2, Errorvar.= 0.92 , Rý = 0.43 (0.081) (0.11) -10.25 8.57

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MLQ_20 = - 0.51*Pillar2, Errorvar.= 1.25 , Rý = 0.17 (0.13) 9.75 MLQ_21 = - 0.66*Pillar2, Errorvar.= 1.05 , Rý = 0.30 (0.11) 9.38 MLQ_22 = - 0.46*Pillar2, Errorvar.= 1.27 , Rý = 0.14 (0.13) 9.82 MLQ_23 = - 0.67*Pillar2, Errorvar.= 0.75, Rý = 0.38 MLQ_24 = - 0.51*Pillar2, Errorvar.= 1.60 , Rý = 0.14 (0.099) (0.16) -5.17 9.77 MLQ_25 = 0.48*Pillar4, Errorvar.= 1.46 , Rý = 0.14 (0.15) 9.79 MLQ_26 = 0.40*Pillar3, Errorvar.= 1.76 , Rý = 0.082 (0.11) (0.18) 3.68 9.83 MLQ_27 = 0.54*Pillar3, Errorvar.= 1.46 , Rý = 0.17 (0.10) (0.15) 5.40 9.48 MLQ_28 = 0.46*Pillar3, Errorvar.= 1.55 , Rý = 0.12 (0.10) (0.16) 4.49 9.69 MLQ_29 = 0.33*Pillar3, Errorvar.= 0.90 , Rý = 0.11 (0.077) (0.092) 4.24 9.74 MLQ_30 = 1.00*Pillar4, Errorvar.= 0.37 , Rý = 0.73 (0.082) (0.11) 12.23 3.49 MLQ_31 = 0.72*Pillar4, Errorvar.= 0.48 , Rý = 0.52 (0.070) (0.072) 10.32 6.69

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MLQ_32 = 0.95*Pillar5, Errorvar.= 1.28 , Rý = 0.41 (0.11) (0.17) 8.68 7.51 MLQ_33 = 0.89*Pillar5, Errorvar.= 0.94 , Rý = 0.46 (0.097) (0.13) 9.16 7.03 MLQ_34 = 0.73*Pillar5, Errorvar.= 1.27 , Rý = 0.30 (0.10) (0.15) 7.18 8.57 MLQ_35 = 0.52*Pillar5, Errorvar.= 1.39 , Rý = 0.16 (0.10) (0.15) 5.14 9.39 MLQ_36 = 0.42*Pillar4, Errorvar.= 1.31 , Rý = 0.12 (0.092) (0.13) 4.59 9.76 Structural Equations iia = - 1.47*Pillar1 + 6.28*Pillar2 + 7.36*Pillar3 - 2.71*Pillar4 + 5.11*Pillar5, Errorvar.= 0.065 , Rý = 0.94 (0.35) (1.21) (1.04) (1.08) (0.95) (0.040) -4.24 5.21 7.11 -2.50 5.39 1.65 iib = - 1.18*Pillar1 + 5.75*Pillar2 + 6.79*Pillar3 - 2.47*Pillar4 + 4.71*Pillar5, Errorvar.= 0.065 , Rý = 0.93 (0.31) (1.11) (0.95) (1.00) (0.87) (0.036) -3.76 5.20 7.14 -2.47 5.40 1.80 im = - 1.36*Pillar1 + 5.79*Pillar2 + 6.88*Pillar3 - 2.45*Pillar4 + 4.72*Pillar5, Errorvar.= 0.089, Rý = 0.91 (0.30) (1.02) (0.80) (0.99) (0.80) -4.48 5.65 8.57 -2.48 5.88 is = - 1.38*Pillar1 + 5.52*Pillar2 + 6.59*Pillar3 - 2.46*Pillar4 + 4.74*Pillar5, Errorvar.= 0.20 , Rý = 0.80 (0.28) (0.70) (0.93) (0.70) (0.063) -4.98 7.89 -2.66 6.74 3.21 ic = - 1.37*Pillar1 + 5.88*Pillar2 + 6.98*Pillar3 - 2.56*Pillar4 + 4.99*Pillar5, Errorvar.= 0.050 , Rý = 0.95

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(0.32) (1.10) (0.91) (1.02) (0.87) (0.034) -4.29 5.36 7.70 -2.51 5.76 1.48 cr = - 1.44*Pillar1 + 6.38*Pillar2 + 7.52*Pillar3 - 2.67*Pillar4 + 5.21*Pillar5, Errorvar.= -0.036 , Rý = 1.03 (0.37) (1.33) (1.22) (1.12) (1.06) (0.040) -3.91 4.78 6.15 -2.38 4.93 -0.91 W_A_R_N_I_N_G : Error variance is negative. mbea = 0.11*Pillar1 - 0.33*Pillar2 - 0.34*Pillar3 + 0.039*Pillar4 - 0.12*Pillar5, Errorvar.= 0.95 , Rý = 0.019 (0.17) (0.56) (0.63) (0.27) (0.46) (0.27) 0.63 -0.58 -0.53 0.15 -0.26 3.55 mbep = 0.64*Pillar1 - 2.72*Pillar2 - 3.07*Pillar3 + 1.14*Pillar4 - 2.15*Pillar5, Errorvar.= 0.83 , Rý = 0.16 (0.56) (0.47) (0.44) (0.18) -4.90 -6.58 2.57 4.56 lf = 1.13*Pillar1 - 4.28*Pillar2 - 4.87*Pillar3 + 1.73*Pillar4 - 3.44*Pillar5, Errorvar.= 0.58 , Rý = 0.42 (0.28) (0.95) (0.89) (0.76) (0.76) (0.15) 4.08 -4.49 -5.44 2.29 -4.53 3.81 NOTE: Rý for Structural Equations are Hayduk's (2006) Blocked-Error Rý Correlation Matrix of Independent Variables Pillar1 Pillar2 Pillar3 Pillar4 Pillar5 -------- -------- -------- -------- -------- Pillar1 1.00 Pillar2 -0.23 1.00 (0.08) -3.03 Pillar3 0.27 -0.78 1.00 (0.08) (0.07) 3.34 -11.66 Pillar4 0.22 -0.44 0.49 1.00 (0.08) (0.07) (0.11) 2.87 -6.05 4.41

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Pillar5 0.33 -0.44 -0.08 0.46 1.00 (0.08) (0.08) (0.13) (0.08) 4.27 -5.55 -0.62 5.75 Global Goodness of Fit Statistics, Missing Data Case -2ln(L) for the saturated model = 31287.786 -2ln(L) for the fitted model = 34577.865 Degrees of Freedom = 1960 Full Information ML Chi-Square = 3290.08 (P = 0.0) Root Mean Square Error of Approximation (RMSEA) = 0.058 90 Percent Confidence Interval for RMSEA = (0.054 ; 0.061) P-Value for Test of Close Fit (RMSEA < 0.05) = 0.00020

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APPENDIX I

STUDY APPROVAL FROM IRB

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