TRANSFORMATIONAL LEADERSHIP FOR SUSTAINABILITY IN 

ARCHITECTURE ENGINEERING AND CONSTRUCTION PROJECT TEAMS 

By 

Faizan Shafique 

 
 
 
 
 
 

 

A DISSERTATION 

Submitted to 

Michigan State University 

in partial fulfillment of the requirements  

for the degree of 

Planning, Design, and Construction – Doctor of Philosophy 

2020 

 

 

 

 

 

 

ABSTRACT 

TRANSFORMATIONAL LEADERSHIP FOR SUSTAINABILITY IN  

ARCHITECTURE ENGINEERING AND CONSTRUCTION PROJECT TEAMS  

By 

Faizan Shafique 

Green  or  sustainable  Architecture  Engineering  and  Construction  (AEC)  projects  have 

become a global phenomenon. To deal with the unique nature of green projects, teams require 

improved design and construction processes. This demands teams to integrate and collaborate 

better.  To  achieve  this,  the  literature  highlights  project  delivery  attributes  such  as  delivery 

methods, contractual conditions, and owner’s commitment. This study explores a new dimension 

in this regard: Leadership. Previous studies have pressed upon the need to explore the role of 

leadership in green AEC project teams. Proponents of leadership believe that it can help align 

team goals and create an encouraging atmosphere for improved performance. 

Transformational leadership is currently one of the most highly regarded and researched 

theories in the literature. Transformational leaders inspire their followers by setting examples, 

give  others  individual  attention,  cater  to  their  needs;  and  stimulate  them  intellectually  by 

encouraging them to take part in discussions and bring in their ideas.  There is a need to explore 

how  transformational  leadership  emerges  in  green  AEC  project  teams  and  can  impact  team 

performance. Some leadership experts believe that the concept of having a single leader for a 

team is not the true representation, and there can be multiple leaders in a team regardless of 

assignment, decision-making power, and position. This form of leadership is known as shared 

leadership. 

 

 

To study this phenomenon in the AEC industry, this study collected data from nine near 

completion, new construction and major renovation projects aiming for a Leadership in Energy 

and  Environmental  Design  (LEED)  certification.  LEED  certification  by  the  United  States  Green 

Building Council (USGBC) is the leading rating system for green buildings, both nationally and 

internationally. The researcher used the shortlist on the USGBC website to identify projects and 

invite team members to participate in this study via emails and phone calls.  

This study uses mixed methods approach to achieve the study aims. A survey was used to 

collect individual-level data (n=103) and quantitatively test the relationship between perceptions 

of transformational leadership and team performance, mediated through team integration. The 

data was analyzed for validity using confirmatory factor analysis and reliability using Cronbach’s 

alpha. The study employed structural equation modeling to test its hypotheses. At the project 

team level, case study methodology was adopted to qualitatively explore the structure and flow 

of transformational leadership in green AEC project teams using social networks. Case studies 

were analyzed using pattern matching, while t-test and Chi-square tests were also employed to 

assess additional leadership characteristics as a follow up to the network study.  

The study provides significant theoretical contributions by developing a modified version 

of  the  Multifactor  Leadership  Questionnaire  (MLQ)  to  measure  transformational  leadership 

specific  to  sustainability  in  AEC  projects.  It  provides  quantitative  evidence  in  support  of 

transformational  leadership  for  team  integration  and  team  performance  improvement.  The 

study is first of its type to report leadership flow in AEC project teams and provides practical 

implications and directions for future research.  

 

 

 
 
 
 
 
 
 
 

 

 

 

 
 
 
 
 
 
 
 

 

Copyright by 
FAIZAN SHAFIQUE 
2020 

 

 
 
 
 
 
 
 
 
 
 

 

 

“Who taught me that humility and empathy are the greatest virtues of all”. 

“Who inspired me to believe in my abilities and never compromise on my values”.  

Both of them passed away in Lahore while I was away for my grad school. 

To my late Dada Abu,  

And my late Uncle Hanif, 

 
 
 
 
 
 
 
 
 
 
 
 

 

v 

 

ACKNOWLEDGEMENTS 

 
It still feels unreal. I’ve finally accomplished the task that once looked overwhelmingly gigantic. 

The last few years have been a constant struggle. There were all sorts of challenges, ranging 

from the complexities of an alien education system to the nuances of the American society. But 

I was lucky to have so many wonderful people around, who helped and supported me in every 

way possible. They will always stay with me in my thoughts and prayers. 

Dr. Sinem Mollaoglu is the best research advisor one can ask for. I couldn’t have achieved this 

feat, had it not been for her extended guidance, inspiration, support and trust. She checks all 

the boxes for transformational leadership: Leads by example, never discourages her students, 

gives them the space to think on their own, and provides them with the individual attention 

they need. Her mentorship has not only transformed me into a confident researcher and 

presenter, but also has taught me the virtues of kindness, empathy, integrity, and courage. I’ve 

the highest of respect and gratitude for her and hope that one day I can make her proud.  

I had a perfect doctoral committee, both in terms of expertise required for my work, as well as 

the level of support extended by each of the professors. Dr. Matt Syal, Dr. Mark Wilson, Dr. Rick 

DeShon and Dr. Zong Dhao pushed and guided me to incorporate diverse points of view. Their 

careful insights added great value to my work, for which I’m grateful. 

I would like to especially mention Dr. Matt Syal, who has been a mentor for me since day one. 

He understood me very well, and always advised what was best for me. I thank him for the 

countless hours he spent in counseling and uplifting. 

 

 

vi 

 

The SPDC staff was super helpful. Jill helped me figure things out on daily basis. So much work, 

but she always smiled. Janelle, Mary, Pat and Lauri, assisted me whenever I needed them. Jenn 

was a wonderful mentor when I volunteered for the writing center.  

My SPDC friends, who were just like my family, were the reason I looked forward to going to 

school every day. Starting with Angelo, who was my academic babysitter for two years. He is 

perhaps the most selfless person I’ve ever met. He would go to any extent helping you, and I 

stand guilty of fully exploiting his kindness. Anthony, Leah, Mohsen and Mel, they were all very 

close to me. As we were all going through the same struggle, we had great empathy for each 

other. I wish them all the best in life. I will never forget our beautiful times together.  

My friends in East Lansing have been the colors and tunes of my life these past few years. I wish 

I could name all of them here. Yasir and Azam, my Fulbright cohort mates from Pakistan, really 

helped me settle when I came, and remained a constant aid throughout. The whole Pakistani 

community provided me and Rabia with a support system that was close to home. My group of 

friends from around the world, with whom I shared the best of adventures and learned a great 

deal about different cultures. Florencia from Argentina, Laura from Finland, Sabawoon from 

Afghanistan, Karn from Thailand, Nouga from Egypt, Bihter from Turkey and Irene from USA. 

Every time we hanged out it was like meeting the world all together. I will always cherish our 

memories. Fulbright provided me with the opportunity to meet friends from over 50 countries; 

places I never knew existed; places I had so many misconceptions about. I thank all of them for 

sharing their stories.  

I cannot miss mentioning the wonderful East Lansing community that welcomed us with arms 

wide open. Joan is like a mother to us. She was always there, celebrating the smallest of our 

 

vii 

 

achievements, comforting us when we got sick, and feeding us whenever she got a chance. 

Mary and Connie from CVIP, who recognized my efforts for international student community 

and always highlighted them in front of university admin. It is not the place, but the people that 

I will miss the most.  

Finally, I come to those who are dearest to me. My family. Especially my parents. I’m their 

product and I owe my life to them. I can’t forget the sacrifices they made in getting me where 

I’m today. My two brothers who are my strength. I’m sure they are super proud of me. My 

parents in law, who supported us throughout. My nephews and nieces, who are the source of 

joy for the whole family. And last, but certainly not the least, my beautiful wife Rabia. She has 

stood by me through thick and thin. Her love makes my life worth living. This is as much her 

achievement as mine. Perhaps more.  

 

 

 

 

 

 

 

 

viii 

 

LIST OF TABLES ........................................................................................................................................... xii 

TABLE OF CONTENTS 

 
 

LIST OF FIGURES ........................................................................................................................................ xiv 

KEY TO ABBREVIATIONS ............................................................................................................................ xvi 

2.1. 

2.2. 

2.3. 

Chapter 1 INTRODUCTION ........................................................................................................................... 1 
Background ................................................................................................................................. 1 
1.1. 
Problem Statement .................................................................................................................... 2 
1.2. 
Research Goal and Objectives .................................................................................................... 3 
1.3. 
1.4. 
Research Scope .......................................................................................................................... 4 
1.5.  Methodological Approach .......................................................................................................... 4 
1.6. 
Deliverables/ Research Contributions ........................................................................................ 4 
1.7. 
Reader’s Guide ........................................................................................................................... 5 
 

Chapter 2 LITERATURE REVIEW ................................................................................................................... 6 
Sustainability in the AEC Industry ............................................................................................... 6 
2.1.1.  Characteristics of Green AEC Projects .................................................................................... 7 
2.1.2.  Complexity in Green AEC Projects: LEED Versions and Certifications .................................... 9 
2.1.3. 
Leadership Requirements for Green AEC Projects ............................................................... 11 
Leadership in Theory ................................................................................................................ 11 
2.2.1. 
Leadership Theories in the Literature .................................................................................. 13 
2.2.2.  Transformational Leadership ............................................................................................... 19 
2.2.3. 
Shared Leadership ................................................................................................................ 21 
2.2.4.  Using Transformational Leadership Theory in Shared Leadership context ......................... 23 
Leadership in Green AEC Projects ............................................................................................ 25 
2.3.1.  Background .......................................................................................................................... 25 
2.3.2.  Roles ..................................................................................................................................... 26 
2.3.3.  Project Delivery Methods .................................................................................................... 30 
 

Chapter 3 STUDY FRAMEWORK ................................................................................................................. 32 
Individual Level Framework ..................................................................................................... 32 
3.1.1.  Hypothesis 1: Transformational leadership and team performance ................................... 33 
3.1.2.  Hypothesis 2: Mediating role of team integration ............................................................... 34 
Team Level Framework ............................................................................................................ 34 
3.2.1.  Research Question 1: Identification of Transformational Leaders ...................................... 36 
3.2.2.  Research Question 2: Flow of Transformational Leadership ............................................... 38 
Summary .................................................................................................................................. 39 

3.2. 

3.1. 

3.3. 
 

Chapter 4 METHODOLOGY ........................................................................................................................ 41 
Summary of Goals and Objectives ............................................................................................ 41 
Overview of Study Phases ........................................................................................................ 41 
Phase 1: Framework Development .......................................................................................... 42 
Study Hypotheses and Research Questions ......................................................................... 42 

4.1. 
4.2. 
4.3. 

4.3.1. 

 

ix 

 

4.4. 

4.5. 

4.3.2. 
Study Variables .................................................................................................................... 43 
4.3.3.  Development of the Measurement Tool ............................................................................. 47 
Phase 2: Expert interviews ....................................................................................................... 47 
4.4.1.  Measurement Tool Revisions ............................................................................................... 48 
4.4.2.  Data Collection Strategies .................................................................................................... 49 
Phase 3: Study of Projects and Team Members ....................................................................... 49 
4.5.1. 
Study Population and Sample .............................................................................................. 49 
4.5.2.  Case Study Selection ............................................................................................................ 50 
4.5.3.  Data Collection ..................................................................................................................... 51 
4.5.4.  Data Analysis and Quality .................................................................................................... 51 
 

Chapter 5 RESULTS FOR EXPERT INTERVIEWS ........................................................................................... 55 
Introduction of Experts ............................................................................................................ 55 
Structured Interview Results .................................................................................................... 56 

5.1. 
5.2. 
 

6.3. 

6.2. 

6.1. 

6.1.1. 
6.1.2. 
6.1.3. 

Chapter 6 RESULTS FOR HYPOTHESES AND RESEARCH QUESTIONS ......................................................... 60 
Sample Characteristics and Data Demographics ...................................................................... 60 
Selection of Case Studies ..................................................................................................... 60 
Summary of Case Study Projects ......................................................................................... 62 
Individual Level Data Demographics .................................................................................... 64 
Individual Level Analysis ........................................................................................................... 66 
6.2.1.  Reliability- Cronbach’s Alpha ............................................................................................... 66 
6.2.2.  Normality of data ................................................................................................................. 67 
6.2.3.  Validity - Confirmatory Factor Analysis ................................................................................ 69 
6.2.4.  Hypothesis 1: Transformational leadership and Team performance .................................. 71 
6.2.5.  Hypothesis 2: Transformational leadership and Team performance mediated by Team 
integration ......................................................................................................................................... 72 
Team Level Analysis .................................................................................................................. 73 
6.3.1. 
Leadership Networks in Case Study Projects ....................................................................... 73 
6.3.2.  Research Question 1: Identification of Transformational Leaders ...................................... 84 
6.3.3.  Research Question 2: Flow of Transformational Leadership ............................................... 89 
6.3.4. 
Summary of Key Findings ..................................................................................................... 94 
 

Chapter 7 DISCUSSIONS ............................................................................................................................. 96 
7.1. 
Flow of Leadership in AEC Projects .......................................................................................... 96 
7.2.  Multi-level Study Framework ................................................................................................... 97 
Team integration through shared transformational leadership .............................................. 97 
7.3. 
7.4. 
Role of emergent leaders ......................................................................................................... 98 
Designers as emergent leaders ................................................................................................ 98 
7.5. 
Role of relevant experience and qualification .......................................................................... 99 
7.6. 
Role of Network Density on Project performance ................................................................. 100 
7.7. 
7.8. 
Generalizability of Findings .................................................................................................... 100 
 

8.  CONCLUSIONS AND FUTURE RECOMMENDATIONS ........................................................................ 102 
Summary of Research Goals and Objectives .......................................................................... 102 
Summary of Study Methods ................................................................................................... 102 
Summary of findings ............................................................................................................... 103 

8.1. 
8.2. 
8.3. 

 

x 

 

Deliverables and Contributions to the Body of Knowledge ................................................... 104 
Limitations .............................................................................................................................. 105 
Recommendations for Future Research ................................................................................. 106 

8.4. 
8.5. 
8.6. 
 

APPENDICES ............................................................................................................................................. 108 
Appendix A: Internal Review Board (IRB) Approval Letter .................................................................. 109 
Appendix B: Structured Interview Questions for Industry Experts ...................................................... 112 
Appendix C: Survey .............................................................................................................................. 113 
Appendix D: Interview questions for Project Managers/Owner’s representatives ............................. 136 
Appendix E: R codes and Results for CFA and SEM ............................................................................. 145 
 

REFERENCES ............................................................................................................................................. 167 
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

xi 

 

 

LIST OF TABLES 

Table 2-1 Leaders versus Managers (adopted from Nahavandi, 2003) ..................................................... 12 

Table 2-2 Relationship Between Transformational Leadership and Owner's commitment ...................... 28 

Table 3-1 Study hypotheses and research questions ................................................................................ 40 

Table 4-1 Individual Level Study Metrics ................................................................................................... 45 

Table 4-2 Changes in the Measurement tools based on Expert Interviews .............................................. 48 

Table 4-3 Attributes of reliability (adopted from Heale & Twycross, 2015) .............................................. 52 

Table 4-4 Types of Validity (adopted from Korb, 2012 & APA, 1974) ........................................................ 52 

Table 4-5 Case Study research quality tests .............................................................................................. 53 

Table 5-1 Expert Introductions .................................................................................................................. 55 

Table 6-1 Projects and their owner types – Available Vs Contacted Vs Participated ................................ 60 

Table 6-2 Basic characteristics of Case Study Projects .............................................................................. 64 

Table 6-3 Individual survey response rate ................................................................................................. 65 

Table 6-4 Respondent roles in case study projects ................................................................................... 65 

Table 6-5 LEED accreditation status for the respondents ......................................................................... 65 

Table 6-6 Cronbach's Alpha values for variable scales .............................................................................. 67 

Table 6-7 Results for normality tests ......................................................................................................... 68 

Table 6-8 Identified Leaders for Case Study 1 ........................................................................................... 75 

Table 6-9 Identified Leaders for Case Study 2 ........................................................................................... 76 

Table 6-10 Identified Leaders for Case Study 3 ......................................................................................... 77 

Table 6-11 Identified Leaders for Case Study 4 ......................................................................................... 78 

Table 6-12 Identified Leaders for Case Study 5 ......................................................................................... 79 

Table 6-13 Identified Leaders for Case Study 6 ......................................................................................... 80 

Table 6-14 Identified Leaders for Case Study 7 ......................................................................................... 81 

 

xii 

 

Table 6-15 Identified Leaders for Case Study 8 ......................................................................................... 83 

Table 6-16 Identified Leaders for Case Study 9 ......................................................................................... 84 

Table 6-17 Theoretical versus identified Leaders in the Case study Projects ............................................ 85 

Table 6-18 Crosstabs for newly identified leaders and design team members ......................................... 85 

Table 6-19 Test for difference of distribution for design team members ................................................. 86 

Table 6-20 Group statistics for Total Professional Experience .................................................................. 86 

Table 6-21 Comparison of means for total professional experience ......................................................... 87 

Table 6-22 Group statistics for LEED Experience ....................................................................................... 87 

Table 6-23 Comparison of means for LEED experience ............................................................................. 88 

Table 6-24 Frequencies of LEED Accreditation .......................................................................................... 88 

Table 6-25 Test for difference of distribution for LEED Accreditation ....................................................... 89 

Table 6-26 Links between design and construction phases in case study projects ................................... 91 

Table 6-27 Network densities and project performance measures in the case studies ............................ 92 

Table 6-28 List of Key Findings ................................................................................................................... 95 

 

 
 
 
 
 
 
 
 
 

 

xiii 

 

 

LIST OF FIGURES 

Figure 2-1 Number of LEED certified projects in the world (2010-2017) .................................................... 9 

Figure 2-2 LEED Project Registrations over years ...................................................................................... 10 

Figure 2-3 Three Waves of Leadership Research in the Journal of Applied Psychology ............................ 13 

Figure 2-4 The leadership quadrants inspired by Flieshman (1953) .......................................................... 15 

Figure 2-5 The continuum of leadership behavior (adopted from Tannenbaum and Schmidt, 1958) ...... 16 

Figure 2-6 The managerial grid (adopted from Blake and Mouton, 1964) ................................................ 16 

Figure 2-7 Grid for neutral leadership styles. (adopted from Reddin, 1970) ............................................ 17 

Figure 2-8 The situational theory grid (adopted from Hersey and Blanchard, 1977) ................................ 18 

Figure 2-9 Example of a leadership network created as a result of peer nominations ............................. 23 

Figure 3-1 Multilevel Framework for the Study in green AEC projects ..................................................... 33 

Figure 3-2 Conceptual network diagram for transformational leadership in construction project team . 35 

Figure 3-3 Construction project team tiers (adopted from Mollaoglu et al., 2014) .................................. 37 

Figure 4-1 Transformational Leadership Exposure .................................................................................... 44 

Figure 6-1 Geographical Locations of Case Study Projects ........................................................................ 62 

Figure 6-2 Study Hypotheses ..................................................................................................................... 66 

Figure 6-3 Confirmatory factor analysis for study model .......................................................................... 70 

Figure 6-4 Hypothesis 1 - transformational leadership and team performance ....................................... 71 

Figure 6-5 Hypothesis H2 - Mediating Effect of Team Integration ............................................................ 72 

Figure 6-6 Leadership Network for Case Study 1 ....................................................................................... 75 

Figure 6-7 Leadership Network for Case Study 2 ....................................................................................... 76 

Figure 6-8 Leadership Network for Case Study 3 ....................................................................................... 77 

Figure 6-9 Leadership Network for Case Study 4 ....................................................................................... 78 

 

xiv 

 

Figure 6-10 Leadership Network for Case Study 5 .................................................................................... 79 

Figure 6-11 Leadership Network for Case Study 6 .................................................................................... 80 

Figure 6-12 Leadership Network for Case Study 7 .................................................................................... 81 

Figure 6-13 Leadership Network for Case Study 8 .................................................................................... 82 

Figure 6-14 Leadership Network for Case Study 9 .................................................................................... 83 

Figure 6-15 Network Densities vs levels of LEED Certifications ................................................................. 93 

Figure 6-16 Network Densities vs owner's perception of project performance ........................................ 94 

 

 

 

 
 
 
 
 
 
 
 
 
 
 
 
 

 

xv 

 

KEY TO ABBREVIATIONS 

Architecture, Engineering and Construction 

Confirmatory Factor Analysis 

Leadership in Energy and Environmental Design 

Structural Equation Modeling 

Social Network Analysis 

 
AEC: 

CFA:  

 

 

LEED:    

SEM:  

SNA:  

 

 

USGBC:  

United States Green Building Council 

 

xvi 

 

Chapter 1 INTRODUCTION 

 
 
1.1. 

Background 

Green Architecture Engineering and Construction (AEC) projects, alternatively known as 

sustainable AEC and green building, aims at improving the environmental, health, economic, 

and productivity performance of buildings through innovative design, construction, and 

operation (USGBC, 2003). Green AEC projects have become a global phenomenon, and it is 

growing exponentially (Dodge Data & Analytics, 2016). LEED by USGBC is the leading rating 

system for green buildings, both nationally and globally (Ewing et al., 2013). Currently, there are 

more than 94,000 participating LEED projects, which account for 2.4 million square feet globally 

(USGBC, 2018).  

Green AEC projects are unique because they are perceived as more complicated due to 

sustainability goals (Magent, 2009). High-tech equipment and components, like photovoltaics 

and smart building technology (Rohracher, 2001), require specialized professionals (Hoffman & 

Henn, 2008).   Due to the broader scope of work and expensive equipment, sustainable AEC 

projects are thought to increase cost and time.  These characteristics are seen on the LEED 

certification stats. Only a small proportion of projects succeed in achieving LEED Platinum, the 

top LEED certification for buildings. Moreover, the new version of LEED (LEED V4) has made 

earning LEED points more challenging (Melton & Andrews, 2016). 

 

1 

 

1.2. 

Problem Statement 
 

Literature has frequently highlighted the unique nature of green AEC projects. The challenges of 

green projects require superior design and construction processes. Sustainability objectives of 

green AEC projects require teams to achieve maximum influence and avoid wasteful activities, 

and therefore the competencies from all relevant disciplines need to be mapped out together 

(Horman et al., 2006). This calls for the teams to integrate so that they can communicate and 

collaborate openly (Korkmaz et al., 2010).  Researchers have mainly approached this through the 

lens of project delivery attributes, such as the owner’s commitment, delivery system, contractual 

conditions, and construction processes (Olanipekun et al., 2017; Korkmaz et al., 2010; Mollaoglu 

et al., 2013). Leadership is another dimension to this, which is less explored so far.  

Leadership skills have been highlighted as crucial for sustainable project performance, as they 

can inspire and direct the project teams towards sustainability goals (Ofori-Boadu et al., 2012). 

Traditionally, leadership has been conceptualized as the trait of a single person in the team, 

usually the functional manager. However, this is considered a misrepresentation of leadership 

(contractor et al., 2012). Recently, more and more researchers are adopting the new concept of 

shared or distributed leadership. It states that leadership is not the property of a single person in 

the team. There can be more than one leader, who may or may not have the positional authority 

(Mehra et al., 2006; Carson et al., 2007)  

Transformational leadership is the most prominent and highly regarded approach for 

leadership in the current era (Ronald, 2014). The spirit of transformational leadership lies in 

inspiring the subordinates and aligning their vision with that of the project; giving them 

 

2 

 

individual attention to cater to their needs; and encourage them to have an open discussion 

and share their ideas (Bass, 1985). Transformational leadership has been identified to positively 

impact team performance numerous times in the literature (Dionne et al., 2004; Braun et al., 

2013). The characteristics of transformational leadership make it a perfect match for large, 

multi-disciplinary green AEC project teams. It is particularly useful for a shared leadership 

approach; however, the network studies of shared leadership have not yet tapped the potential 

of this theory. Thus, there is a need to explore how shared transformational leadership flows in 

green AEC project teams and what role it plays in improving team performance.  

1.3. 

Research Goal and Objectives 

The primary goal of this study was to “Explore the structure and role of transformational 

leadership in AEC project teams, providing a significant contribution to AEC literature.” The 

objectives of the study are as follows: 

1.  Create a multi-level framework for study and measurement tools that: 

a.  Guides the hypothesis development to relate transformational leadership and 

team performance mediated by team integration at the individual level, and  

b.  Provides reasoning for research questions in order to explore the dynamics of 

transformational leadership using social networks at the team level. 

2.  Validate the framework and measurement tool via expert interviews. 

3.  Empirically test the study hypotheses at the individual level. 

Answer the research questions at the team level, exploring the leadership networks in teams 

and assessing various characteristics of leaders.   

 

3 

 

1.4. 

Research Scope 

The subject of this study is leadership in green AEC, which is very broad. To limit the scope, this 

research focused on LEED New Construction and Major Renovation projects in the USA. 

Moreover, only those projects were considered that were registered with the new LEED Version 

4, as it is considered more complicated and hence improves the theoretical requirements of 

transformational leadership. Out of a population of 1512 projects, 152 were contacted for data 

collection, and 9 project owners agreed to participate. Individual responses were sought from 

all team members of owner, design, and construction organizations. 

1.5.  Methodological Approach 

The study adopted a mixed-methods approach to test the study hypothesis at the individual 

level and answer the research questions at the project team level. The quantitative methods 

employed include confirmatory factor analysis, structural equation modeling, independent 

samples t-test, and chi-square test for distribution comparison. For qualitative analysis, pattern 

matching was used to interpret project-level analyses driven by social networks.  

1.6.  Deliverables/ Research Contributions 

The  primary  deliverable  of  this  research  is  the  “Investigation  of  shared  transformational 

leadership for large inert-organizational AEC project teams.” Other deliverables include: 

•  Development of a measurement tool to record shared transformational leadership for 

sustainability in AEC project teams; 

•  Measurement of the effect size of shared transformational leadership on team 

performance; 

 

4 

 

• 

 Assessment of characteristics for sustainability leaders in AEC project teams; 

•  Analysis of sustainability leadership flow in AEC project teams.  

•  Recommendations for: 

o  Best practices in construction project management and management training;  
o  Future research. 

1.7. 

Reader’s Guide 

This dissertation is divided into seven chapters. Chapter 1 forms the ground by providing a quick 

overview  of  literature  leading  to  the  problem  statement,  followed  by  the  research  goals, 

objectives, and deliverables. Chapter 2 provides a detailed literature review regarding the various 

related areas. These include the detailed review of green AEC projects, their characteristics and 

performance; leadership theories, including transformational leadership and shared leadership; 

and transformational leadership in green AEC projects which link the two areas. Chapter 3 creates 

a framework to guide hypotheses and research questions development. Chapter 4 describes the 

methodology  of  research  followed,  including  the  study  variables,  data  collection,  and  survey 

development. Chapter 5 presents the results for expert interviews, including the modification of 

study survey items and answers to questions regarding leadership in green AEC projects. Chapter 

6 provides qualitative and quantitative analysis for the study framework. Hypotheses are tested, 

and research questions are answered. Chapter 7 lists the key findings and discusses the practical 

and theoretical implications of this research. Finally, Chapter 8 concludes the dissertation with 

the summary of findings, contributions of research, limitations, and future recommendation. 

 

 

5 

 

Chapter 2 LITERATURE REVIEW 

 
 
This chapter begins with the introduction to green AEC and the unique nature of green AEC 

projects. After establishing the need for leadership in green teams, the chapter shifts gears to 

discuss leadership theories ending in transformational leadership. The merits of using Shared 

Social Network Analysis for leadership research come next along with basic SNA concepts. 

Finally, transformational leadership in the context of green AEC projects is explored, 

considering various scenarios and structures. 

2.1. 

Sustainability in the AEC Industry 

Green AEC is alternatively referred to as green building, green construction, sustainable building, 

and sustainable construction in the literature (Darko, 2016). There are many definitions available 

for green AEC in the literature.  According to United States Green Building Council (USGBC), green 

AEC aims at improving the environmental, health, economic, and productivity performance of 

buildings through innovative design, construction, and operation (USGBC, 2003).  

Buildings  account  for  a  large  portion  of  global  energy  use  and  emissions.  In  the  USA  alone, 

residential and commercial buildings accounted for 39% of total energy and 72% of electricity 

consumption in 2017(EIA, 2018). Buildings are also a source for greenhouse gas emissions due to 

fossil  fuel  burning,  handling  of  waste,  and  the  use  of  certain  products.  In  2016,  buildings 

accounted  for  producing  11%  emissions  (EPA,  2018).  With  the  increased  awareness  of 

diminishing  resources  and  climate  change,  the  significance  of  green  buildings  has  multiplied. 

Researchers have extensively compared the features of conventional construction with green 

 

6 

 

AEC.  Green  AEC  is  found  to  be  superior  in  thermal  comfort,  health,  productivity,  indoor 

environmental quality, and economy in terms of life cycle costs (Zuo & Zhao, 2014). 

Green AEC projects started gaining fame with both researchers and industry professionals in 

the early nineties (Kibert, 2012). In the current era, green AEC projects have already become a 

global phenomenon.  A 2016 report predicts the global scale of green AEC projects to double by 

2018 (Dodge Data & Analytics, 2016). LEED by USGBC is the leading rating system for green 

buildings, both nationally and globally (Ewing et al., 2013). Currently, there are more than 

90,500 participating LEED projects with 2.2 million square feet covered area around the globe 

(USGBC, 2017). Thus, green AEC projects have already taken over a large portion of the 

construction industry and are estimated to grow significantly in the future.   

2.1.1.  Characteristics of Green AEC Projects 
 
Green AEC projects are deemed to be more complicated in comparison to the traditional ones 

(Myers, 2008). Some of the unique characteristics of green AEC projects are as follows: 

• 

In green AEC projects, the environment is given the status of a stakeholder. In 

comparison to the conventional projects, which set objectives based only on the 

owner/user requirements, green AEC projects have an added dimension to consider. 

Thus, the priorities on the project change. Activities such as life cycle cost analysis and 

energy modeling, which are otherwise ignored, become critical. The design requires a 

greater number of iterations, and construction requires new considerations, such as 

waste management (Horman et al., 2006).  

 

7 

 

•  Due to broader scope/extra activities and expensive material/equipment, green AEC 

projects usually cost more and take longer to complete (Kim et al., 2014). However, the 

proponents of integrated design believe that it costs less to build green if sustainability 

is introduced at the schematic design and planning phase with a holistic approach (7 

Group et al., 2009). Some case studies back this claim, where green buildings were built 

cheaper than their conventional counterparts (Dwaikat & Ali, 2016). 

•  Project sustainability goals increase the level of project complexity (Magent, 2009). They 

may require high-tech equipment and components such as photovoltaics, smart building 

technologies, and high-efficiency mechanical equipment, which are supplied by 

specialized vendors (Rohracher, 2001). The professionals capable of dealing with the 

new technology and techniques are scarce (Hoffmann & Henn, 2008). There is an added 

requirement for new and challenging documentation (France, 2007). 

• 

In addition to the traditional participants in AEC projects such as owners, designers, and 

contractors, there are many new and specialized team members (or traditional 

members with additional roles and responsibilities) involved in green AEC projects. For 

example, sustainability /LEED consultant; Energy Modeler; Commissioning Agent; 

Energy Services Companies; specialized suppliers (like suppliers for FSC certified wood 

products and superior insulation materials); other specialized professionals (Widjaja, 

2016). 

•  Policies to support and facilitate green buildings vary from state to state. For example, 

California requires LEED Silver certification for all state-funded significant projects 

(DuBose et al., 2007).  

 

8 

 

Sustainability objectives of green AEC projects require a systems approach to design and 

construction with input from a wide range of stakeholders as compared to the conventional 

member-based approach (Kibert, 2012). Multidisciplinary collaboration is critical for green 

buildings to produce innovative and effective solutions (Mollaoglu-Korkmaz et al., 2013). 

2.1.2.  Complexity in Green AEC Projects: LEED Versions and Certifications 
 
LEED certification has four different levels based on the number of LEED points achieved. These 

include Certified (40-49 points), Silver (50-59 points), Gold (60-79 points), and Platinum (80+ 

points). A trend of certifications over the years can be seen in figure 2.1. It can be seen that 

despite LEED being around for almost two decades, not many projects have achieved platinum 

certification. On average, only 12 % of projects were able to achieve platinum certification since 

2010.  

9000
8000
7000
6000
5000
4000
3000
2000
1000
0

 

 

LEED Project Certifications

2010

2011

2012

2013

2014

2015

2016

2017

Certified

Silver

Gold

Platinum

Total

Figure 2-1 Number of LEED certified projects in the world (2010-2017) 

9 

 

 

In 2013, USGBC introduced version 4 of LEED. It is stricter and more demanding as compared to 

its predecessors. USGBC states the latest update (LEED V4.1) as the most rigorous green 

building rating system in the world.  Due to the raised bar on sustainability for lower scores, the 

industry witnessed a strategy of risk aversion (Melton & Andrews Jr., 2016). It can be seen in 

Figure 2.2 that despite the launch, very few projects opted for LEED V4. USGBC announced the 

phase-out of LEED V3 in October 2016. More than 20,000 projects got registered in 2016, 

possibly because of this. In the following year (2017), the number of projects registered for 

LEED V4 spiked a little, but the total number went down tremendously.   

LEED Project Registrations

25000

20000

15000

10000

5000

0

2011

2012

2013

2014

2015

2016

2017

LEED V3

LEED V4

Total

 

Figure 2-2 LEED Project Registrations over years 

Green AEC projects are becoming more and more challenging and demanding with time. LEED 

projects always had vast scope for performance improvement, as a tiny proportion of projects 

could achieve top certifications. LEED V4, with its added complexity and requirements, has 

widened this scope further. Hence, there is a need to explore new means and methods in order 

to optimize the performance for LEED projects.  

 

10 

 

2.1.3.  Leadership Requirements for Green AEC Projects 

 

Optimal performance in green AEC projects demands interdisciplinary communication for 

optimized solutions and out of the box thinking for creative ideas. Therefore, team integration 

is highly regarded as the key to green AEC project performance (Widjaja, 2016; Magent et al., 

2009; Korkmaz et al., 2010). Sustainability objectives of green AEC projects require teams to 

achieve maximum influence and avoid wasteful activities, and therefore the competencies from 

all relevant disciplines need to be mapped out together (Horman et al., 2006). The literature 

has advocated for a project delivery approach to achieve integration in green AEC project 

teams. However, it has been observed that the delivery method alone cannot achieve the 

aimed results (Mollaoglu-Korkmaz et al., 2013). 

Ofori-Boadu et al. (2012) highlighted the requirements of leadership in green AEC projects. 

After a thorough review of the dynamic nature of green AEC projects, they advocated for 

improved leadership skills in order to inspire and direct teams towards the sustainability goals 

(Ofori-Boadu et al., 2012). Leadership skills are known to positively impact integration and 

create a climate of creativity and innovation in teams (Zaccaro, 2001; Sarros et al., 2008). 

Effective leadership is one of the top factors of success in cross-functional teams like those in 

green building projects (Olopade & Franz, 2018). 

2.2. 

Leadership in Theory 

There are many definitions of leadership in literature. According to Rost (1993), there are over 

two hundred ideas and concepts regarding leadership. Prominent leadership researchers like 

Bernard M. Bass (2008) are of the view that it is pointless to arrive at a single universal definition, 

 

11 

 

as it varies according to the context and the requirements of the individuals applying it. There 

have been attempts, however. Winston & Patterson (2006) published an article exclusively for 

this purpose. Their basic definition of leadership is as follows: 

“A leader is one or more people who select, equip, train, and influence one 

or more follower(s) who have diverse gifts, abilities, and skills and focuses 

the  follower(s)  to  the  organization’s  mission  and  objectives  causing  the 

follower(s) to willingly and enthusiastically expend spiritual, emotional, and 

physical  energy 

in  a  concerted,  coordinated  effort  to  achieve  the 

organizational mission and objectives.” (Winston & Patterson, 2006, pg. 7) 

It is crucial to differentiate between leadership and management, as both are often confused. 

Firstly, managers can be leaders, but it is not required for leaders to be managers. They can be 

any member of the group/team (Rost, 1993). Some significant differences between leaders and 

managers have been pointed out by Nahavandi (2003).  

Table 2-1 Leaders versus Managers (adopted from Nahavandi, 2003) 

LEADERS 

MANAGERS 

Focus on the future 
Create change  
Create a culture based on shared values 
Establish an emotional link with followers 
Use personal power 

Focus on the present 
Maintain status quo and stability 
Implement policies and procedures 
Remain aloof to maintain objectivity 
Use position power 

12 

 

 

 

 

2.2.1.  Leadership Theories in the Literature 

 

One of the first theories regarding leadership, known as the great-man theory, was promoted by 

a Scottish philosopher, Thomas Carlyle, in the 1840’s (Hook, 1950). The concept has evolved a 

great deal since then. In a recent publication, Lord et al. (2017) reviewed the articles from the 

Journal  of  Applied  Psychology  since  1948  and  identified  three  waves  of  advancement  in 

leadership research. Initially, leadership was limited to personal behavior or style, but gradually 

the  discussion  of  gender,  social  cognition,  situation,  team,  and  transformation  made  the 

literature very rich and broad.  

 

 

 

 

 

Wave 2 (1969-1989) 

Wave 1 (1948-1961) 

•

Focus on personal 
behavior/style 

• Gender differences 
•
•

Social cognition 
Role of 
contingency/situation 

Wave 3 (1999-2007) 

•

•
•
•
•

Leader member 
exchange 
Team leadership 
Trust 
Transformation 
Charisma 

Figure 2-3 Three Waves of Leadership Research in the Journal of Applied Psychology 

As discussed by Rost (1993), there are hundreds of different views and concepts. It is impossible 

to cover every point of view. However, there is a handful of widely known and cited leadership 

theories in the literature (Ronald, 2014; McCleskey, 2014), which are presented in chronological 

order below. 

i) 

Trait Theories 

The advent of trait theories can be traced back to the great-man theory, which stated that great 

men or leaders are simply born with certain traits that enable them to achieve greatness (Hook, 

 

13 

 

1950). One of the biggest proponents of this idea was perhaps Sir Francis Galton. In his book 

“Hereditary Genius”, he presented his studies on high performing judges to even wrestlers in the 

19th century England, trying to prove that extra-ordinary abilities were hereditary, and ran in the 

family (Galton, 1869). Although the great man theory kept on intriguing researchers for at least 

a century (Borgotta et al., 1954), and personal traits still show up in literature with relevance to 

leadership now and then (Kirkpatrick & Locke, 1991), the new theories gradually replaced the 

trait theories because they seemed to be too simplistic with little analytical use and practical 

application (Van & Field, 1990). 

ii) 

Behavioral theories 

The behavioral theories introduced a new focus on behaviors of men carrying out leadership 

roles, against the mental or physical attributes. Ronald (2014) lists seven important works in this 

regard.  

a)  Lewin  et  al.  (1939)  at  Iowa  State  University  studied  three  leadership  styles,  namely: 

Authoritarian  (all  decisions  to  be  made  independently  by  the  leader),  Democratic  (all 

decisions to be taken together through group discussions) and Laissez-faire (Complete 

independence to all group members. No leadership involvement in individual decisions).  

b)  Fleishman (1953) proposed a two-dimensional model of leadership with independent and 

simultaneous  consideration  for  both  task  and  leadership.  A  leader  can  have  any 

combination of high or low task orientation and high or low relationship orientation for 

his group. 

 

14 

 

 

 

 

 

 

 

High relationship orientation 

High relationship orientation 

Low task orientation 

High task orientation 

Low relationship orientation 

Low relationship orientation 

Low task orientation 

High task orientation 

Figure 2-4 The leadership quadrants inspired by Flieshman (1953) 

c)  The  famous  University  of  Michigan  scholar  Rensis  Likert  created  a  similar  model  with 

production  centric  behavior,  with  clear  goal  attaining  attitude,  and  employee-centric 

behavior,  with  strong  interpersonal  relationships  (Likert,  1967).  The  major  difference 

from Fleishman’s model was that Likert’s model was unidirectional, which means that one 

could not have the best of both behaviors at the same time. 

d)  Douglas McGregor, a social psychologist, introduced Theory X and Theory Y in his book 

“The human side of enterprise” (McGregor, 1960). In simple words, theory X assumes that 

all employees are lazy and insecure. On the contrary, theory Y assumes the employees 

look at their work as a source of satisfaction. Both concepts can also co-exist in a leader’s 

mind  and  define  the  leadership  strategy  to  be  used.  Theory  X  supports  a  more 

authoritarian approach, while theory Y leans towards democratic style. 

e)  In continuation of the initial work by Lewin et al. (1939), Tannenbaum and Schmidt (1958) 

developed  a  continuum  of  the  leader-behavior  model,  with  one  end  as  completely 

authoritarian,  while  the  other  end  being  delegation  of  power.  The  leader’s  behavior, 

according to them, could fall anywhere on the line. 

 

15 

 

 

 

 

 

 

 

Authoritative (boss-centered)                                  Participative (sub-ordinate centered) 

Tell  

                 

Sell 

 

 

            Consult                           Share 

Figure 2-5 The continuum of leadership behavior (adopted from Tannenbaum and Schmidt, 1958) 

 

f)  Building upon Fleishman’s two-dimensional theory of task and relationship orientation, 

Blake  and  Mouton  (1964)  came  up  with  the  famous  Managerial  Grid  featuring  five 

different leadership styles. 

 

 

 

 

 

 

 

Figure 2-6 The managerial grid (adopted from Blake and Mouton, 1964) 

g)  The final behavioral theory can also be considered as a situational theory, according to 

Ronald (2014). Reddin (1970) presented a modified version of the managerial grid, called 

the three-dimensional theory. He defined four leadership styles in the grid, as follows: 

 

 

16 

 

 

 

 

 

 

          

 

High people orientation 

Low 
Task  

orientation 

                 

High 
Task  

orientation 

Low people orientation 

Figure 2-7 Grid for neutral leadership styles. (adopted from Reddin, 1970) 

Reddin (1970) further argued that there is a third dimension to this, and that is appropriateness. 

The  same  leadership  style  can  be  highly  effective  if  used  appropriately  and  can  be  counter-

productive if misused. Taking an example of the separated style with low on both task and people 

orientation, if it is used in the form of the missionary, it is ineffective. However, if the same is 

given the shape of bureaucracy, it can be very impactful. 

iii) 

Contingency theories 

Contingency  or  situational  theories  began  with  the  management  psychologist,  Fred  Fiedler’s 

Contingency model (Fiedler, 1967). He came up with an unusual and exciting idea that leaders 

who focus more on task orientation perform better in situations of either high or very low levels 

of  control.  One  the  other  hand,  the  leaders  with  high  people  orientation  perform  better  in 

moderate conditions. 

 

17 

 

Situational theory by Hersey and Blanchard (1977) followed Fiedler’s model. They took the two-

dimensional Fleishman model and added a third dimension of the situation regarding the sub-

ordinate level of maturity. They defined four levels of maturity as follows 

M1 = subordinates are neither capable, nor willing   

M2 = subordinates are willing, but not capable 

M3 = subordinates are capable, but not willing        

M4 = subordinates are both willing and capable 

The authors argued that the level of maturity that sub-ordinates posses decides the task and 

people focus for leadership style. This can be visualized in the following figure 2-8. 

 

 

 

 

 

 

 

 

Figure 2-8 The situational theory grid (adopted from Hersey and Blanchard, 1977) 

Another theory of more abstract nature, known as the path-goal theory, was presented by House 

(1971).  According  to  House,  the  leadership  style  is  a  function  of  the  leader’s  behavior, 

subordinates' needs, and organizational situation. The primary role of a leader is to pave the path 

 

18 

 

for sub-ordinates and organizations to achieve their goals. For the purpose, the leader directs, 

supports, and participates accordingly. 

Finally, the normative decision model by Vroom and Jago (1988) based their theory upon the 

continuum like Lewin et al. (1939) and created a decision-making tool to decide what level of 

leadership (from autocratic to democratic) the situation requires. 

iv) 

Transactional/Relational theories 

The transactional theories, also known as the exchange theories of leadership, are based upon 

the mutually beneficial relationship between the leader and followers. The most famous theory 

in this regard is the LMX (Leader-Member-Exchange) theory. Built upon the vertical dyad 

linkage (VDL) model by Dansereau et al. (1975), the theory proposes a model where leaders 

form close relationships with some sub-ordinates (also known as in-group) and a distant 

relationship with others (out-group).  

2.2.2.  Transformational Leadership  

 

Identified by the famous historian and political scientist James MacGregor Burns (Burns, 1978), 

the transformational leadership theory was majorly developed by Bernard M. Bass (Bass, 1985). 

The spirit of transformational leadership lies in inspiring the subordinates through charisma and 

transforming them. Bass identified 4 I’s in relation to transformational leadership: 

Idealized  influence  refers  to  the  leader  becoming  a  full-fledged  role  model,  acting  out  and 

displaying ideal traits of honesty, trust, enthusiasm, and pride, among others. It is further divided 

into idealized influence attributed and idealized influence behavior. 

 

19 

 

Inspirational  Motivation  by  a  leader  refers  to  providing  the  meaning  of  tasks  to  followers.  It 

usually involves providing a vision or goal. The group is given a reason or purpose to do a task or 

even be in the organization. The leader will resort to charismatic approaches in exhorting the 

group to go forward. 

Leadership behaviors related to idealized influence and inspirational motivation include creating 

pride in the followers for being linked with the leader or team, encouraging the followers to go 

beyond their personal interests in favor of the group cause, assuring that the problems will be 

solved, expressing optimism regarding the future, and sharing the vision in a compelling manner. 

Intellectual stimulation is provided by a leader in terms of challenge to the prevailing order, task, 

and individual. S/he seeks ideas from the group and encourages them to contribute, learn, and 

be  independent.  The  leader  often  becomes  a  teacher.  Some  of  the  behaviors  related  to 

intellectual stimulation include asking for input from followers while making decisions, making 

the followers comfortable to disagree, and encouraging them to think critically.  

Individualized consideration emphasizes on catering to the needs of team members. The leader 

acts as a role model, mentor, facilitator, or teacher to bring a follower into the group and be 

motivated to do tasks. Some of the behaviors related to individualized consideration include 

Spending time and effort in coaching/training, listening attentively to the followers, and taking 

care of their concerns/requirements. 

Significance: Transformational leadership is the most prominent and highly regarded approach 

for leadership in the current era (Ronald, 2014). It is empirically proven to help teams share 

knowledge, vision, commitment, and mental models (Braun et al., 2013; Dionne et al., 2004; 

 

20 

 

Ayoko et al., 2014). Transformational leadership has also been found to impact individual and 

team performance positively many times in the literature (Dvir et al., 2002; Dionne et al., 2004; 

Braun et al., 2013).  

As compared to other leadership styles which are very task-oriented, transformational leadership 

aims to enhance team development, create cohesion, and promote integration in teams (Zaccaro 

et al., 2001). The fact that transformational leadership acknowledges and caters to the social 

context  makes  is  very  suitable  for  temporary  organizations  like  project  teams  (Tyssen  et  al., 

2013).  

2.2.3.  Shared Leadership 

 

Recently, the literature has seen a surge of studies on shared leadership, advocating it as a 

better approach as compared to the traditional concept of a single team leader (Contractor et 

al., 2012). The traditional leadership focusses on the individuality of the leader, which is not the 

accurate representation of leadership in teams. Thus, an expanded unit of analysis is more 

suitable for both researchers and practitioners (Gronn, 2002). Thus, new leadership forms have 

emerged, which recognize leadership as a shared process in the team. These forms are referred 

to as “Shared”, “Collective”, or “Distributed” leadership interchangeably (Avolio et al., 2009).  

There are different approaches researchers have used for shared leadership in teams. 

D'Innocenzo et al. (2016) in their meta-analysis listed down three theoretically distinct forms: 

aggregation (Collective leadership of the complete team as a unit), density (dyadic networks of 

links between team members), and centralization (Distributed form, in which there can be 

many formally appointed and emergent leaders). These forms depend on the type of referent 

 

21 

 

in the studies. If the team members respond to team leadership as a whole or rate themselves 

as leaders, it is aggregated. However, if the team members rate their peers for the leadership 

they provide, then SNA approaches (density and centrality) are used. Chinowsky (2008) has 

defined the two terms as follow: 

•  Network density: This measure indicates the level of interaction that exists between the 

network actors. Conceptually, network density is the number of links that exist between 

the actors in a network in comparison to the total number of links possible between all 

the actors. Thus, a larger number of network density would mean that more members 

in the organization are regularly interacting. 

•  Centrality: The measure of centrality that reflects the distribution of links in the overall 

network. A high value of centrality for a network shows that a small percentage of 

actors have high percentage of links with other actors. On the other hand, a low value 

of centrality shows a relatively uniform distribution of links throughout the network. 

For instance, if a project manager controls most of the communication with the team, it 

is a highly centralized network. 

Shared leadership studies have linked team performance and other relevant constructs with 

higher network densities and lower centralities (or higher leadership distributions). The 

network-based approaches (density and centralization) have received better evaluations in 

terms of effect sizes as compared to aggregation (wang et al., 2014). Density is more widely 

used, as centrality is more susceptible to errors. For example, low centralization scores can also 

be a result of the absence of leadership (D'Innocenzo et al., 2016).  

 

22 

 

In addition to the density and centrality measures, network analysis also provides rich data for 

the study of sources and patterns of leadership in teams (Carson et al., 2007). A hypothetical 

example of leadership network is presented in Figure 2-9 below. 

Figure 2-9 Example of a leadership network created as a result of peer nominations 

 

2.2.4.  Using Transformational Leadership Theory in Shared Leadership context  

Transformational leadership is extensively studied for shared leadership models. In fact, the 

new genre leadership theories – such as transformational and charismatic leadership – are 

found to have a stronger relationship with team performance as compared to the traditional 

leadership theories – such as participative and transactional leadership – when used in the 

context of shared leadership (Wang et al., 2014). However, one category of shared leadership 

studies has not yet specified the form or type of leadership shared in teams. For this, we will 

have to understand the concept of the referent. 

Approaches to shared leadership can be divided into three categories based on the type of 

referent used: i) Team as referent: In these studies, respondents are asked to rate perceived 

leadership experienced in teams; ii) Self-assessment: In these studies, respondents rate 

themselves as leaders; and iii) Other team members as referents: In these studies, respondents 

 

23 

 

rate their team members individually for their leadership qualities. In the first two categories, 

leadership is calculated through aggregation. This means that all responses are added up to 

make one value for the whole team. The third category, in which each respondent rates other 

team members, enables researchers to create social networks and evaluate measures such as 

density and centrality (Wang et al., 2014). This approach not only helps to evaluate individual 

team members more objectively as compared to self-assessment but also provides the 

opportunity for an in-depth analysis of teams.  

Network studies of leadership, that fall in the third referent category, use cumulative overall 

leadership (e.g., Carson et al., 2007; Mehra et al., 2006). This means that they simply ask the 

respondents to give one leadership value for each of their team members. So far, they have not 

taken advantage of leadership theory (or content of leadership), possibly due to the 

cumbersome and time-intensive data collection process it brings to evaluating large teams 

(Conger & Pearce, 2003). The more recent commentaries have, however, stressed the need to 

explore multiple dimensions within the broad theoretical positions (D’Innocenzo, 2016). As 

argued by Schröpfer et al. (2017), having the links alone in networks is not sufficient to evaluate 

leadership in teams; quality and strength of ties are also important. Recommending a future 

framework for shared leadership studies, Sweeney et al. (2019) highlighted the importance of 

specifying the type of leadership in distributed leadership frameworks. Moreover, in the 

prevalent centrality and density studies miss the opportunity of qualitatively studying the roles 

of leaders and followers in teams (Wang et al., 2014).  

 

24 

 

In summary, while transformational leadership is one of the most effective forms in the shared 

leadership context, it is yet to be utilized by network studies in the form where peers in teams 

are used as referents. 

2.3. 

Leadership in Green AEC Projects 

The requirement of leadership in green AEC projects is established in section 2.1. This section 

investigates leadership in the context of the construction industry in general and tries to 

identify the key leadership roles in green AEC project teams. 

2.3.1.  Background 

Management skills have always been the focus of construction literature (Skipper & Bell, 2006). 

Management consists of hard skills, like planning, costing, monitoring, and reporting. Leadership, 

on the other hand, includes soft skills, like vision, motivation, trust-building, and ethics (Rubin et 

al.,  2002).  Leadership  skills  are  not  given  much  importance  in  construction  as  compared  to 

technical skills (Skipper & Bell, 2006). This is because of the culture and mentality prevailing in 

the industry. The focus is only on the day to day transactions to achieve the cost, schedule, and 

quality results. Thus, the managers end up managing the workforce every day to hit the targets, 

rather than leading the teams in continuous improvement towards long term goals (Toor & Ofori, 

2008).  

In a literature review, Toor & Ofori (2008) found that leadership related endeavors date back to 

as early as the 1980s, but progress has been plodding. The reason lies in the distance of industry 

from social science. Neither social scientists understand the dynamics of construction, and nor 

the construction professionals realize the importance of social science (Langford et al., 1995). 

 

25 

 

The majority of studies done for leadership in construction has focused on behaviors and traits 

of  project  managers  and  supervisors  quantitatively.  Moreover,  almost  all  studies  focus  on 

theories  based  on  task  versus  relationship  orientation  of  leaders.  Only  a  few  studies  use 

transformational leadership as their basis for research. No study was found that used shared 

transformational as the leading form. 

Leadership research for green AEC projects is further scarce. Substantial efforts in improving 

management frameworks for sustainable construction exist, such as green project management 

practices (Robichaud and Anantatmula, 2011) and green project management framework 

(Rumaithi & Beheiry, 2016). Despite the immense value of these studies, they do not fulfill the 

requirements of leadership. Leadership for sustainability is the weakest in construction, which 

is a significant gap, given the importance of sustainability in current times (CIOB, 2008). 

Recently, Tabassi et al. (2016) studied the relationship of leadership competencies of project 

managers and the performance of sustainable building projects in Malaysia. The authors found 

a strong relationship for intellectual competencies of project managers and impressed upon the 

need to further the research with moderating and mediating variables to explore in-depth. 

2.3.2.  Roles 
 
A  construction  project  focusing  on  sustainability  can  have  many  additional  roles  and 

responsibilities.  This  section  begins  by  discussing  the  leadership  requirements  of  core 

stakeholders:  Owner,  Architect,  and  Contractor  in  a  traditional  setup.  This  is  followed  by  a 

discussion on how leadership is facilitated differently in various project delivery systems.  

 

 

26 

 

a.  Owner 

The owner or client has been categorized as the single most important stakeholder to 

determine a green AEC approach for the project (Pitt et al., 2009). Owners are the driving force 

behind the success of green AEC projects. Their commitment, or dedication to implementing 

the sustainability features, is translated into the achievement of green project goals (Korkmaz 

et al., 2010; Beheiry, 2006). The type of owners and their motivation behind going green is 

essential in this regard (Korkmaz et al., 2011). The owners can be looking for energy efficiency 

for long term savings, better indoor air and light quality for improved productivities, passion for 

the environment, and marketing. Highly committed owners try to introduce sustainability early 

in the process (Korkmaz et al., 2011).   

The indicators of owner’s commitment leading to the success of green AEC projects include (i) 

Educating project team members (ii) Selecting Project Participants based on their expertise (iii) 

Integrating the team members (iv) Empowering project team participants to develop innovative 

solutions (v) Commissioning of separate experts to guide the project delivery process (vi) 

Support from top management (vii) Encouraging improved performance of project participants 

(viii) Developing and sharing a vision & (ix) Early introduction of sustainability in the project 

(Olanipekun et al., 2017). All of these indicators can be related to one of the dimensions 

presented in the transformational leadership theory. The relationships are presented in Table 

2-2. 

 

 

27 

 

Table 2-2 Relationship Between Transformational Leadership and Owner's commitment 

Transformational Leadership dimensions 

Related Indicators of Owner’s Commitment 

(Bass, 1985) 

Individual Consideration 

(Showing empathy; Paying attention to development 

needs and growth) 

(Olanipekun et al., 2017) 

Educating Project Team Members 
Selecting  Project  Participants  based  on  their 
expertise 
Support from top management 
Integrating the team members 
Empowering project team participants to develop 
innovative solutions 
Commissioning of separate experts to guide the 
project delivery process 
Developing and sharing a vision 
Encouraging  improved  performance  of  project 
participants 
Early introduction of sustainability in the project 

Intellectual Stimulation 

(Soliciting followers’ new ideas; stimulating intellectual 

creativity to solve complex problems) 

Inspirational motivation 

(Show determination and confidence; articulate an 

inspiring vision) 

Idealized influence 

(Setting an example as a role model for followers) 

 

b.  Architect/ Designer 

Architects  are  considered  the  second  most  important  stakeholders  after  owners  for  the 

implementation of sustainability in construction projects (Pitt et al., 2009). The realization of the 

need for leadership skills in architects is very old.  

“The  way  people  work  together  is  the  most  primary  form  of 

communication. Architects should be leaders in this capacity and 

not just presenters of final results.” (Straus & Doyle, 1958) 

Architects were once master builders, but over time their role has been reduced to designing 

only. The construction industry today is more fragmented than ever. This calls for architects to 

be proactive and expand their scope of work to include collaboration and integration (Burr & 

Jones, 2010). Architects of today are required to help devise a vision with the owner (idealized 

influence), communicate extensively with the contractor (individual consideration), and include 

 

28 

 

his skills during the design process (intellectual stimulation) (Burr & Jones, 2010). In addition to 

the owner and contractor, it is critical for architects to lead miscellaneous designers involved in 

the process as well. According to a study, structural engineers feel that the design team fails to 

integrate when architects do not fulfill their leadership role (Uihlein, 2016). 

c.  Contractor 

The contractor’s input is highlighted as not only valuable but critical in the green building 

literature (Riley et al., 2003). Amongst the traits of highly successful construction project 

managers are sharing values (idealized influence), imaging exciting possibilities and inspiring 

(inspirational motivation), and seeking out innovative ways to change and grow (intellectual 

stimulation). Chad Dorgan of McCarthy construction states that they do not rely on mandating 

sustainability on their workforce and try to inspire them (inspirational motivation) so that they 

do it with passion and desire (Slowey, 2017). Notably, the field supervision personnel, who are 

responsible for grass root implementation and play one of the most critical roles in the success 

of the project, should be kept in the communication loop from the very beginning. Kim et al. 

(2017) discovered that the field supervisors on green AEC projects feel that their abilities have 

not been utilized. The authors have advocated for sharing the vision of the project with them, 

and also including their feedback on early design phases of the project, if possible.  

Superintendents are responsible for on-site execution of work. Therefore, they are responsible 

for both the in-house workforce and subcontractor personnel in action. Hagberg (2006) has 

listed key attributes for successful superintendents, and the first of them is leadership. Also 

included in the list are motivational skills and having a vision.   

 

29 

 

d.  LEED/Sustainability Consultants 

 Many green building projects teams have one or more specialized team members known as 

Sustainability/LEED consultants to help them through the LEED process. There is not much 

literature available on these consultants. They, however, have been identified as one of the 

main stakeholders and leaders in green building teams (Opoku, 2015). They are considered very 

important for the LEED certification process to be smooth and effective (Frattari et al., 2012). 

2.3.3.  Project Delivery Methods 

 

As project delivery methods change, so do the responsibilities and consequently, the leadership 

requirements. The design-build (DB) system makes the design-build contractor in charge of 

both design and construction (Widjaja, 2016). The advantages of DB are more integration 

between design and construction professionals. The design-build contractor has excellent 

potential to share the vision, inspire the complete project team, and stimulate the team 

intellectually. The negative aspect is that owner has less control over the design. As the owner 

is the source of the project’s sustainability vision, a single point of contact might make it 

difficult for the owner to practice transformational leadership. 

 Integrated Project Delivery (IPD) seems to have the most potential for transformational 

leadership. In this highly collaborative delivery system, all major participants (owner, 

contractor, architect/designer, and subcontractors) enter in a contract together with financial 

risk-sharing (Widjaja, 2016). In other delivery systems, the owner creates the project vision 

majorly with input from the architect/design-build contractor. In IPD, all major stakeholders, 

including at least the architect/designer and contractor, are actively involved in assisting the 

 

30 

 

owner in vision building. Due to a common, contract the organizational boundaries are faded, 

and there is free communication. Also, more stakeholders are included in communication and 

decision-making processes (like suppliers, future building users, and subcontractors). This 

allows more motivation, individualized consideration, and intellectual stimulation. Thus, IPD 

creates an excellent environment for transformational leadership to not only exist at more 

levels but also to thrive unopposed.  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

31 

 

 

Chapter 3 STUDY FRAMEWORK 

The previous chapter gives an insight into green AEC projects and transformational leadership 

with elaborative details. By reflecting upon the learnings, this chapter develops a multilevel 

framework of transformational leadership and individual/team outcomes. The framework 

provides the basis of the study hypothesis and research questions.  

The framework proposes investigations at the following levels: 

• 

Individual Level: The framework argues that the perceptions of transformational 

leadership are directly related to team performance. Further, this relationship is 

mediated through perceived team integration.  

•  Project Team Level: The framework takes an exploratory approach to observe the 

dynamics of transformational leadership in green AEC project teams. 

The framework (shown in Figure 3-1) is based on the narrative that leadership by its very nature 

is a multilevel phenomenon, and it is necessary to broaden the investigations accordingly (Chun 

et al., 2009).  

Individual Level Framework 

3.1. 
 
Transformational leadership is positively related to team performance (Braun, 2013; Wang & 

Howell, 2010; Dionne, 2004). Also, integration is a significant trait required by the green AEC 

project teams for optimum performance (Widjaja, 2016; Magent et al., 2009; Korkmaz et al., 

2010). Transformational leadership has the potential to be the source of integration in work 

 

32 

 

teams, as shown in previous studies (Zaccaro, 2001; Sarros et al., 2008). Therefore, the 

following hypotheses are proposed at the individual level. 

         

    Figure 3-1 Multilevel Framework for the Study in green AEC projects 

 
3.1.1.  Hypothesis 1: Transformational leadership and team performance 

 

Transformational leadership uses inspiration, individualized consideration, and intellectual 

stimulation to create cohesion and communication in teams leading to improved performance 

in teams (Dionne, 2004). The charisma of transformational leaders inspires the followers to 

transcend above their personal agendas and work wholeheartedly for the team goals (Shamir et 

al., 1993). It also creates a climate of trust amongst the team members, which is a significant 

indicator of team performance (Braun et al., 2013). Thus, the following hypothesis is posed: 

Hypothesis 1. The individual perception of transformational leadership for sustainability in a 

team is positively related to the individual perception of team performance in sustainability. 

 

33 

 

3.1.2.  Hypothesis 2: Mediating role of team integration 

 

Regarding individual perceptions, a team is defined as well-integrated when its members 

understand their roles and are comfortable with them, when they freely contribute to the team 

discussions and decision-making, and when they feel positive about the team’s overall 

functioning (Litchtenstein et al., 1997). Leaders are required not only to influence the followers 

collectively but also to encourage and facilitate them to interact and integrate. Improvement in 

the team process is ignored in earlier leadership theories. Transformational leadership has the 

unique strength of aligning the individual goals with team goals and creating an environment of 

interaction and collaboration (Zaccaro et al., 2001). Team integration is highlighted as the 

primary requirement for optimum performance in green AEC project teams (Mollaoglu-

Korkmaz et al., 2011). Thus, the following hypothesis is posed: 

Hypothesis 2. The individual perception of team integration mediates the relationship 

between the individual perception of transformational leadership for sustainability in the 

team and the individual perception of team performance in sustainability. 

3.2. 

Team Level Framework 
 

As discussed in the previous chapter, shared leadership/team leadership studies have used 

both aggregation and social network techniques for analysis. Aggregation, whether it is in the 

form of collective leadership (such as in Friedrich et al., 2009) or team leadership (such as in 

Braun et al., 2013), is unable to incorporate the dynamics of large inter-organizational work 

teams such as in construction. Social Network Analysis (SNA) is the most suitable approach to 

study the dynamics of shared leadership. Shared leadership is based on relationships, and SNA 

 

34 

 

is relational by its very nature. Tie between actors (typically people) is the unit of analysis in 

SNA. Building upon this unit, SNA has developed various network structure and analysis 

techniques. Therefore, shared leadership can not only be visualized but also be better analyzed 

and explained through SNA (Meindl et al., 2002).  The current study proposes to use the 

perceptions of individual team members regarding the transformational leadership of one or 

more leaders to form social network ties. These ties are used to create leadership networks for 

team-level analysis. Figure 3-2 represents this SNA approach.  

 

Figure 3-2 Conceptual network diagram for transformational leadership in construction project team 

The SNA based shared leadership studies to date have focused on relatively smaller teams. 

Moreover, almost all of them belong to the same functional unit. For example, Mehra et al. 

(2006), in their highly cited distributed leadership paper, used 28 sales teams with an average 

 

35 

 

size of 13 members. Similarly, the shared leadership study by Carson et al. (2007) used 59 

consulting teams with member sizes ranging from 4 to 7. The samples of these studies are in 

high contrast with green AEC project teams, which are not only cross-functional but are also 

much larger. For example, a recent study by Garcia-Cortes (2017) on an educational building 

aiming for LEED certification. This study reported a project team size of more than 160 

members from over 12 functions creating several multi-disciplinary sub-teams.    

Feng et al. (2017) suggested that distributed leadership should be assessed in the context of 

leader attributes, the nature of the task, and the context of occurrence. Traditional methods for 

shared leadership in teams consider a single measure like centrality or density of social 

networks as representative of team leadership. Expanding on the traditional approach, this 

study proposes an exploratory approach for an in-depth understanding of how 

transformational leadership emerges in green AEC project teams and the factors that influence 

it. The next sections review key concepts in the literature to help shape the research questions 

in this pursuit. 

3.2.1.  Research Question 1: Identification of Transformational Leaders 

 

The first concept is of tiers in construction project teams, as discussed by Mollaoglu et al. 

(2014). It states that the construction project teams are distributed into three tiers: (1) A core 

tier consisting representatives of the owner, contractor, and designer/architect; (2) an 

intermediate tier consisting of organizational colleagues of core tier members; and (3) a 

peripheral tier, which includes sub-contractors, suppliers, and various consultants. This tiered 

structure is shown in Figure 3-3. 

 

36 

 

Figure 3-3 Construction project team tiers (adopted from Mollaoglu et al., 2014) 

 

Literature provides a rationale for leadership requirements in the core team members. 

Sustainability in construction is an owner-driven pursuit. The type of owner and the reason for 

pursuing green AEC projects are, therefore, of prime importance (Korkmaz et al., 2011). For 

example, an owner might be interested in a minimum level of LEED certification because of 

legal obligations (such as state or city laws), or to add marketing value (such as real estate 

developers), or maybe an owner is dedicated to the cause of environmental protection and 

wants to go for the top certification level. This background shapes the owner’s commitment, 

one of the most highlighted metrics for the green AEC team and project success (Korkmaz et al., 

2010; Olanipekun, 2017). The owner’s commitment is related closely to transformational 

leadership, as discussed in section 2-2.   

One of the prime features of transformational leadership is sharing vision through inspiration. 

The project vision is created by the owner with the help of the designer(s) (Burr & Jones, 2010). 

 

37 

 

The architect, therefore, is considered the second most important stakeholder in green AEC 

projects, after the owner (Pitt et al., 2009). This vision is later conveyed to the contractor, which 

is the party to lead the construction of a facility. Therefore, as prime carriers of project vision, 

the core team members are expected to demonstrate transformational leadership behaviors. 

Other members that have been indicated in the literature to practice leadership in construction 

teams include the project managers. The project managers have the responsibility of inspiring, 

sharing the vision, and promoting innovation amongst the team (Tabbasi et al., 2016; Slowey, 

2017). Also, the field supervisors are expected to practice leadership behaviors within their 

functional spheres (Hagberg, 2006).  

The literature has identified the team members mentioned above as leaders in AEC project 

teams. However, there is a need to explore their transformational leadership behaviors in the 

context of green AEC projects. Also, there is a possibility of other team members from lower 

tiers of AEC project teams to emerge as transformational leaders. Emergent leaders have been 

identified in other studies like Mehra et al. (2006). Thus, the following research question is 

formed: 

Research Question 1. Who are the transformational leaders for sustainability in green AEC 

project teams and how are they distributed in the project networks? 

3.2.2.  Research Question 2: Flow of Transformational Leadership 

 

The second concept is trickle-down leadership, as discussed by Mayer et al. (2009). The findings 

of this study suggest that there is a trickledown or top-down effect of leadership from top 

leaders to supervisors and, finally, the employees. However, the proponents of shared 

 

38 

 

leadership disagree with this conceptualization. They believe that it is a much more 

complicated phenomenon, and formal authority is not the only source for leadership in teams 

(D’Innocenzo et al., 2016).  

Moreover, as the team member categorization in the tiers proposes, the intermediate tier team 

members are the functional subordinates of core tier team members. It can be assumed that 

the leaders of the core tier will have followers in their respective functions. However, this 

fragmentation has been observed to fade away in the integrated type of contracts like Design-

Build and Integrated Project Delivery (IPD) (such as in Garcia-Cortes, 2017). Therefore, we 

cannot confidently predict the followers of any leader. Thus, the following research question is 

posed: 

Research Question 2. How does transformational leadership for sustainability flow in green 

AEC project teams? Who are the followers of transformational leaders?  

3.3. 

Summary 

Chapter 3 presented a multilevel framework of transformational leadership in green AEC 

project teams at individual and team levels. The individual-level hypotheses and the team level 

research questions developed in the framework are listed again in the table 3-1. 

 

 

 

 

39 

 

Table 3-1 Study hypotheses and research questions 

Individual Level Investigations 

Hypothesis 1. The Individual perception of transformational leadership for sustainability in a team is 
positively related to the individual perception of team performance in sustainability. 
Hypothesis 2. The Individual perception of team integration mediates the relationship between the 
individual perception of transformational leadership for sustainability in a team and individual 
perception of team performance in sustainability. 

Team Level Investigations 

Research Question 1. Who are the transformational leaders for sustainability in green AEC project 
teams and how are they distributed in the leadership network? 
Research Question 2. How does transformational leadership for sustainability flow in green AEC 
project teams? Who are the followers of transformational leaders? 

40 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Chapter 4 METHODOLOGY 

Summary of Goals and Objectives 

4.1. 
 
The primary goal of this study was to “Explore the structure and role of transformational 

leadership in AEC project teams, providing a significant contribution to AEC literature.” The 

objectives of the study are as follows: 

1.  Create a multi-level framework for study and measurement tools that: 

a.  Guides the hypothesis development to relate transformational leadership and 

team performance mediated by team integration at the individual level, and  

b.  Provides reasoning for research questions in order to explore the dynamics of 

transformational leadership using social networks at the team level. 

2.  Validate the framework and measurement tool via expert interviews. 

3.  Empirically test the study hypotheses at the individual level. 

4.  Answer the research questions at the team level, exploring the leadership networks in 

teams and assessing various characteristics of leaders. 

4.2.  Overview of Study Phases 
 
Phase 1. Framework Development: This phase includes the development of a multi-level 

framework that guided the study hypothesis and proposition development, as described in 

Chapter 3. The study variables considered for measurement are further explained in section 

4.3.  

 

41 

 

Phase 2. Expert Interviews: The primary purpose of conducting expert interviews is to seek 

guidance on the topic in general, and the data collection approach in specific. The findings from 

these interviews are presented in Chapter 5. The feedback of experts on measurement tools 

and sampling strategy are discussed in section 4.4. 

Phase 3. Study of Project and Team Members: This phase includes the planning and execution 

of main data collection from projects and individuals that are team members in those projects. 

Section 4.5 includes the characteristics of the study population, sampling strategy, project 

selection criteria, data collection procedure, data analysis techniques and procedures to 

maintain data quality. 

4.3. 

Phase 1: Framework Development 

A multilevel framework was developed in Chapter 3. This section presents study hypotheses 

and research questions, study variables, and development of the measurement tool. 

4.3.1.  Study Hypotheses and Research Questions 

Individual Level Hypotheses: 

Hypothesis 1. Individuals’ perceptions of transformational leadership for sustainability 

in a team is positively related to individuals’ perceptions of team performance in 

sustainability. 

Hypothesis 2. Individuals’ perception of team integration mediates the relationship 

between individual perceptions of transformational leadership for sustainability in team 

and individual perception of team performance in sustainability. 

 

42 

 

Team Level Research Questions 

Research Question 1. Who are the transformational leaders for sustainability in green 

AEC project teams and how are they distributed in the projectnetwork? 

Research Question 2. How does transformational leadership for sustainability flow in 

green AEC project teams? Who are the followers of transformational leaders? 

4.3.2.  Study Variables 

There  are  three  individual  level  variables  in  the  research  model.  Perceived  transformational 

leadership is independent in nature, perceived team integration is the mediating variable, while 

perceived team performance is a dependent variable. Additionally, there is project performance, 

which  was  evaluated  through  both  traditional  (cost,  schedule  and  quality)  and  sustainability 

measures. 

Perceived Transformational Leadership 

This  study  used  a  distributed  leadership  approach  as  proposed  by  Mehra  et  al.  (2006)  for 

Transformational Leadership. Thus, a team can have more than one leader including officially 

designated and emergent leaders. Each team member was asked to nominate one or more fellow 

team  members  (maximum  3)  as  LEED/sustainability  leaders.  The  nominator  was  then 

investigated  further  regarding  transformational  leadership  skills  of  each  nominee.  The  total 

perceived  transformational  leadership  for  each  nominator  was  calculated  by  aggregating  the 

weight of each nominee leader, as depicted in Fig. 3-2.  

 

 

43 

 

 

 

 

 

 

W1 

W2 

W3 

Nominator team member 

Nominee transformational leader 

W  Weight of perceived Transformational 

Leadership  

Total Transformational 

Leadership  

= W1 + W2 + W3 

Figure 4-1 Transformational Leadership Exposure 

The  measures  for  Transformational  leadership  are  inspired  from  the  adapted  version  of  the 

Multifactor Leadership Questionnaire - MLQ 5X (Xirasagar et al., 2005). A total of 10 items - 2 for 

each dimension - are adapted. All responses were collected on a Likert type scale: 1 (Not at all); 

2 (Once in a while); 3 (Sometimes); 4 (Fairly Often); 5 (Always). The metrics are listed in table 4-

1., and the survey questions are given in Appendix C. 

Perceived Team integration 

Green AEC project delivery literature has heavily argued team integration phenomenon, which is 

a  construct  based  on  delivery  attributes  such  as  early  involvement  of  participants,  design 

charrettes, and communication methods used (Mollaoglu-Korkmaz, 2014; Franz et al., 2017). This 

study particularly focuses on the individuals’ perceptions of team integration. In this regard, this 

study followed the approach used by Lichtenstein et al. (1997). According to them, perception of 

team integration has three dimensions:  individual participation, role clarity, and assessment of 

team functioning (Lichtenstein et al., 1997). The metrics are listed in table 4-1., and the survey 

questions are given in Appendix C. 

 

 

44 

 

Perceived Team Performance 

Team performance is a very common terminology in literature, yet there is no agreement on a 

single standard way to measure it (Guzzo & Dickson, 1996). Many studies base it on the project 

outcomes,  some  use  effectiveness  of  communications  within  the  team  as  the  ground,  while 

others claim team functioning should be the dimension to approach this (Yeung, et al., 2007; Hsu 

et al., 2012; Hoegl & Gemuenden, 2001). This study adopts the team performance metrics as 

given  in  Tabassi  et  al.  (2014),  as  they  are  created  specifically  in  the  context  of  construction 

projects. The metrics are listed in table 4-1., and the survey questions are given in Appendix C. 

Table 4-1 Individual Level Study Metrics 

Construct 
Perceived 

Transformational 

Leadership 

Xirasagar et al. 

(2005) 

 

 

Perceived Team 

Integration 

Lichtenstein et al. 

(1997) 

 

 

 

Dimensions 
Idealized 
Influence: 
Attributes 
Idealized 
Influence: 
behaviors 

Inspirational 
Motivation 

Intellectual 
Stimulation 

Individualized 
Consideration 

Individual 

participation with 

team 

Role clarity in 

team 

Individual 

assessment of 
team functioning 

Measures 

Inspires pride in me for being associated to a LEED project. 
Gives respect and regard. 
Communicates the sustainability vision and goals for the 
project. 
Effectively communicates a collective sense of mission 
regarding LEED certification. 
Generates optimism about project’s success in achieving LEED 
certification goals. 
Passionate to work on project’s LEED certification goals. 
Discusses different perspectives on problems related to LEED 
certification. 
Helps evaluate the benefits and liabilities of each potential 
solution related to LEED certification. 
Helps think differently about sustainability. 
Helps in developing strengths related to LEED certification. 
Contributing information about sustainability. 
Interpreting information about sustainability 
Comfort in disagreement 
Contribute in decision making about sustainability. 
Awareness of expectations on sustainability related tasks. 
Awareness of team roles and responsibilities for execution of 
sustainability related tasks. 
Interdependence of team members on sustainability related 
tasks. 
Fitness of sustainability related activities together 

45 

 

 Table 4-1 (Cont’d) 
 

Perceived Team 
Performance 

 

Tabassi et al. (2014) 

 

Project Performance 

 

 

Team collectiveness vs fragmentation on sustainability related 
tasks. 
Sound technical decisions about sustainability on project 
Meeting project sustainability expectations. 
Appropriate courses of action to meet project sustainability 
requirements. 
Choosing the best available strategies for meeting project 
sustainability goals.  
Fewer reworks on sustainability related tasks 
Innovative solutions to the problems related to sustainability. 

Project performance was measured for both traditional and sustainability dimensions. 

•  Traditional Project performance was measured in terms of triple constraints (Time, cost 

and quality). Completion in time, within budget, and as per the required quality has been 

widely recognized as the major criteria of project success (Meng, 2012). Time and cost 

performance were measured using the schedule and cost growth metrics (given below) 

developed by Konchar & Sanvido (1998).  

Schedule growth = (Total Time/Total As-Planned Time)/Total As-Planned Time * 100  

Cost growth = (Final Project Cost – Contract Project Cost)/Contract Project Cost * 100 

For quality, the owner’s satisfaction regarding product is considered the major measure 

as encouraged by Kagioglou et al. (2001). Satisfaction level was measured for the whole 

building, as well as for each building system separately for detailed analysis. 

•  Sustainability Performance was measured based on the LEED performance of the project. 

Performance was measured by comparing the initial and current LEED scorecard/checklist 

 

46 

 

of the project. LEED scorecards have been used previously to measure the performance 

of LEED buildings, such as by Mollaoglu-Korkmaz et al. (2013).  

4.3.3.  Development of the Measurement Tool 
 
In light of the study variables described in section 4.3.2. three data collection and measurement 

tools were developed. Open ended interview questions (Appendix C) were designed for 

Industry experts. A survey (Appendix C) was developed for data collection from all team 

members. Ans structured interview questions (Appendix D) were designed for Owner’s 

representative to elicit information about the project characteristics and performance.  

Institutional Review Board Requirement: As the study uses human subjects, it was subjected to 

a review and approval from the Institutional Review Board (IRB) at Michigan State University 

(MSU). A data collection protocol was therefore submitted for approval before the start of data 

collection. The data was collected through online surveys. (Approval letter in Appendix A) 

4.4. 

Phase 2: Expert interviews 

Expert judgement is recommended to improve the content validity of data collection 

instruments (Korb, 2012). Three industry experts – one from owner, contractor, and designer 

organizations each – with extensive experience in the field of green AEC were interviewed via 

video calling. In addition to feedback on data collection tools, the structured questions (given in 

Appendix B) also gathered experts’ views on: complexity and challenges of green AEC; origin of 

sustainability in projects; and presence, emergence, and role of transformational leaders. 

Results from these interviews were used to modify data collection tool and inform sampling 

strategy for stage 3, survey of project team members. The detailed findings from these 

 

47 

 

interviews are presented in Chapter 5. Here, we present the modifications suggested by experts 

for measurement tool. 

4.4.1.  Measurement Tool Revisions 

The experts had a number of concerns over the clarity of questions in the initial tools. With 

these changes, the new Questionnaire tool for all team members and structured interview 

questions for owner’s representative are given in Appendix C and Appendix D respectively. The 

questions in initial tools which were recommended to be changed/modified/removed are listed 

in table 4.1. below.  

Table 4-2 Changes in the Measurement tools based on Expert Interviews 

Initial Statement 

I frequently interpret information 

Questions for Team Integration 

Recommended change 

Replace with: I’m frequently encouraged to think 
outside my job responsibility. 
Modify into I can comfortably talk about my 
opinions/ideas. 
Both statements rejected. Replaced with the 
following: Members of my team value the roles 
and contributions of all team members. 

Questions for Transformational Leadership 

 

48 

 

I can comfortably disagree with others on my 
team. 
I’m certain about what other members of my 
team expect of me. and 
I’m certain of what other members of my team 
are supposed to do. 
 

We function as a team working for shared goals, 
as compared to fragmented individuals focused 
on their personal agendas. 

The team has made sound technical decisions. 

The output of the team has met project 
expectations. 
The team has chosen the best available strategies 
for meeting project goals.  
The team has succeeded in achieving fewer 
reworks. 
The team has developed innovative solutions to 
the problems. 

New Question: I’m aware of the overall LEED 
goals of the team. 
I feel like I’m an integral part of the collective 
team effort pursuing shared LEED and green 
building goals. 
Questions for Team Performance 

The team has made sound decisions based on 
project’s sustainability principles. 
The output of the team has exceeded the initial 
project performance goals 
The team has explored a wide array of options to 
choose the best available strategies. 
The team has succeeded in achieving fewer 
reworks/change orders. 
The team has developed innovative solutions in 
pursuit of project goals. 

 Table 4-2 (Cont’d) 
This person gives me respect and regard. 

This person communicates the sustainability 
vision and goals for the project. 
This person discusses different perspectives on 
problems. 

 

4.4.2.  Data Collection Strategies 

This person builds respect and regard among all 
team members. 
This person facilitates clarity regarding 
sustainability vision and goals for the project. 
This person helps us explore different perspectives 
on potential solutions related to LEED certification. 

The findings of expert interviews identified factors that impact the performance of 

sustainability projects. These factors included owner and/or tenant types (e.g., private, public, 

government, higher education), and regulations by city/state government. The data collection 

was designed keeping all these factors in mind. An attempt was made through systematic 

sampling to incorporate a variety of owner types and project locations in the sample.  

4.5. 

Phase 3: Study of Projects and Team Members 

4.5.1.  Study Population and Sample 
 
The study population was limited to ongoing sustainable projects in the United States aiming 

for one of the four LEED certifications under LEED Version 4. Certification for sustainability 

provides a standard measure for evaluating and comparing performance. LEED being the most 

common certification globally (Ewing et al., 2013) was therefore considered the most suitable 

option. To control the variability based on facility types, only commercial projects were 

considered. Commercial projects generally larger and more complex as compared to residential 

projects (Senescu et al., 2012), they are expected to have larger and more inter-disciplinary 

teams. The findings of expert interviews (Phase 2) revealed that location (e.g., climate, 

topography, the stringency of local codes and regulations, sensitivity of the public towards 

 

49 

 

sustainability issues) and owner and/or tenant types (e.g., private, public, government, higher 

education) affect the performance of green AEC projects. Therefore, the population was not 

limited in these factors to overcome sampling error and improve the generalizability of study 

results for all population.  

As discussed in section 2.3.2, delivery systems can impact transformational leadership in teams. 

Therefore, an attempt was made to select the projects from three common types of systems, 

namely:  Design-Build,  Design-Bid-Build, 

Integrated  Project  Delivery,  and  Construction 

Management  at  Risk.  Also,  the  level  of  LEED  building  certification  defines  the  level  of 

service/complexity  for  projects,  that  can  impact  transformational  leadership  in  teams.  It  is 

observed  that  Gold  and  Platinum  certifications  require  more  optimization  as  compared  to 

certified and Silver certifications (Kats et al., 2003). Therefore, an attempt was also made to select 

projects  from  each  of  the  4  certification  levels.    9  near  completion  projects  with  similar 

characteristics with reference to specifications and size were targeted for consistency while the 

researcher aimed for variety in location, owner type, aimed LEED certification level, and project 

delivery methods variables as described above. Multiple case studies provide flexibility for cross-

case comparison and synthesis. Moreover, twelve projects generated a cumulative sample size 

of more than 1000 individuals for objective 1, which ensured the quality of quantitative analysis.  

4.5.2.   Case Study Selection  
 
USGBC’s online database (www.usgbc.org/projects) was used for selection of case studies. New 

construction commercial projects aiming for a LEED certification using LEED V4 were filtered for 

United States, and all the projects already having a finish date were neglected as the study 

 

50 

 

required ongoing construction. Systematic sampling process was used, and every 10th project 

on the list was send an invitation to participate in the study, until the required sample size and 

characteristics were achieved.  

4.5.3.  Data Collection  

 

An online survey questionnaire (Survey given in Appendix C) was used to collect data from the 

sample teams. It was distributed amongst all the team members to extract their responses on 

distributed transformational leadership, sustainability vision, and task mental models. A semi-

structured  interview  was  used  to  elicit  information  about  the  project  delivery  attributes  and 

project performance from project managers. The interview (provided in Appendix D) consists of 

both close ended and open-ended questions for a broader exposure to each case study. 

4.5.4.  Data Analysis and Quality 
 
As discussed earlier, the study uses mixed methods to answer the research questions. The 

individual level model uses quantitative approach to the hypotheses, while the project level 

model uses propositions that require qualitative exploration and reasoning. Therefore, this 

section of data analysis is divided into the quantitative and qualitative parts respectively.  

Quantitative Data Analysis and Quality 
The quantitative analysis for the model will begin with quality checks for survey variables 

(Appendix C). These checks include tests for reliability and validity. The statistical analysis 

follows. 

Reliability: mainly depends upon the consistency of a measure. It means that a person providing 

with responses for an instrument should have the same responses each time he/she fills that 

 

51 

 

survey. Although an exact value of reliability cannot possibility be calculated, various measures 

can give us an estimate. The attributes of reliability include homogeneity (also known as internal 

consistency), stability and equivalence. The description of these attributes, along with their tests 

are provided in the table 3-1. 

Table 4-3 Attributes of reliability (adopted from Heale & Twycross, 2015) 

Attribute 
Homogeneity 

Description 

The extent to which all items on a scale 
measure one construct 

Stability 

The consistency of results for an instrument 
with test repetitions.  

Equivalence 

The consistency of responses with multiple-
users or alternative forms of an instrument 

• 

 

Tests 

Item-to-total correlation  

• 
•  Split-half reliability 
•  Kuder-Richardson 

coefficient 

•  Cronbach’s α 
•  Test-retest 
•  Parallel-form reliability 

testing 
Inter-rater reliability 

Validity: Validity means that the instrument is measuring what it was intended to measure. 

There are different forms of validity. The three main types are construct, content and criterion. 

Their descriptions along with tests are given in table 3-2. 

Table 4-4 Types of Validity (adopted from Korb, 2012 & APA, 1974) 

Validity Type 
Construct 

Description 

The tool accurately measures the construct 
under investigation 

Content 

Criterion 

The tool covers all the aspects related to the 
construct  
The measures in the tool are related to the 
outcome 

Tests 

•  Multi-trait Multi-method 

(MTMM) matrix  
•  Factor Analysis 
•  Expert judgement 

•  Concurrent validity test 
•  Predictive validity test 

 

 

52 

 

Although the constructs used in the study are adopted from validated constructs but modified 

to some extent, therefore CFA was employed to validate the constructs before using them for 

statistical analysis in hypotheses testing. 

Statistical Analysis: The study used Structural Equation Modeling (SEM) to test the study 

hypotheses. Independent samples t-test and chi-square tests were also employed to test the 

characteristics of identified leaders.  

Qualitative Data Analysis and Quality 
The second part of the study will assess various propositions at the project level qualitatively. As 

the  proposed  research  will  use  case  studies,  the  relevant  quality  considerations  and  analysis 

techniques will be used.   The reliability, and various forms of validity for case study research, 

along with their descriptions and techniques as presented by Yin (2003) are given in table 3-3.  

Table 4-5 Case Study research quality tests 

Quality Test 
Reliability 

Construct Validity 

Internal Validity 

Description 

Depicts that the operations of 
the research can be repeated 
achieving the same results 
Depicts accurate operational 
measures for the for the areas 
being studied 

Establishes a causal link (For 
Explanatory case studies only) 

External Validity 

Depicts the generalizability of 
research findings 

 

 

Technique for case study 
•  Use case study protocol 
•  Develop case study data 

base 

•  Use multiple sources of 

•  Establish chain of 

evidence 

evidence 

•  Pattern-matching 
•  Explanation-building 
•  Rival explanations 
• 
•  Theory in single case 

Logic models 

studies 

•  Replication logic in 

multiple case studies 

53 

 

Case study data analysis: The proposed study uses the theory to form propositions in order to 

direct the case study towards a specific data. The case study analysis was done using pattern-

matching, explanation-building, and cross-case synthesis techniques (Yin, 2003). 

Explanation Building 

Explanation building is an iterative technique used to demonstrate causal links. First the 

theoretical statements are laid down, and the findings of case study are compared. The 

statements are then revised, if needed, and compared with the findings again. The revisions are 

further carried out until the causal link is identified. This technique needs case as the analysis 

may turn away from the real topic of study.  

Cross Case Synthesis 

Cross Case Synthesis is used in multiple case study researches to find pattern between data. 

This adds to the internal validity of the study. An objective scoring or comparison criteria is 

established to facilitate comparisons across multiple case studies. Objective data is used to find 

similarities and differences in the features of each case study. 

 

 

 

 

 

54 

 

Chapter 5 RESULTS FOR EXPERT INTERVIEWS 

 

 

This chapter covers the results from initial Expert interviews. As mentioned in previous chapter, 

three experts - belonging to contractor, client and consultant organizations - were interviewed 

in phase 2 of this study. The major objective was to validate the subject and content of this 

study. The structured interview questions (Appendix B) were designed to 1) record the 

perceptions of industry experts regarding green AEC projects and the role of transformational 

leadership, and 2) receive the feedback of industry experts on the structure and content of 

questionnaire. In this chapter we begin with the introduction of experts, and then present the 

findings of part 1 in structured interviews. The findings of part 2 and revisions for measurement 

tools are already presented in section 4.4. 

5.1. 

Introduction of Experts 

As mentioned earlier, three experts were interviewed from designer, contractor and owner 

teams. A brief introduction of each expert is presented in Table 5-1. 

 Table 5-1 Expert Introductions 

Expert  Team 

Experience  Highlights 

1 

Designer 

21 Years 

 

 

 

•  Participated as a team member for over 60 LEED 

certified and 80 LEED registered projects. 

•  Co-chaired USGBC’s international task force. 
•  Served as LEED faculty. 
•  Served on LEED steering committee. 
•  Chaired the LEED curriculum committee. 
•  Co-authored a book on integrative design. 

55 

 

 Table 5-1 (Cont’d) 

2 

Contractor  15 Years 

3 

Owner 

18 Years 

 

5.2. 

Structured Interview Results 

•  Directly involved in more than 100 LEED projects 

of various nature.   

•  Served as corporate director of sustainability for 

two large construction companies. 

•  Author of two books on sustainability. 
•  Teaching sustainability at USGBC and other 

renowned platforms. 

•  Working as the director of development for a 

major developer since 2012. 
•  Managed 14 LEED projects. 
•  Also worked in the capacity of project manager 
for 3 years and delivered one of the first LEED 
project in Virginia. 

Structured interview questions were designed to elicit expert views on green AEC and role of 

transformational leadership in it. The findings are presented for each question one by one.  

Question: Can you please shed some light on the complexities of green AEC projects? How are 

they more challenging as compared to their traditional counterparts? 

Experts were unanimous that the complexity of sustainable projects cannot be generalized. It 

depends on many factors including the intention behind going green, the learning curve 

maintained by the team and the level of certification. A lower level LEED certification is not 

much different, and the only added complexity is additional requirements.  

Expert 1 believed that the aim behind going green is really what differentiates in the context of 

this question. If the aim is just to gain points and earn certification alone, a LEED project is not 

much different from traditional project. When the team aims higher, the individual point 

categories start depending on each other and the inclusion of innovation and integration 

 

56 

 

becomes critical. This is where a traditional team and a LEED team become different. “. . . the 

point categories now need to speak to each other. Each of the team members now need to think 

outside their normal area.”  

Expert 3 echoed with this idea, and specified Platinum level certification with LEED Version 4 

more complicated.  “. . . for example, we executed a LEED Platinum project and it was very 

challenging. Version V4 has made it even more difficult. You actually look for innovative 

solutions at a higher certification level or Platinum.”  

Expert 2 believed that LEED or green projects are not more complicated; they are just different. 

“ . . . . It just needs more patience and out of the box thinking sometimes. You need the right 

people for the right roles. Construction projects are very fast paced and green projects bring in 

the requirement of additional learning. Therefore, the important thing is to learn as you go. 

There is no time to stop and wait.” 

Question: How do these projects come alive? In other words, who initiates the idea of going 

green on a project in your experience? Why? 

All experts agreed that it is mostly the owner that initiates sustainability in such projects, 

however the underlying reasons for the owner to take this initiative varies from case to case.  

Expert 1 was of the view that owner is the key, and owner’s aim behind going green plays an 

important role “. . . if the mission of owner or owner organization comes in line with green 

transformational thinking, that really helps in achieving the best results.” 

 

57 

 

Expert 2 also believed that the owners mostly initiate sustainability projects because of reasons 

ranging from government requirements and marketability to sometimes even self-motivation. 

It is also possible for the architect to convince the owner, especially when it is possible without 

spending a lot of money. But normally it is always a business decision, one way or other. “. . . 

there are some owners, architects and contractors out there that truly care, but at the end of 

the day it is always a business decision. Interestingly, building green is often times financially 

profitable too. Also, when it is financially feasible it becomes an easy decision.” 

Expert 3 also suggested that Owner is the major initiator, with many possible reasons behind 

the decision of building green “. . . Nowadays it is mostly local requirement, which is strong 

motivator for basic level sustainability incorporation. Motivation behind going for higher 

certification levels is energy savings and return on investments, branding, and creating a more 

unique product in the marketplace.” 

Question: Is there generally a transformational leader involved in green AEC projects?  

The experts believed that such leaders exist in green AEC project teams, but they are rare.  

Expert 1 responded that there are generally a few team members who facilitate the procedure 

and take the lead for sustainability. There are normally from architect’s or designer’s side. 

Some owner’s do realize the importance of sustainable leadership in green projects and hire a 

third-party consultant for this role.  

Expert 2 also agreed that such leaders exist in teams, sometimes even at lower tier levels. But 

they are very few “. . . I met superintendents. People who managed logistics. Who were very 

passionate about the environmental cause and the spirit of building green?” 

 

58 

 

Expert 3 also had a similar understanding. “. . . Usually just one or two people. There is a person 

from the architect’s side normally that leads. But in better case scenarios, there is one person 

each from owner and architect teams who take up this role.” 

Question: Do you think transformational leaders of green AEC project team can positively 

impact the outcomes?  

Responses of industry experts varied to some extent for this question. Expert 1 strongly agreed 

to the impact and consequently the need of transformational leadership in teams.  Expert 2 and 

3, on the other hand, suggested that it happens sometimes. 

 

 

 

 

 

 

 

 

 

 

 

 

59 

 

Chapter 6 RESULTS FOR HYPOTHESES AND RESEARCH QUESTIONS 

 

Sample Characteristics and Data Demographics 

6.1. 
 
The following sections introduce the study sample. All case studies included in this study are 

educational institute projects. Therefore, first the data collection process is explained that led 

to this unique and unexpected set of projects. Afterwards, the characteristics of case studies 

are discussed one by one. Finally, demographics for data consisting of individual responses is 

presented.  

6.1.1.  Selection of Case Studies 

As per the guidelines laid out in section 4.5, systematic sampling was used to contact every 10th 

project on the list obtained from USGBC website and filtered for respective criteria (see section 

4.5.2). A total of 1512 projects were shortlisted, out of which 152 projects were contacted and 

9 projects eventually participated. Table 6-1 lists the owner categories with respective 

numbers. 

Table 6-1 Projects and their owner types – Available Vs Contacted Vs Participated 

Owner Type 

Business Improvement District 
Community Development Corporation 
Corporate: Privately Held 
Corporate: Publicly Traded 
Educational: College, Private 
Educational: College, Public 
Educational: Community College, Private 
Educational: Community College, Public 
Educational: K-12 School, Private 
Educational: University, Private 
Educational: University, Public 

Number of 
projects 
available 

5 
9 
267 
88 
51 
121 
5 
39 
19 
50 
115 

 

60 

Number of 

Projects contacted 

Number of 
Participants 

0 
1 
30 
9 
8 
15 
0 
6 
0 
4 
16 

 
 
 
 
1 
4 
1 
1 
 
 
2 

 

 Table 6-1 (Cont’d) 

Government Use: Federal 
Government Use: Local, City 
Government Use: Local, County 
Government Use: Local, Public Housing 
Government Use: Other  
Government Use: State 
Investor: Bank 
Investor: Equity Fund 
Investor: Individual/Family 
Investor: Investment Manager 
Investor: Real Estate Investment, publicly traded 
Investor: Real Estate Investment, Non-traded 
Main Street Organization 
Non-Profit (that do not fit into others) 
Religious 
No category 
Total 

 

100 
181 
83 
3 
33 
51 
5 
4 
7 
8 
9 
7 
1 
75 
5 
171 
1512 

12 
13 
10 
0 
2 
5 
0 
0 
3 
0 
0 
1 
0 
7 
2 
8 
152 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9 

It can be seen in Table 6-1 that all individuals that agreed to participate in this study were 

involved in educational institution projects. This can be supported by the fact that 26.4% 

projects in the list belong to educational institutions – more than any other owner category. 

According to a McGraw-Hill report Schools and Universities are highly motivated to build green 

as compared to other owners (McGraw-Hill, 2014). Some reasons mentioned in literature for 

this phenomenon are sustainability perceptions and educational needs (Richardson & Lynes, 

2007). According to the researcher’s experience, another main factor for this uniform sample is 

the organizational structure of universities and easy to reach personnel. Finally, the presence of 

researcher bias can also not be ruled out completely. There is a possibility of researcher to be 

more supportive to educational institutions based on initial success in collecting data (Lüttin, 

2012).  

 

 

61 

 

6.1.2.  Summary of Case Study Projects 
 
Basic characteristics of case study projects that participated in this research are summarized 

one by one in the following sections. A structured interview was conducted with the owner’s 

and designer’s representatives in this regard. All projects were pursuing a LEED New 

Construction/Major Renovation certification under version V4 and were in their late 

construction phases. The geographical distribution of all case studies is demonstrated in figure 

6-1 below. 

 

Figure 6-1 Geographical Locations of Case Study Projects 

 
Case Study 1 is a new construction project for an indoor sports facility located in the Midwest 

region. The budget of the project was $40 million. The building consists of 2 floors and the total 

 

62 

 

area is 11, 401 sq. ft. The project used Design Build method of project delivery. Case Study 2 is a 

major renovation and new addition project of a STEM building in the Midwest region. The total 

budget for the project is $32 million. The building consists of 3 floors with total area of 89,000 

sq. ft. The project used design-build delivery method. Case Study 3 is a new construction project 

for student health center located in the Southeast region. The budget of the project is $14.5 

million. The building consists of 2 floors with the total area of 4,500 sq. ft. Delivery method 

used is Design Bid Build. The project used Design-Bid-Build method of project delivery. Case 

Study 4 is a major renovation and new addition project for a technology center located in the 

Midwest region. The budget of the project is $13.78 million. The work covers a total area of 

187,822 sq. ft. for 2 floors of a 4-story building. Case Study 5 is a new construction project of a 

performing arts center located in the Southeast region. The budget if the project is 69.6 million. 

The total covered area is 80,300 sq. ft. The building has 2 floors for most of the area. Case Study 

6 is new construction project of an academic building located in the Midwest region. The 

budget of the building is $13 million. The total covered area is 30,000 sq. ft. for a 3-story 

building. Case Study 7 is a new construction project of student residential complex located in 

the West region. The budget of the building is $101 million. The total covered area is 197,000 

sq. ft. Case Study 8 is a major renovation and addition project of an academic building located 

in the West region. The budget of the project is 16.5 million. The covered area of the project is 

54,050 sq. ft. The building has two floors. Case Study 9 is a new construction project of a sports 

facility in the Southeast region. Total budget of the project is $50 million. The covered area of 

the project is 88000 sq. ft. The building has 4 stories. These characteristics of case study 

projects are summarized in table 6-2 below. 

 

63 

 

Table 6-2 Basic characteristics of Case Study Projects  

Case Study 

Region 

*Building Use 

Budget ($) 

Area (sq. ft.) 

1 
2 
3 

4 
5 

6 

7 

Midwest 
Midwest 
Southeast 

Midwest 
Southeast 

Sports Facility 
STEM Building 
Health Center 

40 M 
32 M 
14.5 M 

Technology Center 

Performing Arts Center 

13.78 M 
69.6 M 

Midwest 

Academic Building 

West 

Residential Complex 

13 M 

101 M 

11,401 
89,000 
4,500 

187,822 
80,300 

30,000 

197,000 

8 
9 

West 

Southeast 

Academic Building 

Sports Facility 

*All projects belong to educational institutes and are located within college campuses 

16.5 M 
50 M 

54,050 
88,000 

Project 
Delivery 
Method 

Design-Build 
Design-Build 
Design-Bid-

Build 

CM at Risk 
Design-Bid-

Build 

Design-Bid-

Build 

Design-Bid-

Build 

Design-Build 
CM at Risk 

6.1.3.  Individual Level Data Demographics 

An online questionnaire was used to collect data from individuals as explained in section 4.5.3. 

Owner representatives were requested to send out online survey link to team members from 

owner, designer, contractor and main representatives of subcontractor organizations. A total of 

103 responses were received from 9 case studies. The summary of response rates is presented 

in the table 6-3. 

These responses were further categorized according to the project role – owner, designer, 

contractor and subcontractor. LEED consultants and commissioning agents were considered 

separately in the category ‘Others’, unless they identified themselves as a part of one of the 

other categories. Descriptive statistics of data according to project role is presented in table 6-

4. 

 

 

64 

 

Table 6-3 Individual survey response rate 

Case  
Study 

No. of team 
members 

No of responses 

Response Rate 

received 

24 
18 
30 
38 
22 
25 
35 
27 
28 
247 

14 
13 
10 
8 
12 
9 
14 
16 
7 
103 

(%) 
58.3 
72.2 
33.3 
21.1 
54.5 
36 
40 
59.3 
25 
41.7 

Total 

1 
2 
3 
4 
5 
6 
7 
8 
9 

1 
2 
4 
5 
3 
0 
14 

*These numbers include only one representative from subcontractor organizations working at that 
time of the project.   

Table 6-4 Respondent roles in case study projects 

 

Roles 
Owner 
Designer 
Contractor 

Subcontractor 

Other 
Total 

2 
2 
4 
1 
4 
2 
13 

3 
1 
6 
3 
0 
0 
10 

Case Study 

4 
1 
3 
2 
2 
0 
8 

5 
4 
4 
3 
0 
1 
12 

6 
2 
4 
3 
0 
0 
9 

7 
4 
5 
0 
3 
2 
14 

8 
1 
13 
2 
0 
0 
16 

Total 

19 
46 
20 
12 
6 
103 

9 
2 
3 
1 
0 
1 
7 

 
The respondents were also asked to mention if they have a LEED certification (LEED AP BD+C, 

LEED Green Associate or others). The number of certifications with each project role is 

presented in table 6-5 below. Moreover, 80% respondents were male while 20% were female.  

Table 6-5 LEED accreditation status for the respondents 

 

Roles 

Owner 
Designer 
Contractor 

Subcontractor 

Other 
Total 

None 

7 
17 
12 
11 
1 
48 

 

 

Certification 

LEED AP (BD+C) 

LEED Green 
Associate 

3 
4 
1 
1 
0 
9 

9 
24 
6 
0 
6 
45 

65 

LEED 

Neighborhood 

0 
0 
1 
0 
0 
1 

Total 

19 
45 
20 
12 
7 
103 

 

6.2. 

Individual Level Analysis 
 

This section presents the results for hypothesis testing for the two study hypotheses developed 

in chapter 3 – study framework. These hypotheses are as follows: 

Hypothesis 1 Individuals’ perceptions of transformational leadership for sustainability in a team 

is positively related to individuals’ perceptions of team performance in sustainability. 

Hypothesis 2 Individuals’ perception of team integration mediates the relationship between 

individuals’ perceptions of transformational leadership for sustainability in team and 

individuals’ perception of team performance in sustainability. 

 

 

 

 

 

Perceived 

Transformational  

Leadership 

Perceived Team  

Integration 

H2 

H2 

H1 

Perceived Team 
Performance 

Figure 6-2 Study Hypotheses 

The section begins with testing the data for reliability.  Also, the data is checked for normality 

as it defines the methods used in CFA and SEM. Afterwards, CFA is performed for construct 

validity. Finally, the two hypotheses are tested one by one and results are presented using SEM.  

6.2.1.  Reliability- Cronbach’s Alpha 

Cronbach's alpha is the most widely used measure of internal consistency. It is particularly 

preferred when you have a questionnaire with multiple Likert questions forming a scale. A 

value of 0.7 or greater is recommended (Cronbach, 1990). A test was performed for the scale of 

 

66 

 

each latent variable. The alpha values for transformational leadership, team performance and 

team integration came out to be 0.999, 0.911 and 0.888 respectively. Therefore, the data is 

reliable for analysis. No item showed an improvement in alpha value for any of the scales if 

deleted. The summary of results is presented in table 6-6. 

Table 6-6 Cronbach's Alpha values for variable scales 

Latent Variable 

Perceived transformational leadership 

Perceived team performance 
Perceived team integration 

 

6.2.2.  Normality of data 

No. of items 

in scale 

10 
6 
9 

Cronbach’s alpha  Cronbach’s alpha based 
on standardized items 

0.999 
0.911 
0.888 

0.999 
0.916 
0.900 

When using CFA and SEM in analysis, checking for normality of data is very important. For 

statistical models Maximum Likelihood (ML) estimation method is most commonly used. 

However, ML assumes normality of data. If the data is not approximately normal, ML will tend 

to produce biased results in terms of both models fit and parameter estimates (Finney & 

DiStefano, 2006).  

There are three methods available to test the normality of data: graphical, numerical and 

formal normality tests (Razali & Wah, 2011). In this study histograms were used to visually 

assess normality of data, reinforcing the findings with skewness and Kurtosis measures, and the 

formal Shapiro-Wilk test. A z-test is used to test normality for skewness and kurtosis. Z-scores 

are obtained by dividing the skewness and excess kurtosis values (provided by SPSS) by their 

standard errors. For a medium sample size (50<n<300), a z value of above 3.29 shows the data 

is not normal (Kim, 2013). Regarding normality tests, Shapiro-Wilk test is the most powerful 

 

67 

 

test available for all sample sizes (Razali & Wah, 2011), and therefore selected for this study. A 

significance value of 0.05 or greater is needed to pass data for normality. 

It was concluded by visually observing histograms for all items that the data is not normal. This 

deduction was further verified through skewness and Kurtosis z-scores as well as Shapiro-Wilk 

normality test. Results for these tests are listed in table 6-7. It can be seen that all items fail the 

normality testing.  

Table 6-7 Results for normality tests 

Item 

Skewness 

Value 

SE 

Z 

Perceived transformational leadership 

L1 
L2 
L3 
L4 
L5 
L6 
L7 
L8 
L9 
L10 
L11 
L12 
L13 
L14 
L15 
L16 

1.157 
1.114 
1.106 
1.12 
1.113 
1.07 
1.109 
1.134 
1.102 
1.108 
1.134 
1.115 
1.219 
1.234 
1.266 
1.171 

0.238 
0.238 
0.238 
0.238 
0.238 
0.238 
0.238 
0.238 
0.238 
0.238 
0.238 
0.238 
0.238 
0.238 
0.238 
0.238 

Perceived team performance 

P1 
P2 
P3 
P4 
P5 
P6 

-1.613 
-0.627 
-1.28 
-1.408 
-0.539 
-0.808 

0.238 
0.238 
0.238 
0.238 
0.238 
0.238 

Perceived team integration 

4.861 
4.681 
4.647 
4.706 
4.676 
4.496 
4.660 
4.765 
4.630 
4.655 
4.765 
4.685 
5.122 
5.185 
5.319 
4.920 

-6.777 
-2.634 
-5.378 
-5.916 
-2.265 
-3.395 

Value 

0.531 
0.424 
0.414 
0.349 
0.423 
0.302 
0.422 
0.485 
0.344 
0.311 
0.502 
0.397 
0.727 
0.786 
1.005 
0.779 

3.575 
-0.143 
1.86 
2.235 
-0.135 
-0.035 

Kurtosis 

SE 

0.472 
0.472 
0.472 
0.472 
0.472 
0.472 
0.472 
0.472 
0.472 
0.472 
0.472 
0.472 
0.472 
0.472 
0.472 
0.472 

0.472 
0.472 
0.472 
0.472 
0.472 
0.472 

Z 

1.125 
0.898 
0.877 
0.739 
0.896 
0.640 
0.894 
1.028 
0.729 
0.659 
1.064 
0.841 
1.540 
1.665 
2.129 
1.650 

7.574 
-0.303 
3.941 
4.735 
-0.286 
-0.074 

Shapiro-Wilk 

Statistic 

Sig. 

0.833 
0.836 
0.837 
0.831 
0.838 
0.839 
0.837 
0.835 
0.837 
0.834 
0.834 
0.836 
0.826 
0.822 
0.82 
0.835 

0.775 
0.871 
0.811 
0.817 
0.887 
0.873 

0.000 
0.000 
0.000 
0.000 
0.000 
0.000 
0.000 
0.000 
0.000 
0.000 
0.000 
0.000 
0.000 
0.000 
0.000 
0.000 

0.000 
0.000 
0.000 
0.000 
0.000 
0.000 

I1 

-1.069 

0.238 

-4.492 

1.054 

0.472 

2.233 

0.796 

0.000 

 

68 

 

 Table 6-7 (Cont’d) 

I2 
I3 
I4 
I5 
I6 
I7 
I8 
I9 

 

-0.981 
-0.761 
-1.275 
-1.128 
-1.727 
-0.572 
-0.696 
-1.404 

0.238 
0.238 
0.238 
0.238 
0.238 
0.238 
0.238 
0.238 

-4.122 
-3.197 
-5.357 
-4.739 
-7.256 
-2.403 
-2.924 
-5.899 

0.573 
-0.188 
1.101 
1.632 
3.029 
-0.777 
-0.17 
2.633 

0.472 
0.472 
0.472 
0.472 
0.472 
0.472 
0.472 
0.472 

1.214 
-0.398 
2.333 
3.458 
6.417 
-1.646 
-0.360 
5.578 

0.854 
0.777 
0.804 
0.793 
0.672 
0.767 
0.82 
0.789 

0.000 
0.000 
0.000 
0.000 
0.000 
0.000 
0.000 
0.000 

6.2.3.  Validity - Confirmatory Factor Analysis 

CFA assess how well the model fits the data. Since the data was found to be non-normal, 

Maximum Likelihood (ML) estimation that is the default for CFA and assumes normality of data 

cannot be used. Out of all the solutions available for moderate to severe non-normality of data 

in SEM, the most recommended one is using Satorra-Bentler scaling that gives robust estimates 

(Finney & DiStefano, 2006). MLM estimator in lavaan package of R uses this scaling method and 

thus was employed for CFA and SEM in this study. All items had very strong loadings on the 

latent variables, except I6 (0.4), which also showed negative variance. Therefore, I6 was 

removed from the scale. The resulting CFA is shown in figure below. 

 

69 

 

I1

I2

I3

I4

0.870

0.716 0.697

0.758

I5

1.000

Participation

Role Clarity

I7

I8

I9

0.855 0.885 0.692

Team 

Functioning

Idealized 
Influence: 
Attributes

Idealized 
Influence: 
Behaviors

0.999

1.000

0.791

0.887

0.905

Team 

Integration

0.336

Inspirational 
Motivation

0.997

Transformaional 

Leadership

Intellectual 
Stimulation

0.998

0.986

Individualized 
Consideration

0.338

0.882

Team 

Performance

0.864

0.800

0.909
0.798
0.630

0.800

P1

P2

P3

P4

P5

P6

 

Figure 6-3 Confirmatory factor analysis for study model 

Fit indices measures: CMIN/df = 1.42, CFI = 0.974,  

TLI = 0.971, RMSEA = 0.064, SRMR = 0.070 

L1

L2

L3

L4

L5

L6

L7

L8

L9

L10

0.996

1.000

0.999

0.998

0.998

0.999

1.000

0.997

0.992

0.988

 

CFA results are based upon 103 responses. Hu and Bentler (1999) cut-off criteria are employed 

to assess the model fit (0.95 for CFI, 0.8 for TLI, 0.08 for SRMR, and close to 0.06 for RMSEA). 

The Comparative Fit Index (CFI=0.97) is greater than 0.95 and close to 1.00, suggesting that the 

model fits the data well (Hu & Bentler 1999). The Tucker-Lewis Index (TLI=0.97) is greater than 

0.8 and close to 1.00, indicating a good fit as well (Hu and Bentler, 1999). The Root Mean 

Square Error of Approximation (RMSEA) is 0.064 that is close to 0.06 (Hu and Bentler, 1999) and 

equal or lower than 0.8 (Browne and Cudeck, 1993). Overall, the fit indices suggest that the 

model in Figure 6-3 is plausible for the data.  

 

 

70 

 

6.2.4.  Hypothesis 1: Transformational leadership and Team performance 

 

Hypothesis 1 states that individuals’ perceptions of transformational leadership for 

sustainability in a team is positively related to individuals’ perceptions of team performance in 

sustainability. To incorporate the fixed effects of nine project teams from different projects, 

dummy variables were introduced in the model to control for their effect. The results for SEM 

are shown in Fig 6-4 below.  

L1

L2

L3

L4

L5

L6

L7

L8

L9

L10

0.996

1.000

0.999

0.998

0.998

0.999

1.000

0.997

0.992

0.988

 

 

Idealized 
Influence: 
Attributes

Idealized 
Influence: 
Behaviors

0.999

1.000

Inspirational 
Motivation

0.997

Transformaional 

Leadership

0.323

Team 

Performance

Intellectual 
Stimulation

0.998

0.986

Individualized 
Consideration

0.117
0.117

0.038

0.161

0.044
0.147 -0.062

0.091

0.262

0.193

0.223
0.159

0.351

0.332

0.196

0.272

C1

C2

C3

C4

C5

C6

C7

C8

Figure 6-4 Hypothesis 1 - transformational leadership and team performance 

Fit indices measures: CMIN/df = 1.56, CFI = 0.971,  

TLI = 0.967, RMSEA = 0.074, SRMR = 0.038 

0.874

0.796

0.902
0.791
0.641

0.803

P1

P2

P3

P4

P5

P6

The results show a positive relationship with a path coefficient of 0.326 and a p value less than 0.001 

indicating strong relationship. 

 

71 

 

 

6.2.5.  Hypothesis 2: Transformational leadership and Team performance mediated by Team 

integration 
 

Hypothesis 2 states that individuals’ perception of team integration mediates the relationship 

between individual perceptions of transformational leadership for sustainability in team and 

individual perception of team performance in sustainability. Again, SEM was used to test the 

hypothesis and results are shown in Figure 6-5.  

I1

I2

I3

I4

0.873

0.715 0.695

0.758

I5

1.000

Participation

Role Clarity

I7

I8

I9

0.856 0.882 0.693

Team 

Functioning

0.788

0.886

0.908

Team 

Integration

0.341

0.895

Idealized 
Influence: 
Attributes

Idealized 
Influence: 
Behaviors

0.999

1.000

Inspirational 
Motivation

0.997

Transformaional 

Leadership

0.020

Team 

Performance

-0.143

-0.069

0.137

-0.004

-0.004

0.149

-0.088

Intellectual 
Stimulation

0.998

0.986

Individualized 
Consideration

0.087

0.117
0.117

0.038

0.161

0.044
0.147 -0.062

0.091

0.270

0.272

0.206
0.214

0.218

0.225

0.318

0.178

C1

C2

C3

C4

C5

C6

C7

C8

Figure 6-5 Hypothesis H2 - Mediating Effect of Team Integration 

0.866

0.798

0.906
0.794
0.635

0.804

P1

P2

P3

P4

P5

P6

Indirect = 0.305, Direct = 0.020, Total = 0.325 

Fit indices measures: CMIN/df = 1.47, CFI = 0.959,  

TLI = 0.954, RMSEA = 0.068, SRMR = 0.062 

 

L1

L2

L3

L4

L5

L6

L7

L8

L9

L10

0.996

1.000

0.999

0.998

0.998

0.999

1.000

0.997

0.992

0.988

 

The results show a positive relationship between leadership and integration, and between integration 

and performance (coefficients 0.341 and 0.895 respectively). The p value for both these relationships is 

less than 0.001. The direct relationship between leadership and performance is weak coefficient (0.02) 

 

72 

 

 

and p value is greater than 0.05. The results clearly indicate a mediation of relationship between 

transformational leadership and team performance through team integration. 

6.3. 

Team Level Analysis 

This chapter presents the results for team level research questions which are as follows: 

•  Research Question 1. Who are the transformational leaders for sustainability in green 

AEC project teams and how are they distributed in the leadership networks? 

•  Research Question 2. How does transformational leadership for sustainability flow in 

green AEC project teams? Who are the followers of transformational leaders? 

In all 9 case studies, team members were asked to identify transformational leaders and also 

provide with a weightage for each. The chapter begins with a brief description and leadership 

network of each case study. Then, each research question is addressed one by one in the 

following sections. 

6.3.1.  Leadership Networks in Case Study Projects 

In this section a leadership network is drawn for each case study followed by a list of discovered 

leaders and their characteristics. The network diagrams developed with the help of UCINET and 

NETDRAW use color coding for associations of actors. Moreover, the strength of ties is 

translated with the help of tie thickness. For the list of leaders, the following information is 

provided for each:  

• 

Indegree Centrality: Aggregating all leadership scores received from all followers in the 

team. 

 

73 

 

•  Stage at which got involved in the project: The options provided were, Conceptual 

design (0-15% design complete), Schematic design (15-30% design complete), Design 

Development (30-60% design complete), Construction documents (60-99% design 

complete) and Construction.  

•  Experience: Total professional experience in Construction Industry 

•  LEED Projects: Number of LEED projects worked on before the project under discussion. 

•  LEED Accreditation: Any of the accreditations offered by USEFP, if acquired.  

Moreover, a few identified leaders did not respond to our request for data collection, and thus 

their information is missing. They have been pointed out by an asterisk (*) at the end of their 

names both in the networks and tables.  

 

 

 

 

 

 

 

 

 

 

74 

 

Case Study 1 

Figure 6-6 Leadership Network for Case Study 1 

Client 

Design 

Construction 

Other 

Table 6-8 Identified Leaders for Case Study 1 

Leader Role 

Indegree 
centrality 

Principal In-charge (Designer) 

24.1 

Project Director 
(Construction) 
Project Manager 
(Construction) 

University Architect (Owner) 

Senior Project Engineer 

(Construction) 
LEED Consultant 
(Construction) 

LEED Contact (Design) 

22.025 

19.7 

16.8 

8.1 

5.7 

4.9 

 

 

Stage at which 

involved in 

project 

Conceptual 

design 

Conceptual 

design 

Construction 
documents 
Conceptual 

design 

Conceptual 

design 

Construction 

Construction 
documents 

75 

Experience 

(Yrs) 

LEED 
Projects 

LEED  

Accreditation 

42 

37 

10 

29 

9 

17 

20 

7 

10 

1 

8 

1 

60 

6 

None 

LEED AP (ND) 

None 

LEED AP 
(BD+C) 

LEED Green 
Associate 
LEED AP 
(BD+C) 
LEED AP 
(BD+C) 

 

Case Study 2 

Client 

Design 

Construction 

Other 

Figure 6-7 Leadership Network for Case Study 2 

 

Table 6-9 Identified Leaders for Case Study 2 

Leader Role 

Indegree 
centrality 

Head of Sustainability 

(Owner) 

Project Manager 
(Construction) 

Project Manager (Design) 

Sustainability Consultant  

Superintendent 
(Construction) 

Mechanical Engineer (Design) 

Architect/Project coordinator 

(Design) 

(Owner)* 

(Design) 

Owner's representative 

Senior Mechanical Engineer 

25.3 

22.4 

14.8 

13.5 

11.3 

11.3 

11.2 

5.6 

5.3 

Stage at which 

involved in 

project 

Conceptual design 

Schematic design 

Conceptual design 

Schematic design 

Construction 
documents 

Schematic design 

Schematic design 

 

Conceptual design 

 

76 

Experience 

(Yrs) 

LEED 
Projects 

LEED  

Accreditation 

40 

15 

21 

16 

40 

6 

20 

 

14 

1 

18 

6 

60 

0 

8 

0 

 

8 

LEED Green 
Associate 
LEED AP 
(BD+C) 
LEED AP 
(BD+C) 
LEED AP 
(BD+C) 
None 

LEED AP 
(BD+C) 
None 

 

LEED AP 
(BD+C) 

 

Case Study 3 

Client 

Design 

Construction 

Other 

Figure 6-8 Leadership Network for Case Study 3 

 

 

Table 6-10 Identified Leaders for Case Study 3 

Leader Role 

Indegree 
centrality 

Stage at which 

involved in 

project 

Conceptual design 

Construction 

Construction 

10.8 

10.4 

9.5 

Capital Project Coordinator 

(Owner) 

Assistant Project Manager 

(Construction) 
Project Manager 
(Construction) 

Project Manager/Architect 

(Design) 

Director of advanced 
buildings (Design)* 

LEED Facilitator (Design) 

 

 

 

 

 

 

 

 

Experience 

(Yrs) 

LEED 
Projects 

LEED  

Accreditation 

25 

4.5 

20 

28 

 

5 

1 

0 

2 

4 

 

30 

None 

None 

None 

LEED AP 
(BD+C) 

 

LEED AP 
(BD+C) 

8.15 

Conceptual design 

4.8 

4.6 

 

Schematic design 

77 

 

Case Study 4 

Client 

Design 

Construction 

Other 

 

Figure 6-9 Leadership Network for Case Study 4 

 

Table 6-11 Identified Leaders for Case Study 4 

Leader Role 

Indegree 
centrality 

Project Manager 
(Construction) 

Project coordinator (Design) 

Project Manager (Owner) 
Civil Engineer (Design) 

Mechanical Engineer (Design) 

5.9 

5.9 

5.2 
5 

4.8 

 

 

 

 

 

 

project 
Design 

development 

Design 

Stage at which 

involved in 

development 

Conceptual design 

Design 

development 

Conceptual design 

Experience 

(Yrs) 

LEED 
Projects 

LEED  

Accreditation 

28 

15 

16 
9 

35 

2 

0 

1 
9 

47 

LEED AP 
(BD+C) 
None 

None 
LEED AP 
(BD+C) 
LEED AP 
(BD+C) 

78 

 

Case Study 5 

Client 

Design 

Construction 

Other 

 

Figure 6-10 Leadership Network for Case Study 5 

 

Table 6-12 Identified Leaders for Case Study 5 

Leader Role 

Indegree 
centrality 

20.7 

11.4 
6 

5.9 
5.9 

5.9 

5.5 

4.8 

LEED Consultant 

Project Manager (Owner) 
Construction Manager 

(Owner) 

Project Manager (Design) 

Architect (Design) 

Assistant Project Manager 

(Construction) 

Campus Architect (Owner) 

LEED Facilitator (Design) 

 

 

 

 

 

Stage at which 

involved in 

project 
Design 

development 

Conceptual design 

Construction 
documents 

Conceptual design 
Conceptual design 

Construction 
documents 

Conceptual design 

Schematic design 

Experience 

(Yrs) 

LEED 
Projects 

LEED  

Accreditation 

15 

4 
10 

8 
35 

5 

33 

10 

20 

3 
0 

1 
3 

1 

30 

60 

LEED AP 
(BD+C) 
None 
None 

None 
LEED AP 
(BD+C) 
LEED AP 
(BD+C) 
LEED AP 
(BD+C) 
LEED AP 
(BD+C) 

79 

 

Case Study 6 

 

 

Client 

Design 

Construction 

Other 

Figure 6-11 Leadership Network for Case Study 6 

 

Table 6-13 Identified Leaders for Case Study 6 

Leader Role 

Indegree 
centrality 

Project Architect (Design) 

Senior Mechanical Engineer 

(Design) 

Director of Facilities (Owner) 

Mechanical Designer / LEED 

Administrator (Design) 

Project Director 
(Construction) 
Project Manager 
(Construction) 

Superintendent 
(Construction) 
Project Engineer 
(Construction)* 

Project Manager (Design) 

4.2 

Conceptual design 

 

80 

Stage at which 

involved in 

project 

Schematic design 

Conceptual design 

Conceptual design 
Schematic design 

Conceptual design 

Conceptual design 

Construction 

 

17.9 

16.1 

12 
11.9 

6 

6 

6 

6 

Experience 

(Yrs) 

LEED 
Projects 

LEED  

Accreditation 

10 

12 

16 
13 

26 

2 

32 

 

22 

1 

20 

2 
7 

2 

7 

3 

 

6 

LEED Green 
Associate 
LEED AP 
(BD+C) 
None 
LEED AP 
(BD+C) 
LEED AP 
(BD+C) 
LEED AP 
(BD+C) 
None 

 

LEED AP 
(BD+C) 

 

Case Study 7 

 

Client 

Design 

Construction 

Other 

 

 

Figure 6-12 Leadership Network for Case Study 7 

 

Table 6-14 Identified Leaders for Case Study 7 

Leader Role 

Indegree 
centrality 

LEED Consultant Head  

Sustainability Director 

Project Manager/Architect 

(Owner) 

(Design) 

Mechanical Engineer (Design) 

Sustainability planner 

(Design)* 

 

 

21.5 

18.5 

15.5 

11 

5.6 

 

Stage at which 

involved in 

project 

Schematic design 

Conceptual design 

Conceptual design 

Conceptual design 

 

 

81 

Experience 

(Yrs) 

LEED 
Projects 

LEED  

Accreditation 

14 

26 

16 

9 

 

 

100 

67 

7 

30 

 

 

LEED AP 
(BD+C) 
LEED AP 
(BD+C) 
None 

LEED AP 
(BD+C) 

 

 

 

Table 6-14 (cont’d) 

Project Manager (Owner) 

LEED Consultant 

Energy Analyst (Design) 

Project Planner (Owner) 

Project Manager 
(Construction)* 

Construction Manager 

(Owner) 

 
Case Study 8 

5.4 

5.3 

5.2 

5.2 

5 

4.9 

Design 

development 
Construction 

Conceptual 

design 

Conceptual 

design 

 

Schematic design 

27 

2 

5 

26 

 

20 

12 

15 

20 

10 

 

8 

LEED AP 
(BD+C) 
LEED AP 
(BD+C) 
LEED AP 
(BD+C) 
LEED AP 
(BD+C) 

 

None 

 

 

 

 

Client 

Design 

Construction 

Other 

 

Figure 6-13 Leadership Network for Case Study 8 

82 

 

Table 6-15 Identified Leaders for Case Study 8 

Leader Role 

Principal Architect/LEED 
Administrator (Design) 
Director of Planning and 
Construction (Owner) 
Performance Analyst 

(Design) 

Project Manager 
(Construction) 

Electrical Engineer 

(Designer) 

Mechanical Engineer 

(Design) 

Project Superintendent 

(Construction)*  

Preconstruction Manager 

(Construction)* 

 
Case Study 9 

 

 

Indegree 
centrality 

36.65 

22.25 

15.35 

9 

6 

6 

4 

2 

Stage at which 

involved in 

project 

Conceptual 

design 

Conceptual 

design 

Schematic 

design 
Design 

development 
Schematic 

design 

Conceptual 

design 

 

 

 

Experience 

(Yrs) 

14 

37 

15 

29 

2 

13 

 

 

LEED 
Project

s 
11 

4 

30 

5 

2 

11 

 

 

LEED  

Accreditation 

LEED AP 
(BD+C) 
LEED AP 
(BD+C) 
LEED AP 
(BD+C) 
None 

None 

None 

 

 

Client 

 

Design 

Construction 

Other 

Figure 6-14 Leadership Network for Case Study 9 

 

83 

 

Table 6-16 Identified Leaders for Case Study 9 

Leader Role 

Indegree 
centrality 

LEED Consultant 

MEP Designer  

Sustainability Director 

(Owner) 

20.4 

6 

5.9 

Stage at which 

involved in 

project 

Schematic design 

Conceptual design 

Design 

development 

Experience 

(Yrs) 

LEED 
Projects 

LEED  

Accreditation 

35 

19 

20 

115 

20 

3 

LEED AP 
(BD+C) 
LEED AP 
(BD+C) 
None 

 
6.3.2.  Research Question 1: Identification of Transformational Leaders 

This section presents the characteristics of sustainability leaders in LEED project teams. A total 

of 65 team members were identified as transformational leaders across 9 case studies. 7 of 

these identified leaders did not respond to our request for data collection, and therefore their 

information is missing. The tier distribution, professional experience and competencies are 

listed below 

Theoretical Versus Identified Leaders 

As introduced in section 3.2, literature guides us about the potential leaders in AEC teams. 

These leaders include project representatives for owner, contractor and designer organizations, 

project managers and supervisors. A descriptive analysis of the identified leaders inthis study 

informs us that 41 leaders out of 58 belonged to the managerial roles discussed in theory. 17 

leaders however belonged to roles not associated with leadership in AEC literature. The 

frequencies of various roles for both theoretical and identified leaders are given in table 6-17.  

 

 

84 

 

Table 6-17 Theoretical versus identified Leaders in the Case study Projects 

Theoretical Leaders 
Role 

Owner’s representatives/Directors 
Project Manager (Owner) 
Project Manager (Design)/Design 
head 
Project Director (Construction) 
Project Manager/Assistant Project 
Manager (Construction) 
Superintendent (Construction) 

LEED Consultant/ 

Coordinator/Administrator 

TOTAL 

 

Frequency 

4 
6 
10 

Newly Identified Leaders 

Role 

Project Planner (Owner) 
Project coordinator (Owner) 
MEP Engineer (Design) 

2 
7 

2 
10 

41 

Civil Engineer (Design) 
Project coordinator (Design) 

Energy Analyst (Design) 
Project Engineer (Construction) 

TOTAL 

Frequency 

1 
1 
9 

1 
2 

2 
1 

17 

It can be seen that designer team members are most frequently nominated as leaders for 

sustainability in teams amongst the newly identified roles (14 out of 17). This was also tested 

with the help of crosstabs chi-square value for design team members and newly identified 

leaders and the difference of distribution was found to be significant. 

 
Table 6-18 Crosstabs for newly identified leaders and design team members 

Newly_Identified_Leaders * Design_Team_Members Crosstabulation 

Count   

Newly_Identified_Leaders 

0 
1 

Total 

Design_Team_Members 

0 

1 

Total 

56 
4 
60 

30 
13 
43 

86 
17 
103 

85 

 

 

 

 

 

 

 

Table 6-19 Test for difference of distribution for design team members 

Chi-Square Tests 

Asymptotic 

Significance (2-

Exact Sig. (2-

Exact Sig. (1-

df 

sided) 

sided) 

sided) 

 
 
 

 
 

.002 

.002 

Value 
10.094a 
8.457 
10.183 

 

Pearson Chi-Square 
Continuity Correctionb 
Likelihood Ratio 
Fisher's Exact Test 
Linear-by-Linear Association 
N of Valid Cases 
a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 7.10. 
b. Computed only for a 2x2 table 

9.996 
103   

.002   
 

.001   
.004   
.001   

 

1 
1 
1 

1 

 

 

 

 

Moreover, it is observed that transformational leaders can be serving at a variety of roles in 

teams such as coordination, energy analysis and construction engineering.  

Professional Experience 

The total professional experience of leaders varied from 2 years to 42 years. An independent 

samples t-test shows that the mean of total professional experience for leaders is not 

significantly different from the mean of rest of team members. Therefore, total professional 

experience was not found to be a significant predictor of transformational leadership. The 

group statistics and t-test results for experience are presented in tables 6-20 and 6-21 

respectively. 

Table 6-20 Group statistics for Total Professional Experience 

 

 

Experience 

 

Rest of the team members 
Transformational Leaders 

Group Statistics 

N 

Mean 

19.72 
19.28 

45 
58 

86 

Std. Deviation  Std. Error Mean 
1.801 
1.433 

12.081 
10.916 

 

Table 6-21 Comparison of means for total professional experience 

Independent Samples Test 

Levene's Test 
for Equality of 

Variances 

t-test for Equality of Means 

Sig. 
(2-

Mean 

F 

Sig. 

t 

df 

tailed) 

Difference 

95% 

Confidence 
Interval of the 

Difference 

Lower  Upper 

Std. Error 
Difference 

.970 

.327 

.193 

101 

.848 

.438 

2.272 

-4.070 

4.945 

Experi
ence 

 

Equal 
variances 
assumed 

Equal 
variances not 
assumed 

 

 

.190  89.637 

.850 

.438 

2.302 

-4.135 

5.011 

 
The number of LEED projects worked on (LEED experience) for leaders ranged from 0 projects 

to 115 projects. Interestingly, the difference of means independent samples t-test showed that 

the LEED experience for transformational leaders is significantly greater than rest of the team 

members. The group statistics and t-test results for LEED experience are presented in tables 6-

22 and 6-23 respectively. 

 
Table 6-22 Group statistics for LEED Experience 

 

LEED 
Projects 

 

 

 
Rest of the team members 
Transformational Leaders 

Group Statistics 

N 

Mean 

45 
58 

7.42 
16.16 

Std. Deviation  Std. Error Mean 
1.455 
3.154 

9.760 
24.017 

87 

 

Table 6-23 Comparison of means for LEED experience 

Independent Samples Test 

Levene's 
Test for 
Equality of 
Variances 

t-test for Equality of Means 

Sig. 
(2-

Mean 

F 

Sig. 

t 

df 

tailed) 

Difference 

95% Confidence 
Interval of the 

Difference 

Lower 

Upper 

Std. Error 
Difference 

11.702 

.001 

-2.295 

101 

.024 

-8.733 

3.806 

-16.283 

-1.183 

LEED
_Proj 

Equal variances 
assumed 

Equal variances 
not assumed 

 

 

-2.515  79.198 

.014 

-8.733 

3.473 

-15.645 

-1.820 

 

 

LEED Accreditation 

Majority of transformational leaders were found to have a LEED accreditation of some sort by 

USGBC. On the other hand, majority of rest of the team members were found to have no 

accreditation. The frequencies of various accreditations are given in table 6-24 below. 

Table 6-24 Frequencies of LEED Accreditation 

 
   

 

 

Total 

 

 

Rest of the team 
members 
Transformational Leaders 

Accreditation 

None 

29 

LEED AP 
(BD+C) 

10 

LEED Green 
Associate 

6 

19 

48 

35 

45 

3 

9 

88 

LEED 

Neighborhood 

Design 

0 

1 

1 

Total 

45 

58 

103 

 

Chi-square values for these cross tabs show that the two groups are have significantly different 

distributions for accreditations. Therefore, the accreditation rate for transformational leaders is 

greater than rest of the team members. Please refer to table 6-25. 

Table 6-25 Test for difference of distribution for LEED Accreditation 

Chi-Square Tests 

Asymptotic 
Significance 

(2-sided) 

df 

Value 
16.596a 
17.569 
3.942 

3 
3 
1 

 

Pearson Chi-Square 
Likelihood Ratio 
Linear-by-Linear 
Association 
N of Valid Cases 
a. 3 cells (37.5%) have expected count less than 5. The minimum 

.001 
.001 
.047 

103  

expected count is .44. 

 

 

6.3.3.  Research Question 2: Flow of Transformational Leadership 

This section explores the trends of leadership flows in teams through leadership networks. 

First, the cases are checked for trickle-down effect mentioned in literature. Afterwards, the role 

of delivery system is assessed with the help of pattern-matching. 

Trickle-down Flow 

 As presented in section 3.2, the trickle-down leadership literature suggests that leadership 

follows the top down route. It means that for an organization, the top managerial role will act 

as a leader for its immediate subordinates. These subordinates will then transfer it to further 

lower level and so on.  

 

89 

 

A close look at leadership networks (Fig 6-6 to Fig 6-14) reveal that leadership does not 

necessarily follow the organizational trickle-down route. Team members of owner, design and 

construction organizations can be exchange leadership at multiple levels. For instance, in Fig 6-

7 for case study 2, the immediate superior for construction superintendent is project manager 

(construction). But he also nominates the project manager (design) as a transformational 

leader. Another interesting observation is that it is possible for a team member in a sub-

ordinate role to act as a leader for its superior. For instance, In Fig 6-8 for case 3, the Project 

Manager for General Contractor nominated his subordinate as a transformational leader. There 

may be many organizational and phycological factors that may cause these patterns.  

Role of Project Delivery Methods 

The potential impact of delivery methods used for case study projects on the flow of 

transformational leadership was introduced in section 3.2 in light of the literature. Essentially, a 

single contract for both design and construction is expected to have more inter-organizational 

links.  

Looking at patterns of leadership flow across the cases and comparing them with the respective 

delivery system provides us with interesting findings. It was observed that the leadership links 

between design and construction team members appear in the design build and CM at Risk 

type of contract, but they are majorly absent in design-bid-build type of contracts. Case 9 is the 

only exception in this regard.  

 

 

90 

 

Table 6-26 Links between design and construction phases in case study projects 

Case Study 

Delivery method 

Case 1 
Case 2 
Case 3 
Case 4 
Case 5 
Case 6 
Case 7 
Case 8 
Case 9 

Design-Build 
Design-Build 
Design-Bid-Build 
CM at Risk 
Design-Bid-Build 
Design-Bid-Build 
Design-Bid-Build 
Design-Build 
CM at Risk 

Links between design and 
construction 
Yes (multiple) 
Yes (multiple) 
No 
Yes (single) 
No 
No 
No 
Yes (multiple) 
No 

 

There is a clear pattern of design-construction links, in comparison to delivery system, where 

design-build teams show more tendency for leadership flow between design and construction 

personnel.  

Network Density of Leadership Ties and Project Performance 

Studies to date have repeatedly used network density as a measure of shared leadership 

(Carson et al., 2007). Density is preferred over centrality in social network analysis to approach 

leadership (Wang et al., 2014). Density of leadership networks is calculated by aggregating the 

weights of all links present in a network and dividing the sum by number of possible links 

between team members. To compare, we used the level of LEED certification achieved by the 

case study projects and perceived sustainability performance as the measure of performance. 

Table 6-27 below lists the network densities of case studies and those performance measures.  

 

 

91 

 

Table 6-27 Network densities and project performance measures in the case studies 

Perceived 

sustainability 
performance 

Satisfied 
Satisfied 

Case  
study 

LEED 

Certification 

Network  
density 
6 
1.71 
3 
1.55 
9 
1.37 
2 
1.15 
1 
1.11 
7 
1.08 
5 
1 
4 
0.59 
0.53 
8 
Range of performance measures from lowest to highest: 

Level 
Silver 
Gold 
Silver 
Gold 
Silver 
Gold 
Silver 
Silver 
Certified 

Strongly satisfied 
Strongly satisfied 

Satisfied 
Neutral 
Satisfied 

Strongly satisfied 

Satisfied 

 Network density (0.53 – 1.71), LEED Certification Level (Certified, Silver, Gold, Platinum),  

Perceived Sustainability Performance (Strongly dissatisfied, Dissatisfied, Neutral, Satisfied, Strongly 

Satisfied) 

 
Comparing the levels of certification and the density values side by side, we observe a pattern 

where higher densities are observed for higher levels of certification case studies. This trend is 

shown in Figure 6-15. Network density for the only certified case study project was lowest 

amongst all (i-e, 0.53). Five Silver level projects showed the most variation in terms of density 

values. The values ranged from 0.59 all the way up to 1.71, with a mean value of 1.156. There 

are 3 Gold certification level projects. Mean of their network densities came out to be 1.26, 

which is slightly greater than silver certified projects.  

 

92 

 

 

y
t
i
s
n
e
D
k
r
o
w
t
e
N

1.8

1.6

1.4

1.2

1

0.8

0.6

0.4

0.2

0

 

1.156 

1.26 

SD = 0.25 

SD = 0.42 

0.53 

Certified 

Silver 

Gold 

Level of Certification 

Figure 6-15 Network Densities vs levels of LEED Certifications 

 

A similar trend is observed when network densities are compared with the project’s 

sustainability performance perceived by the owner’s representatives (Figure 6-16). Network 

density for the only neutral performance was one of the lowest (i-e, 0.59). Mean network 

density for case studies with “strongly satisfied” performance perception (1.2) was just a little 

bit higher than the mean density for case studies with “Satisfied” performance perception 

(1.18).  

 

93 

 

 

y
t
i
i
s
n
e
D
k
r
o
w
t
e
N

1.8

1.6

1.4

1.2

1

0.8

0.6

0.4

0.2

0

1.18 

1.2 

SD = 0.15 

0.59 

SD = 0.47 

Neutral 

Satisfied 

Strongly 
Satisfied 

Project performance as perceived by owner’s rep. 

 

Figure 6-16 Network Densities vs owner's perception of project performance 

 

These results lead to the acceptance of research proposition that the density of leadership 

network is positively related to a team performance (Carson et al., 2007). Also, they ensure that 

the non-response bias concern at project level is eliminated as variety of certification levels and 

team performance perceptions were observed.  

6.3.4.  Summary of Key Findings 

The results chapter is concluded by listing the key findings, presented in table 6-27. 

 

  

 

 

94 

 

Table 6-28 List of Key Findings 

Research Questions/Study Hypothesis 

Key Findings 

Individual level study hypotheses 

Hypothesis 1: Individuals’ perceptions of 
transformational leadership for sustainability in a 
team is positively related to individuals’ 
perceptions of team performance in 
sustainability. Not Rejected 
Hypothesis 2: Individuals’ perception of team 
integration mediates the relationship between 
individual perceptions of transformational 
leadership for sustainability in team and 
individual perception of team performance in 
sustainability. Not Rejected 

Key Finding 1: Individuals’ perceptions of 
transformational leadership for sustainability in a 
team is positively related to individuals’ 
perceptions of team performance in 
sustainability. 
Key Finding 2: Individuals’ perception of team 
integration mediates the relationship between 
individual perceptions of transformational 
leadership for sustainability in team and 
individual perception of team performance in 
sustainability. 
Team level research questions 

Research Question 1: Who are the 
transformational leaders for sustainability in 
green AEC project teams? 
 
 

Key Finding 3: There can be multiple 
transformational leaders for sustainability in AEC 
project teams serving at a variety of roles. 
 
Key Finding 4: Designers are likely to emerge as 
transformational leaders for sustainability in AEC 
project teams even if they may not be assigned to 
leadership roles. 
 
Key Finding 5: Transformational leaders for 
sustainability are more likely to have past 
experience of working on green AEC projects. 
 
Key Finding 6: Transformational leaders for 
sustainability are more likely to have a green 
credentials such as LEED accreditation offered by 
USGBC.  
 
Key Finding 7: Transformational leadership does 
not necessarily flow from top to bottom in the 
organizational hierarchy. 
 
Key Finding 8: Project delivery systems play a role 
in molding the flow of transformational 
leadership in green AEC projects. 
 
Key Finding 9: Overall network density for shared 
leadership is positively related to the project’s 
sustainability performance. 

95 

 

Research Question 2. How does transformational 
leadership for sustainability flow in green AEC 
project teams? Who are the followers of 
transformational leaders? 

 

 

 

 

Chapter 7 DISCUSSIONS 

 

Chapter 7 provides the theoretical contributions and practical implications for the findings of 

this research. The major themes from the findings are discussed one by one, and all the 

discussions are summarized at the end.  

7.1. 

Flow of Leadership in AEC Projects 

The cascading effect of leadership, as proposed by Bass et al. (1987), and later adopted by 

many authors such as Mayer et al. (2009) as the trickle-down effect of leadership suggests that 

leadership follows a top-down flow in organizations with superiors transferring it to 

subordinates. The findings of this study for AEC teams show otherwise; there is evidence for 

bottom-up flow as well as parallel flow between organizational affiliations. This finding goes in 

line with the proponents of shared leadership, who believe that leaders can emerge at any level 

regardless of their role and position (D’Innocenzo et al., 2016).  

The patterns of cross-organization leadership flow between design and construction team 

members is an interesting observation and can be explained in light of AEC delivery systems 

literature.  Because of the early involvement of construction personnel and the same 

organizational affiliation of design and construction team members, design-build teams are 

expected to facilitate higher levels of team  integration (Mollaoglu-Korkmaz et al., 2013), 

where, members from different organizations are expected to share improved task and team 

mental models. This means that they have a similar understanding of tasks, roles, 

responsibilities, and personality traits of team members (Shafique & Mollaoglu, 2020). Shared 

mental models in teams develop trust, which is required for leadership growth (Dirks, 2000). 

 

96 

 

7.2.  Multi-level Study Framework 

This study employed an approach that enabled it to examine transformational leadership 

quantitatively at the individual level through SEM, and qualitatively at the team level through 

social networks. Both micro and macro findings help draw a more holistic picture of 

organizational behavior (Kozlowski & Klein, 2000). Past researches on leadership in AEC focus 

on leader characteristics (Grill et al., 2017; Tabassi et al., 2016; Rumaithi & Beheiry, 2016). 

These researches are unable to present the team level dynamics of leadership. This study not 

only records the impact of transformational leadership perceived by individual team members 

but also help visualize the distributed structures of leader-follower links that are indicators of 

team outcomes (Mehra et al., 2006).  

7.3. 

Team integration through shared transformational leadership 

With team members coming from various organizations and expertise areas, AEC project teams 

can be fragmented. For this reason, team integration is extensively studied for AEC project 

teams and found to have a strong impact on team outcomes (Franz et al., 2017; Baiden & Price, 

2011). Team integration has special importance in green AEC projects. The complex processes 

require input from all parties of the project, and therefore it is critical for them to freely 

contribute to team discussions and decision making (Mollaoglu-Korkmaz et al., 2013).  

AEC literature has multiple investigations on how project delivery methods (such as contract 

type) and practices (such as partnering) influence team integration (Franz et al., 2017; Eriksson, 

2015; Mollaoglu-Korkmaz et al., 2013). This study explores a new means of integrating teams in 

AEC: transformational leadership. We find many comprehensive investigations on 

transformational leadership in fields as diverse as computing (Carreiro & Oliveira, 2019), 

 

97 

 

manufacturing (Kalsoon et al., 2018), healthcare (Giddens, 2018; Sow, 2017), hotel 

management (Mohamed, 2016) and supply chain (Wang & Cruz, 2018). In AEC, there are only a 

few studies like Tabassi et al. (2016), which focus on the traits of project managers. This 

research is the first of its kind to use shared transformational leadership in the AEC industry.  

7.4. 

Role of emergent leaders 

Using shared leadership helped this study to explore sustainability leaders in AEC for the first 

time. Many of these leaders are emergent: those not with roles associated with leadership in 

AEC literature. Emergent leaders enhance team effectiveness. Teams that create emergent 

leaders are known to outperform teams in which new leaders fail to emerge (Souza & Klein, 

1995). However, the leadership style plays a big role in deciding their impact. Emergent leaders 

positively influence team performance when they practice open communication, care for 

others and optimism (traits similar to modern theories like transformational leadership). In 

comparison, they negatively impact team performance when they practice directive, task-

oriented leadership style (Druskat & Pescosolido, 2016). Therefore, it is very important to 

investigate the content of leadership in teams. Role of Emergent leadership also depends on its 

positioning in the team leadership network. According to Mehra et al. (2006), teams perform 

better when emergent and designated indicate each other as leaders in teams, and work in 

coordination. Hence, to optimize the role of emergent leadership in teams, we also need to 

optimize the team structures that facilitate maximum coordination.  

7.5.  Designers as emergent leaders 

It is interesting to observe that most emergent leaders are design team members. This finding 

goes in line with the theory, as designers are the pioneers of green AEC. The green building 

 

98 

 

revolution began when a group of passionate architects took the initiative and formed a 

committee on the environment within the American Institute of Architects (AIA). Designers 

have since been leading the movement as the biggest proponents of sustainable design and 

construction (Yudelson, J., 2010). Designers (architects and engineers) are trained to consider 

climate change in their practice (Anderson, 2020); hence they take more responsibility. It is also 

observed that MEP design engineers have the most numbers in these emergent leaders. Energy 

efficiency is a major portion of LEED design and construction. Mechanical engineers are the 

head of design development (Orsi & Armiñana, 2018) and therefore take the lead. Energy 

design and modeling not only require a technical effort but also input from various team 

members (Swarup et al., 2011). Their requirement for higher collaboration might be the reason 

they develop and practice transformational leadership. 

7.6. 

Role of relevant experience and qualification 

Relevant experience and knowledge have long been associated with leadership development. 

(Brungardt, 1996; Whitaker et al., 1991). The relationship is best expressed in the articles 

associated with leadership development. Individuals begin their leadership journey based on 

essential personality traits such as self-confidence and adaptability (Judge et al., 2002). Then 

gradually, through experience, education, and training, they gain the confidence and 

knowledge to take leadership initiatives (Day & Dragoni, 2015). In the context of findings 

regarding the LEED accreditation, AEC team members have reported feeling more 

knowledgeable and accomplished to perform their tasks after receiving an accreditation (Bruce 

et al., 2009). The findings add to the AEC literature by informing the importance of specific 

 

99 

 

traits in context of leadership development and can find valuable application in the industrial 

trainings.  

7.7. 

Role of Network Density on Project performance 

Social Network approach to study shared leadership is considered the best in terms of effect 

size and detail of findings. Density is the most widely used network measure associated with 

shared leadership (Carson et al., 2007). This study showed patterns indicating that network 

density was related to project sustainability performance in terms of both the level of 

certification expected to achieve, as well as the owner’s perception. The finding has significant 

implications for the AEC industry, where formal leadership and transitional hierarchical 

structures are prevalent. A change of mindset where all team members are empowered, 

trained, and encouraged to lead can reap significant benefits in terms of team and project 

outcomes.    

7.8.  Generalizability of Findings 

As described in the methodology section, the population for this study consisted of new 

construction and major renovation projects in the US, aiming for a LEED certification under 

version 4. Systematic random sampling was used to contact the project teams. The sample of 

nine project teams varies in location, LEED certification level, team performance perception, 

and leadership network densities, eliminating the possibility of non-response bias that only high 

performing teams participated in the study. However, all projects were owned by educational 

institutions, which can impact the generalizability of findings to the original population.  

 

100 

 

Also, LEED projects outside the US might have different patterns of leadership. Effective 

leadership varies widely from culture to culture. In developing countries, for example, leaders 

are more task oriented. Their goal is to get things done quickly and effectively. They are less 

focused on communication, innovation, and human development (Bersin, 2012). Therefore, we 

can hypothesize the transformational leadership networks for sustainability in developing 

country teams are less dense and strongly hierarchical.  

 

 

 

 

 

 

 

 

 

 

 

 

101 

 

8.  CONCLUSIONS AND FUTURE RECOMMENDATIONS 

 

This chapter briefly summarizes the goals and objectives of this research as well as the 

contributions. The chapter finishes with limitations and some recommendations for future 

research.  

8.1. 

Summary of Research Goals and Objectives 

The primary goal of this study was to “Explore the structure and role of transformational 

leadership in AEC project teams, providing significant contribution to AEC literature”. The 

objectives of the study are as follows: 

1.  Create a multi-level framework for study and measurement tools that: 

a.  Guides the hypothesis development to relate transformational leadership and 

team performance mediated by team integration at the individual level, and  

b.  Provides reasoning for research questions in order to explore the dynamics of 

transformational leadership using social networks at the team level. 

2.  Validate the framework and measurement tool via expert interviews. 

3.  Empirically test the study hypotheses at the individual level. 

4.  Answer the research questions at the team level, exploring the leadership networks in 

teams and assessing various characteristics of leaders. 

8.2. 

Summary of Study Methods 

As mixed-methods research, this study employed several methods for data collection and 

analysis. For the individual-level data, an online survey was used. Project characteristics for 

 

102 

 

each case study were collected through a telephone interview with the owner’s representative. 

For the analysis of study hypotheses at the individual level, CFA was first employed for model 

validation. Afterward, the model was tested using structural equation modeling. The team-level 

analysis included pattern matching for leadership networks created through SNA.   

8.3. 

Summary of findings 

There are several findings of this study emanating from individual and team level analysis 

results. These findings are listed in order below. 

1.  Transformational leadership is positively related to team performance. 

2.  Team integration mediates the relationship between transformational leadership and 

team performance. 

3.  There can be multiple transformational leaders for sustainability in project teams 

serving at a variety of roles. 

4.  Designers are expected to emerge as new leaders for sustainability in AEC project 

teams. 

5.  Transformational leaders for sustainability more likely have past experience of working 

on green AEC projects. 

6.  Transformational leaders for sustainability are more likely to have a green AEC 

accreditation such as LEED offered by USGBC.  

7.  Transformational leadership does not necessarily flow from top to bottom in the 

organizational hierarchy. Many organizational and psychological factors may play a role 

in defining it.  

 

103 

 

8.  Delivery methods (such as Design-Build and Design-Bid-Build) employed for projects 

may play a role in defining the leadership links across organizational boundaries.  

9.  Overall network density for shared leadership in case studies was found to be positively 

related to the project’s sustainability performance in terms of certification level and 

owner’s performance perception. 

8.4.  Deliverables and Contributions to the Body of Knowledge 

The study’s main contribution to the body of knowledge is to investigate shared leadership for 

large inert-organizational AEC project teams for the first time. The individual and team level 

analysis resulted in some novel contributions, which are listed below 

1.  Multifactor Leadership Questionnaire (MLQ) is a heavily utilized tool for 

transformational leadership (Bass & Avolio, 1996). To employ this tool for sustainability 

leadership in AEC project teams, this study developed a modified version of the tool 

with the help of industry experts.  

2.  The effect of leadership on team performance mediated through team integration is 

investigated in literature for other types of leadership – such as servant leadership in 

Sousa & Van Dierendonck (2016). This study uses shared transformational leadership for 

the first time in this relation.  Also, it is the pioneer study that uses any kind of 

leadership to predict integration and performance in AEC teams. 

3.  This study adds to the theory of leadership development by contributing reflections 

from green AEC project teams. LEED project experience and LEED accreditations are 

found to be predictors of leaders for sustainability, which goes in line with Day & 

Dragoni’s conclusions (Dragoni, 2015). 

 

104 

 

4.  This study contributes to the shared leadership literature by adopting transformational 

leadership measurement scales utilizing social network rating approach and examining 

actor attributes for a detailed qualitative analysis. Earlier studies have used the overall 

leadership of peers and thus were limited to only the density and centrality of networks. 

Acting upon the recommendations of leadership researchers, the new approach not 

only added more value in terms of tie content but also provided more context by 

including the leader and follower characteristics. 

5.  The study provides support to emergent leader theory (D’Innocenzo et al., 2016) against 

trickle-down theory (Mayer et al., 2009) for leadership in AEC project teams. It informs 

that the flow of transformational leadership for sustainability depends on many factors 

in addition to the hierarchical dependency of roles. One of these factors is project 

delivery, which is discussed in this study.  

8.5. 

Limitations  

Despite all its findings and contribution, the study has some limitations, which are listed below: 

1.  All case studies belong to educational institutions as owners despite the author’s efforts 

to collect a random sample. This affects the generalizability of study results.  

2.  The response rate from team members from each case study ranged from 21.1% to 

72.2%. The missing data from social networks reduces the quality of team-level analysis 

results as there may be more leaders in the team. Also, this casts away the possibility of 

cross-case comparison of SNA properties such as density and centrality. 

3.  Most of the case study projects were not comfortable sharing objective data on project 

performance metrics, such as LEED checklist, cost over-runs, and schedule. This 

 

105 

 

prevented the author from investigating relationships, such as between team and 

project performance. 

8.6. 

Recommendations for Future Research 

Future researchers can use the methods employed by this study to investigate shared 

transformational leadership in various contexts for AEC or other inter-organizational project 

teams. Some of the themes are presented below: 

Multilevel model: This study failed at creating quantitative or qualitative links between the 

team, sub-team, and individual-level variables. To completely understand organizational 

behavior, both individual and group level investigations are required to relate empirically. 

Future researchers can develop such multilevel models to add further value to their findings.  

Longitudinal studies: This study used cross-sectional data from near completion project teams. 

However, it is imperative to understand the evolution of leadership from the beginning of the 

project cycle to the end. It can be hypothesized that as Mechanical engineers emerged as 

leaders in this study, there might be different professionals filling in the roles of 

transformational leaders at different phases of the project. Therefore, researchers are advised 

to consider using longitudinal data in future studies. 

The response rate: Collecting data from all team members is essential to add more validity to 

the leading networks. This study suffered in terms of response rate, and thus the leadership 

networks might have missed important individuals as leaders or followers. Future researchers 

are advised to make efforts in order to collect responses from all members of the teams.  

 

106 

 

The criterion for leader identification: The findings of this study are in line with the previous 

studies (such as Carson et al., 2007 and Mehra et al., 2006), where most of the team members 

were identified as leaders. However, as we move to more individual-focused studies with this 

research, it is recommended for future investigators to develop a criterion which can 

differentiate between various leaders of a team based on multiple factors, such as the number 

of followers, weightage of leadership, role in the team and position in the social network. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

107 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

APPENDICES 

108 

 

 

 

Appendix A: Internal Review Board (IRB) Approval Letter 

109 

 

 

 

 

110 

 

 

 
 

 

 

111 

 

 

Appendix B: Structured Interview Questions for Industry Experts 

 

1.  Can you please briefly describe your personal history of being involved in sustainable AEC 

projects? 

•  How long have you been working in sustainable projects? 
•  How about LEED projects? 
•  Can you please describe your leadership activities/ efforts in the industry, you can talk 
about the books you wrote, workshops conducted or major events that you spoke at.  
Or do you have a bio that you can share with me if it is easier? 
 

2.  Can you please shed some light on the complexities of AEC projects? How are they more 

challenging as compared to their traditional counterparts? 

 

3.  How do these projects come alive? In other words, who initiates the idea of going green on a 

project in your experience? Why? 
 
Is there generally a transformational leader involved? 
 

4. 

5.  Do you think transformational leaders of green AEC project team can positively impact the 

outcomes? Please explain.  
 
Part 2: Feedback on survey questionnaire 
 

case studies an provide us your feedback. 

6.  Please review the survey instrument in the attached that we will use to collect data from project 

 

 

 

 

 

 

 

 

 

 

 

 

112 

 

 

 

 

 
 
 
 

Appendix C: Survey 

 

 

113 

 
 
 
 
 

 

 

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135 

 

 

 

Appendix D: Interview questions for Project Managers/Owner’s representatives 

 

Section 1: Project General characteristics 

 

 

Please provide the following information about the project: 

1.  Project type:   

     

      New construction 

      Renovation 

      Addition 

 

2.  Building use 

 

 

 

 

 

 

Commercial 

Residential 

Higher education 

K-12 education 

Health care 

Other (Please mention)  

 

 

3.  Building size  

 

 

4.  Total budget $ 

 

5.  Number of floors   

 

 

 

 

 

 

 

 

 

 

 sq. ft.  

 

(City/State) 

6.  Building location  

7.  Who is the owner of the project?  

 

136 

 

 

 

 

 
 

 

 

 
 
Section 2: Project Delivery Characteristics 

 

for each are also given below. 

8.  Which project delivery system which best describes that used on your project? The definitions 

 

 

 

 

Design-Bid- Build 

Design-Build 

Construction Management at risk 

Integrated Project Delivery (IPD) 

Design Bid Build: This is a traditional process in the US construction industry, where the owner contracts 
separately with a designer and a contractor. The owner normally contract with a design company to 
complete design documents. The owner or his/her agent then solicits fixed price bids from contractors 
to perform the work. One contractor is usually selected and enters into an agreement with the owner to 
construct a facility in accordance with the plans and specifications. 
Design Build: This is a single agreement between an owner and a single entity to perform both design 
and construction under a single design build contract. Portions or all of the design and construction may 
be performed by the entity or subcontracted to other companies. 
CM at Risk: The owner contracts with a design company to provide a facility design. The owner 
separately selects a contractor to perform construction a management services and construction work 
in accordance with the plans and specifications for a fee. The contractor usually has significant input in 
the design process and generally guarantees the maximum construction price.  
IPD: Integrated Project Delivery (IPD) is a project delivery approach that brings all major stakeholders 
together from the very beginning of the project though intensive collaboration and risk sharing. Usually 
there is a single legally binding document for all. 

9.  What contractual terms were used for the design-builder or designer and contractor, and 

10. What was the procurement method used for the design-builder or designer and contractor, and 

 

 

subcontractors? 

a)  Architect/Designer 
b)  Contractor                  
c)  Design-Builder           
d)  Subcontractors       

subcontractors? 

a)  Architect/Designer    
                                      
b)  Contractor                  
                                       

c)  Design-Builder      

 Lump-Sum     
 Lump-Sum     
 Lump-Sum     
 Lump-Sum     

 GMP     
 GMP     
 GMP     
 GMP     

 Cost plus fee   
 Cost plus fee   
 Cost plus fee   
 Cost plus fee   

 Not applicable 
 Not applicable 
 Not applicable 
 Not applicable 

 Sole source selection   
 Low bid     
 Sole source selection   
 Low bid     
 Sole source selection     

 Qualification based selection       

 Other ____________________________ 

 Qualification based selection       

 Other ____________________________ 

 Qualification based selection       

 

137 

 

                                     
d)  Subcontractors         
                                       
 

 
 
 

 Low bid      
 Sole source selection     
 Low bid      

 Other ____________________________ 

 Qualification based selection       

 Other ____________________________ 

11. At what stage of the project was each of the major project parties involved? Stages include: i) 
Conceptual design (0-15%), ii) Schematic design (15-30%), iii) Design development (30-60%), iv) 
Construction documents (60-99%), and v) Construction – Put an X in the relevant boxes - 

Phase 

Conceptual 

Design 

Schematic 

Design 

Design 

Development 

Construction 
Documents 

Construction 

Design complete 

0-15% 

15-30% 

30-60% 

60-99% 

100% 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Owner 

Designer - Architect 

Designer - Interior 

Designer - Landscape 

Designer - Other  

(Please mention): 

Engineer - Structural 

Engineer - Geotech 

Engineer - Electrical 

Engineer - Mechanical 

Engineer - Other  

(Please mention): 

General Contractor 

Contractor - Electrical 

Contractor - Mechanical 

Contractor - Roofing 

Contractor - Flooring 

Contractor - Carpenter 

Contractor – Other (Please 
mention): 

Suppliers 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

138 

 

 

 

 

 

 

 

 

Section 3: Project Sustainability Characteristics 

12.  Who initially proposed the idea of incorporating green or sustainable attributes in the project? 

 
  
 
 

Owner 
Developer 
Designer/design-builder 
Other(Please mention): ___________________________ 

 

13. Why is the project team pursuing green building objectives? 

 
 
 
 
 

Mandated by the client or state 
Owner driven factor/vision statement 
Energy use/cost 
Productivity of occupants 
Other(Please mention): ____________________________ 

 

14. At what point during the design phase was the notion of green building introduced? 

 
 
 
 
 

Conceptual design (0-15% design complete) 
Schematic design (15-30% design complete) 
Design Development (30-60% design complete) 
Construction documents (60-99% design complete) 
Bidding (Full completion of design) 

15. Who is/are responsible for the LEED/sustainability implementation of project? How are the 

LEED/sustainability related responsibilities distributed officially? 

 

 

 
 

16. Was there any official training conducted to educate participants regarding LEED? 

 

 
 
 
 
 

139 

 

 

Section 4: Project Cost Performance 

17. What were the following project costs? 

 

 

 
Provide separate Construction Costs if known otherwise enter Total Project Costs only indicating 
whether the cost data provided is estimated (E) or actual (A) Please deduct all property costs, 
owner costs, costs of installed process or manufacturing equipment, furnishings fittings and 
equipment or items not a cost of the base building. 
 

Construction Costs 

Total Project Costs 

Contract Award 

Final Cost 

 

 

E  

A  

E  

A  

 

 

 

 

 

 

E  

A  

E  

A  

 

 

 

 

 Yes       

 No 

 Yes, resolved          

 
Are there any unresolved costs or change orders?       
 
Has the project ever been in litigation?      
 
If applicable, are the costs of litigation and/or claims included in the project costs listed above?                            
                                                   
 
 
 
What is your level of satisfaction with the cost performance of the project? 
 

 Yes, unresolved       

 Yes                    

 No 

 N/A                   

 No        

 Strongly 
Dissatisfied 

 Dissatisfied 

 Neutral 

 Satisfied 

 Strongly 
Satisfied 

 

 

 

 

 

 

 

 

 

140 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Section 5: Project Schedule Performance 

18. Please provide the following schedule information. 
Planned (mm/dd/yyyy) 

 

Actual (mm/dd/yyyy) 

Design Start Date 

(Notice to proceed) 

Construction Start Date 

(Notice to Proceed) 

Construction End Date  

 (Submittal Completion) 

actual or expected 

 

 

 

 

 

 

What is your level of satisfaction with the schedule performance of the project? 

 Strongly 
Dissatisfied 

 Dissatisfied 

 Neutral 

 Satisfied 

 Strongly 
Satisfied 

141 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Section 6: Project Quality Performance 

19. Relative to your expectations, evaluate the quality of the facility and systems (1 = low, 5 = high) 
5 (High) 

Systems 

3 

1 (Low) 

2 

4 

Envelope, Roof, Structure, Foundation 

Interior finishes 

Lights, HVAC 

Exterior aesthetics 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

What is your overall level of satisfaction with the quality performance of the project? 

 Strongly 
Dissatisfied 

 Dissatisfied 

 Neutral 

 Satisfied 

Strongly 
Satisfied 

142 

 

Section 7: Project Sustainability Performance 

 

 

20. What level of LEED certification was planned and awarded/expected to be awarded? 

Planned  

 

 

Achieved/expected  

 

 

 

                          No. of points/credits  

 

 

 

      No. of points/credits  

 

 

What is your overall level of satisfaction with the sustainability performance of the project? 

 Strongly 
Dissatisfied 

 Dissatisfied 

 Neutral 

 Satisfied 

Strongly 
Satisfied 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

143 

 

Section 8: LEED Implementation 

 
 

outcomes of this project? 
 
 

21. What was the process used to align Project Team members around the purpose and targeted 

 

 

 

 
 
 
 
 
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

22. How many multidisciplinary team workshops were convened before design began? 

23. How many design charrettes/workshops have been conducted? 

144 

 

Appendix E: R codes and Results for CFA and SEM 

 
R codes and results for CFA 
 
#Importing data 
library(haven) 
> StudyData <- read_sav("Dropbox/Data Collection/StudyDataNew.sav") 
> View(StudyDataNew) 
 
#loading lavaan package 
> library(lavaan) 
This is lavaan 0.6-5 
lavaan is BETA software! Please report any bugs. 
 
#model definition 
model1 = '#Defining latent variables 
lead1 =~ L1+L2 
lead2 =~ L3+L4 
lead3 =~ L5+L6 
lead4 =~ L7+L8 
lead5 =~ L9+L10 
lead =~ lead1+lead2+lead3+lead4+lead5 
perf =~ P1+P2+P3+P4+P5+P6 
integ1 =~ I1+I2+I3+I4 
integ2 =~ I5 
integ3 =~ I7+I8+I9 
integ =~ integ1+integ2+integ3 
L2 ~~ 0*L2 
L7 ~~ 0*L7 
lead2 ~~ 0*lead2' 
#model fit 
fit1 = lavaan::cfa(model1, data=StudyDataNew, estimator = "MLM") 

 

145 

 

#summary 
summary(fit1, fit.measures=TRUE, standardized=TRUE) 
 
avaan 0.6-5 ended normally after 230 iterations 
 
  Estimator                                         ML 
  Optimization method                           NLMINB 
  Number of free parameters                         55 
                                                       
  Number of observations                           103 
                                                       
Model Test User Model: 
                                              Standard      Robust 
  Test Statistic                               482.696     347.002 
  Degrees of freedom                               245         245 
  P-value (Chi-square)                           0.000       0.000 
  Scaling correction factor                                  1.391 
    for the Satorra-Bentler correction  
 
Model Test Baseline Model: 
 
  Test statistic                              5897.334    4265.313 
  Degrees of freedom                               276         276 
  P-value                                        0.000       0.000 
  Scaling correction factor                                  1.383 
 
User Model versus Baseline Model: 
 
  Comparative Fit Index (CFI)                    0.958       0.974 

 

146 

 

  Tucker-Lewis Index (TLI)                       0.952       0.971 
                                                                   
  Robust Comparative Fit Index (CFI)                         0.974 
  Robust Tucker-Lewis Index (TLI)                            0.971 
 
Loglikelihood and Information Criteria: 
 
  Loglikelihood user model (H0)              -3036.304   -3036.304 
  Loglikelihood unrestricted model (H1)      -2794.956   -2794.956 
                                                                   
  Akaike (AIC)                                6182.607    6182.607 
  Bayesian (BIC)                              6327.517    6327.517 
  Sample-size adjusted Bayesian (BIC)         6153.782    6153.782 
 
Root Mean Square Error of Approximation: 
 
  RMSEA                                          0.097       0.064 
  90 Percent confidence interval - lower         0.084       0.050 
  90 Percent confidence interval - upper         0.110       0.076 
  P-value RMSEA <= 0.05                          0.000       0.050 
                                                                   
  Robust RMSEA                                               0.075 
  90 Percent confidence interval - lower                     0.056 
  90 Percent confidence interval - upper                     0.093 
 
Standardized Root Mean Square Residual: 
 
  SRMR                                           0.070       0.070 

 

147 

 

 
Parameter Estimates: 
 
  Information                                 Expected 
  Information saturated (h1) model          Structured 
  Standard errors                           Robust.sem 
 
Latent Variables: 
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all 
  lead1 =~                                                               
    L1                1.000                               8.204    0.996 
    L2                1.026    0.010  101.082    0.000    8.420    1.000 
  lead2 =~                                                               
    L3                1.000                               8.472    0.999 
    L4                1.008    0.010  100.916    0.000    8.537    0.998 
  lead3 =~                                                               
    L5                1.000                               8.383    0.998 
    L6                1.017    0.008  120.487    0.000    8.524    0.999 
  lead4 =~                                                               
    L7                1.000                               8.355    1.000 
    L8                1.007    0.007  135.498    0.000    8.410    0.997 
  lead5 =~                                                               
    L9                1.000                               7.880    0.992 
    L10               0.998    0.022   44.883    0.000    7.867    0.988 
  lead =~                                                                
    lead1             1.000                               0.999    0.999 
    lead2             1.033    0.011   90.643    0.000    1.000    1.000 
    lead3             1.020    0.019   55.046    0.000    0.997    0.997 

 

148 

 

    lead4             1.017    0.012   87.441    0.000    0.998    0.998 
    lead5             0.948    0.030   31.710    0.000    0.986    0.986 
  perf =~                                                                
    P1                1.000                               0.884    0.864 
    P2                1.196    0.129    9.310    0.000    1.058    0.800 
    P3                1.065    0.088   12.103    0.000    0.942    0.909 
    P4                1.173    0.126    9.350    0.000    1.038    0.798 
    P5                0.948    0.173    5.473    0.000    0.839    0.630 
    P6                1.152    0.146    7.900    0.000    1.019    0.800 
  integ1 =~                                                              
    I1                1.000                               0.754    0.870 
    I2                1.213    0.144    8.417    0.000    0.914    0.716 
    I3                0.675    0.099    6.796    0.000    0.509    0.697 
    I4                1.337    0.151    8.827    0.000    1.008    0.758 
  integ2 =~                                                              
    I5                1.000                               0.829    1.000 
  integ3 =~                                                              
    I7                1.000                               0.590    0.855 
    I8                1.242    0.101   12.301    0.000    0.733    0.885 
    I9                1.241    0.187    6.652    0.000    0.733    0.692 
  integ =~                                                               
    integ1            1.000                               0.791    0.791 
    integ2            1.232    0.197    6.255    0.000    0.887    0.887 
    integ3            0.896    0.137    6.528    0.000    0.905    0.905 
 
Covariances: 
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all 
  lead ~~                                                                

 

149 

 

    perf              2.454    0.718    3.418    0.001    0.338    0.338 
    integ             1.643    0.527    3.118    0.002    0.336    0.336 
  perf ~~                                                                
    integ             0.465    0.117    3.969    0.000    0.882    0.882 
 
Variances: 
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all 
   .L2                0.000                               0.000    0.000 
   .L7                0.000                               0.000    0.000 
   .lead2             0.000                               0.000    0.000 
   .L1                0.501    0.127    3.938    0.000    0.501    0.007 
   .L3                0.123    0.041    3.019    0.003    0.123    0.002 
   .L4                0.311    0.116    2.680    0.007    0.311    0.004 
   .L5                0.265    0.125    2.124    0.034    0.265    0.004 
   .L6                0.166    0.125    1.326    0.185    0.166    0.002 
   .L8                0.366    0.108    3.396    0.001    0.366    0.005 
   .L9                0.983    0.556    1.766    0.077    0.983    0.016 
   .L10               1.527    0.836    1.828    0.068    1.527    0.024 
   .P1                0.266    0.054    4.957    0.000    0.266    0.254 
   .P2                0.630    0.089    7.115    0.000    0.630    0.360 
   .P3                0.187    0.046    4.106    0.000    0.187    0.174 
   .P4                0.616    0.131    4.684    0.000    0.616    0.364 
   .P5                1.067    0.261    4.096    0.000    1.067    0.603 
   .P6                0.585    0.136    4.301    0.000    0.585    0.361 
   .I1                0.182    0.050    3.607    0.000    0.182    0.243 
   .I2                0.792    0.165    4.811    0.000    0.792    0.487 
   .I3                0.273    0.050    5.466    0.000    0.273    0.514 
   .I4                0.753    0.224    3.362    0.001    0.753    0.426 

 

150 

 

   .I5                0.000                               0.000    0.000 
   .I7                0.128    0.035    3.706    0.000    0.128    0.269 
   .I8                0.150    0.040    3.768    0.000    0.150    0.218 
   .I9                0.583    0.125    4.683    0.000    0.583    0.521 
   .lead1             0.102    0.039    2.583    0.010    0.002    0.002 
   .lead3             0.388    0.140    2.776    0.006    0.006    0.006 
   .lead4             0.338    0.080    4.208    0.000    0.005    0.005 
   .lead5             1.683    0.420    4.002    0.000    0.027    0.027 
    lead             67.210   10.450    6.431    0.000    1.000    1.000 
    perf              0.782    0.231    3.385    0.001    1.000    1.000 
   .integ1            0.213    0.058    3.671    0.000    0.374    0.374 
   .integ2            0.147    0.043    3.399    0.001    0.214    0.214 
   .integ3            0.063    0.025    2.517    0.012    0.181    0.181 
    integ             0.356    0.114    3.122    0.002    1.000    1.000 
 
R codes and results for Hypothesis 1 
#model definition 
modelH1 = '#Defining latent variables 
lead1 =~ L1+L2 
lead2 =~ L3+L4 
lead3 =~ L5+L6 
lead4 =~ L7+L8 
lead5 =~ L9+L10 
lead =~ lead1+lead2+lead3+lead4+lead5 
perf =~ P1+P2+P3+P4+P5+P6 
perf~lead 
lead~c1 
lead~c2 

 

151 

 

lead~c3 
lead~c4 
lead~c5 
lead~c6 
lead~c7 
lead~c8 
perf~c1 
perf~c2 
perf~c3 
perf~c4 
perf~c5 
perf~c6 
perf~c7 
perf~c8 
L2 ~~ 0*L2 
L7 ~~ 0*L7 
lead2 ~~ 0*lead2' 
 
#model fit 
fitH1 = lavaan::sem(modelH1, data=StudyDataNew, estimator="MLM") 
 
#model summary 
summary (fitH1, fit.measures = TRUE, standardized=TRUE) 
 
lavaan 0.6-5 ended normally after 301 iterations 
 
  Estimator                                         ML 
  Optimization method                           NLMINB 
  Number of free parameters                         51 
                                                       

 

152 

 

  Number of observations                           103 
                                                       
Model Test User Model: 
                                              Standard      Robust 
  Test Statistic                               449.133     332.275 
  Degrees of freedom                               213         213 
  P-value (Chi-square)                           0.000       0.000 
  Scaling correction factor                                  1.352 
    for the Satorra-Bentler correction  
 
Model Test Baseline Model: 
 
  Test statistic                              5308.508    4402.249 
  Degrees of freedom                               248         248 
  P-value                                        0.000       0.000 
  Scaling correction factor                                  1.206 
 
User Model versus Baseline Model: 
 
  Comparative Fit Index (CFI)                    0.953       0.971 
  Tucker-Lewis Index (TLI)                       0.946       0.967 
                                                                   
  Robust Comparative Fit Index (CFI)                         0.968 
  Robust Tucker-Lewis Index (TLI)                            0.963 
 
Loglikelihood and Information Criteria: 
 
  Loglikelihood user model (H0)              -2208.528   -2208.528 

 

153 

 

  Loglikelihood unrestricted model (H1)      -1983.961   -1983.961 
                                                                   
  Akaike (AIC)                                4519.055    4519.055 
  Bayesian (BIC)                              4653.426    4653.426 
  Sample-size adjusted Bayesian (BIC)         4492.326    4492.326 
 
Root Mean Square Error of Approximation: 
 
  RMSEA                                          0.104       0.074 
  90 Percent confidence interval - lower         0.090       0.060 
  90 Percent confidence interval - upper         0.117       0.087 
  P-value RMSEA <= 0.05                          0.000       0.003 
                                                                   
  Robust RMSEA                                               0.086 
  90 Percent confidence interval - lower                     0.067 
  90 Percent confidence interval - upper                     0.103 
 
Standardized Root Mean Square Residual: 
 
  SRMR                                           0.038       0.038 
 
Parameter Estimates: 
 
  Information                                 Expected 
  Information saturated (h1) model          Structured 
  Standard errors                           Robust.sem 
 
Latent Variables: 

 

154 

 

                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all 
  lead1 =~                                                               
    L1                1.000                               8.204    0.996 
    L2                1.026    0.010  101.636    0.000    8.420    1.000 
  lead2 =~                                                               
    L3                1.000                               8.472    0.999 
    L4                1.008    0.010  101.431    0.000    8.537    0.998 
  lead3 =~                                                               
    L5                1.000                               8.383    0.998 
    L6                1.017    0.008  120.956    0.000    8.524    0.999 
  lead4 =~                                                               
    L7                1.000                               8.355    1.000 
    L8                1.007    0.007  136.050    0.000    8.410    0.997 
  lead5 =~                                                               
    L9                1.000                               7.880    0.992 
    L10               0.998    0.022   45.357    0.000    7.867    0.988 
  lead =~                                                                
    lead1             1.000                               0.999    0.999 
    lead2             1.033    0.011   91.226    0.000    1.000    1.000 
    lead3             1.020    0.019   55.078    0.000    0.997    0.997 
    lead4             1.017    0.012   87.560    0.000    0.998    0.998 
    lead5             0.948    0.030   31.750    0.000    0.986    0.986 
  perf =~                                                                
    P1                1.000                               0.895    0.874 
    P2                1.177    0.129    9.143    0.000    1.053    0.796 
    P3                1.045    0.082   12.738    0.000    0.935    0.902 
    P4                1.150    0.122    9.447    0.000    1.029    0.791 
    P5                0.953    0.171    5.562    0.000    0.853    0.641 

 

155 

 

    P6                1.144    0.146    7.838    0.000    1.024    0.803 
 
Regressions: 
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all 
  perf ~                                                                 
    lead              0.035    0.009    3.764    0.000    0.323    0.323 
  lead ~                                                                 
    c1                2.170    3.035    0.715    0.475    0.265    0.091 
    c2                3.630    2.971    1.222    0.222    0.443    0.147 
    c3               -1.730    2.586   -0.669    0.504   -0.211   -0.062 
    c4                1.354    3.001    0.451    0.652    0.165    0.044 
    c5                0.976    2.825    0.345    0.730    0.119    0.038 
    c6                4.674    3.531    1.324    0.186    0.570    0.161 
    c7                2.803    3.377    0.830    0.407    0.342    0.117 
    c8                2.645    3.174    0.833    0.405    0.323    0.117 
  perf ~                                                                 
    c1                0.683    0.473    1.442    0.149    0.763    0.262 
    c2                0.521    0.469    1.110    0.267    0.582    0.193 
    c3                0.672    0.496    1.356    0.175    0.752    0.223 
    c4                0.530    0.450    1.179    0.238    0.593    0.159 
    c5                0.980    0.459    2.134    0.033    1.095    0.351 
    c6                1.053    0.463    2.271    0.023    1.177    0.332 
    c7                0.512    0.464    1.104    0.270    0.572    0.196 
    c8                0.673    0.503    1.338    0.181    0.752    0.272 
 
Variances: 
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all 
   .L2                0.000                               0.000    0.000 

 

156 

 

   .L7                0.000                               0.000    0.000 
   .lead2             0.000                               0.000    0.000 
   .L1                0.501    0.127    3.947    0.000    0.501    0.007 
   .L3                0.123    0.041    3.014    0.003    0.123    0.002 
   .L4                0.311    0.115    2.696    0.007    0.311    0.004 
   .L5                0.265    0.125    2.129    0.033    0.265    0.004 
   .L6                0.166    0.125    1.326    0.185    0.166    0.002 
   .L8                0.366    0.108    3.392    0.001    0.366    0.005 
   .L9                0.982    0.560    1.753    0.080    0.982    0.016 
   .L10               1.527    0.824    1.853    0.064    1.527    0.024 
   .P1                0.248    0.051    4.852    0.000    0.248    0.237 
   .P2                0.641    0.090    7.109    0.000    0.641    0.367 
   .P3                0.201    0.049    4.059    0.000    0.201    0.187 
   .P4                0.635    0.139    4.560    0.000    0.635    0.375 
   .P5                1.043    0.256    4.078    0.000    1.043    0.589 
   .P6                0.575    0.132    4.371    0.000    0.575    0.354 
   .lead1             0.102    0.039    2.585    0.010    0.002    0.002 
   .lead3             0.388    0.139    2.782    0.005    0.006    0.006 
   .lead4             0.338    0.079    4.272    0.000    0.005    0.005 
   .lead5             1.683    0.422    3.990    0.000    0.027    0.027 
   .lead             64.316    9.677    6.646    0.000    0.957    0.957 
   .perf              0.648    0.188    3.454    0.001    0.810    0.810 
 
R codes and results for Hypothesis 2 
#model definition 
modelH2 = '#Defining latent variables 
lead1 =~ L1+L2 
lead2 =~ L3+L4 

 

157 

 

lead3 =~ L5+L6 
lead4 =~ L7+L8 
lead5 =~ L9+L10 
lead =~ lead1+lead2+lead3+lead4+lead5 
perf =~ P1+P2+P3+P4+P5+P6 
integ1 =~ I1+I2+I3+I4 
integ2 =~ I5 
integ3 =~ I7+I8+I9 
integ =~ integ1+integ2+integ3 
 
#Defining Regressions 
integ ~ a*lead 
perf ~ b*integ + c*lead 
lead~c1 
lead~c2 
lead~c3 
lead~c4 
lead~c5 
lead~c6 
lead~c7 
lead~c8 
perf~c1 
perf~c2 
perf~c3 
perf~c4 
perf~c5 
perf~c6 

 

158 

 

perf~c7 
perf~c8 
integ~c1 
integ~c2 
integ~c3 
integ~c4 
integ~c5 
integ~c6 
integ~c7 
integ~c8 
 
L2 ~~ 0*L2 
L7 ~~ 0*L7 
lead2 ~~ 0*lead2 
 
indirect := a*b 
direct := c 
total := c + (a*b)' 
 
#model fit 
fitH2 = lavaan::sem(modelH2, data=StudyDataNew, estimator="MLM") 
 
#summary 
summary(fitH2, fit.measures=TRUE, standardized=TRUE) 
 
lavaan 0.6-5 ended normally after 284 iterations 
 
  Estimator                                         ML 

 

159 

 

  Optimization method                           NLMINB 
  Number of free parameters                         79 
                                                       
  Number of observations                           103 
                                                       
Model Test User Model: 
                                              Standard      Robust 
  Test Statistic                               719.980     608.070 
  Degrees of freedom                               413         413 
  P-value (Chi-square)                           0.000       0.000 
  Scaling correction factor                                  1.184 
    for the Satorra-Bentler correction  
 
Model Test Baseline Model: 
 
  Test statistic                              6161.153    5232.157 
  Degrees of freedom                               468         468 
  P-value                                        0.000       0.000 
  Scaling correction factor                                  1.178 
 
User Model versus Baseline Model: 
 
  Comparative Fit Index (CFI)                    0.946       0.959 
  Tucker-Lewis Index (TLI)                       0.939       0.954 
                                                                   
  Robust Comparative Fit Index (CFI)                         0.959 
  Robust Tucker-Lewis Index (TLI)                            0.953 

 

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Loglikelihood and Information Criteria: 
 
  Loglikelihood user model (H0)              -3023.036   -3023.036 
  Loglikelihood unrestricted model (H1)      -2663.046   -2663.046 
                                                                   
  Akaike (AIC)                                6204.072    6204.072 
  Bayesian (BIC)                              6412.216    6412.216 
  Sample-size adjusted Bayesian (BIC)         6162.669    6162.669 
 
Root Mean Square Error of Approximation: 
 
  RMSEA                                          0.085       0.068 
  90 Percent confidence interval - lower         0.075       0.057 
  90 Percent confidence interval - upper         0.095       0.078 
  P-value RMSEA <= 0.05                          0.000       0.005 
                                                                   
  Robust RMSEA                                               0.074 
  90 Percent confidence interval - lower                     0.061 
  90 Percent confidence interval - upper                     0.086 
 
Standardized Root Mean Square Residual: 
 
  SRMR                                           0.062       0.062 
 
Parameter Estimates: 
 

 

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  Information                                 Expected 
  Information saturated (h1) model          Structured 
  Standard errors                           Robust.sem 
 
Latent Variables: 
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all 
  lead1 =~                                                               
    L1                1.000                               8.204    0.996 
    L2                1.026    0.010  101.636    0.000    8.420    1.000 
  lead2 =~                                                               
    L3                1.000                               8.472    0.999 
    L4                1.008    0.010  101.416    0.000    8.537    0.998 
  lead3 =~                                                               
    L5                1.000                               8.383    0.998 
    L6                1.017    0.008  120.958    0.000    8.524    0.999 
  lead4 =~                                                               
    L7                1.000                               8.355    1.000 
    L8                1.007    0.007  136.050    0.000    8.410    0.997 
  lead5 =~                                                               
    L9                1.000                               7.880    0.992 
    L10               0.998    0.022   45.352    0.000    7.867    0.988 
  lead =~                                                                
    lead1             1.000                               0.999    0.999 
    lead2             1.033    0.011   91.226    0.000    1.000    1.000 
    lead3             1.020    0.019   55.074    0.000    0.997    0.997 
    lead4             1.017    0.012   87.554    0.000    0.998    0.998 
    lead5             0.948    0.030   31.749    0.000    0.986    0.986 

 

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  perf =~                                                                
    P1                1.000                               0.887    0.866 
    P2                1.190    0.129    9.234    0.000    1.055    0.798 
    P3                1.060    0.084   12.560    0.000    0.940    0.906 
    P4                1.166    0.125    9.312    0.000    1.034    0.794 
    P5                0.952    0.173    5.509    0.000    0.844    0.635 
    P6                1.156    0.147    7.851    0.000    1.025    0.804 
  integ1 =~                                                              
    I1                1.000                               0.756    0.873 
    I2                1.206    0.143    8.435    0.000    0.912    0.715 
    I3                0.670    0.098    6.812    0.000    0.507    0.695 
    I4                1.333    0.152    8.747    0.000    1.008    0.758 
  integ2 =~                                                              
    I5                1.000                               0.829    1.000 
  integ3 =~                                                              
    I7                1.000                               0.591    0.856 
    I8                1.237    0.101   12.210    0.000    0.732    0.882 
    I9                1.241    0.186    6.670    0.000    0.734    0.693 
  integ =~                                                               
    integ1            1.000                               0.788    0.788 
    integ2            1.231    0.194    6.334    0.000    0.886    0.886 
    integ3            0.901    0.135    6.667    0.000    0.908    0.908 
 
Regressions: 
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all 
  integ ~                                                                
    lead       (a)    0.025    0.007    3.517    0.000    0.341    0.341 

 

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  perf ~                                                                 
    integ      (b)    1.332    0.264    5.045    0.000    0.895    0.895 
    lead       (c)    0.002    0.006    0.342    0.733    0.020    0.020 
  lead ~                                                                 
    c1                2.169    3.035    0.715    0.475    0.265    0.091 
    c2                3.632    2.971    1.222    0.222    0.443    0.147 
    c3               -1.730    2.586   -0.669    0.504   -0.211   -0.062 
    c4                1.354    3.001    0.451    0.652    0.165    0.044 
    c5                0.977    2.825    0.346    0.730    0.119    0.038 
    c6                4.674    3.531    1.324    0.186    0.570    0.161 
    c7                2.801    3.377    0.830    0.407    0.342    0.117 
    c8                2.645    3.174    0.833    0.405    0.323    0.117 
  perf ~                                                                 
    c1                0.460    0.302    1.525    0.127    0.519    0.178 
    c2                0.848    0.317    2.671    0.008    0.956    0.318 
    c3                0.674    0.311    2.167    0.030    0.760    0.225 
    c4                0.721    0.358    2.012    0.044    0.813    0.218 
    c5                0.592    0.284    2.080    0.038    0.667    0.214 
    c6                0.648    0.320    2.024    0.043    0.730    0.206 
    c7                0.700    0.321    2.182    0.029    0.789    0.270 
    c8                0.667    0.281    2.369    0.018    0.752    0.272 
  integ ~                                                                
    c1                0.151    0.223    0.678    0.498    0.254    0.087 
    c2               -0.257    0.290   -0.887    0.375   -0.432   -0.143 
    c3               -0.008    0.268   -0.031    0.975   -0.014   -0.004 
    c4               -0.154    0.210   -0.736    0.462   -0.259   -0.069 
    c5                0.277    0.212    1.308    0.191    0.464    0.149 

 

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    c6                0.289    0.203    1.424    0.154    0.486    0.137 
    c7               -0.152    0.216   -0.707    0.479   -0.256   -0.088 
    c8               -0.006    0.253   -0.025    0.980   -0.010   -0.004 
 
Variances: 
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all 
   .L2                0.000                               0.000    0.000 
   .L7                0.000                               0.000    0.000 
   .lead2             0.000                               0.000    0.000 
   .L1                0.501    0.127    3.947    0.000    0.501    0.007 
   .L3                0.123    0.041    3.018    0.003    0.123    0.002 
   .L4                0.311    0.115    2.697    0.007    0.311    0.004 
   .L5                0.265    0.125    2.129    0.033    0.265    0.004 
   .L6                0.166    0.125    1.327    0.185    0.166    0.002 
   .L8                0.366    0.108    3.392    0.001    0.366    0.005 
   .L9                0.982    0.560    1.753    0.080    0.982    0.016 
   .L10               1.528    0.825    1.853    0.064    1.528    0.024 
   .P1                0.262    0.051    5.131    0.000    0.262    0.250 
   .P2                0.637    0.087    7.291    0.000    0.637    0.364 
   .P3                0.192    0.043    4.415    0.000    0.192    0.179 
   .P4                0.625    0.132    4.728    0.000    0.625    0.369 
   .P5                1.058    0.259    4.077    0.000    1.058    0.597 
   .P6                0.573    0.129    4.438    0.000    0.573    0.353 
   .I1                0.178    0.050    3.570    0.000    0.178    0.238 
   .I2                0.795    0.165    4.824    0.000    0.795    0.489 
   .I3                0.275    0.050    5.546    0.000    0.275    0.517 
   .I4                0.753    0.223    3.374    0.001    0.753    0.426 

 

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   .I5                0.000                               0.000    0.000 
   .I7                0.127    0.034    3.741    0.000    0.127    0.267 
   .I8                0.152    0.039    3.868    0.000    0.152    0.222 
   .I9                0.582    0.124    4.695    0.000    0.582    0.519 
   .lead1             0.102    0.039    2.586    0.010    0.002    0.002 
   .lead3             0.388    0.139    2.782    0.005    0.006    0.006 
   .lead4             0.338    0.079    4.271    0.000    0.005    0.005 
   .lead5             1.683    0.422    3.989    0.000    0.027    0.027 
   .lead             64.316    9.678    6.646    0.000    0.957    0.957 
   .perf              0.136    0.053    2.561    0.010    0.173    0.173 
   .integ1            0.217    0.057    3.795    0.000    0.379    0.379 
   .integ2            0.148    0.042    3.523    0.000    0.215    0.215 
   .integ3            0.061    0.023    2.699    0.007    0.175    0.175 
   .integ             0.283    0.089    3.184    0.001    0.795    0.795 
 
Defined Parameters: 
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all 
    indirect          0.033    0.010    3.361    0.001    0.305    0.305 
    direct            0.002    0.006    0.342    0.733    0.020    0.020 
    total             0.035    0.009    3.763    0.000    0.325    0.325 
 
 
 
 
 
 
 

 

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