A Dynamic Decision Support System for Organizational Sustainability Excellence of Construction Firms

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Title:
A Dynamic Decision Support System for Organizational Sustainability Excellence of Construction Firms
Physical Description:
1 online resource (409 p.)
Language:
english
Creator:
Terouhid, Seyyed Amin
Publisher:
University of Florida
Place of Publication:
Gainesville, Fla.
Publication Date:

Thesis/Dissertation Information

Degree:
Doctorate ( Ph.D.)
Degree Grantor:
University of Florida
Degree Disciplines:
Design, Construction, and Planning, Design, Construction and Planning
Committee Chair:
Ries, Robert
Committee Co-Chair:
Kibert, Charles Joseph
Committee Members:
Chini, Abdol Reza
Flood, Ian
Geunes, Joseph Patrick

Subjects

Subjects / Keywords:
construction -- dynamics -- excellence -- organziation
Design, Construction and Planning -- Dissertations, Academic -- UF
Building Construction -- Dissertations, Academic -- UF
Genre:
Design, Construction, and Planning thesis, Ph.D.
Electronic Thesis or Dissertation
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )

Notes

Abstract:
Construction is among the most influential industries in the economy with significant impact on energy, environment, and society; therefore, the role of construction organizations in contributing towards sustainable development objectives is important. As sustainability has increasingly become a strategic business goal in the construction industry, the need for an organizational excellence model for sustainability has become clear. This research aims to find out how construction organizations can achieve excellence in terms of corporate sustainability.Organizational excellence in sustainability is an approach to organizational performance management that aims at the improvement of organizational capabilities in terms of sustainability. Organizational excellence focuses on organizational resources, capabilities, and knowledge management to determine what is driving the long-term success of organizations. The role of different organizational policies in capability building mechanisms will also be investigated. These aims have been achieved by using system dynamics as a modeling tool and by using scenario analysis to identify which enabling factors are more significant for organizational excellence in terms of sustainability, and how these factors are operating and interacting. To satisfy the objective of this research, a decision support tool has been designed and developed based on the EFQM (the European Foundation for Quality Management) model of organizational excellence as the basis for organizational excellence assessment. This decision tool consists of an integrated system dynamics model that each of its sub-models corresponds to one of the main components of the EFQM model, including leadership, policy and strategy, people, partnership and resources, and process. The results of this research can help construction organizations identify effective policies and capability-building programs that improve their organizational capabilities in sustainable practices.
Statement of Responsibility:
by Seyyed Amin Terouhid.
General Note:
In the series University of Florida Digital Collections.
General Note:
Includes vita.
Bibliography:
Includes bibliographical references.
General Note:
Description based on online resource; title from PDF title page.
General Note:
This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Thesis:
Thesis (Ph.D.)--University of Florida, 2013.
General Note:
Adviser: Ries, Robert.
General Note:
Co-adviser: Kibert, Charles Joseph.
General Note:
RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2014-08-31

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Source Institution:
UFRGP
Rights Management:
Applicable rights reserved.
Classification:
lcc - LD1780 2013
System ID:
UFE0045657:00001


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1 A DYNAMIC DECISION SUPPORT SYSTEM FOR ORGANIZATIONAL SUSTAINABILITY EXCELLENCE OF CONSTRUCTION FIRMS B y SEYYED AMIN TEROUHID A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2013

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2 2013 Seyyed Amin Terouhid

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3 To my wife, Maryam, for being a constant source of inspiration, strength, and support And to my pare nts for their prayers and unconditional support

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4 ACKNOWLEDGMENTS I am tremendously grateful for the time, effort, and continued support of my advisor, Dr. Robert Ries. I would never have been able to complete my dissertation without his guidance, thoug htfulness, and patience. My gratitude is extended to my co chair, Dr. Charles Kibert, for his inspirational guidance throughout my Ph. D. studies. Thanks are also extended to Dr. Abdol Reza Chini for his consistent support and advice. I would like to exte nd my sincere appreciation to Dr. Ian Flood for his advisory assistance, and I thank Dr. Joseph Geunes for constructive feedback and suggestions during my committee meetings. I am also grateful for the time and inputs of all the participants who took part in the interviews and research survey conducted for this dissertation. I further wish to extend my appreciation to the University of Florida for providing me with the opportunity to further my academic aspirations.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 9 LIST OF FIGURES ................................ ................................ ................................ ........ 11 LIST OF ABBREVIATIONS ................................ ................................ ........................... 15 ABSTRACT ................................ ................................ ................................ ................... 17 CHAPTE R 1 INTRODUCTION ................................ ................................ ................................ .... 19 Background ................................ ................................ ................................ ............. 19 Motivation ................................ ................................ ................................ ............... 20 Scope of Work ................................ ................................ ................................ ........ 21 Objectives ................................ ................................ ................................ ............... 23 2 LITERATURE REVIEW ................................ ................................ .......................... 26 Organizational Maturity and Excellence ................................ ................................ .. 26 Organizational Resources and Capabilities ................................ ............................ 31 Leadership and Governance ................................ ................................ ............ 37 Policy and Strategy ................................ ................................ .......................... 39 People and Knowledge ................................ ................................ .................... 39 Partnership and Resour ces ................................ ................................ .............. 40 Process ................................ ................................ ................................ ............ 41 Other Organizational Resources ................................ ................................ ...... 42 Sustainable Construction ................................ ................................ ........................ 44 Corporate Sustainability ................................ ................................ .......................... 48 System Dynamics ................................ ................................ ................................ ... 51 O verview ................................ ................................ ................................ .......... 51 The Use of System Dynamics for Organizational Modeling .............................. 53 Building Confidence into Models ................................ ................................ ...... 55 Sustainability Maturity and Excellence Models ................................ ....................... 61 Summary ................................ ................................ ................................ ................ 65 3 METHODOLOGY ................................ ................................ ................................ ... 96 Data Requirements ................................ ................................ ................................ 96 Principal Research Steps ................................ ................................ ........................ 97

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6 4 MODELS AND RESULTS ................................ ................................ ..................... 103 The Preliminary Model ................................ ................................ .......................... 103 Changes Made to the Model after the Interviews ................................ .................. 103 General ................................ ................................ ................................ ........... 104 Leadership ................................ ................................ ................................ ...... 106 Policy and Strategy ................................ ................................ ........................ 107 P eople and Knowledge ................................ ................................ .................. 108 Partnership and Resources ................................ ................................ ............ 110 Process ................................ ................................ ................................ .......... 110 Sub Models ................................ ................................ ................................ ........... 111 Leadership ................................ ................................ ................................ ...... 111 Policy and Strategy ................................ ................................ ........................ 114 People ................................ ................................ ................................ ............ 116 Partnership and Resources ................................ ................................ ............ 118 Process ................................ ................................ ................................ .......... 122 The Overall Excelle nce Score ................................ ................................ ........ 124 Scenarios Tested ................................ ................................ ................................ .. 125 Scenario 1 ................................ ................................ ................................ ...... 125 Scenario 2 ................................ ................................ ................................ ...... 133 Scenario 3 ................................ ................................ ................................ ...... 134 Scenario 4 ................................ ................................ ................................ ...... 135 Scenario 5 ................................ ................................ ................................ ...... 136 Scenario 6 ................................ ................................ ................................ ...... 136 Scenario 7 ................................ ................................ ................................ ...... 138 Scenario 8 ................................ ................................ ................................ ...... 139 Scenario 9 ................................ ................................ ................................ ...... 140 Scenario 10 ................................ ................................ ................................ .... 141 Scenario 11 ................................ ................................ ................................ .... 142 Scenario 12 ................................ ................................ ................................ .... 142 Scenario 13 ................................ ................................ ................................ .... 144 Scenario 14 ................................ ................................ ................................ .... 144 Summar y of the Results ................................ ................................ ........................ 145 5 CONCLUSION ................................ ................................ ................................ ...... 178 Summary of Contributions ................................ ................................ .................... 178 Limitations ................................ ................................ ................................ ............. 179 Concluding Remarks ................................ ................................ ............................. 180 Suggestions for Future Research ................................ ................................ ......... 182 APPENDIX A THE GRI CONSTRUCTION AND REAL ESTATE SECTOR SUPPLEMENT CORPORATE SUSTAINABILITY INDICATORS ................................ .................. 184

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7 B REQUIREMENTS OF ORGANIZATIONAL SUSTAINABILITY EXCELLENCE IN ACCORDA NCE WITH THE IMPROVED EFQM MODEL OF EXCELLENCE ...... 190 C EQUATIONS USED IN THE LEADERSHP SUB MODEL ................................ .... 192 D EQUATIONS USED IN THE POL ICY AND STRATEGY SUB MODEL ................ 195 E EQUATIONS USED IN THE PEOPLE SUB MODEL ................................ ............ 197 F EQUATIONS USED IN THE PARTNERSHIP SUB MODEL ................................ 200 G EQUATIONS USED IN THE PROCESS SUB MODEL ................................ ........ 203 H EQUATIONS USED FOR INTEGRATING THE RESULTS OF SUB MODELS .... 207 I THE RELATIVE IMPORTANCE VALUES OF ORGANIZATONAL CAPABILITIES FOR VARIOUS SUSTAINABILITY ASPECTS OBTAINED FROM INTERVIEWEES THROUGH SURVEY ................................ .................... 210 J SUMMARY OF DSCRIPTIVE STTISTICS OF THE RELATIVE IMPORTANCE VALUES OF ORGANIZATONAL CAPABILITIES FOR VARIOUS SUSTAINABILITY ASPECTS ................................ ................................ ............... 211 K RESEARCH INTERVIEW PLAN ................................ ................................ ........... 214 L RESEARCH QUESTIONNAIRE (PAPER VERSION) ................................ ........... 219 University of Florida Institutional Review Board 02 Approval on the Paper Version of the Research Questio nnaire ................................ ............................. 230 M RESEARCH QUESTIONNAIRE (ONLINE VERSION) ................................ ......... 233 University of Florida Institutional Review Board 02 Approval on the On line Version of the Research Questionnaire ................................ ............................. 235 N POWERPOINT FILE PRESENTED TO INTERVIEWEES ................................ .... 241 O COMMENTS PROVIDED BY INTERVIE WEES ON THE MODEL ....................... 257 P RESULTS OF FITTING AND THE GOODNESS OF FIT ANALYSIS ON THE RELATIVE IMPORTANCE VALUES OF ORGANIZATONAL CAPABILITIES FOR VARIOUS SUSTAINABILITY ASPECTS ................................ ...................... 270 Section 1 ................................ ................................ ................................ ............... 271 Section 2 ................................ ................................ ................................ ............... 272 Section 3. EC_LEA ................................ ................................ ............................... 273 Section 4. EC_POL ................................ ................................ ............................... 281 Section 5. EC_PEO ................................ ................................ .............................. 289 Section 6. EC_PAR ................................ ................................ .............................. 296

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8 Section 7. EC_PRO ................................ ................................ .............................. 304 Section 8. EN_LEA ................................ ................................ ............................... 312 Section 9. EN_POL ................................ ................................ ............................... 320 Section 10. EN_PEO ................................ ................................ ............................ 328 Section 11. EN_PAR ................................ ................................ ............................. 336 Section 12. EN_PRO ................................ ................................ ............................ 344 Section 13. SO_LEA ................................ ................................ ............................. 352 Section 14. SO_POL ................................ ................................ ............................. 360 Section 15. SO_PEO ................................ ................................ ............................ 368 Section 16. SO_PAR ................................ ................................ ............................ 376 Section 17. SO_PRO ................................ ................................ ............................ 3 84 Q LIST OF VARIABLES AND PARAM ETERS OF THE SYSTEM DYNAMICS MODEL ................................ ................................ ................................ ................. 392 LIST OF REFERENCES ................................ ................................ ............................. 402 BIOGRAPHICAL SKETCH ................................ ................................ .......................... 409

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9 LIST O F TABLES Table page 2 1 Summary of the National Quality Award Categories (Criteria) ............................ 78 2 2 c concepts ................................ ..... 79 2 3 A summary of the EFQM criteria ................................ ................................ ........ 80 2 4 ............................... 81 2 5 ................. 82 2 6 nt in organizations .............. 83 2 7 ...... 84 2 8 n and criteria for organizational processes ............................... 85 2 9 Dynamic Complexity ................................ ................................ ........................... 86 2 10 System Dynamics Fit to Organizational Systems ................................ ............... 87 2 11 Business Sustainability Maturity Model BSMM ................................ ................ 88 2 12 Economic aspects of corporate sustainability ................................ ..................... 89 2 13 Internal social aspects of corporate sustainability ................................ ............... 90 2 14 External social aspects of corporate sustainability ................................ ............. 91 2 15 Maturity levels of economic sustainability aspects ................................ .............. 92 2 16 Maturity levels of external social sustainability aspects ................................ ...... 93 2 17 Focus areas of Dow Jones Sustainability Index (DJSI) ................................ ...... 94 2 18 A summary of criteria provided by the reviewed sustainability maturity and excellence models ................................ ................................ .............................. 95 4 1 The list of distribution functions that best represent the variations of weights based on the opinion of the interviewees ................................ ......................... 176 4 2 The effect range of customer relationship management on the number of active customers and the Overall Sustainability Excellence Score ................... 176

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10 4 3 The effect of defining differ ent values for the Skill Level of Entrants on the organizational People Capability and the Overall Sustainability Excellence Score ................................ ................................ ................................ ................ 177 4 4 The obtained organizational People Capability and th e Overall Sustainability Excellence Score with different Training to Skill Conversion Factors ............... 177 A 1 The GRI construction and real estate sector supplement corpo rate sustainability indicators ................................ ................................ ..................... 184 B 1 Requirements of organizational sustainability excellence in accordance with the imp roved EFQM model of excellence ................................ ......................... 190 I 1 The relative importance values of organizational capabilities for various sustainability aspects obtained from interviewees through survey ................... 210 J 1 Summary of descriptive statistics of the relative importance values of organizational capabilities for the economic aspect of sustainability ................ 211 J 2 Summary of descriptive statistics of the relative importance values of organizational capabilities for the environmental aspect of sustainability ......... 212 J 3 Summar y of descriptive statistics of the relative importance values of organizational capabilities for the social aspect of sustainability ...................... 213 L 1 Essential requirement s of organizational sustainability excellence ................... 220 L 2 Question about essential organizational capabilities for sustainability excellence ................................ ................................ ................................ ........ 221 L 3 Questions about the models presented in Figure L 1 through L 5 .................... 222 O 1 General comments provided by interviewees on the model ............................. 257 O 2 Comments provided by interviewees on the Leadership model ........................ 259 O 3 Comments provided by interviewees on the Policy and Strategy model .......... 261 O 4 Comments provided by interviewees on the People model .............................. 263 O 5 Comments provided by interviewees on the Partnership model ....................... 267 O 6 Comments provided by interviewees on the Process model ............................ 269

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11 LIST OF FIGURES Figure page 1 1 Aspec ts of corporate sustainability in accordance with the GRI construction and real estate sector supplement ................................ ................................ ...... 25 2 1 The Five Levels (stages) of Software Process Maturity ................................ ...... 67 2 2 The Maturity Model Structure ................................ ................................ ............. 68 2 3 Baldrige Criteria for Performance Excellence Framework A Systems Perspective ................................ ................................ ................................ ......... 69 2 4 The EFQM Model ................................ ................................ ............................... 69 2 5 The Sustainable Value Framework ................................ ................................ ..... 70 2 6 The revised framework of sustainable construction ................................ ............ 70 2 7 The GRI Construction and Real Estate Sector Supplement corporate sustainability indicators ................................ ................................ ....................... 71 2 8 Organizational knowledge and learning as the aggregation of specific capabilities ................................ ................................ ................................ .......... 72 2 9 The workforce management model ................................ ................................ .... 73 2 10 Hiring, Quits, and Organizational Knowledge ................................ ..................... 74 2 11 High level view of the basic model {of organizational knowledge} ...................... 74 2 12 An alternate view of the basic model {of organizational knowledge} .................. 75 2 13 Change Mechanisms of Operational and Dynamic Capabilities ......................... 76 2 14 Developments in quality management ................................ ................................ 76 2 15 Corporate Sustainability: Capability Maturity Model by Spectrum Innovation Group 2008 ................................ ................................ ................................ ..... 77 4 1 The Leadership sub model ................................ ................................ ............... 147 4 2 Policy and Strategy sub model ................................ ................................ ......... 148 4 3 The People sub mode l The workforce management module ........................ 149 4 4 The People sub model The knowledge management module ....................... 150 4 5 The Partnership sub model ................................ ................................ .............. 151

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12 4 6 The Process sub mode ................................ ................................ .................... 152 4 7 The relative importance values of each of the assumed organizational capabilities for each of the three sustainability aspects ................................ .... 153 4 8 The relative importance values of sustainability aspects (B), and the relative importance values of different organizational capabilities for sustainability aspects (A) ................................ ................................ ................................ ....... 153 4 9 The individual sustainability excellence scores of capabilities (A), and the sustainability excellence score of each capability for each particular sust ainability aspect (B) ................................ ................................ .................... 154 4 10 The normal distribution function that can be fitted to the Overall Sustainability Excellence Scores obtained by running the model 1000 times (scenario 1) ..... 154 4 11 The results of the Leadership sub model in the base scenario (scenario 1) ..... 155 4 12 The results of the Leadership sub model in the base scenario (scenario 1) ..... 155 4 13 The results of the Leadership sub model in the base scenario (scenario 1) ..... 156 4 14 The results of the Policy and Strategy sub model in the base scenario (scenario 1) ................................ ................................ ................................ ....... 156 4 15 The results of the Policy and Strategy sub model in the base scenario (scenario 1) ................................ ................................ ................................ ....... 157 4 16 The results of the Policy and Strategy sub model in the base scenario (scenario 1) ................................ ................................ ................................ ....... 157 4 17 The results of the People sub model in th e base scenario (scenario 1) ........... 158 4 18 The results of the People sub model in the base scenario (scenario 1) ........... 158 4 19 The resul ts of the People sub model in the base scenario (scenario 1) ........... 159 4 20 The results of the People sub model in the base scenario (scenario 1) ........... 159 4 21 The results of the People sub model in the base scenario (scenario 1) ........... 160 4 22 The results of the Partnership sub model in the base scenario (scenario 1) .... 160 4 23 The results of the Partnership sub model in the base scenario (scenario 1) .... 161 4 24 The results of the Partnership sub model in the base scenario (scenario 1) .... 161 4 25 The results of the Process sub model in the base scenario (scenario 1) ......... 162

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13 4 26 The resul ts of the Process sub model in the base scenario (scenario 1) ......... 162 4 27 The results of the Process sub model in the base scenario (scenario 1) ......... 163 4 28 The results of the Process sub model in the base scenario (scenario 1) ......... 163 4 29 The results of the Process sub model in the base scenario (scenario 1) ......... 164 4 30 The effect of changing the sustainability aspect weights on the Overall Sustainability Excellence Score (scenario 2) ................................ .................... 164 4 31 The ef fect of changing the sustainability aspect weights on the Overall Sustainability Excellence Score (scenario 3) ................................ .................... 165 4 32 Average Duration of Stay from 60 to 36 month on the People capability (scenario 4) ................................ ................ 165 4 33 The remediating effect of hiring personnel with higher levels of skill and providing a more intense training program (scenario 4) ................................ ... 166 4 34 The effect of learning effectiveness on People capability (scenario 5) ............. 166 4 35 The normal distribution function tha t can be fitted to the Overall Sustainability Excellence Scores obtained by running the model 1000 times (scenario 6) ..... 167 4 36 The improving trend of the quality of training programs over tim e .................... 167 4 37 The effect of decreasing the Appropriateness of Training Program for Policy Making to 80% of the base scenario (scenario 7) ................................ ............. 168 4 38 The effect of enhancing Leadership capability on the alleviation of declining trends in Policy Capability (scenario 7) ................................ ............................. 169 4 39 The impact of excluding the Partnership Capab ility on the Overall Sustainability Excellence Score (scenario 8) ................................ .................... 169 4 40 One part of the Policy and Strategy sub model ................................ ................ 170 4 41 The trend of the People Capability by setting minimum, median, and maximum values for the Skill Level of Entrant (scenario 10) ............................ 170 4 42 The trend of the People Capability over 10 years in the case of restricted hiring if the plan of training remains the same (scenario 11) ............................ 171 4 43 The trend of the People Capability over a time frame of 10 years in case of restricted hiring if the training plan is adjusted (scenario 12) ............................ 171 4 44 The trend of the People Capability over time with different Training to Skill Conversion Factors (scenario 12) ................................ ................................ ..... 172

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14 4 45 A weak trend of growth followed by a decline of the Overall Sustainability Excellence Score (scenario 13) ................................ ................................ ....... 172 4 46 A declining trend of People Capability in scenario 14 ................................ ....... 173 4 47 The impact of the listed parameters on the People Capability at the end of year 3 ................................ ................................ ................................ ............... 173 4 48 The impact of the listed variables on the Overall Sustainability Excellence Score at the end of year 3 ................................ ................................ ................ 174 4 49 The impact of the listed parameters on the People Capability at the end of yea r 10 ................................ ................................ ................................ ............. 174 4 50 The impact of the listed variables on the Overall Sustainability Excellence Score at the end of year 10 ................................ ................................ .............. 175 L 1 Dynamics of organizational leadership ................................ ............................. 223 L 2 Dynamics of organizational policies and strategies ................................ .......... 224 L 3 Dynamics of people in organizations ................................ ................................ 225 L 4 Dynamics of organizational partnership and resources ................................ .... 226 L 5 Dynamics of organizational processes ................................ ............................. 227 L 6 Relative importance of organizational capabilities for economic performance 228 L 7 Relative importance of organizational capabilities for environmental performance ................................ ................................ ................................ ..... 229 L 8 Relative importance of organizational capabilities for social performance ........ 229

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15 LIST OF ABBREVIATIONS ABC Associated Builders and Contractors, Inc. BEPAC Building Environmental Performance Assessment Criteria BREEAM Building Research Establishment Environmental Assessment Method BSMM Business Sustainability Maturity Level Model CDP Carbon Disclosure Project CIB Conseil International du Batiment CMM Capability Maturity Model CRESS Construction and Real Estate Sector Supplement CS Corporate Sustainability CSR Corporate Social Responsibility DGNB Deutsche Gesellschaft fr Nachhaltiges B auen (German Sustainable Building Council) DJSI Dow Jones Sustainability Index DSS Decision Support System ECSF European Corporate Sustainability Framework EFQM European Foundation for Quality Management EFQM EM European Foundation for Quality Management E xcellence Model GRI Global Reporting Initiative HOS House of Sustainability HR Human Resources IRB Institutional Review Board ISO International Organization for Standardization LEED Leadership in Energy and Environmental Design

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16 MBNQA Malcolm Baldrige Natio nal Quality Award MIT Massachusetts Institute of Technology NGO non governmental organizations NIST National Institute of Standards and Technology OPM3 The Organizational Project Management Maturity Model PMI Project Management Institute QFD Quality Functi on Deployment R&D Research and Development RBV The resource based view of the firm SEI Software Engineering Institute SPI Sustainable Performance Institute TQM Total Quality Management

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17 Abstract of Dissertation Presented to the Graduate School of the Uni versity of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philos ophy A DYNAMIC DECISION SUPPORT SYSTEM FOR ORGANIZATIONAL SUSTAINABILITY EXCELLENCE OF CONSTRUCTION FIRMS By Seyyed Amin Terouhid August 2013 Chair: Robert Ries Cochair: Charles J. Kibert Major: Design, Construction, and Planning Construction is among the most influential industries in the economy with significant impact on energy, environment, and society; therefore, the role of construction organizations in contributing towards sustainable development objectives is important. As sustainability has increasingly become a strategic business goal in the construction industry, the need for an organizational excellence model for sustainability has become clear. This research aims to find out how construction organizations can achieve excellence in terms of corporate sustainability. Organizational excellence in sustainability is an approach to organizational performance management that aims at the im provement of organizational capabilities in terms of sustainability. Organizational excellence focuses on organizational resources, capabilities, and knowledge management to determine what is driving the long term success of organizations. The role of dif ferent organizational policies in capability building mechanisms will also be investigated. These aims have been achieved by using system

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18 dynamics as a modeling tool and by using scenario analysis to identify which enabling factors are more significant for organizational excellence in terms of sustainability, and how these factors are operating and interacting. To satisfy the objective of this research, a decision support tool has been designed and developed based on the EFQM (the European Foundation for Q uality Management) model of organizational excellence as the basis for organizational excellence assessment. This decision tool consists of an integrated system dynamics model that each of its sub models corresponds to one of the main components of the EFQ M model, including leadership, policy and strategy, people, partnership and resources, and process. The results of this research can help construction organizations identify effective policies and capability building programs that improve their organizati onal capabilities in sustainable practices.

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19 1. CHAPTER 1 INTRODUCTION Background Scientists have continuously endeavored to theorize various frameworks to predict and demonstrate the long term consequences of development. Sustainable development can be rega rded as one of these endeavors. Sustainable development has emerged to the extent that corporations can no longer ignore it if they want to be better sustainability recor ds to show that they are competent not only in their field of proficiency, but also in operating in accordance with environmentally friendly business guidelines. In undergoing this adaptation phase, corporations need to smoothly integrate sustainability be st practices into their organizational structures, processes, and policies. Their intention is, first of all, not to fall behind other competitors, but also to capture more market share and further develop new markets. Generally, strategic corporate decisi ons are considered to be complex decisions, because various factors along with uncertainties are associated with them. Expecting the decision process to consider sustainability concerns makes the problem even more complex and studying it more difficult. T his is due to the fact that incorporating sustainability into decision making requires taking not only economic criteria, but also the social and environmental aspects of decisions into consideration. Therefore, policy verification is an important need in corporate sustainability. In this research, system dynamics has been chosen for policy verification purposes as it has the potential to represent the state of an organization by mimicking the behavior of its elements and their interactions in addressing s pecific problems.

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20 Furthermore, various scenarios can be designed and tested to investigate the consequences of various decision policies. Motivation Compliance with the requirements of sustainable development requires proper policies in many areas of an or ganization. Examples include human resource training and development, knowledge acquisition, technology assessment, supplier analysis and management, standardization, and restructuring decisions. These policies cannot be made unless an organization success fully anchors them into its underlying strategic value system. Yet, a key question remains to be answered: how can an organization ensure that a specific set of policies will result in desired performance outcomes? In other words, organizations might have various options in choosing different policies, and those policies can be planned and expressed in different manners and scales; however, how can an organization determine which scenarios end up producing desired performance results? This question is the m ain focus of this research. Simulation techniques, especially system dynamics, due to their ability in dealing with causal relationships among various system components, have great potential to help answer this question. Many studies have been undertaken to measure the sustainability performance of organizations. However, corporations are not able to effectively plan and manage required changes without continuously monitoring and controlling their decisions. Therefore, mechanisms should be in place to eval uate management policies to ensure that they are effective in moving towards sustainability objectives. This dissertation is targeted to investigate what organizational policies make construction organizations capable of undertaking sustainable constructio n best practices. Effective mechanisms

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21 through which construction organizations can build capability in terms of sustainable development will be identified and investigated as part of this research. Scope of Work The scope of work for this research involve s the design and implementation of system dynamics models to investigate mechanisms through which construction organizations can build capability in terms of sustainable development. In other words, this research aims to find out how construction organizat ions can achieve excellence in terms of corporate sustainability. Organizational excellence in sustainability is an approach to organizational performance management that aims at the improvement of organizational capabilities in terms of sustainability. T development capability will also be investigated. Examples of organizational strategic resources include organizational knowledge, human resources, and leadership. Typically, ma ny parties are involved in construction projects. Examples include owners, financiers, designers, consultants, contractors, and inspectors. Each of these parties contributes to meeting sustainable construction goals in different ways. Their degrees of con trol over the implementation of these goals also differ. One approach is to determine which party is the most influential party among all involved in a project, and focus mostly on that party to target the most improvements possible. An alternate approach is to focus on general types of organizational capabilities to find out how those capabilities can be improved and directed towards enabling the organization to achieve excellence in terms of corporate sustainability. In this research, the latter approach has been adopted For this purpose, a wide range of organizational processes and policies will be investigated to determine which

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22 constituents are actually driving superior organizational capability in terms of corporate sustainability. Although no partic ular type of construction organization is targeted in this study, the focus would be placed on contractors and designers in the construction industry. As mentioned before, it is the assumption of this study that best practices for sustainable construction practices exist. Developing new sustainable construction best practices is out of scope of this study. Best practices in different areas of sustainable construction will be used to gain a better insight into what should constitute the model. The objective of this study is not to physically assess the sustainability excellence of any organizations by actually measuring their performance in different areas. By the use of system dynamics, the ultimate objective is to have a set of models as sub systems of an overall decision support system. Sound modeling practices that increase confidence in decision models will be used throughout the modeling process. In preference to precise estimation of all input values used in the model, attention will be given to the m odel structure and architecture. This decision has been made due to the fact that input values are highly case dependent and they vary from organization to organization, whereas, model structures, if designed properly, are less case dependent and can be us ed after some minor adjustments for a range of organizations. The validating phase of the model will comprise of the following steps: Refining the model based on the personal judgment of the modeler Refining the model based on expert judgments from a numbe r of research interviews Testing the model against a dataset representing organizational policies and the circumstances under which a hypothetical organization operates Testing of the model by implementing its suggestions in a construction organization to see how they perform is out of the scope of this study.

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23 Different aspects of corporate sustainability in the construction industry have sector supplement (Global Reporting Initiative, 2011) A brief representation of these aspects is shown in Figure 1 1 In this research, the GRI model for corporate sustainability has been adopted; therefore, the GRI construction and real estate sector supplement is assumed to define the elements of corporate sustainability in the construction industry. A comprehen sive list of indicators introduced in the GRI sector supplement is provided in Appendix A. Objectives The main intention of this dissertation is to answer the following questions: What defines organizational sustainability excellence for construction orga nizations? How can modeling be used to investigate the dynamics of organizational sustainability excellence for construction organizations? What mechanisms and organizational policies affect the capability of construction organizations in terms of sustaina ble practices? Answering these questions can provide a practical base for competency measurement purposes for construction organizations. In other words, this study is aimed at developing models to evaluate how organizational policies contribute to the sus tainability excellence of construction organizations. Using system dynamics, the goal is to determine which enabling factors and organizational resources are essential in making a construction organization capable of performing sustainable construction be st practices. In this study, the definition of construction organizational sustainability excellence is based on the reporting (Global Reporting Initiative, 2011)

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24 The set of system dynamics models can be considered as a decision support system by which organizations can test and verify their organizational policies. What if analysis using the decision support system makes it possible for organizations to predict the corporate sustainability consequences of their policies. Using system dynamics, organizational systems are expected to be modeled to better understand the dynamics of real world business processes. System dynamics models can be designed and developed so that they sufficiently mimic the behavior of interacting processes within construction organizations. It is the intention of this study to utilize the benefits of system dynamics techniques to better understand the d ynamics of organizational sustainability excellence for the construction industry. We expect this study to provide an opportunity for construction organizations to evaluate their organizational capabilities in preparation for the delivery of construction projects with better sustainability records.

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25 Figure 1 1 Aspects of corporate sustainability in accordance with the GRI construction and real estate sector supplement (Global Reporting Initiative, 2011)

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26 2. CHAPTER 2 LITERA TURE REVIEW In this section, a literature review of a number of related topics including organizational maturity and excellence, organizational resources and capabilities, sustainable construction, corporate sustainability, system dynamics, and sustainabil ity maturity and excellence models will be presented. Organizational Maturity and Excellence ning as it investigates whether an organization has advanced in becoming capable of managing organizational processes and effectively proceeding towards its strategies. Organizational maturity considers the quality of organizational processes to be essenti al in producing satisfactory outcomes by the organization. Kaposi and Myers (2001) hold that a mature organization is an organization possessing a well operations. Black box areas are those with no written process representation for which no further operation detail has been provided. Kaposi and Myers focus mainly on how well developed organizational processes and structure are. Others have proposed that maturity also implies whether an appropriate set of tools, techniques, processes, and prerequisites exist in the organization. Many organizational maturity models have been developed to assess the maturity of organizations. Maturity models help organizatio ns realize which areas need to improve to reach the expected organizational performance level. Organizational

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27 maturity models provide a potent tool to make organizations capable of proceeding towards their organizational strategies. The underlying concept behind organizational maturity models is the work of Philip Crosby in quality control, which was further developed in the Capability Maturity Model (CMM) introduced by the Software Engineering Institute (SEI) (Paulk, Curtis, Chrissis, & Weber, 1993) recommended practices in a number of key process areas that have been shown to enhance software Maturity Mod el is currently a broadly accepted framework for capability assessment and capability building in organizations. As defined by the SEI, the main characteristic of a mature organization is e participants (p. 20) The three main underlying definitions used by SEI to demonstrate the Capability Maturity Model (CMM) are as follows: s the range of expected results that can be achieved process. Process performance focuses on achieved results; process capability focuses on exp Evolutionary steps taken by organizations towards higher maturity levels are shown in Figure 2 1 Each level of maturity corresponds to a set of process goals that set the criteria for organizations for that maturity level. Meeting those criteria will make an organization eligible to move to the next maturity level. Each level of maturity

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28 corres ponds to a structure that is demonstrated in Figure 2 2 As shown, each maturity level consists of certain key process areas which are organized into certain common features. These common features represent key pr actices proposed to fulfill the goals of designated key process areas. Various other maturity models with different definitions for organizational maturity have been proposed so far. The Organizational Project Management Maturity Model (OPM3) from the Proj ect Management Institute (PMI) is an example of one of the most widely accepted maturity models. According to the PMI methodology, the purpose of maturity models is to measure the competency level of organizations against a set of best practices (Project Management Institute, 2008) PMI introduces the three main components of its organizational maturity model as being Best Practices Capabilities and Outcomes Bes t Practice is a grouping of related organizational Capabilities Capability Outcome on the Capability (Project Management Institute, 2008) Organizational excellence is also a term with a long history in the literature, and with a definition close to that of maturity. The literature suggests that org anizational excellence has a broader set of requirements, but similar to organizational maturity, organizational excellence investigates the long term success of organizations. However, the difference between organizational excellence and organizational ma turity is that organizational maturity focuses on process management and improvement, whereas,

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29 organizational excellence focuses on organizational resources, capabilities, and knowledge management to achieve its goals. Malcolm Baldrige National Quality Aw ard (MBNQA) is an example of organizational excellence models. MBNQA is an annual award given by the National Institute of Standards and Technology (NIST) to organizations that are considered to achieve exceptional performance excellence. According to NIST (2011) performance management that results in (1) the delivery of ever improving value to customers and stakeholders, contributing t o organizational sustainability, (2) improvement of overall organizational effectiveness and capabilities, and (3) NIST has established a set of criteria to be satisfied in order for an organization to be qua lified to win the award. These criteria offer a framework and an assessment tool for the identification of organizational capabilities and opportunities for further improvements. As shown in Figure 2 3 NIST has c ategorized the requirements of the criteria for performance excellence in seven areas, which are leadership, strategic planning, customer focus, measurement and knowledge management, workforce focus, operations focus, and results. Many other quality system s and national quality awards offer a similar list of criteria. The work of Hui and Chuan (2002) provides a brief summary of the requirements of nine different models of organizational excellence (see Table 2 1 ). Another model that is concerned with performance excellence is the European Foundation for Quality Management Excellence Model (EFQM EM), which was

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30 established between 1989 and 1991 to comprehensively address diffe rent requirements of various models for Total Quality Management (TQM). According to Hillman (1994) means to facilitate compar Figure 2 4 depicts different components of the EFQM model. As indicated in Figure 2 4 the main elements of the EFQM excellence model are leadership, policy and strategy, people, partnership and resources, and process. EFQM not only takes processes and proper process management practices into account, but also accounts for other organizational capabilities and resources that are essential for organizational success. Table 2 2 p rovides a detailed list of concepts and the philosophy behind those concepts in EFQM. Table 2 3 represents a summary of the evaluation criteria defined in the EFQM model. The list of criteria in this table is extr acted from a selection of studies that have compared evaluation criteria of different excellence models. Examples include the works of Conti (2004) Russell (2000) and K arapetrovic and Willborn (2001) and Hui and Chuan (2002) evaluation criteria of the EFQM covered by the EFQM model in any level of evaluation (not necessarily as a key criterion). As Table 2 3 shows, the EFQM model covers almost all criteria covere d by many excellence models, and it takes them into account either as a key evaluation factor or as part of key evaluation factors. The comprehensiveness of the EFQM method and its

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31 inclusive approach in accounting for essential organizational characteristi cs makes the EFQM model a favorable model for organizational excellence studies. Since in this research the EFQM model of organizational excellence has been adopted as the basis for organizational excellence assessment, the following section provides furth er insights into the literature, which is categorized into the main components of the EFQM model. The approach taken in this research for calculating the overall organizational sustainability excellence score is similar to the approach taken in the work of Arditi and Lee (2003) These authors proposed that the service quality performance of design build contractors can be measured by using the QFD (Quality Function Deployment) method. By conducting surveys, they hav e calculated the relative importance of quality components as well as that of quality needs. They have also presented the result in a process matrix for measuring the corporate level of performance. Arditi and Lee have determined the overall quality perfor mance score by calculating the weighted sum of individual quality performance scores in each capability. Organizational Resources and Capabilities Recognizing sources of sustained competitive advantage is one of the key areas of research in strategic manag ement. Inspired by the works of Porter (1998) Wernerfelt (1984) and Barney (1991) many scientists are investigating whic h characteristics and capabilities are associated with the long term superior performance of organizations. The strategy literature suggests that a privileged product market position along with a well chosen and accumulated asset stock in the organization can result in a competitive advantage (Dierickx & Cool, 1989; Ulrich & Lake, 1991)

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32 O rganizational resources are composed of all the assets, capabilities, organizational processes, organizatio n attributes, information, and knowledge controlled by an organization without which the organization will not be able to execute its strategies efficiently and effectively (J. Barney, 1991; Daft, 2009) The resource based view of the firm (RBV) is one of the most cited theories in the literature that can be used to explain what enabling factors are the most significant predictors of the competitive advantage of organizations (Wernerfelt, 1984) Wernerfelt considers the strengths and weaknesses of organizations as their resources. Wernerfelt permanently ources are: brand names, in house knowledge of technology, employment of skilled personnel, trade contacts, machinery, efficient However, organizational performance is not only a result of the variety of the resources that an organization possesses at any point in time, but is also a result of fluctuations of organizational resources due to management policies. In line with this thought, Morecroft et al. (2002) argued tha superior performance stem not only from the uniqueness and variety of the firm's current resources, but also from the ways resource endowments change over time as a result of management policies. This view shifts att ention from static comparisons of resource endowments to dynamic analyses of resource accumulation and the dominant logic of policies and feedback processes that control them and drive their evolution over

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33 The work of Heene and Sanchez (1997) can be considered in line with the notion of Morecroft et al. (2002) These authors demonstrated an organization as a system composing of multiple asset stocks whic h are operating to facilitate the achievement of The aforementioned articles are not the only works that confirm the need for organizational resources. Other research offer similar thoughts. According to Dierickx and Cool (1989) can be adjusted instantaneously, stocks cannot. It takes a consistent pattern of resource flows to accumulate a desired change in strategi Dierickx and Cool (1989) according to to generate sustained competitive advantage {are} value, rareness, imitability, and Barney (1991) has categ orized organization resources into three main types: physical capital resources, human capital resources, and organizational capital technology used in a firm, a firm's plan t and equipment, its geographic location, and its access to raw materials. Human capital resources include the training, experience, judgment, intelligence, relationships, and insight of individual managers and workers in a firm. Organizational capital res ources include a firm's formal reporting structure, its formal and informal planning, controlling, and coordinating systems, as well as informal

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34 (p. 101). According to Gr ant (1991) organizational resources can be classified as tangible, intangible, and personnel based resources. Tangible resources include financial reserves and physical resources; whereas, intangible resources in clude reputation, technology, and human resources (HR). HR includes the culture, training and knowledge of personnel, as well as their commitment and loyalty. The importance of innovation and its role in creating competitive advantage have been emphasized in the literature. Dervitsiotis (2008) develop a sustainable competitive advantage depends on the capability of an organization to explore new opportunities by developing a hi gh quality value innovation efficiently developing new knowledge and the skills requi red to do so on a continual Laszlo et al. (2006) hold that creating value for both stakeholders and shareholders is essential in fostering competitive advantage for organizations (see Figure 2 5 337) that resou rces are considered as tradable and non specific attributes, whereas, capabilities are considered mostly organization specific attributes that are used to

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35 exploit fundamental resources within the organization (Amit & Schoemaker, 1993) Similarly, Barney (2002) include only t hose internal firm attributes that enable a firm to coordinate and exploit its other resources. Although these distinctions among resources, capabilities, and competencies can be drawn in theory, it is likely that they will become badly blurred in practice Collis (1994) lists a number of definitions for organizational capabilities. Collis typology of organizational c Specific attributes of org anizational capabilities have also been addressed in the literature. Schreyogg and Kliesch Eberl (2007) for instance, have listed various characteristics that organizational capabilities should possess. These a uthors hold that in the concert of other resources such as financial assets, technology, or manpower, but rather a distinctive and superior way of allocating resources. It addresses complex processes across the organization such as product development, customer relationship, Some characteristics of capabilities in organizations according to Schreyogg and Kliesch Eberl are as follows: Path dependency and lock in: The possession of this characteristic can mean an organization remains under the significant influence of its past. Schreygg and

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36 Kliesch Eberl (2007) have described this situation a and future decision capabilities are imprinted by past decisions and their underlying reinforcing processes may establish strategic paths which are prone to dramatically narrowing the scope of strategic man agement. In the worst case a (p. 916) Structural inertia: This characteristic refers to a situation in which organizations remain bound to their formed and stabilized configurations. This characteristic is described by Schreygg and Kliesch Eberl (2007) develop greater competence in a particular activity, they engage in that activity more, thus fu organizational characteristic is when it prevents the organization from developing of new capabilities. prerequi As described by Warren (2008) activity that involves complex patterns of coordination and co operation betwee n people and other resources. Capabilities would include research and development expertise, customer service, and high quality manufacturing. Skills, by contrast, are more specific, relating to narrowly defined activities such as typing, machine maintenan ce and book Capabilities are assumed to shape the basis for sustainable competitive advantage and ultimately superior performance of organizations. According to Winter (2003) an organizational capability is a routine or a set of routines that enable the organization to create certain significant outputs given a set of inputs. Based on this definition, capabilities cannot be attained from any external market but are to be developed and accumulat ed within the organization. Although scientists agree that organizations can develop (gain) or lose capabilities, no consensus is reached regarding the mechanisms through which these changes occur.

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37 The literature categorizes organizational capabilities in to two main categories: operational and dynamic (Eisenhardt & Martin, 2000; Teece, Pisano, & Slwen, 2005) Operational capabilities, also known as zero level capabilities, focus on transfor ming inputs into outputs, and they enhance the short term performance of organizations. Due to the environmental uncertainties and volatile circumstances under which organizations operate, the reliance on a particular set of capabilities has been brought i nto question. The new focus has been on the ability of the organization to adapt itself and quickly develop new organizational capabilities to maintain superior (Winter, 2003) Dynamic capabilities represent the ability of the organization to modify its operational capabilities. Dynamic capabilities enhance long term performance of organizations (Winter, 200 3) Research and Development (R&D) capability is an example of a dynamic capability. According to Rahmandad (2012) production, sales, customer service, and design are examples of operational capabilities; wher eas, product development, process improvement, product acquisition, and knowledge creation are examples of dynamic capabilities. Leadership and Governance As shown in Table 2 1 leadership is one of the most common criteria among many different organizational excellence models. In other words, leadership is considered to be an essential component of performance excellence in organizations. Leadership has been one of the ke y areas of interest in the literature of organizational theories. According to Zaccaro and Klimoski (2001) the main function of onal

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38 Jacobs and Jacues (1990) purpose [mean ingful direction] to collective effort, and causing willing effort to be (1992) have also emphasized the policymaking role of leadership in o rganizations. The direction setting function of leadership is considered to be the task of setting the vision for an organization. Kirkpatrick and Locke (1996) ideal that represents shared values; it is often ideological nature and has moral (2003) choose a direction, a leader must first have de veloped a mental image of a possible and desirable future state of the organization. This image, which we call a vision, may be as Leadership is one of the principles of the ISO 9000 q uality management series of standard. According to the International Organization for Standardization, and as cited by Cartha (2004) They sh ould create and maintain the internal environment in which people can become Shahin and Zairi (2007) studied different internal and external elem ents of corporate governance (CG) and corporate social responsibility (CSR). Ocasio and Joseph (2006)

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39 directing, regulating, and controlling the allo cation and distribution of resources within Table 2 4 Policy and Strategy As discussed, one of the functions of leadership is setting direction for the organization. This direction setting can be performed by developing proper strategies and policies. Policies and strategies are considered as key succe ss derivers of organizations because they, as defined, are supposed to guide organizations in the right direction. Table 2 5 Pe ople and Knowledge The role of knowledge management is considered to be essential for corporate sustainability in the literature. Examples include the works of Davenport and Prusak (2000) and Robinson et al. (2006) According to Lee (2001) In the study conducted by Robinson et al. (2006) a maturity roadmap, entitled STEPS, is proposed which illustrates the implementation mechanism for knowledge management in organizations that have set goals towards corporate sustainability. Hub er (2006) introduced the main mechanisms through which organizations can gain technical knowledge. The set of organizational learning mechanisms includes: 1. Acquiring knowledge through boundary spanning employees

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40 2. A cquiring commonplace knowledge by recruiting industry standard knowledge workers 3. Acquiring knowledge by poaching experts from other organizations 4. Acquiring knowledge from consultants 5. Acquiring knowledge through acquisitions 6. Acquiring knowledge through alli ances (1990) According to Cohen and Levinthal (1990) absorptive capacity is defined as "the ability of a firm to recognize the value of new, external information, assimilate it, and apply it to commercial ends." (p. 128) Another definition for absorptive capacity has been offered by Zahra and George (2002) as follows: "a set of organizational routines and processes by which firms acquire, assimilate, transform, and exploit knowledge to produce a dynamic organizational capability." (p. 186) According to the International Organization for Standardization, and as cited by Cartha (2004) involvement enables their abilitie in Table 2 6 Partnership and Resources The role of partnership in providing the opportunities of knowledge sharing between organizations has been emphasized in the literature. Internal organizational undertakings are not the only mechanisms through which organiz ations can gain

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41 knowledge. Organizational knowledge can also be gained through external relationships of the organization; thus, there is a growing interest in working mechanisms of alliances and obtaining new knowledge from partners that has sparked in th e literature (Simonin, 1999) One of the leading examples is the work of Lee (2001) in which the author has listed certain requirements that are necessary in every successf ul partnership. Those learn or acquire the needed knowledge from other organiza Lowndes and Skelcher (2002) have characterized a typical lifecycle of partnership among organizations. According to them, the lifecycle includes the following four stages: pre partnership collabor ation, partnership creation and consolidation, partnership program delivery, and partnership termination or succession. listed in Table 2 7 Process The quality of organizational processes is essential in producing satisfactory outcomes by any organization. Literature suggests that, only organizations with well defined and well implemented organizational processes are capable of predicti ng their performance and undertaking sustained continuous improvements in line with their strategic objectives. Therefore, organizations need to have strategies to identify which process areas need to improve to reach the expected organizational performanc e level. The study of process management has a long history in the literature. Process management performance is a key element in many organizational excellence models,

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42 quality systems, and quality awards. Examples include the European Foundation for Quali ty Management (EFQM), TQM, and ISO 9001 2008. As discussed previously, a process approach is one of the principles of the ISO 9000 series of quality management standards. According to the International Organization for Standardization, and as cited by Car tha (2004) achieved more efficiently when activities and related resources are managed as a in Table 2 8 The importance of well planned, designed, controlled, and implemented processes for superior performance of organizations is so accepted that process capability is considered as a key criterion for organizational maturit y. As defined by the Capability Maturity Model (CMM) (Paulk et al., 1993) a main characteristic of a mature participants understa nd the value of doing so, and an infrastructure exists to support the Ot her Organizational Resources Besides the organizational resources discussed in the previous sections, some other organizational characteristics may also be considered in the assessment of excellence in organizations. Organizational culture is an example of these characteristics that, depending on purpose of studies, may or may not be taken into account for evaluation purposes.

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43 Organizational culture. The works of Hofstede (1980) and Peter and Waterman (1982) are recognized in the literature as the leading studies that sparked the interest in the subject of organizational culture. Siehl et al. (1988) hold that organizational culture consistency in behaviors and patterns. Schein (1984) has offered the following definition ic assumptions that a given group has invented, discovered or developed in learning to cope with its problems of external adaptation and internal integration and that have worked well enough to be considered valid, and therefore, to be taught to new member s as the correct way to perceive, think Based on this definition, organizational culture is a set of assumptions in the form of subconscious values and norms that are inherited by new employees. According t o Golembiewski (2000) the main components of organizational culture are values, beliefs, ideologies attitudes, and cultural artifacts. According to t guide our behavior, whereas, .116) In the work of Golembiewski (2000) attitudes are defined as cultural reflections upon employees expressing their feelings in different occasions. The last component of organizational culture accordi ng to Golembiewski is

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44 Sustainable Construction Another key concept explored in this research is the concept of sustainable development, more specifi cally the concept of sustainable construction. The term Blueprint for Survival (Goldsmith, 1972) and evolved later, resulting in the development of the topic (Kidd, 1992) The Brundtland Report (1987) a United Nations document, has been recognized as one of the prominent publications addressing the topic. The most commonly used needs of the present generation without compromising the ability of future generations cal definition, the definition of sustainability has tended to shift toward a more tangible multi dimensional characterization that takes into account economic, social, and environmental factors in all decisions. Sustainable construction can be considered as the application of sustainable development principles to the construction industry. The construction sector, along with operating homes and offices, accounts for 10% of the global economy (Lenssen & Roodman, 1995) As a result, the construction industry affects the environment, society, and the economy to a large extent. Therefore, sustainable construction can be considered as an ethical and practical way for the building industry to respond to their responsibiliti es in attaining sustainability and sustainable development (Bourdeau, 1999) According to Langston and Ding (2001) sustainable construction is a subset of sustainable d evelopment, which includes design, tendering, site planning and organization, material selection, recycling, and waste minimization. Some of the

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45 practical actions that can be considered in sustainable construction in line with the targets of sustainable de velopment are conserving resources, embracing energy There are different approaches and definitions for sustainable construction in different countries. In 1994, the Conse il International du Batiment (CIB), an international council of research and innovation in building and construction, defined the goal of on resource efficiency and ecolo (Kibert, 2008, p. 8) Seven sustainable construction principles have been identified by the CIB for the entire lifecycle of the construction from planning to deconstruction. Those principles ar e as follows (Kibert, 2008) : 1. Reduce resource consumption (Reduce) 2. Reuse resources (Reuse) 3. Use recyclable resources (Recycle) 4. Protect nature (Nature) 5. Eliminate toxics (Toxics) 6. Apply life cycle costing (Economics) 7. Foc us on quality (Quality) Wyatt (1994) serviceability of a building during its lifetime and eventual deconstruction and recycling of resources to reduce t Bourdeau (1999) listed the key elements of sustainable construction definitions as follows: Reducing the consumption of energy sources and mineral resources M aintaining bio diversity and the natural areas Keeping the quality of indoor environment

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46 Figure 2 6 depicts the revised framework of sustainable construction proposed by Kibert (2008) Sustainable practices and technologies are gaining more popularity among construction practitioners. As an example, more and more governmental agencies and private companies are planning to obtain Leadership in Energy and Environmental De sign (LEED) certification for their new building projects. Construction companies are also incorporating sustainable construction practices into their routine methods and procedures. As owners also begin to consider sustainability goals as part of their o bjectives for their projects, construction organizations such as architects, designers, contractors, and consultants will adapt to satisfy these needs. Corporations are trying to obtain better sustainability records to show that they are competent not only in their field of proficiency but also in operating in accordance with the new environmentally friendly rules of business. In undergoing this adaptation phase, corporations expect to smoothly integrate sustainability best practices into their organization al structures, processes and policies. For that purpose, sustainable construction best practices should be identified and employed by each party, and proper organizational resources should be utilized to support the implementation of those best practices. Kibert (2007) maintenance of a healthy built environment based on resource efficient and ecological tion in mind, it can be conclude d that sustainable construction best practices are those resource efficient methods or

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47 innovative practices that reinforce creation and maintenance of a healthy built environment and are consistent with ecological principles Although some building rating systems, such as BREEAM (Building Research Establishment Environmental Assessment Method), BEPAC (Building Environmental Performance Assessment Criteria), DGNB (German Sustainable Building Certificate), LEED (US Green Building Council, 2010) or Green Globes (Globe, 2006) have not been developed solely for the purpose of providing sustainable practices and processes for construction pr actitioners, they can be used as credible starting points for identifying sustainable construction practices. It is worth mentioning that these rating systems do not include a comprehensive listing of sustainable construction requirements but principally p rovide prescriptive and performance criteria. For instance, LEED concentrates on sustainable sites, water efficiency, energy and atmosphere, materials and resources, indoor environmental quality, innovation in design, and regional priority (US Green Building Council, 2010) The work of Pearce et al. (2010) provides a perspective on what sustainable construction practices are being adopted by construction compa nies. These authors They have categorized construction sustainability best prac tices into the following categories: Sustainability Related Project Implementation Plans Sustainable Procurement Practices Sustainable Contracting Practices Temporary Construction Materials Sustainable Site Management Practices Sustainable Project Manageme nt Practices

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48 Sustainability Audits, Benchmarking, and Metrics Indoor Environmental Quality Management Solid Waste Management Energy Best Practices Alternative Transportation/Equipment Corporate Sustainability Best Practices Corporate Sustainability From a corporate point of view, it is of critical importance to be able to define what corporate sustainability is and how it can be measured and improved. Towards this goal, corporations may plan and implement various types of changes in their organizations and adjust their policies, but a question remains as to how the effectiveness of these policies can be measured and verified. Parnell (2008) identified the following two main components of corporate sustainability : market sustainability and environmental sustainability. Market sustainability is concerned with the sustained market position of the organization, whereas environmental sustainability represents the degree to which the organization is successful in meeti ng the requirements of environmental and social aspects of their business. In other words, Parnell holds the view that that the long term viability of an organization depends upon both market and environmental perspectives. As shown by Marrewijk and Werre (2003) and Marrewijk (2005) organizations develop different manifestations of corporate sustainability due to differing organizational value systems. Marrewi jk and Werre listed different ambition levels of implementing corporate sustainability as follows: pre CS, compliance driven CS, profit

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49 Marrewijk and Werre (2003) proposed an organizational self assessment tool for the institutional development level of corporate sustainability based on certai n criteria concerning profit, planet, and people aspects of organizations. Mukherjee (2009) proposed a framework to facilitate decisions concerning corporate sustainability in the Indian coal sector. By the use of the House of Sustainability (HOS), the author developed a relationship matrix between environmental design attributes and sustainability factors to compute the existing and targeted impact scores of various environmental sustainability factors. Various sustainability performance is gaining more attention from stakeholders. Examples of corporate sustainability rating systems include ASSET4 ESG Ratings, Carbon Disclosure Project (CDP), D ow Jones Sustainability Indexes, and Wal Mart Sustainability Index. Sadowski et al. (2010) have prepared a fairly comprehensive list of green rating systems, which includes a total of 108 rating systems globally Some of these rating systems rate corporations across multiple industries while others are industry specific. In the following, some institutions that are specifically targeting corporate sustainability in the construction industry are introduced. The Su stainable Performance Institute (SPI) has established a certification have the institutional capacity to deliver consistent, high quality sustainability services and proje (Environmental Building News, 2012)

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50 SPI assesses the capability of design and construction organizations in sustainability services and projects in five key performance areas: leadership, infrastruc companies with a roadmap and methodology to improve the effectiveness of their (Environmental Building News, 2012) The SPI certification helps construction organizations to identify key process areas of interest for identification of organizational capabilities in performing sustainable construction practices. Global Reporting Initiative (GRI) is another institution that provides guidance and support to corporations with the goal of making sustainability reporting standard practice. GRI provides sustainability reporting frameworks to different industries. The main component of the framework is the Sustainability Reporting Guidelines, also known as G3 Guidelines, which specifies corporate sustainability criteria as well as sustainability reporting requirements for organizations. In September 2011, GRI issued the Construction and Real Estate Sector Supplement (CRESS), which is a customized version of the sustainability reporting guidelines for the construction and real estate sector. CRESS is one of the sources of information used in this research for identifying criteria that defi ne corporate sustainability in the construction industry. Figure 2 7 illustrates a breakdown structure for the corporate sustainability criteria in accordance

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51 System Dynamics Overview considers complex systems as a holistic set of interrelated components to provide better understanding of the system. The inception of system dynamics occurred due to effort s of Jay Wright Forrester at the Massachusetts Institute of Technology (MIT), which, later on, resulted in his work Industrial Dynamics (Forrester, 1961) The work of Sterman (2000) is also one of the main references in this field. System dynamics has evolved over the last few decades, and many software applications such as Vensim, Stella, and iThink have been produced and made available for researchers and scientists op erating in this field. System dynamics has been used to model issues ranging from population policies to the dynamics of fishery, and from health system issues to management strategies. Four basic questions that a system dynamics modeler should ask before building any model are what is the problem at hand, what is flowing into and gets accumulated in the system representing the problem, where and how does it accumulate, and what factors causes it to flow. Stocks are like storage reservoirs which represent values that accumulate or decay over time. For instance, one organization may accumulate knowledge or may lose it, may build up income or start to spend it, and may acquire workforces or start to release them based on operational policies. Storage levels a re increased or decreased by inflows or outflows respectively causing Units to accumulate or decay. Inflows and outflows are controlled by Rates

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52 Examples of Rate hiring, leaving, retiring, and promot ion rates. The inflows or outflows may originate from or exit into other stocks. They may also discharge to out of boundary environments of the system. All these elements are shown in a graphical representation, which is considered to be a helpful tool for understanding complex problems. According to Sterman (2000) in general there are three types of data needed to develop a system dynamics model, its structure, and decision rules: numerical, written, and mental d series and cross sectional records in various databases. Written data include records such as operating procedures, organizational charts, media reports, emails, and any other archival mate including their impressions, stories they tell, their understanding of the system and how Numerical data can be accessed directly, but mental data cannot. They must be elicited through interviews, observation, and other methods. Sterman (2000) has highlighted the fact that numerical data contain only a tiny fraction of the information in the written databas e and is miniscule compared to the information available only in constructs for which quantitative metrics and numerical data are available. He has called other types of Using proper quantification methods to estimate parameters and evaluating the ability of the model to reproduce historical data when numerical data are available are

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53 important The quantification of soft variables often yields important insight into the dynamics of a system. By quantification of identified variables and establishing the relationship between those variables, a set of differential equations is formed, which can b e solved by numerical methods embedded into the method of system dynamics. System dynamics is particularly helpful in cases where dynamic complexity is involved. It focuses on issues within systems with highly dynamic attributes. Dynamic complexity is a re sult of interactions among interrelated components that evolve over time. The main capabilities of system dynamics to deal with dynamic complexities are stocks and flows, time delays, nonlinearities, and feedback loop structures. Sterman (2000) has prepared a list of constituents that contribute to the dynamic complexity of systems. This list has been reproduced in Table 2 10 The Use of System Dynamics for Organizational Modeling Based on the investigation of systems thinking paradigms, system dynamics has been chosen as an appropriate tool for the investigation of organizational sustainability excellence for constructi on organizations. System dynamics, as a simulation technique, provides not only the benefits of simulation, but also some exclusive advantages because of its capabilities in dealing with dynamic complexities. Based on the work of Sterman (2000) presented in Table 2 9 a list of reasons for suitability of system dynamics for the purpose of this research has been prepared in Table 2 10 Other reasons for the suitability of system dynamics for this research are as follows: Presence of delay in most organizational processes: Delays are inherent in many organizational processes. Delays are the time lag between an action and its consequences, or between an event and its subsequent events. The time lag between a decision and it s consequences in an enterprise, the time lag between

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5 4 placement and its receipts are example of delays that can accurately be modeled in system dynamics. As described in the pr oblem statement, the intention of this research is to investigate how the performance of a construction organization in terms of sustainability is dependent upon its resources and operational policies. System dynamics is an appropriate tool to help us und erstand the implications of causal relationships among variables. System dynamics has frequently been used in the literature to model organizational systems. One of the most apparent applications of system dynamics in modeling organizational systems is te sting resource management policies, and investigating the dynamics of resource accumulation and decay in organizations. Not only physical resources, such as human resources, equipment, and land, but also intangible resources, such as knowledge and reputati on, have been modeled in system dynamics. The work of Morecroft et al. (2002) is an example of how organizational capabilities can be outlined in a high level conceptual model (see Figure 2 8 ). One of the reasons that makes the work of Morecroft et al. (2002) relevant to this research is their fundamental assumption that organizational competence is defined (as shown i n Figure 2 8 where those processes also need certain strategic resources. In the following, a number of example models offered for different organiz ational capabilities or resources are introduced. Morecroft, in his work entitled Strategic Modelling and Business Dynamics has offered an example model of workforce management (see Figure 2 9 ). The work of Rich and Duchessi (2001) is also worth mentioning here because they have addressed the dynamics of organizational knowledge as an organizational capability (see Figure 2 10 ).

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55 A similar work is the work of MacDonald et al. (2003) in which they have demonstrated how human resources and organizational knowledge interact (see Figure 2 11 ). Another view of their model is shown in Figure 2 12 Organizational capabilities constantly change over time; therefore, their (Dierickx & Cool, 1989) Operational capabilities directly affect the performance of operational capabilities (Collis, 1994) Rahmandad (2012) has modeled this process (see Figure 2 13 implicit investments that enable exploration of alternative options and learning by doing. Capabilities erode through forgetting, architectural change in production systems, or te Building Confidence into Models Various researchers, such as Forester and Senge (1978) Richardson and Pugh (1981) Kitching (1983) Sterman (2000) and Coyle and Exelby (2000) have provided a range of methods that can be used for building confidence into system dynamics models. One of the main challenges of the model development process is building confidence in the model. One of the classic studies about the validation of policy making models has been conducted by Greenberger et al. (1976) in which authors reviewed a number of modeling methodologies and documented their observations. The following paragraph from their study is relevant to this section because it defines what modelers need to expect from a validatio No model has ever been or ever will be thoroughly validated. Since, by design, models

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56 are all simplifications of the reference system, they are never entirely valid in the sense of being fully sup The following methods are examples of the methods that can be used to build confidence into syst em dynamics models: Test of model structure. According to Forrester and Senge (1978) the structure verification test, the model structure must not contradict knowledge about the structure of the re (1978) have defined the following steps for the structure verification test: An initial test is conducted on the basis of the model Review m odel assumptions are offered by persons highly knowledgeable about corresponding parts of the real system. The model assumptions are compared to descriptions of decision making and organizational relationships found in relevant literature. Interviews, work shops to solicit expert opinion, archival materials, direct inspection or participation in system processes can be used for the purpose of this test (Sterman, 2000) Interviews have intensively been used in system d ynamics for a wide range of purposes such as model conceptualization, problem definition, and building confidence into models (see, for instance, the works of Andersen et al. (2012) and Luna Reyes et al. (2012) ). Parameter verification test. One of the tests for building confidence in system dynamics models is the parameter verification test. According to Forrester and Senge (1978)

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57 Therefore, one of the validating steps for system dynamics models will be to compar e model parameters to knowledge of the real system to make sure they correspond (2009) s match elements of system Determining whether the model parameter values are consistent with relevant descriptive and numerical knowledge of the system could be answered procedurally (Sterman, 20 00) Methods based on interviews, expert opinion, focus groups, archival materials, and direct experience could also be utilized for this purpose. parameters determine the n eeded level of accuracy for that parameter. As mentioned by Richardson and Pugh (1981) given parameter value, one ought to know how sensitive the behavior of the model is to the value of that parameter. Yet to know that, one must run the model, and that requires parameter values. To resolve the dilemma, the modeler picks some values rather quickly and simulates the model. Initial estimates are made carefull y, to be sure, with as much concern for accuracy as can be easily mustered, but keeping in mind that one can Based on the reasoning provided above, it can be conclude d that for modeling under uncertainty, the modeling process should not be halted due to the lack of proper estimates of model parameters. Examples of useful information for extracting proper estimates for parameters are numerical databases, written databases, and men tal databases (Forrester, 1980)

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58 Mental databases refer to the vast array of information that is stored in the minds of people who have been working with the system for some time. Numerical and written databases need to be followed from the past witness or documented records of the system. It is worth noting that structure verification and parameter verification tests are interrelated, and together they ensure that the right decision making process has been develo ped in the model. Extreme condition test (1978) dynamics model should permit extr eme combinations of levels (state variables) in the system being represented. A model should be questioned if the extreme conditions test conditions test, one must examine each rate equation (policy) in a model, trace it back through any auxiliary equations to the levels (state variable) on which the rate depends, and consider the implications of imaginary maximum and minimum (minus infinity, zero, plus infi nity) values of each state variable and combinations of state variables to According to Coyle and Exelby (2000) t must produce changed behavior which is plausible and explicable; this must still be the

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59 Boundary adequacy test Another im portant validation test in system dynamics models is the boundary adequacy test. This test takes into account structural (1978) have adequacy test asks whether or not model aggregation is needing additional structure is developed, the boundary adequacy test is not passed (p. 14) This test requires an evaluator to perform the test to ensure the designed model includes all relevant components and relationships for its purpose. The modeler also can use model boundary charts, subsystem diagrams, causal diagrams, stock and f low maps, and direct inspection of model equations to ensure the important concepts for addressing the problem are endogenous to the model (Sterman, 2000) The modeler could also change the model boundary to see whet her the policy recommendations change. Dimensional consistency test According to Forrester and Senge (1978) this this test, all model equations could be procedurally inspected for suspect parameters. Test of model behavior According to Forrester and Senge (1978) the test of model behavior evaluates the adequacy of the model structure through analysis of the behavior generated by the structure. It includes behavior reproduction, behavior prediction, family member, surprise behavior, extreme policy, boundary adequacy (behavior) and behavior sensitivity.

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60 The test of model behav ior is not only about ensuring that the model is stable in extreme conditions; it also includes other non extreme conditions. Statistical tests There have been some debates in the literature whether or not individual model equations are to be compared wit h statistical data. According to Forrester and Senge (1978) spread agreement regarding the state that traditional statistical tests should not be considered conclusive enough to reject the causal hypotheses in a system dynamics model, and that while such tests may be useful for discovering possible flaws in model structure, they should be buttr essed by the tests described above. Integration error tests According to Sterman (2000) selection of a numerical integration method and time step that yields an approximation of the underlying continuous dynamic s accurate enough for the purpose of the model is part of any system dynamics modeling process. The results of models should not be sensitive to the choice of time step or integration method. For this purpose, the time step can be cut in half, and the mode l behavior could be checked to see whether any change is seen or not. In addition, a different integration method could be used to investigate whether any changes in behavior is recognizable or not. The benefits of using interviews as an approach for the e volution of system dynamics models has emphasized by Luna Reyes et al. (2012) becomes more important in system dynamics projects where modeler and clients do not have data series, an d the modeling effort relies on the use of qualitative patterns of

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61 readily available to modelers is a common practice in system dynamics. Richardson and Pugh (1981) faced with a dynamic problem in which a key variable is not traditionally quantified or tabulated. It is even more likely, however, that the model er or the client knows the Sustainability Maturity and Excellence Models As sustainability has increasingly become a strategic business need for almost all types of construction organizations the need for the development of an organizational capability assessment model concerning sustainable construction is felt more than ever before. An organizational capability assessment model concerning sustainable construction is expected not only to pr ovide a guideline for construction organizations to improve their capability in implementing sustainable construction, but also to be used as an aid in their counter party selection practices. As discussed, this assessment can be performed by conducting ei ther maturity or excellence assessments. The few instances of organizational capability assessment on sustainable construction have chosen the first approach, i.e. sustainability maturity assessment. The author of this dissertation has not found any compre hensive organizational excellence assessment on sustainable construction in the literature. In addition to general types of organizational resources, a construction organization performing sustainable construction might need to possess some special capabil ities. In this section, first, general types of sustainability capability assessment models in the literature of organizational studies and then those models that have been proposed for construction organizations have been reviewed.

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62 Cagnin et al. (2001) have devised an organizational sustainability maturity model, entitled the business sustainability maturity level model (BSMM). They have based their model on the triple bottom line model as well as the five capita l model, developed as the result of the SIGMA Project (1999) They have also used an evolution of the concept considering the g lobal system within which an organization is operating. The surrounding global system is considered to be much more inclusive than a situation in which only an organization, its suppliers, and its customers are taken into account. In this new setting, the term value has been redefined. According to Cagnin et al. (2005) social value to and by all actors within the network, which is based o n universal principles and shaped by the other three pillars of sustainability: spatial, institutional Accordingly, Cagnin et al. (2005) have defined different organizational matu rity building blocks by which a firm creates products and services valuable to its A direct conclusion from their model is that for organizational maturi ty assessment purposes, organizations can be rated based on the degree to which they successfully adopt value activities corresponding to each maturity level (see Table 2 11 ). Another scientific article with direct relevance to the topic of organizational sustainability maturity is the work of Baumgartner and Ebner (2010) in which authors

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63 have developed different profiles for sustainability strategies so that each profile addresses each particular aspect, economic, ecological, and social, of sustainability Table 2 12 lists different economic aspects of corporate sustainability based on the work of Baumgartner and Ebner. In their study, Baumgartner and Ebner (2010) have introduced the following as the ecological aspects of corporate sustainability: Emissions into the air Emissions into the water Emi ssions into the ground Waste and hazardous waste Impact on biodiversity Environmental issues of the product Table 2 13 and Table 2 14 provide a list of proposed internal and external social aspects of corporate sustainability according to Baumgartner and Ebner. They have also defined suggested maturity levels in each dimens ion of the triple bottom line (see Table 2 15 and Table 2 16 ). A similar model has been presented in the work of Van Marrewijk and Werre (2003) in which authors have proposed multiple levels of corporate sustainability. In addition, corresponding value systems have been introduced. Their model can also be used as a practical measurement tool to audit organizational values un veiling their cultural potential for Corporate Sustainability. In the remainder of this section, some other related studies that are concerned primarily with corporate sustainability programs will be discussed. By studying a vast range of corporate sustain ability programs, Overcash and Twomey (2011) found that business excellence, innovations, human contributions, and

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64 environment are the most significant domains of decision making in promoting industrial sustainability. Dow Jones Sustainability Index (DJS I) is a set of indices collected to evaluate the sustainability performance of the companies listed on the Dow Jones Global Total Stock Market. This program identifies the key areas in which leading sustainability companies are demonstrating higher levels of competence. These areas include strategy, financial performance, customer ad product, governance and stakeholder, and human (DJSI 2012). Hardjono and de Klein (2004) introduced developments in quality management systems (see Figure 2 14 ) in their work on the European corporate sustainability framework (ECSF). As Figure 2 14 shows, organizations might be in different stages of quality management ranging from the quality of product to the q uality of society be society stakeholders as business partners, society as a whole, includi ng the non governmental organizations (NGOs), then the organization arrives at a complexity level that could be called society Hardjono (1995) in the Four Phase Model, which partially forms th e European corporate sustainability framework (ECSF), considers material competencies, commercial competencies, socialization competencies, and intellectual competencies as essential competencies / core assets for corporate sustainability. The following de finitions have been chosen from the work of Hardjono and de Klein (2004) which provides more details about the Four Phase model.

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65 1. res 2. 3. relation ships with stakeholders and the ability to inspire employees, in such a way 4. information and the innovative capacity, which is based on the collec tive intellect of An example of sustainability capability models proposed only for construction organizations is the model offered by the Spectrum Innovation Group. This model proposes a capability maturity mod el with five levels of maturity which each have a corresponding list of criteria (Bedekar & Lawler Kennedy, 2011) Figure 2 15 shows this model. Summary On the basis of the review of literature, the need for more conceptual work in the field of organizational sustainability capability models for construction organizations was identified. The intention is to use the system d ynamics method to model the dynamics of organizational capabilities with respect to sustainable construction. Table 2 18 provides a summary of criteria identified through the review of sustainability maturity and excellence models. This need to model the dynamics of organizational capabilities with respect to sustainable construction is evident based on the following: 1. Sustainable construction practices are increasingly considered as part of organizational strateg ies as opposed to being considered only as operational or tactical approaches in the construction industry. Therefore, appropriate tools that are related strategies are needed mo re than ever before. To ensure that any organization is

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66 pursuing sustainability objectives in a systematic rather than sporadic way, strategy formulation and formation processes should be well planned. Sporadic type of strategy formulation in organizations might result in green washing or other misleading perceptions. 2. Construction organizations cannot verify the effectiveness of their sustainability policies unless a comprehensive sustainability capability model to assess the truction capabilities becomes available to them. The comprehensive organizational assessment tool. If leading performance indicators are considered as those measures that are mor e concerned with organizational processes as opposed to lagging performance indicators that solely look at past leading rather than lagging indicators. 3. Yet organizational exc ellence models are expected to offer a much broader insight than the insight provided by maturity models. Excellence models are expected to offer an overarching system of best practices measures and guidelines that evaluate the extent to which organization s are capable of meeting their strategic objectives in terms of sustainability. In addition, excellence models are expected to offer capability building plans for organizations for achieving their strategic objectives. 4. Organizational strategies are expecte d to be devised so that they effectively work for targeted organizational sustainability performance. Without a reliable strategy verification tool or proper assessment procedures, any chosen strategy might be challenged on effectiveness grounds. The focus of this research will be placed on strategy verification procedures to investigate how the effectiveness of organizational strategies can be supported by the use of system dynamics. In addition to the current literature review, additional insights may be gained from a continued exploration of literature in the following areas: Organizational and business process maturity models, Organizational project management maturity models, Organizational excellence and quality management models, Corporate sustainabil ity, Sustainable Construction, and System modeling and dynamics techniques.

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67 Figure 2 1 The Five Levels (stages) of Software Process Maturity (Adapted from Paulk et al. (1993) )

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68 Figure 2 2 The Maturity Model Str ucture (Adapted from Paulk et al. (1993) )

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69 Figure 2 3 Baldrige Criteria for Performance Excellence Framework A Systems Perspective (Adapted from NIST (2011) ) Figure 2 4 The EFQM Model (Adapted from EFQM (2010) )

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70 Figure 2 5 The Sustainabl e Value Framework (Adapted from Laszlo et al. (2006) ) Figure 2 6 The revised framework of sustainable construction (Developed in 1994 by CIB Group 16; Adapted f rom Kibert (2008) )

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71 Figure 2 7 The GRI Construction and Real Estate Sector Supplement corporate sustainability indicators

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72 Figure 2 8 Organizational knowledge and learning as the aggregation of specific capabilities (Adapted from Morecroft et al. (2002) )

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73 Figure 2 9 The workforce management model (Adapted from Morecroft (2007) )

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74 Figure 2 10 Hiring, Quits, and Organizational Knowledge (Note: OK: Organizational Knowl edge, PK: Personal Knowledge Adapted from Rich and Duchessi (2001) ) Figure 2 11 High level view of the basic model {of organizational knowledge} (Adapted from M acDonald et al. (2003) and leave either to join ventures, or through attrition. Knowledge is represented as a co

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75 Figure 2 12 An alternate view of the basic model {of organizational knowledge} (Adapted from MacDonald et al. (2003) )

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76 Figure 2 13 C hange Mechanisms of Operational and Dynamic Capabilities (Adapted from Rahmandad (2012) ) Figure 2 14 Developments in quality management

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77 Figure 2 15 Corporate Sustainability: Capability Maturity Model by Spectrum Innovation Group 2008

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78 Table 2 1 Summary of the National Quality Award Categories (Criteria) (Ada pted from Hui and Chuan (2002) ) Quality awards and their criteria U.S.: Malcolm Baldrige National Quality Award (2001/2) 1.0 Leadership 2.0 Strategic Planning 3.0 Customer and Market Focus 4.0 Information and Analy sis 5.0 Human Resource Focus 6.0 Process Management 7.0 Business Results Australian Business Excellence Award (2000) 1.0 Leadership and Innovation 2.0 Strategy' and Planning Process 3.0 Data, Information and Knowledge 4.0 People 5.0 Customer and Market Foc us 6.0 Processes, Products and Service! 7.0 Business Results Canadian Award for Excellence (2000) 1.0 Leadership 2.0 Planning 3.0 Customer Focus 4.0 People Focus 5.0 Process Management 6.0 Partner/Supplier Focus 7.0 Organizational Business Performance S ingapore Quality Award (2001) 1.0 Leadership 2.0 Planning 3.0 Information 4.0 People 5.0 Processes 6.0 Customers 7.0 Results European Quality Award (2001) 1.0 Leadership 2.0 Policy and Strategy 3.0 People 4.0 Partnership and Resources 5.0 Processes 6.0 Customer Results 7.0 People Results 8.0 Society Results 9.0 Key Performance Results Japan Quality Award (2000) 1.0 Management Vision and Leadership 2.0 Understanding and Interaction With Customers And Market 3.0 Strategic Planning and Deployment 4.0 Human Resource Development and Learning Environment 5.0 Process Management 6.0 Sharing and Utilization of Information 7.0 Results of Enterprise Activities 8.0 Customer Satisfaction Costa Rica Excellence Award (2000) 1.0 Customer Satisfaction 2.0 Managerial Leadership and Strategic Planning 3.0 Human Resources 4.0 Quality System and Processes 5.0 Innovation and Technology 6.0 Environmental Management South African Excellence Award (2000) I. 0 Leadership 2.0 Policy & Strategy 3.0 Customer & Market Focus 4.0 P eople Management 5.0 Resources & Information Management 6.0 Processes 7.0 Impact on Society 8.0 Customer Satisfaction 9.0 People Satisfaction 10.0 Supplier & Partnership Performance 11. 0 Business Results Jordan: King Abdullah II Award for Excellence (20 00) 1.0 Leadership 2.0 Strategic Planning 3.0 Process Management 4.0 Resources Management 5.0 Results

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79 Table 2 2 Watson and Howarth (2011) ) Concept Description Leadership and modeling of purpose and an environment in which the organization and its people can excel. Policy and strategy A successful organization formulates policy and strategy in collaboration with its people and it is based on relevant, up to date and comprehensive information and research. Continuous learning and Improvement Organizational performance is maxim ized when it is based on the management and sharing of knowledge within a culture of continuous learning innovation and improvement. Partnership development Mutually beneficial relationships, built on trust, sharing of knowledge and integration with partn er organizations are a crucial resource to any effective organization. Management by processes and facts Organizations perform more effectively when all inter related activities are systematically managed and decisions about current operations and improve ments are based on reliable information and stakeholder perception. Customer focus Quality of service and retention of market share are best achieved through a clear focus on the current and potential needs of customers. People development & involvement The full potential of an organization's people is best realized through shared values and I culture of mist and empowerment which involves everyone. Public responsibility The interests of the organization and its people are best served by adopting an ethi cal approach and exceeding the expectations and regulations of the community at large. Results orientation Excellence depends on balancing and satisfying the needs of all relevant stakeholders including employees, customers, suppliers and society as well as the funding organization.

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80 Table 2 3

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81 Table 2 4 organizational leadership (Adapted from Watson & Howarth (2011) ) Definition and Criteria Definition How leaders develop and facilitate the achievement of the mission and vision, develop values required for lon g term success and implement these via appropriate actions and behaviors, and are personally involved in ensuring that continually monitored. Criteria Leaders develop the mission, vision and values and are role models of a culture of excellence. management system is developed, implemented and continuously improved. Leaders are involved with customers, partners and representat ives of society.

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82 Table 2 5 from Watson & Howarth (2011) ) Definition and Criteria Definition How the organization implement s its mission and vision via a clear stakeholder focused strategy, supported by relevant policies, plans, objectives targets and processes. Criteria Policy and strategy are based on the present and future needs and expectations of stakeholders both inter nal and external to the company Policy and strategy are based on information from performance measurement, research, learning and creativity related activities Policy and strategy are developed, reviewed and updated Policy and strategy are deployed through a framework of key processes Policy and strategy are communicated and implemented.

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83 Table 2 6 (Adapted from Watson & Howarth (2011) ) Definition and Criteria Definition How the organization manages, develops and releases the knowledge and full potential of its people at an Individual, team based and organization wide level, and plans these activities in order to support its policy and strategy and the effective operation of its processes, all of which must be customer focused. Criteria People resources are planned, managed and improved. People's knowledge and competencies are identified, developed and sustained in line with organizational activities People are involved and empowered at all levels of the organization People within the organization have an effective dialogue People are rewarded, recognized and cared for

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84 Table 2 7 (Adapted from Watson & Howarth (2011) ) Definition and Criteria Definition How the organization pla ns and manages its external partnerships and internal resources in order to support its policy and strategy and the effective and efficient management of partnerships and resources. Criteria External partnerships are managed. Finances are managed. Buildi ngs, equipment and materials are managed. Technology is managed. Information and knowledge are managed.

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85 Table 2 8 Watson & Howarth (2011) ) Definition and Criteria Definition How the organization manages its operati onal processes Criteria and generate increasing value for all stakeholders. oped based on customer needs and expectations.

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86 Table 2 9 Dynamic Complexity (Adapted from Sterman (2000) ) Dynamic Complexity Arises Because Systems Are: Constantly Changing: nging is, over a longer time horizon, seen to vary. Change in systems occurs at many time scales, and these different scales sometimes interact. A star evolves over billions of years as it burns its hydrogen fuel, then can explode as a supernova in seconds Bull markets can go on for years, then crash in a matter of hours. Tightly Coupled : The actors in the system interact strongly with one another and with the natural world. Everything is connected to everything else. As a famous bumper sticker from the 1 Governed by Feedback : Because of the tight couplings among actors, our actions feedback on themselves. Our decisions alter the state of the world, causing changes in nature and triggering others to act, thus giving rise to a new situation which then influences our next decisions. Dynamics arise from these feedbacks. Nonlinear : Effect is rarely proportional to cause, and what happens locally in a system (near the current operating point) often does not apply in distant regions (other states of the system). Nonlinearity often arises from the basic physics of systems: Insufficient inventory may cause you to boost production, but production can never fall below zero no matter how much excess inventory you have. N onlinearity also arises as multiple factors interact in decision making: Pressure from the boss for greater achievement increases your motivation and effort up to the point where you perceive the goal to be impossible. Frustration then dominates motivation and you give up or get a new boss. History Dependent : Taking one road often precludes taking others and determines where you end up thermodynamics). Stocks a nd flows (accumulations) and longtime delays often mean doing and undoing have fundamentally different time constants: During the 50 years of the Cold War arms race the nuclear nations created more than 250 tons of weapons grade plutonium (239Pu).The half life of 239Pu is about 24,000 years. Self Organizing : The dynamics of systems arise spontaneously from their internal structure. Often, small, random perturbations are amplified and molded by the feedback structure, generating patterns in space and time a nd creating path dependence. The pattern of stripes on a zebra, the rhythmic contraction of your heart, the persistent cycles in the real estate market, and structures such as sea shells and markets all emerge spontaneously from the feedbacks among the age nts and elements of the system. Adaptive : The capabilities and decision rules of the agents in complex systems change over time. Evolution leads to selection and proliferation of some agents while others become extinct. Adaptation also occurs as people le arn from experience, especially as they learn new ways to achieve their goals in the face of obstacles. Learning is not always beneficial, however. Characterized by Trade Offs : Time delays in feedback channels mean the long run response of a system to an intervention is often different from its short run response. High leverage policies often cause worse before better behavior, while low leverage policies often generate transitory improvement before the problem grows worse. Counterintuitive : In complex sy stems cause and effect are distant in time and space while we tend to look for causes near the events we seek to explain. Our attention is drawn to the symptoms of difficulty rather than the underlying cause. High leverage policies are often not obvious. Policy Resistant : The complexity of the systems in which we are embedded overwhelms our ability to understand them. The result: Many seemingly obvious solutions to problems fail or actually worsen the situation.

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87 Table 2 10 System Dynamics Fit to Organizational Systems Dynamic complexity in organizations arise because organizations are: Constantly Changing : The states of organizations change constantly because they are heavily affected by increasing depend ency on both internal and external factors. Constant change in the level of organizational tangible resources, e.g. human resources, technology, and capital; and intangible resources, e.g. potential clients, reputation, and staff loyalty, are major contrib uting factors. Examples of external factors include emerging markets, price competition, and regulations. Tightly Coupled : An organization is best assumed as a whole, rather than in independent units. One organization cannot function effectively unless v arious functionally interrelated processes interact properly. Governed by Feedback : The result of decisions made and actions taken form the situation organizations face in the future. Due to interdependencies of organizational processes, any actions ma ke some reflections on themselves. The state of any organizational unit is constantly changing due not only to the actions of other units but also to self initiated changes made within those units. Nonlinear : Organizations are mostly dealing with disprop ortionate causes and effects, in which, outcomes do not provide a directly proportional output for an alteration in input. Exponential growth and decline are examples. Many outcomes that organizations deal with are nonlinear in nature. For instance, the ra in nature. History Dependent : The state of many organizations is different from that of their counterparts only due to different strategies that they have adopted in early phases of their growth. This means that the state of two similar organizations with fairly equivalent business objectives and comparable circumstances can end up being distant from each other after a few years due to different histories o f organizational decisions and actions. Self Organizing : The internal structure of an organization has a significant impact on its behavior and performance. Many organizational patterns of behavior can be linked to their internal architecture rather than external forces surrounding the organization. Adaptive : Adaption in organizations occurs when organizational learning brings about new solutions in the business environment. Organizations constantly conform themselves to new conditions. Characterized b y Trade Offs : Managers are constantly dealing with situations in which losing or gaining one or more qualities or facets against other options is the case. Desirability associated with multiple options needs to be investigated in organizations when making decisions. Counterintuitive these cases, common sense might suggest so mething seemingly obvious, but in fact something rather distant and aloof might be present. Policy Resistant : Managers often experience unintended consequences to their actions. Sometimes these unfavorable outcomes result due to failure to understand the entire range of feedback structures in play in organizations.

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88 Table 2 11 Business Sustainability Maturity Model BSMM (Adapted from Barthel et al. (1999) )

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89 Table 2 12 Economic aspects of corporate sustainability (Adapted from Baumgartner and Ebner (2010) Aspect Description Innovation and technology Effort in sustainability related R&D in orde r to reduce environmental impacts in new products and in business activities. Use of BAT (best available techniques) and integrated environmental technologies, concentration on cleaner production and zero emission technologies. Collaboration Good cooper ation and active collaboration with various business partners programmes and networks on innovative products and technologies. Exchange of information and knowledge. Knowledge manageme nt Activities and approaches to keep sustainability related knowledge in the organization. Methods to plan, develop, organize, maintain, transfer, apply and measure specific knowledge and to improve the organizational knowledge base. Process Clear proc esses and roles are defined so that business activities are efficiently conducted and that every employee knows what the organization expects from him or her (also concerning sustainability). Adaptation of process management on sustainability necessities t o implement corporate sustainability systematically. Integration of sustainability into daily business life. Purchase Consideration of sustainability issues in purchase. Awareness and consideration of sustainability related issues in the organization as well as alongside the supply chain. Relationship with suppliers focusing also on sustainability. Sustainability reporting Consideration and reporting of sustainability issues within company reports, either in a separate sustainability report or integra ted into the corporate one.

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90 Table 2 13 Internal social aspects of corporate sustainability (Adapted from Baumgartner and Ebner (2010) ) Aspect Description Corporate governance Transparency in all its activities in order to ameliorate relationship towards its stakeholders. Giving insight into all relevant data; following rules of (stock) markets o n corporate governance and defi ning responsibilities and behaviour of the board. Motivation and incentives Active i nvolvement and exemplary function of management on sustainability topics for employees. Awareness of needs, claims and motivation factors of employees in order to implement sustainability suffi ciently into the organization due to support of management for acting in sustainable way (e.g. time, money, resources). Development of incentives and reward systems (monetary, non monetary). Health and safety Guarantee that no health and safety risks occur when working in/for the organization. No negative impact of any time. Operation of programmes for employees to prevent dangers and to stay generally fi t and healthy (e.g. in developing countries). Human capital development Development of human capital for sustainability related is sues through specifi c programmes such as permanent education, mentoring or training. Broad cross working education (job enrichment, job enlargement) in order to become aware of the different challenges and issues of corporate sustainability.

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91 Table 2 14 External social aspects of corporate sustainability (Adapted from Baumgartner and Ebner (2010) ) Aspect Description Ethical behaviour and human rights Ethic al behaviour towards sustainability consisting of well established, basic assumptions and principles relating the cooperation within an organization and the behaviour towards (external) stakeholders. Regarding sustainability, important elements are a cultu re of respect, fair rules and behaviour within an organization (and between its subsidiaries) and fair ideals and needs. No harm of employees, either concerning their religious bel ief, gender, nationality or colour or concerning people who are handicapped or aged. No controversial activities No holding of shares on organizations that are mostly defined as not sustainable (e.g. uranium mining). No use or sale of own assets and goo ds for non sustainable activities. No corruption and cartel Behaving fairly on the market and avoiding manipulating business practices. This includes no rule breaking, no price fixing or joining a cartel and no corruption for gaining advantage. Corpor ate citizenship Being a good corporate citizen on a national level; conservation of subsidiaries in the country and establishment of economic power of a (and others) and their issues on regional level; participation or creation of sustainability related activities for the local community. Orientation on future generations without exploiting the present (or nature).

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92 Table 2 15 Maturity levels of economic sustainability aspects (Adapted from Baumgartner and Ebner (2010) )

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93 Table 2 16 Maturity lev els of external social sustainability aspects (Adapted from Baumgartner and Ebner (2010) ) Aspect Ethical behavior and human rights No controversial activities No corruption and cartel Corporate citizenship Beginning Human rights are generally respected, but no codes and guidelines exist as well as no corporate common behavior/ within the organization. No declaration against controversial activities exists Conformity with laws and regulations regarding corru ption and cartel exists Corporate citizenship is not focused on in the organization. Elementary Human rights are respected. Principal rules how to behave w ithin the organization are defi ned. Firm declares itself to be to be aware of to whom it sells its g oods Compliance with laws and regulation; most important impacts regarding corrupt practices are identified Certain corporate citizenship projects are initiated or supported (mostly in monetary terms). The link between CC projects and the corporate busines s is rarely given. Satisfying Defi nition of corporate codes and guidelines regarding (internal) behavior throughout the whole organization exist Organization is aware to whom it sells its goods and sets measures to reduce controversial activities. Impacts regarding corru pt practices are fully identifi ed and measures set to avoid them. Corporate citizenship is systematically planned and conducted (monetary and non monetary commitment). The link between CC projects and the corporate business is mostly given. Sophisticated/ outstanding Corporate codes and guidelines regarding (internal) behavior throughout the whole organization are defined. Controlling and proactive improvement of these codes Organization is known as non controversial acting firm. It shows c redibility in that it offers and follows possibilities to avoid negative use of its products, based on stakeholder requirements. Impacts regarding corrupt practices are fully identified. Distinct rules exist to demonstrate all kinds and (internal) conseque nces of corrupt practices and measures set to avoid them at all Corporate citizenship is systematically planned and conducted (monetary and non monetary commitment) and focused on long term commitment. Most employees are integrated into the process. The li nk between CC projects and the corporate business is mostly given.

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94 Table 2 17 Focus areas of Dow Jones Sustainability Index (DJSI) (Adapted from DJSI (2012)) Criteria Description Strategy term economic, environmental and social aspects in their business strategies while maintai ning global Financial performance long term economic growth, open communication and Customer & Product oyalty by investing in customer relationship management and product and service innovation that focuses on technologies and systems, which use financial, natural and social resources in an efficient, effective and economic manner over the long Gov ernance and Stakeholder stakeholder engagement, including corporate codes of conduct Human and employee satis faction through best in class organizational learning and knowledge management practices and

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95 Table 2 18 A summary of criteria provided by the reviewed sustainability maturity and excellence models Excellence models and their criteria Innovation Excellence Model (Dahlgaard Park & Dahlgaard, 2008) Spectrum Innovation Group (Bedekar & Lawler Kennedy, 2011) 1. Leadership 2. Customer Orientation 3. Innovativeness 4. Strategies and Plans 5. People 6. Partnership and Resources 7. Innovation Processes 1. Le adership 2. Strategies and Plans 3. Corporate culture 4. People and Skills 5. Environmental awareness Baumgartner and Ebner (2010) Business Sustainability Maturity Model (Cag nin et al., 2005) 1. Innovation and technology 2. Collaboration and Partnership 3. Knowledge management 4. Processes 5. Awareness and consideration of sustainability alongside the supply chain (Purchase) 6. Sustainability reporting 1. Strategy 2. Partnership 3. Motivation 4. Competen ces 5. Communication 6. Technology 7. Operation Four Phase Model competencies (Hardjono, 1995) Dow Jones Sustainability Indexes (2012) 1. Material competencies 2. Commercial competencies 3. Socialization competencies 4. Intellectual competencies 1. Strategy 2. Financial 3. Customer and Product 4. Governance and Stakeholder 5. Human

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96 3. CHAPTER 3 METHODOLOGY Using system dynamics, the dynamics of the organizational excellence in terms of sustainability w ere modeled System dynamics models were designed and built into our study to sufficiently mimic the behavior of interacting processes within construction organizations. These objectives were achieved through the methodology outlined below. In the followin g, data requirements will be discussed first. Next, all principal research steps taken will be listed. Data Requirements In brief, the different types of data that were needed to construct the sustainability excellence model are as follows: The first type of d ata was required for developing causal loop diagrams and system dynamics models The existing literature helped to identify various cause and effect relationships and develop influence diagrams. The personal judgment of the modeler played the leading r ole in gathering, categorizing, and properly using the data. No numerical data was included as part of this step. The second type of d ata was required for estimating model parameters As mentioned earlier, higher priority was given to building the model st ructure rather than the precise estimation of all input values. Since the objective of this study was to develop a decision support system that provides the possibility of testing different scenarios, parameter values were estimated carefully for an exampl e construction organization. This initial parameter estimation made the running of the model possible, having in mind that the model can be run in the future under other scenarios by feeding any new set of input data into the model. Multiple what if scenar ios with varying input

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97 datasets were tested during the model development process to ensure executability of the model for the presumed scenarios. The third type of d ata was required for policy experimentation and policy generation A dataset representing d ifferent organizational policies and corresponding circumstances were built for a hypothetical organization. Policy generation and experimentation were performed by feeding aforementioned input datasets to the system dynamics models. Reports in the literat ure were used, not as a source of data, but to investigate what scenarios are imaginable and can be taken into account. Principal Research Steps The principal research steps in this study were as follows: 1. Review organizational and business process maturity and excellence models 2. Review corporate sustainability literature to gain more insight into the full list of potential criteria for organizational sustainability excellence in the construction industry (focus areas). 3. Review sustainable construction liter ature as well as sustainability reporting guidelines to identify best practices in sustainable construction to gain a better insight into model constituents and key model variables and components 4. Take into account the concept definitions of the EFQM model as the starting point to determine the main components of the model in each focus area. 5. Review sustainable construction literature as well as sustainability reporting guidelines to improve the definitions of the key organizational capabilities in accordanc e with the requirements of sustainable development 6. Develop a system dynamics sub model for each focus area, i.e., leadership, policy and strategy, people, partnership and resources, and process 7. A comprehensive literature review on each focus area was cond ucted to identify influential factors within each focus area. After this identification process, a system dynamics sub model with properly defined structure, components, variables, relations, and feedback loops was developed. This step included: Conceptual design of the model that includes model boundary selection, identification of key variables, needs analysis to determine the time horizon

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98 and time step for the model, and identification and documentation of key Defining a prob lem hypothesis that includes a concise explanation of the dynamics describing the subject of study in terms of the underlying feedback structure of the system Detailed design and development of the model that includes defining all system components, inter relationships and setting all initial conditions Model verification that was accomplished through comprehensive checks to ensure all input variables were given to the model, and there was no computation deficiency in the models. Verification provided confi dence in data integrity. Preliminary model validation that was accomplished through investigating whether the model behaves as expected. Preliminary model validation was accomplished based on: Personal judgment and knowledge of the modeler Through invest igation in literature to ensure appropriate literature support Testing the model against a dataset representing organizational policies and the circumstances under which a hypothetical organization operates In model validation tests, the focus was on testi ng the model structure, extreme condition tests, boundary adequacy tests, dimensional consistency tests and tests of model behavior. 8. For final validation of the model, semi structu red interviews were conducted as an aid in building more confidence in the model (see Appendix K). A research survey is mostly a non face to face method of enquiry in which expert judgments are mostly sought by asking specific questions about a subject. On the other hand, a research interview is mostly conducted face to face, providing the opportunity of

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99 having value adding conversation and interactive discussion between an interviewer and a respondent on the subject matter. Semi structured interviews fall between two extreme types of interviews, structured and unstructured interviews. In structured interviews, interviewers conduct the interview based on a prepared set of questions, whereas unstructured interviews are conducted mostly similar to a journalis tic interview in which the direction of the interview depends upon how the discussion between the researcher and the responder advances. This phase included: Define the objective and scope of in terviews Determine the target population Conduct interviews w ith targeted participants Interviewees were experts who, because of their backgrounds, positions, responsibilities, or activities, had a good understanding of corporate sustainability as or presidents of construction firms, five of the interviewees were project managers, four of the interviewees were sustainability managers or coordinators, and one interviewee was a director of preconstruction services. The interviewees were not necessa rily representing a construction organization, but they all had a major interest in sustainable construction and corporate sustainability. Construction organizations with experience in design or construction of a LEED platinum or gold certified building w ere targeted for this purpose. Some rankings such potential interviewees. Before conducting interviews, a pilot interview was conducted

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100 with one of the faculty members of th e M.E. Rinker Sr. School of Building Construction with interest in corporate sustainability and green building to ensure that the interview process was designed appropriately. After preparing the interview plan and the research questionnaire, a consent for m specifying the rules of the interview and the confidentiality commitment was prepared and sent to the office of the Internal Review Board (IRB) at the University of Florida for approval (see Appendix L). An online version of the research questionnaire wa s also prepared using the Qualtrics online survey tool and a separate approval was obtained from the IRB of the University of Florida for the online version of the questionnaire (see Appendix M). For each interview, the researcher contacted the potential i nterviewee, either permission, schedule an appointment, and agree on where the interview will be held. Each interview lasted between 60 to 90 minutes. The process wa s as follows: The interviewer would introduce himself and remind the interviewee of the topics that were going to be discussed during the interview. The interviewer would tell the interviewee that he or she would be interviewed as an expert in the fields of sustainable construction and corporate sustainability. The interviewer would explain the purpose of the research. As every interview was supposed to be recorded, the interviewee would ask for the would be kept confidential at all times. The interviewer would ask for the signature of the interviewees on the consent form. The interviewer would present a simplified representation of the model structure in the form of ca use and effect diagrams to the interviewee. A printout from the presentation file has been provided in Appendix L. While presenting the PowerPoint file, the modeler would ask the interviewee to compare the structure and behavior of the model with his / her experiences and express his / her comments.

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101 If the interview was held during a face to face meeting, the interviewer would present the presentation file, containing the aforementioned cause and effect diagrams to the interviewee. Another alternative to t he face to face meeting was the use of web conferencing tools such as GoToMeeting, Cisco Webex, or Microsoft Lync. If the interview was held using any web conferencing application, still the same presentation file was being used by the interviewee. During each interview, the interviewer would write down the main themes that emerged as the interviewer asked questions and listened to the answers given by the interviewee. When the interviewer felt that all topics had been discussed, the interviewer would ask t he interviewee if he or she had anything to add. Findings from the literature were presented to experts during the interviews especially when their comments would contradict existing literature. This act facilitated reaching a conclusion in the cases of c ontradictory comments. documented. The following issues were taken into account in deciding on how many interviews to conduct: Range of viewpoints Time and resources: At least eight hours were spent for each interview to prepare, conduct, transcribe, and analyze semi structured interviews. It was also important to consider the fact that interviewees were not always available. Data saturation: After conducting 15 interviews, the resea rcher came to the conclusion that data saturation has almost achieved because interviews did not provide any new or additional insights so data collection from interviewees ended after 15 interviews. Each expert (interviewee) was interviewed only once, and there was no need for making arrangements for a second interview with any of the interviewees. Data collection, classification and analysis: List of suggested changes made by interviewees were categorized and documented (see Appendix O). At the end of eac h interview, the interviewee was asked to fill out the research questionnaire concerning the relative importance of organizational capabilities for making construction organizations capable in each of the three aspects of sustainability, i.e., economic, en vironmental, and social aspects. They were given the option to fill out the questionnaire right after the interview or at their convenience afterwards. Follow up calls were made if it became obvious that an interviewee had not filled out the questionnaire a week after the interview.

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102 9. F inal changes to the system dynamics model were made after reviewing the categorized list of comments from experts obtained from the previous step 10. M ultiple runs of the model were performed to identify organizational policies th at are important for organizational sustainability excellence, and to identify how organizational conditions are affected in response to different organizational policies 11. S cenario development and analysis were conducted to verify multiple scenarios of orga nizational policies. This step was performed by the following: Conducting policy verification and policy design using the system dynamics technique, Testing problem hypotheses, Performing sensitivity analysis of the model, Exploring what if scenarios, and Performing sensitivity analysis to investigate how sensitive model outcomes are to changes in the inputs and assumptions.

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103 4. CHAPTER 4 MODELS AND RESULTS The Preliminary Model The preliminary detailed version of the model was developed and implemented in syst em dynamics in iThink version 9.1.4. The preliminary model contained five sub models, representing five focus areas that were found in the sustainability excellence model, namely leadership, policy and strategy, people, partnership and resources, and proce ss. All sub models include links between sub models. Model verification has been accomplished based on comprehensive checks to ensure all input variables were included in the model, and the process definition were correct, and there was no computational er rors in the models. After verification, the were validated. The paper based version of the Research Questionnaire (see Appendix L) contains the preliminary version of the mo del which passed the verification and preliminary validation test. After conducting the research interviews with experts in the field, as described in Chapter 3, changes were made to the model that are explained in the next section. Changes Made to the Mod el after the Interviews Based on the feedback received during the research interview process, some changes have been made to the preliminary model. As explained in the methodology chapter of this dissertation, simplified representations of the model struct ure, in the form of cause and effect diagrams, were

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104 presented to experts, and they were asked to compare the structure and behavior of the model with their experiences and comment. w round of literature review on some areas of interest touched on by the interviewees, it was decided that the following changes should be made: General As mentioned in the methodology chapter of this dissertation, prior to any official research interview, a pilot interview was conducted with one of the faculty members of the M.E. Rinker Sr. School of Building Construction with research interests in corporate sustainability and green building to ensure that the interview was designed appropriately. The inte rviewee proposed that the model diagrams be simplified, and cause and effect diagrams should be used instead of system dynamics models. This change was made for the future interviews. During the pilot interview, the interviewee also proposed that the ABC for identifying the characteristics of a capable construction firm in terms of sustainable construction. After the pilot interview, the requirements of this certificatio n system was reviewed and it was found that, according to this certification system, company applicants must demonstrate if they are implementing certain green policies in their firms. In other words, this certification system does not assess the capabilit y of construction firms in sustainable construction but it only assesses the sustainability performance of applicants. Since the main question that this dissertation is going to answer, i.e., what it takes in terms of organizational capabilities for constr uction organizations to implement sustainable construction, is different than the question of

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105 whether an organization satisfies certain sustainability performance measures, the aforementioned certification system was found not to be directly relevant to th is research. Some of the comments made by the interviewees suggest that other capabilities need to be added to the sustainability excellence model. In other words, some interviewees proposed that in addition to the capabilities listed in the EFQM model of excellence, other capabilities need to be taken into account, such as organizational culture. As discussed in the literature review chapter of this dissertation, it was found that the mainstream literature suggests that organizational culture entails an in formal set of norms, values, and beliefs governing the interaction style of employees and groups in an organization. In the EFQM definition of leadership, mission, vision, and values can be shaped by leaders of an organization; it is assumed that organizat ional culture is one of the subsets of organizational leadership; therefore, organizational culture has not been added to the list of main capabilities of organizations. Another example of additional capabilities proposed by some interviewees is safety and health. Only one interviewee declared that safety and health should be an essential organizational capability. Based on the definition of organizational capabilities provided in the literature review chapter, measures such as organizational health and saf ety fit better into the category of organizational performance measures than the category of organizational capabilities. A potential list of organizational sustainability measures may include safety and health or any other measures indicating the sustaina bility performance of organizations, such as pollution prevention, energy saving, or waste management, but having this list

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106 does not mean that each of these measures is an organizational capability, i.e., firm specific attributes that are used to exploit fundamental resources within the firm. Other examples of additional capabilities proposed by interviewees include quality, productivity, and marketing capability. All of these factors were already part of the requirements of the sustainability excellence model presented to the interviewees. However, instead of being among the key (top level) requirements, they were among the lower level requirements of the excellence model. These factors were all already included in the system dynamics models. None of the interviewees rejected the choice of the EFQM model of excellence for representing the organizational capabilities needed for successful implementation of sustainable construction in construction organizations. Therefore, no change was made on the overall structure of the excellence model used in this dissertation, and the EFQM model of excellence remained the basis for developing the system dynamics models. Leadership Based on the following comments made by the experts (interviewees) on the leadership sub model, the following changes were made on this sub model: Add a separate set of people resources to the model to reflect the number of leaders in an organization, and define the mechanisms that can result in the change of the number of leaders. Since peopl e resources are modeled in the People sub model, a new reservoir (stock variable) representing the leaders of a typical organization was added to the People sub model. In addition, four new control variables were also added to the model, which represent th e promotion of professional personnel, hiring of leaders, the departure of leaders, and the process of retirement of leaders. The newly added stock

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107 variable along with new control variables can model the working mechanism of leadership succession plans in organizations. Leadership empowerment can be achieved through training of leaders In the L eadership sub model, a new knowledge gaining mechanism was defined to model the mechanism through which leaders are empowered by training. It is assumed that the org anization, as a whole, will benefit from this empowerment; therefore, training of leaders results in gaining more knowledge by the organization. The rate of monthly leadership build up is determined based on the Average Skill Level of Leaders calculated in the L eadership model. The Average Skill Level of Leaders is calculated by dividing the total Leadership Knowledge by the number of leaders at any point in time. T ake into consideration s uccession plan for retiring leaders The inclusion of succession plan for retiring leaders of an organization was proposed to be added to the model. Since a new stock variable along with some new control variables have been added to the model the variation of the number of leaders, it was assumed that succession plan was al so part of this process, and did not need to be addressed separately. Some interviewees emphasized on the personal characteristics of leaders and the fact that some of these characteristics are not gained necessarily through training. It was assumed in th e model that chosen leaders of the organization have already possess personal characteristics needed for leadership, and the model represents the number of people with those characteristics; therefore, no change has been made to the model following this co mment. Policy and Strategy Based on the following comments made by experts (interviewees) on the L eadership sub model, the following changes were made on this sub model: Model the effectiveness of policies instead of the number of policies

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108 Some comments m ade during the interviews were about the fact that the efficiency of policies and strategies are more important than the number of policies and strategies. Therefore, the focus of the model was changed from modeling the number of policies and strategies to modeling the mechanisms that change the efficiency of policies and strategies. Policy Effectiveness is a new variable built into the P olicy and S trategy sub model and it is one of the variables that influences the quality of service. People and Knowledge The People sub model consists of two modules, namely the workforce management module and the knowledge management module. Based on the following comments made by the experts (interviewees) on the People sub model, the following changes were made on this su b model: Include job satisfaction as a factor influencing the departure rate of personnel Some of the interviewees expressed that a number of factors, including job satisfaction, influence the departure rate of personnel in organizations. Job satisfaction can be influenced by many other factors such as the amount of pay and benefit provided to personnel, and workload. Modeling job satisfaction is beyond the scope of this research; therefore, its influence has been captured into the model by introducing onl y one parameter, Job Satisfaction Effect which impacts the departure rate of personnel. This parameter, which is given to the model as an input, has either an escalating or a de escalating effect on the departure rate of personnel. Include th e s kill leve l of current employees A question was raised during one of the interviews on whether or not the skill level of current employees have been taken into account. It is assumed that the initial value of the Organization Knowledge accounts for the skill level of current employees.

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109 In other words, the skill level of employees is already captured by the initial value defined for the stock variable, Organizational Knowledge because organizational knowledge, is mainly affected by the amount of knowledge that the employees possess. Therefore, no additional variable was added to the model to represent the skill level of current employees. However, the P eople sub model includes a variable that tracks changes of the skill level of personnel over time. T his variable is the Average Skill Level of Personnel which is calculated by dividing the overall knowledge level of the organization by the number of Professional Personnel A new stock variable was added to the model to reflect the number of leaders in o rganizations and to model the mechanisms that result in any change in this number over time. The previous section of this chapter provides more information about the changes made to the Leadership sub model. A concern was raised during some of the intervie ws that the term workload is more appropriate for the purpose of this research than the term work pressure Initially, W ork P ressure was among the variables that would accelerate the D eparture R ate of P ersonnel It was discussed during some of the inte rviews that people can leave an organization due to under loading and over loading; therefore, overloading, which results in work pressure, is not always the case. Therefore, the term Work Pressure was replaced with Workload in the model to show that not only over loading but also under loading can accelerate the departure of personnel in organizations. It was discussed during some of the interviews that the average length of stay of employees can reach ten years in some construction firms. As this comm ent was made only by two of the interviewees, and there were no further indication to make a conclusion, no change was made to the model in term of the average length of stay, and the assumed value of this parameter remained at five years. Different employ ee levels, e.g. new hires versus senior personnel, have different efficiencies There was no consensus among interviewees about the criticality of taking into account different efficiency values for different employees. Furthermore, the intention of this r esearch is not to investigate the influence of different groups of employees in an organization. Therefore, this comment has not been considered in the model.

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110 Partnership and Resources Based on the following comments made by the experts (interviewees) on t he P artnership and R esources sub model, the following changes were made on this sub model: Not all potential partners become active partners This was a valid comment made by an interviewee, and it was addressed to better model the partnership mechanisms. I n the P artnership sub model, a control variable has been added to the model to represent potential partners that do not become an active partner. This new variable is Losing Potential Partners Reputation (driven by quality) drives relationships and the ab ility to sustain partners A change was made to the model to model organizational reputation. Reputation was not previously defined as a variable in the model. Reputation was added to the Policy and Strategy sub model and it is driven by the organizational quality of service. Delay factors were also taken into account for the process of reputation building by organization. These delay factors are due to the fact that improving quality of service does not immediately improve the reputation of an organization It takes time for the firm to improve its reputation in the marketplace. On the other hand, the deterioration of quality does not immediately result in the defacement of the organization and it takes time for the reputation of a firm to be affected follo wing a decrease in quality. Process Based on the following the comments made by the experts (interviewees) on the P rocess sub model, the following changes were made on this sub model: Two of the interviewees discussed the variability of the efficiency of employees from one individual to another. They made the comment that taking into account only one single efficiency value for all employees cannot be valid. After reviewing this

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111 comment, it was decided to use one average efficiency value representing the a verage efficiency of personnel. This assumption was made for simplification purposes and to ensure that no unnecessary complexity is added to the model. The comment about using workload instead of work pressure, discussed in the previous section, was also discussed with some of the interviewees during the review of the Partnership sub model. With the same explanation provided earlier, Work Pressure was replaced with Workload in the model. Sub Models As explained in the methodology chapter, the concept defin itions of the EFQM model was taken into account as the starting point to determine what components were needed to be included in each focus area. The definitions of the key organizational capabilities were then improved by conducting a thorough literature review of corporate sustainability and sustainable development. Appendix B shows the requirements of organizational sustainability excellence in accordance with the improved EFQM model of excellence. In the following section, each of the five sub models of the sustainabili ty excellence model, including L eadership, P olicy and S trategy, P eople, P artnership, and P rocess is explained. Leadership The Leadership sub model consists of three main sections (see Figure 4 1 ). The first section concerns leadership knowledge management; the second section models the leadership capability building mechanism; and the third section models the change mechanisms that exist in organizations as a result of Leadership capability. The leadership knowledge management section demonstrates that the organization can gain leadership knowledge through hiring leaders, training of leaders, and the expansion of the leadership team. On the other hand, the organization can lose organizational knowledge due to the retirement or departure of leaders.

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112 The second section, the leadership capability building mechanism, demonstrates that Leadership capability can be gained or lost over time, the result of which would be an overall decrease or increas e in organizational leadership capability. In this sub model, Leadership Capability is shown as a resource reservoir (stock variable). The amount of this stock variable at any point in time depends on the history of gaining and losing knowledge over time b y the organization. As shown in this model, the Target Capability is a given value that is determined based on a managerial decision and represents the target value of leadership capability for the organization in a certain time frame. The difference betw een the Target Capability and the capability in a time step determines if the organization still needs to plan for gaining more capability or not. The Capability Ratio Achieved is the current LEA Capability ( L eadership C apability ) divided by the leadership Target Capability The Growth Rate Adjustment Factor is then calculated as a function of the Capability Ratio Achieved The closer the organization is to the Target Capability (target leadership capability), the lower the Growth Rate Adjustment Factor wil l be, which means a lower level of need for building up capability. The Actual Build up Rate which specifies the amount of L eadership C apability gained over time is determined based on the Desired Monthly Build up Rate Feasible Monthly Build up Rate an d the Growth Rate Adjustment Factor The Desired Monthly Build up Rate represents the buildup rate the organization has decided to gain leadership capability every time step. The Feasible Monthly Build up Rate on the other hand, is determined based on the Average Skill Level of Leaders as well as the Knowledge Impact Factor driven from the overall knowledge level of the organization.

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113 In other words, the Feasible Monthly Build up Rate determines the rate at which th e organization is able to gain L eadership C apability every month. The model then compares the magnitudes of the Feasible Monthly Build up Rate and the Desired Monthly Build up Rate to determine the Actual Monthly Build up Rate The l eadership Capability Build up Rate defined as the Actual Build up Rate multiplied by the LEA Capability ( L eadership C apability ), is the mechanism for leadership capability improvement in organizations. The overall L eadership C apability of an organization over time is also dependent upon the decay rate of the leadershi p capability, which is the result of the Shortage or Surplus of Leaders The Capability Decay Rate models the leadership capability deterioration over time. The assumed initia l values of the organizational L eadership C apability and the related flow variabl es, Capability Build up and Capability Decay ultimately determine the overall organizational Leadership Capability over time. As explained a bove, the third section of the L eadership sub model shows the change mechanisms that exist in organizations as a re sult of the level of L eadership C apability It is assumed that a higher Leadership C apability results in a more successful policy development. It is also assumed that successful policy development means developing policies that comply with the needs of ins ide customers (personnel) and outside customers (clients and stak eholders); therefore, a higher L eadership C apability is assumed to result in a higher Ratio of Policies Complying with the Needs of Inside Customers and a higher Ratio of Policies Complying with the Needs of Outside Customers

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114 Later in this chapter, it will be explained how a higher Ratio of Policies Complying with the Needs of Outside Customers will result in a higher quality of service. On the other hand, a higher Ratio of Policies Complyi ng with the Needs of Inside Customers is assumed to be effective in increasing the throughout the organization. Thus, another consequence of having a higher level of leadership capability is assumed to be a higher Motivation Level in organizations. In addition, an organ ization with a higher level of L eadership C apability is expected to be better able to develop clear organizational missions, visions, and values, resulting in better coordination and more efficient op erations. Furthermore, organizational policies are better communicated with personnel in a more leadership capable organization; therefore, other consequences of having a higher level of L eadership C apability are assumed to be a higher Clarity of Mission, Vision, and Value and a higher Ratio of Properly Communicated Policies As explained before, delay factors have also been added to the model, wherever necessary, to make the model more representative of the real world in which the effect of capability imp roving actions in organizations can be seen only after some time. Policy and Strategy The P olicy and S trategy sub model consists of two main sections (see Figure 4 2 ). The first section concerns the corresponding capability building mechanisms; and the second section models the change mechanisms that exist in organizations as a result of the level of the P olicy and S trategy C apability. The policy and strategy capability building mechanism demonstrates that the Poli cy and Strategy Capability can be gained or lost over time, the result of which would be an overall decrease or increase in the organizational P olicy and S trategy capability.

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115 In this sub model, the Policy and Strategy Capability has shown as a resource res ervoir (stock variable). The amount of this stock variable at any point in time depends of the history of gaining and losing knowledge over time by the organization. As shown in this model, the Target Capability is a given value that is determined based o n a managerial decision and represents the targeted value of the Policy and Strategy Capability for the organization. The difference between the Target Capability and the current achieved level of capability determines if the organization still needs to pl an for gaining more capability. The Capability Ratio Achieved is the current POL Capability ( Policy and Strategy Capability ) divided by the Target Capability for policy and strategy. The Growth Rate Adjustment Factor is then calculated as a function of the Capability Ratio Achieved The closer the organization to the Target Capability (target policy and strategy capability), the lower the Growth Rate Adjustment Factor will be, which means a lower level of need for building up further capability. The Actual Build up Rate, which specifies the amount of P olicy and S trategy C apability gained over time is based on the Desired Monthly Build up Rate Feasible Monthly Build up Rate and Growth Rate Adjustment Factor The D esired Monthly Build up Rate represents the desired build up rate of P olicy and S trategy C apability every time step. The Feasible Monthly Build up Rate on the other hand, is based on the Average Skill Level of Leaders as well as the Knowledge Impact Factor driven from the overall knowledge level of the organization. In other words, the Feasible Monthly Build up Rate determines the rate at which the organization is able to gain P olicy and S trategy C apability every time step. The Average Skill Level of Leaders plays an important role in this section of the model because of the significance of the role of

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116 leaders in developing policies and strategies in organizations. The model then compares the magnitudes of the Feasible Monthly Build up Rate and that of the Desired Monthly Build up Rate to determine the Actual Monthly Build up Rate The Capability Build up Rate for policy and strategy, defined as the Actual Build up Rate multiplied by the POL Capability ( Policy and Strategy Capability ), is the mechanism for capability improvement in terms of policy a nd strategy in organizations. The overall Policy and Strategy Capability of an organization over time is also dependent upon the decay rate of the Policy and Strategy Capability. The Capability Decay rate of the policy and strategy capability is assumed to be equal to the leadership Capability Decay Rate The Capability Decay Rate models the mechanisms through which the Policy and Strategy Capability deteriorates over time. The assumed initial value of the organizational Policy and Strategy Capability the quantities of the related flow variables, the policy and strategy Capability Build up Rate and the Capability Decay Rate ultimately determine the overall organizational capability in terms of policy and strategy over time. People The P eople sub model cons ists of two modules. The first module, workforce management (see Figure 4 3 ), represents the organizational mechanisms related to the hire, promotion, and departure of professional personnel as well as leaders of organizations. The second module of the P eople sub model, knowledge management (see Figure 4 4 ), represents various mechanisms that result in knowledge gain or loss in organizations. These two modules are closely related because many of their flow rates are inter dependent.

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117 The construction organizations make their hiring decisions based on two main variables: the amount of the Professional Personnel Required calculated in the Process sub model, and the Average D eparture Rate determined by calculating the average rate of departure of current professional personnel. The variable Professional Personnel Required represents the number of professional personnel that are required to manage the current project load; the refore, a Correction Value needs to be calculated by subtracting the number of current Professional Personnel from the Professional Personnel Required Ultimately, the number of hires is determined by calculating the summation of the Correction Value and t he Average Departure Rate The Correction Time represents delay factors involved in the process through which the construction organization adjusts the number of professional personnel based on need. The initial number of Professional Personnel and the acc umulation of new hires over time determine how many Professional Personnel are present in an organization at any point in time. Over time, some Professional Personnel leave the organization and some of them are promoted to leadership positions, and the res t remain in the resource pool Professional Personnel It is assumed in the model that the outflow of Personnel Departing depends on an Average Duration of Stay determined based on historical data in construction organizations, the Effect of Workload on De parture defined in the process sub model, and Job Satisfaction Effect a variable that represents the desirability of organizational culture to their personnel. As mentioned before, organizational culture consists of elements such as the organizational mi ssion, vision, and values, which are mainly shaped by the leaders of organizations. In this part of the model, it is assumed that

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118 organizational culture is among the variables that affect the desirability of organizational environments to their personnel. A similar set of mechanisms determines the number of leaders in construction organizations. The leaders are either the promoted professional personnel or new hires. The leaders in any time step will either remain active in their leadership roles, or they leave or retire, represented by Leaders Departing and Leaders Retiring As mentioned before, the second module of the People sub model, knowledge management, represents various mechanisms that result in a knowledge gain or loss in organizations. Basically five different knowledge gaining mechanisms are included in the model: training professional personnel, training new hires, training leaders, gaining knowledge due to the skills that new hires already have, and gaining knowledge through partnership. For each of these cases, corresponding variables and elements have been added to the model. For instance, the Hours of Training Provided to Personnel is calculated by taking into account how many training hours are provided to new entrants and to professional personnel. The number of new hires, for example, is from the workforce module, as previously explained. Knowledge loss occurs if any of the Professional Personnel or Leaders leave the organization, either due to retirement or departure. The Forgetting Effe ct is used in the model to capture the effect of forgetting mechanisms that are influential in increasing the rate of knowledge loss in organizations. Partnership and Resources The P artnership and R esources sub model consists of two main sections (see Figure 4 5 ). The first section concerns the partnership capability building mechanism

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119 and the second section models the change mechanisms that exist in organizations as results of the partnership capability. The part nership capability building mechanism demonstrates that Partnership Capability can be gained or lost over time, the result of which would be an overall decrease or increase in organizational Partnership Capability In this sub model, Partnership Capability is shown as a resource reservoir (stock variable). The amount of this stock variable at any point in time depends on the history of gaining and losing knowledge by the organization over time. Target Capability is a given value that is determined based on a managerial decision and it represents the target value of partnership capability for the organization. The difference between the Target Capability and the current achieved level of capability determines if the organization still needs to plan for gaini ng more capability or not. The Capability Ratio Achieved is the current PAR Capability ( Partnership Capability ) divided by the partnership Target Capability The Growth Rate Adjustment Factor is then calculated as a function of the Capability Ratio Achieve d The closer the organization is to the Target Capability (target partnership capability), the lower the Growth Rate Adjustment Factor will be, which means a lower level of need for building up further capability. The Actual Build up Rate which specifie s the amount of Partnership C apability gained over time, is based on the Desired Monthly Build up Rate Feasible Monthly Build up Rate and Growth Rate Adjustment Factor The Desired Monthly Build up Rate represents the rate at which the organization has d ecided to gain P artnership C apability every month. The Feasible Monthly Build up Rate on the other hand, is based on the

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120 degree of the Appropriateness of Training Program for Partnership as well as the Knowledge Impact Factor driven from the overall know ledge level of the organization. In other words, the Feasible Monthly Build up Rate determines the rate at which the organization is able to gain P artnership C apability every month. The model then compares the magnitudes of the Feasible Monthly Build up Ra te and that of the Desired Monthly Build up Rate to determine the Actual Monthly Build up Rate The partnership Capability Build up Rate defined as the Actual Build up Rate multiplied by the PAR Capability ( P artnership C apability ), is the mechanism for im proving Partnership Capability in organizations. The overall Partnership Capability of an organization over time is also dependent upon the decay rate of partnership capability, which is the result of the turnover of partners ( Partners Turnover ). The Capab ility Decay Rate models the deterioration of P artnership C apability over time. The assumed initial value of the organizational P artnership C apability and the quantities of the related flow variables, the partnership Capability Build up Rate and the partn ership Capability Decay Rate ultimately determine the overall organizational partnership capability over time. As explained above, the second section of the Partnership sub model shows change mechanisms that are in play in organizations as a result of the level of the P artnership C apability It is assumed that the Partnership Capability drives the Initial Partnership Identification Rate which results in the identification of potential partners over time. Since the partnership capability build up does not i mmediately result in a greater ability to identify potential partners for organizations, delay factors are included

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121 in the model. Usually it takes time for organizations to see the effect of their capability improving actions. Potential partners of organi zations become active partners if organizations intend to establish these types of relationships and if they undertake necessary actions required for this transition. As shown in the model, not all potential partners become active partners. In other words, some of the potential opportunities do not result in the ultimate outcome of having active partnership relationships for organizations. On the other hand, active partnership relationships last for certain lengths of time, depending upon the rate of Losing Active Partners In this sub model, it is assumed that Reputation plays a key role in the success of organizations in gaining and retaining proper partners; therefore, the Gaining Rate and the are functi ons of the reputation of organizations. The greater the the greater the Initial is expected to be. Similarly, the greater Identification Rate the greater the Initial Partn ers retaining Rate is expected to be. These two expectations are satisfied by assuming that the Initial Partners Gaining Rate and the Initial Partners Retaining Rate are functions of the Rate This assumption can be made be cause all these three variables, the Initial Initial Partners Gaining Rate and Initial Partners Retaining Rate are expected to change in the same direction. In other words, they are expected to increase or decrease when the overall partnership capability is increasing or decreasing so it is reasonable to assume that the Initial Partners Gaining Rate and the

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122 Initial Partners Retaining Rate are functions of the as explained above. As desc ribed before, delay factors have been added to the model, wherever necessary, to make the model more representative of the real world, in which the effects of capability improving actions in organizations can be seen only after some time. Process The Proce ss sub model consists of three main sections (see Figure 4 6 ). The first section concerns the process capability building mechanism, the second section models the change mechanisms that exist in organizations as a result of the P rocess C apability and the third section defines what factors drive the number of projects that a construction organization is expected to have at any point in time. The process capability building mechanism demonstrates that Process Capab ility can be gained or lost over time, the result of which would be an overall decrease or increase in organizational Process Capability In this sub model, Process Capability is a resource reservoir (stock variable). The amount of this stock variable at a ny point in time depends on the history of gaining and losing knowledge by the organization over time. As shown in this model, the Target Capability is a given value that is determined based on a managerial decision, and it represents the target value of the organizational Process Capability The difference between the Target Capability and the level of capability in a given time step determines if the organization still needs to plan for gaining more capability. The Capability Ratio Achieved is the curren t PRO Capability ( P rocess C apability ) divided by the process Target Capability The Growth Rate Adjustment Factor is then calculated as a function of the Capability Ratio Achieved The

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123 closer the organization is to the Target Capability (target process cap ability), the lower the Growth Rate Adjustment Factor will be, which means a lower level of need for acquiring further capability. The Actual Build up Rate which specifies the amount of P rocess C apability gained over time, is based on the Desired Monthly Build up Rate Feasible Monthly Build up Rate and the Growth Rate Adjustment Factor The Desired Monthly Build up Rate build up rate for P rocess C apability every time step. The Feasible Monthly Build up Rate on the other hand, is determined based on the Appropriateness of Training Program for Process as well as the Knowledge Impact Factor driven from the overall knowledge level of the organization. In other words, the Feasible Monthly Build up Rate determines the ra te at which the organization is able to gain P rocess C apability every month. The model then compares the magnitudes of the Feasible Monthly Build up Rate and that of the Desired Monthly Build up Rate to determine the Actual Monthly Build up Rate The proce ss Capability Build up Rate defined as the Actual Build up Rate multiplied by the PRO Capability ( Process Capability ), is the mechanism for process capability improvement in organizations. The overall Process Capability of an organization over time is als o dependent upon the decay rate of the Process Capability which is defined as a function of the Quality Deficiency Factor The lower the Quality of the service, the higher the Quality Deficiency Factor The Capability Decay Rate models the deterioration me chanism of Process Capability over time.

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124 The assumed initial values of the organizational Process Capability and the related flow variables, the process Capability Build up and Capability Decay ultimately determine the overall organizational Process Capa bility over time. The Overall Excellence Score A separate sub model has been devoted to the calculation of the Overall Sustainability Excellence Score by integrating the results of all individual sub models and by taking into account the relative importanc e values of each of the assumed capabilities for each of the three sustainability aspects, economic, environmental, and social. During the research interviews, the interviewees were asked to identify the relative importance values of each of the assumed ca pabilities for each of the three sustainability aspects (see Figure 4 7 ). The relative importance of the sustainability aspects can be equal or un importa nce to any of the aspects (see Figure 4 8 ). The calculation of the Overall Sustainability Excellence Score is based on the individual sustainability excellence score of each of the capabilities (see Figure 4 9 ). These scores can then be broken down into the sustainability excellence score of each capability for each particular sustainability aspect. (4 1 ) In Equation 4 1, represents the relative importance of the sustainability aspects, represents the relative importance values of each of the assumed capabilities for each of the three sustainability aspects, and repres ents the

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125 sustainability excellence score of the organization in each capability area. determined based on the judgments of the interviewees, and running the system dynamics model. Equation 4 2 is the summ ation of all values in each column associated with each sustainability aspect and can help understand the combined impact of both the relative importance values of different organizational capabilities for sustainability aspects and the capabi lity score values ( ): (4 2 ) The Overall Sustainability Excellence Score (SES) is then calculated using Equation 4 3: (4 3 ) Scenarios Tested Scenario 1 Scenario 1 is the base scenario with the initial and input values defined in the model for an example organization. In brief, the example organization has the following attributes: General description The example organization is a growing construction firm that intends to improve its capabilities in order to improve its sustainability performance. The time step chosen for the model is one month; therefore, the time interval between calculations of the model is one month.

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126 Leadership Based on an initial leadership assessment, the starting leadership capability is 40%, and the organization intends to improve its leadership capability at rates ranging from 0.36% to 0.6% per year. The erosion rate of the leadership cap ability of the organization is determined over time based on the shortage or surplus of leaders. The leaders of the organization continue to make the mission, vision, and values of the organization clear to all employees but it takes about four months for the organization to get the message across. Therefore, they are trying to find better means improve the organizational policies to better satisfy the needs of employee s and customers. The change process associated with policies take about three months to take effect. The leaders also support and motivate the employees in their day to day duties; this leadership support take about four months to be fully perceived by the personnel. Policy and Strategy Based on an initial assessment of the policies and strategies of the organization, the starting capability of the organization in terms of policy and strategy is 40%, and the organization intends to improve its capability i n this area at the rate of 1.7% per year (the Desired Build up Rate = 0.14 per month). The leaders have realized that their improving performance directly affects the effectiveness of the organizational policies, resulting in higher levels of quality of s ervice. It is assumed that organizations are able to determine how the workload of personnel influence the quality of their output. In general, very low or very high workload adversely affect the quality of service provided by the organization. It is furth er assumed that the performance of the customer relationships department has a major impact on

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127 how the customers perceive the quality of service provided by the organization. It is assumed that the current performance level of the customer relationships de partment improves the quality of service by 20% (the performance of the customer relationships department functions as an escalating coefficient). People Currently, the organization has 50 professional personnel and the rate of acquiring projects determin es if the organization needs more personnel to hire. Every three months, the organization reviews this need and decides whether to acquire new hires or not. The average duration of stay of personnel in the organization is about 5 years. Currently, the orga nization has decided to set the promotion rate of personnel to 0.024% per year. The promoted professional personnel will become members of the leadership team of the organization. Under rare circumstances, the organization hires leaders to satisfy needs. T he current rate of hiring is about 0.6% per year. The leaders retire at after about twenty years of providing service to the organization. The average duration of stay of the leaders who leave the organization is about ten years. The new hires, profession al personnel, and leaders are trained in the organization. The organization provides a hour upfront training to new hires. The organization also provides twelve hours of training per month to each of its current professional personnel. Each month, the same duration of training is also provided to the leaders of the organization. It is been determined that each professional skill needs 40 hours of training. However, the average skill level of promoted personnel are twice the skill level of non promoted p ersonnel.

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128 Another knowledge gaining mechanism for the organization is gaining knowledge from partners. It has been estimated that the overall amount of knowledge gained from partners is about twenty skills per month. Partnership Based on an initial part nership capability assessment, the starting partnership capability of the organization is 10%, and the organization intends to improve its capability in this area at the rate of 1% per year (the Desired Build up Rate = 0.08 per month). It is assumed that on the organizational partnership capability at any point in time. It is assumed that the rate Currently, five potential partners have been identified by the organization. The organization is in the process of determining which of these candidates have the potential and are appropriate to be active partners of the organization. It is assumed that organizations can determine how the reputation of their organization impacts the rate of gaining and retaining partners. It is assumed that it take about three to four mont hs for any change in the partnership capability to impact the rate of gaining or retaining partners by the organization. Process Based on an initial process capability assessment, the starting process capability of the organization is 20%, and the organiz ation intends to improve its capability in this area at the approximate rate of 0.5% per year (the Initial Target Build up Rate = 0.04 per month).

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129 By building up reputation, the organization tries to acquire more customers and acquire more contracts. In t he base model, the Average Time Gap between Contracts from each Customer is not deterministic, and it follows the normal distribution with mean 24 and the standard deviation 3 (months). It is assumed that the duration of projects performed by the organizat ion follows a normal distribution with mean 15 and standard deviation 2 (months). The level of process capability determines the overall organizational efficiency. On the other hand, the number of acquired projects determines the overall workload of the or ganization, which then can be translated into the workload of each professional employee by taking into account the total number of professional personnel. Outcome As the base scenario contains a stochastic variable (the Average Time Gap between Contracts from each Customer ), the model was run 1000 times to evaluate its overall performance. Figure 4 10 4 can be fitted to the Ov erall Sustainability Excellence Scores from running the model 1000 times. For calculating the Overall Sustainability Excellence Score in the base scenario, it is assumed that the sustainability aspect weights are equal at 33.33% each. Appendix P Section 1 shows that, at 99% confidence level, the H 0 hypothesis, being the data follows the specified distribution, is not rejected by any of the Chi Square, Anderson Darling, or Kolmogorov Smirnov tests. Figure 4 11 demonstrates that the monthly leadership Capability Build up rate starts with an increasing trend. As time goes by, the achieved Leadership Capability

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130 b ecomes closer to the Target Capability ; therefore, the monthly leadership Capability Build up r ate declines and ultimately reaches stability over time. As the Knowledge Impact Factor and the Average Skill Level of Leaders are improving over time, the Feasible Monthly Build up Rate is also improving (see Figure 4 12 ). The D esired Build up Rate is greater than the Feasible Build up Rate up until about time period 50 but after that point in time, the feasible rate of buildup is slightly greater than the desired rate of capability build up For determining the Actual Monthly Bu ild up Rate the Growth Rate Adjustment Factor Feasible Monthly Build up Rate and Desired Monthly Build up Rate are taken into account; and as previously discussed, the closer the organization is to the Target Capability the lower the Growth Rate Adjust ment Factor will be, which means a lower level of need for building up further capability. Therefore, the values of the Actual Build up Rate after time step 50, which in the case of Figure 4 12 is mostly driven by the Desired Mon thly Build up Rate decline over time mostly due to the decline of the Growth Rate Adjustment Factor over time. Although the Actual Monthly Build up Rate declines slightly, the overall Leadership Capability is still increasing with a slow pace (see Figure 4 13 ). Since the Ratio of Policies Complying with the Needs of Inside Customers and the Ratio of Policies Complying with the Needs of Outside Customers are driven by the overall Leadership Capability the trend of these two rates increase with time (see Figure 4 13 ). The same reason explains the increase of the Clarity of Mission, Vision, and Values over time and the increase of the Motivation Impact Factor over time (see Figure 4 14 ).

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131 Figure 4 15 depicts that the monthly policy and strategy C apability Build up R ate starts with a slightly increasing trend. As time goes by, the achieved capability becomes closer to the Target capability ; therefore, the monthly policy and strategy Capability Build up rate declines. At about time period 60, the Policy and Strategy Capability starts to decline slightly due to the increasing trend of the Capability Decay The increase of the Capability Decay forces the Capability Bu ild up to increase in order to prevent a sharp decline of the POL Capability (Policy and Strategy Capability ). As the POL Capability (Policy and Strategy Capability ), the Ratio of Policies Complying with the Needs of Outside Customers and the Personnel Se nse of Support drive the Policy Effectiveness the trends of change in these variables occur in the same direction. In other words, As Figure 4 16 shows, the increase of the POL Capability (Policy and Strategy Capability) the Ra tio of Policies Complying with the Needs of Outside Customers and the Personnel Sense of Support result in an increase of the Policy Effectiveness. According to the model, a number of factors such as the Policy Effectiveness the Motivation Impact Factor (included in the leadership sub model), and the Effect of Workload on Quality derive the Quality drive the quality. Figure 4 17 shows the influence of these variables on the Quality Figures 4 17 through 4 21 depict the result of the People sub model. Figures 4 17 and 4 18 exhibit hiring, promotion, and departure rates of personnel and leaders respectively. These rate s dete rmine the number of personnel and leaders at any point in time. Based on the number of personnel and leader, the trend of knowledge gaining in organizations, and the overall level of the organizational knowledge at any point in time,

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132 the Average Skill Leve l of Personnel and the Average Skill Level of Leaders can be calculated. For instance, in the case of the base scenario, both the trend of the Average Skill Level of Personnel and the trend of the Average Skill Level of Leaders are increasing with time (se e Figure 4 20 ). The overall trend of the PEO Capability (People Capability ) is depicted in Figure 4 21 The increasing trend of this variable is a result of the human res ource development, training (training of new hires, personnel, and leaders), and partnership (gaining knowledge through partnership) programs. The PEO Capability (People Capability ) then derives the Knowledge Impact Factor which plays a role in other sub m odels (see Figure 4 21 ). Figures 4 22 through 4 24 depict the result of the Partnership sub model. Figure 4 22 demonstrates that the monthly partnership Capability Build up R ate starts with an increasing trend. As time goes by, the achieved Partnership Capability becomes closer to the Target Capability ; therefore, the monthly partnership Capability Build up rate declines and ultimately reaches stability over time. The PAR Capability (Partnership capability ) determines the rate at which potential partners are identified ( Being Identified Potential Partners ), the rate at which the organization loses potential partners ( Losing Potential Partners ), and the rate at which the organization gains active partners ( Gaining Active Partners ). As Figure 4 23 depicts, the greater the Partnership Capability becomes, the more potential partners are identified and gained. As more potential partners are identified, the number of potential partners that do n ot become active partners is also expected to rise but the overall trend of the number of active partners gained over time is increasing (see Figure 4 24 ).

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133 Figures 4 25 through 4 29 depict the result of the Proces s sub model. Figure 4 25 demonstrates that the monthly process Capability Build up rate starts with an increasing trend. As time goes by, the achieved Process Capability becomes closer to the Target Capability ; th erefore, the monthly process Capability Build up rate declines and ultimately reaches stability over time. Figure 4 26 depicts the influence of the organizational Reputation on Attracting Customers Losing Custome rs and the number of Active Customers As this figure shows, these variables change in the same direction and an increased level of Reputation results in increases of the number of Attracting Customers and the number of Active Customers Figure 4 27 depicts the influence of different variables that drive the Organizational Efficiency As this figure shows, the increasing trends of the constituents have resulted in an overall increasing trend in the Organizational Efficiency Figure 4 28 demonstrates the overall increasing trend of the Organizational Monthly Workload in hours and its influence on the number of Professional Personnel at any point in time. The increase of the Organizational Monthly Worklo ad is due to increases in the number of Active Customers and the expected number of projects (see Figure 4 29 ). Figure 4 28 also shows that the Average Workload of each I ndividual starts with a number slightly more than 1 (representing a slight overload) but it flattens with time. In other words, the model adjusts the number of the Professional Personnel in such a way that overloads are minimized to remove the pressure on personnel. Scenario 2 Scenario 2 was run to answer the following question: what difference does changing the sustainability aspect weights to the following amounts make on the Overall

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134 Sustainability Excellence Score ? Economy: 50%, E nvironmental: 25%, and S ocial: 25%. As Figure 4 30 demonstrates, the Overall Sustainability Excellence Score is insignificantly sensitive to the weights of sustainability aspects. The Overall Sustainability Excellence Score obtained by the running of scenario 2 comes out to be about 89.75% at the end of year 10, which is very close to the Overall Sustainability Excellence Score of the base scenario at the end of year 10. This insensitivity is mostly related to the relative importance of organizational capabilities for each sustainability aspect, obtained from the research survey. In other words, the variation of the relative importance values around the average weight factors of capabilities for each individual sustainability aspect are s uch that the relative importance of sustainability aspects make a small difference on the Overall Sustainability Excellence Score Moreover, if the capability scores of an organization obtained from running each sub model are about equal, then the Overall Sustainability Excellence Score is fully insensitive to the weight of sustainability aspects. Scenario 3 Scenario 3 was run to answer the following question: what difference does changing the sustainability aspect weights to the following amounts make on the Overall Sustainability Excellence Score ? Economy: 25%, E nvironmental: 50%, and S ocial: 25%. As Figure 4 31 demonstrates, the Overall Sustainability Excellence Score is insignificantly sensitive to the weight o f sustainability aspects. The Overall Sustainability Excellence Score obtained by the running of scenario 3 comes out to be about 89.76%

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135 at the end of year 10, which is very close to the Overall Sustainability Excellence Score of the base scenario at the e nd of year 10. A conclusion that can be drawn here is similar to the conclusion drawn following scenario 2. In other words, the Overall Sustainability Excellence Score is almost insensitive to the weights of sustainability aspects. Scenario 4 Scenario 4 wa s run to answer the following question: how can an organization maintain its knowledge level during a period during which personnel's departure rate is high (the Average Duration of Stay has dropped from 60 to 36 months)? It is assumed that the performance of the organization at the end of year 10 is the comparison base. Figure 4 32 duration of stay from 60 to 36 month. As this figure depicts, the P eople C apability duration of stay. It is assumed that the organization takes no remediating action initially. However, scenario 4 is designed to also find out what remediating actions can stop the decline of the People Capability. Numerous remediating actions can help organizations stop th e decline of the People Capability In this scenario the effect of hiring personnel with higher skill levels (the Skill Level of Entrants : 20 instead of 16) along with providing a more intense training program (the Average Training Hours Provided to Person nel : 15 instead of 12) is tested. As Figure 4 33 shows, these remediating actions can bring about a People Capability that i s even higher than the initial P eople C apability In other words, if the al length of stay has dropped from 60 to 36 months, the organization can still achieve the same and even higher levels of P eople C apability if the

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136 organization chooses to hire personnel with higher skill levels and provide a more intense training program. In brief, the conclusion drawn following the execution of scenario 4 is as follows: hiring personnel with a higher level of skills (the Skill Level of Entrants : 20 instead of 16) along with a more intense training program (the Average Training Hours Provi ded to Personnel : 15 instead of 12) can provide an enhanced knowledge gaining result for an organization over a ten year horizon. Scenario 5 Scenario 5 was run to answer the following question: how sensitive is the P eople C apability to the Learning Effecti veness parameter defined in the P eople sub model. Figure 4 34 compares the P eople C apability over time when the L earning E ffectiveness takes the following values: 95%, 91%, and 70%. These parameter values correspond to cases 1, 2, and 3 plotted in Figure 4 34 As this figure shows, the People Capability is sensit ive to this parameter, and the P eople C apability declines if the Learning Effectiveness drops in organizations. The sensitivity of the People Capability and the Overall Sustainability Excellence Score to the Le arning Effectiveness parameter have also been plotted in Figures 4 47 through 4 50 for a short term horizon (at year 3) and a long term horizon (at year 10). The result of this scenario highlights the importance of quality training programs in organization s. Scenario 6 Scenario 6 was run to answer the following question: what difference does stochastically re generating the weights of capabilities for sustainability aspects make

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137 on the Overall Sustainability Excellence Score ? In the following, this scenario has been described in more detail. One of the questions that was asked from the interviewees during the research survey was about the relative importance of organizational capabilities for providing a better organizational performance in each sustainabili ty aspect. One way to analyze the collected data pertaining to the question is to determine what distribution functions best represent the distribution of answers given by the interviewees for each individual weighting factor. This analysis has been perfor med by using the professional evaluation version of the statistical tool EasyFit 5.5. EasyFit can be used to determine the distribution function that best represents the distribution of different data points pertaining to each weighting factor. More detai l about the statistical tests performed for data fitting have been provided in Appendix P, Sections 3 through 17. The question that might arise is whether or not the Overall Sustainability Excellence Score will change if these weighting factors are re gene rated using the best fit distribution functions for each individual weighting factor. In other words, the question is: whether the Overall Sustainability Excellence Score will change if, instead of using the average of the weights given by experts in each category, the weights of capabilities for sustainability aspects are reproduced stochastically using the best fitted distribution functions. By running the described scenario, it is seen that the Overall Sustainability Excellence Score will vary from one r un to another, and its distribution is normal with the and Section 2 includes the results of the Kolmogorov Smirnov, Anderson Darling, and Chi Squared tests that show

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138 that the H 0 hypothes is, which is the data follows a normal distribution, is not rejected. Therefore, it can be assume that the dataset follows a normal distribution. Figure 4 35 shows the graphical representation of this distribution function. The difference between the base scenarios (scenario 1) and scenario 6 in terms of the mean sustainability excellence score is that the fitted distribution to the sustainability excellence scores in scenario 6 has shifted to the left side of the x axis. The reason is that most sets of capability weights follow a lognormal distribution (see Table 4 1 ), and lognormal distributions are, by definition, skewed in such a way that the appearance of lower values are more probable than the appearance of higher values. Table 4 1 summarize s the results of the stat istical tests included in Appendix P, Sections 3 through 17. Scenario 7 Scenario 7 was run to answer the following question: how sensitive is the P olicy C apability to the value of the parameter Appropriateness of Training Program for Policy Making ? In othe r words, how much is the policy capability of an organization affected if training programs are appropriate for sustainability purposes, but to a somewhat less degree? It is assumed that the performance of the model at the end of year 3 is the comparison b ase. In the base scenario, it is assumed that the assumed organization is constantly improving the appropriateness of its training program over time (see Figure 4 36 ) whereas in scenario 7 this parameter is set to 80% of the same variable included in the base scenario to evaluate the effects of any changes to this parameter. Figure 4 37 demonstrates the effect of this change on the Policy C apability of the organization.

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139 T he second question that can be asked as part of scenario 7 is what policies have a similar effect in terms of the magnitude of effect on the Policy C apability The structure of the model can be used to answer this question. One of the elements that have a major impact on the Policy C apability of an organization, according to the model, is the L eadership C apability of the organization. Therefore, the effect of increasing the intensity of training for leaders along with increasing the skill level of leader hi res is evaluated. The combined effect of these increases are shown in Figure 4 38 As Figure 4 38 shows, the combined effect of increasing the intensity of training for leaders along with increasing the skill level of leader hires has almost the s ame magnitude of effect on the Policy C apability as changing the quality of training programs. The reason is that applying changes to the model that correspond to these increases have increased the people capability to its initial values. In brief, the conclusion drawn following the execution of scenario 7 is as follows: Policy Capability is sensitive to the quality of training and the appropriateness of training for sustainabilit y purposes in organizations. Declining trends in Policy C apability can be alleviated by enhancing Leadership C apability. Scenario 8 Scenario 8 was run to answer the following question: how much is the Overall Sustainability Excellence Score of an organizat ion affected after 10 years, if the organization chooses to exclude partnership from its sustainability excellence plans? For implementing this change, the magnitude of the parameter the Desired Monthly Build up Rate was set to zero to test how much the ov erall sustainability score declines if an organization does not desire to build up any partnership capability over time.

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140 Figure 4 39 shows the impact of excluding partnership from the list of organizational capabi lities that enable organizations to perform well in terms of sustainable development. This figure shows the trend of this effect on a 10 year long time frame. As it is seen in this figure, the impact of excluding this capability is significant. By implemen ting this change on the base scenario, the approximate sustainability excellence score will drop to about 73% at the end of year 10, which is significantly lower than the sustainability excellence score obtained from the base scenario. In brief, the concl usion drawn following the execution of scenario 8 is as follows: excluding P artnership decreases the Overall Sustainability Excellence Score from 90% (associated with the base scenario) to 73% at the end of the 10 th year. Scenario 9 Scenario 9 was run to a nswer the following question: how significant is the impact of customer relationship management (CRM) programs on the number of customers after 10 years? Figure 4 40 represents one part of the P olicy and S trategy sub model, in which the variable Effect of CRM is considered as a coefficient that affects the quality of service provided by the firm. The variable models customer satisfaction. If customers receive an acceptable level of customer service, their perception of the quality of service provided by the organization improves. Moreover, customer relationship management interac tions with current and future customers are appropriately managed. if an unacceptable level of customer service is provided to customers. Therefore, the

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141 question would be ho w significant are the impacts of this coefficient on the quality and the expected number of customers? It is assumed that the performance of the organization in terms of the number of customers at the end of year ten is the comparison for answering this qu estion. Table 4 2 summarizes the results. Scenario 10 Scenario 10 was run to answer the following question: how significant is the impact of the Skill Level of Entrants on the number of customers after 10 years? Scenario 10 has been run by setting the minimum, median and maximum possible values for the Skill Level of Entrant These value are 12, 16, and 20 respectively. Figure 4 41 depicts the trend of the People Capability over a time frame of 10 years. Table 4 3 summarizes the obtained organizational P eople C apability and the Overall Sustainability Excellence Score at the end of year 10. In brief, the conclusion following the execution of scenario 10 is as follows: the organizational P eople C apab ility is sensitive to the skill level of entrants if training remains the same. The training program is important because organizations can improve the skill level of their personnel by providing more intensive or more effective training programs. However, with the same plan of training, P eople C apability is higher if organizations hire personnel with higher skill levels. The sensitivity of the People Capability and the Overall Sustainability Excellence Score to the Skill Level of Entrants parameter have b een plotted in Figures 4 47 through 4 50 for a short term horizon (at year 3) and a long term horizon (at year 10).

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142 Scenario 11 Scenario 11 was run to answer the following question: if hiring skillful personnel is restricted, how can training new hires pro vide a similar trend of growth in the P eople C apability of an organization? Scenario 11 has been run by defining the minimum, median, and maximum possible values for the Skill Level of Entrants and the Average Training Hours Provided to each Entrant The v alues of 12, 16, and 20 are the Skill Level of Entrants and 400, 240, and 80 hours are the values of Average Upfront Training Hours Provided to each Entrant Figure 4 42 depicts the trend of the P eople C apability over 10 years in the case of restricted hiring in which the Average Skill Level of Entrants have been set at 12, 16, and 20 respectively. The initial assumption in plotting the People Capability on Figure 4 42 is that the plan of training remains the same. However, if organizations adjust their plan of training with respect to the Skill Level of Entrants, the outcome will be different. As Figure 4 43 demonstrates, when h iring skillful personnel is restricted, organizations can maintain their organizational People Capability by hiring new recruits with low levels of skill while providing upfront and intensive training programs to better prepare new hires for the requiremen ts of their job positions. However, this strategy is one of the many strategies that organizations can employ. Other strategies can be designed by taking an appropriate combination of the skill level of entrants and the intensity of upfront or on the job training programs. Scenario 12 Scenario 12 was run to answer the following question: how sensitive is the organizational People Capability to the Hours of Training to Skill Level Conversion

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143 Factor used in the People sub model? It is assumed that the perfo rmance of the organization at the end of year 3 is the comparison base. In the base scenario, the Hours of Training to Skill Level Conversion Factor is set at 40, which means that each skill is worth 40 hours of training. In scenario 12, the sensitivity of the People Capability to this parameter is evaluated. The potential range of this parameter has been assumed to be from 30 to 50 with the mean of 40. For instance, if this parameter takes the value of 30, the overall skill level of the organization tends to be higher than the overall skill level of the organization when the parameter is set at 40. The reason is that providing the same duration of training is worth more in terms of skills. On the other hand, if this parameter takes any values more than 40, the overall skill level of the organization tends to be lower than the overall skill level of the organization when the parameter is set at 40. The reason is that providing the same duration of training is worth less in terms of skills. Figure 4 44 shows the trend of the P eople C apability over time for values of 30, 40, and 50. Table 4 4 summarizes the obtained organizational P eople C apability and the Overall Sustainability Excellence Score at the end of year 3. The sensitivity of the People Capability and the Overall Sustainability Excellence Score to the Hours of Training to Skill Level Conversion Factor parameter have been plotted in Figures 4 47 through 4 50 for a short term horizon (at year 3) and a long term horizon (at year 10). The conclusion drawn for scenario 12 is as follows: the organizational P eople C apability is sensitive to the parameter the Hours of Training to Skill Level Conversion Factor Organizations need to assess their training pr ograms to determine how those

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144 programs improve the skill level of their employees. Based on this evaluation, a proper value can then be assigned to this parameter. Scenario 13 Scenario 13 was run to answer the following question: how sensitive are the Pe ople Capability and the Overall Sustainability Excellence Score of an organization to the intensity of training programs in the organization? For scenario 14, the P eople C apability and the Overall Sustainability Excellence Scores are evaluated over time by changing the following parameters in the model: The Average Training Hours Provided to Personnel set at 6, 12, and 18 The Average Upfront Training Hours Provided to each Entrant is set at 120, 240, and 360 The Average Training Hours Provided to each Leade r set at 6, 12, and 18 The effects of the applying the above changes on the model have been shown in Figure 4 47 through 50 using tornado diagrams. Figure 4 47 and Figure 4 48 depict the results for a short term horizon (at year 3), whereas, Figure 4 49 and Figure 4 50 depict the result for a long term horizon (at year 10). Scenario 14 Scenario 14 was run to answer the following question: how does the model react to a situation where a const ruction organization is not carefully monitoring and improving its organizational capabilities. Certain changes were made to the base scenario to represent the circumstances under which such an organization operates. The combined effect of these changes ar e evaluated on the outcome of the model: An assumed scenario for a construction organization that is not carefully monitoring and improving its organizational capabilities can include the following

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145 attributes: the example organization does not provide trai ning for its personnel and leaders, personnel are hired with a lower average skill level (the Skill Level of Entrants is 12 instead of 16), leaders are hired with a lower average skill level (the Skill Level of New Leaders Hired is 20 instead of 30), the o rganization is reluctant to establish partnership relationships (the partnership Desired Build up Rate is 2% instead of 8%), the organization is interested in improving its policy making capability in a lesser degree (the policy Desired Build up Rate is 7% instead of 14%), and the customer relationship is not provided to the customer as they expect (the Effect of CRM is 1 instead of 1.2). Figure 4 45 depicts how the trend of the overall sustainability excellence s tarts with weak growth and then it declines due to the erosion of essential organizational capabilities such as the People Capability over time. Figure 4 46 depicts the declining trend of the People Capability as a result of the lack of training programs and declining skill levels of personnel. The outcome of running scenarios 5, 10, 12, and 13, in which the sensitivity of the model to the change of certain parameters are evaluated, have been plotted on Figure 4 47 through 50 using tornado diagrams. Summary of the Results Based on the outcomes of different tested scenarios, especially based on the outcomes presented in Figures 4 47 through 4 50, the importance of human resource development and training can be described as follows: The overall knowledge level of construction firms are mainly driven by the knowledge and skill level of professional personnel, leaders, and new hires. Upfront or on the job training programs have also significant roles in enhancing t he knowledge level of construction firms through empowerment of their personnel and leaders. Among these factors, the Skill Level of

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146 Entrants the Effect of CRM (customer relationship management), and the Average Training Hours Provided to Personnel are th e three most important factors with the most influence on the overall sustainability excellence of construction firms. Human resources are the most important asset in firms. Firms can correct many internally or externally imposed organizational deficiencie s by developing appropriate human resource development and training policies.

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147 Figure 4 1 The L eadership sub model

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148 Figure 4 2 The Policy and S trategy sub model

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149 Figure 4 3 The P eople sub model The workf orce management module

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150 Figure 4 4 The P eople sub model The knowledge management module

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151 Figure 4 5 The P artnership sub model

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152 Figure 4 6 The P rocess sub mode

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153 Figure 4 7 The relative importance values of each of the assumed organizational capabilities for each of the three sustainability aspects Figure 4 8 The relative importance values of sustainability aspects (B), and the relative importance values of different organizational capabilities for sustainability aspects (A)

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154 Figure 4 9 The individual sustainability excellence scores of capabilities (A), and the sustainability excellence score of each capability for each particular sustainability aspect (B) Figure 4 10 The normal distribution function that can be fitted to the Overall Sustainability Excellence Scores obtained by running the model 1000 times (scenario 1)

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155 Figure 4 11 The results o f the L eadership sub model in the base scenario (scenario 1) Figure 4 12 The results of the L eadership sub model in the base scenario (scenario 1)

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156 Figure 4 13 The results of the L eadership sub model in the base scenario (scenario 1) Figure 4 14 The results of the P olicy and S trategy sub model in the base scenario (scenario 1)

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157 Figure 4 15 The results of the P olicy and S trategy sub model in the base scenario (scenario 1) Figure 4 16 The results of the P olicy and S trategy sub model in the base sc enario (scenario 1)

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158 Figure 4 17 The results of the P eople sub model in the base scenario (scenario 1) Figure 4 18 The results of the P eople sub mode l in the base scenario (scenario 1)

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159 Figure 4 19 The results of the P eople sub model in the base scenario (scenario 1) Figure 4 20 The results of the P eople sub model in the base scenario (scenario 1)

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160 Figure 4 21 The results of the P eople sub model in the base scenario (scenario 1) Figure 4 22 The results of the P artnership sub model in the base scenario (scenario 1)

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161 Figure 4 23 The results of the P artnership sub model in the base scenario (scenario 1) Figure 4 24 The results of the P artnership sub model in the base scenario (scenario 1)

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162 Figure 4 25 The results of the P rocess sub model in the base scenario (scenario 1) Figure 4 26 The results of the P rocess sub model in the base scenario (scenario 1)

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163 Figure 4 27 The results of the P rocess sub model in the base scenario (scenario 1) Figure 4 28 The results of the P rocess sub model in the base scenario (scenario 1)

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164 Figure 4 29 The results of the P rocess sub model in the base scenario (scenario 1) Figure 4 30 The effect of changing the sustainability aspect weights on the Overall Sustainability Excellence Score (scenario 2) (Note: line 1 represents the base scenario and line 2 repre sents the overall sustainability excellence performance of the organization with the new set of weighting factors )

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165 Figure 4 31 The effect of changing the sustainability aspect weights on the Overall Sus tainability Excellence Score (scenario 3) (Note: line 1 represents the base scenario and line 2 represents the overall sustainability excellence performance of the organization with the new set of weighting factors ) Figure 4 32 A verage D uration of S tay from 60 to 36 month on the P eople capability (scenario 4) (Note: line 1 represents the base scen ario and line 2 represents the P eople capability of the organizati on A verage D uration of S tay )

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166 Figure 4 33 The remediating effect of hiring personnel with higher levels of skill and providing a more intense training program (scenario 4) (Note: line 1 represents the base scenario and line 2 represents the P eople capability of the organization described in scenario 4 after taking remedial actions ) Figure 4 34 The effec t of learning effectiveness on P eople capability (scenario 5) (Note: l ines 1, 2, and 3 represent the P eo ple capability over time when the L earning E ffectiveness is set at 95%, 91%, and 70% respectively )

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167 Figure 4 35 The normal distribution function that can be fitted to the Overall Sustainability Excellenc e Scores obtained by running the model 1000 times (scenario 6) Figure 4 36 The improving trend of the quality of training programs over time

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168 Figure 4 37 The effect of decreasing the Appropriateness of Training Program for Policy Making to 80% of the base scenario (scenario 7)

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169 Figure 4 38 The effect of enhancing L eadership capability on the alleviati on o f declining trends in P olicy C apability (scenario 7) (Note: lines 1 represents the Policy C apability in accordance with the base scenario and line 2 represents the change proposed in scenario 7 after enhancing the Leadership C apability ) Figure 4 39 The impact of excluding the P artnership C apability on the Overall Sustainability Excellence Score (scenario 8) (Note: line 1 represents the Overall Sustainability Excellence Score in accordance with the based scenario, and line 2 represent the situation in which partnership has been

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170 Figure 4 40 One part of the P olicy and S trategy sub model Figure 4 41 The trend of the People Capability by setting minimum, median, and maximum values for the Skill Level of Entrant (scenario 10) (Note: lines 1, 2, and 3 correspond to the cases where the S kill L evel of E ntrants are set at 12, 16, and 20 respectively.)

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171 Figure 4 42 The trend of the P eople C apability over 10 years in the case of restricted hiring if the plan of training remains the same (scenario 11) (Note : lines 1, 2, and 3 correspond to the cases where the Skill L evel of E ntrants has set at 12, 16, and 20 respectively.) Figure 4 43 The trend of the P eople C apability over a time frame of 10 years in ca se of restricted hiring if the training plan is adjusted (scenario 12) (Note: lines 1, 2, and 3 correspond to the cases where the skill level of entrants is set at 12, 16, and 20 respectively, while an appropriate combination of the skill level of entrants and the intensity of upfront or on the job training programs has been selected.)

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172 Figure 4 44 The trend of the P eople C apability over time with different T raining to S kill C onversion F actors (scenario 12) (Note: lines 1, 2, and 3 correspond to the cases where the T raining to S kill C onversion F actors are set at 30, 40, and 50 ) Figure 4 45 A weak trend of growth followed by a decline of the Overall Sustainability Excellence Score (scenario 13) (Note: line 1 represents the base scenario and line 2 represents the overall sustainability excellence performance of an organization that is not carefully monitoring and improving its organizational capabilities.)

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173 Figure 4 46 A declining trend of P eople C apability i n scenario 14 (Note: line 1 represents the base scenario and line 2 represents the People Capability of an organization that is not carefully monitoring and improving its organizational capabilities.) Figure 4 47 The impact of the listed parameters on the P eople C apability at the end of year 3

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174 Figure 4 48 The impact of the listed variables on the Overall Sustainability Excellence Score at the end of yea r 3 Figure 4 49 The impact of the listed parameters on the P eople C apability at the end of year 10

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175 Figure 4 50 The impact of the listed variables on t he Overall Sustainability Excellence Score at the end of year 10

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176 Table 4 1 The list of distribution functions that best represent the variations of weights based on the opinion of the interviewees (Note: EC EN, and SO stand for Economic, Environmental, and Social respectively. Furthermore, LEA, POL, PEO, PAR, and PRO stand for Leadership Policy and strategy, People, Partnership, and Process respectively ) EC EN SO LEA Logno POL PEO PAR PRO Table 4 2 The effect range of customer relationship man agement on the number of active customers and the Overall Sustainability Excellence Score Variable / Parameter Min Median Max The Effect of CRM on Quality 0.8 1.2 1.6 The Number of Active Customers at the end of 10 years 3 4 5 The Overall Sustainability Excellence Score 84% 90% 91 %

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177 Table 4 3 The effect of defining different values for the Skill Level of Entrants on the organizational P eople C apability and the Overall Sustainability Excellence Score Variable / Parameter Min Median Max The Skill Level of Ent rants 12 16 20 The organizational PEO Capability at the end of year 10 91% 93% 95% The Overall Sustainability Excellence Score at the end of year 10 89% 90% 90 % Table 4 4 The obtained organizational P eople C apability and the Overall Sustainability Excellence Score with different T raining to S kill C onversion F actors Variable / Parameter Min Median Max The Hours of Training to Skill Level Conversion Factor 30 40 50 The organizational PEO Capability at the end of year 3 77% 74% 71 % The Overall Sustainability Excellence Score at the end of year 3 65 % 64 % 63 %

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178 5. CHAPTER 5 CONCLUSION With this research, new lines of research are expected to be opened in the area of organizational sustainability excellenc e models, especially for construction organizations. The study contributes to both theory and practice by describing the definition and characteristics of capable organizations in undertaking sustainable construction practices. Summary of Contributions Th rough the use of corporate sustainability, organizational excellence models, and system dynamics, this study contributes to construction res earch in three principal ways. The first contribution of this research was the development of a framework to demonst rate how organizational excellence can be defined in sustainable construction. It was shown how a modified EFQM model of excellence can be used as a starting point for the modeling of organizational capabilities required for the implementation of sustainab le development principles. As discussed in Chapter 4, the EFQM model of excellence can also be considered as an appropriate model for organizational sustainability excellence if a slightly different version of definitions are used for its basic concepts. T he modifications applied to the definitions for basic concepts allows the model to keep its overall structure but make it more suited to the requirements of sustainable development. The second main contribution of this research was dynamic modeling of org anizational capabilities that contribute to the sustainability performance of construction organizations. The system dynamics model consists of a structure, and a set of components, variables, relations, and feedback loops that adequately model

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179 important f actors as well as their interdependencies that shape the sustainability performance of construction organizations. The system modeling and dynamics techniques along with scenario analysis enable organizational capability assessment. The model can be used t o investigate mechanisms through which construction organizations can build capability in terms of sustainable development. The final contribution of this research, is that this model can be used as a decision support tool to investigate the dynamics of or ganizational sustainability excellence. In other words it can be used as an aid in evaluating organizational capabilities and their potential impacts on sustainability performance of construction organizations. Limitations As the initial value of the param eters used in the model and some equations representing the relationship between variables in the model are highly case dependent and as they vary from organization to organization, the model was run for an example organization. The proposal for this resea rch had defined implementing this model in a real world case as an out of scope area of work for this research. As explained in the introduction section of this dissertation, in preference to precise estimation of all input values used in the model by impl ementing the model in a real world case, attention was given to the model structure and architecture. Another limitation of the model presented in this dissertation is the exclusion of some organizational resources such as financial resources and equipmen t. These resources are necessary for organizations to successfully implement their capability building plans but it was decided that taking into account these organizational resources

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180 in the model would add unnecessary complications to the model and detrac t from the main objectives of this research. Concluding Remarks On the basis of a literature review as well as a series of semi structured research interviews, the main organizational capabilities required for construction organizations to implement sustai nable construction practices was identifi ed. These capabilities include L eadership, P olicy and S trategy, P eople, P artnership, and P rocess Each of these capabilities was considered a focus area for which a more focused literature review was conducted. On t he basis of a series of semi structured research interviews, more confidence in the appropriateness of system dynamics as the modeling and decision support tool for the purpose of this research was gained owing to the fact that none of the interviewees rej ected the outcomes generated by system dynamics model. The interviewees made comments on the model but they approved the structure and behavior of the model in principal. The interviewees declared that the outcome of the system dynamics models, representin g the dynamics of organizational capabilities, did not contradict the knowledge about the structure of real organizations. For each of the focus areas, one system dynamics sub model, consisting of all system components, inter relationships, initial conditi ons, and equations, was developed. After a detailed model design and development process, models were run and sensitivity analysis was performed. Based on these steps, the following can be drawn as concluding points: Based on the outcome of the model, esp ecially the outcomes presented in scenarios 5, 7, 10, 11, 12, and 13, it can be concluded that human resource

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181 development and training is the most important enabling factor that enhances sustainability capabilities of construction organizations. Through ru nning these scenarios, it was shown that both workforce management and knowledge management are key components of the People C apability and they play crucial roles in the viability and sustainability performance of construction firms. This conclusion was d rawn, first, based on the high degree of dependency of organizational capabilities on these two components. The substantial sensitivity of the Overall Sustainability Excellence Score to the parameters and variables defined in the P eople model is another re ason for the significance of the People C apability and its role in the viability and sustainability performance of construction firms. Therefore, human resource development and training affect all capability areas of construction organizations without whic h no capability building programs can be planned and implemented effectively. For developing scenario 14, a recent publication, Level Headed: Inside the Walls of One of the Greatest Turnaround Stories of the 21st Century about a business turnaround story o f a construction firm, was used. Chapter 2 of this reference characterizes the factors that contributed to the failure of a construction firm. By developing scenario 14 in accordance with the characteristics of the organization described in chapter 2 of th is reference, it was seen that the system dynamics model reacts in a similar way to what has been described, resulting in the erosion of organizational capabilities in the long term. Although three steps of verification, preliminary validation based on the literature and the knowledge of the modeler, and the final validation based on the inputs from the interviewees were used to build confidence into the system dynamics model, the outcomes of scenario 14 added more confidence

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182 in the model; therefore, the re sults of this research can help construction organizations identify effective policies and capability building programs that improve their organizational capabilities in sustainable practices. Suggestions for Future Research The current dissertation will provide future researchers with new insights into organizational sustainability policy verification through the use of system dynamics. It is suggested that this model be used in real world cases in which construction firm are interested in improving their organizational capabilities to enhance sustainability performance outcomes. The model can be used in case studies as a decision support tool to verify if an appropriate set of capabilities are being planned for and developed by the organization, and if th ey then result in favorable sustainability outcomes. Another research undertaking can be to simulate the sustainability performance of construction firms in each of the main sustainable construction areas of focus. For that purpose, the researcher needs to identify all system components, inter relationships, initial conditions, and equations that shape the sustainability performance of construction firms. Focus areas can include environmental aspects such as energy use, material use, water use, emissions re leased, waste generated, or social responsibility criteria such as health, safety, and ethical operations. Another research undertaking can focus on preparing a complete and comprehensive list of best practices in sustainable construction within each of th e three aspects of sustainability, economic, environmental, and social. That list can potentially be used to assess the compliance of construction firms with the requirements of sustainable construction. The assessment can take into account whether or not a construction firm implements the designated best practices.

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183 Many steps remain to be taken toward creating organizational decision models that integrate all three aspects of sustainable development into decision making. Sustainable development will remai n out of grasp unless all decisions including organizational policy decisions are made based on a systematic understanding of all possible economic, environmental, and social outcomes.

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184 APPENDIX A THE GRI CONSTRUCTION AND REAL ESTATE SECTOR SUPPLEMENT CORPO RATE SUSTAINABILITY INDICATORS Table A 1 The GRI construction a nd real estate sector supplement corporate sustainability indicators Aspects and indicators 1. Economic Aspects Economic Performance EC1. Direct economic value ge nerated and distributed, including revenues, operating costs, employee compensation, donations and other community investments, retained earnings, and payments to capital providers and governments. EC2. Financial implications and other risks and opportuni activities due to climate change. EC4. Significant financial assistance received from government. Market Presence EC5. Range of ratios of standard entry l evel wage compared to local minimum wage at significant locations of operation. EC6. Policies, practices and proportion of spending on locally based suppliers at significant locations of operation. EC7. Procedures for local hiring and proportion of senio r management hired from the local community at significant locations of operation. Indirect Economic Impacts EC8. Development and impact of infrastructure investments and services provided primarily for public benefit through commercial, in kind, or pro bono engagement. EC9. Understanding and describing significant indirect economic impacts, including the extent of impacts. 2. Environmental Aspects Materials EN1. Materials used by weight or volume. EN2. Percentage of materials used that are recycled input materials. Energy EN3. Direct energy consumption by primary energy source. EN4. Indirect Energy Consumption by primary energy source. EN5. Energy saved due to conservation and efficiency improvements. EN6. Initiatives to provide energy efficient or renewable energy based products and services, and reductions in energy requirements as a result of these initiatives. EN7. Initiatives to reduce indirect energy consumption and reductions achieved. CRE1. Building Energy intensity. Water EN8. Total water withdrawal by source. EN9. Water sources significantly affected by withdrawal of water. EN10. Percentage and total volume of water recycled and reused. CRE2. Building water intensity.

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185 Table A 1. Continued. Aspects and indicators Biodiversity EN11. Location and size of land owned, leased, managed in, or adjacent to, protected areas and areas of high biodiversity value outside protected areas. EN12. Description of significant impacts of activities, products, and services on biodiversity in pr otected areas and areas of high biodiversity value outside protected areas. EN13. Habitats protected or restored. EN14. Strategies, current actions, and future plans for managing impacts on biodiversity. EN15. Number of IUCN Red List species and nationa l conservation list species with habitats in areas affected by operations, by level of extinction risk. Emissions, Effluents, and Waste EN16. Total direct and indirect greenhouse gas emissions by weight. EN17. Other relevant indirect greenhouse gas emi ssions by weight. EN18. Initiatives to reduce greenhouse gas emissions and reductions achieved. EN19. Emissions of ozone depleting substances by weight. EN20. NOx, SOx, and other significant air emissions by type and weight. EN21. Total water discharge by quality and destination. EN22. Total weight of waste by type and disposal method. EN23. Total number and volume of significant spills. EN24. Weight of transported, imported, exported, or treated waste deemed hazardous under the terms of the Basel Co nvention Annex I, II, III, and VIII, and percentage of transported waste shipped internationally. EN25. Identity, size, protected status, and biodiversity value of water bodies and related habitats significantly affected by the reporting organization's di scharges of water and runoff. CRE3. Greenhouse gas intensity from building energy. CRE4. Greenhouse gas intensity from construction activity. Land Contamination and Remediation CRE5. Land and other assets remediated and in need of remediation for the e xisting or intended land use according to applicable legal designations. Products and Services EN26. Initiatives to mitigate environmental impacts of products and services, and extent of impact mitigation. EN27. Percentage of products sold and their pac kaging materials that are reclaimed by category. Compliance EN28. Monetary value of significant fines and total number of nonmonetary sanctions for noncompliance with environmental laws and regulations.

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186 Table A 1. Continued. Aspects and indicators Tra nsport EN29. Significant environmental impacts of transporting products and other goods and materials used for the organization's operations, and transporting members of the workforce. Overall EN30. Total environmental protection expenditures and invest ments by type. 3. Social Aspects Labor Practices & Decent Work Employment LA1. Total workforce by employment type, employment contract, and region. LA2. Total number and rate of employee turnover by age group, gender, and region. LA3. Benefits provid ed to full time employees that are not provided to temporary or part time employees, by major operations. Labor/Management Relations LA4. Percentage of employees covered by collective bargaining agreements. LA5. Minimum notice period(s) regarding signi ficant operational changes, including whether it is specified in collective agreements. Occupational Health and Safety LA6. Percentage of total workforce represented in formal joint management worker health and safety committees that help monitor and adv ise on occupational health and safety programs. LA7. Rates of injury, occupational diseases, lost days, and absenteeism, and total number of work related fatalities by region. LA8. Education training, counseling, prevention, and risk control programs in place to assist workforce members, their families, or community members regarding serious diseases. LA9. Health and safety topics covered in formal agreements with trade unions. Training and Education LA10. Average hours of training per year per employe e by employee category. LA11. Programs for skills management and lifelong learning that support the continued employability of employees and assist them in managing career endings. LA12. Percentage of employees receiving regular performance and career de velopment reviews. Diversity and Equal Opportunity LA13. Composition of governance bodies and breakdown of employees per category according to gender, age group, minority group membership, and other indicators of diversity. LA14. Ratio of basic salary o f men to women by employee category. Human Rights Investment and Procurement Practices

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187 Table A 1. Continued. Aspects and indicators HR1. Percentage and total number of significant investment agreements and contracts that include clauses incorporatin g human rights concerns, or that have undergone human rights screening. HR2. Percentage of significant suppliers, contractors, and other business partners that have undergone human rights screening, and actions taken. HR3. Total hours of employee training on policies and procedures concerning aspects of human rights that are relevant to operations, including the percentage of employee trained. Non discrimination HR4. Total number of incidents of discrimination and actions taken. Freedom of Association a nd Collective Bargaining HR5. Operations and significant suppliers identified in which the right to exercise freedom of association and collective bargaining may be violated or at significant risk, and actions taken to support these rights. Child Labor HR6. Operations and significant suppliers identified as having significant risk for incidents of child labor, and measures taken to contribute to the effective abolition of child labor. Forced and Compulsory Labor HR7. Operations and significant supplier s identified as having significant risk for incidents of forced or compulsory labor, and measures to contribute to the elimination of all forms of forced or compulsory labor. Security Practices HR8. Percentage of security personnel trained in the organiz ation's policies or procedures concerning aspects of human rights that are relevant to operations. Indigenous Rights HR9. Total number of incidents of violations involving rights of indigenous people and actions taken. Assessment HR10. Percentage and t otal number of operations in weak governance zones that have been subject to human rights reviews and/or impact assessments. Remediation HR11. Number of grievances related to human rights filed, addressed and resolved through formal grievance mechanisms. Society Local Community SO1. Percentage of operations with implemented local community engagement, impact assessments, and development programs. SO9. Operations with significant potential and actual negative impacts on local communities.

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188 Table A 1. Continued Aspects and indicators SO10. Prevention and mitigation measures implemented in operations with significant potential and actual negative impacts on local communities. CRE6. Number of persons voluntarily and involuntarily displaced and/or reset tled by development, broken down by project. Corruption SO2. Percentage and total number of business units analyzed for risks related to corruption. SO3. Percentage of employees trained in organization's anticorruption policies and procedures. SO4. Actio ns taken in response to incidents of corruption. Public Policy SO5. Public policy positions and participation in public policy development and lobbying. SO6. Total value of financial and in kind contributions to political parties, politicians, and relat ed institutions by country. Anti Competitive Behavior SO7. Total number of legal actions for anti competitive behavior, antitrust, and monopoly practices and their outcomes. Compliance SO8. Monetary value of significant fines and total number of nonmon etary sanctions for noncompliance with laws and regulations. Product Responsibility Customer Health and Safety PR1. Life cycle stages in which health and safety impacts of products and services are assessed for improvement, and percentage of significant products and services categories subject to such procedures. PR2. Total number of incidents of non compliance with regulations and voluntary codes concerning health and safety impacts of products and services, by type of outcomes. Product and Service La beling PR3. Type of product and service information required by procedures, and percentage of significant products and services subject to such information requirements. PR4. Total number of incidents of non compliance with regulations and voluntary code s concerning product and service information and labeling, by type of outcomes. CRE7. Type and number of green building certification, rating and labeling schemes used. PR5. Practices related to customer satisfaction, including results of surveys measuri ng customer satisfaction. Marketing Communications PR6. Programs for adherence to laws, standards, and voluntary codes related to marketing communications, including advertising, promotion, and sponsorship.

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189 Table A 1. Continued. Aspects and indicator s PR7. Total number of incidents of non compliance with regulations and voluntary codes concerning marketing communications, including advertising, promotion, and sponsorship, by type of outcomes. Customer Privacy PR8. Total number of substantiated complai nts regarding breaches of customer privacy and losses of customer data. Compliance PR9. Monetary value of significant fines for non compliance with laws and regulations concerning the provision and use of products and services. Design and Operation CRE 8. Initiatives to ensure efficient design, operation and retrofitting of buildings.

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190 APPENDIX B REQUIREMENTS OF ORGANIZATIONAL SUSTAINABILITY EXCELLENCE IN ACCORDANCE WITH THE IMPROVED EFQM MODEL OF EXCELLENCE Table B 1 Require ments of organizational sustainability excellence in accordance with the improved EFQM model of excellence (Note: Changes made to the original concept definitions are in bold characters.) Capability Sub Criteria Leadership Leaders develop the mission, v ision and values in line with the principles of sustainability, and adopt the highest standards of ethical behavior. They desire to go beyond regulatory compliance. Leaders are personally involved in ensuring the organization's management system is devel oped in line with the principles of sustainability, ethically and efficiently implemented and continuously improved. Leaders are involved with customers, partners and representatives of society, and actively participate in society, with transparency. L eaders motivate, support, recognize the organization's employees, communicate and unite around sustainability. Policy and Strategy Policy and strategy are based on the present and future needs and expectations of stakeholders both internal and external to the company. Policy and strategy are based on information from performance measurement (including sustainability performance measures), research, learning and creativity related activities. Policy and strategy are developed, reviewed and updated in line with the principles of sustainability. Policy and strategy are deployed through a framework of key processes. Policy and strategy are communicated and implemented. People People resources are planned, managed and improved. People are engaged a nd developed. People's knowledge and competencies are identified, developed and sustained in line with organizational activities. Sustainability related knowledge is generated, shared and accumulated in the organization. People are involved and empower ed at all levels of the organization. People within the organization have an effective dialogue. People are rewarded, recognized and cared for. Partnership and Resources Internal and external partnerships are managed. Organization collaborates activ ely with various business partners working on innovative products and technologies. Finances are managed. Organization collaborates actively with all stakeholders in order to reconcile environmental, social and economic priorities. Buildings, equipment and materials are managed efficiently with the consideration of their lifecycle impact. Technology is managed with the consideration of its lifecycle impact. Information and knowledge are managed and sustainability issues are recorded and reported.

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191 Table B 1. Continued. Capability Sub Criteria Process Processes are systematically designed and managed, and they promote the compliance with the requirements of sustainable development. Processes are improved as needed, using innovation in order to fully satisfy and generate increasing value for all stakeholders. Products and services are designed and developed based on customer needs and expectations and they create value for all stakeholders. Products and services are produced, delivered and serviced not only for boosting profit but also to address sustainability needs by going beyond regulatory compliance. Customer relationships are managed and enhanced. Customers are advised on the responsible use of products and services.

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192 APPENDIX C EQU ATIONS USED IN THE LEADERSHP SUB MODEL LEADERSHIP_KNOWLEDGE(T) = LEADERSHIP_KNOWLEDGE(T DT) + (LEADERSHIP_SKILLS_GAINED LEADERSHIP_SKILLS_LOST) DT INIT LEADERSHIP_KNOWLEDGE = 300 INFLOWS: LEADERSHIP_SKILLS_GAINED = (PEO.LEADER_HIRES*SKILL_LEVEL_OF__N EW_LEADERS_HIRED)+(PEO.PROMO TING_PERSONNEL*PEO.AVG_SKILL_LEVEL_OF_PERSONNEL*RELATIVE_SKILL_ VALUE_OF_PERSONNEL_PROMOTED)+((PEO.LEADERS*AVG_TRAINING_HRS_P ROVIDED_TO_EACH_LEADER)/HOURS_OF_TRAINING_TO_SKILL_LEVEL_CONVE RSION_FACTOR) OUTFLOWS: LEADERSHIP_SKILLS_ LOST = (PEO.LEADERS_RETIRING+PEO.LEADERS_DEPARTING)*AVG_SKILL_LEVEL_OF _LEADERS LEA_CAPABILITY(T) = LEA_CAPABILITY(T DT) + (CAPABILITY_BUILD_UP CAPABILITY_DECAY) DT INIT LEA_CAPABILITY = 40 INFLOWS: CAPABILITY_BUILD_UP = LEA_CAPABILITY*ACTUAL_MONTHLY_ BUILD_UP_RATE OUTFLOWS: CAPABILITY_DECAY = LEA_CAPABILITY*CAPABILITY_DECAY_RATE ACTUAL_MONTHLY_BUILD_UP_RATE = IF(FEASIBLE_MONTHLY_BUILD_UP_RATE>DESIRED_MONTHLY_BUILD_UP_RAT E) THEN (DESIRED_MONTHLY_BUILD_UP_RATE*GROWTH_RATE_ADJUSTMENT_FACTO R) ELSE (FEASIBL E_MONTHLY_BUILD_UP_RATE*GROWTH_RATE_ADJUSTMENT_FACTO R) AVG_SKILL_LEVEL_OF_LEADERS = LEADERSHIP_KNOWLEDGE/PEO.LEADERS AVG_TRAINING_HRS_PROVIDED_TO_EACH_LEADER = 12

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193 CAPABILITY_RATIO_ACHIEVED = LEA_CAPABILITY/TARGET_CAPABILITY DELAY_IN_THE_EFFECT_OF_CLARITY_O F_MVV_ON_EFFICIENCY = 2 DELAY_IN_THE_EFFECT_OF_LEADERSHIP_ON_CLARITY_OF_MVV = 4 DELAY_IN_THE_EFFECT_OF_LEADERSHIP_ON_COMMUNICATION = 4 DELAY_IN_THE_EFFECT_OF_LEADERSHIP_ON_MOTIVATION = 4 DELAY_IN_THE_EFFECT_OF_LEADERSHIP_ON_POLICY1 = 3 DELAY_IN_THE_EFFECT_ OF_LEADERSHIP_ON_POLICY2 = 5 HOURS_OF_TRAINING_TO_SKILL_LEVEL_CONVERSION_FACTOR = 50 RELATIVE_SKILL_VALUE_OF_PERSONNEL_PROMOTED = 2 SKILL_LEVEL_OF__NEW_LEADERS_HIRED = 30 TARGET_CAPABILITY = 100 CAPABILITY_DECAY_RATE = GRAPH(ABS(PEO.SHORTAGE_OR_SURPLUS_OF_ LEADERS)) (0.00, 0.00025), (1.00, 0.0025), (2.00, 0.004), (3.00, 0.0085), (4.00, 0.0135), (5.00, 0.0195), (6.00, 0.027), (7.00, 0.0355), (8.00, 0.0515), (9.00, 0.071), (10.0, 0.1) CLARITY_OF_MISSION_VISION_VALUES = GRAPH(DELAY3(LEA_CAPABILITY,DELAY_IN_THE_ EFFECT_OF_LEADERSHIP_O N_CLARITY_OF_MVV)) (0.00, 0.215), (10.0, 0.24), (20.0, 0.27), (30.0, 0.31), (40.0, 0.37), (50.0, 0.51), (60.0, 0.665), (70.0, 0.765), (80.0, 0.82), (90.0, 0.845), (100, 0.85) DESIRED_MONTHLY_BUILD_UP_RATE = GRAPH(TIME) (1.00, 0.03), ( 12.9, 0.03), (24.8, 0.03), (36.7, 0.03), (48.6, 0.03), (60.5, 0.03), (72.4, 0.03), (84.3, 0.035), (96.2, 0.04), (108, 0.045), (120, 0.05) EFFECT_OF_CLARITY_OF_MVV_ON_EFFICIENCY = GRAPH(DELAY3(CLARITY_OF_MISSION_VISION_VALUES,DELAY_IN_THE_EFFEC T_OF_CLARITY_ OF_MVV_ON_EFFICIENCY)) (0.00, 0.59), (0.1, 0.68), (0.2, 0.76), (0.3, 0.84), (0.4, 0.86), (0.5, 0.85), (0.6, 0.85), (0.7, 0.86), (0.8, 0.91), (0.9, 0.98), (1, 1.13) FEASIBLE_MONTHLY_BUILD_UP_RATE = GRAPH(AVG_SKILL_LEVEL_OF_LEADERS*PEO.KNOWLEDGE_IMPACT_FACTO R)

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194 (0.00, 0.0021), (20.0, 0.0135), (40.0, 0.0273), (60.0, 0.0318), (80.0, 0.036), (100, 0.0435), (120, 0.0477), (140, 0.0507), (160, 0.0531), (180, 0.0555), (200, 0.0594) GROWTH_RATE_ADJUSTMENT_FACTOR = GRAPH(CAPABILITY_RATIO_ACHIEVED) (0.00, 1.00), (0.2, 0.94), (0.4, 0.815), (0.6, 0.655), (0.8, 0.36), (1.00, 0.00) MOTIVATION_IMPACT_FACTOR = GRAPH(PERSONNEL_MOTIVATION_LEVEL) (0.00, 0.015), (0.1, 0.055), (0.2, 0.165), (0.3, 0.255), (0.4, 0.37), (0.5, 0.665), (0.6, 0.775), (0.7, 0.885), (0.8, 0.935), (0.9, 0. 955), (1, 0.985) PERSONNEL_MOTIVATION_LEVEL = GRAPH(DELAY3(LEA_CAPABILITY,DELAY_IN_THE_EFFECT_OF_LEADERSHIP_O N_MOTIVATION)) (0.00, 0.61), (10.0, 0.6), (20.0, 0.61), (30.0, 0.63), (40.0, 0.685), (50.0, 0.77), (60.0, 0.84), (70.0, 0.87), (80.0, 0.89), (90.0, 0.935), (100, 0.945) RATIO_OF_POLICIES_COMPLYING_WITH__NEEDS_OF_INSIDE_CUSTOMERS = GRAPH(DELAY3(LEA_CAPABILITY,DELAY_IN_THE_EFFECT_OF_LEADERSHIP_O N_POLICY1)) (0.00, 0.51), (10.0, 0.53), (20.0, 0.565), (30.0, 0.595), (40.0, 0.685), (50.0, 0.755), (60.0, 0. 83), (70.0, 0.895), (80.0, 0.935), (90.0, 0.965), (100, 1.00) RATIO_OF_POLICIES_COMPLYING_WITH__NEEDS_OF_OUTSIDE_CUSTOMERS = GRAPH(DELAY3(LEA_CAPABILITY,DELAY_IN_THE_EFFECT_OF_LEADERSHIP_O N_POLICY2)) (0.00, 0.58), (10.0, 0.585), (20.0, 0.605), (30.0, 0.655 ), (40.0, 0.73), (50.0, 0.81), (60.0, 0.88), (70.0, 0.9), (80.0, 0.915), (90.0, 0.945), (100, 0.96) RATIO_OF_PROPERLY_COMMUNICATED_POLICIES = GRAPH(DELAY3(LEA_CAPABILITY,DELAY_IN_THE_EFFECT_OF_LEADERSHIP_O N_COMMUNICATION)) (0.00, 0.48), (10.0, 0.5), (20.0, 0.53), (30.0, 0.575), (40.0, 0.635), (50.0, 0.765), (60.0, 0.83), (70.0, 0.875), (80.0, 0.895), (90.0, 0.905), (100, 0.905)

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195 APPENDIX D EQUATIONS USED IN THE POLICY AND STRATEGY SUB MODEL POL_CAPABILITY(T) = POL_CAPABILITY(T DT) + (CAPABILITY_BUILD_UP CAPABILITY_DECAY) DT INIT POL_CAPABILITY = 40 INFLOWS: CAPABILITY_BUILD_UP = POL_CAPABILITY*ACTUAL_BUILD_UP_RATE OUTFLOWS: CAPABILITY_DECAY = POL_CAPABILITY*LEA.CAPABILITY_DECAY_RATE ACTUAL_BUILD_UP_RATE = IF(FEASIBLE_MONTHLY_BUILD_UP_RATE>DESIRED_MONTH LY_BUILD_UP_RAT E) THEN (DESIRED_MONTHLY_BUILD_UP_RATE*GROWTH_RATE_ADJUSTMENT_FACTO R) ELSE (FEASIBLE_MONTHLY_BUILD_UP_RATE*GROWTH_RATE_ADJUSTMENT_FACTO R) CAPABILITY_RATIO_ACHIEVED = POL_CAPABILITY/TARGET_CAPABILITY DELAY_IN_REPUTATION_CHANGE = 3 DESIRED_MON THLY_BUILD_UP_RATE = .14 EFFECT_OF_CRM = 1.2 PERSONNEL_SENSE_OF_SUPPORT = LEA.RATIO_OF_POLICIES_COMPLYING_WITH__NEEDS_OF_INSIDE_CUSTOMER S*LEA.RATIO_OF_PROPERLY_COMMUNICATED_POLICIES POLICY_EFFECTIVENESS = (POL_CAPABILITY/100)*PERSONNEL_SENSE_OF_SUPPORT*LEA .RATIO_OF_POL ICIES_COMPLYING_WITH__NEEDS_OF_OUTSIDE_CUSTOMERS QUALITY = IF(EFFECT_OF_CRM*LEA.MOTIVATION_IMPACT_FACTOR*POLICY_EFFECTIVEN ESS*EFFECT_OF_WORKLOAD_ON_QUALITY)>1 THEN 1 ELSE (EFFECT_OF_CRM*LEA.MOTIVATION_IMPACT_FACTOR*POLICY_EFFECTIVENES S*EFFECT_ OF_WORKLOAD_ON_QUALITY) QUALITY_DEFICIENCY_FACTOR = IF(QUALITY<1) THEN (1 QUALITY) ELSE 0

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196 REPUTATION = DELAY3(QUALITY,DELAY_IN_REPUTATION_CHANGE) TARGET_CAPABILITY = 100 APPROPRIATENESS_OF_TRAINING_PROGRAM_FOR_POLICY_MAKING = GRAPH(TIME) (1.00, 0.91), (12. 9, 0.925), (24.8, 0.945), (36.7, 0.95), (48.6, 0.965), (60.5, 0.965), (72.4, 0.965), (84.3, 0.97), (96.2, 0.97), (108, 0.98), (120, 0.99) EFFECT_OF_WORKLOAD_ON_QUALITY = GRAPH(PRO.WORKLOAD_OF_EACH_INDIVIDUAL) (0.00, 0.4), (0.105, 0.45), (0.211, 0.5), (0.31 6, 0.52), (0.421, 0.54), (0.526, 0.6), (0.632, 0.63), (0.737, 0.73), (0.842, 0.95), (0.947, 1.00), (1.05, 1.00), (1.16, 0.95), (1.26, 0.9), (1.37, 0.85), (1.47, 0.81), (1.58, 0.77), (1.68, 0.73), (1.79, 0.6), (1.89, 0.5), (2.00, 0.4) FEASIBLE_MONTHLY_BUILD _UP_RATE = GRAPH(LEA.AVG_SKILL_LEVEL_OF_LEADERS*PEO.KNOWLEDGE_IMPACT_FAC TOR*APPROPRIATENESS_OF_TRAINING_PROGRAM_FOR_POLICY_MAKING) (0.00, 0.0021), (10.0, 0.0364), (20.0, 0.063), (30.0, 0.0812), (40.0, 0.0938), (50.0, 0.102), (60.0, 0.116), (70.0, 0.126), ( 80.0, 0.132), (90.0, 0.134), (100, 0.138) GROWTH_RATE_ADJUSTMENT_FACTOR = GRAPH(CAPABILITY_RATIO_ACHIEVED) (0.00, 1.00), (0.2, 0.8), (0.4, 0.6), (0.6, 0.4), (0.8, 0.2), (1.00, 0.00) REPUTATION_IMPACT_FACTOR = GRAPH(REPUTATION) (0.00, 0.015), (0.1, 0.0675), (0.2, 0.15), (0.3, 0.3), (0.4, 0.405), (0.5, 0.578), (0.6, 0.727), (0.7, 0.922), (0.8, 1.06), (0.9, 1.22), (1, 1.50)

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197 APPENDIX E EQUATIONS USED IN THE PEOPLE SUB MODEL LEADERS(T) = LEADERS(T DT) + (PROMOTING_PERSONNEL + LEADER_HIRES LEADERS_DEPARTING LEADERS_RETIRING) DT INIT LEADERS = 5 INFLOWS: PROMOTING_PERSONNEL = PROFESSIONAL_PERSONNEL*PROMOTION_RATE LEADER_HIRES = RATE_OF_HIRING_LEADERS OUTFLOWS: LEADERS_DEPARTING = LEADERS/LEADERS_AVG_DURATION_OF_STAY LEADERS_RETIRING = LEADERS/AVG_LEGTH_OF_ SERVICE_BEFORE_RETIREMENT ORGANIZATIONAL_KNOWLEDGE(T) = ORGANIZATIONAL_KNOWLEDGE(T DT) + (KNOWLEDGE_GAINED_IN_TERMS_OF_SKILLS KNOWLEDGE_LOST) DT INIT ORGANIZATIONAL_KNOWLEDGE = 800 INFLOWS: KNOWLEDGE_GAINED_IN_TERMS_OF_SKILLS = (SKILLS_GAINED_FROM_NE W_HIRES+(SKILLS_GAINED_FROM_TRAINING*LEAR NING_EFFECTIVENESS)+SKILLS_GAINED_FROM_PARTNERS) {SKILLS} OUTFLOWS: KNOWLEDGE_LOST = PERSONNEL_DEPARTING*AVG_SKILL_LEVEL_OF_PERSONNEL*FORGETTING_E FFECT PROFESSIONAL_PERSONNEL(T) = PROFESSIONAL_PERSONNEL(T DT) + (H IRES PERSONNEL_DEPARTING PROMOTING_PERSONNEL) DT INIT PROFESSIONAL_PERSONNEL = 50 INFLOWS: HIRES = CORRECTION_VALUE+AVG_DEPARTURE_RATE

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198 OUTFLOWS: PERSONNEL_DEPARTING = (PROFESSIONAL_PERSONNEL/PERSONNEL_AVG_DURATION_OF_STAY)*PRO.E FFECT_OF_WORKLOAD_ON_D EPARTURE*JOB_SATISFACTION_EFFECT PROMOTING_PERSONNEL = PROFESSIONAL_PERSONNEL*PROMOTION_RATE UNATTACHED: SKILLS_GAINED_FROM_NEW_HIRES = HIRES*(SKILL_LEVEL_OF_ENTRANTS+(AVG_UPFRONT_TRAINING_HRS_PROVID ED_TO_EACH_ENTRANT/HOURS_OF_TRAINING_TO_SKILL_LEVEL_CONVE RSIO N_FACTOR)) UNATTACHED: SKILLS_GAINED_FROM_PARTNERS = (PAR.ACTIVE_PARTNERS)*AVG_NO_OF_SKILLS_GAINED_FROM_EACH_PARTNE R UNATTACHED: SKILLS_GAINED_FROM_TRAINING = PROFESSIONAL_PERSONNEL*(AVG_TRAINING_HRS_PROVIDED_TO_PERSONN EL/HOURS_OF_TRAINING_TO_SKILL_LEV EL_CONVERSION_FACTOR) ACTUAL_NO_OF_PERSONNEL_PER_LEADER = PROFESSIONAL_PERSONNEL/LEADERS AVG_DEPARTURE_RATE = SMTH1(PERSONNEL_DEPARTING,TIME_TO_AVG_DEPARTURES) AVG_LEGTH_OF_SERVICE_BEFORE_RETIREMENT = 20*12 AVG_NO_OF_SKILLS_GAINED_FROM_EACH_PARTNER = 20 AV G_SKILL_LEVEL_OF_PERSONNEL = ORGANIZATIONAL_KNOWLEDGE/PROFESSIONAL_PERSONNEL AVG_TRAINING_HRS_PROVIDED_TO_PERSONNEL = 12 AVG_UPFRONT_TRAINING_HRS_PROVIDED_TO_EACH_ENTRANT = 240 CORRECTION_TIME = 3 CORRECTION_VALUE = (PRO.PROFESSIONAL_PERSONNEL_REQUIRED PRO FESSIONAL_PERSONNEL)/CORRECTION_TIME

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199 DELAY_IN_THE_EFFECT_OF_KNOWLDGE = 4 DESIRED_NO_OF_EMPLOYEES_PER_LEADER = 15 FORGETTING_EFFECT = 1.03 HOURS_OF_TRAINING_TO_SKILL_LEVEL_CONVERSION_FACTOR = 40 JOB_SATISFACTION_EFFECT = .8 LEADERSHIP_TURNOVER = (LEADERS_DE PARTING+LEADERS_RETIRING)/(LEADER_HIRES+PROMOTING_P ERSONNEL) LEADERS_AVG_DURATION_OF_STAY = 12*10 LEARNING_EFFECTIVENESS = .91 PERSONNEL_AVG_DURATION_OF_STAY = 12*5 PERSONNEL_TURNOVER = (PERSONNEL_DEPARTING+PROMOTING_PERSONNEL)/HIRES PROMOTION_RATE = .002 RATE_OF_HIRING_LEADERS = .05 SHORTAGE_OR_SURPLUS_OF_LEADERS = DESIRED_NO_OF_EMPLOYEES_PER_LEADER ACTUAL_NO_OF_PERSONNEL_PER_LEADER SKILL_LEVEL_OF_ENTRANTS = 16 TIME_TO_AVG_DEPARTURES = 3 KNOWLEDGE_IMPACT_FACTOR = GRAPH(DELAY3(PEO_CAPABILITY*PRO.ORG_EFFICIE NCY,DELAY_IN_THE_EFFE CT_OF_KNOWLDGE)) (0.00, 0.024), (5.26, 0.174), (10.5, 0.372), (15.8, 0.462), (21.1, 0.492), (26.3, 0.528), (31.6, 0.528), (36.8, 0.546), (42.1, 0.594), (47.4, 0.666), (52.6, 0.774), (57.9, 0.864), (63.2, 0.912), (68.4, 0.984), (73.7, 1 .04), (78.9, 1.08), (84.2, 1.12), (89.5, 1.14), (94.7, 1.19), (100, 1.20) PEO_CAPABILITY = GRAPH(AVG_SKILL_LEVEL_OF_PERSONNEL+LEA.AVG_SKILL_LEVEL_OF_LEAD ERS)

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200 APPENDIX F EQUATIONS USED IN THE PARTNERSHIP SUB MODEL ACTIVE_PARTNERS(T) = ACTIVE_PARTNERS(T DT) + (GAINING_ACTIVE_PARTNERS LOSING_ACTIVE_PARTNERS) DT INIT ACTIVE_PARTNERS = 2 INFLOWS: GAINING_ACTIVE_PARTNERS = POTENTIAL_PARTNERS*PARTNERS_GAIN_RATE OUTFLOWS: LOSING_ACTIVE_PARTNERS = ACTIVE_PARTNERS*RATE_OF_LOSING_ACTIVE_PARTNERS PAR_CAPABILITY (T) = PAR_CAPABILITY(T DT) + (CAPABILITY_BUILD_UP CAPABILITY_DECAY) DT INIT PAR_CAPABILITY = 10 INFLOWS: CAPABILITY_BUILD_UP = PAR_CAPABILITY*ACTUAL_BUILD_UP_RATE OUTFLOWS: CAPABILITY_DECAY = PAR_CAPABILITY*CAPABILITY_DECAY_RATE POTENTIAL_PARTNERS(T) = POTENTIAL_PARTNERS(T DT) + (BEING_IDENTIFIED__POTENTIAL_PARTNERS GAINING_ACTIVE_PARTNERS LOSING_POTENTIAL_PARTNERS) DT INIT POTENTIAL_PARTNERS = 5 INFLOWS: BEING_IDENTIFIED__POTENTIAL_PARTNERS = PARTNERS_IDENTIFICATION_RATE OUTFLOWS: GAINING_ACT IVE_PARTNERS = POTENTIAL_PARTNERS*PARTNERS_GAIN_RATE

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201 LOSING_POTENTIAL_PARTNERS = POTENTIAL_PARTNERS*LOSING_RATE_OF_POTENTIAL_PARTNERS ACTUAL_BUILD_UP_RATE = IF(FEASIBLE_MONTHLY_BUILD_UP_RATE>DESIRED_MONTHLY_BUILD_UP_RAT E) THEN (DESIRED_MONTHLY_BUILD_UP_RAT E*GROWTH_RATE_ADJUSTMENT_FACTO R) ELSE (FEASIBLE_MONTHLY_BUILD_UP_RATE*GROWTH_RATE_ADJUSTMENT_FACTO R) CAPABILITY_IMPACT_DELAY = 2 CAPABILITY_RATIO_ACHIEVED = PAR_CAPABILITY/TARGET_CAPABILITY DELAY_IN_GAINING_PARTNERS = 3 DELAY_IN_THE_ABILITY_TO_RETAIN_PARTN ERS = 4 DESIRED_MONTHLY_BUILD_UP_RATE = 0.08 IDENTIFICATION_TO_GAIN_ADJ_MULTIPLIER = .4 IDENTIFICATION_TO_RETAIN_ADJ_MULTIPLIER = .25 LOSING_RATE_OF_POTENTIAL_PARTNERS = RATE_OF_LOSING_ACTIVE_PARTNERS*IDENTIFICATION_TO_GAIN_ADJ_MULTI PLIER PARTNERS_GAIN_RAT E = DELAY3(INITIAL_PARTNERS_GAIN_RATE,DELAY_IN_GAINING_PARTNERS) PARTNERS_IDENTIFICATION_RATE = DELAY3(INITIAL_PARTNERS_IDENTIFICATION_RATE,CAPABILITY_IMPACT_DELA Y) PARTNERS_RETAIN_RATE = DELAY3(INITIAL_PARTNERS_RETAIN_RATE,DELAY_IN_THE_ABILITY_TO_RETAI N_P ARTNERS) PARTNERS_TURNOVER = LOSING_ACTIVE_PARTNERS/GAINING_ACTIVE_PARTNERS TARGET_CAPABILITY = 100 APPROPRIATENESS_OF_TRAINING_PROGRAM_FOR_PARTNERSHIP = GRAPH(TIME)

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202 (1.00, 0.91), (12.9, 0.925), (24.8, 0.945), (36.7, 0.95), (48.6, 0.965), (60.5, 0.965), (7 2.4, 0.965), (84.3, 0.97), (96.2, 0.97), (108, 0.98), (120, 0.99) CAPABILITY_DECAY_RATE = GRAPH(PARTNERS_TURNOVER) (0.00, 0.0035), (0.2, 0.0055), (0.4, 0.008), (0.6, 0.0095), (0.8, 0.013), (1.00, 0.0145), (1.20, 0.0205), (1.40, 0.033), (1.60, 0.046), (1.80 0.0575), (2.00, 0.0775) FEASIBLE_MONTHLY_BUILD_UP_RATE = GRAPH(PEO.KNOWLEDGE_IMPACT_FACTOR*APPROPRIATENESS_OF_TRAININ G_PROGRAM_FOR_PARTNERSHIP) (0.00, 0.0025), (0.12, 0.0425), (0.24, 0.066), (0.36, 0.0775), (0.48, 0.0865), (0.6, 0.0905), (0.72, 0.094), ( 0.84, 0.096), (0.96, 0.096), (1.08, 0.098), (1.20, 0.1) GROWTH_RATE_ADJUSTMENT_FACTOR = GRAPH(CAPABILITY_RATIO_ACHIEVED) (0.00, 1.00), (0.2, 0.94), (0.4, 0.815), (0.6, 0.655), (0.8, 0.36), (1.00, 0.00) INITIAL_PARTNERS_GAIN_RATE = GRAPH(IDENTIFICATION_TO_G AIN_ADJ_MULTIPLIER*POL.REPUTATION_IMPAC T_FACTOR*INITIAL_PARTNERS_IDENTIFICATION_RATE) (0.00, 0.135), (10.0, 0.18), (20.0, 0.22), (30.0, 0.255), (40.0, 0.29), (50.0, 0.34), (60.0, 0.415), (70.0, 0.525), (80.0, 0.61), (90.0, 0.74), (100, 1.00) INITIAL_PARTNE RS_IDENTIFICATION_RATE = GRAPH(PAR_CAPABILITY) (0.00, 0.05), (10.0, 0.12), (20.0, 0.17), (30.0, 0.2), (40.0, 0.31), (50.0, 0.38), (60.0, 0.46), (70.0, 0.61), (80.0, 0.88), (90.0, 1.31), (100, 1.97) INITIAL_PARTNERS_RETAIN_RATE = GRAPH(IDENTIFICATION_TO_RET AIN_ADJ_MULTIPLIER*POL.REPUTATION_IMP ACT_FACTOR*INITIAL_PARTNERS_IDENTIFICATION_RATE) (0.00, 0.015), (10.0, 0.09), (20.0, 0.14), (30.0, 0.185), (40.0, 0.22), (50.0, 0.295), (60.0, 0.37), (70.0, 0.445), (80.0, 0.565), (90.0, 0.69), (100, 1.00) RATE_OF_LOSIN G_ACTIVE_PARTNERS = GRAPH(PARTNERS_RETAIN_RATE) (0.00, 0.1), (0.1, 0.0795), (0.2, 0.0625), (0.3, 0.0505), (0.4, 0.0425), (0.5, 0.0355), (0.6, 0.0285), (0.7, 0.023), (0.8, 0.0165), (0.9, 0.011), (1, 0.0045)

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203 APPENDIX G EQUATIONS USED IN THE PROCESS SUB MODEL ACTIVE_CUSTOMERS(T) = ACTIVE_CUSTOMERS(T DT) + (ATTRACTING_CUSTOMERS LOSING_CUSTOMERS) DT INIT ACTIVE_CUSTOMERS = 3 INFLOWS: ATTRACTING_CUSTOMERS = DELAY3(INITIAL_RATE_OF_ATTRACTING_CUSTOMERS,DELAY_IN_ATTRACTING _CUSTOMERS) OUTFLOWS: LOSING_CUSTOMER S = DELAY3(INITIAL_RATE_OF_ATTRACTING_CUSTOMERS*LOSING_CUSTOMERS_A DJUSTMENT_FACTOR,DELAY_IN_LOSING_CUSTOMERS) PROJECTS_IN_HAND(T) = PROJECTS_IN_HAND(T DT) + (PROJECT_ACQUISITION_RATE PROJECTS_BEING_CLOSED) DT INIT PROJECTS_IN_HAND = 2 COOK TIME = VA RIES CAPACITY = INF FILL TIME = INF INFLOWS: PROJECT_ACQUISITION_RATE = ((ACTIVE_CUSTOMERS/AVG_TIME_GAP_BETWEEN_CONTRACTS_FROM_EACH_ CUSTOMER)) OUTFLOWS: PROJECTS_BEING_CLOSED = CONTENTS OF OVEN AFTER COOK TIME, ZERO OTHERWISE COOK TIME = NORMAL(15,2) PR O_CAPABILITY(T) = PRO_CAPABILITY(T DT) + (CAPABILITY_BUILD_UP CAPABILITY_DECAY) DT

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204 INIT PRO_CAPABILITY = 20 INFLOWS: CAPABILITY_BUILD_UP = PRO_CAPABILITY*ACTUAL_BUILD_UP_RATE OUTFLOWS: CAPABILITY_DECAY = PRO_CAPABILITY*CAPABILITY_DECAY_RATE ACTUAL_BU ILD_UP_RATE = IF(FEASIBLE_MONTHLY_BUILD_UP_RATE>DESIRED_MONTHLY_BUILD_UP_RAT E) THEN (DESIRED_MONTHLY_BUILD_UP_RATE*GROWTH_RATE_ADJUSTMENT_FACTO R) ELSE (FEASIBLE_MONTHLY_BUILD_UP_RATE*GROWTH_RATE_ADJUSTMENT_FACTO R) ACTUAL_MONTHLY_WORKING_HRS_PER_INDIVIDUAL = INITIAL_TARGET_MONTHLY_WORKING_HRS_PER_INDIVIDUAL PEO.AVG_TRAINING_HRS_PROVIDED_TO_PERSONNEL AVG_TIME_GAP_BETWEEN_CONTRACTS_FROM_EACH_CUSTOMER = NORMAL(24,3) CAPABILITY_RATIO_ACHIEVED = PRO_CAPABILITY/TARGET_CAPABILITY DELAY_CLARITY_ON_EFFICIENCY = 4 DEL AY_IN_ATTRACTING_CUSTOMERS = 6 DELAY_IN_LOSING_CUSTOMERS = 24 DESIRED_MONTHLY_BUILD_UP_RATE = 0.04 EFFECT_OF_PROCESS_CAPABILITY_ON_EFFICIENCY_DELAY = 3 INITIAL_ORG_EFFICIENCY = (EFFECT_OF_LOST_PRODUCTIVITY_DUE_TO_UNAVAILABILITY*EFFECT_OF_PR OCESS_CAPABILITY _ON_EFFICIENCY*DELAY3(LEA.EFFECT_OF_CLARITY_OF_ MVV_ON_EFFICIENCY,DELAY_CLARITY_ON_EFFICIENCY)) INITIAL_TARGET_MONTHLY_WORKING_HRS_PER_INDIVIDUAL = 160 LOSING_CUSTOMERS_ADJUSTMENT_FACTOR = .9 MONTHLY_WORKLOAD__PER_PROJECT_IN_HRS = 3000

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205 ORG_EFFICIENCY = IF(I NITIAL_ORG_EFFICIENCY)>1 THEN 1 ELSE INITIAL_ORG_EFFICIENCY ORG_MONTHLY_WORKLOAD_IN_HRS = PROJECTS_IN_HAND*MONTHLY_WORKLOAD__PER_PROJECT_IN_HRS PROFESSIONAL_PERSONNEL_REQUIRED = SMTH1(ORG_MONTHLY_WORKLOAD_IN_HRS/(ACTUAL_MONTHLY_WORKING_H RS_PER_INDIVIDUAL*O RG_EFFICIENCY),WORKFORCE_DECISION_DELAY) TARGET_CAPABILITY = 100 WORKFORCE_DECISION_DELAY = 2 WORKLOAD_OF_EACH_INDIVIDUAL = (ORG_MONTHLY_WORKLOAD_IN_HRS/ORG_EFFICIENCY)/(PEO.PROFESSIONAL _PERSONNEL*ACTUAL_MONTHLY_WORKING_HRS_PER_INDIVIDUAL) APPROPRIATENESS_ OF_TRAINING_PROGRAM_FOR_PROCESS = GRAPH(TIME) (1.00, 0.825), (12.9, 0.86), (24.8, 0.885), (36.7, 0.92), (48.6, 0.92), (60.5, 0.94), (72.4, 0.95), (84.3, 0.955), (96.2, 0.98), (108, 0.995), (120, 1.00) CAPABILITY_DECAY_RATE = GRAPH(POL.QUALITY_DEFICIENCY_FA CTOR) (0.00, 0.00), (0.1, 0.003), (0.2, 0.006), (0.3, 0.011), (0.4, 0.0145), (0.5, 0.019), (0.6, 0.0255), (0.7, 0.033), (0.8, 0.0485), (0.9, 0.0645), (1, 0.0965) EFFECT_OF_LOST_PRODUCTIVITY_DUE_TO_UNAVAILABILITY = GRAPH((INITIAL_TARGET_MONTHLY_WORKING_HRS_ PER_INDIVIDUAL PEO.AVG_TRAINING_HRS_PROVIDED_TO_PERSONNEL)/INITIAL_TARGET_MON THLY_WORKING_HRS_PER_INDIVIDUAL) (0.00, 0.01), (0.1, 0.15), (0.2, 0.26), (0.3, 0.34), (0.4, 0.46), (0.5, 0.53), (0.6, 0.66), (0.7, 0.75), (0.8, 0.84), (0.9, 0.93), (1, 1.00) EFFEC T_OF_PROCESS_CAPABILITY_ON_EFFICIENCY = GRAPH(DELAY3(PRO_CAPABILITY,EFFECT_OF_PROCESS_CAPABILITY_ON_EFF ICIENCY_DELAY)) (0.00, 0.787), (10.0, 0.862), (20.0, 0.915), (30.0, 0.953), (40.0, 0.967), (50.0, 0.982), (60.0, 0.982), (70.0, 0.982), (80.0, 1.01), (90 .0, 1.06), (100, 1.14) EFFECT_OF_WORKLOAD_ON_DEPARTURE = GRAPH(WORKLOAD_OF_EACH_INDIVIDUAL) (0.00, 2.00), (0.2, 1.76), (0.4, 1.34), (0.6, 1.03), (0.8, 1.00), (1.00, 1.00), (1.20, 1.07), (1.40, 1.13), (1.60, 1.41), (1.80, 1.85), (2.00, 2.00)

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206 FEASIBLE_MONTHL Y_BUILD_UP_RATE = GRAPH(PEO.KNOWLEDGE_IMPACT_FACTOR*APPROPRIATENESS_OF_TRAININ G_PROGRAM_FOR_PROCESS) (0.00, 0.00), (0.12, 0.021), (0.24, 0.0435), (0.36, 0.063), (0.48, 0.073), (0.6, 0.0805), (0.72, 0.085), (0.84, 0.089), (0.96, 0.0955), (1.08, 0.099), (1.2 0, 0.1) GROWTH_RATE_ADJUSTMENT_FACTOR = GRAPH(CAPABILITY_RATIO_ACHIEVED) (0.00, 1.00), (0.2, 0.94), (0.4, 0.815), (0.6, 0.655), (0.8, 0.36), (1.00, 0.00) INITIAL_RATE_OF_ATTRACTING_CUSTOMERS = GRAPH(POL.REPUTATION) (0.00, 0.00), (0.1, 0.002), (0.2, 0.0065) (0.3, 0.009), (0.4, 0.013), (0.5, 0.0185), (0.6, 0.024), (0.7, 0.0365), (0.8, 0.0515), (0.9, 0.074), (1, 0.0995)

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207 APPENDIX H EQUATIONS USED FOR INTEGRATING THE RESULTS OF SUB MODELS DETERMINISTIC = 1 EC#LEA = (DETERMINISTIC)*(30/100)+(1 DETERMINISTIC)*(L OGNORMAL(3.3453,0.33453)/100) EC#PAR = (DETERMINISTIC)*(13.933/100)+(1 DETERMINISTIC)*(NORMAL(13.933,9.0591)/100) EC#PEO = (DETERMINISTIC)*(21.333/100)+(1 DETERMINISTIC)*(NORMAL(21.333,8.3381)/100) EC#POL = (DETERMINISTIC)*(17.533/100)+(1 DETERMINISTIC)*(N ORMAL(17.533,5.5917)/100) EC#PRO = (DETERMINISTIC)*(17.2/100)+(1 DETERMINISTIC)*(NORMAL(17.2,3.1214)/100) EN#LEA = (DETERMINISTIC)*(24.666/100)+(1 DETERMINISTIC)*(LOGNORMAL(3.132,0.37142)/100) EN#PAR = (DETERMINISTIC)*(14.46666667/100)+(1 DETERMINISTIC)*(N ORMAL(14.467,8.6592)/100) EN#PEO = (DETERMINISTIC)*(22.66666667/100)+(1 DETERMINISTIC)*(LOGNORMAL(3.0354,0.40594)/100) EN#POL = (DETERMINISTIC)*(21.66666667/100)+(1 DETERMINISTIC)*(LOGNORMAL(3.0096,0.36956)/100) EN#PRO = (DETERMINISTIC)*(16.53333333/100)+( 1 DETERMINISTIC)*(NORMAL(16.533,7.4342)/100) MAX_ASSUMED_LEA_CAP = 100 MAX_ASSUMED_PAR_CAP = 100 MAX_ASSUMED_PEO_CAP = 100 MAX_ASSUMED_POL_CAP = 100 MAX_ASSUMED_PRO_CAP = 100 OVERALL_SUSTAINABILITY_EXCELLENCE_SCORE = (SCORE_EC*WEIGHT_EC)+(SCORE_EN*WEIGHT_E N)+(SCORE_SO*WEIGHT_SO)

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208 SCORE_EC = SCORE_EC#LEA+SCORE_EC#PAR+SCORE_EC#PEO+SCORE_EC#POL+SCORE_ EC#PRO SCORE_EC#LEA = (LEA.LEA_CAPABILITY/MAX_ASSUMED_LEA_CAP)*EC#LEA SCORE_EC#PAR = (PAR.PAR_CAPABILITY/MAX_ASSUMED_PAR_CAP)*EC#PAR SCORE_EC#PEO = (PEO.PEO_CAPABI LITY/MAX_ASSUMED_PEO_CAP)*EC#PEO SCORE_EC#POL = (POL.POL_CAPABILITY/MAX_ASSUMED_POL_CAP)*EC#POL SCORE_EC#PRO = (PRO.PRO_CAPABILITY/MAX_ASSUMED_PRO_CAP)*EC#PRO SCORE_EN = SCORE_EN#LEA+SCORE_EN#PAR+SCORE_EN#PEO+SCORE_EN#POL+SCORE_ EN#PRO SCORE_EN#LEA = (LEA.L EA_CAPABILITY/MAX_ASSUMED_LEA_CAP)*EN#LEA SCORE_EN#PAR = (PAR.PAR_CAPABILITY/MAX_ASSUMED_PAR_CAP)*EN#PAR SCORE_EN#PEO = (PEO.PEO_CAPABILITY/MAX_ASSUMED_PEO_CAP)*EN#PEO SCORE_EN#POL = (POL.POL_CAPABILITY/MAX_ASSUMED_POL_CAP)*EN#POL SCORE_EN#PRO = (PRO.PRO_C APABILITY/MAX_ASSUMED_PRO_CAP)*EN#PRO SCORE_SO = SCORE_SO#LEA+SCORE_SO#PAR+SCORE_SO#PEO+SCORE_SO#POL+SCORE_ SO#PRO SCORE_SO#LEA = (LEA.LEA_CAPABILITY/MAX_ASSUMED_LEA_CAP)*SO#LEA SCORE_SO#PAR = (PAR.PAR_CAPABILITY/MAX_ASSUMED_PAR_CAP)*SO#PAR SCORE_SO#PEO = ( PEO.PEO_CAPABILITY/MAX_ASSUMED_PEO_CAP)*SO#PEO SCORE_SO#POL = (POL.POL_CAPABILITY/MAX_ASSUMED_POL_CAP)*SO#POL SCORE_SO#PRO = (PRO.PRO_CAPABILITY/MAX_ASSUMED_PRO_CAP)*SO#PRO SO#LEA = (DETERMINISTIC)*(28.66666667/100)+(1 DETERMINISTIC)*(NORMAL(28.667,6.6726) /100) SO#PAR = (DETERMINISTIC)*(13.66666667/100)+(1 DETERMINISTIC)*(LOGNORMAL(2.4639,0.55995)/100)

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209 SO#PEO = (DETERMINISTIC)*(30.53333333/100)+(1 DETERMINISTIC)*(LOGNORMAL(3.3512,0.35992)/100) SO#POL = (DETERMINISTIC)*(16.13333333/100)+(1 DETERMINISTIC)*(NO RMAL(16.133,7.3666)/100) SO#PRO = (DETERMINISTIC)*(11/100)+(1 DETERMINISTIC)*(NORMAL(11,6.3246)/100) WEIGHT_EC = 1/3 WEIGHT_EN = 1/3 WEIGHT_SO = 1/3

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210 APPENDIX I THE RELATIVE IMPORTANCE VALUES OF ORGANIZAT I ONAL CAPABILITIES FOR VARIOUS SUSTAINABILITY ASPECTS OBTAINED FROM INTERVIEWEES THROUGH SURVEY Table I 1 The relative importance values of organizational capabilities for various sustainability aspects obtained from interviewees through survey ID EC_ LEA EC_ POL EC_ PEO EC_ PAR EC_ PR O EN_ LEA EN_ POL EN_ PEO EN_ PAR EN_ PRO SO_ LEA SO_ POL SO_ PEO SO_ PAR SO_ PRO 1 20 15 20 30 15 20 10 20 20 30 30 5 30 30 5 2 30 25 25 10 10 30 25 20 15 10 30 20 20 15 15 3 30 20 20 10 20 25 20 20 25 10 30 25 15 10 20 4 20 15 20 30 15 20 20 20 20 20 20 20 20 20 20 5 50 10 10 10 20 10 40 10 10 30 40 10 30 10 10 6 50 15 20 0 15 30 30 30 0 10 30 30 20 10 10 7 35 20 10 20 15 25 20 15 30 10 25 15 40 10 10 8 40 10 20 10 20 20 20 20 20 20 25 25 25 25 0 9 35 20 25 0 20 60 15 10 5 10 40 10 40 5 5 10 25 20 25 10 2 0 25 20 25 10 20 25 20 25 10 20 11 25 15 35 10 15 25 35 25 0 15 35 15 30 10 10 12 15 10 40 20 15 15 10 55 15 5 15 10 65 5 5 13 30 20 10 20 20 20 20 30 10 20 30 15 35 5 15 14 25 18 20 19 18 25 20 20 17 18 25 17 23 20 15 15 20 30 20 10 20 20 20 20 20 20 30 5 40 20 5 Note: EC_LEA: The relative importance of organizational leadership capability for economic performance EC_POL: The relative importance of organizational policy and strategy capability for economic performance EC_PEO: The relative importanc e of organizational people capability for economic performance EC_PAR: The relative importance of organizational partnership capability for economic performance EC_PRO: The relative importance of organizational process capability for economic performance E N_LEA: The relative importance of organizational leadership capability for environmental performance EN_POL: The relative importance of organizational policy and strategy capability for environmental performance EN_PEO: The relative importance of organizat ional people capability for environmental performance EN_PAR: The relative importance of organizational partnership capability for environmental performance EN_PRO: The relative importance of organizational process capability for environmental performance SO_LEA: The relative importance of organizational leadership capability for social performance SO_POL: The relative importance of organizational policy and strategy capability for social performance SO_PEO: The relative importance of organizational people capability for social performance SO_PAR: The relative importance of organizational partnership capability for social performance SO_PRO: The relative importance of organizational process capability for social performance

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211 APPENDIX J SUMMARY OF D E SCRIPTIVE STATISTICS OF THE RELATIVE IMPORTANCE VALUES OF ORGANIZAT I ONAL CAPABILITIES FOR VARIOUS SUSTAINABILITY ASPECTS Table J 1 Summary of descriptive statistics of the relative importance values of organizational capabilities for the economic aspect of sustainability EC_LEA EC_POL EC_PEO EC_PAR EC_PRO Mean 30.00 17.53 21.33 13.93 17.20 Standard Error 2.72 1.44 2.15 2.34 0.81 Median 30 18 20 10 18 Mode 20 20 20 10 20 Standard Deviation 10.52 5.59 8.34 9.06 3.12 Sample Variance 1 10.71 31.27 69.52 82.07 9.74 Kurtosis 0.06 0.42 0.92 0.26 0.00 Skewness 0.74 0.51 0.69 0.36 0.77 Range 35 20 30 30 10 Minimum 15 10 10 0 10 Maximum 50 30 40 30 20 Sum 450 263 320 209 258 Count 15 15 15 15 15 Confidence Level (95.0%) 5.83 3.10 4. 62 5.02 1.73 Note: The relative importance values were collected through survey in terms of percentage

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212 Table J 2 Summary of descriptive statistics of the relative importance values of organizational capabilities for the envi ronmental aspect of sustainability EN_LEA EN_POL EN_PEO EN_PAR EN_PRO Mean 24.67 21.67 22.67 14.47 16.53 Standard Error 2.86 2.11 2.75 2.24 1.92 Median 25 20 20 15 18 Mode 20 20 20 20 10 Standard Deviation 11.09 8.16 10.67 8.66 7.43 Sample Variance 123.10 66.67 113.81 74.98 55.27 Kurtosis 7.96 0.89 6.02 0.42 0.41 Skewness 2.40 0.85 2.03 0.20 0.43 Range 50 30 45 30 25 Minimum 10 10 10 0 5 Maximum 60 40 55 30 30 Sum 370 325 340 217 248 Count 15 15 15 15 15 Confidence Level(95.0%) 6.14 4.52 5 .91 4.80 4.12 Note: The relative importance values were collected through survey in terms of percentage

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213 Table J 3 Summary of descriptive statistics of the relative importance values of organizational capabilities for the so cial aspect of sustainability SO_LEA SO_POL SO_PEO SO_PAR SO_PRO Mean 28.67 16.13 30.53 13.67 11.00 Standard Error 1.72 1.90 3.21 1.98 1.63 Median 30 15 30 10 10 Mode 30 20 30 10 5 Standard Deviation 6.67 7.37 12.45 7.67 6.32 Sample Variance 44.52 5 4.27 154.98 58.81 40.00 Kurtosis 0.44 0.60 3.26 0.26 1.00 Skewness 0.06 0.16 1.52 0.79 0.06 Range 25 25 50 25 20 Minimum 15 5 15 5 0 Maximum 40 30 65 30 20 Sum 430 242 458 205 165 Count 15 15 15 15 15 Confidence Level (95.0%) 3.70 4.08 6.89 4.2 5 3.50 Note: The relative importance values were collected through survey in terms of percentage

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214 APPENDIX K RESEARCH INTERVIEW PLAN

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215 Research Interview Plan SEYYED AMIN TEROUHID UNIVERSITY OF FLORIDA RINKER SCHOOL OF BUILDING CONSTRUCTION

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216 INTRODUCTION Semi structured interviews are considered as one of the methods of gathering qualitative information. In a series of interviews, we are going to investigate organizational dynamics to identify which characteristics and enabling factors are essential in organizational excellence in terms of sustainability, and how these factors are operating and interacting. Organizational excellence in sustainability is an approach to organizational performance management that aims at the improvement of or ganizational capabilities in terms of sustainability. IDENTIFYING INTERVIEWEES Interviewees will be those experts who, because of their backgrounds, positions, responsibilities, or activities, have a good understanding of corporate sustainability as well as sustainable construction. These interviewees will not necessarily represent a construction firm, but they all should have a major interest in sustainable construction and corporate sustainability. Construction firms with experience in design or constru ction of a LEED platinum or gold certified building will be targeted for this potentially used. Here is a preliminary list of potential target firms: The Turner corporation Ho lder Construction Company Clark group DPR Construction JE Dunn Construction Company Evergreen Construction Brown & Cullen, Inc. Charles Perry Partners, Inc. (CPPI) TLC Engineering for Architecture Affiliated Engineers SE Inc. M.M. Parrish Construction Co. Moses & Associates, Inc. (providing mechanical and electrical services) Volkert & Associates, Inc (Engineering, planning and environmental consulting) Ponikvar and Associates Inc. (Architecture firm) Brasfield & Gorrie, LLC (providing general contracting design build, and construction management services) Ajax Building Corporation (providing construction quality services) E M C Engineers Inc. (providing construction project engineering services) NUMBER OF INTERVIEWS The following issues must be taken in to account in deciding on how many interviews to conduct:

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217 Range of viewpoints Time and resources: At least two days are estimated to be needed to prepare, conduct, transcribe and analyze semi structured interviews. The number of interviews scheduled takes into account available time and resources. It is also important to consider the fact that interviewees are not always available. As well, there is the question of the total length of the data collection phase. Data saturation. Under optimal conditions, dat a collection from interviewees should end once data saturation is achieved, i.e. when interviews do not provide any new or additional insights because the information gathered is repetitive. With above considerations in mind, the number of interviews is es timated to be around 1 0 PREPARING INTERVIEWS Some of the main steps are as follows: Review the interview plan Prepare a consent form specifying the rules of the interview and the confidentiality commitment Contact the interviewee: explain the goal of th e interview and obtain his/her permission, schedule an appointment and agree on where the interview will be held INTERVIEWING PROCESS The estimated duration of each interview is between 40 to 90 minutes. The interviewer introduces himself and reminds the interviewee of the topics that will be discussed during the interview It is important to tell the interviewee that he/she will be interviewed as an expert in the fields of sustainable construction and corporate sustainability. Explain the purpose of the re search. As the interview is to be recorded, the interviewee must be asked for his/her written or verbal consent and reminded that his/her statements will be kept confidential at all times. This is a good time to have the respondent sign the consent form. T he interview is conducted in accordance with the agenda

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218 The interviewer writes down the main themes that emerge as they ask questions and listen to the answers given by the interviewee. If the answers are inconsistent with the knowledge of the field, the interviewer can remind the interviewee of what the existing literature indicates. When interviewers feel that all topics have been discussed and that the time set aside for the interview is up, they can ask the interviewee if he/she has anything to add. So on after the interview, what the respondent said is summarized. DATA ANALYSIS The information gathered is classified and analyzed. The comments of experts on the models will be reviewed against the existing literature, and relevant comments that are not f ound to be inconsistent with the existing literature will be incorporated into the research. To integrate the experts' evaluation and calculate weighting factors, interview results will be analyzed using regression method. The overall excellence score will be determined by calculating the weighted sum of individual capability scores in each area of capability. HOUSEKEEPING Experts (interviewees) will be interviewed once. A second interview will be scheduled only if it turns out to be necessary. Interviews will be conversational in style. All interviews will be taped.

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219 APPENDIX L RESEARCH QUESTIONNAIRE (PAPER VERSION) A DYNAMIC DECISION SUPPORT SYSTEM FOR ORGANIZATIONAL SUSTAINABILITY EXCELLENCE OF CONSTRUCTION FIRMS INTRODUCTION This interview is part o f a research in the University of Florida about how construction organizations can achieve excellence in terms of corporate sustainability. In this research, we are investigating organizational dynamics to identify which characteristics and enabling factor s are essential in organizational excellence in terms of sustainability, and how these factors are operating and interacting. Organizational excellence in sustainability is an approach to organizational performance management that aims at the improvement o f organizational capabilities in terms of sustainability. You will be interviewed as an expert in the fields of sustainable construction and corporate sustainability. Thank you, Seyyed "Amin" Terouhid, PMP Ph.D. candidate, Construction Management Progra m M.E. Rinker Sr. School of Building Construction University of Florida 326 Rinker Hall, Gainesville, FL 32611 Voice: +1 (352) 327 8029 | Fax: +1 (805) 309 7534

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220 SECTION 1 REQUIREMENTS OF ORGANIZATIONAL EXCELLENCE Through a review of the literature, it was found that leadership, policy and strategy, people, partnership and resources, and processes are essential organizational capabilities in achieving excellence. To have a better idea of what exactly each of these capabilities entails, a list of evaluat ion criteria is provided in Table L 1 Table L 1 Essential requirements of organizational sustainability excellence Capability Sub Criteria Leadership Leaders develop the mission, vision and values in line with the principles of sustainability, and adopt the highest standards of ethical behavior. They desire to go beyond regulatory compliance. Leaders are personally involved in ensuring the organization's management system is developed in line with the principles of sustaina bility, ethically and efficiently implemented and continuously improved. Leaders are involved with customers, partners and representatives of society, and actively participate in society, with transparency. Leaders motivate, support, recognize the orga nization's employees, communicate and unite around sustainability. Policy and Strategy Policy and strategy are based on the present and future needs and expectations of stakeholders both internal and external to the company. Policy and strategy are ba sed on information from performance measurement (including sustainability performance measures), research, learning and creativity related activities. Policy and strategy are developed, reviewed and updated in line with the principles of sustainability. Policy and strategy are deployed through a framework of key processes. Policy and strategy are communicated and implemented. People People resources are planned, managed and improved. People are engaged and developed. People's knowledge and compe tencies are identified, developed and sustained in line with organizational activities. Sustainability related knowledge is generated, shared and accumulated in the organization. People are involved and empowered at all levels of the organization. Peop le within the organization have an effective dialogue. People are rewarded, recognized and cared for. Partnership and Resources Internal and external partnerships are managed. Organization collaborates actively with various business partners working o n innovative products and technologies. Finances are managed. Organization collaborates actively with all stakeholders in order to reconcile environmental, social and economic priorities. Buildings, equipment and materials are managed efficiently with the consideration of their lifecycle impact. Technology is managed with the consideration of its lifecycle impact. Information and knowledge are managed and sustainability issues are recorded and reported. Process Processes are systematically desig ned and managed, and they promote the compliance with the requirements of sustainable development. Processes are improved as needed, using innovation in order to fully satisfy and generate increasing value for all stakeholders. Products and services ar e designed and developed based on customer needs and expectations and they create value for all stakeholders. Products and services are produced, delivered and serviced not only for boosting profit but also to address sustainability needs by going beyond regulatory compliance. Customer relationships are managed and enhanced. Customers are advised on the responsible use of products and services.

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221 Table L 2 Question about essential organizational capabilities for sustainabili ty excellence Question Do you think there is any other essential capability that is required for organizational excellence that we have not discussed in Table 1? If yes, please explain.

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222 SECTION 2 MODELS Please answer the following questions (Table L 3 ) about the models presented in Figure L 1 through L 5. Table L 3 Questions about the models presented in Figure L 1 through L 5 Main Questions Additional Questions Clarifying Questions o What organizational capabilities, do you think, are essential for organizational excellence in terms of sustainable construction? o Why? o Can you expand a little on this? o Can you give me some examples? o By having a resource based view of firms, we have determined that Leadership, P olicy and Strategy, People, Partnership and Resources, and Processes are essential organizational capabilities for performance excellence in terms of sustainable construction. What do you think? o Do you think any of the listed capabilities are not essential ? o Each capability is considered as a criterion for excellence, for any of which a System Dynamics model has been developed. Do you think the model with its current structure and components adequately represent the role of each criterion? o Are the importan t concepts for addressing the problem modeled? o Is the model structure consistent with relevant descriptive knowledge of the system? o Does the model generate the various modes of behavior observed in the real system? Conclusion of interview o Do you want t o add anything

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223 Figure L 1 Dynamics of organizational leadership

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224 Figure L 2 Dynamics of organizational policies and strategies

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225 Figure L 3 Dynamics of people in organizations

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226 Figure L 4 Dynamics of organizational partnership and resources

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227 Figure L 5 Dynamics of organizational processes

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228 SECTION 3 QUESTIONNAIRE Sustainability performance indi cators for construction corporates can be classified under three pillars of sustainability, i.e., economic, environmental, and social aspects. Figures L 6 through L 8 connect sustainability aspects to organizational capabilities. By filling out the matrix structure of Figures L 6 through L 8 please specify in terms of percentage how important each capability is for each aspect of sustainability. Figure L 6 Relative importance of organizational capabilities for economi c performance

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229 Figure L 7 Relative importance of organizational capabilities for environmental performance Figure L 8 Relative importance of organizational capabilities for social perform ance

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230 University of Florida Institutional Review Board 02 Approval on the Paper Version of the Research Questionnaire

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233 APPENDIX M RESEARCH QUESTIONNAIRE (ONLINE VERSION)

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235 University of Florida Institutional Review Board 02 Approval on the On line Version of the Research Questionnaire

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257 APPENDIX O COMMENTS PROVIDED BY INTERVIEWEES ON THE MODEL Table O 1 General c omments pr ovided by interviewees on the model General comments Confirmatory Explanatory No consensus Beyond the scope Applied Addressed 1 Simplify diagrams and use SmartArts to convey your message more clearly Don't present system dynamics models to interv iewees. It is too complex for most of them. However, you can keep them in your PowerPoint. Explain to interviewees what exactly general EFQM model measures in organizations. Is it market share, performance or something else? Make it clear how you defin e sustainability in your work. Contractor Certificate to find more capabilities 2 How about organizational culture? Organizational culture include components beyond just mission, vision and value 3 Safety and health, and quality are two additional capabilities that need to be included as key organizational capabilities. Productivity is another capability without which organizations cannot achieve success. 4 Quality is another capability that needs to be included as a key organizational capability. For work acquisition, which is very challenging in economic downtu rn, quality and being able to offer a full range of services are crucial. In this respect, marketing capability is also important to show to clients that the firm is really able to perform the job as expected. Awareness, knowledge, and implementation cap abilities are also critical for organizational success. 5 Training is an important program that is definitely required for organizational success. Various materials as well as expertise are also needed for making the firm able to perform sustainable construction.

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258 Table O 1. Continued. General comments Confirmatory Explanatory No consensus Beyond the scope Applied Addressed 6 The model is comprehensive in terms of those capabilities that are essential for organizations in sustainable construction. 7 The decision to make or not to make a project green is, for the most part, a customer driven decision. We, in our organization, don't push necessarily for green {certification}. 8 Training should come to the forefront, and be one of the key organizational capabilities. The ac countability of leaders is essential for making the firm able to reach its targets. 9 Training and knowledge are the most essential capabilities. 10 The model covers all essential capabilities. 11 These categories of organizational capabilities are broad and cover essential capabilities required. 12 People, leadership, and strategy need to be integrated and stressed. More stress also needs to be placed on people. Long term customer relationship is essential for the long ter m success of organizations. 13 Leadership is an intangible capability that has an important role in improving the sustainability performance of the firm. 14 Your list of capabilities is comprehensive. The most important ones are leadership people, communication and having a strategy in place. 15 Every initiative needs to start from leadership. People and training are also very important factors in making a firm able to practice successfully in green construction projects. 1 6 The support of upper management is essential in any organizational undertaking. Implementation of sustainable construction needs budget, training, clear organizational mission, specific committees and group setups, and community involvement and partn ership

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259 Table O 2 C omments provided by interviewees on the L eadership model Leadership Confirmatory Explanatory No consensus Beyond the scope Applied Addressed 1 How about empowering employees and delegating responsibili ty? 2 How about leadership on jobsite? How about training of leaders in the organization? What if leaders don't possess listed capabilities? 3 Leadership mainly depends on personal characteristics. People skills are more i mportant than technical skills for leadership Succession plan for retiring leaders is important for firms to make sure we prepare others for leadership positions 4 Leadership's vision is very important for green construction. 5 Lea ders' personality is essential to get the message across. Leaders need to obtain the organization's buy in during the decision making process. 6 The model and its behavior make sense. 7 Training and knowledge do not necessarily mak e someone a leader. How about the intuition of leadership and personal skills? 8 Accountability of leaders is essential for making the firm able to reach its targets. Distant partners have different implications for the firm. The facilitat ing role of information technology on communication is important to note. Succession plan for leaders is important for the overall leadership capability of the firm. Longevity and exposure of leaders need to be included in the model someh ow. 9 Overall culture of the organization has also an effect on how well the leaders are able to obtain personnel's buy in Internal leaders' ability and reliability has a role on how well an organization performs. The model's outp ut makes sense.

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260 Table O 2 Continued. Leadership Confirmatory Explanatory No consensus Beyond the scope Applied Addressed 10 We have addressed sustainability in our missions and value in a subliminal way. We prefer team recognition to individual recognition. Succession plan for leaders creates a great deal of comfort for our clients and partners. Sustainability for us serves as a core value. Stability of leadership gives more confidence to our clients and p artners, and creates trust. 11 The model and its behavior make sense. 12 The more leaders manage to bring employees into processes, the more they perform. 13 Any initiative's success depends on leadership. Leaders' charisma and personal characteristics play an important role in their capabilities. Many organizations have different levels of leadership in their organizations (for instance, those leaders who implement policies, and those who are mostly involved in pro ject management) Lack of knowledge can be a result of leaving leaders. 14 "More policies" does not necessarily result in a better performance. The effectiveness of policies and having necessary policies are important. Motivation a nd clarity of missions and values are critical functions of leadership. 15 The most important role of leaders are motivating their personnel in their efforts. 16 Capability of leadership improves organizational efficiency in general.

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261 Table O 3 C omments provided by interviewees on the P olicy and S trategy model Policy and strategy Confirmatory Explanatory No consensus Beyond the scope Applied 1 Quality of service can be dependent not only on policy and strategy but also on other capabilities. 2 The model and its behavior make sense. 3 Firms need not only to embrace the change but to chase the change. Having policies in place is not enough. Organizations need to obtain the buy in from personnel, and need to communicate p olicies properly. Multiple personalities are different in this respect 4 The model and its behavior make sense. 5 The model and its behavior make sense. 6 In the case of fluctuating variables (in policy and strategy vs. qua lity), why two charts show a similar behavior initially but then start merging apart? Policies needs to be enforced otherwise the firm might even have some policies but no one is aware of them. 7 Knowledge and experience derive the quality of policies in organizations. 8 Excessive policies will limit innovation and flexibility of the firm Developing more efficient policies should be the target not the number of policies. 9 Employees' retention is another variable th at improves quality of service. I feel a disconnect between different elements of this section, and how they relate to leadership. Why the outcome shows less and less policies being implemented but more and more policies developed but not implemented?

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262 Table O 3. Continued Policy and strategy Confirmatory Explanatory No consensus Beyond the scope Applied Addressed 10 Sustainability in our firm is more subliminal so we allow our personnel to decide in a flexible manner. Policy shouldn't create bureaucracy. Having too many rules and regulations is not favorable. Policies are acceptable as long as they are needed to run the business. Both tactical (1 3 years) and strategic goals (3 5 years) need to be addresse d in organizations. Use the term "Increase number of practices" instead of "Increase number of policies". Accountability is an essential need in dealing with customers. 11 The model and its behavior make sense. 12 High num ber of policies is not favorable. Too many polices decreases efficiency. We need to eliminate unnecessary policies. Good standards and fewer policies are more favorable. "More policies" needs to be replaced with "efficient policies". 13 If personnel are overloaded, policy capability declines. On the other hand, policies need to be in place to ensure personnel are not overloaded. The increase in the number of policies does not necessarily correlate with the increase with higher moti vation level in personnel. Too many policies prevent personnel from being leaders and prevent having enough flexibility for them. If people leave the organization, policies do not go away, but knowledge of the policies will go away. 14 Pol icy capability is a function of not only training and knowledge, but also how well leaders understand the market and set right policies for the organization. 15 Strategy of the firm and its ability to understand the industry and trends of the marke t is essential. 16 Obtaining feedback on policies from the personnel can be important in making the firm able to improve those policies.

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263 Table O 4 C omments provided by interviewees on the People model People Con firmatory Explanatory No consensus Beyond the scope Applied Addressed 1 How about job satisfaction? It impacts sustainability performance because in some cases personnel are not satisfied with some values defined in terms of sustainability and that dissatisfaction impacts their performance. How does this model address long term employees? How about skill level of current (initial) employees? Cyclicality can also depend on whether company self performs. The level of personnel in most construction companies stays flat an d it does not fluctuate very much. 2 A company's culture can make a person to stay or leave the organization. Culture can include the loyalty of personnel. 3 Knowledge loss is not one to one, meaning; an organization cannot rapidl y regain the knowledge lost upon a departure, by hiring new personnel. It takes time. Experience is also important. Variables characterizing the behavior of management team and personnel (hands on people) are different. We, in our organiz ation, spend a significant amount of money just for chasing work, and the firm needs to hire talented people as an investment. 4 Right training is very important for making personnel enabled to perform well for sustainable construction. Wo rk pressure is a negative factor but this statement does not mean that having a challenging work cannot be favorable. In many situations it is favorable. Organizational knowledge is contained in people for the most part. 5 Losing knowledge is the most detrimental when experienced personnel leave the organization. Workload can be too high or too low. Both these cases can impact the departure rate of personnel.

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264 Table O 4. Continued Leadership Confirmatory Explanatory No co nsensus Beyond the scope Applied Addressed 6 How about if we hire someone with certain capabilities but we don't use the person efficiently? Why fluctuation continues for a number of periods? 7 Pay and benefits derive the departure of personnel for the most part. causes. Another mechanism for losing knowledge is when the firm doesn't adopt a new technology and is left behind from its competitors. 8 Tr aining needs to be provided in specific fields, and a general type of training is not enough. The model should recognize the significant difference between the knowledge level of entry level and senior level employees. Economic downturn h as moved many practitioners from the construction industry to other industries. Model should be able to test what will happen if hiring is restricted. Different employee levels (young versus seniors) have different efficiencies. Constant training and upfront training for entry level employees make a significant difference in terms of efficiency. Safe environment, and a good safety culture will improve the personnel's' efficiency. The role of the longevity of personnel in organizational knowledge and efficiency is important. 9 Local economy and market conditions need to be included as factors that impact hiring or departure rates. The initial peak and sudden drop don't make sense. Why a firm needs to hire t hat many personnel initially?

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265 Table O 4. Continued Leadership Confirmatory Explanatory No consensus Beyond the scope Applied Addressed 10 We, in our firm, prefer a non cyclical hiring practice because employees make profit for the firm only after 5 6 years of being with the firm. We prefer to satisfy the long term needs not necessarily immediate needs. People may leave but ultimately come back. We trace these people. Average length of stay in our firm is 10 years. The longe r one has been with the company, the more potential will exist for him to stay longer. We share organizational resources across geographical and product type divisions in the firm (cross pollination). This is also a knowledge gaining mechanism for us. Knowledge sharing happens in our firm partly by using a platform called Springboard to share innovative ideas. Mentorship can also help free exchange of information among individuals. 11 It is difficult to measure organizatio nal knowledge in a firm to see how successful the firm is in term of sustainable construction. 12 Personal accountability is important and we shouldn't look at people as a commodity. They need to feel they are part of the organization. Ave rage length of stay for personnel in our firm has been 10 years. Quality and knowledge correlate closely in our firm. 13 The higher the organizational knowledge, the higher the marketing capability of firms in procuring work, obtaining cus tomers, and business development. The average personal knowledge of personnel are not the same. It varies from one employee to the other. What is the reason for the immediate drop in organization's knowledge (model behavior)? 14 T he model makes sense. The organization needs to focus not only on retaining personnel, but also training and empowering them.

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266 Table O 4. Continued Leadership Confirmatory Explanatory No consensus Beyond the scope Applied Addressed 15 The mo del and its behavior make sense. 16 The essential questions to ask in this section are whether the organization is recruiting right people for the organization to provide continuous improvement? And if we are providing training to them.

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267 Tabl e O 5 C omments provided by interviewees on the Partnership model Partnership Confirmatory Explanatory No consensus Beyond the scope Applied Addressed 1 Not all potential partners become active partners. 2 How ab out the case where the capability of the organization is low in terms of partnership? 3 Reputation is critical in relationships. For example, ethics creates reputation. Building trust is also very important for reputation. Reputation is l inked to the quality of service 4 Partnership for gaining knowledge for sustainable construction can be used only in special cases. I have not seen that happen often. 5 The model and its behavior make sense. 6 Partnership is not tha t important for sustainable construction in construction firms. 7 Different levels of partnership with different levels of cooperation can be assumed. 8 Partnership can help especially during economic downturn. In partnership, both sides of the relations benefit. 9 Policy identification, development, and retention rates are variables that derive how well the partnership capability is, not vice versa. These rates are not outcomes but instead they are deriving the capability. 10 Partnership should cover both the internal and external types of relation. We form product and geographical based strategic relationships with firms to be able to obtain more work, and take over more diversified types of work. 11 Partne rship is very important especially for the economic aspects of sustainability. It is also important in making the firm able to be successful in having a good economic performance. 12 The model and its behavior make sense. 13 Lack of resour ces maybe a result of either having too much work, or not having enough job.

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268 Table O 5. Continued Partnership Confirmatory Explanatory No consensus Beyond the scope Applied Addressed 14 The model and its behavior make sense. The ro le of having active partnership with other firms is one of the main mechanisms of improving the quality of service. 15 As part of partnership, construction firms need to get the owner involved and get their buy ins into issues concerning sustainabl e construction. 16 Community partnership is also very important and needs to be taken into account.

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269 Table O 6 C omments provided by interviewees on the Process model Process Confirmatory No consensus Beyond the scope Applied 1 The model and its behavior make sense. 2 How about efficiency of individual employees? People learn in different rate. 3 The model and its behavior make sense. 4 Product, process of building, and the way that the firm operates need to be taken into consideration in sustainability evaluations. There should be a range for workload. If workload becomes even less than a certain value, it needs to be considered as a negative effect on efficiency of personnel. 5 The model and its behavior make sense. 6 Gathering documentation is very important especially on construction job sites. 7 The model and its behavior make sense. 8 The model and its behavior make sense. 9 Process g oes hand in hand with policies, but there is no indication on how process model and policies model are inter related. I see "quality" as part of the organizational policy not as part of its process. 10 process capability and work hours is going up faster than process capability (upper left chart)? Does that mean more processes result in less efficiency? Being nimble in the market and reacting in a flexible way to the changes of market is also important for obtaining more projects. Product and geographical diversity are also important. 11 The model and its behavior make sense. 12 The model and its behavior make sense. 13 The model and its behavior make sense. 14 T he model and its behavior make sense. 15 The model and its behavior make sense. The most important factor in wok acquisition is the quality of service provided by the firm. 16 The model and its behavior make sense.

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270 APPENDIX P RESULTS OF FITTING AND THE GOODNESS OF FIT ANALYSIS ON THE RELATIVE IMPORTANCE VALUES OF ORGANIZATONAL CAPABILITIES FOR VARIOUS SUSTAINABILITY ASPECTS Note: EC_LEA: The relative importance of organizational leadership capability for economic performance EC_POL: The relative importance of organizational policy and strategy capability for economic performance EC_PEO: The relative importance of organizational people capability for economic performance EC_PAR: The relative importance of organizational partne rship capability for economic performance EC_PRO: The relative importance of organizational process capability for economic performance EN_LEA: The relative importance of organizational leadership capability for environmental performance EN_POL: The relati ve importance of organizational policy and strategy capability for environmental performance EN_PEO: The relative importance of organizational people capability for environmental performance EN_PAR: The relative importance of organizational partnership cap ability for environmental performance EN_PRO: The relative importance of organizational process capability for environmental performance SO_LEA: The relative importance of organizational leadership capability for social performance SO_POL: The relative imp ortance of organizational policy and strategy capability for social performance SO_PEO: The relative importance of organizational people capability for social performance SO_PAR: The relative importance of organizational partnership capability for social p erformance SO_PRO: The relative importance of organizational process capability for social performance

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271 Section 1 The results of the Kolmogorov Smirnov, Anderson Darling, or Chi Squared tests on the Overall Sustainability Excellence Scores obtained by ru nning the model (the base scenario) 1000 times Normal [#40] Kolmogorov Smirnov Sample Size Statistic P Value Rank 1000 0.02272 0.67146 16 0.2 0.1 0.05 0.02 0.01 Critical Value 0.03393 0.03867 0.04294 0.048 0.05151 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 1000 0.29922 15 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No Chi Squared Deg. of freedom Statistic P Value Rank 9 4.937 0.83977 14 0.2 0.1 0.05 0.02 0.01 Critical Value 12.242 14.684 16.919 19.679 21.666 Reject? No No No No No

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272 Section 2 The results of the Kolmogorov Smirnov, Anderson Darling, or Chi Squared tests on the Overall Sustainability Excellence Scores obtained by running the model under the conditions of scenario 6, 1000 times Normal [#40] Kolmogorov Smirnov Sample Size Statistic P Value Rank 1000 0.01784 0.90204 6 0.2 0.1 0.05 0.0 2 0.01 Critical Value 0.03393 0.03867 0.04294 0.048 0.05151 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 1000 0.34012 7 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No Chi Squa red Deg. of freedom Statistic P Value Rank 9 3.1851 0.9565 6 0.2 0.1 0.05 0.02 0.01 Critical Value 12.242 14.684 16.919 19.679 21.666 Reject? No No No No No

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273 Section 3. EC_LEA Fitting Results # Distribution Parameters 1 Exponential 2 Exponential (2P) 3 Lognormal 4 Lognormal (3P) 5 Normal 6 Weibull 7 Weibull (3P) Goodness of Fit Summary # Distribut ion Kolmogorov Smirnov Anderson Darling Chi Squared Statistic Rank Statistic Rank Statistic Rank 2 Exponential (2P) 0.21992 6 2.7618 6 0.00772 1 7 Weibull (3P) 0.10709 1 0.28429 3 0.01509 2 4 Lognormal (3P) 0.11782 2 0.26968 1 0.02185 3 3 Lognormal 0.11863 3 0.27636 2 0.03744 4 6 Weibull 0.13498 4 0.50637 5 0.28257 5 1 Exponential 0.41992 7 3.1824 7 1.152 6 5 Normal 0.16667 5 0.46546 4 1.2213 7

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274 Goodness of Fit Details Exponential (2P) [#2] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.21992 0.40405 6 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37 713 0.4042 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 15 2.7618 6 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? Yes Yes Yes No No Chi Squared Deg. of freedom Statistic P Value Rank 1 0.00 772 0.93 1 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

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275 Weibull (3P) [#7] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.10709 0.98794 1 0.2 0.1 0.05 0.02 0.01 Critical Value 0. 26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 15 0.28429 3 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No Chi Squared Deg. of freedom Statis tic P Value Rank 1 0.01509 0.90222 2 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

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276 Lognormal (3P) [#4] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.11782 0.96941 2 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 15 0.26968 1 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No Chi Squ ared Deg. of freedom Statistic P Value Rank 1 0.02185 0.88248 3 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

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277 Lognormal [#3] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.11863 0.9 6753 3 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 15 0.27636 2 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No Chi Squared Deg. of freedom Statistic P Value Rank 1 0.03744 0.84658 4 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

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278 Weibull [#6] Kolmogorov Smirnov Sample Size Statistic P Valu e Rank 15 0.13498 0.91382 4 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 15 0.50637 5 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3. 2892 3.9074 Reject? No No No No No Chi Squared Deg. of freedom Statistic P Value Rank 1 0.28257 0.59502 5 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

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279 Exponential [#1] Kolmogorov Smirnov Sa mple Size Statistic P Value Rank 15 0.41992 0.00652 7 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? Yes Yes Yes Yes Yes Anderson Darling Sample Size Statistic Rank 15 3.1824 7 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? Yes Yes Yes No No Chi Squared Deg. of freedom Statistic P Value Rank 1 1.152 0.28314 6 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

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280 Normal [ #5] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.16667 0.73954 5 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 15 0.46546 4 0.2 0.1 0 .05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No Chi Squared Deg. of freedom Statistic P Value Rank 1 1.2213 0.26911 7 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

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281 Section 4. EC_POL Fitting Results # Distribution Parameters 1 Exponential 2 Exponential (2P) 3 Lognormal 4 Lognormal (3P) 7.1477 5 Normal 6 Weibull 7 Weibull (3P) Goodness of Fit Summary # Distribution Kolmogorov Smirnov Anderson Darling Chi Squared Statistic Rank Statistic Rank Statistic Rank 1 Exponential 0.43467 7 3.4854 6 1.6486 6 2 Exponential (2P) 0.28507 6 19.578 7 1.9098 7 3 Lognormal 0.18196 2 0.64794 4 0.11443 3 4 Lognorm al (3P) 0.17343 1 0.58518 2 0.15824 4 5 Normal 0.19622 4 0.53393 1 0.00133 1 6 Weibull 0.18456 3 0.63369 3 0.4697 5 7 Weibull (3P) 0.20657 5 0.90055 5 0.05987 2

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282 Goodness of Fit Details Exponential [#1] Kolmogorov Smirnov Sample Size Statistic P V alue Rank 15 0.43467 0.00429 7 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? Yes Yes Yes Yes Yes Anderson Darling Sample Size Statistic Rank 15 3.4854 6 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2. 5018 3.2892 3.9074 Reject? Yes Yes Yes Yes No Chi Squared Deg. of freedom Statistic P Value Rank 1 1.6486 0.19914 6 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? Yes No No No No

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283 Exponential (2P) [#2] Kolmogo rov Smirnov Sample Size Statistic P Value Rank 15 0.28507 0.14275 6 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? Yes No No No No Anderson Darling Sample Size Statistic Rank 15 19.578 7 0.2 0.1 0.05 0.02 0.0 1 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? Yes Yes Yes Yes Yes Chi Squared Deg. of freedom Statistic P Value Rank 1 1.9098 0.16699 7 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? Yes No No No No

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284 Lognormal [#3] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.18196 0.63888 2 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 15 0.6479 4 4 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No Chi Squared Deg. of freedom Statistic P Value Rank 1 0.11443 0.73516 3 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

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285 Lognormal (3P) [#4] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.17343 0.69537 1 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darling Sample S ize Statistic Rank 15 0.58518 2 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No Chi Squared Deg. of freedom Statistic P Value Rank 1 0.15824 0.69078 4 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2 .7055 3.8415 5.4119 6.6349 Reject? No No No No No

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286 Normal [#5] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.19622 0.54591 4 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anders on Darling Sample Size Statistic Rank 15 0.53393 1 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No Chi Squared Deg. of freedom Statistic P Value Rank 1 0.00133 0.97089 1 0.2 0.1 0.05 0.02 0.01 Cri tical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

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287 Weibull [#6] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.18456 0.62173 3 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? N o No No No No Anderson Darling Sample Size Statistic Rank 15 0.63369 3 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No Chi Squared Deg. of freedom Statistic P Value Rank 1 0.4697 0.49313 5 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

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288 Weibull (3P) [#7] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.20657 0.48153 5 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 15 0.90055 5 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No Chi Squared Deg. of freedom Statistic P Value Rank 1 0. 05987 0.80671 2 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

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289 Section 5. EC_PEO Fitting Results # Distribution Parameters 1 Exponential 2 Exponential (2P) 3 Lognorm al 4 Lognormal (3P) 13.07 5 Normal 6 Weibull 7 Weibull (3P) Goodness of Fit Summary # Distribution Kolmogorov Smirnov Anderson Darling C hi Squared Statistic Rank Statistic Rank Statistic Rank 1 Exponential 0.40839 7 3.0 822 6 0.19948 2 2 Exponential (2P) 0.38619 6 20.281 7 0.06471 1 3 Lognormal 0.31049 4 1.1109 4 N/A 4 Lognormal (3P) 0.27836 2 0.96496 2 1.3762 5 5 Normal 0.23648 1 0.93697 1 1.0109 4 6 Weibull 0.30356 3 0.97028 3 0.84836 3 7 Weibull (3P) 0.31085 5 1.1861 5 N/A

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290 Goodness of Fit Details Exponential [#1] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.40839 0.00894 7 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? Yes Yes Yes Yes Yes Anderson Darling Sample Size Statistic Rank 15 3.0822 6 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? Yes Yes Yes No No Chi Squared Deg. of freedom Statistic P Value Rank 1 0.19948 0.65514 2 0.2 0.1 0.05 0.02 0.01 Critical Va lue 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

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291 Exponential (2P) [#2] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.38619 0.01595 6 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? Yes Yes Yes Yes No Anderson Darling Sample Size Statistic Rank 15 20.281 7 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? Yes Yes Yes Yes Yes Chi Squared Deg. of freedom Statistic P Value Rank 1 0.06471 0.7992 1 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

PAGE 292

292 Lognormal [#3] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.31049 0.08796 4 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0 .3376 0.37713 0.4042 Reject? Yes Yes No No No Anderson Darling Sample Size Statistic Rank 15 1.1109 4 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No

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293 Lognormal (3P) [#4] Kolmogorov Smirnov Samp le Size Statistic P Value Rank 15 0.27836 0.16103 2 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? Yes No No No No Anderson Darling Sample Size Statistic Rank 15 0.96496 2 0.2 0.1 0.05 0.02 0.01 Critical Valu e 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No Chi Squared Deg. of freedom Statistic P Value Rank 1 1.3762 0.24074 5 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

PAGE 294

294 Normal [#5] Kolm ogorov Smirnov Sample Size Statistic P Value Rank 15 0.23648 0.31891 1 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 15 0.93697 1 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No Chi Squared Deg. of freedom Statistic P Value Rank 1 1.0109 0.31469 4 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

PAGE 295

295 Weibull [#6] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.30356 0.10081 3 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? Yes No No No No Anderson Darling Sample Size Statistic Rank 15 0.97028 3 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No Chi Squared Deg. of freedom Statistic P Value Rank 1 0.84836 0.35702 3 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reje ct? No No No No No Weibull (3P) [#7] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.31085 0.08734 5 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? Yes Yes No No No Anderson Darling Sample Size St atistic Rank 15 1.1861 5 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No

PAGE 296

296 Section 6. EC_PAR Fitting Results # Distribution Parameters 1 Exponential 2 Exponential (2P) 1.0000 E 14 3 Lognormal 4 Lognormal 5 Normal 6 Weibull 7 Weibull 5 Goodness of Fit Summary # Distribution Kolmogorov Smirnov Anderson Darling Chi Square d Statistic Rank Statistic Rank Statistic Rank 1 Exponential 0.3788 2 4.185 2 0.265 65 1 2 Exponential (2P) 0.3788 3 9.7865 6 0.26565 2 5 Normal 0.26792 1 0.84239 1 0.46518 3 4 Lognormal 0.41041 5 4.5366 4 10.817 4 3 Lognormal 0.41041 4 4.5365 3 10.817 5 6 We ibull 0.43586 6 6.6312 5 N/A 7 Weibull 0.86667 7 354.86 7 N/A

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297 Goodness of Fit Details Exponential [#1] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.3788 0.01919 2 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? Yes Yes Yes Yes No Anderson Darling Sample Size Statistic Ra nk 15 4.185 2 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? Yes Yes Yes Yes Yes Chi Squared Deg. of freedom Statistic P Value Rank 1 0.26565 0.60627 1 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

PAGE 298

298 Exponential (2P) [#2] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.3788 0.01919 3 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? Yes Yes Yes Yes No Ande rson Darling Sample Size Statistic Rank 15 9.7865 6 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? Yes Yes Yes Yes Yes Chi Squared Deg. of freedom Statistic P Value Rank 1 0.26565 0.60627 2 0.2 0.1 0.05 0.02 0.0 1 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

PAGE 299

299 Normal [#5] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.26792 0.19308 1 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Re ject? Yes No No No No Anderson Darling Sample Size Statistic Rank 15 0.84239 1 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No Chi Squared Deg. of freedom Statistic P Value Rank 1 0.46518 0.49521 3 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

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300 Lognormal [#4] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.41041 0.00846 5 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0 .3376 0.37713 0.4042 Reject? Yes Yes Yes Yes Yes Anderson Darling Sample Size Statistic Rank 15 4.5366 4 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? Yes Yes Yes Yes Yes Chi Squared Deg. of freedom Statistic P Value Rank 1 10.817 0.00101 4 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? Yes Yes Yes Yes Yes

PAGE 301

301 Lognormal [#3] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.41041 0.00846 4 0.2 0.1 0.05 0.02 0. 01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? Yes Yes Yes Yes Yes Anderson Darling Sample Size Statistic Rank 15 4.5365 3 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? Yes Yes Yes Yes Yes Chi S quared Deg. of freedom Statistic P Value Rank 1 10.817 0.00101 5 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? Yes Yes Yes Yes Yes

PAGE 302

302 Weibull [#6] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.43586 0.00414 6 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? Yes Yes Yes Yes Yes Anderson Darling Sample Size Statistic Rank 15 6.6312 5 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? Yes Yes Yes Yes Yes

PAGE 303

303 Weibull [#7] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.86667 4.3329E 11 7 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? Yes Yes Yes Yes Yes Anderson Darling Sa mple Size Statistic Rank 15 354.86 7 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? Yes Yes Yes Yes Yes

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304 Section 7. EC_PRO Fitting Results # Distribution Parameters 1 Exponential 2 Exponential (2P) .13889 3 Lognormal 4 Lognormal (3P) 84.301 5 Normal 6 Weibull 7 Weibull (3P) 1.1462E+8 Goodness of Fit Summary # Distribution Kolm ogorov Smirnov Anderson Darling Chi Squared Statistic Rank Statistic Rank Statistic Rank 1 Exponential 0.51526 7 4.8866 7 1.6212 6 2 Exponential (2P) 0.43398 6 4.7291 6 0.56562 5 3 Lognormal 0.27413 2 1.4755 4 0.23342 3 4 Lognormal (3P) 0.28233 4 1.4685 3 0.18701 2 5 Normal 0.28182 3 1.4416 2 0.17926 1 6 Weibull 0.24579 1 1.3204 1 0.25064 4 7 Weibull (3P) 0.31059 5 1.7915 5 4.8948 7

PAGE 305

305 Goodness of Fit Details Exponential [#1] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.51526 3.1756E 4 7 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 R eject? Yes Yes Yes Yes Yes Anderson Darling Sample Size Statistic Rank 15 4.8866 7 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? Yes Yes Yes Yes Yes Chi Squared Deg. of freedom Statistic P Value Rank 1 1.6212 0.2 0292 6 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

PAGE 306

306 Exponential (2P) [#2] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.43398 0.00438 6 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? Yes Yes Yes Yes Yes Anderson Darling Sample Size Statistic Rank 15 4.7291 6 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? Yes Yes Yes Yes Yes Chi Squared Deg. of fre edom Statistic P Value Rank 1 0.56562 0.45201 5 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

PAGE 307

307 Lognormal [#3] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.27413 0.17348 2 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? Yes No No No No Anderson Darling Sample Size Statistic Rank 15 1.4755 4 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? Yes No No No N o Chi Squared Deg. of freedom Statistic P Value Rank 1 0.23342 0.629 3 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

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308 Lognormal (3P) [#4] Kolmogorov Smirnov Sample Size Statistic P Value Ran k 15 0.28233 0.15 4 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? Yes No No No No Anderson Darling Sample Size Statistic Rank 15 1.4685 3 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9 074 Reject? Yes No No No No Chi Squared Deg. of freedom Statistic P Value Rank 1 0.18701 0.66542 2 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

PAGE 309

309 Normal [#5] Kolmogorov Smirnov Sample Size S tatistic P Value Rank 15 0.28182 0.15138 3 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? Yes No No No No Anderson Darling Sample Size Statistic Rank 15 1.4416 2 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1 .9286 2.5018 3.2892 3.9074 Reject? Yes No No No No Chi Squared Deg. of freedom Statistic P Value Rank 1 0.17926 0.67201 1 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

PAGE 310

310 Weibull [#6] Kolmogoro v Smirnov Sample Size Statistic P Value Rank 15 0.24579 0.27683 1 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 15 1.3204 1 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No Chi Squared Deg. of freedom Statistic P Value Rank 1 0.25064 0.61663 4 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

PAGE 311

311 We ibull (3P) [#7] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.31059 0.08779 5 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? Yes Yes No No No Anderson Darling Sample Size Statistic Rank 15 1.7915 5 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? Yes No No No No Chi Squared Deg. of freedom Statistic P Value Rank 1 4.8948 0.02694 7 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Re ject? Yes Yes Yes No No

PAGE 312

312 Section 8. EN_LEA Fitting Results # Distribution Parameters 1 Exponential 2 Exponential (2P) 3 Lognormal 4 Lognormal (3P) 5 Normal 4.667 6 Weibull 7 Weibull (3P) Goodness of Fit Summary # Distribution Kolmogorov Smirnov Anderson Darling Chi Squared Statistic Rank Statistic Rank Statistic Rank 1 Exponential 0.42217 7 3.327 6 1.5552 5 2 Exponential (2P) 0.36097 6 3.684 7 N/A 3 Lognormal 0.22349 1 0.82506 1 0.08359 3 4 Lognormal (3P) 0.22815 2 0.82538 2 0.07724 2 5 Normal 0.28802 5 1.4834 4 N/A 6 Weibull 0.24561 3 1.7423 5 0.1817 4 7 Weibull (3P) 0.2563 4 1.0501 3 0.07586 1

PAGE 313

313 Goodness of Fit Details Exponential [#1] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.42217 0.00612 7 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? Yes Yes Yes Yes Yes Anderson Darling Sample Size Statistic Rank 15 3.327 6 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2. 5018 3.2892 3.9074 Reject? Yes Yes Yes Yes No Chi Squared Deg. of freedom Statistic P Value Rank 1 1.5552 0.21238 5 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

PAGE 314

314 Exponential (2P) [#2] Kolmo gorov Smirnov Sample Size Statistic P Value Rank 15 0.36097 0.02948 6 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? Yes Yes Yes No No Anderson Darling Sample Size Statistic Rank 15 3.684 7 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? Yes Yes Yes Yes No

PAGE 315

315 Lognormal [#3] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.22349 0.38458 1 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 15 0.82506 1 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No Chi Squared Deg. of freedom Statistic P Value Rank 1 0.08359 0 .77249 3 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

PAGE 316

316 Lognormal (3P) [#4] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.22815 0.3601 2 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 15 0.82538 2 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No Chi Squared Deg. of freedom Stat istic P Value Rank 1 0.07724 0.78107 2 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

PAGE 317

317 Normal [#5] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.28802 0.13525 5 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? Yes No No No No Anderson Darling Sample Size Statistic Rank 15 1.4834 4 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? Yes No No No No

PAGE 318

318 Weib ull [#6] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.24561 0.27759 3 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 15 1.7423 5 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? Yes No No No No Chi Squared Deg. of freedom Statistic P Value Rank 1 0.1817 0.66992 4 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

PAGE 319

319 Weibull (3P) [#7] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.2563 0.23428 4 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 15 1.0501 3 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No Chi Squared Deg. of freedom Statistic P Value Rank 1 0.07586 0.78299 1 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5. 4119 6.6349 Reject? No No No No No

PAGE 320

320 Section 9. EN_POL Fitting Results # Distribution Parameters 1 Exponential 2 Exponential (2P) 3 Lognormal 4 Lognormal (3P) 5.6721 5 Normal .165 6 Weibull 7 Weibull (3P) Goodness of Fit Summary # Distribution Kolmogorov Smirnov Anderson Darling Chi Squared Statistic Rank Statistic Rank Statistic Rank 1 Exponential 0.40271 7 3.2698 6 N/A 2 Exponential (2P) 0.37563 6 9.7225 7 N/A 3 Lognormal 0.28508 4 0.99785 2 N/A 4 Lognormal (3P) 0.26824 1 0.96854 1 N/A 5 Normal 0.31421 5 1.0728 5 N/A 6 Weibull 0.2839 3 1.0576 4 N/A 7 Weibull (3P) 0.28163 2 1.0292 3 N/A

PAGE 321

321 Goodness of Fit Details Exponential [#1] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0. 40271 0.0104 7 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? Yes Yes Yes Yes No Anderson Darling Sample Size Statistic Rank 15 3.2698 6 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.907 4 Reject? Yes Yes Yes No No

PAGE 322

322 Exponential (2P) [#2] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.37563 0.02075 6 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? Yes Yes Yes No No Anderson Dar ling Sample Size Statistic Rank 15 9.7225 7 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? Yes Yes Yes Yes Yes

PAGE 323

323 Lognormal [#3] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.28508 0.14272 4 0. 2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? Yes No No No No Anderson Darling Sample Size Statistic Rank 15 0.99785 2 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No

PAGE 324

324 Lognormal (3P) [#4] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.26824 0.19203 1 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? Yes No No No No Anderson Darling Sample Size Statistic R ank 15 0.96854 1 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No

PAGE 325

32 5 Normal [#5] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.31421 0.08165 5 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? Yes Yes No No No Anderson Darling Sample Size Statistic Rank 15 1.0728 5 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No

PAGE 326

326 Weibull [#6] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.2839 0.14581 3 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? Yes No No No No Anderson Darling Sample Size Statistic Rank 15 1.0576 4 0.2 0.1 0.05 0.02 0.01 C ritical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No

PAGE 327

327 Weibull (3P) [#7] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.28163 0.1519 2 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? Yes No No No No Anderson Darling Sample Size Statistic Rank 15 1.0292 3 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No

PAGE 328

328 Section 10. EN_PEO Fitting Results # Distribution Parameters 1 Exp onential 2 Exponential (2P) 3 Lognormal 4 Lognormal (3P) 5 Normal 6 Weibull 7 Weibull (3P) Goodness of Fi t Summary # Distribution Kolmogorov Smirnov Anderson Darling Chi Squared Statistic Rank Statistic Rank Statistic Rank 1 Exponential 0.38619 7 2.9859 6 0.06821 1 2 Exp onential (2P) 0.34592 6 9.4788 7 N/A 3 Lognormal 0.26109 1 0.83445 1 1.4068 4 4 Lognormal (3P) 0.26847 3 0.85016 2 1.4551 5 5 Normal 0.26536 2 1.26 4 0.71606 2 6 Weibull 0.28066 4 1.3114 5 0.95102 3 7 Weibull (3P) 0.3063 5 1.1605 3 N/A

PAGE 329

329 Goodness of Fit Details Exponential [#1] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.38619 0.01595 7 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? Yes Yes Yes Yes No Anderson Darling Sample Size Statistic Rank 15 2.9859 6 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? Yes Yes Yes No No Chi Squared Deg. of freedom Statistic P Value Ra nk 1 0.06821 0.79396 1 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

PAGE 330

330 Exponential (2P) [#2] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.34592 0.04161 6 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? Yes Yes Yes No No Anderson Darling Sample Size Statistic Rank 15 9.4788 7 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? Yes Yes Yes Yes Yes

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331 Log normal [#3] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.26109 0.21658 1 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 15 0.83445 1 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No Chi Squared Deg. of freedom Statistic P Value Rank 1 1.4068 0.23558 4 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

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332 Lognormal (3P) [#4] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.26847 0.19128 3 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? Yes No No No No Anderson Darling Sample Size Sta tistic Rank 15 0.85016 2 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No Chi Squared Deg. of freedom Statistic P Value Rank 1 1.4551 0.22771 5 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3. 8415 5.4119 6.6349 Reject? No No No No No

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333 Normal [#5] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.26536 0.20166 2 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darlin g Sample Size Statistic Rank 15 1.26 4 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No Chi Squared Deg. of freedom Statistic P Value Rank 1 0.71606 0.39744 2 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

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334 Weibull [#6] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.28066 0.15457 4 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? Yes No No N o No Anderson Darling Sample Size Statistic Rank 15 1.3114 5 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No Chi Squared Deg. of freedom Statistic P Value Rank 1 0.95102 0.32946 3 0.2 0.1 0.05 0.0 2 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

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335 Weibull (3P) [#7] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.3063 0.09554 5 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? Yes Yes No No No Anderson Darling Sample Size Statistic Rank 15 1.1605 3 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No

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336 Section 11. EN_PAR Fitting Results # Distribution Parame ters 1 Exponential 2 Exponential (2P) 1.0000E 14 3 Lognormal 4 Lognormal 5 Normal 6 Weibull 7 Weibull Goodness of Fit Summary # Distribution Kolmogorov Smirnov Anderson Darling Chi Squared Statistic Rank Statistic Rank Statistic Rank 1 Exponential 0.29905 5 4.1495 5 0.99512 2 2 Exponential (2P ) 0.29905 6 9.6655 7 0.99512 3 3 Lognormal 0.2168 2 3.9807 3 2.3655 5 4 Lognormal 0.2168 3 3.9807 4 2.3655 6 5 Normal 0.13859 1 0.32066 1 0.66774 1 6 Weibull 0.38888 7 6.4392 6 N/A 7 Weibull 0.23884 4 3.5859 2 1.9953 4

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337 Goodness of Fit Details Exponential [#1] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.29905 0.10996 5 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? Yes No No No No Anderson Darling Sample Size Statistic Rank 15 4.1495 5 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? Yes Yes Yes Yes Yes Chi Squared Deg. of freedom Statistic P Value Rank 1 0.99512 0.318 49 2 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

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338 Exponential (2P) [#2] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.29905 0.10996 6 0.2 0.1 0.05 0.02 0.01 Critical Value 0 .26588 0.30397 0.3376 0.37713 0.4042 Reject? Yes No No No No Anderson Darling Sample Size Statistic Rank 15 9.6655 7 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? Yes Yes Yes Yes Yes Chi Squared Deg. of freedom Statistic P Value Rank 1 0.99512 0.31849 3 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

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339 Lognormal [#3] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.2168 0.4215 2 0.2 0.1 0.0 5 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 15 3.9807 3 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? Yes Yes Yes Yes Yes C hi Squared Deg. of freedom Statistic P Value Rank 1 2.3655 0.12405 5 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? Yes No No No No

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340 Lognormal [#4] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0. 2168 0.42149 3 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 15 3.9807 4 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 R eject? Yes Yes Yes Yes Yes Chi Squared Deg. of freedom Statistic P Value Rank 1 2.3655 0.12404 6 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? Yes No No No No

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341 Normal [#5] Kolmogorov Smirnov Sample Size St atistic P Value Rank 15 0.13859 0.89818 1 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 15 0.32066 1 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1. 9286 2.5018 3.2892 3.9074 Reject? No No No No No Chi Squared Deg. of freedom Statistic P Value Rank 1 0.66774 0.41384 1 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

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342 Weibull [#6] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.38888 0.0149 7 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? Yes Yes Yes Yes No Anderson Darling Sample Size Statistic Rank 15 6.4392 6 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? Yes Yes Yes Yes Yes

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343 Weibull [#7] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.23884 0.30783 4 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.40 42 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 15 3.5859 2 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? Yes Yes Yes Yes No Chi Squared Deg. of freedom Statistic P Value Rank 1 1.9953 0.15 779 4 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? Yes No No No No

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344 Section 12. EN_PRO Fitting Results # Distribution Parameters 1 Exponential 2 Exponential (2P) 3 Lognormal 61 4 Lognormal (3P) 10.431 5 Normal 6 Weibull 7 Weibull (3P) Goodness of Fit Summary # Distribution Kolmogorov Smirnov Anderson Darling Chi Squar ed Statistic Rank Statistic Rank Statistic Rank 1 Exponential 0.38717 7 2.3866 6 1. 3455 4 2 Exponential (2P) 0.28511 6 3.2049 7 0.11124 1 3 Lognormal 0.19826 2 0.77125 5 2.0849 7 4 Lognormal (3P) 0.21563 5 0.73804 4 1.6095 5 5 Normal 0.21025 4 0.72455 3 0.94448 2 6 Weibull 0.18507 1 0.68872 1 0.96553 3 7 Weibull (3P) 0.20355 3 0.7113 2 1.826 1 6

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345 Goodness of Fit Details Exponential [#1] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.38717 0.01556 7 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? Yes Yes Yes Yes No Anderson Darli ng Sample Size Statistic Rank 15 2.3866 6 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? Yes Yes No No No Chi Squared Deg. of freedom Statistic P Value Rank 1 1.3455 0.24607 4 0.2 0.1 0.05 0.02 0.01 Critical Va lue 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

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346 Exponential (2P) [#2] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.28511 0.14262 6 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Rejec t? Yes No No No No Anderson Darling Sample Size Statistic Rank 15 3.2049 7 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? Yes Yes Yes No No Chi Squared Deg. of freedom Statistic P Value Rank 1 0.11124 0.73873 1 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

PAGE 347

347 Lognormal [#3] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.19826 0.53298 2 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 15 0.77125 5 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No Chi Squared Deg. of freedom Statistic P Value Ra nk 1 2.0849 0.14876 7 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? Yes No No No No

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348 Lognormal (3P) [#4] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.21563 0.42815 5 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 15 0.73804 4 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No Chi Squared Deg. of freedom Statistic P Value Rank 1 1.6095 0.20456 5 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

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349 Normal [#5] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.21025 0.45944 4 0. 2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 15 0.72455 3 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No Chi Squared Deg. of freedom Statistic P Value Rank 1 0.94448 0.33113 2 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

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350 Weibull [#6] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.18507 0.61837 1 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 15 0.68872 1 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.90 74 Reject? No No No No No Chi Squared Deg. of freedom Statistic P Value Rank 1 0.96553 0.3258 3 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

PAGE 351

351 Weibull (3P) [#7] Kolmogorov Smirnov Sample Si ze Statistic P Value Rank 15 0.20355 0.49999 3 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 15 0.7113 2 0.2 0.1 0.05 0.02 0.01 Critical Value 1.374 9 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No Chi Squared Deg. of freedom Statistic P Value Rank 1 1.8261 0.17659 6 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? Yes No No No No

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352 Section 13. SO_LEA Fitt ing Results # Distribution Parameters 1 Exponential 2 Exponential (2P) 3 Lognormal 4 Lognormal (3P) 141.66 5 Normal 6 Weibull 7 Weibull (3 P) Goodness of Fit Summary # Distribution Kolmogorov Smirnov Anderson Darling Chi Squared Statistic Rank Statistic Rank Statistic Rank 1 Exponential 0.44859 7 4.2985 7 N/A 2 Exponential (2P) 0.38558 6 4.1658 6 3.3908 6 3 L ognormal 0.21836 3 0.68486 4 0.28079 1 4 Lognormal (3P) 0.21236 1 0.59529 2 0.43894 2 5 Normal 0.22081 4 0.58865 1 0.46261 3 6 Weibull 0.21277 2 0.69909 5 0.59678 5 7 Weibull (3P) 0.22482 5 0.61915 3 0.48133 4

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353 Goodness of Fit Details Exponential [#1] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.44859 0.00284 7 0 .2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? Yes Yes Yes Yes Yes Anderson Darling Sample Size Statistic Rank 15 4.2985 7 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? Yes Yes Yes Yes Yes

PAGE 354

354 Exponential (2P) [#2] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.38558 0.0162 6 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? Yes Yes Yes Yes No Anderson Darling Sample Siz e Statistic Rank 15 4.1658 6 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? Yes Yes Yes Yes Yes Chi Squared Deg. of freedom Statistic P Value Rank 1 3.3908 0.06556 6 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? Yes Yes No No No

PAGE 355

355 Lognormal [#3] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.21836 0.4127 3 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 15 0.68486 4 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No Chi Squared Deg. of freedom Statistic P Value Rank 1 0.28079 0.59619 1 0.2 0.1 0.05 0.02 0 .01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

PAGE 356

356 Lognormal (3P) [#4] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.21236 0.44706 1 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 15 0.59529 2 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No Chi Squared Deg. of freedom Statistic P Value Rank 1 0.43894 0 .50764 2 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

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357 Normal [#5] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.22081 0.39913 4 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 15 0.58865 1 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No Chi Squared Deg. of freedom Statistic P Value Rank 1 0.46261 0.49641 3 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

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358 Weibull [#6] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.21277 0.44462 2 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 15 0.69909 5 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No Chi Squared Deg of freedom Statistic P Value Rank 1 0.59678 0.43981 5 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

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359 Weibull (3P) [#7] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.22482 0.3774 9 5 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 15 0.61915 3 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No Chi Squared Deg. of freedom Statistic P Value Rank 1 0.48133 0.48782 4 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

PAGE 360

360 Section 14. SO_POL Fitting Results # Distribution Parameters 1 Exponential 2 Exponential (2P) 3 Lognormal 4 Lognormal (3P) 56.363 5 Normal 6 Weibull 7 Weibull (3P) Goodness o f Fit Summary # Distribution Kolmogorov Smirnov Anderson Darling Chi Squared Statistic Rank Statistic Rank Statistic Rank 1 Exponential 0.32863 7 2.0793 6 2.9948 7 2 Exponential (2P) 0.25937 6 9.1931 7 0.78487 5 3 Lognormal 0.20259 5 0.56336 5 1.1521 6 4 Lognormal (3P) 0.13573 2 0.29632 2 0.22088 3 5 Normal 0.13079 1 0.26759 1 0.12684 2 6 Weibull 0.18462 4 0.4002 4 0.09557 1 7 Weibull (3P) 0.14231 3 0.34055 3 0.35399 4

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361 Goodness of Fit Details Exponential [#1] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.32863 0.06061 7 0.2 0.1 0.05 0.02 0.01 Critical Value 0 .26588 0.30397 0.3376 0.37713 0.4042 Reject? Yes Yes No No No Anderson Darling Sample Size Statistic Rank 15 2.0793 6 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? Yes Yes No No No Chi Squared Deg. of freedom St atistic P Value Rank 1 2.9948 0.08353 7 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? Yes Yes No No No

PAGE 362

362 Exponential (2P) [#2] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.25937 0.22282 6 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 15 9.1931 7 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? Yes Yes Yes Ye s Yes Chi Squared Deg. of freedom Statistic P Value Rank 1 0.78487 0.37566 5 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

PAGE 363

363 Lognormal [#3] Kolmogorov Smirnov Sample Size Statistic P Value Ra nk 15 0.20259 0.50589 5 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 15 0.56336 5 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No Chi Squared Deg. of freedom Statistic P Value Rank 1 1.1521 0.28311 6 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

PAGE 364

364 Lognormal (3P) [#4] Kolmogorov Smirnov Sa mple Size Statistic P Value Rank 15 0.13573 0.91071 2 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 15 0.29632 2 0.2 0.1 0.05 0.02 0.01 Critical Val ue 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No Chi Squared Deg. of freedom Statistic P Value Rank 1 0.22088 0.63837 3 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

PAGE 365

365 Normal [#5] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.13079 0.93026 1 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 15 0.26759 1 0.2 0.1 0.05 0 .02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No Chi Squared Deg. of freedom Statistic P Value Rank 1 0.12684 0.72173 2 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No N o

PAGE 366

366 Weibull [#6] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.18462 0.62131 4 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 15 0.4 002 4 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No Chi Squared Deg. of freedom Statistic P Value Rank 1 0.09557 0.75721 1 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

PAGE 367

367 Weibull (3P) [#7] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.14231 0.88073 3 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 15 0.34055 3 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No Chi Squared Deg. of freedom Statistic P Value Rank 1 0.35399 0.55186 4 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

PAGE 368

368 Section 15. SO_PEO Fitting Results # Distribution Parameters 1 Exponential 2 Exponential (2P) 3 Lognormal 4 Lognormal (3P) 5 Normal 6 Weibull 7 Weibull (3P) Goodness of Fit Summary # Distribution Kolmogorov Smirnov Anderson Darling Chi Squared Statistic Rank Statistic Rank Statistic Rank 1 Exponential 0.4139 7 2.9942 7 0.06657 5 2 Exponential (2P) 0.20855 6 2.6168 6 0.01068 1 3 Lognormal 0.11146 1 0.28573 2 0.25645 6 4 Lognormal (3P) 0.1188 2 0.26015 1 0.06324 4 5 Normal 0.18375 5 0.64011 4 1.291 7 6 Weibull 0.16182 4 0.89567 5 0.03374 2 7 Weibull (3P) 0.12852 3 0.31437 3 0.0536 3

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369 Goodness of Fit Details Exponential [# 1] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.4139 0.0077 7 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? Yes Yes Yes Yes Yes Anderson Darling Sample Size Statistic Rank 15 2.9942 7 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? Yes Yes Yes No No Chi Squared Deg. of freedom Statistic P Value Rank 1 0.06657 0.79639 5 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No N o No No No

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370 Exponential (2P) [#2] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.20855 0.46956 6 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darling Sample Size Sta tistic Rank 15 2.6168 6 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? Yes Yes Yes No No Chi Squared Deg. of freedom Statistic P Value Rank 1 0.01068 0.91767 1 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

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371 Lognormal [#3] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.11146 0.98179 1 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Ander son Darling Sample Size Statistic Rank 15 0.28573 2 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No Chi Squared Deg. of freedom Statistic P Value Rank 1 0.25645 0.61257 6 0.2 0.1 0.05 0.02 0.01 Cr itical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

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372 Lognormal (3P) [#4] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.1188 0.96711 2 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 15 0.26015 1 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No Chi Squared Deg. of freedom Statistic P Value Rank 1 0.06324 0.80145 4 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

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373 Normal [#5] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.18375 0.62704 5 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 15 0.64011 4 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No Chi Squared Deg. of freedom Statistic P Value R ank 1 1.291 0.25586 7 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

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374 Weibull [#6] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.16182 0.77036 4 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 15 0.89567 5 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No Chi Squared Deg. of freed om Statistic P Value Rank 1 0.03374 0.85427 2 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

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375 Weibull (3P) [#7] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.12852 0.93846 3 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 15 0.31437 3 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No N o Chi Squared Deg. of freedom Statistic P Value Rank 1 0.0536 0.81691 3 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

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376 Section 16. SO_PAR Fitting Results # Distribution Parameters 1 Exponential 2 Exponential (2P) 3 Lognormal 4 Lognormal (3P) 5 Normal 6 Weibull 7 Weibull (3P) Goodness of Fit Summary # Distribution Kolmogorov Smirnov Anderson Darling Chi Squared Statistic Rank Statistic Rank Statistic Rank 1 Exponential 0.31891 7 1.7367 5 3.8196E 4 1 2 Exponential (2P) 0.23838 4 19.373 7 0.98517 6 3 Lognormal 0.21339 3 0.69226 2 0.50568 4 4 Lognormal (3P) 0.21024 2 0.73103 3 0.6131 5 5 Normal 0.28372 6 0.81213 4 4.4061 7 6 Weibull 0.20332 1 0.68228 1 0.12439 3 7 Weibull (3P) 0.269 5 11.939 6 0.00101 2

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377 Goodness of Fit Details Exponential [#1] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.31891 0.0742 7 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0 .3376 0.37713 0.4042 Reject? Yes Yes No No No Anderson Darling Sample Size Statistic Rank 15 1.7367 5 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? Yes No No No No Chi Squared Deg. of freedom Statistic P Value R ank 1 3.8196E 4 0.98441 1 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

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378 Exponential (2P) [#2] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.23838 0.30998 4 0.2 0.1 0.05 0.02 0 .01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 15 19.373 7 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? Yes Yes Yes Yes Yes Chi Squar ed Deg. of freedom Statistic P Value Rank 1 0.98517 0.32093 6 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

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379 Lognormal [#3] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.21339 0.4 4104 3 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 15 0.69226 2 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No Chi Squared Deg. of freedom Statistic P Value Rank 1 0.50568 0.47702 4 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

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380 Lognormal (3P) [#4] Kolmogorov Smirnov Sample Size Statis tic P Value Rank 15 0.21024 0.45951 2 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 15 0.73103 3 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No Chi Squared Deg. of freedom Statistic P Value Rank 1 0.6131 0.43362 5 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

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381 Normal [#5] Kolmogorov Smirn ov Sample Size Statistic P Value Rank 15 0.28372 0.14627 6 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? Yes No No No No Anderson Darling Sample Size Statistic Rank 15 0.81213 4 0.2 0.1 0.05 0.02 0.01 Criti cal Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No Chi Squared Deg. of freedom Statistic P Value Rank 1 4.4061 0.03581 7 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? Yes Yes Yes No No

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382 Weibu ll [#6] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.20332 0.50143 1 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 15 0.68228 1 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No Chi Squared Deg. of freedom Statistic P Value Rank 1 0.12439 0.72432 3 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

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383 Weibull (3P) [#7] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.269 0.18956 5 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? Yes No No No No Anderson Darling Sample Size Statist ic Rank 15 11.939 6 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? Yes Yes Yes Yes Yes Chi Squared Deg. of freedom Statistic P Value Rank 1 0.00101 0.97464 2 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3 .8415 5.4119 6.6349 Reject? No No No No No

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384 Section 17. SO_PRO Fitting Results # Distribution Parameters 1 Exponential 2 Exponential (2P) 1.0000E 14 3 Lognormal 4 Lognormal 5 Normal 246 6 Weibull 7 Weibull Goodness of Fit Summary # Distribution Kolmogorov Smirnov Anderson Darling Chi Squared Statistic Rank Statistic Rank Statistic Rank 1 Exponential 0.2986 5 3.0845 5 0.45055 2 2 Exponential (2P) 0.2986 6 3.3773 6 0.45055 3 3 Lognormal 0.25189 3 2.7194 3 1.038 5 4 Logno rmal 0.2519 4 2.7195 4 1.038 6 5 Normal 0.16282 1 0.51619 1 0.40195 1 6 Weibull 0.35423 7 4.0728 7 2.0605 7 7 Weibull 0.23521 2 2.5538 2 0.89884 4

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385 Goodness of Fit Details Exponential [#1] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.2986 0.11091 5 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? Yes No No No No Anderson Darling Sample Size Statistic Rank 15 3.0845 5 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.907 4 Reject? Yes Yes Yes No No Chi Squared Deg. of freedom Statistic P Value Rank 1 0.45055 0.50207 2 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

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386 Exponential (2P) [#2] Kolmogorov Smirnov Sa mple Size Statistic P Value Rank 15 0.2986 0.11091 6 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? Yes No No No No Anderson Darling Sample Size Statistic Rank 15 3.3773 6 0.2 0.1 0.05 0.02 0.01 Critical Valu e 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? Yes Yes Yes Yes No Chi Squared Deg. of freedom Statistic P Value Rank 1 0.45055 0.50207 3 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

PAGE 387

387 Lognormal [#3] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.25189 0.25148 3 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 15 2.7194 3 0.2 0.1 0 .05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? Yes Yes Yes No No Chi Squared Deg. of freedom Statistic P Value Rank 1 1.038 0.30828 5 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No N o No No

PAGE 388

388 Lognormal [#4] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.2519 0.25146 4 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 15 2.7195 4 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? Yes Yes Yes No No Chi Squared Deg. of freedom Statistic P Value Rank 1 1.038 0.30828 6 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.411 9 6.6349 Reject? No No No No No

PAGE 389

389 Normal [#5] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.16282 0.7641 1 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darling Sampl e Size Statistic Rank 15 0.51619 1 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? No No No No No Chi Squared Deg. of freedom Statistic P Value Rank 1 0.40195 0.52609 1 0.2 0.1 0.05 0.02 0.01 Critical Value 1.642 4 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

PAGE 390

390 Weibull [#6] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.35423 0.03446 7 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? Yes Yes Yes No No Anderson Darling Sample Size Statistic Rank 15 4.0728 7 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? Yes Yes Yes Yes Yes Chi Squared Deg. of freedom Statistic P Value Rank 1 2.0605 0.15116 7 0.2 0.1 0.05 0 .02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? Yes No No No No

PAGE 391

391 Weibull [#7] Kolmogorov Smirnov Sample Size Statistic P Value Rank 15 0.23521 0.32494 2 0.2 0.1 0.05 0.02 0.01 Critical Value 0.26588 0.30397 0.3376 0.37713 0.4042 Reject? No No No No No Anderson Darling Sample Size Statistic Rank 15 2.5538 2 0.2 0.1 0.05 0.02 0.01 Critical Value 1.3749 1.9286 2.5018 3.2892 3.9074 Reject? Yes Yes Yes No No Chi Squared Deg. of freedom Statistic P Value Rank 1 0.89884 0.34309 4 0.2 0.1 0.05 0.02 0.01 Critical Value 1.6424 2.7055 3.8415 5.4119 6.6349 Reject? No No No No No

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392 APPENDIX Q LIST OF VARIABLES AND PARAMETERS OF THE SYSTEM DYNAMICS MODEL

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393 Table Q 1 List of variables and parameters of the system dynamics model

PAGE 394

394 Table Q 1. Continued.

PAGE 395

395 Table Q 1. Continued.

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396 Table Q 1. Continued.

PAGE 397

397 Table Q 1. Continued.

PAGE 398

398 Table Q 1. Continued.

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399 Table Q 1. Continued.

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400 Table Q 1. Continued.

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401 Table Q 1. Con tinued.

PAGE 402

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409 BIOGRAPHICAL SKETCH cation and professiona l background leading to his Ph. D. degree from the University of Florida (UF) is diverse. In 2002, Amin graduated from Tehran Polytec hnic with a B achelor of Science in i ndustrial e ngineering. He then completed a Master of Science in in dustrial engineering with a minor in system engi neering at Tehran Polytechnic. Soon after his admission to the Master program, he started his professional experience by getting involved in industrial and construction projects. His deep interest in this fie ld provided him with the opportunity of being part of, and ultimately heading, a number of construction project planning and control teams in the cement, vehicle manufacturing, gas, and petrochemical industry. In the 3 rd semester of his graduate studies, he was invited to teach in the D epartment of I ndustrial E ngineering in Tafresh University, in which he held a teaching position for more than three years. In 2007, he moved to South Africa to be part of a construction project management team responsible fo r providing project planning and control services to gas and petrochemical projects. After two years of professional practice in South Africa, he applied to the construction management program at UF, and was accepted into the Ph.D. program in the fall of 2 009. In parallel with his Ph. D. studies, he also pursued a Master of Science in civil engineering at UF. He received his Master of Science in civil engineering in 2012 and graduated from the Ph. D. program in 2013. Amin is a Project Management Professional (PMP) and a Green Globe Professional (GGP)