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Identification of Construction Project Problems and Their Impacts on Project Success

Permanent Link: http://ufdc.ufl.edu/UFE0041198/00001

Material Information

Title: Identification of Construction Project Problems and Their Impacts on Project Success
Physical Description: 1 online resource (195 p.)
Language: english
Creator: Kim, Seong
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2009

Subjects

Subjects / Keywords: control, factor, identification, performance, problems, success, weights
Design, Construction, and Planning -- Dissertations, Academic -- UF
Genre: Design, Construction, and Planning Doctorate thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Identification of Construction Project Problems and Their Impacts on Project Success The traditionally accepted success parameters for construction projects are mainly cost and schedule. There are new trends in how to define success and the number of success parameters considered even though cost and schedule are still the most prevalent ones. The number and priority of success parameters heavily depends on the owner. With respect to these trends, determining the success of project is a hard and complicated procedure when a project faces multiple problems simultaneously. The problems that practitioners or researchers face are: 1) What are the relationships between these causes and their impacts on project success parameters?; 2) Is it necessary to concern all problems for each different project success parameter in terms of their negative impacts?; and 3) What problem is really considered when, especially, the project has more than one success parameter with different priorities? The proposed solutions are 1) to determine the relationships between problems and their impacts and 2) to select problems required to be considered for each project success parameter to meet multi-project success parameters within different priorities. This will help ensure that the project meets the required performance targets and adds value for all participants at the early phase of project. Confirmatory factor analysis (CFA) models were used for testing the quality of proposed solutions. The outputs of CFA were used for an application tool to select the most critical problems under given circumstances such as parameter priorities and problems severity. A single multi-attribute rating technique using swing (SMARTS) was the statistical tool used for this study. The results of this research show the selection of critical problem groups under given circumstances. In the future contractors, owners, and other project participants can use this technique to more effectively conduct project reviews during the course of the project.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: 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.
Statement of Responsibility: by Seong Kim.
Thesis: Thesis (Ph.D.)--University of Florida, 2009.
Local: Adviser: Issa, R. Raymond.
Local: Co-adviser: Flood, Ian.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2011-12-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2009
System ID: UFE0041198:00001

Permanent Link: http://ufdc.ufl.edu/UFE0041198/00001

Material Information

Title: Identification of Construction Project Problems and Their Impacts on Project Success
Physical Description: 1 online resource (195 p.)
Language: english
Creator: Kim, Seong
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2009

Subjects

Subjects / Keywords: control, factor, identification, performance, problems, success, weights
Design, Construction, and Planning -- Dissertations, Academic -- UF
Genre: Design, Construction, and Planning Doctorate thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Identification of Construction Project Problems and Their Impacts on Project Success The traditionally accepted success parameters for construction projects are mainly cost and schedule. There are new trends in how to define success and the number of success parameters considered even though cost and schedule are still the most prevalent ones. The number and priority of success parameters heavily depends on the owner. With respect to these trends, determining the success of project is a hard and complicated procedure when a project faces multiple problems simultaneously. The problems that practitioners or researchers face are: 1) What are the relationships between these causes and their impacts on project success parameters?; 2) Is it necessary to concern all problems for each different project success parameter in terms of their negative impacts?; and 3) What problem is really considered when, especially, the project has more than one success parameter with different priorities? The proposed solutions are 1) to determine the relationships between problems and their impacts and 2) to select problems required to be considered for each project success parameter to meet multi-project success parameters within different priorities. This will help ensure that the project meets the required performance targets and adds value for all participants at the early phase of project. Confirmatory factor analysis (CFA) models were used for testing the quality of proposed solutions. The outputs of CFA were used for an application tool to select the most critical problems under given circumstances such as parameter priorities and problems severity. A single multi-attribute rating technique using swing (SMARTS) was the statistical tool used for this study. The results of this research show the selection of critical problem groups under given circumstances. In the future contractors, owners, and other project participants can use this technique to more effectively conduct project reviews during the course of the project.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: 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.
Statement of Responsibility: by Seong Kim.
Thesis: Thesis (Ph.D.)--University of Florida, 2009.
Local: Adviser: Issa, R. Raymond.
Local: Co-adviser: Flood, Ian.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2011-12-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2009
System ID: UFE0041198:00001


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1 IDENTIFICATION OF CONSTRUCTION PROJECT PROBLEMS AND THEIR IMPACTS ON PROJECT S UCCESS By SEONG JIN KIM 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 2009

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2 2009 Seong Jin Kim

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3 To my family ; Keun Ho Kim, Kye Haeng Jo, Won Sook Kim, and Won Jae Kim

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4 ACKNOWLEDGMENTS I really thank all my familie s in Korea for believing in me and supporting me, no matter where I am and what I do. Besides my families, there are many friends who are helping me unconditionally whenever I need help. Without their help, I would not have been able to be who I am right now. Those friends are Seong Hoon (Seth) Lee, Hyang Sil (Bonnie) Park, Sook Hee (Harriet) Choi, Janghowan Choi, Dae Keun Shin, Byung Cheol Kim, Zee Woon Lee, Tae Kook Lee, Ji Yong Kim, and Whan Soon Yang. In addition to these friends, Stan Hudson, Justi n Burns, Ferdinand DeJesus, and Erika Sanchez are also a part of help and support group Finally my old time English teacher Yoon Sik Goh and my former advisor, Stuart Anderson, at Texas A&M University have to also be thanked The special thanks should go to Dr. Raymond Issa. He is the one who has enable d me to reach whre I am right now. Without his help, guidance, and advice, I would not have be en able to pursue my dream. I would also like to thank my committee members who are Dr. Ian Flood, Dr. R. Edwa rd Minchin, Jr., Dr. E. Douglas Lucas, and Dr. Randy Chow as well. They always guide d me into the right direction of my research. Their guidance help ed me improve myself and realize my potential as a scholar I am really lucky to have them all on my com mittee and spend some time with them. All my experience in the Rinker School at the University of Florida is priceless and I love and am proud to be a part of this program. I also thank Dr. Richard Smailes for giving me a chance to be a teaching assista nt of BCN 5789 and BCN 4787 (Capstone). The experience I have had as a TA has been very helpful for in getting me ready to teach classes It gives me an eye opener of teaching world. I really thank all the students who take the capstone class from Spri ng 07 to Spring 08 while I am a TA of that class. All the comments and opinions they provide me are helpful as well. I wish I could write down their names all here but unfortunately

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5 I cannot because it will be more than two hundred students. I am sure t hat they will understand the situation here. But I do remember them all even though their names are not shown here. During this research, I have lots help from my BCN classmates from class es of 07 and 08, especially Nicole Singleton, Alex Curry, Jessica L igator, and Kristi Johnson. They and their colleagues at work unconditionally participate d in this research. Some of my colleagues from Balfour Beatty also help ed me out They are Miles Gibbs, Kurtis Wright, Mike Neumann, Nick Wegener, Joe Smith, and Jo never enough. I had never realized how l ucky I was until I joined this program. I feel that I am really lucky to have good people around me all the time. T he past four years of my educat ional experience the Rinker School showed me the direction of my near future work as a n academic scholar. I have been receiving something from people that I do care for and love. I think that now is the time that I should give something back to them in r eturn. I love to be here and am glad that I did.

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6 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ ............... 4 LIST OF TABLES ................................ ................................ ................................ ......................... 10 LIST OF FIGURES ................................ ................................ ................................ ....................... 14 LIST OF OBJECTS ................................ ................................ ................................ ....................... 16 ABSTRACT ................................ ................................ ................................ ................................ ... 17 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .................. 19 Problem Statement ................................ ................................ ................................ .................. 21 Research Questions ................................ ................................ ................................ ................. 21 Purpose, Objectives, and Approach ................................ ................................ ........................ 22 Layout of Dissertation ................................ ................................ ................................ ............ 23 2 LITERATURE REVIEW ................................ ................................ ................................ ....... 24 Overview ................................ ................................ ................................ ................................ 24 Project Success Factors/Criteria ................................ ................................ ............................. 24 Factors Affecting the Success of a Construction P roject ................................ ................ 24 Framework of Success Criteria for Design/Build Project ................................ ............... 26 Project Performance Measure ................................ ................................ ................................ 27 Balanced Scorecard ................................ ................................ ................................ ......... 27 Project Definition Rating Index ................................ ................................ ....................... 29 Leading Indicators ................................ ................................ ................................ ........... 32 Weight Computations in Index Models ................................ ................................ .................. 34 Background ................................ ................................ ................................ ...................... 34 Concerns with Weights in Index Modeling ................................ ................................ ..... 38 Simple Multi Attribute Rating Technique Using Swing (SMART S ) ................................ ..... 41 3 STRATEGY AND DESIGN ................................ ................................ ................................ .. 45 Overview ................................ ................................ ................................ ................................ 45 Development of Problems ................................ ................................ ................................ ...... 45 Data Collection ................................ ................................ ................................ ....................... 47 Data Transformation ................................ ................................ ................................ ............... 48 Data Analysis ................................ ................................ ................................ .......................... 49 Descriptive Statistics ................................ ................................ ................................ ....... 49 Data Tendency Analysis ................................ ................................ ................................ .. 51 Methodology ................................ ................................ ................................ ........................... 53

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7 Background ................................ ................................ ................................ ...................... 53 Canonical Correlation ................................ ................................ ................................ ...... 55 Overview ................................ ................................ ................................ .................. 55 Computation of canonical correlations ................................ ................................ .... 56 Results of canonical correlations ................................ ................................ .............. 56 Factor Analysis (Exploratory vs. Confirmatory) ................................ ............................. 59 Exploratory Factor Analysis (EFA) ................................ ................................ ................. 61 Overview ................................ ................................ ................................ .................. 61 Number of factors with extraction methods ................................ ............................. 64 Confirmatory Factor Analysis (CFA) ................................ ................................ .............. 67 Overview ................................ ................................ ................................ .................. 67 CFA models for this research ................................ ................................ ................... 70 Procedures for CFA Models ................................ ................................ ............................ 71 Overview ................................ ................................ ................................ .................. 71 ................................ ................................ .................. 73 Average values of groups of each success parameter ................................ .............. 75 Data normality ................................ ................................ ................................ .......... 75 Computer package and goodness of fit test ................................ ............................. 77 Parameter estimate and significance test ................................ ................................ .. 80 4 FACTOR ANALYSIS RESULTS ................................ ................................ ......................... 82 Overview ................................ ................................ ................................ ................................ 82 ................................ ................................ ................................ ................... 83 ................................ ................................ ..................... 83 Improvement of Coefficient Alpha Values ................................ ................................ ..... 84 ................................ ................................ ....................... 87 Average Value of Group ................................ ................................ ................................ ......... 88 Exploratory Factor Analysis (EFA) ................................ ................................ ........................ 89 Overview ................................ ................................ ................................ ......................... 89 Factor Extraction Technique ................................ ................................ ........................... 90 Eigenvalue Greater Than 1.0 Method ................................ ................................ ............. 91 Scree Test Method ................................ ................................ ................................ ........... 94 EFA Result Summary ................................ ................................ ................................ ...... 97 Normality Check ................................ ................................ ................................ ..................... 97 Confirmatory Factor Analysis (CFA) ................................ ................................ ................... 100 Overview ................................ ................................ ................................ ....................... 100 Interpretation of Parameter Estimate Process ................................ ................................ 100 Ini tial Results for Each Success Parameter ................................ ................................ ... 103 CFA Model Trimming ................................ ................................ ................................ ... 105 Overview ................................ ................................ ................................ ................ 105 Critical ratio (CR) method ................................ ................................ ...................... 107 All possible combination of problem groups m ethod ................................ ............ 110 Critical ratio (CR) vs. all possible combination ................................ ..................... 114 Final goodness of fit test indices ................................ ................................ ........... 116 Parameter Estimate and Significance ................................ ................................ ............ 117

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8 5 APPLICATION OF CFA MODEL OUTPUTS ................................ ................................ ... 121 Background ................................ ................................ ................................ ........................... 121 Single Multi Attribute Rating Tec hnique Using Swing Weight (SMART S ) ....................... 122 Overview ................................ ................................ ................................ ....................... 122 Computation of Input Values ................................ ................................ ........................ 122 Independency Properties of Values ................................ ................................ ............... 124 Dominance of Problem Groups ................................ ................................ ..................... 129 Basic Concept of Application ................................ ................................ ............................... 133 Overview ................................ ................................ ................................ ....................... 133 Scenario ................................ ................................ ................................ ......................... 134 Equation Example ................................ ................................ ................................ ......... 135 Denotation of input table ................................ ................................ ........................ 135 Weighted problem group ................................ ................................ ........................ 135 Degrees of problem severities ................................ ................................ ................ 136 Numeric Example ................................ ................................ ................................ .......... 137 Using equal success priority weights ................................ ................................ ..... 137 Using unequal success priority weights ................................ ................................ 138 Using both unequal success priority weights and degrees of problem severity ..... 139 Sum mary ................................ ................................ ................................ ........................ 141 Development of Application ................................ ................................ ................................ 142 Overview ................................ ................................ ................................ ....................... 142 Application Software Program ................................ ................................ ...................... 143 Application Description ................................ ................................ ................................ 143 Validation of Application ................................ ................................ .............................. 149 Overview ................................ ................................ ................................ ................ 149 Validation survey ................................ ................................ ................................ ... 149 Validation survey output ................................ ................................ ........................ 150 6 CONCLUSIONS AND RECOMMENDATIONS ................................ ............................... 157 Conclusions ................................ ................................ ................................ ........................... 15 7 Recommendations for Future Research ................................ ................................ ................ 159 APPENDIX A 43 Potential Problems ................................ ................................ ................................ ........... 162 B Definition of Each Problem Group ................................ ................................ ....................... 164 C Desc riptive Statistics of 43 Problems ................................ ................................ ................... 165 D Summary of Critical Ratio (CR) Method Procedure ................................ ............................ 171 E Summary of Raw Estimate and Its Un ique Variances ................................ .......................... 173 F Plots of Ten Pairs of Project Success Parameters ................................ ................................ 183

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9 G Survey File ................................ ................................ ................................ ............................ 187 H Redrawn Regression Model ................................ ................................ ................................ .. 191 LIST OF REFERENCES ................................ ................................ ................................ ............. 192 BIOGRAPHICAL SKETCH ................................ ................................ ................................ ....... 195

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10 LIST OF TABLES Table page 2 1 General procedure of an index model ................................ ................................ ................ 35 2 2 Example of project definition rati ng index ................................ ................................ ........ 36 2 3 ................................ ........................ 37 2 4 A six point scale used for the questionnaire ................................ ................................ ...... 38 2 5 Generating five different weighted scores for cost parameter ................................ ........... 38 2 6 Summary of three index models on weights ................................ ................................ ...... 39 3 1 Groups of potential problems ................................ ................................ ............................. 46 3 2 A six point scale ................................ ................................ ................................ ................. 48 3 3 Comparisons of current and rescaled ................................ ................................ ................. 49 3 4 Examples of problems with descriptive statistics ................................ .............................. 50 3 5 Mean values of success parameters ................................ ................................ ................... 51 3 6 Canonical correlations of cost parameter ................................ ................................ ........... 57 3 7 Canonical correlations of schedule parameter ................................ ................................ ... 57 3 8 Canonical correlations of quality parameter ................................ ................................ ...... 57 3 9 Canonical correlations of safety parameter ................................ ................................ ........ 58 3 10 Canonical correlations of satisfaction parameter ................................ ............................... 58 3 11 Heuristic EFA data ................................ ................................ ................................ ............. 62 3 12 Bivariate correlation matrix ................................ ................................ ............................... 62 3 13 Eigenvalues for the example ................................ ................................ .............................. 65 3 14 Equations and results of the model ................................ ................................ .................... 69 3 15 Initial CFA model equations ................................ ................................ .............................. 71 3 16 Equations for skewness and kurtosis ................................ ................................ ................. 76 3 17 Ranges of Skewness and Kurtosis ................................ ................................ ..................... 77

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11 3 18 Goodness of fit test categories and its indices ................................ ................................ ... 79 4 1 ................................ ................................ ................... 84 4 2 ................................ .................... 85 4 3 ................................ .... 86 4 4 ................................ ............ 86 4 5 ................................ .............. 87 4 6 ................................ ................ 87 4 7 ................................ ................................ ........................... 88 4 8 EFA summary output of schedule ................................ ................................ ...................... 92 4 9 EFA summary output of quality ................................ ................................ ........................ 93 4 10 EFA summary output of safety ................................ ................................ .......................... 93 4 11 EFA summary output of satisfaction ................................ ................................ ................. 93 4 12 Summary of Eigenvalues of success parameters ................................ ............................... 95 4 13 Output of normality check for cost ................................ ................................ .................... 98 4 14 Output of normality check for schedule ................................ ................................ ............. 98 4 15 Output of normality check for satisfaction ................................ ................................ ........ 98 4 16 Output of normality check for quality ................................ ................................ ............... 99 4 17 Output of normality check for safety ................................ ................................ ................. 99 4 18 Example of statistical significance of schedule using AL as base ................................ ... 102 4 19 Example of statistical significance of schedule u sing CA as base ................................ ... 102 4 20 Example of standard estimate ................................ ................................ .......................... 103 4 21 Initial results of P values of CFA model ................................ ................................ .......... 104 4 22 Summary of critical ratio (CR) procedure for schedule ................................ ................... 108 4 23 Summary of best fitted model using critical ratio (CR) method ................................ ..... 109 4 24 Summary of combinations of seven problem groups ................................ ...................... 111

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12 4 25 Summary of six problem groups combinations for safety ................................ ............... 113 4 26 Summary of best fitted model using possible combination method ................................ 113 4 27 Comparisons of critical ratio and all possible combination ................................ ............. 114 4 28 Summary of goodness of fit indices ................................ ................................ ................ 116 4 29 Summary of standard estimate (factor loading) ................................ ............................... 118 4 30 Equation forms for cost ................................ ................................ ................................ .... 119 5 1 Conversion factor loadings for cost ................................ ................................ ................. 123 5 2 Summary of conversion values for all success parameters ................................ .............. 124 5 3 Value difference between two parameters ................................ ................................ ....... 126 5 4 Value difference between two project s uccess parameters ................................ .............. 127 5 5 Correlations between project success parameters ................................ ............................ 127 5 6 Summary of variability ................................ ................................ ................................ .... 128 5 7 Value difference between two problem groups ................................ ............................... 130 5 8 Impacts of problems on each success parameter ................................ ............................. 135 5 9 Initial impacts with equal weights of 1 ................................ ................................ ............ 137 5 10 Weighted impacts of success parameters ................................ ................................ ......... 139 5 11 Weig hted impacts of both unequal success parameters and degrees of problem severity ................................ ................................ ................................ ............................. 140 5 12 The difference in ranking ................................ ................................ ................................ 142 5 13 Comp arisons of original method and swing weights ................................ ....................... 145 5 14 Computation of proportional ratio of scores ................................ ................................ .... 148 5 15 General summary of pa rticipants and project characteristics ................................ .......... 151 5 16 Priority weights of six projects ................................ ................................ ........................ 151 5 17 Comparison of actual project and tool output ................................ ................................ .. 153 A 1 43 Potential problems ................................ ................................ ................................ ...... 162 C 1 Descriptive statistics of 43 problems ................................ ................................ ............... 165

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13 D 1 Cost ................................ ................................ ................................ ................................ .. 171 D 2 Quality ................................ ................................ ................................ .............................. 171 D 3 Safety ................................ ................................ ................................ ............................... 171 D 4 Satisfaction ................................ ................................ ................................ ....................... 171 E 1 Cost ................................ ................................ ................................ ................................ .. 173 E 2 Schedule ................................ ................................ ................................ ........................... 175 E 3 Qualit y ................................ ................................ ................................ .............................. 177 E 4 Safety ................................ ................................ ................................ ............................... 179 E 5 Satisfaction ................................ ................................ ................................ ....................... 181

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14 LIST OF FIGURES Figure page 1 1 Research approach ................................ ................................ ................................ ............. 22 2 1 Framework for factors affecting project success ................................ ............................... 25 2 2 Criteria for project success ................................ ................................ ................................ 26 2 3 Framework for project success of design/build projects ................................ .................... 27 2 4 Balanced scorecards ................................ ................................ ................................ ........... 28 2 5 PDRI sections, categories, and elements ................................ ................................ ........... 30 2 6 Example of final version of PDRI ................................ ................................ ..................... 32 2 7 Example output of LI tool ................................ ................................ ................................ .. 34 3 1 A screen capture of evaluation worksheet ................................ ................................ ......... 4 7 3 2 Frequency histogram ................................ ................................ ................................ .......... 52 3 3 Input matrix ................................ ................................ ................................ ........................ 56 3 4 A relationship between two variables ................................ ................................ ................ 60 3 5 Summary of latent factors and variables ................................ ................................ ............ 63 3 6 Scree plot. ................................ ................................ ................................ .......................... 66 3 7 Example model. ................................ ................................ ................................ ................. 69 3 8 Initial CFA model for each success parameter ................................ ................................ .. 70 3 9 CFA model procedure ................................ ................................ ................................ ........ 72 3 10 Distributions. ................................ ................................ ................................ ...................... 76 3 11 Screen capture of Amos ................................ ................................ ................................ ..... 78 4 1 The procedure of results ................................ ................................ ................................ ..... 82 4 2 Example of output of EFA for cost ................................ ................................ .................... 91 4 3 Scree plot. ................................ ................................ ................................ .......................... 96 4 4 Example of graphical input for CFA model ................................ ................................ .... 101

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15 4 5 Path diagram. ................................ ................................ ................................ ................... 119 5 1 The plot of safety vs. satisfaction ................................ ................................ .................... 128 5 2 Value differen ce between problem groups part 1 ................................ ............................ 131 5 3 Value difference between problem groups part 2 ................................ ............................ 132 5 4 Screen capture of Instruction wo rksheet for application ................................ ................. 144 5 5 Screen capture of input worksheet of application ................................ ............................ 146 5 6 Screen capture of output worksheet of a pplication ................................ .......................... 147 5 7 Regression model for validation of tool ................................ ................................ ........... 155 F 1 Cost vs. Schedule ................................ ................................ ................................ ............. 183 F 2 Cost vs. Quality ................................ ................................ ................................ ................ 183 F 3 Cost vs. Safety ................................ ................................ ................................ ................. 183 F 4 Cost vs. Satisfaction ................................ ................................ ................................ ......... 184 F 5 Schedule vs. Quality ................................ ................................ ................................ ........ 184 F 6 Schedule vs. Safety ................................ ................................ ................................ .......... 184 F 7 Schedule vs. Satisfaction ................................ ................................ ................................ 185 F 8 Quality vs. Safety ................................ ................................ ................................ ............. 185 F 9 Quality vs. Satisfaction ................................ ................................ ................................ .... 185 F 10 Safety vs. Satisfaction ................................ ................................ ................................ ...... 186 G 1 Worksheet of instruction ................................ ................................ ................................ .. 187 G 2 Worksheet of survey part A through C ................................ ................................ ............ 188 G 3 Worksheet of survey part C through E ................................ ................................ ............ 189 G 4 Worksheet of survey part E ................................ ................................ .............................. 190 H 1 Redrawn regression model ................................ ................................ ............................... 191

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16 LIST OF OBJECTS Object page 5 1 Potential problem identification tool as a Microsoft Excel file (. xls 41kb) ..................... 143

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17 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy IDENTIFICATION OF CO NSTRUCTION PROJECT PROBLEMS AND THEIR IMPACTS ON PROJECT SUCCESS By Seong Jin Kim December 2009 Chair: Raymond Issa Cochair: Ian Flood Major: Design, Construction and Planni n g The traditional ly accepted success parameters for construction projects are mainly cost and schedule. There are new trends in how to define success and the number of success parameters considered even though cost and schedule are still the most prevalent ones The number and priority of success parameters heavily depends on the owner. With respect to these trends, de termining the success of project is a hard and complicated procedure when a project faces multiple problems simultaneously. The problem s that practitioners or researchers face are : 1) W hat are the relationships betw een these causes and their impacts on project success parameters ?; 2) I s it necessary to concern all problems for each different project success parameter in terms of their negative impacts?; and 3) W hat problem is really considered when, especially, the p roject has more than one success parameter with different priorities ? The proposed solutions are 1) to determine the relationships between problems and their impacts and 2) to select problems required to be consider ed for each project success parameter to meet multi project success parameters within different priorities. This will help ensure that the project meets the required performance targets and adds value for all participants at the early phase of

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18 project. Confirmatory factor analysis (CFA) models w ere used for testing the quality of proposed solutions. The outputs of CFA were used for an application tool to select the most critical problems under given circumstances such as parameter priorities and problems severity. A s ingle multi attribute rat ing technique using swing (SMART S ) was the statistical tool used for this study The results of this research show the selection of critical problem groups under given circumstances. In the future contractors, owners, and other project participants can u se this technique to more effectively conduct project reviews during the course of the project.

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19 CHAPTER 1 INTRODUCTION With respect to the development of technologies and new demands on markets, many things in the construction industry have been changing rapidly and some new trends have emerged It appears impossible for the construction industry to break free from these current trends. These trends would be the changes in the contract types, the development of technologies and methodologies to manage t he project problems effectively, and new demands from owners (customers) on project outcomes. For example, e ven though, the highway industry has used the lowest bid system for a long time new innovative methods in contracting such as Method A+B and La ne Rental have been proposed and/or are replacing the lowest / competitive bid (Herbsman et al. 1995; Herbsman and Ellis 1992) A similar situation could be easily found in comme rcial construction as well. There are many computer software programs available that make construction management easier and more effective than ever share the same concepts of project success parameters such as cost and schedule any longer. The success of a project heavily depends on the owner s requirement Apparently there are concerns other than just cost and schedule in terms of project success parameters perspective on projects in our rapidly changing industry e the project problems a contractor fa ce s or will face in the near future because there exist probable relationships between the success of a project success as defined by the owner. Regarding the type of problems impeding success on construction proj ects there are on

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20 The answer to the first question could include material delays, the qualification of subcontractors and architects, request for information (RFIs), and change order, etc. The answer to the second question types of construction projec t problems have been changed that much. Nowadays, contractors must manage multiple problems simultaneously. The traditional project success parameters are cost and schedule. As mentioned above, however, there are new additional success parameters as def ined and prioritized by the owner. The new project success parameters could vary among projects and their owners. These new that any project in the im mediate or near future has more than one or two success parameters. There are numerous articles and abundant research available on project success parameters and/or key performance indicators. This research addresses the fact that different problems have differe nt impacts on success parameters. But unfortunately it does not fully explain the difference. There are two types of variables involved in this research and they are independent and dependent variable s Although the project success index is represented as a dependent different parameters (Griffith et al. 1999) Some dependent variables such as quality, safety, and satisfaction are not a vailable and/or easily accessible to researcher. These kinds of variables are treated as confidential (safety incident rates and related indices) in a company and it is hard to measure the degree of agreement (quality and safety). Therefore, the current studies and research lack multi project success parameters. Most studies have focused on cost and schedule by predicting the outcomes during the course of project. Even though a predicted outcome may

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21 give a guideline of how to manage the project at that moment, it does not specify where to improve or fix the problems if the predicted outcome is not good. And also there are many problems, factors, and indicators addressed without looking at their impacts on the project success parameters. This research is focused on finding the practices of general contractors and owner s to improve project performance during project execution. These practices c ould provide additional insight into the success parameters while complimenting current approaches This will he lp ensure that the project meets the required performance targets and adds value for all participants at an early stage of a project. The primary beneficiaries of this research include owners, contractors, and other project participants Problem Statement The problem statement is : How much could project performance have been improved if the relationships between problems and success parameters had been pre identified in situations whe re there are multi project success parameters each with different priorit ies ? Research Questions F ive research questions have been developed for this research effort. These questions must be investigated to address the research problem. The questions identified are as follows: What are the common problems o n projects? What a re the relationships between problems and their impacts on project success parameters? How different are these relationships when project success parameters have different priorities? How can critical problems be identified under given circumstances of m ultiple success parameters and different priorities?

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22 How can intangible variables (parameters) be measured in practice? Purpose, Objectives, and Approach The main purpose of this research is to identify best practice s to improve project performance throug h the analysis of impacts o f the problems on a project o n project success parameters. To achieve the purpose of this research, the objectives should be identified. The objectives are: 1) Establish a definition of project success parameter; 2) Determine f requent problems and their causes in projects; 3) Identify correlations between problems within each success parameter; 4) Determine all possible significant relationships that affect project success parameters and problems types; and 5) Determine how inta ngible variables (parameters) could be measured. Figure 1 1 shows the overall approach of this research. Figure 1 1 Research approach

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23 Layout of Dissertation Chapter 2 of this report provides an overview and additional backgrou nd information on this research. The review of current literature is presented. Chapter 3 describes the strategy and design of this research. This includes an overview of the research approach and a development of relationships between problems. A desc ription of the data collection process and a discussion of the data analysis are presented Finally the research methodology will be discussed. Chapter 4 presents the general procedure of factor analysis for both exploratory and confirmatory analysis and their results and findings of the study. Chapter 5 provides the development of an application of outputs for reassessment of potential project problems This includes an overview, application, and validation of the methodology Chapter 6 provides concl usions and then recommendations for the usage of the methodology

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24 CHAPTER 2 LITERATURE REVIEW Overview To set up the framework of the research, a review was conducted of other research, current trends, related topics, and methodologies. Although a litera ture review is the first step of this research, it is very hard to find the same or at least similar topics or subjects among current research. Some of the main concerns are performance/key indicators/factors in general construction management and the pro cess of how to develop performance indicators and how to measure them by looking for success parameters. Project control, major issue areas, and how to measure project performances will be discussed. This chapter mainly consists of three sections One i s about the development of project success criteria and another is about the project performance measure, and the last is about weights in index modeling. In addition to the main sections, there will be a brief discussion on decision making theory which m ay apply to the development of an application for this research. More detailed information will follow Project Success Factors/Criteria Factors Affecting the Success of a Construction Project Chan et al. ( 2004) addresse d the number of factors affecting a construction project. The study of previously successful pr oject s and the ir critical success factors (CFSs) is a step t o ward determining means to improve the effectiveness of project s However the concept of project success has remained ambiguously defined in the mind of construction professionals. The researche r reviewed current journals, sorted out the success factors (CFS) for construction project s from their case studies and finally categorized them into five groups. The five groups are : Project Management Actions, Project Procedures, External Environment, Project Related Factors, and Human Related Factors. The Human Related factors group has the largest number,

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25 22, of factors and the Project Procedures has the lowest number two The External Environment has six factors, which are 1) Economic environment; 2) Social environment; 3) Political environment; 4) Physical environment; 5) Industrial relations environment; and 6) Technolog icall y advanced. Figure 2 1 shows the five groups each with its set of factors. Figure 2 1 Framework for factors affecting p roject success (Chan et al. 2004) These factors could be the most influential if the factors affecting a project were considered at the macro viewpoint. The chances to be an important factor for a project would be lower than other factors. Chan et al. (2004) p roject success is a function of project related factors, project procedures, project management actions, human related factors y also pointed out the potential relationships between factors and groups.

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26 Framework of Succ ess Criteria for Design/Build Project Framework of success criteria for design/build projects (Chan et al. 2002) mainly addresse d the concept of a project success and what to measure to achieve project success in design/build projects. The authors mention that measuring project success is a complex task since success is intangible a nd can hardly b e agreed upon. The general concept of project success remains ambiguously defined because of varying perceptions. Each project participant will have his or her own view of success. It depicts that owner s contractor s and architect s have a different perspective of project success. According to the authors, project success is the goal, and the objectives of budget, schedule, and quality are the three normally accepted criteria to achieve the goal. Each project has a set of goals to accomp lish, and they serve as a standard to measure performance. The authors address ed that the criteria for a construction project in general can be classified under two main categories, one being hard, objectives, tangible, and measurable, and the other soft, subjective, intangible, and less measurable. The integration of success and criteria is shown in Figure 2 2. As shown in Figure 2 2, some of examples are provided, for hard, objectives, tangible, and measurable such as time, cost, quality, profitability, technical performance, completion, functionality, health and safety, productivity, and environmental sustainability and for soft, Figure 2 2 Criteria for project success (Chan et al. 2002)

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27 subjective, intangible, and less measurable, such as satisfaction, absence of conflicts, professional aspects. The unique idea proposed by th is article is to add time concept s like phases of construction, to success criteria. With respect to th e time parameter measures of success are different for each phase of the construction process (see Figure 2 3 ) Figure 2 3 Framework for project su ccess of design/build projects (Chan et al. 2002) As far as objective measure s are concerned (see Figure 2 3) a maximum of three items are in the construction phase (current) and only one item is in post construction phase (future). For the subjective measure s two major items with five sub items are at the post construction phase (future) and three items are at pre construction phase (past). The numbers of items in the subjective measure are greater than the objective measure. It depicts that there are more intangible criteria available in design/build projects in terms of succes sful projects. Project Performance Measure Balanced Scorecard T he balanced scorecard method (Kaplan and Norton 1992) tracks the key elem ents of a from continuous improvement and partnerships to teamwork and global

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28 scale The balanced scorecard consists of four important perspectives : customer, financial, innovation and learning perspective, and internal business persp ective It is based on a set of measures that gives top managers a fast but comprehensive view of the business. The balanced scorecard provides answers to four basic questions (Kaplan and Norton 1992) ; How do customers see us? (customer perspective) What must we excel at? (internal perspective) Can we continue to improve and create value? (innovation and learning perspective) How do we look to shareholders? (financial perspective) As shown in Figure 2 4 t he balanced scorecard links among four perspective s and how results are achieved. According to the authors, o ne of the benefits of u sing the balanced scorecard is to show managers the most critical factors on their project There are several companies that have used the balanced scorecard and they have s hown that it meets the ir managerial needs (Kaplan and Norton 1992) The balanced scorecard has two major levels. The first level brings together, in a single Figure 2 4. Balanced scorecards (Kaplan and Norton 1992)

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29 management report, many of the seemingly disparate eleme agenda such as becoming customer oriented, shortening response time, improving quality, and emphasizing teamwork at the manager level. The second level guards against sub optimization at senior manage ment level which means that it forces senior managers to consider all the important operational measures together It makes it possible for senior managers to make sure that they use a balanced approach and to make sure improvement in one area has not been achieved at the expen se of another hence, the nam e balanced scorecard The balanced scorecard is based on management strategies and their measures. It aids managers in tracking their performances based on their strategies or goals and shows the relationship between four diff erent perspectives and the impacts on the results. These concepts could be adopted to develop a n application for this research Project problems have different impacts on project success parameters and each success parameters could have different priorit y weights. The four perspectives of the balanced scorecard are equivalent to project success parameters The authors did not mention how to set up the strategies because strategies can be different for different organizations or companies based on their needs or demand. But it shows how companies or organizations have improved their performances and the degree of improvement using the balanced scorecard. Project Definition Rating Index The project definition rating index (PDRI) was developed by Gibson an d Dumont (1995) for the pre project planning phase It is an easy to helps owners and design contractors interact during pre project planning phase. The first PDRI is designed for the industry or heavy indus try with 70 elements in 15 categories and has three different sections as bas e s for project decision, front end definition, and execution approach. Figure 2 5 shows the list of 3 sections, 15 categories, and 70 elements incorporated in the PDRI.

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30 Figur e 2 5. PDRI sections, categories, and elements (Gibson and Dumont 1995)

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31 The PDRI is based on the scope definition, i.e. th e degree of scope definition at the pre project planning phase. If the scope is well defined at that time, the impact on the project will be positive later. The degree of scope definition consists of five levels ( L evel 1 through 5) and definition of each level is as follow s (Gibson and Dumont 1995) : 1: Completion Definition 2: Minor Deficiencies 3: Some Deficiencies 4: Major Deficiencies 5: Incomplete or Poor Definitions In the PDRI development stages, industry participants were asked to weigh elements b y level of scope definitions and there was no limit on weighing. The w eighting value represents Therefore the lower the value the better project scope de finition is. Finally all the data are normalized by a 1,000 scale as the maximum score for the tool development. It represents the sum of all values (70 elements) in level 5 of the scope definition and is equal to 1,000. The scores of L evel s 1 through 4 are rescaled proportionally compared to L evel 5. Figure 2 6 shows an example of the final version of PDRI There is a difference in the level of scope definition s compared to the initial version. An option of Not Applicable is added into the level of scope definition. As mentioned earlier, the sum of L evel 5 will be 1,000. In the example category L Procurement Strategy is assigned a maximum score of 16 which is the sum at L evel 5 of items, L1, L2, and L3. The maximum score of each item in Section L is 8, 5, and 3 for L1, L2, and L3 respectively. The score range of levels for an item L3 lies in between 0 for L evel 1 and 5 for L evel 5. If any user defines the level of scope definition of 70 items, then fill the column of

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32 s core with each assigned sco re by level of scope definition. The sum of column Score will show the levels of scope definition. The sum of Score will not exceed 1,000. Figure 2 6. Example of final version of PDRI (Gibson and Dumont 1995) The PDRI is a checklist of the project at the pre project planning phase. It is useful for owner, design contractors to check impacts on TIC based on the current si tuation. Although the PDRI is a useful tool for owners and designer s due to its limitation to the pre project planning phase, it cannot be adopted by contractors for project executions. T here will be more than one standpoint for the project checklist du ring project execution other than TIC. The drawback of this measurement tool is that the participant could manipulate the measurement. The participant knows which item s ha ve the most and least impacts on the result so it is possible for them to manipulat e the results. Leading Indicators A leading indicators (LI) project (Choi et al. 2006) was initiated by the Co nstruction Industry Institute (CII) to develop a new tool that can forecast the potential risk of not meeting specific project outcomes based on assessing potential problems (leading indicators). The cators are fundamental project

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33 characteristics and/or events that reflect or predict project health. Revealed in a timely manner, (Choi et al. 2006) Forty three leading indicators were finally developed through three surveys and five different project success parameters were identified. The five different success parameters we re tisfaction. Each LI was evaluated in terms of each success ful outcome using a five point scale. If an LI has the highest negative impact on any success outcome, then five point s will be assigned to it If an LI has no negative impact on any success outc ome, then zero will be assigned. A nd the rest will be in between. Weights of problems are based on aggregated scores. Based on this framework, a LI forecasting tool has been developed. In the tool usage, each problem is assessed as follows (Choi et al. 2006) : 1: Serious (100%) 2: Major (75%) 3: Moderate (50%) 4: Minor (25%) 5: None (0%) Not Applicable The impact of each problem is assumed to be normally distributed. The total score of the tool will be 1,000. Unlike the PDRI, the higher score means po sitive or better and the lower score depicts negative or worse in forecasting project outcomes. This tool provides the score after assessing potential problems in terms of five different project success parameters plus one overall. Figure 2 7 shows an ex ample output for this tool. The overall score represents the combination of each success parameter. The weight of each parameter is equal. The user is able to forecast the project outcome in terms of different success parameters. This tool aids the par ticipants in understanding the degree of success in project outcome during the course of the project.

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34 Figure 2 7. Example output of LI too l (Choi et al. 2006) It is the first tool that considers more than one or two success parameters in the tool development. The tool provides the different success parameters but it fails to provide a guideline on where to fix or what to improve when the expected outcome is not as good as expected or vice versa. During the tool validation process, it was hard for the research team to get information on safety incident rates, quality, and satisfaction. Such i nformation is not readily shared by companies. I nformation on cost and schedule was available to validate the tool but information on safety, quality, and satisfaction was not available. Th is methodology does not provide a clear predicted outcome when the weight of each s uccess parameter is not equal. Weight Computations in Index Models Background The domain of this research originally comes from index modeling. It is becoming popular among construction industry participants to measure performance or to meet needs. A

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35 con struction company will increase their performances. The company will follow a general procedure in developing the index model. An example of an index model for prediction of project success by project problems is shown in Table 2 1. Table 2 1 General procedure of an index model Step Activities Description 1 Develop Problems Select problems for measurements in the model 2 Evaluate Problems on Success Survey Required Evaluate Impac ts/Weights of Problems on Success (Ex. From No Impact: 1 to High Impact: 6) 3 Finalize Impacts or Weights Sum of Survey Use average 4 Decide Degree of Impact or Weights Expertise Just Normal Distribution From 0 % to 100% 5 Application Tool Development (Mapping, Scorecards, Decision Trees, etc.) 6 Tool Validation Required a Survey for Validation Limitations & Recommendation This is the general process for the development of an index m odel. One of the common examples of an index model is the PDRI developed by Dumont and Gibson (1995) It is specifically designed for the pre project planning phase. It is an easy to perspective and this helps owners and design contractors interact during pre project planning phase. The PDRI is based on the scope definition, exactly the degree of scope definition at the pre project planning phase. If the scope is well defined at that time, the impact on the project will be posi tive later. The degree of scope definition consists of five levels. A lower level is

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36 better than a higher level for the project scope definition. All elements are weighted by level of scope definitions and there is no limit on weighing. Weighing point represents that each points, the better project scope definition is. Finally all the data are normalized by 1,000 scale for the tool development. Table 2 2 shows an example of the PDRI. These are the weights of some items on the PDRI before they are normalized by 1,000 scale. Table 2 2 Example of project definition rating index SECTION II FRONT END DEFINITION CATEGORY 1 2 3 4 5 Element J. INFRASTRUCTUR E J1. Water Treatment Requirements 0% 5% 10% 15% 20% J2. Loading/Unloading/Storage Facilities Requirements 1% 4% 8% 12% 14% J3. Transportation Requirements 0% 10% 1 = Complete Definition 2 = Minor Deficiencies 3 = Some Deficiencies 4 = Major Deficiencies 5 = Incomplete or Poor Definition Source: Gibson and Dumont 1995 3 in Table 2 2 on scope definitions, the estimator will add 10% of TIC for the contingen cy. The maximum weight is incomplete or poor definition and it will vary from item to item. In case of J1, the maximum allocated weight is 20% and the minimum is 0%. The final 20% is the average value of participants. Fifty four specialized team member s from 31 companies who are very experienced in the construction industry decide all the weights on the P DRI. According to the PDRI report ( Gibson and Dumont 1995 ) the total number of years of experience is 1,047 (709 years of project management and 338 years of estim ating). The selection of reasonable weights is very important for the PDRI model, which is classified, in general, as an index model. The basis for the PDRI model (1992) definition rating checklist. After weighing items on the list were comp leted the PDRI results were

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37 revised definition rating checklist. The results were similar. Hackney arbitrarily assigned maximum weights to each of the items in his checklist (Gibson and Dumont 1995) The weights shown in Table 2 3 pertain to industrial process plant projects. The magnitude of the weights reflects an estimate of the percentage cost o verrun that might be expected in the overall project if information for an item was completely unknown. In general, the weights represent the relative ability of an item to affect the degree of uncertainty in the project estimate. The intent of the check list was for each item to be scored prior to estimating the cost of the project. Table 2 3 (1992) revised definition rating checklist Items Max. Weight Items Max. Weight General Project Basis Site Information (Continued) Products and By Pr oducts 100 Yard Improvements Available 40 Process Background 200 Review with Operations 25 Raw Materials 100 Review with Construction 25 Utilities & Services 50 Engineering Design Status Ownership Factor (multiplier) Layouts 35 Proc ess Design Status Line Diagrams 50 Flow Balances 70 Auxiliary Equipment, Type and Size 70 Major Equipment, Type and Size 80 Buildings, Type and Size 35 Materials of Construction 50 Yard Improvement, Type and Size 55 Review of Process D esign 70 Hazard Control Specifications 30 Site Information Coating Specifications 20 Surveys 85 Review of Engineering Design 100 Climatological Information 25 Detailed Design Ordinances & Regulations 146 Drawings and Bills of Materials 45 Reusable Equipment 25 Drawing Reviews 35 Reusable Supports, Piping & Electrical 25 Field Performance Status 50 Buildings Available 30 Utilities Available 25 Source: Hackney 1992 The leading indicators project (Choi et al. 2006) was initiated by CII to develop a new tool that can forecast the potential risk of not meeting specific project out comes based on assessing potential problems (leading indicators). 43 leading indicators were developed and five different project success parameters were identified. The five different success parameters are cost, atisfaction. The final product of project is a tool to forecast the project success based on assessment of current problems in a project. There were 84

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38 survey participants. Each problem was evaluated in terms of their negative impact on each project suc cess parameter. The range of scores shown in Table 2 4 is from 0 (no impact) to 5 (very high negative impact) in each potential problem. Table 2 4 A six point scale used for the questionnaire Scale No Very Low Low Moderate High Very High Point 0 1 2 3 4 5 Source: Choi et al. 2006 scores) in each problem. There were issues on this computation with standard deviation of each sum of problem. To minimize the impacts of standard deviation, each sum was divided by its standard deviation for the initial (normal) weight. During the tool testing stage, the normal weight may not be sufficient enough to differentiate between outcome scores. So the weights were recomputed u sing third, fifth, seventh, and ninth power as shown in Table 2 5 Table 2 5 Generating five different weighted scores for cost parameter LI No. Total Score Normal Weight (W1) Third Power Weight (W3) Fifth Power Weight (W5) Seventh Power Weight (W7) Ninth Power Weight (W9) SD Weighted Score SD 3 Weighted Score SD 5 Weighted Score SD 7 Weighted Score SD 9 Weighted Score 1 369 0.62 594 0.24 1,539 0.09 3,987 0.04 10,329 0.01 26,762 2 313 0.83 379 0.56 554 0.39 811 0.26 1,188 0.18 1,739 3 343 0.85 402 0.62 5 52 0.45 759 0.33 1,042 0.24 1,432 4 317 0.81 390 0.54 592 0.35 899 0.23 1,364 0.15 2,070 . . . . . . Source: Choi et al. 2006 Based on these five different weights, the tool validation was processed. After all, t he potential users have options for the usage of these five different weights. Concerns with Weights in Index Modeling T hree different index models have been addressed. The most important part of index modeling is the weighing process. Each model has its ow n way to compute the weights. A summary of the features of each model is shown in Table 2 6

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39 Table 2 6 Summary of three index models on weights Model Participants Method Weights on Weighing Method Hackney ( 1992 ) Personal Experience % of Cost Overrun A rbitrary Personal Experience PDRI ( 1995 ) 54 Industry Expertise Workshops Contingency on TIC Use Average Leading Indicator ( 2006 ) 84 Industry Participants General Surveys Impact on 5 Different Successes Aggregated Score/Standard Deviation (19 92 ) model is based on personal experience and weighs on percentage of cost overrun. Fifty f our industry experts spent a lot of time on e s tablishing weights for TIC in the PDRI. The first index model is based on personal experience and know how and the se cond one has support from 54 specialized industry expertises in the same field for this specific research. value as weights for the PDRI because the par ticipants had enough experiences in the same field and kept communicating on this subject during the workshop. For the leading indicator project, there were 84 industry participants. It is a general research survey such as is done of most cases of this t ype of research In this case, it is not recommended to use the average because participants may have different field of specialties and there may be a gap between their total amount of experience. The aggregated score is the best solution for this proje ct. But as mentioned above, it had problems with standard deviation during weighing process and tool tune up process. The final solution was to use normal, third, fifth, seven, and ninth power as shown in Table 2 5 There are two questions regarding thi second question may be w eights. Based on weights, any kind of tool can be developed. That tool could be similar to the leading indicator, balanced scorecards, mapping, or decision tree

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40 tools The answer to the second question may be controversial. But the importance of weight in the index model is extremely high. What Hackney ( 1992 ) and the PDRI model measure is tangib l e cost s and schedule durations That is why both used percentage as weights. Problems and items on the checklist were developed for these two measures. The re is a demand for new project success parameters. Cost and schedule are the most traditional common project success parameters. But there are some more parameters that impact the success of project s, e.g. satisfaction. The first two models are designed mainly for cost and schedule but the leading indicator project is designed for five different success parameters. The five success parameters are cost, schedule, quality, safety, and satisfaction. Two of these parameters cou ld be tangible like cost and schedule but the rest of parameters cannot be tangible. The leading indicator project has to be consistent in measuring the impacts o f each success parameter. A project should not have more than one method to measure somethin g. For example, the contingency (%) for cost and schedule and degrees of impact for quality, safety, and satisfaction may not be a good solution for weight. In the opinion of th is author, one of the main reasons to use different powers in the leading in dicator project is the range of evaluation is too narrow so that aggregated scores did not make any big difference at the end. But if a wider range of impacts had been used, the aggregated scores could have made a big ger difference and then the standard d eviation could have been larger as well. The range of impacts or measurements is optimal between five and nine (Spector 1992) So the evaluation method of a shown in Table 2 4 is reasonable Users could not tell the degree of diffe rences over this range cases and the last model, the leading indicator, could be considered as a general case from

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41 surveys to tool validation process. From this pe rspective, there are some concerns with weight and their method of computation no matter what the specialized are a is in the index modeling. Si mple Multi Attribute Rating Technique Using Swing (SMART S ) The final deliverable from this research is an easy to use tool for the contractors when they have to make decisions on what to deal with among their potential or current problems under any circumstance of multi project success parameters with different priorities. The first step to using the tool could be a selection of the most critical problems based on the priority weights of project success parameters. The second step of the process is additionally applied to the degrees of severity of problems. In t he second step it is assumed that the degree of sever ity is normally distributed. Considering the first step, the methodology of simple multi attribute rating technique using swing (SMART S ) (Edwards and Barron 1994) would be applied. SMART S is based on an elicitation procedure for weights. SMART S uses linear approximations to single utility functions, an additive aggrega tion model, and swing weights. It is assumed that a decision maker has a project called Project A and has two potential job sites, Site s 1 and 2 and that their main selection criteria for a job site are cost and schedule. Under this circumstance, the pr ocedure of SMART S will be as follows: Step 1: Purpose and Decision Makers The selection of best job site for Project A with options of Site 1 and 2 Step 2: Develop a List of Attributes (Criteria) Cost and schedule of Project A Step 3: Objects of Evaluatio ns Evaluation of two job sites in terms of cost and schedule. Suppose, Site 1 is better than Site 2 in cost the weights are 90 for Site 1 and 70 for Site 2. On the other hand, Site 2 is better than Site 1 for the schedule the weights are 60 for Site 1 and 95

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42 for Site 2 The numbers represent the degree of preferences (value dimension) of each job site in cost and schedule. T he higher value, the higher the preference is. Based on this evaluation, the following matrix is obtained: Cost Schedule Si te 1 90 60 Site 2 70 95 Step 4: Determine Swing Weights Edwards and Barron ( 1994 ) use the term swing to refer to the operation of changing the score of some object of evaluation on some dimension from one value to a different value. Suppose that the schedule is more important than the cost. If the range of weights is in between 0 and 100, the weight of schedule would be 100. Then how much important is cost, compared to schedule, 100? One thing is sure that the weight of cost cannot be 100. Because cost is less important than schedule and the weight of schedule is 100. It is just assumed that the decision maker thinks the weight of cost is 80, compared to schedule, 100. Step 5: Calculate Swing Weights In Step 4, the weight of each attribute is de cided as 100 for schedule and 80 for cost. Using these weights, swing weights will be computed as follows: Weights Swing Weights Cost 80 80/180 = 0.45 Schedule 100 100/180 = 0.55 Total 180 1.00 Step 6: Calculate All Multi Attribute Utilities Using the matrix from Step 3 and swing weights, all multi attribute utilities (aggregated score) are computed The computation s for Site s 1 and 2 are as follows:

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43 Site 1: 90 0.45 + 60 0.55 = 73.50 Site 2: 70 0.45 + 95 0.55 = 83.75 Step 7: Make a Decisio n Based on the computation in Step 6, the decision maker has to come up with a final decision. The utility values of this example depict the preference. It means that the higher value is more favorable than the lower value and therefore, the higher aggre gated score from Step 2 would be the first best option. The aggregated score for Sites 1 and 2 are 73.50 and 83.75 for respectively. Under the given conditions for the selection criteria, evaluation of sites in terms of criteria, and weights of criteria, Site 2 would be a better choice than Site 1. The general procedure of SMART S has been addressed so far. Regarding options, there are two more considerations in this method. One is the domina nce option and the other is independence of options (Edward 197 7, Edwards and Barron 1994, Oyetunji 2001) Although there are some ways to check the dominance, it c ould be possibl y done by visual checking. In addition to the dominance, all options have to be independent of each other. It means that they have to hav e low correlations between them. The options that have the same correlations would be combined to one option or one of these options would be eliminated These two issues will be addressed in more detail in Chapter 5 Edwards and Barron (1994) address ed two key ideas underlying SMART S One is the multi attribute utility and the other is the strategy of heroic approximation. In terms of the multi attribute utility if anything is valued at all, it is valued for more than one reason. A ny outcome of a dec ision is most naturally described by a vector of numbers that relate to value. Edwards and Barron (1994) developed SMART S based on two principles One is that simpler tools are easier to use and so more likely to be useful. The second is that the key to appropriate selection

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44 of methods is concern about the trade off between modeling error and elicitation error. I n i ts previous version, SMART, included the judgments of the indifference between pairs of hypothetical options which seemed difficult and unst able. The a uthors believe d that more nearly direct assessments of the desired quantities wer e easier and less likely to produce elicitation errors and call that view the strategy of heroic approximation. Users of that strategy do not identify formally ju stifiable judgments and then figure out how to elicit them. R ather they identify the simplest possible judgments that have any hope of meeting the underlying requirements of multi attribute utility measurement, and try to determine whether they will lead to substantially suboptimal choices in the problem at hand. I f not, they try to avoid elicitation errors by using those methods. In this chapter, the literature review for this research has been presented in terms of its relevance to the research. The fi rst part of this chapter addresses the major factors and/or problems of projects. The major issue is to define factors and/or problems without their impacts on projects. A nother part focuses on the current trends or research of how to measure project per formances and its usage as a tool or index model in terms of project success parameters. T his section clearly shows the current research needs in this area for some more work on multi project success parameters and their relationships with problems. In o ther words, the current research focuses on a single or two success parameters. T he last part of this section address es some considerations on weight computation and a methodology for the application.

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45 CHAPTER 3 STRATEGY AND DESIGN Overview This chapte r will mainly address the overall strategy and design of this research. Th is include s the data collection, the methodologies for this research such as canonical correlations and factor analysis ( exploratory factor analysis and confirmatory factor analysis ). Regarding data collection, the background for the data collection will be discussed Canonical correlations will address concepts and how it relates to this research and their outputs. The concepts of exploratory factor analysis (EFA) and confirmator y factor analysis (CFA) will also be addressed Development of Problems All the necessary data were obtained from the leading indicators project (Choi et al. 2006) It developed 43 potential problems with five project outcomes (success parameters). The 43 problems are listed in Appendix A. The defined five success parameters by the research team were 1) Cost; 2) Schedule; 3) Quality/Operability (Quality); 4) Safety; and 5) Stakeholder satisfaction. These five parameters are generally and traditionally used to measure project success in many industries. The definition s for each parameter are as follows (Choi et al. 2006) : Cost : Cost performance is viewed in terms of overall actual final cost versus the established project budget. Secondary cost outcomes can include cost/cash flow deviation (compliance with spending plans), cost efficiency (how efficiently an asset is design and constructed verses similar facilities in industry), and consumption of contingency or reserves. Schedule : Schedule performance is viewed in terms of overall actual final duration versus the planned project duration. Secondary schedule performance can include outage duration performances and overall engineering and construction cycle time (for certain fast rack projects). Quality/Operability : Quality and Operability are outcomes that are based upon a facility being capable of operating per its intended function and that the quality of the facility and construction craftsmanship matches the intended asset lift. (For example, if we build a facility that is intended to make 100 widgets a day, the facility should be capable of making 100 w idgets a day).

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46 Safety : Safety as an outcome is a combination of construction safety during the course of the project and the overall safety considerations of the new facility that will enable it to operate safely over its production life cycle. Constructi on safety involves the accidents to personnel within the construction zone and is genera lly viewed in terms of recordable or Days Away or Restricted Time (DART) cases. Facility safety is focused on a more long term outcome and is based upon the facility h aving the equipment, protections, and or warning/safety devices, safe job procedures, energy control procedures etc. required for the facility to operate in a safe manner. Stakeholder Satisfaction : Stakeholder satisfaction is the overall pride, satisfacti on, contentment and/or happiness that the stakeholders have with the outcome of the project. It is somewhat a measure of the potential for future repeat business. After the 43 potential problems were finalized, eight different groups we re formed based o n the characteristics of thes e potential problems. The definition of each problem group (Choi et al. 2006) is listed in Appendix B. Due to grouping, each problem is renamed based on its characteristics (group). For example, L1, which is the project team is lacking in the necessary expertise, experience, breadth and depth to successfully executed the project, now is renamed as AL1. The label indicates that it belongs to Alignment (AL) group and its assigned the number is 1. The rest of problems will be renamed based on their characteristics. The renamed problems by group and with assigned number s are shown in Appendix A. Based on these 43 problems, this research will be addressed. Table 3 1 shows the number of groups of problems and their number of pr oblems. Table 3 1 Groups of potential problems Groups Number of Problems Alignments (AL) 8 Constructability (CA) 4 Change Management (CM) 4 Contracting (CO) 3 Quality Management (QM) 5 Safety Practice (SP) 7 Project Control (PC) 8 Team Building (T B) 4 Total 43

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47 Alignment (AL) and Project Control (PC) have the most number of problems and Contracting (CO) has the least number of problems. Safety Practice (SP) has seven problems which is the second highest number of problems among groups. Data Coll ection As mentioned in the previous section, the initial data set come from leading indicator project (Choi et al. 2006) The survey file consisted of five worksheets. The five worksheets we re Introduction, Evaluation Sample, Evaluation, Definition of LI, and Project Outcomes Definition. Figure 3 1 shows a screen capture of the Evaluation worksheet of the surve y. There is an explanation of the general concept of the survey about how to evaluate LIs as well as contact information for the author in the Introduction worksheet. The Evaluation Sample worksheet shows how to evaluate LIs in the sheet. The Evaluatio n worksheet is the main data collection form for survey participants. Two definition worksheets show the definition of LI and project outcomes to help the survey participants understand the concepts. Figure 3 1 A screen capture of evaluation worksheet (Choi et al. 2006)

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48 A total of 84 respondents completed the final survey: 26 were owners and 58 were contractor /engineer/designer respondents. These respondents represented 32 companies: 14 owners companies and 18 contractor/engineer/designer companies (Choi et al. 2006) Participants were asked to evaluate each problem using a six point scale with the following response options: no, very low, low, moderate, high, and very high negative impact on five success parameters. If any LI has no impact on any success parameter, it will be marked as 0 and vi c e versa. Table 3 2 shows a six point scale used for questionnai re. Table 3 2 A six point scale Scale No Very Low Low Moderate High Very High Point 0 1 2 3 4 5 Source: Choi et al. 2006 In addition to the original 84 data sets, another 16 data sets were collected during the summer 2008 timeframe via email and phone interviews. This data collection used the same survey form as the leading indicator project (Choi et al. 2006) as shown in Figure 3 1. The respondents represent 9 companies of contractor s The final number of survey responses for this research is 100, which came from 14 owners companies and 27 contractor/engineer/designer companies. Data Transformation The rang e of evaluation for the impact on any success parameter is based on a six point scale. The minimum value is 0 with no negative impact on any success parameter and the maximum value is 5 with the highest negative impact on any success parameter. The main methodology of leading indicator study (Choi et al. 2006) is the aggregat ion of the scores for each problem ev aluated by each participant. The sum of the score s is the basis of the measurement tool. In this case the range of between 0 and 5 works well. The proposed analysis methodology for this research is confirmatory factor analysis. In the data set for this statistics,

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49 the numbers 0 and 1 usually represent opposite values based on the characteristics of data set set, there are some data points with values of 0 This may lead to a misunderstanding in terms of data interpretation. Therefore, it is necessary to revise the current scale. If any problem has no negative impact on any success parameter, it will be marked as 1 and if any problem has the highest nega tive impact on any success parameter, it will be marked as 6. To change the scale of evaluation, the current evaluation of each problem is incremented by 1 For example if a problem has an evaluation value of 1, it will be changed into 2. This research uses this rescaled data set. Table 3 3 shows the comparison o n a six point scale. Table 3 3 Comparisons of current and rescaled Scale No Very Low Low Moderate High Very High Current 0 1 2 3 4 5 Rescaled 1 2 3 4 5 6 Data Analysis Descriptive Statisti cs In this section, all the survey data will be analyzed in terms of each problem within each success parameter, using descriptive statistics. It is assumed that each problem has a different negative impact on each success parameter. Table 3 4 shows some of problems with their descriptive statistics. A full set of descriptive statistics for all 43 problems is available in Appendix C. The highest mean value is SP1 in Safety with 5.85 and the lowest mean value is CA4 in Safety with 2.56. The range of sta ndard deviations is between 0.56 and 1.486 for PC2 in Schedule and for AL2 in Safety respectively. Each problem has different mean values for its success parameters. It shows that the negative impacts of problems on success parameters varies For exampl e CA4 has a mean value of 5.62, which is pretty high in Cost but has a mean value of 2.56, which is much lower than that of Cost in Safety.

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50 Table 3 4 Examples of problems with descriptive statistics No. Outcome Total Sum Mean S.D 1 C.V 2 AL1 Cost 54 2 5.42 0.619 11.43 Schedule 540 5.40 0.600 11.11 Quality 507 5.07 0.803 15.84 Safety 471 4.71 1.211 25.71 Satisfaction 522 5.22 0.844 16.16 AL2 Cost 481 4.81 0.880 18.29 Schedule 475 4.75 0.187 18.68 Quality 451 4.51 1.063 23.57 Safety 353 3 .53 0.421 42.10 Satisfaction 491 4.91 1.150 23.42 . . . . . . . TB4 Cost 481 4.81 1.036 21.54 Schedule 504 5.04 0.958 19.01 Quality 415 4.15 1.236 29.78 Safety 387 3.87 1.309 33.82 Satisfaction 507 5. 07 0.951 18.76 Notes: 1. S.D = Standard Deviation 2 C.V = Coefficient of Variation (%) Regarding safety as a success parameter, there may be some disagreement between problems and its impacts. The value of standard deviation in Safety appears generally high er than the others. It is hard to have a clear overview of standard deviations because the standard deviation is based on the mean values. Forty three problems have different mean values so it is impossible to compare standard deviation on an apple to ap ple basis To have a clearer view of standard deviations, the coefficient of variation is used. It is the ratio of standard deviation to the mean. Sometimes it is represented as ratio (digits) or percentage. The equation for coefficient of variation is shown in Equation 3 1 (Weisstein 2009b) Coefficient of Variation = Standard Deviation 100 (%) ( Equation 3 1 ) Mean

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51 The values of the coefficient of variation are different from those for standard deviation because it considers the mean values. The highest coefficient of variation is CM4 in Safety with a valu e of 43.14 and the lowest coefficient of variation is SP1 in Safety with a value of 9.78. In the previous paragraph, the statistics data were provided in terms of each problem for any success parameters. At this point, one question rises. How about the values of the descriptive statistics in terms of success parameter? It would be helpful for understanding the data in depth. This information show ed what the survey participants unintentionally th ought in terms of their priorities of project success pa rameters even though they were not directly asked to set the priority of each success parameter. If any success parameter has a higher mean value than others, it means that the success parameter has a higher priority (impact) than th e others. Based on da ta collected from 100 survey s the computed mean values of success parameters are shown in Table 3 5. Table 3 5 Mean values of success parameters Success Parameter Cost Schedule Quality Safety Satisfaction Mean 4.76 4.82 4.13 3.73 4.68 The mean value of Schedule is the highest at 4.82 and the mean value of Safety is the lowest at 3.73. Cost and Schedule have the two highest mean values among project success parameters. The survey participants evaluate problems consider ing Cost and Schedule the most among the five success parameters. Data Tendency Analysis As mentioned ear l ier each problem has different responses to each success parameter in terms of mean values. It is evident that each problem has different priorities that affect success para meters. The tendency of data is defined a s the number of frequencies of each point during

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52 the evaluation process of each problem in terms of each success parameters. Figure 3 2 shows the frequency histogram of each success parameter. A) Cost B) Schedule C) Quality D) Safety E) Sat isfaction Figure 3 2 Frequency h istogram

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53 Regarding data tendency, cost, schedule, and satisfaction have the similar tendency as shown in Figures 3 2 A, B, and E Q uality and safety show similar tendency as shown in Figure s 3 2 C and D. The frequency his tograms of cost, schedule, and satisfaction are skewed to the left and the frequency histograms of quality and safety look like they are normally distributed. It shows that the cost, schedule, and satisfaction groups have more problems with a value of 5 o r greater than those of the group of quality and safety. The two most frequent values are 5 and 6 for Cost, Schedule, and Satisfaction and 4 and 5 for Quality and Safety. From the descriptive statistics and frequency histograms, we can conclude that the re are two main characteristics of data. One is that each problem has a different response to any success parameters and the other is that each success parameter has a different priority. There are no problems that always have the highest score or lowest score to all five success parameters. frequency of values over 5 in Cost, Schedule, and Satisfaction are higher than those for Quality and Safety. Methodo logy Background The data available for this research are for 43 potential problems with 8 groups of parameters The input data mainly are provided by industrial project companies so it may not be suitable for other types of construction projects such as c ommercial, residential, and heavy civil. It is important to understand the relationships within each success parameter before identifying a relationship between problems and success parameter. To define the relationships between variables (problems), the re are several statistical methods (techniques) available to define the relationships between variables such as 1) correlations, 2) multiple regression, 3) factor analysis, and 4) canonical correlations. Regarding the methodology for the analysis of relat ionships

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54 between problem groups and success parameters, SMART S will not be mentioned in this chapter because it wa s already addressed in Chapter 2. Correlations show the strength and direction of a linear relationship between two variables. It could be id eal for this data analysis. But there are 43 variables for this research and it means the generated matrix will be a matrix of 43 43. Since 43 variables are grouped by their characteristics i t would be better comparing group to group not a variable to variable. C orrelation does not allow for group to group analysis hence it is not recommended for this data analysis. Multiple regression consists of one set of dependent (criterion) variables and more than two sets of independent (predictor) variables. It will provide one linear combination of dependent and independent variables. One of the main purposes of multiple regressions is to develop a prediction model. For the purposes of this study multiple regression is not required Factor analysis is co mmonly used to determine the variable. The main function of this method is to determine the underlying factor(s) and data reduction. There are two types of methods in this analysis. They are exploratory factor an alysis (EFA) and confirmatory factor analysis (CFA) Regarding confirmatory factor analysis, it will be addressed later in this study The data has been grouped as eight different groups. It is not necessary to perform factor analysis to determine facto rs for this research but is necessary to check how many factors would be extracted before performing CFA. Canonical correlations show the relationships between two variable sets. Each set has more than two variables. Unlike multiple regressions, canonic al correlations generate two linear combinations, one for criterion and the other for predictor. This is the main difference between multiple regression and canonical correlations. Canonical correlation provides the maximum correlations between two sets. It is suitable for this research to define relationships between groups of multiple variables.

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55 Canonical Correlation Overview Canonical correlation analysis is used to explore relationships between two variable sets when each variable set has at least two variables (Thompson 1984) One variable set represents correlation is optimized such that the linear correlation between two latent variables is maximized. Whereas multiple regressio ns is used for many to one relationships, canonical correlation is used for many to many relationships (Garson 2008a) Due to many to many relationships, it is possible to have more than one correlation relating two sets of variables. A total number of possible correlations are set by a number of smaller variable set s For example, if th ere are two variable sets available which are a set of three variables and another set of five variables, then the number of possible correlation s between these two sets is three. With respect to the characteristics of computation of correlations, the fir st extracted correlation usually has the highest value and the last correlation has the lowest value. To test the significance of canonical follows (Garson 2008a) : i n which the within set correlation has been controlled. There are two canonical variates per canonical correlation. One is the dependent canonical variable, while the one for the independent v a riable nonical correlation: A canonical correlation, also called a characteristic root, is a form of correlation relating two sets of variables. As with factor analysis, there may be more than one significant dimension (more than one canonical correlation), each representing an Eigenvalue s: The Eigenvalue s are approximately equal to the canonical correlations squared. They reflect the proportion of variance in the canonical vari ate explained by the canonical correlation relating two sets of variables. This is only useful when there is more than one extracted canonical correlation and more than one Eigenvalue one for each

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56 sets of variables are significantly associated by canonical correlation. Degree of freedom equal P Q, where P = number of variables in variable set 1 and Q = number of variables in variable set 2. This test establishes the significance of the first canonical correlation but Computation of canonical correlations To compute canonical correlations, the data will be computed as a correlation matrix and the program uses this as an input data as shown in Figure 3 3 where there are two subsets of variables x and y R xx R xy R yx R yy Figure 3 3 Input matrix (Ainsworth 2008) The correlation matrix of x is denoted as R xx and the correlation matrix of y is denoted as R yy R xy and R yx are the cross correlation matrices of the two subsets. To find canonical correlati ons, Equation 3 2 (Ainsworth 2008; Dunteman 1984) is applied: = R xx 1 R xy R yy 1 R xy T = R yy 1 R xy T R xx 1 R xy ( Equation 3 2 ) To solve this equation, the determinant of R xx 1 R xy R yy 1 R xy T I has to va nish since the columns of this matrix has to be linearly dependent to meet the conditions of the characteristic equation (Dunteman 1984) I is the identification matrix. From the values of the canonical variates will be computed by each extracted for the subset of x and y Results of canonical correlations The correlations are the strength and direction of a linear relationship between two variables. The degree of linear correlation varies in the range of 1 and +1. If = 0.6 the relationship is considered as a modera te relationship and if = 0.9 ,the relationship is considered a strong relationship (Olson 1987) T he results will be categorized as follow s:

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57 No Relationship : 0.6 Low Moderate : 0.6 0.7 Medium Moderate : 0.7 0.8 High Moderate : 0.8 0.9 If the absolute values of correlation are less than 0.6, the n it will be considered that there is no relationship. The ranges of the results in this study lie between 0.541 as a minimum and 0.898 as a maximum. M ost of correlations fall into the category of moderate. There is no negative or opposite direction of values in correlation. The detailed results are shown in Table s 3 6 through 3 10. Table 3 6 Canonical correlations of cost parameter AL CA CM CO PC QM SP TB AL 1.000 CA 0.781 1.000 CM 0.690 0.648 1.000 CO 0.658 0.541 0.694 1.000 PC 0.819 0.861 0.729 0.666 1.000 QM 0.816 0.722 0.673 0.594 0.782 1.000 SP 0.772 0.755 0.584 0.556 0.781 0.794 1.000 TB 0.757 0.687 0.619 0.573 0.736 0.585 0.724 1.000 Table 3 7 Canonical correlations of schedule parameter AL CA CM CO PC QM SP TB AL 1.000 CA 0.712 1.000 CM 0.836 0.631 1.000 CO 0.632 0.580 0.612 1.000 PC 0.777 0.688 0.756 0.657 1.000 QM 0.768 0.674 0.773 0.669 0.778 1.000 SP 0.753 0.705 0.666 0.646 0.637 0.746 1.000 TB 0.784 0.713 0.743 0.6 47 0.705 0.739 0.727 1.000 Table 3 8 Canonical correlations of quality parameter AL CA CM CO PC QM SP TB AL 1.000 CA 0.674 1.000 CM 0.789 0.670 1.000 CO 0.691 0.670 0.639 1.000 PC 0.785 0.808 0.773 0.712 1.000

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58 T able 3 8. Continued AL CA CM CO PC QM SP TB QM 0.660 0.639 0.658 0.591 0.628 1.000 SP 0.748 0.718 0.675 0.601 0.855 0.637 1.000 TB 0.789 0.661 0.737 0.679 0.783 0.605 0.786 1.000 Table 3 9 Canonical correlations of safety parameter AL CA CM CO PC QM SP TB AL 1.000 CA 0.812 1.000 CM 0.779 0.774 1.000 CO 0.831 0.831 0.761 1.000 PC 0.832 0.898 0.883 0.867 1.000 QM 0.797 0.850 0.868 0.811 0.889 1.000 SP 0.694 0.626 0.556 0.595 0.596 0.562 1.000 TB 0.847 0.80 9 0.771 0.759 0.807 0.719 0.580 1.000 Table 3 10 Canonical correlations of satisfaction parameter AL CA CM CO PC QM SP TB AL 1.000 CA 0.803 1.000 CM 0.836 0.817 1.000 CO 0.811 0.762 0.815 1.000 PC 0.842 0.866 0.883 0.822 1.00 0 QM 0.834 0.735 0.831 0.800 0.862 1.000 SP 0.774 0.762 0.785 0.710 0.836 0.778 1.000 TB 0.874 0.732 0.721 0.718 0.781 0.733 0.664 1.000 As shown in Table 3 6 through 3 10 above, each problem group has different correlations between problem grou ps within success parameters. This trend already addressed in the section 3.5 in this chapter. The highest and lowest correlations in Cost are the combination of PC and CA with a correlation value of 0.861 and the combination of CO and CA with a correlat ion value of 0.541. In Schedule, there is no significantly high moderate correlation except for the correlation of CM and AL with a correlation value of 0.836, which is the highest correlation in Schedule. The lowest correlation is produce d between CO an d CA with a correlation of 0.580. There are two high moderate correlations in Quality. They are the combinations of PC and CA and SP and PC with correlations of 0.808 and 0.855 respectively. On the other hand, the lowest

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59 correlation is 0.591 of the comb ination of QM and CO. The highest correlation in Safety is 0.898, which is the combination of PC and CA. The lowest correlation in Safety is 0.556 from the combination of SP and CM. There are more correlations of less than or equal to 0.6 in Safety, esp ecially the most of combinations with SP, compared to other success parameters. The highest correlation is the combination of PC and CM with a correlation of 0.883 and the lowest correlation is 0.664 by the combination of TB and SP in Satisfaction. There is no correlation less than or equal to 0.6 in this parameter. In Safety and Satisfaction parameters, the most of problem groups show medium and high moderate correlations. The number of high moderate correlations is larger than that of Cost, Schedule, and Quality parameter. It clearly shows that a few combinations of problem groups have high correlations in Cost, Schedule, and Quality parameters. In other words, some problem groups have some tendencies for high correlations in those success parameters But most of combinations in Safety and Satisfaction parameters usually have high correlations. It depicts that most of problems in these parameters have tendencies for high correlations, compared to another three parameters. Factor Analysis (Explorator y vs. Confirmatory) One of the main purposes of this research is to define the relationships between problem groups and success parameters. To achieve this goal, the proper methodology has to be chosen, considering the data available. The data available are the negative impacts of problems on each success parameter. To satisfy the data set and purpose of this research, confirmatory factor analysis (CFA) has been chosen. Before CFA is addressed, the general concepts of factor analysis will be discussed. F actor analysis is commonly used in education, sociology, and psychology because it is sometimes impossible to measure directly the variables effects in these disciplines. F actor analysis is a statistical procedure for uncovering factors by observed varia bles. The main

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60 purpose of this method is to reproduce the relationships among observed variables by factors (Brown 2006) The factors are something that cannot be directly observed or measured such as depression but can be me asured indirectly by their effects on observed variables. It is evident why it is common in education, sociology, and psychology. There are two types of factor analysis. One is exploratory factor analysis (EFA) and the other is confirmatory factor analy sis (CFA). Both of them share the basic concept s of factor analysis but its application is different. EFA is a data driven approach and there is no specification such as number of factors and relationships between factors and variables (Brown 2006) Therefore, the main purpose of EFA is to reduce the number of data into small number of data set by factors. Sometimes it is used for the early stage of CFA to check the number of factors and validation. One of the best examples of EFA is the intelligence quotient (IQ) test. Some of problems in IQ can be categorized by factors or latent variables when it is calculated. Unlike EFA, CFA is a theory driven approach based on such parameters as the number of factors and the pattern of indicators and factor loadings, etc (Brown 2006) The researcher has to decide, at least expect the number of factors and o ther related matters, or has a theory to test before he/she can perform CFA (Brown 2006; Thompson 2004) In factor analysis, the relationships between latent variables and indicator variables are shown as arrows. For example there is a latent variable, A, and an indicator variable, B. The relationship between two vari ables would be represented by an arrow as shown in Figure 3 4. Figure 3 4 A r elationship b etween t wo v ariables

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61 A relationship depicted by an arrow in factor analysis is considered as a causal relationship (Brown 2006) In Figure 3 4, the relationship between A and B is a causal relationship. It mean s that the latent variable, A, causes the indicator variable, B. In another words, the variable, CFA model (S) reproduces the sample covariance matrix (S) of the (Brown 2006) The researche r has to find the best fitted model explained by the sample covariance matrix. In this section, the overall concepts of factor analysis have been discussed. Although the main methodology of this research is CFA, it is necessary to address the concept of EFA in detail because both of them share the same methodology and terminologies Exploratory Factor Analysis (EFA) Overview T he overall concepts of factor analysis in EFA and CFA have been discussed above Before CFA is discussed in detail, it is helpful to discuss EFA first to understand CFA. All the terminologies and components are common to both EFA and CFA In this section, the main concepts and purposes of EFA will be shown with some examples. The concepts of factor or latent variables have already been addressed previously The main purposes of factor analysis can be summarized as shown below (Thompson 2004) : 1) Evaluation of score validation 2) Development of theory regarding nature of construct 3) Summary of relationships using factor These three purposes do not always fall into the category of EFA. The last two are l relationships or look for a pattern, trend, or characteristics using EFA in a set of data. Based on this exploration, the researcher could develop and test a theory using CFA. During an EFA process,

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62 it is possible to define the relationships and charac teristics of a data set and it is finally also possible to reduce the number of data set s Table 3 11 shows an example of EFA data. This is a hypothetical data set for the evaluation of the author done by seven students. Each student evaluate d the autho r using values ranging from most agree with a value of 9 and the least agree with a value of 1. Table 3 11 Heuristic EFA data Student Measured Variable Handsome Beautiful Ugly Brilliant Smart Dumb Barb a ra 6.00 5.00 4.00 8.00 6.00 2.00 Debora 8.00 7.00 2.00 7.00 5.00 3.00 Jan 9.00 8.00 1.00 9.00 7.00 1.00 Kelly 5.00 4.00 5.00 9.00 7.00 1.00 Murray 4.00 3.00 6.00 9.00 7.00 1.00 Susan 7.00 6.00 3.00 7.00 5.00 3.00 Wendy 3.00 2.00 7.00 7.00 5.00 3.00 Mean 6.00 5.00 4.00 8.00 6.00 2.00 S.D. 1 2.16 2.1 6 2.16 1.00 1.00 1.00 Notes: 1. Standard Deviation. Source: Thompson 2004 The data in Table 3 11 have six measured variables and seven data points. These six measured variables are indicator variables in factor analysis. One of the purposes of factor analysis is to explore the relationships and then to summarize the relationships into a smaller number of latent constructs (Thompson 2004) There are several statistical methods available to summarize the relationships including corre lations. Table 3 12 shows the bivariate correlation matrix for Table 3 11. Table 3 12 Bivariate correlation matrix Variables Measured Variable Handsome Beautiful Ugly Brilliant Smart Dumb Handsome 1.00 1.00 (1.00) 0.00 0.00 0.00 Beautiful 1.00 1.00 ( 1.00) 0.00 0.00 0.00 Ugly (1.00) (1.00) 1.00 0.00 0.00 0.00 Brilliant 0.00 0.00 0.00 1.00 1.00 (1.00) Smart 0.00 0.00 0.00 1.00 1.00 (1.00) Dumb 0.00 0.00 0.00 (1.00) (1.00) 1.00 Source: Thompson 2004

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63 All the values in Table 3 12 are bivariate correl ations between measured variables. The correlations in the parenthesis depict negative correlation From this matrix, the relationships among measured variables are easily defined. There are two possible latent factors found here. One latent factor is related to handsome, beautiful, and ugly and the other latent factor is related to brilliant, smart, and dumb. The first factor could be labeled the second factor could be labeled (Thompson 2004) There is no significant relationship between these latent factors. The raw data initially have six measured variables as shown in Table 3 11 and 3 12. Now this data set could be summarized by two latent factors instead of six variabl es. This is a reduction of number of variables based on latent factors. This is commonly used in EFA to reduce the number of variables. The second finding from this example is to show the relationships between measured variables within each latent facto r. It is evident that there is no significant relationship between latent factors in the above example. In addition to this, the correlations in Table 3 12 show the relationships between measured variables. Regarding the variables related to Physical At S imilar interpretation s can be found in the variables of Intellectual Prowess. These f indings can be summarized as shown in Figure 3 5. Figure 3 5 Summary of latent factors and variables

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64 From the original six measured variables, two latent factors are found and the characteristics of variables are defined within each latent factor. This example has only six variables and seven participants. It is very clear and easy to categorize variables by their meaning or wording such as handsome, beautiful, ugly, brilliant, smart, or dumb. In actual research projects there will be more variables with more survey participants. It is impossible to figure out latent factors and characteristics of variables from the variables directly by wordings. Through t his example, the overall concept of EFA is addressed clearly. Number of factors with extractio n methods In the previous section, the general concepts of EFA are addressed with an example. From the example, two factors are extracted from six variables. How many factors can be extracted from a set of data? The maximum possible number of extracted factors is equal to the number of variables. If there are six variables available as example above, the maximum extracted number of factors is six or less than six If the number of extracted factors is equal to the number of variables, then it will not be necessary to perform factor analysis. There are five methods available to extract factors. The five methods are 1) Statistical Significance Tests; 2) Eigenvalue Greater Than 1.0 Rule; 3) Scree Test; 4) Inspection of the Residual Correlation Matrix; a nd 5) Parallel Analysis (Thompson 2004) Each method has its Eigenvalue Eigenvalue method is the default function to extract factors in mos t statistical packages (Thompson 2004) The definition of Eigenvalue has already been addressed in the previous section. Recall the example with six variables with two latent factors. Table 3 13 shows Eigenvalue s of the example.

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65 Tab le 3 13 Eigenvalue s for the example Measured Variable Factor I II Handsome 1.00 0.00 Beautiful 1.00 0.00 Ugly (1.00) 0.00 Brilliant 0.00 1.00 Smart 0.00 1.00 Dumb 0.00 (1.00) Sum of Squared Column Values 3.0 3.00 Source: Thompson 2 004 The sum of squared column values is an Eigenvalue of each extracted factor. Each value in T able 3 13 is a pattern/structure coefficient. The sum of the Eigenvalue s is equal to the number of measured variables. Here in this example, 3.0 + 3.0 = 6.0. Each factor reproduces 50% (3.0/6.0 = 0.5) of information regarding the original data. So the sum of these two Eigenvalue s is equal to 6 or 100%. Thompson (2004) made the following four statements about Eigenvalue s in EFA : 1) The number of Eigenvalue s is equal to the number of measured variables. 2) The sum of Eigenvalue s is equal to the number of measured variables. 3) An Eigenvalue divided by the number of measured variables indicates the proportion of information. 4) The sum of the Eigenvalue s for the extract ed factors divided by the number of measured variables indicates the proportion of the information. The fundamental concept of using the Eigenvalue greater than 1.0 rule is based on the logical thinking behind these four observations The computation of an Eigenvalue is based on the sum of squared pattern/structure coefficients of measured variables within an extracted factor. There may be one or more than one variable available for an extracted factor. If there is only one variable available for that e xtracted factor with a coefficient value of 1.0 and the coefficient values of rest of variables are zero, then the Eigenvalue of that factor would be 1.0 (Thompson

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66 2004) Eigenvalue method i s based on the numerical value to extract factors while the S cree test is based on a graphical test to determine the number of factors. The extracted factors will be plotted on the x axis (horizontal) and their Eigenvalue s on y axis (vertical). A line c an be drawn to connect the coordinates of each factor and its Eigenvalue (Brown 2006; Thompson 2004) The Scree test is basically looking for a spot where there is a sharp or big change in slope of the line on the grap pencil can be laid on the rightmost portion of the relevant graph to determine where the elbow or (Thompson 2004) Besides the statistical significance, it is an easy process to extract factors using the S cree test when there is clearly a steep point that changes the slope in the line. Two examples are shown in Figure s 3 6 A and B A) Qual ity B) Made up example Figure 3 6 Scree plot. Figure 3 6 A is associated with one of project success parameters, Quality, and Figure 3 6 B is a made up example to compare the slope of lines in S cree plots. Both of them have the same number of measured va riables and the sum of Eigenvalue s is equal to eight. According to the Scree test method, only one factor could be extracted in Figure 3 6 A because there is a big slope

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67 il the last 6 B has no significant changes in slope. In this case, it would be impossible to extract factors using the Scree test method. The Eigenvalue greater than 1.0 and the Scree test are two m ost common methods to extract factors. These two methods are available on most statistical computer packages such as the Statistical Analysis Software (SAS ) and the Statistical Package for the Social Sciences (SPSS ). EFA will be performed as the prelim inary stage of CFA even though the main methodology of this research is CFA. The method to be used to extract factors would be the combined Eigenvalue method and Scree test. Confirmatory Factor Analysis (CFA) Overview The overall concepts of factor analys is for EFA and CFA have been addressed and the background of EFA has been discussed in more detail so far. The reason why CFA has been chosen for this research has been explained as well. In this section, the parameter s of factor analysis models and an e xample relating to CFA factor analysis will be discussed. There are five terms in general factor analysis models. They are factor loadings ( ), factor variances ( 2 ), unique variances ( ), exogenous (independent) variables, and endogenous (dependent) va riables. The explanation of these are as follows (Brown 2006) : Factor Loadings ( ): Normally factor loadings are regression slopes between latent variables and indicator variables. Sometimes they can be correlations when there is no cross loading between latent variables and indicator variables. Facto r Variances ( 2 ): Factor variances are variances explained by latent factors. Unique Variances ( = 1 2 ): Unique variances are variances that are not explained by latent factors. This is the difference between factor analysis and multiple regressions. Factor analysis considers the measurement error.

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68 Exogenous (Independent) Variables: These are independent (latent) variables in the model. They are not caused by other variables and normally latent variables are exogenous variables in the model. An o val or circle represents exogenous variables in the factor analysis models. An arrow starts from exogenous variables. Endogenous (Dependent) Variables: These are dependent (indicator) variables caused by other variables in the model. The indicator vari ables are almost always endogenous variables. But sometimes, latent variables are also endogenous variables. It depends upon the model specifications but not for this research. A box or square represents endogenous variables in the factor analysis model s. An arrow ends endogenous variables. Regarding factor loadings, there are some issues on the interpretation of factor loadings. It may be a little bit different from each perspective (Thompson 2004) These represent the impact of l atent variables on indicator variables on many excellence models (Bassioni et al. 2008; Eskildsen et al. 2001) So for this research, the interpretation of factor loading will be the same as excellence models. The values in Table 3 12 would be factor loadings on each latent factor Prowess are the exogenous variables and three measured variables for each exogenous variable are the endog enous variables. Figure 3 4 shows an example of exogenous and endogenous A n e xample of the factor analysis model (Brown 2006) relating to CFA will be discussed from here to help understand CFA in depth. four indicator (measured) variables; 1) D 1 : Hopelessness; 2) D 2 : Feelings of Worthlessne ss/Guilt; 3) D 3 : Psychomotor Retardation; and 4) D 4 : Sleep Disturbance. The depression cannot be measured directly so it can be measured only indirectly from its effects on each indicator. It means that the latent variable (depression) is manifested by t hese four indicator variables. Thi s

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69 is based on 300 samples There are various methods to denote the factor analysis model but in this research, the denotations and results are chosen as shown in Figure 3 7 A) Denotation of model. B) Results of mod el Figure 3 7 Example model (Brown 2006) In F igure 3 7 A a latent factor (depression) is denoted as 1 within an oval and four indicator variables are denoted as Y N where N = 4. The factor loading of each indicator variable is denoted as N1 where N = 4. Unique variances are denoted as N where N = 4. A ll the related equations and results in this model are shown in Table 3 14. Table 3 14 Equations and results of the model Equations Numerical Results Y 1 = 11 1 + 1 D 1 = 0.83 1 + 0.31 Y 2 = 21 1 + 2 D 2 = 0.84 1 + 0.29 Y 3 = 31 1 + 3 D 3 = 0. 79 1 + 0.37 Y 4 = 41 1 + 4 D 4 = 0.75 1 + 0.44 Source: Brown 2006 The results of the example model shown in Figure 3 7 indicate that the most impact on depressions among four indicators is D 2 (Feelings of Worthlessness/guilt) with = 0.84 and = 0.29 The least impact on the latent variable is D 4 (Sleep Disturbance) with = 0.75 and = 0.44. At this point the researcher may conclude that depression is manifested by four indicator variables and D 2 has the highest impact and D 4 has the least impact on the latent variable. To satisfy the results requirement of a CFA model, it is required to satisfy the goodness fit test

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70 because one of the purposes of CFA is to test any theory and all the data has to be reproduced based on latent variables. The goodn ess of fit test is used to show the gap between the original data and the data reproduced by factors. It is an evaluation process of how well the factor model reproduces the original data. So it is necessary to check the goodness of fit test in CFA model s. There are various methods and indices to perform the goodness of fit test and it will be discussed later on in this study CFA models for this research The data set available for this research has 43 problems, categorized into eight different problem g roups. The data has 100 observations. These problems are evaluated in terms of their negative impacts on five different success parameters. The range of impacts is from no impact (1) to highest impact (6). From this background, it is clear to infer tha t each success parameter represents a latent variable and eight problem groups represent indicator variables for each success parameter. There are five different CFA models for this research because this research addresses five different success parameter s and eight different problems groups. The five latent variables are Cost, Schedule, Quality, Safety, and Satisfaction and the eight problems groups are Alignment (AL), Constructability (CA), Change Management (CM), Contracting (CO), Quality Management (Q M), Project Control (PC), Safety Practices (SP), and Team Building (TB). The initial CFA model for each parameter is shown in Figure 3 8. Figure 3 8 Initial CFA model for each success parameter

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71 These five initial CFA models can be rewritten in an equat ion format similar to that shown in Table 3 14. These equations for each success parameter are shown in Table 3 15, where P = each success parameter (Cost, Schedule, Quality, Safety, and Satisfaction). Table 3 15 I nitial CFA model equations Equations AL = 1p p + 1 QM = 5p p + 5 CA = 2p p + 2 PC = 6p p + 6 CM = 3p p + 3 SP = 7p p + 7 CO = 4p p + 4 TB = 8p p + 8 As shown in the descriptive statistics in Chapter 3, each problem reacts differently to each different success parameter It may be questionable whether each success parameter gets affected by all eight problem groups but it is evident that eight problem groups do not have the same impact on five success parameters. The hypotheses to test, using these models are as follows: H o: Each latent factor (success parameter) is manifested by the eight problem groups. H o: Each problem group will have a different impact on each success parameter if the statement above would be true. The main hypothesis is to test whether or not each l atent factor (success parameter) would be manifested by the eight problem groups. This is for a number of problem groups needed for each success parameter, for example eight problem groups or less than that. And then a degree of impact on each success pa rameter of related problem groups would be determined. Procedure s for CFA Models Overview The generalized procedure for the CFA model is shown in Figure 3 9. There are three key eck, and the third one is the goodness of fit test. The articles reviewed (Bassioni et al. 2008; Cheng 2001;

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72 Eskildsen et al. 2001; Garson 2008c; Ko et al. 2005) have performed these steps for their CFA CFA models in micro perspective and for factor analysis in macro perspective. The normality check is used to aid in selecting the CFA model method such as Maximum Likelihood (ML) and Weighted Least Squares (WLS). The normality of data affects the CFA models in terms of estimating parameters. The parameter estimates can be underestimated or overestimated when a wrong method is chosen (Brown 2006) So they have to be chosen carefully. The Goodness of Figure 3 9 CFA model procedure fit test is the last step of CFA models. This step shows how well a CFA model explains the raw data set in terms of latent factors. To satisfy this step, the null hypothesis should fail to reject in

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73 the case of 2 for exa There are various methods for the goodness of fit tests and they will be discussed later. There is a difference between all the articles reviewed and the procedure in Figure 3 9. Only one article ad dresses the improvement of CFA model goodness of fit test by deleting some of paths in the model (Garso n 2008c) The rest of articles do not address the model trimming The reasons why they do not explain this step may be : The CFA model satisfies the goodness of fit test. The CFA model does not satisfy the goodness of fit test but the goodness of fit te st result or official guidelines for this). One of the purposes of CFA models is to test a theory. If the null hypothesis gets rejected and/or the goodness of f it test does not meet, then the theory is not right regarding at least what it says. So it is not necessary to improve CFA models in this case because it is proven to be wrong. If the goodness of test does not meet the required criteria then there is a difference between a model and a set of data and it could mean that the theory to be tested is wrong. As mentioned earl ier if any CFA models do not meet the goodness of fit test, then the CFA models will be trimmed until the goodness of fit test is satis fied. A ll eight problem groups may not result in major impacts on each success parameter even though each problem group may have different impacts. The model trimming procedure will be necessary to find those problem groups that may not manifest in impac ts on each success parameter. More detailed information on each step will be discussed in the following sections. ) which (Garson 2008b) The is a measure of the internal consistency of a scale and the high er the alpha value is the better (Garson 2008c; Spector 1992)

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74 s obtained at the same time correlate highly wi (Garson 2008b) The alpha value represents a direct function of both the number of items and t heir magnitude of intercorrelation. The range of alpha is from 0 to 1 (Garson 2008c) The number of alpha normally has higher values when there is a higher number of items. The main use of way Th e ch e cking process is sometimes referred to as check ing (Garson 2008b) It is assumed that there is an error if an item is less correlated with others (Spector 1992) For example, if everybody measures something in common, then the measurement s will be highly correlated w ith each other. The computation of alpha value is shown in Equation 3 3 (Spector 1992) = k T 2 I 2 ( Equation 3 3 ) k 1 T 2 Where, k : Number of items T 2 : Total variance of the sum of the items I 2 : Total variance of an individual item To use the raw data for CFA models, the coefficient alpha values have to be checked. The acceptable range of coeffici ent alpha values varies by authors, with requiring a value greater than 0.6 (Bassioni et al. 2008; Hair et al. 1998) or 0.7 (Garson 2008b; Garson 2008c) even though all agree that the higher t he value is the better. For this research, the minimum acceptable va lue is greater than 0.6. If some value is less than 0.6, then it will be retained and not deleted because there are only eight problem groups available here. In other words, there are not too many variables available for CFA models. The values will be interpreted as the guidelines for each success parameter.

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75 In each problem group of each success parameter, the coefficient alpha values will be computed. If an initial alpha value is less than 0.6, then one or more items w ill be deleted until a recomputed alpha value is greater than 0.6. The statistical computer program, SPSS provides the expected alpha value if an item is deleted. Even though the researcher could delete as many items as he/she wants to satisfy the mini mum coefficient values, at least, two items need to be retain ed in each problem group (Bassioni et al. 2008) After this procedure, the number of items in each problem group of each success parameter will be determined for the next step. Average values of groups of each success parameter There will be maximum 43 problems available for eight problem groups after the computation of the coefficient alpha. There is a chance that some success parameters have less than 43 problems. The number of items in each group will vary depending on the alpha values. So the same concept that is based on the problem groups not the individual problems of canonical correlation is applied to this step. To get the value of each problem group, the average value is computed and ap plied for each success parameter. After the computation of the the original value of remaining items will be used for the computation of the average value of each problem group in each success parameter. Eight average values for each su ccess parameter will be available for normality check. Data normality There are two major estimation methods in CFA models. They are the Maximum Likelihood (ML) and the Weighted Least Squares (WLS) method. Both of them are popular in CFA models. But the major difference in between these two methods is the data normality. To use ML method, the data should be normal otherwise WLS has to be used for CFA models. As mentioned earlier there will be a underestimated or overestimated parameter if a wrong esti mate method is chosen for a set of data (Brown 200 6) Regarding the data normality, there are two

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76 aspects to be addressed. One is skewness and the other is kurtosis. Skewness is a measure of the symmetr y of a distribution and kurtosis is a measure of having a peak or flat of distribution (NIST/SEMATECH 2006; Wynne 1982) Skewness mainly refers to the location of the center point of a distribution such as being skew ed to the right or being skewed to the left and kurtosis depicts the height of the distribution such as high (peak) or low (flat). The equations for skewness and kurtosis are shown in Table 3 16 and an example distributions of skewness and kurtosis are sh own in Figure 3 10. Table 3 16 Equations for skewness and kurtosis Skewness Kurtosis or Where, : Mean s : Standard deviation N : Number of data Source: NIST/SEMATECH 2006 A) Skewness. B) Kurtosis Figure 3 10 Distrib utions (Wynne 1982)

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77 If the values of skewness are negative, then the distribution is skewed to the left as shown in Figure 3 10 A or vice versa. If Kurtosis has high values, then the distribution is near at the peak, Leptokurtic (A) in Figure 3 10 B A fla t form of a distribution may have a low kurtosis value, Platykurtic (C) in Figure 3 10 B To be normal, the values of Skewness and Kurtosis have to be zero or near zero. The range of normal, moderately non normal, and severely non normal distributions are defined (Curran et al. 1996) Table 3 17 shows the rang es of Skewness and Kurtosis of each segment. Table 3 17 Ranges of Skewness and Kurtosis Segment Ranges Skewness Kurtosis Normal 0 and 2 0 and 7 Moderately Non normal 2 and 3 7 and 21 Severely Non normal Greater than 3 Greater than 21 Source: Curran et al. 1996 According to Curran et al. (1996), the cutoff for being normal may be too narrow if only the segment of normal is included. It will be too wide if the segment of moderately non normal is included as normal. So it may be necessary to find an other method to check for data normality. To check for data normality in this research, the critical region (CR) method is applied. The data is assumed to be normal if the value of CR falls into greater than 2 and smaller than 2 (SPSS 2008) It clearly addresses the range of cutoff for data normality. The CR values are computed by dividing the Skewness or Kurtosis value by their standard err or. These values will be provided by SPSS and the Amos TM packages. After checking the normality of data, the estimation method will be determined. Computer package and goodness of fit test This research will use a set of raw data as input for the comput er program. There are a couple of computer packages available for CFA models such as LISREL, Amos, SAS, and

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78 Mplus. There is a subtle difference in functions among these computer packages. Each program has different functions, depending upon its version but all these programs are capable to perform this calculation All the computer software packages discussed earlier allow users to do this parameter estimate. The Amos computer package will be chosen for this research because this package has two option s to perform CFA models. One is a traditional syntax input command method and the other is a graphical method. A screen capture of Amos is shown in Figure 3 11. Figure 3 11 Screen capture of Amos This capture shows an example of a graphical method. A mos is m ore user friendl y than other programs and gives users more versatility in the usage of the package. This research is to use this graphical method. After running Amos, the goodness of fit test will be performed. There are four fit indices for thr ee major categories and it is recommended to use at least four indices from three different categories (Brown 2006) The three categories are Absolute Fit, Parsimony Correction, and Comparative Fit. E ach category, its index, and its recommended cutoff values are shown in Table 3 18, including the 2 va lues. The cutoff values of each index are a little bit subjectively different from authors and articles and also the acceptable ranges of cutoff values are various. Brown (2006) addresses the

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79 role of p value of 2 in CFA model as index. The p value would be satisfied, greater than 0.05 at = 0.05 if the cutoff values meet the certain value point or certain ranges and a large number, greater than 300 or more, of samples is available. A large number of samples could lead to a satisfied p value for 2 That is why Brown (2006) mentions that the p value is not a good index Table 3 18 Goodness of fit test categories and its indices Category Index Cutoff Value 2 H 0 : S H a : S or no restriction Should fail to reject H 0 at 0.05 > 0.05 Absolute Fit Standardized Root Mean Square Resid ual ( SRMR ) < 0.08 Parsimony Correction Root Mean Square Error of Approximation ( RMSEA ) < 0.10 Comparative Fit Comparative Fit Index ( CFI ) > 0.95 Tucker Lewis Index ( TLI ) > 0.95 Source: Brown 2006 and it is recommended to check more indices to compens ate for the p value. From this perspective, it is inferred that a CFA model at least has to satisfy the p value first and then other indices. Although all indices are recommended to be address ed in CFA models, the main index has to be the p value of 2 a nd other indices are supplementary to check the models for this research. T he number of samples for this research is 100 which is just enough for a CFA model but this is not considered a large number of samples. If the initial results of Amos do not satis fy the 2 value, model trimming will be performed until it satisfies the model fitting criteria The model trimming is originally designed to delete an unnecessary path in the model to improve the model fitting (Garson 2008c) Any unnecessary path will be defined as statistically insignificant parameter estimate among all parameters in the model. It mea ns any item that fails to reject the null hypothesis (z value < 1.96, = 0.05) has to be deleted. As shown in Figure 3 8, the CFA model for this research is not complicated and has

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80 a maximum of eight indicator variables. So the concept of model trimming is revised to delete an item (variable) that is less significant than others in the model even though a parameter estimate of that item is statistically significant. M odel trimming will be performed until the final revised model satisfies the goodness of fit test. A detailed method for deleting variables will be addressed in C hapter 4. Parameter estimate and significance test In the previous section, the main concern was a CFA model fitting using the goodness of fit test. This is a fitting for a whole m odel without testing for the statistical significance of an individual variable. If a CFA model satisfies the goodness of fit test as a whole model, and then the statistical significance test has to be performed for each individual remaining variable in t he model. During the model trimming process, some indicator variables may be deleted to satisfy the goodness of fit test. For the remaining indicator variables, each parameter estimate has to be checked. Even though there are some indicators remaining i n the model after the model trimming, there is a chance that any indicator may not be statistically significant at = 0.05. The model cannot be finalized until the parameter estimate is completed. During this procedure, the researcher could sort out the finalized indicators in the model and its parameter estimates. All statistical significance test in this research is at = 0.05 unless specifie d otherwi s e In this chapter, all the strategies and design for this research have been laid out. Major met hodologies used for this research are 1) Canonical correlation for relationships between problem groups; 2) EFA for the preliminary stage of CFA; and 3) CFA for the final model. This chapter provides some detailed information on each method and the potent ial issues are already addressed. Based on this information presented in this chapter, all the outputs of this research will be discussed in Chapter 5, except for canonical correlations that were already discussed in

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81 this chapter. In addition to methodol ogies presented in this chapter, the methodology for the application of CFA outputs is Simple Multi Attribute Rating Technique Using Swing Weight (SMART S ). It is briefly reviewed in Chapter 2 and it will be discussed again in Chapter 5.

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82 CHAPTER 4 FACTOR ANALYSIS RESULTS Overview The overall strategies and design, including methodologies have been discussed so far. In this chapter, all the results of these addressed methods will be explained. They are the Average Value of Group, EFA, No rmality Check, and CFA. The workflow for this chapter is shown in Figure 4 1 and the brief discussion of each step will follow Figure 4 1 The procedure of results The first step is to a lpha The coefficient values have to be grea ter than 0.6 (Hair et al. 1998) to retain any variables in a group. Even though any variables could be deleted to improve the coefficient values, there have to be at least two variables in a group (Bassioni et al. 2008) The valu es will be computed using SPSS. The average values for a group will be calculated after the is determined From this point forward all the value addressed will be the average value of each group for each success parameter. EFA will be p erformed after the computation of average value of a group as a

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83 preliminary stage of CFA. This is a good process to check how many factors could be extracted before CFA is performed. It will provide the overview of CFA. The method used for EFA is princi pal axis factoring, using SPSS. The final stage of this chapter is to perform CFA. During this stage, the hypotheses mentioned in C hapter 3 will be tested along with the goodness of fit test and the parameter estimate. The program used for CFA is Amos. Initial Alpha Values The initial va lues are computed based on 43 problems groups. The computation is completed for each group of each success parameter. There are eight groups available for five different su ccess parameters. This means that the computations w ill be done at least 40 times (8 groups 5 success parameters). Table 3 1 Groups of Potential Problems already addresses the number of variables (problems) in each group so it is not necessary to addr ess it again here. The computation of a lpha is done in SPSS. T he rules followed by the researcher in this portion of analysis were as follows: It is necessary to retain as many problems as possible in a group for the computation of average val ue and as many groups as it possible for the CFA models. If the values of a lpha meet the minimum (0.6), then all variables in a group are retained. This rule is applied when the initial value satisfies the minimum and also the coefficient valu e is greater than 0.6 In this case, all the variables will be retained because the initial value already meets the minimum. If t a lpha value is smaller than the minimum some problems are deleted. The item deletion process will be stopped when the improved coefficient alpha value meets the minimum. The computations of coefficient alpha values are based on problem s within each group but the results are shown as group. There are eight problem groups available for each project succe ss parameter. If any of problem group does not meet the minimum a lpha and

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84 so is completely deleted a project success may ha ve few er problem group s than eight (8) for CFA models. As ment ioned in C hapter 3, this process will be mainly interpret ed as the guidelines for each problem group. Table 4 1 shows the initial results of Table 4 1 Initial results Problem Group Cost Schedule Quality Safety Satisfaction AL 0.776 0.762 0.795 0.823 0.839 CA 0.617 0.512 0.590 0.736 0.779 CM 0.633 0.629 0.671 0.828 0.844 CO 0.451 0.469 0.555 0.585 0.620 PC 0.839 0.784 0.888 0.897 0.900 QM 0.738 0.719 0.643 0.896 0.807 SP 0.881 0.833 0.845 0.559 0.908 TB 0.761 0.737 0.745 0.796 0.737 Some of initial a lph a values did not satisfy the minimum as shown in Table 4 1. They are 1) CA in Schedule and Quality; 2) CO in Cost, Schedule, Quality, and Safety; and 3) SP in Safety. The worst case is for group CO. It does not satisfy almost every project success param eter. All these a lpha values have to be improved. There are two limitations on improv a lpha values. One is a chance that the initial value may be the highest coefficient alpha value. It means that there is no way to improve the initial coefficient alpha value. The other is, as mentioned earlier in this section, is that there are at least two variables remaining in a group. Improvement of Coefficient Alpha Values The computations of a lpha values are done in SPSS. SPS S provides the user with the expected a lpha values when any of items in a group is deleted. It is helpful to decide which item has to be deleted to improve the a lpha value. Table 4 2 shows AL for Cost as an example of SPSS output. The initial alpha value of AL for Cost is 0.776, which is greater than 0.6. So it is not necessary to delete any item in that group for

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85 improvement. But the alpha value will be 0.783 which is greater than 0.776 if AL1 is deleted. S o it is a guideline map for the improvement of alpha value. Table 4 2 Example of output of value in SPSS AL Scale Mean if Item Deleted Scale Variance if Item Deleted Corrected Item Total Correlation Alpha if Item Del eted AL1 31.76 22.184 0.231 0.783 AL2 32.37 19.124 0.520 0.745 AL3 33.14 18.728 0.468 0.753 AL4 32.27 19.654 0.508 0.749 AL5 32.46 17.948 0.583 0.733 AL6 32.61 18.806 0.470 0.753 AL7 33.18 16.371 0.619 0.724 AL8 32.47 18.433 0.423 0.764 It is nec essary to improve seven problems groups, which are CA in Schedule and Quality, CO in Cost, Schedule, Quality, and Safety, and SP in Safety as shown in Table 4 1. The values of CA in Schedule and Quality and CO in Safety can be improved by deleting some of items in each group. On the other hand, it is impossible to improve the values of CO in Cost, Schedule and Quality and SP in Safety. The initial values are the highest values that be accomplished The values of CO in Cost and Schedule are 0.451 and 0.469 respectively and the values of CO in Quality and SP in Safety are 0.555 and 0.559 respectively. The first two values are not even close to 0.6 but the latter two values are close enoug h to 0.6. So the latter two values from CO in Quality and SP in Safety could possibly be retained The problem occurs with CO in Cost and Schedule. Even though their values are a little bit far from 0.6 but close to 0.5, they have to be retained because there only eight problem groups. If this doe s not fit to the model, they may be trimmed in CFA models. Therefore, at this stage, these two groups have to be retained. Table 4 3 shows the output of CO in Quality as example of a Cronbach value that is impossible to improve.

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86 Table 4 3 Incapability of improvement of alpha value of SPSS CO Scale Mean if Item Deleted Scale Variance if Item Deleted Corrected Item Total Correlation if Item Deleted CO1 8.41 4.285 0.314 0.527 CO2 8.02 3.212 0.470 0.271 CO3 8.09 3.820 0.321 0.525 The initial value of CO in Quality is 0.555 as shown in Table 4 1 but all potential alpha values after deleting any variable are less than 0.555 if any item is del eted. In this case, it is not necessary to delete any item. The values of CA in Schedule and Quality and CO in Safety can be improved by deleting some of items in each group. Their initial values are 0.512, 0.590, and 0. 585 for CA in Schedule and Quality and CO in Safety respectively. Table s 4 4, 4 5, and 4 6 show how the initial values are able to be improved by using the SPSS output. As shown in Table 4 4, the initial alpha value can be impr oved to 0.621 by deleting item CA3. So while the initial value is 0.512 for this problem group, The improved value is 0.621, which is greater than 0.6. The process of improvement has to stop here because the revised value meets the minimum and CA still has three variables remaining, which exceeds the minimum requirement of two variables (Bassioni et al. 2008) Table 4 4 Improvement of alpha value for CA in schedule CA Scale Mean if Item Deleted Scale Variance if Item Deleted Corrected Item Total Correlation if Item Deleted CA1 14.08 4.054 0.501 0.280 CA2 13.88 4.410 0.425 0.354 CA3 14.79 4.652 0.124 0.621 CA4 15.33 4.244 0.257 0.487 The initial value of CA in Quality can be improved to 0.701 by deleting CA3 as shown in Table 4 5. The improved value of CA in Quality is 0.701

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87 and it meets the minimum value of 0.6. As CA in Schedule, the remaining problems in that group are still three, which is greater than two (Bassioni et al. 2008) Table 4 5 Improvement of Cronbac a lpha value for CA in quality CA Scale Mean if Item Deleted Scale Variance if Item Deleted Corrected Item Total Correlation if Item Deleted CA1 12.14 6.263 0.520 0.414 CA2 12.77 5.310 0.546 0.361 CA3 12.59 7.820 0.123 0.701 CA4 13. 65 6.614 0.356 0.530 The final discussion on improvement of value is shown in Table 4 6. The initial value is 0.585 and the improved value is 0.612 by deleting CO3 to improve. The revised value and the remaining number of problems in a group meet the requirement s All three values are finally improved by deleting one of variables in each group. CA3 has been deleted in Schedule and Quality and CO3 has been deleted in Safety. Table 4 6 Improvement of a lpha value for CO in safety CO Scale Mean if Item Deleted Scale Variance if Item Deleted Corrected Item Total Correlation if Item Deleted CO1 6.74 5.245 0.303 0.605 CO2 6.68 3.291 0.600 0.126 CO3 6.18 4.311 0 .316 0.612 Final Values is used to check the degree of consistency o f measurement. If the degree of consistency is high, then the measurements will be correlated to each other (Spector 1992) The alp ha shows the degree of consis tency of measurement. The higher the value is the higher the consistency is a lpha values can be categorized into three types. One is based on most of a lpha values show ing a good consistency, which means meeting the minimum values. Another is that it is necessary to improve a lpha values and that they are capable of being improv ed The last is that

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88 there is no way to improve their initial values. It indicate s that the i nitial values that do not meet the minimum are the possible highest values. Even though there are lower value groups, it is necessary to retain all possible groups for the CFA model s The a lpha will be considered as a guidelin e for checking its measurement consistency. So no matter what the initial and final coefficient values are, all the groups are intact at this stage for the purposes of this study Table 4 7 shows the final coefficient value of each group for each project success parameter. Table 4 7 Final a lpha values Problem Group Cost Schedule Quality Safety Satisfaction AL 0.776 0.762 0.795 0.823 0.839 CA 0.617 0.621 0.701 0.736 0.779 CM 0.633 0.629 0.671 0.828 0.844 CO 0.451 0.469 0.555 0.612 0.620 PC 0.839 0.784 0.888 0.897 0.900 QM 0.738 0.719 0.643 0.896 0.807 SP 0.881 0.833 0.845 0.559 0.908 TB 0.761 0.737 0.745 0.796 0.737 As shown in Table 4 7, t he improved values are 0.621, 0.701, and 0.612 for CA in Schedule and Quality and CO in Safety re spectively Except for the groups with lower a lpha values, most of the rest show a good consistency of measurement. The highest value is 0.908 for SP in Satisfaction and the second highest is 0.900 for PC in Satisfaction. All eight groups wil l be retained for the next step of this research. Average Value of Group There are 43 problems available for this research. These 43 problems are br oken down into eight different groups. The number of problems in a group is different from each other as s hown in Table 3 1. Each detailed expression of a problem with its group is found in Appendix A. As mentioned in C hapter 3 from this step, everything is based on the average value of group not values of individual problems. Forty three problems could pr ovide as detailed information as

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89 possible but it is hard to provide an overview of problem group for each project success parameter. One of the objectives of this research is to define the relationships between project problems and their impacts on projec t success parameters. To achieve this goal, it is more appropriate to use the average value of a group than to use the value of an individual problem, especially dealing with multi project success parameters. As discussed in Chapter 3, each problem has a different impact on a success parameter such as frequency histogram and canonical correlations. After studying the relationships between problems and project success parameters, using the problem group perspective, then the individual level will be studi ed later but not in this research because there is not enough data on the multi project success parameter at the problem group perspective. This research will provide an overview of the relationships between problems and their impacts on project success p arameters from the group level and will be a first step for similar future studies. Another reason for using the average value of a group is related to the method for CFA but not for EFA. It is easier and more reliable to build a CFA model using eight gro ups than using 43 problems. So the average of each group value will be used for this research and everything will be based on the group level and not on the individual problem. The deleted items like CA in Schedule and Quality for improvement of the Cron a lpha value are excluded when the computation of the average value is performed. Exploratory Factor Analysis (EFA) Overview It is not a requirement to perform the EFA before the CFA is developed but it is a good preliminary stage for CFA (Brown 2006; Thompson 2004) The theory or number of factors to be tested cou ld be assumed until the CFA is completed f or example, when only one factor is expected for CFA I f there are two factors extracted using EFA, then CFA should be modified to

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90 a two factor model instead of one factor model. On the other hand, if only one f actor is extracted by EFA then the one factor model will be tested in CFA P value or other ind ices in CFA represents how different the degree of gap between the raw data and the CFA model based on a theory is. It will be statistically acceptable when t he gap between the raw data and the CFA model is small That is why it is necessary and beneficial to perform EFA before CFA even though it is not required For this research, each success parameter is the expected factor because every problem is evaluat ed in terms of its impact on each success parameter. If only one factor is extracted, then it will be tested using CFA. If not, the model has to be modified. The factor extraction method between EFA and CFA is similar. It means that there is a better c hance to get a small gap between the raw data and the CFA model. Factor Extraction Technique The factor extraction methods have been addressed in C hapter 3. The two methods are Eigenvalue greater than 1.0 and the Scree test. T here are mainly two techni ques available in EFA t hey are principal component analysis (PCA) and common factor analysis (CFA). Both of them are use d for similar purpose s of data reduction but the underlying assumptions are different (ACITS 1995) In CFA the variance of each variable is decomposed into common variance that is shared by other variables such as the factor variances in CFA (ACITS 1995) It means that it consi ders only common variance and has a unique variance for each variable. On the other hand, PCA considers the total variance, which is 1.0 and therefore, there is no distinction between common and unique variance (ACITS 1995) Confirmatory factor analysis (CFA) is based on the common factor analysis (CFA) model addressed in Cha pter 3 (Floyd and Widaman 1995) The purpose of EFA for this research is to facilitate the preliminary stage of CFA to explore the construct in the data set. So the technique for EFA factor extraction has to be CFA not PCA.

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91 EFA will have been done using the principal axis factoring technique in SPSS. Principal axis factoring is another name for common fa ctor analysis. Eigenvalue Greater Than 1.0 Method One of two factor extraction methods is Eigenvalue greater than 1.0 and it will be discussed in this section. Although there are various options to set up the output of EFA, three options that are commun alities, total variance explained and factor loadings are addressed here. All options to extract factors are set up to Eigenvalue greater than 1.0. Figure 4 2 shows the capture of output of Cost for EFA as an example. In Figure 4 2, communalities are t he variances explained by factor for that variance. For example, 0.758 for AL is 75.80% of the AL variance explained. The highest variance explained is PC with 0.799 and the lowest is 0.377 for CO. Figure 4 2 shows th at Total Variance Explained table whi ch contains the Eigenvalue of each possible extracted factor, which is the column of Total under Initial Eigenvalue s. According to this table, the only factor that has an Eigenvalue greater than 1.0 is Factor 1. It shows that only one factor can be extra cted using this method. The Eigenvalue of Factor 1 is 5.301 and the rest of the values are less than 1.0. Then Factor 1 can be named as Cost because this is the Figure 4 2 Example of output of EFA for cost

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92 measurement of p roblem impacts on cost and the only factor extracted. The column of % of Variance shows that the variance explained by that factor out of all potential factors. The Factor 1 is the highest with 66.261% and the second highest is factor 2 with 10.477%. Th e first extracted factor has the highest Eigenvalue and highest % variance explained and the last extracted factor has the lowest Eigenvalue and lowest % variance explained. This is a partial explanation of why the Eigenvalue has to be greater than 1.0. T he last table in Figure 4 2 shows the factor loading of each variable for the extracted factor, called Factor Matrix. The highest factor loading comes from PC with 0.894 and the lowest loading comes from 0.614. Form this table, it is evident that PC has the highest impact on Cost and CO has the lowest impact on Cost. So from this example, only one factor is extracted in Cost with 66.261% of variance explained. The rest of project success parameters, Schedule, Quality, Safety, and Satisfaction, will be a s analyzed similarly to the way Cost has been done. Table 4 8 shows the EFA output summary f or Schedule using SPSS. Table 4 8 EFA summary output of schedule Factor Eigenvalue s Factor Loading Total % of Variance Cumulative % 1 5.249 65.616 65.616 AL 0 .861 2 0.657 8.208 73.825 CA 0.657 3 0.495 6.191 80.016 CM 0.791 4 0.471 5.889 85.905 CO 0.690 5 0.433 5.418 91.323 PC 0.766 6 0.292 3.655 94.928 QM 0.814 7 0.224 2.795 97.772 SP 0.788 8 0.178 2.228 100.000 TB 0.854 The number of extracted factor using the Eigenvalue greater than 1.0 method is one for Schedule as shown Table 4 8. Only one factor (Factor 1 in Table 4 8) is extracted with an Eigenvalue of 5.249 and its communality (% of variance) is 65.616%. The extracted factor would be named as S chedule because only one factor is extracted and this is a measurement about Schedule. The two columns on the right

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93 highest is AL with 0.861 and the lowest is CA with 0.657. AL has the highest impacts on S chedule (Factor 1) and CA has the lowest impact on Schedule (Factor 1). The EFA output summary of Quality, Safety, and Satisfaction is show n in Table s 4 9, 4 10, and 4 11 respectively. Table 4 9 EFA summary output of quality Factor Eigenvalue s Factor Loa ding Total % of Variance Cumulative % 1 5.411 67.638 67.638 AL 0.869 2 0.605 7.559 75.197 CA 0.707 3 0.564 7.054 82.251 CM 0.817 4 0.480 5.994 88.245 CO 0.736 5 0.364 4.549 92.794 PC 0.877 6 0.266 3.327 96.122 QM 0.635 7 0.165 2.058 98.179 SP 0.8 15 8 0.146 1.821 100.000 TB 0.876 Table 4 10 EFA summary output of safety Factor Eigenvalue s Factor Loading Total % of Variance Cumulative % 1 5.889 73.613 73.613 AL 0.868 2 0.723 9.036 82.648 CA 0.884 3 0.436 5.455 88.104 CM 0.865 4 0.276 3.447 91.551 CO 0.817 5 0.244 3.051 94.601 PC 0.920 6 0.185 2.313 96.914 QM 0.879 7 0.135 1.685 98.599 SP 0.563 8 0.112 1.401 100.000 TB 0.858 Table 4 11 EFA summary output of satisfaction Factor Eigenvalue s Factor Loading Total % of Variance Cumulativ e % 1 6.119 76.484 76.484 AL 0.890 2 0.510 6.374 82.859 CA 0.852 3 0.387 4.834 87.693 CM 0.891 4 0.334 4.172 91.864 CO 0.852 5 0.198 2.477 94.341 PC 0.914 6 0.174 2.175 96.516 QM 0.835 7 0.160 1.999 98.515 SP 0.829 8 0.119 1.485 100.000 TB 0.775 From the summary of EFA output of Quality, Safety, and Satisfaction, it is evident that only one factor (Factor 1 from Table 4 9, 4 10, 4 11) can be extracted using the Eigenvalue greater than 1.0 method for each success parameter. The communality of Saf ety and Satisfaction is greater than the rest of three project success parameters at 73.613% and 76.484% respectively.

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94 The highest factor loading among three project success parameters comes from PC with 0.877, 0.920, and 0.914 for Quality, Safety, and Sa tisfaction respectively and the lowest factor loadings among them are different from each other. Even though PC has the highest impact on these three success parameters, the rest of project problem group has different factor loadings on each success param eter. It is clearly shown that each problem group has a different impact on a project success parameter. Using the Eigenvalue greater than 1.0 method, only one factor from each project success parameter could be extracted. As Thompson (2004) indicated n o cases exist of Eigenvalue s that are right above 1.0 like 1.002 or 1.001 and right below 1.0 like 0.998 or 0.995 when using this method for factor selection. All five project success parameters have clear cutoff Eigenvalue s for their factor selection. Sc ree Test Method The Eigenvalue s depicts the amount of information represented within a given factor (Thompson 2004) The first extracted factor has the highest Eigenvalue and the last extracted factor has the lowest Eigenvalue is the rubble of loose rock and boulders not solidly attached to mountains that collects at the feet of the mountains. Cattell thought of solid, big, intact mountains as being analogous to solid, noteworthy factors that researchers should recognize and re tain. Trivial factors, however, are analogous to Scree and should be left behind in the extraction process (Thompson 2004) Trivial factors show the trend of flattening of slopes in the graph of Eigenvalue s and factors. Scree test l ooks for a spot at where the slope of line starts flattening or changes drastically such as an elbow because after the slope changes significantly, there is no big difference in slope among factors. It represents that these are the trivial factors such as Scree in the mountains. Table 4 12 shows the summary of Eigenvalue s for all possible factors of each success parameter. The Scree plot for each success parameter will be drawn based on the Eigenvalue s shown in Table 4 12. The difference in Eigenvalue s between

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95 Factor 1 and Factor 2 for each success parameter is large. It will be visually clearer in the plots than in the tables. Figure 4 3 shows the Scree plot of each success parameter. Table 4 12 Summary of Eigenvalue s of success parameters Factor S uccess Parameters Cost Schedule Quality Safety Satisfaction 1 5.301 5.249 5.411 5.889 6.119 2 0.838 0.657 0.605 0.723 0.510 3 0.483 0.495 0.564 0.436 0.387 4 0.413 0.471 0.480 0.276 0.334 5 0.325 0.433 0.364 0.244 0.198 6 0.249 0.292 0.266 0.185 0. 174 7 0.209 0.224 0.165 0.135 0.160 8 0.182 0.178 0.146 0.112 0.119 Although the Scree plot of Quality has already been shown in Figure 3 6 A it is presented again as Figure 4 3 for the convenience of discussion and comparison. All five Scree plots fo r project success parameters clearly show one major pattern or trend in slope change. The largest slope change occurs at Factor 2 in the plot of all parameters. The plot of Cost and Safety ha s shown a similar change in slope between Factor 1, Factor 2, a nd Factor 3. Both of them share the similar trend in between Factor 2 and Factor 3 before having less slope change than before. The slope starts more flattened at Factor 3 in Cost and at Factor 4 in Safety. The plot of Schedule, Quality, and Satisfactio n shows the same pattern in slope change. There is the largest slope change between Factor 1 and Factor 2. After Factor 2, there is no big difference in slope change between f actors. The slope really flattens from Factor 2 to Factor 8. It looks like on e horizontal line in the plot. With respect to the Scree test, due to the large slope change, the factor extraction process has to stop at Factor 2. Therefore, the number of extracted factors, using the Scree test, is only one for each project success pa rameter and slope changes in the plots are very discernible.

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96 A) Cost B) Schedule C) Quality D) Safety E) Satisfaction Figure 4 3 Scree plot.

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97 EFA Result Summary As the preliminary stage of CFA, the factor extraction has been performe d, using EFA. The two methods used for the factor extraction are Eigenvalue greater than 1.0 and S cree test. Both methods rely on Eigenvalue s because the Eigenvalue is an index of information of a factor (Thompson 2004) The first met hod depends upon a numerical output such as greater than or less than 1.0 and the latter one is visually inspected such as the spot where the largest slope change occurs at. Through the EFA factor selection process using two methods, it is concluded that the only one factor could be extracted from each project success parameter. It is a good sign for the positive output of CFA models later in this chapter because as mentioned earlier, the basic mechanism of EFA and CFA are the same. Normality Check This i s the last preparation step for CFA models. In t his section the normality check of data set will be performed It will be helpful to decide the method for CFA models. There are two major methods available. One is ML and the other is WLS. The ML metho d will be used if the data set is normally distributed. WLS will be preferable when the data set does not meet the normality check The decision on whether to choose ML or WLS depends upon the range of the critical region. To meet the normality check t he CR range has to fall into between 2 and 2 (SPSS 2008) The computation of CR values is done by using Amos and Table s 4 13, 4 14, 4 15, 4 1 6, and 4 17 show the output of CR values for Cost, Schedule, Satisfaction, Quality, and Safety respectively. Each table has the minimum and the maximum value for each problem group and the value of problem group for skewness and kurtosis and its CR values All the values shown in the tables in parenthes e s are negative or less than zero. The outputs of five different project success parameters can be categorize d into two groups. The first group consists of Cost, Schedule, and Satisfaction and the second g roup consists of

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9 8 Quality and Safety. As shown in Table s 4 13, 4 14, and 4 15, the CR ranges for skewness of the first group are less than 2. For skewness, the lowest CR value is 6.619 and the highest CR value is 2.232. Even though the CR values of ku rtosis are better than those of skewness, they are mostly still out of range. So the method of CFA for these groups will be WLS. Table 4 13 Output of normality check for cost Variable Min. Max. Skew CR Kurtosis CR AL 2.250 5.875 (0.752) (3.071) 1.422 2. 903 CA 2.500 6.000 (1.621) (6.619) 3.853 7.865 CM 3.500 6.000 (0.645) (2.635) 0.211 0.431 CO 3.333 6.000 (0.888) (3.624) 1.192 2.434 PC 2.625 5.875 (1.287) (5.254) 2.635 5.379 QM 2.200 5.800 (1.112) (4.538) 2.888 5.894 SP 1.286 5.714 (0.770) (3.143) 0.951 1.940 TB 1.500 5.750 (1.209) (4.937) 2.840 5.798 Table 4 14 Output of normality check for schedule Variable Min. Max. Skew C.R. Kurtosis C.R. AL 2.750 5.750 (0.582) (2.376) 0.383 0.783 CA 2.000 6.000 (1.499) (6.120) 3.838 7.834 CM 3.250 6.000 (0.646) (2.639) 0.126 0.258 CO 2.667 6.000 (0.989) (4.039) 1.484 3.029 PC 3.250 6.000 (1.253) (5.116) 1.771 3.616 QM 2.400 5.800 (0.863) (3.523) 1.256 2.564 SP 1.571 5.571 (0.681) (2.781) 0.655 1.337 TB 1.500 5.750 (1.272) (5.195) 2.832 5.780 Table 4 15 Output of normality check for satisfaction Variable Min. Max. Skew C.R. Kurtosis C.R. AL 3.000 6.000 (0.598) (2.440) (0.323) (0.659) CA 2.250 6.000 (1.000) (4.081) 0.344 0.701 CM 1.500 6.000 (1.051) (4.290) 0.974 1.988 CO 2.333 6.000 (0.547) (2.2 32) (0.129) (0.263) PC 2.250 5.875 (0.904) (3.689) (0.021) (0.043) QM 2.000 5.800 (0.718) (2.933) 0.199 0.407 SP 2.000 6.000 (0.981) (4.006) 0.340 0.694 TB 2.500 6.000 (0.719) (2.936) 0.212 0.433 The method for Cost, Schedule, and Satisfaction has be en determined as WLS. As shown in Table 4 16 and 4 17 for Quality and Safety respectively, the values of CR for skewness are mostly in between 2 and 2. Four out of eight problem groups in Quality and seven out of eight groups in Safety are in this range Even though the number of CR values in that range in

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99 skewness of Quality is a little bit higher than that of Safety, only one group has an out of range CR value for kurtosis as shown in Table 4 16. Regarding CR values for kurtosis, both of them fall in the normal range except for only one group, which is CM in Quality with CR value of 2.488 So it is recommended to use ML method for CFA. Table 4 16 Output of normality check for quality Variable Min. Max. Skew C.R. Kurtosis C.R. AL 2.125 5.875 (0.623) (2.542) 0.267 0.544 CA 1.000 6.000 (0.602) (2.456) 0.557 1.136 CM 1.250 5.750 (0.699) (2.854) 1.219 2.488 CO 2.000 6.000 (0.284) (1.160) (0.467) (0.953) PC 1.500 5.750 0.102 0.415 (0.408) (0.833) QM 3.400 6.000 (0.359) (1.467) (0.338) (0.690) SP 1.2 86 5.714 (0.307) (1.253) (0.441) (0.901) TB 1.250 6.000 (0.685) (2.795) 0.509 1.039 Table 4 17 Output of normality check for safety Variable Min. Max. Skew C.R. Kurtosis C.R. AL 1.500 5.750 (0.405) (1.655) (0.110) (0.224) CA 2.000 5.250 (0.398) (1.62 5) (0.776) (1.583) CM 1.000 5.000 (0.188) (0.768) (0.786) (1.605) CO 1.000 5.000 (0.310) (1.264) (0.593) (1.211) PC 1.000 4.750 (0.119) (0.485) (0.724) (1.477) QM 1.000 5.200 0.044 0.178 (0.783) (1.598) SP 4.429 6.000 (0.534) (2.179) (0.636) (1.297) TB 1.000 6.000 (0.421) (1.721) (0.344) (0.702) In this section the normality check of the data set for CFA models using Amos and CR range values was addressed For the project success parameter s of Cost, Schedule, and Satisfaction, WLS is the optimal op tion to use for CFA models because most of their CR values for skewness and kurtosis are out of range to be considered as normal. In this case WLS is suitable for CFA. On the other hand, for Quality and Safety, the CR values for skewness and kurtosis are mostly in between 2 and 2. So it is acceptable to use the ML method for the CFA model.

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100 Confirmatory Factor Analysis (CFA) Overview All the procedures addressed in this chapter so far were in preparation for this section. The a lpha values h ave shown the consistency of the data and some of items were deleted to improve the a lpha values. Based on the results of a lpha EFA has been performed to extract possible factors before CFA, using the Eigenvalue greater than 1.0 and Scree test method. Outputs of EFA support one factor model for the equivalent CFA model of each project success parameter. Finally, the normality check of data helps decide the method for CFA models like WLS for Cost, Schedule, and Satisfaction and ML f or Quality and Safety. The main focus of this section is how well a latent factor (each project success parameter) is manifested by eight problem groups. If they are, then what is the degree of impact of each problem group on each success parameter? If not, how many problem groups are related to a latent factor? And then what are the impacts of each related problem group on each success parameter? In this section, the more detailed information on CFA models will be discussed in the following order : inte rpretation of the parameter estimate process ; initial results ; model trimming, and final results. Interpretation of Parameter Estimate Process There are two terminologies available for any CFA model in the parameter estimate process One is the raw estima te and the other is the standard estimate. All the parameter estimates found in articles are normally standard estimated (Garson 2008c) The difference between a raw estimate and a standard estimate is the base. The r aw estimate is based on one of variables in the model and the standard estimate is based on the latent factor. The mechanism for computat ion of the parameter estimate is to compute the raw estimate first and then the raw estimate will be converted into standard estimate. Each unique variance will be computed based

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101 on the standard estimate. To compute the raw estimate, the researcher has t o choose or the computer program automatically chooses one variable as a base of the computation for the parameter estimate as shown in Figure 4 4. Even though this notation is based on Amos, the notation graphic may var y among different computer software packages. Figure 4 4 Example of graphical input for CFA model The latent factor in Figure 4 4 is Schedule and it has eight different problem groups. Each problem group has its own unique variance denoted as er01, er02 i n the path in between a latent factor (Schedule) and a group (AL) indicates that the raw estimate will indicates for the computation of the between problem a group and its unique variance is interpreted as the path between a latent factor and each problem group. The r aw esti mate and the unique variance are tested for their statistical significance using the p value and critical ratio (CR) but the standard estimate does not have them. Regarding the statistical significance test and critical ratio, they can be different depend ing on how the base is chosen. Table s 4 18 and 4 19 show examples of statistical difference based on the base.

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102 Table 4 18 Example of statistical significance of schedule using AL as base Problem Groups Estimate Standard Error (S.E.) Critical Ratio (C.R.) P Value AL Schedule 1.000 CA Schedule 0.633 0.181 3.499 *** CM Schedule 0.982 0.082 11.962 *** CO Schedule 1.010 0.120 8.452 *** PC Schedule 0.770 0.078 9.878 *** QM Schedule 1.169 0.082 14.265 *** SP Schedule 1.229 0.069 17.711 ** TB Schedule 1.071 0.070 15.313 *** Table 4 19 Example of statistical significance of schedule using CA as base Problem Groups Estimate Standard Error (S.E.) Critical Ratio (C.R.) P Value AL Schedule 1.580 0.452 3.498 *** CA Schedule 1.000 --------CM Schedule 1.551 0.382 4.060 0.002 CO Schedule 1.596 0.518 3.083 0.002 PC Schedule 1.217 0.388 3.137 *** QM Schedule 1.847 0.531 3.477 *** SP Schedule 1.943 0.587 3.310 *** TB Schedule 1.692 0.495 3.419 *** The parameter es timate method is WLS and is done using Amos. The estimate shown in both tables are raw estimate s The statistical significance values shown in Table 4 18 are based on AL and those shown in Table 4 19 are based on CA. As shown in both tables, the statist ical significances are different from the base such as the column of standard error and critical ratio (CR). To be statistically significant, the critical ratio (CR) has to be greater than 1.96 at = 0.05. The values in the P Value column presented in the table. The critical ratio (CR) is computed by dividing estimate by its standard error and it will be the main index in the model trimming process. Even though the raw estimate s are differe nt from each other based on the base variable, the standard estimate as shown in Table 4 20 is the same

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103 Table 4 20 Example of standard estimate Problem Groups Estimate AL Schedule 0.837 CA Schedule 0.491 CM Schedule 0.910 CO Schedule 0.730 PC Schedule 0.729 QM Schedule 0.902 SP Schedule 0.820 TB Schedule 0.838 The major difference between Table 4 18 4 19 and 4 20 in the parameter estimate s is that the first two tables are based on one of any variables in the model and the las t one is based on the latent factor. That is why the standard estimate has to look for the interpretation of CFA models. The unique variances will be computed based on the standard estimate. So there is no change in statistical significance test of uniq ue variances for each variable no matter what a variable is selected as a base. For this research, AL will be the base of the computation of raw estimate for the initial run and then standard estimate will be computed using Amos. Initial Result s for Each Success Parameter Before discussing the initial results of the CFA models for each success parameter, the goodness of fit test has to be addressed. The CFA models will be evaluated based on how well they represented the data set in the form of the CFA mod els (Brown 2006) The goodness of fit test is a part of that process. As shown in Table 3 18, to be an acceptable model, the null hypothesis fails to reject the hypothesis using 2 at = 0.05 first and all other goodness of fit indices will be explained later in this chapter. To fail to reject the n ull hypothesis, the p value for each CFA model has to be greater than 0.05 at = 0.05. If not, it will reject the null hypothesis, which means the CFA model is different from the data. Each CFA model of success parameter is shown in Figure 3 8 and a CFA model for Schedule is shown in Figure 4 4. Using these

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104 models, all five initial results of p values of CFA model for success parameter s are summarized in Table 4 21. Table 4 21 Initial results of P values of CFA model Item Success Parameters Cost Sche dule Quality Safety Satisfaction Method WLS WLS ML ML WLS P Value 0.007 0.001 *** *** 0.002 s are smaller than 0.05. It represents that the null hypothesis has to be rejected at = 0.05. The models and the data are statistically different at this alpha level. In this case, it is not necessary to look for the parameter estimate (factor loading ) because there is a significant difference between model and data. In the previous section, the results of EPA have clearly shown that there is only one possible extracted factor in each success parameter using the Eigenvalue greater than 1.0 and Scree t est methods. The range of communality of each extracted factor is from 65% for Schedule to 76% for Satisfaction. It indicates that each extracted factor contained information worth 65% through 75%, compared to the original data. The unexplained and rema ining 35% and 24% of communality after extracting factors may affect the results of the CFA model because, in general, the CFA model has more parsimonious form than the EFA model in terms of the number of factors and factor loadings, etc (Brown 2006) Then what does the rejection of null hypothesis mean ? This could be summarized as follows: The number of latent factors would be only one ( 1) whether it is EFA or CFA. With respect to the parsimonious form of CFA, there cannot be more than one latent factor in CFA models. EFA outputs already show the pos sible extracted number of factors. Each success parameter has not been manifested by eight problem groups. Some of problem groups are not significant enough to fail to reject the null hypothesis of the model.

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105 The main hypothesis of this research address ed in the previous section is that success parameter is this hypothesis has to be rejected for all five project success parameters. It indicates that each project success param eter has some problem groups, which are less consistent than the others on the degree of impact using CFA models. It is another benefit of the usage of CFA models to choose better variables. It is sometimes hard to select some variables among a group of variables in a reasonable way. The average value and ranking by summation could be used the most for the selection, but the cutoff values or the range will be based on the personal or groups opinion to choose. In terms of this, the CFA model is useful to filter the variables. From the discussion above, all p values make the null hypothesis rejected for five CFA models. It is evident that each project parameter has not been manifested by eight problem groups. It is necessary to trim the CFA models to fin d the variables that are more consistent than others among the eight problem groups in satisfying the statistical significance test CFA Model Trimming Overview As mentioned earlier, it is necessary to trim the CFA models. This process will be a filtering process to find any problem groups that affect the failure to reject the null hypothesis and also be a process to choose the best fitted model for each success parameter. Those problem groups will be removed from the CFA models. There are two ways to fi nd out the best fitted model. One is using critical ratio (CR) and the other is using a combination of each problem group to form a model with less than eight variable s per CFA model. Garson (2008c) addresses the usage of critical ratio (CR) for improveme nt of model fitting. Deleting a statistically insignificant path in the model can improve the model fitting. The value of the critical ratio (CR) is found by divid ing the parameter estimate by its standard error. T he

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106 cutoff value is 1.96 at = 0.05. A ny paths with critical ratio (CR) value less than 1.96 will be removed from the model. But for this research, the CFA models are not that complicated and have only a maximum of eight variables for each success parameter. So this concept will be revised a s deleting any variables that are statistically insignificant or less significant than others when all variables are significant until the null hypothesis fails to be reject ed But the critical ratio (CR) will change depending upon what base is set up for each model as shown in Table s 4 18 and 4 19. So the critical ratio (CR) method will be performed, using eight possible models for each success parameter and each model will have a different base. The other method is generating all possible combinations of problem groups consist ing of seven variables or less in the model until the null hypothesis fails to be reject ed The first method is deleting any variables in the model to improve model fit. On the other hand the second method is to find the best fi tted model using all possible combination s of problem groups. Since the CFA model for the eight problem groups fails to be reject ed the maximum number of problem groups in the model will be seven. Due to the all possible number of combination models for seven or less problem group for each CFA model, this is a time consuming process, compared to the critical ration (CR) method. For example there are eight possible combinations that consist of seven problem groups for each success parameter and there are 2 8 possible combinations that consist of six problem groups for each success parameter. A combination of six problem groups will be performed only if there is no best fitted model within seven problem groups of combination. A total number of combination s of five problem groups for each success parameter will have more than those of the seven and six problem groups Deleting any variables in the model will stop when the p value is greater than 0.05 in a different base model, using the critical ratio (CR) method. All combination method s will

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107 generate all possible models with the seven problem groups first, and then six problem groups only if there is no combination to meet the p value greater than 0.05 with seven problem groups. The selection of best fitt ed model for both methods is based on the p value and number of problem groups retained in the model. In the best fitted model selection, the first priority among fitted models is any model with the highest p value and the largest number of problem groups retained. The second priority is any model with the minimum satisfied p value and the largest number of problem groups retained. For example there are two models available having the same p value but a different number of problem groups retained. One has seven problem groups and the other has six problem groups in the model. Then the best fitted model would be the model with the seven problem groups. In most cases, any model with the largest number of problem groups and that has satisfied the p value would be chosen for the best fitted model because the model would be better fitted when there are less variables in the model. Critical r atio (CR) m ethod The values of the critical ratio are different from the base of each CFA model. Eight different CFA models with a different base are expected. For example, using AL as a base, the model will be tested until the p value is greater than 0.05. During this process, a problem group that is not statistically significant will be deleted to improve the model f it. If all values of the critical ratio (CR) are significant, then a problem group that is comparatively less significant than other problem groups will be removed from the model. To be statistically significant, the critical ratio (CR) values have to be greater than 1.96 at = 0.05. Once the best fitted model has been found, then this process stops for a base and the same procedure is repeated using a different base at a time and so forth. Table 4 22 shows the results of this procedure for Schedule.

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108 Table 4 22 Summary of critical ratio (CR) procedure for schedule Base First Second Third Fourth Fifth P Value Del. Var P Value Del. Var P Value Del. Var P Value Del. Var P Value AL 0.001 CA *** CO *** PC *** CM 0.195 CA 0.001 CO 0.001 PC 0.001 QM 0.04 0 SP 0.316 CM 0.001 CA *** CO *** QM 0.02 0 PC 0.034 CO 0.001 CA *** CM 0.022 PC 0.22 0 PC 0.001 CA *** CO *** QM 0.02 0 CM 0.899 QM 0.001 CA *** CM 0.022 PC 0.22 0 SP 0.001 CA *** CO *** QM 0.02 0 CM 0.899 TB 0.001 CA *** CO *** QM 0.02 0 PC 0.034 T he first column on the left shows the base of each model. It means that for Schedule there are eight models available. The P Value indicates that the p value is that was delet e d to impr ove the model fit. The column heading represents the initial output with eight problem groups. For example base AL, the p value is 0.001 at the first output and CA is deleted to improve the model fit. The critical ratio (CR) value of CA could be statistically insignificant or less significant than that of model was r un with seven variables here without CA. The p value without CA is too small as and CO are removed from the model. The column run with six variables i.e without CA and CO. The p value is still too small and PC is chosen to be deleted and is deleted. In column of CA, CO, and PC. After the model is run with five variables, the p value is still small and CM is selected to be removed and is removed from the model. Finally, column model retains only four variables after the deletion of CA, CO, PC, and CM consecutively and the p value of this model is finally 0.195 which is greater than 0.05. The model improvement

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109 process stops here using AL as the base. The rest of model s using different bases such as CA, CM, CO, PC, QM, PC, SP, and TB will repeat the same procedure as described above To select the best fitted models among all tested models, the p value has to be greater than 0.05 and the model has to retain as many problem groups as it possible According to this process, the models using bases of C M and C O were chosen for the best f itted model and the results for both models are the same in terms of p value and deleted problem groups. Their p value is 0.220 and there are five remaining problem groups (AL, CO, QM, SP, and TB) in the model. The p value is the highest value and, at th e same time, the largest number of problem groups retained among all tested models. Even though p values of model with base s of CA, PC, and SP are higher than that of CO and CM, these models have only four problem groups remaining not five. These models have reach ed the required level of p values after four problem group eliminations. That is why the model with a base of CO and CM is chosen for the best fitted model. For Schedule, using AL as base, the overall procedures to find the best fitted model di scussed previously are followed The rest of models using different bases will follow the same procedures. A summary of the critical ratio (CR) method for each project success parameters excluding Schedule is shown in Appendix D. The final selected best fitted model for each project success parameter is shown in Table 4 23. Table 4 23 Summary of best fitted model using critical ratio (CR) method Success Parameter P Value Problem Groups Retained Deleted Cost 0.065 AL, CA, CM, CO, PC, QM, and SP (7) TB (1) Schedule 0.220 AL, CO, QM, SP, and TB (5) CA, CM, and PC (3) Quality 0.061 AL, CM, CO, PC, and TB (5) CA, QM, and SP (3) Safety 0.067 AL, CA, CM, CO, PC, and QM (6) SP and TB (2) Satisfaction 0.251 AL, CA, CM, CO, and PC (5) QM, SP, and TB (3) All the p values of selected models are greater than 0.05. The highest is 0.251 for Satisfaction and the lowest is 0.065 for Cost. The minimum retained number of problem groups

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110 is five for Schedule, Quality, and Satisfaction. The maximum retained numbe r of problem groups is seven for Cost. Safety has six remaining problem groups in the model. According to the critical ratio (CR) method, each success parameter is manifested by the remaining problem groups. The deleted problem groups may affect the mod el fitting of the original eight problem group model. The critical ratio (CR) method was originally intended to delete a path that is not significant but has been revised to delete a problem group that is not significant or less significant than others. With respect to this revision, it does not seem that it is a good index to find the best fitted model. One of the reasons is that most of time the p value does not improve whenever a problem group is removed from the model. There is a significant improve ment after three or more problem groups have been deleted. Before that point, it is hard to notice the improvement of model. The revision of the concept of critical ratio (CR) method may not apply effectively to this process. It may be necessary to chec k all possible combinations of problem groups for the best fitted model of each success parameter with different numbers of problem groups. All possible combination of problem groups m ethod There are originally eight problem groups per project success para meter. If each CFA model for success parameter has seven problem groups, all possible combination of seven or less problem groups will be computed using E quation 4 1 (Weisstein 2009a) below. ( Equation 4 1) The total number of set s is n and the number of possible combination is k out of n If each model has seven problem groups then the total numbe r of combination will be 8 (8!/[7!(8 7)!]) for each success parameter. On the other hand, if any CFA model has six problem groups, then the total number of possible combination will be 28 (8!/[6!(8 6)!]). The total number of

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111 combination s will be come larg er and larger as the number of problem groups gets smaller and smaller. All models with seven problem groups will be tested first and then the process will continue with six or less problem groups when any model cannot fit into seven variables. The selec tion of best fitted model is the same as the critical ratio (CR) method. The p value of model has to be greater than 0.05 and contain the largest number of problem groups in the model. First of all the seven problem groups are tested. It has a total of e ight combination model for each success parameter. Table 4 24 shows the summary of all combinations of seven problem groups of each success parameter. The upper part of table shows the original eight problem groups and the combination of seven problem gr oups for each success parameter. Each combination is labeled as combination 1 through 8. The lower part of table shows the p values of the counterpart of each combination model for each success parameter. If the p value is too small to present, it will be show n Table 4 24 Summary of combinations of seven problem groups Original Combinations 1 2 3 4 5 6 7 8 AL AL AL AL AL AL AL AL CA CA CA CA CA CA CA CA CM CM CM CM CM CM CM CM CO CO CO CO CO CO CO CO PC PC PC PC PC PC PC PC QM QM QM QM QM QM QM QM SP SP SP SP SP SP SP SP TB TB TB TB TB TB TB TB Success Parameter P Values Cost 0.011 0.005 0.189 0.003 0.004 0.015 0.092 0.065 Schedule 0.005 *** 0.079 0.001 0.004 0.054 0.188 0.027 Quality 0.006 0.001 *** *** 0.129 *** 0.010 *** Safety 0.001 *** *** *** *** *** *** 0.002 Satisfaction 0.089 0.001 0.002 *** 0.003 0.001 0.004 0.040 Regarding the combination 1, it excludes AL and has all eight problem groups. Its p values are 0.011, 0.005, 0.006, 0.001, and 0.089 f or Cost, Schedule, Quality, Safety, and Satisfaction respectively. The combination 2 through 8 would be interpreted similar to

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112 combination 1. The highest p value of each success parameter is 0.189, 0.188, 0.129, 0.002, and 0.089 for Cost, Schedule, Quali ty, Safety, and Satisfaction respectively. Only the p value of Safety is less than 0.05 and others satisfy the minimum of 0.05. The best fitted model for Cost would be the combination 3 with the p value of 0.189 that is the highest even though the p valu e of combination 7 and 8 is greater than 0.05. The best fitted model for Schedule would be the combination 7. The p value s of combination s 3 and 6 are greater than 0.05 but less than that of combination 7. Quality has the only one combination that has t he p value greater than 0.05 so combination 5 is the best fitted model for Quality. The best fitted model for Satisfaction is the combination 1 with the p value of 0.089 that is the only one p value greater than 0.05. Unfortunately there is no best fitte d model for Safety, using the seven problems in combination. It is evident that Safety has to look at six problem groups combination to find the best fitted model. So far there are seven problem groups per model available for Cost, Schedule, Quality, and Satisfaction using the combination method. The best fitted model is found for Cost, Schedule, Quality, and Satisfaction using seven problem groups combination. There is no best fitted model using seven problem groups combination for Safety and so it is necessary to find a six problem groups combination for Safety if possible. A total number of possible six problem groups combination s is 28 as indicated earlier. The results of 28 combinations for Safety are shown in Table 4 25. Each row shows a combin The combination 22 has the highest p value among 28 combination models. This model does not include AL and TB. The second highest is 0.130 from the combination 16 with out CA and TB. So the best fitted model for Safety is the combination 22 with the problem groups of CA, CM, CO, PC, QM, and SP. Using all possible combination methods, the best fitted model

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113 Table 4 25 Summary of six problem groups combinations for safet y Original AL CA CM CO PC QM SP TB P Value Combinations 1 AL CA CM CO PC QM 0.067 2 AL CA CM CO PC SP 0.002 3 AL CA CM CO PC TB *** 4 AL CA CM CO QM SP 0.003 5 AL CA CM CO QM TB *** 6 AL CA CM CO SP TB 0.112 7 AL CA CM PC QM SP ** 8 AL CA CM PC QM TB *** 9 AL CA CM PC SP TB *** 10 AL CA CM QM SP TB *** 11 AL CA CO PC QM SP *** 12 AL CA CO PC QM TB *** 13 AL CA CO PC SP TB 0.004 14 AL CA CO QM SP TB 0.004 15 AL CA PC QM SP TB *** 16 AL CM CO PC Q M SP 0.130 17 AL CM CO PC QM TB *** 18 AL CM CO PC SP TB *** 19 AL CM CO QM SP TB *** 20 AL CM PC QM SP TB *** 21 AL CO PC QM SP TB *** 22 CA CM CO PC QM SP 0.147 23 CA CM CO PC QM TB 0.004 24 CA CM CO PC SP TB 0.003 25 CA CM CO QM SP TB 0.001 26 CA CM PC QM SP TB *** 27 CA CO PC QM SP TB 0.001 28 CM CO PC QM SP TB 0.079 for each success parameter has been found. Table 4 26 shows the final results of all combination method s Cost, Schedule, Quality, and S atisfaction have seven problem groups in the model and Safety has six problem groups in the model. AL was removed from the initial model twice in total from Safety and Satisfaction. Other removed problem groups have been removed only once. Table 4 26 Su mmary of best fitted model using possible combination method Success Parameter P Value Problem Groups Retained Deleted Cost 0.189 AL, CA, CO, PC, QM, SP, and TB (7) CM (1) Schedule 0.188 AL, CA, CM, CO, PC, QM, and TB (7) SP (1) Quality 0.129 AL, CA, CM, CO, QM, SP, and TB (7) PC (1) Safety 0.147 CA, CM, CO, PC, QM, and SP (6) AL and TB (2) Satisfaction 0.089 CA, CM, CO, PC, QM, SP, and TB (7) AL (1)

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114 Critical ratio (CR) vs. all possible combination There are two methods that can be used to find th e best fitted model for each success parameter because the original eight problem group model rejects the null hypothesis. T he t wo methods are the critical ratio (CR) method and the all possible combination s method. The first one is to revise to apply th e concept and the second one is to perform all possible combinations of problem groups. The critical ratio (CR) method stops the model trimming once a model is found to satisfy the p value, using a different base, no matter how many problem groups are ava ilable in the model. The final selection for the best fitted model is performed among trimmed models of different bases. On the other hand, the all possible combination method tests all possible combination models in terms of seven problem groups first a nd then six problem group or less than that until the model is found to satisfy the p value. Table 4 27 shows the comparisons between these two methods. Table 4 27 Comparisons of critical ratio and all possible combination Success Parameter Critical Rati o All Possible Combination P Value # of Remaining Problem Groups P Value # of Remaining Problem Groups Cost 0.065 7 0.189 7 Schedule 0.220 5 0.188 7 Quality 0.061 5 0.129 7 Safety 0.067 6 0.147 6 Satisfaction 0.251 5 0.089 7 There are more problem groups available in the model with the satisfied p values using the all possible combination s method than th at using the critical ratio (CR) method. There are more p values in the all possible combination method that are higher than those of the critical methods. The p values of critical methods for Schedule and Satisfaction are higher than those of combination method. But the number of remaining problem groups in the model using the critical ratio (CR) method is smaller than those of combination method Schedule and Satisfaction have five problem groups for each using the critical ratio (CR) method and have

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115 seven problem groups for each using the all combination method. Although the total number of tested model s using the all combination method would be larger than those for the critical ratio (CR) method where there are less problem group combinations per model, it clearly shows that all combination is preferred to find the best fitted model with a higher number of problem groups in the model. The re vised concept of critical ratio (CR) method may not work effectively in this model trimming process. To finalize the model for each success parameter, the selection priority will go to the highest p value with the largest number of problem groups in the mo del. There is no issue on selection of best fitted model for Cost, Quality, and Safety. All combination models satisfy the highest p value and the largest number of problem groups. So three models from all combination method will be chosen as the best f itted model for Cost, Quality, and Safety. On the other hand, there may be some issues with Schedule and Satisfaction. The p values of both success parameters of the critical ratio (CR) method are higher than those of all combination method but fewer num ber of problem groups than those of all combination. Which model has to be chosen? Recalling why all this modeling process is needed for the first time. The initial original eight problem group model has rejected the null hypothesis. If any project suc cess parameter is not manifested by the eight problem groups, then how many possible problem groups can manifest the project success parameter? If there is any model that meets the minimum p value with a larger number of problem groups, then that model wi ll have to be selected for the best fitted model. So the model of Schedule and Satisfaction from the all combination s method has to be chosen to be the best fitted model. If the all combination s method had been performed first for Schedule and Satisfacti on, the model trimming process using the critical ratio (CR) method would not have been necessary because there is a model that meet s the p value with seven

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116 problem groups for Schedule and Satisfaction. Finally the best fitted model for each success param eter is the model from all combination method. Final goodness of fit test indices Thus far, only the p value has been used for the selection of the best fitted model for each success parameter however, it is necessary and recommended that other indices be reviewed as well (Brown 2006) There are three categories and four different types of goodness of fit test indices available besides the p value. The three categories are Absolute Fit, Parsimony Correction, and Comparative Fit. The four types of indices are Standardized Root Mean Square Residual (SRM R), Root Mean Square Error of Approximation (RMSEA), Comparative Fit Index (CFI), and Tucker Lewis Index (TLI). Table 4 28 shows the summary of these indices of the final CFA model for each success parameter. Table 4 28 Summary of goodness of fit indi ces Category Index Cutoff Value 1 Success Parameter Cost Schedule Quality Safety Satisfaction 2 H 0 : S H a : S or no restriction Should fail to reject H 0 at 0.05 > 0.05 0.189 0.188 0.129 0.147 0.089 Absolute Fit Standardized Root Mean Square Re sidual ( SRMR ) < 0.08 0.039 0.065 0.034 0.032 0.062 Parsimony Correction Root Mean Square Error of Approximation ( RMSEA ) < 0.10 0.056 0.056 0.066 0.070 0.074 Comparative Fit Comparative Fit Index ( CFI ) > 0.95 0.856 0.881 0.980 0.986 0.832 Tucker Lewis Index ( TLI ) > 0.95 0.904 0.921 0.987 0.991 0.888 Notes: 1. Source: Brown 2006

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117 All indices satisfy the cutoff values except for comparative fit. The values of Quality and Safety satisfy the comparative fit but the rest of the project success parameters d o not meet the cutoff values that are greater than 0.95 for both indices. The index values of Cost and Schedule are close to the cutoff value and that of Satisfaction is a little bit further away than the other two success parameters. The two indices val ues of the comparative fit of Satisfaction will be increased to 0.919 and 0.951 for CFI and TLI respectively if the model has six problem groups without AL and CA in the model. In this case the model fit is increased to satisfy one of two indices but ther e are only six problem groups not seven. The fewer number of problem groups, the better model fit is. But for this research, even though a good model fit is important, the number of problem groups in the model explained by CFA is important as well. So t he current selected model for each success parameter is good enough for the goodness of fit tests even though some of them do not satisfy the comparative fit by itself. Parameter Estimate and Significance The process of the computation of parameter estimat e is already addressed in the previous section. In this section, there are two main topics presented. One is the parameter estimate (factor loading) and its statistical significance. Based on the best fitted model for each success parameter, the paramet er estimate is performed. The raw estimate is computed and then the raw estimate will be converted into the standard estimate. In this process, the significance of the parameter has to be checked. To be statistically significant, the critical ratio (CR) value has to be greater than 1.96 at = 0.05. All the raw estimates of each project success parameter using different bases are shown in Appendix E. It also includes unique variances for each success parameter. Even though the raw estimate is differen t from the base problem group, the standard estimate and the unique variance s are not. All the raw estimates using different bases and unique

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118 variances are statistically significant at = 0.05 which means the critical ratio values are greater than 1.96. Table 4 29 shows the summary of the standard estimate (factor loading) for each success parameter. Table 4 29 Summary of standard estimate (factor loading) Problem Groups Success Parameters Cost Schedule Quality Safety Satisfaction AL 0.867 0.865 0.90 3 CA 0.804 0.498 0.670 0.848 0.938 CM 0.813 0.835 0.877 0.931 CO 0.574 0.726 0.707 0.821 0.870 PC 0.859 0.746 0.948 0.939 QM 0.846 0.846 0.616 0.919 0.860 SP 0.806 0.799 0.504 0.865 TB 0.788 0.846 0.898 0.776 From the model trimming process, the number of problem groups per success parameter has been set up. Cost, Schedule, Quality, and Satisfaction have seven problem groups and Safety has only six problem groups as shown in Table 4 29 and also one of the graphical path diagrams with denotat ions and output results as example for Cost is shown in Figure 4 5. The path diagrams shown in Figure 4 5 are based on the latent factor (Cost). All the values in both figures are rounded up to two decimal places for convenience. All the denotations in p lot A of Figure 4 5 are the counterpart of values for factor loadings and unique variances in plot B of Figure 4 5. Each problem group consists of a factor loading and a unique variance. The unique variance for Cost is available in Appendix E as mentione d early. The path diagram can be presented by equation shown in Table 4 30. The equations are based on each individual problem group and are in the form of a regression model. The largest slope is from AL with 0.87 and the smallest slope is from CO with 0.57. Although the interpretation of factor loading varies among researchers (Thompson 2004) these factor loadings are regression slopes between a latent factor and indicator variables (Brown 2006; Garson 2008c) A latent factor is a project success parameter and indicator variables are

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119 A) Denotations B) Outputs Figure 4 5 Path diagram. Table 4 30 Equation forms for cost Denotation Real Output AL = 1C C + 1 AL = 0.87 C + 0.07 CA = 2C C + 2 CA = 0.80 C + 0.10 CO = 4C C + 4 CO = 0.57 C + 0.20 QM = 5C C + 5 QM = 0.86 C + 0.08 PC = 6C C + 6 PC = 0.85 C + 0.08 SP = 7C C + 7 SP = 0.81 C + 0.20 TB = 8C C + 8 TB = 0.79 C + 0.15 project pro blem groups in this research. These regression slopes can be interpreted as the impact on a latent factor (Bassioni et al. 2008; Eskildsen et al. 2001) The raw data s hows the evaluations of the that negative ly impact on each success parameter. From this perspective, the factor loadings shown in Table 4 29 are negative impacts on each project success parameter. The highest value means the highest negative impact on any success

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120 parameter and the lowest value means the lowest impact on any success parameter. Most of problem groups have different impacts on each project success parameter. It supports one of the hypotheses of this research, ach problem group will have a different impact on each The CFA model procedures for this research have been addressed. The five initial CFA models, from the 100 point data set, have eight problem groups to be tested whether they are manifested by all eight problem groups or not. At the end of the process, none of them are manifested by eight problem groups but it is found that each project success parameter is manifested by six or seven problem groups. The impacts of problems on each project success parameter are different from each other. M ost of problem groups dynamically and differently response to each project success parameter. This information on the number of problem groups within each project success parameter and impacts of the problem groups on success parameter s will be useful and can be a good guideline for further studies on the multiple project success parameters. The n ext chapter will be dis cuss ed how this information can be used for developing a tool for potential project problem identification.

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121 CHAPTER 5 APPLICATION OF CFA M ODEL OUTPUTS Background In the preceding chapter, the relationship between certain project success parameter s and som e problem groups has been discussed. Each problem group has a different impact on a project success parameter. It is clear and easy to manage or focus on which problem group has the most negative impact when there is only one project success parameter. In the reality, there are more than one project success parameters. The two most common project success parameters are cost and schedule. A project is considered as successful when it is completed under budget and within schedule. So there are many arti cles available on these two project success parameters such as index modeling (Gibson and Dumont 1995) ; performance measur e and project outcome forecasting (Choi et al. 2006; Russell et al. 1996) These studies focus ed on cost and schedule. As society changes rapidly, the demand s on the construction industry are not focusing on only cost and schedule any more. There are additional success parameters other than cost and schedule such as quality and safety. Even though Choi et al (2006) addresses multiple project success paramete rs and project outcome forecasting, it mainly discusses each individual success parameter. It does not see the multiple project success parameters as one whole project success when a project has more than two success parameters with a different weight on each success parameter. Griffith et al (1999) shows how project success index could be computed and changed depending upon the criteria and their weights. If there are multiple project success parameters available with different weight priorities for a p roject, then how can this project be controlled? When the project has its own potential problems, which problems should be consider ed as the most serious or critical for the project success? Using outputs of the previous

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122 chapter and the SMART S technique, this chapter will show the process involved in coming up with the answer those questions. Single Multi Attribute Rating Technique Using Swing Weight (SMART S ) Overview In Chapter 2, the basic procedure of SMART S has been discussed. This method is help ful to narrow down the options based on the evaluation of options related to attributes as discussed in Chapter 2. Attributes would be project success parameters and options would be eight project problem groups for this research. To use this technique, ther e are two concerns about the independence properties of the measurement attributes. One is the value independence and the other is defined as environmental independence (Edwards 1977; Oyetunji 2001 ) Another concern with the values is the dominance among options (Edwards and Barron 1994; Winterfeldt and Edwards 1986) Any option dominating others should be eliminated. All of the input values should meet these criteria to use this technique. In this section, the computation of input values, independence properties, dominance option, an d tool development and its validation process will be discussed. Computation of Input Values To perform SMART S there needs to be a set of input values for multi attributes (project success parameters) and their options (problem groups). In the preceding chapter, the relationships between project success parameters and eight problem groups is defined as shown in Table 4 29. The values shown in Table 4 29 consider the impact of each problem group on each success parameter. The higher the values, the more negative the impact on a project success parameter. So the values in Table 4 29 are good enough as input values for SMART S T he raw values are showing as values with three decimal places so they have to be converted into whole number s for the convenience of further work in this process. There are two

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123 approaches to convert the raw values into whole numbers. One approach is a value of option A over a value of option B when the value of option B is the highest value among options and the other value is a v alue of option A over a value of option B when the value of option B is the sum of all options. The first approach is the computation of a relative ratio between a value and the highest value among values (Oyetunji 2001) Obviously the highest value would be 100 and others will be less than 100 if 100 is set to be the highest value The second approach is the computation of the proportional ratio between a value of an option and the sum of all options (Bassioni et al. 2008; Eskildsen et al. 2001) The first approach is useful when all attributes have the same highest values. The second approach is useful when all attributes have different h ighest values. The same input values of the first approach will come out if the input values of second approach are computed as the first one. So there is no bi g difference in the input value computations. For this study the second approach is chosen f or the conversion of raw data into whole numbers and the computation example of Cost from Table 4 29 shown in Table 5 1. Table 5 1 Conversion factor loadings for cost Problem Group Factor Loading Proportional Ratio Final Impact AL 0.867 0.867/5.544 = 0 .156 156 CA 0.804 0.804/5.544 = 0.145 145 CO 0.574 0.574/5.544 = 0.104 104 PC 0.859 0.859/5.544 = 0.155 155 QM 0.846 0.846/5.544 = 0.153 153 SP 0.806 0.806/5.544 = 0.145 145 TB 0.788 0.788/5.544 = 0.142 142 Total 5.544 1.000 1,000 There are seven problem groups available for Cost and CM is excluded. The total sum of factor loading in Table 5 1 is 5.544. Each factor loading for AL, CA, CO, PC, QM, SP, and TB is 0.867, 0.804, 0.574, 0.859, 0.846, 0.806, and 0.788 respectively. The proportional ra tio for AL is 0.867/5.544 = 0.156. The final impact values are multiplied by 1,000 since it is easier to use 156 than 0.156 (Bassioni et al. 2008; Eskildsen et al. 2001) The highest conversion value is

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124 156 from AL and the lowest conversion value is 104 from CO. The index value of 156 stands for the maximum negative impact on Co st from AL and the index value 104 stands for the maximum negative impact on Cost from CO. The sum of converted values of problem groups on each project success parameter will be 1,000. It means that the index value of maximum negative impact on each suc cess parameter is 1,000. Using this method, all the raw values of factor loadings in Table 4 29 are converted into the whole number values as shown in Table 5 2 Table 5 2 Summary of conversion values for all success parameters Problem Groups Success Para meters Cost Schedule Quality Safety Satisfaction AL 156 162 166 CA 145 93 123 172 152 CM 152 154 178 151 CO 104 136 130 167 141 PC 155 140 193 152 QM 153 158 113 187 139 SP 145 147 103 140 TB 142 158 165 126 Sum 1,000 1,000 1,000 1,000 1,0 00 The conversion numbers shown in the table 5 2 is the final input for SMART S The sum of each project success parameter is 1,000 and each success parameter has its highs and lows for the maximum impacts on a given success parameter. Next, t hese conve rsion values will be checked to determine whether they satisfy the independence properties and dominance of the options. Independency Properties of Values As mentioned early, there are two types of checks to be performed to determine the independency prop erties of the values (Edwards 1977; Oyetunji 2001) One is the value independence and the other is defined as environmental independence. The aggregation rule on the SMART S process is based on th e assumptions of independence of the measurement attributes considered in a selection analysis (Oyetunji 2001) Regarding value independence, Oyetunji

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125 attributes that are assess ed As shown in Table 4 29, the val ues of factor loadings are showing relationships between success parameters and the problem groups. The values in Table 4 29 show that there is no relationship between problem groups. Due to the relationship between success parameters and problem groups, each problem group is naturally independent from each other. It means that they satisf y the value independency. For the convenience of the application, all input values are computed into scales out of 1,000. Edwards (1977) defines the value independenc e means that the extent of an impact for problem A over B of a success parameter is unaffected by the position of the entity being evaluated on other success parameters. The final conversion values satisfy the value independency because all factor loading s are converted into maximum impact using the proportional ratio on success parameters. A conver sion value is an impact for problem group over the overall impacts. This meets the definition of the value independency as well. The other aspect of the inde pendence properties is environmental independence. The environmental independence indicates the lack of correlation between the levels of problem groups with respect to two project success parameters (Oyetunji 2001) For example the values of Cost and Schedule are compared to problem group by problem group. If the wa ys the values exist by problem groups are similar, then the correlation between Cost and Schedule will be high. If the pattern of values by problem groups is the same to both, the correlation will be 1.0. In this case, a perfect correlation is a violatio n of the environmental independence and it can lead to double counting (Edwards 1977) Due to this double counting, if two project parameters are perfectly environmentally correlated, only one of them need be included in the evaluation

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126 process (Edwards 1977) Edwards (1977) also indicates that the acceptable range or cutoff value of correlations with respect to the environmental independence has not been determined yet. There are three appr oach es to determine the environmental independence among project success parameters. One approach is to compare values of a pair of success parameters (Oyetunji 2001) Another is to compute correlations between success parameters. And the third approach is to plot the values of a pair success parameters (Oyetunji 2001) The first one is easy to define the environmental independen ce. If a pair of success parameters has the same value pattern and the same value by problem group, which is a perfect correlation, the value will be zero when the subtraction is done between them There are 10 pair combinations of project success param eters among five project success parameters. A pair from Cost and Schedule and its value difference is shown in Table 5 3 as example. Table 5 3 Value difference between two parameters Problem Group Success Parameter Value Difference (Cost Schedule) C ost Schedule AL 156 162 (6) CA 145 93 52 CM 0 152 (152) CO 104 136 (32) PC 155 140 15 QM 153 158 (6) SP 145 0 145 TB 142 158 (16) T able 5 3 clearly shows that there is no pattern in between two project success parameters with respect to the prob lem groups. All the negative values are in the parenthesis. The output of rest of the nine pair combinations of two project success parameters among five project success parameters are shown in Table 5 4. Similar to the pair of Cost and Schedule, the re st of pairs have no pattern in the value difference with respect to problem groups. Each pair of parameters shows the irregular pattern in value difference. These outputs show that there are no high environmental correlations between project success para meters in the selection process.

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127 Table 5 4 Value difference between two project success parameters Problem Group Value Difference Cos Cos Cos Sch Sch Sch Qua Qua Saf Qua Saf Sat Qua Saf Sat Saf Sat Sat AL (10) 156 156 (4) 162 162 1 66 166 0 CA 22 (27) (7) (30) (79) (59) (49) (28) 21 CM (154) (178) (151) (2) (26) 2 (25) 3 28 CO (27) (63) (37) 6 (31) (5) (37) (11) 26 PC 155 (38) 3 140 (53) (12) (193) (152) 41 QM 39 (34) 13 45 (28) 19 (73) (26) 48 SP (2) 43 5 (147) (103) (140) 45 7 (37) TB (23) 142 17 (7) 158 33 165 40 (126) Cos (Cost), Sch (Schedule), Qua (Quality), Saf (Safety), and Sat (Satisfaction) The second one is to compute the correlations between project success parameters. Table 5 5 shows the correlations between p roject success parameters using SPSS. Table 5 5 Correlations between project success parameters Cost Schedule Quality Safety Satisfaction Cost 1.000 Schedule (0.181) 1.000 Quality (0.266) (0.059) 1.000 Safety (0.308) (0.079) (0.588) 1.000 Satisfaction (0.284) (0.282) (0.390) 0.721 1.000 The negative correlations In Table 5 5 are presented in the parenthesis. All correlations are negative except for the pair for Safety and Satisfaction. The correlation between Safety and Satisfaction has the highest correlation at 0.721 and the correlation between Schedule and Quality has the lowest correlation at 0.059. Except for the correlation of Satisfaction and Safety, all correlations are less than 0.6. According to the range of correlation by Ol son (1987), the correlation between Safety and Satisfaction is moderately correlated and the rest of correlations have no relationship. It is evident that that there is no relationship between pairs of project success parameters even though one of the cor relation value s is moderately correlated. The last one is to plot the values of a pair of project success parameters with respect to problem groups. If two success parameters are perfectly correlated, all the values will be plotted on the trend line and i ts variability (R 2 ) will be 100% (Oyetunji 2001) The total numb er of pairs

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128 of project success parameters is ten. Each pair of success parameters will be plotted as a y = a x + b relationship Table 5 6 shows the summary of variability of ten pairs and Figure 5 1 shows the plot of Satisfaction and Safety as example T able 5 6 Summary of variability Variability Pairs of Success Parameters 1 2 3 4 5 6 7 8 9 10 Cos Cos Cos Cos Sch Sch Sch Qua Qua Saf Sch Qua Saf Sat Qua Saf Sat Saf Sat Sat R 2 (%) 3.29 7.09 9.48 8.05 0.34 0.63 7.95 34.52 15.20 52.03 where Cos (Cos t), Sch (Schedule), Qua (Quality), Saf (Safety), and Sat (Satisfaction) Figure 5 1 The plot of safety vs. satisfaction P air 10 comprised of Safety and Satisfaction in Table 5 6 has the highest variability among pairs with 52.03% and its plot is shown in Figure 5 1. All the values of Safety are plotted on the x axis and all the values of Satisfaction are plotted on the y axis. After plotting values of both success parameters, a trend line can be drawn as shown in Figure 5 1. If both success parameters are perfectly correlated, then all the values of both parameters will be on the trend line and the variability of the trend line will be 100%. As shown in Figure 5 1, none of the values is on the

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129 trend line. For the references, all plots with trend lines and variability of the ten pairs are available in Appendix F. The second highest variability is in P air 8 consisting of Quality and Safety with 34.52%. The third highest variability is 15.20% for P air 9 consisting of Quality and Satisfaction. The rest of the variabilities are too small to expect the trend and relationship. The maximum variability is 52.03% and the lowest is 0.34% among all ten pairs of variabilities Even though 52.03% is the highest variability, it is still not statistically enough t o explain the relationship between two project success parameters. From this method, there is no significant relationship between all pairs of success parameters. It is clearly evident that all pairs of project success parameters are environmentally inde pendent. Dominance of Problem Groups The value independency mainly addresses the value independency between project success parameters. On the other hand, the dominance focuses on the problem groups. Even though the results of the CFA models already show that problem groups respond differently to each project success parameter, the dominance check has to be performed. Problem groups are alternatives showing the degree of being critical to a given set of project success parameters. A problem group is cla ssified as dominant if the problem group is superior to other problem groups with respect to all project success parameters. In this case, the dominant problem group has to be eliminated because the problem group is always out performed so there is no cha nce to change in positioning for other problem groups (Edwards 1977; Oyetunji 2001) To satisfy the SMART S process, there are no dominant problem groups among problem groups The easiest way to ch eck the dominance among problem groups is to check the value difference between two problem groups with respect to five project success parameters (Oyetunji 2001) The possible combination of pairs of problem groups is 28. The order in a pair does not matter here for example AL CA or CA AL. If the value differenc e between two problem

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130 groups is larger than zero or smaller than zero with respect to all five project success parameters, then one of problem groups that have a larger value is the dominant problem group. The out performed problem group has to be conside r ed for removal from the alternatives because there is no chance to change in positioning with respect to project success parameters. Table 5 7 shows the value difference between two problem groups. All the value differences less than zero are shown in t he parenthesis. To avoid the dominance problem group, the value difference of each pair in the row in Table 5 7 has to be a combination of larger and less than zero values If all the values are larger or smaller than zero, then as stated earlier, one of problem groups will be considered as dominant. Table 5 7 Value difference between two problem groups No. Problem Pairs Success Parameters Cost Schedule Quality Safety Satisfaction 1 AL CA 11 69 43 (172) (152) 2 AL CM 156 10 13 (178) (151) 3 AL CO 53 26 36 (167) (141) 4 AL PC 1 22 166 (193) (152) 5 AL QM 4 4 53 (187) (139) 6 AL SP 11 162 19 (103) (140) 7 AL TB 14 4 1 0 (126) 8 CA CM 145 (59) (30) (6) 1 9 CA CO 41 (43) (7) 5 11 10 CA PC (10) (46) 123 (20) 0 11 CA QM (8) ( 65) 10 (14) 13 12 CA SP 0 93 (24) 70 12 13 CA TB 3 (65) (42) 172 26 14 CM CO (104) 16 24 11 10 15 CM PC (155) 13 154 (14) (1) 16 CM QM (153) (6) 40 (9) 11 17 CM SP (145) 152 7 76 11 18 CM TB (142) (6) (12) 178 25 19 CO PC (62) (4) 1 69 (18) (9) 20 CO QM (59) (22) 22 (14) 1 21 CO SP (50) 136 (22) 45 1 22 CO TB (46) (22) (46) 117 12 23 PC QM 2 (19) (113) 6 13 24 PC SP 10 140 (147) 90 12 25 PC TB 13 (19) (165) 193 26 26 QM SP 7 158 (34) 84 (1) 27 QM TB 10 0 (52) 1 87 14 28 SP TB 3 (158) (18) 103 14

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131 Every pair of problem groups has its ups (above zero) and downs (below zero) with respect to five project success parameters. It does not look like that there are serious dominant problem groups existing among proble m groups. Regarding AL and its pair combinations, due to the highest value among problem groups in three success parameters, AL is out performed in Cost, Schedule, and Quality but not in Safety and Satisfaction. AL would be removed if it were out perform ed in Safety and Satisfaction as well. The value differences in Table 5 7 are plotted into two graphs as shown in Figure 5 2 and 5 3 for visual checking. Figure 5 2 shows the combination No. 1 through 13 and Figure 5 3 shows the rest of combinations. F igure 5 2 Value difference between problem groups part 1

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132 Figure 5 3 Value difference between problem groups part 2 In addition to Table 5 7, visual checking is performed using Figure 5 2 and 5 3. The two graphs support the conclusion there are no domi nant problem groups among themselves. All the property for the input values of the SMART S process has been tested. Those properties are the independency property and dominance of problem groups. The independence property has two categories. One is the value independency and the other is the environmental correlation. The input values for the SMART S from the CFA models satisfy these properties. The next property checked is the dominance problem groups among themselves. The value difference between two problem groups and visual checking using two plots show that there are no dominant problem groups among themselves. So the final conversion values in Table 5 2 satisfy all the property of the SMART S process and are ready to be used in tool development.

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133 B asic Concept of Application Overview As discussed in the section 2.5 of the Chapter 2, the SMART S process shows the best alternative (problem group) based on the preferences or weights of factors (project success parameters) as a form of aggregated scores. The preceding section show ed the whole process of testing the property of input values for the SMART S process. The input values are the output of CFA models. Each value in the table is the maximum negative impact of the index value on each success para meter. The values are various among problem groups and project success parameters. From this point, there are five project success parameters available with a different weight or preference and there are eight potential problem groups available with maxi mum impact on each success parameter. Using the SMART S process, the degrees of being critical problem groups can be computed by changing the weight or preference of project success parameters. The aggregated scores represent the maximum degree of being c ritical of a problem group with respect to the preference or weights of project success parameters. The higher the aggregated score of a problem group, the most critical problem group is. The SMART S process has only one criterion in this study which is project success parameter. As stated early, all the values of problem groups are their maximum negative impact on project success parameters. It would be impossible to have all eight problem groups as the most critical as they are at the same time for a project. There will be certainly a difference in the degrees of severity of the problem groups. So in addition to the project success parameters, it is necessary to add the degree of severity of the problem group into the application development. The fi nal application has two criteria which are five project success parameters and eight project problem groups. In this section, the process of the computation of aggregated scores using the SMART S process and the

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134 degree of severity of problem group to sele ct the most critical problem group will be showed using equations and numerical examples for a given scenario. Scenario The following is a scenario on the explanation of the SMART S process and the degree of severity of the problem for the basic concept of the application. T he process will be di s cussed based on this scenario and the issues related to this study A contractor has been awarded a project. The contractor uses a system that classifies the problems it encounters on projects into eight (8) problem groups AL (alignment), CA (constructability), CM (change management), CO (contracting), PC (project controls), QM (quality), SP (safety practices), and TB (team building). The severity of problems such as how serious or minor they are varies among the p roblem groups. The owner of the project is very demanding and evaluates their projects based on five different success parameters cost, schedule, quality, safety, and satisfaction. The contractor wants to know which problems he has to be concerned with the most and the least under any given conditions (severity of the problem the project. The issues related to this scenario are : 1) How does the contractor know which of the eight problem groups is the most critical?; 2) How do the problem groups impact each success parameter?; 3) How does the contractor manage the problems to minimize the impacts of problems?; and 4) How do priority weights of the success parameters affect the impacts of problems?

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135 Equation Example Denotation of input table From the CFA models, there are maximum impacts of problem groups on each success parameter. All the impacts of each problem group are denoted as shown in Table 5 8. Table 5 8 I mpacts of problems on each success parameter Problem Group Cost Schedule Quality Safety Satisfaction Total AL COS AL SCH AL QUA AL SAF AL SAT AL AL TOTAL CA COS CA SCH CA QUA CA SAF CA SAT CA CA TOTAL CM COS CM SCH CM QUA CM SAF CM SAT CM CM TOTAL CO COS CO SCH CO QUA CO S AF CO SAT CO CO TOTAL PC COS PC SCH PC QUA PC SAF PC SAT PC PC TOTAL QM COS QM SCH QM QUA QM SAF QM SAT QM QM TOTAL SP COS SP SCH SP QUA SP SAF SP SAT SP SP TOTAL TB COS TB SCH TB QUA TB SAF TB SAT TB TB TOTAL The total impacts of each problem group on success parameters are s hown in Table 5 8. The weight (priority) of each success parameter is denoted as W COS W SCH W QUA W SAF and W SAT for cost, schedule, quality, safety, and satisfaction respectively. The success of the project is the combination of each weight of success parameter and the maximum sum of these weights is equal to 5 or the weight would be the proportional ratio (%) of the total sum of weight. Five (5) are chosen for this example. The s uccess of project is expressed as : Success = W COS + W SCH + W QUA + W SAF + W SAT w here W COS W SCH W QUA W SAF and/or W SAT 0. Weighted problem group The weight of each success parameter is defined, and then the weighted total of the problem group is calculated as follows: W ALTOTAL = W COS COS AL + W SCH SCH AL + W QUA QUA AL + W SAF SAF AL + W SAT SAT AL

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136 W CATOTAL = W COS COS CA + W SCH SCH CA + W QUA QUA CA + W SAF SAF CA + W SAT SAT CA W CMTOTAL = W COS COS CM + W SCH SCH CM + W QUA QUA CM + W SAF SAF CM + W SAT SAT CM W COTOTAL = W COS COS CO + W SCH SCH CO + W QUA QUA CO + W SAF SAF CO + W SAT SAT CO W PCTOTAL = W COS COS PC + W SCH SCH PC + W QUA QUA PC + W SAF SAF PC + W SAT SAT PC W QMTOTAL = W COS COS QM + W SCH SCH QM + W QUA QUA QM + W SAF SAF QM + W SAT SAT QM W SPTOTAL = W COS COS SP + W SCH SCH SP + W QUA QUA SP + W SAF SAF SP + W SAT SAT SP W TBTOTAL = W COS COS TB + W SCH SCH TB + W QUA QUA TB + W SAF SAF TB + W SA T SAT TB The original total sum of each problem and its success parameters is based on equal weight among success parameters. That is why each success was assigned a weight of 1 and the sum of weights is 5. If the total sum of the success parameters is less than 5, then it will be adjusted to equal to the number of success parameters. Degrees of problem severities The range of degrees of severity of problems is from 0% to 100%. A 0% value means that there are no problems and a 100% value indicates that very serious problems exist. The degree of severity of each problem group is denoted as D AL D CA D CM D CO D PC D QM D SP and D TB Using the degree of problems, the final impact of AL as an example can be computed as follows:

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137 AL = D AL W ALTOTAL = D AL W COS COS AL + D AL W SCH SCH AL + D AL W QUA QUA AL + D AL W SAF SAF AL + D AL W SAT S AT AL = D AL ( W COS COS AL + W SCH SCH AL + W QUA QUA AL + W SAF SAF AL + D AL W SAT S AT AL ) Numeric Example Using equal success priority weights The conversion values shown in Table 5 2 are based on the equal weight of each success parameter. The sum of each success parameter is equal to 1,000. Table 5 2 can be revised as shown in Table 5 9, which includes the colu mn of the degree of severity, total, and rank and the row of weight s The impacts shown in Table 5 9 are the maximum impacts of the problem groups on each success parameter. Table 5 9 is similar to Table 5 8 except for the weights success parameters. Th each problem and the sum of the weights for each success parameter. The range of degrees of problems severities is from 0% to 100%. A 0% value means that there are no problem s and a 1 00% value indicates that the most serious problems exist. The values of the column labeled Table 5 9 Initial impacts with equal weights of 1 Problem Group Degrees of Problem Group Severity Success Parameters (Equal Priority Weights=1) Ranking Cost Sc hedule Quality Safety Satisfaction Total 1 1 1 1 1 5 AL 100% 156 162 166 485 8 CA 100% 145 93 123 172 152 686 2 CM 100% 152 154 178 151 635 5 CO 100% 104 136 130 167 141 678 3 PC 100% 155 140 193 152 639 4 QM 100% 153 158 113 187 139 751 1 SP 100% 145 147 103 140 535 7 TB 100% 142 158 165 126 592 6 Total 1,000 1,000 1,000 1,000 1,000 5,000

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138 ex values in Table 5 9 are their maximum impacts on each success parameter. In the case of impacts, the h igher its value the more critical it is. The weight of each success parameter in Table 5 9 is equal to 1 because they are all equally important in this example With respect to the equal weights among success parameters in th is example, the highest impact on project success is for the QM group with an index value of 751 and the lowest impact on project success is AL with an index value of 485 It indicates that a contractor has to be concern ed about the QM group the most and the AL group the least if a contractor has a project which has five equally weighted success parameters. Using unequal success priority weights In the preceding section success parameters of equal weights (1.0) were addressed. Next success parameters with unequal priority weights will be discussed. For example, a contractor has a project for which five success parameters a re used to measure the project success. The owner of project wants a project within budget and schedule On the other hand, the owner s do not care about Safety and Satisfaction as much as t he y do about Cost and Schedule Safety is the owners least concern as a success parameter Quality is somewhere in the middle range of success parameter In this case, the two highest priorities f or the project are Cost and S chedule and the lowest priority is Safety among five success parameters. These priorities are converted into weights such as 1. 3 1. 1 1.0, 0. 9 and 0. 7 for Schedule, Cost Quality, Satisfaction, and Sa fety respectively The sum total of the success parameters remains equal to 5. Schedule is the highest priority with a weight of 1.3 and Safety is the lowest priority with a weight of 0.7. Table 5 10 shows the different weights among success parameters and the change in rankin g based upon the different weights. For the selected of priority weights, the QM group has the highest impact on the project with an index value of 743 and the SP group has the least impact on project with an index value of 505 It shows that the contract or has to be concern ed with QM the most

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139 Table 5 10 Weighted impacts of success parameters Problem Group Degrees of Problem Group Severity Success Parameters ( Une qual Priority Weights) Ranking Cost Schedule Quality Safety Satisfaction Total 1.1 1.3 1.0 0.7 0.9 5 AL 100% 172 211 166 0 0 549 7 CA 100% 160 121 123 121 137 662 3 CM 100% 0 198 154 125 136 612 6 CO 100% 114 177 130 117 127 664 2 PC 100% 170 182 0 135 137 624 5 QM 100% 168 206 113 131 125 743 1 SP 100% 160 0 147 72 126 505 8 TB 10 0% 156 206 165 0 113 641 4 Total 1,100 1,300 1,000 700 900 5,000 and SP the least to meet owner s demand for project success. For the maximum impacts of each problem group (100% Severity) and the unequal weights of the success parameter, the ranking s in Table 5 10 are different from that in Table 5 9 It shows that the weights of success parameter affect the impacts of the problem group s. Using this approach, i t is possible for the contractor to predict how the impact s of the problem groups change, depending upon changes in the success parameter s priority. For example, t he computation of the impact of the AL problem group is as follows: AL = W COS COS AL + W SCH SCH AL + W QUA QUA AL + W SAF SAF AL + W SAT SAT AL = 1. 1 1 56 + 1. 3 162 + 1.0 166 + 0.7 0 + 0.9 0 = 172 + 211 + 1 66 + 0 + 0 = 549 Using both unequal success priority weights and degrees of problem severity It is assumed that the contractor previously mentioned is the same contractor here The situation h e/she has faced regarding the weights of project success parameters is the same as before. The contractor figures out which problem groups he /she has to be concern ed with the

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140 most and the least based on the priority weight of success. So far he /she is co ncern ed only with the weights of success parameters but not his /her own potential problems on the project. The contractor checks the degree severity for each of the eight problem groups for the project. The contractor expects to have the two most serious problems with the PC and CA problem groups but he /she does not seem to have serious problems with the CO problem group The rest of problem groups are somewhere in the middle. If a d egree of s everity value of 100% i ndicates maximum impact while a value of 0% i ndicates no impact on any success parameters, the contractor s problem groups PC, CA, AL, QM, SP, TB, CM, and CO will be assigned the values of 90%, 85%, 75%, 70%, 70%, 7 0%, 50%, and 20% respectively The PC problem group has the highest degree of severity ( 90% ) and the CO group has the lowest degree of severity ( 20% ) Table 5 11 shows the different degrees of problems severities for the same success parameters weights that were used in Table 5 10 Table 5 11 Weighted impacts of both unequal suc cess parameters and degrees of problem severity Problem Group Degrees of Problem Group Severity Success Parameters ( Une qual Priority Weights) Ranking Cost Schedule Quality Safety Satisfaction Total 1.1 1.3 1.0 0.7 0.9 5 AL 75% 129 158 125 0 0 412 4 CA 85% 136 103 105 103 116 562 1 CM 50% 0 99 77 62 68 306 7 CO 20% 23 35 26 23 25 133 8 PC 90% 153 163 0 121 123 561 2 QM 70% 118 144 79 92 88 520 5 SP 70% 112 0 103 50 88 353 6 TB 70% 109 144 116 0 79 449 3 Total 780 847 631 452 587 3,297 W ith the combination of priority weight s and degree s of severity, the CA group has the highest impact on project with an index value of 562 and the PC group follows with an index value of 5 61 The C O group has the least impact on project with an index valu e of 133 It

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141 means that the contractor has to monitor the CA and PC groups the most and the CO and CM groups the least to meet owner s demand. The ranking of problems is different from Table 5 9 and 5 10. Table 5 9, 5 10, and 5 11 s how how the degrees o f problem severities and the weights assigned to the success parameters affect the final impact of each problem group The computation of AL as an example is as follows : AL = D AL W COS COS AL + D AL W SCH SCH AL + D AL W QUA QUA AL + D AL W SAF SAF AL + D AL W SAT S AT AL = 75% 1. 1 1 56 + 75% 1. 3 162 + 75% 1.0 166 + 75% 0.7 0 + 75% 0.9 0 = 75% 172 + 75% 211 + 75% 1 66 + 75% 0 + 75% 0 = 129 + 1 58 + 1 25 + 0 + 0 = 412 Summary Changes in impacts of problem groups on a project have been discussed so far. During this process, it is possible for the contractor to predict what problem groups will be critical for project success based on the owner s success priority and his /her degree of problems seve rity. As shown in Table 5 11 using the approach described above the contractor will be able to determine which problem has the most and the least impact on the project success parameters b ased on the current degree of problems severities and s uccess prior it y weights The contractor may utilize the degrees of problems to minimize the impacts on a certain success parameter. If there is a change in the priorities of success parameters during construction, the contractor will be able to determine which probl em group he/she has to be more concerned with T able 5 11 is useful to help both owners and contractors focus on certain potential problem groups in order to satisfy the

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142 success of project This whole process shows that there is a change in ranking of pr oblem groups between the degree of severity with concerning project success parameters and without concerning project success parameters. Table 5 12 shows the difference in ranking. Table 5 12 The difference in ranking Problem Groups Project Success Para meters Without Concerning With Concerning Degree of Severity Ranking Index Value Ranking AL 75% 3 412 4 CA 85% 2 562 1 CM 50% 7 306 7 CO 20% 8 133 8 PC 90% 1 561 2 QM 70% 4 520 5 SP 70% 4 353 6 TB 70% 4 449 3 Without considering the project s uccess parameters, all the degrees of severity of problem groups are quantitative oriented because there is no consideration of the impact of problem groups on project success parameters. On the other hand, the index values concerned with the project succ ess parameters are qualitative oriented because all the index values are combination of project success parameters and degrees of severity of problems. This will provide a contractor and/or owner with an in depth analysis of their potential problems. In this section, all the basic concepts of the application process have been discussed. Based on this process, the application will be developed. Development of Application Overview Reassessment of potential project problems has been addressed in the precedi ng section. Based on the concept of reassessment, the application is developed for the potential users to aid them in reassess ing project problems. The application has to be simple and easy to use. In this section, all the application development is add resse d in terms of the software, the contents, the

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143 difference between the basic concepts explained and the application, and finally the validation of application. Application Software Program The objective of application development is to have an easy and simple to use software package for the potential users. To keep the application accessible to the largest number of users, it was developed using Microsoft Office Excel 2003. Application Description This application is named the Potential Project Problem Identification Tool because the final output shows the degrees of being critical of problem groups based on project success parameters and problems. The tool is available as shown in Object 5-1 below. Object 5-1. Potential problem identification tool as a Microsoft Excel file (.xls 41kb) There are four worksheets available in the application file. They are Instruction, Input, Output, Success Parameters, and Problem Group. The Instruction worksheet gives the users general information about the application, the Input worksheet is used to assess the project success parameters and the degrees of severity of problem groups. The output shows a histogram chart as the final results based on the input. The worksheet of Success Parameters and Problem Groups addresses the definition of success parameters and problem groups. In the Instruction worksheet, there are three main subtitles available. The three subtitles are Overview, Project Success Parameters, and Project Problem Severity. In Overview, the overall purposes and the basic concepts of this application are addressed. What the outputs mean addresses as well. In Project Success Parameters worksheet, the instruction of weighing project success parameters is explained with an example. It will be explained more detailed later. Finally, the instruction for the degrees of severity of problem groups is explained with an example in Project

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144 Problem Severity. Figure 5 4 shows a screen capture of the Instruction worksheet for this application. Figure 5 4 Sc reen capture of Instruction worksheet for application In the Input worksheet there are two main activities going on. One is to select the project success parameters with priority weights and the other is to assess project problem groups. As previously s tated, there is a difference in weighing the priorities of project success parameters between the application and the original method as described in the preceding section s. The total sum of priority weight is five because each success parameter is equal to one. It will be inconvenient and complicated if this weighing process such as 1.2 or 1.3 is used in the application because there is no limit for the highest weight and also the sum of priority weights has to be five. Setting priority weights and keep ing the sum equal to five are not that easy. I n

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145 this application, the SMART S process is applied to compute the weights using swing weights (Edwards and Barron 1994) Table 5 13 shows the computation of swing weights and its counterpart of the original method. The procedure for computing swing weights is followed; 1. Choos e the highest success parameter and its weight should be 100 for this application. 2. Set the priority weights of the rest of project success parameter comparing to the highest one, which is 100 here. 3. Sum all the priority weights of project success paramete rs 4. Compute the proportional ratio of each priority weight of success parameter over the total sum of project success parameters 5. Apply the computed proportional ratio of priority weight of each success parameter to the calculation of aggregated scores Ta ble 5 13 Comparisons of original method and swing weights Success Parameters Original Method Swing Weights Weights 1 Weights 2 Proportional Ratio (%) Weights Computation Proportional Ratio (%) Cost 1.1 .22 22% 85 85/385 = 0.22 22% Schedule 1.3 .26 26% 100 100/385 = 0.26 26% Quality 1.0 .20 20% 77 77/385 = 0.20 20% Safety 0.7 .14 14% 54 54/385 = 0.14 14% Satisfaction 0.9 .18 18% 69 69/385 = 0.18 18% Sum 5.0 1.0 100% 385 100% The proportional ratio will be the same from both methods because the pr oportional ratio is based on the total sum of weights. If the sum of the priority weights shown in Table 5 11 is set to one, then the weight of each success parameter will be the proportional ratio as shown in Table 5 13. The weights in swing weights in Table 5 13 are the counterpart of those of the original method. In the original method, Schedule is the highest priority with a weight of 1.3. The weight of Cost in the original method can be computed compared to Schedule. The computation of Cost is 1.1 /1.3 = 0.85 (85%). The weight of Quality can be computed by 1.0/1.3 = 0.77 (77%). The rest of weights of the success parameters can be computed in the same way. The rest of the computed weights are 0.54 (54%) and 0.69 (69%) for Safety and Satisfaction

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146 r espectively. These computed values are shown in the column of Weights in the Swing Weights side in Table 5 13. From the user perspective, swing weights are easy to use in set ting the priority weights among success parameters by setting one as the highest value and comparing the rest of the weight s to the highest. The original method uses the real priority weight for the computation of aggregated scores. But in the swing weights, the proportional ratio will be used. So the final application will be usin g the proportional ratio for the calculation of the aggregated scores. This is the main difference between the method in the preceding section and the final application. Figure 5 5 shows the screen capture of the input worksheet. Figure 5 5 Screen cap ture of input worksheet of application The assessment of severity of project problems is in the Input worksheet. The range of the degrees of severity is zero ( 0 ) through 100. A value of zero means that a project does not have a

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147 problem in a specific prob lem group and on the other hand, a value of 100 means that a project has a serious problem in that category. So the value of zero ( 0 ) that represents no problem is the minimum and the value of 100 that is most serious is the maximum. Based on the assessm ent of the two categories of project success priority and problem severity, the final output will be shown as a histogram form in the Output worksheet. Figure 5 6 shows the screen capture of the O utput worksheet. Figure 5 6 Screen capture of output wor ksheet of application The histogram is drawn based on the proportional ratio of each problem group. During assessment of the project success parameter weights and problem groups, the aggregated scores will be computed as discussed in the section 5.3 using the proportional ratio of each success parameter weight. The aggregated scores indicate the degree of critical ity of problem groups. But it is hard to tell how serious a problem group is and also there is a limitation in showing the

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148 degrees of critical ity The proportional ratio of each problem group provides the big picture for all problem groups. Each ratio tells the proportion of itself with respect the total negative impact problem groups. The proportional ratio still provides the ranking with th e degrees serious ness and critical ity with respect to the total negative impact of problem groups. The interpretation of output format of the histogram is explained in the Instruction worksheet Table 5 14 shows the computation of the proportional ratio of each problem group shown in Figure 5 6. Table 5 14 Computation of proportional ratio of scores Problem Group Ranking Aggregated Scores Proportional Ratio AL 3 79 79/559 = 0.14 (14%) CA 1 124 124/559 = 0.22 (22%) CM 5 62 62/559 = 0.11 (11%) CO 6 54 54/559 = 0.10 (10%) PC 4 79 79/559 = 0.14 (14%) QM 2 107 107/559 = 0.19 (19%) SP 7 32 32/559 = 0.06 (6%) TB 8 23 23/559 = 0.04 (4%) Sum 559 As shown in Table 5 14, the proportional ratio provides the proportion of each problem group into the to tal negative impact on the project and the ranking of each problem group. So the final user can tell the degrees of critical ity of each problem group with respect to the overall project in qualitative as well as quantitative terms According to Figure 5 6 and Table 5 14, CA and QM are the most critical problem groups and SP and TB are the least critical problem groups among eight problem groups. In the Success Parameters and Problem Groups worksheet the definitions of success parameters and problem group s are explained for those who are not familiar with the ir definitions.

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149 Validation of Application Overview The potential problem identification tool is a decision making aid, designed for use by project participants especially project managers and owners in the early phase of projects. Under given circumstances priority weights of success parameters and expected problems severity, this tool will provide project participants with a deep insight of potential project problems. So it is necessary to valida te this tool as a useful guideline for participants. The main output of this tool is to show the possible future problems based on the combination of priority weights of success parameters and problem severity. To validate this tool, the comparison of the output of tool of project problems and actual project problem cases is necessary. If the output of tool is close to the actual project case, then the tool will be validated. To do so, it is required to collect some information on projects such as priori ty weights of success parameters and severity of project problems, etc. It will be an intense process to validate the tool. There are numerous causes and reasons out there that affect the project performance. There is no perfect tool to forecast or pred ict every project problem however, any tool that provides guideline s for solving project problems and improve performance at the early phase of projects should be welcome In next section, the whole validation process is addressed, including the validati on survey. Validation survey To compare the output of the tool and the actual project data, three criteria have to be included in the survey. They are 1) priority weights of project success; 2) degree of severity of project problems at the early phase o f project or the time a participant joined the project; and 3) the actual critical problems at the end of project or at the project review phase. The first two criteria are necessary to calculate the values in the tool and the last criterion is needed to compare the output of tool. The first two criteria are based on the early stage of construction and

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150 the last criterion is based on the end of project. In addition to this information, participants were asked to fill in their experience in the constructio n industry, their position, and the characteristics of projects they participate or participated in. The total duration for a project to be described in the survey is from the contract phase (0%) to the substantial completion (100%). Each participant is asked to fill in the time they join ed the project and the time they leave the project using percentages in multiples of 10 If any participant join ed the project at a stage earlier than the cont r act phase of project or at the contract phase, the value in the survey will be zero (0). If any participant le ft the project at the substantial completion or later than that time period, the value will be 100. It is very useful to see how long a participant works on a project to notice any changes in project prob lems during the whole project duration if any changes occur. One of the survey questions ask ed whether there we re any changes in priority weight of project success parameters during the project period. Only one project has some changes in priority weight s of its success parameters. So this information will be discarded in the validation process. A full set of survey questions is available in Appendix G. The survey responses were analyzed using the previously described Microsoft Excel tool. Appendix G h as the Instruction and Survey worksheet as shown in Figure G 1 through G 4. Validation survey output The total number of collected project data sets is six (6) for the tool validation process One of them had some changes in the priority weights of the success parameters but as stated earlier, these changes will not be used during the validation process. Each project is labeled as A, 15 shows the project characteristics and location, par of experience, and duration of their participation on a project.

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151 Table 5 15. General summary of participants and project characteristics Project Characteristics Project Location # of Years in Experience Duration (%) Joining (A) Leaving (B) Working (B A) A Commercial & Government Facilities Florida 10 0 100 100 B Hospitalities & Residential Florida 19 30 100 70 C Government Facilities Washington D.C. 7 0 100 100 D Civil and Heavy Korea, South 10 10 70 60 E Civil and Heavy Georgia 16 0 80 80 F Commercial & Hospitalities Florida 34 65 90 25 The characteristics of projects are various such as commercial, government facilities, hospitalities & residential, and heavy civil and h ighway The six projects come from four differ ent locations that are Florida, Washington D.C., Georgia, and South Korea. There are three (3) projects from Florida and one project is from each of the rest of the location s The lowest number of years of experience is seven (7) and the highest number o f years of experience is 34. Most participants join the project close to the contract time or earlier phase of construction and leave the project close to the substantial completion. The longest working duration is 100% from Project A and C and the short est working duration is 25% from Project E. To reassess the potential project problems using the tool, the priority weights of success parameters are necessary. Table 5 16 shows the priority weights of the six (6) projects. Table 5 16. Priority weights of six projects Project Success Parameters Cost Schedule Quality Safety Satisfaction A 85 72 90 72 100 B 90 100 85 75 85 C 90 85 100 72 85 D 100 95 90 90 80 E 100 100 80 90 90 F 75 85 80 90 100 As shown in Table 5 16, each project has different pri ority weights for itself. Cost, schedule, satisfaction have two top priority over other success parameters. Quality has one top

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152 priority over other parameters. Safety has no top priority over other parameters and most of them are marked as the lowest pr iority. Table 5 16 shows that each project has its own priorities for project success. It supports that there should be more research on the multi project success parameters. Due to the variety of priority weights for success parameters and the number o f success parameters involved for a project, the current norm for the project control concept may not be as accurate as it used to be. The current norm is as usual concerned about cost and schedule. But now there are more parameters other than just cost and schedule to be considered in classifying a project as successful in the construction industry. The severity of the project problems of the six projects is shown in Table 5 17. The final critical ranking in the project review process or the time a part icipant leaves a project and the critical ranking computing from the application are shown in Table 5 17 as well. The highest severity is 100 for Project E and F. The problem groups are CO and PC for Project E and CA for Project F. The lowest severity i s zero (0) for SP in Project A. using the tool based on the survey input. The Difference column shows the difference in rank ing. The easiest way to validate the tool is to compare the actual critical ranking assigned by a participant to the critical ranking resulting from the tool based upon the survey input. If the tool output is close to the actual output, then it will be a good validation of tool. If the output of tool 17 will be zero (0). Even though the output from the tool is not exactly the same as the actual ranking, the o Table 5 17 are smaller. For example, a ranking from the tool is eighth and an actual ranking

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153 Table 5 17. Comparison of actual project and tool output Project Problem Cr itical Ranking Group Severity Review (A) Tool (B) Difference (A B) A AL 60 3 3 0 CA 40 1 4 (3) CM 30 6 5 1 CO 20 4 6 (2) PC 65 2 1 1 QM 50 5 2 3 SP 0 7 8 (1) TB 10 8 7 1 B AL 90 1 1 0 CA 20 4 5 (1) CM 20 5 6 (1) CO 10 6 8 (2) PC 6 0 3 3 0 QM 40 7 4 3 SP 20 8 7 1 TB 70 2 2 0 C AL 80 2 2 0 CA 20 7 8 (1) CM 50 6 4 2 CO 35 4 5 (1) PC 60 1 3 (2) QM 70 3 1 2 SP 31 8 7 1 TB 30 5 6 (1) D AL 30 6 6 0 CA 40 4 4 0 CM 50 3 3 0 CO 35 5 5 0 PC 80 1 2 (1) QM 70 2 1 1 SP 20 7 7 0 TB 5 8 8 0 E AL 60 8 8 0 CA 80 6 4 2 CM 90 3 3 0 CO 100 1 1 0 PC 100 2 2 0 QM 70 5 6 (1) SP 90 7 7 0 TB 90 4 5 (1) F AL 50 4 7 (3) CA 100 1 1 0 CM 60 7 5 2 CO 30 6 8 (2) PC 40 8 6 2 QM 80 3 2 1 SP 90 2 3 (1) TB 70 5 4 1

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154 is first for a problem group. The ranking difference will be seven. This is the maximum ranking difference between actual and tool output. In this case, the tool output may not be validated because the gap between actual and tool output is much different. But if the ranking difference between actual review and tool output is small enough such as one (1) or two (2), then the output of tool is still considered to being close enough to the actual ranking. The values in the Difference colum n in Table 5 17 have the minimum of zero (0) and the maximum of three (3) in absolute value form. Only four problem groups have a three (3) difference out of 48 problem groups could be found and the rest of values in differences fall in the zero (0), one (1), or two (2) groups The output of the tool predicts very close the actual ones. In addition to the ranking difference between tool output and actual output for the validation of the tool, another way to validate the tool is to use a regression model. The evaluation of a regression model is to use the R 2 value (variability). The higher the R 2 value, the better the validation is for the model. A regression model for this case falls into the expectation or forecasting model. If all the values of actu al critical ranking in Table 5 17 are on the y axis and the values of tool critical rankings in Table 5 17 are on the x axis, then a regression model with R 2 can be drawn as shown in Figure 5 7. The R 2 value of this regression model is 0.6747 (67.47%). I t is not a high R 2 value but it is still serves validating the tool. The equation of this regression model is shown in Equation 5 1 : y = 0.8214x + 0.8036 ( Equation 5 1) where y = actual cri tical ranking ; x = tool critical ranking Even though there is a small difference in critical ranking as shown in Table 5 17, the R 2 value does not seem high. This is because the four (4) combination pairs of ranking have a difference of three ( 3 ) CA and QM in Project A, QM in Project B, and AL in Project F in Table 5 17.

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155 Figure 5 7. Regression model for validation of tool These pairs are considered as outliers in the model that are far from the trend line in Figure 5 7. The further the distance from the trend line, the less the R 2 value is. If the regression model is redrawn without these four (4) pairs, the R 2 value of redrawn regression model will be increased to 0.7844 (78.44%) from 0.6747 (67.47%). This value is a lot higher than that of the ori ginal value. Therefore the original R 2 value is still a worth y validating tool. The redrawn regression model is shown in Appendix H. As stated earlier in this section, there is no perfect reassessment tool For example, a project manager considers some critical problems out of eight problem groups. But he /she does not consider all eight problem groups critical but he /she may consider the top three or four problem groups seriously. In this case, the critical ranking among the top three or four problem g roups may not mean anything to him /her In other words, three or four problem groups would be treated as one big critical group but not individual groups. Once he considers them all as

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156 critical. From this perspective, the output of this tool is able to provide an overview of the critical problem groups based on the situations. Although based on six case studies, this tool is not perfectly the same as the actual ranking it still will be able to provide the potential assessment of project problems with a priority weight of success parameters.

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157 CHAPTER 6 CONCLUSIONS AND RECO MMENDATIONS Conclusions Our society is getting more demanding in every aspect of our lives as technology has developed and our way of life has improved. It is hard for seller s or s ervice providers to satisfy customers due to the high expectation from customers on the service. From this perspective, the construction industry has been changing rapidly as well. The customers (owners) have more demands on their service (construction) and the service providers (general contractors) try to keep the customers satisfied to have them come again for another service (repeat customers). The demands on service in construction come from two perspectives in terms of time. One is for the constru ction phase and something tangible and objective and the other is after the construction and something intangible and subjective. Within the five project success parameters, the first one is related to cost, schedule, and safety, while the second one is r elated to quality and satisfaction. Traditionally, cost and schedule are two favorite success parameters used for construction projects in order to classify them as a successful project. As discussed based on Table 5 16 most projects require more than co st and schedule to be successful with the s demands and at the same time minimize the potential f or current problems, in order to deliver a successful project. How well project problems are managed is the key to satisfying the customers. To do so, it is necessary to research the relationship bet ween project success parameters and project problems and to integrate them as one project control and management perspective.

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158 Many articles have been written about project management and controls and most of them are focused on cost and schedule as keys to success. These two parameters are clearly an objective measurement to judge the success of a project. This could be why many studies on cost and schedule have been conducted On the other hand, there are not so many studies available on other parameter s for project success. Besides cost and schedule, safety, quality, and satisfaction could be used, but they are subjective and they can be difficult to measure Even though these parameters would be subjective and hard to measure, the request to satisfy these parameters in addition to cost and schedule are rising in the construction industry as shown in the data gathered in Table 5 16. In this study the relationship between project success parameters and project problems are mainly discussed using various methods such as canonical correlations and factor analysis. During this process, project problems respond to each proj ect success parameter differently. Their responses are not constant for the five project success parameters. Once the relationship between a project success parameter and project problems are defined using confirmatory factor analysis (CFA), this stud y performs the integration of five project success parameters as one project success parameter depending on their priority weights. To accomplish this, the SMART S (Edwards and Barron 1994) technique is applied. During this integration of the five project success parameters with their priority weights, all the re lationships between a project success parameter and project problems are integrated as well. Based on the priority weights of the project success parameters, the potential critical problem groups can be identified. Lastly, the degree of problem severity is applied to the previous stage to reassess the critical project problem groups at the early phase of construction or earlier than that. Using this process, the project manager could identify the critical problem groups using the priority weight s of the success parameters and the degree of problem severity the project faces.

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159 The final output of this research is a simple measurement tool called Potential Problem Identification Tool. This tool is a decision making aid for project managers at the earl y phase of a project. The tool gives you a degree of overview for being critical of project problems based on priority weights of success parameters and the degree of project problem severity. It will be helpful to projects manage rs in managing proj ect problems when there are limited resources available to manage them. As mentioned in the validation process, this is a good guideline for a decision making tool but not an absolute way for the selection of critical problem groups. Combining personal e xperience and this tool would result in the best selection of critical problems for a project. Recommendations for Future Research This research mainly concerns the relationships between project problems and project success parameters and its application. This study is not the first study of multi project success parameters but one of a few studies done so far. All the processes addressed in this study will be helpful for further studies on multi project success parameters. There are five p roject success parameters discussed in this study and there could be more than five and possibly other kinds of parameters available. Regarding project problem groups, this research focuses on the project problem group level but it may be necessary to stud y these relationships at the project problem level as well. Even though the project group level provides the overview of relationship between problem groups and success parameters, with respect to the applications of the results, there would be more poten tial ways at the project problem level, compared to the project group level. It will give more opportunities to study the relationship between project success parameters and problems in depth and provides more versatility in its application. If both proj ect problem group level and project problem level are available, then the project problem group level would be

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160 useful for the early phase of project to set up the overall direction of project. On the other hand, the project problem level would be useful f or the project control during the whole period of project execution. The main concept of the identification tool is to decide the weigh t of the priority of success parameters and to identify the degree of severity. Even though it is designed to assist a project manager in the decision making process for the selection of critical problem groups under the given circumstances the weights for success parameters and the degree of severity may not be the same among all project participants in the same projec t if all participants are asked to decide the weights of success parameters and the degree of severity because everybody has their own perspective of a project and interpret s it in their ways. In this case, the output of a tool can be different f or dif ferent participant s To minimize this discrepancy on perception s for the usage of tool among project participants, even it is recommended that fuzzy weighting (Seo et al. 2004) investigate d for rank ing the priority of weights and the severity of problems This c ould possibl y result in less biased output to choose from for the project manager. One of the objectives of this research is to define the relationship betwee n project problems and project success parameters. Through this relationship, this research provides the guideline in identifying the potential project problems using the priorities of success weights and the degree of problem severities. The identificat ion of potential problem groups for the project is a part of the project risk mitigation plans. The project risk is different f or different project contracting strateg ies and project delivery method s It means a problem does not have the same degree of negative impact for every contracting strategy and project delivery method all the time. A negative impact of a problem on a project in a lump sum contract may not be the same as the one in a cost plus contract In addition to contract ing strategy and project delivery the project types

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161 will be considered as well. A different type of project such as commercial, residential, industry, and heavy civil has its own characteristics to be concerned for a project success. If every type of pr oject has a problem, then the negative impact of that problem cannot be the same to all different type s of project because its impact on project could be different from the type of projects. The final output and application of the output for this research would be a boilerplate but not a tailor made for a specific type of contracting strategy project delivery method, or project type These issues sh ould be explored in futu re studies This research mainly focuses on the projects and practices in the United Sates. Every country has different cultu re and practices. It means that the output of current tool for a foreign country may not be as useful as here. With respect to the globalization, the research on overseas projects w ould provide deep insi ght into international project ma nagement

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162 APPENDIX A 43 POTENTIAL PROB LEMS Table A 1. 43 Potential problems No. Group Potential Problems 1 AL1 The project team is lacking in the necessary expertise, experience, breadth and depth to successfully execute the project. 2 TB1 The project team is experiencing a high turnover rat e and instability in team membership. 3 CM1 The project teams response to Requests for Information, questions, and changing events that can significantly impact project results is slow, inadequate or very late. 4 PC1 The project team is losing confidence in the accuracy and validity of the schedule due to constantly changing activity durations and repeated slippages from one reporting period to the next. 5 PC2 Design milestones are not met and achieving future phases milestones are not confirmed in relat ion to the impact of factors beyond information provided in current progress and status reports. 6 CO1 Construction is bid or commences before completion of project design resulting in an incomplete scope definition at time of award. 7 AL2 Business goals project objectives, and critical success factors are vague and/or inconsistent relative to project team and key stakeholder understanding. 8 CM2 Owner and/or contractor is requesting an excessive number of contract changes during project execution (deta iled design, procurement, construction, and start up). 9 CO2 Project scope items are omitted from bid packages. 10 CO3 Some project participant companies are not financially stable. 11 QM1 The project is experiencing a high level of detailed engineering /design/specification errors and changes. 12 QM2 A project specific quality plan for construction is not completely developed that is consistent with the contract documents, including plans and specifications, and project participant roles and responsibil ities. 13 QM3 The project fails to follow the quality plan for construction in relation to the roles and requirements of those who are responsible for that plan. 14 SP1 The project is experiencing a high level of safety incidents. 15 SP2 Design reviews fail to include qualified personnel that can analyze safety and loss prevention features of plans and specifications. 16 SP3 Project team personnel lack involvement in safety inspections, awareness of safety issues, and education in safety practices. 17 SP4 Potential safety related problems are not resolved in a timely manner. 18 SP5 Drastic actions (e.g., fines, dismissals, work stoppages) are often needed to address non compliance in safety practices. 19 SP6 The project is not following the requiremen ts of a project specific safety plan during construction. 20 TB2 Owner and contractor project personnel are not properly integrated into the project team. 21 CA1 The project lacks sufficient skilled craft and is experiencing high craft turnover due to co mpetition from other projects, low wages, and shorter work schedules.

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163 Table A 1. Continued No. Group Potential Problems 22 CA2 The project lacks sufficient manpower, materials, small tools and construction equipment to adequately support planned act ivities. 23 AL3 The level of maintenance personnel involvement in detailed design is low and maintenance personnel are not aligned with other project team personnel with respect to maintenance issues for the facility. 24 CA3 The project is using new tech nology or construction practices that are unproven in commercial use. 25 CM3 The project team is failing to identify and/or address missing requirements during design reviews. 26 PC3 The level of detail and the scope covered in the budget estimate are no t clear. 27 AL4 The project manager (or team leader) is lacking in the required level of experience and skills. 28 CM4 The project is not following an appropriate change management that includes defining cost and mark up rates, evaluating schedule impact and/or initiating dispute resolution procedures. 29 TB3 Key project stakeholder(s) is (are) exhibiting poor relationships and pursuing private agendas. 30 AL5 Commitments are increasingly not made with the intention of being met and are almost always n ot met. 31 PC4 The project is experiencing difficulties in integrating schedules between participants. 32 AL6 The project is asking vendors to perform functions outside their areas of expertise and experience. 33 SP7 Hazard and Operability (HAZOP) plan is late or is experiencing an excessive number of operational/support items that are not complete during the design phase. 34 TB4 The project team is not being encouraged to be realistic and truthful when project circumstances are unfavorable. 35 PC5 Act ual installed bulk material quantities are greater than estimated or forecasted total bulk material quantities (e.g., steel, concrete, straight run pipe, electrical wire and cable) 36 PC6 Float for project activities is being used up at an increasingly hi gh rate 37 PC7 Actual schedule activities are lagging behind planned schedule activities over several reporting periods 38 PC8 Forecasts to complete based on actual project experience, actual commitments, and actual expenditures are projecting overruns. 39 QM4 The project is experiencing an above normal level of construction rework hours and costs when compared to target levels of rework included in the total budget or schedule. 40 QM5 Project quality control results are reflecting high rejection rates for equipment and materials under fabrication in the factory and/or materials in place through testing in the field. 41 AL7 The project is experiencing difficulties due to the lack of understanding cultural differences. 42 CA4 Material and/or equipment p rices are increasing rapidly for certain types of materials/equipment that represent a high percent of the project cost. 43 AL8 The client and/or upper management is frequently making unreasonable requests (includes setting unrealistic goals).

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164 APPENDIX B DEFINITION OF EACH P ROBLEM GROUP Alignment (AL): These are practices associated with the overall alignment of the project team with respect to project goals and objectives. The make up of project teams can change considerably from the Pre Project Plann ing Phase to the Execution Phase. The owner project team generally changes from business planning personnel to those responsible for implementation. New contractors and suppliers are also usually added at this time. Both owner and contractor teams are g enerally expanded to address the increasing volume of work. How these new team members and contractors understand and are aligned to common goals plays key role to project success. Constructability (CA): Constructability generally involves construction r elated methodology and planning. The ability to efficiently plan and execute the construction of a facility is a major driver behind project success. Change Management (CM): CII and others have accumulated large amounts of research regarding the effects of late project scope changes and high volumes of rework to poor project outcomes. How the project team makes decisions on, controls, tracks, and implements change on a project can have a significant effect on project outcomes. Contracting (CO): Contract ing in terms of a practice is based on the matching of contract types to project risks. It is not an endorsement of any one particular contract type. There is no weighting of the tool that values Turnkey versus Lump Sum versus Design Build versus Cost Re imbursable. It is purely a measure of whether the project team is seeing potential issues between the contracts in place and the scope that needs to be executed. Project Control (PC): Project control involves the tools and techniques used to track, evalu ate and improve schedule and cost performance. In terms of this tool, it is not simply the use of a project schedule and cost reporting. It is a measure of how accurate the schedule is; how effective the schedule is in tracking work and identifying gaps; whether the cost reporting is utilized in future decision making; and whether or not the team is effectively using the information as a planning tool. Too often, schedule and cost reports become deliverables themselves instead of tools to be used in plan ning the work. Quality Management (QM): Quality Management includes items such as quality of engineering, construction quality and rework, equipment inspections and testing and facility start up. Safety Practices (SP): This is a measure of whether or not the project team is fully engaged in the practices that drive project safety (see CII Target Zero practices). Team Building (TB): People implement projects. The core competencies of the people that constitute the project team and how the people that mak e up the project team play a very key role in the success of any project. Good project teams overcome gaps in scope, risk events, design issues, project changes etc., in a proactive way to minimize the negative effects on project outcomes. Poor teams do not.

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165 APPENDIX C DESCRIPTIVE STATISTI CS OF 43 PROBLEMS Table C 1. Descriptive statistics of 43 problems No. Outcome Total Sum Mean S.D 1 C.V 2 AL1 Cost 542 5.42 0.619 11.43 Schedule 540 5.40 0.600 11.11 Quality 507 5.07 0.803 15.84 Safety 471 4 .71 1.211 25.71 Satisfaction 522 5.22 0.844 16.16 AL2 Cost 481 4.81 0.880 18.29 Schedule 475 4.75 0.187 18.68 Quality 451 4.51 1.063 23.57 Safety 353 3.53 0.421 42.10 Satisfaction 491 4.91 1.150 23.42 AL3 Cost 404 4.04 1.019 25.22 Schedule 40 3 4.03 1.053 26.13 Quality 496 4.96 0.999 20.15 Safety 335 3.35 1.284 38.31 Satisfaction 466 4.66 0.839 18.01 AL4 Cost 491 4.91 0.801 16.32 Schedule 504 5.04 0.871 17.28 Quality 433 4.33 1.158 26.75 Safety 406 4.06 1.256 30.92 Satisfaction 4 84 4.84 0.967 19.97 AL5 Cost 472 4.72 1.001 21.20 Schedule 528 5.28 0.991 18.76 Quality 403 4.03 1.300 32.25 Safety 353 3.53 1.374 38.94 Satisfaction 499 4.99 1.109 22.22 AL6 Cost 457 4.57 1.003 21.94 Schedule 456 4.56 0.840 18.43 Quality 507 5.07 0.886 17.48 Safety 414 4.14 1.289 31.12 Satisfaction 434 4.34 1.124 25.91 AL7 Cost 400 4.00 1.208 30.21 Schedule 422 4.22 1.180 27.95 Quality 395 3.95 1.299 32.89 Safety 416 4.16 1.239 29.78 Satisfaction 419 4.19 1.391 33.19 AL8 Cost 47 1 4.71 1.143 24.26 Schedule 487 4.87 1.074 22.05 Quality 401 4.01 1.269 31.64

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166 Table C 1. Continued No. Outcome Total Sum Mean S.D 1 C.V 2 AL8 Safety 381 3.81 1.339 35.15 Satisfaction 484 4.84 1.155 23.87 CA1 Cost 497 4.97 0.877 17.65 Schedule 528 5.28 0.884 16.74 Quality 491 4.91 1.059 21.57 Safety 455 4.55 1.071 23.54 Satisfaction 474 4.74 1.036 21.85 CA2 Cost 471 4.71 1.032 21.92 Schedule 548 5.48 0.842 15.37 Quality 428 4.28 1.273 29.75 Safety 424 4.24 1.258 29.67 S atisfaction 464 4.64 1.063 22.91 CA3 Cost 469 4.69 1.055 22.50 Schedule 457 4.57 1.160 25.38 Quality 446 4.46 1.220 27.35 Safety 416 4.16 1.181 28.39 Satisfaction 427 4.27 1.173 27.48 CA4 Cost 562 5.62 0.596 10.61 Schedule 403 4.03 1.100 27.29 Quality 340 3.40 1.175 34.55 Safety 256 2.56 1.098 42.90 Satisfaction 455 4.55 1.135 24.94 CM1 Cost 507 5.07 0.840 16.56 Schedule 532 5.32 0.786 14.77 Quality 431 4.31 0.945 21.94 Safety 326 3.26 1.213 37.22 Satisfaction 466 4.66 1.142 24.51 CM2 Cost 548 5.48 0.818 14.93 Schedule 522 5.22 1.016 19.46 Quality 403 4.03 1.144 28.39 Safety 321 3.21 1.314 40.93 Satisfaction 486 4.86 1.158 23.82 CM3 Cost 510 5.10 0.671 13.15 Schedule 488 4.88 0.778 15.95 Quality 473 4.73 1.066 22.54 Safety 332 3.32 1.232 37.11 Satisfaction 469 4.69 1.055 22.50 CM4 Cost 539 5.39 0.720 13.35 Schedule 527 5.27 0.786 14.91 Quality 389 3.89 1.256 32.29

PAGE 167

167 Table C 1. Continued No. Outcome Total Sum Mean S.D 1 C.V 2 CM4 Safety 287 2.87 1. 238 43.14 Satisfaction 492 4.92 1.007 20.46 CO1 Cost 537 5.37 0.730 13.60 Schedule 497 4.97 0.877 17.65 Quality 385 3.85 1.099 28.54 Safety 306 3.06 1.094 35.75 Satisfaction 435 4.35 1.043 23.97 CO2 Cost 554 5.54 0.590 10.65 Schedule 527 5.27 0.847 16.07 Quality 424 4.24 1.258 29.67 Safety 312 3.12 1.329 42.59 Satisfaction 490 4.90 1.127 23.00 CO3 Cost 472 4.72 1.068 22.64 Schedule 496 4.96 0.937 18.90 Quality 417 4.17 1.241 29.77 Safety 362 3.62 1.362 37.63 Satisfaction 454 4.5 4 1.117 24.61 PC1 Cost 468 4.68 0.915 19.56 Schedule 559 5.59 0.722 12.92 Quality 350 3.50 1.261 36.03 Safety 318 3.18 1.244 39.12 Satisfaction 479 4.79 0.941 19.65 PC2 Cost 496 4.96 0.836 16.85 Schedule 563 5.63 0.560 9.94 Quality 360 3.60 1 .039 28.87 Safety 298 2.98 1.095 36.75 Satisfaction 488 4.88 0.941 19.28 PC3 Cost 537 5.37 0.770 14.34 Schedule 471 4.71 0.941 19.98 Quality 400 4.00 1.068 26.69 Safety 287 2.87 1.222 42.58 Satisfaction 487 4.87 1.036 21.27 PC4 Cost 443 4.43 0.863 19.49 Schedule 538 5.38 0.732 13.60 Quality 354 3.54 1.268 35.83 Safety 325 3.25 1.169 35.98 Satisfaction 449 4.49 1.153 25.68 PC5 Cost 539 5.39 0.733 13.61 Schedule 453 4.53 1.135 25.06 Quality 318 3.18 1.220 38.35

PAGE 168

168 Table C 1. Co ntinued No. Outcome Total Sum Mean S.D 1 C.V 2 PC5 Safety 261 2.61 1.057 40.51 Satisfaction 417 4.17 1.327 31.82 PC6 Cost 441 4.41 1.114 25.27 Schedule 537 5.37 1.016 18.93 Quality 325 3.25 1.268 39.01 Safety 293 2.93 1.151 39.29 Satisfac tion 427 4.27 1.103 25.84 PC7 Cost 450 4.50 0.943 20.96 Schedule 560 5.60 0.632 11.29 Quality 327 3.27 1.057 32.32 Safety 297 2.97 1.053 35.46 Satisfaction 463 4.63 0.891 19.23 PC8 Cost 556 5.56 0.739 13.29 Schedule 492 4.92 1.046 21.26 Quali ty 357 3.57 1.160 32.49 Safety 297 2.97 1.118 37.63 Satisfaction 500 5.00 1.058 21.17 QM1 Cost 539 5.39 0.646 11.99 Schedule 519 5.19 0.771 14.85 Quality 471 4.71 1.032 21.92 Safety 311 3.11 1.224 39.35 Satisfaction 489 4.89 1.122 22.94 QM2 C ost 442 4.42 0.862 19.51 Schedule 412 4.12 1.003 24.34 Quality 507 5.07 0.919 18.13 Safety 275 2.75 1.143 41.58 Satisfaction 452 4.52 0.995 22.01 QM3 Cost 418 4.18 0.963 23.04 Schedule 405 4.05 0.994 24.54 Quality 512 5.12 0.909 17.75 Safety 301 3.01 1.253 41.63 Satisfaction 447 4.47 1.044 23.35 QM4 Cost 536 5.36 0.714 13.33 Schedule 511 5.11 0.823 16.11 Quality 427 4.27 1.173 27.48 Safety 313 3.13 1.074 34.31 Satisfaction 459 4.59 1.030 22.45 QM5 Cost 478 4.78 0.934 19.53 Sched ule 532 5.32 0.676 12.72 Quality 504 5.04 1.104 21.90

PAGE 169

169 Table C 1. Continued No. Outcome Total Sum Mean S.D 1 C.V 2 QM5 Safety 303 3.03 1.253 41.34 Satisfaction 476 4.76 1.097 23.04 SP1 Cost 449 4.49 1.082 24.09 Schedule 440 4.40 1.140 25.91 Quality 351 3.51 1.367 38.96 Safety 585 5.85 0.572 9.78 Satisfaction 496 4.96 1.104 22.25 SP2 Cost 424 4.24 0.971 22.90 Schedule 401 4.01 1.100 27.43 Quality 416 4.16 1.317 31.66 Safety 520 5.20 0.970 18.64 Satisfaction 449 4.49 1.063 23.67 SP3 Cost 383 3.83 1.068 27.89 Schedule 367 3.67 1.049 28.59 Quality 356 3.56 1.267 35.60 Safety 556 5.56 0.637 11.47 Satisfaction 437 4.37 1.163 26.62 SP4 Cost 398 3.98 1.058 26.59 Schedule 401 4.01 1.145 28.54 Quality 344 3.44 1.219 35 .44 Safety 562 5.62 0.579 10.31 Satisfaction 465 4.65 1.143 24.59 SP5 Cost 441 4.41 1.226 27.79 Schedule 464 4.64 1.204 25.96 Quality 393 3.93 1.336 34.00 Safety 554 5.54 0.842 15.19 Satisfaction 466 4.66 1.227 26.32 SP6 Cost 390 3.90 1.196 3 0.66 Schedule 398 3.98 1.183 29.72 Quality 344 3.44 1.177 34.23 Safety 562 5.62 0.596 10.61 Satisfaction 453 4.53 1.162 25.64 SP7 Cost 437 4.37 1.064 24.36 Schedule 452 4.52 1.034 22.88 Quality 438 4.38 1.215 27.73 Safety 438 4.38 1.377 31.4 3 Satisfaction 444 4.44 1.244 28.01 TB1 Cost 468 4.68 0.937 20.02 Schedule 488 4.88 0.920 18.84 Quality 457 4.57 1.022 22.37

PAGE 170

170 Table C 1. Continued No. Outcome Total Sum Mean S.D 1 C.V 2 TB1 Safety 397 3.97 1.220 30.74 Satisfaction 47 7 4.77 1.018 21.35 TB2 Cost 423 4.23 0.926 21.89 Schedule 443 4.43 0.993 22.40 Quality 412 4.12 1.211 29.38 Safety 354 3.54 1.292 36.49 Satisfaction 473 4.73 1.076 22.74 TB3 Cost 459 4.59 1.059 23.08 Schedule 453 4.53 1.063 23.46 Quality 402 4.02 1.334 33.18 Safety 345 3.45 1.459 42.28 Satisfaction 510 5.10 1.063 20.84 TB4 Cost 481 4.81 1.036 21.54 Schedule 504 5.04 0.958 19.01 Quality 415 4.15 1.236 29.78 Safety 387 3.87 1.309 33.82 Satisfaction 507 5.07 0.951 18.76 Notes: 1. St andard Deviation. 2: Coefficient of Variation

PAGE 171

171 APPENDIX D SUMMARY OF CRITICAL RATIO (CR) METHOD PR OCEDURE Table D 1. Cost Base First Second Third Fourth Fifth P Value Del. Var P Value Del. Var P Value Del. Var P Value Del. Var P Value AL 0.007 CO 0.003 CM 0.233 CA 0.007 CO 0.003 CM 0.233 CM 0.007 CO 0.003 CA 0.001 CO 0.007 TB 0.065 PC 0.007 CO 0.003 CM 0.233 QM 0.007 TB 0.065 SP 0.007 CO 0.003 CM 0.233 TB 0.007 CO 0.003 QM 0.016 Table D 2. Quality Base First Second Third Fourth Fifth P Value Del. Var P Value Del. Var P Value Del. Var P Value Del. Var P Value AL *** QM *** CA *** CO *** PC 0.069 CA *** QM *** CO *** CM *** SP 0.001 CM *** QM *** CA *** CO *** PC 0.069 CO *** QM *** CA *** SP 0.061 PC ** QM *** CA *** CO *** CM 0.002 QM *** CA 0.001 CO 0.004 CM 0.014 PC 0.259 SP *** QM *** CA *** CO *** PC 0.069 TB *** QM *** CA *** CO *** PC 0.069 Table D 3. Safety Base First Second Third Fourth Fifth P Value Del. Var P Value Del. Var P Value De l. Var P Value Del. Var P Value AL *** SP *** CO *** TB 0.01 0 CA 0.568 CA *** SP *** CO *** CM *** TB 0.003 CM *** SP *** CO *** AL *** TB 0.513 CO *** SP *** TB 0.067 PC *** SP *** TB 0.067 QM *** SP *** TB SP *** CO *** TB *** AL 0.10 8 TB *** SP *** CO *** AL *** CA 0.081 Table D 4. Satisfaction Base First Second Third Fourth Fifth P Value Del. Var P Value Del. Var P Value Del. Var P Value Del. Var P Value AL 0.002 CO *** SP 0.001 TB 0.024 QM 0.361 CA 0.002 QM 0.001 CO *** TB 0.012 CM 0.545

PAGE 172

172 Table D 4. Continued Base First Second Third Fourth Fifth P Value Del. Var P Value Del. Var P Value Del. Var P Value Del. Var P Value CM 0.002 QM 0.001 TB 0.029 SP 0.251 CO 0.002 SP 0.004 TB 0.044 CA 0.18 0 PC 0.002 CO ** QM *** TB 0.012 SP 0.361 QM 0.002 TB 0.04 0 SP 0.044 CM 0.071 SP 0.002 QM 0.001 TB 0.029 CM 0.085 TB 0.002 QM 0.001 SP 0.025 CO 0.005 CM 0.007

PAGE 173

173 APPENDIX E SUMMARY OF RAW ESTIM ATE AND ITS UNIQUE V ARIANCES Table E 1. Cost AL Base Problem Groups Estimate Standard Error (S.E.) Critical Ratio (C.R.) P Value TB Cost 1.058 0.188 5.626 *** SP Cost 1.283 0.135 9.508 *** QM Cost 0.969 0.089 10.870 *** PC Cost 0.990 0.095 10.476 *** CO Cost 0.671 0.113 5.961 *** CA Cost 0.889 0.125 7.083 *** AL Cost 1.000 CA Base Problem Groups Estimate Standard Error (S.E.) Critical Ratio (C.R.) P Value TB Cost 1.190 0.215 5.526 *** SP Cost 1.443 0.161 8.941 *** QM Cost 1.090 0.163 6.667 *** PC Cost 1.114 0.111 10.029 *** CO Cost 0 .755 0.164 4.613 *** CA Cost 1.000 AL Cost 1.125 0.159 7.083 *** CO Base Problem Groups Estimate Standard Error (S.E.) Critical Ratio (C.R.) P Value TB Cost 1.576 0.408 3.864 *** SP Cost 1.911 0.368 5.192 *** QM Cost 1.443 0.233 6.192 ** PC Cost 1.475 0.270 5.460 *** CO Cost 1.000 CA Cost 1.324 0.287 4.613 *** AL Cost 1.490 0.250 5.961 *** PC Base Problem Groups Estimate Standard Error (S.E.) Critical Ratio (C.R.) P Value TB Cost 1.068 0.169 6.304 *** SP Cost 1.29 5 0.108 12.040 *** QM Cost 0.978 0.117 8.336 *** PC Cost 1.000 CO Cost 0.678 0.124 5.460 *** CA Cost 0.897 0.089 10.029 ***

PAGE 174

174 Table E 1. Continued PC Base Continued Problem Groups Estimate Standard Error (S.E.) Critical Ratio (C.R.) P Valu e AL Cost 1.010 0.096 10.476 *** QM Base Problem Groups Estimate Standard Error (S.E.) Critical Ratio (C.R.) P Value TB Cost 1.092 0.251 4.344 *** SP Cost 1.324 0.170 7.807 *** QM Cost 1.000 PC Cost 1.023 0.123 8.336 *** CO Cost 0.69 3 0.112 6.192 *** CA Cost 0.918 0.138 6.667 *** AL Cost 1.032 0.095 10.870 *** SP Base Problem Groups Estimate Standard Error (S.E.) Critical Ratio (C.R.) P Value TB Cost 0.825 0.120 6.890 *** SP Cost 1.000 QM Cost 0.755 0.097 7.806 *** PC Cost 0.772 0.064 12.040 *** CO Cost 0.523 0.101 5.192 *** CA Cost 0.693 0.077 8.941 *** AL Cost 0.779 0.082 9.508 *** TB Base Problem Groups Estimate Standard Error (S.E.) Critical Ratio (C.R.) P Value TB Cost 1.000 SP Cost 1.212 0.176 6.890 *** QM Cost 0.915 0.211 4.344 *** PC Cost 0.936 0.148 6.304 *** CO Cost 0.634 0.164 3.864 *** CA Cost 0.840 0.152 5.526 *** AL Cost 0.945 0.168 5.626 *** Unique Variance Problem Groups Estimate Standard Error (S.E.) Critical R atio (C.R.) P Value TB (er08) 0.153 0.030 5.110 *** SP (er07) 0.198 0.026 7.576 *** QM (er06) 0.083 0.016 5.121 *** PC (er05) 0.078 0.011 7.104 *** CO (er04) 0.204 0.029 7.129 ***

PAGE 175

175 Table E 1. Continued Unique Variance Continued Problem Groups Estim ate Standard Error (S.E.) Critical Ratio (C.R.) P Value CA (er02) 0.096 0.017 5.728 *** AL (er01) 0.074 0.011 6.788 *** Table E 2. Schedule AL Base Problem Groups Estimate Standard Error (S.E.) Critical Ratio (C.R.) P Value TB Schedule 1.080 0.07 0 15.492 *** QM Schedule 1.057 0.082 12.812 *** PC Schedule 0.775 0.075 10.281 *** CO Schedule 0.963 0.118 8.169 *** CM Schedule 0.906 0.080 11.390 *** CA Schedule 0.594 0.185 3.204 0.001 AL Schedule 1.000 CA Base Problem Groups Esti mate Standard Error (S.E.) Critical Ratio (C.R.) P Value TB Schedule 1.820 0.571 3.187 QM Schedule 1.780 0.590 3.015 *** PC Schedule 1.306 0.440 2.966 *** CO Schedule 1.622 0.584 2.777 *** CM Schedule 1.527 0.422 3.614 *** CA Schedule 1. 000 *** AL Schedule 1.685 0.526 3.205 *** CM Base Problem Groups Estimate Standard Error (S.E.) Critical Ratio (C.R.) P Value TB Schedule 1.192 0.119 10.052 *** QM Schedule 1.166 0.119 9.794 *** PC Schedule 0.856 0.110 7.809 *** CO Sched ule 1.063 0.148 7.201 *** CM Schedule 1.000 CA Schedule 0.655 0.181 3.614 *** AL Schedule 1.103 0.097 11.390 *** CO Base Problem Groups Estimate Standard Error (S.E.) Critical Ratio (C.R.) P Value TB Schedule 1.122 0.142 7.911 ***

PAGE 176

176 Table E 2. Continued CO Base Continued Problem Groups Estimate Standard Error (S.E.) Critical Ratio (C.R.) P Value QM Schedule 1.097 0.115 9.561 *** PC Schedule 0.805 0.111 7.229 *** CO Schedule 1.000 CM Schedule 0.941 0.131 7.201 *** CA Sche dule 0.616 0.222 2.777 0.005 AL Schedule 1.038 0.127 8.169 *** PC Base Problem Groups Estimate Standard Error (S.E.) Critical Ratio (C.R.) P Value TB Schedule 1.393 0.136 10.284 *** QM Schedule 1.363 0.142 9.630 *** PC Schedule 1.000 CO Schedule 1.242 0.172 7.229 *** CM Schedule 1.169 0.150 7.809 *** CA Schedule 0.766 0.258 2.966 0.003 AL Schedule 1.290 0.125 10.281 *** QM Base Problem Groups Estimate Standard Error (S.E.) Critical Ratio (C.R.) P Value TB Schedule 1.022 0.0 97 10.495 *** QM Schedule 1.000 PC Schedule 0.734 0.076 9.630 *** CO Schedule 0.911 0.095 9.561 *** CM Schedule 0.858 0.088 9.794 *** CA Schedule 0.562 0.186 3.015 0.003 AL Schedule 0.946 0.074 12.812 *** TB Base Problem Groups Estim ate Standard Error (S.E.) Critical Ratio (C.R.) P Value TB Schedule 1.000 QM Schedule 0.978 0.093 10.495 *** PC Schedule 0.718 0.070 10.284 *** CO Schedule 0.891 0.113 7.911 *** CM Schedule 0.839 0.083 10.052 *** CA Schedule 0.549 0.17 2 3.187 0.001 AL Schedule 0.926 0.060 15.492 ***

PAGE 177

177 Table E 2. Continued Unique Variance Problem Groups Estimate Standard Error (S.E.) Critical Ratio (C.R.) P Value TB (er08) 0.108 0.022 4.953 *** QM (er06) 0.103 0.019 5.362 *** PC (er05) 0.112 0 .020 5.654 *** CO (er04) 0.194 0.029 6.780 *** CM (er03) 0.099 0.025 3.962 *** CA (er02) 0.250 0.062 4.045 *** AL (er01) 0.079 0.012 6.598 *** Table E 3. Quality AL Base Problem Groups Estimate Standard Error (S.E.) Critical Ratio (C.R.) P Value TB Quality 1.267 0.093 13.578 *** SP Quality 1.138 0.107 10.646 *** QM Quality 0.634 0.091 7.008 *** CO Quality 0.961 0.112 8.609 *** CM Quality 1.021 0.088 11.587 *** CA Quality 0.967 0.122 7.928 *** AL Quality 1.000 CA Base Probl em Groups Estimate Standard Error (S.E.) Critical Ratio (C.R.) P Value TB Quality 1.311 0.166 7.893 *** SP Quality 1.178 0.164 7.163 *** QM Quality 0.656 0.116 5.677 *** CO Quality 0.994 0.154 6.433 *** CM Quality 1.056 0.142 7.432 *** CA Quality 1.000 AL Quality 1.035 0.131 7.928 *** CM Base Problem Groups Estimate Standard Error (S.E.) Critical Ratio (C.R.) P Value TB Quality 1.242 0.108 11.481 *** SP Quality 1.116 0.117 9.529 *** QM Quality 0.622 0.093 6.658 *** CO Q uality 0.941 0.118 7.984 *** CM Quality 1.000 CA Quality 0.947 0.127 7.432 *** AL Quality 0.980 0.085 11.587 ***

PAGE 178

178 Table E 3. Continued CO Base Problem Groups Estimate Standard Error (S.E.) Critical Ratio (C.R.) P Value TB Quality 1.319 0. 154 8.565 *** SP Quality 1.185 0.155 7.654 *** QM Quality 0.660 0.112 5.912 *** CO Quality 1.000 CM Quality 1.062 0.133 7.984 *** CA Quality 1.006 0.156 6.433 *** AL Quality 1.041 0.121 8.609 *** QM Base Problem Groups Estimate Stan dard Error (S.E.) Critical Ratio (C.R.) P Value TB Quality 1.997 0.286 6.984 *** SP Quality 1.795 0.278 6.463 *** QM Quality 1.000 CO Quality 1.514 0.256 5.912 *** CM Quality 1.609 0.242 6.658 *** CA Quality 1.524 0.268 5.677 *** AL Quality 1.576 0.225 7.008 *** SP Base Problem Groups Estimate Standard Error (S.E.) Critical Ratio (C.R.) P Value TB Quality 1.113 0.105 10.563 *** SP Quality 1.000 QM Quality 0.557 0.086 6.463 *** CO Quality 0.844 0.110 7.654 *** CM Q uality 0.896 0.094 9.529 *** CA Quality 0.849 0.119 7.163 *** AL Quality 0.878 0.083 10.646 *** TB Base Problem Groups Estimate Standard Error (S.E.) Critical Ratio (C.R.) P Value TB Quality 1.000 SP Quality 0.898 0.085 10.563 *** QM Qu ality 0.501 0.072 6.984 *** CO Quality 0.758 0.089 8.565 *** CM Quality 0.805 0.070 11.481 *** CA Quality 0.763 0.097 7.893 *** AL Quality 0.789 0.058 13.578 ***

PAGE 179

179 Table E 3. Continued Unique Variance Problem Groups Estimate Standard Error ( S.E.) Critical Ratio (C.R.) P Value TB (er08) 0.159 0.032 5.011 *** SP (er07) 0.303 0.049 6.194 *** QM (er06) 0.273 0.040 6.749 *** CO (er04) 0.382 0.058 6.565 *** CM (er03) 0.188 0.032 5.938 *** CA (er02) 0.474 0.071 6.652 *** AL (er01) 0.093 0.019 4.890 *** Table E 4. Safety CA Base Problem Groups Estimate Standard Error (S.E.) Critical Ratio (C.R.) P Value SP Safety 0.304 0.057 5.334 *** QM Safety 1.259 0.099 12.707 *** PC Safety 1.127 0.084 13.479 *** CO Safety 1.161 0.112 10.351 *** CM Safety 1.219 0.105 11.632 *** CA Safety 1.000 CM Base Problem Groups Estimate Standard Error (S.E.) Critical Ratio (C.R.) P Value SP Safety 0.250 0.046 5.410 *** QM Safety 1.033 0.075 13.844 *** PC Safety 0.925 0.062 14.854 *** CO Safety 0.952 0.087 10.939 *** CM Safety 1.000 CA Safety 0.820 0.071 11.632 *** CO Base Problem Groups Estimate Standard Error (S.E.) Critical Ratio (C.R.) P Value SP Safety 0.262 0.050 5.262 *** QM Safety 1.085 0.092 11.819 *** PC Safety 0.971 0.078 12.434 *** CO Safety 1.000 CM Safety 1.050 0.096 10.939 *** CA Safety 0.862 0.083 10.351 ***

PAGE 180

180 Table E 4. Continued PC Base Problem Groups Estimate Standard Error (S.E.) Critical Ratio (C.R.) P Value SP Safety .270 0 .049 5.566 *** QM Safety 1.117 0.065 17.271 *** PC Safety 1.000 CO Safety 1.030 0.083 12.434 *** CM Safety 1.081 0.073 14.854 *** CA Safety 0.887 0.066 13.479 *** QM Base Problem Groups Estimate Standard Error (S.E.) Critical Ratio (C.R .) P Value SP Safety 0.242 0.044 5.507 *** QM Safety 1.000 PC Safety 0.896 0.052 17.271 *** CO Safety 0.922 0.078 11.819 *** CM Safety 0.968 0.070 13.844 *** CA Safety 0.794 0.063 12.707 *** SP Base Problem Groups Estimate Standard E rror (S.E.) Critical Ratio (C.R.) P Value SP Safety 1.000 QM Safety 4.134 0.751 5.507 *** PC Safety 3.702 0.665 5.566 *** CO Safety 3.811 0.724 5.262 *** CM Safety 4.003 0.740 5.410 *** CA Safety 3.284 0.616 5.334 *** Unique Variance Problem Groups Estimate Standard Error (S.E.) Critical Ratio (C.R.) P Value SP (er07) 0.145 0.021 6.929 *** QM (er06) 0.155 0.030 5.220 *** PC (er05) 0.077 0.019 4.157 *** CO (er04) 0.348 0.055 6.376 *** CM (er03) 0.237 0.040 5.951 *** CA (er02) 0.2 09 0.034 6.217 ***

PAGE 181

181 Table E 5. Satisfaction CA Base Problem Groups Estimate Standard Error (S.E.) Critical Ratio (C.R.) P Value TB Satisfaction 0.763 0.076 10.046 *** SP Satisfaction 0.986 0.062 16.026 *** QM Satisfaction 0.778 0.066 11.801 *** PC Satisfaction 0.963 0.050 19.259 *** CO Satisfaction 0.844 0.064 13.107 *** CM Satisfaction 1.013 0.057 17.706 *** CA Satisfaction 1.000 CM Base Problem Groups Estimate Standard Error (S.E.) Critical Ratio (C.R.) P Value TB Satisf action 0.753 0.075 9.985 *** SP Satisfaction 0.973 0.065 14.906 *** QM Satisfaction 0.768 0.063 12.175 *** PC Satisfaction 0.951 0.048 20.022 *** CO Satisfaction 0.834 0.058 14.398 *** CM Satisfaction 1.000 CA Satisfaction 0.988 0.056 17.707 *** CO Base Problem Groups Estimate Standard Error (S.E.) Critical Ratio (C.R.) P Value TB Satisfaction 0.903 0.097 9.336 *** SP Satisfaction 1.167 0.088 13.219 *** QM Satisfaction 0.921 0.065 14.221 *** PC Satisfaction 1.141 0.076 14. 929 *** CO Satisfaction 1.000 CM Satisfaction 1.199 0.083 14.398 *** CA Satisfaction 1.184 0.090 13.107 *** PC Base Problem Groups Estimate Standard Error (S.E.) Critical Ratio (C.R.) P Value TB Satisfaction 0.792 0.072 10.965 *** SP Sa tisfaction 1.023 0.065 15.697 *** QM Satisfaction 0.808 0.055 14.605 *** PC Satisfaction 1.000 CO Satisfaction 0.877 0.059 14.929 *** CM Satisfaction 1.051 0.053 20.022 *** CA Satisfaction 1.038 0.054 19.259 ***

PAGE 182

182 Table E 5. Continued QM Base Problem Groups Estimate Standard Error (S.E.) Critical Ratio (C.R.) P Value TB Satisfaction 0.980 0.109 9.017 *** SP Satisfaction 1.267 0.110 11.518 *** QM Satisfaction 1.000 PC Satisfaction 1.238 0.085 14.605 *** CO Satisfaction 1.085 0.076 14.221 *** CM Satisfaction 1.301 0.107 12.172 *** CA Satisfaction 1.285 0.109 11.801 *** SP Base Problem Groups Estimate Standard Error (S.E.) Critical Ratio (C.R.) P Value TB Satisfaction 0.774 0.090 8.566 *** SP Satisfaction 1. 000 QM Satisfaction 0.789 0.069 11.518 *** PC Satisfaction 0.977 0.062 15.697 *** CO Satisfaction 0.857 0.065 13.219 *** CM Satisfaction 1.027 0.069 14.906 *** CA Satisfaction 1.014 0.063 16.026 *** TB Base Problem Groups Estimate Stand ard Error (S.E.) Critical Ratio (C.R.) P Value TB Satisfaction 1.000 SP Satisfaction 1.292 0.151 8.566 *** QM Satisfaction 1.020 0.113 9.016 *** PC Satisfaction 1.263 0.115 10.965 *** CO Satisfaction 1.107 0.119 9.336 *** CM Satisfacti on 1.327 0.133 9.985 *** CA Satisfaction 1.311 0.130 10.046 *** Unique Variance Problem Groups Estimate Standard Error (S.E.) Critical Ratio (C.R.) P Value TB (er08) 0.240 0.034 7.047 *** SP (er07) 0.205 0.035 5.870 *** QM (er06) 0.133 0.022 5.959 *** PC (er05) 0.078 0.016 4.771 *** CO (er04) 0.143 0.026 5.433 *** CM (er03) 0.099 0.031 3.181 0.001 CA (er02) 0.086 0.035 2.431 0.015

PAGE 183

183 APPENDIX F PLOTS OF TEN PAIRS O F PROJECT SUCCESS PA RAMETERS Figure F 1. Cost vs. Schedule Figure F 2. Cos t vs. Quality Figure F 3. Cost vs. Safety

PAGE 184

1 84 Figure F 4. Cost vs. Satisfaction Figure F 5. Schedule vs. Quality Figure F 6. Schedule vs. Safety

PAGE 185

185 Figure F 7. Schedule vs. Satisfaction Figure F 8. Quality vs. Safety Figure F 9. Qua lity vs. Satisfaction

PAGE 186

186 Figure F 10. Safety vs. Satisfaction

PAGE 187

187 APPENDIX G SURVEY FILE Figure G 1. Worksheet of instruction

PAGE 188

188 Figure G 2. Worksheet of survey part A through C

PAGE 189

189 Figure G 3. Worksheet of survey part C through E

PAGE 190

190 Figure G 4. Workshee t of survey part E

PAGE 191

191 APPENDIX H REDRAWN REGRESSION M ODEL Figure H 1. Redrawn regression model

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192 LIST OF REFERENCES ACITS. (1995). "Factor Analysis Using SAS PROC FACTOR (Stat 53)." < http://www.utexas.edu/cc/docs/stat53.html > (Jun. 4, 2009). Ainsworth, A. (2008). "Canonical Correlation: Equation Psy 524." < http://www.csun.edu/~ata20315/psy524/docs/Psy524%20Lecture%208%20CC.ppt > (May 31, 2009) Bassioni, H. A., Hassan, T. M., and Price, A. D. F. (2008). "Evaluation and analysis of criteria and sub criteria of a construction excellence model." Engineering, Construction and Architectural Management 15(1), 21 41. Brown, T. (2006). Confirmatory Factor Analysis for Applied Research Guilford Press, New York. Chan, A. P. C., Scott, D., and Chan, A. P. L. (2004). "Factors Affecting the Success of a Construction Project." Journal of Construction Engineering and Management 130(1), 153 155. Chan, A. P. C., Scott, D., and Lam, E. W. M. ( 2002). "Framework of Success Criteria for Design/Build Projects." Journal of Management in Engineering 18(3), 120 128. Cheng, E. W. L. (2001). "SEM being more effective than multiple regression in parsimonious model testing for management development res earch." Journal of Management Development 20(7), 650 667. Choi, J., Anderson, S., and Kim, S. J. T. (2006). "Forecasting Potential Risks Through Leading Indicators to Project Outcome." Curran, P. J., West, S. G., and Finch, J. F. (1996). "The Robustness of Test Statistics to Nonnormality and Specification Error in Confirmatory Factor Analysis." Psychological Methods 1(1), 16 29. Dunteman, G. H. (1984). Introduction to Multivariate Analysis Sage Publications, Beverly Hills. Edwards, W. (1977). "How to Use Multiattribute Utility Measurement for Social Decisionmaking." IEEE Transactions on Systems, Man, and Cybernetics SMC 7(5), 326 340. Edwards, W., and Barron, F. H. (1994). "SMARTS and SMARTER: Improved Simple Methods for Multiattribute Utility Measu rement." Organizational Behavior and Human Decision Processes 60, 306 325. Eskildsen, J. K., Kristensen, K., and Juhl, H. J. (2001). "The criterion weights of the EFQM excellence model." International Journal of Quality & Reliability Management 18(8), 7 83 795.

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193 Floyd, F. J., and Widaman, K. F. (1995). "Factor Analysis in the Development and Refinement of Clinical Assessment Instruments." Psychological Assessment 7(3), 286 299. Garson, D. (2008a). "PA 765: Canonical Correlation Canonical Correlation." < http://www2.chass.ncsu.edu/garson/pa765/canonic.htm > (Feb. 18, 2008) Garson, D. (2008b). "Scales and Standard Measures < http://faculty.chass.ncsu.edu/garson/PA765/standard.htm > (Jul. 16, 2008) Garson, D. (2008c). "Structural Equation Modeling: Statnotes < http://faculty.chass.ncsu.edu/garson/PA765/structur.htm > (Jul. 7, 2008) Gibson, E. J., and Dumont, P. (1995). "Project Definition Rating Index (PDRI) for Industrial Proje ct." Construction Industry Institute (CII) Research Report 113 11 Griffith, A. F., Gibson, E. J., Tortora, A. L., and Wilson, C. T. (1999). "Project Success Index for Capital Facility Construction Project." Journal of Performance of Constructed Facilitie s 13(1), 39 45. Hackney, J. W. (1992). Control & Management of Capital Projects Second Edition McGraw Hills, Inc, New York. Hair, J. F., Anderson, R. E., Tatham, R. L., and Black, W. (1998). Multivariate Data Analysis Prentice Hall, Englewood, New J ersey. Herbsman, Z., Chen, W. T., and Epstein, W. C. (1995). "Time Is Money: Innovative Contracting Methods in Highway Construction." Construction Engineering and Management 121(3), 273 281. Herbsman, Z., and Ellis, R. (1992). "Multiparameter Bidding Sy stem Innovation in Contract Administration." Construction Engineering and Management 118(1), 142 150. Kaplan, R. S., and Norton, D. P. (1992). "The Balanced Scorecard Measures That Drive Performance." Harvard Business Review January February, 71 75. Ko, H., Cho, C. H., and Roberts, M. S. (2005). "Internet Uses and Gratifications/A Structural Equation Model of Interactive Advertising." Journal of Advertising 34(2), 57 70. NIST/SEMATECH. (2006). "e Handbook of Statistical Methods." < http://www.itl.nist.gov/div898/handbook/eda/section3/eda35b.htm > (Dec. 10, 2008) Olson, C. L. (1987). Statistics: Making Sense of Data Allyn and Bac on, Boston. Oyetunji, A. A. (2001). "Methodology for Selecting Project Delivery and Contracting Strategies for Capital Projects," Texas A&M University, College Station, TX.

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194 Russell, J. S., Jaselskis, E. J., Lawrence, S., Tserng, H. P., and Prestine, M. ( 1996). "Development of a predictive tool for continuous assessment of project performance." The Construction Industry Institute, The University of Texas Austin, Austin, TX. Seo, S., Aramaki, T., Hwang Y ., and Hanaki K ( 2004 ). Fuzzy Decision Making Too l for Environmental Sustainable Buildings ." Journal of Construction Engineering and Management 13 0 ( 3 ), 415 423 Spector, P. E. (1992). Summated Rating Scale Construction: An Introduction SAGE Publications, Newbaury Park, California. SPSS. (2008). "AMOS ." Thompson, B. (1984). Canonical Correlation Analysis: Uses and Interpretation Sage Publications, Beverly Hills. Thompson, B. (2004). Exploratory and Confirmatory Factor Analysis: Understanding Concepts and Applications American Psychological Associat ion, Washington, DC. Weisstein, E. W. (2009a). "Combination." < http://mathworld.wolfram.com/Combination.html > (Ju n. 14, 2009) Weisstein, E. W. (2009b). "Variation Coefficient." < http://mathworld.wolfram.com/Variatio nCoefficient.html > (Jan. 20, 2009) Winterfeldt, D., and Edwards, W. (1986). Decision Analysis and Behavioral Research Cambridge University Press, Cambridge; New York. Wynne, J. D. (1982). Learning Statistics: A Common Sense Approach Macmillan (New Yo rk) and Collier Macmillan (London), New York and London.

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195 BIOGRAPHICAL SKETCH Seong Jin Kim was born in Seoul, Korea. He has four family members (two parents and two sisters). He grew up in most his time in Koyansi, Kyunggido. The schools he has been to are : Shin Do elementary school, Youn Shin middle school, and Dae Shin high school. Right after he graduated from high school, he went to Soong Sil University for four years of college, majoring in architectural engineering. During the college years, he joined the army for two years as other Korean men do. He loved the design of buildings and houses and won a couple of design competitions during his college years. After graduation he started working with a general contractor for t he next five years, three years in Malaysia and two years in Seoul, Korea. When he worked for a general contractor in Korea and Malaysia, he had a chance to meet a lot of people from all over the world and gained experienced in the construction industry. Even though he loved his job and work, he decided to study more to be come a better person in the construction industry. After five years of work, he continued studying for his master degree in civil engineering at Texas A&M University. At that time To mmy decided to be a professor at a university and that is something he w ould like to do better than anything in the Florida to seek his Ph.D in Design, Constructio n and Planning His interests in the construction industry are project control s and planning with estimating His specialties are related to cost and schedule. It has been a long journey to accomplishing his goals but it has still been worth doing Tommy likes to cook for his friends and families and loves to see movies. He has been a lecturer in the department of construction management at East Carolina University since August 2009.