ABSTRACT 
 
 
 
 

Title of Dissertation: ANALYZING BID PRICES QUANTITATIVELY 
AND PROTEST DECISIONS QUALITATIVELY 
TO REDUCE PROJECT-RELATED DISPUTES 
IN ADVANCE 

  
 Young Joo Kim 
  
Dissertation directed by: Professor Miroslaw Skibniewski, Department of 

Civil and Environmental Engineering 
 

Parties to a construction contract can consume significant resources in dealing with project-

related disputes. Therefore, it is advantageous for project stakeholders to identify potential 

issues earlier to avoid such problems as much as possible. This dissertation research 

explored evidence-based approaches to reduce project-related disputes before 

commencing construction projects. The research was carried out by examining a cost 

dataset from a state Department of Transportation that prioritizes the lowest-priced bid and 

by investigating a bid protest dataset from a Federal Government office that typically 

prioritizes the best value. With the coefficient of variation of bids as an independent variable 

of interest, the cost dataset was quantitatively studied using Welch’s t-test, correlation and 

regression analyses, and the K-nearest neighbors classification. Then, the Government 

Accountability Office’s decisions on denied bid protests against the U.S. Army Corps of 

Engineers were qualitatively meta-summarized. The observations showed the limited 

usefulness of collective intelligence provided by bidders at the time of bid opening in 

identifying projects likely to experience more significant project cost changes upon 

completion, as well as the effectiveness of the thematic findings in limitedly helping small 

businesses fore-test the validity of their cases before filing bid protests. The results could 

be applied beyond the Architecture, Engineering, and Construction industries as projects 

occur in all industries and industry sectors. 

 



 

 
 
 
 
 
 
 
 
 
 
 
 
 
 

ANALYZING BID PRICES QUANTITATIVELY AND PROTEST DECISIONS 
QUALITATIVELY TO REDUCE PROJECT-RELATED DISPUTES IN ADVANCE 

 
 
 
 
 

by 
 
 

Young Joo Kim 
 
 
 
 
 
 
 

Dissertation submitted to the Faculty of the Graduate School of the 
University of Maryland, College Park, in partial fulfillment 

of the requirements for the degree of 
Doctor of Philosophy 

2022 
 
 
 
 
 
 
 
 
 
 
 
 

Advisory Committee: 
Professor Miroslaw Skibniewski, Chairman 
Assistant Professor Michelle Bensi 
Professor Qingbin Cui 
Professor Emeritus Gerald Galloway 
Associate Professor Gideon Mark, Dean’s Representative  

 



ii 

Table of Contents 

Table of Contents .................................................................................................................ii 

List of Tables .......................................................................................................................vi 

List of Figures .................................................................................................................... xiv 

1 Justification of dissertation research .......................................................................... 1 

1.1 Introduction to project-related claims and disputes ........................................... 1 

1.2 Research questions ........................................................................................... 5 

1.3 Justification of proposed research questions .................................................... 7 

2 Usefulness of coefficient of variation of bids in identifying projects likely to experience 
more significant project cost changes upon completion ..................................................... 9 

2.1 Chapter introduction .......................................................................................... 9 

2.2 Literature review .............................................................................................. 10 

2.3 Research method ............................................................................................ 12 

2.4 Results and discussion .................................................................................... 18 

2.5 Classification using the K-nearest neighbors algorithm .................................. 42 

2.6 Chapter conclusion .......................................................................................... 47 

3 Effectiveness of meta-summarized thematic findings in fore-testing validity of bid 
protests.............................................................................................................................. 51 

3.1 Chapter introduction ........................................................................................ 51 

3.2 Research method ............................................................................................ 55 

3.3 Results of meta-summary analysis and discussion ......................................... 59 

3.4 Validation ......................................................................................................... 93 



iii 

3.5 Chapter conclusion .......................................................................................... 96 

4 Conclusion ................................................................................................................ 99 

Appendix A: 898 bids on 210 projects ............................................................................ 104 

Appendix B: Outcomes of correlation and regression analyses ..................................... 107 

C.V. and Project Cost Change (Actual) ...................................................................... 107 

C.V. and Project Cost Change (Absolute) .................................................................. 108 

Planned Project Duration and Project Cost Change (Actual) ..................................... 110 

Planned Project Duration and Project Cost Change (Absolute) ................................. 111 

Actual Project Duration and Project Cost Change (Actual) ........................................ 113 

Actual Project Duration and Project Cost Change (Absolute) .................................... 114 

Project Duration Change and Project Cost Change (Actual) ..................................... 116 

Project Duration Change and Project Cost Change (Absolute) ................................. 117 

Number of Bidders and Project Cost Change (Actual) ............................................... 119 

Number of Bidders and Project Cost Change (Absolute) ........................................... 120 

Average Bid Value and Project Cost Change (Actual) ............................................... 122 

Average Bid Value and Project Cost Change (Absolute) ........................................... 123 

Winning Bid and Project Cost Change (Actual) .......................................................... 125 

Winning Bid and Project Cost Change (Absolute) ...................................................... 126 

Final Construction Cost and Project Cost Change (Actual) ....................................... 128 

Final Construction Cost and Project Cost Change (Absolute) ................................... 129 



iv 

Cumulative Rate of Inflation and Project Cost Change (Actual) ................................. 131 

Cumulative Rate of Inflation and Project Cost Change (Absolute)............................. 133 

Appendix C: Outcomes of regression analyses with transformations ............................ 135 

Linear-log model ......................................................................................................... 135 

Log-linear model ......................................................................................................... 163 

Log-log model ............................................................................................................. 177 

Appendix D: Synopses of analyzed bid protest cases .................................................... 192 

Matter of Netizen Corporation (Case 1) ...................................................................... 192 

Matter of Pond Constructors, Inc. (Case 2) ................................................................ 192 

Matter of Facility Services Management, Inc. (Case 3) .............................................. 193 

Matter of NIKA Technologies, Inc. (Case 4) ............................................................... 193 

Matter of J&J Worldwide Services (Case 5) ............................................................... 194 

Matter of Prestige Lawncare, Inc. (Case 6) ................................................................ 195 

Matter of Benaka Inc. (Case 7) ................................................................................... 196 

Matter of Re-Engineered Business Solutions, Inc. (Case 8) ...................................... 196 

Matter of Artek Construction (Case 9) ........................................................................ 197 

Matter of Pacific Dredge and Construction, LLC (Case 10) ....................................... 197 

Matter of C.I. Lovell, Inc. (Case 11) ............................................................................ 198 

Matter of Sevenson Environmental Services, Inc. (Case 12) ..................................... 198 



v 

Matter of W-T, Joint Venture (Case 13) ...................................................................... 199 

Matter of Wolff-Mueller Government Services GmbH & Company KG (Case 14) ..... 200 

Matter of KMK Construction, Inc. (Case 15) ............................................................... 200 

Matter of Bodell Construction Company (Case 16) .................................................... 201 

Matter of Butt Construction Company, Inc. (Case 17) ................................................ 201 

Appendix E: Findings ...................................................................................................... 203 

Appendix F: Codebook.................................................................................................... 275 

5 Bibliography ............................................................................................................ 282 

 

  



vi 

List of Tables 

Table 1 Characteristics of case projects ........................................................................... 15 

Table 2 Analysis Results Summary .................................................................................. 19 

Table 3 t-Test: Two-Sample Assuming Unequal Variances ............................................. 19 

Table 4 Comparison of Means of Project Cost Changes against the Number of Bidders and 

CV Percentile .................................................................................................................... 20 

Table 5 Correlation coefficients between variables .......................................................... 35 

Table 6 Regression and single variable linear regression results .................................... 36 

Table 7 Multiple variable linear regression results ............................................................ 40 

Table 8 Regression analysis with a dummy variable results ........................................ 41 

Table 9 Top 10 best accuracies over 100 KNN trails (n-neighbors =5) ............................ 45 

Table 10 Number of findings per case .............................................................................. 60 

Table 11 Meta-summary with frequency effect sizes ........................................................ 90 

Table 12 Regression analysis output summary - C.V. and Project Cost Change (Actual)

 ........................................................................................................................................ 107 

Table 13 Correlation between C.V. and Project Cost Change (Actual) .......................... 108 

Table 14 Regression analysis output summary - C.V. and Project Cost Change (Absolute)

 ........................................................................................................................................ 109 

Table 15 Correlation between C.V. and Project Cost Change (Absolute) ...................... 110 

Table 16 Regression analysis output summary - Planned project duration and Project Cost 

Change (Actual) .............................................................................................................. 111 

Table 17 Correlation between planned project duration and Project Cost Change (Actual)

 ........................................................................................................................................ 111 

Table 18 Regression analysis output summary - Planned project duration and Project Cost 

Change (Absolute) .......................................................................................................... 112 

Table 19 Correlation between planned project duration and Project Cost Change ....... 113 

Table 20 Regression analysis output summary - Actual project duration and Project Cost 

Change (Actual) .............................................................................................................. 114 



vii 

Table 21 Correlation between actual project duration and Project Cost Change (Actual)

 ........................................................................................................................................ 114 

Table 22 Regression analysis output summary - Actual project duration and Project Cost 

Change (Absolute) .......................................................................................................... 115 

Table 23 Correlation between actual project duration and Project Cost Change (Absolute)

 ........................................................................................................................................ 116 

Table 24 Regression analysis output summary- Project duration change and Project Cost 

Change (Actual) .............................................................................................................. 117 

Table 25 Correlation between project duration change and Project Cost Change (Actual)

 ........................................................................................................................................ 117 

Table 26 Regression analysis output summary - Project duration change and Project Cost 

Change (Absolute). ......................................................................................................... 118 

Table 27 Correlation between project duration change and Project Cost Change (Absolute)

 ........................................................................................................................................ 119 

Table 28 Regression analysis output summary - Number of bidders and Project Cost 

Change (Actual) .............................................................................................................. 120 

Table 29 Correlation between number of bidders and Project Cost Change (Actual) .... 120 

Table 30 Regression analysis output summary - Number of bidders and Project Cost 

Change (Absolute) .......................................................................................................... 121 

Table 31 Correlation between Number of Bidders and Project Cost Change (Absolute)

 ........................................................................................................................................ 122 

Table 32 Regression analysis output summary - Average bid value and Project Cost 

Change (Actual) .............................................................................................................. 122 

Table 33 Correlation between Average bid value and Project Cost Change (Actual) .... 123 

Table 34 Regression analysis output summary - Average bid value and Project Cost 

Change (Absolute) .......................................................................................................... 124 

Table 35 Correlation between Average Bid Value and Project Cost Change (Absolute)

 ........................................................................................................................................ 125 



viii 

Table 36 Regression analysis output summary - Average bid value and Project Cost 

Change (Actual) .............................................................................................................. 126 

Table 37 Correlation between winning bid value and Project Cost Change (Actual) ..... 126 

Table 38 Regression analysis output summary - Average bid value and Project Cost 

Change (Absolute) .......................................................................................................... 127 

Table 39 Correlation between winning bid value and Project Cost Change (Actual) ..... 128 

Table 40 Regression analysis output summary – Final construction cost and Project Cost 

Change (Actual) .............................................................................................................. 128 

Table 41 Correlation between final construction cost and Project Cost Change (Actual)

 ........................................................................................................................................ 129 

Table 42 Regression analysis output summary – Final construction cost and Project Cost 

Change (Absolute) .......................................................................................................... 130 

Table 43 Correlation between final construction cost and Project Cost Change (Absolute)

 ........................................................................................................................................ 131 

Table 44 Regression analysis output summary – Cumulative inflation rate and Project Cost 

Change (Actual) .............................................................................................................. 132 

Table 45 Correlation between inflation rate and Project Cost Change (Actual) ............. 133 

Table 46 Regression analysis output summary – Cumulative inflation rate and Project Cost 

Change (Absolute) .......................................................................................................... 134 

Table 47 Correlation between inflation rate and Project Cost Change (Absolute) ......... 134 

Table 48 Regression analysis output summary – Log (C.V.) and Project Cost Change 

(Actual) ............................................................................................................................ 136 

Table 49 Correlation between Log (C.V.) and Project Cost Change (Actual) ................ 137 

Table 50 Regression analysis output summary – Log (C.V.) and Project Cost Change 

(Absolute) ........................................................................................................................ 138 

Table 51 Correlation between Log (C.V.) and Project Cost Change (Absolute) ............ 138 

Table 52 Regression analysis output summary – Log (Planned project duration) and 

Project Cost Change (Actual) ......................................................................................... 139 



ix 

Table 53 Correlation between Log (C.V.) and Project Cost Change (Actual) ................ 140 

Table 54 Regression analysis output summary – Log (Planned project duration) and 

Project Cost Change (Absolute) ..................................................................................... 141 

Table 55 Correlation between Log (Planned project duration) and Project Cost Change 

(Absolute) ........................................................................................................................ 141 

Table 56 Regression analysis output summary – Log (Actual project duration) and Project 

Cost Change (Actual) ...................................................................................................... 142 

Table 57 Correlation between Log (Actual project duration) and Project Cost Change 

(Actual) ............................................................................................................................ 143 

Table 58 Regression analysis output summary – Log (Actual project duration) and Project 

Cost Change (Absolute) .................................................................................................. 144 

Table 59 Correlation between Log (Actual project duration) and Project Cost Change 

(Absolute) ........................................................................................................................ 144 

Table 60 Regression analysis output summary – Log (Project duration change) and Project 

Cost Change (Actual) ...................................................................................................... 145 

Table 61 Correlation between Log (Project duration change) and Project Cost Change 

(Actual) ............................................................................................................................ 146 

Table 62 Regression analysis output summary – Log (Project duration change) and Project 

Cost Change (Absolute) .................................................................................................. 147 

Table 63 Correlation between Log (Project duration change) and Project Cost Change 

(Absolute) ........................................................................................................................ 148 

Table 64 Regression analysis output summary – Log (Number of bidders) and Project Cost 

Change (Actual) .............................................................................................................. 149 

Table 65 Correlation between Log (Number of bidders) and Project Cost Change (Actual)

 ........................................................................................................................................ 149 

Table 66 Regression analysis output summary – Log (Number of bidders) and Project Cost 

Change (Absolute) .......................................................................................................... 150 



x 

Table 67 Correlation between Log (Number of bidders) and Project Cost Change 

(Absolute) ........................................................................................................................ 151 

Table 68 Regression analysis output summary – Log (Average bid value) and Project Cost 

Change (Actual) .............................................................................................................. 152 

Table 69 Correlation between Log (Average bid value) and Project Cost Change (Actual)

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

Table 70 Regression analysis output summary – Log (Average bid value) and Project Cost 

Change (Absolute) .......................................................................................................... 153 

Table 71 Correlation between Log (Average bid value) and Project Cost Change (Absolute)

 ........................................................................................................................................ 154 

Table 72 Regression analysis output summary – Log (Winning bid) and Project Cost 

Change (Actual) .............................................................................................................. 155 

Table 73 Correlation between Log (Winning bid) and Project Cost Change (Actual) .... 155 

Table 74 Regression analysis output summary – Log (Winning bid) and Project Cost 

Change (Absolute) .......................................................................................................... 156 

Table 75 Correlation between Log (Winning bid) and Project Cost Change (Absolute) 157 

Table 76 Regression analysis output summary – Log (Final construction cost) and Project 

Cost Change (Actual) ...................................................................................................... 158 

Table 77 Correlation between Log (Final construction cost) and Project Cost Change 

(Actual) ............................................................................................................................ 158 

Table 78 Regression analysis output summary – Log (Final construction cost) and Project 

Cost Change (Absolute) .................................................................................................. 159 

Table 79 Correlation between Log (Final construction cost) and Project Cost Change 

(Absolute) ........................................................................................................................ 160 

Table 80 Regression analysis output summary – Log (Cumulative rate of inflation) and 

Project Cost Change (Actual) ......................................................................................... 161 

Table 81 Correlation between Log (Cumulative rate of inflation) and Project Cost Change 

(Actual) ............................................................................................................................ 161 



xi 

Table 82 Regression analysis output summary – Log (Cumulative rate of inflation) and 

Project Cost Change (Absolute) ..................................................................................... 162 

Table 83 Correlation between Log (Cumulative rate of inflation) and Project Cost Change 

(Absolute) ........................................................................................................................ 163 

Table 84 Regression analysis output summary – C.V. and Log (Project Cost Change 

(Absolute)) ....................................................................................................................... 164 

Table 85 Correlation between C.V. and Log (Project Cost Change (Absolute)) ............ 165 

Table 86 Regression analysis output summary – Planned project duration and Log (Project 

Cost Change (Absolute))................................................................................................. 166 

Table 87 Correlation between planned project duration and Log (Project Cost Change 

(Absolute)) ....................................................................................................................... 166 

Table 88 Regression analysis output summary – Actual project duration and Log (Project 

Cost Change (Absolute))................................................................................................. 167 

Table 89 Correlation between actual project duration and Log (Project Cost Change 

(Absolute)) ....................................................................................................................... 168 

Table 90 Regression analysis output summary – Project duration change and Log (Project 

Cost Change (Absolute))................................................................................................. 169 

Table 91 Correlation between project duration change and Log (Project Cost Change 

(Absolute)) ....................................................................................................................... 169 

Table 92 Regression analysis output summary – Number of bidders and Log (Project Cost 

Change (Absolute)) ......................................................................................................... 170 

Table 93 Correlation between number of bidders and Log (Project Cost Change (Absolute))

 ........................................................................................................................................ 171 

Table 94 Regression analysis output summary – Average bid value and Log (Project Cost 

Change (Absolute)) ......................................................................................................... 172 

Table 95 Correlation between average bid value and Log (Project Cost Change (Absolute))

 ........................................................................................................................................ 172 



xii 

Table 96 Regression analysis output summary – Winning bid and Log (Project Cost 

Change (Absolute)) ......................................................................................................... 173 

Table 97 Correlation between winning bid and Log (Project Cost Change (Absolute)) . 174 

Table 98 Regression analysis output summary – Final construction cost and Log (Project 

Cost Change (Absolute))................................................................................................. 175 

Table 99 Correlation between final construction cost and Log (Project Cost Change 

(Absolute)) ....................................................................................................................... 175 

Table 100 Regression analysis output summary – Cumulative inflation rate and Log 

(Project Cost Change (Absolute)) ................................................................................... 176 

Table 101 Correlation between cumulative inflation rate and Log (Project Cost Change 

(Absolute)) ....................................................................................................................... 177 

Table 102 Regression analysis output summary – Log (C.V.)and Log (Project Cost Change 

(Absolute)) ....................................................................................................................... 178 

Table 103 Correlation between Log (C.V.) and Log (Project Cost Change (Absolute)) . 179 

Table 104 Regression analysis output summary – Log (Planned project duration) and Log 

(Project Cost Change (Absolute)) ................................................................................... 180 

Table 105 Correlation between Log (Planned project duration) and Log (Project Cost 

Change (Absolute)) ......................................................................................................... 180 

Table 106 Regression analysis output summary – Log (Actual project duration) and Log 

(Project Cost Change (Absolute)) ................................................................................... 181 

Table 107 Correlation between Log (Actual project duration) and Log (Project Cost Change 

(Absolute)) ....................................................................................................................... 182 

Table 108 Regression analysis output summary – Log (Project duration change) and Log 

(Project Cost Change (Absolute)) ................................................................................... 183 

Table 109 Correlation between Log (Project duration change) and Log (Project Cost 

Change (Absolute)) ......................................................................................................... 184 

Table 110 Regression analysis output summary – Log (Number of bidders) and Log 

(Project Cost Change (Absolute)) ................................................................................... 185 



xiii 

Table 111 Correlation between Log (Number of bidders) and Log (Project Cost Change 

(Absolute)) ....................................................................................................................... 185 

Table 112 Regression analysis output summary – Log (Average bid value) and Log (Project 

Cost Change (Absolute))................................................................................................. 186 

Table 113 Correlation between Log (Average bid value) and Log (Project Cost Change 

(Absolute)) ....................................................................................................................... 187 

Table 114 Regression analysis output summary – Log (Winning bid) and Log (Project Cost 

Change (Absolute)) ......................................................................................................... 188 

Table 115 Correlation between Log (Winning bid) and Log (Project Cost Change 

(Absolute)) ....................................................................................................................... 188 

Table 116 Regression analysis output summary – Log (Final construction cost) and Log 

(Project Cost Change (Absolute)) ................................................................................... 189 

Table 117 Correlation between Log (Final construction cost) and Log (Project Cost Change 

(Absolute)) ....................................................................................................................... 190 

Table 118 Regression analysis output summary – Log (Cumulative inflation rate) and Log 

(Project Cost Change (Absolute)) ................................................................................... 191 

Table 119 Correlation between Log (Cumulative inflation rate) and Log (Project Cost 

Change (Absolute)) ......................................................................................................... 191 

Table 120 A summary of interactions between the protester and the agency before and 

after the bid due date ...................................................................................................... 195 

 

 



xiv 

List of Figures 

Figure 1 Parallel coordinate chart comparing the number of bidders and Project Cost 

Change .............................................................................................................................. 23 

Figure 2 Multiple plots (C.V. and other variables) ............................................................. 24 

Figure 3 Multiple plots (Planned project duration and other variables) ............................ 25 

Figure 4 Multiple plots (Actual project duration and other variables) ................................ 26 

Figure 5 Multiple plots (Project duration change and other variables) ............................. 27 

Figure 6 Multiple plots (Number of bidders and other variables) ...................................... 28 

Figure 7 Multiple plots (Average bid value and other variables) ....................................... 29 

Figure 8 Multiple plots (Winning bid and other variables) ................................................. 30 

Figure 9 Multiple plots (Cumulative rate of inflation and other variables) ......................... 31 

Figure 10 Multiple plots - Comparing Project Cost Change (Actual) and Project Cost 

Change (Absolute) ............................................................................................................ 32 

Figure 11 Number of bidders (more than) and R-square scatter diagram ........................ 41 

Figure 12 KNN diagram .................................................................................................... 43 

Figure 13 Python code used for the KNN classification .................................................... 43 

Figure 14 Scattergram of KNN accuracies over 100 trials (n-neighbors =5) .................... 46 

Figure 15 Average accuracy and number of n-neighbors scattergram ............................ 46 

Figure 16 Meta-summary workflow ................................................................................... 56 

Figure 17 Word cloud from analyzed bid protest decisions .............................................. 59 

Figure 18 Word cloud from relevant findings (Substantive flaws) .................................... 61 

Figure 19 Word cloud from relevant findings (Unequal treatment) ................................... 62 

Figure 20 Word cloud from relevant findings (Without more) ........................................... 62 

Figure 21 Word cloud from relevant findings (Action or inaction) ..................................... 63 

Figure 22 Explore diagram 1 ............................................................................................. 63 

Figure 23 Word cloud from relevant findings (Ambiguity or unambiguous) ...................... 64 

Figure 24 Word cloud from relevant findings (Plain language) ......................................... 65 

Figure 25 Word cloud from relevant findings (Entirety) .................................................... 65 



xv 

Figure 26 Word cloud from relevant findings (Express requirements) ............................. 66 

Figure 27 Explore diagram 2 ............................................................................................. 67 

Figure 28 Word cloud from relevant findings (Post-protest) ............................................. 67 

Figure 29 Word cloud from relevant findings (Contemporaneous) ................................... 68 

Figure 30 Word cloud from relevant findings (Review of the record) ............................... 69 

Figure 31 Explore diagram 3 ............................................................................................. 69 

Figure 32 Word cloud from relevant findings (No re-evaluation of proposals) ................. 70 

Figure 33 Word cloud from relevant findings (Reasonable and consistent) ..................... 71 

Figure 34 Explore diagram 4 ............................................................................................. 72 

Figure 35 Word cloud from relevant findings (Well-written) .............................................. 73 

Figure 36 Word cloud from relevant findings (Your word against your word) ................... 74 

Figure 37 Word cloud from relevant findings (In compliance) .......................................... 74 

Figure 38 Explore diagram 5 ............................................................................................. 75 

Figure 39 Word cloud from relevant findings (Form the basis of the protest) ................... 76 

Figure 40 Word cloud from relevant findings (Not an interested party) ............................ 77 

Figure 41 Explore diagram 5 ............................................................................................. 78 

Figure 42 Word cloud from relevant findings (Prejudiced) ................................................ 79 

Figure 43 Word cloud from relevant findings (Even if) ...................................................... 79 

Figure 44 Explore diagram 7 ............................................................................................. 80 

Figure 45 Word cloud from relevant findings (Broad discretion) ...................................... 81 

Figure 46 Word cloud from relevant findings (Logically encompassed) ........................... 82 

Figure 47 Explore diagram 8 ............................................................................................. 82 

Figure 48 Word cloud from relevant findings (Fail to rebut).............................................. 83 

Figure 49 Explore diagram 9 ............................................................................................. 84 

Figure 50 Word cloud from relevant findings (Untimely) ................................................... 85 

Figure 51 Explore diagram 10 ........................................................................................... 85 

Figure 52 Word cloud from relevant findings (Theme 11) ................................................ 86 

Figure 53 Explore diagram 11 ........................................................................................... 87 



xvi 

Figure 54 Word cloud from relevant findings (Theme 12) ................................................ 88 

Figure 55 Explore diagram 12 ........................................................................................... 89 

Figure 56 C.V. and Project Cost Change (Actual) scatter diagram ................................ 107 

Figure 57 C.V. and Project Cost Change (Absolute) scatter diagram ............................ 109 

Figure 58 Planned project duration and Project Cost Change (Actual) scatter diagram 110 

Figure 59 Planned project duration and Project Cost Change (Absolute) scatter diagram

 ........................................................................................................................................ 112 

Figure 60 Actual project duration and Project Cost Change (Actual) scatter diagram ... 113 

Figure 61 Actual project duration and Project Cost Change (Absolute) scatter diagram115 

Figure 62 Project duration change and Project Cost Change (Actual) scatter diagram . 116 

Figure 63 Project duration change and Project Cost Change (Absolute) scatter diagram

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

Figure 64 Number of bidders and Project Cost Change (Actual) scatter diagram ......... 119 

Figure 65 Number of bidders and Project Cost Change (Absolute) scatter diagram ..... 121 

Figure 66 Average bid value and Project Cost Change (Actual) scatter diagram .......... 122 

Figure 67 Average bid value and Project Cost Change (Absolute) scatter diagram ...... 124 

Figure 68 Winning bid value and Project Cost Change (Actual) scatter diagram .......... 125 

Figure 69 Winning bid value and Project Cost Change (Absolute) scatter diagram ...... 127 

Figure 70 Final construction cost and Project Cost Change (Actual) scatter diagram ... 128 

Figure 71 Final construction cost and Project Cost Change (Absolute) scatter diagram130 

Figure 72 Cumulative inflation rate and Project Cost Change (Actual) scatter diagram 132 

Figure 73 Cumulative inflation rate and Project Cost Change (Absolute) scatter diagram

 ........................................................................................................................................ 133 

Figure 74 Log (C.V.) and Project Cost Change (Actual) scatter diagram ....................... 136 

Figure 75 Log (C.V.) and Project Cost Change (Absolute) scatter diagram ................... 137 

Figure 76 Log (Planned project duration) and Project Cost Change (Actual) scatter diagram

 ........................................................................................................................................ 139 



xvii 

Figure 77 Log (Planned project duration) and Project Cost Change (Absolute) scatter 

diagram ........................................................................................................................... 140 

Figure 78 Log (Actual project duration) and Project Cost Change (Actual) scatter diagram

 ........................................................................................................................................ 142 

Figure 79 Log (Actual project duration) and Project Cost Change (Absolute) scatter 

diagram ........................................................................................................................... 143 

Figure 80 Log (Project duration change) and Project Cost Change (Actual) scatter diagram

 ........................................................................................................................................ 145 

Figure 81 Log (Project duration change) and Project Cost Change (Absolute) scatter 

diagram ........................................................................................................................... 147 

Figure 82 Log (Number of bidders) and Project Cost Change (Actual) scatter diagram 148 

Figure 83 Log (Number of bidders) and Project Cost Change (Absolute) scatter diagram

 ........................................................................................................................................ 150 

Figure 84 Log (Average bid value) and Project Cost Change (Actual) scatter diagram . 151 

Figure 85 Log (Average bid value) and Project Cost Change (Absolute) scatter diagram

 ........................................................................................................................................ 153 

Figure 86 Log (Winning bid) and Project Cost Change (Actual) scatter diagram ........... 154 

Figure 87 Log (Winning bid) and Project Cost Change (Absolute) scatter diagram ....... 156 

Figure 88 Log (Final construction cost) and Project Cost Change (Actual) scatter diagram

 ........................................................................................................................................ 157 

Figure 89 Log (Final construction cost) and Project Cost Change (Absolute) scatter 

diagram ........................................................................................................................... 159 

Figure 90 Log (Cumulative rate of inflation) and Project Cost Change (Actual) scatter 

diagram ........................................................................................................................... 160 

Figure 91 Log (Cumulative rate of inflation) and Project Cost Change (Absolute) scatter 

diagram ........................................................................................................................... 162 

Figure 92 C.V. and Log (Project Cost Change (Absolute)) scatter diagram ................... 164 



xviii 

Figure 93 Planned project duration and Log (Project Cost Change (Absolute)) scatter 

diagram ........................................................................................................................... 165 

Figure 94 Actual project duration and Log (Project Cost Change (Absolute)) scatter 

diagram ........................................................................................................................... 167 

Figure 95 Project duration change and Log (Project Cost Change (Absolute)) scatter 

diagram ........................................................................................................................... 168 

Figure 96 Number of bidders and Log (Project Cost Change (Absolute)) scatter diagram

 ........................................................................................................................................ 170 

Figure 97 Average bid value and Log (Project Cost Change (Absolute)) scatter diagram

 ........................................................................................................................................ 171 

Figure 98 Winning bid and Log (Project Cost Change (Absolute)) scatter diagram ....... 173 

Figure 99 Final construction cost and Log (Project Cost Change (Absolute)) scatter 

diagram ........................................................................................................................... 174 

Figure 100 Cumulative inflation rate and Log (Project Cost Change (Absolute)) scatter 

diagram ........................................................................................................................... 176 

Figure 101 Log (C.V.)and Log (Project Cost Change (Absolute)) scatter diagram ........ 178 

Figure 102 Log (Planned project duration) and Log (Project Cost Change (Absolute)) 

scatter diagram ............................................................................................................... 179 

Figure 103 Log (Actual project duration) and Log (Project Cost Change (Absolute)) scatter 

diagram ........................................................................................................................... 181 

Figure 104 Log (Project duration change) and Log (Project Cost Change (Absolute)) scatter 

diagram ........................................................................................................................... 183 

Figure 105 Log (Number of bidders) and Log (Project Cost Change (Absolute)) scatter 

diagram ........................................................................................................................... 184 

Figure 106 Log (Average bid value) and Log (Project Cost Change (Absolute)) scatter 

diagram ........................................................................................................................... 186 

Figure 107 Log (Winning bid) and Log (Project Cost Change (Absolute)) scatter diagram

 ........................................................................................................................................ 187 



xix 

Figure 108 Log (Final construction cost) and Log (Project Cost Change (Absolute)) scatter 

diagram ........................................................................................................................... 189 

Figure 109 Log (Cumulative inflation rate) and Log (Project Cost Change (Absolute)) 

scatter diagram ............................................................................................................... 190 

 



1 

1 Justification of dissertation research 

1.1 Introduction to project-related claims and disputes 

Civil engineering and construction contracts almost always generate conflict. If one party 

to a contract believes that it is in a position to exercise its contractual rights, a written notice 

of its intent should be given to make a claim. A claim becomes a dispute when the claim is 

rejected by the other, and such rejection is not accepted by the claim-issuing party (Brunet 

& César, 2021). In the context of construction project management, claims are amounts 

above agreed contract prices that a contractor seeks to collect from its clients or others for 

client-caused delays, errors in specifications and designs, contract terminations, change 

orders that are either in dispute or are unapproved as to both scope and price, or other 

causes of unanticipated additional contract costs or delays (Wallace, 2008). Research has 

shown a high rate of contractual claims in construction projects, negatively impacting the 

construction industry’s progress (Asadi, Rotimi, & Wilkinson, 2022).  

At a high level, claims can be categorized into performance-related, payment-related, and 

delay-related claims (Brunet & César, 2021). Without pretension of being exhaustive, 

researchers have identified various causes of claims:  

• Acceleration, restricted access, weather/cold, and scope increase (Semple, 

Hartman, & Jergeas, 1994);  

• High uncertainty, up-front allocation of contractual responsibility, and opportunistic 

behavior (Mitropoulos & Howell, 2001);  

• Payment of completed work, owner interference, slow decision-making, unrealistic 

contract, site management, improper planning, inadequate contractor experience, 

mistakes during construction, improper construction methods, delays caused by 

subcontractors, contract management, preparation and approval of drawings, 

quality assurance/control, long waiting time for approval of tests and inspections, 



2 

material quality and shortage, labor supply, labor productivity, equipment 

availability and failure, change orders, mistakes and discrepancies in contract 

document, major disputes and negotiation during construction, inappropriate 

organizational structure, lack of communication, weather conditions, regulatory 

change, problems with neighbors, and site conditions (Odeh & Battaineh, 2002);  

• Poorly defined scope, establishment of unrealistic schedules, design changes, and 

coordination problems as contributors to reworks (Love, Edwards, Irani, & Goh, 

2011); and,  

• Collaborative conflict, risk, uncertainty, opportunistic behavior, affective conflict, 

ambiguity, efficiency, inconsistency, and defectiveness (Cheung & Pang, 2013). 

The relationship between claims and time delays was also investigated. Based on 780 

projects conducted by the Colorado Department of Transportation between 1997 and 2012, 

it was reported that delays had a significant impact on claims occurrence, and the 

association between cost overruns and time delays was confirmed by correlation analyses 

(Hashem M. Mehany & Grigg, 2015).  

During the bidding process, typically, a project owner receives several offers, and bidders 

perceive that they are in a weakened position in terms of bargaining power. However, once 

the contract is signed, the dynamics are reversed as the contractor is in a position to exploit 

the lock-in situation. Opportunism is often well-thought-out and planned to acquire 

maximum value from the exchange (Brunet & César, 2021). Zack, in his 1993 article, 

unveils opportunistic maneuvers used by contractors and project owners; for example, 

eleven claimsmanship tactics employed by contractors are:  

• Preparing their offer based on a low-cost material and renegotiating if their client 

makes an adjustment request;  

• Willingly decreasing their margin to win a contract, hoping to regain their margin 

from change orders;  



3 

• Negotiating all financial claims at the very end of a contract without attempting to 

determine causality;  

• Contending that they own the float on the project’s critical path and allocating all 

delays and cost overruns to their client;  

• Asserting that they would have finished earlier if their client had not forced them to 

revise their schedule, claiming the difference;  

• Claiming the costs of acceleration for the entire acceleration period, even if only 

part of the time allocated had been utilized;  

• Claiming for loss of efficiency or productivity suffered due to their customer’s 

actions;  

• Claiming the difference between their actual cost and initial cost estimate;  

• Claiming that changes required by their customer are so extensive that the work 

no longer corresponds to the initial contract terms. 

• Shifting the responsibility for delays or faults to the subcontractors that their 

customer forced them to use; and,  

• Waiting for a change order at the end of a project in a last-ditch attempt to recoup 

losses (Zack, 1993). 

In response to such an opportunistic mode of operations, ways to promote cooperation 

between disputing parties have been studied too. For example, the proponents of the 

proactive approach to legal and management issues consider the law as an enabling 

instrument to foster sustainable and ethical relationships rather than as a means to protect 

one’s interests against others’ harmful behaviors (Berger-Walliser, 2012). In construction 

management, parties have tried to neutralize opportunistic behaviors by adopting 

partnering as a cooperation scheme (Brunet & César, 2021). With respect to the 

effectiveness of partnering, there was no statistically significant difference in mean values 

of project claims costs between agencies that formally partnered most of their major 

projects and agencies that no longer employed formal partnering; however, it was argued 



4 

that the salient principles of partnering, such as increased collaboration, communication, 

trust-building, and dispute escalation were fully institutionalized by the agencies that no 

longer employed formal partnering (Pinto Nunez, López Del Puerto, & Gransberg, 2018).  

The relationship between claims and project delivery methods also garnered researchers' 

interest. Based on actual construction documents from 137 U.S. southeastern public 

schools, Carpenter and Bausman analyzed different project delivery methods against cost, 

time, quality, and claims performance. The authors reported that the performance of the 

Design-Bid-Build deliver method was superior across cost metrics. In contrast, the 

Construction Manager at Risk delivery method produced higher levels of product and 

service quality (N. Carpenter & Bausman, 2016). Meanwhile, based on survey data 

collected from several Departments of Transportation, using descriptive and inferential 

statistics, it was reported that project delivery methods had no significant impact on the 

claim and dispute performance (Mehany, Bashettiyavar, Esmaeili, & Gad, 2018).  

Meanwhile, standardized contracts were examined to mitigate adversarial relationships 

that claims and disputes can cause. The American Institute of Architects’ standard 

conditions were analyzed to highlight what contract conditions were to be conducive to the 

speedy administration of claims and what contract conditions were to be conducive to 

tedious disputes (Barakat, Abdul-Malak, & Khoury, 2018). Various claim and dispute 

administration and resolution provisions per standard conditions were compared to 

illustrate the operational variations to guide project owners (Barakat, Abdul-Malak, & 

Khoury, 2019). However, projects are complex, and virtually all contracts have dispute-

related clauses; it is practically impossible to anticipate every eventuality, thus making 

project contracts incomplete (Brunet & César, 2021). 

Claims were also studied in different geographical contexts. By conducting a 

comprehensive literature survey and interviews with nine experts, Jalal et al. (2019) 

identified the most common claims in Iranian construction projects and their root causes 

through Ishikawa Diagram. It was reported that the four major sources of claims were 



5 

delays, changes, defects in documents, and discord within parties (Jalal, Noorzai, & 

Roushan, 2019). The FIDIC standard conditions were analyzed in the Egyptian civil law 

context to identify gaps and requirements for successfully applying the FIDIC’s clauses to 

help minimize disputes resulting from delays, claims for extensions of time, or additional 

payment (Fawzy, El-adaway, Perreau-Saussine, Abdel Wahab, & Hamed, 2018). By 

analyzing 573 claims and dispute cases from 77 highway construction contracts in India, 

Parikh et al. (2019) classified and grouped the claim causes responsible for claims and 

disputes between contractors and clients (Parikh, Joshi, & Patel, 2019).  

Parties to a contract can consume significant resources in dealing with claims and disputes. 

Therefore, it is critical for project owners, contractors, or project stakeholders to identify 

potential issues earlier to avoid them as much as possible. Therefore, this work asks 

specific research questions to explore evidence-based approaches to reduce project-

related disputes before commencing construction projects.  

1.2 Research questions  

Specifically, two research questions are asked.  

1.2.1 Are there any meaningful differences in construction cost changes between 

projects that received dispersed estimates and those that received more 

clustered estimates at the bidding stage? 

Under the typical lowest-price sealed-bid project award auction environment, unsuccessful 

bids are typically discarded once bids are open. Interestingly, however, bidders usually 

consume significant resources to prepare their bids. Thus, unsuccessful bids are likely to 

convey essential information that the owner organization can take advantage of, given that 

the winning bidder is expected to be the one who underestimates the final cost more than 

his competitors (Chang et al. 2014). Such information could indicate uncertainties the 

bidders have noted, but the owner has glossed over (Wright & Williams, 2001). Further, 

there appeared to be some relationship between the bidding ratios (second-lowest bid 



6 

ratio, mean bid ratio, and maximum bid ratio) and the completed cost of construction 

projects in Texas (Williams, 2005). It was found that the value of the ratios (second-lowest 

bid ratio, mean bid ratio, maximum bid ratio, and the coefficient of variation) tended to be 

more significant for the projects in which the completed cost significantly deviated from the 

original bid (Williams, Lakshminarayanan, & Sackrowitz, 2005; Williams, 2007). Thus, it is 

asked whether there are any meaningful differences in project cost changes between 

projects that received dispersed estimates and projects that received more clustered 

estimates at the bidding stage.  

1.2.2 What are the most frequently referred quasi-legal reasons for bid dispute-

related decisions? 

Claims and disputes occur between contracted parties, and contracts are made with those 

with the resources to spend, e.g., U.S. Federal Government. Firms interested in doing 

business with the federal government may have reasons to believe they have been denied 

fair opportunities to compete for federal government contracts. The U.S. Government 

Accountability Office (GAO hereafter) is where dissatisfied parties can challenge the terms 

of a solicitation or the award of a federal government contract (GAO, n.d.-a). Disappointed 

bidders can object to the conduct of federal agencies in acquiring supplies and services 

via the means of bid protests (D. H. Carpenter & Schwartz, 2018). Further, small 

businesses, which might feel more-significant financial burdens to file bid protests through 

legal counsels, need to be helped early enough to see if they have valid cases (Arena et 

al., 2018). It is argued, therefore, that identifying and spreading the quasi-legal bases that 

the GAO takes in its denials of bid protest decisions is crucial to support the operations of 

less resourceful contracting firms in the business of federal government acquisition. 

Although the GAO’s decisions are not legal documents like a court’s verdict, those 

decisions display certain legal instrument-like traits. For instance, the GAO’s decisions set 

precedents and influence future decisions. Thus, this study refers to the GAO’s decisions 

as quasi-legal.  



7 

1.3 Justification of proposed research questions 

Identifying worrisome contractors, locating projects plagued with uncertainties, calling out 

industry-wide errors, and supporting less-resourceful small businesses are critical to the 

health of the Architecture, Engineering, and Construction (AEC) industries. In addition, it is 

essential for owners to reduce claim- and dispute-related spending, for contractors to check 

their offers before entering agreements, for the related industries to learn from their failures, 

and for governments to improve procurement processes.   

The anticipated contributions to the body of knowledge from this work include the following:  

• Validating the usefulness of collective intelligence provided by bidders at the time 

of bidding opening in identifying projects likely to experience more significant 

project cost changes upon completion; and, 

• Quantifying the representative quasi-legal reasons for bid dispute-related 

decisions by the GAO.  

Evaluating bids mainly involves two methods: 1) Prioritizing the lowest-priced bid and 2) 

prioritizing the best value determined by set criteria (Chen, Zhang, Rodríguez, Pedrycz, & 

Martínez, 2021). In this study, both methods are relevant. The first research question is 

answered by analyzing a quantitative dataset that prioritizes the lowest-priced bid. In 

contrast, the second research question is answered by studying a qualitative dataset that 

generally prioritizes the best value.  

It can be argued that people or organizations learn more from defeats than victories. Most 

industrial standards such as ISO 10006 Quality management - Guidelines for quality 

management in projects, Capability Maturity Model Integration program, and PRINCE2 

method recommend performance reviews to learn from past experiences (Brunet & César, 

2021). Players in the AEC industries could learn from their failures, such as failed bids and 

denied protests. The findings could be applied beyond the AEC industries as projects occur 

in all industries and industry sectors, including information technology, engineering and 



8 

manufacturing, consulting, professional services, international development and 

cooperation, etc.  



9 

2 Usefulness of coefficient of variation of bids in 

identifying projects likely to experience more significant 

project cost changes upon completion 

2.1 Chapter introduction 

With limited information available, bidders are forced to make decisions based on a small 

sample of comparable cases and, therefore, are likely to make estimation errors, leading 

them to underestimate project risk (Brunet & César, 2021). Still, any responsive and 

responsible bidder would put together a most reasonable bid in their judgment. They would 

consume significant resources for calculations, correspondence, conversations, document 

studies, meetings, deskwork, and travel (Laryea & Hughes, 2011). However, unsuccessful 

bids are typically discarded under the typical lowest-price sealed-bid project award auction 

environment. Also, the winning bidder is likely to be the one who underestimates the final 

cost more than his competitors (Chang, Chen, & Salmon, 2014). Therefore, such 

unsuccessful bids could convey important information about the proposed project the 

owner organization can take advantage of; such information could indicate uncertainties 

that the bidders have noted but that the owner has glossed over. For example, it is possible 

that the scope of work or field conditions may not be clearly stated in bid documents; 

availability of labor, equipment, or material in neighboring areas may be uncertain; or there 

may be potential issues in terms of right-of-way, site access, or third-party involvement 

(railroads, utilities. etc.) (Wright & Williams, 2001). Thus, the following research question 

is asked: Are there any meaningful differences in terms of construction cost changes 

between projects that received dispersed estimates and those that received more clustered 

estimates at the bidding stage? 

This research question merits an investigation as it is critical for project owners, 

contractors, or any project stakeholders to locate projects with uncertainties earlier, taking 

advantage of collective intelligence delivered by bidders at the time of bidding. Then, if 



10 

necessary, owners could cancel projects and reinvite bidders or increase contingency; that 

is, project stakeholders could take corrective actions earlier. Therefore, the outcome of this 

study could be helpful for owner organizations and contracting organizations to identify 

projects with perceived uncertainties—projects that require a second look into their 

scope—using bidding data.  

2.2 Literature review 

Prior research has looked into project bidding data. For example, Skitmore et al. (2001) 

investigated bid-spread, the difference between the lowest and second-lowest bids, money 

left on the table, or foregone profit, in a “lowest wins” auction (Skitmore, Drew, & Ngai, 

2001). Kuprenas (2005) studied the relationship between construction cost performance 

and bid characteristics. The construction cost performance was measured through change 

orders (as a percentage of bid value). The bid characteristics were measured through the 

percentage of the bid budget, the number of bidders, the bid date, the date since the last 

bid, the bid cost per square foot, and the bid amount. After a series of regression analyses 

of twenty-four public sector projects from a multi-billion dollar school construction program 

in Los Angeles, California, it was found that the percentage of bid budget, the number of 

bidders, bid date, date since the last bid, and bid amount were correlated with change order 

amounts (Kuprenas, 2005). Also, Minchin et al. (2013) used contractor bid unit prices to 

estimate the impact of night construction on cost and productivity for transportation 

projects. Assuming that a contractor’s bid amount reflected expected relative project 

productivity, with a few exceptions, granting contractors maximum flexibility in deciding 

what construction activities to be done at night would result in substantial cost savings to 

transportation agencies (Minchin, Thurn, Ellis, & Lewis, 2013).  

Williams and colleagues extensively examined bidding data to predict final construction 

costs. The New Jersey Department of Transportation data for 298 highway construction 

projects showed that median bid and normalized median absolute deviation were the best 

predictors of completed construction cost (Wright & Williams, 2001). The variability of the 



11 

submitted bids did not seem to indicate the completed construction cost (Williams, 2002). 

However, there appeared to be some relationship between the bidding ratios (second-

lowest bid ratio, mean bid ratio, and maximum bid ratio) and the Texas Department of 

Transportation highway construction projects' completed cost. The bidding ratios appeared 

to increase when uncertainty about the completed project cost increased. Higher values of 

the bidding ratios were observed both for the projects that were constructed at a much 

greater cost than the original low bid amount and for the projects that were constructed for 

amounts significantly less than the original low bid amount (Williams, 2005). Significantly, 

it was found that the value of the ratios (second-lowest bid ratio, mean bid ratio, maximum 

bid ratio, and the coefficient of variation) tended to be more significant for projects where 

the completed cost deviated significantly from the original low bid (Williams et al., 2005; 

Williams, 2007).  

The association between the number of bidders and competition has also been examined. 

Bid price competition was measured through pre-bid project estimates, actual bid prices, 

and the number of bidders. The impact of reduced competition on project bid prices was 

studied by quantitatively analyzing public project bids under a condition of free, open, and 

unfettered competition. After examining a single building type, designed by a single firm, 

with pre-bid estimates prepared over a limited time period, it was found that reducing the 

number of bidders would result in increased project bid prices (Carr, 2005). Meanwhile, Li 

et al. (2008) reported that owners could offset the effect of a reduced number of bidders in 

part by timing their projects to periods of construction slowdown or by bundling their 

projects together into a single larger project after analyzing building projects in the state of 

Utah (Li, Foulger, & Philips, 2008).  

Other researchers have studied bidding data with various interests. For example, Shrestha 

et al. (2010) analyzed 435 bids on 113 public street projects and showed a strong 

correlation between the lowest bid price and the final construction cost but no correlation 

between the lowest bid price and the construction cost growth; also, the indication was that 



12 

the higher the number of bidders there were, the lower the bid price became (Pramen 

Prasad Shrestha & Pradhananga, 2010). Baek et al. (2018) used historical bid data of 

highway projects in the State of Georgia between 2005 and 2015 and reported that that 

total contract price, number of pay item, duration, the annual total value of projects, 

architecture billings index, annual total number of projects, asphalt cement price index, and 

number of bids were significant factors that contributed to the degree of 

competition/number of bidders (Baek & Ashuri, 2018) and that the following variables had 

statistically significant relations with submitted unit price bids: the quantity of the bid item, 

the number of nearby asphalt plants, total contract price, Georgia asphalt cement price 

index, producer price index for construction machinery manufacturing, GDP, crude oil 

prices, ratio of bid item, pavement length, population, hauling distance between quarry and 

asphalt plants, number of bidders, total monthly asphalt size of resurfacing and widening 

projects awarded in the same month at the level of the county, and number of hires (Baek 

& Ashuri, 2019).  

Several previous pieces of research, especially Williams’ work, have investigated bidding 

data to predict final project costs. In contrast, this chapter aims not to predict final project 

costs from bidding data but to validate the usefulness of collective intelligence provided by 

bidders at the time of bid opening in identifying projects with more perceived uncertainties 

in their scope.  

2.3 Research method 

2.3.1 Theoretical framework 

Collective Intelligence is a framework for the investigation of collectives. Collectives are 

“systems where each agent aims to optimize its own performance, but there is a well-

defined set of system-level performance criteria.” There is no centralized control, and 

underlying systems, agents, or neurons modify their behavior to maximize rewards. It is a 

behaviorist approach; the framework is broad enough to encompass the real-world 



13 

collectives that are often too complex to represent in a tractable model (Tumer & Wolpert, 

2004; Wolpert, Tumer, & Frank, 1999; Wolpert, 2004). The Collective Intelligence 

framework was deemed suitable for this study as: 

• In bidding for winning a project, there is no centralized control, i.e., bidders are free 

to submit any bids at their discretion;  

• bidders can be seen as agents/underlying systems/neurons trying to maximize the 

reward, i.e., winning a contract;  

• there are system-level performance criteria, i.e., the lowest price; and,  

• collective efforts by bidders foster good global performance, i.e., economical 

project delivery.  

2.3.2 Coefficient of variation 

It is aimed to explore the possibility of using the dispersion of bids to determine the 

meaningful difference in project cost changes between projects that received dispersed 

estimates and those that received more clustered estimates at the bidding. Thus, adopting 

the coefficient of variation (C.V.) was deemed justified, as explained below.  

According to the Encyclopedia of Research Design, the coefficient of variation (C.V.), a 

primarily descriptive statistic, eliminates the unit of measurement from the standard 

deviation of a series of numbers by dividing it by the mean of the series of numbers 

(Salkind, 2010). The C.V. helps compare the relative variability of positive random variable 

distributions and has been widely used in many scientific areas. For example, the C.V. is 

appropriate when the objective is to compare the risk of alternative investments, and a 

lower C.V. ratio represents a lower risk (Curto & Pinto, 2009). The C.V. has been widely 

used to measure relative risk in engineering, medicine, agricultural economics, 

archaeology, and financial management (Weber, Shafir, & Blais, 2004). In finance, the C.V. 

evaluated project risks in an uncertain situation (Brief & Owen, 1969). The C.V. was also 

used as an indicator of risk in bank lending environments to show that a bank with a 



14 

relatively high C.V. for the environmental portfolio was likely to fail at a higher rate (Gunther 

& Robinson, 1999). The C.V. was also used to investigate risk, return, and portfolio 

diversification in major painting markets from 1976–2001 (Worthington & Higgs, 2004). 

Meanwhile, in sociology, the C.V. was applied to compare different nations’ geographic 

mobility and homicide rates by regions in the U.S. (Martin & Gray, 1971). In addition, the 

C.V. has been applied as an index of diversity (Bedeian & Mossholder, 2000).  

More relevant to this study, the C.V. was used to measure the spread of the submitted bids 

(Williams et al., 2005). The C.V. is an indicator of bidders' uncertainty about the value of a 

project. It can be postulated that a project with a high CV indicates considerable uncertainty 

among bidders about the project's cost. In contrast, a project with a low C.V. shows little 

disagreement among bidders (Wright & Williams, 2001).  

2.3.3 Data collection 

In general, private contractors’ project cost data are considered trade secrets and, thus, 

usually difficult to access; therefore, project cost data in the public domain were used in 

this endeavor. First, from the Ohio Department of Transportation, bid estimates (Ohio 

Department of Transportation, n.d.-a) and project final cost data (Ohio Department of 

Transportation, n.d.-b) were obtained; then, two datasets were matched via uniquely 

assigned project identification numbers. It was decided to investigate projects that cost the 

agency more than $ 5 million, assuming that larger projects would have more uncertainties 

perceived by bidders. Nine hundred ten (910) bids on 222 case projects completed 

between 2008 and 2018 were identified for further investigation.  

The following algebraic formula was adopted to quantify the Project Cost Change (see 

Equation (1)). An absolute value is used because:  

• An increase in the cost of a project would be considered a risk to an owner 

organization (Wright & Williams, 2001); and, 



15 

• A decrease in the cost of a project below the award bid would be viewed as a risk 

to a contractor for reduced revenue and the suboptimal use of public funds.  

Thus, in this research, a change in project cost was deemed unfavorable regardless of its 

direction.   

𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 𝐶𝐶𝑃𝑃𝐶𝐶𝑃𝑃 𝐶𝐶ℎ𝑎𝑎𝑎𝑎𝑎𝑎𝑃𝑃 = �
(𝐹𝐹𝐹𝐹𝑎𝑎𝑎𝑎𝐹𝐹 𝐶𝐶𝑃𝑃𝑎𝑎𝐶𝐶𝑃𝑃𝑃𝑃𝐶𝐶𝑃𝑃𝑃𝑃𝐹𝐹𝑃𝑃𝑎𝑎 𝐶𝐶𝑃𝑃𝐶𝐶𝑃𝑃 –  𝑊𝑊𝐹𝐹𝑎𝑎𝑎𝑎𝐹𝐹𝑎𝑎𝑎𝑎 𝐵𝐵𝐹𝐹𝐵𝐵)

𝑊𝑊𝐹𝐹𝑎𝑎𝑎𝑎𝐹𝐹𝑎𝑎𝑎𝑎 𝐵𝐵𝐹𝐹𝐵𝐵
� Equation (1) 

Then, each project’s C.V. of bids was calculated. Twelve (12) sole-bid projects were 

dropped from the dataset due to the lack of the C.V. value. The remaining 210 case projects 

were further processed. Please see Appendix A.  

The characteristics of 210 case projects are summarized in Table 1. 

Table 1 Characteristics of case projects 

Description Unit Value Remark 

Number of Case Projects  ea. 210  

Mean, Award Bid $ 20,916,801.39 S.D. 31,430,304.83 

Mean, Project Final Cost $ 21,324,687.90 S.D. 31,831,462.92 

Mean, Number of Bidders ea. 4.28 S.D. 2.13 

Mean, CV of Bids  % 7.42 S.D. 4.76 

Mean, Project Cost Change % 5.36 S.D. 5.99 

 



16 

2.3.4 Data analysis 

Collected data were analyzed using Welch’s t-test, and correlation and regression analysis.   

2.3.4.1 Welch’s t-test 

The unequal variance t-test, or Welch’s t-test (Welch, 1947), is a modified version of the 

Student’s t-test to see whether two sample means are significantly different; the null 

hypothesis for the test is that the means are equal. In other fields, the unequal variance t-

test has been widely used to statistically test whether the central tendencies, e.g., mean 

values, of two groups are different from each other based on samples of the two groups 

(Delacre, Lakens, & Leys, 2017; Ruxton, 2006; Standaert, 2019). However, the t-test does 

not specify the direction, i.e., the outcome of the test does not tell which mean value is 

greater than the other. In this study, the null hypothesis to be rejected was that there was 

zero difference between the means of project cost changes between the above-average 

C.V. projects and the below-average C.V. projects. The significance level, alpha, or the 

probability of rejecting the null hypothesis when true (Minitab, 2015), was set to 0.05 (5%).  

2.3.4.2 Correlation and regression analysis 

Regression analysis is a statistical process for estimating the probabilistic relationship 

between variables. The relationship obtained from a regression analysis does not 

necessarily present any cause-and-effect relation between the variables. However, such a 

relationship may be used to predict the value of statistics of one variable based on the 

value of the other (Ang & Tang, 2006).  

Regression analysis allows for dealing with an arbitrarily chosen explanatory variable 

(Sykes, 1993). Regression analysis models take the form of 𝑦𝑦� = 𝑏𝑏0 + 𝑏𝑏1𝑥𝑥1 , where 𝑦𝑦� 

denotes estimated value of the dependent variable, 𝑏𝑏0 denotes the estimated intercept, 𝑏𝑏𝑖𝑖 

denote estimate slope coefficients, and 𝑥𝑥𝑖𝑖 denote independent variables.  



17 

For this research question, the tentative hypothesis is that more significant project cost 

changes are determined by higher C.V. of bids and by an aggregation of omitted factors 

that is termed “noise” (Sykes, 1993). A linear regression identifies a linear relationship 

between one or more predictors and an outcome; that is, a regression shows the 

relatedness of predictor(s) with the outcome (Howard, n.d.).  

The strength of linearity underlying a regression analysis is measured by the corresponding 

correlation coefficient (Ang & Tang, 2006). The correlation coefficient is shown as 0 if not 

correlated and 1 if matched. A correlation coefficient between 0.1 and 0.3 is considered a 

weak correlation; a value between 0.3 and 0.7 is a median correlation, and a value of 0.7 

or higher is a strong correlation (Rumsey, 2011).  

Correlation analysis has been utilized by researchers in the field of construction 

engineering and management. For instance, based on the Engineering News-Record 

(ENR) data and financial reports, it was reported that the international business 

diversification levels showed a clear positive correlation with sales revenue (Sung, Lee, Yi, 

& Son, 2017). Correlation analysis was performed on field survey data to determine the 

relationship between motivational factors and construction crew performance (Raoufi & 

Fayek, 2018). From 120 educational facilities projects in the United Kingdom, the 

correlation between the initial tender price amount, overrun cost, and delay was examined. 

As a result, a positive correlation between initial tender price amounts and cost overruns 

was interpreted as larger projects incurring more considerable overruns (Eke, Elgy, & 

Wedawatta, 2019). Correlation analysis was used to identify key project complexity factors 

of complex construction projects. It was suggested that project complexity is significantly 

negatively correlated with project success in complex Chinese construction projects (Luo, 

He, Xie, Yang, & Wu, 2017). A significant correlation was reported between change orders 

with cost growth and schedule growth for new highway projects in Texas (Pramen P. 

Shrestha & Maharjan, 2018). Correlation analysis revealed significant relations between 

risk groups and disputes in subcontractor contracts (Artan Ilter & Bakioglu, 2018). 



18 

Correlation analysis also showed a negative impact of project-level change order 

percentages on construction intensity (Pramen P. Shrestha, Shrestha, & Kandie, 2017). 

Strong inverse correlations between aggregate dollar amounts of claims and one or more 

liquidity-measuring financial ratios in the majority of case firms were reported (Y. J. Kim & 

Skibniewski, 2020).  

The relationship developed from regression and correlation analyses simply establishes 

the statistical relationship on the basis of the observed data (Ang & Tang, 2006). 

2.4 Results and discussion 

2.4.1 Welch’s t-test 

The case projects were sorted from the largest to the smallest according to the value of 

their C.V. Then, they were divided into two groups: The projects with above-average C.V. 

and those with below-average C.V. to compare the means of Project Cost Change in each 

group. The hypothesis was that there would be statistically significant differences between 

the mean value of Project Cost Changes of the projects that received dispersed estimates 

and the mean value of Project Cost Changes of the projects that received more clustered 

estimates at the bidding stage. 

The average value of C.V. of the entire dataset was 7.42%. This value is a specific value 

tied to this particular dataset. The average value of C.V. will change as the pool of projects 

changes.  

An independent samples t-test was performed to compare, assuming unequal variances, 

the means of Project Cost Change values of the above-average C.V. projects and the 

below-average C.V. projects. The null hypothesis to be rejected was that the difference 

between the means was zero. It was found that the p-value was less than 0.05.; therefore, 

the null hypothesis was rejected. There was a statistically significant difference in Project 



19 

Cost Change between the projects with above-average C.V. and those with below-average 

C.V. Please see Table 2.   

Table 2 Analysis Results Summary 

Description Unit Value 

Number of Case Projects ea. 210 

Mean, Number of Bidders (S.D.) ea. 4.28 (2.13) 

Mean, CV (S.D.) % 7.42 (4.76) 

Mean, Project Cost Change (S.D.) % 5.36 (5.99) 

Mean, Project Cost Change, Above-Average CV 

(S.D.) [A] 

% 6.49 (6.73) 

Mean, Project Cost Change, Below-Average CV 

(S.D.) [B] 

% 4.24 (4.92) 

p-value between [A] and [B] - 0.01 

 

As a comparison, when adopting actual values, not absolute values, for Project Cost 

Change, it was found that the p-value was greater than 0.05 (0.51). Therefore, there was 

no statistically significant difference between the projects with above-average C.V. and 

those with below-average C.V. Please see Table 3 below.  

Table 3 t-Test: Two-Sample Assuming Unequal Variances 

  

Project Cost Change, Above-

Average CV (actual value) 

Project Cost Change, Below-

Average CV (actual value) 

Mean 0.027 0.019 



20 

Table 3 t-Test: Two-Sample Assuming Unequal Variances 

  

Project Cost Change, Above-

Average CV (actual value) 

Project Cost Change, Below-

Average CV (actual value) 

Variance 0.008 0.005 

Observations 81 129 

Hypothesized 

Mean Difference 
0  

df 134  

t Stat 0.661  

P(T<=t) one-tail 0.255  

t Critical one-tail 1.656  

P(T<=t) two-tail 0.510  

t Critical two-tail 1.978   

 

Further, another round of analysis was carried out, taking the number of bidders as a 

distinguishing factor for splitting the data with varying C.V. percentile. Please see Table 4.  

Table 4 Comparison of Means of Project Cost Changes against the Number of Bidders 
and CV Percentile 

Description 
2-Bid 

Projects 

3-Bid 

Projects 

4-Bid 

Projects 

5-Bid 

Projects 

6-Bid 

Projects 

7+Bid 

Projects 

50% 

Mean, Top 50 

% CV, % 
7.61 4.45 6.75 4.24 6.39 7.98 

Mean, Bottom 

50% CV, % 
3.66 6.20 3.91 3.93 3.03 5.03 

p-value 0.01 0.36 0.16 0.87 0.15 0.24 



21 

Table 4 Comparison of Means of Project Cost Changes against the Number of Bidders 
and CV Percentile 

Description 
2-Bid 

Projects 

3-Bid 

Projects 

4-Bid 

Projects 

5-Bid 

Projects 

6-Bid 

Projects 

7+Bid 

Projects 

40% 

Mean, Top 40 

% CV, % 
7.64 4.62 6.52 4.26 6.09 8.94 

Mean, Bottom 

40% CV, % 
3.28 5.47 3.24 4.24 3.13 4.50 

p-value 0.02 0.70 0.12 0.99 0.33 0.14 

30% 

Mean, Top 30 

% CV, % 
6.98 4.15 6.97 4.75 7.27 9.24 

Mean, Bottom 

30% CV, % 
3.36 5.63 3.20 5.27 2.94 2.50 

p-value 0.04 0.60 0.15 0.87 0.27 0.05 

20% 

Mean, Top 20 

% CV, % 
6.11 4.76 8.62 3.18 3.99 6.19 

Mean, Bottom 

20% CV, % 
3.61 5.61 2.97 6.34 4.00 2.19 

p-value 0.31 0.84 0.15 0.35 0.99 0.01 

10% 

Mean, Top 10 

% CV, % 
7.59 8.04 12.67 2.13 2.80 7.30 

Mean, Bottom 

10% CV, % 
4.22 8.54 3.97 8.58 2.95 1.17 

p-value 0.44 0.95 0.26 0.55 0.96 0.02 

 

Generally, as the sample size decreases, the p-value increases (Thiese, Ronna, & Ott, 

2016). When broken down by the number of bidders, the difference between means of 

Project Cost Changes between the higher C.V. projects and the lower C.V. projects lost 



22 

statistical significance; several comparisons did not reveal any statistically significant 

differences. However, an interesting observation was made: For the projects with seven or 

more bidders, as the difference between average C.V. values became larger between 

comparing groups, the p-value decreased from not being statistically significant to being 

statistically significant. On the other hand, for the projects with only two bidders, as the 

difference between average C.V. values became larger between comparing groups, the p-

value increased from being statistically significant to not statistically significant.  

Indeed, Figure 1 below generally depicts that the projects that received a large number of 

bids experienced less substantial Project Cost Changes; on the other hand, the projects 

that experienced more significant Project Cost Changes generally received fewer bids. 

With caution, therefore, it could be posited that “the wisdom of crowds” is more trustworthy 

to the dependability of a democratic judgment than one might have anticipated (Galton, 

1907); as averaging cancels error, combining judgments can isolate the collective’s view 

of the truth (Mannes, Larrick, & Soll, 2012). 

 



23 

Figure 1 Parallel coordinate chart comparing the number of bidders and Project Cost 

Change 

 

2.4.2 Correlation and regression analysis 

A series of other correlation and regression analyses were examined in the following 

sections: Project Cost Change (dependent variable) and C.V, planned project duration, 

actual project duration, project duration change, number of bidders, average bid value, 

winning bid, final construction cost, and the cumulative rate of inflation (independent or 

control variables). See Appendix B for the outcomes of correlation and regression analyses.  

2.4.2.1 Correlation coefficients between variables  

First, it was tested to see whether the proposed independent variables were genuinely 

independent of each other.  

• First of all, it was revealed that the variable C.V. had no significant correlation with 

the other proposed independent variables. See Figure 2 and Table 5.   

 



24 

 

Figure 2 Multiple plots (C.V. and other variables) 

 

• On the other hand, the variable planned project duration showed a strong 

correlation with the variable actual project duration (correlation coefficient = 0.804) 

and the cumulative rate of inflation (0.740). Also, it showed a median correlation 

with the variable average bid value (0.651), winning bid (0.639), and final 

construction cost (0.644). See Figure 3 and Table 5.   

 



25 

 

Figure 3 Multiple plots (Planned project duration and other variables) 

 

• The variable actual project duration showed a strong correlation with the 

cumulative rate of inflation (0.878). Also, it showed a median correlation with the 

variable average bid value (0.625), winning bid (0.613), and final construction cost 

(0.616). See Figure 4 and Table 5.   



26 

 

Figure 4 Multiple plots (Actual project duration and other variables) 

 

• The variable project duration change showed an inverse median correlation with 

the variable planned project duration (-0.320). See Figure 5 and Table 5.   



27 

 

Figure 5 Multiple plots (Project duration change and other variables) 

 

• The variable number of bidders showed a weak correlation with the variable 

planned project duration (0.201), actual project duration (0.202), and the 

cumulative rate of inflation (0.195). See Figure 6 and Table 5.  



28 

 

Figure 6 Multiple plots (Number of bidders and other variables) 

 

• The variable average bid value strongly correlated with the winning bid (0.999) and 

final construction cost (0.997). Also, it showed a median correlation with the 

cumulative rate of inflation (0.562). See Figure 7 and Table 5. 



29 

 

Figure 7 Multiple plots (Average bid value and other variables) 

 

• The variable winning bid showed a strong correlation with the variable final 

construction cost (0.998). Also, it showed a median correlation with the cumulative 

rate of inflation (0.548). See Figure 8 and Table 5. 



30 

 

Figure 8 Multiple plots (Winning bid and other variables) 

 

• The variable final construction cost showed a median correlation with the 

cumulative rate of inflation (0.556). See Figure 9 and Table 5. 



31 

 

Figure 9 Multiple plots (Cumulative rate of inflation and other variables) 

 

As a result, the following strongly correlated variables—correlation coefficient 0.7 or 

higher—were to be dropped from the further analysis: Final construction cost, winning bid, 

actual project duration, and planned project duration. Nevertheless, for the sake of 

academic curiosity, all the proposed control variables were studied.  

Table 5 lists the correlation coefficients between and among the independent (control) and 

dependent variables. Table 6 shows single variable linear regression results, and Figure 

10 is the combined multiple plots depicting the comparison between the relationships of 



32 

Project Cost Change (Absolute) and Project Cost Change (Actual) with the proposed 

independent variables, respectively.  

Project Cost Change (Actual) Project Cost Change (Absolute) 

 

 

Figure 10 Multiple plots - Comparing Project Cost Change (Actual) and Project Cost 

Change (Absolute) 

 



33 

Project Cost Change (Actual) Project Cost Change (Absolute) 

 

Figure 10 Multiple plots - Comparing Project Cost Change (Actual) and Project Cost 

Change (Absolute) (cont’d) 

 

  



34 

Project Cost Change (Actual) Project Cost Change (Absolute) 

 

Figure 10 Multiple plots - Comparing Project Cost Change (Actual) and Project Cost 

Change (Absolute) (cont’d) 

 



35 

Table 5 Correlation coefficients between variables 

Independent variable 

(Non-retrospective variable in shade) 

Correlation coefficient with  

(a) (b) (c) (d) (e) (f) (g) (h) (i) (j) (k) 

C.V. (a) 1           

Planned project duration (in days) (b) 0.010 1          

Actual project duration (in days) (c) -0.022 0.804 1         

Project duration change (%) (d) -0.005 -0.320 0.190 1        

Number of bidders (e) 0.105 0.201 0.202 0.006 1       

Average bid value ($) (f) -0.070 0.651 0.625 -0.067 -0.067 1      

Winning bid ($) (g) -0.092 0.639 0.613 -0.066 -0.083 0.999 1     

Final construction cost ($) (h) -0.094 0.644 0.616 -0.067 -0.079 0.997 0.998 1    

Cumulative rate of interest (%) (i) 0.030 0.740 0.878 0.104 0.195 0.562 0.548 0.556 1   

Project Cost Change (Actual) (j) 0.054 0.110 0.186 0.085 0.149 -0.023 -0.025 0.012 0.181 1  

Project Cost Change (Absolute) (k) 0.174 -0.062 0.054 0.143 0.031 -0.064 -0.070 -0.062 0.049 0.433 1 

 



36 

Table 6 Regression and single variable linear regression results 

 Project Cost Change (Actual) Project Cost Change (Absolute) 

Proposed Independent Variable 

(Non-retrospective variable in shade) 
R2 Adj. 

R2 
Std. 
Error Obs. 

Sig. 
F 

Corr. 
Coeff. R2 Adj. R2 Std. 

Error Obs. 
Sig. 
F 

Corr. 
Coeff. 

C.V. 0.003 -0.002 0.077 210 0.433 0.054 0.030 0.026 0.059 210 0.011 0.174 

Planned Project Duration (day) 0.012 0.007 0.077 210 0.112 0.110 0.004 -0.001 0.060 210 0.375 -0.062 

Actual Project Duration (day) 0.035 0.030 0.076 210 0.007 0.186 0.003 -0.002 0.060 210 0.440 0.054 

Project Duration Change 0.007 0.002 0.077 210 0.222 0.085 0.020 0.016 0.059 210 0.039 0.143 

Number of Bidders 0.022 0.018 0.077 210 0.030 0.149 0.001 -0.004 0.060 210 0.653 0.031 

Average Bid Value ($) 0.001 -0.004 0.077 210 0.741 -0.023 0.004 -0.001 0.060 210 0.359 -0.064 

Winning Bid ($) 0.001 -0.004 0.077 210 0.722 -0.025 0.005 0.000 0.060 210 0.316 -0.070 

Final Construction Cost ($) 0.000 -0.005 0.077 210 0.857 0.012 0.004 -0.001 0.060 210 0.375 -0.062 

Cumulative Rate of Inflation 0.033 0.028 0.076 210 0.009 0.181 0.002 -0.002 0.060 210 0.480 0.049 

 0 

  1 



37 

Six of the nine proposed control variables examined stood out and are discussed 

hereunder.  

• First, the variable actual project duration was weakly correlated with Project Cost 

Change (Actual); the correlation coefficient was 0.186. This control variable, 

however, is retrospective in nature. The value is unknown at the time of bid opening.  

• Second, the variable project duration change was weakly correlated with Project 

Cost Change (Absolute); the correlation coefficient was 0.143. This control variable, 

however, is retrospective in nature. The value is unknown at the time of bid opening. 

• Third, the cumulative rate of inflation was weakly correlated with Project Cost 

Change (Actual); the correlation coefficient was 0.181. This control variable, 

however, is retrospective in nature. The value is unknown at the time of bid opening. 

• Fourth, the variable number of bidders was weakly correlated with Project Cost 

Change (Actual); the correlation coefficient was 0.149.  

 

This relationship between the Project Cost Change (Actual) and the number of 

bidders needs to be explained (Figure 10). Reducing the number of bidders would 

result in increased project bid prices (Carr, 2005). But, then, the competition 

created increased interest shown by the higher number of bidders might have led 

to the implementation of some of the aforementioned claimsmanship tactics. For 

instance, contractors might willingly decrease their margin to win a contract, hoping 

to regain their margin from change orders (Zack, 1993). It is not the intention to 

“blame” contractors for opportunistic tactics. Instead, this explanation simply 

presents a reasonably realistic scenario based on prior research. 

 

To this end, the game theory might provide an interesting framework. Game theory 

is defined as “the study of mathematical models of conflict and cooperation 

between intelligent rational decision-makers.(Myerson, 1997)” Construction 

project management researchers have embraced game theory to explain 



38 

contractors' bidding behavior. For instance, without pretending to be exhaustive, 

Ahmed et al. (2016) analyzed the effects of the winner’s curse in construction 

bidding by identifying the degree of the winner’s curse in single-stage bidding and 

multistage bidding to compare bidding environments and to determine how 

learning from past bidding decisions and experiences could mitigate suffering from 

the winner’s curse (Ahmed, El-adaway, Coatney, & Eid, 2016). However, game 

theory performs best in an open auction, where multiple competitors continually 

bid until they reach a value that encourages no one to bid lower. It limits the 

applicability of the game theory in construction bidding, which is generally closed, 

and no bidder knows its competitors’ prices until the bid opening (Abotaleb & El-

adaway, 2017). On the other hand, Signor et al. (2020) sought to explain the bid-

rigging in public infrastructure procurement with game theory by determining the 

confessions of collusion are consistent with what has transpired from an economic 

viewpoint (Signor, Love, & Ika, 2020). Although the game theory approaches have 

been increasingly attracting project management researchers, a recent state-of-

the-art review article identified the following research gaps that can be further 

investigated from the game theory perspective: Project governance and 

cooperation, project financing, risk management, project bidding, and project 

scheduling (Narbaev, Hazır, & Agi, 2022).  

• Fifth, the variable planned project duration was weakly correlated with Project Cost 

Change (Actual). One possible explanation put forward for this phenomenon is 

optimism bias. It was reported that the contractors exhibited optimism bias-like 

time-dependent estimating behavior. When the contractors’ estimates were 

compared to the actual cost of the projects, there was a statistically significant 

difference in cost growth between the long-duration projects compared to the 

short-duration projects (Y. J. Kim & Skibniewski, Accepted). 

• Finally, the C.V. of bids was weakly correlated to Project Cost Change (Absolute). 

The R-square value of the C.V. of bids was 3%. It is opined that how impactful the 



39 

3% is is subject to debate. It is essential to highlight that the C.V. of bids can be 

obtained befor