- Email: [email protected]
Comparing contributors to time and cost performance in building projects
\ PERGAMON
Building and Environment 23 "0888# 20Ð31
Comparing contributors to time and cost performance in building projects Sunil M[ Dissanayaka \ Mohan M[ Kumaraswamy Dept[ of Civil + Structural Engineering\ The University of Hong Kong\ Pokfulam Road\ Hong Kong Received 3 June 0886^ accepted 07 September 0886
Abstract Identifying the relative strengths of the linkages between procurement sub!systems and project performance was initially hypo! thesized to be useful for developing guidelines for the formulation of a performance!oriented construction procurement system[ A comprehensive model was developed to incorporate all signi_cant procurement sub!systems variables\ together with {non!pro! curement| related "or {intervening|# variables to explore and evaluate such linkages[ The multiple regression technique was applied to analyze the data from 21 Hong Kong!based building projects and the results were compared with reality[ The results suggest that procurement sub!systems variables are less signi_cant than the non!procurement related variables in predicting time and cost performance levels on Hong Kong building projects[ Þ 0887 Elsevier Science Ltd[ All rights reserved[ Keywords] Procurement^ Performance^ Modelling^ Regression^ Hong Kong
0[ Introduction Along with the increasing complexities of construction projects\ for example related to their administration and _nancial management\ most clients now also encounter di.culties in selecting optimal procurement options for their projects[ The choice of a procurement system has been considered to be one of the most important decisions that a client will make during the project[ One of the principal reasons for the construction industry|s poor performance has been attributed to the inappropriateness of the chosen procurement systems ð0Ł[ The selection of a suitable procurement system is thus a necessary con! dition for project success^ but not a su.cient condition\ because other intervening variables such as project characteristics\ project team characteristics and their per! formance\ as well as external conditions\ are also associ! ated with overall project performance[ Rowlinson ð1Ł\ Naoum ð2\ 4Ł and Walker ð5\ 6Ł concluded that manage! ment\ organizational and contextual related variables are highly associated with overall project performance[ Rwel! amila and Hall ð7Ł argued that the management of the {human aspects| contributed more to {poor| project per! formance\ than the initial procurement related decisions[ In procurement related research\ it is important to Corresponding author[ Tel[] 99741 17481554^ fax] 99741 14484226^ e!mail] h8484930Ýhkusua[hku[hk
initially de_ne the precise scope of the envisaged pro! curement systems\ since a procurement system can be de_ned on the basis of various aspects of the construction process ð0\ 8\ 09Ł[ In the context of construction\ the scope of procurement has been based on the CIB W81 de_nition as {{the framework within which construction is brought about\ acquired or obtained|| ð00Ł[ A procurement system can then be considered to consist of a collection of sub! systems within such a framework[ In this research\ the following procurement sub!systems were chosen] "0# {{Work Packaging|| may be formulated so that the packages are large enough to attract international interest\ if needed for purposes of price competition or for deploying advanced technologies[ Alter! natively\ the large and:or complex work packages may be {sliced|\ to keep them within the capabilities of local construction organizations[ "1# {{Functional Groupings|| * of the design\ con! struction and management functions * may be based on either] "a# a {separated| approach of independent design and construction as in the traditional method\ whether within a sequential or {{fast!track|| structure^ "b# an {{integrated|| approach such as in {Design and Construct| or {Build Operate Transfer| "BOT#^ or * a {{Management!led|| approach such as in {{Man! agement Contracting||[ "2# {{Payment Modality|| could vary from {Cost Reim!
9086Ð9075:88:,*see front matter Þ 0887 Elsevier Science Ltd[ All rights reserved PII] S 9 2 5 9 Ð 0 2 1 2 " 8 6 # 9 9 9 5 7 Ð 0
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S[M[ Dissanayaka\ M[M[ Kumaraswamy:Buildin` and Environment 23 "0888# 20Ð31
bursement| to {Lump Sum Fixed Price|[ The timing and currency of payments could also vary[ "3# {{Selection Methodologies|| of consultants and con! tractors may be open\ or based on registered or pre! quali_ed short lists^ and di}erent weightings may be assigned to technical or price parameters[ "4# Standard sets of {{Conditions of Contracts|| "for building works\ Civil Engineering works\ or for {Design and Build|#\ may be chosen for convenience or because they have been tried and tested and now trusted by the industry[ Alternatively special sets of conditions may be designed for a given programme as in the New Airport Core programme in Hong Kong\ or for use by a particular large client or a consultant[ Using the hierarchy of possible procurement sub systems\ and options developed by Kumaraswamy and Dissanayaka ð01Ł "further expanded in Fig[ 0#\ di}erent procurement systems can be assembled by collecting options from each sub!system[ The {most appropriate| procurement system for a project\ should be selected to be compatible with the project scenario because there is no {best procurement route|[ The type of client and his priorities for the particular project generally lead to some routes being better than others ð02Ł[ Some conclusions of other researchers such as Row! linson ð1Ł\ Naoum and Musthapa ð4Ł\ Akintoye ð03Ł\ Dulaimi and Dalziel ð04Ł\ Hashim ð05Ł and Ho et al[ ð06Ł regarding the impact of procurement systems on various aspects of project performance "e[g[ time\ cost\ satis! faction\ claims and disputes# provide a platform for laun! ching an investigation into the relative strengths of the links between the procurement system and project out! comes[ However\ most procurement research has been focused on just one particular sub!system of the pro! curement system "such as the {functional grouping|# in any given research project[ Incorporating all signi_cant procurement sub!systems and intervening variables into one comprehensive model^ and then identifying the relative strengths of the linkages between procurement sub!systems\ non!procurement related variables and the project outcomes could provide valuable guidelines to improve project performance levels[ A research project was thus formulated to explore those linkages[ This paper] "a# outlines the methodology and principal methods used during this research and "b# brie~y summarizes the data analysis\ results and con! clusions[ The former identi_es some signi_cant non!pro! curement related variables\ while the latter points to future steps that would be useful in continuing this inves! tigation[ 1[ Objectives of this Research project The main objective of this research is to identify the relative strengths of the linkages between procurement
sub!systems\ any other relevant variables and project out! comes in Hong Kong based building projects[ The fol! lowing sub!objectives are set out to satisfy this main objective] "0# To identify and select particular {{procurement|| and {{non!procurement|| related "{{intervening||# variables that may be signi_cantly related to project perform! ance[ "1# To model the principal variables "{{procurement|| sub!systems and {{non!procurement|| related or {{intervening||# in~uencing project performance "in terms of meeting cost and time targets#\ and to ident! ify the most signi_cant variables and linkages which may govern project performance[ 2[ Methodology 2[0[ Identifying variables Identi_cation of the variables which may have some in~uence on project performance was based on the con! clusions of previous researchers Rowlinson ð1Ł\ Naoum ð2\ 3Ł\ Walker ð5Ł\ Hashim ð05Ł\ Ashley et al[ ð07Ł\ Hughes ð08Ł\ Liu ð19Ł and initial interviews with industry prac! titioners[ These were grouped into macro variables\ each of which then {covered| a large number of micro variables "as indicated in Table 0#[ All the selected micro variables may not be of the same importance in every project but may vary with the client|s objectives\ priorities\ project conditions\ constraints and complexities and the quality of the project team[ The initial variables and their sug! gested measurement criteria:scales are listed in Appendix 0[ 2[1[ The Research model A model was formulated as in Fig[ 1\ to explore the potential relationships between project performance and procurement sub!systems "e[g[ work packaging\ func! tional grouping and payment method# and other inter! vening variables "e[g[ project characteristics\ client characteristics\ and team performance#[ Identifying the relative strengths of association of procurement sub!sys! tems with project performance\ as compared to other intervening variables\ would provide useful guidelines for the formulation of a performance oriented procurement strategy for a project[ This will also identify the signi_cant variables which in~uence the project performance levels[ In this research\ the relationships of particular interest are the procurement sub!systems variables and the mea! sures of project performance[ The feedback link from the project performance to the procurement sub!systems variables indicates the potential implications of such _n! dings in improving the procurement selection process[ The model also shows other relationships of procurement
S[M[ Dissanayaka\ M[M[ Kumaraswamy:Buildin` and Environment 23 "0888# 20Ð31
22
Fig[ 0[ A hierarchy of procurement options[ Notes] + contains the {sub!sub!systems| within each sub!system "i[e[ indicates a break!down of the sub!system#[ Each sub!system has series of options as indicated above[ All possible {sub!sub systems| and options are not indicated here[
sub!systems variables and intervening variables] project characteristics\ client characteristics\ priorities and objec! tives\ and external conditions which are important cri! teria for the procurement selection process[ Di}erent external conditions may exert in~uence at the procurement selection stage and also at the project execution stage[ Therefore the changed external con! ditions are considered to be in~uential variables in this research[ Project performance indicators are both quan!
titative "e[g[ {{time index|| and {{cost index||# and quali! tative "e[g[ client satisfaction#[ Measures chosen for other intervening variables are quantitative:qualitative depend! ing on the nature of the variables[ 2[2[ Data collection The collection of su.cient and relevant data is a crucial factor in ensuring the quality of the research and should
23
S[M[ Dissanayaka\ M[M[ Kumaraswamy:Buildin` and Environment 23 "0888# 20Ð31
Table 0 The initially identi_ed Macro and Micro variables
Macro variables
No of Micro variables
"a# Project Characteristics
01
Building type\ Original cost estimate\ Gross ~oor area\ Number of storeys\ Programme duration\ Project complexity
"b# Procurement System
4
Work packaging\ Functional Grouping\ Selection Method\ Payment Method\ Contract conditions
"c# Project Team Performance "i# Contractor Team "ii# Management Team "iii# Design Team
0 0 0
Overall assessment Overall assessment Overall assessment
Examples of micro variables
"d# Client:Client representative|s Characteristics
27
Client type^ Experience\ Client Priority\ Client source of _nance
"e# Contractor Characteristics
19
Reponse to instruction\ Quality of management and supervision of contractor|s sta}\ Financial di.culties\ Availability of competent work! force
"f# Design Team Characteristics
4
Experience\ Accuracy of detailing in drawings\ E}ective com! munication of design details
"g# External Conditions
5
Legal\ Economical\ Political\ Social
Fig[ 1[ Model of basic linkages between procurement system\ project performance and related factors[
be compatible with the preferred analysis technique[ In this study\ a structured questionnaire was designed in two parts to collect information on both qualitative and quantitative measurements[ Part A queries the respon! dent|s experiential knowledge of procurement systems and its sub!systems\ while Part B seeks detailed infor! mation on a recently completed project in which the
respondent participated[ The questionnaires were dis! tributed among a selected sample and\ those who agreed to discuss their experiences on this subject were sub! sequently interviewed to elicit further details[ After some feedback from the initial questionnaires\ the format of the original questionnaire was modi_ed and new questionnaires were dispatched to a random
24
S[M[ Dissanayaka\ M[M[ Kumaraswamy:Buildin` and Environment 23 "0888# 20Ð31
sample covering clients\ contractors\ consultants\ archi! tects and quantity surveyors in the Hong Kong con! struction industry[ 114 questionnaires were distributed to the random sample and only 04 questionnaires were _lled and returned before the deadline[ After follow!up with the latter organizations on the telephone\ another _ve responses were received\ yielding a total of 19 responses from the random sample[ The response rate to the ques! tionnaire was fairly low\ perhaps due to the relatively detailed nature and considerable length of the ques! tionnaire "09 pages#[ Meanwhile 29 questionnaires were distributed among a {{selected sample|| "of senior industry personnel who were known to these researchers#\ of which 15 questionnaires were returned[ Fourteen project man! agers from the selected sample were also interviewed[ The foregoing questionnaire survey was conducted during AprilÐDecember 0885[ 2[3[ Project performance measures Time\ cost\ quality targets and participant satisfaction criteria are commonly used project performance measures[ A project is considered an overall success if the project meets the technical performance speci_cations and:or ful_ls the speci_ed mission\ to be performed\ and if there is a high level of satisfaction concerning the pro! ject outcome among principal participants in the project team and the key users ð10Ł[ In this study\ time and cost performance indicators were used as _rst!order project performance measures for simplicity[ Time Index
Actual duration 099 Programmed duration
Cost Index
Final cost 099 Initial cost
Time indices exceeding 099 indicate projects that exceed the scheduled times\ while those less than 099 correspond to projects completed before the scheduled time[ An index of 099 implies completion exactly on time[ In this context\ the programmed duration for a project is considered to span from the agreed construction com! mencement date to the planned project completion date[ The main reason for considering this duration is that project activities are not usually continuous from project inception to construction commencement date "e[g[ some projects undergo delays while awaiting funding approval#[ Another reason is the di.culty of getting adequate information from those who are involved both in pre!construction and construction periods\ hence the focus on the construction period alone[ On the other hand\ initial cost is taken as the tender price[ If projects were executed at di}erent periods\ it may be necessary * in the case of certain {{absolute|| comparisons * to con! sider the discounted values of project cost in relation to a particular year[ However\ in this study\ all prices were
considered as prices at the current years of the respective projects\ because the cost indicators were taken as ratios i[e[ in {relative| terms[ The projects having been at some! what di}erent periods is thus immaterial^ most projects in the sample were very recently completed or at an almost completed stage "e[g[ some projects were in the com! missioning stage#[ 2[4[ Initial analysis According to the large number of micro variables and the limited sample size\ neither statistical nor knowledge! based techniques could be directly applied to analyze the model because of the di.culty of identifying actual correlations between variables[ Even though a total of 35 data sets were available\ only 21 of them were complete and could be {{adequately|| used for the analysis[ The excluded data sets had some of the important information missing\ or were related to non!building projects[ Thus\ given the large numbers of hypothesized variables in relation to the sample size\ the _rst task of the research was to reduce this large number of variables to a man! ageable size[ The criteria and:or scales of measurements of the initial micro variables are shown in Appendix 0[ {{Principal Component Analysis|| and {{Factor Analy! sis|| were found to be potentially useful variable reduction techniques ð11Ł[ In this study\ the Factor Analysis tech! nique was applied to reduce the ordinal scale micro vari! ables to a manageable number\ but did not yield satisfactory results because of the low correlation that was found between variables\ perhaps due to the small sample size[ The results are thus not reported in this paper[ An alternative method was next adopted] sets of micro variables were identi_ed in relation to each of the selected macro variables[ The micro variables corresponding to each macro variable were next separated according to whether they were on an ordinal scale or not[ Finally\ all the {measures| of micro variables on an ordinal 0Ð4 scale "{{4|| indicating very high and {{0|| indicating very low# were combined to form a representative value[ This rep! resentative value was computed as follows] First\ Spearman|s rho correlation coe.cients between ordinal scale micro variables and the time "and cost# indices were computed "r0 [ [ [ rn#[ The signs of the coe.cients were reversed and they were then divided by their maximum absolute value to derive the cor! responding weightings to the ordinal scale micro vari! ables "w0 −r0:rmax [ [ [ wn −rn:rmax#[ Next\ the original scores of the micro variables were consolidated to form a collective representative value for the cor! responding macro variable as follows] N
& ' s WiXi
Representative value
i l
M(N
×099
25
S[M[ Dissanayaka\ M[M[ Kumaraswamy:Buildin` and Environment 23 "0888# 20Ð31
where Xi score of ith variable^ N number of vari! ables^ M highest possible score^ and Wi weighting of ith variable[ The micro variables were thus consolidated using the above formula and the remaining residual micro variables "which were measured on nominal and interval scales# are shown in Table 1[ However\ two micro variables:sub! systems "selection methodology and work packaging# were removed from the analysis in the case of the pro! curement system macro variable because their variations were found to be insigni_cant in the available sample[
tor|| "VIF# which measure the strength of inter!relation! ships among the explanatory variables in the model were used to check the multicollinearity[ For example\ if all the variables are orthogonal to each other\ both TOL and VIF are 0 "unity#[ If\ on the other hand\ a variable is closely related to other variables\ the TOL tends towards zero and the VIF becomes large[ There was no signi_cant correlation between the independent variables in both the time and cost performance models[ Another model assumption * that the sample distribution is normal\ was checked by Kolmogorov|s test ð12Ł[ Satisfying model assumptions\ the time and cost performance models were found to be signi_cant at 88) con_dence interval and the explanation capabilities of models were reasonably good after removing some outliers which were identi_ed by Cook|s D!test and residual plots[ The ensuing model equations thus considered su.ciently reliable are as fol! lows]
3[ Multiple Regression Analysis "MRA# In the context of the main objective of the research\ Multiple Regression Analysis "MRA# was considered to be a potentially useful statistical approach to analyze the strength of association between independent variables and dependent variables[ Therefore MRA was carried out to regress the 08 independent variables\ against each of the _rst two selected dependent variables * time and cost indices * using SAS statistical software[ One of the most important achievements of this regression analysis was to check and validate the model assumptions[ One of the problems often associated with MRA is the multicollinearity that may camou~age the results[ This is caused when an explanatory variable is virtually a linear combination of other explanatory variables in the model ð12Ł[ {{Tolerance|| "TOL# and {{variance of in~ation fac!
3[0[ Time index equation Time Index 66[7764¦3[9462 PCOMPLEX −0[9462 PDUR−10[2111 P!0 −05[9892 P!1−03[2414 P!2¦16[1825 P!3 where] PDUR Programme duration^ PCOM! PLEX Project Complexity representative value^ CLTYPE Client Type] P!0 Property + Devel!
Table 1 The selected macro and micro variables for further analysis Macro variables
Residual micro variables
"a# Project Characteristics
"0# Building Type "1# Number of Storeys "2# Number of Blocks "3# Project Complexity Representative Value "4# Programmed Duration "5# Original Cost Estimate
"b# Procurement System
"6# Functional Grouping "7# Payment Method "8# Contract Conditions
"c# Project Team Performance
"09# Contractor "00# Design Team "01# Management Team
"d# Client:Client Representative|s Characteristics
"02# Client Type "03# Client Priority "04# Client Source of Finance "05# Client Characteristics Representative Value
"e# Contractor Characteristics
"06# Contractor Characteristics Representatives Value
"f# Design Team Characteristics
"07# Design Team Characteristics Representative Value
"g# External Conditions
"08# External Conditions Representative Value
Di}erent representative values were used for the time and cost indices analysis[
26
S[M[ Dissanayaka\ M[M[ Kumaraswamy:Buildin` and Environment 23 "0888# 20Ð31 Table 2 Comparing the actual and predicted time and cost indices Time index No[
Actual
Predicted
Error
0 1 2 3 4 5 6 7 8 09 00 01 02 03 04 05 06 07 08 19 10 11 12 13 14 15 16
099[9 099[9 099[9 094[4 096[0 099[9 199[9 096[6 049[9 099[9 022[2 092[3 019[1 098[0 099[9 019[9 003[29 026[89 025[49 039[99 092[99 043[99 098[49 099[99 003[19 019[99 099[99
78[2 093[0 009[0 096[6 88[9 092[0 081[1 007[3 023[3 002[4 010[8 003[2 019[8 008[4 097[0 009[4 005[5 026[7 033[0 028[1 84[5 043[4 003[6 001[4 099[6 093[8 85[8
09[6 −3[0 −09[0 −1[1 7[0 −2[0 6[7 −09[6 04[6 −02[4 00[3 −09[8 −9[6 −09[3 −7[0 8[4 −1[2 9[0 −6[5 9[7 6[3 −9[4 −4[1 −01[4 02[4 04[0 2[0
Fig[ 2[ Comparing the actual and predicted time indices[
Cost index No[
Actual
Predicted
Error
0 1 2 3 4 4 6 7 8 09 00 01 02 03 04 05 06 07 08 19 10 11 12 13 14
092[2 090[8 090[2 099[9 099[9 099[9 006[0 60[3 89[1 029[9 099[9 86[0 88[1 094[7 094[8 019[9 66[7 009[9 78[0 86[9 019[9 75[1 87[7 74[9 090[1
090[6 82[3 090[0 094[9 096[5 89[4 002[6 65[6 87[4 029[0 095[4 89[8 091[0 85[4 099[7 006[6 74[6 093[4 82[0 77[8 004[8 75[2 87[2 82[3 097[3
0[5 7[3 9[1 −4[9 −6[5 8[4 2[3 −4[2 −7[2 −9[0 −5[4 5[1 −1[8 8[2 4[0 1[2 −7[9 4[4 −3[9 7[0 3[0 −9[1 9[4 −7[3 −6[1
Fig[ 3[ Comparing the actual and predicted cost indices[
27
S[M[ Dissanayaka\ M[M[ Kumaraswamy:Buildin` and Environment 23 "0888# 20Ð31
opment Company^ P!1 Investor^ P!2 Local:central Government Authority^ P!3 Occupier[ Properties] R−square 9[7382^ Adj R−square 9[7939^ Root MSE 09[2077^ Sample size 16^ F Sig[ 9[9990[ The above individual variables were signi_cant at the following levels] programme duration "P ³ 9[9903#\ pro! ject complexity representative value "P ³ 9[9990#\ and Client type "P ³ 9[9990#[ 3[1[ Cost index equation Cost Index 60[6272¦0[3126 PCOMPLEX −0[3027 CLCHAR−9[9976 ORICOST ¦1[2555 CONCHAR where] PCOMPLEX Project Complexity rep! resentative value^ CLCHAR Client Characteristics representative value^ ORICOST Original Cost Estimate^ CONCHAR Contractor Characteristics representative value[ Properties] R−square 9[6775^ Adj R−square 9[6353^ Root MSE 5[5010^ Sample size 14^ F Sig[ 9[9990[ The above individual variables were signi_cant at the following levels] Project complexity representative value "P ³ 9[9990#\ Client:Client representative|s charac! teristics representative value "P ³ 9[9917#\ Contractor Characteristics representative value "P ³ 9[9931# and original cost estimate "P ³ 9[0955#[ Table 2 shows the time and cost indices\ which indicate the achieved times:costs against those predicted\ along with the magnitudes of the errors "deviations#[ The dis! tributions are also graphically illustrated in Figs 2 and 3[ An alternative approach to re!establish and compare the foregoing relationships was attempted * by using the available data to develop an Arti_cial Neural Network "ANN#[ In general ANNs are well suited for analysis and prediction of complex patterns from highly erratic data with a large number of independent variables ð13Ł[ How! ever in this case it did not yield satisfactory results\ prob! ably because of the small sample size and the relatively large number of input variables[ It was therefore decided to rely on the Multiple Regression approach alone in this scenario[
client type are highly correlated with the time index^ whilst the cost index equation indicates that project com! plexity representative value\ client:client representative|s characteristics representative value and contractor characteristics representative value are highly correlated with the cost index[ Referring to Fig[ 1\ these results appear to con_rm the relative strengths of the direct relationships between the time and cost performance levels and project characteristics\ client:client representative|s characteristics\ and contractor charac! teristics[ Similar conclusions have also been reached by previous researchers\ although through di}erent approaches[ For example\ it has been found that the experience of the building team with the building process has signi_cantly in~uenced the time and cost over!run ð4Ł[ Walker ð6Ł concluded that the contract type does not signi_cantly a}ect speed of construction\ while several client related factors and team communications proved more signi_! cant[ The quality of the relationships between client\ cli! ent representative\ design team and construction management team were thus found to be signi_cant in governing construction time performance[ Organ! izational and contextual variables have also been found to be more strongly associated with performance ð1Ł[ It was thus not surprising that non!procurement related variables appeared to exert a more signi_cant in~uence on time and cost than procurement related vari! ables in the building projects studied in Hong Kong[ Although some of the variables identi_ed in the time and cost indexes equations can not be quanti_ed prior to the commencement of a project\ the knowledge that they would in~uence potential time and cost\ enables project managers to pay special attention to controlling the cor! responding variables more e}ectively[ It is also noted that allowances are needed for special scenarios and variables which are di.cult to incorporate satisfactorily in such quantitatively derived general models[ Further testing and investigations are envisaged following the collection and analysis of detailed data from more building projects in order to increase the sample size and hence improve the reliability of the derived relationships[ Once vali! dated\ these relationships can be useful in reducing time and cost over!runs by way of more focused planning and control of building projects\ i[e[ by paying closer attention to the identi_ed critical variables[
4[ Discussion and Concluding Observations According to the foregoing results and derived relationships\ the in~uence of procurement sub!systems by themselves on time and cost performance\ is not as signi_cant as that of other variables[ For example\ it appears from the time index equation that project com! plexity representative value\ programme duration and
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S[M[ Dissanayaka\ M[M[ Kumaraswamy:Buildin` and Environment 23 "0888# 20Ð31
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ð04Ł Dulami MF\ Dalziel RC[ The E}ects of the Procurement Method on the Level of Management Synergy in Construction Projects[ CIB W81\ East Meets West\ Procurement Systems Symposium\ CIB Publication No 064\ Hong Kong 0883^42Ð59[ ð05Ł Hashim M[ The E}ects of Procurement Systems on Performance of Construction Projects in Malaysia[ CIB W81\ North Meets South\ Procurement Systems Symposium\ Durban\ South Africa 0885^081Ð88[ ð06Ł Ho DCW\ Chau KW\ Leung HF[ Are Contractors prepared to take on design and build contract< CIB W81\ North Meets South\ Procurement Systems Symposium\ Durban\ South Africa 0885^102Ð13[ ð07Ł Ashely DB\ Lurie CS\ Jaselis EJ[ Determinants of Construction Project Success[ Project Management Journal 0876^07"1#]58Ð68[ ð08Ł Hughes WP[ Identifying the Environments of Construction Projects[ Construction Management and Economics 0878^6"0#]18Ð 39[ ð19Ł Liu MMA[ Evaluation of the Outcome of Construction Projects[ PhD thesis\ University of Hong Kong 0884[ ð10Ł De Wit A[ Measurement of project success[ Project Management Journal 0877^5"2#]053Ð69[ ð11Ł Beck MSL[ Factor Analysis and Related Techniques[ International Handbooks of Quantitative Applications in the Social Sciences\ Vol[ 4[ SAGE Publications 0883[ ð12Ł SAS:INSIGHT User|s guide\ Version 5\ 1nd edn[ SAS Institute Inc\ USA 0882[ ð13Ł McKim R\ Adas A\ Handa VK[ Construction Firm organizational E}ectiveness] A Neural Network!Based Prediction Methodology[ The Organization and Management of Construction] Shaping theory and practice\ CIB W54 Conference\ Glasgow 0885^2]136Ð 45[
Appendix 0] Initial variables and their evaluation criteria:measures Macro variables
Micro variables
Code
Criteria:measures
Project characteristics
"A[0# Type of building
0 1 2 3 4 5
Residential Commercial:O.ce Hospital School Industrial Other
"A[1# Type of construction
0 1 2
New Refurbishment Extension
"A[2# Original Cost Estimate "A[2a# Final Cost "A[3# Programmed Duration "A[3a# Actual Duration Size of Project "A[4# No of Storeys "A[5# No of Blocks "A[6# Total Gross Floor Area Project complexity "A[7# Location\ "A[8# Design\ "A[09# Construction\ "A[00# Coordinative and "A[01# Changes "with time#
HK, Months
In numbers In numbers Square feet 4
Very high
3 2 1 0
Moderately high Average Slightly low Very low
39
S[M[ Dissanayaka\ M[M[ Kumaraswamy:Buildin` and Environment 23 "0888# 20Ð31
Appendix 0] contd[ Macro variables
Micro variables
Code
Criteria:measures
Procurement system
"B[0# Functional grouping
0 1 2
Traditional "Sequential# Traditional "Fast Track# Design + Build
"B[1# Payment Method
0 1 2
Lump Sum Fixed price "without ~uctuations# Lump sum Fixed price "with ~uctuations# Remeasure
"B[2# Selection Procedure used for Consultant + Contractor
0 1 2
Tenders from prequali_ed parties + lowest bid Open tenders + Lowest bid Other means
"B[3# Contract conditions used
0 1 2
HK Government Conditions of Contract HKIA Other
Team performance
"C[0# Contractor Team "C[1# Design Team "C[2# Management Team
4 3 2 1 2
Very Good Good Average Poor Very Poor
Client characteristics
"D[0# Client type
0 1 2 3 4
Developer Investor Local:Central government Authority Occupier Other "mixed#
"D[1# Client sources of Finance
0 1 2
Public Private Mixed
0Ð09
Scale of 0Ð09 "09 is highest#
0
Yes
9
No
4
Very high
3 2 1 0
High Average Low Very Low
"D[2# Priority On Time On Cost "D[3# Did the client communicate his requirements and priorities clearly at beginning of the project< "D[4# Were the client requirements and priorities realistic in terms of! "a# Time "b# Cost "c# Quality "d# Scope "D[5# Client involvement in the project in the following aspects "a# Planning "b# Decision Making "c# Controlling "D[6# Project Manager capabilities to handle the project "D[7# Project Manager experience on project of this type "D[8# Project Manager authority to authorise "a# Time extension "b# Extra costs "c# Modi_ed speci_cation "D[09# Project Manager ability to make binding:authoritative decisions "D[00# Project Manager ability to contribute ideas to the construction process "D[01# Speed of decision making "D[02# Client con_dence in the design team "D[03# Client con_dence in the construction team
S[M[ Dissanayaka\ M[M[ Kumaraswamy:Buildin` and Environment 23 "0888# 20Ð31 Appendix 0] contd[ Macro variables
Micro variables
Code
Criteria:measures
4 3
Very high High
2
Average
1 0
Low Very Low
"D[04# E}ectiveness of information ~ow in "a# Reporting "b# Changes "variations# "c# Instructions "D[05# E}ectiveness of Communication with "a# Management Team:Construction Team "b# Management Team:Design Team "D[06# Clience incentives to accelerate the project "D[07# Project Team motivation and goal orientation "D[08# Frequency of project meetings "D[19# Frequency of schedule adjustments "D[10# Client interest in safety measures "D[11# Level "degree# of Risk allocated to the contractor "D[12# Level "degree# of di.culty of getting interim payments "D[13# Di.culty of getting Payments for additional works "D[14# Possibility of long term relationship "D[15# Level "degree# of the co!operation between the contractor team and the client team "D[16# Level "degree# of the co!operation between the design team and the client team "D[17# Level "degree# of the co!operation between the project management team and the client|s main organization Contractor characteristics "E[0# Contractor Management Sta} number "E[1# Quality of Management and supervision of contractor|s sta} "E[2# Contractor|s top management involvement in the Project "E[3# Availability of Equipment "E[4# Availability of competent workforce "E[5# Response to instructions "E[6# E}ective communications "a# within contractor|s own team "b# between contractor|s team and design:supervision team "c# between contractor|s team and project management team "E[7# Financial di.culties during the construction "E[8# Number of subcontractors involved "E[09# Reporting Frequency "a# Progress "b# Budget "c# Changes and claims "E[00# Contractor team turnover "E[01# Control systems E}ectiveness "a# Budget "b# Resources "E[02# Contractor involvement in general decision making "E[03# Trustworthiness of contractor "E[04# Level of decentralization of contractor project organization
30
31
S[M[ Dissanayaka\ M[M[ Kumaraswamy:Buildin` and Environment 23 "0888# 20Ð31
Appendix 0] contd[ Macro variables
Micro variables
Code
Criteria:measures
Design Team characteristics
"F[0# Design team experience "F[1# Frequency of changes in drawings "F[2# Accuracy of detailing in drawings "F[3# E}ective Communication of design details to contractor "F[4# Complexity level of drawings
4 3 2 1
Very high High Average Low
0
Very Low
"G[0# Physical "G[1# Cultural "G[2# Social "G[3# Political "G[4# Legal "G[5# Economical "G[6# Others "specify#
4 3 2 1 0
Very high High Average Low Very Low
External conditions
{{Project Manager|| is taken here to mean the client|s representative "either in!house or authorised#\ overseeing both {{design|| and {{construction|| aspects[
Building and Environment 23 "0888# 20Ð31
Comparing contributors to time and cost performance in building projects Sunil M[ Dissanayaka \ Mohan M[ Kumaraswamy Dept[ of Civil + Structural Engineering\ The University of Hong Kong\ Pokfulam Road\ Hong Kong Received 3 June 0886^ accepted 07 September 0886
Abstract Identifying the relative strengths of the linkages between procurement sub!systems and project performance was initially hypo! thesized to be useful for developing guidelines for the formulation of a performance!oriented construction procurement system[ A comprehensive model was developed to incorporate all signi_cant procurement sub!systems variables\ together with {non!pro! curement| related "or {intervening|# variables to explore and evaluate such linkages[ The multiple regression technique was applied to analyze the data from 21 Hong Kong!based building projects and the results were compared with reality[ The results suggest that procurement sub!systems variables are less signi_cant than the non!procurement related variables in predicting time and cost performance levels on Hong Kong building projects[ Þ 0887 Elsevier Science Ltd[ All rights reserved[ Keywords] Procurement^ Performance^ Modelling^ Regression^ Hong Kong
0[ Introduction Along with the increasing complexities of construction projects\ for example related to their administration and _nancial management\ most clients now also encounter di.culties in selecting optimal procurement options for their projects[ The choice of a procurement system has been considered to be one of the most important decisions that a client will make during the project[ One of the principal reasons for the construction industry|s poor performance has been attributed to the inappropriateness of the chosen procurement systems ð0Ł[ The selection of a suitable procurement system is thus a necessary con! dition for project success^ but not a su.cient condition\ because other intervening variables such as project characteristics\ project team characteristics and their per! formance\ as well as external conditions\ are also associ! ated with overall project performance[ Rowlinson ð1Ł\ Naoum ð2\ 4Ł and Walker ð5\ 6Ł concluded that manage! ment\ organizational and contextual related variables are highly associated with overall project performance[ Rwel! amila and Hall ð7Ł argued that the management of the {human aspects| contributed more to {poor| project per! formance\ than the initial procurement related decisions[ In procurement related research\ it is important to Corresponding author[ Tel[] 99741 17481554^ fax] 99741 14484226^ e!mail] h8484930Ýhkusua[hku[hk
initially de_ne the precise scope of the envisaged pro! curement systems\ since a procurement system can be de_ned on the basis of various aspects of the construction process ð0\ 8\ 09Ł[ In the context of construction\ the scope of procurement has been based on the CIB W81 de_nition as {{the framework within which construction is brought about\ acquired or obtained|| ð00Ł[ A procurement system can then be considered to consist of a collection of sub! systems within such a framework[ In this research\ the following procurement sub!systems were chosen] "0# {{Work Packaging|| may be formulated so that the packages are large enough to attract international interest\ if needed for purposes of price competition or for deploying advanced technologies[ Alter! natively\ the large and:or complex work packages may be {sliced|\ to keep them within the capabilities of local construction organizations[ "1# {{Functional Groupings|| * of the design\ con! struction and management functions * may be based on either] "a# a {separated| approach of independent design and construction as in the traditional method\ whether within a sequential or {{fast!track|| structure^ "b# an {{integrated|| approach such as in {Design and Construct| or {Build Operate Transfer| "BOT#^ or * a {{Management!led|| approach such as in {{Man! agement Contracting||[ "2# {{Payment Modality|| could vary from {Cost Reim!
9086Ð9075:88:,*see front matter Þ 0887 Elsevier Science Ltd[ All rights reserved PII] S 9 2 5 9 Ð 0 2 1 2 " 8 6 # 9 9 9 5 7 Ð 0
21
S[M[ Dissanayaka\ M[M[ Kumaraswamy:Buildin` and Environment 23 "0888# 20Ð31
bursement| to {Lump Sum Fixed Price|[ The timing and currency of payments could also vary[ "3# {{Selection Methodologies|| of consultants and con! tractors may be open\ or based on registered or pre! quali_ed short lists^ and di}erent weightings may be assigned to technical or price parameters[ "4# Standard sets of {{Conditions of Contracts|| "for building works\ Civil Engineering works\ or for {Design and Build|#\ may be chosen for convenience or because they have been tried and tested and now trusted by the industry[ Alternatively special sets of conditions may be designed for a given programme as in the New Airport Core programme in Hong Kong\ or for use by a particular large client or a consultant[ Using the hierarchy of possible procurement sub systems\ and options developed by Kumaraswamy and Dissanayaka ð01Ł "further expanded in Fig[ 0#\ di}erent procurement systems can be assembled by collecting options from each sub!system[ The {most appropriate| procurement system for a project\ should be selected to be compatible with the project scenario because there is no {best procurement route|[ The type of client and his priorities for the particular project generally lead to some routes being better than others ð02Ł[ Some conclusions of other researchers such as Row! linson ð1Ł\ Naoum and Musthapa ð4Ł\ Akintoye ð03Ł\ Dulaimi and Dalziel ð04Ł\ Hashim ð05Ł and Ho et al[ ð06Ł regarding the impact of procurement systems on various aspects of project performance "e[g[ time\ cost\ satis! faction\ claims and disputes# provide a platform for laun! ching an investigation into the relative strengths of the links between the procurement system and project out! comes[ However\ most procurement research has been focused on just one particular sub!system of the pro! curement system "such as the {functional grouping|# in any given research project[ Incorporating all signi_cant procurement sub!systems and intervening variables into one comprehensive model^ and then identifying the relative strengths of the linkages between procurement sub!systems\ non!procurement related variables and the project outcomes could provide valuable guidelines to improve project performance levels[ A research project was thus formulated to explore those linkages[ This paper] "a# outlines the methodology and principal methods used during this research and "b# brie~y summarizes the data analysis\ results and con! clusions[ The former identi_es some signi_cant non!pro! curement related variables\ while the latter points to future steps that would be useful in continuing this inves! tigation[ 1[ Objectives of this Research project The main objective of this research is to identify the relative strengths of the linkages between procurement
sub!systems\ any other relevant variables and project out! comes in Hong Kong based building projects[ The fol! lowing sub!objectives are set out to satisfy this main objective] "0# To identify and select particular {{procurement|| and {{non!procurement|| related "{{intervening||# variables that may be signi_cantly related to project perform! ance[ "1# To model the principal variables "{{procurement|| sub!systems and {{non!procurement|| related or {{intervening||# in~uencing project performance "in terms of meeting cost and time targets#\ and to ident! ify the most signi_cant variables and linkages which may govern project performance[ 2[ Methodology 2[0[ Identifying variables Identi_cation of the variables which may have some in~uence on project performance was based on the con! clusions of previous researchers Rowlinson ð1Ł\ Naoum ð2\ 3Ł\ Walker ð5Ł\ Hashim ð05Ł\ Ashley et al[ ð07Ł\ Hughes ð08Ł\ Liu ð19Ł and initial interviews with industry prac! titioners[ These were grouped into macro variables\ each of which then {covered| a large number of micro variables "as indicated in Table 0#[ All the selected micro variables may not be of the same importance in every project but may vary with the client|s objectives\ priorities\ project conditions\ constraints and complexities and the quality of the project team[ The initial variables and their sug! gested measurement criteria:scales are listed in Appendix 0[ 2[1[ The Research model A model was formulated as in Fig[ 1\ to explore the potential relationships between project performance and procurement sub!systems "e[g[ work packaging\ func! tional grouping and payment method# and other inter! vening variables "e[g[ project characteristics\ client characteristics\ and team performance#[ Identifying the relative strengths of association of procurement sub!sys! tems with project performance\ as compared to other intervening variables\ would provide useful guidelines for the formulation of a performance oriented procurement strategy for a project[ This will also identify the signi_cant variables which in~uence the project performance levels[ In this research\ the relationships of particular interest are the procurement sub!systems variables and the mea! sures of project performance[ The feedback link from the project performance to the procurement sub!systems variables indicates the potential implications of such _n! dings in improving the procurement selection process[ The model also shows other relationships of procurement
S[M[ Dissanayaka\ M[M[ Kumaraswamy:Buildin` and Environment 23 "0888# 20Ð31
22
Fig[ 0[ A hierarchy of procurement options[ Notes] + contains the {sub!sub!systems| within each sub!system "i[e[ indicates a break!down of the sub!system#[ Each sub!system has series of options as indicated above[ All possible {sub!sub systems| and options are not indicated here[
sub!systems variables and intervening variables] project characteristics\ client characteristics\ priorities and objec! tives\ and external conditions which are important cri! teria for the procurement selection process[ Di}erent external conditions may exert in~uence at the procurement selection stage and also at the project execution stage[ Therefore the changed external con! ditions are considered to be in~uential variables in this research[ Project performance indicators are both quan!
titative "e[g[ {{time index|| and {{cost index||# and quali! tative "e[g[ client satisfaction#[ Measures chosen for other intervening variables are quantitative:qualitative depend! ing on the nature of the variables[ 2[2[ Data collection The collection of su.cient and relevant data is a crucial factor in ensuring the quality of the research and should
23
S[M[ Dissanayaka\ M[M[ Kumaraswamy:Buildin` and Environment 23 "0888# 20Ð31
Table 0 The initially identi_ed Macro and Micro variables
Macro variables
No of Micro variables
"a# Project Characteristics
01
Building type\ Original cost estimate\ Gross ~oor area\ Number of storeys\ Programme duration\ Project complexity
"b# Procurement System
4
Work packaging\ Functional Grouping\ Selection Method\ Payment Method\ Contract conditions
"c# Project Team Performance "i# Contractor Team "ii# Management Team "iii# Design Team
0 0 0
Overall assessment Overall assessment Overall assessment
Examples of micro variables
"d# Client:Client representative|s Characteristics
27
Client type^ Experience\ Client Priority\ Client source of _nance
"e# Contractor Characteristics
19
Reponse to instruction\ Quality of management and supervision of contractor|s sta}\ Financial di.culties\ Availability of competent work! force
"f# Design Team Characteristics
4
Experience\ Accuracy of detailing in drawings\ E}ective com! munication of design details
"g# External Conditions
5
Legal\ Economical\ Political\ Social
Fig[ 1[ Model of basic linkages between procurement system\ project performance and related factors[
be compatible with the preferred analysis technique[ In this study\ a structured questionnaire was designed in two parts to collect information on both qualitative and quantitative measurements[ Part A queries the respon! dent|s experiential knowledge of procurement systems and its sub!systems\ while Part B seeks detailed infor! mation on a recently completed project in which the
respondent participated[ The questionnaires were dis! tributed among a selected sample and\ those who agreed to discuss their experiences on this subject were sub! sequently interviewed to elicit further details[ After some feedback from the initial questionnaires\ the format of the original questionnaire was modi_ed and new questionnaires were dispatched to a random
24
S[M[ Dissanayaka\ M[M[ Kumaraswamy:Buildin` and Environment 23 "0888# 20Ð31
sample covering clients\ contractors\ consultants\ archi! tects and quantity surveyors in the Hong Kong con! struction industry[ 114 questionnaires were distributed to the random sample and only 04 questionnaires were _lled and returned before the deadline[ After follow!up with the latter organizations on the telephone\ another _ve responses were received\ yielding a total of 19 responses from the random sample[ The response rate to the ques! tionnaire was fairly low\ perhaps due to the relatively detailed nature and considerable length of the ques! tionnaire "09 pages#[ Meanwhile 29 questionnaires were distributed among a {{selected sample|| "of senior industry personnel who were known to these researchers#\ of which 15 questionnaires were returned[ Fourteen project man! agers from the selected sample were also interviewed[ The foregoing questionnaire survey was conducted during AprilÐDecember 0885[ 2[3[ Project performance measures Time\ cost\ quality targets and participant satisfaction criteria are commonly used project performance measures[ A project is considered an overall success if the project meets the technical performance speci_cations and:or ful_ls the speci_ed mission\ to be performed\ and if there is a high level of satisfaction concerning the pro! ject outcome among principal participants in the project team and the key users ð10Ł[ In this study\ time and cost performance indicators were used as _rst!order project performance measures for simplicity[ Time Index
Actual duration 099 Programmed duration
Cost Index
Final cost 099 Initial cost
Time indices exceeding 099 indicate projects that exceed the scheduled times\ while those less than 099 correspond to projects completed before the scheduled time[ An index of 099 implies completion exactly on time[ In this context\ the programmed duration for a project is considered to span from the agreed construction com! mencement date to the planned project completion date[ The main reason for considering this duration is that project activities are not usually continuous from project inception to construction commencement date "e[g[ some projects undergo delays while awaiting funding approval#[ Another reason is the di.culty of getting adequate information from those who are involved both in pre!construction and construction periods\ hence the focus on the construction period alone[ On the other hand\ initial cost is taken as the tender price[ If projects were executed at di}erent periods\ it may be necessary * in the case of certain {{absolute|| comparisons * to con! sider the discounted values of project cost in relation to a particular year[ However\ in this study\ all prices were
considered as prices at the current years of the respective projects\ because the cost indicators were taken as ratios i[e[ in {relative| terms[ The projects having been at some! what di}erent periods is thus immaterial^ most projects in the sample were very recently completed or at an almost completed stage "e[g[ some projects were in the com! missioning stage#[ 2[4[ Initial analysis According to the large number of micro variables and the limited sample size\ neither statistical nor knowledge! based techniques could be directly applied to analyze the model because of the di.culty of identifying actual correlations between variables[ Even though a total of 35 data sets were available\ only 21 of them were complete and could be {{adequately|| used for the analysis[ The excluded data sets had some of the important information missing\ or were related to non!building projects[ Thus\ given the large numbers of hypothesized variables in relation to the sample size\ the _rst task of the research was to reduce this large number of variables to a man! ageable size[ The criteria and:or scales of measurements of the initial micro variables are shown in Appendix 0[ {{Principal Component Analysis|| and {{Factor Analy! sis|| were found to be potentially useful variable reduction techniques ð11Ł[ In this study\ the Factor Analysis tech! nique was applied to reduce the ordinal scale micro vari! ables to a manageable number\ but did not yield satisfactory results because of the low correlation that was found between variables\ perhaps due to the small sample size[ The results are thus not reported in this paper[ An alternative method was next adopted] sets of micro variables were identi_ed in relation to each of the selected macro variables[ The micro variables corresponding to each macro variable were next separated according to whether they were on an ordinal scale or not[ Finally\ all the {measures| of micro variables on an ordinal 0Ð4 scale "{{4|| indicating very high and {{0|| indicating very low# were combined to form a representative value[ This rep! resentative value was computed as follows] First\ Spearman|s rho correlation coe.cients between ordinal scale micro variables and the time "and cost# indices were computed "r0 [ [ [ rn#[ The signs of the coe.cients were reversed and they were then divided by their maximum absolute value to derive the cor! responding weightings to the ordinal scale micro vari! ables "w0 −r0:rmax [ [ [ wn −rn:rmax#[ Next\ the original scores of the micro variables were consolidated to form a collective representative value for the cor! responding macro variable as follows] N
& ' s WiXi
Representative value
i l
M(N
×099
25
S[M[ Dissanayaka\ M[M[ Kumaraswamy:Buildin` and Environment 23 "0888# 20Ð31
where Xi score of ith variable^ N number of vari! ables^ M highest possible score^ and Wi weighting of ith variable[ The micro variables were thus consolidated using the above formula and the remaining residual micro variables "which were measured on nominal and interval scales# are shown in Table 1[ However\ two micro variables:sub! systems "selection methodology and work packaging# were removed from the analysis in the case of the pro! curement system macro variable because their variations were found to be insigni_cant in the available sample[
tor|| "VIF# which measure the strength of inter!relation! ships among the explanatory variables in the model were used to check the multicollinearity[ For example\ if all the variables are orthogonal to each other\ both TOL and VIF are 0 "unity#[ If\ on the other hand\ a variable is closely related to other variables\ the TOL tends towards zero and the VIF becomes large[ There was no signi_cant correlation between the independent variables in both the time and cost performance models[ Another model assumption * that the sample distribution is normal\ was checked by Kolmogorov|s test ð12Ł[ Satisfying model assumptions\ the time and cost performance models were found to be signi_cant at 88) con_dence interval and the explanation capabilities of models were reasonably good after removing some outliers which were identi_ed by Cook|s D!test and residual plots[ The ensuing model equations thus considered su.ciently reliable are as fol! lows]
3[ Multiple Regression Analysis "MRA# In the context of the main objective of the research\ Multiple Regression Analysis "MRA# was considered to be a potentially useful statistical approach to analyze the strength of association between independent variables and dependent variables[ Therefore MRA was carried out to regress the 08 independent variables\ against each of the _rst two selected dependent variables * time and cost indices * using SAS statistical software[ One of the most important achievements of this regression analysis was to check and validate the model assumptions[ One of the problems often associated with MRA is the multicollinearity that may camou~age the results[ This is caused when an explanatory variable is virtually a linear combination of other explanatory variables in the model ð12Ł[ {{Tolerance|| "TOL# and {{variance of in~ation fac!
3[0[ Time index equation Time Index 66[7764¦3[9462 PCOMPLEX −0[9462 PDUR−10[2111 P!0 −05[9892 P!1−03[2414 P!2¦16[1825 P!3 where] PDUR Programme duration^ PCOM! PLEX Project Complexity representative value^ CLTYPE Client Type] P!0 Property + Devel!
Table 1 The selected macro and micro variables for further analysis Macro variables
Residual micro variables
"a# Project Characteristics
"0# Building Type "1# Number of Storeys "2# Number of Blocks "3# Project Complexity Representative Value "4# Programmed Duration "5# Original Cost Estimate
"b# Procurement System
"6# Functional Grouping "7# Payment Method "8# Contract Conditions
"c# Project Team Performance
"09# Contractor "00# Design Team "01# Management Team
"d# Client:Client Representative|s Characteristics
"02# Client Type "03# Client Priority "04# Client Source of Finance "05# Client Characteristics Representative Value
"e# Contractor Characteristics
"06# Contractor Characteristics Representatives Value
"f# Design Team Characteristics
"07# Design Team Characteristics Representative Value
"g# External Conditions
"08# External Conditions Representative Value
Di}erent representative values were used for the time and cost indices analysis[
26
S[M[ Dissanayaka\ M[M[ Kumaraswamy:Buildin` and Environment 23 "0888# 20Ð31 Table 2 Comparing the actual and predicted time and cost indices Time index No[
Actual
Predicted
Error
0 1 2 3 4 5 6 7 8 09 00 01 02 03 04 05 06 07 08 19 10 11 12 13 14 15 16
099[9 099[9 099[9 094[4 096[0 099[9 199[9 096[6 049[9 099[9 022[2 092[3 019[1 098[0 099[9 019[9 003[29 026[89 025[49 039[99 092[99 043[99 098[49 099[99 003[19 019[99 099[99
78[2 093[0 009[0 096[6 88[9 092[0 081[1 007[3 023[3 002[4 010[8 003[2 019[8 008[4 097[0 009[4 005[5 026[7 033[0 028[1 84[5 043[4 003[6 001[4 099[6 093[8 85[8
09[6 −3[0 −09[0 −1[1 7[0 −2[0 6[7 −09[6 04[6 −02[4 00[3 −09[8 −9[6 −09[3 −7[0 8[4 −1[2 9[0 −6[5 9[7 6[3 −9[4 −4[1 −01[4 02[4 04[0 2[0
Fig[ 2[ Comparing the actual and predicted time indices[
Cost index No[
Actual
Predicted
Error
0 1 2 3 4 4 6 7 8 09 00 01 02 03 04 05 06 07 08 19 10 11 12 13 14
092[2 090[8 090[2 099[9 099[9 099[9 006[0 60[3 89[1 029[9 099[9 86[0 88[1 094[7 094[8 019[9 66[7 009[9 78[0 86[9 019[9 75[1 87[7 74[9 090[1
090[6 82[3 090[0 094[9 096[5 89[4 002[6 65[6 87[4 029[0 095[4 89[8 091[0 85[4 099[7 006[6 74[6 093[4 82[0 77[8 004[8 75[2 87[2 82[3 097[3
0[5 7[3 9[1 −4[9 −6[5 8[4 2[3 −4[2 −7[2 −9[0 −5[4 5[1 −1[8 8[2 4[0 1[2 −7[9 4[4 −3[9 7[0 3[0 −9[1 9[4 −7[3 −6[1
Fig[ 3[ Comparing the actual and predicted cost indices[
27
S[M[ Dissanayaka\ M[M[ Kumaraswamy:Buildin` and Environment 23 "0888# 20Ð31
opment Company^ P!1 Investor^ P!2 Local:central Government Authority^ P!3 Occupier[ Properties] R−square 9[7382^ Adj R−square 9[7939^ Root MSE 09[2077^ Sample size 16^ F Sig[ 9[9990[ The above individual variables were signi_cant at the following levels] programme duration "P ³ 9[9903#\ pro! ject complexity representative value "P ³ 9[9990#\ and Client type "P ³ 9[9990#[ 3[1[ Cost index equation Cost Index 60[6272¦0[3126 PCOMPLEX −0[3027 CLCHAR−9[9976 ORICOST ¦1[2555 CONCHAR where] PCOMPLEX Project Complexity rep! resentative value^ CLCHAR Client Characteristics representative value^ ORICOST Original Cost Estimate^ CONCHAR Contractor Characteristics representative value[ Properties] R−square 9[6775^ Adj R−square 9[6353^ Root MSE 5[5010^ Sample size 14^ F Sig[ 9[9990[ The above individual variables were signi_cant at the following levels] Project complexity representative value "P ³ 9[9990#\ Client:Client representative|s charac! teristics representative value "P ³ 9[9917#\ Contractor Characteristics representative value "P ³ 9[9931# and original cost estimate "P ³ 9[0955#[ Table 2 shows the time and cost indices\ which indicate the achieved times:costs against those predicted\ along with the magnitudes of the errors "deviations#[ The dis! tributions are also graphically illustrated in Figs 2 and 3[ An alternative approach to re!establish and compare the foregoing relationships was attempted * by using the available data to develop an Arti_cial Neural Network "ANN#[ In general ANNs are well suited for analysis and prediction of complex patterns from highly erratic data with a large number of independent variables ð13Ł[ How! ever in this case it did not yield satisfactory results\ prob! ably because of the small sample size and the relatively large number of input variables[ It was therefore decided to rely on the Multiple Regression approach alone in this scenario[
client type are highly correlated with the time index^ whilst the cost index equation indicates that project com! plexity representative value\ client:client representative|s characteristics representative value and contractor characteristics representative value are highly correlated with the cost index[ Referring to Fig[ 1\ these results appear to con_rm the relative strengths of the direct relationships between the time and cost performance levels and project characteristics\ client:client representative|s characteristics\ and contractor charac! teristics[ Similar conclusions have also been reached by previous researchers\ although through di}erent approaches[ For example\ it has been found that the experience of the building team with the building process has signi_cantly in~uenced the time and cost over!run ð4Ł[ Walker ð6Ł concluded that the contract type does not signi_cantly a}ect speed of construction\ while several client related factors and team communications proved more signi_! cant[ The quality of the relationships between client\ cli! ent representative\ design team and construction management team were thus found to be signi_cant in governing construction time performance[ Organ! izational and contextual variables have also been found to be more strongly associated with performance ð1Ł[ It was thus not surprising that non!procurement related variables appeared to exert a more signi_cant in~uence on time and cost than procurement related vari! ables in the building projects studied in Hong Kong[ Although some of the variables identi_ed in the time and cost indexes equations can not be quanti_ed prior to the commencement of a project\ the knowledge that they would in~uence potential time and cost\ enables project managers to pay special attention to controlling the cor! responding variables more e}ectively[ It is also noted that allowances are needed for special scenarios and variables which are di.cult to incorporate satisfactorily in such quantitatively derived general models[ Further testing and investigations are envisaged following the collection and analysis of detailed data from more building projects in order to increase the sample size and hence improve the reliability of the derived relationships[ Once vali! dated\ these relationships can be useful in reducing time and cost over!runs by way of more focused planning and control of building projects\ i[e[ by paying closer attention to the identi_ed critical variables[
4[ Discussion and Concluding Observations According to the foregoing results and derived relationships\ the in~uence of procurement sub!systems by themselves on time and cost performance\ is not as signi_cant as that of other variables[ For example\ it appears from the time index equation that project com! plexity representative value\ programme duration and
References ð0Ł Masterman JWE[ An Introduction to Building Procurement Systems[ E and FN Spon\ London 0881[ ð1Ł Rowlinson SM[ An analysis of factors a}ecting project per! formance in industrial building[ PhD thesis\ Brunel University\ UK 0877[ ð2Ł Naoum SG[ Procurement Systems and Project Performance[
S[M[ Dissanayaka\ M[M[ Kumaraswamy:Buildin` and Environment 23 "0888# 20Ð31
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Occasional Paper No[ 34\ The Chartered Institute of Building\ UK 0880[ Naoum SG[ Critical Analysis of Time and Cost of Management and Traditional Contractions[ Journal of Construction Engin! eering + Management 0883^019"3#]576Ð694[ Naoum SG\ Mustapha FH[ In~uences of the Client\ Designer and Procurement Methods on Project Performance[ CIB W81\ East Meets West\ Procurement Systems Symposium\ CIB Publication No 064\ Hong Kong 0883^110Ð7[ Walker DHT[ An investigation into factors that determine building construction time performance[ PhD thesis\ RMIT\ Australia 0883[ Walker DHT[ The in~uence of Client and Project Teams relation! ships upon construction time performance[ Journal of Con! struction Procurement 0884^0"0#]3Ð19[ Rwelamila PD\ Hall KA[ An Inadequate Traditional procurement system< Where do we go from here< East Meets West\ Procurement Systems Symposium\ CIB No 064\ Hong Kong 0883^096Ð03[ Franks J[ Building Procurement Systems[ The Chartered Institute of Building\ UK 0873[ Love P[ Fast Building] An Australian perspective[ CIB W81\ North Meets South\ Procurement Systems Symposium\ Durban\ South Africa 0885^208Ð17[ Sharif A\ Morledge R[ Procurement Strategies] The Dependency Linkage[ CIB W81\ North Meets South\ Procurement Systems Symposium\ Durban\ South Africa 0885^455Ð66[ Kumaraswamy MM\ Dissanayaka SM[ Procurement by Objec! tives[ Journal of Construction Procurement 0885^1"1#]27Ð40[ Turner A[ Building Procurement[ Macmillan Education Ltd[\ Lon! don 0889[ Akintoye SA[ Design and Build Procurement Method in the UK Construction Industry[ CIB W81\ East Meets West\ Procurement Systems Symposium\ CIB No 064\ Hong Kong 0883^0Ð09[
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ð04Ł Dulami MF\ Dalziel RC[ The E}ects of the Procurement Method on the Level of Management Synergy in Construction Projects[ CIB W81\ East Meets West\ Procurement Systems Symposium\ CIB Publication No 064\ Hong Kong 0883^42Ð59[ ð05Ł Hashim M[ The E}ects of Procurement Systems on Performance of Construction Projects in Malaysia[ CIB W81\ North Meets South\ Procurement Systems Symposium\ Durban\ South Africa 0885^081Ð88[ ð06Ł Ho DCW\ Chau KW\ Leung HF[ Are Contractors prepared to take on design and build contract< CIB W81\ North Meets South\ Procurement Systems Symposium\ Durban\ South Africa 0885^102Ð13[ ð07Ł Ashely DB\ Lurie CS\ Jaselis EJ[ Determinants of Construction Project Success[ Project Management Journal 0876^07"1#]58Ð68[ ð08Ł Hughes WP[ Identifying the Environments of Construction Projects[ Construction Management and Economics 0878^6"0#]18Ð 39[ ð19Ł Liu MMA[ Evaluation of the Outcome of Construction Projects[ PhD thesis\ University of Hong Kong 0884[ ð10Ł De Wit A[ Measurement of project success[ Project Management Journal 0877^5"2#]053Ð69[ ð11Ł Beck MSL[ Factor Analysis and Related Techniques[ International Handbooks of Quantitative Applications in the Social Sciences\ Vol[ 4[ SAGE Publications 0883[ ð12Ł SAS:INSIGHT User|s guide\ Version 5\ 1nd edn[ SAS Institute Inc\ USA 0882[ ð13Ł McKim R\ Adas A\ Handa VK[ Construction Firm organizational E}ectiveness] A Neural Network!Based Prediction Methodology[ The Organization and Management of Construction] Shaping theory and practice\ CIB W54 Conference\ Glasgow 0885^2]136Ð 45[
Appendix 0] Initial variables and their evaluation criteria:measures Macro variables
Micro variables
Code
Criteria:measures
Project characteristics
"A[0# Type of building
0 1 2 3 4 5
Residential Commercial:O.ce Hospital School Industrial Other
"A[1# Type of construction
0 1 2
New Refurbishment Extension
"A[2# Original Cost Estimate "A[2a# Final Cost "A[3# Programmed Duration "A[3a# Actual Duration Size of Project "A[4# No of Storeys "A[5# No of Blocks "A[6# Total Gross Floor Area Project complexity "A[7# Location\ "A[8# Design\ "A[09# Construction\ "A[00# Coordinative and "A[01# Changes "with time#
HK, Months
In numbers In numbers Square feet 4
Very high
3 2 1 0
Moderately high Average Slightly low Very low
39
S[M[ Dissanayaka\ M[M[ Kumaraswamy:Buildin` and Environment 23 "0888# 20Ð31
Appendix 0] contd[ Macro variables
Micro variables
Code
Criteria:measures
Procurement system
"B[0# Functional grouping
0 1 2
Traditional "Sequential# Traditional "Fast Track# Design + Build
"B[1# Payment Method
0 1 2
Lump Sum Fixed price "without ~uctuations# Lump sum Fixed price "with ~uctuations# Remeasure
"B[2# Selection Procedure used for Consultant + Contractor
0 1 2
Tenders from prequali_ed parties + lowest bid Open tenders + Lowest bid Other means
"B[3# Contract conditions used
0 1 2
HK Government Conditions of Contract HKIA Other
Team performance
"C[0# Contractor Team "C[1# Design Team "C[2# Management Team
4 3 2 1 2
Very Good Good Average Poor Very Poor
Client characteristics
"D[0# Client type
0 1 2 3 4
Developer Investor Local:Central government Authority Occupier Other "mixed#
"D[1# Client sources of Finance
0 1 2
Public Private Mixed
0Ð09
Scale of 0Ð09 "09 is highest#
0
Yes
9
No
4
Very high
3 2 1 0
High Average Low Very Low
"D[2# Priority On Time On Cost "D[3# Did the client communicate his requirements and priorities clearly at beginning of the project< "D[4# Were the client requirements and priorities realistic in terms of! "a# Time "b# Cost "c# Quality "d# Scope "D[5# Client involvement in the project in the following aspects "a# Planning "b# Decision Making "c# Controlling "D[6# Project Manager capabilities to handle the project "D[7# Project Manager experience on project of this type "D[8# Project Manager authority to authorise "a# Time extension "b# Extra costs "c# Modi_ed speci_cation "D[09# Project Manager ability to make binding:authoritative decisions "D[00# Project Manager ability to contribute ideas to the construction process "D[01# Speed of decision making "D[02# Client con_dence in the design team "D[03# Client con_dence in the construction team
S[M[ Dissanayaka\ M[M[ Kumaraswamy:Buildin` and Environment 23 "0888# 20Ð31 Appendix 0] contd[ Macro variables
Micro variables
Code
Criteria:measures
4 3
Very high High
2
Average
1 0
Low Very Low
"D[04# E}ectiveness of information ~ow in "a# Reporting "b# Changes "variations# "c# Instructions "D[05# E}ectiveness of Communication with "a# Management Team:Construction Team "b# Management Team:Design Team "D[06# Clience incentives to accelerate the project "D[07# Project Team motivation and goal orientation "D[08# Frequency of project meetings "D[19# Frequency of schedule adjustments "D[10# Client interest in safety measures "D[11# Level "degree# of Risk allocated to the contractor "D[12# Level "degree# of di.culty of getting interim payments "D[13# Di.culty of getting Payments for additional works "D[14# Possibility of long term relationship "D[15# Level "degree# of the co!operation between the contractor team and the client team "D[16# Level "degree# of the co!operation between the design team and the client team "D[17# Level "degree# of the co!operation between the project management team and the client|s main organization Contractor characteristics "E[0# Contractor Management Sta} number "E[1# Quality of Management and supervision of contractor|s sta} "E[2# Contractor|s top management involvement in the Project "E[3# Availability of Equipment "E[4# Availability of competent workforce "E[5# Response to instructions "E[6# E}ective communications "a# within contractor|s own team "b# between contractor|s team and design:supervision team "c# between contractor|s team and project management team "E[7# Financial di.culties during the construction "E[8# Number of subcontractors involved "E[09# Reporting Frequency "a# Progress "b# Budget "c# Changes and claims "E[00# Contractor team turnover "E[01# Control systems E}ectiveness "a# Budget "b# Resources "E[02# Contractor involvement in general decision making "E[03# Trustworthiness of contractor "E[04# Level of decentralization of contractor project organization
30
31
S[M[ Dissanayaka\ M[M[ Kumaraswamy:Buildin` and Environment 23 "0888# 20Ð31
Appendix 0] contd[ Macro variables
Micro variables
Code
Criteria:measures
Design Team characteristics
"F[0# Design team experience "F[1# Frequency of changes in drawings "F[2# Accuracy of detailing in drawings "F[3# E}ective Communication of design details to contractor "F[4# Complexity level of drawings
4 3 2 1
Very high High Average Low
0
Very Low
"G[0# Physical "G[1# Cultural "G[2# Social "G[3# Political "G[4# Legal "G[5# Economical "G[6# Others "specify#
4 3 2 1 0
Very high High Average Low Very Low
External conditions
{{Project Manager|| is taken here to mean the client|s representative "either in!house or authorised#\ overseeing both {{design|| and {{construction|| aspects[