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Group Decision and Negotiation

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01-09-2015

A Group Decision-Making Aggregation Model for Contractor Selection in Large Scale Construction Projects Based on Two-Stage Partial Least Squares (PLS) Path Modeling

Authors: Bingsheng Liu, Tengfei Huo, Pinchao Liao, Jie Gong, Bin Xue

Published in: Group Decision and Negotiation | Issue 5/2015

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Abstract

Modern projects are normally characterized by huge investments, long construction periods, and complex technology. Therefore, the selection of an appropriate contractor for smooth project delivery is challenging. Previous studies attempted to develop suitable frameworks for contractor selection. However, correlation among indicators, the subjectivity of indicator weights, and heterogeneity among experts’ professional capabilities for selecting contractors were not successfully removed from the decision-making process. Typical partial least square (PLS) path modeling can solve these problems. However, it can only solve problems with the same direction correlation among the indicators (e.g., the correlation coefficients of the indicators are all positive). For indicators with different direction correlations, path modeling is helpless. Aiming to overcome this limitation, this research introduces a group decision model based on two-stage PLSs path modeling. This decision model can eliminate correlation among indicators and the impact of subjectivity and heterogeneity among experts on the reliability of weighting schemes; more importantly, it can effectively solve the problem of having correlation indicators with different directions. Through a literature review, we first established an indicator system of contractor selection on large scale construction projects. Second, a two-stage PLS path modeling combined with the maximization of deviations principle was proposed as an aggregation approach for performance evaluation. Finally, a comparison was made between the two-stage and typical PLS path modeling methods through a case study, which was conducted to validate the reliability of the new approach.

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Latent variables: indicate the variables that cannot be subjectively measured directly and that can influence and be reflected or measured by manifest variables, which represent the five categories in Table 1, i.e. finance, tendered price, technology, past project performance, and health/safety/environment.

Manifest variables: indicate the variables that can be measured through indicator scoring directly, which represent the 15 specific indicators in Table 1.

A comprehensive evaluation variable: indicates a kind of synthetic variable that can be explained by latent variables, with the synthetic relationship between the comprehensive evaluation variable and the other latent variables being clearly shown in Eq. (2).

Note: In order to ensure the research objectively, the indicators’ weights of this two-stage PLS path modelling are obtained through indicators’ attributes scoring conducted by more than 20 experts from different professional backgrounds (i.e. financial, tender price, technology, past project performance and HSE) firstly and then information aggregating of decision indicators, which realize the objective presentation and consensus results of all the experts’ suggestion and solve the problem of subjectivity.

Aibinu AA, Al-Lawati AM (2010) Using PLS-SEM technique to model construction organizations’ willingness to participate in e-bidding. Autom Constr 19(6):714–724CrossRef

Alencar LH, Almeida AT (2008) Multi-criteria decision group model for the selection of suppliers. Pesqui Operacional 28:321–337. doi:10.1590/S0101-74382008000200009 CrossRef

Alencar LH, Almeida AT (2010) A model for selecting project team members using multi-criteria group decision making. Pesqui Operacional 30(1):221–236. doi:10.1590/S0101-74382010000100011 CrossRef

Alencar LH, Almeida AT, Morais DC (2010) A multicriteria group decision model aggregating the preferences of decision-makers based on ELECTRE methods. Pesqui Operacional 30:687–702. doi:10.1590/S0101-74382010000300010 CrossRef

Al-Harbi KM (2001) Application of the AHP in project management. Int J Proj Manag 19(1):19–27CrossRef

Almeida AT (2007) Multi-criteria decision model for outsourcing contracts selection based on utility function and ELECTRE method. Comput Oper Res 34(12):3569–3574. doi:10.1016/j.cor.2006.01.003 CrossRef

Al-Reshaid K, Kartam N (2005) Design-build pre-qualification and tendering approach for public projects. Int J Proj Manag 23(4):309–320CrossRef

Alsugair AM (1999) Framework for evaluating bids of construction contractors. J Manag Eng 15(2):72–78CrossRef

Arslan G, Kivrak S, Birgonul MT, Dikmen I (2008) Improving sub-contractor selection process in construction projects: web-based sub-contractor evaluation system (WEBSES). Autom Constr 17(4):480–488CrossRef

Bayazit O (2006) Use of analytic network process in vendor selection decisions. Benchmarking Int J 13(5):566–579CrossRef

Brito AJM, Filho ATA, Almeida AT (2010) Multi-criteria decision model for selecting repair contracts by applying utility theory and variable interdependent parameters. IMA J Manag Math 21(4):349–361. doi:10.1093/imaman/dpn014 CrossRef

Cabrerizo FJ, Herrera-Viedma E, Pedrycz W (2013) A method based on PSO and granular computing of linguistic information to solve group decision-making problems defined in heterogeneous contexts. Eur J Oper Res 230(3):624–633CrossRef

Chan APC, Chan APL (2004) Key performance indicators for measuring construction success. Benchmarking Int J 11(2):203–221CrossRef

Cheng EWL, Li H (2004) Contractor selection using the analytic network process. Constr Manag Econ 22(10):1021–1032CrossRef

Chiclana F, Herrera-Viedma E, Herrera F, Alonso S (2007) Some induced ordered weighted averaging operators and their use for solving group decision-making problems based on fuzzy preference relations. Eur J Oper Res 182(1):383–399CrossRef

Demirtas EA, Üstün Ö (2008) An integrated multi-objective decision making process for supplier selection and order allocation. Omega 36(1):76–90CrossRef

Dempsey WA (1978) Vendor selection and the buying process. Ind Mark Manag 7(4):257–267CrossRef

Dickson GW (1966) An analysis of supplier selection systems and decisions. J Purch 2(1):5–17

Faisal MN, Banwet DK, Shankar R (2007) Quantification of risk mitigation environment of supply chains using graph theory and matrix methods. Eur J Ind Eng 1(1):22–39CrossRef

Guinot C, Latreille J, Tenenhaus M (2001) PLS path modelling and multiple table analysis. Application to the cosmetic habits of women in lle-de-France. Chemometr Intell Lab Syst 58(2):247–259CrossRef

Harris P, Holt G (1999) The management of sustainable social housing strategies in the West Midlands region of the UK. In: The ARCOM fifteenth annual conference, vol 1, pp 203–210

Hatush Z, Skitmore M (1997) Assessment and evaluation of contractor data against client goals using PERT approach. Constr Manag Econ 15(4):327–340CrossRef

Hatush Z, Skitmore M (1998) Contractor selection using multicriteria utility theory: an additive model. Build Environ 33(2):105–115CrossRef

Holt GD (1996) Applying cluster analysis to construction contractor classification. Build Environ 31(6):557–568

Holt GG (1998) Which contractor selection methodology? Int J Proj Manag 16(3):153–164CrossRef

Holt GD, Olomaiye PO, Harris FC (1994) Factors influencing UK construction clients choice of contractor. Build Environ 29(2):241–248CrossRef

Isik Z, Arditi D, Dikmen I, Birgonul MT (2009) Impact of corporate strengths/weaknesses on project management competencies. Int J Proj Manag 27(6):629–637CrossRef

Jakobowicz E, Derquenne C (2007) A modified PLS path modeling algorithm handling reflective categorical variables and a new model building strategy. Comput Stat Data Anal 51(8):3666–3678CrossRef

Jaskowski P, Biruk S, Bucon R (2010) Assessing contractor selection criteria weights with fuzzy AHP method application in group decision environment. Autom Constr 19(2):120–126CrossRef

Jennings P, Holt GD (1998) Prequalification and multi-criteria selection: a measure of contractors’ opinions. Constr Manag Econ 16(6):651–660CrossRef

Kadefors A, Björlingson E, Karlsson A (2007) Procuring service innovations: contractor selection for partnering projects. Int J Proj Manag 25(4):375–385CrossRef

Kashiwagi D, Byfield RE (2002) Selecting the best contractor to get performance: on time, on budget, meeting quality expectations. J Facil Manag 1(2):103116

Keeney RL, Raiffa H (1976) Decisions with multiple objectives: preferences and value tradeoffs. Wiley series in probability and mathematical statistics. Wiley, New York

Khosrowshahi F (2001) Neural network model for contractors’ prequalification for local authority projects. Eng Constr Archit Manag 6(3):315–328CrossRef

Kumaraswamy MM (1996) Contractor evaluation and selection: a Hong Kong perspective. Build Environ 31(3):273–282CrossRef

Lai KK, Liu SL, Wang SY (2004) A method used for evaluating bids in the Chinese construction industry. Int J Proj Manag 22(3):193–201CrossRef

Lam KC, Ng ST, Hu T, Skitmore RM, Cheung SO (2000) Decision support system for contractor pre-qualification-artificial neural network model. Eng Constr Archit Manag 7(3):251–266

Lambropoulos S (2007) The use of time and cost utility for construction contract award under European Union Legislation. Build Environ 42(1):452–463CrossRef

Li Y, Nie X, Chen S (2007) Fuzzy approach to prequalifying construction contractors. J Constr Eng Manag 133(1):40–49CrossRef

Li YC, Suo JJ, Zhou SJ (2008) New safety assessment model for civil engineering structure. In: International workshop on modelling, simulation and optimization, 2008. WMSO’08. IEEE, pp 143–145

Liu BS, Huo TF, Wang XQ, Shen QP, Chen Y (2013) The decision model of the intuitionistic fuzzy group bid evaluation for urban infrastructure projects considering social costs. Can J Civ Eng 40(3):263–273CrossRef

Liu BS, Shen YH, Chen XH, Sun H, Chen Y (2014) A complex multi-attribute large-group PLS decision-making method in the interval-valued intuitionistic fuzzy environment. Appl Math Model. doi:10.1016/j.apm.2014.02.023

Liu BS, Wang XQ, Li B (2011) Analysis of Chinese construction industry competitiveness formation mechanism—empirical research of PLS-based structural equation model. J Appl Stat Manag 11:12–22

LohmLoller JB (1989) Latent variables path modeling with partial least squares. Physica-Verlag, HeildelbergCrossRef

Lopes M, Flavell R (1998) Project appraisal—a framework to assess nonfinancial aspects of projects during the project life cycle. Int J Proj Manag 16(4):223–233CrossRef

Mahdi IM, Riley MJ, Fereig SM, Alex AP (2002) A multi-criteria approach to contractor selection. Eng Constr Archit Manag 9(1):19–37

McCabe B, Tran V, Ramani J (2005) Construction prequalification using data envelopment analysis. Can J Civ Eng 32(1):183–193CrossRef

Missbauer H, Hauber W (2005) Bid calculation for construction projects: regulations and incentive effects of unit price contracts. Eur J Oper Res 171(3):1005–1019CrossRef

Ng ST, Skitmore RM (1999) Client and consultant perspectives of prequalification criteria. Build Environ 34(5):607–621CrossRef

Ng ST, Skitmore RM (2001) Contractor selection criteria: a cost-benefit analysis. IEEE Trans Eng Manag 48(1):96–106CrossRef

Ng ST, Thomas S, Skitmore RM (1995) CP-DSS: decision support system for contractor prequalification. Civ Eng Environ Syst 12(2):133–159CrossRef

Padhi SS, Mohapatra PKJ (2009) Contractor selection in government procurement auctions: a case study. Eur J Ind Eng 3(2):170–186CrossRef

Padhi SS, Pratap KJ, Mohapatra PKJ (2010) Centralized bid evaluation for awarding of construction projects—a case of India government. Int J Proj Manag 28(3):275–284CrossRef

Palaneeswaran E, Kumaraswamy M (2001) Recent advances and proposed improvements in contractor prequalification methodologies. Build Environ 36(1):73–87CrossRef

Pérez IJ, Cabrerizo FJ, Alonso S, Herrera-Viedma E (2013) A new consensus model for group decision-making problems with non-homogeneous experts. IEEE Trans Syst Man Cybern Syst. doi:10.1109/TSMC.2013.2259155

Pérez IJ, Cabrerizo FJ, Herrera-Viedma E (2010) A mobile decision support system for dynamic group decision-making problems. IEEE Trans Syst Man Cybern Part A Syst Humans 40:6(6):1244–1256CrossRef

Plebankiewicz E (2009) Contractor prequalification model using fuzzy sets. J Civ Eng Manag 15(4):377–385CrossRef

Proverbs DG, Holt GD, Olomolaiye PO (1997) Factors influencing the choice of concrete supply methods. Build Res Inform 25(3):176–184CrossRef

Ravi V, Shankar R, Tiwari MK (2005) Analyzing alternatives in reverse logistics for end-of-life computers: ANP and balanced scorecard approach. Comput Ind Eng 48(2):327–356CrossRef

Singh D, Tiong RLK (2005) A fuzzy decision framework for contractor selection. J Constr Eng Manag 131(1):62–70CrossRef

Skitmore M, Hatush Z (1997) Criteria for contractor selection. Constr Manag Econ 15(1):19–38CrossRef

Tenenhaus A, Tenenhaus M (2011) Regularized generalized canonical correlation analysis. Psychometrika 76(2):257–284CrossRef

Tenenhaus M, Vinzi VE, Chatelin YM, Lauro C (2005) PLS path modeling. Comput Stat Data Anal 48(1):159–205CrossRef

Topcu YI (2004) A decision model proposal for construction contractor selection in Turkey. Build Environ 39(4):469–481CrossRef

Tran VHQ (2002) Practical frontier in construction pre-qualification using data envelopment analysis. Master thesis, University of Toronto

Wang YM (1998) Using the method of maximizing deviations to make decision for multi-indices. Syst Eng Electr 7:24–26

Wang HW, Fu LH (2004) The application research of partial least square path modeling on establishing synthesis evaluation index. Syst Eng Theory Pract 10(10):80–86

Wang X, Triantaphyllou E (2008) Ranking irregularities when evaluating alternatives by using some ELECTRE methods. Omega 36(1):45–63CrossRef

Wang ZF, Zhang Y (2010) The project bid evaluation model based on entropy method. Sci Technol Manag Res 3:47–48 (in Chinese)

Wang W, Wang H, Lai Y, Li JC (2006) Unit-price-based model for evaluating competitive bids. Int J Proj Manag 24(2):156–166CrossRef

Watt DJ, Kayis B, Willey K (2009) Identifying key factors in the evaluation of tenders for projects and services. Int J Proj Manag 27(3):250–260CrossRef

Watt DJ, Kayis B, Willey K (2010) The relative importance of tender evaluation and contractor selection criteria. Int J Proj Manag 28(1):51–60CrossRef

Weber C, Current JR, Benton WC (1991) Vendor selection criteria and methods. Eur J Oper Res 50(1):2–18CrossRef

Wold H (1985) Partial least squares. In: Kotz S, Johnson NL (eds) Encyclopedia of statistical sciences, vol 6. Wiley, New York, pp 581–591

Wong CH, Holt GD, Cooper PA (2000) Lowest price or value? Investigation of UK construction clients’ tender selection process. Constr Manag Econ 18(7):767–774CrossRef

Wong CH, Holt GD, Harris P (2001) Multi-criteria selection or lowest price? Investigation of UK construction clients’ tender evaluation preferences. Eng Constr Archit Manag 8(4):257–271

Wong CH, Nicholas J, Holt GD (2003) Using multivariate techniques for developing contractor classification models. Eng Constr Archit Manag 10(2):99–116CrossRef

Works Branch (1990) Approved contractors for public works. Rules for administration of lists I and II. Works Branch technical circular no. 2/90

World Bank (1992) Guidelines. Procurement under IBRD Loans and IDA credits

Xiao H, Proverbs D (2003) Factors influencing contractor performance: an international investigation. Eng Constr Archit Manag 10(5):322–332CrossRef

Zavadaskas EK, Vilutiene T (2006) A multiple criteria evaluation of multi-family apartment block’s maintenance contractors: I—model for maintenance contractor evaluation and the determination of its selection criteria. Build Environ 41(5):621–632CrossRef

Title: A Group Decision-Making Aggregation Model for Contractor Selection in Large Scale Construction Projects Based on Two-Stage Partial Least Squares (PLS) Path Modeling
Authors: Bingsheng Liu
Tengfei Huo
Pinchao Liao
Jie Gong
Bin Xue
Publication date: 01-09-2015
Publisher: Springer Netherlands
Published in: Group Decision and Negotiation / Issue 5/2015
Print ISSN: 0926-2644
Electronic ISSN: 1572-9907
DOI: https://doi.org/10.1007/s10726-014-9418-2

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