Skip to main content
SpringerLink
Log in

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

  • Published:
Group Decision and Negotiation Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Notes

  1. 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.

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

  3. 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).

  4. 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.

References

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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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):480–488

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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):624–633

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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):383–399

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

  • 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):22–39

    Article  Google Scholar 

  • 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):247–259

    Article  Google Scholar 

  • 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):327–340

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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):153–164

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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):3666–3678

    Article  Google Scholar 

  • 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):120–126

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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

    Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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):263–273

    Article  Google Scholar 

  • 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, Heildelberg

    Book  Google Scholar 

  • 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):223–233

    Article  Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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):275–284

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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):1244–1256

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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):327–356

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

  • 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):250–260

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

  • 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

    Google Scholar 

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

    Article  Google Scholar 

  • 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):322–332

    Article  Google Scholar 

  • 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):621–632

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Management and Economics, Tianjin University, Tianjin, China

    Bingsheng Liu

  2. Business School of Hohai University, #8 Focheng West Road, Jiangning District, Nanjing, 211100, Jiangsu, People’s Republic of China

    Tengfei Huo

  3. Department of Construction Management, School of Civil Engineering, Tsinghua University, Beijing, 100084, China

    Pinchao Liao

  4. Civil and Environmental Engineering, Rutgers, The State University of New Jersey, 96 Frelinghuysen Road, Piscataway, NJ, 08854-8018, USA

    Jie Gong

  5. School of Management and Economics, Tianjin University, Tianjin, 300072, People’s Republic of China

    Bin Xue

Authors
  1. Bingsheng Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tengfei Huo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pinchao Liao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jie Gong
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Bin Xue
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pinchao Liao.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, B., Huo, T., Liao, P. et al. A Group Decision-Making Aggregation Model for Contractor Selection in Large Scale Construction Projects Based on Two-Stage Partial Least Squares (PLS) Path Modeling. Group Decis Negot 24, 855–883 (2015). https://doi.org/10.1007/s10726-014-9418-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10726-014-9418-2

Keywords

Search

Navigation