Assessing Collusion Risks in Public Construction Projects: A Case of China

Shan, Ming; Le, Yun; Chan, Albert P. C.; Hu, Yi

doi:10.1007/978-981-13-9550-5_8

Ming Shan⁵,
Yun Le⁶,
Albert P. C. Chan⁷ &
…
Yi Hu⁸

491 Accesses

Abstract

Collusion has been defined as a set of behaviours where competitors coordinate their market behaviour surreptitiously, which is contrary to the principles of free competition (Chotibhongs and Arditi 2012a, b; Zarkada-Fraser 2000).

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

Access this chapter

Log in via an institution

Chapter: USD 29.95; Price excludes VAT (USA)

eBook: USD 84.99; Price excludes VAT (USA)

Softcover Book: USD 109.99; Price excludes VAT (USA)

Hardcover Book: USD 109.99; Price excludes VAT (USA)

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

Al-Sobiei, O. S., Arditi, D., & Polat, G. (2005). Predicting the risk of contractor default in Saudi Arabia utilizing artificial neural network (ANN) and genetic algorithm (GA) techniques. Construction Management and Economics, 23(4), 423–430. https://doi.org/10.1080/01446190500041578.
Article Google Scholar
Alutu, O. (2007). Unethical practices in Nigerian construction industry: Prospective engineers’ viewpoint. Journal of Professional Issues in Engineering Education and Practice, 133(2), 84–88.
Article Google Scholar
Alutu, O., & Udhawuve, M. (2009). Unethical practices in Nigerian engineering industries: Complications for project management. Journal of Management in Engineering, 25(1), 40–43.
Article Google Scholar
Ameh, O. J., & Odusami, K. T. (2010). Professionals’ ambivalence toward ethics in the Nigerian construction industry. Journal of Professional Issues in Engineering Education and Practice, 136(1), 9–16. https://doi.org/10.1061/(asce)1052-3928(2010)136:1(9).
Article Google Scholar
Ameyaw, E. E., Hu, Y., Shan, M., Chan, A. P. C., & Le, Y. (2016). Application of Delphi method in construction engineering and management research: A quantitative perspective. Journal of Civil Engineering and Management, 22(8), 991–1000. https://doi.org/10.3846/13923730.2014.945953.
Article Google Scholar
Ballesteros-Perez, P., Carmen Gonzalez-Cruz, M., Canavate-Grimal, A., & Pellicer, E. (2013). Detecting abnormal and collusive bids in capped tendering. Automation in Construction, 31, 215–229. https://doi.org/10.1016/j.autcon.2012.11.036.
Article Google Scholar
Berry, M. J. A., & Linoff, G. (1997). Data mining techniques. New York: Wiley.
Google Scholar
Boussabaine, A. H. (1996). The use of artificial neural networks in construction management: A review. Construction Management and Economics, 14(5), 427–436. https://doi.org/10.1080/014461996373296.
Article Google Scholar
Bowen, P., Akintoye, A., Pearl, R., & Edwards, P. J. (2007a). Ethical behaviour in the South African construction industry. Construction Management and Economics, 25(6), 631–648.
Article Google Scholar
Bowen, P., Pearl, R., & Akintoye, A. (2007b). Professional ethics in the South African construction industry. Building Research and Information, 35(2), 189–205.
Article Google Scholar
Bowen, P. A., Edwards, P. J., & Cattell, K. (2012). Corruption in the South African construction industry: A thematic analysis of verbatim comments from survey participants. Construction Management and Economics, 30(10), 885–901.
Article Google Scholar
Brown, J., & Loosemore, M. (2015). Behavioural factors influencing corrupt action in the Australian construction industry. Engineering, Construction and Architectural Management, 22(4), 372–389.
Google Scholar
Carr, R. I., & Maloney, W. F. (1983). Basic research needs in construction engineering. Journal of Construction Engineering and Management, 109(2), 181–189. https://doi.org/10.1061/(ASCE)0733-9364(1983)109:2(181).
Article Google Scholar
Cheng, M. Y., Tsai, H. C., & Sudjono, E. (2011). Evaluating subcontractor performance using evolutionary fuzzy hybrid neural network. International Journal of Project Management, 29(3), 349–356. https://doi.org/10.1016/j.ijproman.2010.03.005.
Article Google Scholar
Cheng, M. Y., Tsai, H. C., & Sudjono, E. (2012). Evolutionary fuzzy hybrid neural network for dynamic project success assessment in construction industry. Automation in Construction, 21(1), 46–51. https://doi.org/10.1016/j.autcon.2011.05.011.
Article Google Scholar
Cheng, M. Y., Wu, Y. W., & Wu, C. F. (2010). Project success prediction using an evolutionary support vector machine inference model. Automation in Construction, 19(3), 302–307. https://doi.org/10.1016/j.autcon.2009.12.003.
Article Google Scholar
Cheung, S. O., Tam, C. M., & Harris, F. C. (2000). Project Dispute Resolution Satisfaction classification through neural network. Journal of management in engineering, 16(1), 70–79. https://doi.org/10.1061/(ASCE)0742-597X(2000)16:1(70).
Article Google Scholar
Cheung, S. O., Wong, P. S. P., Fung, A. S. Y., & Coffey, W. V. (2006). Predicting project performance through neural networks. International Journal of Project Management, 24(3), 207–215. https://doi.org/10.1016/j.ijproman.2005.08.001.
Article Google Scholar
Chotibhongs, R., & Arditi, D. (2012a). Analysis of collusive bidding behaviour. Construction Management and Economics, 30(3), 221–231.
Google Scholar
Chotibhongs, R., & Arditi, D. (2012b). Detection of collusive behavior. Journal of Construction Engineering and Management, 138(11), 1251–1258.
Google Scholar
De Jong, M., Henry, W. P., & Stansbury, N. (2009). Eliminating corruption in our engineering/construction industry. Leadership and Management in Engineering, 9(3), 105–111. https://doi.org/10.1061/(ASCE)1532-6748(2009)9:3(105).
Article Google Scholar
Demuth, D., & Beale, M. (2000). Neural network toolbox for use with MATLAB. Natick, Massachusetts: Math Works.
Google Scholar
Dikmen, I., Birgonul, M. T., & Kiziltas, S. (2005). Prediction of organizational effectiveness in construction companies. Journal of Construction Engineering and Management, 131(2), 252–261. https://doi.org/10.1061/(ASCE)0733-9364(2005)131:2(252).
Article Google Scholar
Dorée, A. G. (2004). Collusion in the Dutch construction industry: an industrial organization perspective. Building Research & Information, 32(2), 146–156.
Article Google Scholar
Georgy, M. E., Chang, L. M., & Zhang, L. (2005). Prediction of engineering performance: A neurofuzzy approach. Journal of Construction Engineering and Management-ASCE, 131(5), 548–557. https://doi.org/10.1061/(asce)0733-9364(2005)131:5(548).
Article Google Scholar
Goh, A. T. C. (1995). Back-propagation neural networks for modeling complex systems. Artificial Intelligence in Engineering, 9(3), 143–151. https://doi.org/10.1016/0954-1810(94)00011-S.
Article Google Scholar
Goh, Y. M., & Binte Sa’Adon, N. F. (2015). Cognitive factors influencing safety behavior at height: A multimethod exploratory study. Journal of Construction Engineering and Management, 141(6). https://doi.org/10.1061/(asce)co.1943-7862.0000972.
Article Google Scholar
Goh, Y. M., & Chua, D. (2013). Neural network analysis of construction safety management systems: A case study in Singapore. Construction Management and Economics, 31(5), 460–470. https://doi.org/10.1080/01446193.2013.797095.
Article Google Scholar
Gunduz, M., & Onder, O. (2013). Corruption and internal fraud in the Turkish construction industry. Science and Engineering Ethics, 19(2), 505–528. https://doi.org/10.1007/s11948-012-9356-9.
Article Google Scholar
Gunduz, M., & Yahya, A. M. A. (2018). Analysis of project success factors in construction industry. Technological and Economic Development of Economy, 24(1), 67–80. https://doi.org/10.3846/20294913.2015.1074129.
Article Google Scholar
Hallowell, M. R., & Gambatese, J. A. (2010). Qualitative research: Application of the Delphi method to CEM research. Journal of Construction Engineering and Management, 136(1), 99–107. https://doi.org/10.1061/(asce)co.1943-7862.0000137.
Article Google Scholar
Hartley, R. (2009). Fighting corruption in the Australian construction industry: The national code of practice. Leadership and Management in Engineering, 9(3), 131–135.
Article Google Scholar
Hon, C. K. H., Chan, A. P. C., & Yam, M. C. H. (2012). Empirical study to investigate the difficulties of implementing safety practices in the repair and maintenance sector in Hong Kong. Journal of Construction Engineering and Management, 138(7), 877–884. https://doi.org/10.1061/(asce)co.1943-7862.0000497.
Article Google Scholar
Hu, Y., Chan, A. P., Le, Y., & Jin, R.-Z. (2013). From construction megaproject management to complex project management: Bibliographic analysis. Journal of Management in Engineering, 31(4), 04014052.
Article Google Scholar
Hussain, M. A., Aroua, M. K., Yin, C. Y., Rahman, R. A., & Ramli, N. A. (2010). Hybrid neural network for prediction of CO₂ solubility in monoethanolamine and diethanolamine solutions. Korean Journal of Chemical Engineering, 27(6), 1864–1867. https://doi.org/10.1007/s11814-010-0270-z.
Article Google Scholar
Hwang, B. G., & Ng, H. B. (2016). Project network management: Risks and contributors from the viewpoint of contractors and sub-contractors. Technological and Economic Development of Economy, 22(4), 631–648. https://doi.org/10.3846/20294913.2015.1067847.
Article Google Scholar
Hwang, B.-G., Zhao, X., & Goh, K. J. (2014). Investigating the client-related rework in building projects: The case of Singapore. International Journal of Project Management, 32(4), 698–708. https://doi.org/10.1016/j.ijproman.2013.08.009.
Article Google Scholar
Hwang, B. G., Zhao, X., & Ong, S. Y. (2015a). Value management in Singaporean building projects: Implementation status, critical success factors, and risk factors. Journal of management in engineering, 31(6). https://doi.org/10.1061/(asce)me.1943-5479.0000342.
Article Google Scholar
Hwang, B. G., Zhao, X., See, Y. L., & Zhong, Y. (2015b). Addressing risks in green retrofit projects: the case of Singapore. Project Management Journal, 46(4), 76–89. https://doi.org/10.1002/pmj.21512.
Article Google Scholar
Hwang, B.-G., Zhao, X., & Yu, G. S. (2016). Risk identification and allocation in underground rail construction joint ventures: Contractors’ perspective. Journal of Civil Engineering and Management, 22(6), 758–767. https://doi.org/10.3846/13923730.2014.914095.
Article Google Scholar
Ika, L. A. (2012). Project management for development in Africa: Why projects are failing and what can be done about it. Project Management Journal, 43(4), 27–41. https://doi.org/10.1002/pmj.21281.
Article Google Scholar
Jamieson, S. (2004). Likert scales: How to (ab)use them. Medical Education, 38(12), 1217–1218. https://doi.org/10.1111/j.1365-2929.2004.02012.x.
Article Google Scholar
Jha, K. N., & Chockalingam, C. T. (2011). Prediction of schedule performance of Indian construction projects using an artificial neural network. Construction Management and Economics, 29(9), 901–911. https://doi.org/10.1080/01446193.2011.608691.
Article Google Scholar
Jin, X. H., & Zhang, G. (2011). Modelling optimal risk allocation in PPP projects using artificial neural networks. International Journal of Project Management, 29(5), 591–603. https://doi.org/10.1016/j.ijproman.2010.07.011.
Article Google Scholar
Ke, Y., Wang, S., Chan, A. P. C., & Cheung, E. (2011). Understanding the risks in China’s PPP projects: Ranking of their probability and consequence. Engineering, Construction and Architectural Management, 18(5), 481–496. https://doi.org/10.1108/09699981111165176.
Article Google Scholar
Ko, C. H., & Cheng, M. Y. (2007). Dynamic prediction of project success using artificial intelligence. Journal of Construction Engineering and Management, 133(4), 316–324. https://doi.org/10.1061/(ASCE)0733-9364(2007)133:4(316).
Article Google Scholar
Le, Y., & Shan, M. (2014). Research trend of collusion in top construction journals. Paper presented at the Proceedings of the 17th International Symposium on Advancement of Construction Management and Real Estate.
Google Scholar
Le, Y., Shan, M., Chan, A. P., & Hu, Y. (2014). Investigating the causal relationships between causes of and vulnerabilities to corruption in the Chinese public construction sector. Journal of Construction Engineering and Management, 140(9), 05014007.
Google Scholar
Lo, W., Krizek, R. J., & Hadavi, A. (1999). Effects of high prequalification requirements. Construction Management and Economics, 17(5), 603–612.
Article Google Scholar
Molenaar, K. R. (2005). Programmatic cost risk analysis for highway megaprojects. Journal of Construction Engineering and Management, 131(3), 343–353. https://doi.org/10.1061/(ASCE)0733-9364(2005)131:3(343).
Article Google Scholar
Moselhi, O., Hegazy, T., & Fazio, P. (1991). Neural networks as tools in construction. Journal of Construction Engineering and Management, 117(4), 606–625. https://doi.org/10.1061/(ASCE)0733-9364(1991)117:4(606).
Article Google Scholar
National Bureau of Corruption Prevention of China, N. (2011). Analysis on typical corruption cases in the construction sector and instructions for corruption prevention. Fangzheng Press of China, Beijing, China.
Google Scholar
OECD. (2009). Guidelines for fighting bid rigging in public procurement. Retrieved from http://www.oecd.org/daf/competition/cartels/42851044.pdf.
OECD. (2012). Recommendation of the OECD council on fighting bid rigging in public procurement. Retrieved from http://www.oecd.org/daf/competition/RecommendationOnFightingBidRigging2012.pdf.
Patel, D. A., & Jha, K. N. (2015a). Neural network approach for safety climate prediction. Journal of management in engineering, 31(6). https://doi.org/10.1061/(asce)me.1943-5479.0000348.
Article Google Scholar
Patel, D. A., & Jha, K. N. (2015b). Neural network model for the prediction of safe work behavior in construction projects. Journal of Construction Engineering and Management, 141(1). https://doi.org/10.1061/(asce)co.1943-7862.0000922.
Article Google Scholar
Priemus, H. (2004). Dutch contracting fraud and governance issues. Building Research and Information, 32(4), 306–312. https://doi.org/10.1080/0961321042000221089.
Article Google Scholar
Ray, R. S., Hornibrook, J., Skitmore, M., & Zarkada-Fraser, A. (1999). Ethics in tendering: A survey of Australian opinion and practice. Construction Management and Economics, 17(2), 139–153. https://doi.org/10.1080/014461999371646.
Article Google Scholar
Rumelhart, D. E., Widrow, B., & Lehr, M. A. (1994). The basic ideas in neural networks. Communications of the ACM, 37(3), 87–92. https://doi.org/10.1145/175247.175256.
Article Google Scholar
Samarasinghe, S. (2007). Neural networks for applied sciences and engineering. Boca Raton: Taylor & Francis.
Google Scholar
Shan, M., Chan, A. P. C., Le, Y., & Hu, Y. (2015a). Investigating the effectiveness of response strategies for vulnerabilities to corruption in the Chinese public construction sector. Science and Engineering Ethics, 21(3), 683–705. https://doi.org/10.1007/s11948-014-9560-x.
Article Google Scholar
Shan, M., Chan, A. P. C., Le, Y., Xia, B., & Hu, Y. (2015b). Measuring corruption in public construction projects in China. Journal of Professional Issues in Engineering Education and Practice, 141(4). https://doi.org/10.1061/(asce)ei.1943-5541.0000241.
Article Google Scholar
Shan, M., Chan, A. P. C., Le, Y., Hu, Y., & Xia, B. (2017). Understanding collusive practices in Chinese construction projects. Journal of Professional Issues in Engineering Education and Practice, 143(3). https://doi.org/10.1061/(asce)ei.1943-5541.0000314.
Article Google Scholar
Shen, L. Y., Wu, G. W. C., & Ng, C. S. K. (2001). Risk assessment for construction joint ventures in China. Journal of Construction Engineering and Management, 127(1), 76–81. https://doi.org/10.1061/(ASCE)0733-9364(2001)127:1(76).
Article Google Scholar
Sichombo, B., Muya, M., Shakantu, W., & Kaliba, C. (2009). The need for technical auditing in the Zambian construction industry. International Journal of Project Management, 27(8), 821–832.
Article Google Scholar
Sohail, M., & Cavill, S. (2008). Accountability to prevent corruption in construction projects. Journal of Construction Engineering and Management, 134(9), 729–738.
Article Google Scholar
Tabish, S., & Jha, K. N. (2011). Analyses and evaluation of irregularities in public procurement in India. Construction Management and Economics, 29(3), 261–274.
Article Google Scholar
The National People’s Congress of People’s Republic of China, T. (1999). Bidding law of People’s Republic of China. Retrieved from http://www.npc.gov.cn/wxzl/gongbao/2000-12/05/content_5004749.htm.
Vee, C., & Skitmore, C. (2003). Professional ethics in the construction industry. Engineering, Construction and Architectural Management, 10(2), 117–127.
Article Google Scholar
Wang, Y. R., & Gibson, G. E., Jr. (2010). A study of preproject planning and project success using ANNs and regression models. Automation in Construction, 19(3), 341–346. https://doi.org/10.1016/j.autcon.2009.12.007.
Article Google Scholar
Wang, J., Liu, J., Liao, Z., & Tang, P. (2009). Identification of key liability risks of supervision engineers in China. Construction Management and Economics, 27(12), 1157–1173. https://doi.org/10.1080/01446190903222395.
Article Google Scholar
Xia, B., & Chan, A. P. C. (2012a). Identification of selection criteria for operational variations of the design-build system: A Delphi study in China. Journal of Civil Engineering and Management, 18(2), 173–183. https://doi.org/10.3846/13923730.2012.657417.
Article Google Scholar
Xia, B., & Chan, A. P. C. (2012b). Measuring complexity for building projects: A Delphi study. Engineering, Construction and Architectural Management, 19(1), 7–24. https://doi.org/10.1108/09699981211192544.
Article Google Scholar
Xinhua Net, X. (2014). Irregularities in biddings of three gorges project: Nepotism, and waste of public money. Retrieved from http://news.xinhuanet.com/finance/2014-02/19/c_126159860.htm.
Xinhua Net, X. (2015). How many inside stories of bidding have been refracted by huge kickbacks in small projects? Retrieved from http://news.xinhuanet.com/fortune/2015-09/19/c_1116615043.htm.
Yi, W., & Chan, A. P. C. (2014). Critical review of labor productivity research in construction journals. Journal of Management in Engineering, 30(2), 214–225. https://doi.org/10.1061/(asce)me.1943-5479.0000194.
Article Google Scholar
Zarkada-Fraser, A. (2000). A classification of factors influencing participating in collusive tendering agreements. Journal of Business Ethics, 23(3), 269–282.
Article Google Scholar
Zarkada-Fraser, A., & Skitmore, M. (2000). Decisions with moral content: Collusion. Construction Management & Economics, 18(1), 101–111.
Article Google Scholar
Zhang, G., Eddy Patuwo, B., & Y. Hu, M. (1998). Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting, 14(1), 35-62. https://doi.org/10.1016/s0169-2070(97)00044-7.
Article Google Scholar
Zhang, B., Le, Y., Xia, B., & Skitmore, M. (2016). Causes of business-to-government corruption in the tendering process in China. Journal of management in engineering.
Google Scholar
Zhao, X., Hwang, B.-G., & Low, S. P. (2013a). Developing fuzzy enterprise risk management maturity model for construction firms. Journal of Construction Engineering and Management, 139(9), 1179–1189. https://doi.org/10.1061/(asce)co.1943-7862.0000712.
Article Google Scholar
Zhao, X., Hwang, B. G., & Low, S. P. (2013b). Critical success factors for enterprise risk management in Chinese construction companies. Construction Management and Economics, 31(12), 1199–1214. https://doi.org/10.1080/01446193.2013.867521.
Article Google Scholar
Zhao, X., & Singhaputtangkul, N. (2016). Effects of firm characteristics on enterprise risk management: Case study of Chinese construction firms operating in Singapore. Journal of Management in Engineering, 32(4). https://doi.org/10.1061/(asce)me.1943-5479.0000434.
Article Google Scholar
Zou, P. X. W. (2006). Strategies for minimizing corruption in the construction industry in China. Journal of Construction in Developing Countries, 11(2), 15–29.
Google Scholar
Zou, P. X. W., & Zhang, G. (2009). Comparative study on the perception of construction safety risks in China and Australia. Journal of Construction Engineering and Management, 135(7), 620–627. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000019.
Article Google Scholar

Download references

Author information

Authors and Affiliations

School of Civil Engineering, Central South University, Changsha, Hunan, China
Ming Shan
School of Economics and Management, Tongji University, Shanghai, China
Yun Le
Department of Building and Real Estate, The Hong Kong Polytechnic University, Kowloon, Hong Kong
Albert P. C. Chan
School of Economics and Management, Tongji University, Shanghai, China
Yi Hu

Authors

Ming Shan
View author publications
You can also search for this author in PubMed Google Scholar
Yun Le
View author publications
You can also search for this author in PubMed Google Scholar
Albert P. C. Chan
View author publications
You can also search for this author in PubMed Google Scholar
Yi Hu
View author publications
You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ming Shan .

Rights and permissions

Reprints and permissions

Copyright information

About this chapter

Cite this chapter

Shan, M., Le, Y., Chan, A.P.C., Hu, Y. (2020). Assessing Collusion Risks in Public Construction Projects: A Case of China. In: Corruption in the Public Construction Sector. Springer, Singapore. https://doi.org/10.1007/978-981-13-9550-5_8

Download citation

DOI: https://doi.org/10.1007/978-981-13-9550-5_8
Published: 13 July 2019
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-9549-9
Online ISBN: 978-981-13-9550-5
eBook Packages: Political Science and International StudiesPolitical Science and International Studies (R0)

Publish with us

Policies and ethics