Predicting the litigation outcome of PPP project disputes between public authority and private partner using an ensemble model
Abstract
Apart from the loss of time and money, disputes between public authority and private partner in China’s public-private partnership (PPP) projects are destroying the government’s image of PPP support and the private partner’s investment confidence. This article aims to explore the main causes for PPP disputes, present the results of disputes, and then predict the litigation outcomes. Based on 171 PPP litigation cases from China Judgements Online within 2013–2018, the research identified 17 legal factors and explained how these factors influence the litigation outcomes, which are named as “prediction approach” in this study. Nine machine learning (ML) models were trained and validated using the data from 171 cases. The ensemble model of gradient boosting decision tree (GBDT), k-nearest neighbor (KNN) and multi-layer perceptron neural network (MLP) performed best compared with other nine individual ML models, and obtained a prediction accuracy of 96.42%. This study adds meaningful insights to PPP dispute avoidance, such as high compensation of expected revenues could prevent the government from terminating the contract unilaterally. To some extent, if parties consider the case litigation outcome, they are more likely prefer a rational settlement out of court to avoid further aggravation of the dispute, and would also alleviate the pressure of litigation in China.
Keyword : public-private partnership (PPP), project management, dispute causes, court decisions, litigation prediction, artificial intelligence, case study
How to Cite
Zheng, X., Liu, Y., Jiang, J., Thomas, L. M., & Su, N. (2021). Predicting the litigation outcome of PPP project disputes between public authority and private partner using an ensemble model. Journal of Business Economics and Management, 22(2), 320-345. https://doi.org/10.3846/jbem.2021.13219
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Arditi, D., & Pulket, T. (2010). Predicting the outcome of construction litigation using an integrated artificial intelligence model. Journal of Computing in Civil Engineering, 24(1), 73–80. https://doi.org/10.1061/(ASCE)0887-3801(2010)24:1(73)
Arditi, D., & Tokdemir, O. (1999). Using case-based reasoning to predict the outcome of construction litigation. Computer-Aided Civil and Infrastructure Engineering, 14(6), 385–393. https://doi.org/10.1111/0885-9507.00157
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Chan, A. P. C., Chan, D. W. M., & Ho, K. S. K. (2003). Partnering in construction: Critical study of problems for implementation. Journal of Management in Engineering, 19(3), 126–135. https://doi.org/10.1061/(asce)0742-597x(2003)19:3(126)
Chan, A. P. C., Lam, P., Wen, Y., Ameyaw, E., Wang, S. Q., & Ke, Y. (2015). Cross-sectional analysis of critical risk factors for PPP water projects in China. Journal of Infrastructure Systems, 21(1), 04014031. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000214
Chaphalkar, N. B., Iyer, K.C., & Patil, S. K. (2015). Prediction of outcome of construction dispute claims using multilayer perceptron neural network model. International Journal of Project Management, 33(8), 1827–1835. https://doi.org/10.1016/j.ijproman.2015.09.002
Chau, K. W. (2007). Application of a PSO-based neural network in analysis of outcomes of construction claims. Automation in Construction, 16(5), 642–646. https://doi.org/10.1016/j.autcon.2006.11.008
Cheng, Z., Ke, Y., Lin, J., Yang, Z., & Cai, J. (2016). Spatio-temporal dynamics of public private partnership projects in China. International Journal of Project management, 34(7), 1242–1251. https://doi.org/10.1016/j.ijproman.2016.05.006
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Cheung, S. O., Yiu, T. W., & Chan, H. W. (2010). Exploring the potential for predicting project dispute resolution satisfaction using logistic regression. Journal of Construction Engineering and Management, 136(5), 508–517. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000157
China Judgements Online. (2018). Retrieved October 31, 2018, from http://wenshu.court.gov.cn/
China Public Private Partnerships Center. (2019). National PPP Information Platform. Retrieved June 30, 2019, from https://www.cpppc.org:8082/inforpublic/homepage.html#/projectPublic
Chou, J. S., & Lin, C. (2013). Predicting disputes in public-private partnership projects: Classification and ensemble models. Journal of Computing in Civil Engineering, 27(1), 51–60. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000197
Cruz, C. O., & Marques, R. C. (2013a). Exogenous determinants for renegotiating public infrastructure concessions: Evidence from Portugal. Journal of Construction Engineering and Management, 139(9), 1082–1090. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000710
Cruz, C. O., & Marques, R. C. (2013b). Endogenous determinants for renegotiating concessions: Evidence from local infrastructure. Local Government Studies, 39(3), 352–374. https://doi.org/10.1080/03003930.2013.783476
Dansoh, A., Frimpong, S., Ampratwum, G., Oppong, G. D., & Osei-Kyei, R. (2020). Exploring the role of traditional authorities in managing the public as stakeholders on PPP projects: a case study. International Journal of Construction Management, 20(6), 628–641. https://doi.org/10.1080/15623599.2020.1725722
Dewulf, G., & Garvin, M. J. (2020). Responsive governance in PPP projects to manage uncertainty. Construction Management and Economics, 38(4), 1–15. https://doi.org/10.1080/01446193.2019.1618478
Ding, J. Y., Jia, J. Y., Jin, C. H., & Wang, N. (2018). An innovative method for project transaction mode design based on case-based reasoning: A Chinese case study. Sustainability, 10(11), 4127. https://doi.org/10.3390/su10114127
Dixon, T., Pottinger, G., & Jordan, A. (2005). Lessons from the private finance initiative in the UK: Benefits, problems and critical success factors. Journal of Property Investment & Finance, 23(5), 412–423. https://doi.org/10.1108/14635780510616016
Doğan, S. Z., Arditi, D., & Gunaydin, H. M. (2008). Using decision trees for determining attribute weights in a case-based model of early cost prediction. Journal of Construction Engineering and Management, 134(2), 146–152. https://doi.org/10.1061/(ASCE)0733-9364(2008)134:2(146)
Feng, K., Xiong, W., Wang, S., Wu, C. & Xue, Y. (2017). Optimizing an equity capital structure model for public-private partnership projects involved with public funds. Journal of Construction Engineering and Management, 143(9), 04017067. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001349
Ferri, C., Hernández-Orallo, J., & Modroiu, R. (2009). An experimental comparison of performance measures for classification. Pattern Recognition Letters, 30(1), 27–38. https://doi.org/10.1016/j.patrec.2008.08.010
Guasch, J. L. (2004). Renegotiating infrastructure concessions: Doing it right. World Bank Institute. https://doi.org/10.1596/0-8213-5792-1
Haugen, T., & Singh, A. (2015). Dispute resolution strategy selection. Journal of Legal Affairs and Dispute Resolution in Engineering and Construction, 7(3), 05014004. https://doi.org/10.1061/(ASCE)LA.1943-4170.0000160
Ke, Y. J., Wang, S. Q., & Chan, A. P. C. (2010). Risk allocation in public-private partnership infrastructure projects: Comparative study. Journal of Infrastructure Systems, 16(4), 343–351. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000030
Kim, Y. S. (2010). Performance evaluation for classification methods: A comparative simulation study. Expert Systems with Applications, 37(3), 2292–2306. https://doi.org/10.1016/j.eswa.2009.07.043
Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In 14th International Joint Conference on Artificial Intelligence (pp. 1137–1143). San Francisco, California, USA.
Kwon, N., Park, M., Lee, H. S., Ahn, J., & Kim, S. (2017). Construction noise prediction model based on case-based reasoning in the preconstruction phase. Journal of Construction Engineering and Management, 143(6), 04017008. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001291
Lee, M. C. (2009). Using support vector machine with a hybrid feature selection method to the stock trend prediction. Expert Systems with Applications, 36(8), 10896–10904. https://doi.org/10.1016/j.eswa.2009.02.038
Lin, J. Y., Cheng, C. T., & Chau, K. W. (2006). Using support vector machines for long-term discharge prediction. Hydrological Sciences Journal, 51(4), 599–612. https://doi.org/10.1623/hysj.51.4.599
Mahfouz, T., & Kandil, A. (2009). Factors affecting litigation outcomes of differing site conditions (DSC) disputes: A logistic regression model (LRM). In 2009 Construction Research Congress (pp. 239–248). ASCE, Reston, Virginia. USA. https://doi.org/10.1061/41020(339)25
Mahfouz, T., & Kandil, A. (2012). Litigation outcome prediction of differing site condition disputes through machine learning models. Journal of Computing in Civil Engineering, 26(3), 298–308. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000148
Marques, R. C. (2018). Is arbitration the right way to settle conflicts in PPP arrangements? Journal of Management in Engineering, 34(1), 05017007. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000564
Marques, R. C., & Berg, S. (2010). Revisiting the strengths and limitations of regulatory contracts in infrastructure industries. Journal of Infrastructure Systems, 16(4), 334–342. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000029
Marques, R. C., & Berg, S. (2011). Risks, contracts, and private-sector participation in infrastructure. Journal of Construction Engineering and Management, 137(11), 925–932. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000347
Mitropoulos, P., & Howell, G. (2001). Model for understanding, preventing, and resolving project disputes. Journal of Construction Engineering and Management, 127(3), 223–231. https://doi.org/10.1061/(ASCE)0733-9364(2001)127:3(223)
Mustapha, I. B., & Saeed, F. (2016). Bioactive molecule prediction using extreme gradient boosting. Molecules, 21(8), 983. https://doi.org/10.3390/molecules21080983
Opitz, D., & Maclin, R. (1999). Popular ensemble methods: An empirical study. Journal of Artificial Intelligence Research, (11), 169–198. https://doi.org/10.1613/jair.614
Osei-Kyei, R., & Chan, A. P. C. (2017). Empirical comparison of critical success factors for public-private partnerships in developing and developed countries: A case of Ghana and Hong Kong. Engineering, Construction and Architectural Management, 24(6), 1222–1245. https://doi.org/10.1108/ECAM-06-2016-0144
Osei-Kyei, R., Chan, A. P. C., Yu, Y., Chen, C., & Dansoh, A. (2019). Root causes of conflict and conflict resolution mechanisms in public-private partnerships: Comparative study between Ghana and China. Cities, (87), 185–195. https://doi.org/10.1016/j.cities.2018.10.001
Pulket, T., & Arditi, D. (2009). Universal prediction model for construction litigation. Journal of Computing in Civil Engineering, 23(3), 178–187. https://doi.org/10.1061/(ASCE)0887-3801(2009)23:3(178)
Robert, K. Y. (2014). Case study research: Design and methods (pp. 5–8). Sage.
Rogova, G. (1994). Combining the results of several neural network classifiers. Neural Networks, 7(5), 777–781. https://doi.org/10.1016/0893-6080(94)90099-X
Rokach, L. (2010). Ensemble-based classifiers. Artificial Intelligence Review, 33(1–2), 1–39. https://doi.org/10.1007/s10462-009-9124-7
Shawe-Taylor, J., & Cristianini, N. (2000). Support vector machines and other kernel-based learning methods. Cambridge University Press. https://doi.org/10.1017/CBO9780511801389
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