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Predicting the litigation outcome of PPP project disputes between public authority and private partner using an ensemble model

    Xiaoxiao Zheng Affiliation
    ; Yisheng Liu Affiliation
    ; Jun Jiang Affiliation
    ; Linda M. Thomas Affiliation
    ; Nan Su Affiliation

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
Published in Issue
Feb 1, 2021
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This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Albert, J. M., Durepos, G., & Wiebe, E. (2010). Encyclopedia of case study research. Sage.

Alexandre, L. A., Campilho, A. C., & Kamel, M. (2001). On combining classifiers using sum and product rules. Pattern Recognition Letters, 22(12), 1283–1289. https://doi.org/10.1016/S0167-8655(01)00073-3

Almarri, K., Alzahrani, S., & Boussabaine, H. (2019). An evaluation of the impact of risk cost on risk allocation in public private partnership projects. Engineering, Construction and Architectural Management, 26(8), 1696–1711. https://doi.org/10.1108/ECAM-04-2018-0177

Arditi, D., Oksay, F., & Tokdemir, O. (1998). Predicting the outcomes of construction litigation using neural networks. Computer-Aided Civil and Infrastructure Engineering, 13(2), 75–81. https://doi.org/10.1111/0885-9507.00087

Arditi, D., & Pulket, T. (2005). Predicting the outcome of construction litigation using boosted decision trees. Journal of Computing in Civil Engineering, 19(4), 387–393. https://doi.org/10.1061/(ASCE)0887-3801(2005)19:4(387)

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

Carrillo, P., Robinson, H., Foale, P., Anumba, C., & Bouchlaghem, D. (2008). Participation, barriers, and opportunities in PFI: The United Kingdom experience. Journal of Management in Engineering, 24(3), 138–145. https://doi.org/10.1061/(ASCE)0742-597X(2008)24:3(138)

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

Cheung, S. O., Suen, H. C. H., & Lam, T. I. (2002). Fundamentals of alternative dispute resolution processes in construction. Journal of Construction Engineering and Management, 128(5), 409–417. https://doi.org/10.1061/(asce)0733-9364(2002)128:5(409)

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)

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

Steen, R. H. (1994). Five steps to resolving construction disputes-without litigation. Journal of Management in Engineering, 10(4), 19–21. https://doi.org/10.1061/(ASCE)9742-597X(1994)10:4(19)

Treacy, T. B. (1995). Use of alternative dispute resolution in the construction industry. Journal of Management in Engineering, 11(1), 58–63. https://doi.org/10.1061/(ASCE)0742-597X(1995)11:1(58)

Vassallo, J. M. (2006). Traffic risk mitigation in highway concession projects: the experience of Chile. Journal of Transport Economics and Policy, 40(3), 359–381. https://www.jstor.org/stable/20053991

Wang, J. D., Li, P., Ran, R., Che, Y. B., & Zhou, Y. (2018). A short-term photovoltaic power prediction model based on the gradient boost decision tree. Applied Sciences, 8(5), 689. https://doi.org/10.3390/app8050689

Wu, X. H., Fu, H. J., Tian, X. Y., & Sun, J. (2017). Prediction of pork storage time using Fourier transform near infrared spectroscopy and Adaboost-ULDA. Journal of Food Process Engineering, 40(6), 12566. https://doi.org/10.1111/jfpe.12566

Zhang, L. H., Fenn, P., & Fu, Y. C. (2019). To insist or to concede? Contractors’ behavioural strategies when handling disputed claims. Engineering, Construction and Architectural Management, 26(3), 424–443. https://doi.org/10.1108/ECAM-05-2018-0219

Zhou, F., Zhang, Q., Sornette, D., & Jiang, L. (2019). Cascading logistic regression onto gradient boosted decision trees for forecasting and trading stock indices. Applied Soft Computing Journal, 84, 105747. https://doi.org/10.1016/j.asoc.2019.105747

Zhou, J., Li, W., Wang, J. X., Ding, S., & Xia, C. Y. (2019). Default prediction in P2P lending from highdimensional data based on machine learning. Physica A: Statistical Mechanics and its Applications, 534, 122370. https://doi.org/10.1016/j.physa.2019.122370