IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i17p12836-d1224527.html
   My bibliography  Save this article

The Adoption of a Big Data Approach Using Machine Learning to Predict Bidding Behavior in Procurement Management for a Construction Project

Author

Listed:
  • Wuttipong Kusonkhum

    (Department of Civil Engineering, Northeastern University, Khon Kaen 40000, Thailand)

  • Korb Srinavin

    (Department of Civil Engineering, Khon Kaen University, Khon Kaen 40002, Thailand)

  • Tanayut Chaitongrat

    (Construction and Project Management Center, Faculty of Architecture, Urban Design and Creative Arts, Mahasarakham University, Maha Sarakham 44150, Thailand)

Abstract

Big data technologies are disruptive technologies that affect every business, including those in the construction industry. The Thai government has also been affected and attempted to use machine learning techniques with the analytics of big data technologies to predict which construction projects have a winning price over the project budget. However, this technology was never developed, and the government did not implement it because they had data obtained via a traditional data collection process. In this study, traditional data were processed to predict the behavior in Thai government construction projects using a machine learning model. The data were collected from the government procurement system in 2019. There were seven input data, including the project owner department, type of construction project, bidding method, project duration, project scale, winning price overestimated price, and winning price over budget. A range of classification techniques, including an artificial neural network (ANN), a decision tree (DC), and a K-nearest neighbor (KNN), were used in this study. According to the results, after hyperparameter tuning, the ANN had the greatest prediction accuracy of 78.9 percent. This study confirms that the data from the Thai government procurement system can be investigated using machine learning techniques from big data technologies.

Suggested Citation

  • Wuttipong Kusonkhum & Korb Srinavin & Tanayut Chaitongrat, 2023. "The Adoption of a Big Data Approach Using Machine Learning to Predict Bidding Behavior in Procurement Management for a Construction Project," Sustainability, MDPI, vol. 15(17), pages 1-19, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:17:p:12836-:d:1224527
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/17/12836/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/17/12836/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rajat Roy & Margaret Low & John Waller, 2005. "Documentation, standardization and improvement of the construction process in house building," Construction Management and Economics, Taylor & Francis Journals, vol. 23(1), pages 57-67.
    2. Canhoto, Ana Isabel & Clear, Fintan, 2020. "Artificial intelligence and machine learning as business tools: A framework for diagnosing value destruction potential," Business Horizons, Elsevier, vol. 63(2), pages 183-193.
    3. David L. Olson & Dursun Delen, 2008. "Advanced Data Mining Techniques," Springer Books, Springer, number 978-3-540-76917-0, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Vangelis Marinakis & Themistoklis Koutsellis & Alexandros Nikas & Haris Doukas, 2021. "AI and Data Democratisation for Intelligent Energy Management," Energies, MDPI, vol. 14(14), pages 1-14, July.
    2. Mark Gilchrist & Deana Lehmann Mooers & Glenn Skrubbeltrang & Francine Vachon, 2012. "Knowledge Discovery in Databases for Competitive Advantage," Journal of Management and Strategy, Journal of Management and Strategy, Sciedu Press, vol. 3(2), pages 2-15, April.
    3. Bavaresco, Rodrigo Simon & Nesi, Luan Carlos & Victória Barbosa, Jorge Luis & Antunes, Rodolfo Stoffel & da Rosa Righi, Rodrigo & da Costa, Cristiano André & Vanzin, Mariangela & Dornelles, Daniel & J, 2023. "Machine learning-based automation of accounting services: An exploratory case study," International Journal of Accounting Information Systems, Elsevier, vol. 49(C).
    4. Fernandez Martinez, Roberto & Lostado Lorza, Ruben & Santos Delgado, Ana Alexandra & Piedra, Nelson, 2021. "Use of classification trees and rule-based models to optimize the funding assignment to research projects: A case study of UTPL," Journal of Informetrics, Elsevier, vol. 15(1).
    5. Ayat Sami ODEIBAT, 2021. "The Effect Of Technology Evolution On The Future Of Jobs," Network Intelligence Studies, Romanian Foundation for Business Intelligence, Editorial Department, issue 17, pages 57-67, June.
    6. Neubert, Mitchell J. & Montañez, George D., 2020. "Virtue as a framework for the design and use of artificial intelligence," Business Horizons, Elsevier, vol. 63(2), pages 195-204.
    7. Marina Johnson & Abdullah Albizri & Serhat Simsek, 2022. "Artificial intelligence in healthcare operations to enhance treatment outcomes: a framework to predict lung cancer prognosis," Annals of Operations Research, Springer, vol. 308(1), pages 275-305, January.
    8. Li, Chia-Ying & Zhang, Jin-Ting, 2023. "Chatbots or me? Consumers’ switching between human agents and conversational agents," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    9. Mehri, Ali & Darooneh, Amir H. & Shariati, Ashrafalsadat, 2012. "The complex networks approach for authorship attribution of books," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(7), pages 2429-2437.
    10. Alina Köchling & Marius Claus Wehner, 2020. "Discriminated by an algorithm: a systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development," Business Research, Springer;German Academic Association for Business Research, vol. 13(3), pages 795-848, November.
    11. Kamoonpuri, Sana Zehra & Sengar, Anita, 2023. "Hi, May AI help you? An analysis of the barriers impeding the implementation and use of artificial intelligence-enabled virtual assistants in retail," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    12. Michał Jasiński & Tomasz Sikorski & Zbigniew Leonowicz & Klaudiusz Borkowski & Elżbieta Jasińska, 2020. "The Application of Hierarchical Clustering to Power Quality Measurements in an Electrical Power Network with Distributed Generation," Energies, MDPI, vol. 13(9), pages 1-19, May.
    13. Beni Rohrbach & Sharolyn Anderson & Patrick Laube, 2016. "The effects of sample size on data quality in participatory mapping of past land use," Environment and Planning B, , vol. 43(4), pages 681-697, July.
    14. Eliza Nichifor & Adrian Trifan & Elena Mihaela Nechifor, 2021. "Artificial Intelligence in Electronic Commerce: Basic Chatbots and Consumer Journey," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 23(56), pages 1-87, February.
    15. Arias-Pérez, José & Vélez-Jaramillo, Juan, 2022. "Ignoring the three-way interaction of digital orientation, Not-invented-here syndrome and employee's artificial intelligence awareness in digital innovation performance: A recipe for failure," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    16. Jingwen Dong & Siti Nurulain Mohd Rum & Khairul Azhar Kasmiran & Teh Noranis Mohd Aris & Raihani Mohamed, 2022. "Artificial Intelligence in Adaptive and Intelligent Educational System: A Review," Future Internet, MDPI, vol. 14(9), pages 1-11, August.
    17. Simsek, Serhat & Dag, Ali & Tiahrt, Thomas & Oztekin, Asil, 2021. "A Bayesian Belief Network-based probabilistic mechanism to determine patient no-show risk categories," Omega, Elsevier, vol. 100(C).
    18. Sebastian Büsch & Volker Nissen & Arndt Wünscher, 0. "Automatic classification of data-warehouse-data for information lifecycle management using machine learning techniques," Information Systems Frontiers, Springer, vol. 0, pages 1-15.
    19. Chen Yang & Jing Hu, 2022. "When do consumers prefer AI-enabled customer service? The interaction effect of brand personality and service provision type on brand attitudes and purchase intentions," Journal of Brand Management, Palgrave Macmillan, vol. 29(2), pages 167-189, March.
    20. Yucel, Ahmet & Dag, Ali & Oztekin, Asil & Carpenter, Mark, 2022. "A novel text analytic methodology for classification of product and service reviews," Journal of Business Research, Elsevier, vol. 151(C), pages 287-297.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:17:p:12836-:d:1224527. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.