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The Impact of Information Load on Predicting Success in Electronic Negotiations

Author

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  • Muhammed-Fatih Kaya

    (University of Hohenheim)

  • Mareike Schoop

    (University of Hohenheim)

Abstract

The exchange of information is an essential means for being able to conduct negotiations and to derive situational decisions. In electronic negotiations, information is transferred in the form of requests, offers, questions and clarifications consisting of communication and decisions. Taken together, such information makes or breaks the negotiation. Whilst information analysis has traditionally been conducted through human coding, machine learning techniques now enable automated analyses. One of the grand challenges of electronic negotiation research is the generation of predictions as to whether ongoing negotiations will success or fail at the end of the negotiation process by considering the previous negotiation course. With this goal in mind, the present research paper investigates the impact of information load on predicting success and failure in electronic negotiations and how predictive machine learning models react to the successive increase of negotiation data. Information in different data combinations is used for the evaluation of various classification techniques to simulate the progress in negotiation processes and to investigate the impact of increasing information load hidden in the utility and communication data. It will be shown that the more information the merrier the result does not always hold. Instead, data-driven ML model recommendations are presented as to when and based on which data density certain models should or should not be used for the prediction of success and failure of electronic negotiations.

Suggested Citation

  • Muhammed-Fatih Kaya & Mareike Schoop, 2025. "The Impact of Information Load on Predicting Success in Electronic Negotiations," Group Decision and Negotiation, Springer, vol. 34(3), pages 487-521, June.
    • Handle: RePEc:spr:grdene:v:34:y:2025:i:3:d:10.1007_s10726-025-09920-5
    DOI: 10.1007/s10726-025-09920-5
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    References listed on IDEAS

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    1. Zhi-Xue Zhang & Yu-Lan Han, 2007. "The effects of reciprocation wariness on negotiation behavior and outcomes," Group Decision and Negotiation, Springer, vol. 16(6), pages 507-525, November.
    2. Marina Sokolova & Guy Lapalme, 2012. "How Much Do We Say? Using Informativeness of Negotiation Text Records for Early Prediction of Negotiation Outcomes," Group Decision and Negotiation, Springer, vol. 21(3), pages 363-379, May.
    3. Tine Bertoncel & Ivan Erenda & Mirjana Pejić Bach & Vasja Roblek & Maja Meško, 2018. "A Managerial Early Warning System at a Smart Factory: An Intuitive Decision‐making Perspective," Systems Research and Behavioral Science, Wiley Blackwell, vol. 35(4), pages 406-416, July.
    4. John Zeleznikow, 2021. "Using Artificial Intelligence to provide Intelligent Dispute Resolution Support," Group Decision and Negotiation, Springer, vol. 30(4), pages 789-812, August.
    5. Gérard Biau & Erwan Scornet, 2016. "A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 197-227, June.
    6. Vivi Nastase & Sabine Koeszegi & Stan Szpakowicz, 2007. "Content Analysis Through the Machine Learning Mill," Group Decision and Negotiation, Springer, vol. 16(4), pages 335-346, July.
    7. Citroen, Charles L., 2011. "The role of information in strategic decision-making," International Journal of Information Management, Elsevier, vol. 31(6), pages 493-501.
    8. Wendi L. Adair & Jeanne M. Brett, 2005. "The Negotiation Dance: Time, Culture, and Behavioral Sequences in Negotiation," Organization Science, INFORMS, vol. 16(1), pages 33-51, February.
    9. Matthias Schonlau & Rosie Yuyan Zou, 2020. "The random forest algorithm for statistical learning," Stata Journal, StataCorp LLC, vol. 20(1), pages 3-29, March.
    10. Douglas P. Twitchell & Matthew L. Jensen & Douglas C. Derrick & Judee K. Burgoon & Jay F. Nunamaker, 2013. "Negotiation Outcome Classification Using Language Features," Group Decision and Negotiation, Springer, vol. 22(1), pages 135-151, January.
    11. Gérard Biau & Erwan Scornet, 2016. "Rejoinder on: A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 264-268, June.
    12. Hamid Saadatfar & Samiyeh Khosravi & Javad Hassannataj Joloudari & Amir Mosavi & Shahaboddin Shamshirband, 2020. "A New K-Nearest Neighbors Classifier for Big Data Based on Efficient Data Pruning," Mathematics, MDPI, vol. 8(2), pages 1-12, February.
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