The impact of information load on predicting success in electronic negotiations

dc.contributor.authorKaya, Muhammed-Fatih
dc.contributor.authorSchoop, Mareike
dc.contributor.corporateKaya, Muhammed-Fatih; Intelligent Information Systems, Institute of Information Systems, University of Hohenheim, Schwerzstr. 40, Osthof-Nord, 70599, Stuttgart, Germany
dc.contributor.corporateSchoop, Mareike; Intelligent Information Systems, Institute of Information Systems, University of Hohenheim, Schwerzstr. 40, Osthof-Nord, 70599, Stuttgart, Germany
dc.date.accessioned2025-08-05T11:15:11Z
dc.date.available2025-08-05T11:15:11Z
dc.date.issued2025
dc.date.updated2025-07-25T08:56:38Z
dc.description.abstractThe 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.en
dc.description.sponsorshipOpen Access funding enabled and organized by Projekt DEAL.
dc.description.sponsorshipUniversität Hohenheim (3153)
dc.identifier.urihttps://doi.org/10.1007/s10726-025-09920-5
dc.identifier.urihttps://hohpublica.uni-hohenheim.de/handle/123456789/18012
dc.language.isoeng
dc.rights.licensecc_by
dc.subjectMachine learning
dc.subjectInformation growth
dc.subjectNegotiation outcome
dc.subjectClassification
dc.subjectPrediction performance
dc.subjectModel selection
dc.subject.ddc650
dc.titleThe impact of information load on predicting success in electronic negotiationsen
dc.type.diniArticle
dcterms.bibliographicCitationGroup decision and negotiation, 34 (2025), 487-521. https://doi.org/10.1007/s10726-025-09920-5. ISSN: 1572-9907 Dordrecht : Springer Netherlands
dcterms.bibliographicCitation.issn1572-9907
dcterms.bibliographicCitation.journaltitleGroup decision and negotiation
dcterms.bibliographicCitation.originalpublishernameSpringer Netherlands
dcterms.bibliographicCitation.originalpublisherplaceDordrecht
dcterms.bibliographicCitation.pageend521
dcterms.bibliographicCitation.pagestart487
dcterms.bibliographicCitation.volume34
local.export.bibtex@article{Kaya2025, doi = {10.1007/s10726-025-09920-5}, author = {Kaya, Muhammed-Fatih and Schoop, Mareike}, title = {The Impact of Information Load on Predicting Success in Electronic Negotiations}, journal = {Group Decision and Negotiation}, year = {2025}, volume = {34}, pages = {487--521}, }
local.title.fullThe Impact of Information Load on Predicting Success in Electronic Negotiations

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