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010   $a9783658405342$b(electronic bk.)
010   $z9783658405335
024 7 $a10.1007/978-3-658-40534-2
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035   $a(DE-He213)978-3-658-40534-2
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100   $a20230119d2023      u|         0
101 0 $aeng
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181   $ctxt$2rdacontent
182   $cc$2rdamedia
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200 10$aAutomated Pattern Recognition of Communication Behaviour in Electronic Business Negotiations /$fby Muhammed Fatih Kaya
205   $a1st ed. 2023.
210  1$aWiesbaden :$cSpringer Fachmedien Wiesbaden :$cImprint: Springer Gabler,$d2023.
215   $a1 online resource (178 pages)
225 1 $aGabler Theses,$x2731-3239
311 08$aPrint version: Kaya, Muhammed Fatih Automated Pattern Recognition of Communication Behaviour in Electronic Business Negotiations Wiesbaden : Springer Fachmedien Wiesbaden GmbH,c2023 9783658405335 
320   $aIncludes bibliographical references.
327   $aIntroduction -- Application of Data Mining Methods for Pattern Recognition in Negotiation Support Systems -- Advanced Maintenance of Data Richness in Business Communication Data ? An Evaluation of Dimensionality Reduction Techniques -- Analytical Comparison of Clustering Techniques for the Recognition of Communication Patterns -- Pattern Labelling of Business Communication Data -- Discussion and Outlook -- References.
330   $aThe world of digitalisation is changing the way how people and business companies communicate with each other. Electronic negotiations represent one of the most important forms of business communication and can influence the successes and failures of companies in a significant way, whether in interorganisational or intraorganisational processes. Analysing negotiation interactions to determine pattern-based peculiarities in the communication offers new value-adding information concerning the management of optimised communication processes, even though the machine-based processing of communication data bears a series of challenges. The present book develops a new approach to analyse the automated pattern recognition potential of Machine Learning methods in unstructured negotiation communication. It presents holistic research frameworks for the effective detection of structural patterns and reveals the pattern labelling potential in high-dimensional communication data by analytically implementing a series of Machine Learning methods. About the author Dr. Muhammed Fatih Kaya is a postdoctoral researcher at the Information Systems 1 department (Institute of Interorganisational Management and Performance) at the University of Hohenheim in Germany. His research interests include Machine Learning, Natural Language Processing, Text Mining, Recommender Systems and Electronic Negotiations.
410  0$aGabler Theses,$x2731-3239
606   $aTechnological innovations
606   $aEconomics$xPsychological aspects
606   $aInnovation and Technology Management
606   $aBehavioral Economics
615  0$aTechnological innovations.
615  0$aEconomics$xPsychological aspects.
615 14$aInnovation and Technology Management.
615 24$aBehavioral Economics.
676   $a658.45
700   $aKaya$b Muhammed Fatih$01275740
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
912   $a9910645890303321
996   $aAutomated Pattern Recognition of Communication Behaviour in Electronic Business Negotiations$93006304
997   $aUNINA