Automated Pattern Recognition of Communication Behaviour in Electronic Business Negotiations

Description

The 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. Foreword Preface Contents Abbreviations List of Figures List of Tables 1 Introduction 1.1 The Importance of Communication in Electronic Business Negotiations 1.2 The Need for the Application of Machine Learning 1.3 Overall Research Objective and Research Questions 1.4 Structure of the Thesis 2 Application of Data Mining Methods for Pattern Recognition in Negotiation Support Systems 2.1 Introduction 2.2 The Application Field of Negotiation Support Systems 2.3 The Implementation of KDD Using Data Mining 2.4 Data Processing 2.4.1 Processing Textual Negotiation Messages Using LIWC 2.4.2 Consistent Utility Values 2.5 Results 2.5.1 Association Rule Discovery 2.5.2 Decision Tree 2.6 Discussion and Outlook 3 Advanced Maintenance of Data Richness in Business Communication Data—An Evaluation of Dimensionality Reduction Techniques 3.1 Introduction 3.2 Theoretical Background 3.2.1 Application Field: Electronic Business Negotiations 3.2.2 Methodology of Knowledge Discovery in Databases 3.3 Research Framework 3.4 Results 3.5 Discussion 3.6 Conclusion and Outlook 4 Analytical Comparison of Clustering Techniques for the Recognition of Communication Patterns 4.1 Motivation 4.2 Theoretical Background 4.2.1 The Importance of Communicative Interactions in Electronic Negotiations 4.2.2 Clustering of High-dimensional Negotiation Messages 4.3 Research Approach 4.3.1 Dimensionality Reduction 4.3.2 Calculation of Similarity Measure 4.3.3 Evaluation of Optimal Cluster Number 4.3.4 Clustering Techniques 4.3.5 Performance Evaluation 4.4 Results 4.5 Discussion 4.6 Conclusion and Outlook 4.7 Appendix 5 Pattern Labelling of Business Communication Data 5.1 Introduction 5.2 Theoretical Background 5.2.1 Support of E-Negotiations 5.2.2 Negotiation Behaviour 5.2.3 Ways of Pattern Labelling in High-Dimensional Business Communication Data 5.3 Research Procedure 5.4 Results 5.5 Discussion 5.6 Summary and Outlook 5.7 Appendix 5.7.1 Functionality of used ML-methods 5.7.2 ML-based Process Pipeline of the Research Procedure 6 Discussion and Outlook 6.1 Summary 6.2 Discussion 6.3 Limitations 6.4 Research Contribution 6.5 Future Research References