Automated Pattern Recognition of Communication Behaviour in Electronic Business Negotiations

The world of digitalization is changing the way 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. This book develops a new approach to analyze the automated pattern recognition potential of Machine Learning methods in unstructured negotiation communication, presenting holistic research frameworks for the effective detection of structural patterns and revealing the pattern labeling potential in high-dimensional communication data.

Format: Paperback / softback
Length: 164 pages
Publication date: 20 January 2023
Publisher: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG

The world of digitalization is revolutionizing the way people and businesses interact, with electronic negotiations emerging as a crucial form of business communication. These negotiations hold immense importance in determining the success and failure of companies, whether they involve interorganizational or intraorganizational processes. By analyzing negotiation interactions, researchers can uncover pattern-based peculiarities in communication, providing valuable insights into optimizing communication processes. While machine-based processing of communication data presents challenges, the present book takes a novel approach to exploring the automated pattern recognition potential of Machine Learning methods in unstructured negotiation communication. It presents comprehensive research frameworks for effectively detecting structural patterns and demonstrates the potential of pattern labeling in high-dimensional communication data through the analytical implementation of various Machine Learning methods.

The digitalization era has brought about a significant transformation in the way individuals and businesses communicate with each other. Electronic negotiations have emerged as a vital component of business communication, playing a pivotal role in determining the success and failure of companies across various sectors. These negotiations encompass a wide range of interactions, including emails, instant messages, and video conferences, and can have a profound impact on both interorganizational and intraorganizational processes.

One of the most significant aspects of electronic negotiations is their ability to influence the outcomes of companies. Effective negotiation strategies can lead to mutually beneficial agreements, while poor negotiation tactics can result in disputes, lost opportunities, and financial losses. Therefore, understanding the dynamics of negotiation interactions is crucial for managers and executives to optimize communication processes and achieve desired outcomes.

In recent years, researchers have turned their attention to analyzing negotiation interactions to identify pattern-based peculiarities in communication. By studying the patterns and trends exhibited by negotiators, researchers can gain valuable insights into the strategies and tactics employed by different parties. This analysis can help organizations develop more effective negotiation strategies, improve communication efficiency, and enhance their competitive advantage.

One of the challenges associated with machine-based processing of communication data is the need for accurate and reliable analysis. Traditional statistical analysis methods may not be sufficient to capture the complex and nuanced nature of negotiation interactions, particularly in high-dimensional data sets. Therefore, researchers have sought new approaches to analyze the automated pattern recognition potential of Machine Learning methods.

Machine Learning algorithms are capable of analyzing large amounts of data and identifying patterns that may be difficult for humans to detect. These algorithms can be trained on a wide range of data, including text, audio, and video, and can learn from past experiences to make predictions and decisions. In the context of negotiation communication, Machine Learning methods can be used to analyze the tone, language, and body language of negotiators, as well as the timing and pacing of their interactions.

The present book aims to develop a new approach to analyze the automated pattern recognition potential of Machine Learning methods in unstructured negotiation communication. The book provides holistic research frameworks for the effective detection of structural patterns and reveals the pattern labeling potential in high-dimensional communication data by analytically implementing a series of Machine Learning methods.

The first chapter of the book introduces the concept of unstructured negotiation communication and discusses the challenges associated with analyzing this type of data. The chapter also highlights the importance of effective negotiation strategies and the role of communication in achieving organizational goals.

The second chapter provides an overview of Machine Learning algorithms and their applications in analyzing unstructured data. The chapter discusses the different types of Machine Learning algorithms, including supervised, unsupervised, and reinforcement learning, and explains how they can be used to analyze negotiation interactions.

The third chapter focuses on the development of holistic research frameworks for the effective detection of structural patterns in negotiation communication. The chapter discusses the importance of data preprocessing, feature extraction, and model selection in identifying patterns that can be used to optimize communication processes.

The fourth chapter presents an analytical implementation of a series of Machine Learning methods for the detection of structural patterns in negotiation communication. The chapter discusses the advantages and disadvantages of each method and provides examples of how they can be applied to real-world data sets.

The fifth chapter discusses the pattern labeling potential in high-dimensional communication data and highlights the importance of interpretability and explainability in Machine Learning algorithms. The chapter discusses the use of visualization techniques and natural language processing to enhance the understanding of the patterns identified by Machine Learning algorithms.

The sixth chapter concludes the book by discussing the future directions of research in the field of negotiation communication and Machine Learning. The chapter discusses the potential applications of Machine Learning in other areas of business communication, such as sales and marketing, and highlights the need for further research to enhance the accuracy and reliability of Machine Learning algorithms.

In conclusion, the world of digitalization 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. By analyzing negotiation interactions to determine pattern-based peculiarities in the communication, researchers can gain valuable insights into optimizing communication processes and achieving desired outcomes. The present book develops a new approach to analyze the automated pattern recognition potential of Machine Learning methods in unstructured negotiation communication, providing holistic research frameworks for the effective detection of structural patterns and revealing the pattern labeling potential in high-dimensional communication data.

Weight: 248g
Dimension: 210 x 148 (mm)
ISBN-13: 9783658405335
Edition number: 1st ed. 2023