Today, value is created by making use of technologies based on artificial intelligence (AI) in almost all parts of the value chain. Business students need to acquire AI competencies to unleash the full potential of AI in organisations. Therefore, institutions of higher education need to teach AI to business students. The present research aims to identify which AI-related competencies are relevant for business graduates and which courses exist to teach respective skills. To this end, we firstly provide a comprehensive overview of the AI competence demand in today's business world by means of a literature review and a qualitative study. Secondly, we conduct a quantitative study to analyse the curricula of leading universities and online platforms providing AI teaching. The results reveal an AI competence model for business students, an overview of current curriculum offers, and the gaps in current curricula.

Business negotiations are conducted through communication enabling the declaration of negotiation objectives and active implementation of negotiation strategies to achieve pre-defined goals. The processing of exchanged textual communication enables the automatic transformation of unstructured data into processable structured datasets and subsequently the analysis of textual content without losing the data richness of exchanged communication messages. For this purpose, this paper discusses Text Mining-based pre-processing approaches by comparing TF-IDF and frequency measures. Furthermore, dimensionality reduction techniques from Feature Extraction, Feature Selection from Machine Learning and an additional statistical approach are evaluated to be able to counteract the curse of dimensionality in textual processing. In doing so, the maintenance of data richness in communication data is considered to be the overall goal to determine the dataset with minimal information loss. Therefore, various pre-processed and transformed communication datasets derived from dimensionality reduction are integrated as input data into selected classification models to measure the prediction performance with ROC analysis. The overall results of ROC show that quantified business communication data reduced by PCA delivers the most valuable data based on Porter's stemming algorithm followed by the quantified data combinations of Optimize Selection using a SVM classifier.

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 via the communication channel in the form of requests, offers, questions, and clarifications. 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 e-negotiation research is the generation of future-oriented predictions whether ongoing negotiations will be accepted or rejected at the end of the negotiation process by considering the previous negotiation course. With this goal in mind, the present research paper investigates how predictive machine learning models react to the successive increase of negotiation data. Information in different data combinations is used for the evaluation of classification techniques to simulate the progress in negotiation processes and to investigate the impact of utility and communication data. It will be shown that the more information the merrier 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 use for the analysis of electronic negotiations.

Systematic pattern recognition as well as the corresponding description of determined patterns entail numerous challenges in the application context of high-dimensional communication data. These can cause increased effort, especially with regard to machine-based processing concerning the determination of regularities in underlying datasets. Due to the increased expansion of dimensions in multidimensional data spaces, determined patterns are no longer interpretable by humans. Taking these challenges into account, this paper investigates to what extent pre-defined communication patterns can be interpreted for the application area of high-dimensional business communication data. An analytical perspective is considered by taking into account a holistic research approach and by subsequently applying selected Machine Learning methods from Association Rule Discovery, Topic Modelling and Decision Trees with regard to the overall goal of semi-automated pattern labelling. The results show that meaningful descriptions can be derived for the interpretation of pre-defined patterns.

The systematic processing of unstructured communication data as well as the milestone of pattern recognition in order to determine communication groups in negotiations bears many challenges in Machine Learning. In particular, the so-called curse of dimensionality makes the pattern recognition process demanding and requires further research in the negotiation environment. In this paper, various selected renowned clustering approaches are evaluated with regard to their pattern recognition potential based on high-dimensional negotiation communication data. A research approach is presented to evaluate the application potential of selected methods via a holistic framework including three main evaluation milestones: the determination of optimal number of clusters, the main clustering application, and the performance evaluation. Hence, quantified Term Document Matrices are initially pre-processed and afterwards used as underlying databases to investigate the pattern recognition potential of clustering techniques by considering the information regarding the optimal number of clusters and by measuring the respective internal as well as external performances. The overall research results show that certain cluster separations are recommended by internal and external performance measures by means of a holistic evaluation approach, whereas three of the clustering separations are eliminated based on the evaluation results.

Data mining methods have long been used to support organisational decision making by analysing organisational data from large databases. The present paper follows this tradition by discussing two different data mining techniques that are being implemented for pattern recognition in Negotiation Support Systems (NSSs), thereby providing process assistance to human negotiators. To this end, data from several international negotiation experiments via NSS Negoisst is used. Consequently, a suitable data representation of the underlying utility data and communication data has to be created for the applicability of data mining. Each generated data type needs individual processing treatments and almost all data mining methods lose their feasibility without a correct data representation as consequence. Once a correct data representation is found, the potential for pattern recognition in electronic negotiation data can be evaluated using descriptive and predictive methods. Whilst Association Rule Discovery is used as a descriptive technique to generate essential sets of strategic association patterns, the Decision Tree is applied as a supervised learning technique for the prediction of classification patterns. The extent to which reliable as well as valuable patterns can be derived from the electronic negotiation data and valuable predictions can be generated is examined in this paper.