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AbstractOrganisations are involved in various types of negotiation. As digitalisation advances, such business negotiations are to a large extent electronic negotiations. Consequently, dedicated training for such electronic negotiations is important for mastering negotiation skills. We designed a gamified negotiation system used in e-negotiation training to increase participants' motivation, engagement, use of the system's negotiation support features and to improve their decision making. The quantitative evaluation using students as subjects shows higher motivation, engagement and better system and decision-making skills for participants in the gamified training compared to a conventional training. Furthermore, female participants show higher engagement in the gamified training than males. An analysis of the individual elements in the system provides insights into participants' perceptions and shows that the inclusion of a domain-specific feedback element yields motivational results that are almost similar compared to those using traditional game elements. Organisations can employ the designed artefact for fundamental and effective e-negotiation training.

AbstractThe 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.