Negotiation effectiveness is a skill required especially in organisational contexts. To become an effective negotiator and to gain negotiation competence, practice is essential. In addition, reflection on the good and the bad of those negotiation processes is vital. In particular, to learn from mistakes and to learn from successes, negotiators need to receive feedback. Feedback must be accurate, targeted towards the negotiators, and purpose-driven to genuinely support training of negotiation effectiveness. Digital system support in the form of negotiation support systems (NSSs) can provide such support for self-reflection. Whilst NSSs document all the data of a negotiation process such as preferences, utilities, message, and concessions, the question as to which feedback elements using the rich negotiation data promote self-reflection remains unanswered. As yet, NSSs do not provide dedicated self-reflection elements. The present paper designs and evaluates seven feedback elements using a design science research approach. Four of these designed elements (namely TKI evaluation, behaviour evaluation, history graph, and feedback from the partner) can be shown to be useful in supporting participants to self-reflect on effectiveness in their concluded negotiations.

Transformer-based pre-trained language models (TPLMs) have demonstrated strong performance across a wide range of natural language processing tasks. However, their transferability to highly specialised domains with highly dynamic communication interactions such as digital negotiations remains unexplored. The current paper analyses the extent to which TPLMs can predict negotiation outcomes based on exchanged textual communication data in digital negotiations. A systematic literature review is conducted to identify suitable TPLMs followed by experimental evaluations based on real-world digital negotiation data. The findings provide empirical insights into the strengths and limitations of applying TPLMs to domain-specific negotiation data. More generally, the research advances predictive digital negotiation analysis.

Knowing one’s own negotiation style and recognising the partner’s negotiation style are fundamental prerequisite to negotiate effectively. Since novice negotiators often fail to state these styles correctly, a dedicated training to support the identification of negotiation styles is required. In this paper, an AI-based training approach is presented. As a first step, software agents acting as training partners have to be designed. We implemented a genetic algorithm to shape the agents’ behaviour. As a first implication, the designed agents act according to their intended negotiation styles without the need for extensive datasets of previous negotiations to specify those styles.

In order to act and to react effectively in (digital) negotiations, it is essential that negotiators recognise and apply diverse negotiation behaviour. However, there is a lack of research regarding IT-based training tools that support negotiators in implementing such behaviour. Consequently, training becomes essential for acquiring the skills to recognise and apply various negotiation approaches. Our objective is to enhance the accessibility of IT-based training for novice negotiators.The training employs software agents capable of exhibiting different negotiation behaviour, thereby enabling negotiators to identify these patterns and respond appropriately during business negotiations. The training encompasses business negotiations with a variety of negotiation partners representing businesses. An artefact was designed and implemented in accordance with the principles and methodologies of design science research. The artefact underwent empirical evaluation through surveys, wherein negotiators identified specific displayed behaviour and reflected upon their individual learning outcomes. Despite the inherent complexity of negotiation behaviour training, novice negotiators were able to successfully recognise behaviour whilst maintaining a consistently high level of intrinsic motivation. Furthermore, the study demonstrated positive effects on learning outcomes.

Blockchain Technology (BCT) is the backbone of the next generation of the internet and thus affects how electronic business (e-business) is conducted. While the usage of BCT for the initiation and transaction phases in e-business has been studied, the negotiation aspect has not been considered in a comprehensive manner. The current literature on the utilisation of BCT in electronic negotiations (e-negotiations) primarily focuses on autonomous agents and lacks research on the support of e-negotiations conducted by human negotiators using negotiation support systems (NSSs). This results in the issue that the consequences of a transition to Web3.0-based NSSs are unclear, while other areas of e-business already apply Web3.0 technologies. We address this lack of knowledge following a design-oriented approach in three steps exploring the opportunities and challenges of using BCT for e-negotiations via NSSs. Firstly, the well-established negotiation support system Negoisst is extended by BCT features resulting in the development of a Web3.0-based NSS called NegoisstBCT to demonstrate the technical feasibility of this approach. Secondly, the potential opportunities and challenges of a Blockchain-based NSS are analysed referring to its technical architecture. Thirdly, a generalised view of the application of Web3.0-based NSSs in different settings is taken, discussing future research on BCT in e-negotiations. The present research thus fosters the application of Blockchain-based NSSs in e-negotiations and of NSSs in BCT application areas.

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 in the form of requests, offers, questions and clarifications consisting of communication and decisions. 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 electronic negotiation research is the generation of predictions as to whether ongoing negotiations will success or fail at the end of the negotiation process by considering the previous negotiation course. With this goal in mind, the present research paper investigates the impact of information load on predicting success and failure in electronic negotiations and how predictive machine learning models react to the successive increase of negotiation data. Information in different data combinations is used for the evaluation of various classification techniques to simulate the progress in negotiation processes and to investigate the impact of increasing information load hidden in the utility and communication data. It will be shown that the more information the merrier the result 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 be used for the prediction of success and failure of electronic negotiations.

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.

Blockchain Technology (BCT) offers several possible applications in the field of electronic commerce (e-commerce), such as decentralised marketplaces or payments in cryptocurrencies. Even though these applications of BCT have already been explored in the academic literature, a comprehensive collection along the whole e-commerce value chain is still missing. Furthermore, the existing comprehensive reviews are based on the academic literature whilst the evolution and further development of BCT is highly driven by practitioners. Therefore, we aim to understand how and why BCT is used in e-commerce based on a qualitative content analysis of news articles, i.e., we apply scientific methods to content which reports the latest developments in the field. As a result, we describe the multiple application domains of BCT along the e-commerce value chain. Subsequently, we discuss the main underlying principles of BCT usage across all the value chain steps.

Negotiation are essential in every day’s life, increasingly via electronic media. Due to a huge amount of negotiation styles, strategies, and tactics presented in the literature, it is unclear which combinations of strategies and tactics for a concrete style can be applied in (electronic) negotiations. Therefore, we conducted a systematic literature review identifying combinations of negotiation styles, strategies, and tactics in electronic negotiations. The findings were consolidated into a taxonomy and patterns of the combinations were generated.

In electronic business (e-business), innovative technologies such as blockchain technology (BCT) have a fundamental impact on activities along the value chain. The perspective of the value chain in the context of blockchain technology has been explored, but contributions are mainly focused on supply chain management and tangible goods. Hence, the e-business domain lacks research, even though many beneficial features for service value chains and the transaction of intangible goods exist. Therefore, the current paper focuses on (1) how and why value chain activities are supported by using BCT and (2) how the stakeholder’s responsibilities change for value chain activities that are affected by BCT. A multiple case analysis of four e-business cases, i.e., Theta, OpenBazaar, Presearch, and Crypviser, is conducted. Based on four ideal value chains by Wirtz (2019) (cf. 4C-Net Model), steps that depend on BCT or that are supported by BCT are outlined. By conducting a cross-case analysis, we derive eight blockchain technology propositions that enlarge the existing knowledge base.

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.

Organisations 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.

Negotiators communicate with each other and decide on offers or requests. Whilst the decision side of negotiations has long been a focus of negotiation research, the communication side has not been extensively supported. The current paper revisits the need for a communication perspective in business negotiations and reviews current research on negotiation communication. Both strands of relevant work are then integrated to provide a concept of electronic negotiation communication and to discuss how this concept was implemented in the system Negoisst and thus operationalised.

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.