Abstract Many solution concepts have been proposed within the Graph Model for Conflict Resolution to represent many behaviors that decision makers (DMs) can have in strategic settings. Among these solution concepts, the limited move stability stands out because it allows analyzing the conflict a variable number of steps forward, taking into account the preferences of all DMs involved in the conflict. However, for conflicts involving more than two DMs, there are two definitions of limited move stability that we argue to have undesired properties. For that reason, we present two alternative definitions for limited move stability for conflicts with multiple DMs, called: Alternative Limited-Move stability with Cooperative Coalition ( $$L_h^{CC}$$ ) and Alternative Limited-Move stability with Individualistic Coalition ( $$L_h^{CI}$$ ). Moreover, we obtained results relating these concepts to classical GMCR stability concepts. Furthermore, we provide results that establish the equivalence among the stability notions $$L_h^{CC}$$ , $$L_h^{CI}$$ and $$\text {Maximin}_h$$ , for conflicts in which the DMs belonging to the opposing coalition have equal and completely opposite preferences to those of the focal DM. An application to the Elmira conflict illustrates the usefulness of the proposed concepts in a real conflict situation.

Abstract This research examines how third parties recognize bias blind spots in business decision-making by using matrix-based graph models. We use the mathematical, theoretical, and philosophical foundations of the Graph Model for Conflict Resolution to emphasize the significance of recognition of bias blind spots as a means to enhance informed decision-making. We introduce the Bias Blind Spots Graph Model and provide a mathematical framework for modeling bias blind spots. Additionally, we provide evidence of the effectiveness of accounting for the presence of bias blind spots that influence decision-making within the context of Japan’s Skymark Airlines . In showcasing the theoretical underpinnings and practical applicability of this model, we contribute to identifying bias blind spots in reasoning and judgment. Further, we extend the model by providing mathematical foundations for considering decision-maker preference and outcome state stability/equilibrium. Ultimately, our findings offer valuable insights for both researchers and third parties to model and evaluate the influences of cognitive bias blind spots in decision-making processes in organizational settings.

Abstract The digitalization of supply chains enhances efficiency and aligns with the expectations of customers and suppliers. In data-intensive industries, such as Oil and Gas (O&G), blockchain technology, a key tool for supply chain digitalization, offers substantial benefits. This study investigates how the latest advancements in blockchain technology can be utilized in Supply Chain Management (SCM) to enhance transparency, improve resilience, and enable automation. In particular, we focus on the adoption of blockchain in the O&G sector within the context of enterprise and business applications. To guide decision-making in uncertain conditions, we present a Data Envelopment Analysis (DEA)-based framework. To the best of the authors’ knowledge, this study represents the inaugural research endeavor to evaluate blockchain deployment strategies under deterministic and non-deterministic environments utilizing data-driven optimization models. This framework assesses blockchain adoption strategies for industries based on criteria associated with organization, supply chain, technology, and pressures and drivers. The findings highlight the framework’s applicability under uncertain conditions by implementing it to a case study of the Norwegian O&G sector. The results indicate that “Single Use” and “Substitution” strategies are ranked first and second, followed by “Localization” and “Transformation”.

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.

Framed within facilitated modelling approaches and grounded on multi-criteria decision aiding concepts, Decision Conferencing (DCing) combines group facilitation, preference modelling and decision analytical software to help groups tackle problems characterized by high complexity, uncertainty, and diverse, conflicting perspectives. Despite its widespread application over the past decades, scholarly knowledge on DCing remains under-systematized and poorly explored. This study addresses this gap through a systematic literature review (SLR) and selected bibliometric analyses of DCing literature, and by identifying major challenges and promising research directions to advance DCing practice. The SLR adhered to the PRISMA guidelines, with structured searches applied to Web of Science, Scopus, EBSCO, ScienceDirect and Google Scholar. Seventy-one studies met the pre-defined criteria and were categorised by year, publication source, country, and type of DCing study. Information on analytical methods/frameworks/tools, software used, and evidence of effectiveness was extracted from DCing case studies. Selected bibliometric analyses included co-authorship and keyword co-occurrence network maps and were conducted to gain further insights into the DCing scientific landscape. Results show that DCing has been applied in multiple contexts and that there is diversity in facilitation practices and methods used; few studies have formally evaluated DCing effectiveness or reported its use in virtual settings. Based on the review findings and on literature from adjacent areas, we propose a roadmap for advancing DCing practice, highlighting the need for research on DCing effectiveness, for comparative studies on methods/software, for investigating biases in DCing settings, for developing empirical research on virtual DCing, and for sharing of best facilitation practices.

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.