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This paper deals with cooperative games whose characteristic functions are fuzzy intervals, i.e. the worth of a coalition is not a real number but a fuzzy interval. This means that one observes a lower and an upper bound of the considered coalitions. This is very important, for example, from a computational and algorithmic viewpoint. The authors notice that the approach is general, since the characteristic function fuzzy interval values may result from solving general optimization problems.

This paper deals with cooperative games whose characteristic functions are fuzzy intervals, i.e. the worth of a coalition is not a real number but a fuzzy interval. A situation in which a finite set of players can obtain certain fuzzy payoffs by cooperation can be described by a cooperative fuzzy interval game.

In this paper, the authors extend a class of solutions for cooperative games that all have some egalitarian flavour in the sense that they assign to every player some initial payoff and distribute the remainder of the worth v(N) of the grand coalition N equally among all players under fuzzy uncertainty.

In this paper, the authors extend a class of solutions for cooperative games that all have some egalitarian flavour in the sense that they assign to every player some initial payoff and distribute the remainder of the worth v(N) of the grand coalition N equally among all players under fuzzy uncertainty. Examples of such solutions are the centre-of-gravity of the imputation-set value, shortly denoted by CIS value, egalitarian non-separable contribution value, shortly denoted by ENSC value and the equal division solution. Further, the authors discuss a class of equal surplus sharing solutions consisting of all convex combinations of the CIS value, the ENSC value and the equal division solution. The authors provide several characterizations of this class of solutions on variable and fixed player set. Specifications of several properties characterize specific solutions in this class.

The purpose of this paper is to illustrate how game theoretic solution concepts inform what classes of problems will be amenable to artificial intelligence and machine learning (AI/ML), and how to evolve the interaction between human and artificial intelligence.

The approach addresses the development of operational gaming to support planning and decision making. It then provides a succinct summary of game theory for those designing and using games, with an emphasis on information conditions and solution concepts. It addresses how experimentation demonstrates where human decisions differ from game theoretic solution concepts and how games have been used to develop AI/ML. It concludes by suggesting what classes of problems will be amenable to AI/ML, and which will not. It goes on to propose a method for evolving human/artificial intelligence.

Game theoretic solution concepts inform classes of problems where AI/ML 'solutions' will be suspect. The complexity of the subject requires a campaign of learning.

Though games have been essential to the development of AI/ML, practitioners have yet to employ game theory to understand its limitations.

Artificial intelligence and machine learning have spread rapidly across every aspect of business and social activity. The purpose of this paper is to examine how this rapidly growing field of analytics might be put to use in the area of reputation risk management.

The approach taken was to examine in detail the primary and emerging applications of artificial intelligence to determine how they could be applied to preventing and mitigating reputation risk by using machine learning to identify early signs of behaviors that could lead to reputation damage.

This review confirmed that there were at least two areas in which artificial intelligence could be applied to reputation risk management – the use of machine learning to analyze employee emails in real time to detect early signs of aberrant behavior and the use of algorithmic game theory to stress test business decisions to determine whether they contained perverse incentives leading to potential fraud.

Because of the fact that this viewpoint is by its nature a thought experiment, the authors have not yet tested the practicality or feasibility of the uses of artificial intelligence it describes.

Should the concepts described be viable in real-world application, they would create extraordinarily powerful tools for companies to identify risky behaviors in development long before they had run far enough to create major reputation risk.

By identifying risky behaviors at an early stage and preventing them from turning into reputation risks, the methods described could help restore and maintain trust in the relationship between companies and their stakeholders.

To the best of the author’s knowledge, artificial intelligence has never been described as a potential tool in reputation risk management.

This paper aims to clarify the notion of artificial intelligence (AI), reviewing the present scope of the phenomenon through its main applications. It aims at describing the various applications while assessing the markets, highlighting some of the leading industrial sectors in the field. Therefore, it identifies pioneering companies and the geographical distribution of AI companies.

The paper builds upon an in-depth investigation of public initiatives focusing mostly on the EU. It is based on desk research, a comprehensive review of the main grey and scientific literature in this field.

The paper notes that there is no real consensus on any definition for this umbrella term, that the definition does fluctuate over time but highlights some of the main changes and advances that took place over the past 60 years. It stresses that, in spite of the hype, on both the business and consumer sides, the demand appears uncertain. The scope of the announced disruptions is not easy to assess, technological innovation associated with AI may be modest or take some time to be fully deployed. However, some companies and regions are leading already in the field.

The paper, based on desk research, does not consider any expert opinions. Besides, the scientific literature on the phenomenon is still scarce (but not the technical one in the specific research sectors of AI). Most of the data come from consultancies or government publications which may introduce some bias, although the paper gathered various, often conflicting viewpoints.

The paper gives a thorough review of the available literature (consultancies, governments) stressing the limitations of the available research on economic and social aspects. It aims at providing a comprehensive overview of the major trends in the field. It gives a global overview of companies and regions.

An abundance of literature suggests that organisations adopting a cooperative approach achieve greater rewards than those that act in opposition or isolation. An emerging body of work also highlights the multiple actors involved in servitization. Despite this, in some contexts the benefits of servitization are not apparent. This paper examines business relationships in the oil industry and how they affect levels of servitization.

A mixed method study employing qualitative and quantitative methods was used to fully explore the context. In the quantitative phase, 48 oil industry specialists responded to a scenario based on game theory. This aimed to determine if the relationships between their respective organisations are cooperative or adversarial. Abduction drove a second qualitative phase. This consisted of a series of semi-structured interviews used to explore the servitization level and influence of servitization on relationships and vice versa.

The statistical results suggest that all parties used adversarial strategies despite the publicised intent to work cooperatively. The interviews suggested that increasing (decreasing) servitization could increase (decrease) cooperation and, in turn, value co-creation but revealed nuances to this effect. It also adds to our understanding of the darker side of servitization by illustrating the impact of mimetic isomorphism.

The findings add to understanding of the complex dynamics around servitization by showing that it is only at advanced levels of servitization that cooperative behaviour is observed, and base and intermediate levels result in non-cooperative behaviour and thus illustrate the importance of adopting a multi-actor lens to explore servitization.

The emergence of new manufacturing technologies, spurred by intense competition, will lead to dramatically new products and processes. New management systems, organizational structures, and decision‐making methods will also emerge as complements to new products and processes. This paper attempts to investigate technologies, systems and paradigms for the effective management of networked enterprise (supply chain networks), especially long supply chains. In doing so, the paper presents not only an exhaustive literature review to identify the complexities, gaps and challenges associated with long supply chains but also the emerging enabling technologies to support these gaps and challenges.

The approach takes the form of an interview of industrials, researchers and a literature review.

“Competition in the future will not be between individual enterprises but between competing supply chains.” Business opportunities are captured by groups of enterprises in the same enterprise network. This is due to the global competition that forces enterprises to focus on their core competences.

The paper presents a vision of the future technical issues relating to long supply chains and an insight into the future scientific and industrial advances required to meet future market and public demands.

This research work highlights the research issues and discusses the key enabling features, which will need to evolve and be perfected in industry in the future manufacturing networked enterprises and especially long manufacturing supply chains.

Several inferential procedures are advocated in the literature. The most commonly used techniques are the frequentist and the Bayesian inferential procedures. Bayesian methods afford inferences based on small data sets and are especially useful in studies with limited data availability. Bayesian approaches also help incorporate prior knowledge, especially subjective knowledge, into predictions. Considering the increasing difficulty in data acquisition, the application of Bayesian techniques can be hugely beneficial to managers, especially in analysing limited data situations like a study of expert opinion. Another factor constraining the broader application of Bayesian statistics in business was computational power requirements and the availability of appropriate analytical tools. However, with the increase in computational power, connectivity and the development of appropriate software programmes, Bayesian applications have become more attractive. This chapter attempts to unravel the applications of the Bayesian inferential procedure in marketing management.

This paper aims to provide empirically derived insights into trust and its predictors within a cyber-physical system context of a household service.

The methodology comprises an innovative mixed methods design encompassing a videographic animated film portraying a potential “slice of life” household service-system scenario that was subsequently incorporated into a quantitative survey. A total of 400 responses were then used to examine trust dimensions and their hypothesised predictors.

Findings suggest trust is two-dimensional, with “online networking competency”, “perceptions of risk”, “propensity to trust technology in general” and “concerns about security” being significant predictors. Surprisingly, “concerns about privacy” do not have a significant effect.

The contribution of this research is twofold. Firstly, from a theoretical perspective, the paper offers empirical insights into trust and its predictors within a cyber-physical system context of a household service. Secondly, and from a pragmatic perspective, the model derived from this study may aid practitioners in developing trust strategies and trust management systems within such contexts.