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The change of production methods, the industrial revolutions, technological developments, and digital transformation have affected almost all functions in the enterprises. Accounting and auditing areas are also quite affected by this transformation. Another important result of technology and digitalization is the rapid increase in errors, frauds, and irregularities. Enterprises are looking for new solutions and investigations against irregularities and frauds. Audits for errors, frauds, or irregularities are among the interests of forensic accounting. Many methods are used to identify errors and frauds in the forensic accounting. However, it is inevitable that digital technologies should be utilized in forensic accounting applications as a result of the rapid spread of automation and computer programs in enterprises within the framework of digitalized business activities. Hence, enterprises will be able to get more effective results through computer programs and artificial intelligence in terms of fraud audit in forensic accounting. Expert system applications use artificial intelligence to enable computer programs to behave just like people. One of the most widely used, most easily applicable, and most understandable types of expert system is rule-based expert system. The aim of this study is to determine the accounting fraud that may occur in enterprises within the framework of forensic accounting through rule-based expert systems. For this purpose, various applications have been implemented in a large-scale production enterprise through the use of rule-based expert systems for the determination of accounting fraud. Benford’s Law, risk levels, and various other criteria were used in the creation of expert systems. According to the results obtained from the study, it has been seen that by means of rule-based expert system applications, enterprises can better detect existing frauds and prevent further irregularities in the future. The study is important and it is expected that the study will contribute to the literature because it is shown in the study that the rule-based expert systems, applied in many fields under the title of social sciences, can also be applied in the field of forensic accounting and auditing.

Due to the increasing size and complexity of construction projects, construction engineering and management involves the coordination of many complex and dynamic processes and relies on the analysis of uncertain, imprecise and incomplete information, including subjective and linguistically expressed information. Various modelling and computing techniques have been used by construction researchers and applied to practical construction problems in order to overcome these challenges, including fuzzy hybrid techniques. Fuzzy hybrid techniques combine the human-like reasoning capabilities of fuzzy logic with the capabilities of other techniques, such as optimization, machine learning, multi-criteria decision-making (MCDM) and simulation, to capitalise on their strengths and overcome their limitations. Based on a review of construction literature, this chapter identifies the most common types of fuzzy hybrid techniques applied to construction problems and reviews selected papers in each category of fuzzy hybrid technique to illustrate their capabilities for addressing construction challenges. Finally, this chapter discusses areas for future development of fuzzy hybrid techniques that will increase their capabilities for solving construction-related problems. The contributions of this chapter are threefold: (1) the limitations of some standard techniques for solving construction problems are discussed, as are the ways that fuzzy methods have been hybridized with these techniques in order to address their limitations; (2) a review of existing applications of fuzzy hybrid techniques in construction is provided in order to illustrate the capabilities of these techniques for solving a variety of construction problems and (3) potential improvements in each category of fuzzy hybrid technique in construction are provided, as areas for future research.

This study focuses on ad hoc policy advisory committees that bring together experts and stakeholders to propose public policies on the basis of consensus. These kinds of committees are often considered to be a typical governance mechanism of the social democratic model of regulation and policy-making known from the Nordic countries. We challenge this view by comparing the Norwegian system of committee governance with those of Germany and the European Union and point out the central role of coordination and consensus in all three systems. Relying on existing and original research, and contrary to the assumption of a distinct Nordic regime, we find significant similarities between the three committee governance systems when it comes to organisational features, the kind of expertise produced and the committees' governance functions. Most remarkable is the prevalence of hybrid, tripartite committees that draw together interest groups, civil servants and researchers in all three systems. We show that these kinds of ad hoc advisory committees tend to generate a kind of coordinated, negotiated expertise where notions of validity and objectivity are connected not only to cognitive quality but also to the breadth of viewpoints that are integrated. Moreover, the Nordic committee system of Norway stands out with only few distinctive qualities, and it is not obvious how the notion of ‘social democracy’ helps illuminating these features. To help shed light on the striking resemblances we find across systems, we develop a notion of consensus-oriented political and epistemological systems, which may be a useful complement to the notion of Nordic social democracy.

Construction is a highly dynamic environment with numerous interacting factors that affect construction processes and decisions. Uncertainty is inherent in most aspects of construction engineering and management, and traditionally, it has been treated as a random phenomenon. However, there are many types of uncertainty that are not naturally modelled by probability theory, such as subjectivity, ambiguity and vagueness. Fuzzy logic provides an approach for handling such uncertainties. However, fuzzy logic alone has some limitations, including its inability to learn from data and its extensive reliance on expert knowledge. To address these limitations, fuzzy logic has been combined with other techniques to create fuzzy hybrid techniques, which have helped solve complex problems in construction. In this chapter, a background on fuzzy logic in the context of construction engineering and management applications is presented. The chapter provides an introduction to uncertainty in construction and illustrates how fuzzy logic can improve construction modelling and decision-making. The role of fuzzy logic in representing uncertainty is contrasted with that of probability theory. Introductory material is presented on key definitions, properties and methods of fuzzy logic, including the definition and representation of fuzzy sets and membership functions, basic operations on fuzzy sets, fuzzy relations and compositions, defuzzification methods, entropy for fuzzy sets, fuzzy numbers, methods for the specification of membership functions and fuzzy rule-based systems. Finally, a discussion on the need for fuzzy hybrid modelling in construction applications is presented, and future research directions are proposed.

Experts respond to the same incentives as people in other areas of human action, and in the same ways. This insight is a truism: Experts are ordinary people, not otherworld creatures. The disciplined pursuit of this common sense observation helps us to reach conclusions about experts that might be surprising or counterintuitive.

Purpose — This chapter illustrates and explains the ambiguity and vagueness of the term social search and aims at describing and classifying the heterogeneous landscape of social search implementations on the WWW.

Methodology/approach — We have looked at different definitions as well as the context of social search by carrying out an extensive literature review, and tried to unify and enhance existing ideas and concepts. Our definition of social search is illustrated by a general review of existing social search engines, which are analyzed and described by their specific features and social aspects.

Findings — The chapter presents a discussion of social search as well as a comparison of existing social search engines.

Social implications — The definition of social search and the comparison of social search engines summarize the many ways people can search the web together and allow for an assessment of future developments in this area.

Originality/value of paper — Although different attempts to define social search have been made in the past, we present an argumentation that unifies some existing definitions and which is different from other interpretations of the social search concept. We present an overview and a comparison of the different genres of social search engines.

Several different simulation techniques, such as discrete event simulation (DES), system dynamics (SD) and agent-based modelling (ABM), have been used to model complex construction systems such as construction processes and project management practices; however, these techniques do not take into account the subjective uncertainties that exist in many construction systems. Integrating fuzzy logic with simulation techniques enhances the capabilities of those simulation techniques, and the resultant fuzzy simulation models are then capable of handling subjective uncertainties in complex construction systems. The objectives of this chapter are to show how to integrate fuzzy logic and simulation techniques in construction modelling and to provide methodologies for the development of fuzzy simulation models in construction. In this chapter, an overview of simulation techniques that are used in construction is presented. Next, the advancements that have been made by integrating fuzzy logic and simulation techniques are introduced. Methodologies for developing fuzzy simulation models are then proposed. Finally, the process of selecting a suitable simulation technique for each particular aspect of construction modelling is discussed.

The underpinnings of knowledge management theories is that finding, keeping and leveraging an organization’s information assets are critical to productivity, efficiency of operation and successful competition. Following a brief introduction of the knowledge management systems, this essay examines the corollary relationship between knowledge management and appreciative inquiry, and subsequently points to critical areas in which knowledge management practices can benefit from adopting the appreciative inquiry perspective. More particularly, we submit that appreciative inquiry can motivate organizational-wide adoption and it can provide language-based mechanisms that facilitate effective knowledge exchange. The development of an appreciative inquiry based mode of knowledge management as an alternative to the prevailing approaches opens new horizons and uncovers previously overlooked possibilities, which eventually can contribute to the overall organizational well-being.

The nature of technologies that are recognised as Artificial Intelligence (AI) has continually changed over time to be something more advanced than other technologies. Despite the fluidity of understanding of AI, the most common theme that has stuck with AI is ‘human-like decision making’. Advancements in processing power, coupled with big data technologies, gave rise to highly accurate prediction algorithms. Analytical techniques which use multi-layered neural networks such as machine learning and deep learning have emerged as the drivers of these AI-based applications. Due to easy access and growing information workforce, these algorithms are extensively used in a plethora of industries ranging from healthcare, transportation, finance, legal systems, to even military. AI-tools have the potential to transform industries and societies through automation. Conversely, the undesirable or negative consequences of AI-tools have harmed their respective organisations in social, financial and legal spheres. As the use of these algorithms propagates in the industry, the AI-based decisions have the potential to affect large portions of the population, sometimes involving vulnerable groups in society. This chapter presents an overview of AI’s use in organisations by discussing the following: first, it discusses the core components of AI. Second, the chapter discusses common goals organisations can achieve with AI. Third, it examines different types of AI. Fourth, it discusses unintended consequences that may take place in organisations due to the use of AI. Fifth, it discusses vulnerabilities that may arise from AI systems. Lastly, this chapter offers some recommendations for industries to consider regarding the development and implementation of AI systems.

In recent years, there has been a growing dialogue around community-based and systems-based approaches to security risk management through the introduction of top-down and bottom-up knowledge acquisition. In essence, this relates to knowledge elicited from academic experts, or security subject-matter experts, practitioner experts, or field workers themselves and how much these disparate sources of knowledge may converge or diverge. In many ways, this represents a classic tension between organisational and procedural perspectives of knowledge management (i.e. top-down) versus more pragmatic and experience focussed perspectives (i.e. bottom-up).

This chapter considers these approaches and argues that a more consistent approach needs to address the conflict between procedures and experience, help convert field experience into knowledge, and ultimately provide effective training that is relevant to those heading out into demanding work situations. Ultimately, ethics and method are intricately bound together in whichever approach is taken and the security of both staff and at-risk populations depends upon correctly managing the balance between systems and communities.