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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.

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.

This chapter considers the role of entrepreneurship theory in the development of ideation techniques for entrepreneurship education. It begins by considering how metatheories impact theory construction in entrepreneurship research and discusses the role of ontology, epistemology, axiology, as well as the role of assumptions about human nature and social change. The chapter presents four different paradigms of thought that apply different philosophies and illustrates how these different paradigms conceptualize entrepreneurship and entrepreneurial opportunity differently. The four paradigms include the equilibrium paradigm, the disequilibrium paradigm, the disruptive innovation paradigm, and the social constructionism paradigm. Within each paradigm, the nature of entrepreneurial opportunity is discussed, and the chapter provides examples to show how different ideation techniques can be generated from these different conceptualizations. Forms of ideation technique are presented and explained, as they relate to each paradigm, and the chapter concludes by explaining the value of these techniques for ideation, opportunity discovery, and creation, in the entrepreneurial process.

The growing displacement of theory and other forms of wide-ranging knowledge of social phenomena by empirical research methods in economics is widely noted by economists and historians of economic knowledge. Less attention has been devoted, however, to understand the materialization of such changes in the scientific practices. This article studies the recent transformations in the epistemological practices at CEDE, a research center in Colombia. I use a machine learning technique called Topic Modeling, interviews to CEDE researchers, and exegesis of papers to characterize a shift in the production of knowledge in microeconometrics at CEDE during the years 2000 and 2018. I explain this shift by characterizing two sets of epistemological practices that implies a recent tendency to disdain research that cannot make a “strong” causal inference.

The Government of Eswatini (GoE) civil service wage bill has continued to rise in recent years. The personnel budget is still the largest single recurrent expenditure item in the budget in Eswatini. To control the civil service wage bill, the GoE introduced Lean Service Principle through Management Services Division (MSD). The civil service wage bill continues to rise despite the implementation of the Lean Service Principle. So far, there are no tangible outcomes that indicate that the principles applied have been effective in the reduction of the wage bill. Thus, this research aims to examine the question of why the Lean Service Principle failed to effectively slow the rampant growth of the civil service wage bill in the Kingdom of Eswatini. This study used the quantitative research approach to collect data on amounts spent on wages, the percentage increase of the wage bill for the period 2010–2017, and the percentage increase in the number of civil servants as per the Establishment Registers, 2010–2017. The study investigated the wage bill push factors, the shortcomings of the Lean Service Principle, and the Just-In-Time (JIT) Technique in the management and reduction of the GoE civil service wage bill. The study found the MSD has been applying the wrong methodologies in wage bill control, which has been evident in the continued yearly increase of the wage bill. The study recommends that the MSD should consider the utilisation of Human Resources Forecasting and Planning Techniques instead of using the Lean Service Principle and the JIT technique. This study will enable the Cabinet to make an appropriate decision on the mandate and future of the MSD, as there have been growing calls to disband the division due to the failure to reduce and/or control the wage bills as that is the core mandate of the division.

Assessment and identification of children with emotional and behavioral disorders (EBD) is complex and involves multiple techniques, levels, and participants. While federal law sets the general parameters for identification in school settings, these criteria are vague and may lead to inconsistencies in selection and interpretation of assessment measures. Assessment practice across school settings is greatly influenced by clinical guidelines such as the DSM-IV, which more specifically defines emotional and behavioral disorders and highlights the issue of co-morbidity. Before a student is assessed for special education eligibility under the IDEIA category of emotional disturbance, screening techniques and pre-referral interventions are needed. Positive Behavioral Supports and Response to Intervention models provide empirically supported frameworks for establishing the need for formal psychological assessment. Collaboration among members of the multidisciplinary team, including parents, helps to ensure that identification and intervention efforts have ecological validity. Tests and techniques vary considerably, but developmental histories, interviews, observations across settings, and behavioral checklists and rating scales are recommended, along with cognitive and achievement testing. While problems exist in the reliability and validity of projective techniques, they continue to be used in school-based assessment for EBD. Multitrait, multisetting, and multimethod approaches are essential for culturally fair assessment and reduction of bias in identification and placement.

The psychological analysis of strategic management issues has gained a great deal of momentum in recent years. Much can be learned by entering the black box of strategic thinking of senior executives and bring new insights on how they see, make sense of, and interpret their everyday strategic experiences. This chapter will focus on a powerful cognitive mapping tool called the Repertory Grid Technique and demonstrate how it has been used in the strategy literature along with how a new and more refined application of the technique can enhance the elicitation of complex strategic cognitions for strategy and Board of Directors research.

The purpose of this study is to explore the learning and teaching techniques that accounting professors use in their courses to educate students. In this chapter, we answer the following questions: (1) What methods are accounting faculty currently using in the classroom? (2) To what extent are active learning techniques being utilized relative to passive techniques? (3) What are the perceptions of accounting faculty regarding the use of active learning in the classroom?

To answer these questions, we conducted an Internet-based survey of accounting educators (n = 300). We found that, on average, passive learning methods (e.g., lectures) comprise approximately 50% of class time, active learning methods cover slightly more than 35% of class time, while assessment activities (e.g., exams) use about 15% of class time. Regarding faculty perceptions of the usefulness of various learning methods, we found that the faculty recommend the use of every learning method included in the survey at higher levels than are currently being used. Our findings provide a baseline profile of the current use of both passive and active learning methods in accounting and their perceived usefulness by accounting educators. This baseline should enable future research to track changes and trends in accounting pedagogy, particularly the learning and teaching techniques employed in the classroom.

The robust appraisal of exploration drilling concepts is essential for establishing the economic viability of a prospective recovery field. This study evaluates the different concept selection methods that were considered for drilling operations at the Trym field in Norway. The construction of drilling rigs is a capital-intensive process, and it involves high levels of economic risk. These risks can be broadly categorised as aleatoric (i.e. those related to chance) and epistemic (i.e. those related to knowledge). Evaluating risks in the investment appraisal process tends to be a complicated process. Project risks are evaluated using Monte Carlo simulation (MCS) and are based on the fuzzy analytic hierarchy process (AHP). MCS provides a useful means of evaluating variabilities (i.e. aleatoric risks) in oil drilling operations. However, many of the economic risks in oil drilling processes are unanticipated, and, in some cases, are not readily expressible in quantitative values. The fuzzy AHP is therefore used to appraise the qualitatively defined indirect revenues comprising risks that affect future flexibilities, schedule certainty and health and safety performance. Both the Monte Carlo technique and the fuzzy AHP technique found that a cumulative revenue variation of up to 30% is possible in any of the considered drilling options. The fuzzy AHP technique estimates that the chances of profitability being less than NOK 1 billion over a five-year period is 0.5%, while the Monte Carlo technique estimates suggest a more conservative proportion of 10%. Overall, the fuzzy AHP technique is easy to use and flexible, and it demonstrates increased robustness and improved predictability.