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We use design thinking in the context of accounting pedagogy to exploit recent advances in cybernetics in the form of generative artificial intelligence technology. Relying on the intuition that supplementing or augmenting human argumentation (natural intelligence or NI) with parallel AI output can produce better student written assignments, we posit the “augmentation premise,” that is, ((NI + AI) > AI > NI). To test the augmentation premise, we compare student written submissions in an Accounting Information Systems (AIS) course with and without the benefit of parallel generative AI output. We then evaluate how the generative AI output enhances student-crafted revisions to their initial submissions. Using a summative quality improvement index (QII) consisting of quantitative and qualitative assessments, we present preliminary evidence supporting the augmentation premise. The augmentation premise likely extends to other accounting subdisciplines and merits generalization for enriching accounting pedagogy.

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 chapter presents the survey of selected linear and mixed integer programming multi-objective portfolio optimization. The definitions of selected percentile risk measures are presented. Some contrasts and similarities of the different types of portfolio formulations are drawn out. The survey of multi-criteria methods devoted to portfolio optimization such as weighting approach, lexicographic approach, and reference point method is also presented. This survey presents the nature of the multi-objective portfolio problems focuses on a compromise between the construction of objectives, constraints, and decision variables in a portfolio and the problem complexity of the implemented mathematical models. There is always a trade-off between computational time and the size of an input data, as well as the type of mathematical programming formulation with linear and/or mixed integer variables.

Advanced information technologies, and particularly big data, provide new affordances to facilitate inter-organizational collaboration. Rich flows of real-time data provide transparency across organizational boundaries and enable greater automation of inter-organizational routines. Taking stock of the literature and building on observations from the research in an industrial setting, the authors introduce the concept of technological embeddedness as an important characteristic of inter-organizational relationships, denoting the degree of monitoring, control, and optimization of intra- and inter-organizational tasks accomplished through technology at the interface of the inter-organizational relationship. The authors theorize how increasing technological embeddedness created by big data technologies affects the development of inter-organizational trust, mutual adaptation, and temporal structuring of collaboration. The propositions elaborate how greater technological embeddedness enables collaboration, and warn about the potential limiting effects of technological embeddedness on the development of interpersonal trust, strategic learning, and long-term orientation.

We argue that in order to address the contemporary challenges that organizations and societies are facing, the field of organization development (OD) requires frameworks and skills to focus on the eco-system as the level of analysis. In a world that has become economically, socially, and technologically highly connected, approaches that foster the optimization of specific actors in the eco-system, such as individual corporations, result in sub-optimization of the sustainability of the natural and social system because there is insufficient offset to the ego-centric purposes of the focal organization. We discuss the need for OD to broaden focus to deal with technological advances that enable new ways of organizing at the eco-system level, and to deal with the challenges to sustainable development. Case examples from healthcare and the agri-foods industry illustrate the kinds of development approaches that are required for the development of healthy eco-systems. We do not suggest fundamental changes in the identity of the field of organizational development. In fact, we demonstrate the need to dig deeply into the open systems and socio-technical roots of the field, and to translate the traditional values and approaches of OD to continue to be relevant in today’s dynamic interdependent world.

This chapter addresses quality management (QM) content on the process quality management (PQM) level in the high-technology industry of semiconductor manufacturing. Identifying critical components of a manufacturing or service process and improving them to ensure superior quality at economic costs is the overall goal of PQM. Deming was a prominent proponent of PQM as a means to optimize the performance of a product or process. In optimizing the performance of a product or process, good design practices require the evaluation of designs from a process perspective. Advanced design techniques, namely design of experiments (DOEs), are cornerstone to the optimization process, to design management, and in turn to PQM. This chapter investigates the use of DOEs in the manufacture of semiconductors. Specifically, two underlying assumptions impact operations managers using DOEs: solution differences/similarities in underlying DOE optimization methods and marginal rates of substitution. Perhaps unknown to the user, DOE optimization techniques carry strong assumptions regarding these characteristics. This chapter investigates two commonly used DOE optimization approaches applied to the operational control of semiconductor wafer production, and demonstrates that each method contains assumptions about these characteristics, which are not intuitively evident to a user.

By taking conventionalist view of the evolution of biotechnology, we suggest that the process by which entrepreneurs determined what made biotechnology valuable and figured out how to organize around such an economic logic was contested. The shape that biotechnology has ultimately taken emerged from the resolution of these contests. Convention theory – as elaborated in Boltanski and Thévenot's (2006) On Justification ¹ – argues that our economy is shaped by participants affecting the rules of economic action. Whereas most economists would argue that the assignment of value underpins any system of exchange, conventionalists suggest that this value is not only given by the principles of optimization but instead can be derived from many possible spheres such as civic duty, attainment of fame, proof of technologic performance, and demonstration of creativity. More specifically, Boltanski and Thévenot (2006, p. 43) claim that the establishment of a particular logic “comes about as a part of a coordinated process that relies on two supports: a common identification of market goods, whose exchange defines the course of action, and a common evaluation of these objects in terms of prices that make it possible to adjust various actions.” Simply put, economic logics embody principles of economic coordination or conventions that guide interpretation of the technology and its value.

Supply chain is an important aspect for all the companies and can affect many aspects of companies. Especially the disruption in supply chain is causing huge impacts and consequences that are difficult to deal with. This chapter presents a review of selected multiple criteria problems used in supply chain optimization. Research analyzed the multiple criteria decision-making methods to tackle the problem of supplier evaluation and selection. It also focuses on the problem of supply chain when a disruption happens and presents strategies to deal with the issue of disruptions in supply chain and how to mitigate the impact of disruptions. Prevention, response, protection, and recovery strategies are explained. Practical part is focused in the risk-averse models to minimize expected worst-case scenario by single sourcing. Computational experiments for practical examples have been solved using CPLEX solver.