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This chapter presents an overarching overview of how the rather recent technological phenomena, like data mining, machine learning, and artificial intelligence, are applied in the field of education. The author provides examples of how technological developments associated with the so-called Fourth Industrial Revolution are applied in education and considers the benefits and challenges they may bring regarding the economic system, as education (at least in the higher education sector) tends to be monetized and commercialized. The framework for education is perceived in the context of the economic intelligence of states, which is instrumental in ensuring their economic security. It is further expanded to the global scale, as Digital Education is crossing national borders and is being implemented in broader national processes.

As manufacturers continue to increase their level of automation, the issue of how to allocate machinery costs to products becomes increasingly important to product profitability. If machine costs are allocated to products on a basis that is incongruent with the realities of machine use, then income and product profitability will be distorted. Adding complexity to the dilemma of identifying an appropriate method of allocating machine costs to products is the changing nature of machinery itself. Depreciation concepts were formulated in days when a machine typically automated a single operation on a product. Today’s collections of computer numerically controlled machines can perform a wide variety of operations on products. Different products utilize different machine capabilities which, depending on the function used, put greater or less wear and tear on the equipment. This paper presents a mini-case that requires management accountants to consider alternative machine cost allocation methods. The implementation of an activity-based method allows managers to better match machine cost consumption to products. Better matching of machine costs to products enables better strategic decisions about pricing, mix, customer retention, capacity utilization, and equipment acquisition.

Three themes dominate Hunting Causes. The first is that cause is a plural concept. The methods and metaphysics of causation, Cartwright believes, are context dependent. Different causal accounts seem to be at odds with one another only because the same word means different things in different contexts. Every formal approach to causality uses a conceptual framework that is “thinner” than causal reality. She lists a bewildering variety of approaches to causation: probabilistic and Bayes-net accounts (of, for example, Patrick Suppes, Clive Granger, Wolfgang Spohn, Judea Pearl, and Clark Glymour), modularity accounts (Pearl, James Woodward, and Stephen LeRoy), invariance accounts (Woodward, David Hendry, and Kevin Hoover), natural experiments (Herbert Simon, James Hamilton, and Cartwright), causal process accounts (Wesley Salmon and Philip Dowe), efficacy accounts (Hoover), counterfactual accounts (David Lewis, Hendry, Paul Holland, and Donald Rubin), manipulationist accounts (Peter Menzies and Huw Price), and others. The lists of advocates of various accounts overlap. Nevertheless, she sometimes treats these accounts as if they were so different that it is not clear why they should be the subject of a single book. And she fails to explain what they have in common. If, as she apparently believes, they do not have a common essence, do they have a Wittgensteinian family resemblance? She fails to explore in any systematic way the complementarities among the different approaches – for example, between invariance accounts, Bayes-nets, and natural experiments – that frequently make their advocates allies rather than opponents.

This chapter introduces a risk control framework on credit card fraud instead of providing a solely binary classifier model. The anomaly detection approach is adopted to identify fraud events as the outliers of the reconstruction error of a trained autoencoder (AE). The trained AE shows fitness and robustness on the normal transactions and heterogeneous behavior on fraud activities. The cost of false-positive normal transactions is controlled, and the loss of false-negative frauds can be evaluated by the thresholds from the percentiles of reconstruction error of trained AE on normal transactions. To align the risk assessment of the economic and financial situation, the risk manager can adjust the threshold to meet the risk control requirements. Using the 95th percentile as the threshold, the rate of wrongly detecting normal transactions is controlled at 5% and the true positive rate is 86%. For the 99th percentile threshold, the well-controlled false positive rate is around 1% and 83% for the truly detecting fraud activities. The performance of a false positive rate and the true positive rate is competitive with other supervised learning algorithms.

Anthropomorphism in Artificial Intelligence (AI)-powered devices is being used increasingly frequently in consumer-facing situations (e.g., AI Assistants such as Alexa, virtual agents in websites, call/chat bots, etc.), and therefore, it is essential to understand anthropomorphism in AI both to understand consequences for consumers and to optimize firms' product development and marketing. Extant literature is fragmented across several domains and is limited in the marketing domain. In this review, we aim to bring together the insights from different fields and develop a parsimonious conceptual framework to guide future research in fields of marketing and consumer behavior.

We conduct a review of empirical articles published until November 2021 in Financial Times Top 50 (FT50) journals as well as in 41 additional journals selected across several disciplinary domains: computer science, robotics, psychology, marketing, and consumer behavior.

Based on literature review and synthesis, we propose a three-step guiding framework for future research and practice on AI anthropomorphism.

Our proposed conceptual framework informs marketing and consumer behavior domains with findings accumulated in other research domains, offers important directions for future research, and provides a parsimonious guide for marketing managers to optimally utilize anthropomorphism in AI to the benefit of both firms and consumers.

We contribute to the emerging literature on anthropomorphism in AI in three ways. First, we expedite the information flow between disciplines by integrating insights from different fields of inquiry. Second, based on our synthesis of literature, we offer a conceptual framework to organize the outcomes of AI anthropomorphism in a tidy and concise manner. Third, based on our review and conceptual framework, we offer key directions to guide future research endeavors.

The concept of Industry 5.0 is not just one more revolution but calls for a tectonic shift in digitization and operationalizing technology with connected value chain across sectors. It is human centric that promotes talents, diversity and empowerment coupled with resilience leading to agile and adaptable technologies with prime focus on sustainability. The COVID-19 pandemic has given impetus to digital transformation and accelerated the focus on other challenges of present time and with extended importance on people, planet and societal concepts. This study shall attempt to examine the nature of association between revolution of Industry 5.0 with perspectives to digital innovation and its implications toward bringing sustainable business model. The main objective of this chapter shall be to uncover interrelated questions in terms of sustainability perspectives of industries in framing business models. This study shall serve as a primer to significance of digital transformation with relevance to businesses that can lead to efficient use of scarce resources and optimal feasible solutions to the business models, given the institutional and organizational frameworks. Further, an attempt shall also be made to underpin the key facets of effects of Industry 5.0 on the knowledge economy. It shall delve into how digital innovations can yield benefits to industry in terms of competitiveness and sustainability with focus on Society 5.0 that attempts to balance economic development with the resolution of societal and environmental problems. It is not restricted to the manufacturing sector but addresses larger social challenges based on the integration of physical and virtual spaces.

Businesses are rapidly automating workplaces with new technologies (e.g., driverless cargo trucks, artificially intelligent mortgage approvals, machine-learning-based paralegals, algorithmic managers). Such technological advancement raises a host of questions for business and society. As Thomas Donaldson recently remarked, “It’s instance of a problem that more sophisticated engineering cannot solve, and that requires a more sophisticated approach to values” (Ufberg, 2017). In this chapter, we explore the value questions as follows: What is the purpose of business in the machine age? What model for business will best serve society in coming decades: profit maximization, stakeholder theory, or another conception entirely? Is it time for a new social contract between business and society? Do firms have a natural duty to offer employment? Are existing concepts of responsibility/liability adequate for an age in which companies use autonomous robots as scapegoats? How can we protect our humanity and dignity in an algorithm-based society? Do we need to teach ethics to robots?

Utility strikes have spawned companies specializing in providing a priori analyses of the underground. Geophysical techniques such as Ground Penetrating Radar (GPR) are harnessed for this purpose. However, analyzing GPR data is labour-intensive and repetitive. It may therefore be worthwhile to amplify this process by means of Machine Learning (ML). In this work, harnessing the ADR design science methodology, an Intelligence Amplification (IA) system is designed that uses ML for decision-making with respect to utility material type. It is driven by three novel classes of Convolutional Neural Networks (CNNs) trained for this purpose, which yield accuracies of 81.5% with outliers of 86%. The tool is grounded in the available literature on IA, ML and GPR and is embedded into a generic analysis process. Early validation activities confirm its business value.