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While uncertainty has always been a feature of the healthcare environment, its pace and scope are rapidly increasing, fueled by myriad factors such as technological advancements, the threat and frequency of disruptive events, global economic developments, and increasing complexity. Contemporary healthcare organizations thus persistently face what is known as “deep uncertainty,” which obscures their ability to predict outcomes of strategic action and decision-making, presenting them with novel challenges and threatening their survival. Persistent, deep uncertainty challenges us to revisit and reconsider how we think about uncertainty and the strategic actions needed by organizations to thrive under these circumstances. Simply put, how can healthcare organizations thrive in the face of deeply uncertain environments? We argue that healthcare organizations need to employ both adaptive and creative strategic approaches in order to effectively meet patients' needs and capture value in the long-term future. The chapter concludes by offering two ways organizations can build the dynamic capabilities needed to employ such approaches.

Certain elements of Hayek’s work are prominent precursors to the modern field of complex adaptive systems, including his ideas on spontaneous order, his focus on market processes, his contrast between designing and gardening, and his own framing of complex systems. Conceptually, he was well ahead of his time, prescient in his formulation of novel ways to think about economies and societies. Technically, the fact that he did not mathematically formalize most of the notions he developed makes his insights hard to incorporate unambiguously into models. However, because so much of his work is divorced from the simplistic models proffered by early mathematical economics, it stands as fertile ground for complex systems researchers today. I suggest that Austrian economists can create a progressive research program by building models of these Hayekian ideas, and thereby gain traction within the economics profession. Instead of mathematical models the suite of techniques and tools known as agent-based computing seems particularly well-suited to addressing traditional Austrian topics like money, business cycles, coordination, market processes, and so on, while staying faithful to the methodological individualism and bottom-up perspective that underpin the entire school of thought.

This study provides a comprehensive framework of adaptation in triadic business relationship settings in the service sector. The framework is based on the industrial network approach (see, e.g., Axelsson & Easton, 1992; Håkansson & Snehota, 1995a). The study describes how adaptations initiate, how they progress, and what the outcomes of these adaptations are. Furthermore, the framework takes into account how adaptations spread in triadic relationship settings. The empirical context is corporate travel management, which is a chain of activities where an industrial enterprise, and its preferred travel agency and service supplier partners combine their resources. The scientific philosophy, on which the knowledge creation is based, is realist ontology. Epistemologically, the study relies on constructionist processes and interpretation. Case studies with in-depth interviews are the main source of data.

This chapter examines key drivers of variation in adaptive capacity of project network organizations (PNOs). PNOs are defined as strategically coordinated sets of longer-term, yet project-based relationships, which provide for both stability and change in volatile project businesses. While prior research has emphasized the adaptive role of flexible structures and agency, the author focuses on the role of project variety and contextual embedding and disembedding in building adaptive capacity. Comparing two PNOs in TV movie production, the author argues that differences in adaptive capacity are a function of inter-context connectivity, that is, the level of task and team linkages among diverse project contexts, and the degree to which network ties and relational practices have “dual quality” in being valuable both within and beyond specific project contexts. Findings have important implications for project, network, and organization research.

For organizational neuroscience to progress, it requires an overarching theoretical framework that locates neural processes appropriately within the wider context of organizational cognitive activities. In this chapter, we argue the case for building such a framework on two foundations: (1) critical realism, and (2) socially situated cognition. Critical realism holds to the importance of identifying biophysical roots for organizational activity (including neurophysiological processes) while acknowledging the top-down influence of higher-level, emergent organizational phenomena such as routines and structures, thereby avoiding the trap of reductionism. Socially situated cognition connects the brain, body, and mind to social, cultural, and environmental forces, as significant components of complex organizational systems. By focusing on adaptive action as the primary explanandum, socially situated cognition posits that, although the brain plays a driving role in adaptive organizational activity, this activity also relies on the body, situational context, and cognitive processes that are distributed across organizational agents and artifacts. The value of the framework that we sketch out is twofold. First, it promises to help organizational neuroscience become more than an arena for validating basic neuroscience concepts, enabling organizational researchers to backfill into social neuroscience, by identifying unique relations between the brain and social organization. Second, it promises to build deeper connections between neuroscience and mainstream theories of organizational behavior, by advancing models of managerial and organizational cognition that are biologically informed and socially situated.

To provide librarians with a better understanding of what makes online content truly accessible and to increase awareness of the current common accessibility issues found in library web sites and vendor supplied electronic resources.

A through and in-depth literature review takes a look at the web accessibility problems historically found in library web sites. It then briefly explains the basics of web accessibility and delves more deeply into structural content access, which is the hallmark of true accessibility. The simple mechanics as well as the pros and cons of traditional methods of providing keyboard access to web content is discussed. The chapter then provides a gentle introduction to HTML5 and the Web Accessibility Initiative-Accessible Rich Internet Applications (WAI-ARIA), current use, techniques for use, and application opportunities. The chapter also provides the research results of a broad examination of the basic structural accessibility state of many current database providers.

The research shows that the accessibility of library web sites is improving; however many library vendor database products still have significant accessibility problems.

Through the practical accessibility explanations, the chapter points out the ways librarians can use this knowledge to work with users and communicate with product providers regarding the accessibility of library resources.

This chapter provides a rich resource for understanding and implementing web accessibility, particularly as it applies to keyboard navigation and the new accessibility features in HTML5 and WAI-ARIA.

This paper conceives of Hayek's overall project as presenting a theory of sociocognition, explication of which has a two-fold purpose: (1) to locate Hayek within the non-Cartesian tradition of cognitive science, and (2) to show how Hayek's philosophical psychology infuses his social theory.

Advances in Artificial Intelligence (AI) technologies and Autonomous Unmanned Vehicles are shaping our daily lives, society, and will continue to transform how we will fight future wars. Advances in AI technologies have fueled an explosion of interest in the military and political domain. As AI technologies evolve, there will be increased reliance on these systems to maintain global security. For the individual and society, AI presents challenges related to surveillance, personal freedom, and privacy. For the military, we will need to exploit advances in AI technologies to support the warfighter and ensure global security. The integration of AI technologies in the battlespace presents advantages, costs, and risks in the future battlespace. This chapter will examine the issues related to advances in AI technologies, as we examine the benefits, costs, and risks associated with integrating AI and autonomous systems in society and in the future battlespace.

Purpose: This chapter aims to examine machine learning (ML) models for predicting credit card fraud (CCF).

Need for the study: With the advance of technology, the world is increasingly relying on credit cards rather than cash in daily life. This creates a slew of new opportunities for fraudulent individuals to abuse these cards. As of December 2020, global card losses reached $28.65billion, up 2.9% from $27.85 billion in 2018, according to the Nilson 2019 research. To safeguard the safety of credit card users, the credit card issuer should include a service that protects customers from potential risks. CCF has become a severe threat as internet buying has grown. To this goal, various studies in the field of automatic and real-time fraud detection are required. Due to their advantageous properties, the most recent ones employ a variety of ML algorithms and techniques to construct a well-fitting model to detect fraudulent transactions. When it comes to recognising credit card risk is huge and high-dimensional data, feature selection (FS) is critical for improving classification accuracy and fraud detection.

Methodology/design/approach: The objectives of this chapter are to construct a new model for credit card fraud detection (CCFD) based on principal component analysis (PCA) for FS and using supervised ML techniques such as K-nearest neighbour (KNN), ridge classifier, gradient boosting, quadratic discriminant analysis, AdaBoost, and random forest for classification of fraudulent and legitimate transactions. When compared to earlier experiments, the suggested approach demonstrates a high capacity for detecting fraudulent transactions. To be more precise, our model’s resilience is constructed by integrating the power of PCA for determining the most useful predictive features. The experimental analysis was performed on German credit card and Taiwan credit card data sets.

Findings: The experimental findings revealed that the KNN achieved an accuracy of 96.29%, recall of 100%, and precision of 96.29%, which is the best performing model on the German data set. While the ridge classifier was the best performing model on Taiwan Credit data with an accuracy of 81.75%, recall of 34.89, and precision of 66.61%.

Practical implications: The poor performance of the models on the Taiwan data revealed that it is an imbalanced credit card data set. The comparison of our proposed models with state-of-the-art credit card ML models showed that our results were competitive.