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Failure to rescue events, or events involving preventable deaths from complications, are a significant contributor to inpatient mortality. While many interventions have been designed and implemented over several decades, this patient safety issue remains at the forefront of concern for most hospitals. In the first part of this study, the development and implementation of one type of highly studied and widely adopted rescue intervention, algorithm-based patient assessment tools, is examined. The analysis summarizes how a lack of systems-oriented approaches in the design and implementation of these tools has resulted in suboptimal understanding of patient risk of mortality and complications and the early recognition of patient deterioration. The gaps identified impact several critical aspects of excellent patient care, including information-sharing across care settings, support for the development of shared mental models within care teams, and access to timely and accurate patient information.

This chapter describes the use of several system-oriented design and implementation activities to establish design objectives, model clinical processes and workflows, and create an extensible information system model to maximize the benefits of patient state and risk assessment tools in the inpatient setting. A prototype based on the product of the design activities is discussed along with system-level considerations for implementation. This study also demonstrates the effectiveness and impact of applying systems design principles and practices to real-world clinical applications.

Telehealth has been playing a progressively significant role in the management of the COVID-19 crisis. The enforcement of social distancing measures has had the consequence of reduced technology distance in almost every walk of life. In this chapter, based primarily on the still-unfolding experiences of deploying it during the current situation, we argue that telehealth has finally come of age and that it is time to move it from the peripheries to the center of the twenty-first-century healthcare. To provide a live context to the discussion, several instances of how telehealth strengthened our healthcare systems during the COVID-19 crisis are presented.

Transformational, charismatic, and related leadership theories play an important role in understanding how leaders motivate better performance. However, these approaches have paid surprisingly little attention to the management of error in organizations. In fact, current studies in transformational leadership tend to define the management of error as one of the negative features of leadership. Preventing error and learning from error is a high profile leadership role in a wide variety of global industries, and therefore, it is important that leadership theories encompass this critical task. We draw on different streams of research to provide a more integrated and positive approach to leadership and the management error. Studies of error management culture provide insights into the organizational systems that are important for responding and learning from error. We discuss how error learning culture can inform the leadership behaviors that will enhance learning from error. We also draw on regulatory focus theory to show how managing error can be differentiated from other leadership activities. The integration of these ideas with current leadership theory provides a more comprehensive framework for understanding the role of leadership when error management is critical. We present this integrated framework and discuss how cultural factors are likely to shape the role of error management in a variety of global contexts.

This chapter critically evaluates the opportunities and challenges in adopting information technology for enhancing transportation in developing countries. The conception framework emerging from this study highlights four key stakeholders and the country context in developing and integrating information technology for improving transportation in developing countries – Tech developers, Transporters, Transport Tech start-ups and Travellers. This study makes cogent theoretical contributions to the growing body of works around technological innovation, smart mobility and intelligent transport systems, albeit from a developing country perspective. This study has also emerged from managerial implications for stakeholders, especially the tech developers, transporters and transport tech start-ups. There is a need for innovative ideas to address the inherent transportation challenges in Africa, and the customers are counting on these stakeholders to make the technology readily available and accessible.

This chapter presents a typology, a comprehensive overview and a deconstruction of food counterfeits. In this chapter, common targets, defined by type of commodity, supply chain (node) and location, are identified based on incident reports and vulnerability assessments in global food supply chains. As a second step, the effects of counterfeiting on brand owners, consumers and governments are detailed, which is followed by the characterisation of the groups of buyers and suppliers. To comprehend the counterfeiting process and its most important factors in greater detail, counterfeiting is disassembled into and analysed for existing motivational drivers, opportunities and control measures. Lastly, various strategies are proposed to deter counterfeiting and disrupt these practices.

A somewhat underappreciated aspect of the burgeoning rush to regional trade agreements (RTAs) is a discrepancy between the dispute settlement procedure (DSP) embodied in the original World Trade Organization (WTO) Dispute Settlement Understanding (DSU) and that found in the language of many RTAs. This chapter explores the issue in the context of a dynamic repeated game of trade agreements. As is well known, the institutional alternatives available in negotiating multilateral freer trade agreements – regional agreements, side agreements, trade dispute settlement punishments, and so on – can proscribe the limits and shape the nature of self-enforcing trade agreements. Here, we suggest the extent to which deviations from the WTO DSP embodied in RTAs – for example, “private interest access,” “third party procedures,” and “choice of forum” – can not only work against the interests of “weaker parties” but furthermore undermine multilateral agreements closer to free trade.

The growth of social media and the sharing economy is generating abundant unstructured image and video data. Computer vision techniques can derive rich insights from unstructured data and can inform recommendations for increasing profits and consumer utility – if only the model outputs are interpretable enough to earn the trust of consumers and buy-in from companies. To build a foundation for understanding the importance of model interpretation in image analytics, the first section of this article reviews the existing work along three dimensions: the data type (image data vs. video data), model structure (feature-level vs. pixel-level), and primary application (to increase company profits vs. to maximize consumer utility). The second section discusses how the “black box” of pixel-level models leads to legal and ethical problems, but interpretability can be improved with eXplainable Artificial Intelligence (XAI) methods. We classify and review XAI methods based on transparency, the scope of interpretability (global vs. local), and model specificity (model-specific vs. model-agnostic); in marketing research, transparent, local, and model-agnostic methods are most common. The third section proposes three promising future research directions related to model interpretability: the economic value of augmented reality in 3D product tracking and visualization, field experiments to compare human judgments with the outputs of machine vision systems, and XAI methods to test strategies for mitigating algorithmic bias.

Purpose – The purpose of this chapter is to introduce new methods to behavioral research on group negotiation.

Design/methodology/approach – We describe three techniques from the field of Machine Learning and discuss their possible application to modeling dynamic processes in group negotiation: Markov Models, Hidden Markov Models, and Inverse Reinforcement Learning. Although negotiation research has employed Markov modeling in the past, the latter two methods are even more novel and cutting-edge. They provide the opportunity for researchers to build more comprehensive models and to use data more efficiently. To demonstrate their potential, we use scenarios from group negotiation research and discuss their hypothetical application to these methods. We conclude by suggestions for researchers interested in pursuing this line of work.

Originality/value – This chapter introduces methods that have been successfully used in other fields and discusses how these methods can be used in behavioral negotiation research. This chapter can be a valuable guide to researchers that would like to pursue computational modeling of group negotiation.