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Constraints on human-machine systems’ performance are generally treated as due to anatomy, physiology, and cognitive or behavioral limits. It is assumed that research findings can be universally applied to the design of such systems. It is now clear that social and cultural constraints are equally important, even in simple work systems. Context and culture are at least as important as limits of cognitive ability, and in many situations social and cultural factors are the dominant constraints on performance. This is particularly true in the cross-cultural transfer of advanced technological systems. A particularly clear example is given by population stereotypes of stimulus-response relations.

This chapter focuses on networks comprised of explicit data sources and information and non-human machines as actors. As non-human actors, we include intelligent agents, robotics, and other forms of interactive artificial intelligence. All six facets of knowledge networks are explored. Given these networks’ peculiar nature, three facets have particular importance, including geography, topology, and relationships. The authors provide profiles of seven networks, including semantic and citation networks, webpage networks, communications and computer networks, and energy grids.

The diffusion and adoption (D&A) of innovation propels today's technological landscape. Crisis situations, real or perceived, motivate communities of people to take action to adopt and diffuse innovation. The D&A of innovation is an inherently human activity; yet, artificially intelligent techniques can assist humans in six different ways, especially when operating in fifth generation ecosystems that are emergent, complex, and adaptive in nature.

Humans can use artificial intelligence (AI) to match solutions to problems, design for diffusion, identify key roles in social networks, reveal unintended consequences, recommend pathways for scaling that include the effects of policy, and identify trends for fast-follower strategies. The stability of the data that artificially intelligent systems rely upon will challenge performance; nevertheless, the research in this area has positioned several promising techniques where classically narrow AI systems can assist humans. As a result, human and machine interaction can accelerate the D&A of technological innovation to respond to crisis situations.

Results of a previous study showed that use of health information technology (HIT) significantly reduced potential medication prescribing errors. However, the results also revealed a less than 100% rate of HIT adoption by primary care physicians. The current study reports on personal interviews with participating physicians that explored the barriers they faced when attempting to fully adopt a particular HIT. Content analysis of qualitative interviews revealed three barrier themes: time, technology, and environment. Interviews also revealed two other areas of concern; specifically, the compatibility of the HIT with the physician's patient mix and the physician's own attitude toward the use of HIT. A theoretical model of technology acceptance and use is used to discuss and further explain the data derived from the physician interviews. With a better understanding of these issues, health care administrators can develop successful strategies for adoption of HIT across their health care organizations.

The competence-based management approach has shed light on how firms represent open systems that link assets, strategic logic, and capabilities in order to create new competences. Nonetheless, we find that there are too few empirical studies that illustrate how competences are distributed within an industry. The following case study is based on an in-depth analysis of innovation and standards-formation in industrial automation. Two examples, the standard-setting community PROFIBUS and the field bus-related sensor consortium IO-Link are used to analyze partnership arrangements and competence-distribution patterns.

This study is based on qualitative interviews and it uses patent data to judge competences of a standard-setting community's partner firms. Referring to the empirical case of IO-Link, we show how the integrator firms’ competence-leveraging can be significantly affected by new technology approaches that reason a novel deployment of capabilities. It seems that the deployment of resources depends not only on industry segmentation, but also on the firms’ coordinated agenda concerning innovative, new functionality of a given standard. Our patent analysis also mirrors the variety of knowledge within a standard-setting community. Furthermore, we develop a concept of layered business systems, that is, a terminology of knowledge, organizational, and technology domains. Standard-setting communities bundle complementarity assets, they make their member firms create both proprietary and open technology, and they integrate knowledge across industry boundaries.

Introduction: Artificial intelligence (AI) is being extensively used to solve complex problems in the industry. AI provides several benefits such as providing visibility in the processes, reducing time, improving accuracy, saving time, helping in the decision-making process, etc. Due to the range of benefits of AI technologies, organisations readily adopt this technology. However, there are several challenges that the organisation faces during the implementation of AI. These challenges are in context to human resource (HR) development for successful implementation of AI across different functions and are discussed in this chapter.

Purpose: Although we know that AI technology is widely accepted in human resource management (HRM) due to its various benefits. But the organisations face many challenges during the implementation of AI. The focus of the study is to explore the literature on AI in HRM, identify the challenges of implementing AI and provide potential future research direction based on a systematic literature review.

Methodology: To explore the literature on AI in HRM, the study undertakes a systematic literature review. The study identifies, analyse and classifies the literature to provide a holistic view of HR challenges in implementing AI. The study is built on a review of 47 documents, including the articles, book chapters and conference papers using the Scopus database for the past 10 years (2012–27 January 2022).

Findings: The study provides an overview of the documents published in Scopus in this area through a systematic literature review. The study reveals that a significant amount of growth in the publication has been shown in the past 10 years. The maximum and continuous growth is shown after 2017. The maximum number of papers are published in India, the USA and China. The study identifies major eight challenges of AI implementation in HRM. The study also provides a secondary case to deep dive in this area based on a systematic literature review.

Research Limitation/Implication: The challenges identified in the study are not empirically tested. Each of the identified challenges should be empirically examined. This study has expanded the body of knowledge of AI in HRM. This study will help the academicians and practitioners work on the identified challenges and help the organisations ease in adopting AI.

Originality/Value: This study represents the first work that integrates AI implementation challenges in HRM.

To better understand contributing factors and mediating mechanisms related to team dynamics in isolated, confined, and extreme (ICE) environments.

Literature review.

Our primary focus is on cohesion and adaptation – two critical aspects of team performance in ICE environments that have received increased attention in both the literature and funding initiatives. We begin by describing the conditions that define ICE environments and review relevant individual biological, neuropsychiatric, and environmental factors that interact with team dynamics. We then outline a unifying team cohesion framework for long-duration missions and discuss several environmental, operational, organizational, and psychosocial factors that can impact team dynamics. Finally, we end with a discussion of directions for future research and countermeasure development, emphasizing the importance of temporal dynamics, multidisciplinary integration, and novel conceptual frameworks for the inherently mixed work and social setting of long-duration missions in ICE environments.

A better understanding of team dynamics over time can contribute to success in a variety of organizational settings, including space exploration, defense and security, business, education, athletics, and social relationships.

We promote a multidisciplinary approach to team dynamics in ICE environments that incorporates dynamic biological, behavioral, psychological, and organizational factors over time.

Purpose – Driver distraction and other forms of driver inattention remain significant road safety problems. The purpose of this chapter is to explore recent developments in theoretical and empirical research on driver distraction and inattention and provide the reader with a sense for, and understanding of, the key issues.

Methodology – Key references from the literature are reviewed and discussed.

Findings – First, we discuss one way of conceptualising the distinction between driver distraction and other forms of inattention, as well as the mechanisms which may underlie these forms of inattention. Second, we underscores how driver distraction may derive from a plethora of sources, and how the potential for performance degradation deriving from driver interaction with these sources may be moderated by a range of factors. Third, we review recent literature on the types of impairments in driving performance and safety associated with driver distraction. Fourth, we outline recent literature on driver distraction and inattention in the realm of highly automated vehicles that will drive the transport future. Finally, we discuss some promising strategies aimed at preventing and mitigating the impact of driver distraction.

Research implications – There are many gaps in the driver distraction literature that need to be addressed. In addition, further research needs to be undertaken to examine the role of driver distraction in the realm of highly automated vehicles.

Practical implications – The findings point towards of a range of injury prevention countermeasures that have potential to prevent and mitigate driver distraction.