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In complex environments, the use of technology to enhance the capability of people is commonplace. In rapidly changing and often unpredictable environments, it is not enough that these human-automated “teams” perform well when events go as expected. Instead, the human operators and automated aids must be flexible, capable of responding to rare or unanticipated events. The purpose of this chapter is to discuss the Framework of Automation Use (Dzindolet, Beck, Pierce, & Dawe, 2001) as it relates to adaptive automation. Specifically, our objectives are to: (1) examine a number of factors that determine how people can effectively integrate their activities with their machine partners in fluid environments and (2) consider the implications of these findings for future research.

This chapter considers how social capital is evolving in the era of globalization today especially under COVID-19 pandemic conditions globally. Definitions of social capital have varied: some broad others narrow. The Organisation for Economic Co-operation and Development (OECD), for example, currently has a broad research project on social capital. These researchers have defined social capital as comprising four key areas. These areas are:

• Personal relationships, referring to the structure of people’s social networks.

• Depth and breadth of social network support available to each person in their networks.

• Civic engagement activities such as volunteering and community action.

• Beliefs, attitudes, and action frames of reference such as trust and cooperative norms, of reciprocity.

Personal relationships, referring to the structure of people’s social networks.

Depth and breadth of social network support available to each person in their networks.

Civic engagement activities such as volunteering and community action.

Beliefs, attitudes, and action frames of reference such as trust and cooperative norms, of reciprocity.

Thus, there are tacit as well as explicit aspects of social capital though some of these may seldom if ever be articulated and delineated for others.

As Claridge (2020) indicates, there are distinct, but dynamically interrelated, levels of social capital. These levels range between the micro- or individual level. That is personal “habitus” – which Bourdieu (1977) describes as a person’s “taken-for-granted” – ways of being, thinking, and reacting to events and to other people. Then, the next level above the individual is the meso-level, which is “how things are done here amongst us,” that is, the level of a group’s social capital (such as a team, or an organizational or local community level). Lastly, and wider still, the top level is the macro- or cultural-societal structural level of the nation.

The social capital systems in any location encompasses sets of acceptable or culturally legitimated behavioral norms and rules of engagement between community members which include types of greetings, forms of cooperation, communications, and signaling between diverse members. Thus, social capital may be present in the tacit, or unspoken/taken-for-granted assumptions as much as in explicit or formalized codes of behavior. The forms of social interactions at each of the levels may have norms for specific types communication and address in particular sets of circumstances such as social gatherings at home or in public or when attending communal gatherings or ceremonial occasions, or between people of different social status. Social capital generates trust and social cohesion and some level of cultural and attitudinal consensus and interest, which in turn delivers a stable environment for the local community or larger society, business, or the economy.

• (1)

  Social capital is the development of relationships that help contribute to a more efficient production of goods and services as there is embedded trust, embodied in practice, that is, in behaviors regarded as trustworthy and socially helpful.

• (2)

  There are three types of social capital at each level of interaction – bonding, bridging, and linking. Bridging and linking are similar though they operate in different directions socially. Bonding social capital describes the connections between people in similar social levels or groups of people who share the same characteristic norms and beliefs, whereas linking social capital facilitates connects between different groups.

• (3)

  Social capital can therefore make or break businesses, especially small businesses or start-ups as those with the right kind and amount of social capital, such as good connections and contacts in the trade or profession, can usually thrive as they are able to get work done more quickly, effectively, and efficiently. Conversely, a lack of social capital denoting some distrust between groups can undermine social stability.

Social capital is the development of relationships that help contribute to a more efficient production of goods and services as there is embedded trust, embodied in practice, that is, in behaviors regarded as trustworthy and socially helpful.

There are three types of social capital at each level of interaction – bonding, bridging, and linking. Bridging and linking are similar though they operate in different directions socially. Bonding social capital describes the connections between people in similar social levels or groups of people who share the same characteristic norms and beliefs, whereas linking social capital facilitates connects between different groups.

Social capital can therefore make or break businesses, especially small businesses or start-ups as those with the right kind and amount of social capital, such as good connections and contacts in the trade or profession, can usually thrive as they are able to get work done more quickly, effectively, and efficiently. Conversely, a lack of social capital denoting some distrust between groups can undermine social stability.

The meso- or macro-levels of bridging type social capital ensures acceptance of established social roles locally and linking forms of social capital boost levels of acceptance of other roles such as those of leaders and followers.

All three forms of social capital and the three levels are not mutually exclusive but instead are mutually inclusive and interrelated. That is, they co-evolve, each impacting the other while dynamically interacting with the social capital anchored as it is emerging from the complex and interwoven fields of tacit and explicit norms of social interaction underpinning each of the levels of interaction over time.

The purpose of this chapter is to provide a first step towards determining the piloting skills needed for the operation of advanced automated aircraft. Using an information-processing (IP) approach, categorization of the problems observed in interactions between human operators and advanced automated systems were determined. Based on these issues, potential training areas were supplied which may aid to ameliorate many of these interactions between humans and automated systems. Following the identification of potential training areas were brief discussions of the potential efficacy of traditional approaches to performance improvements in these areas. Using the IP framework, numerous problems were identified through examining current literature and research findings, technical reports, as well as incident and accident reports and databases. Five problem areas were identified which, not surprisingly, paralleled the major dimensions of the IP model. These five problem areas included mismatches between hardware and software and the capabilities of the human sensory/perceptual system, increased requirements for vigilance, attention-sharing, and distribution, the development of accurate knowledge in long-term memory, overload of large resource requirements in working memory, and problems relating to decision making. The breakdown of these automation-related problems is important as this step must be completed to determine the foci of any training programs to address these issues. While some believe automation has introduced a whole new array of interaction problems, analyses such as this supply a different snapshot, in our case by using a well investigated information processing model to categorize such problems at their most core level.Investigations should be conducted to determine the effectiveness of the training approaches we recommended. While Wickens' IP model is closed-loop in nature, the model does describe a certain path which information follows throughout the process. Future studies should therefore also examine how training solutions targeting at the earlier stages (i.e. sensation and perceptual) of the model might have a cascading effect in relation to the elimination of problems further along in the processing model.

This chapter focuses upon operators' automation usage decisions (AUDs), choices in which people have the option of relying on automation or employing a manual or less technologically advanced means of control. Misuse, the over-utilization of automation, and disuse, the under-utilization of automation, result from inappropriate AUDs. Three causes of misuse and disuse were identified. Operators may: (1) not recognize that both automated and manual alternatives are available (recognition errors), (2) inaccurately estimate the utilities of the automated and/or manual options (appraisal errors), or (3) knowingly select the alternative with the lowest expected outcome (action errors). A decision-making model was used to organize the literature, identify new areas of inquiry, and examine the effects of a number of variables known to affect AUDs. These included machine reliability and consistency, performance feedback, trust in automation, self-confidence in manual control, perceived utility, and the automation bias.

Digital technologies provide an opportunity for the rail sector to achieve social, economic and environmental sustainability, if implemented correctly. Unlocking the full potential of technology, however, will require significant changes beyond the technological. Physical assets will need to link with digital assets, making best use of data, simulation and modelling. Transformational leadership informed by systems engineering will be necessary to deliver the change process required to innovate across the whole railway life cycle. Each of these digital railway elements – technology, data, simulation, transformational leadership and systems engineering – presents challenges to be overcome. The authors believe that by instilling core values alongside technical expertise, by being open, resilient, responsive, customer-centric and valuing people, the digital railway has the power to transform the sector. It will enable improved railway processes; safer, faster and more reliable trains; better customer experience; cost-effectiveness; and reduced carbon emissions and more. The digital railway will not just realise the current vision but form the foundation for a sustainable railway to meet changing mobility needs well beyond 2050.

Technology for training military teams has evolved through a convergence of advances in simulation technology for individual and collective training, methods for analyzing teamwork and designing training solutions, and intelligent tutoring technologies that adapt training to the student, to accelerate learning. A number of factors have slowed this evolution toward intelligent team tutoring systems (ITTS), including the challenges of processing communications data, which are the currency of teamwork, and the paucity of automated and generalizable measures of team work. Several systems fulfill a subset of the features required of an ITTS, namely the use of team training objectives, teamwork models, measures of teamwork, diagnostic capability, instructional strategies, and adaptation of training to team needs. We describe these systems: the Advanced Embedded Training System (AETS), Synthetic Cognition for Operational Team Training (SCOTT), the AWO Trainer, the Benchmarked Experiential System for Training (BEST), and the Cross-Platform Mission Visualization Tool. We close this chapter with recommendations for future research.

The purpose of the research is to determine perspectives and spheres of application of innovational managerial technologies of decision-making in the sphere of digital medicine in modern business systems in the interests of improvement of the healthcare system.

The methods of systemic, problem, logical, and structural & functional analysis and the methods of modeling of economic processes and systems and formalization are used.

It is shown that the most perspective innovational managerial technologies include digital marketing, digital collection of feedback, digital selection of models of managerial decisions, intellectual technologies of decision support, and digital monitoring and control over making and implementation of managerial decisions. The spheres of application of digital technologies in the sphere of digital medicine include collection of feedback from patients, collection of feedback from medical workers, usage of successful experience of other medical organizations, automated selection of managerial decisions, automated selection of the optimal decision of the existing alternatives according to the set criteria, and electronic reports of medical organizations and its administration.

Wide opportunities of usage of innovational managerial technologies of decision-making in the sphere of digital medicine in modern business systems are determined; they allow improving the healthcare system. These technologies could be applied at all stages of the process of making of managerial decisions. A conceptual model of application of innovational managerial technologies of decision-making in the sphere of digital medicine in modern business systems is developed.

This article reviews the quantitative marketing literature on artificial intelligence (AI) through an economics lens. We apply the framework in Prediction Machines: The Simple Economics of Artificial Intelligence to systematically categorize 96 research papers on AI in marketing academia into five levels of impact, which are prediction, decision, tool, strategy, and society. For each paper, we further identify each individual component of a task, the research question, the AI model used, and the broad decision type. Overall, we find there are fewer marketing papers focusing on strategy and society, and accordingly, we discuss future research opportunities in those areas.