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This chapter examines various definitions and perceptions of Research Management and Administration (RMA) from individuals both from within and outside the profession to gain a wider understanding of this field. These definitions and perceptions are expected to trigger reflections on where the boundaries of the profession are more likely to be.

To do so, the authors utilise a mixed method that begins with a discussion of different definitions of RMA. Next, we move from conceptualisation to action and engage the reader by presenting empirical insights from an analysis of specific training programmes within RMA, shedding light on the profession’s distinctive features from an insider’s perspective. Lastly, we delve into the case study of the project foRMAtion, a training program that introduces RMAs as the ‘Professionals at the Interface of Science.’ This case study allows us to explore how individuals outside the RMA profession, such as teachers and students participating in its training courses, perceive and understand RMA.

High turnover rates, delay and dissatisfaction among PhD students about the high efforts and low rewards are common problems in doctoral education. Research shows that many different factors are associated with the mental health crisis in graduate education, but these diverse aspects have not often been studied in relation to talent management and human resource management (HRM) strategies. Based on questionnaires and in-depth interviews, this chapter critically assesses the factors that influence doctoral students’ well-being, using as theoretical framework the self-determination theory, concerned with the social and other conditions that facilitate or hinder human well-being and flourishing, and the job demands–resources model, an occupational stress model that suggests strain is a response to imbalance between demands on the individual and the resources he or she has to deal with those demands. These theoretical frameworks help to explore the perceived job demands and resources, and motivations of a sample of 25 PhD students in the Netherlands, in order to recommend adequate talent management strategies to improve PhD work conditions at universities and reduce the increasing levels of ill-being. The study proposes a collegial model, focussing on the enjoyment of work, instead of the current managerial model, which focusses on strengthening knowledge and skills, and stimulating performance-oriented behaviour. A differentiated approach is needed, offering customized talent development for each PhD student in order to respond to his or her specific qualities, improving general well-being. This radical shift in talent management is needed to counter the mental health crisis in doctoral studies.

This paper investigates effective human-robot collaboration (HRC) and presents implications for Human Resource Management (HRM). A brief review of current literature on HRM in the smart industry context showed that there is limited research on HRC in hybrid teams and even less on effective management of these teams. This book chapter addresses this issue by investigating factors affecting intention to collaborate with a robot by conducting a vignette study. We hypothesized that six technology acceptance factors, performance expectancy, trust, effort expectancy, social support, organizational support and computer anxiety would significantly affect a users' intention to collaborate with a robot. Furthermore, we hypothesized a moderating effect of a particular HR system, either productivity-based or collaborative. Using a sample of 96 participants, this study tested the effect of the aforementioned factors on a users' intention to collaborate with the robot. Findings show that performance expectancy, organizational support and computer anxiety significantly affect the intention to collaborate with a robot. A significant moderating effect of a particular HR system was not found. Our findings expand the current technology acceptance models in the context of HRC. HRM can support effective HRC by a combination of comprehensive training and education, empowerment and incentives supported by an appropriate HR system.

This chapter explores the results of an international survey (RAAAP-2) to provide global insight into research management and administration (RMA) as a relatively new field of investigation within the area of higher education management (HEM). Building on that extensive survey, the purpose of this chapter is to investigate qualitatively how and why people become and remain research managers and administrators, focussing primarily on their skills, roles, and career paths.

Findings from the analysis confirm that a career in RMA is rarely an intentional choice and can be described as labyrinthine, which could be even compared and contrasted with a concertine academic career described by Whitchurch et al. (2021). While conclusions confirm the gender implications of the profession, which is overall highly ‘female’; further conclusion sheds light on RMAs across regions and suggests how this varied ecosystem could even undermine the recognition of RMA as a profession.

This chapter presents results from the ‘How I Became a Research Manager and Administrator’ (HIBARMA) section of the 2022 Research Administration as a Profession (RAAAP-3) global survey of Research Managers and Administrators (RMAs). Here we focus on routes into the profession, the skills that were useful in gaining that first RMA role and the career satisfaction of individuals. In addition, we look at some of the qualitative feedback from the survey questions to present an overall picture of the variety of backgrounds and routes that can lead people to the field of RMA. Finally, illustrative vignettes highlight the diversity of routes into the profession and some common themes attracting professionals to ‘the best job of all’ (Andreson, 2016) – Research Management and Administration.

Given that the workforce constitutes a principal resource of primary care, appraisal of models of care requires thorough investigation of the health workforce in all Models of Child Health Appraised (MOCHA) countries. This chapter explores this in terms of workforce composition, remuneration, qualifications and training in relation to the needs of children and young people. We have focused on two principal disciplines of primary care; medicine and nursing, with a specific focus on training and skills to care for children in primary care, particularly those with complex care needs, adolescents and vulnerable groups. We found significant disparities in workforce provision and remuneration, in training curricula and in resultant skills of physicians and nurses in European Union and European Economic Area Countries. A lack of overarching standards and recognition of some of the specific needs of children reflected in training of physicians and nurses may lead to suboptimal care for children. There are, of course, many other professions that also contribute to primary care services for children, some of which are discussed in Chapter 15, but we have not had resources to study these to the same detail.

Organizations across industries are increasingly using Artificial Intelligence (AI) systems to support their innovation processes, supply chains, marketing and sales and other business functions. Implementing AI, firms report efficiency gains from automation and enhanced decision-making thanks to more relevant, accurate and timely predictions. By exposing the benefits of digitizing everything, COVID-19 has only accelerated these processes. Recognizing the growing importance of AI and its pervasive impact, this chapter defines the “social value of AI” as the combined value derived from AI adoption by multiple stakeholders of an organization. To this end, we discuss the benefits and costs of AI for a business-to-business (B2B) firm and its internal, external and societal stakeholders. Being mindful of legal and ethical concerns, we expect the social value of AI to increase over time as the barriers for adoption go down, technology costs decrease, and more stakeholders capture the value from AI. We identify the contributions to the social value of AI, by highlighting the benefits of AI for different actors in the organization, business consumers, supply chain partners and society at large. This chapter also offers future research opportunities, as well as practical implications of the AI adoption by a variety of stakeholders.

The chapter concludes the section on cross-innovation and convergence processes between audiovisual media industries and the education sector. It addresses, first, that these processes are not driven by any specific technology, but by two broad and interdependent processes – individualisation that makes people in insecure careers search for personalised learning opportunities and the experience economy that produces expectations for learning experiences to be pleasurable and fun, that is, gamified. The chapter demonstrates the emergence of EdTech as a new dialogic subsector operating between the publicly operating education sector and the private media and information and communication technology industries. It demonstrates the inherent institutional diversity in and around this subsector and discusses the nature of the dialogues constituting it. It, lastly, addresses the risks deriving from global platformisation to the education sector and demonstrates how Estonia’s government-run platforms, effectively cross-innovation systems linking teachers, learners and content providers in dynamic ways, could present feasible alternatives to the global platforms.