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Future Research – Thesis Guide

Table of Contents

Future Research

Definition:

Future research refers to investigations and studies that are yet to be conducted, and are aimed at expanding our understanding of a particular subject or area of interest. Future research is typically based on the current state of knowledge and seeks to address unanswered questions, gaps in knowledge, and new areas of inquiry.

How to Write Future Research in Thesis

Here are some steps to help you write effectively about future research in your thesis :

• Identify a research gap: Before you start writing about future research, identify the areas that need further investigation. Look for research gaps and inconsistencies in the literature , and note them down.
• Specify research questions : Once you have identified a research gap, create a list of research questions that you would like to explore in future research. These research questions should be specific, measurable, and relevant to your thesis.
• Discuss limitations: Be sure to discuss any limitations of your research that may require further exploration. This will help to highlight the need for future research and provide a basis for further investigation.
• Suggest methodologies: Provide suggestions for methodologies that could be used to explore the research questions you have identified. Discuss the pros and cons of each methodology and how they would be suitable for your research.
• Explain significance: Explain the significance of the research you have proposed, and how it will contribute to the field. This will help to justify the need for future research and provide a basis for further investigation.
• Provide a timeline : Provide a timeline for the proposed research , indicating when each stage of the research would be conducted. This will help to give a sense of the practicalities involved in conducting the research.
• Conclusion : Summarize the key points you have made about future research and emphasize the importance of exploring the research questions you have identified.

Examples of Future Research in Thesis

SomeExamples of Future Research in Thesis are as follows:

Future Research:

Although this study provides valuable insights into the effects of social media on self-esteem, there are several avenues for future research that could build upon our findings. Firstly, our sample consisted solely of college students, so it would be beneficial to extend this research to other age groups and demographics. Additionally, our study focused only on the impact of social media use on self-esteem, but there are likely other factors that influence how social media affects individuals, such as personality traits and social support. Future research could examine these factors in greater depth. Lastly, while our study looked at the short-term effects of social media use on self-esteem, it would be interesting to explore the long-term effects over time. This could involve conducting longitudinal studies that follow individuals over a period of several years to assess changes in self-esteem and social media use.

While this study provides important insights into the relationship between sleep patterns and academic performance among college students, there are several avenues for future research that could further advance our understanding of this topic.

• This study relied on self-reported sleep patterns, which may be subject to reporting biases. Future research could benefit from using objective measures of sleep, such as actigraphy or polysomnography, to more accurately assess sleep duration and quality.
• This study focused on academic performance as the outcome variable, but there may be other important outcomes to consider, such as mental health or well-being. Future research could explore the relationship between sleep patterns and these other outcomes.
• This study only included college students, and it is unclear if these findings generalize to other populations, such as high school students or working adults. Future research could investigate whether the relationship between sleep patterns and academic performance varies across different populations.
• Fourth, this study did not explore the potential mechanisms underlying the relationship between sleep patterns and academic performance. Future research could investigate the role of factors such as cognitive functioning, motivation, and stress in this relationship.

Overall, there is a need for continued research on the relationship between sleep patterns and academic performance, as this has important implications for the health and well-being of students.

Further research could investigate the long-term effects of mindfulness-based interventions on mental health outcomes among individuals with chronic pain. A longitudinal study could be conducted to examine the sustainability of mindfulness practices in reducing pain-related distress and improving psychological well-being over time. The study could also explore the potential mediating and moderating factors that influence the relationship between mindfulness and mental health outcomes, such as emotional regulation, pain catastrophizing, and social support.

Purpose of Future Research in Thesis

Here are some general purposes of future research that you might consider including in your thesis:

• To address limitations: Your research may have limitations or unanswered questions that could be addressed by future studies. Identify these limitations and suggest potential areas for further research.
• To extend the research : You may have found interesting results in your research, but future studies could help to extend or replicate your findings. Identify these areas where future research could help to build on your work.
• To explore related topics : Your research may have uncovered related topics that were outside the scope of your study. Suggest areas where future research could explore these related topics in more depth.
• To compare different approaches : Your research may have used a particular methodology or approach, but there may be other approaches that could be compared to your approach. Identify these other approaches and suggest areas where future research could compare and contrast them.
• To test hypotheses : Your research may have generated hypotheses that could be tested in future studies. Identify these hypotheses and suggest areas where future research could test them.
• To address practical implications : Your research may have practical implications that could be explored in future studies. Identify these practical implications and suggest areas where future research could investigate how to apply them in practice.

Applications of Future Research

Some examples of applications of future research that you could include in your thesis are:

• Development of new technologies or methods: If your research involves the development of new technologies or methods, you could discuss potential applications of these innovations in future research or practical settings. For example, if you have developed a new drug delivery system, you could speculate about how it might be used in the treatment of other diseases or conditions.
• Extension of your research: If your research only scratches the surface of a particular topic, you could suggest potential avenues for future research that could build upon your findings. For example, if you have studied the effects of a particular drug on a specific population, you could suggest future research that explores the drug’s effects on different populations or in combination with other treatments.
• Investigation of related topics: If your research is part of a larger field or area of inquiry, you could suggest potential research topics that are related to your work. For example, if you have studied the effects of climate change on a particular species, you could suggest future research that explores the impacts of climate change on other species or ecosystems.
• Testing of hypotheses: If your research has generated hypotheses or theories, you could suggest potential experiments or studies that could test these hypotheses in future research. For example, if you have proposed a new theory about the mechanisms of a particular disease, you could suggest experiments that could test this theory in other populations or in different disease contexts.

Advantage of Future Research

Including future research in a thesis has several advantages:

• Demonstrates critical thinking: Including future research shows that the author has thought deeply about the topic and recognizes its limitations. It also demonstrates that the author is interested in advancing the field and is not satisfied with only providing a narrow analysis of the issue at hand.
• Provides a roadmap for future research : Including future research can help guide researchers in the field by suggesting areas that require further investigation. This can help to prevent researchers from repeating the same work and can lead to more efficient use of resources.
• Shows engagement with the field : By including future research, the author demonstrates their engagement with the field and their understanding of ongoing debates and discussions. This can be especially important for students who are just entering the field and want to show their commitment to ongoing research.
• I ncreases the impact of the thesis : Including future research can help to increase the impact of the thesis by highlighting its potential implications for future research and practical applications. This can help to generate interest in the work and attract attention from researchers and practitioners in the field.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Introducing the Future of Work: Key Trends, Concepts, Technologies and Avenues for Future Research

• Open Access
• First Online: 30 July 2023

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• Theo Lynn 7 ,
• Pierangelo Rosati 9 ,
• Edel Conway 8 &
• Lisa van der Werff 8  

Part of the book series: Palgrave Studies in Digital Business & Enabling Technologies ((PSDBET))

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The Future of Work is a projection of how work, working, workers and the workplace will evolve in the years ahead from the perspective of different actors in society, influenced by technological, socio-economic, political and demographic changes. In addition to defining the Future of Work, this chapter discusses some of the main trends, themes and concepts in the Future of Work literature before discussing the different topics covered in the remainder of the book. The chapter concludes with a call for greater inter- and multidisciplinary research, evidence to validate assumptions and hypotheses underlying extant Future of Work research and policy, greater use of futures methodologies and a future of research agenda that is even in its coverage of workspaces, population and employment cohorts, regions, sectors, and organisation types.

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The Changing World of Work in Developing and Emerging Economies

Back to the Future of Work: Old Questions for New Technologies

The Present and Future of Work: Some Concluding Remarks and Reflections on Upcoming Trends

• Future of Work
• Digital technologies
• Digital transformation
• Artificial Intelligence

1.1 Introduction

The Future of Work is not a new idea; however, following the Covid-19 pandemic, it has become not only a major discourse in all aspects of life but a central pillar of government policy worldwide. The pandemic has mainstreamed a plethora of terms (see Table 1.2 ) for how we work in a post-Covid world—hybrid working, remote working and co-working are just some of artefacts that have travelled from the Future of Work to the now of work.

The Future of Work is both a short-term and long-term concern, and while central to industrial strategy, it is by no means limited to this domain. This is particularly evidenced in the European Union where the Future of Work plays a central role in the updated European Industrial Strategy and the European Pillar of Social Rights Action Plan, and is a field of action for the European Research Area and its policy agenda (European Commission, 2022 ). At the time of writing, the European Commission has invested c. €1.9 billion in areas related to the Future of Work, including research and innovation, economic competitiveness and social protection measures (European Commission, 2022 ). It should not be a surprise therefore that the Future of Work is of significant interest to scholars. Despite this interest, it would seem to be something everybody understands but nobody can explain.

This chapter seeks to provide greater clarity on what the Future of Work is or might be. The remainder of the chapter begins with a discussion on the definition of the Future of Work and proposes a working definition for the purposes of this book. This is followed by a brief overview of key trends, themes and concepts on the Future of Work before providing an overview of the topics discussed in the remaining chapters of this book. We conclude with a discussion on some potential future avenues for research and highlight the need for inter- and multidisciplinary research, evidence to validate the many assumptions and hypotheses underlying extant Future of Work research and policy, greater use of futures methodologies and a future of research agenda that is even in its coverage of population and employment cohorts, regions, sectors, workspaces and organisation types.

1.2 What Is the Future of Work?

The term “Future of Work” in itself poses at least three significant challenges for researchers, practitioners and policymakers alike. Firstly, the study of the future requires boundaries. Predicting the future in the social sphere is particularly difficult as there are no strong laws (as in the sciences), and identifying and aggregating relevant information is complicated by its dispersal across different people and organisations (Chen et al., 2003 ). In particular, one needs to be careful not to fall foul of the so-called futures fallacies (Dorr, 2017 ). Thus, any future projection should not:

assume a simple and steady extension of past trends (linear projection fallacy);

consider only one single aspect of change while holding “all else equal” ( ceteris paribus fallacy); and

envision possible futures as static objects rather than as a dynamic process, an ongoing procession of changes (the arrival fallacy) (Dorr, 2017 ).

The second challenge relates to what we mean when we say “work.” A quick review of the literature will reveal that when we talk about the Future of Work it may be related to a particular activity (what), the process of working (how), the worker (who) and the workplace (where), or any combination of these. Thirdly, the Future of Work can be viewed from a variety of perspectives from macro to micro, from a society, industry, firm or an individual level (Stoepfgeshoff, 2018 ).

When dealing with the future, it is always a movable feast. The Future of Work is not new but rather is the latest iteration of an established phenomenon where the current wave of interest is largely driven by the impact of Covid-19 on accelerating technology adoption and new flexible work arrangements. To paraphrase Webster ( 2006 ), there is both change and persistence.

Given its prominence in the public discourse, it is unsurprising that increasingly scholars are arriving at the conclusion that there is no clear understanding about what the Future of Work is (Stoepfgeshoff, 2018 ; Santana & Cobo, 2020 ). The scholarly literature is remarkably scarce on precise definitions of the Future of Work. Instead, the literature on the Future of Work is defined by characteristics or narratives. This is even a feature of reviews of Future of Work research. For example, Balliester and Elsheikhi ( 2018 ) define the Future of Work along five dimensions in which changes brought about by megatrends such as technology, climate change, globalisation and demography impact the world of work, namely (1) the future of jobs; (2) the quality of jobs; (3) wage and income inequality; (4) social protection systems; and (5) social dialogue and industrial relations. Mitchell et al. ( 2022 ) do not define the Future of Work but categorise the most influential research into four key research streams: (1) workplace relations, (2) workplace change, (3) diversity and (4) personal skills. Similarly, in their review, Kolade and Owoseni ( 2022 ) do not define the Future of Work but rather identify three underlying theoretical perspectives from the literature, namely (1) socio-technical systems theory, (2) skill-biased technological change and (3) political economy of automation and digital transformation.

This is not to say that there are no definitions but perhaps one must look elsewhere, for example, to practice. Gartner ( 2022 ) defines the Future of Work as “[…] the changes in how work will get done over the next decade, influenced by technological, generational and social shifts.” The Society for Human Resource Management (SHRM) defines the Future of Work as “a projection of how work, workers and the workplace will evolve in the years ahead” (SHRM, 2022 ). In the same vein, Deloitte defines the Future of Work as “encompass(ing) changes in work, the workforce, and the workplace” (Schwartz et al., 2019 ). While Gartner ( 2022 ) puts a specific, albeit moving, time horizon of ten years, both Gartner ( 2022 ) and SHRM ( 2022 ) include a consideration of a time still to come unlike Schwartz et al. ( 2019 ). However, while Gartner’s definition focuses exclusively on how work (the what) will be done in the future, the SHRM and Deloitte definitions are wider including how workers (the who) and the workplace (the where) will evolve. Moreover, Gartner recognises that the Future of Work is impacted by the outside world and accommodates these shifts. None of these definitions recognise that the Future of Work may be inflected by the actor perspective. As such, for the purposes of this book, we propose the following definition of the Future of Work which accommodates these existing definitions as well as important dimensions recognised in scholarly literature, namely technological, socio-economic, political and demographic changes (Balliester & Elsheikhi, 2018 ; Anner et al., 2019 ; Santana & Cobo, 2020 ; Mitchell et al., 2022 ):

The Future of Work is a projection of how work, working, workers and the workplace will evolve in the years ahead from the perspective of different actors in society, influenced by technological, socio-economic, political, and demographic changes.

1.3 Key Trends, Themes and Concepts in the Future of Work

Based on our discussion on the definitions of the Future of Work, it is clear that extant thinking is heavily inflected by a number of predominant trends, themes, concepts and technologies which can be viewed at different levels of granularity. At a high level, technology, climate change, globalisation and demographic changes are common megatrends cited in the literature (Balliester & Elsheikhi, 2018 ). At a more granular level, the focus breaks out into a wide range of trends—the impact of restructuring on efficiency including supply chain optimisation and outsourcing, ageing populations, increased migration and mobility, greater emphasis on work-life balance and wellness, amongst others. More recently, of course, the role and impact of Covid-19, and indeed, future pandemics, has become more prominent and is likely to remain part of the discourse for some time.

In a recent article, Paul Deane ( 2021 ) said: “when thinking about the future, we often overemphasise the role of technology and underestimate where technology fits in a social context.” This has undoubtedly been true in the case of the Future of Work. The predominant theme of literature, from the academy, industry and policymakers, has focussed on the implications of greater digitalisation, automation and analytics on the Future of Work. Unsurprisingly, much of this discourse focuses on advancements in Artificial Intelligence (AI) and associated labour-market and societal effects, although more often than not the distinction between narrow task-focussed AI and more wide-ranging artificial general intelligence (AGI) is ignored.

Academia is neither ignorant of these trends nor deaf to concerns. In their recent review of the 32 most influential publications in the field, Mitchell et al. ( 2022 ) categorise the research into four themes. These are further subdivided into 11 sub-themes—workplace relations (well-being, job insecurity, grievance process, mentoring); workplace changes (evolution of the workplace, telecommuting); diversity (workplace diversity, gender diversity, age discrimination) and personal skills (people skills and storytelling). Echoing Dorr ( 2017 ), it is important to remember that Future of Work research merely provides “snapshots of an inherently dynamic process.” Santana and Cobo ( 2020 ) discuss the thematic evolution of Future of Work research over four periods from 1959 to 2019 based on a systematic mapping of 2286 documents, which is largely consistent with Mitchell et al. ( 2022 ). These are summarised in Table 1.1 . While it is clear that specific perspectives, fears, insights and recommendations are of their age, there are also persistent themes (e.g., telework) and themes that go in and out of vogue (e.g., employment).

In addition to thematically analysing the evolution of Future of Work research, Santana and Cobo ( 2020 ) further categorise themes into four dimensions—technological, social, economic and political/institutional. Technologies such as automation, digitalisation, platformisation and AI are both creating new forms of work (e.g., gig working) and enabling flexible work arrangements (e.g., hybrid, remote and shared working) (Santana & Cobo, 2020 ). Furthermore, AI is introducing new forms of management through algorithmic management, which in turn require new types of skills to train, monitor and optimise such tools. Key terms and concepts in the Future of Work are presented in Table 1.2 .

The transformative effect of technologies, and specifically digital technologies, on how society operates and how social actors interact with each other is well-documented and much-discussed (Martin, 2008 ; Reis et al., 2018 ; Lynn et al., 2022 ). The technological impact on work has a knock-on effect on individuals and citizens. There are real and serious concerns about how new forms of work and working arrangements affect the social dimension of the Future of Work (Santana & Cobo, 2020 ) and social cohesion more generally (Anner et al., 2019 ). While benefitting some parts of society, innovations such as remote working and gig working may exacerbate other social problems and anxieties such as work-life conflict and burnout, as well as other outcomes including career development and progression and job satisfaction (Santana & Cobo, 2020 ). Weil ( 2014 ) has argued that innovations such as the gig economy can result in “fissured workplaces” where the bulk of employees are no longer central to the operation of the company due to outsourcing, franchising, and supply chain optimisation. Furthermore, the adoption of algorithmic management and other analytical techniques for employee surveillance while improving efficiency, performance and productivity may have adverse effects on employee voice and individual autonomy (Anner et al., 2019 ; Figueroa, 2018 ). Weil ( 2014 ), Anner et al. ( 2019 ), ILO ( 2017 ) and others argue that such advancements may, if not checked, result in a decline in wages and working conditions, while increasing levels of precarity and vulnerability experienced by workers. In contrast, Willcocks ( 2020 ), while suggesting that there will be considerable workforce and skill disruption due to technological advancements, suggests that claims on net job loss are exaggerated. Indeed, he argues that not only do extant studies fail to factor in dramatic increases in the amount of work to be done, they also fail to consider ageing populations, productivity gaps and skills shortages. Increasingly, this view is finding increasing support from several leading academics (Bessen et al., 2020 ; Malone et al., 2020 ).

The social and economic dimensions of work are inexplicably linked. When discussing the economic dimension of the Future of Work, the impact is different whether taking the perspective of the economy, sector, the firm or the individual worker. While technological advancements and increased efficiency, performance and productivity have a significant positive impact for economies and firms, the extant Future of Work literature highlights some major risks related to employment, wage inequality and job polarisation (Anner et al., 2019 ). As discussed, the impact of automation, robotics and AI on job numbers and wages is a significant topic of debate. Undoubtedly, some jobs will be replaced and some tasks automated, but equally new jobs and tasks will be created and to some extent AI will augment human capabilities (Bessen, 2018 ; Malone et al., 2020 ). Some commentators highlight some of the serious risks that a more globalised, gig- and remote working future might present to ensuring decent working conditions, minimum standards for workers and social cohesion (Anner et al., 2019 ). For example, Balliester and Elsheikhi ( 2018 ) note that the combination of labour-market changes and technological trends represent at least eight risks to existing working conditions. These include flexibility in hours and location, short-term and casual contracts, longer working hours, low pay and payment uncertainty, reduced occupational safety and health policies, dissolution of workers’ organisation and bargaining power, erosion and absence of legal protection, and informality (Balliester & Elsheikhi, 2018 ).

“The future is already here, it’s just not evenly distributed,” a quote ascribed to the American science-fiction writer, William Gibson, foreshadows a key aspect of the discourse on the Future of Work and particularly the unevenness of the potential impact of technology on work (see, for example, Bessen, 2018 and Malone et al., 2020 ). Managing the adoption, and associated disruption, of these transformative technologies requires policymakers, political institutions and organisations to develop new organisational forms, policies and regulations to support and incentivise socially responsible adoption and use (Santana & Cobo, 2020 ; Willcocks, 2020 ), but also to retrain and transition workers to new occupations (Bessen, 2018 ; Malone et al., 2020 ; Mindell & Reynolds, 2022 ). This requires a significant multi-stakeholder effort and investment not only to train and upskill the workforce of the future and avoid potential skills inequities but to reduce adverse effects from disruption to longstanding societal norms and expectations. It may require not only a re-imagination of work but education, social protection, regulations and the role of institutions in the design and safeguarding the Future of Work. Given the delicate balance between social and economic policy, and the wide range of stakeholders affected by the Future of Work, governments need to consult and liaise with all stakeholders. This should not be limited to employers and labour organisations but should include the public, community organisations, education providers, data protection authorities and civil liberties advocates as early and transparently as possible so that suitable governance mechanisms are put in place to provide not only input but oversight on Future of Work initiatives.

1.4 Perspectives on the Future of Work

The nine remaining chapters in this book provide perspectives and insights that advance our understanding and help make sense of the Future of Work. They demonstrate that while there has been substantial intellectual effort in the conceptualisation of the Future of Work, we are still at an early stage in theorisation, exploratory and explanatory research, and more importantly actionable outcomes for practice. They are presented as follows.

Chapters 2 and 3 are dedicated to the impact of the increasing adoption of digital technologies in the workplace on employees’ well-being and professions, respectively. More specifically, Chap. 2 focuses on new ways of working (NWW) which are defined as work practices that are enabled by complex information systems and virtualised organisational formations. The authors adopt self-determination theory (SDT) as a lens to explore the impact of NWW on three employees’ universal needs, namely autonomy, competence and relatedness and the actual and potential implications for employees’ well-being. The findings of this review suggest that relatedness is set to play a critical role in supporting the needs for autonomy and competence in increasingly digital workplaces.

Chapter 3 responds to an ongoing and growing debate on how professional roles are impacted and somewhat threatened by technology. This chapter looks at two professions that have been listed by the World Economic Forum ( 2018 ) among the most “at risk,” namely accounting and law, and how they may be impacted by the shift from process and knowledge-oriented activities as a result of the adoption of AI and data analytics. The authors point out that professionals do not always face “standard” situations that can be solved using predetermined rules. On the contrary, most cases require individual professionals to make decisions based on their own judgement; this cannot and should not be replaced by an algorithm. The authors argue that while advancements in digital technologies can supplement and support human judgement, professionals must continue to apply autonomy and reflexive considerations to form independent judgments.

Chapter 4 turns the attention to the so-called gig-economy and related flexible and contingent forms of working that are enabled by digital platforms. More specifically, this chapter delves into how “gig-work” organisations have developed digitally enabled control systems that leverage AI and Machine Learning (ML) to manage their workforce. While the use of algorithmic management provides clear benefits for digital platforms in terms of higher efficiency and lower risks and labour costs, it also creates challenges for management practices, legislators and policymakers, as well as for workers. These challenges are discussed in more detail in the chapter, but they essentially point to the fact that the perceived independence from managerial control that is typical of gig work does not necessarily result in increased autonomy for workers and that closer attention needs to be paid to a number of aspects of gig work, such as the lack of various forms of support, that may detrimental for both gig workers and organisations.

Trust is arguably the cornerstone of any work relationship and the foundation of any social interaction. The increasing use of digital technologies, particularly those systems that leverage advancements in AI and ML, is likely to change the trust dynamics between employees and the organisation. This is the topic of Chap. 5 , which is built on the argument that common practices of advocating the benefits and strengths of new technology are unlikely to be effective in building/protecting employees’ trust as they fall short when it comes to supporting perceptions of organisational character or capability. The authors identify and discuss various challenges posed by the use of smart technology in the workplace (e.g., automation of leadership) and highlight a number of pathways to maximise the benefits of smart technology without undermining organisational trust.

Chapter 6 is dedicated to the role of leadership in the Future of Work. Leadership heavily relies on a leader’s social presence which consists of three dimensions, namely co-presence, behavioural engagement and psychological involvement. While there is an extensive body of research exploring the factors that affect any of these three dimensions, little is known about how leadership dynamics change in a virtual and distributed workplace. The authors present a review of academic literature on leadership and the Future of Work and highlight and discuss four underexplored areas which represent avenues for future research, namely leadership in the context of virtual teams, leader-follower relationships in a digital workplace, the development of human and social capital in the digital world, and leadership in the platform-mediated economy. The authors point out the need for organisations’ leaders to pay closer attention to both the range of digital technologies available and how these can be used to achieve organisational goals.

One of the main consequences of increasing globalisation is the growing diversity of the workforce in terms of race, ethnicity, gender, age, religion, culture, nationality and language. In addition to this, the use of digital technologies has facilitated the implementation of virtual and distributed teams implying that many organisations no longer have a dominant, traditional or homogenous pool of workers, nor do they have universal structures or approaches to work and working time. This poses both opportunities and challenges for organisations and these are presented and discussed in Chap. 7 . The authors argue that the combination of a more diverse workforce, organisational leaders who are more aware of detrimental discriminatory attitudes and behaviour, and digital technologies that can transform the nature of work provides organisations with a unique opportunity to rethink their definition of success and what roles individual workers can play within the organisation to help organisations succeed.

The adoption of digital technologies not only changes how and where people work but also the skills required to play an active role in the digital economy and how these skills are acquired and developed. Chapters 8 and 9 discuss the learning aspects of the Future of Work. Chapter 8 delves into key skills required for the Future of Work and explores how these skills can be developed and co-created through formal yet flexible higher education and the potential impact this may have on the higher education system. The authors first outline the growing demand and pressure coming from the evolution of work and how this is affecting the higher education system and then highlight the need for universities to move away from a technical focus on skill development to a more holistic view of human-centred development. To conclude, the authors argue that higher education institutions should focus on providing students with innate capabilities and strategic awareness which will help them to identify and ask the right questions, to think critically, to explore silences and inequities, and to seek their own wisdom. In so doing, universities will prepare students for the various “futures of work” that they may be facing rather than a predetermined Future of Work that is based on current fixed disciplinary knowledge and predetermined career trajectories.

Chapter 9 discusses the role of digital technologies in the context of human resource development, specifically their role in learning and development (L&D). In this chapter, the authors highlight how, despite the growing attention received over the last few years and particularly during the Covid-19 pandemic, digital learning is still defined in a rather general all-encompassing way in the L&D literature. They provide an overview of L&D technology-based applications that would fall under this definition (e.g., AI, augmented and virtual reality, analytics, learning management systems, etc.) and describe their current use in this field. The authors then discuss how the drive for shorter, faster and less costly training and learning methods may undermine learning quality if digital learning methods are not designed with learning pedagogy in mind and call out the need for further research on synchronous and informal digital learning capabilities and effectiveness before conclusions can be reached concerning the effectiveness of digital learning in the context of human resource development.

Finally, Chap. 10 is dedicated to ethical considerations for the Future of Work. It considers how the adoption of digital technologies generates a new set of ethical questions regarding their contribution to workers’ personal flourishing and to the good of society. In this chapter the authors argue that there is a need for an agent-centred approach to ethics, based on goods, norms and virtues, to analyse the ethical implications of digital technologies on the Future of Work.

1.5 Conclusions and Future Avenues for Research

This chapter introduces some of the challenges with Future of Work research, not least the lack of common definition in the scholarly literature. To address this gap, we define the Future of Work as “a projection of how work, working, workers, and the workplace will evolve in the years ahead from the perspective of different actors in society, influenced by technological, socio-economic, political and demographic changes.” While we summarise the key trends, themes and concepts in the literature, this is largely from a social science perspective. Given that technology, and specifically digitalisation, automation, robotics and AI, is the predominant theme in the Future of Work discourses, we call for more inter- and multidisciplinary collaboration so that a more nuanced discourse on the impact of specific technologies or types of technologies on both jobs and tasks emerges. In particular, with the exception of a relatively small number of authors (see, for example, Malone et al., 2020  and Selenko et al., 2022 ), the differences between narrow AI and artificial general intelligence are under-appreciated and consequently under-researched.

The increased acceptance of new forms of working including remote working, hybrid working and other forms of teleworking during the Covid-19 pandemic has led to a renewed interest in where work is performed and how this may impact the design of workspaces. During the pandemic, work was increasingly performed in spaces beyond the commuting distance to the employer’s work site, typically in their homes. However, there were notable increases in workers not only working remotely in holiday accommodation but also co-working spaces. In some instances, these co-working spaces were other workers’ homes although not necessarily workers of the same employer (Rossitto et al., 2017 ). This so-called hoffice network phenomenon, in itself, may provide significant opportunities for future research. Contemporaneously, there has been a surge in interest in how extended reality (XR) technologies in all its various forms can be applied to work. Technologies such as virtual reality (VR), augmented reality (AR), mixed reality (MR), telepresence and mirror worlds have the potential to transform how we conceptualise workers and workspaces but also how we train, reskill and transition workers (see, for example, Anderson & Rainie, 2022 ). We encourage researchers to consider how these new technologies and workspaces impact how workers conceptualise where and how they perform work and the implications for workspace design, social interactions, management and organisational forms, amongst others.

Given the size and scope of the book, each chapter provides only a selected snapshot of a given topic. Notwithstanding this, each chapter identifies a potentially rich vein of research to validate or invalidate the hypotheses and arguments made to support a given academic or policy position. This does not mean one should be bound to the arguments of today and the timeline of the future. While there is an increasingly mature set of tools in social sciences for conceptualising the future, these are often not employed in scholarly research on the Future of Work or rather social science research, to echo Bainbridge ( 2003 ), is constrained by methodological rigour or value commitments. Thus, we call for not only greater use of futures methodologies but also research across more specific and longer-term time horizons. For policymakers, in particular, this will enable greater consideration of actionable interventions that can be taken within a more realistic timeframe.

Future of work literature, like much scholarly research, is often led by the more developed countries often focussing on the larger and more advanced commercial entities worldwide. This is particularly the case when discussing technological innovation and disruption. Small and medium-sized enterprises represent approximately 90% of businesses and more than 50% of employment worldwide and even higher in rural areas (World Bank, 2021 ). The Future of Work will impact different regions, sectors and organisation types in different ways and at different time scales. Similarly, the changes brought about by the Future of Work will impact different demographics and population cohorts, directly and indirectly, at different times. Successful adoption of new forms of work, workplaces or working arrangements is likely to depend on the worker’s mindset at a given time. Accordingly, we call on researchers to ensure that Future of Work research is equally distributed across population demographics and cohorts, regions, sectors and organisation types.

Earlier in this chapter, we described the Future of Work as a movable feast characterised by persistence and change. For each generation, there is a new generation of Future of Work research, and for each Future of Work scholar, to borrow from Chambers ( 2010 ), a “cornucopia of potentials.”

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Lynn, T., Rosati, P., Conway, E., van der Werff, L. (2023). Introducing the Future of Work: Key Trends, Concepts, Technologies and Avenues for Future Research. In: Lynn, T., Rosati, P., Conway, E., van der Werff, L. (eds) The Future of Work. Palgrave Studies in Digital Business & Enabling Technologies. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-031-31494-0_1

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Future of work in 2050: thinking beyond the COVID-19 pandemic

• Carlos Eduardo Barbosa   ORCID: orcid.org/0000-0001-8067-7123 1 , 2 ,
• Yuri Oliveira de Lima 1 ,
• Luis Felipe Coimbra Costa 1 ,
• Herbert Salazar dos Santos 1 ,
• Alan Lyra 1 ,
• Matheus Argôlo 1 ,
• Jonathan Augusto da Silva 1 &
• Jano Moreira de Souza 1  

European Journal of Futures Research volume  10 , Article number:  25 ( 2022 ) Cite this article

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Work has been continuously changing throughout history. The most severe changes to work occurred because of the industrial revolutions, and we are living in one of these moments. To allow us to address these changes as early as possible, mitigating important problems before they occur, we need to explore the future of work. As such, our purpose in this paper is to discuss the main global trends and provide a likely scenario for work in 2050 that takes into consideration the recent changes caused by the COVID-19 pandemic. The study was performed by thirteen researchers with different backgrounds divided into five topics that were analyzed individually using four future studies methods: Bibliometrics, Brainstorming, Futures Wheel, and Scenarios. As the study was done before COVID-19, seven researchers of the original group later updated the most likely scenario with new Bibliometrics and Brainstorming. Our findings include that computerization advances will further reduce the demand for low-skill and low-wage jobs; non-standard employment tends to be better regulated; new technologies will allow a transition to a personalized education process; workers will receive knowledge-intensive training, making them more adaptable to new types of jobs; self-employment and entrepreneurship will grow in the global labor market; and universal basic income would not reach its full potential, but income transfer programs will be implemented for the most vulnerable population. Finally, we highlight that this study explores the future of work in 2050 while considering the impact of the COVID-19 pandemic.

Introduction

Work has been continuously changing throughout history. Industrial revolutions represent “profound changes in the means of production,” and they change work in a short period. We had three industrial revolutions: the first was the implementation of factory-based production using steam-powered machines, the second was characterized by changes in production provided by electricity, and the third was triggered by information and computation technologies [ 1 , 2 ].

New technologies and their combined use, such as artificial intelligence (AI), robotics, biotechnology, and nanotechnology, are seen as the starting point of the 4th industrial revolution [ 3 ]. These technologies are intrinsically associated with socioeconomic changes which, combined, will bring new possibilities for the future of work. Hence, the goal of this study is to use a long-term analysis perspective that considers technological development and socioeconomic changes to explore what work will look like in 2050. The future of work is a challenging topic due to its importance ranging from the global economy to social well-being. Therefore, we hope to help decision-makers from companies, governments, and elsewhere to recognize the changes ahead to better guide our society.

The methodology of this study is based on Foresight. We use four methods from future research—namely Bibliometrics, Brainstorming, Futures Wheel, and Scenarios—to present the main global trends that are most relevant for the future of work. These trends were then further analyzed and consolidated in a likely scenario for work in 2050. According to Grupp and Linstone [ 4 ], several countries utilize foresight for policymaking such as EUA, Germany, France, the UK, Spain, Austria, the Republic of Korea [ 5 ], Hungary, South Africa, Thailand, Indonesia, Japan [ 6 ], Canada [ 7 ], India [ 8 ], and Brazil [ 9 ]. Therefore, this study contributes to the understanding of the current situation of work, and its current future trends—the needed knowledge to perform policymaking changes.

In recognition of the tremendous impact of the pandemic on work [ 10 ] and its future, our study was updated with the most recent academic research about the COVID-19 pandemic by new Bibliometrics, Brainstorming, and an update of the Scenarios previously created. However, even before the COVID-19 pandemic, several studies [ 11 , 12 ] attempted to understand the dynamics behind the future of work and developed sets of scenarios.

Methodology

This section details the methodology used in this work. First, we present the study dynamics, detailing who participate in the study, how the study was placed in time (number and types of sessions), the foresight methods used, specific goals for each method, and how each method contributed to achieving our main goal, which is to provide scenarios for the future of work in the 2050 horizon. Second, we introduce each method used and explain how they were used in the context of this work.

Foresight studies follow no specific methodology, each study tailor the methodology according to its goals. However, the literature indicates that Foresight becomes more reliable when different and complementary methods are combined [ 13 ], once they provide multiple perspectives for the analysis, reducing the probability of a biased result. Therefore, we used a Foresight framework that generalizes Foresights as workflows [ 14 ] to structure this study.

In this study, we decided to present our results as scenarios, which is a method to develop consistent evolutions of the future based on a set of assumptions [ 15 ] and present the results efficiently to third parties, such as decision-makers. We use the following methods to build the scenarios: Bibliometrics, Brainstorming, and Futures Wheel, respectively. These methods are mostly qualitative; thus, the participants were oriented to base their conclusions on the data gathered in the Bibliometrics method.

The study dynamic

The study was performed by thirteen researchers (including the moderators) with different backgrounds, for example, in computer engineering, production engineering, public management, architecture and urbanism, and design. These researchers were divided into five groups, taking into consideration their expertise and interests, corresponding to different topics regarding the future of work: computerization/automation (2 researchers), employment (2 researchers), education (3 researchers), social welfare (2 researchers), and economy (2 researchers). The topics were defined by a literature review [ 16 ] that was previously done by researchers of the future of work. The other moderator is an expert in future studies. These two moderators participated in all groups, guiding the participants to follow the methodology and providing suggestions as necessary. It is also worth noting that the moderators presented and discussed the results of each step of the methodology in meetings with all the participants of the study. Furthermore, the guidance provided by the moderators and the participation of, at least, two participants for each topic helped to reduce biases and ensured that even though each topic had a relatively small number of researchers. Methods such as Brainstorming and Futures Wheel were performed as group activities, integrating the entire interdisciplinary group of thirteen people in the collaboration efforts to ensure the quality of the results.

The study was conducted during 8 sessions, once per week. Each session had an approximate duration of 2 hours. Since the duration and format of the sessions were defined before the execution of the study, there were few absences from the participants, and most of them were communicated previously. In absences, the moderators explained the work to be done by e-mail and were available to answer any question. Since most of the work was done in the week between the sessions, each group could perform meetings to produce their contributions. However, the Brainstorming for all groups was performed in a single 5-h session with mandatory attendance.

This study was performed with the aid of software, named Tiamat [ 17 ]. Tiamat software is a modular collaborative Foresight Support System, designed to support on-site and remote (through the Internet) studies using the concept of Foresight method workflow [ 17 ]. The software was used in several studies [ 12 , 18 , 19 , 20 , 21 ] before and allowed the moderators to orchestrate the study, all participants to communicate asynchronously, and serve as a repository that offers traceability to the intermediary results. The Tiamat framework offers a process that can be followed independently from the software; therefore, although we used the computer system to support the study, no special software is required to perform our methodology.

In the first session, the moderators presented the methodology and the Tiamat software and also divided the researchers into the aforementioned five groups. Between the first and the second session, the participants were responsible to search the literature and gather relevant material to be analyzed. The use of bibliometrics is useful to level the knowledge among the interdisciplinary participants, gather and store the state of the art of the topics of the study and select primary citations for further writing future scenarios. Details of the results are presented in the next section.

In the second session, each group presented its findings and the participants had the opportunity to recommend papers to other groups. Each participant would then have two weeks to fully read the papers considered important for their topics.

The third session was focused on discussing their findings until the moment. Each participant would share how the documents that were read up until that session contributed to understanding the future of the topic assigned to their group. Also, the participants were encouraged to share any trends found in the reading that was related to the topic studied by another group, thus stimulating the collaboration between groups.

In the fourth session, the moderators performed five Brainstorming sessions (one for each group) in which the researchers presented the main events they found. For example, the employment group found the event “more flexibility in the employment contract.” In the Brainstorming, we discussed the impacts of each event and proposed new events from the discussion. The Brainstorming ended with voting on which events should be included in the next steps. The moderators lead the brainstorming sessions according to Osborn’s brainstorming guidelines for the generation of ideas [ 22 ]. The participants from the other topics were allowed to participate since many papers that they read also discuss other participants’ topics and their different perspectives may contribute to the topic in discussion. The output of the brainstorming is a list of possible future events regarding the topic of the group. These possible future events were used as input for the next method, Futures Wheel. Between the fourth and fifth sessions, the groups were invited to develop a Futures Wheel [ 23 ], a method that stimulates participants to discover events that are consequences of other events. The Futures Weel also establishes cause-consequence relationships among events which are highlighted in a graph format. We started the Futures Wheel of each group with the events discovered in the brainstorming as initial events , allowing the participants to include, remove, or modify events while they indicate cause-consequence relationships between events, i.e., discovering primary and secondary consequences of events.

In the fifth session, each group presented and discussed their Futures Wheel. At the end of the fifth session, the moderators asked the groups to use the list of expanded events, developed during the Brainstorming and Futures Wheel to identify and develop the main trends for each topic using the scenarios technique and the literature already gathered to support their research—new literature could be added further. We call trend a set of possible future events that told a cohesive matter, heavily supported by the literature—not only the literature gathered in the bibliometrics step. Each group developed trends related to the topic of their study.

In the sixth and seventh sessions, the participants presented the trends for each topic. After the seventh session, the moderators dismissed the topic division, joining all participants to develop 3 scenarios: one which considers the best outcome for each event, one that considers the worst outcome for each event, and one that considers the most likely outcome for each event. The participants were asked to check the consistency of each scenario produced.

Therefore, the eighth session was focused to check the produced scenarios. The moderators provided one extra week for the participants to fix all spelling and formatting errors and analyze the proposed suggestions from the eighth session. The final version of the scenarios was delivered using the Tiamat software. This study encounters dynamic is presented in Fig. 1 .

Study dynamic diagram

With the COVID-19 pandemic, we knew that our study had to be updated to consider its impact. The update was done in 2 months, from June to August 2020, by gathering seven of the researchers involved in the first part of the study and assigning at least one of them to each of the five topics of the research. The methodologies applied to explore the impact of COVID-19 on the future of work and update the trend scenarios were Bibliometrics and Brainstorming. The Bibliometrics was based on 42 papers about the impact of COVID-19 on the different topics that were being studied with several reports serving as a support to grasp the scenario that was unfolding during the pandemic. Then, the group reviewed the likely scenario to consider how the combination of these individual impacts would change the future of work in 2050. We performed the update without face-to-face sessions, as expected during the COVID restrictions.

The resulting trend scenarios presented in Section 3, and the likely scenario presented in Section 4 already consider the COVID-19 pandemic impact on the future of work.

The methodology in practice

In this section, we formalize the study dynamics: first, we provide a global view of the study, in the form of a workflow; second, we explain how each method work and provide results for methods from one topic as an example of the methodology. The topic of Employment will be used to illustrate the methodology in this section. Since trends and scenarios are the main findings presented in detail in this work, we present in this section only example results from Bibliometrics, Brainstorming, and Futures Wheel.

We highlight that the participants were trained and guided during the study to produce high-quality data and guarantee its uniformity and consistency. In Fig. 2 , we present our methodology in the form of a workflow following the Foresight framework proposed by Barbosa [ 14 ].

Study workflow

The five topics were analyzed individually using four methods: Bibliometrics, Brainstorming, Futures Wheel, and Scenarios.

Bibliometrics

Bibliometric analysis is the analysis of large numbers of scientific documents. Bibliometric analysis is usually taken from patent and scientific publication databases [ 24 ] and should be combined with other measures or expert opinions to be balanced [ 25 ]. The bibliometric analysis summarizes document characteristics for statistical analysis and infers linkage among documents, where it may be used to find indirect links among concepts [ 26 ]. For inferring the linkage among documents, there are a few approaches: co-citation analysis, co-word analysis, and mapping. Co-citation increases the linkage between documents as they cite the same references. Co-word increases the linkage between documents as they use the same relevant words. Mapping presents the bibliometric data and findings, facilitating its interpretation by humans.

In this study context, we used bibliometrics to perform a simple literature review, using both scientific databases and documents available on the Internet, such as governmental reports. Therefore, the researchers gathered data in the literature to build a knowledge base, used to identify trends and support scenarios. Moderators did not enforce the use of systematic reviews of the literature; therefore, we infer the use of random search on several bases and Google to include gray literature. Snowballing was also allowed. We expected to reach beyond the academic literature, including data from technical reports from governmental organizations, non-governmental organizations, think tanks, and companies to capture early signals of change and enrich the study by providing plural views about the future of work. Due to the time restriction between the sessions, we back down from performing a mapping of the literature. The summary of the Bibliometrics results is presented in Table 1 .

As an example of an output of the Bibliometrics method, Table 2 presents the results from the employment group .

Brainstorming

Brainstorming [ 22 ] is a group technique focused on idea generation that frees its participants from criticism [ 27 ]. Osborn’s brainstorming guidelines for the generation of ideas: no immediate concern for quality or evaluation, in a set time frame, encourage building on the ideas of others, and recorded by a non-idea-contributing facilitator/scribe [ 28 ]. Although brainstorming is a very old concept, it is still widely used. Putman and Paulus [ 29 ] proposed a set of rules based on the original Osborn’s rules but extended for interactive groups. Putman and Paulus [ 29 ] proposed rules to avoid criticism, stimulate freewheeling in other participants’ ideas, stimulate quantity over quality of ideas, stimulate the combination and improvement of ideas, avoid losing focus on the task, avoid moments of silence, and stimulate review previously ideas and categories.

In this study context, we used Brainstorming to raise possible future events of each group research topic—computerization/automation, employment, education, social welfare, and economy—on work, based on the literature analyzed in the previous step (Bibliometrics). Moderators place these events are the starting point for the further analysis performed by each research group, following the Putman and Paulus rules. Participants were stimulated to use the literature to develop the events, which were not limited to the Bibliometrics results—i.e, the participants could perform snowballing for example to gather more information. However, the main source of possible future events comes from their understanding of the complex scenario and their further reasoning into ideas. Such ideas—even if they exist—are not easily findable in the literature. Finally, we voted on the list of proposed ideas, developing basic trends to be further analyzed. We present the selected brainstorming events from the Employment group in Table 3 .

Futures wheel

The Futures Wheel [ 23 ] is a method to identify the consequences of trends and events. For the sake of simplicity, we will refer to trends or events only as events. Starting initial events, the participants define a set of primary consequences. The participants should ask themselves three questions to discover the consequences: “If this event occurs, then what happens next?”, “What necessarily goes with this event?”, and “What are the impacts or consequences?”. The Futures Wheel analysis continues recursively, i.e., each primary consequence is analyzed to generate a set of secondary consequences. Although the Futures Wheel may go on indefinitely, rarely does it go further than the tertiary consequences, mostly because the complexity of the analysis grows exponentially. Contradictory consequences may also occur and the participants must consider them.

The participants of the Futures Wheel map the event to its consequences, producing concentric graphs, which highlight the potential complexity of interactions, showing that the consequences do not happen all at once, but in an evolutionary, interactive sequence [ 23 ].

In this study context, we used Futures Wheel to further discussed the events listed in the Brainstorms. Therefore, the Futures Wheel mapped the events to their consequences, producing concentric graphs of primary, secondary, and tertiary consequences. New events were included as a result of this analysis. The Futures Wheel from the Employment group is shown in Fig. 3 .

Futures wheel from the employment group

Scenarios are possible evolutions of the future consistent with some set of assumptions [ 15 ]. Scenarios have been termed the “archetypal product of futures studies” [ 30 ]. They can be achieved through creative thinking about future possibilities (explorative scenarios) as well as through active working towards the production of a desirable future or set of futures (normative scenarios) [ 31 ].

Scenarios represent the combination of a set of extrapolated current trends or projections, and these must be internally consistent, i.e., not contradict each other. For example, when analyzing possible futures related to ATM usage, a scenario where an increase in cashless money transfer and an increase in the usage of ATMs by the general population should be pruned, as these events are mutually exclusive, therefore making the scenario inconsistent [ 32 ]. Indeed, Shoemaker [ 33 ] suggests that three tests of internal consistency are especially useful. Firstly, remove scenarios with trends whose time frames do not match. Secondly, remove scenarios in which predicted outcomes are inconsistent with each other. Lastly, remove scenarios in which major players are placed in unlikely positions.

In this study context, we used scenarios to analyze the events and developed the trend scenarios that are presented in detail in Section 3, using each group Futures Wheel and literature gathered. Therefore, the trend scenarios discuss trends for each topic of this study, and they are heavily based on the literature.

Finally, we also use scenarios to develop three scenarios for work in 2050: an optimistic/positive scenario, a pessimist/negative scenario, and a likely scenario. To develop such scenarios, we dismissed the division of groups into topics, since the scenarios must consider all topics. The Scenarios for work in 2050 were built on all the knowledge gathered in all previous steps. Therefore, the scenarios are based on the joint analysis of the trend scenarios to understand how they interact. We also classify the trends as more or less likely to happen and if a trend can be considered good or bad for society. Due to space limitations, we present the likely scenario in Section 4, which considers the combination of the trends for the future of work that the participants considered as most probable .

Trend scenarios for future work

This section will present the future trends for the areas analyzed in this study: computerization/automation, employment, education, social welfare, and economics.

Computerization/automation

The last century started a transition in industrial automation as machines are increasingly better to make decisions, not only performing manual activities but allowing more activities to be automated. The most cited paper concerning the topic estimated that 47% of the US workforce was under a high probability of computerization (automation by computer technologies, mainly AI and Robotics) in the next decades [ 34 ]. Later studies that applied the same methodology showed that the number of workers in occupations that are likely to suffer computerization varies from country to country. In developing economies such as Brazil, the percentage reaches 60% [ 35 ] while in advanced economies such as the UK, the number drops to 35% [ 36 ].

Areas such as the retail market, archiving, data collection and processing, and line assembly operations will be highly impacted. Still, even for workers at higher risk, adopting automation is not simple: it requires analysis of some key points, such as technical feasibility; development and implementation costs; labor market dynamics, considering its demand, costs, and social characteristics; economic benefits, such as governmental policies; and social acceptance [ 37 ].

As automation increases, it will require policies to protect unprepared and vulnerable workers, allowing them to migrate to the new model of production [ 38 ]. Underdeveloped nations face higher risks since they are rarely part of the discussion about this topic and are outside of the focus of studies. Erroneous interventions also leave underdeveloped nations incapable to compete against developed nations, producing economic, social, and political inequalities along with technological advancement [ 38 ]. It is important to note, however, that unemployment levels have remained stable in the long run, despite disruptions caused by industrial revolutions, as workers migrated to new jobs sometimes enabled by new technologies or the number of jobs was increased because of a higher consumption [ 39 , 40 ].

The increasing adoption of automation technologies results in ever-lower costs of hardware, sensors, network, processing, and storage; a more refined and accurate set of data allowing tests and studies even without human supervision; and a great expansion and absorption of knowledge unprecedented [ 41 ].

The last 20 years have brought remarkable progress in AI, one of the most important technologies in the current wave of automation, and now, we can build machines capable of learning even when humans are unable to teach them, producing new knowledge faster than humans [ 41 ]. Due to these advances, it will be possible to have AI working with humans as assistants, from reading e-mails to driving cars. However, it will also raise privacy, security, and ethical issues, with unintended consequences if we cannot identify these challenges promptly [ 42 ].

The Internet of Things (IoT) is another important automation technology that has been experiencing considerable expansion, especially in areas such as medical and health care, smart building, intelligent transportation, industry, and logistics [ 43 ]. IoT includes low-cost and high-performance processors attached to low-cost sensors; they usually include some form of analytical software, many of them in highly distributed architectures, i.e., cloud computing [ 44 ]. IoT is a central element of Industry 4.0 where the integration between humans and machines can speed up the production systems by 30%, raising their efficiency by 25%, and allowing a new scale of product customization [ 45 ].

IoT evolved into the development of smart medical devices creating the concept of the Internet of Medical Things (IoMT). IoMT allows real-time monitoring of the health condition of a person using smart sensors and connected devices and can also help the medical staff at the hospitals by remote monitoring chronic-conditions patients at home. Thus, IoT reduces the workload on medical staff and becomes a necessity instead of a luxury [ 46 ].

The COVID-19 pandemic affects several automation-related segments such as telemedicine, IoMT, manufacturing, and supply networks, AI, and smart payments. In telemedicine, the increased adoption of telemedicine to keep patients and medical staff safe can be highlighted. One example is Tele-Critical Care (TCC), a tool related to telemedicine that enables intensivists in traditional intensive care units (ICU) to speed up critically ill triage, thus improving ICU bed management; in hospitals without ICU, TCC enables remote care for critically ill patients, preventing transferring these patients. Another telemedicine tool is Telementoring, in which experts from low-demand areas help their high-demanded peers. During the COVID-19 crisis, intensivists used tiered telementoring to provide consultation on patients with a higher risk from experiencing respiratory and organ failure. One intensivist was capable to oversee 100–250 patients through telementoring [ 47 ].

Manufacturing is also being adapted for the post-COVID era, as workplace standard practices are adapted to the physical-distancing policy, thus stimulating concepts such as Smart Manufacturing and Industry 4.0 [ 48 , 49 ]. AI will be the backbone of automated transportation systems, both on the streets (driverless trucks and cars), and in factories and warehouses (Automated Guided Vehicles) [ 49 ]. AI is related to the development of cleaning and disinfecting robots as well [ 46 ]. Such innovations make manufacturing and supply chains more resilient to human-related interruptions.

Finally, smart payment (also known as contactless payment) technologies minimize human contact during cash payments; their demand tends to continue high in the post-COVID era [ 46 ].

The COVID-19 pandemic accelerated the adoption of several technologies—such as Big Data, robotics, AI, and IoT—as they help companies and society in general to mitigate the impacts of the pandemic. In some cases, the adoption of technologies was necessary to maintain the operation of businesses, making digital literacy an essential skill, and allowing workers to see technologies more as a tool than a replacement [ 50 ], a movement that started even before the pandemic as digital skills were already becoming an important determinant of employability in the digital age [ 51 , 52 ]. The adoption of automation and digitalization tends to intensify during and after the pandemic as essential activities will use more automation to safely attend to their customers and activities that can be moved online such as retail, entertainment, and recreation will be digitalized [ 53 ].

The world is entering its 4th Industrial Revolution where technologies such as AI, nanotechnology, 3D printing, robotics, and biotechnology are being used in combination and creating new possibilities for production [ 54 ]. Technological unemployment is once again a preoccupation in this new industrial revolution and it can be defined as “non-employment due to our discovery of ways of saving the use of labor, exceeding the pace at which we can find new uses for work” [ 55 ]. On the other hand, the displacement theory of work affirms that automation will provoke the end of certain careers and the creation of new ones, thus causing little or no harm to employment.

Globalization, another major force in the future of employment, has created two trends in the markets: outsourcing and immigration. Remote work is already a reality, even in traditional enterprises such as IBM, where only 42% of employees work in IBM’s location [ 56 ]. Remote work was only recently adopted by large companies, but in startups, it is already common. The distribution of offices in different places or even spaces for co-work will promote a reduction of expenses for the companies, becoming an alternative to the central offices in costly commercial locations [ 57 ]. However, illegal immigration from underdeveloped nations will be motivated by the combination of unemployment, food scarcity, wars, and other extreme situations [ 58 ].

There is also a trend for greater flexibility for workers, making it possible for them to mix different part-time jobs. In this scenario, virtual reality (VR) and augmented reality (AR) may be used to amplify immersion and collaboration, allowing workers to be “where” they are needed [ 59 ].

The return of the elderly to the workforce will be motivated by the increased difficulty in fulfilling their retirement plans, in general, and also by the sense of helping society with their experience [ 60 , 61 ]. This trend produces a significant impact on society since organizations can continue to be competitive by having access to a larger pool of qualified professionals, reducing the scarcity of specialists, and the impact on social security systems [ 60 ]. Considering advances in the automation of health care, increased use of continuous health tracking devices, reduced health costs, and human errors being reduced due to automation, life expectancy, and the time that a person will be able to perform work and actively participate in society tend to increase [ 62 , 63 ].

According to the International Labor Organization (ILO) [ 64 ], “non-standard forms of employment have become a contemporary feature of labor markets around the world.” In South America, 6 of the 10 young people working in the informal economy today [ 65 ]. This trend is not exclusive to developing countries [ 66 ].

Recognizing the inevitable growth of non-standard forms of employment, a policy proposed by Harris and Krueger [ 67 ] introduces a new “self-employed” designation that is not eligible for overtime payment and unemployment insurance but protects workers by antidiscrimination statutes and gives them the right to organize and withhold taxes [ 67 ]. Their employers, be they online or offline, would make tax contributions to the payroll [ 67 ].

The labor movement suffered recent changes influenced by globalization and technological change but has managed to remain relevant as new forms of work and challenges for workers appeared [ 68 ]. Some examples of how the labor movement is being organized by digital platforms’ workers are the App-Based Driver Association, a group from Seattle-US of app-based (e.g., Lyft, and Uber) drivers, and Turkopticon, an initiative by the University of California San Diego that gives the possibility of Amazon Mechanical Turk workers to evaluate their Human Intelligent Tasks [ 69 ]. Another way that new labor movements can be created and empowered is by seeking support from traditional unions and other social actors. An example is the FairCrowdWork Watch, a platform developed by the IG Metall (dominant metalworkers’ union in Germany)—that allows workers to rate platforms, compare their payments with others, and receive legal advisory [ 70 ]. The diversity of new workers’ movements and the importance of their agenda show that these organizations are likely to continue existing in the future by adapting themselves to each new challenge with the support of technology. Nevertheless, this trend does not mean that traditional unions will become more relevant in the future as digital platform workers tend to feel a certain apathy towards unions partly explained by their identification with entrepreneurship [ 68 ].

The job losses caused by the current pandemic are expected to be worse than the 2008 crisis because around 38% of the global workforce is in economic sectors that are suffering a collapse in demand such as manufacturing, hospitality, tourism, and transportation. The crisis is also expected to increase unemployment rates around the world to two-digit numbers even in places that had very low rates before the pandemic as the USA and developing countries can experience even worse outcomes [ 53 ].

In Italy, an analysis of the impact of the coronavirus on 7800 companies shows that the aggregate shock on a 3-month horizon is −21% and −16% in twelve months with companies canceling 44% of the preexisting scheduled R&D plans. When it comes to employment, the expected aggregate drop is −6.5% [ 71 ]. In the USA, a survey of 10,000 households shows the impact of the virus from January to April of this year. The employment rate fell by 5%; overall spending dropped by US$1000 per month (a 31% drop), especially with mortgages, student, and auto loans which indicates the possibility of a wave of defaults soon; 42% of employed respondents lost earnings due to the virus (an average of over US$5000) [ 72 ].

Another challenge brought by the pandemic is the asymmetry of the impact on jobs as it will disproportionately affect entire social categories as low-skilled, low-wage jobs usually held by minorities, immigrants, women, and other disadvantaged groups will suffer the effects of the crisis in the long term [ 50 , 53 ].

COVID-19 showed the importance of the low-wage workforce which comprises a considerable portion of the essential sectors. Immigration systems in advanced economies tend to be more open to high-skilled workers and more restrictive when it comes to low-skilled workers. In the UK, 16.1% of essential workers are foreign-born. Specifically in the health industry, 18.6% of the workforce is foreign and 13.4% of the workforce is not from the European Union. In a “sharp” crisis like the one caused by COVID-19, immigration systems cannot change quickly enough to supply immigrant workers to needed areas. Forty-six percent of foreign-born essential workers in the UK do not meet the post-Brexit immigration rules that stipulate minimum thresholds for immigrants’ jobs’ skills and wages [ 73 ].

Many unemployed people will seek jobs in other cities and countries, leading to migration. Massive migrations to other countries may cause two impacts: anti-immigration laws in the countries receiving immigrants and a lack of young workers to develop the countries losing their workforce [ 74 ]. According to Goniewicz et al. [ 75 ], future policy should incorporate lessons learned from the COVID-19 pandemic. Granting refugee status to immigrants is controversial and pro-immigration policies can cause confusion and conflicts [ 74 ]. A policy for long-term social distancing and the gradual personal interactions of low-risk individuals should be implemented. Workplaces should be adapted to facilitate physical distancing.

At the micro-level, as the measures to control the coronavirus spread involve social distancing, society is experiencing a surge in remote work, specifically working from home [ 76 ]. This change brings new challenges to workers as unplugging from work demands is one of the new work-life conflicts [ 50 , 76 , 77 , 78 ].

The changes in the world of work will force education to be adapted and advancements in technology may help teachers to achieve this goal. The education system needs to train increasingly specialized workers, due to the end of some careers and the emergency of new ones [ 79 ]. This cycle will be more active and impactful in the future, bringing the need for lifelong learning to adapt workers to different jobs. However, the reactive characteristic of changes in education (that trains workers for an almost obsolete job to bring them to the current market) needs to be adapted, continuously updating their curricula on new job trends, and providing relevant competencies for job opportunities [ 79 ]. Governments and education institutions have a key role in keeping education updated for new workers. Governments are also responsible for stimulating the creation of new jobs. In this way, they create initiatives such as short-term higher education courses focused on a faster insertion into the labor market [ 80 ] and Massive Open Online Courses (MOOC) capable of teaching and training thousands of workers at the same time, complementing traditional educational methods to provide faster adaptation of education in the future [ 81 ].

As work will need less time to be performed due to automation [ 79 ], workers will have more free time, which may be used to learn, rest, or work on a second job. Information becomes cheaper (in many cases free), brought by the expansion of knowledge through the Internet, and this trend boosts the use of MOOCs by workers. Thus, we will see teachers becoming advisors, directing students through the knowledge freely available [ 79 ]. Education will be personalized, with tailored learning plans to fulfill the worker’s needs, interests, and preferences stimulating students to spend more time learning the skills related to their interests [ 82 ]. Besides, projects such as the open access initiative will help the sharing of research facilitating free access to knowledge [ 83 ]. MOOCs and other educational online environments have also the capability to train people looking for self-employment, either to supplement their monthly income or as the only existing employment option.

New jobs will require highly skilled, knowledge-intensive workers [ 84 ]. Science, Technology, Engineering, and Mathematics (STEM) skills play a key role in any country’s economic success and require years of investment in education [ 85 ]. This higher demand for highly skilled workers will also affect low-skill jobs, mainly those in services [ 85 ]. Fundamental skills such as literacy, numeracy, communication, and team working are required for most jobs [ 85 , 86 ].

In addition, the worker of the future will need to learn the following skills: critical thinking and problem-solving (cognitive skills), presentation and conflict resolution (interpersonal skills), and adaptability and self-development (intrapersonal skills) [ 87 ]. Jobs with a lower risk of automation rely on social and creative skills [ 84 ]. Therefore, the most important skills needed for these jobs are collaboration, self-regulation, knowledge construction, communication, real-world problem-solving, and the use of technology for learning [ 88 ].

During the COVID-19 pandemic, millions of students were unable to go to school and received general recommendations to use digital tools such as online study platforms [ 89 , 90 , 91 ].

Before the COVID-19 pandemic, we assessed that as free information becomes more prevalent in society, we may see teachers replaced by MOOCs if people became more self-taught. From mid-March to mid-May 2020, Coursera, one of the most used MOOC platforms, saw a growth of ten million new users [ 92 ].

Regarding the impact on schools in the USA, Dutta [ 93 ] discusses several consequences of prolonged stay-at-home and school closures for children. Prolonged isolation from their grandparents, teachers, classmates, and friends is likely to cause sadness and stress. The unexpected transition to distance learning makes students struggle to learn the required knowledge for their respective grades, while schools face difficulties with the standardized COVID-19 test requirements. Schools also play an integral role in promoting healthy eating and maintaining an active and healthy lifestyle—which may lead to sedentary behaviors and, thus, increased rates of child obesity. Inequality is also an issue: some students will have problems accessing the distance learning web modules, and children in need will lose access to school meals, facing starvation. Kneale et al. [ 94 ] list other consequences: children losing access to school-based health care, increased injury risks due to self-care or inadequate care, higher risk of child abuse or violence, and even increased child labor and marriage rates.

The World Bank produced a report on the impact of the pandemic on education financing. They projected declines in government revenue due to slower economic activity. On the other hand, countries are overspending their budgets on health and social protection. Such a combination will deteriorate the fiscal balances in most countries during 2020. Therefore, governments will tend to reprioritize their budgets, reducing the education budget, reducing the per capita education spending in almost all country income groups and regions, and impacting future education outcomes. Even in a scenario of economic growth for 2021, the education budget tends to stay stagnate or fall in most countries [ 95 ].

• Social welfare

Technological progress will affect how we work. In healthcare, technology will increase the quality of diagnoses and improve people’s quality of life. The population is proportionately aging [ 96 ], and, as a result, people’s productive ages will increase, raising the economically active population. Besides, a bigger population implies changing pension systems as well as workers who exceed the minimum/normal retirement age for a better pension.

In general, eligibility rules for retiring are complex and the pension benefit varies according to the objectives of each government. In 2014, the normal age of the normal pension was 64.0 years (men)/63.1 years (women), assuming entry into the labor market at age 20 [ 97 ]. The forecast for 2054 is a rise in the normal retirement age, with more countries raising the normal retirement age to above 65 years while reducing the gender disparities in retirement age [ 97 ].

Figure 4 shows a forecast that economic inequality tends to grow in the future, bringing negative consequences for the distribution of wealth, since previously accumulated wealth grows faster than production and wages. Thus, the current wealthy people tend to become the dominant rentiers over those who do not own properties but only their work [ 98 ].

After-tax rate of return on capital vs. growth rate at the world level, until 2100 [ 98 ]

Gender equality influences the return on capital rate and the growth of income and output. An increase in women’s participation in the economy results in more political power. If the trend toward increasing gender equality is sustained in the coming decades, a slower increase in economic inequality can be expected [ 99 ].

Gender equality has improved in the last 50 years, but several countries still fail to provide even fundamental rights to women, especially in North Africa and the Middle East. The gender gap has slowly reduced in the past decade. Health and education subindexes reached values close to 1 (equality), while economic and political show much lower values. At the current rate, the gender gap will only be closed by the year 2100 [ 100 ].

Some researchers argue that growing inequality is a result of the exponential shift in technology. The new technology provides the economic reward for the winners of our modern economy, while the losers become increasingly expendable and less resourceful [ 101 ]. Racial inequality should be also considered. For example, Native Americans, Africans, and Latin Americans present a lower Human Development Index (IDH) than Asian and white Americans [ 102 ]. Sexual orientation is also a taboo subject in many countries, making it difficult to develop evidence-based policies. LGBTI rights are also necessary for an equal society. Anti-discrimination laws are necessary to make people more tolerant of the LGBTI community.

For McGahey [ 103 ], technology or computerization job losses, demographic changes, and rising costs of social benefits are challenges for social welfare states. Thus, social welfare states should offer new types of social benefits or build a new way out of this problem. McGahey suggests that there is a way for introducing Universal Basic Income (UBI) as a floor income, providing basic subsistence, complementing the existing welfare state policies, or, in some cases, substituting it [ 103 ]. Universal Basic Assets (UBA), sometimes considered an evolution of UBI [ 104 ], is defined as a basic set of resources every person is entitled to have: housing, education, health, and financial security.

The COVID-19 pandemic affected social welfare in several ways because it causes an economic crisis and global recession. Although each country tries to stimulate its economy, methods vary due to financial and political constraints. US unemployment raised to its highest level since the great depression. Most households have insufficient savings to live through this type of adversity. Governments provided liquidity to the most vulnerable households through penalty-free withdrawals from retirement savings accounts and stimulus checks, among others. The consequences of such actions include large government debts [ 105 ].

Massive unemployment led many families with young children to become food insecure. Household Food Insecurity (HFI) increases the risk of chronic undernutrition and infectious diseases in children, maternal anemia, obesity, and type 2 diabetes [ 106 ]. According to Pérez-Escamilla et al. [ 106 ], COVID-19 HFI will affect more vulnerable groups such as young children, pregnant, and lactating women. Many disadvantaged students lost their access to free school meals [ 74 ]. Half of them are in low- and lower-middle-income countries; losing this meal also reduces the most vulnerable families’ income [ 106 ]. Under such circumstances, children in some nations are at higher risk of child marriage and child labor [ 74 ]. COVID-19-related stockpiling and panic buying have also affected food security. The just-in-time supply chains are vulnerable to disruptions; this sudden rise in demand caused empty shelves and higher prices for some products. Poor availability of food in supermarkets forced households to access food from food banks that already suffering from the sudden increase in demand and reduced volunteer numbers. Some independent food banks achieved their “breaking point” and others closed entirely [ 107 ]. According to Power et al. [ 107 ], the food aid system seems unable to face health and economic emergencies simultaneously. According to Pérez-Escamilla et al. [ 106 ], COVID-19 has shown “how unprepared the world is to protect populations against hunger, food, nutrition, and health insecurity during global emergency situations.”

The current demographic changes will highly impact the economy in 2050. The first demographic change will be caused by world population growth, which will rise from the current 7.6 billion to about 10 billion people by 2050 [ 108 ]. This worldwide increase in population is a challenge as it shows that hundreds of millions (or a few billion) jobs must be created [ 108 ].

A second demographic change that will impact economies is population aging. By 2050, the population of developing countries will still be younger than those of developed countries [ 109 ]. As the worker live longer, they must also have to work for a longer period to support their pension schemes. The extension of working years will also impact youth employment as more experienced workers will dispute a few job opportunities with them.

A third demographic change is an increase in urbanization. Besides the population growth, people in rural areas are migrating to medium and large cities. According to Schwettmann [ 108 ], 64% of the population of developing countries and 86% of the population of developed countries will be urbanized by 2050. This trend has mixed impacts, as urbanization may cause unplanned city growth, pollution-related health hazards, and unemployment. However, urbanization may reduce the costs of transport and education, and create cultural diversity [ 108 ].

The 2050 economy will be based on knowledge-intensive work. Knowledge-intensive services now include business services such as finances, accounting and software, medical services, and engineering [ 110 ]. Knowledge-intensive careers have fewer jobs; mostly because they require very high skills and advanced degrees in the fields of science and engineering. Thus, the unskilled and less educated, which represent most of the population of many countries, are excluded from most of the opportunities in knowledge-intensive production [ 110 ].

Technology is only a factor to determine economic results such as growth, inequality, or employment—however, technology is the main driver of Gross Domestic Product (GPD) growth per capita. Leading economies have access to similar technologies which resulted in different economic results throughout history, mostly because they have different policies and institutions [ 111 ].

According to the McKinsey Global Institute [ 37 ], in the USA, 46% of the time spent on work activities is technically automatable, using the current technologies. They estimate that the current automation technologies could replace 50% of working hours on a global scale. Increasing computerization will affect almost every occupation, not limited to factory workers. This automation potential represents 1.2 billion workers, which wages about US$ 14.6 trillion [ 37 ].

Rises in labor productivity usually translate into increased average wages, providing the opportunity for workers to reduce their working hours and increase the offer of goods and services [ 111 ].

Another important trend related to the economy is a phenomenon named “Rise of The Rest,” which describes the shift of the GDP from developed countries to developing countries. Nowadays, global economic activity is already shifting from the G7 to the G20 [ 109 , 112 ]. As a consequence, developing countries have a faster increase in their technology, capital, and people [ 109 , 112 ]. Figure 5 presents the projected average GDP growth from 2016 to 2050. We highlight that COVID-19 and the recent Russian invasion of Ukraine [ 113 , 114 ] may cause an impact on these long-term GDP projections, especially for Russia.

Projected average real GDP growth 2016–2050 [ 109 ]

The COVID-19 pandemic produced a health and economic crisis with unprecedented scale and magnitude producing an unforeseen combination of supply and demand shocks for the global economy that will affect it for a long time even after the coronavirus control policies [ 71 ]. Almost 90% of the world economy went under some sort of lockdown measures by mid-April and an economic crisis unparalleled to none since the great depression has taken place. Blockades of national frontiers imposed by governments have paralyzed economic activities in general, laying off millions of workers worldwide and having a major impact on the world economy. Global GDP is forecast to decline by 3.2%, reaching a drop of 5.0% among developed countries; and production losses projected for 2020 and 2021—almost US$ 8.5 trillion—will eliminate almost all production gains of the previous 4 years [ 53 , 115 , 116 ].

Among developing countries, large fiscal deficits and high levels of public debt will pose significant challenges, particularly for economies dependent on commodities and tourism. The severity of the economic impact depends mainly on two factors: the duration of the restrictions (economy, circulation, and transport) and the size and effectiveness of fiscal responses to the crisis [ 117 ].

Households are strongly affected due to lockdown restrictions, causing job losses. This situation reflects in a decreased consumer power, being perceived as more accentuated in sectors such as tourism/hospitality and clothing [ 72 ]. The greatest concern on financial health at the family level is due to the uncertainty as to whether financial reserves will cope with an extended lockdown period [ 117 ]. For this reason, governments have introduced financial support programs for groups of people who are economically vulnerable to the effects of the pandemic; however, not all have established norms regarding credit scores, which also have an influence on consumption and the granting of future credit [ 118 ].

The global production chain was also impacted: mainly affected by the closure of countries’ borders, this network of international economic relations proved to be highly dependent on a small number of countries, such as China—causing the absence of industrial inputs and unbalancing the trade balance of the countries under its influence [ 117 ].

The COVID-19 pandemic also impacts the financial sector. Risk management models built over the past few decades have not been able to guarantee global financial stability and contain the effects of this financial crisis [ 119 ]. Stock exchanges are experiencing a period of high volatility around the world, mainly in Asia—investor uncertainty reflects the pandemic’s effect on the economy [ 120 ].

The governmental response involves a set of strategic actions, learning lessons from this event to build resilience for possible future crises. At the household level, governmental actions include proposals for financial support for those who had income losses and for stimulating family cost savings to build emergency finance reserves. At the business level, governmental actions include preventing corporate bankruptcy and mass layoffs by identifying companies that are in the most critical stage to support loans and investments so that they can rebuild. At the local economy level, governmental actions include identifying interventions to improve business recovery after COVID-19 and prioritizing investment in critical economic sectors and businesses, based on the added value to the local community [ 121 ].

Stronger development cooperation, supporting efforts to contain the pandemic, and extending economic and financial assistance to countries most affected by the crisis will be of utmost importance to accelerate the recovery and put the world back on the path of sustainable development [ 115 ].

Likely scenario for work in 2050

In this section, we use the trends and trend scenarios presented in Section 3 to build a most-likely scenario of how working will be in the year 2050. First, a short story of this scenario is told, followed by a discussion of the actions that the social actors (government, companies, and workers) would take to lead us into this future.

Working in 2050

The advancement of computerization will make some jobs obsolete while new ones will be created, as happened in previous industrial revolutions. The benefits generated by automation are important and induce widespread improvements in society as society has taken the right actions to guarantee that technology adoption does so, at least in most cases.

Occupations that involve social and creative intelligence, and/or advanced perception and manipulation tasks will be less affected by computerization due to the technology limitation to emulate these behaviors.

Society will see a job shift from low-skill to knowledge-intensive occupations. Many people will face unemployment and companies will be stimulated by governments to help reduce the transition impact by training workers in new skills before completely automating their jobs. Those that cannot be helped by these measures will receive a basic income from the state.

Communication will be globally improved due to the reduction of costs in Internet access in most countries. Better communication will further improve the integration between national and international markets, allowing more people to offer their services on the internet. The COVID-19 pandemic and the measures adopted to contain it still have an impact on work because by 2050 employers will be aware that they may be forced to move their production to remote work at any time. Thus, we expect some jobs to have at least one “remote day.”

Workers will not be associated with traditional trade unions as new forms of worker organization movements will be recognized by governments and employers. These organizations will show that new technologies can be used to innovate not only the way people work but also how they defend their rights as workers.

As populations age, the minimum retirement age will increase. This will not represent a big impact on companies for, as jobs become more knowledge-intensive, it will be increasingly easier and even profitable to keep senior workers in their jobs.

Gender equality will increase, but even in 2050, many countries will still be far from the ideal equality between men and women. Another group that will see advancements in their rights, despite some resistance from far-right groups, will be the LGBTQIA+ as more initiatives such as anti-discrimination laws are created. Racial discrimination will also reduce.

The income distribution will be another factor for social inequality reduction. Several UBI and UBA trials will happen worldwide, but few societies are going to adopt these social welfare policies permanently.

The world population will be close to 10 billion, and governments will face the challenge of creating jobs for hundreds of millions or, at least, providing means for their survival. As some countries will fail to do so, workers will either migrate or use the internet to offer their services globally. Both developing and developed countries will see a reduction in their rural population as urbanization grows.

In 2050, the economy will be more knowledge-intensive as low-skilled jobs are reduced and new ones, based on more creative and social activities, are created. Rises in labor productivity will provide increased average wages, allowing workers to reduce their working hours while the offer of goods and services is increased.

The COVID-19 crisis will affect the global production chain in the long term. Many governments will consider the production of some fundamental industrial inputs as strategic, demanding local production by law, even with higher costs. This will reduce their dependence on other countries.

Several countries will face large fiscal deficits and high levels of public debt, aggravated by the COVID-19 pandemic. The severity of the economic impact will be related to how strongly each government acted to break the spreading of the SARS-CoV-2 virus. Another effect to be seen is the increased migration in the search for better economic opportunities, which will lead to an increase in nationalist and far-right movements in Europe and the USA.

Actions leading to the most-likely scenario

In this section, we present a compilation of a set of actions that could be taken to make the most-likely scenario come to fruition. Most of these actions are related to public policy and have been mapped during the different steps of the research.

For the most-likely scenario to become true, governments will have to make sure that technology meets not only the capital demands but also the population’s needs. As such, governments that promote regulatory policies to control the advancement of computerization, avoiding mass unemployment without stopping innovation, will perceive better results—both in economic growth and welfare. These regulatory policies include the ones aimed to protect vulnerable people, those with few resources and less educated, who must be prepared for this transformation. Governments will be able to respond to the job shift by investing in broader and better education and teaching new skills for the new occupations that will arise while providing the displaced workers with, at least, the minimum living conditions during this period of transition [ 122 ].

As companies perceive the consumer market reduction risk due to unemployment, they will promote reducing the working hours, thus maintaining a stable employment rate, and allowing economic growth. This type of action benefits not only the consumer market but can also relief the pressure on the social safety net by reducing the unemployed [ 123 ]. More than 90 decades ago, Keynes made a famous prediction that the duration of the workweek would be of only 15h by the time his grandchildren came to age but more modern predictions consider that a reduction limited to 20–40% of the workweek would be more realistic [ 55 , 123 ].

In many countries, non-standard employment workers will have minimum rights granted through government regulation after a series of disputes with their employers. This will improve the quality of services provided and reduce the insecurity of workers in this category. Disputes surrounding the employment contract of platform workers are already a reality around the world with mixed results and different proposals are already being put forward and can be expected to increase in the future to create at least some intermediary set of rights that place NSE workers in a situation of less insecurity than the current one [ 67 ].

By progressively increasing taxes for larger corporations, and taxing great fortunes and heritages, the government will distribute wealth to the poorer sections of society, including the promotion of basic income efforts. Wealth distribution efforts would allow the reduction of poverty and the stabilization of inequality. The implementation of international tax cooperation could also help with the reduction of inequality between developed and developing countries with the payment of universal basic income as a way of transferring resources from the first group to the second [ 124 ]. This is certainly a challenge that can be seen as a stretch considering the current reality of global cooperation, but even if no international circulation of financial resources is not possible in the future, at least some cooperation in terms of knowledge regarding actions to deal with the negative consequences of technological change can be considered in the future [ 124 ].

Work has changed in the past, and it is continuously changing. Future studies are important to highlight which important changes must be made now to prevent future problems. Therefore, the future of work is a relevant subject for future study because we are currently going through the 4th Industrial Revolution—focused on robotics, AI, biotechnology, and nanotechnology—and a global pandemic which are events that produce fast and profound changes to society and work.

In this study, we analyzed the future of work on the horizon of 2050. We divided our scope into five groups: computerization/automation, employment, education, social welfare, and economy. As a result, we point out trend scenarios about the future of work—with some conflicting trends. Therefore, we developed and integrated these trends into three scenarios of Work in 2050: an optimistic/positive scenario, a pessimist/negative scenario, and a likely scenario which were presented in detail. The likely scenario combined the trends which we consider the most probable future outcomes, including the impacts caused by the COVID-19 pandemic.

Results show that computerization and automation continues to advance in industry and will reduce the demand for low-skill and low-wage jobs; non-standard employment tends to be better regulated, with minimum worker rights granted; new technologies will allow a transition to a personalized education process; the workload to the workers will reduce due this personalized education and computerization; automation will impact all types of work, and workers will receive knowledge-intensive training to make them able to perform the fewer available jobs; the self-employment and entrepreneurship will grow in the global labor market; society will demand more transparency and participation in political matters using new technologies; population will age and legislations will be amended so that pensions have increased ages; universal basic income would not reach its full potential, but income transfer programs will be implemented for the most vulnerable population; knowledge-intensive work and services will become more advanced; and extreme poverty will decrease but inequality will be slightly higher than it is nowadays.

This study contributes to the understanding of the current situation of work and its current future trends. We contribute to the discussion of problems related to work, to help decision-makers to understand them and take efficient actions to mitigate them.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Artificial intelligence

Augmented reality

Gross Domestic Product

Household Food Insecurity

Human Development Index

Intensive care unit

International Labor Organization

Internet of Medical Things

Internet of Things

Massive Open Online Courses

Science, Technology, Engineering, and Mathematics

Tele-critical care

Universal basic assets

Universal basic income

Virtual reality

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Acknowledgements

We would like to thank Rosa Alegria for many useful suggestions that improved the contents of the paper.

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Brasil (CAPES), Finance Code 001.

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CEB designed the framework, system, and methodology and lead the experiment to produce the scenario described in this work. YOL was the main researcher on employment trends. LFCC was the main researcher on social welfare trends. HS and JAS developed education trends. AL and MA contributed to the COVID-19 update. JMS supervised and coordinated this research. The authors read and approved the final manuscript.

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Barbosa, C.E., de Lima, Y.O., Costa, L.F.C. et al. Future of work in 2050: thinking beyond the COVID-19 pandemic. Eur J Futures Res 10 , 25 (2022). https://doi.org/10.1186/s40309-022-00210-w

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Evans D, Coad J, Cottrell K, et al. Public involvement in research: assessing impact through a realist evaluation. Southampton (UK): NIHR Journals Library; 2014 Oct. (Health Services and Delivery Research, No. 2.36.)

Public involvement in research: assessing impact through a realist evaluation.

Chapter 9 conclusions and recommendations for future research.

• How well have we achieved our original aim and objectives?

The initially stated overarching aim of this research was to identify the contextual factors and mechanisms that are regularly associated with effective and cost-effective public involvement in research. While recognising the limitations of our analysis, we believe we have largely achieved this in our revised theory of public involvement in research set out in Chapter 8 . We have developed and tested this theory of public involvement in research in eight diverse case studies; this has highlighted important contextual factors, in particular PI leadership, which had not previously been prominent in the literature. We have identified how this critical contextual factor shapes key mechanisms of public involvement, including the identification of a senior lead for involvement, resource allocation for involvement and facilitation of research partners. These mechanisms then lead to specific outcomes in improving the quality of research, notably recruitment strategies and materials and data collection tools and methods. We have identified a ‘virtuous circle’ of feedback to research partners on their contribution leading to their improved confidence and motivation, which facilitates their continued contribution. Following feedback from the HS&DR Board on our original application we did not seek to assess the cost-effectiveness of different mechanisms of public involvement but we did cost the different types of public involvement as discussed in Chapter 7 . A key finding is that many research projects undercost public involvement.

In our original proposal we emphasised our desire to include case studies involving young people and families with children in the research process. We recruited two studies involving parents of young children aged under 5 years, and two projects involving ‘older’ young people in the 18- to 25-years age group. We recognise that in doing this we missed studies involving children and young people aged under 18 years; in principle we would have liked to have included studies involving such children and young people, but, given the resources at our disposal and the additional resource, ethical and governance issues this would have entailed, we regretfully concluded that this would not be feasible for our study. In terms of the four studies with parental and young persons’ involvement that we did include, we have not done a separate analysis of their data, but the themes emerging from those case studies were consistent with our other case studies and contributed to our overall analysis.

In terms of the initial objectives, we successfully recruited the sample of eight diverse case studies and collected and analysed data from them (objective 1). As intended, we identified the outcomes of involvement from multiple stakeholders‘ perspectives, although we did not get as many research partners‘ perspectives as we would have liked – see limitations below (objective 2). It was more difficult than expected to track the impact of public involvement from project inception through to completion (objective 3), as all of our projects turned out to have longer time scales than our own. Even to track involvement over a stage of a case study research project proved difficult, as the research usually did not fall into neatly staged time periods and one study had no involvement activity over the study period.

Nevertheless, we were able to track seven of the eight case studies prospectively and in real time over time periods of up to 9 months, giving us an unusual window on involvement processes that have previously mainly been observed retrospectively. We were successful in comparing the contextual factors, mechanisms and outcomes associated with public involvement from different stakeholders‘ perspectives and costing the different mechanisms for public involvement (objective 4). We only partly achieved our final objective of undertaking a consensus exercise among stakeholders to assess the merits of the realist evaluation approach and our approach to the measurement and valuation of economic costs of public involvement in research (objective 5). A final consensus event was held, where very useful discussion and amendment of our theory of public involvement took place, and the economic approach was discussed and helpfully critiqued by participants. However, as our earlier discussions developed more fully than expected, we decided to let them continue rather than interrupt them in order to run the final exercise to assess the merits of the realist evaluation approach. We did, however, test our analysis with all our case study participants by sending a draft of this final report for comment. We received a number of helpful comments and corrections but no disagreement with our overall analysis.

• What were the limitations of our study?

Realist evaluation is a relatively new approach and we recognise that there were a number of limitations to our study. We sought to follow the approach recommended by Pawson, but we acknowledge that we were not always able to do so. In particular, our theory of public involvement in research evolved over time and initially was not as tightly framed in terms of a testable hypothesis as Pawson recommends. In his latest book Pawson strongly recommends that outcomes should be measured with quantitative data, 17 but we did not do so; we were not aware of the existence of quantitative data or tools that would enable us to collect such data to answer our research questions. Even in terms of qualitative data, we did not capture as much information on outcomes as we initially envisaged. There were several reasons for this. The most important was that capturing outcomes in public involvement is easier the more operational the focus of involvement, and more difficult the more strategic the involvement. Thus, it was relatively easy to see the impact of a patient panel on the redesign of a recruitment leaflet but harder to capture the impact of research partners in a multidisciplinary team discussion of research design.

We also found it was sometimes more difficult to engage research partners as participants in our research than researchers or research managers. On reflection this is not surprising. Research partners are generally motivated to take part in research relevant to their lived experience of a health condition or situation, whereas our research was quite detached from their lived experience; in addition people had many constraints on their time, so getting involved in our research as well as their own was likely to be a burden too far for some. Researchers clearly also face significant time pressures but they had a more direct interest in our research, as they are obliged to engage with public involvement to satisfy research funders such as the NIHR. Moreover, researchers were being paid by their employers for their time during interviews with us, while research partners were not paid by us and usually not paid by their research teams. Whatever the reasons, we had less response from research partners than researchers or research managers, particularly for the third round of data collection; thus we have fewer data on outcomes from research partners‘ perspectives and we need to be aware of a possible selection bias towards more engaged research partners. Such a bias could have implications for our findings; for example payment might have been a more important motivating factor for less engaged advisory group members.

There were a number of practical difficulties we encountered. One challenge was when to recruit the case studies. We recruited four of our eight case studies prior to the full application, but this was more than 1 year before our project started and 15 months or more before data collection began. In this intervening period, we found that the time scales of some of the case studies were no longer ideal for our project and we faced the choice of whether to continue with them, although this timing was not ideal, or seek at a late moment to recruit alternative ones. One of our case studies ultimately undertook no involvement activity over the study period, so we obtained fewer data from it, and it contributed relatively little to our analysis. Similarly, one of the four case studies we recruited later experienced some delays itself in beginning and so we had a more limited period for data collection than initially envisaged. Research governance approvals took much longer than expected, particularly as we had to take three of our research partners, who were going to collect data within NHS projects, through the research passport process, which essentially truncated our data collection period from 1 year to 9 months. Even if we had had the full year initially envisaged for data collection, our conclusion with hindsight was that this was insufficiently long. To compare initial plans and intentions for involvement with the reality of what actually happened required a longer time period than a year for most of our case studies.

In the light of the importance we have placed on the commitment of PIs, there is an issue of potential selection bias in the recruitment of our sample. As our sampling strategy explicitly involved a networking approach to PIs of projects where we thought some significant public involvement was taking place, we were likely (as we did) to recruit enthusiasts and, at worst, those non-committed who were at least open to the potential value of public involvement. There were, unsurprisingly, no highly sceptical PIs in our sample. We have no data therefore on how public involvement may work in research where the PI is sceptical but may feel compelled to undertake involvement because of funder requirements or other factors.

• What would we do differently next time?

If we were to design this study again, there are a number of changes we would make. Most importantly we would go for a longer time period to be able to capture involvement through the whole research process from initial design through to dissemination. We would seek to recruit far more potential case studies in principle, so that we had greater choice of which to proceed with once our study began in earnest. We would include case studies from the application stage to capture the important early involvement of research partners in the initial design period. It might be preferable to research a smaller number of case studies, allowing a more in-depth ethnographic approach. Although challenging, it would be very informative to seek to sample sceptical PIs. This might require a brief screening exercise of a larger group of PIs on their attitudes to and experience of public involvement.

The economic evaluation was challenging in a number of ways, particularly in seeking to obtain completed resource logs from case study research partners. Having a 2-week data collection period was also problematic in a field such as public involvement, where activity may be very episodic and infrequent. Thus, collecting economic data alongside other case study data in a more integrated way, and particularly with interviews and more ethnographic observation of case study activities, might be advantageous. The new budgeting tool developed by INVOLVE and the MHRN may provide a useful resource for future economic evaluations. 23

We have learned much from the involvement of research partners in our research team and, although many aspects of our approach worked well, there are some things we would do differently in future. Even though we included substantial resources for research partner involvement in all aspects of our study, we underestimated how time-consuming such full involvement would be. We were perhaps overambitious in trying to ensure such full involvement with the number of research partners and the number and complexity of the case studies. We were also perhaps naive in expecting all the research partners to play the same role in the team; different research partners came with different experiences and skills, and, like most of our case studies, we might have been better to be less prescriptive and allow the roles to develop more organically within the project.

• Implications for research practice and funding

If one of the objectives of R&D policy is to increase the extent and effectiveness of public involvement in research, then a key implication of this research is the importance of influencing PIs to value public involvement in research or to delegate to other senior colleagues in leading on involvement in their research. Training is unlikely to be the key mechanism here; senior researchers are much more likely to be influenced by peers or by their personal experience of the benefits of public involvement. Early career researchers may be shaped by training but again peer learning and culture may be more influential. For those researchers sceptical or agnostic about public involvement, the requirement of funders is a key factor that is likely to make them engage with the involvement agenda. Therefore, funders need to scrutinise the track record of research teams on public involvement to ascertain whether there is any evidence of commitment or leadership on involvement.

One of the findings of the economic analysis was that PIs have consistently underestimated the costs of public involvement in their grant applications. Clearly the field will benefit from the guidance and budgeting tool recently disseminated by MHRN and INVOLVE. It was also notable that there was a degree of variation in the real costs of public involvement and that effective involvement is not necessarily costly. Different models of involvement incur different costs and researchers need to be made aware of the costs and benefits of these different options.

One methodological lesson we learned was the impact that conducting this research had on some participants’ reflection on the impact of public involvement. Particularly for research staff, the questions we asked sometimes made them reflect upon what they were doing and change aspects of their approach to involvement. Thus, the more the NIHR and other funders can build reporting, audit and other forms of evaluation on the impact of public involvement directly into their processes with PIs, the more likely such questioning might stimulate similar reflection.

• Recommendations for further research

There are a number of gaps in our knowledge around public involvement in research that follow from our findings, and would benefit from further research, including realist evaluation to extend and further test the theory we have developed here:

• In-depth exploration of how PIs become committed to public involvement and how to influence agnostic or sceptical PIs would be very helpful. Further research might compare, for example, training with peer-influencing strategies in engendering PI commitment. Research could explore the leadership role of other research team members, including research partners, and how collective leadership might support effective public involvement.
• More methodological work is needed on how to robustly capture the impact and outcomes of public involvement in research (building as well on the PiiAF work of Popay et al. 51 ), including further economic analysis and exploration of impact when research partners are integral to research teams.
• Research to develop approaches and carry out a full cost–benefit analysis of public involvement in research would be beneficial. Although methodologically challenging, it would be very useful to conduct some longer-term studies which sought to quantify the impact of public involvement on such key indicators as participant recruitment and retention in clinical trials.
• It would also be helpful to capture qualitatively the experiences and perspectives of research partners who have had mixed or negative experiences, since they may be less likely than enthusiasts to volunteer to participate in studies of involvement in research such as ours. Similarly, further research might explore the (relatively rare) experiences of marginalised and seldom-heard groups involved in research.
• Payment for public involvement in research remains a contested issue with strongly held positions for and against; it would be helpful to further explore the value research partners and researchers place on payment and its effectiveness for enhancing involvement in and impact on research.
• A final relatively narrow but important question that we identified after data collection had finished is: what is the impact of the long periods of relative non-involvement following initial periods of more intense involvement for research partners in some types of research, particularly clinical trials?

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• Cite this Page Evans D, Coad J, Cottrell K, et al. Public involvement in research: assessing impact through a realist evaluation. Southampton (UK): NIHR Journals Library; 2014 Oct. (Health Services and Delivery Research, No. 2.36.) Chapter 9, Conclusions and recommendations for future research.
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Work of the Past, Work of the Future

Labor markets in U.S. cities today are vastly more educated and skill-intensive than they were five decades ago. Yet, urban non-college workers perform substantially less skilled work than decades earlier. This deskilling reflects the joint effects of automation and international trade, which have eliminated the bulk of non-college production, administrative support, and clerical jobs, yielding a disproportionate polarization of urban labor markets. The unwinding of the urban non-college occupational skill gradient has, I argue, abetted a secular fall in real non-college wages by: (1) shunting non-college workers out of specialized middle-skill occupations into low-wage occupations that require only generic skills; (2) diminishing the set of non-college workers that hold middle-skill jobs in high-wage cities; and (3) attenuating, to a startling degree, the steep urban wage premium for non-college workers that prevailed in earlier decades. Changes in the nature of work—many of which are technological in origin—have been more disruptive and less beneficial for non-college than college workers.

I am indebted to Daron Acemoglu, David Dorn, Amy Finkelstein, Juliette Fournier, Claudia Goldin, Colin Gray, Gordon Hanson, Lawrence Katz, James Poterba, Anna Salomons, and Evan Soltas for ideas, insights, and critiques that enriched this work. Anne Beck, Emiel Van Bezooijen, Pepe (Jose Ignacio Velarde) Morales, Edwin Song, and Sunny (Liang) Tan provided abundant and ingenious hard work to put these ideas to the test. I thank Accenture LLP, the IBM Global Universities Program, the Schmidt Futures Foundation, and the Smith Richardson Foundation, for generous financial support. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

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• Cities' Bright Lights and Big Promises Dim for the Less-Educated Author(s): David Autor Non-college-educated workers in cities are far less likely to work in middle-skill occupations than in the past, and the urban wage...

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ORIGINAL RESEARCH article

Evaluation of vegetable green logistics in lanling county of china based on dematel-anp-fce model provisionally accepted.

• 1 Qingdao Agricultural University, China

The final, formatted version of the article will be published soon.

Introduction: Due to the problems of greenhouse gas emissions, noise pollution, and vegetable waste pollution during the transportation of vegetables, it is not conducive to the sustainable development of the environment. To mitigate the pollution of the environment during transportation, vegetable green logistics plays a pivotal role in promoting both environmental sustainability and high-quality economic development. Therefore, it is crucial to evaluate the development of vegetable green logistics.Methods: This research builds a DEMATEL-ANP-FCE model to scientifically assess the development of vegetable green logistics in Lanling County. In the first place, the model uses DEMATEL to verify the relationship and degree of influence between the primary indexes. In the second place, the ANP approach with Super Decisions software to determine the weights of the indexes at each level. Lastly, evaluating and scoring vegetable green logistics in Lanling County based on FCE.Results and Discussion: The results of the study show that there is an interaction relationship between the primary evaluation indexes, and its evaluation score is low, the vegetable logistics in Lanling County has not reached the degree of greening. Accordingly, the evaluation results obtained by the DEMATEL-ANP-FCE model in this work are in line with the actual situation of vegetable green logistics in Lanling County, which verifies that the model has good applicability. Moreover, managerial contributions for promoting the development of vegetable green logistics in Lanling County are put forward in response to the evaluation situation. expecting to enhance the greening level of vegetable logistical development and advance agricultural sustainability. Finally, all four questions raised in this paper are well addressed. This study also provides a new perspective and evaluation model for future related research.

Keywords: Lanling County1, vegetables2, green logistics3, DEMATEL-ANP-FCE model4, evaluation5

Received: 08 Mar 2024; Accepted: 26 Apr 2024.

Copyright: © 2024 Wang, Liu, Lu and Cui. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Prof. hongzhi Wang, Qingdao Agricultural University, Qingdao, Shandong Province, China Miss. Zhaoli Liu, Qingdao Agricultural University, Qingdao, Shandong Province, China Dr. Hailong Cui, Qingdao Agricultural University, Qingdao, Shandong Province, China

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Focusing on Los Angeles, this paper introduces the idea of “transit vanity projects” as a new way of understanding the relationship between space and social (in)justice. It proposes five qualities that define a transit vanity project: they are primarily focused on their stylistic quality; they are functionally unnecessary; they are isolated and disconnected from other transit; they serve a small, bounded population; they reflect and reproduce a selective understanding of the past or future. The paper contrasts the case studies of The Getty Tram, The Grove Trolley, and the Angel’s Flight Funicular with the embodied realities of using public transit in LA. It is based upon 3 weeks spent in Los Angeles relying only on public transit, a total of 75 hours on the bus and train. The resulting ethnographic observations provided me a first-hand understanding of how the public transportation services upon which poor and working-class Angelenos depend differ from transit vanity projects. The paper's analysis draws upon work done in the field spatial justice studies, and specifically upon Edward Soja’s 2010 book Seeking Spatial Justice. Spatial justice, in this context, refers to the principle that everyone has a right to safe and reliable transportation. The paper analyzes transit projects on a continuum from brutal or realistic necessity to vanity transit, and explains how these opposing approaches towards transit development should be recognized in future transit planning in order to create more equitable public transit.

Public transit, Spatial justice, Urban sociology

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Johnson, Luca, "Transit Vanity Projects in Los Angeles" (2024). Research Symposium . 23. https://digitalcommons.oberlin.edu/researchsymp/2024/presentations/23

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