future research definition

What is 'futures studies' and how can it help us improve our world?

A futurist explains.

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• Futures studies is the systematic study of possible, probable and preferable futures.
• It can be used to help leaders and communities manage uncertainties and increase their resilience and innovation.
• We spoke with futurist Dr. Stuart Candy about the latest developments in this field and how it can help us solve pressing global challenges.

Futures studies, or futures research, is the systematic study of possible, probable and preferable futures. The field has broadened into an exploration of alternative futures and deepened to investigate the worldviews and mythologies that underlie our collective prospects.

Governments and leaders around the world are increasingly looking to systemic foresight to manage uncertainty and build resilience. For example, the government of the United Arab Emirates has a Ministry for the Future , and the UN Secretary General recently proposed a global Summit of the Future in 2023.

Futurists collaborate with businesses, governments and other partners to explore future scenarios and help people think about –– and prepare for –– things that haven’t happened yet. Dr. Stuart Candy, USC Berggruen Fellow and Associate Professor of Design at Carnegie Mellon University, is a professional futurist and experience designer known for pioneering experiential futures , a range of practices for bringing possible scenarios to life through tangible artifacts and immersive storytelling.

As we welcome Dr. Candy into the Forum Expert Network , we discuss his motivations to explore this domain, what developments have him most excited, what he wishes people knew about his work, and how we could make the concept of the future more inclusive and accountable.

What drew you to the field of foresight and speculative design?

I happened across the foresight field, or futures studies, back in high school. It was immediately inspiring to me –– wide-ranging and imaginative, analytically insightful, ethically engaged and practically applied. However, over some years of working with foresight in government, I found that policymakers had limited capacity to envision alternative futures, and even where the field had a certain currency, its legacy methods weren’t necessarily having great impact.

So, I began re-visiting longstanding creative interests of mine that had perhaps begun to fall away during my formal education in history and law –– making things, films, theatre, games –– and asked: how might thinking about futures be made more accessible and compelling through these modes?

What began as a trickle has, over time, become more like a flood: practitioners, scholars, activists, and others around the world are now working in countless different ways on these intersections. A range of these are documented in our recent collection Design and Futures .

What global challenge does your work address?

The central challenge this work addresses could certainly be called global, but equally, it's psychological. It is an aspect of the human condition that exists at every scale of action and institution, from the personal to the planetary. That challenge is: how to engage the various possible worlds we might find ourselves in later –– not just intellectually, in the abstract, but more deeply as potential lived realities? The field traditionally has been very strong on frameworks for organizing thought, but less so on converting those anticipations into embodied insights and making them stick.

Design and futures were largely non-overlapping worlds when we started joining the dots in the mid-2000s, and a decade ago, the term "speculative design" wasn’t even in the mix. However, new framings that speak to different groups are part of the vitality of how the work has taken off, and I’m glad to help people explore futures more effectively under any banner. I have now spent well over a decade bringing futures often into new spaces, especially by growing and gardening those connections between foresight and media, arts, and design, which is intended to help acculturate –– build into our cultures –– these ways of thinking.

I would add that to my mind, designers have special duties because they create fragments of the future on behalf of everyone. Similarly, to the extent that a leader in any context has an outsized capacity to shape things, they have a commensurate responsibility to practice and enable high quality futures thinking.

What is the most critical challenge that you face as a futurist?

Perhaps the most critical challenge is the need for futures literacy in the culture. Take politics and journalism, institutions that inherently deal with the future but that do not have a well-established habit of " rigorous imagining ". Lack of futures literacy is apparent when otherwise discerning journalists demand that you provide predictions for their piece on "the future" (note the singular form) of any issue they are covering.

It is also apparent when policymakers, technologists, pundits, and other public figures issue a constant stream of authoritative-sounding forecasts, but no one checks back later to see how they fared, or asks how this diet of images of the future might be exerting influence and serving some interests more than others. Raising collective futures literacy, or "social foresight", not just across organizations but also throughout society, is an essential way for us all to navigate the predicaments that we face as a species.

What is the most exciting new development in collective foresight and why?

The greatest development right now is the rapid widening of those who initiate, run, and take part in foresight work. It’s incredibly exciting. People in various sectors, bringing diverse cultural, organizational, and disciplinary backgrounds and sensibilities, are picking up the tools to build strategic foresight and experiential futures approaches in particular, and adapting them for their own contexts and needs.

There’s more participation and interest than there has ever been, which is as thrilling as it is overdue. Organization leaders and governments, too, are taking the cue to improve their foresight approaches which is necessary in this time.

How are emerging technologies in the sphere of media (such as AR/VR) enabling this?

Playing with emerging media tools and technologies is a fun and productive aspect of opening up new ways of thinking through experiential futures. For instance, for the World Economic Forum’s own Global Technology Governance Summit this year, with my Carnegie Mellon students we designed online media –– websites and podcasts that behaved as if they were "from" decades out, each examining technology governance dilemmas and interventions that might be waiting in the wings.

Another project, for the UNESCO Futures Summit, pictured a future after the Sustainable Development Goals are achieved, via a digital showcase of world-changing organizations and initiatives in the year 2045. Here, we created a digital trade show for visitors to wander and explore at their leisure, using an online collaboration software Miro . Earlier this year, we created TikToks from the future , just as an experiment. The result was a range of wonderfully mundane, sometimes provocative or hilarious, vignettes of everyday futures, made with zero budget, and exploring food, autonomous vehicles, real estate, travel, and more.

Yet, the medium itself does not necessarily need to be cutting-edge or experimental to be effective. To support the UN Development Programme’s annual innovation gathering, mid-pandemic, my collaborators and I created physical artifacts from alternative futures for global development and sent them in the mail for people to receive at their homes, ahead of a global event that took place entirely online.

Every storytelling approach offers different ways to think and feel into what alternative scenarios might be like. Since no one can visit the future to get hard information about it, we must use whatever it takes to stoke our collective imaginative and deliberative capacities.

What is most misunderstood about your work? What do you wish people knew?

The role of a futurist is more like that of an artist or writer than an accountant or lawyer. It’s as much an art or craft as a profession, and there are as many kinds of futurists as there are ways of thinking about the future. The tradition I identify with is notable for being radically imaginative, critical, inclusive, and democratic. And to me, taking words like "future" and "futurist" back from the ways they have been abused, pre-populated or colonized with a tremendous amount of baggage is part of the project in hand.

It could also be helpful for more people to be aware that experts in the field generally don’t call it " futurism " –– that word refers more to an art movement early last century that’s unrelated.

What has been the biggest impact of mapping futures?

Building the habit of mapping futures can be life changing. For institutions or organizations, it can really shift how they operate. Likewise at an individual level –– and it’s remarkable to get to see this among my students. I think a reason it can have such impact is that it’s a way of situating the "what" and "how" of daily effort within the larger "whys" in our lives. Investing in foresight capacity helps to knit vital day-to-day work to the meaningful longer-term and bigger-picture questions, and to keep those ties alive.

I believe the biggest collective impact of all this is unfolding right before our eyes, but it’s a large story, so you must look for it on a timescale of decades or generations rather than months or years. We, as humans, are learning how to codesign our futures. This is ultimately a transformation in culture and governance.

How can we democratize futures studies and make it more accessible?

Well, I love that question. It’s central to what we have been up to. My own approach to developing and socializing experiential futures widely has been to keep several hats at the ready, sometimes wearing more than one at once. As a creative, I devise projects and interventions to make particular questions, and new horizons of thought, available for particular occasions and audiences

As an educator, I learn from these experiments to devise new frameworks, and distribute them to emerging practitioners and whoever else can use them in their own context. And as a strategic consultant, I collaborate with organizations, governments and communities on their challenges to apply what we are learning, and show how it can work, which helps address those challenges while also earning greater legitimacy and visibility on behalf of a wider futures community, growing the audience of users and learners for the underlying practices.

If you’re wondering about what a broader "we" can do, just about every organization has potential to grow their foresight capacity , and make more space to engage with alternative futures, which can help support creativity and innovation on one hand as well as risk mitigation and resilience on the other.

One project we’ve developed over some years which I think exemplifies this hybrid activity rather well, is a card deck called The Thing From The Future . It’s a tool for diversifying and deepening imagination. We’ve used it with UN agencies and the International Red Cross, as well as the BBC, NASA JPL, US Conference of Mayors, Skoll World Forum and other partners all over the world. It is a game that has the purpose of lowering the bar to using imagination with skill, and having conversations that matter, but playfully.

The future is not just something that happens to us, it is something we have the ability to shape. And part of what is interesting is, the more people and institutions tune in, participate, and act, the truer this becomes.

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• GETTING STARTED
• Introduction
• FUNDAMENTALS
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• Research limitations
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FUTURE RESEARCH

Types of future research suggestion.

The Future Research section of your dissertation is often combined with the Research Limitations section of your final, Conclusions chapter. This is because your future research suggestions generally arise out of the research limitations you have identified in your own dissertation. In this article, we discuss six types of future research suggestion. These include: (1) building on a particular finding in your research; (2) addressing a flaw in your research; examining (or testing) a theory (framework or model) either (3) for the first time or (4) in a new context, location and/or culture; (5) re-evaluating and (6) expanding a theory (framework or model). The goal of the article is to help you think about the potential types of future research suggestion that you may want to include in your dissertation.

Before we discuss each of these types of future research suggestion, we should explain why we use the word examining and then put or testing in brackets. This is simply because the word examining may be considered more appropriate when students use a qualitative research design; whereas the word testing fits better with dissertations drawing on a quantitative research design. We also put the words framework or model in brackets after the word theory . We do this because a theory , framework and model are not the same things. In the sections that follow, we discuss six types of future research suggestion.

Addressing research limitations in your dissertation

Building on a particular finding or aspect of your research, examining a conceptual framework (or testing a theoretical model) for the first time, examining a conceptual framework (or testing a theoretical model) in a new context, location and/or culture.

• Expanding a conceptual framework (or testing a theoretical model)

Re-evaluating a conceptual framework (or theoretical model)

In the Research Limitations section of your Conclusions chapter, you will have inevitably detailed the potential flaws (i.e., research limitations) of your dissertation. These may include:

An inability to answer your research questions

Theoretical and conceptual problems

Limitations of your research strategy

Problems of research quality

Identifying what these research limitations were and proposing future research suggestions that address them is arguably the easiest and quickest ways to complete the Future Research section of your Conclusions chapter.

Often, the findings from your dissertation research will highlight a number of new avenues that could be explored in future studies. These can be grouped into two categories:

Your dissertation will inevitably lead to findings that you did not anticipate from the start. These are useful when making future research suggestions because they can lead to entirely new avenues to explore in future studies. If this was the case, it is worth (a) briefly describing what these unanticipated findings were and (b) suggesting a research strategy that could be used to explore such findings in future.

Sometimes, dissertations manage to address all aspects of the research questions that were set. However, this is seldom the case. Typically, there will be aspects of your research questions that could not be answered. This is not necessarily a flaw in your research strategy, but may simply reflect that fact that the findings did not provide all the answers you hoped for. If this was the case, it is worth (a) briefly describing what aspects of your research questions were not answered and (b) suggesting a research strategy that could be used to explore such aspects in future.

You may want to recommend that future research examines the conceptual framework (or tests the theoretical model) that you developed. This is based on the assumption that the primary goal of your dissertation was to set out a conceptual framework (or build a theoretical model). It is also based on the assumption that whilst such a conceptual framework (or theoretical model) was presented, your dissertation did not attempt to examine (or test) it in the field . The focus of your dissertations was most likely a review of the literature rather than something that involved you conducting primary research.

Whilst it is quite rare for dissertations at the undergraduate and master's level to be primarily theoretical in nature like this, it is not unknown. If this was the case, you should think about how the conceptual framework (or theoretical model) that you have presented could be best examined (or tested) in the field . In understanding the how , you should think about two factors in particular:

What is the context, location and/or culture that would best lend itself to my conceptual framework (or theoretical model) if it were to be examined (or tested) in the field?

What research strategy is most appropriate to examine my conceptual framework (or test my theoretical model)?

If the future research suggestion that you want to make is based on examining your conceptual framework (or testing your theoretical model) in the field , you need to suggest the best scenario for doing so.

More often than not, you will not only have set out a conceptual framework (or theoretical model), as described in the previous section, but you will also have examined (or tested) it in the field . When you do this, focus is typically placed on a specific context, location and/or culture.

If this is the case, the obvious future research suggestion that you could propose would be to examine your conceptual framework (or test the theoretical model) in a new context, location and/or culture. For example, perhaps you focused on consumers (rather than businesses), or Canada (rather than the United Kingdom), or a more individualistic culture like the United States (rather than a more collectivist culture like China).

When you propose a new context, location and/or culture as your future research suggestion, make sure you justify the choice that you make. For example, there may be little value in future studies looking at different cultures if culture is not an important component underlying your conceptual framework (or theoretical model). If you are not sure whether a new context, location or culture is more appropriate, or what new context, location or culture you should select, a review the literature will often help clarify where you focus should be.

Expanding a conceptual framework (or theoretical model)

Assuming that you have set out a conceptual framework (or theoretical model) and examined (or tested) it in the field , another series of future research suggestions comes out of expanding that conceptual framework (or theoretical model).

We talk about a series of future research suggestions because there are so many ways that you can expand on your conceptual framework (or theoretical model). For example, you can do this by:

Examining constructs (or variables) that were included in your conceptual framework (or theoretical model) but were not focused.

Looking at a particular relationship aspect of your conceptual framework (or theoretical model) further.

Adding new constructs (or variables) to the conceptual framework (or theoretical model) you set out (if justified by the literature).

It would be possible to include one or a number of these as future research suggestions. Again, make sure that any suggestions you make have are justified , either by your findings or the literature.

With the dissertation process at the undergraduate and master's level lasting between 3 and 9 months, a lot a can happen in between. For example, a specific event (e.g., 9/11, the economic crisis) or some new theory or evidence that undermines (or questions) the literature (theory) and assumptions underpinning your conceptual framework (or theoretical model). Clearly, there is little you can do about this. However, if this happens, reflecting on it and re-evaluating your conceptual framework (or theoretical model), as well as your findings, is an obvious source of future research suggestions.

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• Published: 27 January 2022

The future of human behaviour research

• Janet M. Box-Steffensmeier 1 ,
• Jean Burgess 2 , 3 ,
• Maurizio Corbetta 4 , 5 ,
• Kate Crawford 6 , 7 , 8 ,
• Esther Duflo 9 ,
• Laurel Fogarty 10 ,
• Alison Gopnik 11 ,
• Sari Hanafi 12 ,
• Mario Herrero 13 ,
• Ying-yi Hong 14 ,
• Yasuko Kameyama 15 ,
• Tatia M. C. Lee 16 ,
• Gabriel M. Leung 17 , 18 ,
• Daniel S. Nagin 19 ,
• Anna C. Nobre 20 , 21 ,
• Merete Nordentoft 22 , 23 ,
• Aysu Okbay 24 ,
• Andrew Perfors 25 ,
• Laura M. Rival 26 ,
• Cassidy R. Sugimoto 27 ,
• Bertil Tungodden 28 &
• Claudia Wagner 29 , 30 , 31  

Nature Human Behaviour volume  6 ,  pages 15–24 ( 2022 ) Cite this article

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Human behaviour is complex and multifaceted, and is studied by a broad range of disciplines across the social and natural sciences. To mark our 5th anniversary, we asked leading scientists in some of the key disciplines that we cover to share their vision of the future of research in their disciplines. Our contributors underscore how important it is to broaden the scope of their disciplines to increase ecological validity and diversity of representation, in order to address pressing societal challenges that range from new technologies, modes of interaction and sociopolitical upheaval to disease, poverty, hunger, inequality and climate change. Taken together, these contributions highlight how achieving progress in each discipline will require incorporating insights and methods from others, breaking down disciplinary silos.

Genuine progress in understanding human behaviour can only be achieved through a multidisciplinary community effort. Five years after the launch of Nature Human Behaviour , twenty-two leading experts in some of the core disciplines within the journal’s scope share their views on pressing open questions and new directions in their disciplines. Their visions provide rich insight into the future of research on human behaviour.

Artificial intelligence

Kate Crawford

Much has changed in artificial intelligence since a small group of mathematicians and scientists gathered at Dartmouth in 1956 to brainstorm how machines could simulate cognition. Many of the domains that those men discussed — such as neural networks and natural language processing — remain core elements of the field today. But what they did not address was the far-reaching social, political, legal and ecological effects of building these systems into everyday life: it was outside their disciplinary view.

Since the mid-2000s, artificial intelligence (AI) has rapidly expanded as a field in academia and as an industry, and now a handful of powerful technology corporations deploy these systems at a planetary scale. There have been extraordinary technical innovations, from real-time language translation to predicting the 3D structures of proteins 1 , 2 . But the biggest challenges remain fundamentally social and political: how AI is widening power asymmetries and wealth inequality, and creating forms of harm that need to be prioritized, remedied and regulated.

The most urgent work facing the field today is to research and remediate the costs and consequences of AI. This requires a deeper sociotechnical approach that can contend with the complex effect of AI on societies and ecologies. Although there has been important work done on algorithmic fairness in recent years 3 , 4 , not enough has been done to address how training data fundamentally skew how AI models interpret the world from the outset. Second, we need to address the human costs of AI, which range from discrimination and misinformation to the widespread reliance on underpaid labourers (such as the crowd-workers who train AI systems for as little as US $2 per hour) 5 . Third, there must be a commitment to reversing the environmental costs of AI, including the exceptionally high energy consumption of the current large computational models, and the carbon footprint of building and operating modern tensor processing hardware 6 . Finally, we need strong regulatory and policy frameworks, expanding on the EU’s draft AI Act of 2021.

By building a more interdisciplinary and inclusive AI field, and developing a more rigorous account of the full impacts of AI, we give engineers and regulators alike the tools that they need to make these systems more sustainable, equitable and just.

Kate Crawford is Research Professor at the Annenberg School, University of Southern California, Los Angeles, CA, USA; Senior Principal Researcher at Microsoft Research New York, New York, NY, USA; and the Inaugural Visiting Chair of AI and Justice at the École Normale Supérieure, Paris, France.

Anthropology

Laura M. Rival

The field of anthropology faces fundamental questions about its capacity to intervene more effectively in political debates. How can we use the knowledge that we already have to heal the imagined whole while keeping people in synchrony with each other and with the world they aspire to create for themselves and others?

The economic systems that sustain modern life have produced pernicious waste cultures. Globalization has accelerated planetary degradation and global warming through the continuous release of toxic waste. Every day, like millions of others, I dutifully clean and prepare my waste for recycling. I know it is no more than a transitory measure geared to grant manufacturers time to adjust and adapt. Reports that most waste will not be recycled, but dumped or burned, upset me deeply. How can anthropology remain a critical project in the face of such orchestrated cynicism, bad faith and indifference? How should anthropologists deploy their skills and bring a sense of shared responsibility to the task of replenishing the collective will?

To help to find answers to these questions, anthropologists need to radically rethink the ways in which we describe the processes and relations that tie communities to their environments. The extinction of experience (loss of direct contact with nature) that humankind currently suffers is massive, but not irreversible. New forms of storytelling have successfully challenged modernist myths, particularly their homophonic promises 7 . But there remain persistent challenges, such as the seductive and rampant power of one-size-fits-all progress, and the actions of elites, who thrive on emulation, and in doing so fuel run-away consumerism.

To combat these challenges, I simply reassert that ‘nature’ is far from having outlasted its historical utility. Anthropologists must join forces and reanimate their common exploration of the immense possibilities contained in human bodies and minds. No matter how overlooked or marginalized, these natural potentials hold the key to what keeps life going.

Laura M. Rival is Professor of Anthropology of Nature, Society and Development, ODID and SAME, University of Oxford, Oxford, UK .

Communication and media studies

Jean Burgess

The communication and media studies field has historically been animated by technological change. In the process, it has needed to navigate fundamental tensions: communication can be understood as both transmission (of information), and as (social) ritual 8 ; relatedly, media can be understood as both technology and as culture 9 .

The most important technological change over the past decade has been the ‘platformization’ 10 of the media environment. Large digital platforms owned by the world’s most powerful technology companies have come to have an outsized and transformative role in the transmission (distribution) of information, and in mediating social practices (whether major events or intimate daily routines). In response, digital methods have transformed the field. For example, advances in computational techniques enabled researchers to study patterns of communication on social media, leading to disciplinary trends such as the quantitative description of ‘hashtag publics’ in the mid-2010s 11 .

Platforms’ uses of data, algorithms and automation for personalization, content moderation and governance constitute a further major shift, giving rise to new methods (such as algorithmic audits) that go well beyond quantitative description 12 . But platform companies have had a patchy — at times hostile — relationship to independent research into their societal role, leading to data lockouts and even public attacks on researchers. It is important in the interests of public oversight and open science that we coordinate responses to such attempts to suppress research 13 , 14 .

As these processes of digital transformation continue, new connections between the humanities and technical disciplines will be necessary, giving rise to a new wave of methodological innovation. This next phase will also require more hybrid (qualitative and quantitative; computational and critical) methods 15 , not only to get around platform lockouts but also to ensure more careful attention is paid to how the new media technologies are used and experienced in everyday life. Here, innovative approaches such as the use of data donations can both aid the ‘platform observability’ 16 that is essential to accountability, and ensure that our research involves the perspectives of diverse audiences.

Jean Burgess is Professor of Digital Media at the School of Communication and Digital Media Research Centre (DMRC), Queensland University of Technology, Brisbane, Queensland Australia; and Associate Director at the Australian Research Council Centre of Excellence for Automated Decision-Making and Society (ADM+S), Melbourne, Victoria, Australia .

Computational social science

Claudia Wagner

Computational social science has emerged as a discipline that leverages computational methods and new technologies to collect, model and analyse digital behavioural data in natural environments or in large-scale designed experiments, and combine them with other data sources (such as survey data).

While the community made critical progress in enhancing our understanding about empirical phenomena such as the spread of misinformation 17 and the role of algorithms in curating misinformation 18 , it has focused less on questions about the quality and accessibility of data, the validity, reliability and reusability of measurements, the potential consequences of measurements and the connection between data, measurement and theory.

I see the following opportunities to address these issues.

First, we need to establish privacy-preserving, shared data infrastructures that collect and triangulate survey data with scientifically motivated organic or designed observational data from diverse populations 19 . For example, longitudinal online panels in which participants allow researchers to track their web browsing behaviour and link these traces to their survey answers will not only facilitate substantive research on societal questions but also enable methodological research (for example, on the quality of different data sources and measurement models), and contribute to the reproducibility of computational social science research.

Second, best practices and scientific infrastructures are needed for supporting the development, evaluation and re-use of measurements and the critical reflection on potentially harmful consequences of measurements 20 . Social scientists have developed such best practices and infrastructural support for survey measurements to avoid using instruments for which the validity is unclear or even questionable, and to support the re-usability of survey scales. I believe that practices from survey methodology and other domains, such as the medical industry, can inform our thinking here.

Finally, the fusion of algorithmic and human behaviour invites us to rethink the various ways in which data, measurements and social theories can be connected 20 . For example, product recommendations that users receive are based on measurements of users’ interests and needs: however, users and measurements are not only influenced by those recommendations, but also influence them in turn. As a community we need to develop research designs and environments that help us to systematically enhance our understanding of those feedback loops.

Claudia Wagner is Head of Computational Social Science Department at GESIS – Leibniz Institute for the Social Sciences, Köln, Germany; Professor for Applied Computational Social Sciences at RWTH Aachen University, Aachen, Germany; and External Faculty Member of the Complexity Science Hub, Vienna, Austria .

Criminology

Daniel S. Nagin

Disciplinary silos in path-breaking science are disappearing. Criminology has had a longstanding tradition of interdisciplinarity, but mostly in the form of an uneasy truce of research from different disciplines appearing side-by-side in leading journals — a scholarly form of parallel play. In the future, this must change because the big unsolved challenges in criminology will require cooperation among all of the social and behavioural sciences.

These challenges include formally merging the macro-level themes emphasized by sociologists with the micro-, individual-level themes emphasized by psychologists and economists. Initial steps have been made by economists who apply game theory to model crime-relevant social interactions, but much remains to be done in building models that explain the formation and destruction of social trust, collective efficacy and norms, as they relate to legal definitions of criminal behaviour.

A second opportunity concerns the longstanding focus of criminology on crimes involving the physical taking of property and interpersonal physical violence. These crimes are still with us, but — as the daily news regularly reports — the internet has opened up broad new frontiers for crime that allow for thefts of property and identities at a distance, forms of extortion and human trafficking at a massive scale (often involving untraceable transactions using financial vehicles such as bitcoin) and interpersonal violence without physical contact. This is a new and largely unexplored frontier for criminological research that criminologists should dive into in collaboration with computer scientists who already are beginning to troll these virgin scholarly waters.

The final opportunity I will note also involves drawing from computer science, the primary home of what has come to be called machine learning. It is important that new generations of criminologists become proficient with machine learning methods and also collaborate with its creators. Machine learning and related statistical methods have wide applicability in both the traditional domains of criminological research and new frontiers. These include the use of prediction tools in criminal justice decision-making, which can aid in crime detection, and the prevention and measuring of crime both online and offline, but also have important implications for equity and fairness due to their consequential nature.

Daniel S. Nagin is Teresa and H. John Heinz III University Professor of Public Policy and Statistics at the Heinz College of Information Systems and Public Policy, Carnegie Mellon University, Pittsburgh, PA, USA .

Behavioural economics

Bertil Tungodden

Behavioural and experimental economics have transformed the field of economics by integrating irrationality and nonselfish motivation in the study of human behaviour and social interaction. A richer foundation of human behaviour has opened many new exciting research avenues, and I here highlight three that I find particularly promising.

Economists have typically assumed that preferences are fixed and stable, but a growing literature, combining field and laboratory experimental approaches, has provided novel evidence on how the social environment shapes our moral and selfish preferences. It has been shown that prosocial role models make people less selfish 21 , that early-childhood education affects the fairness views of children 22 and that grit can be fostered in the correct classroom environment 23 . Such insights are important for understanding how exposure to different institutions and socialization processes influence the intergenerational transmission of preferences, but much more work is needed to gain systematic and robust evidence on the malleability of the many dimensions that shape human behaviour.

The moral mind is an important determinant of human behaviour, but our understanding of the complexity of moral motivation is still in its infancy. A growing literature, using an impartial spectator design in which study participants make consequential choices for others, has shown that people often disagree on what is morally acceptable. An important example is how people differ in their view of what is a fair inequality, ranging from the libertarian fairness view to the strict egalitarian fairness view 24 , 25 . An exciting question for future research is whether such moral differences reflect a concern for other moral values, such as freedom, or irrational considerations.

A third exciting development in behavioural and experimental economics is the growing set of global studies on the foundations of human behaviour 26 , 27 . It speaks to the major concern in the social sciences that our evidence is unrepresentative and largely based on studies with samples from Western, educated, industrialized, rich and democratic societies 28 . The increased availability of infrastructure for implementing large-scale experimental data collections and methodological advances carry promise that behavioural and experimental economic research will broaden our understanding of the foundations of human behaviour in the coming years.

Bertil Tungodden is Professor and Scientific Director of the Centre of Excellence FAIR at NHH Norwegian School of Economics, Bergen, Norway .

Development economics

Esther Duflo

The past three decades have been a wonderful time for development economics. The number of scholars, the number of publications and the visibility of the work has dramatically increased. Development economists think about education, health, firm growth, mental health, climate, democratic rules and much more. No topic seems off limits!

This progress is intimately connected with the explosion of the use of randomized controlled trials (RCTs) and, more generally, with the embrace of careful causal identification. RCTs have markedly transformed development economics and made it the field that it is today.

The past three decades (until the COVID-19 crisis) have also been very good for improving the circumstances of low-income people around the world: poverty rates have fallen; school enrolment has increased; and maternal and infant mortality has been halved. Although I would not dare imply that the two trends are causally related, one of the reasons for these improvements in the quality of life — even in countries where economic growth has been slow — is the greater focus on pragmatic solutions to the fundamental problems faced by people with few resources. In many countries, development economics researchers (particularly those working with RCTs) have been closely involved with policy-makers, helping them to develop, implement and test these solutions. In turn, this involvement has been a fertile ground for new questions, which have enriched the field.

I imagine future change will, once again, come from an unexpected place. One possible driver of innovation will come from this meeting between the requirements of policy and the intellectual ambition of researchers. This means that the new challenges of our planet must (and will) become the new challenges of development economics. Those challenges are, I believe, quite clear: rethinking social protection to be better prepared to face risks such as the COVID-19 pandemic; mitigating, but unfortunately also adapting to, climate changes; curbing pollution; and addressing gender, racial and ethnic inequality.

To address these critical issues, I believe the field will continue to rely on RCTs, but also start using more creatively (descriptively or in combination with RCTs) the huge amount of data that is increasingly available as governments, even in poor countries, digitize their operations. I cannot wait to be surprised by what comes next.

Esther Duflo is The Abdul Latif Jameel Professor of Poverty Alleviation and Development Economics at the Department of Economics, Massachusetts Institute of Technology, Cambridge MA, USA; and cofounder and codirector of the Abdul Latif Jameel Poverty Action Lab (J-PAL) .

Political science

Janet M. Box-Steffensmeier

Political science remains one of the most pluralistic disciplines and we are on the move towards engaged pluralism. This takes us beyond mere tolerance to true, sincere engagement across methods, methodologies, theories and even disciplinary boundaries. Engaged pluralism means doing the hard work of understanding our own research from the multiple perspectives of others.

More data are being collected on human behaviour than ever before and our advances in methods better address the inherent interdependencies of the data across time, space and context. There are new ways to measure human behaviour via text, image and video. Data creation can even go back in time. All these advancements bode well for the potential to better understand and predict behaviour. This ‘data century’ and ‘golden age of methods’ also hold the promise to bridge, not divide, political science, provided that there is engaged methodological pluralism. Qualitative methods provide unique insights and perspectives when joined with quantitative methods, as does a broader conception of the methodologies underlying and launching our research.

I remain a strong proponent of leveraging dynamics and focusing on heterogeneity in our research questions to advance our disciplines. Doing so brings in an explicit perspective of comparison around similarity and difference. Our questions, hypotheses and theories are often made more compelling when considering the dynamics and heterogeneity that emerges when thinking about time and change.

Striving for a better understanding of gender, race and ethnicity is driving deeper and fuller understandings of central questions in the social sciences. The diversity of the research teams themselves across gender, sex, race, ethnicity, first-generation status, religion, ideology, partisanship and cultures also pushes advancement. One area that we need to better support is career diversity. Supporting careers in government, non-profit organizations and industry, as well as academia, for graduate students will enhance our disciplines and accelerate the production of knowledge that changes the world.

Engaged pluralism remains a foundational key to advancement in political science. Engaged pluralism supports critical diversity, equity and inclusion work, strengthens political scientists’ commitment to democratic principles, and encourages civic engagement more broadly. It is an exciting time to be a social scientist.

Janet M. Box-Steffensmeier is Vernal Riffe Professor of Political Science, Professor of Sociology (courtesy) and Distinguished University Professor at the Department of Political Science, Ohio State University, Columbus OH, USA; and immediate past President of the American Political Science Association .

Cognitive psychology

Andrew Perfors

Cognitive psychology excels at understanding questions whose problem-space is well-defined, with precisely specified theories that transparently map onto thoroughly explored experimental paradigms. That means there is a vast gulf between the current state of the art and the richness and complexity of cognition in the real world. The most exciting open questions are about how to bridge that gap without sacrificing rigour and precision. This requires at least three changes.

First, we must move beyond typical experiments. Stimuli must become less artificial, with a naturalistic structure and distribution. Similarly, tasks must become more ecologically valid: less isolated, with more uncertainty, embedded in natural situations and over different time-scales.

Second, we must move beyond considering individuals in isolation. We live in a rich social world and an environment that is heavily shaped by other humans. How we think, learn and act is deeply affected by how other people think and interact with us; cognitive science needs to engage with this more.

Third, we must move beyond the metaphor of humans as computers. Our cognition is deeply intertwined with our emotions, motivations and senses. These are more than just parameters in our minds; they have a complexity and logic of their own, and interact in nontrivial ways with each other and more typical cognitive domains such as learning, reasoning and acting.

How do we make progress on these steps? We need reliable real-world data that are comparable across people and situations, reflect the cognitive processes involved and are not changed by measurement. Technology may help us with this, but challenges surrounding privacy and data quality are huge. Our models and analytic approaches must also grow in complexity — commensurate with the growth in problem and data complexity — without becoming intractable or losing their explanatory power.

Success in this endeavour calls for a different kind of science that is not centred around individual laboratories or small stand-alone projects. The biggest advances will be achieved on the basis of large, rich, real-world datasets from different populations, created and analysed in collaborative teams that span multiple domains, fields and approaches. This requires incentive structures that reward team-focused, slower science and prioritize the systematic construction of reliable knowledge over splashy findings.

Andrew Perfors is Associate Professor and Deputy Director of the Complex Human Data Hub, University of Melbourne, Melbourne, Victoria, Australia .

Cultural and social psychology

Ying-yi Hong

I am writing this at an exceptional moment in human history. For two years, the world has faced the COVID-19 pandemic and there is no end in sight. Cultural and social psychology are uniquely equipped to understand the COVID-19 pandemic, specifically examining how people, communities and countries are dealing with this extreme global crisis — especially at a time when many parts of the world are already experiencing geopolitical upheaval.

During the pandemic, and across different nations and regions, a diverse set of strategies (and subsequent levels of effectiveness) were used to curb the spread of the disease. In the first year of the pandemic, research revealed that some cultural worldviews — such as collectivism (versus individualism) and tight (versus loose) norms — were positively associated with compliance with COVID-19 preventive measures as well as with fewer infections and deaths 29 , 30 . These worldview differences arguably stem from different perspectives on abiding to social norms and prioritizing the collective welfare over an individual’s autonomy and liberty. Although in the short term it seems that a collectivist or tight worldview has been advantageous, it is unclear whether this will remain the case in the long term. Cultural worldviews are ‘tools’ that individuals use to decipher the meaning of their environment, and are dynamic rather than static 31 . Future research can examine how cultural worldviews and global threats co-evolve.

The pandemic has also amplified the demarcation of national, political and other major social categories. On the one hand, identification with some groups (for example, national identity) was found to increase in-group care and thus a greater willingness to sacrifice personal autonomy to comply with COVID-19 measures 32 . On the other hand, identification with other groups (for example, political parties) widened the ideological divide between groups and drove opposing behaviours towards COVID-19 measures and health outcomes 33 . As we are facing climate change and other pressing global challenges, understanding the role of social identities and how they affect worldviews, cognition and behaviour will be vital. How can we foster more inclusive (versus exclusive) identities that can unite rather than divide people and nations?

Ying-yi Hong is Choh-Ming Li Professor of Management and Associate Dean (Research) at the Department of Management, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China .

Developmental psychology

Alison Gopnik

Developmental psychology is similar to the kind of book or band that, paradoxically, everyone agrees is underrated. On the one hand, children and the people who care for them are often undervalued and overlooked. On the other, since Piaget, developmental research has tackled some of the most profound philosophical questions about every kind of human behaviour. This will only continue into the future.

Psychologists increasingly recognize that the minds of children are not just a waystation or an incomplete version of adult minds. Instead, childhood is a distinct evolutionarily adaptive phase of an organism, with its own characteristic cognitions, emotions and motivations. These characteristics of childhood reflect a different agenda than those of the adult mind — a drive to explore rather than exploit. This drive comes with motivations such as curiosity, emotions such as wonder and surprise and remarkable cognitive learning capacities. A new flood of research on curiosity, for example, shows that children actively seek out the information that will help them to learn the most.

The example of curiosity also reflects the exciting prospects for interdisciplinary developmental science. Machine learning is increasingly using children’s learning as a model, and developmental psychologists are developing more precise models as a result. Curiosity-based AI can illuminate both human and machine intelligence. Collaborations with biology are also exciting: for example, in work on evolutionary ‘life history’ explanations of the effects of adverse experiences on later life, and new research on plasticity and sensitive periods in neuroscience. Finally, children are at the cutting edge of culture, and developmental psychologists increasingly conduct a much wider range of cross-cultural studies.

But perhaps the most important development is that policy-makers are finally starting to realize just how crucial children are to important social issues. Developmental science has shown that providing children with the care that they need can decrease poverty, inequality, disease and violence. But that care has been largely invisible to policy-makers and politicians. Understanding scientifically how caregiving works and how to support it more effectively will be the most important challenge for developmental psychology in the next century.

Alison Gopnik is Professor of Psychology and Affiliate Professor of Philosophy at the Department of Psychology, University of California at Berkeley, Berkeley, CA, USA .

Science of science

Cassidy R. Sugimoto

Why study science? The goal of science is to advance knowledge to improve the human condition. It is, therefore, essential that we understand how science operates to maximize efficiency and social good. The metasciences are fields that are devoted to understanding the scientific enterprise. These fields are distinguished by differing epistemologies embedded in their names: the philosophy, history and sociology of science represent canonical metasciences that use theories and methods from their mother disciplines. The ‘science of science’ uses empirical approaches to understand the mechanisms of science. As mid-twentieth-century science historian Derek de Solla Price observed, science of science allows us to “turn the tools of science on science itself” 34 .

Contemporary questions in the science of science investigate, inter alia, catalysts of discovery and innovation, consequences of increased access to scientific information, role of teams in knowledge creation and the implications of social stratification on the scientific enterprise. Investigation of these issues require triangulation of data and integration across the metasciences, to generate robust theories, model on valid assumptions and interpret results appropriately. Community-owned infrastructure and collective venues for communication are essential to achieve these goals. The construction of large-scale science observatories, for example, would provide an opportunity to capture the rapidly expanding dataverse, collaborate and share data, and provide nimble translations of data into information for policy-makers and the scientific community.

The topical foci of the field are also undergoing rapid transformation. The expansion of datasets enables researchers to analyse a fuller population, rather than a narrow sample that favours particular communities. The field has moved from an elitist focus on ‘success’ and ‘impact’ to a more-inclusive and prosopographical perspective. Conversations have shifted from citations, impact factors and h -indices towards responsible indicators, diversity and broader impacts. Instead of asking ‘how can we predict the next Nobel prize winner?’, we can ask ‘what are the consequences of attrition in the scientific workforce?’. The turn towards contextualized measurements that use more inclusive datasets to understand the entire system of science places the science of science in a ripe position to inform policy and propel us towards a more innovative and equitable future.

Cassidy R. Sugimoto is Professor and Tom and Marie Patton School Chair, School of Public Policy, Georgia Institute of Technology, Atlanta, GA, USA .

Sari Hanafi

In the past few years, we have been living through times in which reasonable debate has become impossible. Demagogical times are driven by the vertiginous rise of populism and authoritarianism, which we saw in the triumph of Donald Trump in the USA and numerous other populist or authoritarian leaders in many places around the globe. There are some pressing tasks for sociology that can be, in brief, reduced to three.

First, fostering democracy and the democratization process requires disentangling the constitutive values that compose the liberal political project (personal liberty, equality, moral autonomy and multiculturalism) to address the question of social justice and to accommodate the surge in people’s religiosity in many parts in the globe.

Second, the struggle for the environment is inseparable from our choice of political economy, and from the nature of our desired economic system — and these connections between human beings and nature have never been as intimate as they are now. Past decades saw rapid growth that was based on assumptions of the long-term stability of the fixed costs of raw materials and energy. But this is no longer the case. More recently, financial speculation intensified and profits shrunk, generating distributional conflicts between workers, management, owners and tax authorities. The nature of our economic system is now in acute crisis.

The answer lies in a consciously slow-growing new economy that incorporates the biophysical foundations of economics into its functioning mechanisms. Society and nature cannot continue to be perceived each as differentiated into separate compartments. The spheres of nature, culture, politics, social, economy and religion are indeed traversed by common logics that allow a given society to be encompassed in its totality, exactly as Marcel Mauss 35 did. The logic of power and interests embodied in ‘ Homo economicus ’ prevents us from being able to see the potentiality of human beings to cultivate gift-giving practices as an anthropological foundation innate within social relationships.

Third, there are serious social effects of digitalized forms of labour and the trend of replacing labour with an automaton. Even if digital labour partially reduces the unemployment rate, the lack of social protection for digital labourers would have tremendous effects on future generations.

In brief, it is time to connect sociology to moral and political philosophy to address fundamentally post-COVID-19 challenges.

Sari Hanafi is Professor of Sociology at the American University of Beirut, Beirut, Lebanon; and President of the International Sociological Association .

Environmental studies (climate change)

Yasuko Kameyama

Climate change has been discussed for more than 40 years as a multilateral issue that poses a great threat to humankind and ecosystems. Unfortunately, we are still talking about the same issue today. Why can’t we solve this problem, even though scientists pointed out its importance and urgency so many years ago?

These past years have been spent trying to prove the causal relationship between an increase in greenhouse gas concentrations, global temperature rise and various extreme weather events, as well as developing and disseminating technologies needed to reduce emissions. All of these tasks have been handled by experts in the field. At the same time, the general public invested little time in this movement, probably expecting that the problem would be solved by experts and policy-makers. But that has not been the case. No matter how much scientists have emphasized the crisis of climate change or how many clean energy technologies engineers have developed, society has resisted making the necessary changes. Now, the chances of keeping the temperature rise within 1.5 °C of pre-industrial levels — the goal necessary to minimize the effects of climate change — are diminishing.

We seem to finally be realizing the importance of social scientific knowledge. People need to take scientific information seriously for clean technology to be quickly diffused. Companies are more interested in investing in newer technology and product development when they know that their products will sell. Because environmental problems are caused by human activity, research on human behaviour is indispensable in solving these problems.

Reports by the Intergovernmental Panel on Climate Change (IPCC) have not devoted many pages to the areas of human awareness and behaviour ( https://www.ipcc.ch/ ). The IPCC’s Third Working Group, which deals with mitigation measures, has partially spotlighted research on institutions, as well as on concepts such as fairness. People’s perception of climate change and the relationship between perception and behavioural change differ depending on the country, societal structure and culture. Additional studies in these areas are required and, for that purpose, more studies from regions such as Asia, Africa and South America, which are underrepresented in terms of the number of academic publications, are particularly needed.

Yasuko Kameyama is Director, Social Systems Division, National Institute for Environmental Studies, Tsukuba, Japan .

Sustainability (food systems)

Mario Herrero

The food system is in dire straits. Food demand is unprecedented, while malnutrition in all its forms (obesity, undernutrition and micronutrient deficiencies) is rampant. Environmental degradation is pervasive and increasing, and if it continues, the comfort zone for humanity and ecosystems to thrive will be seriously compromised. From bruises and shapes to sell-by dates, we tend to find many reasons to exclude perfectly edible food from our plates, whereas in other cases not enough food reaches hungry mouths owing to farming methods, pests and lack of adequate storage. These types of inequalities are common and — together with inherent perverse incentives that maintain the status quo of how we produce, consume and waste increasingly cheap and processed food — they are launching us towards a disaster.

We are banking on a substantial transformation of the food system to solve this conundrum. Modifying food consumption and waste patterns are central to the plan for achieving healthier diets, while increasing the sustainability of our food system. This is also an attractive policy proposition, as it could lead to gains in several sectors. Noncommunicable diseases such as obesity, diabetes and heart disease could decline, while reducing the effects of climate change, deforestation, excessive water withdrawals and biodiversity loss, and their enormous associated — and largely unaccounted — costs.

Modifying our food consumption and waste patterns is very hard, and unfortunately we know very little about how to change them at scale. Yes, many pilots and small examples exist on pricing, procurement, food environments and others, but the evidence is scarce, and the magnitude of the change required demands an unprecedented transdisciplinary research agenda. The problem is at the centre of human agency and behaviour, embodying culture, habits, values, social status, economics and all aspects of agri-food systems. Certainly, one of the big research areas for the next decade if we are to reach the Sustainable Development Goals leaving no one behind.

Mario Herrero is Professor, Cornell Atkinson scholar and Nancy and Peter Meinig Family Investigator in the Life Sciences at the Department of Global Development, College of Agriculture and Life Sciences and Cornell Atkinson Center for Sustainability, Cornell University, Ithaca, NY, USA .

Cultural evolution

Laurel Fogarty

Humans are the ultimate ‘cultural animals’. We are innovative, pass our cultures to one another across generations and build vast self-constructed environments that reflect our cultural biases. We achieve things using our cultural capacities that are unimaginable for any other species on earth. And yet we have only begun to understand the dynamics of cultural change, the drivers of cultural complexity or the ways that we adapt culturally to changing environments. Scholars — anthropologists, archaeologists and sociologists — have long studied culture, aiming to describe and understand its staggering diversity. The relatively new field of cultural evolution has different aims, one of the most important of which is to understand the mechanics in the background — what general principles, if any, govern human cultural change?

Although the analogy of culture as an evolutionary process has been made since at least the time of Darwin 36 , 37 , cultural evolution as a robust field of study is much younger. From its beginnings with the pioneering work of Cavalli-Sforza & Feldman 38 , 39 , 40 and Boyd & Richerson 41 , 42 , the field of cultural evolution has been heavily theoretical. It has drawn on models from genetic evolution 40 , 43 , 44 , 45 , ecology 46 , 47 and epidemiology 40 , 48 , extending and adapting them to account for unique and important aspects of cultural transmission. Indeed, in its short life, the field of cultural evolution has largely been dominated by a growing body of theory that ensured that the fledgling field started out on solid foundations. Because it underpins and makes possible novel applications of cultural evolutionary ideas, theoretical cultural evolution’s continued development is not only crucial to the field’s growth but also represents some of its most exciting future work.

One of the most urgent tasks for cultural evolution researchers in the next five years is to develop, alongside its theoretical foundations, robust principles of application 49 , 50 , 51 . In other words, it is vital to develop our understanding of what we can — and, crucially, cannot — infer from different types of cultural data. Where do we draw those boundaries and how can we apply cultural evolutionary theory to cultural datasets in a principled way? The tandem development of robust theory and principled application has the potential to strengthen cultural evolution as a robust, useful and ground-breaking inferential science of human behaviour.

Laurel Fogarty is Senior Scientist at the Department of Human Behaviour, Ecology, and Culture, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany .

Over the past decade, research using molecular genetic data has confirmed one of the main conclusions of twin studies: all human behaviour is partly heritable 52 , 53 . Attempts at examining the link between genetics and behaviour have been met with concerns that the findings can be abused to justify discrimination — and there are good historical grounds for these concerns. However, these findings also show that ignoring the contribution of genes to variation in human behaviour could be detrimental to a complete understanding of social phenomena, given the complex ways that genes and environment interact.

Uncovering these complex pathways has become feasible only recently thanks to rapid technological progress reducing the costs of genotyping. Sample sizes in genome-wide association studies (GWAS) have risen from tens of thousands to millions in the past decade, reporting thousands of genetic variants associated with different behaviours 54 , 55 , 56 , 57 . New ways to use GWAS results have emerged, the most important one arguably being a method to aggregate the additive effects of many genetic variants into a ‘polygenic index’ (PGI) (also known as a ‘polygenic score’) that summarizes an individual’s genetic propensity towards a trait or behaviour 58 , 59 . Being aggregate measures, PGIs capture a much larger share of the variance in the trait of interest compared to individual genetic variants 60 . Thus, they have paved the way for follow-up studies with smaller sample sizes but deeper phenotyping compared to the original GWAS, allowing researchers to, for example, analyse the channels through which genes operate 61 , 62 , how they interact with the environment 63 , 64 , and account for confounding bias and boost statistical power by controlling for genetic effects 65 , 66 .

Useful as they are, PGIs and the GWAS that they are based on can suffer from confounding due to environmental factors that correlate with genotypes, such as population stratification, indirect effect from relatives or assortative mating 67 . Now that the availability of genetic data enables large-scale within-family GWAS, the next big thing in behaviour genetic research will be disentangling these sources 68 . While carrying the progress further, it is important that the field prioritizes moving away from its currently predominant Eurocentric bias by extending data collection and analyses to individuals of non-European ancestries, as the exclusion of non-European ancestries from genetic research has the potential to exacerbate health disparities 69 . Researchers should also be careful to communicate their findings clearly and responsibly to the public and guard against their misappropriation by attempts to fuel discriminatory action and discourse 70 .

Aysu Okbay is Assistant Professor at the Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands .

Cognitive neuroscience

Anna C. Nobre

Since the ‘decade of the brain’ in the 1990s, ingenuity in cognitive neuroscience has focused on measuring and analysing brain signals. Adapting tools from statistics, engineering, computer science, physics and other disciplines, we studied activity, states, connectivity, interactions, time courses and dynamics in brain regions and networks. Unexpected findings about the brain yielded important insights about the mind.

Now is a propitious time to upgrade the brain–mind duumvirate to a brain–mind–behaviour triumvirate. Brain and mind are embodied, and their workings are expressed through various effectors. Yet, experimental tasks typically use simple responses to capture complex psychological functions. Often, a button press — with its limited dimensions of latency and accuracy — measures anticipating, focusing, evaluating, choosing, reflecting or remembering. Researchers venturing beyond such simple responses are uncovering how the contents of mind can be studied using various continuous measures, such as pupil diameter, gaze shifts and movement trajectories.

Most tasks also restrict participants’ movements to ensure experimental control. However, we are learning that principles of cognition derived in artificial laboratory contexts can fail to generalize to natural behaviour. Virtual reality should prove a powerful methodology. Participants can behave naturally, and experimenters can control stimulation and obtain quality measures of gaze, hand and body movements. Noninvasive neurophysiology methods are becoming increasingly portable. Exciting immersive brain–mind–behaviour studies are just ahead.

The next necessary step is out of the academic bubble. Today the richest data on human behaviour belong to the information and technology industries. In our routines, we contribute data streams through telephones, keyboards, watches, vehicles and countless smart devices in the internet of things. These expose properties such as processing speed, fluency, attention, dexterity, navigation and social context. We supplement these by broadcasting feelings, attitudes and opinions through social media and other forums. The richness and scale of the resulting big data offer unprecedented opportunities for deriving predictive patterns that are relevant to understanding human cognition (and its disorders). The outcomes can then guide further hypothesis-driven experimentation. Cognitive neuroscience is intrinsically collaborative, combining a broad spectrum of disciplines to study the mind. Its challenge now is to move from a multidisciplinary to a multi-enterprise science.

Anna C. Nobre is Chair in Translational Cognitive Neuroscience at the Department of Experimental Psychology, University of Oxford, UK; and Director of Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, UK .

Social and affective neuroscience

Tatia M. C. Lee

Social and affective neuroscience is a relatively new, but rapidly developing, field of neuroscience. Social and affective neuroscience research takes a multilevel approach to make sense of socioaffective processes, focusing on macro- (for example, social environments and structures), meso- (for example, social interactions) and micro (for example, socio-affective neural processes and perceptions)-level interactions. Because the products of these interactions are person-specific, the conventional application of group-averaged mechanisms to understand the brain in a socioemotional context has been reconsidered. Researchers turn to ecologically valid stimuli (for example, dynamic and virtual reality instead of static stimuli) and experimental settings (for example, real-time social interaction) 71 to address interindividual differences in social and affective responses. At the neural level, there has been a shift of research focus from local neural activations to large-scale synchronized interactions across neural networks. Network science contributes to the understanding of dynamic changes of neural processes that reflect the interactions and interconnection of neural structures that underpin social and affective processes.

We are living in an ever-changing socioaffective world, full of unexpected challenges. The ageing population and an increasing prevalence of depression are social phenomena on a global scale. Social isolation and loneliness caused by measures to tackle the current pandemic affect physical and psychological well-being of people from all walks of life. These global issues require timely research efforts to generate potential solutions. In this regard, social and affective neuroscience research using computational modelling, longitudinal research designs and multimodal data integration will create knowledge about the basis of adaptive and maladaptive social and affective neurobehavioural processes and responses 72 , 73 , 74 . Such knowledge offers important insights into the precise delineation of brain–symptom relationships, and hence the development of prediction models of cognitive and socioaffective functioning (for example, refs. 75 , 76 ). Therefore, screening tools for identifying potential vulnerabilities can be developed, and timely and precise interventions can be tailored to meet individual situations and needs. The translational application of social and affective neuroscience research to precision medicine (and policy) is experiencing unprecedented demand, and such demand is met with unprecedented clinical and research capabilities.

Tatia M. C. Lee is Chair Professor of Psychology at the State Key Laboratory of Brain and Cognitive Sciences and Laboratory of Neuropsychology and Human Neuroscience, The University of Hong Kong, Hong Kong Special Administrative Region, China .

Maurizio Corbetta

Focal brain disorders, including stroke, trauma and epilepsy, are the main causes of disability and loss of productivity in the world, and carry a cumulative cost in Europe of about € 500 billion per year 77 . The disease process affects a specific circuit in the brain by turning it off (as in stroke) or pathologically turning it on (as in epilepsy). The cause of the disabling symptoms is typically local circuit damage. However, there is now overwhelming evidence that symptoms reflect not only local pathology but also widespread (network) functional abnormalities. For instance, in stroke, an average lesion — the size of a golf ball — typically alters the activity of on average 25% of all brain connections. Furthermore, normalization of these abnormalities correlates with optimal recovery of function 78 , 79 .

One exciting treatment opportunity is ‘circuit-based’ stimulation: an ensemble of methods (optogenetic, photoacoustic, electrochemical, magnetic and electrical) that have the potential to normalize activity. Presently, this type of therapy is limited by numerous factors, including a lack of knowledge about the circuits, the difficulty of mapping these circuits in single patients and, most importantly, a principled understanding of where and how to stimulate to produce functional recovery.

A possible solution lies in a strategy (developed with G. Deco, M. Massimini and M. Sanchez-Vivez) that starts with an in-depth assessment of behaviour and physiological studies of brain activity to characterize the affected circuits and associated patterns of functional abnormalities. Such a multi-dimensional physiological map of a lesioned brain can be then fed to biologically realistic in silico models 80 . A model of a lesioned brain affords the opportunity to explore, in an exhaustive way, different kinds of stimulation to normalize faulty activity. Once a suitable protocol is found it can be exported first to animal models, and then to humans. Stimulation alone will not be enough. Pairing with behavioural training (rehabilitation) will stabilize learning and normalize connections.

The ability to interface therapy (stimulation, rehabilitation and drugs) with brain signals or other kinds of behavioural sensor offers another exciting opportunity, to open the ‘brain’s black box’. Most current treatments in neuroscience are given with no regard to their effect on the underlying brain signals or behaviour. Giving patients conscious access to their own brain signals may substantially enhance recovery, as the brain is now in the position to use its own powerful connections and learning mechanisms to cure itself.

Maurizio Corbetta is Professor and Chair of Neurology at the Department of Neuroscience and Director of the Padova Neuroscience Center (PNC), University of Padova, Italy; and Principal Investigator at the Venetian Institute of Molecular Medicine (VIMM), Padova, Italy .

Merete Nordentoft

Schizophrenia and related psychotic disorders are among the costliest and most debilitating disorders in terms of personal sufferings for those affected, for relatives and for society 81 . These disorders often require long-term treatment and, for a substantial proportion of the patients, the outcomes are poor. This has motivated efforts to prevent long-lasting illness by early intervention. The time around the onset of psychotic disorders is associated with an increased risk of suicide, of loss of affiliation with the labour market, and social isolation and exclusion. Therefore, prevention and treatment of first-episode psychosis will be a key challenge for the future.

There is now solid evidence proving that early intervention services can improve clinical outcomes 82 . This was first demonstrated in the large Danish OPUS trial, in which OPUS treatment — consisting of assertive outreach, case management and family involvement, provided by multidisciplinary teams over a two-year period — was shown to improve clinical outcomes 83 . Moreover, it was also cost-effective 84 . Although the positive effects on clinical outcomes were not sustainable after five and ten years, there was a long-lasting effect on use of supported housing facilities (indicating improved ability to live independently) 85 . Later trials proved that it is possible to maintain the positive clinical outcomes by extending the services to five years or by offering a stepped care model with continued intensive care for the patients who are most impaired 86 . However, even though both clinical and functional outcomes (such as labour market affiliation) can be improved by evidence-based treatments 82 , a large group of patients with first-episode psychosis still have psychotic symptoms after ten years. Thus, there is still an urgent need for identification of new and better options for treatment.

Most probably, some of the disease processes start long before first onset of a psychotic disorder. Thus, identifying disease mechanisms and possibilities for intervention before onset of psychosis will be extremely valuable. Evidence for effective preventive interventions is very limited, and the most burning question — of how to prevent psychosis — is still open.

The early intervention approach is also promising also for other disorders, including bipolar affective disorder, depression, anxiety, eating disorders, personality disorders, autism and attention-deficient hyperactivity disorder.

Merete Nordentoft is Clinical Professor at the Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark; and Principal Investigator, CORE - Copenhagen Research Centre for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark .

Epidemiology

Gabriel M. Leung

In a widely anthologized article from the business field of marketing, Levitt 87 pointed out that often industries failed to grow because they suffered from a limited market view. For example, Kodak went bust because it narrowly defined itself as a film camera company for still photography rather than one that should have been about imaging writ large. If it had had that strategic insight, it would have exploited and invested in digital technologies aggressively and perhaps gone down the rather more successful path of Fujifilm — or even developed into territory now cornered by Netflix.

The raison d’être of epidemiology has been to provide a set of robust scientific methods that underpin public health practice. In turn, the field of public health has expanded to fulfil the much-wider and more-intensive demands of protecting, maintaining and promoting the health of local and global populations, intergenerationally. At its broadest, the mission of public health should be to advance social justice towards a complete state of health.

Therefore, epidemiologists should continue to recruit and embrace relevant methodology sets that could answer public health questions, better and more efficiently. For instance, Davey Smith and Ebrahim 88 described how epidemiology adapted instrumental variable analysis that had been widely deployed in econometrics to fundamentally improve causal inference in observational epidemiology. Conversely, economists have not been shy in adopting the randomized controlled trial design to answer questions of development, and have recognized it with a Nobel prize 89 . COVID-19 has brought mathematical epidemiology or modelling to the fore. The foundations of the field borrowed heavily from population dynamics and ecological theory.

In future, classical epidemiology, which has mostly focused on studying how the exposome associates with the phenome, needs to take into simultaneous account the other layers of the multiomics universe — from the genome to the metabolome to the microbiome 90 . Another area requiring innovative thinking concerns how to harness big data to better understand human behaviour 91 . Finally, we must consider key questions that are amenable to epidemiologic investigation arising from the major global health challenges: climate change, harmful addictions and mental wellness. What new methodological tools do we need to answer these questions?

Epidemiologists must keep trying on new lenses that correct our own siloed myopia.

Gabriel M. Leung is Helen and Francis Zimmern Professor in Population Health at WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong; Chief Scientific Officer at Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park; and Dean of Medicine at the University of Hong Kong, Hong Kong Special Administrative Region, China .

Bahdanau, D., Cho, K. & Bengio, Y. Neural machine translation by jointly learning to align and translate. Preprint at https://arxiv.org/abs/1409.0473 (2014).

Jumper, J. et al. Nature 596 , 583–589 (2021).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Dwork, C., Hardt, M., Pitassi, T., Reingold, O. & Zemel, R. Fairness through awareness. In Proc. 3rd Innovations in Theoretical Computer Science Conference , 214–226 (Association for Computing Machinery, 2012).

Chouldechova, A. & Roth, A. Commun. ACM 63 , 82–89 (2020).

Article   Google Scholar  

Hara, K. et al. A data-driven analysis of workers’ earnings on Amazon Mechanical Turk. In Proc. 2018 CHI Conf. on Human Factors in Computing Systems , 1–14 (Association for Computing Machinery, 2018).

Strubell, E., Ganesh, A. & McCallum, A. Energy and policy considerations for deep learning in NLP. Preprint at https://arxiv.org/abs/1906.02243 (2019).

Rival, L. Anthropol. Today 37 , 9–12 (2021).

Carey, J. W. Communication as Culture: Essays on Media and Society (Unwin Hyman, 1985).

Williams, R. Television: Technology and Cultural Form (Fontana, 1974).

Helmond, A. Soc. Media Soc . 1 , https://doi.org/10.1177/2056305115603080 (2015).

Rambukkana, N. (Ed.). Hashtag Publics: The Power and Politics of Discursive Networks (Peter Lang, 2015).

Burrell, J. Big Data Soc. 3 , 1–12 (2016).

Bruns, A. et al. Facebook shuts the gate after the horse has bolted, and hurts real research in the process. Internet Policy Review , https://go.nature.com/3IP2xYr (25 April 2018).

Article 19. EU: Stop platforms from suppressing public interest research. article.19.org , https://go.nature.com/3DTWgXT (13 September 2021).

Burgess, J. et al. Critical simulation as hybrid digital method for exploring the data operations and vernacular cultures of visual social media platforms. Preprint at https://doi.org/10.31235/osf.io/2cwsu (2021).

Rieder, B. & Hofmann, J. Internet Policy Review 9 , 1–28 (2020).

Vosoughi, S., Roy, D. & Aral, S. Science 359 , 1146–1151 (2018).

Article   CAS   PubMed   Google Scholar  

Hussein, E., Juneja, P. & Mitra, T. Measuring misinformation in video search platforms: an audit study on YouTube. In Proc. of the ACM on Human–Computer Interaction 4 (CSCW1) (eds. Lampe C. et al.) 1–27 (Association for Computing Machinery, 2020).

Lazer, D. M. J. et al. Science 369 , 1060–1062 (2020).

Wagner, C. et al. Nature 595 , 197–204 (2021).

Kosse, F., Deckers, T., Pinger, P., Schildberg-Hörisch, H. & Falk, A. J. Polit. Econ. 128 , 434–467 (2020).

Cappelen, A., List, J., Samek, A. & Tungodden, B. J. Polit. Econ. 128 , 2739–2758 (2020).

Article   PubMed   PubMed Central   Google Scholar  

Alan, S., Boneva, T. & Ertac, S. Q. J. Econ. 134 , 1121–1162 (2019).

Google Scholar  

Almås, I., Cappelen, A. & Tungodden, B. J. Polit. Econ. 128 , 1753–1788 (2020).

Müller, D. & Renes, S. Soc. Choice Welfare 56 , 679–711 (2021).

Falk, A. et al. Q. J. Econ. 133 , 1645–1692 (2018).

Almås, I., Cappelen, A., Sørensen, E. Ø. & Tungodden, B. Global evidence on the selfish rich inequality hypothesis. Proc. Natl Acad. Sci. USA (in the press).

Henrich, J., Root, H. & Henrich, J. The WEIRDest People in the World: How the West Became Psychologically Peculiar and Particularly Propserous (Farrar, Strauss, and Giroux, 2020).

Gelfand, M. J. et al. Lancet Planet. Health 5 , e135–e144 (2021).

Lu, J. G., Jin, P. & English, A. S. Proc. Natl Acad. Sci. USA 118 , e2021793118 (2021).

Hong, Y. Y., Morris, M. W., Chiu, C. Y. & Benet-Martínez, V. Am. Psychol. 55 , 709–720 (2000).

Chan, H.-W. et al. Polit. Psychol. 42 , 767–793 (2021).

Gollwitzer, A. et al. Nat. Hum. Behav. 4 , 1186–1197 (2020).

Article   PubMed   Google Scholar  

de Solla Price, D. Little Science, Big Science (Columbia Univ. Press, 1963).

Mauss, M. 1925. in Sociologie et Anthropologie (Réédition 1978) 143–275 (Presses Universitaires de France).

Darwin, C. The Descent of Man, and Selection in Relation to Sex (Penguin 2004) (reprint of 2nd edn, 1879).

Mesoudi, A. Proc. Natl Acad. Sci. USA 114 , 7853–7860 (2017).

Cavalli-Sforza, L. & Feldman, M. W. Theor. Popul. Biol. 4 , 42–55 (1973).

Cavalli-Sforza, L. L. & Feldman, M. W. Am. J. Hum. Genet. 25 , 618–637 (1973).

CAS   PubMed   PubMed Central   Google Scholar  

Cavalli-Sforza, L. L. & Feldman, M. W. Cultural Transmission and Evolution (Princeton Univ. Press, 1981).

Richerson, P. J. & Boyd, R. J. Social Biological Syst. 1 , 127–154 (1978).

Boyd, R. & Richerson, P. J. Culture and the Evolutionary Process (Chicago Univ. Press, 1985).

Shennan, S. J. Camb. Archaeol. J. 11 , 5–16 (2001).

Strimling, P., Sjöstrand, J., Enquist, M. & Eriksson, K. Theor. Popul. Biol. 76 , 77–83 (2009).

Aoki, K., Lehmann, L. & Feldman, M. W. Theor. Popul. Biol. 79 , 192–202 (2011).

Kandler, A. & Laland, K. N. Theor. Popul. Biol. 76 , 59–67 (2009).

Laland, K. N. & O’Brien, M. J. Biol. Theory 6 , 191–202 (2011).

Richerson, P. J. & Boyd, R. Not By Genes Alone: How Culture Transformed Human Evolution (Univ. Chicago Press, 2005).

Kandler, A. & Powell, A. Phil. Trans. R. Soc. Lond. B 373 , 20170056 (2018).

Kandler, A., Wilder, B. & Fortunato, L. R. Soc. Open Sci. 4 , 170949 (2017).

McElreath, R. et al. Phil. Trans. R. Soc. Lond. B 363 , 3515–3528 (2008).

Turkheimer, E. Curr. Dir. Psychol. Sci. 9 , 160–164 (2000).

Polderman, T. J. C. C. et al. Genet. 47 , 702–709 (2015).

CAS   Google Scholar  

Visscher, P. M. et al. Am. J. Hum. Genet. 101 , 5–22 (2017).

Karlsson Linnér, R. et al. Nat. Genet. 51 , 245–257 (2019).

Article   PubMed   CAS   Google Scholar  

Lee, J. J. et al. Nat. Genet. 50 , 1112–1121 (2018).

Liu, M. et al. Nat. Genet. 51 , 237–244 (2019).

Wray, N. R., Goddard, M. E. & Visscher, P. M. Genome Res. 17 , 1520–1528 (2007).

The International Schizophrenia Consortium. Nature 460 , 748–752 (2009).

Article   CAS   Google Scholar  

Becker, J. et al. Nat. Hum. Behav . (2021).

Belsky, D. W. et al. Psychol. Sci. 27 , 957–972 (2016).

Young, A. I., Benonisdottir, S., Przeworski, M. & Kong, A. Science 365 , 1396–1400 (2019).

Barcellos, S. H., Carvalho, L. S. & Turley, P. Proc. Natl Acad. Sci. USA 115 , E9765–E9772 (2018).

Papageorge, N. W. & Thom, K. J. Eur. Econ. Assoc. 18 , 1351–1399 (2020).

DiPrete, T. A., Burik, C. A. P. & Koellinger, P. D. Proc. Natl Acad. Sci. USA 115 , E4970–E4979 (2018).

Rietveld, C. A. et al. Science 340 , 1467–1471 (2013).

Kong, A., Benonisdottir, S. & Young, A. I. Family analysis with Mendelian imputations. Preprint at https://doi.org/10.1101/2020.07.02.185181 (2020).

Howe, L. J. et al. Within-sibship GWAS improve estimates of direct genetic effects. Preprint at https://doi.org/10.1101/2021.03.05.433935 (2021).

Martin, A. R. et al. Nat. Genet. 51 , 584–591 (2019).

Nuffield Council on Bioethics. Genetics and Human Behaviour: The Ethical Context (Nuffield Council on Bioethics, 2002).

Redcay, E. & Schilbach, L. Nat. Rev. Neurosci. 20 , 495–505 (2019).

Roeckner, A. R., Oliver, K. I., Lebois, L. A. M., van Rooij, S. J. H. & Stevens, J. S. Transl. Psychiatry 11 , 508 (2021).

Sevgi, M., Diaconescu, A. O., Henco, L., Tittgemeyer, M. & Schilbach, L. Biol. Psychiatry 87 , 185–193 (2020).

Wittmann, M. K. et al. Neuron 91 , 482–493 (2016).

Gao, M. et al. Neuroimage 223 , 117290 (2020).

Lee, K. M., Ferreira-Santos, F. & Satpute, A. B. Neurosci. Biobehav. Rev. 131 , 211–228 (2021).

Olesen, J., Gustavsson, A., Svensson, M., Wittchen, H. U. & Jönsson, B. Eur. J. Neurol. 19 , 155–162 (2012).

Corbetta, M., Siegel, J. S. & Shulman, G. L. Cortex 107 , 229–237 (2018).

Griffis, J. C., Metcalf, N. V., Corbetta, M. & Shulman, G. L. Cell Rep. 28 , 2527–2540 (2019).

Deco, G. et al. J. Neurosci. 33 , 11239–11252 (2013).

Patel, V. et al. Lancet 392 , 1553–1598 (2018).

Correll, C. U. et al. JAMA Psychiatry 75 , 555–565 (2018).

Petersen, L. et al. Br. Med. J. 331 , 602 (2005).

Hastrup, L. H. et al. Br. J. Psychiatry 202 , 35–41 (2013).

Secher, R. G. et al. Schizophr. Bull. 41 , 617–626 (2015).

Albert, N. et al. Br. Med. J. 356 , i6681 (2017).

Levitt, T. Harv. Bus. Rev. 38 , 45–56 (1960).

Davey Smith, G. & Ebrahim, S. Int. J. Epidemiol. 32 , 1–22 (2003).

Callaway, E. ‘Randomistas’ who used controlled trials to fight poverty win economics Nobel. Nature, https://www.nature.com/articles/d41586-019-03125-y (14 October 2019).

Topol, E. J. Cell 157 , 241–253 (2014).

Subrahmanian, V. S. & Kumar, S. Science 355 , 489 (2017).

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Aguinis, H., Boyd, B. K., Pierce, C. A., & Short, J. C. (2011). Walking new avenues in management research methods and theories: Bridging micro and macro domains. Journal of Management, 37 (2), 395–403.

Article   Google Scholar  

Ansoff, H. I. (1979). Strategic management . Macmillan.

Book   Google Scholar  

Ansoff, H. I. (1984). Implanting strategic management . Prentice Hall.

Google Scholar  

Ansoff, H. I. (1987). The emerging paradigm of strategic behavior. Strategic Management Journal, 8 (6), 501–515.

Bagozzi, R., & Phillips, L. (1982). Representing and testing organizational theories: A holistic construal. Administrative Science Quarterly, 27 , 459–489.

Barnard, C. I. (1938). The functions of the executive . Harvard University Press.

Bascle, G. (2008). Controlling for endogeneity with instrumental variables in strategic management research. Strategic Organization, 6 (3), 285–327.

Bluedorn, A. C., Kalliath, T. J., Strube, M. J., & Martin, G. D. (1999). Polychronicity and the inventory of polychronic values (IPV) the development of an instrument to measure a fundamental dimension of organizational culture. Journal of Managerial Psychology, 14 (3/4), 205–231.

Cardinal, L. B. (2001). Technological innovation in the pharmaceutical industry: The use of organizational control in managing research and development. Organization Science, 12 (1), 19–3.

Cardinal, L. B., Sitkin, S. B., & Long, C. P. (2004). Balancing and rebalancing in the creation and evolution of organizational control. Organization Science, 15 (4), 411–431.

Cyert, R. M., & March, J. G. (1963). A behavioral theory of the firm . Prentice Hall.

Dess, G. G., Newport, S., & Rasheed, A. M. A. (1993). Configuration research in strategic management: Key issues and suggestions. Journal of Management, 19 (4), 775–795.

Eckert, C., & Hohberger, J. (2022). Addressing endogeneity without instrumental variables: An evaluation of the Gaussian Copula approach for management research. Journal of Management , forthcoming.

Eisenhardt, K. M. (1985). Control: Organizational and economic approaches. Management Science, 31 (2), 134–149.

Eisenhardt, K. M., Furr, N. R., & Bingham, C. B. (2010). Microfoundations of performance: Balancing efficiency and flexibility in dynamic environments. Organization Science, 21 (6), 1263–1273.

Evans, K. R., Landry, T. D., Li, P. C., & Zou, S. (2007). How sales controls affect job-related outcomes: The role of organizational sales-related psychological climate perceptions. Journal of the Academy of Marketing Science, 35 (3), 445–459.

Evans, M. G. (1985). A Monte Carlo study of the effects of correlated method variance in moderated multiple regression analysis. Organizational Behavior and Human Decision Processes, 36 (3), 305–323.

Fayol, H. (1949). General and industrial management . Pitman Publishing.

Fiegenbaum, A., Hart, S., & Schendel, D. (1996). Strategic reference point theory. Strategic Management Journal, 17 (3), 219–235.

Fischer, R. (2004). Standardization to account for cross-cultural response bias a classification of score adjustment procedures and review of research in JCCP. Journal of Cross-Cultural Psychology, 35 (3), 263–282.

Frey, B. S., Homberg, F., & Osterloh, M. (2013). Organizational control systems and pay-for-performance in the public service. Organization Studies, 34 (7), 949–972.

Gaba, V., & Greve, H. R. (2019). Safe or profitable? The pursuit of conflicting goals. Organization Science, 30 (4), 647–667.

Gagné, M., & Deci, E. L. (2005). Self-determination theory and work motivation. Journal of Organizational Behavior, 26 (4), 331–362.

Govindarajan, V. (1988). A contingency approach to strategy implementation at the business-unit level: Integrating administrative mechanisms with strategy. Academy of Management Journal, 31 (4), 828–853.

Govindarajan, V., & Fisher, J. (1990). Strategy, control systems, and resource sharing: Effects on business-unit performance. Academy of Management Journal, 33 (2), 259–285.

Greckhamer, T., Furnari, S., Fiss, P. C., & Aguilera, R. V. (2018). Studying configurations with qualitative comparative analysis: Best practices in strategy and organization research. Strategic Organization, 16 (4), 482–495.

Harris, M. L., Collins, R. W., & Hevner, A. R. (2009). Control of flexible software development under uncertainty. Information Systems Research, 20 (3), 400–419.

Kaplan, R. S., & Norton, D. P. (1996). The balanced scorecard: Translating strategy into action . Harvard Business School Press.

Kirsch, L. J. (1996). The management of complex tasks in organizations: Controlling the systems development process. Organization Science, 7 (1), 1–21.

Kreutzer, M., Cardinal, L. B., Walter, J., & Lechner, C. (2016). Formal and Informal control as complement or substitute? The role of the task environment. Strategy Science, 1 (4), 235–255.

Kreutzer, M., Walter, J., & Cardinal, L. B. (2015). Organizational control as antidote to politics in the pursuit of strategic initiative performance. Strategic Management Journal, 36 (9), 1317–1337.

Levinthal, D. A., & Rerup, C. (2020). The plural of goal: Learning in a world of ambiguity. Organization Science, 32 (3), 527–543.

Li, H., & Zhang, Y. (2007). The role of managers political networking and functional experience in new venture performance: Evidence from Chinas transition economy. Strategic Management Journal, 28 (8), 791.

Lindenberg, S., & Foss, N. J. (2011). Managing joint production motivation: The role of goal framing and governance mechanisms (article). Academy of Management Review, 36 (3), 500–525.

Locke, E. A., & Latham, G. P. (1990). A theory of goal setting and task performance . Prentice Hall.

Locke, E. A., Shaw, K. N., Saari, L. M., & Latham, G. P. (1981). Goal setting and task performance: 1969–1980. Psychological Bulletin, 90 (1), 125.

Marginson, D. E. W. (2002). Management control systems and their effects on strategy formation at middle-management levels: evidence from a U.K. organization. Strategic Management Journal , 23(11), 1019–1031.

Misangyi, V. F., Greckhamer, T., Furnari, S., Fiss, P. C., Crilly, D., & Aguilera, R. (2017). Embracing causal complexity: The emergence of a neo-configurational perspective. Journal of Management, 42 (1), 255–282.

Ouchi, W. G. (1977). The relaionship between organizational structure and organizational control. Academy of Management Journal, 22 (1), 95–113.

Papalexandris, A., Ioannou, G., Prastacos, G., & Soderquist, K. E. (2005). An integrated methodology for putting the balanced scorecard into action. European Management Journal, 23 (2), 214–227.

Phillips, L. W. (1981). Assessing measurement error in key informant reports: A methodological note on organizational analysis in marketing. Journal of Marketing Research, 18 (4), 395–415.

Podsakoff, P. M., MacKenzie, S. B., Lee, J., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88 (5), 879.

Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55 (1), 68–78.

Shinkle, G. A., Kriauciunas, A. P., & Hundley, G. (2013). Why pure strategies may be wrong for transition economy firms. Strategic Management Journal, 34 (10), 1244–1254.

Shinkle, G. A., Yang, M. M., Yang, F., Elshaw, J., & Schleicher, D. J. (2021). Enacting formal controls in information system development: Process guidance and goal importance. Information & Management, 58 , 103430.

Short, J. C., Payne, G. T., & Ketchen, D. J. (2008). Research on organizational configurations: Past accomplishments and future challenges. Journal of Management, 34 (6), 1053–1079.

Siemsen, E., Roth, A., & Oliveira, P. (2010). Common method bias in regression models with linear, quadratic, and interaction effects. Organizational Research Methods, 13 (3), 456–476.

Simon, H. A. (1947). Administrative behavior: A study of decision-making processes in administrative organizations . Macmillan.

Sitkin, S. B., Cardinal, L. B., & Bijlsma-Frankema, K. M. (2010). Organizational control . Cambridge University Press.

Snell, S. A. (1992). Control theory in strategic human resource management: The mediating effect of administrative information. Academy of Management Journal, 35 (2), 292–327.

Turner, K. L., & Makhija, M. V. (2006). The role of organizational controls in managing knowledge. Academy of Management Review, 31 (1), 197–217.

Weber, M. (1947). Theory of social and economic organization . Free Press.

Weibel, A. (2007). Formal Control and Trustworthiness Shall the Twain Never Meet? Group & Organization Management, 32 (4), 500–517.

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Home » Future Studies – Types, Approaches and Methods

Future Studies – Types, Approaches and Methods

Table of Contents

Future Studies

Definition:

Future studies, also known as foresight or future thinking, is an interdisciplinary field that seeks to understand and anticipate possible future developments in various aspects of human society and the environment. It draws upon a range of disciplines, including sociology , economics , political science , psychology , and technology studies, among others, to identify and analyze emerging trends, patterns, and drivers of change that may shape the future.

Types of Future Studies

Types of Future Studies are as follows:

Exploratory Future Studies

Exploratory future studies are focused on identifying and exploring a wide range of potential future scenarios and their implications. These studies often involve scenario planning, horizon scanning, and trend analysis to identify potential drivers of change and possible future outcomes. They are useful for developing a broad understanding of the possible futures and the key factors that could shape them.

Normative Future Studies

Normative future studies are focused on identifying the most desirable or preferred future and developing strategies to achieve it. These studies are often informed by values and ethical considerations and involve the development of visions or goals for the future. They are useful for guiding policy development and decision-making and for inspiring action towards a desired future.

Predictive Future Studies

Predictive future studies are focused on forecasting or predicting the future based on current trends and data. These studies often involve quantitative analysis and modeling to project future outcomes. They are useful for providing insights into potential future scenarios and for informing planning and decision-making.

Participatory Future Studies

Participatory future studies involve engaging stakeholders in the process of envisioning and shaping the future. These studies are often focused on developing a shared vision or goals for the future and involve collaborative and participatory approaches to decision-making. They are useful for building consensus and developing strategies that are aligned with the values and priorities of different stakeholders.

Future Studies Approaches

Future Studies Approaches are as follows:

Forecasting

Forecasting is a common approach used in future studies. It involves using data and statistical methods to predict future trends and events. This approach is often used in business and economics to forecast market trends and financial performance. Forecasting can also be used in other areas, such as weather forecasting, demographic forecasting, and technology forecasting.

There are different methods of forecasting, including trend extrapolation, simulation models, and expert opinion. Trend extrapolation involves using historical data to predict future trends. Simulation models use mathematical models to simulate the behavior of complex systems, such as the economy. Expert opinion involves using the opinions of subject matter experts to predict future events.

Scenario planning

Scenario planning is another approach used in future studies. It involves creating multiple scenarios or possible futures based on different assumptions and factors. Scenario planning is often used in strategic planning and risk management. It helps organizations prepare for different futures and make better decisions in uncertain environments.

Scenario planning involves identifying key drivers of change, such as technology, demographics, and environmental factors. These drivers are used to create different scenarios, each with a different set of assumptions and outcomes. The scenarios are then used to explore different possibilities and identify potential risks and opportunities.

Visioning is a more creative approach used in future studies. It involves imagining a desirable future and working towards it. Visioning is often used in community planning and social innovation. It helps individuals and organizations create a shared vision of the future and work towards a common goal.

Visioning involves engaging stakeholders and using participatory methods to create a shared vision. It also involves identifying key barriers and opportunities and developing strategies to achieve the vision. Visioning is a powerful tool for inspiring change and creating a more positive future.

Future Studies Methods

There are several methods that can be used in future studies, some of which include:

• Trend Analysis: This involves examining past and present trends to predict future developments. This method can be used to analyze demographic, economic, technological, and other trends that may impact the future.
• Scenario Planning: This involves developing multiple scenarios or stories about possible futures, based on different assumptions and factors. It can help identify potential risks and opportunities and aid decision-making.
• Delphi Method : This is a structured process that involves gathering opinions and feedback from a panel of experts over multiple rounds. It can be used to explore complex or uncertain topics and can help identify areas of agreement and disagreement.
• Horizon Scanning : This involves scanning the environment for signals of change, such as emerging technologies, social trends, or policy changes, and analyzing their potential impact on the future.
• Backcasting : This involves starting from a desired future outcome and working backward to identify the steps needed to achieve that outcome. It can help identify alternative paths and actions needed to reach a particular goal.
• Simulation and Modeling : This involves using computer models to simulate and test different scenarios and outcomes. It can help predict the likely outcomes of different decisions and policies and identify potential risks and opportunities.
• Wild Cards: This involves identifying low-probability, high-impact events or trends that could significantly impact the future. It can help organizations prepare for unexpected events and develop more resilient strategies.

Example of Future Studies

Here are some examples of future studies:

• Technology foresight: This involves the analysis of emerging technologies and their potential impacts on society and the economy. This could include the development of artificial intelligence, quantum computing, nanotechnology, and biotechnology.
• Environmental foresight : This focuses on the potential impacts of climate change, resource depletion, and environmental degradation on human societies and the planet. It involves exploring possible solutions and strategies for sustainable development.
• Social foresight: This involves the study of social and cultural trends, including changing demographics, urbanization, and globalization. It explores the potential impacts of these trends on society and how they may shape the future.
• Economic foresight: This involves the analysis of economic trends and developments, including globalization, trade, and the rise of new industries. It explores the potential impacts of these trends on the economy and society.
• Political foresight: This involves the study of political trends and developments, including changes in governance structures, geopolitical shifts, and the rise of new political movements. It explores the potential impacts of these trends on society and the world order.

Applications of Future Studies

Here are some applications of future studies:

• Business : Future studies can help companies anticipate market trends, identify emerging technologies, and develop strategies to stay ahead of the competition. It can also help them identify potential risks and opportunities, and plan for long-term growth.
• Public Policy: Future studies can inform policy decisions by identifying potential problems and opportunities that may arise in the future. This can include analyzing demographic trends, emerging technologies, and changes in the global economy.
• Education : Future studies can help educators prepare students for the future by identifying the skills and knowledge they will need to succeed in a rapidly changing world. It can also help them identify emerging trends in education, such as online learning, personalized learning, and new teaching methods.
• Environment : Future studies can help governments and organizations anticipate and prepare for environmental changes, such as climate change and natural disasters. It can also help them identify solutions to environmental problems and promote sustainability.
• Healthcare : Future studies can help healthcare providers anticipate and prepare for changes in healthcare delivery, such as new technologies and treatments. It can also help them identify potential health risks and develop preventive measures.
• Technology : Future studies can help companies and governments anticipate and prepare for new technological advances, such as artificial intelligence, quantum computing, and biotechnology. It can also help them identify potential risks and ethical concerns associated with these technologies.

Purpose of Future Studies

The purposes of future studies can be broadly categorized as follows:

• Anticipation : Future studies aims to identify and anticipate potential future trends, developments, and scenarios. By studying past and present trends, analyzing data and expert opinions, and using various forecasting methods, future studies can help individuals, organizations, and societies prepare for the future and take proactive measures to shape it.
• Innovation : Future studies can help stimulate innovation by identifying emerging opportunities, challenges, and gaps in knowledge or technology. By exploring alternative futures and challenging current assumptions, future studies can inspire new thinking and creativity that can lead to breakthrough solutions.
• Strategy : Future studies can inform strategic decision-making by helping individuals and organizations identify and evaluate different future scenarios and their potential consequences. By considering multiple possible futures, future studies can help individuals and organizations develop more robust and adaptable strategies that can withstand a range of future challenges and uncertainties.
• Policy : Future studies can inform policy development and implementation by providing insights into the potential long-term impacts of different policy options. By analyzing the likely consequences of different policies under different future scenarios, future studies can help policymakers make more informed and sustainable decisions.
• Education : Future studies can help educate individuals and communities about the potential future challenges and opportunities they may face. By raising awareness about the potential consequences of different actions or inactions, future studies can empower individuals and communities to take proactive measures to shape their own futures.

Advantages of Future Studies

Here are some advantages of future studies:

• Better decision-making : By analyzing potential future scenarios and trends, organizations and individuals can make more informed decisions about investments, strategies, and operations. Future studies can help decision-makers understand the potential risks and opportunities of different paths, enabling them to make more informed choices.
• Anticipation of change: Future studies can help individuals and organizations anticipate changes in their environment, allowing them to prepare for and adapt to changes more effectively. This can help prevent or mitigate negative consequences and take advantage of new opportunities.
• Innovation and creativity : Future studies can help stimulate innovation and creativity by providing new perspectives and ideas. By thinking about possible futures, individuals and organizations can develop new products, services, and processes that are better adapted to the changing environment.
• Improved resilience: Future studies can help individuals and organizations build resilience by identifying potential risks and developing strategies to mitigate them. By anticipating potential threats and challenges, organizations can prepare contingency plans and adapt more quickly to changing circumstances.
• Increased competitiveness: Future studies can help organizations gain a competitive advantage by identifying emerging trends and opportunities before their competitors. By understanding potential future scenarios, organizations can make strategic investments and position themselves for future success.

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The past, present, and future of consumer research

Maayan s. malter.

1 Columbia Business School, Columbia University, New York, NY USA

Morris B. Holbrook

Barbara e. kahn.

2 The Wharton School, University of Pennsylvania, Philadelphia, PA USA

Jeffrey R. Parker

3 Department of Marketing, University of Illinois at Chicago, Chicago, IL USA

Donald R. Lehmann

In this article, we document the evolution of research trends (concepts, methods, and aims) within the field of consumer behavior, from the time of its early development to the present day, as a multidisciplinary area of research within marketing. We describe current changes in retailing and real-world consumption and offer suggestions on how to use observations of consumption phenomena to generate new and interesting consumer behavior research questions. Consumption continues to change with technological advancements and shifts in consumers’ values and goals. We cannot know the exact shape of things to come, but we polled a sample of leading scholars and summarize their predictions on where the field may be headed in the next twenty years.

Introduction

Beginning in the late 1950s, business schools shifted from descriptive and practitioner-focused studies to more theoretically driven and academically rigorous research (Dahl et al. 1959 ). As the field expanded from an applied form of economics to embrace theories and methodologies from psychology, sociology, anthropology, and statistics, there was an increased emphasis on understanding the thoughts, desires, and experiences of individual consumers. For academic marketing, this meant that research not only focused on the decisions and strategies of marketing managers but also on the decisions and thought processes on the other side of the market—customers.

Since then, the academic study of consumer behavior has evolved and incorporated concepts and methods, not only from marketing at large but also from related social science disciplines, and from the ever-changing landscape of real-world consumption behavior. Its position as an area of study within a larger discipline that comprises researchers from diverse theoretical backgrounds and methodological training has stirred debates over its identity. One article describes consumer behavior as a multidisciplinary subdiscipline of marketing “characterized by the study of people operating in a consumer role involving acquisition, consumption, and disposition of marketplace products, services, and experiences” (MacInnis and Folkes 2009 , p. 900).

This article reviews the evolution of the field of consumer behavior over the past half century, describes its current status, and predicts how it may evolve over the next twenty years. Our review is by no means a comprehensive history of the field (see Schumann et al. 2008 ; Rapp and Hill 2015 ; Wang et al. 2015 ; Wilkie and Moore 2003 , to name a few) but rather focuses on a few key thematic developments. Though we observe many major shifts during this period, certain questions and debates have persisted: Does consumer behavior research need to be relevant to marketing managers or is there intrinsic value from studying the consumer as a project pursued for its own sake? What counts as consumption: only consumption from traditional marketplace transactions or also consumption in a broader sense of non-marketplace interactions? Which are the most appropriate theoretical traditions and methodological tools for addressing questions in consumer behavior research?

A brief history of consumer research over the past sixty years—1960 to 2020

In 1969, the Association for Consumer Research was founded and a yearly conference to share marketing research specifically from the consumer’s perspective was instituted. This event marked the culmination of the growing interest in the topic by formalizing it as an area of research within marketing (consumer psychology had become a formalized branch of psychology within the APA in 1960). So, what was consumer behavior before 1969? Scanning current consumer-behavior doctoral seminar syllabi reveals few works predating 1969, with most of those coming from psychology and economics, namely Herbert Simon’s A Behavioral Model of Rational Choice (1955), Abraham Maslow’s A Theory of Human Motivation (1943), and Ernest Dichter’s Handbook of Consumer Motivations (1964). In short, research that illuminated and informed our understanding of consumer behavior prior to 1969 rarely focused on marketing-specific topics, much less consumers or consumption (Dichter’s handbook being a notable exception). Yet, these works were crucial to the rise of consumer behavior research because, in the decades after 1969, there was a shift within academic marketing to thinking about research from a behavioral or decision science perspective (Wilkie and Moore 2003 ). The following section details some ways in which this shift occurred. We draw on a framework proposed by the philosopher Larry Laudan ( 1986 ), who distinguished among three inter-related aspects of scientific inquiry—namely, concepts (the relevant ideas, theories, hypotheses, and constructs); methods (the techniques employed to test and validate these concepts); and aims (the purposes or goals that motivate the investigation).

Key concepts in the late - 1960s

During the late-1960s, we tended to view the buyer as a computer-like machine for processing information according to various formal rules that embody economic rationality to form a preference for one or another option in order to arrive at a purchase decision. This view tended to manifest itself in a couple of conspicuous ways. The first was a model of buyer behavior introduced by John Howard in 1963 in the second edition of his marketing textbook and quickly adopted by virtually every theorist working in our field—including, Howard and Sheth (of course), Engel-Kollat-&-Blackwell, Franco Nicosia, Alan Andreasen, Jim Bettman, and Joel Cohen. Howard’s great innovation—which he based on a scheme that he had found in the work of Plato (namely, the linkages among Cognition, Affect, and Conation)—took the form of a boxes-and-arrows formulation heavily influenced by the approach to organizational behavior theory that Howard (University of Pittsburgh) had picked up from Herbert Simon (Carnegie Melon University). The model represented a chain of events

where I = inputs of information (from advertising, word-of-mouth, brand features, etc.); C = cognitions (beliefs or perceptions about a brand); A = Affect (liking or preference for the brand); B = behavior (purchase of the brand); and S = satisfaction (post-purchase evaluation of the brand that feeds back onto earlier stages of the sequence, according to a learning model in which reinforced behavior tends to be repeated). This formulation lay at the heart of Howard’s work, which he updated, elaborated on, and streamlined over the remainder of his career. Importantly, it informed virtually every buyer-behavior model that blossomed forth during the last half of the twentieth century.

To represent the link between cognitions and affect, buyer-behavior researchers used various forms of the multi-attribute attitude model (MAAM), originally proposed by psychologists such as Fishbein and Rosenberg as part of what Fishbein and Ajzen ( 1975 ) called the theory of reasoned action. Under MAAM, cognitions (beliefs about brand attributes) are weighted by their importance and summed to create an explanation or prediction of affect (liking for a brand or preference for one brand versus another), which in turn determines behavior (choice of a brand or intention to purchase a brand). This took the work of economist Kelvin Lancaster (with whom Howard interacted), which assumed attitude was based on objective attributes, and extended it to include subjective ones (Lancaster 1966 ; Ratchford 1975 ). Overall, the set of concepts that prevailed in the late-1960s assumed the buyer exhibited economic rationality and acted as a computer-like information-processing machine when making purchase decisions.

Favored methods in the late-1960s

The methods favored during the late-1960s tended to be almost exclusively neo-positivistic in nature. That is, buyer-behavior research adopted the kinds of methodological rigor that we associate with the physical sciences and the hypothetico-deductive approaches advocated by the neo-positivistic philosophers of science.

Thus, the accepted approaches tended to be either experimental or survey based. For example, numerous laboratory studies tested variations of the MAAM and focused on questions about how to measure beliefs, how to weight the beliefs, how to combine the weighted beliefs, and so forth (e.g., Beckwith and Lehmann 1973 ). Here again, these assumed a rational economic decision-maker who processed information something like a computer.

Seeking rigor, buyer-behavior studies tended to be quantitative in their analyses, employing multivariate statistics, structural equation models, multidimensional scaling, conjoint analysis, and other mathematically sophisticated techniques. For example, various attempts to test the ICABS formulation developed simultaneous (now called structural) equation models such as those deployed by Farley and Ring ( 1970 , 1974 ) to test the Howard and Sheth ( 1969 ) model and by Beckwith and Lehmann ( 1973 ) to measure halo effects.

Aims in the late-1960s

During this time period, buyer-behavior research was still considered a subdivision of marketing research, the purpose of which was to provide insights useful to marketing managers in making strategic decisions. Essentially, every paper concluded with a section on “Implications for Marketing Managers.” Authors who failed to conform to this expectation could generally count on having their work rejected by leading journals such as the Journal of Marketing Research ( JMR ) and the Journal of Marketing ( JM ).

Summary—the three R’s in the late-1960s

Starting in the late-1960s to the early-1980s, virtually every buyer-behavior researcher followed the traditional approach to concepts, methods, and aims, now encapsulated under what we might call the three R’s —namely, rationality , rigor , and relevance . However, as we transitioned into the 1980s and beyond, that changed as some (though by no means all) consumer researchers began to expand their approaches and to evolve different perspectives.

Concepts after 1980

In some circles, the traditional emphasis on the buyer’s rationality—that is, a view of the buyer as a rational-economic, decision-oriented, information-processing, computer-like machine for making choices—began to evolve in at least two primary ways.

First, behavioral economics (originally studied in marketing under the label Behavioral Decision Theory)—developed in psychology by Kahneman and Tversky, in economics by Thaler, and applied in marketing by a number of forward-thinking theorists (e.g., Eric Johnson, Jim Bettman, John Payne, Itamar Simonson, Jay Russo, Joel Huber, and more recently, Dan Ariely)—challenged the rationality of consumers as decision-makers. It was shown that numerous commonly used decision heuristics depart from rational choice and are exceptions to the traditional assumptions of economic rationality. This trend shed light on understanding consumer financial decision-making (Prelec and Loewenstein 1998 ; Gourville 1998 ; Lynch Jr 2011 ) and how to develop “nudges” to help consumers make better decisions for their personal finances (summarized in Johnson et al. 2012 ).

Second, the emerging experiential view (anticipated by Alderson, Levy, and others; developed by Holbrook and Hirschman, and embellished by Schmitt, Pine, and Gilmore, and countless followers) regarded consumers as flesh-and-blood human beings (rather than as information-processing computer-like machines), focused on hedonic aspects of consumption, and expanded the concepts embodied by ICABS (Table ​ (Table1 1 ).

Extended ICABS Framework after 1980

Methods after 1980

The two burgeoning areas of research—behavioral economics and experiential theories—differed in their methodological approaches. The former relied on controlled randomized experiments with a focus on decision strategies and behavioral outcomes. For example, experiments tested the process by which consumers evaluate options using information display boards and “Mouselab” matrices of aspects and attributes (Payne et al. 1988 ). This school of thought also focused on behavioral dependent measures, such as choice (Huber et al. 1982 ; Simonson 1989 ; Iyengar and Lepper 2000 ).

The latter was influenced by post-positivistic philosophers of science—such as Thomas Kuhn, Paul Feyerabend, and Richard Rorty—and approaches expanded to include various qualitative techniques (interpretive, ethnographic, humanistic, and even introspective methods) not previously prominent in the field of consumer research. These included:

• Interpretive approaches —such as those drawing on semiotics and hermeneutics—in an effort to gain a richer understanding of the symbolic meanings involved in consumption experiences;
• Ethnographic approaches — borrowed from cultural anthropology—such as those illustrated by the influential Consumer Behavior Odyssey (Belk et al. 1989 ) and its discoveries about phenomena related to sacred aspects of consumption or the deep meanings of collections and other possessions;
• Humanistic approaches —such as those borrowed from cultural studies or from literary criticism and more recently gathered together under the general heading of consumer culture theory ( CCT );
• Introspective or autoethnographic approaches —such as those associated with a method called subjective personal introspection ( SPI ) that various consumer researchers like Sidney Levy and Steve Gould have pursued to gain insights based on their own private lives.

These qualitative approaches tended not to appear in the more traditional journals such as the Journal of Marketing , Journal of Marketing Research , or Marketing Science . However, newer journals such as Consumption, Markets, & Culture and Marketing Theory began to publish papers that drew on the various interpretive, ethnographic, humanistic, or introspective methods.

Aims after 1980

In 1974, consumer research finally got its own journal with the launch of the Journal of Consumer Research ( JCR ). The early editors of JCR —especially Bob Ferber, Hal Kassarjian, and Jim Bettman—held a rather divergent attitude about the importance or even the desirability of managerial relevance as a key goal of consumer studies. Under their influence, some researchers began to believe that consumer behavior is a phenomenon worthy of study in its own right—purely for the purpose of understanding it better. The journal incorporated articles from an array of methodologies: quantitative (both secondary data analysis and experimental techniques) and qualitative. The “right” balance between theoretical insight and substantive relevance—which are not in inherent conflict—is a matter of debate to this day and will likely continue to be debated well into the future.

Summary—the three I’s after 1980

In sum, beginning in the early-1980s, consumer research branched out. Much of the work in consumer studies remained within the earlier tradition of the three R’s—that is, rationality (an information-processing decision-oriented buyer), rigor (neo-positivistic experimental designs and quantitative techniques), and relevance (usefulness to marketing managers). Nonetheless, many studies embraced enlarged views of the three major aspects that might be called the three I’s —that is, irrationality (broadened perspectives that incorporate illogical, heuristic, experiential, or hedonic aspects of consumption), interpretation (various qualitative or “postmodern” approaches), and intrinsic motivation (the joy of pursuing a managerially irrelevant consumer study purely for the sake of satisfying one’s own curiosity, without concern for whether it does or does not help a marketing practitioner make a bigger profit).

The present—the consumer behavior field today

Present concepts.

In recent years, technological changes have significantly influenced the nature of consumption as the customer journey has transitioned to include more interaction on digital platforms that complements interaction in physical stores. This shift poses a major conceptual challenge in understanding if and how these technological changes affect consumption. Does the medium through which consumption occurs fundamentally alter the psychological and social processes identified in earlier research? In addition, this shift allows us to collect more data at different stages of the customer journey, which further allows us to analyze behavior in ways that were not previously available.

Revisiting the ICABS framework, many of the previous concepts are still present, but we are now addressing them through a lens of technological change (Table ​ (Table2 2 ). In recent years, a number of concepts (e.g., identity, beliefs/lay theories, affect as information, self-control, time, psychological ownership, search for meaning and happiness, social belonging, creativity, and status) have emerged as integral factors that influence and are influenced by consumption. To better understand these concepts, a number of influential theories from social psychology have been adopted into consumer behavior research. Self-construal (Markus and Kitayama 1991 ), regulatory focus (Higgins 1998 ), construal level (Trope and Liberman 2010 ), and goal systems (Kruglanski et al. 2002 ) all provide social-cognition frameworks through which consumer behavior researchers study the psychological processes behind consumer behavior. This “adoption” of social psychological theories into consumer behavior is a symbiotic relationship that further enhances the theories. Tory Higgins happily stated that he learned more about his own theories from the work of marketing academics (he cited Angela Lee and Michel Pham) in further testing and extending them.

ICABS framework in the digital age

Present Methods

Not only have technological advancements changed the nature of consumption but they have also significantly influenced the methods used in consumer research by adding both new sources of data and improved analytical tools (Ding et al. 2020 ). Researchers continue to use traditional methods from psychology in empirical research (scale development, laboratory experiments, quantitative analyses, etc.) and interpretive approaches in qualitative research. Additionally, online experiments using participants from panels such as Amazon Mechanical Turk and Prolific have become commonplace in the last decade. While they raise concerns about the quality of the data and about the external validity of the results, these online experiments have greatly increased the speed and decreased the cost of collecting data, so researchers continue to use them, albeit with some caution. Reminiscent of the discussion in the 1970s and 1980s about the use of student subjects, the projectability of the online responses and of an increasingly conditioned “professional” group of online respondents (MTurkers) is a major concern.

Technology has also changed research methodology. Currently, there is a large increase in the use of secondary data thanks to the availability of Big Data about online and offline behavior. Methods in computer science have advanced our ability to analyze large corpuses of unstructured data (text, voice, visual images) in an efficient and rigorous way and, thus, to tap into a wealth of nuanced thoughts, feelings, and behaviors heretofore only accessible to qualitative researchers through laboriously conducted content analyses. There are also new neuro-marketing techniques like eye-tracking, fMRI’s, body arousal measures (e.g., heart rate, sweat), and emotion detectors that allow us to measure automatic responses. Lastly, there has been an increase in large-scale field experiments that can be run in online B2C marketplaces.

Present Aims

Along with a focus on real-world observations and data, there is a renewed emphasis on managerial relevance. Countless conference addresses and editorials in JCR , JCP , and other journals have emphasized the importance of making consumer research useful outside of academia—that is, to help companies, policy makers, and consumers. For instance, understanding how the “new” consumer interacts over time with other consumers and companies in the current marketplace is a key area for future research. As global and social concerns become more salient in all aspects of life, issues of long-term sustainability, social equality, and ethical business practices have also become more central research topics. Fortunately, despite this emphasis on relevance, theoretical contributions and novel ideas are still highly valued. An appropriate balance of theory and practice has become the holy grail of consumer research.

The effects of the current trends in real-world consumption will increase in magnitude with time as more consumers are digitally native. Therefore, a better understanding of current consumer behavior can give us insights and help predict how it will continue to evolve in the years to come.

The future—the consumer behavior field in 2040 1

Niels Bohr once said, “Prediction is very difficult, especially if it’s about the future.” Indeed, it would be a fool’s errand for a single person to hazard a guess about the state of the consumer behavior field twenty years from now. Therefore, predictions from 34 active consumer researchers were collected to address this task. Here, we briefly summarize those predictions.

Future Concepts

While few respondents proffered guesses regarding specific concepts that would be of interest twenty years from now, many suggested broad topics and trends they expected to see in the field. Expectations for topics could largely be grouped into three main areas. Many suspected that we will be examining essentially the same core topics, perhaps at a finer-grained level, from different perspectives or in ways that we currently cannot utilize due to methodological limitations (more on methods below). A second contingent predicted that much research would center on the impending crises the world faces today, most mentioning environmental and social issues (the COVID-19 pandemic had not yet begun when these predictions were collected and, unsurprisingly, was not anticipated by any of our respondents). The last group, citing the widely expected profound impact of AI on consumers’ lives, argued that AI and other technology-related topics will be dominant subjects in consumer research circa 2040.

While the topic of technology is likely to be focal in the field, our current expectations for the impact of technology on consumers’ lives are narrower than it should be. Rather than merely offering innumerable conveniences and experiences, it seems likely that technology will begin to be integrated into consumers’ thoughts, identities, and personal relationships—probably sooner than we collectively expect. The integration of machines into humans’ bodies and lives will present the field with an expanding list of research questions that do not exist today. For example, how will the concepts of the self, identity, privacy, and goal pursuit change when web-connected technology seamlessly integrates with human consciousness and cognition? Major questions will also need to be answered regarding philosophy of mind, ethics, and social inequality. We suspect that the impact of technology on consumers and consumer research will be far broader than most consumer-behavior researchers anticipate.

As for broader trends within consumer research, there were two camps: (1) those who expect (or hope) that dominant theories (both current and yet to be developed) will become more integrated and comprehensive and (2) those who expect theoretical contributions to become smaller and smaller, to the point of becoming trivial. Both groups felt that current researchers are filling smaller cracks than before, but disagreed on how this would ultimately be resolved.

Future Methods

As was the case with concepts, respondents’ expectations regarding consumer-research methodologies in 2030 can also be divided into three broad baskets. Unsurprisingly, many indicated that we would be using many technologies not currently available or in wide use. Perhaps more surprising was that most cited the use of technology such as AI, machine-learning algorithms, and robots in designing—as opposed to executing or analyzing—experiments. (Some did point to the use of technologies such as virtual reality in the actual execution of experiments.) The second camp indicated that a focus on reliable and replicable results (discussed further below) will encourage a greater tendency for pre-registering studies, more use of “Big Data,” and a demand for more studies per paper (versus more papers per topic, which some believe is a more fruitful direction). Finally, the third lot indicated that “real data” would be in high demand, thereby necessitating the use of incentive-compatible, consequential dependent variables and a greater prevalence of field studies in consumer research.

As a result, young scholars would benefit from developing a “toolkit” of methodologies for collecting and analyzing the abundant new data of interest to the field. This includes (but is not limited to) a deep understanding of designing and implementing field studies (Gerber and Green 2012 ), data analysis software (R, Python, etc.), text mining and analysis (Humphreys and Wang 2018 ), and analytical tools for other unstructured forms of data such as image and sound. The replication crisis in experimental research means that future scholars will also need to take a more critical approach to validity (internal, external, construct), statistical power, and significance in their work.

Future Aims

While there was an air of existential concern about the future of the field, most agreed that the trend will be toward increasing the relevance and reliability of consumer research. Specifically, echoing calls from journals and thought leaders, the respondents felt that papers will need to offer more actionable implications for consumers, managers, or policy makers. However, few thought that this increased focus would come at the expense of theoretical insights, suggesting a more demanding overall standard for consumer research in 2040. Likewise, most felt that methodological transparency, open access to data and materials, and study pre-registration will become the norm as the field seeks to allay concerns about the reliability and meaningfulness of its research findings.

Summary - Future research questions and directions

Despite some well-justified pessimism, the future of consumer research is as bright as ever. As we revised this paper amidst the COVID-19 pandemic, it was clear that many aspects of marketplace behavior, consumption, and life in general will change as a result of this unprecedented global crisis. Given this, and the radical technological, social, and environmental changes that loom on the horizon, consumer researchers will have a treasure trove of topics to tackle in the next ten years, many of which will carry profound substantive importance. While research approaches will evolve, the core goals will remain consistent—namely, to generate theoretically insightful, empirically supported, and substantively impactful research (Table ​ (Table3 3 ).

Future consumer behavior research questions

At any given moment in time, the focal concepts, methods, and aims of consumer-behavior scholarship reflect both the prior development of the field and trends in the larger scientific community. However, despite shifting trends, the core of the field has remained constant—namely, to understand the motivations, thought processes, and experiences of individuals as they consume goods, services, information, and other offerings, and to use these insights to develop interventions to improve both marketing strategy for firms and consumer welfare for individuals and groups. Amidst the excitement of new technologies, social trends, and consumption experiences, it is important to look back and remind ourselves of the insights the field has already generated. Effectively integrating these past findings with new observations and fresh research will help the field advance our understanding of consumer behavior.

1 The other papers use 2030 as a target year but we asked our survey respondents to make predictions for 2040 and thus we have a different future target year.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

• Beckwith NE, Lehmann DR. The importance of differential weights in multiple attribute models of consumer attitude. Journal of Marketing Research. 1973; 10 (2):141–145. doi: 10.1177/002224377301000203. [ CrossRef ] [ Google Scholar ]
• Belk RW, Wallendorf M, Sherry JF., Jr The sacred and the profane in consumer behavior: theodicy on the odyssey. Journal of Consumer Research. 1989; 16 (1):1–38. doi: 10.1086/209191. [ CrossRef ] [ Google Scholar ]
• Dahl, R. A., Haire, M., & Lazarsfeld, P. F. (1959). Social science research on business: product and potential . New York: Columbia University Press.
• Ding, Y., DeSarbo, W. S., Hanssens, D. M., Jedidi, K., Lynch Jr., J. G., & Lehmann, D. R. (2020). The past, present, and future of measurements and methods in marketing analysis. Marketing Letters, 10.1007/s11002-020-09527-7.
• Farley JU, Ring LW. An empirical test of the Howard-Sheth model of buyer behavior. Journal of Marketing Research. 1970; 7 (4):427–438. doi: 10.1177/002224377000700401. [ CrossRef ] [ Google Scholar ]
• Farley JU, Ring LW. “Empirical” specification of a buyer behavior model. Journal of Marketing Research. 1974; 11 (1):89–96. [ Google Scholar ]
• Fishbein M, Ajzen I. Belief, attitude, intention, and behavior: an introduction to theory and research. Reading: Addison-Wesley; 1975. [ Google Scholar ]
• Gerber, A. S., & Green, D. P. (2012). Field experiments: design, analysis, and interpretation . New York: WW Norton.
• Gourville JT. Pennies-a-day: the effect of temporal reframing on transaction evaluation. Journal of Consumer Research. 1998; 24 (4):395–408. doi: 10.1086/209517. [ CrossRef ] [ Google Scholar ]
• Higgins ET. Promotion and prevention: regulatory focus as a motivational principle. Advances in Experimental Social Psychology. 1998; 30 :1–46. [ Google Scholar ]
• Howard JA, Sheth J. The theory of buyer behavior. New York: Wiley; 1969. [ Google Scholar ]
• Huber J, Payne JW, Puto C. Adding asymmetrically dominated alternatives: violations of regularity and the similarity hypothesis. Journal of Consumer Research. 1982; 9 (1):90–98. doi: 10.1086/208899. [ CrossRef ] [ Google Scholar ]
• Humphreys A, Wang RJH. Automated text analysis for consumer research. Journal of Consumer Research. 2018; 44 (6):1274–1306. doi: 10.1093/jcr/ucx104. [ CrossRef ] [ Google Scholar ]
• Iyengar, S. S., & Lepper, M. R. (2000). When choice is demotivating: can one desire too much of a good thing? Journal of Personality and Social Psychology, 79 (6), 995–1006. [ PubMed ]
• Johnson EJ, Shu SB, Dellaert BG, Fox C, Goldstein DG, Häubl G, et al. Beyond nudges: tools of a choice architecture. Marketing Letters. 2012; 23 (2):487–504. doi: 10.1007/s11002-012-9186-1. [ CrossRef ] [ Google Scholar ]
• Kruglanski AW, Shah JY, Fishbach A, Friedman R, Chun WY, Sleeth-Keppler D. A theory of goal systems. Advances in Experimental Social Psychology. 2002; 34 :311–378. [ Google Scholar ]
• Lancaster K. A new theory of consumer behavior. Journal of Political Economy. 1966; 74 :132–157. doi: 10.1086/259131. [ CrossRef ] [ Google Scholar ]
• Laudan L. Methodology’s prospects. In PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association. 1986; 1986 (2):347–354. [ Google Scholar ]
• Lynch Jr., J. G. (2011). Introduction to the journal of marketing research special interdisciplinary issue on consumer financial decision making. Journal of Marketing Research, 48 (SPL) Siv–Sviii.
• MacInnis DJ, Folkes VS. The disciplinary status of consumer behavior: a sociology of science perspective on key controversies. Journal of Consumer Research. 2009; 36 (6):899–914. doi: 10.1086/644610. [ CrossRef ] [ Google Scholar ]
• Markus HR, Kitayama S. Culture and the self: implications for cognition, emotion, and motivation. Psychological Review. 1991; 98 (2):224–253. doi: 10.1037/0033-295X.98.2.224. [ CrossRef ] [ Google Scholar ]
• Payne JW, Bettman JR, Johnson EJ. Adaptive strategy selection in decision making. Journal of Experimental Psychology: Learning, Memory, and Cognition. 1988; 14 (3):534–552. [ Google Scholar ]
• Prelec D, Loewenstein G. The red and the black: mental accounting of savings and debt. Marketing Science. 1998; 17 (1):4–28. doi: 10.1287/mksc.17.1.4. [ CrossRef ] [ Google Scholar ]
• Rapp JM, Hill RP. Lordy, Lordy, look who’s 40! The Journal of Consumer Research reaches a milestone. Journal of Consumer Research. 2015; 42 (1):19–29. doi: 10.1093/jcr/ucv011. [ CrossRef ] [ Google Scholar ]
• Ratchford BT. The new economic theory of consumer behavior: an interpretive essay. Journal of Consumer Research. 1975; 2 (2):65–75. doi: 10.1086/208617. [ CrossRef ] [ Google Scholar ]
• Schumann, D. W., Haugtvedt, C. P., & Davidson, E. (2008). History of consumer psychology. In Haugtvedt, C. P., Herr, P. M., & Kardes, F. R. eds.  Handbook of consumer psychology (pp. 3-28). New York: Erlbaum.
• Simonson, I. (1989). Choice based on reasons: the case of attraction and compromise effects. Journal of Consumer Research, 16 (2), 158–174.
• Trope Y, Liberman N. Construal-level theory of psychological distance. Psychological Review. 2010; 117 (2):440–463. doi: 10.1037/a0018963. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wang X, Bendle NT, Mai F, Cotte J. The journal of consumer research at 40: a historical analysis. Journal of Consumer Research. 2015; 42 (1):5–18. doi: 10.1093/jcr/ucv009. [ CrossRef ] [ Google Scholar ]
• Wilkie WL, Moore ES. Scholarly research in marketing: exploring the “4 eras” of thought development. Journal of Public Policy & Marketing. 2003; 22 (2):116–146. doi: 10.1509/jppm.22.2.116.17639. [ CrossRef ] [ Google Scholar ]

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Suggestions for Future Research

Your dissertation needs to include suggestions for future research. Depending on requirements of your university, suggestions for future research can be either integrated into Research Limitations section or it can be a separate section.

You will need to propose 4-5 suggestions for future studies and these can include the following:

1. Building upon findings of your research . These may relate to findings of your study that you did not anticipate. Moreover, you may suggest future research to address unanswered aspects of your research problem.

2. Addressing limitations of your research . Your research will not be free from limitations and these may relate to formulation of research aim and objectives, application of data collection method, sample size, scope of discussions and analysis etc. You can propose future research suggestions that address the limitations of your study.

3. Constructing the same research in a new context, location and/or culture . It is most likely that you have addressed your research problem within the settings of specific context, location and/or culture. Accordingly, you can propose future studies that can address the same research problem in a different settings, context, location and/or culture.

4. Re-assessing and expanding theory, framework or model you have addressed in your research . Future studies can address the effects of specific event, emergence of a new theory or evidence and/or other recent phenomenon on your research problem.

My e-book,  The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step assistance  offers practical assistance to complete a dissertation with minimum or no stress. The e-book covers all stages of writing a dissertation starting from the selection to the research area to submitting the completed version of the work within the deadline. John Dudovskiy

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How the American middle class has changed in the past five decades

The middle class, once the economic stratum of a clear majority of American adults, has steadily contracted in the past five decades. The share of adults who live in middle-class households fell from 61% in 1971 to 50% in 2021, according to a new Pew Research Center analysis of government data.

From 2020: Are you in the American middle class? Find out with our income calculator

The shrinking of the middle class has been accompanied by an increase in the share of adults in the upper-income tier – from 14% in 1971 to 21% in 2021 – as well as an increase in the share who are in the lower-income tier, from 25% to 29%. These changes have occurred gradually, as the share of adults in the middle class decreased in each decade from 1971 to 2011, but then held steady through 2021.

The analysis below presents seven facts about how the economic status of the U.S. middle class and that of America’s major demographic groups have changed since 1971. A related analysis examines the impact of the coronavirus pandemic on the financial well-being of households in the lower-, middle- and upper-income tiers, with comparisons to the Great Recession era. (In the source data for both analyses, demographic figures refer to the 1971-2021 period, while income figures refer to the 1970-2020 period. Thus, the shares of adults in an income tier are based on their household incomes in the previous year.)

This report analyzes data from the Annual Social and Economic Supplements (ASEC) of the Current Population Survey (CPS) to study how the economic status of the American middle class has changed since 1971. It also examines the movement of demographic groups in and out of the American middle class and across lower- and upper-income tiers from 1971 to 2021.

The CPS is the U.S. government’s official source for monthly estimates of unemployment ; the ASEC, conducted in March each year, is the official source for its estimates of income and poverty . The COVID-19 outbreak has affected data collection efforts by the U.S. government in its surveys, limiting in-person data collection and affecting the response rate. It is possible that some measures of economic outcomes and how they vary across demographic groups are affected by these changes in data collection. This report makes use of updated weights released by the Census Bureau to correct for nonresponse in 2019, 2020 and 2021.

In this analysis, “middle-income” adults in 2021 are those with an annual household income that was two-thirds to double the national median income in 2020, after incomes have been adjusted for household size, or about $52,000 to $156,000 annually in 2020 dollars for a household of three. “Lower-income” adults have household incomes less than $52,000 and “upper-income” adults have household incomes greater than $156,000.

The income it takes to be middle income varies by household size, with smaller households requiring less to support the same lifestyle as larger households. The boundaries of the income tiers also vary across years with changes in the national median income. Read the methodology for more details.

The terms “middle income” and “middle class” are used interchangeably in this analysis for the sake of exposition. But being middle class can refer to more than just income, be it the level of education, the type of profession, economic security, home ownership, or one’s social and political values. Class also could simply be a matter of self-identification.

Household incomes have risen considerably since 1970, but those of middle-class households have not climbed nearly as much as those of upper-income households. The median income of middle-class households in 2020 was 50% greater than in 1970 ($90,131 vs. $59,934), as measured in 2020 dollars. These gains were realized slowly, but for the most part steadily, with the exception of the period from 2000 to 2010, the so-called “ lost decade ,” when incomes fell across the board.

The median income for lower-income households grew more slowly than that of middle-class households, increasing from $20,604 in 1970 to $29,963 in 2020, or 45%.

The rise in income from 1970 to 2020 was steepest for upper-income households. Their median income increased 69% during that timespan, from $130,008 to $219,572.

As a result of these changes, the gap in the incomes of upper-income and other households also increased. In 2020, the median income of upper-income households was 7.3 times that of lower-income households, up from 6.3 in 1970. The median income of upper-income households was 2.4 times that of middle-income households in 2020, up from 2.2 in 1970.

The share of aggregate U.S. household income held by the middle class has fallen steadily since 1970. The widening of the income gap and the shrinking of the middle class has led to a steady decrease in the share of U.S. aggregate income held by middle-class households. In 1970, adults in middle-income households accounted for 62% of aggregate income, a share that fell to 42% in 2020.

Meanwhile, the share of aggregate income accounted for by upper-income households has increased steadily, from 29% in 1970 to 50% in 2020. Part of this increase reflects the rising share of adults who are in the upper-income tier.

The share of U.S. aggregate income held by lower-income households edged down from 10% to 8% over these five decades, even though the proportion of adults living in lower-income households increased over this period.

Older Americans and Black adults made the greatest progress up the income ladder from 1971 to 2021. Among adults overall, the share who were in the upper-income tier increased from 14% in 1971 to 21% in 2021, or by 7 percentage points. Meanwhile, the share in the lower-income tier increased from 25% to 29%, or by 4 points. On balance, this represented a net gain of 3 percentage points in income status for all adults.

Those ages 65 and older made the most notable progress up the income ladder from 1971 to 2021. They increased their share in the upper-income tier while reducing their share in the lower-income tier, resulting in a net gain of 25 points. Progress among adults 65 and older was likely driven by an increase in labor force participation , rising educational levels and by the role of Social Security payments in reducing poverty.

Black adults, as well as married men and women, were also among the biggest gainers from 1971 to 2021, with net increases ranging from 12 to 14 percentage points.

On the other hand, not having at least a bachelor’s degree resulted in a notable degree of economic regression over this period. Adults with a high school diploma or less education, as well as those with some college experience but no degree, saw sizable increases in their shares in the lower-income tier in the past five decades. Although no single group of adults by education category moved up the income ladder from 1971 to 2021, adults overall realized gains by boosting their education levels . The share of adults 25 and older who had completed at least four years of college stood at 38% in 2021, compared with only 11% in 1971.

Progress up the income ladder for a demographic group does not necessarily signal its economic status in comparison with other groups at a given point in time. For example, in 2021, adults ages 65 and older and Black adults were still more likely than many other groups to be lower income, and less likely to be middle or upper income.

Married adults and those in multi-earner households made more progress up the income ladder from 1971 to 2021 than their immediate counterparts. Generally, partnered adults have better outcomes on a range of economic outcomes than the unpartnered. One reason is that marriage is increasingly linked to educational attainment , which bears fruit in terms of higher incomes.

Married men and women were distributed across the income tiers identically to each other in both 1971 and 2021. Both groups nearly doubled their shares in the upper-income tier in the past five decades, from 14% in 1971 to 27% in 2021. And neither group experienced an increase in the share in the lower-income tier.

Unmarried men and women were much more likely than their married counterparts to be in the lower-income tier in 2021. And unmarried men, in particular, experienced a sizable increase in their share in the lower-income tier from 1971 t0 2021 and a similarly large decrease in their share in the middle-income tier. Nonetheless, unmarried men are less likely than unmarried women to be lower income and more likely to be middle income.

Adults in households with more than one earner fare much better economically than adults in households with only one earner. In 2021, some 20% of adults in multi-earner households were in the lower-income tier, compared with 53% of adults in single-earner households. Also, adults in multi-earner households were more than twice as likely as adults in single-earner households to be in the upper-income tier in 2021. In the long haul, adults in single-earner households are among the groups who slid down the income ladder the most from 1971 to 2021.

Despite progress, Black and Hispanic adults trail behind other groups in their economic status. Although Black adults made some of the biggest strides up the income tiers from 1971 to 2021, they, along with Hispanic adults, are more likely to be in the lower-income tier than are White or Asian adults. About 40% of both Black and Hispanic adults were lower income in 2021, compared with 24% of White adults and 22% of Asian adults.

Black adults are the only major racial and ethnic group that did not experience a decrease in its middle-class share, which stood at 47% in 2021, about the same as in 1971. White adults are the only group in which more than half (52%) lived in middle-class households in 2021, albeit after declining from 63% in 1971. At the top end, only about one-in-ten Black and Hispanic adults were upper income in 2021, compared with one-in-four or more White and Asian adults.

The relative economic status of men and women has changed little from 1971 to 2021. Both experienced similar percentage point increases in the shares in the lower- and upper-income tiers, and both saw double-digit decreases in the shares who are middle class. Women remained more likely than men to live in lower-income households in 2021 (31% vs. 26%).

Adults 65 and older continue to lag economically, despite decades of progress. The share of adults ages 65 and older in the lower-income tier fell from 54% in 1971 to 37% in 2021. Their share in the middle class rose from 39% to 47% and their share in the upper-income tier increased from 7% to 16%. However, adults 65 and older are the only age group in which more than one-in-three adults are in lower-income households, and they are much less likely than adults ages 30 to 44 – as well as those ages 45 to 64 – to be in the upper-income tier.

All other age groups experienced an increase in the shares who are lower income from 1971 to 2021, as well as a decrease in the shares who are middle income. But they also saw increases in the shares who are upper income. Among adults ages 30 to 44, for instance, the share in upper-income households almost doubled, from 12% in 1971 to 21% in 2021.

There is a sizable and growing income gap between adults with a bachelor’s degree and those with lower levels of education. In 2021, about four-in-ten adults with at least a bachelor’s degree (39%) were in the upper-income tier, compared with 16% or less among those without a bachelor’s degree. The share of adults in the upper-income tier with at least a bachelor’s degree edged up from 1971 to 2021, while the share without a bachelor’s degree either edged down or held constant.

About half or a little more of adults with either some college education or a high school diploma only were in the middle class in 2021. But these two groups, along with those with less than a high school education, experienced notable drops in their middle class shares from 1971 to 2021 – and notable increases in the shares in the lower-income tier. In 2021, about four-in-ten adults with only a high school diploma or its equivalent (39%) were in the lower-income tier, about double the share in 1971.

Note: Here is the methodology for this analysis.

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• China releases world's first high-definition lunar geologic atlas

(ECNS) -- China released a set of 1:2.5 million lunar geologic atlas on Sunday. It is the first complete high-definition lunar geologic atlas in the world, providing basic map data for future lunar research and exploration.

The atlas was jointly compiled by academician Ouyang Ziyuan and researcher Liu Jianzhong from the Institute of Geochemistry, Chinese Academy of Sciences, along with scientists and cartographers from Jilin University, Shandong University, China University of Geosciences (Beijing), the Geological Institute of the Chinese Academy of Geological Sciences, and the Institute of Geographical Sciences and Natural Resources Research of the Chinese Academy of Sciences. The compilation work began in 2012.

The research and compilation team stated that the high-definition lunar geologic atlas is compiled in both Chinese and English.

The main map identifies and marks 12,341 impact craters, 81 impact basins, 17 types of rocks, and 14 types of structures on the global Moon, which  established a classification system for the subcategories of basin construction.

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