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Imagining the (Distant) Future of Work

June 2023
Academy of Management Discoveries 10(3)

DOI:10.5465/amd.2022.0130

Authors:

Nicky Dries

KU Leuven

Joost Luyckx

IESEG School of Management

Philip Rogiers

Universitat Ramon Llull, Esade

Across two datasets—a corpus of 485 print media articles and a multi-actor survey of Tech/Innovation experts, Authors/Journalists, Economy/Labor Market experts, Policy Makers/Public Administrators, and Engaged Citizens (N=570)—we build the case that the future of work is a fiction, not a fact; or better yet, a series of competing fictions prescribing what the future will or should look like. Using an abductive and curiosity-driven mixed-method analysis process we demonstrate that different narratives about the future of work stand in direct relation to specific actors in the public debate, both through framing tactics used by narrators in the media, and through political and dispositional processes of narrative subscription. From these findings, we infer that research on the future of work is in need of a paradigm shift: from ‘predictions’ to ‘imaginaries’. This, we argue, will help counter deterministic and depoliticized understandings of the future of work. We propose an integration of theory around framing contests, field frames, narrative subscription, and corresponsive mechanisms to offer a plausible account of our empirical discoveries and develop an agenda for further research. As the practical implications of our research show, the future of work does not need to be something that happens ‘to us’—instead, the future can be what we ‘make it’.

Competing Imaginaries of the Future of Work in the Public Debate.

…

Continued.

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Imagining the (Distant) Future of Work

Journal:

Academy of Management Discoveries

Manuscript ID

AMD-2022-0130.R2

Manuscript Type:

Special Research Forum: The Human Side of the Future of Work

Keywords:

.....Science and Society, Sense-making/giving < Cognition & Neuro,

Technology < Human Resource Management & Work Design,

Power/Influence/Politics < Interpersonal & Team Processes, Occupations

& Professions, Social Movements < Organizational & Management

Theory, Innovation Diffusion & Adoption < Technology & Innovation

Management, Innovation & Technology Policy < Technology & Innovation

Management, Technological Change, Search, & Types of Innovation <

Technology & Innovation Management

Abstract:

Across two datasets—a corpus of 485 print media articles and a multi-

actor survey of Tech/Innovation experts, Authors/Journalists,

Economy/Labor Market experts, Policy Makers/Public Administrators, and

Engaged Citizens (N=570)—we build the case that the future of work is a

fiction, not a fact; or better yet, a series of competing fictions prescribing

what the future will or should look like. Using an abductive and curiosity-

driven mixed-method analysis process we demonstrate that different

narratives about the future of work stand in direct relation to specific

actors in the public debate, both through framing tactics used by

narrators in the media, and through political and dispositional processes

of narrative subscription. From these findings, we infer that research on

the future of work is in need of a paradigm shift: from ‘predictions’ to

‘imaginaries’. This, we argue, will help counter deterministic and

depoliticized understandings of the future of work. We propose an

integration of theory around framing contests, field frames, narrative

subscription, and corresponsive mechanisms to offer a plausible account

of our empirical discoveries and develop an agenda for further research.

As the practical implications of our research show, the future of work

does not need to be something that happens ‘to us’—instead, the future

can be what we ‘make it’.

Academy of Management Discoveries

Imagining the (Distant) Future of Work

Nicky Dries

KU Leuven, Belgium

BI Norwegian Business School Oslo, Norway

nicky.dries@kuleuven.be

Joost Luyckx

IÉSEG School of Management (LEM-CNRS UMR 9221)

Lille, France

j.luyckx@ieseg.fr

Philip Rogiers

University Ramon Llull, Esade, Barcelona, Spain

KU Leuven, Belgium

philip.rogiers@esade.edu

Acknowledgements. This research was funded by an FWO project grant (G074418N) and a C1 grant from

the KU Leuven (DON-D2880-C14/17/014), both held by Nicky Dries. The authors wish to express their

gratitude towards Lauren Howe, the editor for this paper, and two anonymous reviewers. Not only were

their comments constructive and insightful, they were also kind.

Page 1 of 60 Academy of Management Discoveries

IMAGINING THE (DISTANT) FUTURE OF WORK

ABSTRACT

Across two datasets—a corpus of 485 print media articles and a multi-actor survey of

Tech/Innovation experts, Authors/Journalists, Economy/Labor Market experts, Policy

Makers/Public Administrators, and Engaged Citizens (N=570)—we build the case that the future

of work is a fiction, not a fact; or better yet, a series of competing fictions prescribing what the

future will or should look like. Using an abductive and curiosity-driven mixed-method analysis

process we demonstrate that different narratives about the future of work stand in direct relation

to specific actors in the public debate, both through framing tactics used by narrators in the

media, and through political and dispositional processes of narrative subscription. From these

findings, we infer that research on the future of work is in need of a paradigm shift: from

‘predictions’ to ‘imaginaries’. This, we argue, will help counter deterministic and depoliticized

understandings of the future of work. We propose an integration of theory around framing

contests, field frames, narrative subscription, and corresponsive mechanisms to offer a plausible

account of our empirical discoveries and develop an agenda for further research. As the practical

implications of our research show, the future of work does not need to be something that happens

‘to us’—instead, the future can be what we ‘make it’.

Keywords. Future of work, Narratives, Counternarratives, Imaginaries, Framing contests,

Corresponsive mechanisms, AI, Artificial intelligence, Automation, Robots, Technology

INTRODUCTION

The research reported in this paper started out as a series of anecdotal observations that

culminated into curiosity-driven data collection. Around 2018, we started to read a lot of books

on the Fourth Industrial Revolution and disruptive innovation (e.g., Bregman, 2017; Harari,

2017; Hinssen, 2017; Janssens, 2018; Kurzweil, 2005), which spurred three (existential)

questions about the research we had been doing: First, if this is all true, will our field not very

soon become very outdated in terms of how we understand and study the world of work?

Second, how can we as social scientists (and not AI or robotics specialists) understand and

contribute something of value to this topic area? And third, how can we as researchers

empirically study the future (rather than being ‘futurism gurus’ who only write think-pieces);

what data would we use for that? In this period, we started having conversations with people

from different walks of life about the books and news articles we had been reading about the

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future. Something struck us: people reacted very differently (even emotionally) to different

scenarios of the future of work depending on what type of person they were. Our rational,

economist Dean was quick to dismiss stories about disruptive innovation as ‘science fiction’; our

sweet, people-loving colleague expressed a fear that we would lose our humanity through novel

technologies; our working-class Dad wished he could build a time machine and transport himself

back to the society he knew when he was younger; and our misanthropic, environmentalist friend

told us the world could blow up for all she cared, as humanity is asking for it.

Within our own author team, we also held different perspectives on the future of work,

which we could trace back to our different educational backgrounds—psychology, history, and

business economics. A crucial insight that arose from our team conversations was that

historically, work has always been a highly politicized phenomenon, especially at points in

history when radical transformations were taking place, such as during the Industrial Revolution

(Lucassen, 2021). Contemporary debates around the future of work, however, strike us as both

technologically deterministic and depoliticized. Deterministic in the sense that there seems to be

a belief that it is the technological capabilities and innovations themselves that determine the

nature of social, economic, and political institutions shaping work; depoliticized in that the

human-societal root causes and processes of change are largely invisibilized in the public debate

(Kelly, 2022). Where conflict and struggle are acknowledged, the framing seems to be one of

mankind standing united against an externalized and socially disembodied enemy (Kenis &

Mathijs, 2014), like hyperintelligent AI or robots coming to steal our jobs (Fleming, 2019). It is

clear, however, that technology does not invent or implement itself, nor does it fire people—

people do (Dinerstein & Pitts, 2021; Howcroft & Taylor, 2022).

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Interestingly, under the guise of expertise and backed by ‘objective’ arguments, numbers,

and figures, current debates seem to be going in circles as competing schools of experts are all

adamant that their predictions about the future of work are more right (Cave & Dihal, 2019). Not

only are these competing predictions confusing to the public—and thus likely to hurt issue

engagement (Miller, Krosnick, & Fabrigar, 2017)—this ‘prediction paradigm’ also renders

invisible the fact that the future is construed over time, as we go along, within a social and

political space (Gümüsay & Reinecke, 2022). An additional challenge is posed by what Harari

(2015) calls second-order chaos, referring to the crucial difference between future events of

which the outcome is not influenced by predictions—like the weather—and future events that are

influenced by the predictions themselves—like elections, or the stock market. This implies that to

predict the future is to (attempt to) influence its outcomes, casting an entirely different, political

light on the discursive struggles around the future of work taking place between different groups

of actors in the public debate (Urry, 2016). Such politicized explanations for why very different

predictions about the future of work exist in the public domain have to date been largely absent

from the literature (Kelly, 2022).

In the present paper, we thus set out to examine the following research question: How

and why are competing narratives of the future of work construed, and subscribed to, by different

actors in the public debate? Following an abductive and interpretive process, we uncover seven

different narratives about the future of work in the public debate: Dataism, Exterminism,

Re/Upskilling, Augmentation, the Singularity, Job Destruction, and Work Deintensification.

Using two distinct sets of data—a selected corpus of print media articles and a multi-actor

survey—we show that each of these narratives stands in direct relation to specific actors in the

public debate, both through framing tactics used by narrators in the print media data (Kaplan,

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2008), and through political and dispositional processes of narrative subscription (i.e., the belief

of actors in narratives; Miller, 2019) in the survey data.

THEORETICAL BACKGROUND

What will the future of work look like? All throughout history, people have been

imagining, and telling stories about the future—driven, it seems, in equal parts by hope and fear

(Schoemaker, 2020). The word ‘robot’, for instance, was used for the very first time in 1920, in

Karl Čapek’s play Rossum’s Universal Robots. In it, robots end up revolting against their human

overlords, killing them, as they gain intelligence and become aware of their status as cheap labor

(Czarniawska & Joerges, 2019). Before that, in the late 19th century, Karl Marx already warned

his readers about the rise of technology in the workplace, which he saw as a tool of capitalism

that would reduce the worker to an extension or component of the machine. Other great thinkers

were more optimistic. John Maynard Keynes, for instance, predicted in the 1930s that by

century’s end, countries like the UK and the US would introduce a 15-hour work week thanks to

advances in technology (which, clearly, did not happen; cf. Graeber, 2013). Currently, the Fourth

Industrial Revolution—characterized by a fusion of technologies blurring the lines between the

physical, the digital, and the biological—is said to bring about changes in the world of work so

disruptive in scope and so exponential in pace, that they have no historical precedent (Schwab,

2018).

That said, not everyone agrees on the nature and implications of the changes on the

horizon. Generally speaking, three groups can be identified: optimists, pessimists, and sceptics

(Kelly, 2022; Muzio, Doh, Sarala, & Prescott, 2020). While optimists tend to focus on the

potential of new technologies to augment human labor (Raisch & Krakowski, 2020), pessimists

focus on the threat of automation and the massive job loss that would come with it (Frey &

Osborne, 2013). Sceptics, finally, believe that claims about the velocity and scope of projected

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transformations in the world of work are vastly exaggerated, and that the future will look more or

less like the present, with a few tweaks here and there (Schlogl, Weiss, & Prainsack, 2021).

So who is right: the optimists, the pessimists, or the sceptics? We argue that this is

actually the wrong question to ask. According to a new generation of economists, what these

different groups in the public debate are doing is not so much making objective predictions about

what the future will look like, but rather, pushing their preferred narratives—or in the case of the

pessimists, cautionary tales—about what they think the future should look like (Beckert &

Bronk, 2019). Conceptually, the ‘future of work’ can thus be understood as a set of competing

narratives, representing different views held by different actors (Cave & Dihal, 2019; Roux-

Rosier, Azambuja, & Islam, 2018). Narratives typically take the form of compelling stories, that

help people make sense of complexity by creating some sort of order in the ambiguous

information available around a specific issue (Buchanan & Dawson, 2007). Beckert and Bronk

(2019), in their essay advocating for a narrative turn in economics, write that competing

narratives of the future should be understood as instruments of political or market power, since

they— when internalized by sufficient numbers—influence outcomes.

We propose that news media analysis, although not a traditional method in management

research, is particularly well-suited for studying current-day public debates around the future of

work (Patton & Johns, 2007). Theoretically, media texts can be understood as sensegiving

devices, which are defined as “discursive devices that influence the sensemaking and meaning

construction of others, by providing important cues for sensemaking and a focal point for

interaction about these cues” (Maitlis & Christianson, 2014, p. 67). Research has consistently

demonstrated the central role of the mass media in shaping the knowledge, beliefs, values, and

social identities of the public (Fairclough, 1995). We might also add that, methodologically, the

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analysis of publicly available discourses and narratives is one of the only conceivable methods

for studying the future empirically—as the future has not happened yet, we cannot rely on the

data sources we would typically use to study phenomena relevant to the management field

(Augustine, Soderstrom, Milner, & Weber, 2019; Gümüsay & Reinecke, 2022).

Although the future of work is by all indicators a ‘booming’ topic, to date, empirical

research—especially research based on primary data—has been quite rare. The reason for this,

we believe, is obvious; it is hard to imagine how to collect empirical data on the future, as it is

not yet observable in reality today (Augustine et al., 2019). As a result, most existing studies on

the future of work can be classified as belonging to one of two types: a first type of study, that

looks at the implementation and usage of novel technologies in the present-day workplace; and a

second type of study, that attempts to predict the future of work based on macroeconomic labor

market indicators.

We argue that although both types of studies are certainly helpful in charting the

future of work both as a concept and a research area, they offer an incomplete understanding.

One might actually argue that the first type of study—that looks, for instance, at telework, e-

HRM, new organizational forms, gig work, and job redesign (see the review by Santana & Cobo,

2020)—is not about ‘the future’ at all, but about innovations in the present workplace. It is often

unclear whether these sorts of studies are labelled as ‘future of work’ because of the hot-topic

nature of the subject, or whether the intention of these studies is to describe patterns of

technology usage that are currently new, but expected to become commonplace in the future.

The prototypical example of the second type of study—and the one that has certainly

received most attention both in academia and in the media—is Frey and Osborne’s (2013)

simulation study of the percentage of jobs threatened by automation in a range of occupations. In

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contrast to the first type of study, these studies are clearly focused on the future (Frey &

Osborne’s timeframe was 10 to 20 years). They have also been instrumental in opening up the

scholarly and public debate about the future of work, and increasing issue engagement (Miller et

al., 2017). However, although Frey and Osborne (2013) acknowledge and describe historical acts

and processes of human resistance against automation (e.g., the ‘Luddite’ riots during the British

Industrial Revolution in the early 19th century) in their literature review, their empirical analysis

of the future of work is mostly driven by projections of what will be technologically possible in

the future—and thus deterministic (Kelly, 2022). We argue that what is missing in their analysis

is the political nature of the act of predicting the future, and the values and ideologies underlying

the discursive struggles taking place in the public domain (Jones, Shanahan, & McBeth, 2014).

We argue that such discursive struggles are best studied through media analysis, as the

popular press mirrors and shapes public opinion. First of all, the news media ‘mirrors’ the social

norms and values that exist around a given topic at a given point in history. It thus represents a

central forum for public debate about ideological issues, giving “voice to multiple and often

conflicting legitimacy judgments of various prominent social actors” (Luyckx & Janssens, 2020,

p. 116). Second, it has the power to ‘shape’ public opinion and organizational practice, by staging

particular actors as narrators (e.g., tech billionaires; politicians; best-selling authors), and

selecting and editing their judgments and messages (Luyckx & Janssens, 2020). Patton and Johns

(2007) make the argument that business leaders read major newspapers, trade press periodicals,

and bestsellers much more regularly than they do academic journals. Consequently,

organizational policies are often influenced by what members of management have recently read

in the popular press.

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Narratives about the future of work found in the media are thus not just ‘stories’ or

‘theories’ of the future (Cave & Dihal, 2019). The more prominent a given narrative is in the

public debate, the more likely it will be to affect society (Levy & Spicer, 2013). Narratives about

the future—often in the form of predictions— can thus be argued as already being in the process

of ‘becoming real’ through their pre-representations in the public domain (Beckert & Bronk,

2019). Consequently, say critical management scholars, the future cannot so much be accurately

predicted, than that these predictions are ‘willed into being’ through the dominant narratives of

powerful actors (Levy & Spicer, 2013; Roux-Rosier et al., 2018). Thus, rather than one prediction

being objectively more ‘right’ than another, what the future of work will look like, according to

this view, will depend on which narratives gain dominance over time, and the collective actions

taken based on those dominant narratives (Beckert & Bronk, 2019).

METHODS

Both the data collection and the data analysis for this project were distinctly mixed-

method, abductive, and curiosity-driven. In a first, qualitative study we performed a content

analysis of all print media articles that appeared in the Belgian press related to the topic of the

future of work between 2015 and 2021 (K = 485) (more details on the print media analysis are

reported in the Appendix, see Step 1 and Table A1). Referring back to our research question, in

analyzing our qualitative data we focused specifically on how competing narratives of the future

of work are construed by different actors in the public debate. Our content analysis uncovered

seven different narratives about the future of work (see Tables 1 and 2), and identified five major

actors in the public debate around the future of work—i.e., Tech/Innovation experts,

Authors/Journalists, Economy/Labor Market experts, Policy Makers/Public Administrators, and

Engaged Citizens (see Table A2 in the Appendix).

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In a second, quantitative study we developed a survey around these future of work

narratives, which was administered to respondents from these five major actor groups (N = 570),

using a targeted sampling strategy (more details on the survey and the sample are reported in the

Appendix, see Step 2 and Tables A3, A4, and A5). Referring back to our research question, in

analyzing our quantitative data we focused specifically on how competing narratives of the

future of work that exist in the public debate are subscribed to by different actors (i.e., believed

in; Miller, 2019).

By identifying data patterns across both datasets using a convergent parallel mixed-

method design (Creswell & Pablo-Clark, 2011), while simultaneously looking for explanations

in the literature, we were ultimately able to formulate a tentative answer to our guiding research

question as to not only how, but also why, competing narratives of the future of work are

construed and subscribed to by different actors in the public debate. Methodological details on

our data collection and analysis process are reported in the Appendix, along with additional

descriptive background analyses and context information.

FINDINGS

In what follows, we describe the findings that emerged from the mixed-method analysis

of our two datasets. We organize our findings in three sections. First, we describe what narratives

about the future of work were found in our print media analysis, how they were framed, and by

whom—i.e., what groups of actors were typically featured as narrators. Second, we discuss how

specific narratives about the future of work, in the public debate, were construed as

counternarratives to each other, and the resultant tensions between different narrators. Third, we

dig deeper into the topic of narrative subscription by developing a data-driven explanation for

why the different actor groups in our data held different beliefs about the future of work.

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In our Findings, we thus make the distinction between ‘actors-as-narrators’ (in the print

media data) and ‘actors-as-subscribers’ (in the multi-actor survey data)—in both cases referring

to the same five groups of actors that inductively emerged from our print media analysis (see

Tables A2 and A3 in the Appendix) .

Future of Work Narratives and Narrators

Through content analysis of our print media data (Patton & Johns, 2007; see Appendix,

Step 1), we identified seven main narratives about the future of work in the public debate, listed

here in order of their descending prevalence in the data: Dataism, Exterminism, Re/Upskilling,

Augmentation, the Singularity, Job Destruction, and Work Deintensification. Definitions—

developed through iterative engagement with the data and the literature—along with examples of

underlying phenomena for each narrative can be found in Table 1. Illustrative excerpts for each

narrative are found in Table 2. It is important to note that almost all articles featured multiple

narratives simultaneously. We revisit this point later in our analysis of narratives and

counternarratives.

Insert Table 1 about here

Each article was coded for date, title, publication, future of work phenomenon or

phenomena discussed in the article, projected timeframe (near future, distant future, or already

happening in the present), framing of a phenomenon (positive, negative, or mixed), outlook

(positive, negative, or mixed), and narrators featured (see Table 2; more details in the Appendix,

Step 1).

Timeframe. Timeframe was coded as whether a given narrative was construed as near-

future or distant-future (e.g., “in a few years” versus “a century from now”); when specific

indications of years (e.g., “in 10 years”) or dates (e.g., “by 2050”) were given, we also coded

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those. We found that an overall majority of articles (266/485) construed ‘the future of work’ as a

distant-future (55% of articles) as opposed to a near-future phenomenon (210/485 articles, or

43%). Only a handful of articles discussed future of work narratives that were described as

already taking place in the present (9/485, or 2%).

Table 2 offers a more detailed overview of projected timeframes at the level of the

individual narratives. We see, indeed, that five of the seven narratives were predominantly

construed as distant-future (i.e., Work Deintensification, the Singularity, Exterminism, Job

Destruction, and Re/Upskilling). Only two narratives (i.e., Dataism and Augmentation) were

predominantly construed as near-future. In articles that included mention of a specific year in

which a given narrative was predicted to occur (k = 93, 19%), the distant future was projected as

taking place on average 40.51 years (SD = 118.48) from ‘now’ (taking into account the

publication date of the article), while the near future was positioned as 7.58 years removed (SD =

16.84). Across the whole corpus, the average article positioned the future of work 33.78 years

into the future (SD = 106.64). All in all, these findings indicate that there was in fact no lack of

attention to distant-future trends in the media, something we had initially expected based on the

literature on temporal construal level (D’Argembeau & Van der Linden, 2004) and the media’s

typical reliance on ‘here and now’ frames (Schoemaker, 2020). This is an interesting observation

as near-future bias does in fact seem to exist in the academic literature, which has been found to

focus its future of work research mostly on trends in the present-day workplace (Santana & Cobo,

2020).

Framing and outlook. Framing was coded in the data in two ways: first, whether a

narrative was construed as overall ‘good’ (i.e., societally desirable) versus ‘bad’ (i.e., societally

undesirable), and second, whether a narrative was construed as having a positive versus a

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negative outlook (i.e., the likelihood of a societally desirable versus undesirable outcome).

Crossing (positive or negative) framing with (positive or negative) outlook, we found that

Dataism and Exterminism—the two narratives with both a negative framing and a negative

outlook, meaning that they are considered ‘bad and likely’—were the two most prevalent

narratives in our article set (see Table 2). Specifically, Dataism was typically construed as likely

to occur in the near future, while Exterminism was construed as likely to occur in the distant

future. The overall view across all narratives was more nuanced; the other two negatively framed

narratives—i.e., the Singularity and Job Destruction—were construed as ‘bad but unlikely’, while

of the three positively framed narratives, two were construed as having a positive outlook (i.e.,

‘good and likely’)—i.e., Augmentation in the near future, and Re/Upskilling in the distant

future—and only one (i.e., Work Deintensification) as ‘good but unlikely’. Thus, although we

found some indications of negativity bias in the media, this did not emerge as a clear pattern,

since there was an overall higher prevalence of narratives with a positive outlook in our data

(Patton & Johns, 2007).

Insert Table 2 about here

Narrators. Finally, we coded which actors were featured in the articles as narrators

(Pentland, 1999). Narrators were typically represented in one of three ways: either they were

talked to, for instance as interviewees; or their narratives were talked about, for instance when

they had written a book; or they did the talking, for instance when they wrote an op-ed

(Buchanan & Dawson, 2007). Five groups of actors emerged from the data inductively—i.e.,

Tech/Innovation experts, Authors/Journalists, Economy/Labor Market experts, Policy

Makers/Public Administrators, and ‘Other’. (In the ‘Other’ category, the most common actors

were lawyers and regulators specialized in AI, trade union representatives, factory workers, and

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social movement actors). In almost all cases, their profession or expertise was explicitly named

in the article (e.g., “economist David Autor”; “computer scientist Pattie Maes”), but where it was

not, we performed additional Google searches.

Overall, Tech/Innovation experts (in 185/485 articles, or 38%), Authors/Journalists (in

130/485 articles, or 27%), and Economy/Labor Market experts (in 98/485 articles, or 20%) were

the main narrators in the majority of articles in our corpus, meaning that they were most often

featured and thus, had the most opportunity to push their preferred narratives about the future of

work in the public debate (Jones & Crow, 2017). In line with their respective expertise,

Tech/Innovation experts dominated the debate around Dataism, Augmentation, and the

Singularity; Authors/Journalists dominated the debate around Exterminism; and Economy/Labor

Market experts dominated the debate around Re/Upskilling, Job Destruction, and Work

Deintensification (see Table 2). Policy Makers/Public Administrators (in 41/485 articles, or 9%),

and ‘Others’ (in 31/485 articles, or 6%) were featured much less often. More details on the

narrators identified in our print media corpus can be found in the Appendix (Table A2).

Future of Work Narratives and Counternarratives

Throughout our content analysis of the different future of work narratives found in the

print media, we observed that almost all articles contained multiple narratives simultaneously,

and that specific narratives were often featured as running counter to each other (Bamberg &

Wipff, 2020). Based on this observation, we decided to add another layer to our content analysis

(Patton & Johns, 2007) and specifically look at how, in the print media data, specific narratives

of the future of work that exist in the public debate are construed as counternarratives to other

narratives, by the different actors.

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The literature on counternarratives positions them as running counter to ‘master’

narratives, meaning that specific narratives at some point are chosen or enforced as being

legitimate, official, or correct, which may or may not be contested and opposed by

counternarratives (Czarniawska, 2016). However, in our print media data, different master

narratives seemed to exist, pushed by different actors. What became clear in our analysis was

that the different narrators explicitly positioned themselves against one another (calling out other

actors as being wrong, sometimes by actor category, sometimes by name), and their preferred

narratives against others, for instance by referring to studies that (in their view) debunked studies

cited by ideological opponents (Bamberg & Wipff, 2020).

Interestingly, in the process of identifying the exact points of contention between the

different narratives and narrators, we concluded that if we were to identify a central master

narrative in the public debate around the future of work, it would have to be the Job Destruction

narrative—although it was not among the most prevalent overall in the print media data (Table

2), nor strongly subscribed to in the survey data (Tables 3 and 4). That is, in analyzing the

narratives and counternarratives of the different groups of actors, we experimented with different

points at which their narratives ‘branched off’ and diverged from one another, and found that the

best point of departure was whether or not a narrator believed in Job Destruction.

Contention around the Job Destruction narrative. In almost all cases where narrators

discussed the Job Destruction narrative, the seminal study by Frey and Osborne (2013) was

invoked either as simple fact or as point of contention, while typically also being reduced to a

single headline, i.e. “47 percent of jobs are at high risk of automation in the next 10 to 20 years”.

Overall, Economy/Labor Market experts and Tech/Innovation experts did not believe in Job

Destruction (positioning Re/Upskilling and Augmentation as counternarratives to Job

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Destruction, respectively), while Authors/Journalists did (positioning Job Destruction as a

harbinger of Exterminism).

Consider the following set of excerpts, in which the first represents a quote from an

Economy/Labor Market expert (i.e., Fons Leroy, managing director of the public employment

service of Flanders, VDAB), the second from a Tech/Innovation expert (i.e., Luc Steels, director

of the Artificial Intelligence Lab at the university of Brussels), and the third from an

Author/Journalist (i.e., Philipp Blom, historian, journalist, and author of the book ‘The vertigo

years’):

[Economy/Labor Market expert] “Those—mostly American—studies you are referring to have since been

debunked. They made a crucial mistake: they confused ‘tasks’ with ‘jobs’. Yes, in every job there will be

tasks that will be taken over by robots, but that doesn’t mean the whole job will disappear. In the worst cases

that will be true, of course—purely industrial line work can be completely taken over by machines—but for

other jobs that will be only partly the case. … Employers are still thinking in boxes. They publish job ads

saying ‘we are looking for someone with a degree in x, y years of relevant experience and competencies a,

b, and c’. … We are living in exciting but fast-changing times. Degrees are worthless in this new reality.

What people need are what I call 21st-century skills. They need to be agile, resilient, autonomous, and be

able to find their own way in an ever-evolving labor market.”

(‘Robots will not replace us, they will make our work meaningful again’, Humo, Oct 9th, 2018)

[Tech/Innovation expert] “The fear that AI will make jobs redundant is extremely overblown. … Most

people, including CEOs and public administrators simply do not understand what AI is at all. … Jobs will

either become better, easier, and more optimal thanks to AI, or they will evolve into new kinds of jobs. AI

cannot replace human intelligence. Real intelligence requires creative thinking and looking beyond patterns.

I can’t see that happening anytime soon. In the best-case scenario, AI will be able to learn from human

behavior how to best solve problems. But man will, for a long time to come, still be the main source of

knowledge. The future lies in the collaboration between man and machine … Possibilities in traffic control,

healthcare, education, public safety, and poverty reduction are endless. … We need a sort of ecosystem that

allows researchers, companies, funders, and governments to come together to weave AI into our society as

optimally as possible.”

(‘AI is not a major threat, but also not a magic wand’, De Morgen, Apr. 16th, 2018)

[Author/Journalist] “This time it really is different. Those who can’t see that, are enormously wrong. The

West is a society on the verge of collapse. … The machines are coming, and they will win. Those who think

they cannot be replaced by a machine are wrong, and that goes for all occupations. …What alternative is

there to a basic income in a society with 40 or 50 percent unemployment? We have to question the growth

model itself, because it has become toxic. … All of my pessimistic claims about climate change, the millions

of refugees that will result from it, and the harmful effects of digitization and robotization are made based

on the best research I know and many conversations with scientists from a variety of disciplines. … We are

now at the point where we as a society can make mistakes that we can make only once.”

(‘People ask me: Your book is interesting, but things aren’t that bad, are they? But they áre that bad’, Humo, Jul. 31st, 2018)

Contention around the Work Deintensification narrative. Another major point of

contention between the different actors in our print media data was the Work Deintensification

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narrative. Although this was, in relative terms, the least prevalent narrative in our corpus, the

debate around this narrative—especially the idea of a universal basic income (UBI)—tended to

get the most heated. For instance, several Economy/Labor Market experts called UBI and the

idea of degrowth ‘nonsense’, and actors who claimed they were viable alternatives to the current

capitalist order ‘naive’. Proponents of Work Deintensification were mostly found among

Tech/Innovation experts—as introducing a UBI would, at least partially, solve society’s moral

concerns around automation and augmentation—as well as among Authors/Journalists, who saw

the UBI as the only solution to mass-scale Job Destruction.

Consider the following two excerpts. The first one explicitly contrasts the (pro-UBI)

position of prominent Silicon Valley Tech/Innovation experts with the (anti-UBI) position of

Economy/Labor Market experts (the quote is by Marc De Vos, director of Belgian conservative

think tank Itinerary), while the second represents a quote from an Author/Journalist (i.e., David

Graeber, professor of anthropology and author of the best-selling book ‘Bullshit jobs’):

[Economy/Labor Market expert] “If robots start taking over our jobs en masse, we will have to find

another way to provide an income to the growing army of unemployed. One way that is gaining traction: a

universal basic income. Silicon Valley tech gurus like Tesla CEO Elon Musk and Facebook founder Mark

Zuckerberg are a fan of the idea of ‘free money for everyone’. A basic income, in their view, can prevent

automation from pushing an ever-larger group of unemployed into poverty. A recent study by economic

think tank Itinera, however, razes the ‘free-money utopia’ to the ground. ‘A basic income means we

collectively become poorer and wealth creation declines. Instead, we need to do all we can to activate people

in the labor market, through education and targeted investments’. … ‘It may sound nice as a purely

philosophical exercise, but when tested against reality, it turns out the basic income doesn’t work on any

level. It may sound appealing to give everyone free money, but to quote former British Prime Minister

Margaret Thatcher, eventually you run out of other people's money’.”

(‘The basic income discounted: The math does not add up’, De Tijd, Jun. 17th, 2017)

[Author/Journalist] “Almost all economists tell us: ‘people love working, even if the work is pointless’.

Because we are apparently rational beings that pursue maximal profit for minimal effort. … Economics, as

a science, is ill-equipped to deal with climate change, pollution, or overproduction. We need to rethink the

entire system. The old narrative is all about maximal growth and profit, but now it should be about how to

keep things running without destroying our planet. … Keynes was right, we should have had a fifteen-hour

work week by now. However, the jobs that were automated have been replaced by bullshit jobs. Most people

have been doing only 15 hours of meaningful work a week for years. The rest of their time is spent on

completely useless activities. … The fifteen-hour work week is not a fetish, you know. We can also continue

to work forty hours a week and take four months off. … I am in favor of the basic income. Since the Second

World War, we somehow have started to believe that workers with too much spare time will start drinking

or get depressed. I think it is very condescending to assume that working people are unable to meaningfully

spend their time on other activities.”

(‘The private sector has even more bureaucracy than the civil service’, Knack, Aug. 1st, 2018)

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Contention around the Exterminism narrative. In addition to the discursive struggles

between actors in the public debate about Job Destruction and Work Deintensification, what was

interesting is the way in which historical time was invoked by the different actors to make their

point. Rather than using overtly emotional tactics in their construal of future of work narratives

(and in their delegitimation of the counternarratives construed by other actors; Deetz, 2007), in

almost all articles narrators claimed to rely on facts, and facts only, in making predictions about

the future of work (i.e., scientific research, numbers and figures, and historical trends). Very

often, history—especially the Industrial Revolution—was invoked as an empirical source of

evidence from which future trends could be inferred. This was particularly noticeable in articles

featuring the Exterminism narrative, with contention centering around the question as to whether

the world is getting better (the narrative preferred by Tech/Innovation experts and

Economy/Labor Market experts) or worse (the narrative preferred by Authors/Journalists).

Interestingly, however, the same historical trends were framed in a different way by

narrators from different actor groups. Both Economy/Labor Market experts and Tech/Innovation

experts had a positive reading of history—the former emphasizing the importance of economic

growth (as part of their Re/Upskilling master narrative), the latter that of technological progress

(as part of their Augmentation master narrative). Authors/Journalists, however, focused more on

the negative side-effects of historical trends, and on the plight of low-skilled workers and people

from poorer areas of the world—with their master narrative, Exterminism, positioned as the

unavoidable outcome, if mankind continues along this path.

Consider the following set of excerpts, in which the first represents a quote from an

Economy/Labor Market expert (i.e., Peter De Keyzer, chief economist of strategic

communication agency Growth Inc.), the second from a Tech/Innovation expert (i.e., Françoise

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Chombar, founder of Belgian microchip producer Melexis), and the third from an

Author/Journalist (i.e., Ryan Avent, senior editor at The Economist and author of the book ‘The

wealth of humans’):

[Economy/Labor Market expert] “Our capitalist system needs an overhaul. Only then can we build a

sustainable society. That, in brief, is what the Post-Growth movement stands for. Today, the European

Parliament is hosting a debate on ‘the end of growth’. We asked our panel of leading economists for their

views. … ‘Absolutely not. Growth is a fairly recent phenomenon that only started with the Industrial

Revolution. Up until about 1800 there was no economic growth, or any other growth for that matter. …

Since the Industrial Revolution, per capita income has increased more than tenfold and global life

expectancy has doubled. You have economic growth to thank for that. Who says no to growth, says no to

our human nature to seek solutions to problems. … Imposing basic incomes ánd maximum incomes ánd

redistributing the time people spend, looks an awful lot like the communist experiments from the Soviet

days. That didn’t exactly end well’.”

(‘Putting an end to growth? Nonsense’, De Morgen, Sep. 18th, 2018)

[Tech/Innovation expert] “I am sick and tired of this idea that the best is behind us, that it’s all downhill

from here. … Even though we are the most successful generation in history, our society is overcome by

homesickness to a time that was objectively less prosperous and peaceful. … Our aspirations are what have

brought us here; on the edge of an era in which energy can be cheap, abundant, and clean. In which illnesses

like cancer and dementia can be conquered by developments in bio- and nanotechnology. In which we can

feed 10 billion people, in which we can make the water in the oceans drinkable, in which education can be

a basic right for the entire world population. … A few decades ago such a society would have been described

as a utopia. Tomorrow it will become reality.”

(‘We need to heal ourselves from defeatism’, De Tijd, Jul. 26th, 2016)

[Author/Journalist] “In Britain's first century of industrialization, wages did not increase. For a long time,

the new technologies did not benefit the workers. They lived in terrible conditions and died of diseases like

cholera. The cry for revolution could ultimately only be silenced through the creation of new political

systems, centered around social institutions, workers’ parties, and voting rights. It was the only way to

ensure that the profits from the increased productivity were distributed fairly. That was a long and painful

process, which we will have to go through again soon.”

(‘Even if we do everything right, there will still be a lot of losers’, De Standaard, Jul. 22th, 2017)

Contention around the role of Policy Makers/Public Administrators. The final point of

contention uncovered in our content analysis of narratives and counternarratives was about the

role of Policy Makers/Public Administrators in the future of work. Although not directly related

to a single (counter)narrative, this was the major point of contention between Tech/Innovation

experts and Economy/Labor Market experts, who were otherwise quite aligned. By and large,

Tech/Innovation experts portrayed Policy Makers/Public Administrators (as part of the

Augmentation narrative) as out of touch with new technologies, and as slowing down progress

and innovation with their focus on centralized regulation. Economy/Labor Market experts and

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Authors/Journalists, on the other hand, portrayed Policy Makers/Public Administrators as lacking

long-term vision due to their myopic focus on election cycles, and urged them to regulate new

technologies and labor market trends more strictly (as part of the Job Destruction narrative).

Consider the following two excerpts, in which the first represents a quote from a

Tech/Innovation expert (i.e., Sebastian Thrun, founder of the Google X research lab), and the

second from an Economy/Labor Market expert (i.e., Mariana Mazzucato, professor of

Economics of Innovation & Public Value at University College London):

[Tech/Innovation expert] “In Silicon Valley, a new elite is emerging that does not only want to control

what we consume, but also how we should live. It wants to change the world, and will not accept any rules

in doing so. Politicians are struggling with this evolution. … The headquarters of this new world government

is not on Wall Street, but in Silicon Valley… They loathe politics, and consider regulation both an

obstruction and an anachronism. If societal values like privacy get in the way of progress, they say, maybe

we should create new values. ‘Rules are made to cement existing structures. We try to work around them.

The state, the government is ultimately just another system that can be reinvented. Everything is global, but

all laws are local. That doesn’t make sense anymore. At some point, you have to sit down and have a serious

discussion about more efficient and democratic forms of government’. … Some time ago, he [Sebastian

Thrun] was received as a guest by German president Joachim Gauck. After dinner, Gauck pulled him aside

with the words: ‘Mister Thrun, you scare me’.”

(‘The new overlords: The era of globalization ánd digitalization’, Knack, Apr. 22nd , 2015)

[Economy/labor market] “Yes, you can realize flashy innovations without the intervention of the state, but

not meaningful innovations that address major social problems. We need the government. But don't get me

wrong, I'm not saying the government should do everything. It is the task of the government to clearly map

out the route and deploy all possible resources to achieve the goal. … We have two fundamental problems

right now. The first is the lack of goals. That's what you get when politicians have no vision and are really

only concerned with the next elections. The second problem is that politicians, even if they're interested in

something like a Green New Deal, aren’t mission-oriented in their thinking. Then there is no catalyst for

innovations. The public-private partnership today is often parasitic. A lot of money flows to the private

sector, but the profits are rarely returned. The question is: how do you socialize not only the risk, but also

the benefit? On the risk side, you have to ensure that the innovations are effectively aimed at major societal

challenges. As for the benefits, we need to make sure we manage innovation in such a way that it benefits

people.”

(‘Innovation economist knows how we can reform capitalism’, De Morgen, Dec. 31st, 2021)

Turning to the topic of narrative subscription (Miller, 2019)—which we analyzed using a

mixed-method analysis of our survey data and our print media data—we found that the same

patterns of narratives and counternarratives emerged in the correlation table (Table 3). Moreover,

(non-)subscription of different actors to the different narratives was highly similar to the patterns

found on the narrator side (Table 4), as we discuss in the final findings section below.

(Non-)Subscription of Actors to Future of Work Narratives

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In this final section of the Findings we set out to achieve two goals. First, to examine the

extent to which people from different groups of actors believe in (subscribe to) different future of

work narratives. And second, to come up with explanations as to why differences in narrative

subscription are found between actors. To be clear, while in the previous section we focused on

actors-as-narrators—and the related sub-question of how competing narratives of the future of

work are construed by different actors in the public debate—in this final section we will focus on

actors-as-subscribers, and the related sub-question of how competing narratives of the future of

work that exist in the public debate are subscribed to by different actors.

We first looked at the relationship between how narratives were framed in the media and

how they were responded to in the survey data in the overall sample (see Appendix, Step 2)1. We

found that the four most prevalent narratives in the print media were also most strongly

subscribed to by the actors in our survey, although in the survey the ‘good and likely’ narratives

(i.e., Re/Upskilling and Augmentation) ranked higher than the ‘bad and likely’ narratives (i.e.,

Exterminism and Dataism), while in the media data it was the other way around (see Tables 2

and 3). The three narratives that were construed in the print media as unlikely to happen (in

terms of coded outlook) were also rated lowest in the survey. Interestingly, this applied both to

the negatively framed narratives (i.e., Job Destruction and the Singularity), and to the positively

framed narrative of Work Deintensification. We thus tentatively conclude that the framing of

narratives by narrators in the public debate is logically related to narrative subscription by

respondents from the same actor groups, although subscription skews a bit more optimistically.

Insert Table 3 about here

1 From the ‘Other’ category that emerged from our print media analysis (and that contained a variety of actors including lawyers

and regulators specialized in AI, trade union representatives, factory workers, and social movement actors), we decided to focus

on what we called ‘Engaged Citizens’ as a final subsample for the survey study (more details in Appendix, Step 2).

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Differences in narrative subscription between actor groups. To analyze differences in

subscription of the different actors to the different future of work narratives, we started by

running a series of ANOVAs to examine differences between the actor groups in terms of how

they rated each narrative. Overall, the ranking of the narratives within each actor group

corresponded to that of the total sample (Table 3)—that is, Re/Upskilling and Augmentation (the

two ‘good and likely’ narratives) were rated highest by all groups, followed by Exterminism and

Dataism (the ‘bad and likely’ narratives), and these four were also the most prevalent narratives

featured in the print media data. Looking at the relative differences between actors in

subscription to the narratives, however, some interesting patterns were found (Table 4).

In fact, actors only agreed on two narratives—i.e., Dataism (which was rated as likely to

occur in the distant future) and the Singularity (which was rated as unlikely to ever occur). Not

only did we find that some actors believed significantly more in certain narratives than other

actors did; we also found that (non-)subscription to specific narratives correlated with specific

individual differences, that systematically differed between actors. In other words, the different

groups of actors could be characterized by specific individual difference profiles, that in turn

were logically related to specific narratives.

Throughout the process of analyzing our quantitative survey data, we noticed that these

individual difference profiles were also mirrored in the print media data. For example, narrators

characterized themselves as optimists or pessimists or made statements indicative of a certain

locus of control. In other words, the individual difference profiles identified on the subscriber

side in the survey data were supported by self-referential statements made on the narrator side in

the print media data. Consequently, alongside our quantitative findings presented below, we

incorporated a selection of excerpts that demonstrate how individual differences between the

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actor groups were reflected in the news articles. We also encourage readers to revisit the quotes

from the different actors in the previous sections and in Table 2 in the main body of the article to

verify the individual difference profiles identified below—as we see it, the pattern is quite

consistent throughout.

We are, of course, not saying that all economists or all technologists are the same. Rather,

what our analyses showed was that each group of actors had an archetypical profile—as we

explain in the Discussion, likely caused by corresponsive mechanisms in their life course

personality development (Woods, Edmonds, Hampson, & Lievens, 2020)—that explained why

they believed in certain narratives, and not others. Since belonging to a specific group of actors

seemed to be the dominant predictor of (non-)subscription to a given narrative—that could in

turn be explained by individual differences—in this section we organize our findings per actor

group2.

Insert Table 4 about here

Individual difference profile of Tech/Innovation experts. The Tech/Innovation experts

exhibited a particularly strong archetypical profile, that was also clearly linked to their preferred

narratives. They scored highest of all actors in the sample on trait optimism (F(4, 565)=3.96, p =

.00), technology readiness (F(4, 565)=16.56, p = .00), and openness to experience (F(4,

565)=7.30, p = .00), and lowest of all actors on resistance to change (F(4, 565)=5.44, p = .00)

(Table 4). These individual differences were, in turn, significantly correlated (Table 3) with the

2 Surprisingly, Policy Makers/Public Administrators were noticeably ‘absent’ in both datasets. In the print media articles, they

fulfilled the narrative role of antagonist (Pentland, 1999) to practically every other actor. They were hardly ever interviewed or

used as informants; rather, they were spoken about and against. As illustrated earlier in the section on counternarratives,

Tech/Innovation experts portrayed them as a nuisance to technological progress while Economy/Labor Market experts and

Authors/Journalists urged them to develop a much-needed vision of the future, beyond the current election cycle. In the survey

data, as well, Policy Makers/Public Administrators had a much less pronounced profile than the other groups. The Engaged

Citizens were also rarely featured. As this was the most heterogeneous group of actors in our data, perhaps this should come as

no surprise.

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two narratives that Tech/Innovation experts subscribed to significantly more than the other

actors—i.e., Augmentation (F(4, 565)=8.36, p = .00) and Work Deintensification (F(4,

565)=2.66, p = .03) (Table 4). In the print media data, a typical talking point of Tech/Innovation

experts was their mission to build a better world, driven by big dreams and visionary ideas. They

often lamented that other actors (especially policy makers and politicians) lacked imagination

and courage, and optimism was often explicitly mentioned as a personal trait. Consider the

following two excerpts, both citing Pieter Abbeel (director of the Berkeley Robot Learning Lab):

[Tech/Innovation expert] “Typical of Silicon Valley is a boundless belief in technology making the world

a better place, almost as a moral duty. I am very optimistic about technology [optimism]. Ultimately, people

still have to make decisions about how to use the technology in the right way. It shouldn’t be possible for

people with bad intentions to misuse the technology. … Artificial intelligence should be an extension of

individual free will, in the spirit of freedom, and distributed as fairly and widely as possible. … That fear

and concern lives mostly among people who are not closely involved with robots. … My ultimate dream is

that we’ll see, in our lifetimes, robots functioning among us and helping us [technology readiness].”

(‘Technology should make the world a better place’, De Tijd, Dec. 15th, 2015)

“My motivation and passion are intrinsic, I want to understand how literally everything works. How do you

build a bridge? How does an insect work? A computer? A car? Pure curiosity. All of that is super intriguing

to me. And the culmination of that is: how does intelligence work [openness to experience]? … As for the

apocalyptic tone: it’s OK to be a little sceptic and concerned. But that shouldn’t stop us for pursuing benign

innovations. If people get too concerned, it may start limiting us [low resistance to change]. And there is

so much good we can do. It would be sad if the doom and gloom of the Terminator led us to live with car

accidents for years to come because we’re afraid intelligent self-driving cars will take over the world.”

(‘The man who makes robots think’, De Tijd, May 6th, 2017)

Individual difference profile of Authors/Journalists. The Authors/Journalists were the

pessimists in our data. Their survey data showed a future-negative time orientation (F(4,

565)=2.71, p = .03) and a powerful other locus of control (F(4, 565)=2.50, p = .04), meaning that

they believe important decisions in the world are taken by an elite behind closed doors. They

also scored highest of all actors on misanthropy (F(4, 565)=2.51, p = .04) (Table 4). These

individual differences were, in turn, significantly correlated (Table 3) with the dystopian

narratives of Job Destruction (F(4, 565)=4.22, p = .00) and Exterminism (F(4, 565)=2.98, p =

.02), which Authors/Journalists rated significantly higher than other actors (Table 4). In the print

media data, Authors/Journalists juxtaposed their fear of system collapse with pleas for system

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change, highlighting anti-capitalist ideas challenging the hegemony of the global elite. From

their position in society, they saw it their mission to warn others (especially policy makers) about

the huge challenges on the horizon, while bearing little hope of being able to spur them into

action before it is too late. Consider the following excerpt, citing Yuval Noah Harari (historian

and author of best-selling books such as ‘Sapiens’ and ‘Homo Deus’):

[Author/Journalist] “If you’re already concerned about the state of the world, don’t read Yuval Noah

Harari’s new book. In ‘Lessons for the 21st century’, the Israeli intellectual declares liberal ideology dead

and buried. … according to Harari, three catastrophes hang over us: ecological destruction, technological

disruption and the biological enhancement of the human race [future-negative time orientation]. On the

condition, of course, that we can first stop global warming. But even if we succeed, the merging of computer

science and biotechnology, according to Harari, will render most of us economically irrelevant and

politically powerless as a biologically engineered superclass emerges, the ‘homo deus’ [powerful others

locus of control]. … Harari continues to attack his familiar specters: nationalism, populism and religion.

For example, he enthusiastically joins the debate around fake news. He does so from the perspective he

already described in ‘Sapiens', namely that society is held together by fictitious creations such as money,

nation and religion. 'If a thousand people accept a made-up story as the truth for a month, we call it fake

news. If millions of people believe it for a thousand years, we call it religion’ [misanthropy].”

(‘Provocations of an intellectual rockstar’, De Tijd, August 18th, 2018)

Individual difference profile of Economy/Labor Market experts. The Economy/Labor

Market experts displayed a distinct future-positive time orientation (F(4, 565)=4.47, p = .00) and

the highest internal locus of control (F(4, 565)=3.55, p = .01) of all actors in the sample. They

also had a significantly higher education level (i.e., a Master, MBA, or PhD degree) than the

other groups (F(4, 565)=27.64, p = .00), and faced the lowest automation risk based on their job

characteristics (F(4, 565)=7.34, p = .00) (Table 4). These individual differences were, in turn,

significantly correlated (Table 3) with the Re/Upskilling narrative, which the Economy/Labor

Market experts rated highest of all actors (F(4, 565)=12.08, p = .00) (Table 4). In the print media

articles featuring Economy/Labor Market experts as narrators, they emphasized the age-old

economic law that increases in productivity (for instance, as a result of partial automation) lead

to economic growth and thus increases in employment. They were adamant that humans will

always find ways to create new work. They also emphasized the important role of work in

people’s lives, citing studies that found a relationship between unemployment and clinical

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depression. While they often held similar views to those of the Tech/Innovation experts—except

about Work Deintensification—their views were more informed by economic ratio. For instance,

they emphasized that full employment is important to keep social security and pensions

affordable. Consider the following excerpt, citing Anna Salomons (professor of Economics at

Utrecht University):

[Economy/Labor Market expert] “This doom scenario, the 'robocalypse' in which intelligent machines

make humans redundant, is facing more and more opposition from science. … Since the onset of the

Industrial Revolution, the trend has been that as productivity increases, employment follows. New goods

and services emerge, in turn creating new demand. There is no reason to assume that this trend will stall in

the future [future-positive time orientation]. … Robots taking over all our work? Forget it. Then you

underestimate the power of humans to adapt. People are creative. They adapt and they can learn. They will

always come up with new work [internal locus of control]. … From an economic perspective, disruptive

innovation caused by new technology is a good thing, in that rapid change creates more productivity growth

and thus more wealth. It would be great if machine learning becomes the new breakthrough technology that

boosts the entire economy, like the invention of electricity or the internal combustion engine before it. But

what is good for the whole, is not necessarily good for the individual. A trucker had better hope that the

adjustments are gradual, so that he doesn’t find himself without a job overnight. The costs of adaptation will

not be equally distributed among everyone [job automation risk]. … The risk of automation is not so much

that there will be no jobs left, but that inequality and polarization in the labor market will increase further.

These are good times to be highly educated. Highly educated people benefit the most from automation, see

their wages rise the most, and are also better able to adapt to the innovation [educational level].”

(‘Robotization divides society’, De Tijd, May 12th, 2018)

To conclude, we summarize the key discoveries from our two datasets—linking each

discovery to a specific sub-question derived from our overall research question, and to a specific

theoretical mechanism—in Table 5.

Insert Table 5 about here

DISCUSSION

We started this research project from the observation that the future of work has

historically been a highly political topic, while current understanding is characterized by both

determinism and depoliticization (Howcroft & Taylor, 2022; Gümüsay & Reinecke, 2022); and

from our belief that the ‘prediction paradigm’ offers only a very limited understanding of the

future of work (Schoemaker, 2010). Through an abductive and curiosity-driven, mixed-method

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analysis process, we set out to examine how—and more importantly, why—competing narratives

of the future of work are construed and subscribed to by different actors in the public debate.

We found that members from different groups of societal actors—Tech/Innovation

experts, Authors/Journalists, Economy/Labor Market experts, Policy Makers/Public

administrators, and Engaged Citizens—differ both in terms of the narratives they push in the

public debate (as narrators), and in the extent to which they are (non-)believers of certain future

of work narratives (as subscribers). These differences appear to be motivated not only by the

expertise and vested interests of each group, but also by these actor groups having distinct

individual difference profiles. We were intrigued to find quite consistent patterns across our two

datasets, which were very different in nature: a set of 485 print media articles on the future of

work; and a survey of 570 respondents representing the different future of work actors.

Proposing a Paradigm Shift—from ‘Predictions’ to ‘Imaginaries’

Across the key discoveries in this paper, we have built the case that the future of work is

a fiction, not a fact; or better yet, a series of competing fictions depicting what the future will or

should look like. Consequently, we propose that what research on the future of work needs, as a

rapidly emerging field, is a paradigm shift—from ‘predictions’ to ‘imaginaries’. Imaginaries are

defined as shared socio-semiotic systems that articulate and structure a field around a set of

shared understandings that provide a sense of coherence and link actors into a network around

the issue. They both describe how current institutions and economic activities are organized and

structured, and prescribe how they ought to be organized and structured in the future (Levy &

Spicer, 2013). Within the context of the present study, the seven narratives identified in the

public debate around the future of work can be seen as narrative ‘building blocks’ that can be

configured by societal actors in different ways to form an internally consistent imaginary

(Augustine et al., 2019; Cave & Dihal, 2019). Based on our analysis of counternarratives and

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points of contention between actors, and their distinct individual difference profiles, we can

distinguish three major future of work imaginaries in the public debate (Figure 1).

The first imaginary uncovered in our data was that construed by, and subscribed to by

Tech/Innovation experts, which we labeled Effective Accelerationism (Technocracy). Effective

accelerationism refers to the idea that technological progress cannot—and should not—be

stopped, and should instead be harnessed and accelerated in order to force societal change such

as technocratic modes of governance, for the benefit of mankind and to reduce existential risk

(Bareis & Katzenbach, 2022). As can be seen in Figure 1, the master narrative behind this

imaginary is Augmentation, which is construed as a counternarrative to Job Destruction and

Exterminism, but is seen as requiring Work Deintensification. That is, as Tech/Innovation

experts see technological progress as inherently positive, they imagine a future of work in which

humans will have to work less but are simultaneously exponentially more productive through the

use of AI and ‘cobots’ (i.e., collaborative robots). Potential loss of income that would result from

automation would, in their imaginary, need to be compensated for through the provision of a

basic income, an idea many in Silicon Valley are proponents of.

The second imaginary uncovered in our data was that construed by, and subscribed to by

Authors/Journalists, which we labeled Degrowth (System Change). Degrowth refers to the idea

that system change is needed to tackle the pitfalls of capitalism, such as increasing levels of work

intensification and burnout, the exploitation of workers, rampant consumerism, and a focus on

economic growth at the expense of the environment (Carruthers, 2023; Le Fevre et al., 2015).

The master narrative behind this imaginary is Exterminism, with Job Destruction construed as a

key driver, the solution of which requiring Work Deintensification. That is, as

Authors/Journalists see the world as going down a dangerous path, they propose an alternative

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future for mankind in which we lower our demands for consumption and productivity

(effectively causing economic growth to stabilize or decline)—thus decreasing the need for

human labor while safeguarding an, at the very least, acceptable standard of living for all

people—and with a renewed focus on craftwork, the arts, and the commons. Like the

Tech/Innovation experts, the Authors/Journalists see a universal basic income (UBI) as the best

solution to the threat of mass-scale automation. The relationship between both actor groups,

however, is antagonistic, as Tech/Innovation experts tend to despise pessimists and any ideas

that would slow down progress, whereas Authors/Journalists see Silicon Valley as a playground

for sociopaths without a social conscience, so detached from reality that they believe even

wicked problems like climate change can be fixed overnight by technology (cf. ‘Technofix’;

Augustine et al., 2019).

The third imaginary uncovered in our data was that construed by, and subscribed to by

Economy/Labor Market experts, which we labeled Capitalist Realism (TINA). TINA refers to a

well-known speech held by Conservative British prime minister Margaret Thatcher in the ‘80s,

in which she said “there is no alternative” to capitalism. Capitalist realism, then, refers to the

idea that capitalism is the only viable political and economic system, and that it is impossible to

even imagine a coherent alternative to it (Fisher, 2009). Of all actors, Economy/Labor Market

experts advocate most for ‘business as usual’, with most of them strongly opposing the idea of

system change (i.e., anti-capitalist, post-workist, and degrowth movements). Their rock-solid

belief in capitalism and economic growth causes Economy/Labor Market experts to discard

Work Deintensification as a ridiculous and childish idea—or likening it to communism in the

former Soviet Union—while they counter the Job Destruction narrative with their master

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narrative of Re/Upskilling, in their view a more realistic outcome of automation based on similar

trends in history.

Insert Figure 1 about here

A paradigm shift towards imaginaries, we believe, would open up entirely new avenues

for research on the future of work both in theoretical and methodological terms. As we have

discussed earlier, existing research on the future of work tends to focus on trends in the present-

day workplace as researchers struggle with the challenge of how to collect empirical data on

phenomena that do not yet exist, and events that have not yet happened (Santana & Cobo, 2020).

Adopting an imaginaries paradigm, using interpretivist research methods, is one promising way

forward for those who want to focus on research questions beyond the prediction of automation

rates of different occupations (see Table 6).

Theoretical Implications

The discoveries from our two studies prompt several further questions: Why is it, that

people belonging to the same group of actors not only hold similar, collective views about the

future of work, but also exhibit high levels of similarity to each other (and dissimilarity to other

groups) in their individual differences? Is this the result of people with similar individual

difference profiles sorting into the same professions, as reflected in our sampling? Alternatively,

do individuals belonging to the same actor group tend to become more similar over time? And if

so, what are the implications of these mechanisms? How will they impact the future of work both

in research and practice—what are the implications for policy makers, business leaders, experts,

and workers?

In what follows, we use framing contests to explain how different actors construe

different narratives about the future of work; field frames to explain why different actors

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construe different narratives about the future of work; narrative subscription to explain how

different actors subscribe to different narratives about the future of work; and corresponsive

mechanisms to explain why different actors subscribe to different narratives about the future of

work (Table 6).

Framing contests. It is clear from the three imaginaries uncovered in our data that

different societal actors tend to construe very different narratives about what the future will (or

should) look like. Moreover, as most of their viewpoints are fundamentally incompatible, it is

also hard to imagine them reaching agreement at some point in time (Deetz, 2007). Instead, what

our findings show is that the public debate around the future of work is an arena for values,

politics, and ideology, in which competing imaginaries battle for enactment (Levy & Spicer,

2013). This theoretical angle opens up entirely new avenues for further research, as we discuss

further down.

A highly relevant theoretical framework for understanding this type of macro mechanism

is that of framing contests (Kaplan, 2008). This framework is particularly helpful in that it offers

a theoretical bridge between individual and collective sensemaking (Maitlis & Christianson,

2014). Framing contests are defined as activities in which societal actors engage in an attempt to

mobilize others around a specific point of view (Kaplan, 2008). The theory suggests that actors

with conflicting interests create, reproduce, and challenge certain frames in a strategic attempt to

gain control of the public debate (Cornelissen & Werner, 2014). Typically, this is done by

selecting specific aspects of a perceived reality and making them more salient in a way that

promotes a particular problem definition, causal interpretation, moral evaluation, and treatment

recommendation (Entman, 1993).

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It is important not to underestimate the implications of framing—a future that cannot be

imagined, cannot be acted towards (Schlogl et al., 2021). Deetz (2007) called this ‘discursive

closure’; the subtle and uncontested shutdown of certain conversations in the public domain

(through tactics like neutralization, subjectification, and denial) driven by hidden hegemonic

forces. Especially the lack of subscription to the Work Deintensification narrative can plausibly

be attributed to a successful delegitimization (Luyckx & Janssens, 2020), mostly by

Economy/Labor Market experts in the public debate, of the idea of universal basic income (UBI)

and the possibility of a ‘post-work’ society. Please note that it is not necessarily the case that

actors have an explicit agenda in delegitimizing certain future of work narratives. Rather, we

argue, they have been occupationally and dispositionally socialized into a specific worldview, as

we explain in the section on narrative subscription and corresponsive mechanisms.

Field frames. A likely explanation for why different actors construe different future of

work narratives is the existence of field frames. Field frames are political constructions that

provide order and meaning to fields of activity by creating a status ordering that deem some

practices—and the narratives associated with them—as more appropriate than others (Lounsbury

et al., 2003). In other words, they represent taken-for-granted cognitive frames that structure

expectations and scripts within a specific field, occupied by a specific group of societal actors

(Jones et al., 2014). To this end actors tend to use rhetorical devices like metaphors, stereotypes,

or slogans. Consequently, actors from the same group tend to develop a shared vocabulary—and

more importantly, a sense of social identity—that motivates their collective actions. We found

multiple examples of this in our data, such as the phrase that “this time is different”—referring to

the idea that historical fears around mass-scale automation will come true this time—typically

used by Authors/Journalists and explicitly opposed by Economy/Labor Market experts.

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Thus, field frames provide a macro-structural underpinning for actors’ motivations,

cognitions, and discourses at the micro level (Cornelissen & Werner, 2014). This also explains

why actors have different ideas as to what type of sources or arguments are considered credible,

or why they interpret the same studies, numbers and figures, and historical trends in a

fundamentally different way than other groups—that is nonetheless consistent within their group.

The fact that each actor group is convinced to be more ‘right’ based on what their field does and

does not consider valid ‘evidence’, our findings suggest is a possible driver of depoliticization in

the public debate about the future of work, since all groups claim their views—but not those of

other fields—are neutral and based on objective evidence (Kelly, 2022).

Narrative subscription. Narrative subscription is a concept from critical policy studies

that refers to the agreement of a person with a narrative; the extent to which he or she is

convinced by that narrative (Miller, 2019). Research has shown that narrative subscription is not

only achieved through logic and evidence, but that motivated reasoning (Slothuus & De Vreese,

2010) and biased assimilation (Jones & Crow, 2017) play a key role. That is, people process

information selectively such as to defend and maintain their values, identities, and attitudes. For

instance, they may agree or disagree with science communication about climate change

depending on whether being a believer or non-believer is more aligned with their vested interests

(Hart & Nisbet, 2012).

A first way in which selective information search manifests itself is in the type of

newspaper or periodical people prefer to read—i.e., those that have an editorial stance in line

with their own profile (see Appendix, Step 1)—which further contributes to polarization of the

public debate (Hart & Nisbet, 2012; Patton & Johns, 2007). A second way is through

intersubjective processes related to social identities and group memberships. That is, people do

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not make sense of competing narratives in the public domain in isolation; rather, meaning is

negotiated, contested, and co-constructed between individual actors, through their interactions

with each other, for instance through education or occupational membership (e.g., actors from a

specific group interacting at sectorial workshops and conferences; policy reports published

towards a specific actor audience; disciplinary paradigms; Maitlis & Christianson, 2014). This

explains our finding that actors tend to subscribe more to narratives pushed by members of their

own actor group (Cornelissen & Werner, 2014). As an aside, although it is often assumed that

highly educated people are less biased because of their broader knowledge base, research on

biased assimilation has in fact shown that higher levels of education and expertise often lead to

more bias, as a result of field-level socialization creating ‘tunnel vision’ (Jones & Crow, 2017).

All of this implies that the future of work is not only characterized by competing imaginaries or

narratives, but also by competing ‘tribes’ inhabiting these possible futures. (In addition to these

‘tribal’ effects, we also found more universal narrative subscription effects cutting across actor

groups, relating to optimism bias, accessibility bias, and framing bias; see Appendix Step 3).

Corresponsive mechanisms. Finally, we propose that the ‘why’ behind actors being

(non-)believers of specific future of work narratives—and especially our finding that distinct

individual difference profiles align with subscription to specific narratives—can be explained by

corresponsive mechanisms in life course personality development. This theoretical concept has

its origins in personality and vocational psychology, and refers to the reciprocal influences of

work on personality through the mechanisms of selectivity and corresponsive reactivity

(Schneider, Smith, & Goldstein, 2000). That is, people have been found to systematically select

into fields that are consistent with their individual differences (i.e., selectivity), which are

consequently further developed, reinforced, and strengthened by their experiences in that field

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(i.e., reactivity). The term ‘corresponsive’ refers to the observation that the individual differences

that are most reactive to field socialization correspond to the ones that led the person to select the

field in the first place (Woods et al., 2020). In other words, occupational choice is at least in part

driven by a person’s individual difference profile, with positive reinforcement cycles—e.g.,

educational and career success—deepening and reinforcing these traits across different life

stages and transitional events, like high school graduation and labor market entry. Over time, this

leads to homogeneity within that field, both at the level of occupations and organizations

(Roberts, 2006). This theory, thus, offers a plausible account for the similarities and differences

we found within and between the groups of actors in our data. It also explains why it is so

difficult for actors to engage in perspective taking (i.e., the ability to understand an issue from

another group’s perspective; Galinsky & Moskowitz, 2000). Not only do different actors hold

different views about the future of work; the individual differences they have that are logically

associated with these views have specifically caused them to select into tech, economics,

journalism, or policy making, where over the years they have been reinforced more and more.

The difficulty of understanding the perspective of a group of people who is different to you in

every way—not only today but as a result of their cumulative life experiences since childhood—

is another possible explanation for our initial observation that all actors in the public debate

around the future of work believe to be ‘right’ (Kelly, 2022).

Avenues for Further Research on the Future of Work

Based on all of the above, and through the acknowledgement of the limitations of the

present study, we can now formulate a research agenda for further research on the future of work

adopting an imaginaries paradigm (see Table 6). The present study was conducted in a specific

country (Belgium), at a specific point in time (2015-2021), and among those actors that were

determined—within this geographical and temporal context—to be most prominent in the public

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debate around the future of work. As we demonstrate in the Appendix (Table A2), however,

many of the narrators identified in the print media data were international (most notably from the

US and Europe), such that we cannot conclude that our findings would only hold in the tiny

country of Belgium. That said, it is clear that the narratives and imaginaries represented in our

data were distinctly Western. Future research could look at imaginaries from non-Western

countries, or run comparative studies of imaginaries found in different cultural settings facing

different demographic, political, and economic realities, and that may hold different societal

attitudes toward technology (Bareis & Katzenbach, 2022).

As for the temporal context, it is clear that the future of work is by all accounts a moving

target. At the time of writing this paper, stories were in the news every day about OpenAI’s

ChatGPT-4 and competing LLMs (i.e., large language models) like Sydney (by Bing), Bard (by

Google), and Ernie (by the Chinese Baidu)—fanning fears of unaligned AGI (i.e., artificial

general intelligence) and existential risk (Cave & Dihal, 2019). One possible implication of this

is that the Singularity narrative, that was not believed in by any of the actors in our data, will

gain prominence in coming years—or at the very least, that if we were to run our survey again

today, subscription to this narrative would now be significantly higher.

Incidentally, this may also explain why the Job Destruction narrative was pivotal, but not

very prevalent in our print media data. As the seminal Frey and Osborne study appeared in 2013

and our analysis only covered articles from 2015 onwards, perhaps this narrative had already

passed its prime at this point, and became a point of contention in the public debate (and of

course, at this point, the study itself is already 10 years old). An interesting avenue for further

research would be to run longitudinal follow-up studies on prominent future of work narratives,

tracing how narrative subscription evolves over time, and adding new narratives as they emerge

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in the public domain. At the very least, the present study offers an interesting snapshot—a time-

stamped document containing different narratives of the future of work as they existed in the

year 2023, for future historians to look back at, and conduct retrospective scenario reviews

(Schoemaker, 2020). A more fundamental concern that arose during the process of doing this

research is whether the academic literature will be able to keep up with the rapid evolutions in

the wider future of work space. While it typically takes years to publish an academic study,

technologies such as AI and robotics are evolving at a very different speed, that is believed (at

least by Technology/Innovation experts) to increase exponentially in coming years. These past

few months, we have found ourselves using Twitter as a source of information as least as much

as the academic literature. Along with other trends and issues in academic publishing—i.e., open

science, preregistration, paywalls, increasing difficulties in finding reviewers—it is conceivable

that future of work researchers will start (pre-)publishing their work through different, faster

dissemination channels, like open-access whitepapers or preprint servers like arXiv and Sci-Hub.

Future research could also look at sources of narrative data beyond the print media, for

example policy documents, government reports, or press releases; interviews or focus groups

with people from different actor groups—or recorded debates or conversations between them—

on the radio, on TV, or as part of a research study; or works of fiction, such as science-fiction

films or books (Czarniawska & Joerges, 2019). It is very well possible that different types of

actors and narratives would emerge from looking at different sources, which is all the more

relevant as we could not distill any relevant insights about Policy Makers/Public Administrators

and Engaged Citizens from our data. We should add that not only top-down sensegiving by

powerful societal actors—as suggested by Kaplan (2008)—is an interesting topic for further

research; of equal interest are bottom-up acts and processes of conflict and resistance against

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dominant imaginaries of the future of work (Cameron & Rahman, 2022; Kellogg, Valentine &

Christin, 2020; Mumby, Thomas, Martí, & Seidl, 2017). Recent experiments in the field of

political psychology have even found no effect of so-called ‘agenda-setting’ (i.e., the deliberate

framing of an issue by powerful actors through the media) on citizens’ issue engagement—which

was in fact mostly determined by the interests of their in-group—while a causal effect was found

of public opinion polls on the attention devoted to issues by legislators (Miller et al., 2017). Such

findings drive home Kaplan’s (2008) view that frames are continually transformed through series

of interactions.

We also propose that the shift from a prediction to an imaginaries paradigm should be

accompanied by a shift in epistemology. Studies on the future of work situated within the

prediction paradigm tend to be highly positivistic in nature. When adopting an imaginaries

paradigm, in contrast, an interpretivist stance makes more sense. Different strands of

interpretivism open up different avenues for further research; hermeneutics, for instance, would

lead researchers to the study of cultural artefacts such as texts, symbols, stories, and images.

Phenomenology focuses our attention on people’s lived experiences, recollections, and

interpretations. Symbolic interactionism offers fertile grounds for studying meaning that emerges

out of interactions between people. Researchers who wish to adopt an activist stance and affect

change will likely adopt a radical humanist or critical epistemology (Burrell & Morgan, 2017).

Methods could consist of media analysis, historical analysis, strategic scenario planning,

ethnographies (for instance of robotics development labs), or even the rendering of imaginaries

of the future of work in virtual reality (VR) experiments, allowing actors to ‘pre-experience’

different futures first-hand (Galloway & Caudwell, 2018).

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These proposed epistemologies and methods guide us away from a deterministic, and

toward a repoliticized view of the future (Kenis & Mathijs, 2014; Kelly, 2020), in which the

future of work is understood to be shaped through a dialectic process between members from

different actor groups, each representing different perspectives and vested interests (Howcroft &

Taylor, 2022). Together, these ideas for further research point to the crucial difference between

seeing the future as something that can be ‘objectively predicted’ versus something that must be

‘subjectively imagined’ (Tapinos & Pyper, 2018). This approach, we hope, will significantly

open up the debate about the human side of the future of work.

Insert Table 6 about here

Practical Implications

The present study may puzzle practitioners: it proposes studying stories rather than

predictions, and the distant future rather than the near future. In both cases, it would seem the

latter alternative is more actionable than the former (Weitzman, 1998). We would rebut that the

Fourth Industrial Revolution can be expected to play out over a long period of time, one that will

not feel clearly delineated while we are going through it. According to Carlota Pérez’ important

book (2002) on the matter, cycles of technological revolution typically last 50 to 60 years. So

although in the short term, the future of work can indeed be approached as a series of predictions

about gradual changes (preferably competing ones, however, organized along a decision-tree

type structure), in the long term it must be understood as a very different phenomenon,

characterized by radical and disruptive changes, and likely requiring a full societal

transformation (Augustine et al., 2019; Levy & Spicer, 2013; Roux-Rosier et al., 2018).

One advantage of switching gears and focusing on the distant future is that it has the side-

effect of creating discursive opening (Reinig & Borda, 2023). People may not believe that a

universal basic income is realistic in the next 10 years, but what about 100 years from now? We

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argue that a distant-future frame shifts the focus away from the eternal ‘yes but’ debates around

realism and affordability, towards a more democratic societal debate envisioning the society we

want in the future. Paradoxically, focusing on realism at all times can create inertia and inaction

(Schoemaker, 2020). We recommend getting input from as wide an array of actors as possible—

perhaps even organize referenda or create participative citizen forums (Schoemaker, 2020).

To business leaders, we recommend hiring strategic scenario planners (Kahane, 2012)

such that they can prepare for all possible futures simultaneously, regardless of whether they

personally believe a given ‘imaginary’ to be realistic or not. Predictions of the future can and

should be updated in real time as reality changes around them, as explained by Webb (2019) in

Harvard Business Review, outlining the difference between tactics, strategy, vision, and system

change—often poorly understood. In doing so, we recommend against an overreliance on the

views and data provided by only a few experts or even types of experts. The advantage to this is

that whenever the prediction model of one particular expert (or group of experts) goes off the

rails, alternative models for understanding this new data will be immediately available.

As for experts (such as the actor groups in our data), our findings imply that working in

transdisciplinary teams may be the best way forward, not only because our analysis shows that

the future of work covers many different (highly contextualized) phenomena, but also because

this would reduce the risk of cognitive blind spots (Jones & Crow, 2017; Schoemaker, 2020). As

our findings show, the facts and figures invoked by experts in the public debate are often less

objective as they appear to be (Beckert & Bronk, 2019). We would thus encourage more

reflexivity among experts, based on our insights around field frames (Lounsbury et al., 2003) and

corresponsive mechanisms (Woods et al., 2020). This call for reflexivity comes with a

disclaimer, however—studies have found that when research areas are (re)politicized,

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polarization among the general public can increase, as competing views expressed by experts

undermine their faith in science (Hart & Nisbet, 2012; Kenis & Mathijs, 2014).

To workers, we would like to say that whenever they read or hear something about the

future of work from now on, they should not just look at what is predicted and by when, but also

who is saying it and why (Levy & Spicer, 2013; Roux-Rosier et al., 2018). What is their

disciplinary background? What narratives are they pushing or contesting? What are their vested

interests? Who is funding their research or paying their salaries? What society do they want and

how does it benefit them (Urry, 2016)? One need only look at the daily Twitter updates by

prominent technologists, economists, and subject-matter authors to see that the patterns

identified in the present paper fit in many cases. We would also encourage workers to look into

creating or joining social movements organized around shared imaginaries of the future

(Gümüsay & Reinecke, 2022), and the vested interests of their in-groups (Miller et al., 2017).

CONCLUSION

When asked to summarize the findings of this big and complex research project in a

single sentence, we have typically quipped that “we can’t predict the future of work, but we can

predict your prediction”. (Another phrase that comes to mind is that “we see things not as they

are, but as we are”). The point we have sought to drive home with this project is that the future of

work is an issue of high societal importance, that is currently being dominated by a largely

deterministic discourse. The phenomenon of exponentially increasing technological capability,

however, is only one aspect shaping the future of work. In addition to that, the future of work

will also be the result of a political and democratic process shaped by framing contests driven by

the competing interests of different actors in society—some of which holding more power than

others to dominate the debate. A lack of issue engagement especially among non-experts, due to

the distant-future nature of the topic area, creates the risk that the future of work is something

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that will happen ‘to us’—as an avalanche of novel and disruptive technologies (accompanied by

a host of ethical concerns that are better anticipated than remedied) that we as citizens, experts,

and policy makers will be forced to undergo without adequate mental or practical preparation.

The imaginaries paradigm proposed here, in contrast, proposes that the future will be what we

‘make it’. As the future of work has not happened yet, now is the time to systematically map the

viewpoints and interests of different actors in society, and create a joint awareness, democratic

debate, and long-term vision of where we want to go.

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Page 46 of 60Academy of Management Discoveries

TABLE 1. Common Narratives about the Future of Work in the Public Debate (Study 1).

Definitions (from literature)

Examples of underlying phenomena (from data)

Narrative 1:

Dataism

The wide-scale acceptance of the authority of algorithms and Big Data,

effectively transforming all political and social structures into data-processing

systems based on real-time tracking and predictive analysis (Van Dijck, 2014)

The emergence of a fully data-driven society, the demise of privacy, growing

dependence on technology, the rise of a powerful elite of technocrats

Narrative 2:

Exterminism

The rich hoarding all the resources while the earth burns, in the process

eliminating all people who produce little or no economic value (Frase, 2016)

Climate change and ecological disasters, an increased risk of war, growing

economic inequality

Narrative 3:

Re/Upskilling

The need for continuous retraining in response to the obsolescence of existing

skills (reskilling), and/or the necessity to learn entirely new skills (upskilling) in

adaptation to changing demands (Schlagle, Weiss, & Prainsack, 2021)

Workers needing to keep up with new technologies, partial automation (of tasks),

the emergence of new types of jobs and industries, humans learning to

collaborate with robots

Narrative 4:

Augmentation

Computers (algorithms) and humans working together, by design, to enhance

one another, such that the intelligence of the resulting system improves (Jain,

Padmanabhan, Pavlou, & Raghu, 2021)

Algorithms making human work faster and easier, increased productivity,

technology enhancing rather than replacing human labor

Narrative 5:

Singularity

Artificial intelligence (AI) transcending human intelligence, as a result of

exponential growth in the technology leading up to a ‘point of no return’

(Kurzweil, 2005)

Intelligent machines surpassing human capacities in every way, full automation

(of jobs), humanity entering a new historical era

Narrative 6:

Job Destruction

The mass unemployment of workers in many industries as a result of

automation and structural shifts in the labor market (Frey & Osborne, 2017)

The disappearance of jobs and industries (that are not compensated for by new

job creation), mass unemployment

Narrative 7:

Work Deintensification

The opposite of the historical phenomenon of work intensification (Le Fevre,

Boxall, & Macky, 2015)—that is, a decrease in the need for human labor and

long work hours, calling into question the role of work in the human life and

work as a primary source of income (Bregman, 2017).

More leisure, time for side gigs and craftwork, and the introduction of a universal

basic income (UBI)

Page 47 of 60 Academy of Management Discoveries

TABLE 2. Identification and Initial Coding of Future of Work Narratives Featured in the Print Media (Study 1).

Study 1: Qualitative data (print media articles)

Future of work narratives

Frequency

within

article set

Coding categories1

Frequency

within coding

category

Example of coded excerpt

Narrative 1: Dataism

276 (57%)

(‘People have no idea of the extent of it’, De Tijd, Sept. 14th, 2019)

Timeframe

Present

8 (3%)

Near future

155 (56%)

Distant future

113 (41%)

I’m glad that the Cambridge Analytica affair received a lot of publicity. The constant

media attention helps. We’ve been talking a lot more about privacy for several years

now, and that’s good. The more it is in the news, the more people will think about it and

can influence tomorrow’s policy. … We have to set standards as a society so that, these

next few years, our personal data stops being used in inappropriate ways. Only if we can

ensure that our data are handled according to ethical guidelines, will the data revolution

be a good thing.

Framing

Positive

58 (21%)

Negative

196 (71%)

Mixed

22 (8%)

New practices are constantly popping up. … There are plenty of examples of how this

technology could lead to even more serious personal infractions. Take health apps that

women use to track their menstrual cycles. The intimate data that women share in the app

could be used to predict when they will get pregnant, in order to then show them ads for

baby stuff. The data could even end up with employers, who could use it to predict if and

when current or prospective employees may take maternity leave. They could then

decide not to hire someone, or fire them before they announce their pregnancy, to save

money.

Outlook

Positive

126 (46%)

Negative

131 (47%)

Mixed

19 (7%)

This is a failure of the market. There is asymmetry of information. Consumers do not

have all the information they need to make informed decisions. And that is by design: the

companies that do this have an incentive not to give consumers that information. … Look

at the settlements that Facebook and YouTube recently reached in the US. They both had

to pay fines that were lower than the profits they made from their violations (Facebook

had to pay $5 billion and YouTube $170 million, ed.). If you only have to pay a fraction

of what you stole as a fine, that is not punishment. That is a reason to carry on. If I rob

a bank and I only have to return half of the loot, I have found a very profitable model.

Then I’m going to rob another one.

Narrator

Tech

117 (42%)

Author

80 (29%)

Econ

43 (16%)

Policy

19 (7%)

Other

17 (6%)

Ask your lawyer. That is the answer Serge Egelman [Research Director at UC

Berkeley’s International Computer Science Institute] received from a Google

daughter company that helps app makers trace bugs. Egelman had inquired about their

law, but got the friendly suggestion to go ask my own lawyer. That was…weird. Let’s

just say that response did not instill much trust’.

Narrative 2: Exterminism

235 (49%)

(‘Optimism is something for the elite’, De Morgen, Aug. 16th, 2019)

Timeframe

Present

4 (2%)

Near future

76 (32%)

‘By 2050 there are predicted to be hundreds of millions of refugees. We are entering a

dystopian future. ... We in the West are trapped by presentism. Our generations are

bombarded with information every second of the day, so that we are only concerned with

the present moment. We are no longer able to think about our future and history. In this

sense, capitalism, especially Silicon Valley companies, has taken complete control of us.

Western politicians, as well, only think in the short term. That is simply what

parliamentary democracy is set up to do. Politicians are in power for about four to five

years, then someone else takes over. This is very different in China. There, the plans for

the next twenty years are already in place. I am not saying that China is a utopian

society, on the contrary. But China is much better at thinking ahead.’

Page 48 of 60Academy of Management Discoveries

Distant future

155 (66%)

Framing

Positive

5 (2%)

Negative

228 (97%)

Mixed

2 (1%)

‘You can't escape it anymore … Capitalism is advancing in its worst forms’. Welcome

to Horvat's dark world, where it is five to twelve. Capitalism has extended its poisonous

tentacles into all levels of the world and is holding humanity captive. Unwittingly, we are

heading for major climatic and humanitarian disasters. It is time for a revolution....

‘Only when we face up to the magnitude of the problems that await us, will it dawn

on us what drastic changes are needed’.

Outlook

Positive

77 (33%)

Negative

139 (59%)

Mixed

19 (8%)

Horvat distances himself from all the misery that communist systems have produced.

Nevertheless, he refuses to throw in the ideological towel. Precisely because it is very

clear to him which system represents the true evil: capitalism. ... He muses about

revolutions, though he harbors little hope that they will actually take place. ... ‘Optimism

is something for the elite’.

Narrator

Tech

68 (29%)

Author

85 (36%)

Econ

43 (18%)

Policy

28 (12%)

Other

11 (5%)

Philosophizing about the dystopian world that awaits us, is what Croatian prophet of

doom Srecko Horvat loves most: ‘It makes you combative’. So he argues in his latest

book ‘Poetry from the Future - Why a Global Liberation Movement is Our Civilization's

Last Chance’.

Narrative 3: Re/Upskilling

220 (45%)

(‘The Brits invented the tank, but the Germans won battles with it’, De Standaard, Sept. 29th, 2018)

Timeframe

Present

2 (1%)

Near future

99 (45%)

Distant future

119 (54%)

No, Arnold Schwarzenegger’s Terminator will not steal your job tomorrow nor decades

from now. … It isn’t the robots or supercomputers themselves that will change the way

we live in the future. … For new technology to create a breakthrough we humans

needed to adapt and reorganize around it.

Framing

Positive

178 (81%)

Negative

29 (13%)

Mixed

13 (6%)

Improvements in productivity make our lives better, maybe not spiritually, but

definitely materially. In general technological progress has given us freedom and made

our lives more comfortable.

Outlook

Positive

134 (61%)

Negative

64 (29%)

Mixed

22 (10%)

‘Look, I can’t predict the future, but I can look to the past. It’s easy to forget how many

everyday things, like the light bulb, have improved our lives. It’s tempting to fall into

despair and try and stop progress, but then we’re not paying enough attention to what we

have achieved already.’

Narrator

Tech

77 (35%)

Author

43 (20%)

Econ

78 (36%)

Policy

10 (4%)

‘The classical example is the electrical engine, that started to replace the steam engine at

a certain point in time. Those giant steam engines powered factories centrally, and

operations in the factories were organized around them. Simply replacing these

unwieldly machines with an electrical version would hardly change that’, says economist

Tim Harford. ‘It wasn’t until they started to adopt smaller electrical engines—which

wasn’t possible with steam engines—that workplaces became more dynamic, workers

gained control over their work, and the industrial revolution gained momentum’.

Page 49 of 60 Academy of Management Discoveries

Other

12 (5%)

Narrative 4: Augmentation

175 (36%)

(‘You think, the computer does: New technology controls computers with your brain’, Het Laatste Nieuws, Feb. 29th, 2020)

Timeframe

Present

3 (2%)

Near future

91 (52%)

Distant future

81 (46%)

The technological foundations are laid and today there are dozens of companies working

on a brain-computer interface that—or so they hope—could be mainstream in a few

years already.

Framing

Positive

171 (98%)

Negative

2 (1%)

Mixed

2 (1%)

As he [Mark Zuckerberg] recently said in an interview: ‘The way in which our phones

and computer systems operate today, organized around apps and tasks, is fundamentally

not how our brains work and how we approach the world’. In other words, if in the future

we can control computers with our minds, we won’t need keyboards, mice, or

touchscreens anymore. Even the whole visual computer interface will become

redundant.

Outlook

Positive

121 (69%)

Negative

38 (22%)

Mixed

16 (9%)

Recently, new companies are emerging that claim to have made a major breakthrough

in transferring electrical brainwaves into computer data, using a device that reads the

waves correctly and is not too difficult to carry.

Narrator

Tech

85 (49%)

Author

31 (18%)

Econ

39 (22%)

Policy

9 (5%)

Other

11 (6%)

Mark Zuckerberg, founder and CEO of Facebook, is very excited about the future

prospects offered by the technology. … Tesla founder Elon Musk, as well, is interested

in computers that can read brainwaves, although he wants to take it a bit further; his

company Neuralink is working on chips that can be implanted in our brains.

Narrative 5: Singularity

171 (35%)

(‘We must dare make the choice for ethical technology’, De Standaard, Apr. 27th, 2020)

Timeframe

Present

2 (1%)

Near future

46 (27%)

Distant future

123 (72%)

The AI science fiction tells us to be afraid of is general AI, intelligence that looks like

human intelligence and is superior. Even if it were possible, that is something for the

distant future.

Framing

Positive

61 (36%)

Negative

101 (59%)

Mixed

9 (5%)

One of the most urgent problems is: who is responsible when automation goes wrong?

… And to what extent do we want technology to make decisions for us? Humans have

to have ultimate accountability … I feel strongly that Europe should not join the AI

accelerationism race. Europe should say: these are the values we stand for, and set an

example for the rest of the world.

Outlook

Positive

101 (59%)

Negative

56 (33%)

Mixed

14 (8%)

Instead of waiting for a new technology to emerge and problems to arise, we are now

making it so that the technology is developed in an ethical way from the onset. … A lot

of countries outside of Europe are looking to the GDPR as an example of how to

regulate technology without destroying the economy. I think we can do something

similar for AI.

Narrator

Tech

82 (48%)

Author

41 (24%)

‘AI is sneaking into our lives. We can’t stop that, but we can guide it in the right

direction’, says Mark Coeckelbergh, member of the High-Level Expert Group on

Artificial Intelligence which advises the European Commission, the Foundation for

Responsible Robotics, and the IEEE Global Initiative for Ethical Considerations in

Artificial Intelligence and Autonomous Systems.

Page 50 of 60Academy of Management Discoveries

Econ

32 (19%)

Policy

6 (3%)

Other

10 (6%)

Narrative 6: Job Destruction

107 (22%)

(‘Robots, jobs, and leisure’, De Morgen, Oct. 9th, 2018)

Timeframe

Present

1 (1%)

Near future

40 (37%)

Distant future

66 (62%)

Some will say: this time it’s different. That was also what was said about the previous

technological revolutions. Each time, it turned out to be the wrong prediction. I firmly

believe that the current IT-revolution will indeed make many jobs disappear, but that the

development of new activities, goods, and services will create a lot of new jobs; jobs that

don’t even exist yet today.

Framing

Positive

1 (1%)

Negative

103 (96%)

Mixed

3 (3%)

People are very worried. Will there be enough jobs left when the IT revolution is

making so much human labor redundant? … The old world, that is disappearing, is what

we know; the new world is in the future and is largely unknown, creating a lot of

uncertainty.

Outlook

Positive

61 (57%)

Negative

31 (29%)

Mixed

15 (14%)

I am actually very optimistic about the capacity of the market to create new jobs. …

Historically, Job Destruction was more than compensated for by a phenomenal rise in

employment in industry, with the result that the total amount of jobs did not decrease,

but increase.

Narrator

Tech

28 (26%)

Author

20 (19%)

Econ

51 (48%)

Policy

5 (5%)

Other

3 (2%)

‘Every time I give a speech somewhere about current economic issues I get questions

about the future of employment in a world where robots and artificial intelligence

threaten to take over all human labor’, says Paul De Grauwe, professor at the London

School of Economics.

Narrative 7: Work Deintensification

61 (13%)

(‘Working isn’t working’, De Standaard, Feb. 17th, 2018)

Timeframe

Present

0 (0%)

Near future

13 (21%)

Distant future

48 (79%)

Post-workists are convinced that in a few centuries, mankind will look back on our work

ethic as an aberration in history. … They are convinced that working from 9 to 5 is

barbaric and that one day, we will look back on our current workday like we now look at

19th-century child labor.

Framing

Positive

55 (90%)

Negative

2 (4%)

Mixed

4 (6%)

A basic income for everyone (paid for by taxes) is a conditio sine qua non for [the post-

workists’] theory. In their ideal future, we live in a peer-to-peer economy: an open and

participative commons-based economy. They expect AI and robots to take over

traditional jobs, so that people can devote themselves to a new artisanal economy

centered on self-expression and creativity. We’ll have all the time in the world to

indulge in art, dance, literature, pottery, or creating objects with 3D printers. The drastic

reduction in work hours will create time for political and social engagement, and to

strengthen our relationships.

Outlook

Positive

20 (33%)

Negative

30 (49%)

Mixed

11 (18%)

We have always worked and we will always work. The idea that work will disappear, is

as old as work itself. … In the 1960s it was also proclaimed that we were evolving to a

leisure society, they were wrong then, too. … Sixty years ago, they said work was a

problem, until mass-scale unemployment came, and they realized ‘no work’ was even

more of a problem. … A society with few or no jobs would be a dystopia.

Page 51 of 60 Academy of Management Discoveries

Narrator

Tech

13 (21%)

Author

20 (33%)

Econ

25 (41%)

Policy

2 (3%)

Other

1 (2%)

'Up until now, we’ve always converted time gained into even more work and more

consumption’, says Ive Marx, professor of social-economic sciences at University of

Antwerp. ‘I don’t see that changing any time soon, human needs are insatiable. And so

there will always be more and more work.’

Notes. K=485 print media articles; 1 Timeframe = whether a given narrative was construed as near-future or distant-future, Framing = whether a narrative was

construed as ‘good’ (i.e., societally desirable) versus ‘bad’ (i.e., societally undesirable), Outlook = whether a narrative was construed as having a positive versus

a negative outlook (i.e., the likelihood of a societally desirable versus undesirable outcome), Narrator = actors that were talked to, for instance as interviewees; or

their narratives were talked about, for instance when they had written a book; or they did the talking, for instance when they wrote an op-ed; Tech =

Tech/Innovation experts, Author = Authors/Journalists, Econ = Economy/Labor Market experts, Policy = Policy Makers/Public Administrators. In the ‘Other’

category, the most common actors were lawyers and regulators specialized in AI, trade union representatives, factory workers, and social movement actors.

Page 52 of 60Academy of Management Discoveries

TABLE 3. Descriptives and Correlations (Study 2).

M (SD)

Narrative subscription1:

Dataism

2.98 (.98)

(.71)

Exterminism

3.63 (.99)

.39**

(.77)

Re/Upskilling

4.16 (.45)

.17**

.04

(.60)

Augmentation

4.04 (.71)

-.00

-.09*

.45**

(.69)

Singularity

1.99 (.64)

.43**

.16**

.30**

.08

(.80)

Job Destruction

2.77 (.98)

.29**

.30**

-.03

-.21**

.30**

(.70)

Work Deintensification

2.64 (.95)

.04

-.13**

.20**

.27**

.23**

0.05

(.72)

Individual differences:

Perceived temporal distance:

to the ‘near’ future (years)

9.20 (42.93)

.00

.03

-.10*

-.12**

-.05

-.01

.02

to the ‘distant’ future (years)

78.08 (215.28)

-.05

-.01

-.18**

-.11**

-.06

.02

.01

.68**

Time orientation:

past-negative

3.34 (1.35)

.08

.15**

-.13**

-.11*

.04

.15**

-.06

.03

(.83)

past-positive

4.78 (1.14)

-.02

.01

.12**

-.02

-.09*

.04

.00

-.02

-.11**

(.75)

future-negative

2.95 (1.17)

.14**

.21**

-.14**

-.16**

.09*

.23**

-.11**

.06

.05

.48**

-.09*

(.77)

future-positive

5.04 (1.06)

-.10*

-.11**

.14**

.13**

.01

-.06

.04

-.07

.00

-.21**

.11**

-.31**

(.70)

Trait optimism

5.00 (1.01)

-.12**

-.19**

.16**

.21**

-.08*

-.22**

.14**

-.03

-.02

-.47**

.24**

-.52**

.21**

(.83)

Misanthropy

3.04 (1.06)

.18**

.29**

-.13**

-.17**

.13**

.19**

-.14**

.01

-.04

.29**

-.25**

.35**

-.15**

-.39**

(.76)

Openness to experience

5.30 (.77)

.09*

.08

.18**

.08

.07

0.01

.14**

-.06

-.07

-.15**

.12**

-.12**

.07

.25**

-.09*

(.81)

Resistance to change

3.02 (.90)

-.01

.12**

-.32**

-.23**

-.11**

0.08

-.22**

.01

.02

.35**

-.08

.37**

-.16**

-.40**

.23**

-.44**

(.85)

Educational level

4.37 (1.22)

-.02

-.12**

.22**

.12**

-.11*

-.13**

-.03

-.08

-.11**

-.13**

-.06

-.16**

.15**

.13**

-.05

.13**

-.17**

Job automation risk

2.59 (.72)

.07

.13**

-.11*

-.17**

.08

.15**

-.10*

.02

.01

.24**

-.13**

.35**

-.32**

-.38**

.20**

-.28**

.28**

Technology readiness

4.27 (.92)

-.03

-.25**

.28**

.38**

.09*

-.25**

.21**

-.04

-.08

-.27**

-.02

-.31**

.11**

.32**

-.19**

.25**

-.40**

Locus of control:

internal

5.08 (.86)

-.03

-.07

.13**

.18**

.01

-.17**

.11**

-.02

-.05

-.29**

.15**

-.37**

.23**

.44**

-.18**

.14**

-.20**

chance

3.62 (.93)

.10*

.14**

-.11**

-.19**

.08

.17**

-.08

.05

.06

.36**

-.13**

.50**

-.23**

-.39**

.24**

-.14**

.28**

powerful others

3.09 (1.33)

.20**

-.09*

-.17**

.11**

.25**

-.09*

.07

.04

.38**

-.15**

.43**

-.15**

-.46**

.32**

-.19**

.36**

Notes. N = 570; 1 Respondents rated each of the items on the following response scale: 1 = No, I can't see this happening; 2 = Maybe, in the very distant future; 3

= Yes, in the distant future; 4 = Yes, in the near future; 5 = Yes, and much faster than we believe now; Cronbach's alpha inter-item reliabilities reported on the

diagonal where applicable; ** p < .01; * p < .05.

Page 53 of 60 Academy of Management Discoveries

TABLE 3. Continued.

Narrative subscription1:

Dataism

Exterminism

Re/Upskilling

Augmentation

Singularity

Job Destruction

Work Deintensification

Individual differences:

Perceived temporal distance:

to the ‘near’ future (years)

to the ‘distant’ future (years)

Time orientation:

past-negative

past-positive

future-negative

future-positive

Trait optimism

Misanthropy

Openness to experience

Resistance to change

Educational level

Job automation risk

-.20**

(.82)

Technology readiness

.22**

-.24**

(.87)

Locus of control:

internal

.04

-.33**

.27**

(.74)

chance

-.10*

.23**

-.30**

-.44**

(.61)

powerful others

-.14**

.28**

-.31**

-.41**

.49**

(.87)

Page 54 of 60Academy of Management Discoveries

TABLE 4. Means, Standard Deviations, and ANOVAs (Study 2).

‘Future of work’ actors

Tech/innovation

experts T

Authors/

journalists A

Economy/Labor

Market experts E

Policy

Makers/Public

Administrators P

Engaged

citizens C

Narrative subscription1:

1: Dataism

3.08 (1.08)

2.91 (1.00)

2.99 (.98)

3.03 (.94)

2.98 (.94)

2: Exterminism

3.37*(1.16)A

3.77*(.95)T

3.50 (.87)

3.55 (1.21)

3.71 (.96)

3: Re/Upskilling

4.36**(.37)A,C

4.03**(.47)T,E,P

4.22**(.39)A

4.39**(.41)A,C

4.08**(.46)T,P

4: Augmentation

4.40**(.56)A,C

3.87**(.73)T,E

4.14**(.63)A

4.04 (.68)

3.96**(.74)T

5: Singularity

2.17 (.66)

1.98 (.68)

1.93 (.57)

1.84 (.61)

2.03 (.66)

6: Job Destruction

2.44**(.97)A,C

2.96**(1.02)T

2.69 (.94)

2.57 (1.03)

2.83*(.91)T

7: Work Deintensification

2.93**(.94)E

2.60 (.89)

2.50**(.87)T

2.52 (.94)

2.68 (1.03)

Individual differences:

Perceived temporal distance:

to the ‘near’ future (years)

9.06 (25.89)

14.60 (81.83)

5.29 (6.23)

6.67 (7.54)

8.32 (10.57)

to the ‘distant’ future (years)

101.97 (268.09)

89.49 (270.68)

55.62 (93.48)

56.98 (79.64)

80.06 (224.14)

Time orientation:

past-negative

3.29 (1.17)

3.57 (1.41)

3.23 (1.23)

2.99 (1.18)

3.34 (1.48)

past-positive

4.97 (1.05)

4.83 (1.18)

4.78 (1.12)

4.88 (1.07)

4.64 (1.17)

future-negative

2.79 (.93)

3.19*(1.26)E

2.76*(1.15)A

2.97 (1.24)

2.93 (1.15)

future-positive

5.05 (.95)

4.75**(1.24)E,C

5.23**(.99)A

4.95 (1.12)

5.14**(.92)A

Trait optimism

5.23**(.81)A

4.77**(1.06)T,E

5.13**(.96)A

5.20 (.95)

4.95 (1.05)

Misanthropy

2.99 (1.17)

3.27 (1.06)

2.92 (.98)

2.86 (.98)

2.99 (1.07)

Openness to experience

5.58**(.63)C

5.41**(.72)C

5.31 (.79)

5.34 (.70)

5.08**(.80)T,A

Resistance to change

2.75**(.75)A,C

3.11**(.85)T

2.84**(.86)C

2.94 (.85)

3.21**(.99)T,E

Educational level

4.67**(1.20)A,C

3.86**(1.25)T,E,P

5.07**(.87)A,C

4.93**(.78)A,C

4.04**(1.19)T,E,P

Job automation risk:

2.43**(.61)C

2.64**(.73)E

2.37**(.59)P,A,C

2.73**(.61)E

2.77**(.81)E

Technology readiness

5.02**(.78)E,P,A,C

4.04**(.94)T

4.31**(.77)T

4.29**(.83)T

4.14**(.92)T

Locus of control:

internal

5.23 (.78)

4.99*(.86)E

5.27*(.79)P,A,C

4.91* (.81)E

4.98*(.93)E

chance

3.39*(.90)A

3.77*(.95)T

3.51 (.81)

3.71 (.96)

3.64 (.96)

powerful others

2.92 (1.27)

3.21*(1.39)E

2.83*(1.19)A,C

3.08 (1.33)

3.25*(1.38)E

Notes. N = 570; 1 Respondents rated each of the items on the following response scale: 1 = No, I can't see this

happening; 2 = Maybe, in the very distant future; 3 = Yes, in the distant future; 4 = Yes, in the near future; The

subscripts indicate which groups of actors differed significantly from each other for each variable, based on a series

of ANOVAs with Bonferroni post-hoc tests; ** p < .01; * p < .05

Page 55 of 60 Academy of Management Discoveries

TABLE 5. Overview of Key Discoveries.

Actor role

Narrator

Subscriber

How are competing narratives of the future of work construed by different actors

in the public debate?

How are competing narratives of the future of work that exist in the public debate

subscribed to by different actors?

Key discoveries:

1. The public debate around the future of work is an arena for discursive struggles

between different groups of actors, each pushing different narratives about the future

that align with their vested interests, and each trying to ‘win’ the debate by getting

the public to subscribe to their master narrative.

1. The narrative subscription of actors to a specific narrative of the future of work in

the public debate is the result of selective information processing, motivated by

whether or not that narrative fits their vested interests.

2. Discursive closure tactics are used by all actors in the public debate around the

future of work, to undermine and delegitimize narratives that run counter to their

master narrative. Delegitimation of a narrative typically leads to lower narrative

subscription, reducing the chance of that narrative coming true.

2. In addition to universal biases like optimism bias, accessibility bias, and framing

bias, narrative subscription goes hand in hand with group membership, such that

members of a given actor group subscribe more to narratives pushed by actors of that

same actor group. This is because members from the same actor group tend to interact

more often, thus reinforcing field frames.

How?

Theoretical mechanism:

Framing contests (Macro)

Theoretical mechanism:

Narrative subscription (Micro)

Why are competing narratives of the future of work construed by different actors

in the public debate?

Why are competing narratives of the future of work that exist in the public debate

subscribed to by different actors?

Key discoveries:

1. Members of a specific actor group hold taken-for-granted cognitive frames typical

of their field of expertise (of which they are often unaware), that translate into a

shared vocabulary, and determine how they construe narratives of the future of work.

1. Actors select into fields of expertise based on pre-existing individual differences that

are typical of that field, and are subsequently continually reinforced through field

socialization. Over time, fields become more homogenous in terms of the type of

people that work in them.

2. All actor groups in the public debate about the future of work believe their master

narratives are based on neutral facts and objective numbers, figures, and trends—and

believe that those of the other groups are not. This is because actors from different

fields hold different understandings of how to interpret data and what constitutes

‘evidence’.

2. Individual differences stand in logical relation to membership of a specific actor

group, which in turn influences subscription to different narratives about the future of

work. People from one actor group often find it hard to understand the perspective of

another actor group, as a result of their lifelong socialization into distinct fields.

Research sub-question

Why?

Theoretical mechanism:

Field frames (Macro)

Theoretical mechanism:

Corresponsive mechanisms (Micro)

Page 56 of 60Academy of Management Discoveries

FIGURE 1. Competing Imaginaries of the Future of Work in the Public Debate.

Figure legend

… goes hand in hand with…

… counters …

… delegitimizes …

… is believed to lead to …

… legitimizes …

Re/Upskilling narrative:

Since the Industrial Revolution, increases in productivity have consistently

led to increases in employment. Mass-scale automation and job destruction

have been predicted many times in history, but these predictions have

always been wrong. The Fourth Industrial Revolution will be a good thing

for mankind. Routine tasks and dangerous or dirty work will be outsourced

to machines and AI, while new jobs and industries will be created as a

result of technological progress. Constant re/upskilling of workers will be

needed, finally creating a labor market climate of lifelong learning.

Augmentation narrative:

Post-World War II technology has given us the Moon landing, the personal

computer, and the smartphone. Only by dreaming and by taking grand leaps

of faith can mankind advance itself. Fears about robots and AI are

understandable, but unnecessary, and government’s pesky regulations are

only hindering technological progress. Technology in itself is never

inherently ‘good’ or ‘bad’; it is what mankind does with it that matters.

Job Destruction narrative:

For the first time in human history the age-old economic law, that an

increase in productivity will lead to an increase in employment, will not

hold. If capital owners can achieve growth without labor costs—thanks to a

workforce of robots and algorithms that is never sick, never needs a break,

and never complains—they will do exactly that. Historically, the onset of

any Industrial Revolution has been characterized by terrible circumstances

for workers. We need a big state, with strict government oversight and

regulations, to prevent this.

Exterminism narrative:

The wealth of Western nations was built on the backs of modern slaves in

low-wage countries; job destruction caused by large-scale automation will

only make things worse. If left unregulated, tech companies—who tend to

lack a social conscience and are currently freely experimenting with AI—

may open a Pandora’s box and unleash unimaginable technologies upon

mankind. Governments, on their end, may exploit citizens’ fears around

safety in an ever-changing world to implement social credit systems.

Tech/Innovation master narrative

Economy/Labor Market expert master narrative

(central master narrative)

Author/journalist master narrative

Capitalist Realism

(TINA)

Degrowth (System Change)

Work Deintensification narrative:

Keynes predicted that by the end of the 20th century, Western countries

would have a 15-hour work week. Instead, work intensity and burn-outs—

driven by an unequal division of income between capital and labor—have

only increased in recent decades. By introducing a universal basic income

(UBI), we can counter potential negative effects of automation, and re-

humanize human labor—in the process creating a post-scarcity society with

decentralized governance.

Effective Accelerationism

(Technocracy)

Page 57 of 60 Academy of Management Discoveries

TABLE 6. Avenues for Further Research on the Future of Work Adopting an Imaginaries Paradigm.

Theoretical mechanisms

Framing contests

Field frames

Narrative subscription

Corresponsive mechanisms

Possible research

questions

How can we determine what future of

work narratives are dominant in the

public debate? (How would we

measure or quantify ‘dominance’?)

How are field-specific rhetorical

devices—such as metaphors,

stereotypes, slogans, and

vocabularies—used by actors to

construe future of work narratives?

How does subscription to specific

future of work narratives fluctuate

over time, as narratives ‘rise and

fall’, and new narratives emerge?

How does subscription to specific

future of work narratives evolve

across an individual’s life course

and/or career?

How do dominant future of work

narratives become ‘real’ (i.e., translate

into collective action) over time?

How is the construal of, and

subscription to future of work

narratives influenced by field-related

social identity markers?

How does subscription to specific

future of work narratives differ

along geographical and cultural

faultlines?

What are the events or processes that

are most likely to strengthen the

individual differences associated with

specific future of work narratives?

How do counternarratives about the

future of work emerge in the public

debate, and how might they ‘crowd

out’ or alter master narratives over

time?

How are future of work master

narratives construed and subscribed to

by actors belonging to certain fields

expressed and reified through

institutionalized practices?

How is narrative subscription

influenced by cognitive versus

emotional responses to framings in

future of work narratives?

What identity cues are most relevant

to predict an individual’s (non-)

subscription to a specific future of

work narrative (e.g., personality,

socio-demographics, political values)?

How do societal actors mobilize

others around a specific future of

work narrative?

How are future of work narratives

embodied in the inventions and

prototypes of technology creators

from different fields (e.g., engineers,

software developers, designers,

artists)?

How might policy makers create

increased narrative subscription to

future of work narratives they deem

important, but for which issue

engagement is low among citizens?

To what extent can corresponsive

mechanisms explain polarization of

the future of work debate over time?

What are possible acts of resistance—

by workers or other actors—against

(the reification of) future of work

narratives that go against their

interests?

What are the drivers of determinism

and depoliticization in the future of

work debate? And what could be the

drivers of repoliticization?

Can narrative subscription be

increased by asking people from

different actor groups to construe

their own future of work narratives?

Can perspective taking between future

of work actors with different

individual difference profiles be

increased through transdisciplinary

collaboration?

Possible data/

methods

Text mining of large-scale media data

News sentiment analysis

Historical document analysis

Science fiction books or films

Policy documents and press releases

Professional associations websites

Text mining of Twitter posts

Video analysis of keynotes

Discourse analysis of debates

Interviews (radio, TV, in-person)

Ethnography

Multi-wave surveys

Country-comparative studies

Vignette (scenario) experiments

Virtual reality (VR) experiments

Essay-writing contests

Longitudinal surveys

(Event-based) diary studies

Repertory grid technique

Field experiments

Focus groups

Relevant

literature

streams

Counterfactual history

(Schoemaker, 2010)

Counternarratives

(Bamberg & Wipff, 2020)

Discursive closure

(Deetz, 2007)

Discursive opening

(Reinig & Borda, 2023)

Narrative policy framework

(Jones et al., 2014)

Epistemic authority

(Aytac & Rossi, 2022)

Framing analysis

(Cornelissen & Werner, 2014)

Paradigmatic thinking

(Béland & Cox, 2013)

Biased assimilation

(Jones & Crow, 2017)

Distant-future inaction

(Marshall, 2015)

Emotions in science communication

(Chapman et al., 2017)

Motivated reasoning

(Slothuus & De Vreese, 2010)

Attraction-Selection-Attrition

(Schneider et al., 2000)

Identity cues

(Hart & Nisbet, 2012)

Perspective taking

(Galinsky & Moskowitz, 2000)

Polarization

(Baldwin & Lammers, 2016)

Page 58 of 60Academy of Management Discoveries

Resistance

(Mumby et al., 2017)

Social movements

(Lounsbury et al., 2003)

Speculative design

(Galloway & Caudwell, 2018)

Strategic scenario planning

(Kahane, 2012)

Policy issue salience

(Miller et al., 2017)

Temporal construal level

(D’Argembeau & Van der

Linden, 2004)

Page 59 of 60 Academy of Management Discoveries

Nicky Dries (nicky.dries@kuleuven.be) is a full professor of organizational behavior at KU

Leuven (department of Work & Organisation Studies) and at BI Norwegian Business School in

Oslo (department of Leadership & Organizational Behaviour). In Leuven, she runs the Future of

Work Lab within the Faculty of Economics, that studies social imaginaries for the future. Research

within the Lab builds on methods aimed at triggering people's imagination about the future, using

media analysis, robotic art and design, virtual reality, and science-fiction movies. The mission of

the Lab is to re-politicize the future of work, and stimulate democratic debate.

Joost Luyckx (j.luyckx@ieseg.fr) is an assistant professor of business and society at IESEG

School of Management (LEM-CNRS UMR 9221) and a research fellow at KU Leuven. His

research focuses on legitimacy struggles over multinational companies in the global public debate,

a more desirable future of work, alternative organizations, and neutralization of social movement

activism.

Philip Rogiers (philip.rogiers@esade.edu) is an assistant professor of organizational behavior and

organizational theory at Univ. Ramon Llull, Esade and a research fellow at KU Leuven. His

research focuses on the transformation and deconstruction of jobs, along with the exploration of

alternative organizational forms that can lead us towards a more human-centered future of work.

Page 60 of 60Academy of Management Discoveries

AMD-2022-0130.R2. final Appendix.docx

Data

July 2023

Nicky Dries · Joost Luyckx · Philip Rogiers

Download

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Sep 2024
J MANAGE

McNamara and Schleicher (2024) have identified four principal paths for contributing to the Journal of Management (JOM): theoretical insights, phenomenon-driven research, research methodologies, and review papers. This editorial focuses on phenomenon-based research, emphasizing its potential for enhancing management knowledge by offering a nuanced understanding of real-world phenomena. Unlike traditional approaches, phenomenon-based research prioritizes the complexity of phenomena over the immediate generation of theoretical contributions. Grounded in established theory, phenomenon-based research utilizes the phenomenon itself as the primary source of insight, facilitating the development of relevant organizational frameworks. We propose a multistep framework encompassing phenomenon selection, framing, data collection, and study constraints, highlighting criteria—Pertinence, Reach, Insightfulness, Magnification, and Expediency (PRIME)—to guide scholars in identifying meaningful phenomena. Additionally, we discuss constraints that may limit research, including cultural, logistical, ethical, academic, and resource-related challenges (CLEAR). By addressing these considerations, we encourage management scholars to explore diverse and impactful phenomena, ultimately aiming to position JOM as a leading platform for phenomenon-based research and its contributions to real-world organizational challenges. This editorial advocates for a balanced approach that values both theory-driven and phenomenon-driven research in advancing management scholarship.

Der Faktor Mensch in wirtschaftlichen Prozessen

Chapter

Sep 2024

Perspective-Taking: Decreasing Stereotype Expression, Stereotype Accessibility, and In-Group Favoritism

Article

Full-text available

Apr 2000

Using 3 experiments, the authors explored the role of perspective-taking in debiasing social thought. In the 1st 2 experiments, perspective-taking was contrasted with stereotype suppression as a possible strategy for achieving stereotype control. In Experiment 1, perspective-taking decreased stereotypic biases on both a conscious and a nonconscious task. In Experiment 2, perspective-taking led to both decreased stereotyping and increased overlap between representations of the self and representations of the elderly, suggesting activation and application of the self-concept in judgments of the elderly. In Experiment 3, perspective-taking reduced evidence of in-group bias in the minimal group paradigm by increasing evaluations of the out-group. The role of self–other overlap in producing prosocial outcomes and the separation of the conscious, explicit effects from the nonconscious, implicit effects of perspective-taking are discussed.

Distinguishing Optimism From Neuroticism (and Trait Anxiety, Self-Mastery, and Self-Esteem): A Reevaluation of the Life Orientation Test

Article

Full-text available

Dec 1994

Research on dispositional optimism as assessed by the Life Orientation Test (Scheier & Carver, 1985) has been challenged on the grounds that effects attributed to optimism are indistinguishable from those of unmeasured third variables, most notably, neuroticism. Data from 4,309 subjects show that associations between optimism and both depression and aspects of coping remain significant even when the effects of neuroticism, as well as the effects of trait anxiety, self-mastery, and self-esteem, are statistically controlled. Thus, the Life Orientation Test does appear to possess adequate predictive and discriminant validity. Examination of the scale on somewhat different grounds, however, does suggest that future applications can benefit from its revision. Thus, we also describe a minor modification to the Life Orientation Test, along with data bearing on the revised scale’s psychometric properties.

Ideology Critique without Morality: A Radical Realist Approach

Article

Full-text available

Dec 2022

What is the point of ideology critique? Prominent Anglo-American philosophers recently proposed novel arguments for the view that ideology critique is moral critique, and ideologies are flawed insofar as they contribute to injustice or oppression. We criticize that view and make the case for an alternative and more empirically oriented approach, grounded in epistemic rather than moral commitments. We make two related claims: (a) ideology critique can debunk beliefs and practices by uncovering how, empirically, they are produced by self-justifying power and (b) the self-justification of power should be understood as an epistemic rather than moral flaw. Drawing on the recent realist revival in political theory, we argue that this genealogical approach has more radical potential, despite being more parsimonious than morality-based approaches. We demonstrate the relative advantages of our view by discussing the results of empirical studies on the contemporary phenomenon of neopatriarchy in the Middle East and North Africa.

Automation and the future of work: A social shaping of technology approach

Imagining the (Distant) Future of Work

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