About the author: Nicky Dries is a tenured Research Professor (senior BOFZAP) at KU Leuven, Belgium, and an Adjunct Professor at BI Norwegian Business School, Norway. You can contact her at firstname.lastname@example.org
The study reported in this blog post is not yet published. Please contact the author before citing or linking to the materials below to ensure that the work is cited correctly and in line with intellectual property law.
What will the future of work look like? It depends who you ask. According to optimists, new technologies will help us work faster and more efficiently such that we will have more free time. Perhaps governments will even introduce a universal basic income to compensate for the (partial) loss of income. Pessimists, on the other hand, predict a future in which advanced automation will cause many jobs to disappear, causing massive unemployment, and further increasing the inequality between rich and poor. Finally, there are the skeptics who believe that change will not happen that fast and that the future will still look very much like the present, with some tweaks here and there.
So: who is right? The optimists, the pessimists or the skeptics?
According to some authors, this is the wrong question to ask, for two reasons.
First of all, under circumstances such as the present ones, where there is radical uncertainty about the future, it is much more difficult to make ‘objective’ predictions. That is, we should not simply assume that trends from the past will continue in the future. (A typical example are the ‘exponentially’ rapid evolutions in the field of artificial intelligence or AI.) Therefore, the future should be understood not as a fact, but as a fiction—as a diverging set of scenarios that currently exist only in our collective imagination. In addition, the ideas of different stakeholder groups do not weigh equally on reality. Those who have more power and influence also have more opportunity to turn their ideas into reality. This makes it even more difficult to objectively predict the future. After all, ‘imaginaries’ about the future—often with a strong ideological basis—and what people believe will actually happen, are interrelated.
Second, it is not necessarily true that consensus about what the future will (or should) look like is a necessary precondition to meaningful action, although this is often assumed to be the case. In fact, experts in the field of scenario planning say that the opposite is true; that tensions between conflicting scenarios trigger people to think more deeply about the future, and (in the long term) arrive at better, more widely supported decisions. Research on sensemaking shows that people pay more attention to information that deviates strongly from their frame of reference (through a kind of surprise effect), and that this leads to a deeper processing of, and reflection on that information.
Based on all of the above, we decided to develop a set of extreme scenarios about the future of work (rather than making predictions about it), and to use these in different ways to collect data.
In a first study, we analyzed 485 Belgian newspaper and magazine articles from the last five years, on the topic of the future of work. We compiled a list of 32 phenomena that often appeared in these articles. We also analyzed whether these phenomena were discussed with a positive or negative tone, and whether they were positioned in the near or distant future. We developed four contrasting scenarios: ‘Business-as-usual’ (Utopian / Human-driven), ‘Lifelong learning & cobots’ (Utopian / Technology-driven), ‘Extinction’ (Dystopian / Human-driven), and ‘Singularity/robocalypse’ (Dystopian / Technology Driven) (see Figure 1).
In a second study, we presented these scenarios to a sample of 570 Belgian respondents from the following five stakeholder groups: committed citizens, labor market and/or HR experts, innovation and/or technology experts, journalists and policymakers/politicians. They were asked to assess the extent to which the different scenarios evoked hope versus fear, as well as the likelihood that they could actually happen. We found inspiration for this approach in the field of climate change, in which experts strongly disagree as to whether hopeful versus frightening scenarios about the future most encourage action. Finally, participants in this second study were asked to complete a survey about the characteristics of their job, as well as about their personal views and personal characteristics.
We summarize the main findings of both studies here:
1. The articles showed no bias towards negative reporting—something the press is often accused of. To the contrary, a larger proportion of articles was positive in tone. It was noticeable that the tone of the reporting was mainly influenced by the experts that were consulted or interviewed for the articles—these were often ‘techno-optimists’. One potential concern however was ‘framing bias’—people’s tendency to interpret information as it is presented to them (positive or negative). This bias can affect both journalists and readers.
2. Phenomena about the future of work that were more often discussed in the media and/or that were discussed more positively, were also rated as more realistic and closer in time in the survey study. This may indicate an ‘optimism bias’ (the motivation to believe that positive outcomes are more likely than negative outcomes), as well as ‘accessibility bias’, where messages that are repeated more often become more accessible in people’s working memory thereby influencing their judgment, opinions and decisions.
3. Although the academic literature suggests that people think more often and in more concrete terms about the near future than about the distant future, we found that more references were made in both the articles (study 1) and the survey data (study 2) (and with more positivity) to the distant future of work (2050 or later). The observation that phenomena in the distant future were estimated more positively can be explained by psychological research on the mental construction of time. For example, research has shown that people have less and less confidence in a good outcome as a ‘moment of truth’ approaches. (This is sometimes referred to as ‘distal optimism’ and ‘proximal pessimism’.)
4. As expected, we found that the two utopian scenarios triggered hope, and the two dystopian scenarios triggered fear. All five stakeholder groups considered ‘Business-as-usual’ the most realistic scenario. However, there was a difference in hope for the two utopian scenarios: only engaged citizens and journalists preferred the ‘Business-as-usual’ scenario, while labor market and/or HR experts, innovation and/or technology experts and policymakers/politicians preferred the ‘Lifelong learning & cobots’ scenario. This is an interesting finding, as the latter stakeholder groups weigh more on policy than the former.
5. There was a clear relationship between the emotional response to a scenario—hope versus fear—and its estimated likelihood. For the two utopian scenarios, only hope was a good predictor of likelihood, whereas for the two dystopian scenarios, the degree of fear also predicted the extent to which people believed they could really happen. This is in line with findings from climate science. Both hope and fear influence people’s ideas about the future, and this in turn is related to their motivation to work towards an imaginary of the future (in the case of hope) or away from it (in the case of fear). Although we cannot prove on the basis of our data that negative scenarios indeed lead to deeper information processing in people, we did notice that it was mainly the negative information that ‘stuck by’ people—at the end of the survey, respondents had the opportunity to leave comments in a text box. Many people indicated that the survey severely affected them, made them anxious, and gave them a lot of food for thought.
6. Finally, we found significant relationships between people’s job and personal characteristics and their ideas about the future. Especially people’s relationship to technology (the extent to which someone likes technology and stays informed about it), misanthropy (the extent to which one thinks negatively about humankind) and openness (the degree of imagination and openness to new ideas) were predictive of how they rated the scenarios. We also found that people in professions with a higher risk of automation had more fear of the future. This indicates that not only the stakeholder group to which one belongs, but also individual characteristics influence how one sees the future. This underscores, again, the difficulty of ‘objectively’ predicting the future. It is thus crucially important that people, especially policy and opinion makers, reflect on their own beliefs and biases before assuming that the way they see the future is ‘the right one’. As it is sometimes said, “We don’t see things as they are, but as we are”.
Our study also formulates some recommendations for practice.
First of all, we recommend mixing positive and negative scenarios in reporting on the future of work (both in the media and in the business world) within one message, instead of between different messages. This can be done by giving a platform to experts with very different views, so as to actively prevent people from writing off scenarios, in which they personally do not believe, all too quickly. Research suggests that audience engagement will be highest when messages first attract attention using a negative framing, while including more positive (hopeful) nuances later on in that same message. This should trigger a widely shared motivation to avoid dystopian scenarios.
Second, people’s relationship to technology, in particular, seems to be something that governments—for example through continued education and training—need to work on, especially among those people who have little or no access to basic education. It appears that this variable (as well as the risk of job automation) will play a major role in people’s resistance to changes in the future of work. Belgian precedents such as the introduction of the ‘career vouchers’ (loopbaancheques) in 2005 come to mind, through which people were encouraged to enroll in free career counseling—would such an initiative also be possible for technological adaptability? In terms of the risk of automation, it seems crucial to educate young people towards their chosen profession, not only based on what that profession requires today, but also training them for what it will look like in the future. (A typical example is car mechanics who will have to be able to repair electronic self-driving cars).
Finally, our findings point to the important role of voice, power and control in public engagement around the future of work. For example, we found that stakeholder groups with more influence on policy preferred the scenario ‘Lifelong learning & cobots’, while citizens and journalists preferred ‘Business-as-usual’. Another finding was that people who believe their lives are largely determined by powerful others (instead of being within their own control) report more fears about the future of work. A possible recommendation is to encourage people to speak up about the future of work, for example through polls, think tanks and referendums. Historical examples are the Mont Fleur scenarios distributed among the South African population after the Apartheid regime, or (very differently) the Child Development Studies in the UK in the 1950s and 1960s, where 13,669 eleven-year-old students wrote an essay on how they imagined their life as a twenty-five year old, and afterwards were regularly surveyed about their views on life over their life course. These types of initiatives could potentially be used to engage citizens from all walks of life to have their voices (and imaginaries) about the future heard.
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