We’re starting up a new mailing list for Centre for the Edge. It will be low volume, only announcements for C4tE-hosted events or new publications, with a quarterly summary.
If you’re interested then you can subscribe using the form below.
We’re starting up a new mailing list for Centre for the Edge. It will be low volume, only announcements for C4tE-hosted events or new publications, with a quarterly summary.
If you’re interested then you can subscribe using the form below.
The concluding report from Deloitte Centre for the Edge and Geelong Grammar Schools’ collaboration looking into digital skills in the workplace, Digital agency and the skills gap,1)Evans-Greenwood, P & Patston, T 2019, Digital agency and the skills gap, Deloitte, Australia, <https://www2.deloitte.com/au/en/pages/public-sector/articles/to-code-or-not-to-code-coding-competence.html>. has been published by Deloitte, Australia. This report pulls together the results from across the project to provide an overview of the journey and the findings.
There’s a huge amount of angst in the community that our education system cannot keep up with rapid technological change, however this project shows that this is likely not the case. What we’re seeing is, in many cases, not a lack of skills, but an inability to navigate an increasingly complex digital environment. While digital skills are important, knowing when and why to use these skills is more important, particularly in a world where new knowledge is no further away than a search engine accessed via a smart phone.
Workers are unable to make the connection between the skills they have and the problem infant of them, making this a problem of unknown knowns. It’s not that workers lack skills, what they lack is discernment, the ability to read the digital environment around them and make sharp judgements about when and why particular digital tools and skills should be used. A lack of discernment limits a worker’s digital agency, their ability to act freely in a digital environment.
Solving this problem is not simply a question of teaching students and workers more, and more relevant digital skills. We need to focus on fostering in them the discernment required for them to develop the work habits that will enable them to make the most of digital technology.
The project was a long and fascinating journey so this concluding report itself is quite long, around 12,000 words. A much shorter business-friendly summary, The digital ready worker,2)Evans-Greenwood, P, Patston, T, & Flouch, A 2019, ‘The digital-ready worker: Digital agency and the pursuit of productivity’, Deloitte Insights, <https://www2.deloitte.com/us/en/insights/focus/technology-and-the-future-of-work/learned-helplessness-workforce.html>. was published last week by Deloitte Insights. A lot of valuable insights were dropped on the cutting room floor to create that summary, hence this report.
This report provides a summary of project’s journey, from the initial provocation through the roundtables, the more recent workshops, to the development of the project’s conclusions, as well as providing a detailed exploration of the findings. If you’re an educator (K12 or post-secondary) you might find this longer report more valuable as it digs into the details of the models presented, and does a better job of exploring the implications of the findings. If you were involved in any of the projects, the this report will join a number of dots and provide that ah-ha moment.
Revisiting the concept of learned helplessness, the report shows how the solution to learned helplessness is not to teach students more digital skills, but to foster their digital agency, their capacity to act independently and make their own free choices in the digital workplace. The concept of digital natives is explored in light of what the project discovered, resulting in a new model of digital competence in the workplace the identifies four archetypes: the digital naïf, digital pragmatist, digital explorer, and digital evangelist.
Finally, the report develops a progression capturing how one’s digital agency changes over time, and explores how digital agency might be fostered in both students and workers, and the changes this implies.
|1.||↑||Evans-Greenwood, P & Patston, T 2019, Digital agency and the skills gap, Deloitte, Australia, <https://www2.deloitte.com/au/en/pages/public-sector/articles/to-code-or-not-to-code-coding-competence.html>.|
|2.||↑||Evans-Greenwood, P, Patston, T, & Flouch, A 2019, ‘The digital-ready worker: Digital agency and the pursuit of productivity’, Deloitte Insights, <https://www2.deloitte.com/us/en/insights/focus/technology-and-the-future-of-work/learned-helplessness-workforce.html>.|
We have a new essay published on Deloitte Insights, The digital-ready worker: Digital agency and the pursuit of productivity, which is the result of a collaboration between Centre for the Edge and Geelong Grammar School.1)Evans-Greenwood, P, Patston, T, & Flouch, A 2019, ‘The digital-ready worker: Digital agency and the pursuit of productivity’, Deloitte Insights, <https://www2.deloitte.com/us/en/insights/focus/technology-and-the-future-of-work/learned-helplessness-workforce.html>. As the blurb says, this essay looks into how:
To be effective in an increasingly technological workplace, workers must know, not just how to use digital tools, but when and why to use them. Critical to this ability is digital agency: the judgment and confidence required to navigate and be effective in unfamiliar digital environments.
There’s a lot of concern at the moment of a growing skills gap, the gap between the the skills held by graduates and those demanded by employers. Studies have been done to measure this growing gap, and significant resources have been devoted to updating curricula in an attempt to close the gap, all to no avail.
If we peal the lid of these studies we see they rely on aggregate skills data, typically from O*NET, which means that they’re limited to seeing a single negative view of how technology affects jobs, one where technology automates skills making workers redundant. The problem is that this isn’t the only pathway for technology to affect work. There’s also a positive pathway, where technology automates skills making the workers’ remaining skills more valuable, as well as a “no net change” pathway (or collection of pathways) which we have empirical evidence for but are yet to pull apart and understand.2)Spenner, KI 1983, ‘Deciphering Prometheus: Temporal Change in the Skill Level of Work’, American Sociological Review, vol. 48, no. 6, p. 824, <http://www.jstor.org/stable/2095328?origin=crossref>.
One of the key insights, if not the key insight, from Centre for the Edge and Geelong Grammar Schools’ To code or not to code collaboration, was that many of the problems we’re seeing in the workplace are likely due to learned helplessness,3)The term “learned helplessness” is borrowed from the psychology literature, drawing upon the work of Martin Seligman and many others. See, for instance, Martin E. P. Seligman, “Learned helplessness,” Annual Review of Medicine 23, no. 1 (1972): pp. 407–12. where a person suffers from a sense of powerlessness arising from a persistent failure to succeed. We’re teaching students how to use particular digital tools in particular ways, but we’re also teaching them that these tools are fragile and using them the wrong often results in problems and might even ‘brick’ the device. Rather than framing the problems we’re seeing in the workplace as the result of a growing skills gap due to the destruction of skills, it might be more appropriate to frame them as a problem of unknown knowns. It’s not that the worker doesn’t have the skills required, their problem is making the connection between the skill and the current problem they’re working on.
The solution to this problem isn’t to provide students with more, and more relevant, digital skills. Indeed, that approach is unlikely to help as the students are not lacking in skills. While it’s important to know how to use particular digital tools, it’s more important to know when and why these digital tools should be used. What students lack is discernment, the knowledge and experience required to make observations and sharp judgements about which digital tools might be useful and how they will affect the work. We need to foster in students the attitudes and behaviours—something we’ve taken to calling a predilection—that help them navigate the digital workplace and develop the habits that enable them to integrate digital tools into their work. Ultimately the solution is to foster digital agency in students, to help them develop the literacies, knowledge, skills and predilections the need to act independently and make their own free choices in the digital workplace.
The essay explores, in some detail, the concept of learned helplessness in the digital workplace, and how we might might foster digital agency in both students and workers. There’s also a few of useful models for thinking about this problem, helping us move beyond misleading dichotomies like Digitial Native vs Digital Immigrant which have proven to be wrong.
You can find the entire text over at Deloitte Insights. Feel free to leave a comment here with your thoughts.
|1.||↑||Evans-Greenwood, P, Patston, T, & Flouch, A 2019, ‘The digital-ready worker: Digital agency and the pursuit of productivity’, Deloitte Insights, <https://www2.deloitte.com/us/en/insights/focus/technology-and-the-future-of-work/learned-helplessness-workforce.html>.|
|2.||↑||Spenner, KI 1983, ‘Deciphering Prometheus: Temporal Change in the Skill Level of Work’, American Sociological Review, vol. 48, no. 6, p. 824, <http://www.jstor.org/stable/2095328?origin=crossref>.|
|3.||↑||The term “learned helplessness” is borrowed from the psychology literature, drawing upon the work of Martin Seligman and many others. See, for instance, Martin E. P. Seligman, “Learned helplessness,” Annual Review of Medicine 23, no. 1 (1972): pp. 407–12.|
A team at Harvard has released a new version of the Atlas of Economic Complexity, an index of ‘economic complexity’. Journalists have pounced on the model to make that case—as they often do—that Australia is a second class country run by second rate politicians. The problem is that the model seems rather bland, only proving that Australia is a large country with a small population (and correspondingly small market) a long way from the major markets. We already knew this.
The atlas “interpret[s] trade data as a bipartite network in which countries are connected to the products they export, and show that it is possible to quantify the complexity of a country’s economy by characterizing the structure of this network”.1)Hidalgo, C.A. & Hausmann, R., 2009. The Building Blocks of Economic Complexity. Available at <http://www.tinyurl.com/y55fxc8s>. So complexity is a measure of integration into global and regional supply chains. This is assumed to correlate with the complexity of an economy.
Given that, any small populous country that is geographically situated near a large market (or cluster of markets) should do well. These countries are too small to export resources (due to lack of land and resources) while their domestic market is too small to soak up many finished goods. They are, however, well situated to be part of supply chains that feed the large market that they’re adjacent too, both importing and exporting intermediate goods. In a case of “no shit Sherlock”, countries like Singapore and Switzerland score quite well.
Large populous countries, such as the US, do ok as they can export products supported by their large domestic market as well as the large domestic market being a sink, importing products from other countries. Not as ‘complex’ as a less populous country importing and exporting intermediate goods, but there’s still a bit going on.
Small to mid-sized countries (in terms of population) that are far from major markets will do poorly. They’re too far from global or regional value chains to participate in them, and their domestic market is too small to support the development of finished goods for export. Here’s looking at you Australia.
Countries such as South Africa sort of fall into this bucket too, though being surrounded by a number of small markets does alleviate their problem somewhat. Australia, as we like to point out, is both a continent and an island. Being small and far from major markets is also why Australia doesn’t have a domestic car manufacturing industry: we’re not big enough to support a car assembly plant with domestic sales, while being too far away from major markets to export.
So the atlas does show a correlation, but it’s with population and geography more than anything else. Also, as the atlas is based on correlation, rather than a causal model, it don’t have anything to say about the future as they’re just extrapolating trends.
It’s a bit annoying that there’s not much to be learnt from the atlas. What is more annoying though is the colonial mindset in Australia that assumes that nothing good can come out of the colonies (Australia) as all good things come out of the colonial power (being Europe and the US).
In 2016, Deloitte Centre for the Edge and Geelong Grammar School hosted a series of roundtables looking into digital skills and the challenges of the digital workplace. The project that emerged from those roundtables took three years to peel back layer after layer of assumptions to discover that it’s likely that our graduates are suffering from a lack of discernment, rather than a lack of digital skills.
On the 30th of October Deloitte is hosting an event in the Melbourne office around close of business to launch the reports that lay out the project’s findings, and to explore where to next. Fill out the contact form at the bottom of this post if you’re interested in attending.
The future will be digital and mastery of digital technology is seen as an essential skill. The growing skills gap—the gap between skills held by graduates and those employers seek—is a cause of great concern. Prescriptions range from new curricula, new technology-driven pedagogy, through to blowing it all up and starting again.
Our research shows that while digital skills, knowing how to use digital tools, is important, knowing when and why to use them is more important. The challenges graduates experience in the workplace a more likely due to a lack of digital agency. They suffer from learned helplessness, struggling to navigate a workplace saturated in, even defined by, digital tools.
The event will consist of a plenary discussing the findings and a panel to dig into the details. Can we fix the education system? Or do we need to disrupt it?
Fear of AI-based automation forcing humans out of work or accelerating the creation of unstable jobs may be unfounded. AI thoughtfully deployed could instead help create meaningful work.
This is a 25 minute presentation providing an overview of the report Reconstructing jobs (published in 2018) from the Edge Session just after the report was published.
This third essay in the series, Reconstructing jobs, takes a step back and looks these jobs of the future might look like. The narrative is built around a series of concrete examples – from contact centres through wealth management to bus drivers – to show how we might create this next generation of jobs. These are jobs founded on an new division of labour: humans creating new knowledge, making sense of the world to identify and delineate problems; AI plans solutions to these problems; and good-old automation to delivers. To do this we must create good jobs, as it is good jobs that make the most of our human abilities as creative problem identifiers. These jobs are also good for firms as, when combined suitably with AI, they will provide superior productivity. They’re also good job for the community, as increased productivity can be used to provide more equitable services and to support learning by doing within the community, a rising tide that lives all boats.
In 2017 Deloitte Centre for the Edge hosted a public lecture by James C. Kaufman, PhD; a professor of educational psychology at the University of Connecticut as well as a creativity & education expert, where he discussed the challenges of teaching and assessing creativity. This is a 20 minute bite-sized version of the 90-minute lecture.
We noticed the similarity between creativity and our recent work on digital competency, which we published in “From coding to competence”. Both are depend more on attitudes and behaviours than knowledge and skills. Both are also tightly tied to context, and don’t transfer easily between domains.
The lecture is derived from Dr Kaufman’s cutting-edge psychological research and debunks common misconceptions about creativity, describe how learning environments can support creativity, while providing insights into teaching and assessing creativity within the established curriculum.
The lecture covers:
I, along with Alan Marshall and Robert Hillard, have a new essay published by Deloitte Insights – The new division of labor: On our evolving relationship with technology1)Evans-Greenwood, P, Hillard, R, & Marshall, A 2019, ‘The new division of labor: On our evolving relationship with technology’, Deloitte Insights, <https://www2.deloitte.com/insights/us/en/focus/technology-and-the-future-of-work/the-new-division-of-labor.html>.. This is the latest in an informal series that looks into how artificial intelligence (AI) is changing work. The other essays (should you be interested) are Cognitive collaboration,2)Guszcza, J, Lewis, H, & Evans-Greenwood, P 2017, ‘Cognitive collaboration: Why humans and computers think better together’, Deloitte Review, no. 20, viewed 14 October 2017, <https://dupress.deloitte.com/dup-us-en/deloitte-review/issue-20/augmented-intelligence-human-computer-collaboration.html>. Reconstructing work3)Evans-Greenwood, P, Lewis, H, & Guszcza, J 2017, ‘Reconstructing work: Automation, artificial intelligence, and the essential role of humans’, Deloitte Review, no. 21, <https://dupress.deloitte.com/dup-us-en/deloitte-review/issue-21/artificial-intelligence-and-the-future-of-work.html>. and Reconstructing jobs.4)Evans-Greenwood, P, Marshall, A, & Ambrose, M 2018, ‘Reconstructing jobs: Creating good jobs in the age of artificial intelligence’, Deloitte Insights, <https://www2.deloitte.com/insights/us/en/focus/technology-and-the-future-of-work/creating-good-jobs-age-of-artificial-intelligence.html>.
Over the last few essays we’ve argued that humans and AI might both think but they think differently, though in complimentary ways, and if we’re to make the most of these differences we need to approach work differently. This was founded on the realisation that there is no skill – when construed within a task – that is unique to humans. Reconstructing work proposed that rather than thinking about work in terms of products, processes and tasks, it might be more productive to approach human work as a process of discovering what problems need to be solved, with automation doing the problem solving. Reconstructing jobs took this a step further and explored how jobs might change if we’re to make the most of both human and AI-powered machine using this approach, rather than simply using the machine to replace humans.
This new essay, The new division of labour, looks at what is holding us back. It’s common to focus on what’s known as the “skills gap”, the gap between the knowledge and skills the worker has and those required by the new technology. What’s often forgotten is that there’s also an emotional angle. The introduction of the word processor, for example, streamlined the production of business correspondence, but only after managers became comfortable taking on the responsibility of preparing their own correspondence. (And there’s still a few senior managers around who have their emails printed out so that they can draft a reply on the back for their assistant to type.) Social norms and attitudes often need to change before a technology’s full potential can be realised.
We can see something similar with AI. This time, though, the transition is complicated as the new tools and systems are not passive tools anymore. We’re baking decisions into software then connecting these automated decisions to the levers that control our businesses: granting loans, allocating work and so on. These digital systems are no longer passive tools, they have some autonomy and, consequently, some agency. They’re not human, but they’re not “tools” in the traditional sense.
This has the interesting consequence that we relate to them as sort-of humans as their autonomy and agency affects our own. They’re consequently taking on roles in the organogram as we find ourselves working for, with and on machines. This also works the other way around, and machines find themselves working for, with and on humans. Consider how a ride-sharing driver has their work assigned to them, and their competence is measured, by an algorithm that is effectively their manager. A district nurse negotiates their schedule with a booking and work scheduling system. Or it might be more of a peer relationship, such as when a judge consult a software tool when determining a sentence. We might even find humans and machines teaching each other new tricks.
As with the word processors, we can only make the most of this new technology if we address the social issues. With the word processor it was managers seeing typing as being below their station. The challenge with AI is much more difficult though, as making the most of this new generation of technology requires us to value humans to do something other than complete tasks.
The essay uses the example of superannuation. Nobody wants retirement financial products, they want a happy retirement, the problem is that ‘happy retirement’ is no more than a vague idea for most of us. We need to go on a journey through sorting out if what we think will make us happy will actually make us happy, setting reasonable expectations, and adjusting our attitudes and behaviours to balance our life today with the retirement we want to work toward. This is something like a Socratic dialogue, a conversation with others where we create the knowledge of what ‘happy retirement’ means for us. Only then can we engage the robots-advisor to crunch the numbers and create an investment plan.
The problem is the disconnect between how the client and firm derive value from this journey. The client values discovering what happy retirement means, and adjusting their attitudes and behaviours to suit. The firm values investments made. This disconnect means that firms focus their staff on clients later in life, once the kids have left home and the house is paid off. The client, on the other hand, would realise the most value by engaging early to establish the attitudes and behaviours that will enable the magic of compound interest to work.
As we say in the conclusion to the report:
However, successfully adopting the next generation of digital tools, autonomous tools to which we delegate decisions and that have a limited form of agency, requires us to acknowledge this new relationship. At the individual level, forming a productive relationship with these new digital tools requires us to adopt new habits, attitudes, and behaviors that enable us to make the most of these tools. At the enterprise level, the firm must also acknowledge this shift, and adopt new definitions of value that allow it to reward workers for contributing to the uniquely human ability to create new knowledge. Only if firms recognize this shift in how value is created, if they are willing to value employees for their ability to make sense of the world, will AI adoption deliver the value they promise.
You can find the entire essay over at Deloitte Insights.
|1.||↑||Evans-Greenwood, P, Hillard, R, & Marshall, A 2019, ‘The new division of labor: On our evolving relationship with technology’, Deloitte Insights, <https://www2.deloitte.com/insights/us/en/focus/technology-and-the-future-of-work/the-new-division-of-labor.html>.|
|2.||↑||Guszcza, J, Lewis, H, & Evans-Greenwood, P 2017, ‘Cognitive collaboration: Why humans and computers think better together’, Deloitte Review, no. 20, viewed 14 October 2017, <https://dupress.deloitte.com/dup-us-en/deloitte-review/issue-20/augmented-intelligence-human-computer-collaboration.html>.|
|3.||↑||Evans-Greenwood, P, Lewis, H, & Guszcza, J 2017, ‘Reconstructing work: Automation, artificial intelligence, and the essential role of humans’, Deloitte Review, no. 21, <https://dupress.deloitte.com/dup-us-en/deloitte-review/issue-21/artificial-intelligence-and-the-future-of-work.html>.|
|4.||↑||Evans-Greenwood, P, Marshall, A, & Ambrose, M 2018, ‘Reconstructing jobs: Creating good jobs in the age of artificial intelligence’, Deloitte Insights, <https://www2.deloitte.com/insights/us/en/focus/technology-and-the-future-of-work/creating-good-jobs-age-of-artificial-intelligence.html>.|
I, along with a Robert Hillard and Peter Williams, have a new essay published by Deloitte Insights, Digitalizing the construction industry: A case study in complex disruption1)Evans-Greenwood, P et al. 2019, ‘Digitalizing the construction industry: A case study in complex disruption’, Deloitte Insights,<https://www2.deloitte.com/insights/us/en/topics/digital-transformation/digitizing-the-construction-industry.html>.. The case study elaborates on one of the examples we used in Your next future.2)Evans-Greenwood, P & Leibowitz, D 2017, Your next future: Capitalising on disruptive change, Deloitte University Press, <https://dupress.deloitte.com/dup-us-en/focus/disruptive-strategy-patterns-case-studies/capitalising-on-disruptive-change.html>.
In that essay we made the distinction between simple disruption – disruption due to a particular disruptive technology, the thing the comes to mind first for most people when they think of disruption – and complex disruption – where the disruption is due to a confluence of (mainly social) factors. Think the telegraph (simple disruption) vs the global multi-modal container network (complex disruption). Many current disruptions – artificial intelligence, blockchain, etc – tend to be complex (rather than simple) disruption. We’re seeing an environmental shift, as individuals and firms realise that the current environment (with many things available cheap and on-demand) presents opportunities to find new ways to use old technologies to create new ‘disruptive’ operating models, rather than there being a massive wave of new technologies as many pundits claim.
One of examples we used to illustrate the shift was the building industry. There’s a lot of noise about technologies such as 3D printing or brick-laying robots disrupting the building industry, but this is unlikely as the industry’s product is the building process, not the buildings it produces. Builders will simply integrate these new technologies into their process if and when they become commercially viable. The invention of a new building process, however, where a builder uses old technologies in new ways to create a new, and superior, operating model has the potential to disrupt the industry.
Your next future mentioned a design for manufacture and assembly (DFMA) process – where a building is completely modelled in 3D before the model is split up and feed to numerically controlled machines in a factory, with the components shipped to the construction site for assembly – as potentially disruptive. Versions of the process current at the time of publication were roughly 30% faster than a conventional build (due to moving some work to the controlled environment of a factory where rain delays aren’t a problem, and enabling the optimisation of vertical transport on site). They were slightly cheaper, and had the potential to be much cheaper. And there’s the possibility to integrating new materials into the process, materials which couldn’t be used in a conventional process due to on-site restrictions.
Since that essay was published what was then a potential disruption looks like it might be about to tip into actual disruption. This is the subject of the case study.
In 2018 a project in the Melbourne CBD hit problems as the cranes and trucks required to move materials onto the site would block a lane that was the sole access to the homes of many local residents. The solution the builder (Hickory) came up with was to build at night: the machinery would arrive around 9 pm and lift DFMA components (via the Hickory Building System) onto the site, installing an entire floor in four-six hours. Once a floor is complete the floor below is weather-proof and there are no lives edges. The machines are gone before the residents wake. During the day the trades go through the completed floor and finish the interior. There was some skepticism as building is considered noisy, though a trial one night showed that the residents would hardly notice the nighttime construction.
And here’s where we might be seeing a potential complex disruption crystallise into actual disruption. The build proceeded, and the city council was so happy they are considering that all high-rise building to be done at night. This would, with the stroke of a pen, bar conventional builders from the market until they undertake the multi-year journey to develop their own operating model based a DFMA process.
The case study looks at the development of DFMA building processes, the challenges they faced and how they’ve been overcome, and the potential impact on the market. It also looks at how firms might also anticipate similar complex disruptions in their own market, pointing out that conventional market-scanning practices looking for disruptive technologies can actually be counter productive as they cannot predict complex disruption, and we’re in a market with there appears to be more complex disruption than simply disruption.
It’s an interesting story, and a local story which is nice, so head over the the Deloitte web site to read Digitalizing the construction industry: A case study in complex disruption.
|1.||↑||Evans-Greenwood, P et al. 2019, ‘Digitalizing the construction industry: A case study in complex disruption’, Deloitte Insights,<https://www2.deloitte.com/insights/us/en/topics/digital-transformation/digitizing-the-construction-industry.html>.|
|2.||↑||Evans-Greenwood, P & Leibowitz, D 2017, Your next future: Capitalising on disruptive change, Deloitte University Press, <https://dupress.deloitte.com/dup-us-en/focus/disruptive-strategy-patterns-case-studies/capitalising-on-disruptive-change.html>.|
I and a coauthor have a new report out on DU Press: Your next future: Capitalising on disruptive change.1)Evans-Greenwood, P & Leibowitz, D 2017, Your next future: Capitalising on disruptive change, Deloitte University Press, <https://dupress.deloitte.com/dup-us-en/focus/disruptive-strategy-patterns-case-studies/capitalising-on-disruptive-change.html>. Disruption is something we’d been puzzling for some time as it’s a fuzzy and poorly defined concept despite all the noise it generates. It’s also concerning that few, if any, of the theories have much predictive power.
Our contribution is fairly straight forward.
First we make that point that disruption, as the term is commonly used, covers a broad range of phenomena. This creates tension between our desire for a comprehensive definition, one encompassing this broad scope, and the need for a precise definition, so that we are all clear on what we’re talking about. Many academic theories (such as Clayton Christensen’s) come unstuck when it’s pointed out that the theory might refer to some disruptive phenomenon, but they don’t account for many other phenomena that can also be considered disruptive.
Consequently we must acknowledge that disruption operates are at least three different levels of abstraction:
It was the observation that value used to be objective and defined relative to the market, in terms of product feature-function, but now value is more commonly defined subjectively, relative to the firm and the firm-customer relationship, that prompted us to look at disruption with a wider lens and make this subjective disruption the subject of a our essay.
Next we wanted to create a model of disruption that was predictive, which could be fed into a strategy-formation process to enable a firm to identify concrete actions that would enable a firm to prepare for a (potential) disruption and either capitalise on it or defuse it (i.e. neuter the disruption). The resulting model relies on three observations.
The result a model that shows us why we we cannot predict disruption by identifying ‘disruptive technologies’, but which does enable us to do something about shaping how we approach disruption.
We’re pretty happy with the result, which you can find at DU Press.
|1.||↑||Evans-Greenwood, P & Leibowitz, D 2017, Your next future: Capitalising on disruptive change, Deloitte University Press, <https://dupress.deloitte.com/dup-us-en/focus/disruptive-strategy-patterns-case-studies/capitalising-on-disruptive-change.html>.|
|2.||↑||Evans-Greenwood, P & Williams, P 2014, Setting aside the burdens of the past: The possibilities of technology-driven change in Australia, Deloitte Australia, viewed 26 October 2017, <https://www2.deloitte.com/au/en/pages/technology/articles/shift-index-key-findings.html>.|
|3.||↑||Hagel, ,J, Seely Brown, J, Wooll, M, & de Maar, A 2015, Patterns of disruption: Anticipating disruptive strategies in a world of unicorns, black swans, and exponentials, Deloitte University Press, <http://dupress.com/articles/ anticipating-disruptive-strategy-of-market-entrants/>.|