The leadership challenges of homeworking.

Our notion of work and the office have changed forever. Whilst a positive experience for some, research shows we shouldn’t discount the benefits of an office environment. Will Venters and Enrico Rossi explain.

One lesson in leadership is not to make long-term decisions based on short-term experiences, while remaining ever mindful of world events. The last couple of months have provided many amazing examples of the success of homeworking and have certainly dispelled many criticisms. In the most part the technology works, we can meet people via Zoom and Microsoft Teams with relative ease (though we have also coined the term “Zoom-fatigue”) and most office work has continued well. But companies who see this as a tectonic shift towards a world where office-space is rendered unnecessary may be disappointed. While process and transactional work seems effective, the socialising, team building, learning and networking have been drastically reduced.

Our ongoing research has been examining how a very large global company’s high-level customer support staff went from being co-located in an office to all working from home. The lessons, based on numerous interviews, are stark with some staff hoping never to return to the office (often those with caring responsibilities at home, a long commute, or extensive experience), others desperate to get back (often those with small homes or partners also working from home, and with social or learning needs).

Not all workers enjoy the same conditions at home, and this can reintroduce disparities and injustices.

Two key changes are around learning and knowledge sharing. In offices, lots of knowledge is shared through serendipity; chance meetings in corridors or by the water cooler, the tea-break, the lunchtime chat[1]. These are crucial for knowledge sharing, generating new ideas, and bringing new staff into communities of shared practice and developing their identity as team-members[2-4]. Another form of informal efficient knowledge sharing is the “quick question”, where workers lean-back in their office chair and ask the people around them for help (a reason financial traders sit so closely together). These two forms of informal knowledge sharing have proved hard to replicate online. They are not however, always wanted in the office as they can be distracting (particularly for experienced staff who are constantly asked) and stressful (e.g. when managers constantly check their team). The transition to homeworking, and the consequential loss of informal knowledge sharing, was therefore perceived as a negative for some, but as a liberation for others.

Given this it was unsurprising that the very experienced staff we interviewed saw their productivity rise. One told us “I do more work at home… my workload has gone up massively” because in the office people were always coming up to her asking for help. Another told us “I am hitting targets more… without the office distractions…”,  “[in the office] I have nowhere to go because they ask face to face”. With homeworking, this person can choose which queries on the Instant Messenger app to answer and disengage herself whenever she feels the need to focus. Consequentially though, less skilled staff must spend more time finding the answer from the manuals and support tools which slows them down. As a junior person explained:

“[homeworking has] pushed me to educate myself more on what I don’t know… search for the information rather than just going ‘do you know this’…. It sinks in more if I have to find the knowledge myself”.

From a leadership perspective this feels very positive by fostering self-reliance, but the impact on new staff needs managing. More effort is needed to help them become part of the community at work. And, as online training proved less productive and harder to deliver, so more time is needed for training. Ideas like mentoring and regular-checkins seems sensible leadership responses here.

Our research also revealed the double-edge nature of digital technologies: they encourage people to share problems with a wider community (beyond their local desks) through platforms such as Microsoft Teams and Instant Messaging, but it also allowed workers to disengage and disconnect themselves from the wider social context by simply ignoring the chat, or putting it “in mute”- free riding on others. It was also harder for managers to keep track of staff – in particular, managers could not see the stress on people’s faces at home and so step in to help. And the lack of visibility increased pressure on some staff who felt the need to “prove something”, whist others were relieved and “liberated” from oversight.

Leaders should think about how to build a complex homeworking and office-working mix which maximises efficiency, equity and innovation.

Homeworking is also a great leveller – everyone had the same access to each other which proved a boon for staff who are usually located away from the main office. At the same time, not all workers enjoy the same conditions at home, and this can reintroduce disparities and injustices. Their work was impacted by different family and personal conditions (e.g. caring responsibilities, disabilities), different IT facilities (e.g. internet connection and hardware), but also physical facilities such as space for a desk, amounts of light, and whether other family members were trying to work from home at the same time. Leaders need to balance the disparity between equality of work for both office environments and homeworking. Consider, for example, the married couple we interviewed who are both now homeworking – the husband cooped up in the bedroom working for a bank, his wife downstairs in the open-plan living space. Neither could enter the room when the other was working and both worked long-shifts.

In summary, when designing physical office space architects consider balancing the need for efficient transactional work with socialised knowledge sharing and such design is linked to a wide range of factors such as the role and personality of the workers. For example, silicon-valley technology companies favour innovation over transactional work and so invest in fancy fun offices which foster interruption and informal gatherings and interaction. In contrast, many call-centres are physically organised to limit this kind of innovative interaction and focus staff on processing transactional work. When planning homeworking arrangements, the same thought for architecture, team-structures and support are needed. It is almost certainly not enough to simply replicate the same team and office structure online.

In the long-term, leaders should think about how to build a complex homeworking and office-working mix which maximises efficiency, equity and innovation, and should realise that this will vary considerably from individual to individual.

References

1. Fayard, A.-L. and J. Weeks, Photocopiers and Water-coolers: The Affordances of Informal Interaction. Organization Studies, 2007. 28(5): p. 605-634.

2. Orr, J., Talking about Machines: An Ethnography of a Modern Job. 1996, Ithaca, NY: IRL Press.

3. Wenger, E., Communities of practice : Learning, meaning and identity. 1st ed. Learning in Doing: Social, Cognitive and Computational Perspectives, ed. R. Pea, J.S. Brown, and J. Hawkins. 1998, Cambridge: Cambridge University Press.

4. Wenger, E., Communities of Practice and Social Learning Systems. Organization, 2000. 7(2): p. 225-246.

Feature image by Annie Spratt on Unsplash

What automation can do for small businesses

What does automation mean for small businesses? And what processes can they automate today using current technologies?

Cloud computing provides small businesses with direct access to a huge range of automation solutions. But rather than automating their existing admin processes, small businesses may be better examining whether standardised processes already exist in the cloud through specialist tailored services.

Small businesses should be wary though as costs can escalate. Return on investment (ROI) analysis should be undertaken before committing, and automation services themselves need carefully managing. Small businesses must be particularly careful that they don’t end up locked-into a spaghetti like arrangement of different cloud services with escalating costs. One advantage of automation in the cloud however is that costs are linked to usage and thus scale as the business scales.

Where small businesses have existing processes that need automating, companies like AnotherMonday and UIPath have Robotic Process Automation (RPA) products targeting small business. Similarly, Low-Code providers like Mendix, Pega and even ZoHo support point and click application design. However, the skills needed to use these products are by no means trivial and the challenge is not just building the automation but maintaining and evolving it as business changes. Our own research suggests that many small businesses across Europe choose vendors based on the proximity of support staff during adoption[1].

Is the future of small business management creating entities to help owners/managers better run their businesses?

For decades small business managers have harnessed software such as spreadsheets to manage accounting or word-processors for mail-merges. The difference today is that new software is transforming processes allowing the integration of parts of the business using RPA, Low-Code development and AI. RPA automates robotic-human-processes in the sense that they are most capable of doing things which humans find robotic – the boring, repetitive, easy, precise work. Indeed since research suggests (Willcocks and Lacity 2016) companies do not fire staff when replacing their work with RPA but instead redeploy them to more value-added work, so many employees may welcome RPA as it frees them to do the exciting, interesting and value-creating work rather than the drudgery. I believe the future of small business is about focusing on core-capabilities (the stuff the business does best and adds value) and seeking to streamline the none-core capabilities through automation, cloud services and better organisation.

One way of achieving this streamlining is through collaboration. Small businesses should collaborate to automate capabilities which are not core and build ecosystems of connected services which adds value to all. For example, small UK businesses in a sector might collaborate to produce standard automation for a problem they all face (for example specific customs-related administration post Brexit). These businesses are well placed to understand the specific requirements for the automation solution for their sector and thus develop something which works for all. Government pump-priming support for such initiatives might also be helpful.

What are the key challenges facing the small and micro business owner wanting to use more automation? Isn’t AI and Machine Learning only for big businesses?

Advanced AI and automation are already available as cloud services from the likes of Amazon AWS, Google’s Tensorflow, and Microsoft Azure. While these obviously require some skills to exploit, they do not require a PhD in AI. Indeed, innovative small companies may be better placed to create innovative AI solution as they are unencumbered by legacy IT processes and can move quickly.

There are however five major issues: Data, skills, interfacing and bias:

  • Small businesses will struggle to acquire the data to train machine learning (ML) algorithms whereas big businesses often have data-lakes from which they can use ML to extract insight and value. It is these vast data-lakes which are driving the growth in AI. Small companies may struggle to have enough data to train the algorithms to work effectively.
  • Small businesses may struggle to find the skills even with todays cloud-AI framework. My own research (Venters, Sorensen et al. 2017) suggested 71% of IT departments have lost revenue due to lack of cloud skills alone. Building an AI automation solution requires cloud and AI skills as well as those of analysing the business problem being are automated. Maintenance of the application will also be needed over the long term. Small consulting practices and agencies will likely emerge which are better placed to assist specific types of small business in this work.
  • A solution to the skills and data challenges is interfacing: connecting small company’s systems together to pool the data they have, share the cost of innovation, and build system which, by connecting companies together, can complete with large enterprise. For example, firms within a supply chain might collectively (obviously within the limits of competition law) build automation which, through the collective sharing of production and usage data, improves the production processes of all of them. Managing this interfaced relationship will obviously require work (and is the focus of my own research[2]).
  • One of the significant challenges of AI is that it can only learn from historic data which may embed pre-existing biases, or fail to reflect current (and planned future) organisational realities. Keeping an open mind, examining critically and carefully calibrating AI solutions for today’s reality is vital. Given that organisations will change and evolve this final issue requires careful skills. It is easy to build a successful solution to yesterday’s problem.

Is a bot, or other Machine Learning-based entity, a small business’s next employee?

A bot or Machine-Learning based entity is not like an employee. But, for this very reason, they need carefully managing by human employees. Bots won’t question their work; they hold no ethical compass; they cannot easily explain how they arrived at a decision; and they cannot understand the biases they might be applying. Further, they work so quickly that the problems they cause can rapidly scale out of control. For this reason, managing bots requires frequent checks to ensure they are working in the company’s best interest and delivering a clear ROI.

As significant may be that the a small-businesses’ future customers may be AI-led bots that negotiate and place orders automatically with complex market analysis. This may change the way small businesses transact their businesses and derive profit.

Will some freelancers and micro business eventually be competing against AI-based services?

It’s easy to overplay the success of AI-services compared to a human. AI suffers from the frame problem (Boden 2016) in that they cannot understand what it is to be human and make human-decisions – they can only apply robotic action or make inferences from existing limited data. The most likely emerging reality then is not that a bot or AI will replace employees, but that employees’ capabilities will be drastically improved by working alongside AI and technology. Seeing human and machine as entwined rather than opposed is a more productive perspective.

What’s somewhat more interesting is how many micro-businesses today are becoming human-intelligence based services (forms of mechanical turks[3]) provided to large AI-reliant platform companies. Consider Uber; for the “micro-business” person driving the car their “company” is entirely controlled by the Uber algorithm which allocates their piece-work contracts to drive. All traditional business functions – marketing, quality-control, advertising, sales etc. that a small mini-cab company might have provided are subsumed into the Uber platform leaving the drive-business reduced to just the intelligent driving skill. Ubers seem closer to self-driving cars than to mini-cab-firms in that the human only provides the driving-intelligence. Similarly, eBay and Amazon Marketplace have reduced retail to mere product acquisition.

What does small business automation look like in 2025?

I think many small businesses will prove highly efficient as their administrative overhead reduces allowing them to complete more easily and efficiently with larger companies. As small business automation matures so the cost of doing business will be reduced allowing more exciting and innovative businesses emerge which are both efficient and agile. The rise of Harry’s Razor for example shows how a plucky start-up harnessing online tools in marketing can build a business to complete with global multinationals.

It would be great to see government departments focused on supporting this innovation. For example, if they used next five years to focus on producing standardised APIs that allow small businesses to easily comply with “red-tape” rather than through mountains of form-filling. This would require considerable change in the way Government built and used their IT[4].

We should also not underestimate the growth of blockchain alongside automation. By providing mechanisms for establishing trust the technology could allow small businesses to collaborate more fluidly with greater safety. Smart contracts, for example, might ensure that payments happen immediately and automatically once a product is shipped.

Physical robots are also rapidly improving and may form part of physical-automation processes alongside digital ones. For example, robots, like the early Baxter, can now safely work alongside humans and are easily trained for repeated activities. If coupled with a process automation application one can envisage such robots undertaken key tasks (e.g. picking, boxing and labelling) unaided. Further out, as robotics takes the place of humans, so the economics of manufacturing may shift in favour of producing closer to the consumer rather than overseas – a move which may benefit small business.

Finally, once automation becomes accepted within the small business community, I think we will see the rise of ecosystems of small businesses working together to take on larger enterprise. By drastically reducing the transaction costs involved in collaborating there is little reason such ecosystems cannot outcompete.

References

Boden, M. A. (2016). AI: Its nature and future, Oxford University Press.

  • Venters, D. W., C. Sorensen and Rackspace (2017). The Cost of Cloud Expertise, Rackspace and Intel.
  • Willcocks, L. and M. C. Lacity (2016). Service Automation: Robots and the future of work. Warwickshire, UK, Steve Brookes Publishing.

[1] Polyviou, Pouloudi and Venters, (Forthcoming),” Sensemaking and proximity in cloud adoption decisions” (Working paper).

[2] https://binaryblurring.com/2017/12/04/win-of-6-million-to-research-digital-interfacing/

[3] https://en.wikipedia.org/wiki/The_Turk also https://en.wikipedia.org/wiki/Amazon_Mechanical_Turk

[4] Fishenden, J., M. Thompson and W. Venters (2018). Better Public Services: The Green Paper accompanying Better Public Services, A Manifesto. Launched at the Institute for Government on 27th March 2018.

Feature image by James Pond on Unsplash. (With thanks).

The World Turned Upside Down … The future of digital innovation research post COVID-19

To register (free) via eventbrite click here!

25th June from 1:00pm (BST – UK Summer time) until 5:30 pm (includes a 1hr break from around 2:30-3:30)

As part of the The Digital Infrastructure, Innovation and Economy Series London (DIIESL.org) which I co-run I would like to announce a seminar on the 25th June 2020.

In 2020 the world turned upside down: one particular consequence has been the most marked shift to digital forms of social interaction we are likely to see in our lifetimes. We would like to hold a workshop to explore how research in Information Systems and related fields might (and perhaps should) change in response, by bringing together a range of information systems researchers to engage in reflection and futurology.

Dr Will Venters
Chairperson:
Dr Will Venters – LSE

We will not dwell on COVID, choosing to focus instead on imagining what sort of changes may ensue – and speculating on how these are likely to affect our disciplines. The workshop will be informed by futures research (Chiasson, Davidson et al. 2018) and consist of short presentations on imagined changes, followed by structured discussion and debate.

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Speakers

Prof. Michael Barrett – Judge Business School

Why IS Practice Essential for Digital Innovation Research

Prof. François-Xavier de Vaujany – Université Paris Dauphine-PSL

The Birth of Digitality: Computing for a World in Crisis in the 40s

Dr Mareike Möhlmann – Warwick Business School

App-based surveillance – Lessons from the algorithmic management of Uber drivers

Prof. Mark Thompson – Exeter University

Public services during COVID:  the curious case of a ‘digital’ council

Dr Edgar Whitley – London School of Economics

Managing digital identities online: Public, private and interoperable?

Prof. Mike Chaisson – The University of British Columbia

TBA

Dr Wendy Günther – INDEX

Wendy will act as rapporteur and provide a summary at the end of the event.

Topics for discussion include

  1. Methodological innovations.
  2. Changes to Privacy and Identity.
  3. Digital infrastructure and the emerging role of the state .
  4. Changes in Information Systems development practices.
  5. Digital nomadism and emerging organisational forms.
  6. Globalised supply chains and outsourcing.
  7. Practices, processes and performances.
  8. Materiality in a dematerialising world.

For administrative Questions please contact f.white@lse.ac.uk

Chiasson, M., Davidson, E., and Winter, J. 2018. “Philosophical Foundations for Informing the Future(S) through Is Research,” European Journal of Information Systems (27:3), pp. 367-379.

Sprint Week – Launch with Jake Knapp

It was fantastic to launch the annual “Sprint Week” I run, with Carsten Sorensen, as part of our MSc in Management, Information Systems and Digital Innovation . During the week our students follow Jake Knapp’s Sprint book (albeit with changes to use more complex modelling tools) to develop a solution for VISA. The week is supported by Roland Berger consultants and various outside speakers.

We were pleased to launch the week with myself interviewing Jake via Skype about his Sprint techniques. In the talk (30minutes) is below.

 

Presentation of our research in Map Camp 2019

Roser Pujadas presented in Map Camp 2019 some of the findings of the research she is conducting with Will Venters and Mark Thompson on Wardley Maps.

Map Camp (map-camp.com/) is a yearly event that brings together a community interested in Wardley Maps, a tool used to support strategic decision-making by helping organizations develop situational awareness. More than 600 people from across the globe attended this year’s Map Camp in Sadler’s Wells Theatre (London). Presentations were recorded and will be available @map_camp.

Roser’s presentation explored the social dimensions of mapping and sensemaking. Instead of thinking about Wardley Maps as a tool, she suggests instead to consider mapping as an active doing, an ongoing process that involves making knowledge explicit through visualisation. Wardley Maps constitute a common language that facilitate communication and collaboration, and can also be used to gain legitimacy. As our research shows, most mappers see the main value of mapping in the process, in the discussions that are generated through mapping and interpreting maps, and not so much in the production of ‘a perfect map’. In fact, as Roser went on to argue, all maps are partial, and reveal some things but obscure others.

Recruiting Again – Research Fellow.

I am looking to recruit someone who can work in the interdisciplinary space between information systems and formal modelling. The aim is to work with a large industrial partner to build and test models (of various kinds) of systems at scale. Ideally we would find someone directly able to do that – but realistically the candidate is likely to have a stronger flavour in one of these areas.

Research Fellow – Programming Principles, Logic and Verification Group, – Ref:1820659

University College London is seeking to appoint a Research Fellow for examining modelling of large enterprise systems and architectures. This fellowship will involve working with industry partners to analyse and model their digital ecosystems. The Information Systems and Innovation group (ISIG) at the London School of Economics is well known for its research in the social, political and economic dimensions of information and communications technology.

The Interface Reasoning for Interacting Systems (IRIS) project, led by Prof. David Pym, uses logical and algebraic methods, as well as management research theory, to understand the compositional structure of systems and their communications, seeking to develop analyses at all scales, from code through distributed systems to organizational structure, generically and uniformly. The role will be jointly managed by David Pym at UCL and Will Venters at LSE’s Department of Management — a world-leading centre for Information Systems research.

The PPLV group at UCL conducts world-leading research in logical and algebraic methods and their applications to program and systems modelling and verification.

While based at UCL, the role will involve working at the LSE for around two days per week where you will have a desk.

The post is funded for 12 months in the first instance with a possible extension up to 36 months.

Ideally you will have a technical/engineering background with experience in business modelling,  business analysis, programming, and formal methods, and an understanding of qualitative and quantitative research techniques. An understanding of information systems and management research would be highly desirable, as would experience of action research or design science.

Good communication skills are essential.

Applicants must hold, or be about to receive, a PhD in information systems, logic, theoretical computer science, or a closely related area. An interest in systems modelling verification, together with underlying logical and mathematical theory, is essential. Advanced programming skills and knowledge of, or some interest in, distributed systems and/or information and systems security are highly desirable.

Appointment at Grade 7 is dependent upon having been awarded a PhD; if this is not the case, initial appointment will be at research assistant Grade 6B (salary £30,922 – £32,607 per annum) with payment at Grade 7 being backdated to the date of final submission of the PhD thesis.

Further details and how to apply:

https://atsv7.wcn.co.uk/search_engine/jobs.cgi?owner=5041404&ownertype=fair&jcode=1820659&vt_template=965&adminview=1

 

 

Professor of Information, Logic, and Security Head of Programming Principles, Logic, and Verification University College London

 

Turing Fellow, The Alan Turing Institute, London

 

d.pym@ucl.ac.uk

www.cs.ucl.ac.uk/people/D.Pym.html

www.cs.ucl.ac.uk/staff/D.Pym/

 

Assistant: Julia Savage, j.savage@ucl.ac.uk, +44 (0)20 7679 0327

 

Control-Generativity Paradox – Visiting student Michael Blaschke is working with me for the next year.

For the next year Michael Blaschke, is visiting me at the LSE. He is a final year PhD student from University of St Gallen (HSG) and the SAP Innovation Center St.Gallen. His research mainly focuses on digital platforms and value co-creation.

The following  paper-summary written by Michael gives an idea of his research. 

Abstract

The platform economy represents the most profound global macroeconomic change since the industrial revolution. Digital platforms afford organizations to synergistically co-create value in digital third-party ecosystems. Considering these ecosystems’ specificities, digital platforms require a delicate balance of two conflicting ends: control and generativity. While pure control makes adaptation difficult, pure generativity suffers the costs of experimentation without gaining associated benefits. In turn, embracing the complementary benefits of simultaneous control and generativity is challenging given its inherent contradictions. Beyond summarizing the control-generativity paradox of digital platforms, this blog post makes four alternative modes of balancing control and generativity available to platform managers. The publication can be found here.

The Control-Generativity Paradox

Key Takeaways

What? Digital platforms—digital core technologies upon which third parties add peripheral derivatives—afford organizations to co-create value in networked business ecosystems.

So What? While platform owners aim for stabilization to exploit their third-party ecosystem (control), third parties aim for autonomy to explore unanticipated avenues of innovation (generativity).

Now What? Platform owners draw upon at least four modes of balancing control and generativity in digital platforms—contextual, structural, temporal, and domainal balance.

Managerial and scholarly interest in digital platforms is mounting. Some of the most valued companies—including Alibaba, Amazon, and Alphabet—embrace the platform logic with surprisingly short histories. At the same time, many long-lived companies are considering how they can adopt the platform logic to improve performance. Prominent digital platform exemplars are social media platforms (e.g., Facebook and LinkedIn), mobile operating system platforms (e.g., Android and iOS), payment platforms (e.g., PayPal and Apple Pay), and peer-to-peer platforms (e.g., Uber and Airbnb).

Digital platforms are characterized by synergistic value co-creation in digital third-party ecosystems. These ecosystem make digital platforms subject to a delicate tension between (1) maintaining control and, at the same time, (2) stimulating—not directly managing—generativity through dynamically recombining third-party resources (Blaschke and Brosius 2018). While control captures mechanisms that encourage desirable outputs or behaviors by third parties (Tiwana et al. 2010), generativity describes a technology’s overall capacity to produce unprompted change driven by large, varied and uncoordinated audiences (Zittrain 2006).

Notably, control and generativity are not incompatible or mutually exclusive goals. Successful digital platforms meet both ends as pure control makes adaptation difficult and pure generativity suffers the costs of experimentation without gaining associated benefits. As balancing has in fact become the innate mindset of digital platform management, we ask: How do digital platforms balance simultaneous control and generativity?

Balancing Control and Generativity

Based on our research, we extracted a set of four modes of balancing control and generativity in digital platforms, namely contextual, organizational, temporal, and domanial balance. This set of modes is drawn based on the premise that digital platforms seek both (1) for stabilization to exploit the given ecosystem of third-party actors (through control) and (2) for dynamism to explore new avenues of resource integration in adapting to third-party actors’ external stimuli (through generativity). Next, we summarize these modes of balancing control and generativity in digital platforms.

 

 

Contextual balance denotes a situation-dependent combination of concurrent control and generativity. It is a form of contextual buffering, whereby the platform owner maintains control and generativity activities (1) situation-dependent for each platform partner individually and (2) simultaneously at any given organizational level. For instance, Microsoft (Windows) employs contextually configures control and generativity within the contexts of either exchanging, adding, or synergistically integrating third-party resources.

Structural balance refers to different types of partners that are subject to either control or generativity. It is a form of spatial buffering, whereby the platform owner maintains control and generativity (1) simultaneously on the platform ecosystem level, but (2) are situated within distinct organizational units for distinct partner types (e.g., new and existing partners), respectively. For instance, SAP (SAP Cloud Platform) runs one unit to negotiate and onboard new partners (control), while a different unit explores novel software with already existing partners (generativity).

Temporal balance denotes sequential shifts over time from control to generativity, and vice versa. It is a form of temporal buffering, whereby control and generativity (1) coexist for the same given platform partner but (2) at different points in time, so that the platform owner switches sequentially between control and generativity for each platform partner. To illustrate, Alibaba Group (Alibaba.com) predominantly maintained generativity to become a two-sided platform (1994-2004), relied on control to mitigate the threat of platform envelopment (2005-2006), and fostered generativity again to pursue a digital ecosystem strategy (2007-present).

Domainal balance denotes control in one domain with simultaneous generativity in another domain. It is a form of domanial buffering, whereby any given platform partner is subject to both control and generativity (1) organizational domains while (2) the platform owner balances these domain-dependent control and generativity activities globally across domains. For instance, Microsoft (LinkedIn) differentiates a platform’s core, interfaces, and complements as key architectural domains, each of which require different control-generativity configurations.

Recommendations

  1. Thriving platforms simultaneously seek (1) for stabilization to exploit the given digital ecosystem (through control) and (2) for dynamism to explore innovation in adapting to third parties’ external stimuli (through generativity).
  2. Thriving platforms balance control and generativity through a platform-specific adoption and adaption of the four proposed balancing modes.
  3. Effective platform mangers identify novel modes and mechanisms to achieve the targeted control-generativity balance.

About the paper

References

Blaschke, M., and Brosius, M. 2018. “Digital Platforms: Balancing Control and Generativity,” in: 39th International Conference on Information Systems (ICIS2018). San Francisco, US.

Tiwana, A., Konsynski, B., and Bush, A. A. 2010. “Platform Evolution: Coevolution of Platform Architecture, Governance, and Environmental Dynamics,” Information Systems Research (21:4), pp. 675-687.

Zittrain, J. L. 2006. “The Generative Internet,” Harvard Law Review (119:7), pp. 1974-2040.

 

I’m recruiting! Post-doc position at UCL/LSE: Interface Reasoning for Interacting Systems Research Fellow

I am recruiting for a joint UCL/LSE position examining the modelling of interfacing. The role will be split between UCL and LSE (managed by Prof. David Pym and myself) and will focus on modelling complex distributed systems. It would strongly suit an information systems PhD with a technical/computing/IT consulting type background. 

Post-doc position UCL/LSE: Interface Reasoning for Interacting Systems Research Fellow in Programming Principles, Logic, and Verification

Interface Reasoning for interacting Systems (IRIS) — a project funded by the UK’s EPSRC.

https://interfacereasoning.com

Today’s large enterprises are harnessing a complex mix of cloud computing services, APIs, legacy applications and service-oriented architectures to build complex information systems. You will work with an interdisciplinary team consisting of computer scientists, Information Systems researchers, logicians and modellers to explore the modelling of such complex distributed digital ecosystems. This fellowship will involve working with industry partners to analyse and model their ecosystems. Ideally you will have a technical/engineering background with experience in programming, formal methods, business modelling and business analysis, and an understanding of qualitative and quantitative research techniques. An understanding of information systems and management would be highly desirable, as would experience of action research or design science. Good communication skills are essential.

The role will be jointly managed by David Pym at UCL and Will Venters at LSE.

While based at UCL, the role will involve working at the LSE for around two days per week where you will have a desk.

Applicants must hold, or be about to receive, a PhD with relevant expertise and research interests; for example, in systems modelling, software engineering, formal methods, business analysis, and/or information systems. Advanced programming skills and knowledge of, or some interest in, distributed systems and/or information and systems security are highly desirable.

Appointment at Grade 7 (£35,328 – £42,701 per annum) is dependent upon having been awarded a PhD; if this is not the case, initial appointment will be at research assistant Grade 6B (salary £30,922 – £32,607 per annum) with payment at Grade 7 being backdated to the date of final submission of the PhD thesis.

Appointment is subject to UCL’s terms and conditions.

Th post is funded for 12 months in the first instance with a possible extension up to 36 months.

Closing date 23 June 2019.

Informal enquires to David Pym (d.pym@ucl.ac.uk; http://www0.cs.ucl.ac.uk/staff/D.Pym/)

or Will Venters (w.venters@lse.ac.uk; https://www.willventers.com).

 

For full details and to apply, please see UCL’s recruitment page for this position:

https://atsv7.wcn.co.uk/search_engine/jobs.cgi?SID=amNvZGU9MTgwNzA2NSZ2dF90ZW1wbGF0ZT05NjUmb3duZXI9NTA0MTE3OCZvd25lcnR5cGU9ZmFpciZicmFuZF9pZD0wJnZhY194dHJhNTA0MTE3OC41MF81MDQxMTc4PTkyNzg2JnZhY3R5cGU9MTI3NiZwb3N0aW5nX2NvZGU9MjI0

The 5-Es of AI potential: What do executives and investors need to think about when evaluating Artificial Intelligence?

I spent last week in Berlin as part of a small international delegation of AI experts convened by the Konrad-Adenauer Foundation[1]. In meetings with politicians, civil servants and entrepreneurs, over dinners, conferences and a meeting in the Chancellery[2], we discussed in detail the challenges faced in developing AI businesses within Germany.

A strong theme was the difference between AI as a “thing” and as “component”. Within most commercial sales-pitches AI is a “thing” developed by specialist AI businesses to be evaluated for adoption. Attention is focused on what I will term efficacy. Such efficacy aligns with pharmacology definitions as “the performance of an intervention under ideal and controlled circumstances” and is contrasted with effectiveness which “refers to its performance under ‘real-world’ conditions.[3]. AI efficacy is demonstrated through sales-pitch presentations based on specially tagged data or upon human-selected data-sets honed for the purpose.

As a “component” however, AI only becomes when it is incorporated into real-world consequential and ever evolving business processes. To be effective not just efficacious, AI must bring together real-data sources in real-world physical technology (usually involving cloud services, complex networking and physical devices) for consequential action. AI “components” then become part of a complex digital ecosystem within the Niagara-like flow of real businesses rather than a “thing” isolated from it. Since business processes, data-standards, sensors and devices, evolve and change so the AI must evolve as well while continuing to meet the needs of this flow.

To be effective (not efficacious) AI components must also be:

  • efficient in terms of energy and time (providing answers sufficiently quickly as to be useful.
  • economic in terms of cost-benefit for the company (particularly as the cost of human tagging of training data can be extreme),
  • ethical by making correct moral judgements in the face of bias in data and output, ensuring effective oversight of the resultant process, and transparency of the way the algorithm works. For example resent research shows image classifier algorithms may work by unexpected means (for example identifying horse pictures from copyright tags or train types by the rails). This can prove a significant problem when new images are introduced.
  • established in that it will continue to run long-term without disruption for real world business processes and data-sets.

The final two of these Es are particularly important: Ethics because business data is usually far from pure, clean and will likely include many biases. And Established because any process delay or failure can cause a pile-ups and overflow to other processes, and thus cause disaster. In my opinion, only if all these 5Es are achieved should a business move AI into core business processes.

For business leaders seeking to address these Es the challenges will not be in acquiring PhDs in AI-algorithms but instead in (1) hiring skilled business analysts with knowledge of AI’s opportunity but also knowledge of real-world IT challenges, (2) hiring skilled Cloud-AI implementors who can ensure these Es are met in a production environment and (3) appointing AI ethics people to focus on ensuring bias, data-protection laws, and poor data quality do not lead to poor ineffective AI. Given the significant competition for AI skills, digital transformation skills and for cloud-skills [4] this will not be easy.

So while it is fun to see interesting wizz-bang demos of AI products at industry AI conferences like those in Berlin this week, in my mind executives should remain mindful that really harnessing the potential of AI represents a much deeper form of digital transformation. Hopefully my 5Es will aid those navigating such transformation.

(C) 2019 W. Venters

[1] https://www.kas.de/ also https://www.kas.de/veranstaltungen/detail/-/content/international-perspectives-on-artificial-intelligence

[2] https://en.wikipedia.org/wiki/Federal_Chancellery_(Berlin)

[3] Efficacy : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3912314/  Singal AG, Higgins PD, Waljee AK. A primer on effectiveness and efficacy trials. Clin Transl Gastroenterol. 2014;5(1):e45. Published 2014 Jan 2. doi:10.1038/ctg.2013.13. I acknowledge drawing on Peter Checkland’s SSM for 3Es (Efficacy ,Efficiency and Effectiveness) in systems thinking.

[4] Venters, D. W., Sorensen, C., and Rackspace. 2017. “The Cost of Cloud Expertise,” Rackspace and Intel.

[5] https://en.wikipedia.org/wiki/Industry_4.0

Wardley Mapping and building situational awareness in the age of service ecosystems.

How do executives make sense of their complex digital ecosystem of cloud services? How do they gain situational awareness? One method gaining increasing popularity in a large number of organisations is Simon Wardley’s “Wardley Mapping” technique. With Simon, and with Roser Pujadas and Mark Thompson, we have been developing and researching of how and why this technique is used. The following paper, to be presented in June at ECIS Stockholm[1], outlines the basics of the technique and our early findings.

Pujadas, R, Thompson, M., Venters, W., Wardley, S. (2019) Building situational awareness in the age of service ecosystems. 27th European Conference on Information Systems, Stockholm & Uppsala, June 2019. 

Paper Abstract:

We discuss the little-explored construct of situational awareness, which will arguably become increasingly important for strategic decision-making in the age of distributed service ecosystems, digital infrastructures, and microservices. Guided by a design science approach, we introduce a mapping artefact with the ability to enhance situational awareness within, and across, horizontal value chains, and evaluate its application in the field amongst both IS practitioners and IS researchers. We make suggestions for further research into both construct and artefact, and provide insights on their use in practice.

Keywords: Situational awareness, Distributed systems, Design Science, Strategy, Digital Ecosystems, Digital Infrastructure, modularity, servitization.

[1] ECIS, the European Conference on Information Systems, is the meeting platform for European and international researchers in the field of Information Systems. This 27th edition will take place in Sweden. We will present our paper in the “Rethinking IS Strategy and Governance in the Digital Age” research track.

For more on Simon’s Wardley Mapping see: https://en.wikipedia.org/wiki/Wardley_map or https://www.wardleymaps.com/