By Dr Alex Zarifis, originally published in The Conversation

I have a confession to make. Despite being an academic, I do not actually read many books. In truth, I don’t often find books about what I like. My interest is on how the latest technologies affect business, and this topic is usually covered better by research articles and the press.

In the days when I was a student and money was tight, I was even less likely to buy books. But one exception was Wikinomics: How Mass Collaboration Changes Everything (2006). Written by Canadian tech thinkers Dan Tapscott and Anthony D. Williams, it captured my interest in how new innovations can change our personal and professional lives – and how exciting this change can be. Clearly I was by no means the only one that felt this way, as the book became a tremendous success.

The title, a compound of Wikipedia pages and economics, followed in the style of the equally successful Freakonomics (2005), but Wikinomics is very much a landmark in its own right. What it conveyed powerfully was that the level of mass collaboration and sharing online was about to move way beyond what we had seen in the first 15 years of the internet, transforming how people did business.

These were much more than mere technological advances, the book argued, and would require a completely different business mindset and philosophy. This was all about openness, sharing, freedom to innovate, and acting globally.

Wikinomics highlights seven new models of collaboration:

1. Open-source software: Software whose source code is made available for everyone to use and build on, which provides a way for firms and coders to coalesce around the same standard. One of the key early driving forces was the Linux operating system, while the book also points to Wikipedia as the archetypal example of the collaborative mindset.

Today, we see many examples of standalone coders coming together from around the world to build applications that are decentralised, meaning they’re not owned by anyone or based anywhere. Decentralised finance (defi), for instance, is offering a new way for people to do everything from trading financial assets to taking out mortgages.

2. Crowdsourcing innovative talent: This allows organisations to solve problems with ideas from outside, typically from other parts of the world. The example given in the book is InnoCentive (now Wazoku Crowd), a site where organisations post scientific challenges and offer rewards for their solution.

3. Prosumers: These are forward-thinking consumers who co-create products and services. In 2006, for instance, users of the virtual world, Second Life, were creating virtual buildings then renting them to other users. Today, fans of computer games such as Total War: Warhammer III are creating new characters and environments in a similar way.

4. Innovators sharing information: Making data widely available for others to use has particular importance in helping solve humanity’s greatest challenges, such as climate change. The increasing popularity of open-access publishing of academic research has been an important step in this direction.

5. Open platforms: Software that allows largely unrestricted access to its content and data gives businesses and individuals more room to collaborate and create new products. One example in the book is Google Maps, which was used by US entrepreneur Paul Rademacher to create a service called HousingMaps. It took data from Craigslist about homes for sale in a given area and pinned them on a map so that anyone searching for a home could see all the available locations at the same time.

Combining capabilities in this way became known as mash-ups, and can be seen today in a service like online bank Revolut. Revolut brings together services and information from a broad variety of organisations and offers them in an integrated way that is easy to use.

6. Mass collaboration in manufacturing: The book noted how a manufacturer like Boeing had shifted from designing everything in-house and sourcing specified parts from individual suppliers to instead having suppliers working together to design parts themselves and then assemble them in teams in Boeing factories.

This switch in emphasis from supply chains to ecosystems has more recently been typified by Shenzhen in China, where collaborative manufacturing in everything from circuits to touchscreens blurs the boundaries between the companies involved.

7. Modern workplaces that avoid hierarchies and silos: Instead of rigid structures, the driving force is social connectivity and fun.

Pros and cons

As is often the case with hugely successful books, Wikinomics was a combination of a great title, good writing and timing. By 2006 many of these trends were well underway. For instance, it was already common for coders to use open-source software like Linux for mass global collaboration. But if the book had been more original, it would not have been so well timed for mass market appeal.

A common criticism of Wikinomics is that it created many obscure terms that will only be familiar to those who have read the book. For example, its fourth collaborative model is called “ideagoras”, which hasn’t exactly caught on. No doubt the authors could have used simpler existing terms, but this is not the main weakness of the book.

With the benefit of hindsight, Wikinomics emphasised the positives of mass collaboration but did a poor job of foreseeing the challenges. Openness has made the world much more vulnerable to cybersecurity hacks, frauds and privacy breaches. Our behaviour online is now endlessly recorded and analysed, making people feel both distrustful and powerless.

What the book did do very well was to frame the issues around the new collaborative economy and explain them clearly. It helped readers to organise the new and old information in their minds, making it easier for them to analyse developments and be part of the revolution.

As a lecturer that teaches business on an executive MBA course to experienced managers, this is something I can appreciate. I cannot always tell them something they have not heard before, but if I can frame the issues well and communicate them clearly, it’s still useful to them. This is ultimately what Wikinomics did: it helped clarify the issues readers already had some understanding of, helping to shape the business zeitgeist for web 2.0.

Reference

Zarifis A. (2024) ‘Books that shook the business world: Wikinomics by Dan Tapscott and Anthony D. Williams’, The Conversation. Available from: https://theconversation.com/books-that-shook-the-business-world-wikinomics-by-dan-tapscott-and-anthony-d-williams-233587

New research!

Central Bank Digital Currencies (CBDC) are digital money issued, and backed, by a central bank. Consumer trust can encourage or discourage the adoption of this currency, which is also a payment system and a technology. CBDCs are an important part of the new Fintech solutions disrupting finance, but also more generally society. This research attempts to understand consumer trust in CBDCs so that the development and adoption stages are more effective, and satisfying, for all the stakeholders. This research verified the importance of trust in CBDC adoption, and developed a model of how trust in a CBDC is built (Zarifis & Cheng 2023).

Figure 1. Model of how trust in a Central Bank Digital Currencies (CBDC) is built in six ways

There are six ways to build trust in CBDCs. These are: (1) Trust in government and central bank issuing the CBDC, (2) expressed guarantees for the user, (3) the positive reputation of existing CBDCs active elsewhere, (4) the automation and reduced human involvement achieved by a CBDC technology, (5) the trust building functionality of a CBDC wallet app, and (6) privacy features of the CBDC wallet app and back-end processes such as anonymity. The first three trust building methods relate to trust in the institutions involved, while the final three relate to trust in the technology used. Trust in the technology is like the walls of a new building and institutional trust is like the buttresses that support it.

This research has practical implications for the various stakeholders involved in implementing and operating a CBDC but also the stakeholders in the ecosystem using CBDCs. The stakeholders involved in delivering and operating CBDCs such as governments, central banks, regulators, retail banks and technology providers can apply the six trust building approaches so that the consumer trusts a CBDC and adopts it.

Dr Alex Zarifis

Reference

Zarifis A. & Cheng X. (2023) ‘The six ways to build trust and reduce privacy concern in a Central Bank Digital Currency (CBDC)’. In Zarifis A., Ktoridou D., Efthymiou L. & Cheng X. (ed.) Business digital transformation: Selected cases from industry leaders, London: Palgrave Macmillan, pp.115-138. https://doi.org/10.1007/978-3-031-33665-2_6

New research!

Fintech is changing the services to consumers, and their relationship with the organizations that offer them. This change is neither top-down nor bottom-up, but is being driven by many different stakeholders in many different parts of the world, making it hard to predict its final form. This research identifies five business models of Fintech that are ideal for AI adoption, growth and building trust (Zarifis & Cheng, 2023).

The five models of Fintech are (a) an existing financial organization disaggregating and focusing on one part of the supply chain, (b) an existing financial organization utilizing AI in the current processes without changing the business model, (c) an existing financial organization, an incumbent, extending their model to utilize AI and access new customers and data, (d) a startup finance disruptor only getting involved in finance, and finally (e) a tech company disruptor adding finance to their portfolio of services.

Figure 1. The five Fintech business models that are optimised for AI

The five Fintech business models give an organization five proven routes to AI adoption and growth. Trust is not always built at the same point in the value chain, or by the same type of organization. The trust building should usually happen where the customers are attracted and on-boarded. This means that while a traditional financial organization must build trust in their financial services, a tech focused organization builds trust when the customers are attracted to other services.

This research also finds support that for all Fintech models the way trust is built, should be part of the business model. Trust is often not covered at the level of the business model and left to operation managers to handle, but for the complex ad-hoc relationships in Fintech ecosystems this should be resolved before Fintech companies start trying to interlink their processes.

Alex Zarifis

Reference

Zarifis A. & Cheng X. (2023) ‘The five emerging business models of Fintech for AI adoption, growth and building trust’. In Zarifis A., Ktoridou D., Efthymiou L. & Cheng X. (ed.) Business digital transformation: Selected cases from industry leaders, London: Palgrave Macmillan, pp.73-97. https://doi.org/10.1007/978-3-031-33665-2_4

New research!

Digital transformation is being driven by AI that is acting as a catalyst for business advancement. We looked at eight cases of digital transformation and found nine key themes. We looked at cases of digital transformation in finance, tourism, transport, entertainment and social innovation (Zarifis et al. 2023).

Figure 1. The tightly coiled ‘spring’ of digital transformation leader’s innovation, and the followers

The first of the nine main themes identified here is: (1) Digital transformation leaders will constantly innovate, while digital transformation laggards will have a stop-start approach. Digital transformation leaders will rapidly innovate going through regular iterative evolutions of their technologies, moving through repeated cycles of agile developments metaphorically forming a ‘spring’. New innovations and in-house skills are built up in this process of constant innovation. Continuing with the metaphor this tightly coiled ‘spring’ will store ‘energy’ propelling the organization forward. Digital transformation laggards will have a stop-start approach copying certain solutions of the leaders but not keeping up. Metaphorically a far less tightly coiled ‘spring’.

The other eight themes identified are: (2) There are no simple answers, or a single way to go forward, with digital transformation. (3) Each sector of the economy has its own opportunities, challenges and must find its own path forward. (4) Changes in one sector of the economy, such as the financial sector, will send a ripple of change across other sectors of the economy. (5) Change needs a shared vision, and digital transformation needs leaders to create the shared vision. (6) Digital transformation needs trust and cooperation on every level: Teams, organizations, governments and super-organizations like the EU. (7) People will still have a role: Staff, customers and other stakeholders are still important. (8) There is a dark side of digital transformation that may have not been fully revealed to us yet. (9) Digital transformation should happen hand in hand with sustainability and resilience.

Those are the nine main themes of digital transformation identified based on the cases we looked at. A leader of digital transformation must disassemble the technology, processes, business models and strategies, involved and then put together their own collage of what they want to achieve, and their own montage of the journey there.

Dr Alex Zarifis

Reference

Zarifis A., Efthymiou L. & Cheng X. (2023) ‘Sustainable digital transformation in finance, tourism, transport, entertainment and social innovation’. In Zarifis A., Ktoridou D., Efthymiou L. & Cheng X. (ed.) Business digital transformation: Selected cases from industry leaders, London: Palgrave Macmillan, pp.1-16. https://doi.org/10.1007/978-3-031-33665-2_1

Artificial intelligence (AI) and related technologies are creating new opportunities and challenges for organizations across the insurance value chain. Incumbents are adopting AI-driven automation at different speeds, and new entrants are attempting to use AI to gain an advantage over the incumbents. This research explored four case studies of insurers’ digital transformation. The findings suggest that a technology focused perspective on insurance business models is necessary and that the transformation is at a stage where we can identify the prevailing approaches. The findings identify the prevailing five insurance business models that utilize AI for growth: (1) focus on a smaller part of the value chain and disaggregate, (2) absorb AI into the existing model without changing it, (3) incumbent expanding beyond existing model, (4) dedicated insurance disruptor, and (5) tech company disruptor adding insurance services to their existing portfolio of services (Zarifis & Cheng 2022).

Figure 1. Updated model of five business models in insurance with disruptors split into two types

In addition to the five business models illustrated in Figure 1, this research identified two useful avenues for further exploration: Firstly, many insurers combined the two first business models. For some products, often the simpler ones, such as car insurance, they focused and disaggregated. For other parts of their organization, they did not change their model, but they absorbed AI into their existing model. Secondly, new entrants can be separated into two distinct subgroups: (4) disruptor focused on insurance and (5) disruptor focused on tech but adding insurance.

Reference

Zarifis A., & Cheng X. (2022). AI Is Transforming Insurance With Five Emerging Business Models. In Encyclopedia of Data Science and Machine Learning (pp. 2086–2100). IGI Global. https://www.igi-global.com/chapter/ai-is-transforming-insurance-with-five-emerging-business-models/317609 (open access)

Dr Alex Zarifis

Universities, like many other organizations, are going through a disruptive digital transformation. The alure of AI and automation, allowing smarter, more responsive and scalable universities is clear. What is less clear is what a university will look like five years into this process. We identified four business models that can give leaders a destination for the digital transformation journey (Zarifis and Efthymiou 2022):

(1) This first education business models that is optimized for AI is to focus and disaggregate: In addition to the classroom the successful delivery of education requires a supply chain. With the changes in this supply chain caused by AI an educator can chose to focus on one part of this supply chain. They can focus on the part of the supply chain where their skills are best suited and build an ecosystem for the rest.

Figure 1. Four education business models that are optimised for AI (adapted from (Zarifis, Holland, and Milne 2019))

(2) The second model that is optimized for AI is to keep the existing education model and add AI: Despite the transformational nature of AI, some universities use AI to make the existing model more effective without changing it fundamentally. This may involve more back-office AI applications and less student facing applications.

(3) The third education model that is optimized for AI is an educator expanding beyond the current model: In this model the educator takes advantage of new opportunities emerging from AI and digital transformation. The educator keeps their existing part of the education supply chain, but they also add new processes that take advantage of AI to reach more students and more data.

(4) The fourth model that is optimized for AI is the model of a disruptor entering education: As technology plays a more decisive role in many areas, including education, tech savvy companies can use their advanced systems and existing user base and add other new services. Education can be added as a new feature to a platform in a similar way that banking and insurance services have been added.

The four models presented give a strategic direction and make it easier for the leader of the digital transformation to communicate it. The leader of digital transformation will have to make many choices along this journey, so it is important that all the decisions are compatible with the chosen education business model.

References

Zarifis A. & Efthymiou L. (2022) ‘The four business models for AI adoption in education: Giving leaders a destination for the digital transformation journey’, IEEE Global Engineering Education Conference (EDUCON), pp.1866-1870. https://doi.org/10.1109/EDUCON52537.2022.9766687

Zarifis A., Holland C.P. & Milne A. (2019) ‘Evaluating the impact of AI on insurance: The four emerging AI and data driven business models’, Emerald Open Research, pp.1-17. https://emeraldopenresearch.com/articles/1-15/ (open access)