estelleBy Estelle Roiena, Associate (Financial Services & Blockchain)

Let’s talk about Blockchain again!

“Blockchain is everywhere, blockchain is the hot stuff, blockchain, blockchain, blockchain…” Everybody is talking about blockchain all the time.. but is it really the case?

When I conducted my MBA research on blockchain for bond trading, I tried to interview both blockchain experts and people whose jobs are likely to be impacted the most by the implementation of this technology within the capital markets – I am talking mainly about sales and traders (having worked as a broker for several years, they unsurprisingly form my main professional network!). The idea was to get a holistic non-biased vision and compare divergent opinions. You can see me coming: most of them had barely heard about blockchain and the potential implications it will have for their job. For those who know me, you will think I am exaggerating as usual! But, if you did your own investigation you would be surprised!

In reality if you think about it, it should be predictable. I have dug into the concept of technology diffusion cycles and this is what I found:

  1. The blockchain technology and its applications are still at an early stage of the S-curves for technological improvement and technology diffusion, meaning the rate of performance has a lot of room for improvement;
  2. According to the Abernathy-Utterback Model, the blockchain technology is still in its fluid phase, meaning that there is a lack of clear ideas of the potential applications for the technology.
  3. The more people are educated on the subject, the quicker the diffusion… (and cost-saving achievements). So, let’s talk about it some more…!

Look at the life scheme of a technology: A new technology first emerges, then evolves in terms of performance improvement’s rate; it is adopted by one or several firms and again progresses in terms of rate of diffusion. Finally, it becomes obsolete and is being replaced by other technologies. This path is known by technology trajectory.

S-Curves in Technological Improvement

The S-Curve in technological improvement of a new technology describes its improvement performance against the amount of effort and money invested in the technology. It’s possible to compare performance with time; however, it often happens that the effort is not constant, so the true relationship between performance and effort is biased.

The S-Curve is so-called because it usually portrays a curve in a S shape:

Figure 1: S-Curve in Technological improvement


Source: Schilling, 2013

The graph shows that just after the emergence of the technology, the performance is low while the effort invested in time and money is growing very slowly. Indeed, the adoption of a disruptive new technology like blockchain first needs to be fully understood. Only firms that embrace a full comprehension of the technology would want to adopt it.

Education takes time. Moreover, firms would want to assess the technology before investing time, money, capabilities and skills into it. In these early stages, very few assessments have been completed. In this respect, the blockchain technology is still at an early stage. I would say that it is particularly true for buy-side players, who are very conservative and slow to catch up with innovation.

Once the technology has been understood and assessed, then it starts to be accepted. This is the second stage where firms begin to invest efforts and R&D, leading ultimately to better performance. Several banks are in this stage, having started to massively invest in R&D, both individually and in consortium. Globalisation and competition hasten the process: firms would want to be first adopters of a new revolutionary technology. At a certain point, the technology will reach its limit and the performance to effort ratio will decrease, flattening the S-Curve.

Alternatively, a discontinuous technology can emerge, rending the incumbent technology obsolete. Schilling defines a discontinuous technology as ‘a technology that fulfils a similar market need by building on an entirely new knowledge base.’ Because of the location of the incumbent and the discontinuous technologies on the s-curve, it is not unusual that, at the first stages, the effort invested in the new technology do not generate as much returns as the old technology. Therefore, firms are often unwilling to switch. However, afterwards, two scenarios are plausible:

  • The new technology has a steeper s-curve, meaning that, as the technology evolves, less effort will be required for the same rate of performance
  • The new technology will produce a better rate of performance for the same amount of effort

Figure 2: Technology S-Curve – Introduction of a new technology


Source: Schilling, 2013

In those cases, high legacy firms are confronted to a strategic decision. The first choice is to continue investing in their current technology and face the possibility that it becomes rapidly obsolete. In parallel, they might lose market share, as new firms entering the market or competition adopt the new technology and therefore, are able to better serve ever changing and increasingly demanding customers’ needs.

The second choice they have is to switch to the new technology, meaning that they would need to invest a lot of efforts and money in the transition process, without knowing the future performance to effort rate.

S-Curve in technology diffusion

It is interesting to try and understand the rate of diffusion of a new technology against time. As we mentioned above, the educational part is the most important: if firms do not properly understand the benefits of adopting and implementing a disruptive technology, the latter can appear as being a burden.

The second factor influencing the rate of adoption is the technical requirement the adoption may require. It is a long and costly process both in terms of money and effort since it may require the need to change the overall architecture of processes in the case of a radical, architectural innovation, such as blockchain technology. So, it can take years before the diffusion takes off. However, when this stage is reached, the diffusion can spread quickly: the technology and the requirements for adoption are better understood, firms are better prepared and have gathered the right resources and capabilities with more experts available to share their knowledge.

At some point the market becomes saturated and the s-curve flattens.

Technology cycles

Finally, a quick word about the Abernathy-Utterback Model defining and describing the three main phases of a technology life:

  1. The fluid phase: a phase of uncertainties and experimentations. No firms can know for sure which market niche the technology will be suited for and what the outcomes of the change will be. There is a lack of any clear ideas and best practices of the potential applications for the technology, so competition is quite gentle and firms base their competitive advantage on differentiation.
  2. The transitional phase: as firms and experts learn more about specificities of this new technology and about customers’ needs, a standardisation emerges. The experimentations ultimately lead to a consensus about the specifications of the product or process architecture, denominated dominant design by Utterback.
  3. The specific phase: once the dominant design has been established, companies can now focus their attention on production effectiveness and efficiency. Competition then becomes intense.

Needless to say that blockchain technology is currently in its fluid phase as a standardisation has not yet been reached – and that means there are a lot exciting developments ahead of us!

Source: the body of knowledge entirely derives from Schilling, M. (2013). Strategic management of technological innovation. 4th ed. New York, US: McGraw-Hill.