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Blockchain and the technology diffusion cycle

31 October 2016  |  9287 views  |  0

“Blockchain is everywhere, blockchain is the hot stuff, blockchain, blockchain, blockchain…”

It seems like everybody is talking about blockchain all the time.. but is it really the case?

 

When I conducted my research on blockchain for bond trading, I interviewed both blockchain experts and people whose jobs are likely to be impacted the most by the implementation of the technology in 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 barely heard about blockchain and its potential implications for their jobs. 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 that the rate of performance has a large 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 idea of the potential applications for the technology;

3. The more people are educated on the subject, the quicker the diffusion... (and cost savings achievement). 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 of technological improvement of a new technology describes its improvement performance against the amount of efforts and money invested in this technology. It is possible to compare performance with time; however, it often happens that the effort is not constant and therefore 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.

Unfortunately I couldn't upload the figure but the S-curve 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 such as the DLT first requires to be fundamentally 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. Globalization 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

 

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 efforts as it may require to change the overall architecture of processes in the case of a radical, architectural innovation such as the blockchain technology. Therefore, it can take years before the diffusion take 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 gathered the right resources and capabilities and more experts are available to share their knowledge.

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

 

  Technology cycles 

 

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

 

-       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 will be the outcomes of the change. There is a lack of clear idea of the potential applications for the technology, therefore, competition is quiet gentle and firms base their competitive advantage on differentiation.

 

-       The transitional phase: as firms and experts learn more about specificities of this new technology and about customers’ needs, a standardization emerges: The experimentations ultimately lead to a consensus about the specifications of the product or process architecture, denominated dominant design by Utterback.

 

-       The specific phase: once the dominant design has been established, companies can now focus their attention on production effectiveness and efficiency. Competition becomes intense.

 

Needless to say that the blockchain technology is in its fluid phase - as a standardization has not been reached - and that a highway of exciting developments is ahead of us!

 

Estelle Roiena

 

 

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

 

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