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Friday, 1 July 2016

Openness, Innovation and Patents

Image via IMDB
The September issue of Research Policy, a multi-disciplinary journal focusing on economic, policy, management and related issues posted by innovation, is out. Research Policy often has IP-related papers, and this issue has a special section devoted to patents edited by Professor Suma Athreye.  I’ve selected three papers for a closer look.

"The paradox of openness revisited: Collaborative innovation and patenting by UK innovators" by Ashish Arora, Suma Athreye and Can Huang (open access.)  Examining the "paradox of openness," in which "opening up to outside sources of knowledge to innovate may weaken the firm's power to capture rents from that knowledge." (Rent, in very simplified economic terms, is excess returns. E.g. the legal profession earns rent by requiring lawyers meet membership criteria and therefore restricting competition.) The authors use comprehensive UK survey data to study how firms set out their strategies. One interesting finding was that firms with incremental innovations (i.e. not breakthroughs), "may be less willing to patent because it makes them a less attractive open partner and perhaps also less able to derive value from collaboration." Firms that are too bombastic about protecting their IP are off-putting (I've heard similar things in the creative industries and non-disclosure agreements.) The authors describe two phenomena in the interaction between patenting and open innovation/collaboration. In "spillover prevention," firms patent to prevent unintended knowledge spillovers in collaborations, in "organizational openness," firms don't patent as it makes them a less attractive partner. Firms are described as leaders and followers in innovation races (followers are sometimes called imitators.)  Leaders are more likely to patent due to "spillover prevention" and followers, due to "organizational openness."

Types and reasons for patent non-use (Walsh et al)
Innovation collaboration and appropriability by knowledge-intensive business services firms” by Marcela Miozzoa, Panos Desyllas, Hsing-fen Leec, Ian Miles (also open access.) The researchers look at the “paradox of formal appropriability mechanisms.” Appropriability is the ability to reap, or appropriate, the returns from investment; in patenting terms it’s the ability of a patent to translate the success of the innovation into financial benefits for the innovator.  Examining a survey of UK knowledge-intensive business services (KIBS) firms, which consistently do not prioritise formal appropriability mechanisms such as patents, the authors find KIBS do use these mechanisms when collaborating.  It’s a nuance on open innovation, and a possible explanation for this paradox is that, “leading innovators may be more likely to stress formal appropriability mechanisms in order to prevent knowledge leakages, but may not value strongly innovation collaboration.”  The answer, is as often the case in economics, is "it depends," and the paper shines an empirical light on collaboration.

Win, lose or draw? The fate of patented inventions” by John P. Walsh, You-Na Leea, and Taehyun Jung focuses on non-use of patents (pay walled.) The study looks a ‘triadic’ patents (patents which have been applied for at the EPO, JPO and granted by the USPTO.)  The key finding is that, “55% of triadic patents are commercialized. We also find that 17% of all triadic patents are not commercialized but are at least partially for preemption, though only 3% of all triadic patents are purely preemptive patents.” Preemption is patenting for strategic purposes, rather than commercial. (You could argue the two are one and the same, but the paper focuses on preemptive non-use, as in strategic patenting with no intention to use the patent.) The paper goes into much more detail, but the punchline is that nearly half of triadic patents are not used, but ‘strategic’ patenting may be less prevalent than popular discourse would have you believe.

Over to you, IPKat readers, as I know you have quite strong feelings about patent, and economic analysis of!

3 comments:

Ashley Roughton said...

Nicola

I think that the problem which I have with the first paper is that it is (presumably) Gumbel distribution based, in which case you tend not to be able to produce publishable results until the model in question has been tested by many for some time. The authors in the extreme distribution paper say "If we assume that the payoff from each choice has an additive error term that is [identically and independently distributed] and distributed with a Generalized Extreme Value (GEV) distribution, the probability of the different combination of choices can be written as follows." What this means in English (and there is no reason why the authors could not have used English) is that:-

1. each choice a firm makes about information sharing and protection has profit implications which are different, depending upon the choice.
2. It would be nice if we could predict this mathematically (preferably using complicated looking mathematics).
3. That there is an "average" profit which you will always make whatever choice you make.
4. There is also a plus or minus which is choice dependent.
5. It is that plus or minus which we model.
6. me putting it slightly more poetically, if I say so myself) We do so using the same mathematics used to predict "big" floods or very damaging earthquakes every 100 (or so) years, though we are not sure why.
6. We do not relate our model to any real world situation.
7. We have not taken account of bounded rationality (the ability to know, really, what the present holds or what the future might hold or, indeed, what the past has held).

Put like that then the extreme distribution paper is clearly a first step (and not, perhaps, in the right direction).

Ashley

The Pigs said...

Distillation:

Academics seeking publication.

Nothing to see here, move along now.

Nicola Searle said...

Ashley - many thanks for taking a closer look.

I'm not entirely sure how to respond as much of your critique could be levied at all economic modelling as a whole. Yes, there are flaws and challenges, but these are acknowledged by the discipline. (A bit like Rumsfield's known unknowns.) The choice is between doing imperfect work or none at all.

I think the way forward it slowly but surely to test methods, develop new ones and keep at it. Or should we just scrap the whole endeavour?

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