Creating Value From Intangible Assets in the Context of University Spinouts

A review of two papers that seek to define what qualifies as an intangible asset, and provide actionable insights into value extraction for university spinouts

Erratum: The source document to my previous article contained several factual errors with respect to the University of Alberta Innovation fund. These were corrected on November 6.

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If you are following the debate around the value of intellectual property, you have probably come across the idea that intangible assets now make up 90% of the value of the S&P 500. While that number is not very useful in practice, the conclusion derived from it—that a more thoughtful approach to intangible asset management is key to overcoming Canada’s productivity woes—is sound. Coming to that conclusion in a way that provides actionable insight requires a more detailed, bottom-up approach to defining intangible assets.

I came across two papers written by Andrew Park, Elicia Maine, and their team, that attempts to do exactly this in the context of university spinouts, where almost the entirety of the value is intangible. Enroute, the authors provide a wealth of actionable insight into how to think about intangible assets, including several kinds that are not often part of the discourse; the roles best played by academics in the spinout process; methods by which university-sourced IP can be turned into economic and societal benefit; and suggestions for effective metrics to assess long-term impact arising from commercialization of early-stage IP portfolios. You can find these two papers below. Both are open access. Throughout this article, I refer to these papers as Park et al 2024a and Park et al 2024b, as appropriate.

• Park, A., Maine, E., Fini, R., Rasmussen, E., Di Minin, A., Dooley, L., Mortara, L., Lubik, S., & Zhou, Y. (2024). Science-based innovation via university spin-offs: the influence of intangible assets. R&D Management, 54(1), 178–198. https://doi.org/10.1111/radm.12646

• Park, A., Goudarzi, A., Yaghmaie, P., Jon Thomas, V., & Maine, E. (2024). The role of pre-formation intangible assets in the endowment of science-based university spin-offs. International Journal of Technology Management, 4(96). https://doi.org/10.1504/IJTM.2024.140712

Defining Intangible Assets

The 90% figure that is so often thrown around in policy debates is estimated by summing the value of the tangible assets of all the companies in the S&P 500 list and subtracting it from the sum of their market caps. That number therefore includes investor hype and fear of missing out among the intangible assets, on an equal footing with patents and trade secrets. It also allows for manipulation via creative accounting on the tangible assets (with depreciated tangible assets appearing as intangible ones), and only considers large, publicly traded companies in its assessment, making it a poor measure of intangible value generally, and for startups in particular. Moreover, the definition of intangible assets as “the sum of everything that isn’t a tangible asset” is not useful in practice, since it provides us no insight into the components of intangible value and therefore no actionable means by which to optimize it.

The two papers take a bottom-up approach toward identifying specific sources of intangible value, first providing a useful definition from which to work. That definition alone deserves some consideration as the basis for ongoing discussions around the value of intangible assets.

“An intangible asset is a resource that is nonphysical, non-­financial, has long life, and has potential to provide future benefits to the owner.” (Park et al 2024a)

This is already an obvious improvement in that the requirement for long life excludes market noise from the calculation. These papers also highlight that there is a complex relationship between intangible assets and firm performance. Unlike the word “asset” implies by itself, mere ownership is not enough for value generation. There are additional mediating variables, relating to the ability to operationalize the asset, that turns potential into value.

The Role of Academics in Entrepreneurship

Both papers are focused on utility and value extraction from intangible assets in the context of university spinouts, and touches on the importance of a number of asset classes that are not often discussed in more established industry contexts. While all the usual suspects are present, including patents and trade secrets, in the world of academic spinouts, there are additional key value sources to consider. Among these are the entrepreneurial capabilities of the participants, the strength of their networks, their scientific reputation and prestige, their publication and patent record, their access to experienced mentors, and even their creativity. While these assets are undeniably of potential value, the ability to operationalize them in a business context gates value creation.

There is an interesting ongoing debate among early stage innovation supporters about the role of professors and academics in spinout activity which I have discussed in some detail previously. On the one hand, professors are rarely interested in giving up a tenured position for a risky startup company; academic thinking is generally poorly suited to the mindset of building a startup; “part-time founder” is an oxymoron; and many academic spinouts fail to launch for reasons related more to the founding team than any problem with the underlying technology. This is the basis for many early-stage VC funds preferring that professors simply not be involved in the startup except in an advisory capacity. On the other hand, the catalogue of intangible assets identified in these papers, and the discussion around methods to operationalize their value, makes a strong case for thoughtful inclusion of academic participants in the spinout process:

“While extant research has often suggested that academic scientists may lack the skills needed for science commercialisation (Gurdon and Samsom, 2010), other scholars observe that academic scientists have rich and deep collaboration networks which can be measured through proxies such as patents and papers (Murray, 2004; Schiffauerova and Beaudry, 2009, 2012). Our evidence supports the latter view and that these collaboration networks can be considered as pre-formation intangible assets which can be leveraged to endow university spin-offs.” (Park et al 2024a)

Academics focus pre-formation intangible assets, and are important for market matching given their ecosystem knowledge. In other words, the involvement of an academic founder can be the mediating variable that operationalizes and extracts value from intangible assets that might otherwise be wasted, and different types of academic founders bring to the table different capabilities with respect to that operationalization. This case is made in Park et al 2024b through three case studies of successful Canadian biotech companies that began as academic spinouts and successfully navigated the valley of death, in no small part thanks to their academic origins.

This should not be overly surprising. As acknowledged explicitly by the authors, much of the value that underlies scientific knowledge and the basis for academic spinouts is in the minds of the inventors, even in cases where it has been made public through patents or papers.

“[…] publishing activity by inventors, founders and later the firms themselves is particularly relevant in the context of biotechnology spin-offs as the techniques and tools they seek to commercialise often have a significant element of newness and thus may be incompletely codified and have higher levels of tacit knowledge particularly in the early stages of technology development. […] Published papers may thus be viewed as the tip of the iceberg with much relevant and related tacit knowledge embedded in inventors’ minds and not easily accessible.” (Park et al 2024b)

Taken together, the papers make the case that, managed well, the inclusion of academic participants in the spinout process is of great value, particularly inventors whose minds hold much of the knowledge needed to realize the potential of a technology portfolio and professors who can use their deep domain knowledge and international networks in support of the commercialization efforts. At the same time, the authors acknowledge the need to address the mismatch that exists between the skills learned as a scientist and those needed to build a company. This mismatch is the basis for a number of training programs spearheaded by Elicia Maine herself, including the Invention to Innovation (i2I) National Network that seeks to provide entrepreneurial training to Canadian academics on being effective mediators of scientific impact beyond the lab, as well as Commercialization Postdocs, aimed at providing similar training to the up and coming scientists that are their mentees.

Measuring Impact

A common theme in many of the essays on this site is the importance of carefully constructing metrics by which to assess the impact of various approaches to deep tech commercialization, and warnings that Goodhart’s Law applies and is directly responsible for many cases of innovation policy failure. The authors have much to say on the topic:

“Based on this systematic review, we suggest that the field requires a much more sophisticated understanding of what constitutes science innovation success through [University Spin Outs] USOs. A simple tally of the number of USOs created does little to advance our knowledge of how intangible assets can be leveraged to enhance economic and societal outcomes. […] we encourage the incorporation of less easily quantified variables such as societal impacts, as suggested by Fini et al. (2018). Such nuanced variables, while they require more effort to operationalize, drive management scholarship forward more meaningfully.” (Park et al 2024a)

Their approach to value measurement is a good example of the kind of long-term thinking that is required to properly assess the economic and societal impact of deep tech commercialization generally.

Thus, our study which examines the 10-year survival status, and the 10th year revenue of the selected biotechnology spin-off sets a high bar for the measurement of firm performance particularly for spin-offs emerging from a university setting. (Park et al 2024b)

It is not practically possible to assess impact of deep tech without acknowledging the timelines involved. Quantum technologies have been in development for 20 years and still have ground to cover before impact is felt. Biotech timelines are measured in decades as well. These two papers make clear that simplistic metrics that fail to take these timelines into account are not useful in assessing program impact, but that the exercise of developing thoughtful means to measure impact on longer timescales is a requirement for effective program design.

Conclusions

For policy makers and administrators designing support programs for technology commercialization projects, these papers are a clear blueprint for how to think about intangible assets beyond patents and trade secrets, as well as how to more effectively measure the impact of related programs. Avoid using shortcuts for impact measurement. Doing it right is not easy, but doing it wrong is worse than pointless; Goodhart’s Law (and real-world Canadian innovation performance) teach us that it actively leads to the wrong outcomes. Understanding this, and building programming that acknowledges that mere ownership is not enough for value extraction and that long-term projects require long-term metrics is the key to improving the myriad of innovation support programs that exist in Canada.

For academics conducting research with potential for commercialization and aspiring entrepreneurs, these papers provide a perspective on the value that you bring to the technology commercialization process that you may not have considered before. If seeing the impact of your research beyond of the lab interests you, I strongly encourage you to get involved in the Invention to Innovation (i2I) National Network. There are numerous ways to be effective mediators of impact beyond the lab that do not necessarily involve being a founder yourself, and the training is intended to provide guidance on how to best operationalize the intangible assets that you bring to the table, regardless of your choice of role. For postdocs considering a move to industry, commercialization postdocs developed by the same team provide a similar base of training, both for those considering the entrepreneurial path as well as those interested in going directly to established industry.

This commentary is an incomplete summary of these works, and I encourage anyone involved in commercializing deep tech and university IP to make a study of these papers. There is a wealth of actionable knowledge in them.

Properly defining intangible assets and assessing their long-term contribution to value creation in commercial enterprise is a complex challenge. Thank you to the authors for doing the hard work required to add a data-backed perspective to these critical issues.