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The propensity and speed of technology licensing.

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There has been much research interest in the speed of innovation, although few consistent findings have emerged. We unpack the innovation process, focus on the commercialization stage and examine the determinants and impact of the speed of technology licensing. More specifically, using data for the biopharmaceutical industry, we examine: Which licensor and patent characteristics determine the speed of licensing? How does the speed of licensing impact the post agreement payments to licensors? We address these questions using a theoretical premise based on the effects of licensor prominence (size and experience), licensor knowledge structuration (technological depth, technological breadth and experience), and patent appeal (forward citations, scope and complexity). We predict and find that strength in these variables helps licensors discover and be discovered by licensees, resulting in a wealth of agreement options and a strong licensing-out position for licensors. This not only increases the size, complexity and duration of the licensing out-task, but allows licensors to take their time to review, negotiate and select the most attractive offer. This finding is counter to arguments for a fast-paced innovation strategy, as it suggests that the commercialization stage of the innovation process rewards a ‘less haste, more value’ approach.

To understand why some patents get licensed and others do not we estimate a portfolio of firm- and patent-level determinants for why a particular licensor’s patent was licensed over all technologically similar patents held by other licensors. Using data for licensed biopharmaceutical patents, we build a set of alternate patents that could have been licensed-in using topic modeling techniques. This provides a more sophisticated way of controlling for patent characteristics and analysing the attractiveness of a licensor and the characteristics of the patent itself. We find that patents owned by licensors with technological prestige, experience at licensing, and combined technological depth and breadth have a greater chance at being chosen by licensees. This suggests that a licensor’s standing and organizational learning rather than the quality of its patent alone influence the success of outward licensing.

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The propensity and speed of technology licensing.

  1. 1. The propensity and speed of technology licensing Seminar at Ceará State University September 30th 2017 Ian P. McCarthy
  2. 2. PAPER 1:
  3. 3. “Licensing speed: Its determinants and payoffs” with Karen Ruckman, under review at the Journal of Engineering and Technology Management PAPER 2:
  4. 4. THE AUTHORS Karen Ruckman Beedie School of Business, Simon Fraser University Ian P. McCarthy Beedie School of Business, Simon Fraser University
  5. 5. 5 MY BACKGROUND: BUSINESS Pentagon Radiators (Alcan) Philips Electronics Footprint Tools
  6. 6. 6 MY BACKGROUND - ACADEMIC
  7. 7. 7 MY BACKGROUND - OTHER STUFF
  8. 8. 8 MY BACKGROUND - OTHER STUFF
  9. 9. 9 MY BACKGROUND - OTHER STUFF
  10. 10. WHAT IS LICENSING? • Licensing is an activity where the owner of IP (the licensor), allows another party (the licensee) the rights to use, adapt and commercialize the IP. • A patented technology has IP protection for a finite period of time. • Parties can earn monopoly profits until this patent protection period expires.
  11. 11. WHAT IS LICENSING? As of 2017, the university had received more than $320 million from royalties (20%) The 9 inventors (6 scientists, two trainers and a lab technician) have received $1.3 billion.
  12. 12. LICENSING INCOME- US UNIVERSITIES: LIFE SCIENCES 2015 B Huggett - Nature Biotechnology, 2017 - Nature Research
  13. 13. THE MARKET FOR PATENTED IDEAS: THE ECONOMIST • In 2005: – U.S. patent licensing revenue is $45 billion annually – Worldwide, the figure $100 billion and growing fast. – IBM earning over $1 billion annually from its intellectual- property portfolio in 2005 – HP's revenue from licensing has quadrupled in less than three years • The global licensing market was estimated to be worth $200 billion in 2011 (Alvarez and Lopez, 2015) • There continues to be increasing supply and demand for technology
  14. 14. THE RISE OF LICENSING Adapted from Chesborough 2004
  15. 15. THE RISE OF LICENSING Adapted from Chesborough 2004
  16. 16. RESEARCH ON LICENSING (AND PATENTING) • Why firms license technologies (e.g., Rockett, 1990) • What factors affect the propensity to engage in licensing (e.g., Fosfuri, 2006; Nagaoka & Kwon, 2006) • How different approaches to licensing impact licensing outcomes (e.g., Gallini & Wright, 1990). • The duration and timing of the patent application and approval process (Popp, Juhl & Johnson, 2003; Gans, Hsu & Stern, 2008)
  17. 17. IN OUR RESEARCH WE ….. • Surprisingly no research attention has been paid to: – Why some patents get licensed and others do not? (paper 1) – The determinants and payoffs of licensing speed (paper 2)
  18. 18. PAPER 2: WHY SOME PATENTS GET LICENSED AND OTHERS DO NOT • Despite the considerable market for patented technologies, many patents remain unlicensed. • We seek to understand why some patents get licensed, while others do not.
  19. 19. WE KNOW THAT PATENT CHARACTERISTICS ARE IMPORTANT. The complexity, breadth and perceived importance of a patent make it more attractive to licensees and increase its chances of being licensed.
  20. 20. BUT WHAT ABOUT THE OWNER OF THE PATENT? We address this gap. It’s important that companies know which characteristics to capitalise on so as to be better at licensing out.
  21. 21. Patent Characteristics Patent complexity (+) Patent age (+) Patent scope (+) Patent citations (+) Licensor Characteristics Licensor size (+/-) Licensor experience (+) Licensor research intensity (+) Licensor research age (+) Past relationships between a licensor and licensee (+) The likelihood a patent will be licensed. WHAT WE LOOKED AT
  22. 22. OUR APPROACH We identified a set of biopharmaceutical patents that were licensed. For each of these licensed patents we identified a set of 19 alternate patents that could have been licensed but were not. To do this, we used topic modeling to analyse each licensed patent and then identify remarkably similar patents that were not licensed
  23. 23. DATA • Biopharmaceutical licensing agreements from the RECAP database by Deloitte. • An initial search = 297 non-university licensing agreements (1993 and 2008) • After data matching across three databases, the default sample was reduced to 93 agreements. • RECAP provided information about the past relationship history of each partner. • Accounting data (revenues and R&D expenditures) from Compustat and filings with the US Securities and Exchange Commission. • Data for patent-based variables came from the NBER US patent citation data file (Hall et al., 2001) and its 2006 update.
  24. 24. SOME OF THE LICENSOR VARIABLES • Technological prestige = Licensor’s average number of non- self forward citations on patent stock within 5 years after patent granted • Licensor depth = Number of patents granted to licensor in same IPC as licensed patent during 5 years before agreement, logged • Licensor breadth = Number of different IPC classes in licensor patents that were granted within 5 years before agreement • Licensor experience = Number of license agreements during 5 years before agreement involving the licensor
  25. 25. THE PATENT VARIABLES • Patent complexity = Number of technological claims made by the patent • Patent age = Year of agreement minus year patent was granted • Patent scope = Number of IPC categories listed on the patent • Patent citations = Number of backwards citations listed on the patent
  26. 26. LICENSOR CHARACTERISTICS THAT IMPACT THE LIKELIHOOD A PATENT WILL BE LICENSED HOW THE LICENSOR CHARACTERISTIC IMPACTS THE LIKELIHOOD A PATENT WILL BE LICENSED Licensor prestige: The extent to which a licensor is viewed favorably by licensees. Prestigious licensors have a greater chance of licensing-out as licensees are more likely to know about and be attracted to them due to the increases in licensor standing, visibility, credibility, and the benefits by association Licensor experience at licensing: The extent to which a licensor is practiced at licensing. Experienced licensors learn from prior licensing activity which makes them more proficient at licensing-out and in turn increasing the likelihood their patents will be licensed. FINDINGS
  27. 27. LICENSOR CHARACTERISTICS THAT IMPACT THE LIKELIHOOD A PATENT WILL BE LICENSED HOW THE LICENSOR CHARACTERISTIC IMPACTS THE LIKELIHOOD A PATENT WILL BE LICENSED Licensor technological depth: The extent to which a licensor specializes in a particular technological area. Individually technological depth and breadth have no impact on licensing likelihood. However, licensors strong in both technological depth and breadth have greater learning and innovation capabilities and these also combine to increase a licensor’s ability to license- out. Licensor technological breadth: The technological scope of a licensor’s past patenting efforts. FINDINGS
  28. 28. THE TAKEAWAYS 1. The halo effect. Licensees are not just making licensing decisions based on quality of the patented technology, but also on the reputation of and technological fit with the licensor. Prestige makes a licensor more visible to licensees. It is also makes licensors more legitimate and attractive to licensees.
  29. 29. THE TAKEAWAYS 2. Experience matters. Experience allows licensors to accumulate the specific knowledge, people, and routines required to find and do deals with licensees Experience increases a licensor's ability to be known to and selected by potential licensees.
  30. 30. THE TAKEAWAYS 3. Organizational learning enhances licensing likelihood. A licensor’s technological depth and breadth balance the knowledge processes for technology transfer transactions. Depth and breadth combined, signal to prospective licensees that a licensor possesses the efficient ability to convey and transmit technical ideas.
  31. 31. “Licensing speed: Its determinants and payoffs” with Karen Ruckman, under review at the Journal of Engineering and Technology Management PAPER 2:
  32. 32. INNOVATION SPEED • Fast innovation = – greater revenue returns (Ringel, Taylor and Zablit, 2015) – more new product development (Acur, Kandemir, Weerd‐Nederhof and Song, 2010) – growth in sales and initial public offerings (Eisenhardt, 1989). • Fast innovation = – less impactful and profitable outcomes (Steen and Dhondt, 2010) – mistakes and inefficiencies (Crawford, 1992). • To help resolve this conflict, we explore the determinants and impact of licensing speed.
  33. 33. INNOVATION SPEED • Research on innovation speed has focused on the speed for all three stages of the innovation process: – the conception of an idea – the development that idea, – and the eventual commercialization of that idea
  34. 34. WHY THE SPEED OF LICENSING? • The market for licensing patents is big and growing. • Patented technologies are protected for a finite time period: – licensing slowly reduces the value of the technology to both licensor and licensee (Hegde, 2014; Markman et al. 2005). – fast licensing = hurried sub-optimal deal and lower price and reduced returns (Allain, Henry, and Kyle 2011; Mauleon, Vannetelbosch and Vergari, 2013).
  35. 35. IN OUR RESEARCH WE ….. • Surprisingly no research attention has been paid to licensing duration or speed. • In response we examine: – Which licensor and patent characteristics determine the speed of licensing? – How does the speed of licensing impact the post agreement payments to licensors? Licensing speed = the elapsed time from when a patent for a technology is filed to when the licensing deal for the technology is announced. Patent filed Patent approved Patent licensed
  36. 36. LICENSOR PROMINENCE • Licensor prominence = a licensor’s standing or status relative to other licensors, as perceived by potential licensees. • It is a signal of the latent value of a licensor’s patent technology offerings, which impacts licensing speed through different kinds of visibility, appeal and power effects. – Hypothesis 1a. Licensor age increases the time it takes to reach a licensing agreement. – Hypothesis 1b. Licensor size increases the time it takes to reach a licensing agreement – Hypothesis 1c. Licensor prestige increases the time it takes to reach a licensing agreement.
  37. 37. LICENSOR KNOWLEDGE STRUCTURATION • Knowledge structuration is the extent to which a licensor’s knowledge portfolio is based on multiple or a few technology domains (George at al 2008). • Knowledge structuration indicates a licensor’s breadth and depth of technological expertise, which is linked to learning and exploration capabilities (e.g. Ahuja and Lampert, 2001; Rosenkopf and Nerkar, 2001). – Hypothesis 2a. Licensor technological depth increases the time it takes to reach a licensing agreement. – Hypothesis 2b. Licensor technological breadth increases the time it takes to reach a licensing agreement.
  38. 38. PATENT DESIRABILITY • Forward citations = knowledge impact of a patent (Duguet and MacGarvie 2005), an indicator of the value and desirability of the patent (Ceccagnoli et al. 2010; Harhoff, Scherer, and Vopel 2003). • Patent scope = breadth of technological areas cited by the patent’s technology (Ruckman and McCarthy, 2017; Sakakibara, 2010). • Claims = building blocks of a patented invention, and they are indicative of the purview of the invention (Hall, Jaffe, and Trajtenberg 2001). – Hypothesis 3a. Patent forward citations increase the time it takes to reach a licensing agreement. – Hypothesis 3b. Patent scope increases the time it takes to reach a licensing agreement. – Hypothesis 3c. Patent complexity increases the time it takes to reach a licensing agreement.
  39. 39. PAYOFFS TO LICENSOR • The payoff to a licensor is typically an up-front lump sum and/or royalty rate paid as a percentage of the final sales. • Previous research looks at which type of payoff will predominate (see: Kamien and Tauman, 1986). • We focus on how licensing speed affects the payoffs. – Hypothesis 4. A slower licensing speed increases the payoffs to the licensor.
  40. 40. DATA • Same industry and data sources as for Paper 1 • Different variables for the hypotheses
  41. 41. MODEL • Dependent variable = (Time 1 – Time 0) – Time 0: date of patent application – Time 1: date of licensing agreement • Survival analysis • Log-logistic model
  42. 42. RESULTS • We find that strength in licensor prominence (H1), licensor knowledge structuration (H2) and patent desirability (H3), all work to slow down the time it takes to licensing-out a patented technology. • This suggests that strength in these factors endow licensors with an abundance of licensing opportunities that: – increases the size, complexity and duration of the licensing out-task, and – allows licensors to take their time to review, negotiate and select the most attractive offer
  43. 43. RESULTS • The results show that slower licensing speeds are associated with higher royalty rates (H4). • There also evidence that licensor size, research intensity, and prestige and patents with a high number of forward citations and scope, reduce royalty rates. • Licensee size and patent complexity act to increase lumpsums.
  44. 44. TAKEAWAYS • While innovation speed is important, when it comes to licensing, it is beneficial for licensors to slowly and carefully license-out patented technologies. • There are different stages and aspects to the innovation process, and the benefits of being fast or slow can vary for these. • Licensing is not just a functional transaction based solely on the quality and suitability of a patent. Perceptions about the licensor matter. • A licensor’s knowledge base and related organizational learning capabilities (knowledge structuration) influence the speed of licensing.
  45. 45. TIMELINE PAPER 1 • May 2009 = Idea for paper • Dec 2012 = First submission to the Strategic Management Journal • Aug 2013 = Second submission to the Strategic Management Journal • May 2014 = First submission to Research Policy • April 2015 = Second submission to Research Policy • May 2015 = First submission to Industry and Corporate Change • Jan 2016 = Second submission to Industry and Corporate Change • June 2016 = Third submission to Industry and Corporate Change • July 2016 = Accepted by Industry and Corporate Change !!!! • July 2017 = Published in Industry and Corporate Change !!!!
  46. 46. Dr. Ian McCarthy @toffeemen68 Professor, Technology and Operations Management Beedie School of Business Simon Fraser University Ian McCarthy It Depends Blog

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