When Theodore Maiman invented the laser technology in May 1960, neither he himself, nor his supervisors and colleagues with Hughes Research Laboratories would recognize the huge potential of this invention. In fact, Maiman left his employer soon after his invention because they refused to further support his research: “The laser is a solution to a problem that does not exist“, he was told by one of his supervisors. And indeed, Maiman struggled with finding commercially attractive applications to the laser technology. Only after he had published about his invention in “Nature“, the technology started to diffuse. Other scientists, who stumbled upon Maiman’s publication, realized that the laser technology was a solution to specific problems in their very domains and thus started to re-build the laser apparatus.
Maiman`s story is not only a nice little anecdote. The case of Maiman and the laser technology is representative of thousands of other cases and holds at least two major insights with regard to knowledge and technology transfer (KTT):
1.) Inventors are often not the ones to identify (the most interesting) application fields to their technologies.
2.) Broadcasting a technology (i.e., presenting it to a wider, heterogeneous audience) might lead to viable application ideas
Many technology-driven organizations account for these insights in their KTT efforts. Today, it is state-of-the-art to have an own, dedicated organizational unit that plans, organizes, and conducts KTT activities. It is not the internal inventors (i.e., the R&D staff) anymore who are solely responsible for commercializing their inventions and turning them into real innovations with economic impact.
Another best practice in KTT is to broadcast R&D results, e.g., via publications, filing a patent and presenting it to the public on platforms and databases like yet2.com, or by publishing descriptions of newly developed technologies on their own websites. In addition to these online activities, technology-driven organizations might also try to generate interest in their technologies and attract potential users by showcasing them in conferences, industry exhibitions, etc. Some organizations even hire technology scouts to identify new market opportunities to their inventions. The idea is always the same: Get out the word about your technology and have potential users realize how good a solution it might be to specific problems in their very domains.
All these activities are pretty much in line with the insights from the Maiman case. And still, KTT often does not work very well! Many organizations fail in mastering what I call the fuzzy front-end of KTT, i.e., the identification and evaluation of completely new and – more importantly – commerically attractive application fields to their new or existing technologies. Interestingly, the people in charge of KTT usually excel at legal tasks, e.g. filing a patent or setting up a licensing agreement with potential users. The might also help with contacts to specific industries or monetary funds to support the development of a prototype. What they usually do not know about is how to find the most promising market opportunities.
But why is it that taking this first step of every KTT endeavor is that hard? And what can be done about that? Lets start with the why. From my experience, there are two major reasons prohibiting organizations from successfully identifying new market opportunities to their technologies.
The first barrier to effective KTT is the technological perspective of not only the inventors but all members of a technolog-driven organization. Employees in such organizations “live, think, and talk technology“. They have learned to focus on and cherish the specifications, features, and functionalities of their technologies. Consequently, when presenting their technologies, they highlight those technical specifications and use domain-specific language. It’s almost as if they recited a patent! Obviously, this makes it hard for potential users from other domains to understand what the technology is actually able to do, which problem it solves, and which benefits it might deliver to them. Thus, potential users will hardly be able to recognize the technology as a solution to a problem they might have. Let me give you an example: A couple of years ago, a leading Austrian high-tech company approached me to help them with the commercialization of a new device they had developed. They provided me with a description of the technology they thought would help me in the KTT process. The description read the following:
“The HDDM (High Definition Distance Measurement) is a statistical time-of-flight measurement technology. It allows for statistical evaluation of several 100 pulses in each measurement cycle to get one measurement value. It offers economical but highly accurate and reliable time-of-flight measurement. Coded sending sequences of Dx35 assure unique measurement and avoid cross talk of several sensors“.
Based on this description, can you think of any reasonable application fields? Do you think you understood what the technology is about? In case you are lacking technical training (just like I did), this description is hard to understand. And it is even harder to come up with ideas of where to apply this technology, right? What if the description had read like this:
“The HDDM technology is a small and lightweight, portable device that you can place in the middle of a room of up to 100 square meters. It is capable of generating a 3D model of this room in the fraction of a second, basically in real-time“.
When describing the technology based on its capabilities and/or the benefits it delivers rather than on technical specifications, it gets much easier for us to imagine applications. The HDDM technology might for example be used in the real estate industry by estate agents to offer digital visits and inspections of objects to their customers. The technology might also be used as a gesture-based remote control for Smart-TVs since it might also be capable of tracking human gestures by constantly scanning the environment and checking for specific changes (the movements of the person sitting in front of the TV set).
This example illustrates that the technological perspective (and language) of inventors makes it much harder for them to convey what they actually have, what problems they might solve, and how users might benefit from their solution. Oftentimes, users not only don’t understand the technology. In most cases, they don’t even care about the technology. Or as Theodore Levitt, an American economist and professor at Harvard Business school had put it: “People don’t want a quarter-inch drill, they want a quarter-inch hole.“
The second barrier to mastering the fuzzy-front end of KTT is each and every human’s limited ability to think outside of the box. Scientific literature refers to this phenomenon as “functional fixedness“ (at an individual level) or “local search bias“ (at an organizational level). Functional fixedness means that we “tend to search for solutions in the neighborhood of our current expertise or knowledge“. In other words: Humans as well as organizations are very bad in coming up with completely new solutions to problems. When looking for new market opportunities to a technology, inventors might easily find near-analogous application fields, that is markets that are very similar to their current target market(s). However, because of their specific training, their experiences, and their professional focus, they will face huge challenges in imagining far-analogous, really new application fields.
Take the example of a highly specialized OEM in the automotive industry that I have worked with a couple of years ago. The company had developed a high-performance gearing technology for sports cars. Everything worked out well, the technology was superior over solutions by competitors. However, the company had set itself the goal to generate 20% of their revenues in other markets than the automotive industry by 2025. Thus, they were looking for new applications to their technology. Because of their functional fixedness, they had come up with near-analogous application ideas only, e.g., motorcycles, agricultural utility vehicles and so on. The problem was: They considered themselves as a manufacturer of gearing technology for vehicles. Consequently, they could only think about applications somehow linked to vehicles and mobility. Little did they know about all the promising, far-analogous markets out there that were desperately looking for a solution like their technology. One example is off-shore wind parks. The wind mills in such off-shore wind parks have an increased risk of getting damaged because of very demanding wind profiles, i.e., because of gusty winds. In case of strong gusts, the gearing mechanism would not decouple fast enough, causing the gearing mechanism to break. Such incidents obviously are very costly, not only because of the repair work to be conducted but also because of the discontinuation of the energy production. The high-performance gearing technology of the automotive OEM was a potential solution to the wind park operators’ problems, but neither side did know of each other. And because of their functional fixedness, they would not have found each other easily.
The cases described above illustrate how the inventor’s technological perspective and functional fixedness negatively affect the fuzzy front-end of KTT processes, i.e., the identification and evaluation of new business opportunities to technologies. In my next blog post, I will elaborate on how to overcome these obstacles and to master the fuzzy front-end of KTT activities.