There is a lot of research on how teams make disasters happen, and the answer is clear: teams use cues to make sense of the situation, and disasters happen when sensemaking differs from reality. That’s useful to know, but we would also like to know how it can be prevented. We know that expertise and experience do not help. Experienced commercial pilots, space shuttle subcontractor engineers, chemical plant operators, and fighter pilots have all been studied and found to do faulty sensemaking. The examples I just gave have led to a total of 4,000 confirmed deaths and more than 10,000 likely deaths.
Finally, an article in Administrative Science Quarterly by Marlys Christianson has some answers. She studied how medical teams went through an emergency room training procedure – treating a young asthma patient with increasing breathing failure – in a simulation designed to invite incorrect sensemaking in the beginning, so they would need to recover later. Fortunately, in simulations the patients are not real, because one quarter of them would have died. Even among the teams that managed to identify and correct the problem (replacing a piece of broken equipment), the speed of doing so varied a lot, so thanks to this research we now know a lot more about how sensemaking can recover.
Teams are in organizations for doing work, not for solving puzzles. Whenever a situation involves a puzzle that needs to be solved, such as faulty sensemaking that needs to be corrected, the regular work done by the team takes effort and attention away from the correction. This means that cues that may look obvious to someone outside the team are not at all clear to team members who are focused on the regular work and who do this work premised on their sensemaking. In an emergency room, the team will look for cues to how the patient is doing, but they spend much of their time treating the patient. Treating and observing clues are related, but they compete for time.
This means that emergency room teams can solve puzzles only if they manage two trajectories at once – the regular treatment and the interpretation of cues from the patient’s condition. The interpretation trajectory is how sensemaking is updated, and it is complex because it moves from noticing cues that suggest something is wrong, to interpreting them to indicate what the problem is, to acting to check the interpretation. Usually the actions involve changing the treatment, so treatment and interpretation need to be in sync. The trajectory management can fail in multiple places. For example, the treatment takes too much time so cues are not interpreted, or the treatment is based on current sensemaking so changing it to check interpretation does not make sense.
The emergency room teams had a sensemaking problem because the simulation was designed to involve treatment equipment that did not work correctly, so the usual sensemaking (“our equipment works, so all problems can be found in the patient”) was faulty. Similar sensemaking problems are found in many places. In the Black Hawk shooting incident, the fighter pilots saw helicopters without correct friend–foe identification signals and concluded they would be hostile because friendlies signal who they are. Any cues they could see were drowned out by the tasks of flying the aircraft low in mountainous terrain, keeping alert for possible threats, and going through a modified foe identification and engagement procedure while communicating with each other.
Trajectory management can easily fail, with tragic consequences. Now that we know more about the differences between teams that succeed and teams that fail, we may be able to work to make teamwork more reliable, especially when lives are at stake.
Christianson, Marlys K. 2017. More and Less Effective Updating: The Role of Trajectory Management in Making Sense Again. Administrative Science Quarterly, forthcoming.
On a personal note, I’ve experienced the benefits of the sort of updated sensemaking described in the article. When I was in the emergency room after an accident, the team scanned me to look for internal bleeding based on their experience of how body folding from being hit by a car while riding a motorcycle can break blood vessels. They found none. The cue of falling blood pressure after closing the external wounds made them re-scan over a broader range, and they found the broken vessel and fixed it. I am alive, thanks to the team’s updated sensemaking.
We are supposed to like innovations. They drive the world forward, with effects that range from the pleasant (like the camera on your phone) to the vital (like portable ultrasound in developing nations). In fact, many of the heroes in business are known because of their innovations. A classic example is Steve Jobs launching the multi-function iPhone, which relied on knowledge of music storage and playoff, as well as internet connectivity, that previously had not been part of mobile phone technology. This is one of the two classical stories on how to innovate: combine existing knowledge in new ways, or create completely new knowledge.
The only problem with the iPhone story is that it makes us think the world rewards innovation and that firms doing it get Apple-like fame and fortunes. That happens to be the exception. A research paper by Matt Theeke, Francisco Polidoro, and James Fredrickson in Administrative Science Quarterly has shown that firms using new kinds of knowledge for making innovations face a surprising form of risk: they may end up getting ignored.
The details of this research help us see exactly what happens. All kinds of firms want stock brokerage firms to issue analyst reports on them, because that means investors will pay attention to them, which helps them gain financing. This is especially important for firms that rely on innovations, because making innovations means paying money now to get money later, which is exactly what financing is used for. In fact, there are entire industries that are so dependent on innovations that analyst reports are essential. Theeke, Polidoro, and Fredrickson studied medical devices, which is a good example of an innovation-driven industry. Brokerage firms covering that industry need to understand research and knowledge use, because otherwise they cannot estimate future profits well.
So what is the problem? Well, the brokerage firms have expertise in the conventional use of knowledge, which means that use of new knowledge – innovative use of knowledge – is something they understand less well. As a result, firms incorporating new knowledge are more likely to be ignored, as brokerages drop them from their coverage. The newer the knowledge is, and the more expertise the brokerage firm has in covering other firms in the industry using conventional knowledge, the worse the situation is. Just as expertise makes some firms rigid in their knowledge use, it makes brokerage firms rigid in their knowledge valuation.
So our tales of heroic unconventional innovators are good examples of exceptions, because business rewards convention. Does that mean it is better to follow convention and just make minor improvements? Not really, because easier access to financing is very different from more successful product launches. It just means that firms planning to use new knowledge in making innovations should check their bank accounts first, because they may have to pay the cost themselves.
Theeke, Matt, Jr. Francisco Polidoro, and James W. Fredrickson. 2017. "Path-dependent Routines in the Evaluation of Novelty: The Effects of Innovators’ New Knowledge Use on Brokerage
Open innovation is heralded as a way to advance technology and product innovation quickly and cheaply. It is modeled on the open source software movement, which is based on computer programmers donating their time to build software components, check their own work, check others’ work, and correct mistakes. Among the famous software suites made through open source, Linux is a computer operating system that is used in everything from cellular phones to web servers, and is often involved when you are retrieving and reading blog posts like this one. Open innovation extends this model to innovations outside computer programming by organizations posting problems that anyone interested can help solve.
The idea is to use volunteer efforts to get innovations for free (almost a Dire Straits lyric), which sounds like a good deal. Unfortunately, this has proven difficult for many organizations, and research in Administrative Science Quarterly by Hila Lifshitz-Assaf has found out why. Her careful study looks at an open innovation initiative in a very innovative high-tech organization: NASA. In 2009, NASA tried an open innovation experiment that led to some speedy, inexpensive, and impressive solutions. But its relationship with open innovation since then has been inconsistent, with some NASA professionals using it to great success and some not. Why the difference?
In a word, the difference is identity. Innovations are typically done by highly educated people who are trained to follow careful processes specific to their organization and to their scientific and technological specialization. These people have a professional identity built around their unique skills as problem solvers for the organization. For people with such an identity, what does it feel like to have amateurs solve problems instead of them? Open innovation draws much of its strength from individuals who may lack formal education, don’t follow the predefined process, and aren’t even employees of the organization. Naturally there is an inherent conflict between the insiders and the open innovation use of outsiders, and some insiders are tempted to seal the organization off from the outside sources of innovations.
Why did some parts of NASA embrace open innovation? Again the answer is identity. Those who could redefine their professional identity to be a solution seeker, not a problem solver, became adept users of open innovation. For a solution seeker, the existence of a solution is what matters – not who made it, and not how it was made. It is a completely different way of thinking of oneself and of solving problems.
The division between problem solvers and solution seekers resulted in NASA professionals adopting various approaches to the open innovation initiatives advocated by their leadership. Problem solvers maintained boundaries, either explicitly or through the pretense of openness but actual closure. That way they could maintain their focus on their individual efforts and internal innovations. Solution seekers looked for outside solutions, sometimes simply embracing externally developed solutions, and sometimes adapting external solutions so that the final solution became a mixture of outside and inside effort. Problem solvers may hold tight to their identity, but open innovation is sure to continue gaining ground. “Get your innovations for nothing, get your praise for free” is an appealing tune.
Lifshitz-Assaf, Hila.2017. "Dismantling Knowledge Boundaries at NASA: The Critical Role of Professional Identity in Open Innovation." Administrative Science Quarterly, Forthcoming.
This blog is devoted to discussions of how events in the news illustrate organizational research and can be explained by organizational theory. It is only updated when I have time to spare.