When Networks Merge: The Other Benefit of Firm Acquisitions

Here are two facts we have known for a long time. One is that a firm acquiring another firm combines their assets, and that can give synergies if they have something that works better together than apart. That something can be any kind of asset, by the way, including knowledge or intellectual property. The other is that firms establish and change interfirm networks through forming and dissolving alliances with other firms, and they use alliances to gain synergies too. So far everything sounds conventional and straightforward.
But these two facts don’t tell the whole story. A firm acquiring another firm also combines their networks, and that can create synergies when the combined network is better than the original ones. In fact, it can change a network much more radically than just forming and dissolving alliances one by one. This third fact is the start of an article in Administrative Science Quarterly by Exequiel Hernandez and J. Myles Shaver, but the article does not end there. It also checks whether firms are deliberately choosing acquisition targets based on these network synergies.
The question is important. It speaks to how smart firms are in maneuvering and modifying interfirm networks, which is useful to know, especially because people are not particularly smart in modifying interpersonal networks. But firms are not people, and acquisitions are not normal firm actions – they are analyzed carefully, and networks could be one of the factors taken into account. They could even be more important for acquisitions than some of the assets that researchers have long obsessed over. After all, alliances are observable in advance, like physical assets are, but they are unique and therefore more strategic. The other unique and strategic assets in play are often people with knowledge, but they are known to sometimes leave a firm after it has been acquired, so it is pretty risky to acquire to get people. Alliances usually stay. 
Network synergies are especially potent if they make the combined firm a better broker of knowledge because they connect to firms that are not themselves connected and do not have other shared connections (that’s called gaining structural holes). Brokers of knowledge can help create novelty and can reap more of its benefits. Synergies are also potent if they make the combined firm more central in the overall network, giving it higher status. Pretty much any combination of firm networks will improve brokerage and status, however, so it is not enough to see that this happens after an acquisition. What we need to know in order to look for deliberate choice of network synergy is whether the increase from the firm network that got acquired was better than the increase would have been for other firms that could have been acquired. Strategy is about choices, so a choice has to be compared with what was not chosen. 
This is where Hernandez and Shaver make a fully convincing case for network synergies as an important factor in acquisitions. They studied biotech, where networks are very important, and so are assets like people and intellectual capital. Their analysis is impressive and leaves no doubt that the opportunity to combine networks and gain network synergies was an important factor in the choice of acquisition targets. That means we now have a new way of looking at acquisitions, and we are better able to tell what firms may get acquired, and what they were acquired for. 

Hernandez, E., & Shaver, J. M. Network Synergy. Administrative Science Quarterly, forthcoming.

From Tigger to Eeyore: How Gig Economy Workers Cultivate Work Identities

When some people hear the term “gig economy,” they think of temps working for agencies, but that misses 90 percent of the picture. The gig economy consists of people who act as independent workers, contract firm workers, on-call workers, and temporary help agency workers. Fully half are independent workers, and with the gig economy growing fast and now encompassing one-sixth of the U.S. workforce, independent workers have become important in society. But what is it like to be an independent worker in a world of organizations and employees?
An article in Administrative Science Quarterly by Gianpiero Petriglieri, Susan Ashford, and Amy Wrzesniewski finds that the answer is… complicated. The reasons give an interesting look at both the world of employees and the world of independent workers. Studying the world of organizational employees, scholars and observers of society have long been interested in how people’s identities become shaped by their affiliation with an organization, and how some organizations strengthen this and make use of it. The classic book “The Organization Man” by William Whyte was part of a greater conversation on how people’s identities and actions may become too connected to the safety of being in an organization as part of a collective.
If the idea of collectives as capturing and constraining people captured employees’ reality, for independent workers, contracting and independent work should mean freedom and the ability to express one’s individuality. And that should be a good thing. The problem is that Whyte may have been right when he suggested that people like collectives and fear the freedom of individualism. The independent workers interviewed by Petriglieri, Ashford, and Wrzesniewski expressed an unmoored existence with wildly fluctuating emotions – like the Tigger and Eeyore fluctuations that one of them mentioned. They also experienced uncertainty about their personal identity, economic position, and the recognition of their work. All of this because independent workers don’t have an organization as a holding environment that defines their identity, determines their economy, and recognizes their work.
So what do the independent workers do? The key finding is not that they have found one clear solution, but instead that they seize on all sorts of ways to secure their work identities. They make routines for anchoring themselves, not necessarily because the routines help the work. They connect to places where they do the work, almost as a ritual of establishing the place as a context that helps define their individuality. They connect to people who can provide interpretations of what they do and affirmations of who they are. They find ways of connecting their work to higher goals for society, so they can define a purpose of life. Some of their actions are hard to understand for people who work for organizations. I don’t know how a writer can revere the public library as a workspace, but maybe that’s because I don’t know enough about writing. I did software development much earlier in my life, though, and it is a mystery to me that an independent software engineer can describe his home office as a “fighter pilot cockpit” – to me, part of the beauty of software engineering is that it is completely portable, so places are unimportant.
When actions are hard for outsiders to understand, have seemingly precarious links to outcomes, and are highly varied across person, time, and place, they display all the signs of having a function. In this case, the function is to hold on to and cultivate an identity in the absence of a collective, and to manage the emotions that come with independence. We may find that the new economy has many more unmoored people holding onto their identities in ways we’ve not seen before.

Petriglieri, G., Ashford, S. J., & Wrzesniewski, A. 2018. Agony and Ecstasy in the Gig Economy: Cultivating Holding Environments for Precarious and Personalized Work Identities. Administrative Science Quarterly, forthcoming.

Improving Social Science: What Can Journals and Editors Do?

We all know that correct science is needed for much of what we do. So many parts of our modern world—things we buy, use, or interact with in various ways—are based on science and work as they should only when the science is correct. Social science does not build things, but it teaches us how society works and how it can be improved. This gives value to social science, including to organization and management theory. One could even argue that organization theory has special motivations to be a correct science, because so many parts of our modern world—things we buy, use, or interact with in various ways—are made and operated by organizations and work as they should only when the science of managing is correct.
In an editorial essay in Administrative Science Quarterly,William Starbuck argues that we need to improve social science. The argument has multiple parts, and I will discuss just one here. It is a simple issue with a lot of complications: Research is done by people. People have all sorts of thoughts, feelings, actions, and reactions, which are often different from the cold and objective look at evidence that science calls for.
People want safe jobs, success, and recognition. They often feel under pressure to do well, both from what they want and from rules such as the promotion system we have, which is strongly driven by publications. This drives some of them to deviate from the true reporting of findings, which could involve selective modeling to strengthen findings, holding back findings that go against expectations, making up theory for unexpected findings, or even editing or falsifying data. A very important problem is that many researchers who are selective, hold back, or make up theory believe that they are better than those who edit or falsify. But they are not. Any one of the actions I just mentioned is a misleading departure from scientific standards. We understand why some people do it, but we need them to stop.
People assess scientific results, but they also think about the people doing the science, and that colors their assessment because people have stronger feelings about people than they have about scientific findings. Much of social science is aware of this effect and tries to shield authors through double blind review, to hide their identity from those who assess them. The fields that don’t do this end up rewarding the already famous over and over again. But double blind is not enough. People are good readers and can make all sorts of guesses about who the writer is, or at least what kind of person the writer is. The guesses are

usually accurate, but the assessments that follow the guesses are biased and disadvantage those who have low status. We understand why some assessments are shaped by status, but we need it to stop.

When people make the science, with bias, and people assess the science, with bias, how can we improve it? Starbuck has a wide range of suggestions that can help improve our procedures. At ASQ we are thinking carefully about these problems, and we have wondered whether part of the problem is that the evidence is not presented with enough appeal and transparency. We have changed our invitation to contributors to encourage more display of data before showing models and to encourage using graphical approaches that show the reader exactly how much is explained by the theory and how much is not yet explained. I have also written a blog post on this issue.People will still be people. The solution has to involve better procedures, including those that allow the data we work with to become more prominent.

When Others Mine Bitcoin, You Can Make Money on Its Ecosystem

What is a similarity between a Bitcoin day trader or an Etherium miner of the 21stcentury and a Californian gold digger in the 19th century? 

The answer is that they are both looking for gold—digital or physical. Another similarity is that their exploits will benefit the ecosystem of providers of complementary products or services. During the Gold Rush period, Levi Strauss made money selling jeans to the gold diggers. Jeans were a piece of a gold digger’s ecosystem at that time. Today, wallets to store coins or computer chips which solve math problems play the same role as the jeans back then.

Making complementary products can be a way to benefit from someone’s risk-taking. The value of complements frequently rises with the value of the products that they are supporting. 
Take the crypto-wallet Ledger Nano S. This is a USB-like device with a cryptographic protection that allows the owner to store digital currencies off-line without the risk of hackers stealing the funds from an online wallet. 
One could have ordered this product in November 2017 in France for the price of 60 euros. By the end of December 2017, the price was 80 euros and then the Ledger Wallet, the company that makes this device, even stopped shipping until March 2018. It simply ran out of stock. Ebay.fr now has these wallets on sale for 400 euros, although Amazon now sells them for 199 euros. By the time you read this post, the price can be different.
Clearly, many people bought a lot of digital gold (silver or “dogcoin”) during the bit- and alt-coin trading frenzy over the Christmas break. Fearful of the hacker attacks, the owners of the digital currency started to look for a secure way to store them, and the stock of the Ledger Nano S was gone. If the price of crypto-currencies goes up or down, the maker of the wallet will still make money because people will need to store their more (or less) valuable coins somewhere.
Nvidia is another case in point. Its share price is up over 200% since last year. A large part of this growth can be explained by the market for its graphics processing unit chips, which were used by the “miners” to run the operations that generate the digital currencies. In other words, Nvidia benefited from the fact that people were not using its chips to play video games, they were using the chips to mine digital gold. A perfect parallel to Levi Strauss.

Despite the volatility of the crypto-currencies, there is a non-zero probability that some of them are here to stay. What their price will be in 12 months is anybody’s guess. Could be big, coule be close to zero. But what is more or less certain is that there was a lot of money made last year not on buying and selling digital coins, but rather on the making of complementary products to them. Perhaps the market for complements to digital gold will have a good 2018!

Between Guardrails: How Organizations Handle Mission Contradictions

Digital Divide Data (DDD) is a commercial enterprise doing data-entry work for profit. It is also a social enterprise that trains Cambodians to obtain better jobs than the ones they do for DDD. Is that a contradiction? Maybe it is not fully contradictory but instead just a tension—one that many social enterprises handle because they need to sustain themselves commercially, not just do good work. We have long known that the dual purpose of social enterprise is seen as a contradiction internally and can lead to various problems and coping strategies, but we have not known much about the long-term effects.
Now we know more, thanks to an article in Administrative Science Quarterly by Wendy Smith and Marya Besharov. They followed DDD for more than ten years, seeing it as a great example of the effects of how such contradictions are dealt with over a long time. It is a great example both because DDD has coped with them well, while many other organizations break apart or fail, and because it has faced a particularly difficult tension between its commercial and social work, as the commercial work has slim margins and some of the social activities can undermine it seriously.
What do we learn from DDD? As you might expect, the answer to such contradictions has more than one part, but here I want to describe just one: guardrails. Establishing guardrails is a way to set up the organization that follows some old organization theory almost to the book, although the DDD founders may not have been aware of it. In a hybrid organization like DDD, whose commercial and social activities are both important, one of the many possible solutions is to make sure that the organization holds strong advocates of each one and is not set up to let one type of activity dominate. That setup results in a battle for dominance between these advocates and between the coalitions they can muster for support whenever a critical problem arises.
That sounds like a noisy and costly way to organize, and it is. But its key feature is that the battles arise whenever one coalition sees the organization as going too far in one direction and neglecting the other, and the battles help to pull it back to the center. As long as the organization can balance its activities, it is peaceful. That’s why competing advocates and coalitions function as guardrails – they keep the organization from going off track and favoring one mission over the other.
The reason this is important is that hiring advocates for contradictory positions without giving priority to one looks like a way to generate problems for the organization. There is not one overriding mission, there is not a clear organizational identity, it is not possible to predict when conflicts will start, and it is hard to predict how they will end. But all these frightful sources of noise help stabilize the organization and resolve the tension between its contradictory goals and activities.

The Different Uses of Training: Training to Retain First-time Workers

Firms often train workers, and nearly always for very specific reasons. We are most familiar with how they teach specific skills for their equipment and procedures, including re-training people when these change. Whether we’ve taken such training ourselves or have worked with assistants or administrators who have done so, we understand that such training is important for both the organization and the worker: it should help the worker produce more valuable output (which they can share), and it is more valuable if the organization can retain the worker on the job longer after training.
But the reality is that many workers don’t stay on the job very long, either because they experience a lack of fit with the employer or they have difficulty meeting the organization’s expectations of them as an employee and continuing to manage their responsibilities outside of work. This is especially true for women entering the workforce for the first time whose domestic roles haven’t prepared them for work. It is also true for people with self-employment backgrounds such as families doing farming or craftwork. Such first-time workers are the focus of an article in Administrative Science Quarterly by Aruna Ranganathan, who studied women entering the workforce for the first time as employees of a garment factory in India. Many of these women didn’t last long on the job: about a third left within three months of hiring.
As expected, the employer provided training to help new workers get up to speed. But what was unusual is that the content of the training differed depending on the trainers’ experience, and the content made a big difference in attrition rates. Ranganathan found that less-experienced trainers in the factory focused on teaching the new employees assigned to them only job-specific skills, such as how to use a sewing machine. These trainers saw their goal as teaching the “equipment and procedures” knowledge I referred to before. More-experienced trainers taught job-specific skills and also provided more general work-readiness training that focused on skills related to self-presentation, interpersonal communication, work–life separation, and self-reliance.
Clearly this is a different form of training because it is a way of socializing the first-time women workers, helping them feel comfortable in their workplace, behave as expected, communicate well when needed, and work independently when needed. These activities are natural for many people who are socialized into workplaces early in life through exposure to an organization such as a university or a business. The women studied by Ranganathan came from rural villages, where such socialization is hard to get. Successful work-readiness training, which decreased the numbers of women quitting shortly after they were hired, was important both for the firm and the employees: re-hiring is costly for the firm, and leaving paid work as a result of lack of fit hurts these women’s income and further employment chances.
Socialization training was different from job-specific training because the trainers didn’t work from a checklist of skills to impart. Instead, the experienced trainers seemed to have a natural understanding of what the new workers could experience as problems; they taught new employees how to get to work on time in the morning, showed them where the bathroom was, and encouraged them to take breaks for drinks of water, for example. Without the benefit of a checklist of such seemingly simple (yet clearly important) skills to teach, less-experienced trainers didn’t teach them, perhaps because they didn’t understand the importance of such work-readiness skills.
We rarely think of training as having such general goals to help employees feel ready to work. We rarely think of socialization as happening through training rather than through workers interacting formally. We rarely study how developing nations modernize through having people who were earlier engaged in farming or housework taking on the role of paid employees. Ranganathan’s research is eye-opening because it is right in the middle of so many important and neglected topics.

The Different Uses of Training: Training to Retain First-time Workers

Firms often train workers, and nearly always for very specific reasons. We are most familiar with how they teach specific skills for their equipment and procedures, including re-training people when these change. Whether we’ve taken such training ourselves or have worked with assistants or administrators who have done so, we understand that such training is important for both the organization and the worker: it should help the worker produce more valuable output (which they can share), and it is more valuable if the organization can retain the worker on the job longer after training.
But the reality is that many workers don’t stay on the job very long, either because they experience a lack of fit with the employer or they have difficulty meeting the organization’s expectations of them as an employee and continuing to manage their responsibilities outside of work. This is especially true for women entering the workforce for the first time whose domestic roles haven’t prepared them for work. It is also true for people with self-employment backgrounds such as families doing farming or craftwork. Such first-time workers are the focus of an article in Administrative Science Quarterly by Aruna Ranganathan, who studied women entering the workforce for the first time as employees of a garment factory in India. Many of these women didn’t last long on the job: about a third left within three months of hiring.
As expected, the employer provided training to help new workers get up to speed. But what was unusual is that the content of the training differed depending on the trainers’ experience, and the content made a big difference in attrition rates. Ranganathan found that less-experienced trainers in the factory focused on teaching the new employees assigned to them only job-specific skills, such as how to use a sewing machine. These trainers saw their goal as teaching the “equipment and procedures” knowledge I referred to before. More-experienced trainers taught job-specific skills and also provided more general work-readiness training that focused on skills related to self-presentation, interpersonal communication, work–life separation, and self-reliance.
Clearly this is a different form of training because it is a way of socializing the first-time women workers, helping them feel comfortable in their workplace, behave as expected, communicate well when needed, and work independently when needed. These activities are natural for many people who are socialized into workplaces early in life through exposure to an organization such as a university or a business. The women studied by Ranganathan came from rural villages, where such socialization is hard to get. Successful work-readiness training, which decreased the numbers of women quitting shortly after they were hired, was important both for the firm and the employees: re-hiring is costly for the firm, and leaving paid work as a result of lack of fit hurts these women’s income and further employment chances.
Socialization training was different from job-specific training because the trainers didn’t work from a checklist of skills to impart. Instead, the experienced trainers seemed to have a natural understanding of what the new workers could experience as problems; they taught new employees how to get to work on time in the morning, showed them where the bathroom was, and encouraged them to take breaks for drinks of water, for example. Without the benefit of a checklist of such seemingly simple (yet clearly important) skills to teach, less-experienced trainers didn’t teach them, perhaps because they didn’t understand the importance of such work-readiness skills.
We rarely think of training as having such general goals to help employees feel ready to work. We rarely think of socialization as happening through training rather than through workers interacting formally. We rarely study how developing nations modernize through having people who were earlier engaged in farming or housework taking on the role of paid employees. Ranganathan’s research is eye-opening because it is right in the middle of so many important and neglected topics.

Teams at Work and Lives at Stake: How to Handle Fast-Paced Complexity

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.

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.

A Paradox of Innovation: Those Who Do It May Be Ignored

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.

Open Innovation and Closed Minds: Why NASA Used Open Innovation Sometimes but Not Always

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.