We spend way too much time focusing on success. How much space in popular press is spent on the centi-billionaires and the firms they founded? How much academic research is drawn from successful enterprises, those who founded and financed them, and the CEOs currently leading them? Let’s talk about failure for a little while.
We understand that entrepreneurship success, the founding of enterprises that survive and grow, in most cases has a big skill component, though luck is needed too. Is the same true for entrepreneurship failure? Probably not, as Diego Zunino, Gary Dushnitsky, and Mirjam van Praag point out in research published in Academy of Management Journal. Skill is so important for success that we can be pretty sure it is present along with some luck. But by the same token, bad luck can sink an enterprise regardless of skill, so failure does not mean that skill is absent. It does, however, raise the possibility that skill is absent.
Why is this important? Well, the successful entrepreneur often does not form any new enterprises because managing growth and ensuring continued success is already plenty of work, and it is rewarding work too. Failed entrepreneurs often wants to form a new enterprise, because they naturally believe that they are highly capable and just got unlucky. After all, entrepreneurship does go along with a high self-image and willingness to risk other people’s money, and these days “serial entrepreneur” is something of a badge of honor.
But what about the investors who are asked to fund enterprises? Do they look at the track record of the entrepreneur? How do they assess it? First, we need to understand that very few investors face the situation of those who were asked to help fund Amazon. Jeff Bezos told them that they had a 70 percent chance of losing their money, which is fairly realistic (actually the percentage is higher). More importantly, he had no past failures because he had never founded an enterprise – he had been an employee. Most entrepreneurs asking for funding will have a short or long track record of dead or moribund enterprises.
One simple and incorrect decision rule is to view any failure as a sign to stay away. Clearly that will exclude many skilled founders and promising enterprise. Another is to ignore past failures. Clearly that means not seeding out some entrepreneurs who really ought to get a job instead. But can potential investors thread a reasonable middle path?
Fortunately, the researchers found that they can. When assessing a potential venture investment, how promising people found it and how much they could be willing to invest was influenced by past failure, but not so much that past failure ruled out investment. Instead, past failure made the potential investors more sensitive to clues about whether they entrepreneur had skills that would help the venture. So, neither of the simple and incorrect decision rules are at work, but instead some form of middle path. This is what we want to see.
So, does that mean all is well? Not quite. We have to remember that the research shows average investor reactions, and averages are usually smarter than individuals when making judgments like this. This means that entrepreneurial failure does not cut off funding for new ventures. It does not mean that all individual investors avoid the simple and incorrect decision rules. Good news for entrepreneurship, less so for investment.
Zunino D, Dushnitsky G, Praag Mv. 2021. How Do Investors Evaluate Past Entrepreneurial Failure? Unpacking Failure Due to Lack of Skill versus Bad Luck. Academy of Management Journal forthcoming.
We know that discrimination is common in organizations, in the economy, and in our social life. People are treated differently depending on a broad range of criteria, starting with race and gender, and there seems to be no form of training, qualification, or accomplishment that can help people escape discrimination. A classic example are Asian-Americans, who are a so-called “model minority” with a well-known taste for higher education. They suffer discrimination first through the accusation that they somehow do not deserve the education they have earned and then, more nastily, through violent attacks following the Covid-19 pandemic.
The fact of discrimination is well known, but the reasons are less clear – in part because there are too many explanations, and they contradict each other. Two well-known ones are taste discrimination and statistical discrimination. Taste discrimination is simple: people discriminate because they dislike, usually because others (parents? friends?) have told them who to dislike. Statistical discrimination is more complicated because the idea here is that some of those who are discriminated against should be assessed negatively, but it is hard to tell who, so the safe option is to discriminate against all. For example, an employer may think that some young women will get pregnant and quit soon and may decide that all young women should be thought of as short-term employees who do not need to be trained for promotion.
To many of us, statistical discrimination sounds like an excuse that may be true occasionally, but we assume most discrimination is based on cultural beliefs. But is that really so? Bryan Stroube has some interesting findings in research published in Administrative Science Quarterly. The findings were based on the discovery of transactions that offered reasons for statistical discrimination in one period, but these were removed later. In a peer-to-peer lending platform, there is always the concern that the loan may not be repaid, so statistical discrimination could be used to fund loans only to the most trusted social group. If the platform issues repayment guarantees, this motive for discrimination goes away. That is exactly what happened in the platform he studied.
So, what happened to the discrimination? This was a platform in China, where discrimination against women is common in economic arenas, even though women are thought to be reliable in paying back loans. You can probably suspect what happened. Women were discriminated against before the loan guarantee. After the loan guarantee, the economic security of women as lenders was no longer an issue, so women were even more strongly discriminated against.
Where does that leave the explanation of discrimination? Clearly people are capable of considering economic consequences and adjusting to them, and this affects the degree of discrimination. But at its core, discrimination is based on distaste and is culturally determined. Money is no excuse.
Stroube, Bryan K. 2021. Economic Consequences and the Motive to Discriminate. Administrative Science Quarterly, forthcoming.
Does knowledge help innovation? This is a simple question that is difficult to answer. In science, training people well enough to build on the knowledge of others is essential for advancing knowledge. But also, knowing too much forces thinking into established streams, making incremental additions easier but radical innovation harder. In business, most firms will place their bets on knowing more, to the extent of locating R&D in places with expertise, such as Los Angeles for video games or Silicon Valley for electronics and software more generally. Some firms even scatter their R&D around to have multiple listening posts to capture local expertise.
It is exactly this practice of multiple R&D teams that has helped us learn more about knowledge and innovation. In a paper published in Administrative Science Quarterly, Alex Vestal and Erwin Danneels analyze breakthrough innovations in the nanotech industry. This industry has multiple places with expertise (“hotspots”), such as San Jose, Boston, and Los Angeles, and firms have a blend of R&D teams that are in these hotspots or in places with less concentrated expertise.
So, does it help to be near expertise? It turns out that being too close to a hotspot with the same expertise as the firm is a drawback, just as scientists believe, but if the hotspot has slightly different expertise, the firm is more likely to produce a technological breakthrough. If the hotspot has expertise that is too different, a breakthrough is much less likely. The insight here is that one learns the most by being near, but not too near, the expertise of others. Maybe this is because being too close to the outside expertise means that there is little outside knowledge that needs to be moved inside the firm?
The explanation is not so simple as that. Instead, a hotspot with the same type of expertise as the firm may generate so much knowledge that it becomes difficult to process internally. But some firms had very close personal networks within their R&D team in the hotspot, which makes processing and integration of knowledge easier. For firms like that, there is no cost to being in a hotspot with the same expertise as the firm, because this makes technological breakthroughs much more likely.
Close networks among the local R&D team are not all good, however. Closely connected R&D teams are prone to ignoring knowledge gained from R&D teams in other locations, so they can fail to move knowledge that is already inside the organization but outside their specific location. As a result, the teams with close networks are less likely to make technological breakthroughs based on knowledge from outside their local hotspot.
This is interesting because it shows how the creative spark leading to innovation depends on how knowledge is moved around and processed. We have long known that hotspots for technology and innovation have knowledge moving quite freely, so firms can locate there to detect interesting knowledge and move it inside. Getting knowledge into the firm is not the same as using it effectively though. It needs to be moved to the right place in the firm, and it needs to be processed effectively.
The key to gaining advantages is the social network inside the firm. Location relative to a hotspot of knowledge looks like an easy solution to the problem of facilitating innovations, but the firm also has to be able to move knowledge internally and process it internally. That means having employees who are willing and able to share knowledge.
Vestal, Alex and Erwin Daneels. 2021. Technological Distance and Breakthrough Inventions in Multi-Cluster Teams: How Intra- and Inter-Location Ties Bridge the Gap. 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.