How to forecast success
You probably know someone who owns an Apple Watch, or maybe you own one yourself. Is it a creative idea? Well, the multi-function watch was creative the first time it appeared in science fiction, but that was a long time ago. Technologically, a watch with the Apple Watch functionality has been possible for a while, but firms have waited because they were unsure if it could become a success. If fewer and fewer people wear watches, because Smartphones do the same job and much more, why make a watch?
In fact, the potential for success of the Apple Watch was in dispute as soon as it was launched and it is still not settled. This is an issue that surfaces again and again – firms need to estimate the potential success of ideas, both creative and more conventional ones. Innovative solutions are increasingly granting a competitive edge, but managers cannot decide to invest in every novel idea an organization generates. At the same time, organizations can’t afford to miss out on potentially transformational ideas.
There are many examples of managers rejecting novel ideas that go on to be huge successes. How can organizations make better decisions about which ideas to pursue? In a forthcoming paper in Administrative Science Quarterly, Justin Berg, of the Stanford Graduate School of Business, examines this question in the context of who in the organization might be best placed to decide what is most likely to be a creative success: creators, managers or customers?
The question is important because it reflects an ongoing tension in firms. Creators think managers don’t have the right kind of thinking to appreciate their work and managers think creators are poor decision makers, especially when evaluating their own work. Theoretically, the key difference is between the divergent thinking that underlies creativity (generating ideas by exploring many possible solutions) and the convergent thinking that underlies analysis and decision-making (giving “correct” answers to standard questions).
In his paper, Berg turned to the circus industry and drew upon 339 circus professionals, spanning creators, managers and people who occupied roles with both duties across 43 countries, as well as 150 non-circus people. Participants were then asked to watch online videos of circus acts and forecast how successful the videos would be with the audience. Creators and those with dual roles also submitted videos and were able to forecast the success of their own work as well as that of others. Berg also surveyed the audience, asking them to rate the videos.
It turns out that the creators are much better at assessing creative success than managers are. Managers could be the worst – even laypeople outperform their predictions in one measure of assessment accuracy.
But managers still had an edge in some respects: creators are bad at assessing the success of their own work (you get no points for guessing that they overestimate it). Even more interestingly, a creator with a strong track record of past successes is especially bad at assessing the acts, probably due to overconfidence. For organizations to better forecast the success of creative ideas, this study gives a good rule of thumb for managers. If a creator says, “I know this product idea will succeed/fail because [insert own success story here]”, it would be worth considering whether they are talking about their own creation or others’ and what their past record has shown. It would also be useful for managers to ignore themselves occasionally. Having creators assess each other’s work could be the best way of forecasting whether an idea will be a hit or not. If that’s not possible, ask the audience.
When customers panic
In 2011, the depositors of two banks in Latvia, Swedbank and SEB, rushed to empty their bank accounts after a rumor spread on Twitter claiming that the banks were experiencing financial trouble. The banks quickly addressed the rumors as false and police investigations were launched to track down the source of the claims. The episode showed how quickly bank runs can happen, even to entirely solvent institutions, and how, if enough people withdraw their cash, a bank run based only on rumor can become self-fulfilling. Such runs can turn into a systemic customer panic and affect other banks in the same lines of business – essentially made guilty by association. Similar panic in other industries is not uncommon. Even today, Chinese consumers prefer to buy their baby milk formula overseas after the melamine scandal in 2008, even though not every milk producer was found to be tampering with their products.
In our recent paper, Ripples of Fear: The Diffusion of a Bank Panic, Jay Kim, Daphne Teh and I examined the largest customer-driven bank panic in US history to understand why customer fears grow into serious bank runs. In our bank-specific sample, we found that customers do not actually lose trust in all banks. Instead, they target individual banks based on their assessment of which banks are similar to those that have already experienced a run. It only takes a run on a few banks to spark a systemic breakdown, which has disproportionate economic knock-on effects.
The 1893 bank panic triggered a devastating economic crisis that saw real earnings decline by 18 percent from 1892 to 1894. Despite the efforts made to control the panic and limit the damage for individual banks, 503 banks were suspended. Two-thirds of them failed. But they weren’t all insolvent. It was also surprising that paid-in capital made banks more vulnerable to bank runs. This, perversely, made them more prominent targets of bank runs despite being more financially stable.
Why a bank run turns into panic
In our study, we found three factors that influenced the likelihood of panic spreading. First, bank runs were more likely to occur in communities with similar compositions of race, national origin, religion or wealth, suggesting a role of prejudice, even in such important economic decisions as withdrawing money. Essentially, people were more ready to believe members of their community who found reason to panic than they were members of different communities.
Thus, the more diverse the population, the less vulnerable it is to a bank run, as weaker social ties reduce the likelihood of members agreeing on whether something, in this case a bank, was in crisis or not. Secondly, we noted how customers drew associations between banks of a certain type, increasing the likelihood of panic at banks of a similar form. Customers tend to rely on heuristics rather than deeper reasoning when assessing a bank’s vulnerability.
This was seen in the 1985 Ohio savings and loan crisis, which spread to savings and loans in other states but not to other forms of financial institutions in Ohio. Similarly, in the 1873 bank panic in New York City, the loss of depositor confidence was confined to savings banks, with nearly all savings banks experiencing a run, while only one national bank did. Thirdly, customers also distinguished between banks that were structurally similar, in this case whether they had a shared position in the network of correspondent banks. The correspondent banking system did not directly transfer vulnerabilities between banks during the 1893 panic, but customers viewed the links between their banks and the ones that experienced a run as a threat. It is also interesting to note that customers made these decisions despite the efforts of the banks to avoid being stigmatized by the news of runs on other banks.
An article in the Aspen Daily on July 21, 1893, read: “There is no reason why Aspen people should get excited over the situation here. All of the Aspen banks are backed by conservative businessmen whose business careers have not been marked by wild speculations or daring ventures.” Despite this quote, typical of newspapers at the time, the panic continued.
Protecting your firm
Bank panics are an ideal setting to observe customer reactions to perceived threats, because they are consequential but with weak economic rationale for each run. But these findings have broad applicability to other organizations. Crucially, customer panics remove organizations from the narrative about their financial health. We find that customers make distinctions based on easily observable, even if superficial, characteristics, which can disproportionately and adversely affect the firm – in this case, irrespective of a bank’s financial health. Organizations can protect themselves against falling victim to a run of customer panic by differentiating themselves from their competitors as much as possible.
It is common for businesses to create connections with their peers to gain acceptance as a legitimate player. But, as our research shows, if one organization in the group breaks its customer’s trust, suspicion can fall on all.
Henrich R. Greve is Professor of Entrepreneurship at INSEAD.