Did the members of the crowd reach the verdict?

There are some professions that inspire awe just by the sound of their name. Professional fact-checker, a person tech companies such as Facebook and Twitter hire to identify false or misleading claims, is one of them. Isn’t it cool to be someone who always knows the truth? How special do you have to be to tell other folks what to believe in?

It turns out that there is nothing special in professional fact-checkers after all. An international team of authors explored a crowdsourcing approach to fact-checking. Participants, recruited from an online labor market, were asked to rate randomly selected articles just by reading the headline and the lede (the opening sentence or paragraph) of the article. The authors found that a crowd of untrained laypeople as small as 10 people was as good or even better at fact-checking than three professional fact-checkers who, incidentally, had a chance to read the full article.  

This finding can surprise only someone who never heard about crowdsourcing (or still confuses it with crowdfunding). Over the past 10-15 years, numerous organizations–including corporations, governmental agencies, and nonprofits–have adopted crowdsourcing as a tool to help them address their most pressing technical and business challenges.

Crowdsourcing has also been applied to public policymaking: from writing state and city constitutions to creating “smart cities.”

Now, a group of authors led by Kyle Bozentko from the Center for New Democratic Processes want to make crowdsourcing an intrinsic part of our democratic decision-making process. They call their idea Citizens’ Juries.

As the name implies, Citizens’ Juries will be comprised of small groups of people (12-24 members) and be convened for 3-8 days of moderated deliberations on a specific topic. Depending on the topic, the jury could include people from the general population or be “custom-selected” based on characteristics relevant to the policy issue at hand like age, gender, or race. (For example, a youth citizen’s jury could be convened to study student loans.)  The expectation is that serving as a microcosm of the public, Citizens’ Juries will bring the diversity of perspectives, interpretations, and heuristics that is so desperately lacking in our traditional, “expert-based,” decision-making process.

Sure, to make Citizens’ Juries work, a series foundation of organizational and legal bricks needs to be built first, and the vehement resistance from professional nay-sayers is all but assured. But as someone who witnessed crowdsourcing creating value in real life—and as someone who feels that our political ecosystem is rapidly becoming uninhabitable—I’d give the idea a chance.

Image credit: https://www.dreamstime.com/royalty-free-stock-images-female-attorney-addressing-jury-image29662899

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3 best innovation team formulas (and when to ignore them)

This article first appeared on Qmarkets blog in 2017 and is reproduced here with some modifications.

You’ve heard this cliché many times before: innovation is all about people. Even if you’re an avid fan of AI, you hardly expect robots replacing humans as innovators any time soon. And if you agree with another popular cliché, the one saying that innovation is a team sport, you will come to a natural conclusion: to pursue a corporate innovation project, you need a dedicated innovation team.

Do you need an innovation team?

Or maybe you don’t. Many folks don’t believe in structured innovation arguing that any ‘structure’ kills creativity and stifles innovation. Even some innovation experts claim that ‘innovation is everyone’s job.’

The notion that “innovation is everyone’s job” is quite popular in many companies. Why? Because it allows their leadership teams to adopt a hands-off approach to innovation process. It obviously takes time and effort to formulate the company’s innovation strategy, align it with the corporate strategic goals, and identify key business problems to solve. In contrast, it’s much easier to announce an open season for ‘ideas,’ launch an innovation hackathon or two and then claim that the collective wisdom of the whole company has been harnessed. (The popularity of this hands-off approach to innovation can be at least partially attributed to the proliferation of easy-to-use innovation management platforms.)

And yet, most corporate innovation leaders do understand the need and value of creating a dedicated innovation team. To be sure, every employee in an organization should ideally take part in innovation projects, but it’s the ultimate responsibility of the innovation team to take ownership of the process: to make it efficient, measurable, and accountable. Anyone with a glimpse of corporate experience knows that when ‘everyone’ is responsible for something, no one is.

Innovative people for innovative teams

And here we come to an important question: how this innovation team should be formed? Several approaches exist.

The first approach emphasizes personal skills of the team members. That’s why you can often hear that the best way to staff your innovation team is to hire…innovative people. Great advice, of course, but unfortunately, with limited practical value. This is not to say, however, that more specific directions are completely lacking. For example, a 2017 publication lists five ‘innovative’ qualities each member of the innovation team is supposed to possess:

  • Leapfrogging mindset: a desire to view the world with the goal of changing it.
  • Complementary knowledge: the expertise that will help your organization create new technology or a new business model.
  • Strategic relationships: the existence of a strong network of business partners.
  • Ambiguity tolerance: the capacity to make decisions based on limited data.
  • Optimistic persistence: the risk-taking mindset needed to persist through the tough times.

Although I wholeheartedly agree with all five suggestions, I nevertheless suspect that most of corporate HR departments, even equipped with advanced evaluation tests, will have troubles with finding enough candidates meeting such a high standard.

Who is your partner?

The second approach pays little attention to the individual skill sets of the team members, but stresses instead the need for an optimal mix of individuals the team is composed of. This approach specifically focuses on the functional roles each member of the team plays in the project. For example, it was suggested that each innovation team should include nine innovation roles of which the most important are the following five:

  • Revolutionary: team member generating and sharing ideas.
  • Connector: team member bringing people together.
  • Customer Champion: team member responsible for interactions with customers.
  • Magic Maker: team member responsible for implementing developed ideas and solutions.
  • Evangelist: team member creating a ‘buzz’ about the project and its results within the organization.

This approach is obviously much more practical than the first. In fact, many organizations have already adopted the ‘spirit’ (if not the exact ‘letter’) of this approach by creating innovation ‘joint task forces’ composed of representatives from different corporate units and functions (or locations, if appropriate): R&D, sales and marketing, customer service, finance, legal, HR, etc.

Implicit in the formation of an innovation team composed of members belonging to different parts of an organization is a belief that this team can only be successful if it includes people with diverse professional expertise and experience. In recent years, this concept of diversity was augmented by a growing body of evidence showing that socially diverse groups (that is, those with a diversity of race, ethnicity, gender, and sexual orientation) are more innovative than socially homogeneous groups.

Research shows that socially diverse groups are better at solving complex problems not only because people with different backgrounds bring new information, but also because the mere presence of individuals with alternative viewpoints forces group members to work harder to get their own points across.

This is good news for HR managers in charge of innovation teams: in our rapidly globalizing workforce environment, finding people with diverse professional, personal, and social attributes is much easier than chasing rare individuals with nebulous qualities such as ‘leapfrogging mindset.’

It’s all about process

There is the third approach to the formation of innovation teams. This approach emphasizes not the team composition or individual skills of its members, but the way the team operates. The logic behind this approach was eloquently articulated in a 2015 article about Google. The article argues that the composition of a team matters much less for its success than how the team members interact, structure their work, and view their contribution. The article listed five key factors that set apart successful Google teams:

  • Psychological safety: team members taking risks without feeling insecure or embarrassed.
  • Dependability: team members counting on each other to do high-quality work.
  • Structure & clarity: the availability of clear goals, roles, and execution plans for each team member.
  • Meaning of work: team members working on something that is personally important for them.
  • Impact of work: team members believing that their work matters.

Characteristically, it is the first factor, psychological safety, which was by far the most important of the five. The safer team members felt with one another, the more likely they were to admit mistakes, work together, and take on new roles. All this obviously positively affected pretty much every aspect of their work.

The power of this specific example in large part derives from the fact that it comes from Google, arguably one of the world’s most innovative companies, because the very notion that innovation requires taking risks without fear of negative career repercussions is hardly new. We all used to hearing calls to ‘fail fast and fail often’ (or even to ‘celebrate failure’) as a surrogate invitation to innovate.

Unfortunately, while voiceful in promoting risk-taking, relentless experimentation, and learning from mistakes (all being parts of the elusive ‘culture of innovation’), companies fail to introduce specific corporate policies that would encourage and reward such a behavior of their employees.

What can be done?

Previously, I proposed two such specific and actionable corporate policies.

  • First, I proposed placing employees involved in strategic innovation projects on fixed-term (tenure-like) employment contracts, as opposed to employment-at-will. This proposal is based on a 2001 study showing that labor laws making it more difficult to fire employees increase their participation in corporate innovation activities.
  • Second, I proposed making stock option grants, as opposed to cash bonuses and other monetary rewards, the principal incentive for engaging employees in innovation projects. This proposal is based on a 2015 finding that companies offering stock options to non-executive employees were more innovative and that the positive effect of stock options on innovation was more pronounced for longer-term grants.

In other words, even before starting to form individual innovation teams, provide all employees with incentives to engage in innovation activities along with immunity for failed innovation projects. Who knows, you may immediately discover that the number of innovative people in your organization is larger than you expected.

Image credit: Leon on Unsplash

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A Case of Innovation Foreboding: 3 Things That Can Damage U.S. Innovation Long-Term

When it comes to complex things, the proverbial glass is never full; it’s only half-empty. On the other hand, the glass is never empty; it’s always half-full.

The glass analogy perfectly applies to U.S. innovation.

In fits and starts, the U.S. economy has begun recovering from the devastating effects of the COVID-19 pandemic. Yet, the pandemic shocks, which will be felt for a long time ahead, have already forced many organizations to change the ways they do business. Almost every operation has been affected: from the manner firms talk to their customers to the logistics of product delivery to maintaining channels of communication between employees.

Will corporate innovation be spared the troubles of adjusting to the ‘new normal’?

Some people don’t even think that anything particularly bad has happened to U.S. innovation at all. Folks who prefer to believe that the glass of U.S. innovation is at least half-full point to the lightning-speed rollout of the RNA-based COVID-19 vaccines and impressive list of ‘fast and frugal’ innovations developed in response to the pandemic. They also celebrate the unprecedented level of the pandemic-driven cooperation between U.S. academic institutions and private companies. And, hey, did the Global Innovation Index 2020, an annual ranking of the world’s innovation capacities, not name the United States the 3rd most innovative country after Switzerland and Sweden?

Houston, do we have a problem?

We do. In fact, we have a host of problems. One of them, obviously pandemic-related, is ‘covidization’ of science, a dangerous ongoing trend of shifting research funding and, consequently, research activities to the field of infectious diseases at the expense of other areas of fundamental medical research. Others have deep and systemic roots in U.S. business and political environment, and in the space below, I highlighted three problems that, in my opinion, can damage U.S. innovation long term.

The well of innovative ideas is drying up due to insufficient R&D funding

Everyone would agree that ideas are the livelihood of innovation. Many also are used to believe that novel innovative ideas are plentiful and cheap, a notion solidified in a popular slogan “ideas are a dime a dozen.”

As I argued before, this wide-spread conviction that we are swimming in an ocean of cheap innovative ideas is no more than a myth. Available evidence shows that the U.S. is facing a growing shortage of novel ideas. Worse, the cost of getting these ideas is growing while their quality seems to be declining. Consider this: by the end of 2019, the venture capital industry had accumulated a whopping $121 billion in so-called “dry powder,” the money for which venture capitalists failed to find ideas to invest in. In other words, ideas might be plentiful and cheap but at the same time not worth of investing money in them.

Where should novel ideas come from in the first place? The answer looks obvious: from R&D, where else?

Exactly, and here is the root of the problem. In the decades that followed World War II, entirely new sectors of the U.S. economy (jet aircraft, modern-day pharmaceuticals, microelectronics, satellites, digital computers, etc.) have been created, thanks to a heavy infusion of public money, with the federal government contributing more than 50% of R&D expenses.

Things have changed since. Although the total U.S. spending on R&D has remained steady for the past years, at 2.5% of GDP, only about 30% of the money now comes from the federal government, whereas 70% of it is contributed by the private sector. With its focus on rapid ROI and competition, will private sector spend money on fundamental and, therefore, potentially risky R&D projects? No.

Sure, the industry can still generate incrementally innovative combinations of old ideas–which indeed may be plentiful and cheap–but it will likely fail to create breakthrough innovations.

President Biden’s proposal to dramatically increase funding for fundamental research along with the elevation of the Director of the White House Office of Science and Technology Policy to the cabinet-level position are promising steps in the right direction. Unfortunately, there is no institutional protection for the increased R&D budget, which may be easily slashed again by a future Republican administration.

The pandemic has disrupted existing innovation networks

A major question, the answers to which are about to start emerging, is to which extent massive shift to remote work has affected the country’s ability to innovate.

Following the initial euphoria over the fact that remote work did not immediately destroy the corporate world, sober voices of concern are heard. Experts warn that online communications, the hallmark of remote work, are characterized by lower information sharing; that means reduced exchange of ideas between members of the innovation teams.

Besides, and perhaps, more consequential, remote work has essentially eliminated serendipitous interactions, unplanned encounters between employees working in close proximity to each other. Serendipity is believed to play a central role in the development of new collaborative partnerships that are crucial for the sustained corporate innovation process—and, as such, serendipity represents one of the driving forces of innovation.

American history already knows one example of a sudden disruption of innovation networks, Prohibition of 1920-1933, when government actions abruptly intervened in the established pattern of people-to-people interactions. This had a profound negative effect on the U.S. corporate innovation. 

Prohibition-induced effects on innovation had one characteristic feature: they didn’t change the scale or the identity of the individuals within innovation networks. They just disrupted established ways people belonging to the networks communicated with each other, and that was enough to damage the whole innovation process.

This is exactly what we see today: innovation budgets are still there (or at least most of them), people involved in the innovation activities, too. But the way these people interact has been dramatically changed by pushing them behind computer screens in their home offices.

The Prohibition case teaches us another lesson: while the innovation input fell dramatically in the years immediately after the Prohibition onset, it rebounded over time, meaning that affected individuals gradually rebuilt their informal social networks. As America gradually opens, and folks return to offices, innovation networks will be re-established. How long it will take, and how innovative the restored innovation network will be, remains to be seen.

The growing politicization of science

In general, Americans trust science. In fact, they trust scientists as highly as military and much higher than religious and business leaders, and, not surprisingly, elected officials.

Unfortunately, a troubling trend has emerged over the past 40 years: the growing distrust in science that has been specifically driven by conservatives. (The trust in science remains steady among moderates and the liberals.)

So far, this distrust in science among the conservatives has been mostly manifested by their skepticism about anthropogenic climate change. However, the COVID-19 pandemic has expanded the front lines of this ‘war on science.’ In December 2020, the Pew Research Center reported that while 84% of Democrats considered COVID-19 as a major threat to public health, only 43% of Republicans agreed. The same ‘blue vs. red’ divide can be seen over seemingly non-partisan issues like wearing masks and COVID-19 vaccination.

Research shows that distrust in science among the conservatives correlates with their unwillingness to support it. Taking to the extreme, this trend may result in increasing ‘defunding science’ both in the Republican-controlled states and, worse, at the federal level.

Image credit: DEVN on Unsplash

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Does predicting the future have a future?

A popular joke attributed to a bunch of historical figures says: “It is difficult to make predictions, especially about the future.”

How true. Writing for the WIRED magazine, Paul Ford described his recent experience of reading The Book of Prediction, a 1980 anthology about what life would be in 50 years. Ford’s summary of the Book’s predictions is short and unambiguous: “All predictions are wrong.” 

It always amuses me how seemingly smart and competent people can say something that is so blatantly stupid (in hindsight, of course).

“Everything that can be invented has been invented” (Charles Duell, Commissioner of the U.S. Patents Office, 1899)

“The atomic bomb will not go off, and I speak as an expert in explosives” (Adm. William Leahy to President Truman, 1945).

“There is no reason anyone would want a computer in their home,” (Ken Olson, President of Digital Equipment Corp., 1977).

But let’s not ridicule folks living a hundred or even 40 years ago, for the human ability to predict the future has hardly improved ever since. Take, for example, the March-April 2020 issue of the MIT Technology Review. Headlined “The Prediction Issue,” it features a dozen or so leading futurologists predicting which technology trends will dominate in 2020-2030. Characteristically, none of the proposed trends even mentioned the threat of a worldwide pandemic like the one that is currently ravaging the globe.

It appears that with the exception of Bill Gates (who, being neither a professional futurologist nor, for that matter, a professional epidemiologist, predicted a pandemic caused by a highly-infectious virus back in 2015), experts have particularly tough time with foreseeing disease outbreaks. A case in point is the 2019 Global Health Security Index, the first comprehensive assessment of the health security capabilities across 195 nations. The Index specifically focused on nations’ preparedness for infectious disease outbreaks that can lead to international epidemics and pandemics. (Sounds to me exactly like COVID-19.)

To the credit of its authors, the Index finds no single fully prepared country: the average overall score among all 195 countries was 40 of a possible 100.

But what genuinely surprised me were the scores that the Index assigned to individual countries. The United States led the world in the overall preparedness (with a score of 83.5). The U.S. also scored the highest in a few specific categories, including prevention of the emergence of pathogens, early detection and reporting of epidemics, and sufficient and robust system to protect health workers. The U.S. was second to the U.K., though, in the category of rapid response to the spread of an epidemic.

I wonder what the predictive power of the Index has been given that the U.S. was among the countries with the highest per capita numbers of COVID-19 infections and COVID-19-related deaths? (Of note: New Zealand, a poster boy for handling the pandemic, came only 35th with the overall score of 54.0.)

Like every normal human being, I love guessing about what will happen tomorrow. And I know that organizations need to peek into the future to foresee upcoming threats and opportunities and to plan for the next steps. Yet, it worries me how fast some self-appointed Cassandras have rushed to tell us about our next future (a.k.a. “next normal”). Did you guys have enough time to figure out first what happened in the near past?

*     *     *

Why can experts be so wrong when predicting the future?

A 2019 neurobiology study provides a useful, if provocative, insight into the issue. A team of scientists analyzed neuronal activity in the brains of mice forced to learn new decision-making skills. As the mice progressed through learning new tricks, more and more neurons in their brains got involved. However, the neuronal activity rapidly became very selective:the individual neurons only responded when the mice made one choice and not another. This pattern became even stronger as the mice learned how to do a task better (i.e., became “experts”). Moreover, when the expertise was fully achieved, the mouse’s brain was ready for that expert decision even before the mouse began executing the task.

In other words, “expert” mice knew how to solve the problem even before starting to solve it! In contrast, the neuronal activities in the brains of “non-expert” mice remained non-selective, meaning that the mice were approaching the task with an “open mind.”

If we take the risk of extrapolating these results to humans, the implication would be that experts approach the problem with the patterns that are already pre-formed in their brains by their prior experience. So, when predicting the future, they see it as a slightly different version of the past and present they’re already familiar with.

*     *     *

To me, that means that predicting the future has a future only if the prediction process will begin systematically engaging large groups of people, experts and non-experts alike. I believe that the more people can be brought together (figuratively speaking, of course), the more granular picture of the future will emerge.

One useful prototype of such an approach could be Wikistrat, a geostrategic business consultancy that leverages crowdsourcing to deliver strategic intelligence. Many Wikistrat’s clients include government agencies interested in geopolitical scenario planning (and, on occasion, in the location of the next black swan hatching).

Corporations that value operational agility—and, therefore, unable to use a large-scale crowdsourcing format all the time—could use another, hugely underappreciated form of utilizing the proverbial “wisdom of crowds”: prediction markets, internal platforms allowing employees to speculate on future events and outcomes. A few large companies included Google have used corporate prediction markets to dramatically improve the quality of their decision-making.

And what is left to regular folks like me who can’t afford the expense of running a crowdsourcing campaign or prediction market? I don’t know about others, but I train myself not to be surprised with anything that tomorrow can bring, a task that was made much easier by watching the past five years of U.S. politics.

Image Credit:  Javier Allegue Barros on Unsplash

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Why ask your future customers only once about new product development, not twice?

More and more firms have begun practicing the consumer-centric approach to new product development (NPD). As part of this approach, firms use sophisticated market research to identify unmet customer needs that could be converted into successful consumer products. Now, it’s time to take the next step and use crowdsourced NPD research to also ask consumers to help design the new products.

(This article was first published on Crowdsourcing Week)

My wife often complains that her hair dryer has been designed by a bunch of bald males. I see her point. True, the device does produce a stream of hot air (accompanied by a loud noise). But it is heavy, and its handle is way too thick for a small-size female’s hand. Besides, control buttons on the handle are positioned in such a way that you cannot operate the device with one hand. When asked if she would buy a hair dryer from the same brand again, my wife answered with a simple “no.”

If you build it, they will come. Will they?

My wife’s hair-dryer experience is hardly an exception. A list of failed innovations in the consumer area is depressingly long. In fact, consumers reject new products at an alarmingly high rate: the late Harvard Business School professor Clayton Christensen calculated that of more than 30,000 new consumer products launched every year, up to 95% fail.

What’s going on? Unfortunately, many firms still practice poor customer research supporting NPD, especially when it comes to specific design and features of new models. “Let’s build it, and they (customers) will come,” the thinking seems to go. But customers, overwhelmed with the sheer number of new offerings and spoiled with the flood of online reviews and recommendations, are not rushing to open their wallets (physical and digital alike) for subpar newcomers. Crowdsourced NPD research and product design certainly has scope to make improvements.

The dawn of customer-centricity approach?

Things have begun to change. Customer centricity—a framework that places the end user at the center of customer experience—is gradually becoming a leading paradigm for new product and services development. Many companies began employing a variety of novel, more sophisticated market research tools, including ethnography and netnography, to identify unmet customer needs (“jobs-to-be-done”) that could be potentially addressed by new and supposedly improved offerings.

One of such new tools elegantly combines product research and crowdfunding. Instead of offering finished products, companies now test consumer demand by collecting online “pre-orders” for products that are still in early development. Once used exclusively by startups, crowdfunding is rapidly becoming a part of the market research toolbox of grand brands like Amazon.

Thank you very much. See you later.

Interestingly, however, that after customer input has been collected and systematized through crowdsourced NPD research (“thank you very much”), customer centricity gets rapidly forgotten, and firms turn to internal R&D teams to address the newly identified customer needs. As the prevailing thinking has it, it is only the firm’s own professionals (marketers, product developers, engineers, etc.)—and no one else–who has knowledge and experience to transform customer needs into working ideas that could be eventually realized into commercially successful products (“see you later at the counter”).

Ironically, the assumption that customers know what they need, but don’t know how to make it, is seldom tested—and when tested, is proven wrong. In a 2012 article published in The Journal of Product Innovation Management, Poetz and Schreier compared novel product ideas generated by a firm’s professionals with those submitted by a crowd of users. The field of innovation was baby feeding products, and all the ideas were evaluated, blindly to their source, in terms of novelty, customer benefit, and feasibility.

The study showed that the ideas generated by the users scored significantly higher in terms of novelty and customer benefits–and only slightly lower in terms of feasibility–than those proposed by the firm’s own designers. Moreover, it was found that the ideas that received the highest overall marks came predominantly from the outside users. So much for internal expertise!

One more time about experts vs. crowds

Of course, one could argue that Poetz and Schreier’s study is an exception. As mentioned above, the field of innovation was baby feeding; the analysis of the users who took part in the idea generation process revealed that about 90% of them were females, many with firsthand experience in feeding babies and a sound technical knowledge of the related products. It is easy to imagine a recent mom with a solid technical background who can come up with better ideas for baby feeding products than a team of professional designers—the majority of whom, I suspect, were males with less than perfect knowledge of the baby-feeding process.

Although I’d love to see Poetz and Schreier’s study replicated in different settings and product areas, I do believe that it has much broader implications. It yet again dispels a popular myth that crowds of problem solvers are composed of “amateurs” and that when posed with a question that requires knowledge and expertise, not just an opinion, crowds are becoming useless (or, worse, outright stupid).

The truth that many experts are reluctant to accept is that properly assembled crowds are composed of experts. They may not work for your company or in your field, but they are experts, nonetheless. Take, for example, InnoCentive, a commercially available crowdsourcing platform with a solid track record of solving difficult scientific and business problems for corporate and non-profit clients. The InnoCentive proprietary crowd is composed of 400,000+ solvers, with almost 70% of them holding advanced degrees. I strongly suspect that some of them are women with experience in feeding babies; I’m also sure that many of them have strong opinions about their hair dryers and other household products.

Firms would be wise to ask them—and other solvers—about products they need through crowdsourced NPD research. Firms would be even wiser to take the next step to ask for help in actually designing these new products.

Check out my eBook, “We the People of the Crowd…,” a collection of stories about crowdsourcing reflecting my personal experience in working with corporate and nonprofit clients.

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Don’t bring me eggs…Sorry, I meant problems.

It appears that the resistance to the time-tested management wisdom “Don’t bring me problems, bring me solutions” has reached a critical mass. Writing back in 2017 for Harvard Business Review, Sabina Nawaz suggested to “retire the saying” and replace this type of mentality with a process of bringing up problems “in a more productive way.”

By now, the fallacy of prioritizing solutions over problems is evident to many. A recent piece warns corporate leaders that the “solutions-only thinking” damages innovation. Worse, it can blind leaders to potential downsides that can eventually culminate in a crisis.

I’m happy to say that innovation managers, myself included, have always hated the “don’t bring me problems” line. We insisted that a thorough investigation of the underlying problems must precede every innovation project; collecting solutions can only start when the problems are spotted, defined, and properly articulated. (Those with a habit of quoting Albert Einstein on every occasion like to mention this one in this context: “If I had an hour to solve a problem, I’d spend 55 minutes thinking about the problem and 5 minutes thinking about solutions.”)

And yet, I’m not ready to swing myself to the “problem-first” extreme of the pendulum. To me, the discussion of what is more important, problems or solutions, reminds of the centuries-old philosophical battle over which came first, the chicken or the egg. I believe that instead of choosing sides—and even inventing better ways of bringing up problems—managers should take a more holistic approach and establish a sustained problem-solving process.

With such a process in place, the question of which is more important, a problem or a solution, is simply irrelevant. Small teams and large organizations alike will be constantly looking for problems, both old and emerging, and defining these problems in a systematic and actionable way. A solution-generating phase, involving various techniques (brainstorming, co-creation with customers, internal and external crowdsourcing, etc.) will follow, with the best solutions being selected and implemented. A solved problem will be automatically replaced by the next waiting for a solution.

The existence of a sustainable portfolio of problems-to-be-solved should also extract the best from the firms’ employees. Some people are better at sensing troubles and spotting trends, whereas others excel at finding fixes. With a constant flow of problems and solutions, everyone will find something to get excited and engaged.

But what are we going to do with the “don’t bring me problems” line itself? I’d suggest trying this instead: “Bring me problems, then solutions, then problems again…” Or anyone can propose a shorter version of the same?

Check out my eBook, “We the People of the Crowd…,” a collection of stories about crowdsourcing reflecting my personal experience in working with corporate and nonprofit clients.

Image credit: Daniel Tuttle on Unsplash

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A sober look at drinking and creativity

In one of my prior lives, I was a researcher in a Russian academic lab. A colleague of mine was studying the effects of ethanol alcohol on yeast cells. Every time she presented project updates to the rest of the lab, an atmosphere of joy would fill the meeting room. Regardless of the results, folks would smile, giggle, and drop witty notes, which in turn triggered a new round of smiling and giggling. At the end of each presentation, another colleague would always ask the same question: “Did you try brandy instead?” A burst of laughter would follow.

I suspect that my previous posts addressing the connection between alcohol and human creativity resulted in the same smiling and giggling, however muffled by the restraints of the social media channels. And I almost heard a sound of suppressed laugh by Alison Beard, a senior editor of HBR, when she interviewed, in 2018, with Prof. Andrew Jarosz of Mississippi State University who led a study of the effects of alcohol consumption on creativity.

Compare this frivolous attitude with a stern academic tone of another HBR article, published in 2017, that studied the effects on creativity of 10 minutes of mindfulness meditation. The authors concluded their article by providing a 10-step, “do-it-yourself” guidance to conducting mindfulness meditation sessions. Can one imagine an article on the effects of alcohol on creativity that would conclude with providing a step-by-step instruction to fixing a drink?

I also suspect that such a humorous perception of the topic is a reason of a discernible pause in the academic research on chemically induced ways to influence human creativity. For example, a 2017 study attempted to systematically review all published (by that time) articles that focused on the relationship between psychoactive substances and creativity/creative artistic process. A total of only 19(!) studies were identified that met inclusion criteria. Little surprise that the results were difficult to summarize because of different study designs, diverse methods used, and various substances examined. A conclusion of the review, nevertheless, was that an association between creativity and substance use did exist.

As I argued before, alcohol represents a convenient experimental model to study chemically induced ways to affect human creativity. As opposed to many narcotics or drugs, ethyl alcohol is a simple chemical molecule, whose behavior in the body is reasonably well studied. Using this model, researchers may start identifying specific neurochemical reactions in the brain that are responsible for creativity.

My preliminary review of the corresponding academic literature allows to make two basic conclusions. First, moderate doses of alcohol (resulting in a blood alcohol content of approximately .075, i.e., just below the U.S. legal limit) did improve creating problem solving in affected individuals. Second, alcohol did not influence performance on measures unrelated to creative problem solving, suggesting that alcohol influenced specifically creative performance.  

One article attracted my attention. It is widely accepted that the creative process goes through four distinct stages:

  • Preparation. At this stage, your brain is gathering information.
  • Incubation. It is at this stage that you let your mind wander around.
  • Illumination. This is “eureka!” moment. Connections in your brain collide, and you realize that you got an idea.
  • Verification. Your critical thinking skills return at this stage, and you start “packaging” your newly born idea in a consumable way.

Back in 2011, Torsten Norlander of Karlstad University in Sweden showed that alcohol consumption specifically stimulated the incubation and illumination stages of the creative process but inhibited its preparation and verification stages. This seems to be exactly what Prof. Jarosz’s team observed a year later: intoxicated individuals who solved more creative problems in less time that the control (sober) group perceived their solutions as the result of a sudden insight. Eureka, in other words.

Taken together, the data aligns with Prof. Jarosz’s hypothesis that people under the influence are more susceptible to so-called mind wandering, which results in losing some focus but gaining instead the ability to see a “bigger picture.” This effect, of course, can be harmful in many situations requiring concentration but it might be helpful in others where the ability to connect the proverbial dots is more important than the ability to collect them.

One can further hypothesize that creativity improves when an individual is capable of engaging additional, dormant before parts of the brain in the creative process. It is tempting to take one more step and speculate that the outcome of the creative process can be dramatically improved by the engagement of not just additional parts of a single brain but of additional brains of multiple, previously unengaged individuals. This is what happens when we use crowdsourcing: engaging multiple brains instead of one.

I will return to this idea in my following posts.

Check out my eBook, “We the People of the Crowd…,” a collection of stories about crowdsourcing reflecting my personal experience in working with corporate and nonprofit clients.

Image credit: https://www.dailyartmagazine.com/drunk-absinthe/

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Prohibition, disrupted networks, and innovation

The best answer to the question “Do government regulations hurt innovation?” seems to be “It depends.”

The suspicions against regulations are fueled by wide-spread belief that they damage economic growth, with which innovation is intimately connected. Yet, academic research on the topic paints much more nuanced picture. For example, several studies show that corporate innovation is fostered by laws that limit firms’ ability to discharge their employees at will. This phenomenon is called an insurance effect: feeling increased protection from negative consequences of failure, employees are more committed to engaging in risky innovative projects.

However, there is a shining example of a massive government regulation that had profound negative effect on innovation: Prohibition in the United States in 1920-1933. In a brilliant 2020 paper, “Bar Talk: Informal Social Interactions, Alcohol Prohibition, and Invention,” Michael Andrews provides a detailed description of what happened when a government action abruptly intervened in the established pattern of people-to-people interactions. 

Prohibition and patents

Before the passage of federal prohibition, states and counties could determine for themselves whether or not to allow alcohol consumption in bars and saloons. When federal prohibition went into effect, counties that were previously wet saw an 8-18% drop in patenting relative to consistently dry counties in the same state.

As a former researcher, I admire the rigorous checks Andrews applies to prove that the observed effects were caused by preventing people from going to bars rather than by other factors. For example, he shows that the drop in patenting was smaller for groups that did not typically attend saloons, such as women and ethnic groups that preferred to drink in private.

More importantly, Andrews presents evidence that prohibition did not appreciably reduce the total alcohol consumption in newly dry counties (surprise!). And this leads to Andrew’s major point: the negative effect of prohibition on invention was caused not by preventing people from drinking alcohol, but by disrupting natural social networks.  

Prohibition and disrupted networks

Prohibition presents itself as a particularly useful model to study the role of social networks in innovation. Prior to prohibition, the saloons acted as a social hub in which individuals could exchange information in an informal setting. Prohibition is so useful to studying the effects of social interactions on innovation because it disrupted the structure of social networks but not its scale or the identities of the individuals within the network.

In my opinion, one of the study’s findings carries special weight. If networks facilitated invention by simply making it easier for individuals to find collaborators, then only patents with multiple inventors would have declined. Instead, Andrews found that solo-inventor patents declined as well. That means that networks serve not only to bring people together but also as a venue to exchange ideas between them

COVID-19 and innovation

Andrews’s study is especially important considering massive disruption of global innovation network caused by the COVID-19 pandemic. Like prohibition, the pandemic did not change the scale or the identity of the individuals within the network. But by massive shifting to remote work (to “drinking from home,” so to speak), it disrupted informal interactions, and we can only guess about the long-term consequences of this disruption.

Following the initial euphoria over the fact that remote work did not result in the immediate end of the corporate world, voices of caution and concern might be already heard. In particular, experts warn that online communication are characterized by lower information sharing—and that means reduced exchange of ideas between innovators, a major factor in prohibition-induced patenting slump. To believe that this will not affect innovation in some negative way in the future is to be a techno-optimist on steroids.

Innovation and post-COVID

There is one more finding in Andrews’s study that deserves mentioning. While patenting fell dramatically in the years immediately after the prohibition onset, it rebounded over time, meaning that affected individuals gradually rebuilt their informal social networks.

Interestingly, however, when folks began rebuilding their social networks after prohibition, they did not collaborate with the same individuals as they did in the past. Instead, they connected with new people in new ways, being exposed to different ideas as a result. This was manifested in a long-lasting change in the types of inventions these individuals created, as measured by patent classes. In other words, while the rate of innovation will restore over time following disruption, the direction of innovation may change.

Sure, innovation will recover post-COVID. But it will be different innovation. Will we like it more? Less?

Check out my eBook, “We the People of the Crowd…,” a collection of stories about crowdsourcing reflecting my personal experience in working with corporate and nonprofit clients.

Image credit: http://www.reddit.com/r/reddeadredemption/comments/9w5ehq/you_can_shutdown_the_saloon/

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Innovation and spirit. Yes, that spirit.

Recent evidence strongly suggests that the U.S. is facing a growing shortage of novel ideas. Worse, the cost of getting these ideas is growing while their quality seems to be declining. Left unchanged, this trend may have serious negative consequences for American innovation.

One of the possible approaches to reversing this trend could be strengthening the ideation process by stimulating creativity. In a recent post, I described a study showing that creativity could be boosted by moderate procrastination. The authors of the study argued that moderate procrastination sets in motion a mechanism of problem restructuring, which results in the production of more out-of-the-box ideas.

Promoting procrastination, however moderate, goes against the established cultural norms that force us to always stay (or at least pretend to be) busy. What the scientific data seems to be telling instead is that treating our brain with occasional spells of a quiet, unrushed deliberation may make us more creative.

Another way to stimulate creativity—while, again, going against multiple social boos and taboos–might be to consume moderate amounts of alcohol. This seemingly fancy (and even offensive to many) idea stems from a scientific study described in a 2018 article in Harvard Business Review. The authors of the study treated a group of men aged 21-30 to a vodka/cranberry juice mix in three drinks over a 30-minute period until their blood alcohol level reached near legal intoxication level (0.075). Then they were given a series of word association problems to solve.

The result? Tipsy subjects solved 13% to 20% more problems—and did it faster–than their sober peers in the control group. The authors of the study hypothesized that people under the influence were more susceptible to so-called mind wandering, which results in losing some focus but gaining instead the ability to see a “bigger picture.” This effect, of course, can be harmful in many situations requiring concentration but it might be helpful in others where the ability to connect the proverbial dots is needed more than the ability to focus on a single dot.

I like this study for one simple reason. Ethyl alcohol, as opposed to many narcotics or drugs, is a simple chemical molecule, whose behavior in the human body is quite well understood. Using this relatively simple model, researchers may start identifying specific neurochemical reactions in the brain that are responsible for creativity.

It turns out that the benefits of alcohol consumption may extend to our social life—all despite the widely-held assumption that drinking causes serious social problems. A study conducted back in 2006 found that self-reported drinkers earned 10-14% more than abstainers. Moreover, males who frequented bars at least once per month—so-called social drinkers–earned an additional 7% on top of the 10-14% drinkers’ premium.

The authors of this study hypothesized that the factor leading to higher earnings by drinking people was their increased social capital. Wikipedia defines social capital as “the networks of relationships among people who live and work in a particular society, enabling that society to function effectively.” To me, the key word in this definition is “networks.” Social drinkers might be more successful because they form and maintain networks with other folks—and do this better than non-drinkers.

A 2019 study linked alcohol-consumption-based social networks (and their disruption) to innovation. After the imposition of state-level alcohol prohibition in the U.S. in 1920-1933, previously wet counties had 8-18% fewer patents per year relative to consistently dry counties. The effect was largest in the first three years after the imposition of prohibition and rebounded thereafter. The author attributes this effect to the disruption of existing social interactions and subsequent formation of new, non-alcohol-based ones.

I suspect that when the final tally of the effects of the COVID-19 pandemic on innovation is tabulated, we’ll be shocked by the results, by the damage that the Lockdown of the Century has caused to our ability to generate new products and business models. Sure, disrupted networks will be eventually restored but how shall we make up for the irreversible loss of human interactions over the past year?

But I don’t want to end this piece on a sour note. A national survey in September 2020 found that American adults have increased their consumption of alcohol during the pandemic: the overall frequency of alcohol consumption increased by 14% among adults over age 30, compared to the same time last year. Who knows, a spike in creativity caused by alcohol consumption may compensate for the negative effect of disrupted networks.

If so, not all is lost for American innovation.

Check out my eBook, “We the People of the Crowd…,” a collection of stories about crowdsourcing reflecting my personal experience in working with corporate and nonprofit clients.

Image credit: “Absinthe Lover” by Pablo Picasso

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Does labor regulation hurt innovation?

Those who believe that government regulations, including labor laws, hurt innovation, please, raise your hand. Wow, quite a few hands raised!

Indeed, the suspicion against regulations is fueled by common belief that they damage economic growth, with which innovation is intimately connected. For example, some scholars have expressed concerns that slower economic growth in Southern European and Latin America countries with heavy labor regulation could be due to reluctance to fund innovation programs because of the burden of labor laws (for references, see here). However, a point of view has been strongly articulated too that if applied effectively, regulation can foster a thriving, competitive marketplace where innovation and technological progress flourish. 

Interestingly, academic literature on the effects of regulation, in particular labor laws, on innovation seems to favor the second, more sanguine point of view.

For example, a 2010 study compared the innovation output in five countries—the U.S., U.K., France, Germany, and India–and found that stronger labor laws positively correlated with a country’s innovation output. Interestingly, this effect was more pronounced in innovation-intensive industries, such as medical devices, than in more traditional industries, such as textile. Equally importantly, the study found that the only dimension of labor laws that had a tangible impact on innovation was the “regulation of dismissal” component, i.e., the ease/difficulty with which employers could dismiss employees.

Another study published by the same authors in 2014 analyzed the impact on innovation of the U.S. wrongful discharge laws (WDL). These laws provide employees with greater protection than employment-at-will, where employees can be terminated with or without just cause. The staggered passage of WDL across the U.S. states created a natural experiment assessing their impact on the innovation output. The study found increase in the number and improved quality of patents issued in the states that adopted WDL, with the effect starting to emerge two years after the WDL passage. As in the previous work, the positive impact on innovation was significant only in highly innovation-intensive industries.

Taken together, both studies indicate that innovation is fostered by laws that limit firms’ ability to discharge their employees at will. The authors call this phenomenon an “insurance effect”: feeling increased protection from negative consequences of failure, employees are more committed to engaging in risky innovative projects.

These findings are fully consistent with a theoretical concept proposed by Berkeley’s Gustavo Manso in 2011. The concept postulates that the optimal incentives motivating employees to innovate must include a combination of tolerance for failures in the short term and reward for success in the long term. Tolerance for early failures allows the employees to take risks at the initial stages of the innovation process without incurring the negative consequences of failed projects. The reward for long-term success encourages the employees to explore risky ideas that may allow them to achieve innovation breakthroughs in more distant future.

In other words, the best way to encourage risk-taking and experimentation is not to “celebrate failures,” as often suggested, but to remove the proverbial Sword of Damocles of punishment for them, something that any firm can easily do by modifying its termination policies.

A recent study adds an additional layer of complexity to the relationship between innovation and regulation. An international team of economists analyzed innovation outputs in France where many labor regulations apply to firms with as few as 50 employees. The authors found that regulations do negatively affect innovation, but in an interesting twist they saw that this regulation negatively affected only incremental, but not radical innovation. Using a sports metaphor, they concluded that “[a] more regulated economy may have less innovation, but when firms do innovate, they tend to ‘swing for the fence’ with more radical…breakthroughs.”

On emotional level, I hate regulations (hey, I grew up in the Soviet Union!). But regulations are part of our lives. There is no point in crying that “structure stifles innovation.” We should instead constantly look for ways to create conditions favoring innovation. Through regulations and otherwise.

Check out my eBook, “We the People of the Crowd…,” a collection of stories about crowdsourcing reflecting my personal experience in working with corporate and nonprofit clients.

Image credit: Paweł Czerwiński on Unsplash

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