A sober truth about drunk people

Harvard Business Review is a serious periodical not known for publishing frivolous content. Yet the tone of one of its features, “Professor X, defend your research,” in the latest (May-June 2018) issue, was far from academic.

Defending his research was Professor Andrew Jarosz of Mississippi State University, and the title of the HBR piece read “Drunk People Are Better at Creative Problem Solving.” Prof. Jarosz told how he treated a group of men ages 21 to 30 to vodka/cranberry juice in three drinks over a 30-minute period until their blood alcohol level reached near legal intoxication point of 0.075. He then gave them a series of word association problems to solve. The result? Tipsy subjects solved 13% to 20% more problems—and did it faster–than sober subjects in the control group.

Prof. Jarosz hypothesizes that people under the influence are more susceptible to the so-called mind wandering, which results in losing some focus but gaining the ability to see a “bigger picture.” This effect can be harmful in many situations requiring concentration but might be helpful in others where the ability to connect “dots” is more relevant.

Prof. Jarosz’ research is yet another, still very rare, piece of evidence showing the unexpected benefits of alcohol consumption. Back in 2006, Bethany Peters and Edward Stringham found that drinking earned 10-14% more than abstainers. Peters and Stringham hypothesized that the factor leading to higher earnings by drinking people was their increased social capital. Indeed, there was a difference between the so-called social and non-social drinkers: males who frequented bars at least once per month earned an additional 7% on top of the 10% drinkers’ premium.

Jokes aside, Prof. Jarosz’ research may turn out to be an important milestone in the study of human creativity. Ethyl alcohol, as opposed to many narcotics or drugs, is a simple chemical molecule, whose behavior in the body is reasonably well studied. Using this model, researchers may well start identifying specific neurochemical reactions in the brain which stimulate creativity, regardless of whether the creative individuals prefer Scotch or Perrier.

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Innovation: for and against

I like Jeff Bezos’ line: “Good intentions don’t work, mechanisms do.” To me, it sounds like a full support of my conviction that endless talks about establishing a “culture of innovation” is a distraction, rather than an enabler, in fostering corporate innovation. Instead of chasing chimeras, organizations should start implementing concrete corporate policies helping innovation take root. Over the past few months, I’ve posted a series of pieces (here, here, here, here, here, here, here, here, and here) outlining specific socioeconomic factors that favor or obstruct innovation.

When looking for these factors, I closely followed a theoretical framework created by Gustavo Manso in 2011 postulating 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 this post, I’m listing these specific factors organized in two (“for“ and “against”) groups.

AFactors promoting innovation

A1. Stricter labor laws, such as 1988 Worker Adjustment and Retraining Notification Act (Acharya et al., 2010) and the U.S. wrongful discharge laws (Acharya et al., 2013) (but see B1).

A2. Firms’ family ownership (Kammerlander and van Essen, 2017).

A3.  Firm-friendly bankruptcy laws (Acharya and Subramanian, 2009) (see also B4).

A4. Engaging risk-tolerant VC investors (Tian and Wang, 2011).

A5. Greater use of long-term incentives, such as stock-option grants, as a way to compensate employees involved in innovation activities: CEOs (Francis et al., 2011), heads of corporate R&D (Lerner and Wulff, 2006), and non-executive employees (Chang et al., 2015).

A6. Better employee treatment, as measured by the KLD Socrates (Chen et al., 2016 and Mao and Weathers, 2016) or MSCI ESG STATS (Mayer et al., 2016) databases.

B. Factors obstructing innovation

B1. Unionization (Bradley et al., 2015) (but see discussion in Doucouliagos, 2017 below).

B2. Income inequality (Doucouliagos, 2017).

B3. IPO (Bernstein, 2017).

B4. Creditor-friendly bankruptcy laws (Acharya and Subramanian, 2009) (see also A3).

The results of the above studies suggest that firms may increase the efficiency of their corporate innovation by modifying its termination and compensation policies. Here, I want to offer two specific recommendations:

  • To place employees involved in strategic innovation projects on fixed-term employment contracts (as opposed to employment-at-will). Alternatively, tenure-like positions may be created for the same employees. Whatever the arrangement, employees should be assured that they have a fixed “window of opportunities”—say, five-six years—to make progress before any administrative decisions regarding their employment will be considered.
  • To make stock option grants the principal incentive for engagement in innovation projects–as opposed to cash bonuses and multiple non-monetary recognition and rewards.

Admittedly, capitalizing on the effects of bankruptcy laws and VC investors’ risk tolerance isn’t straightforward. However, firms should consider local bankruptcy codes when choosing the location of their innovation centers. And startup companies ought to be aware of the failure tolerance level of VC investors they choose.

A larger point, however, is that we must finally move from words to deeds when dealing with innovation. Implementing specific corporate policies is a much better way to promote it than finding topics for meaningless discussions.

The image credit: http://matthewgates.co/tipping-scales/

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Innovation “quid pro quo”: firms that treat workers better are more innovative

In my previous post, I described studies showing that giving stock option grants to both executive and non-executive employees fosters innovation, which points to the important role compensation plays in defining corporate innovation performance. However, compensation is only one factor employees consider when assessing the quality of their jobs. Can other factors contributing to the overall job satisfaction influence corporate innovation?

A series of recent academic publications give a strong positive answer to this question. Two groups of researchers, one led by Chen Chen and the other by Connie X. Mao, used the KLD Socrates database to explore the relationship between employee treatment and corporate innovation. The KLD Socrates database collects firm-level performance scores—expressed as employee treatment index (ETI)–on employee treatment standards, such job satisfaction, work environment, demographic makeup, diversity, management credibility, internal opportunities, benefit programs, etc.

Both groups found that firms with higher ETI produced more patents (and more quality patents). Similar results were obtained when the two groups used Fortune’s list of “100 Best Companies to Work for” as an alternative proxy for employee treatment and satisfaction.

Chen and co-authors further found that firms with high ETI have a higher market value of their patents and that impact of these patents in operating performance was greater than in firms with lower ETI. They also demonstrated that better employee treatment positively influenced innovation through increased inventor productivity (generated, in part, by lower inventor turnover), improved teamwork, and better internal communication and knowledge sharing.

Another group of researchers, led by Roger C. Mayer, studied the relationship between employee treatment and corporate innovation using different parameters. They measured corporate innovation in term of innovation efficiency, which they defined as the ratio of innovation output (the number of patents and the frequency of their citation) to economic input (R&D expenses). And to assess employee treatment, they used the MSCI ESG STATS database, which ranks U.S. publicly-traded firms in seven broad categories, including environment, employee relations, diversity, and corporate governance.

Like the two other groups, Mayer and co-authors showed that corporate policies that result in better treatment of employees enhance innovation efficiency. Interestingly, they found that innovation efficiency was enhanced by more pro-diverse and inclusive culture, specifically by equal treatment of women and minorities.

The above results show that corporate policies that improve employee’s well-being and perceived job security allow them to focus their attention and efforts on value-producing activities. This is consistent with the positive effect that “tolerance for failure” has on corporate innovation, the topic I covered in previous posts (for example, here).

Equally importantly, these results reinforce the idea that policies that promote diversity and inclusion in the workplace have a strong positive influence on corporate innovation, a key point that deserves further exploration.

The image credit: www.incarabia.com/lead/10-cheap-and-simple-steps-to-happy-employees/

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Innovate today, get paid tomorrow

Theoretical analysis conducted by Gustavo Manso in 2011 suggests 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. 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 the long-term success encourages the employees to explore risky ideas that may allow them to achieve innovation breakthroughs in more distant future.

In a follow-up study, Florian Ederer & Manso employed a series of laboratory experiments to provide empirical support to the above hypothesis. They showed that the best performance during an experiment was demonstrated by the participants who could explore, risk-free, different options during the first part of the experiment while being compensated for the results achieved in its second part.

Field data are supporting Manso’s theoretical conclusions, too. In a 2006 paper, Josh Lerner & Julie Wulf pointed out that beginning in the late 1980s, U.S. corporations began to increasingly link the compensation of R&D personnel to the strategic objectives of the firms. Specifically, the compensation of the heads of corporate R&D shifted towards much greater use of long-term incentives, such as stock options.

Lerner & Wulf looked at the relationship between this shift in compensation and the corporate innovation outputs. They showed that among firms with a centralized R&D organization—in which the head of corporate R&D has a greater authority over strategic decisions—a clear relationship emerges: more long-term incentives are associated with more heavily (i.e., more high-quality) patents issued to these firms. Lerner & Wulf found no relationship between patent quality and incentives offered to CFOs or heads of HR.

Similar results were obtained by Bill Francis, Iftekhar Hasan, and Zenu Sharma when the authors analyzed the relationship between innovation output and CEO compensation. In a sample of 1,106 firms operating during 1992–2005, they found that CEO compensation that enforces long-term commitment (new options grants and previously granted unvested and vested options) had a positive relationship with innovation as judged by the number of issued patents and the frequency of their citation. Further confirming Manso’s reasoning, Francis and co-authors found that so-called golden parachutes–a large payment guaranteed to the CEO in case of dismissal following a merger or takeover—also have a positive effect on corporate innovation.

It turns out that the corporate innovation benefits from granting stock options not only to CEOs and heads of corporate R&D but to the rank-and-file (non-executive) employees as well. This was the finding by Xin Chang and co-authors published in 2015. Interestingly, Chang et al. found that the effect of stock options on the employee innovation performance was stronger when the average expiration period of stock options was longer and when firms had broad-based (as opposed to targeted) non-executive option plans, which enhanced cooperation among employees.

There appear to be at least two reasons accounting for the beneficial effect of stock options on innovation. First, it’s the asymmetric payoff structure of stock options, which not only rewards employees with unlimited upside potential when innovation succeeds, and stock prices increase, but also protects them with limited downside loss when innovation fails, and stock prices fall.  Second, innovation projects are long-term, multi-stage, and labor intensive. Employee stock options with long vesting period encourage employees to stay with their firms longer while investing their intellectual capital in innovation.

A 2010 study highlighted a positive effect of non-executive stock option grants on corporate operating performance. The authors attribute this effect to increased cooperation and mutual monitoring among co-workers. The results of the above studies highlight another important function of broad-based stock option plans: fostering innovation.

The image credit: http://www.orthospinenews.com/2017/01/19/aurora-spine-corporation-grants-stock-options/

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Crowdsourcing “in reverse”: asking crowds to ask questions

It’s important to understand that crowdsourcing is first and foremost a question, a question that you ask a large and, ideally, diversified crowd of people. And for as long as it’s well-thought-out, properly defined, and clearly articulated, it doesn’t really matter what this question is about. It can be a question about a solution to a problem, something crowdsourcing is mostly known for; it can also be a question about the problem itself.

I call asking questions about a problem crowdsourcing “in reverse.” A few years ago, researchers at Harvard Medical School proved the effectiveness of this approach. They asked the crowd the following question: what do we not know to cure Type 1 diabetes? In other words, are there “neglected” problems that for whatever reasons were off the radars of the existing Type 1 diabetes research groups? What questions need to be asked to accelerate the rate of Type 1 diabetes research?

Interestingly, among 12 winning contributions, there was one submitted by a diabetes patient. Although lacking appropriate scientific background, this person has provided a unique perspective on the type of challenges faced by diabetes patients, a perspective that can’t be offered by a healthy individual.

Such approach—using crowdsourcing to combine scientific knowledge of doctors with experiential knowledge of patients—has been expanded and further developed by researchers at the Open Innovation in Science Center in Vienna, Austria. They addressed the issue of mental health and illness, a subject that is highly relevant from the public health, economic and policy points of view, yet relatively under-researched when compared to other medical conditions (such as cancer, for example).

During the submission period, which lasted for 11 weeks in spring-summer 2015, patients, their caregivers, doctors, and other medical professionals were asked to highlight unresolved problems and open research questions in the field of mental health. Characteristically, 40% of the received contributions were submitted by people who described themselves as patients.

The crowdsourcing campaign has identified an area of mental health research that hasn’t received sufficient attention so far: mental health of children and adolescents with mentally ill parents. To fill the gap, two research projects addressing this issue have since been launched with a total funding of six million euros over the period of four years.

The Center’s next target is the field of orthopedic traumatology. By launching a crowdsourcing campaign called “Tell Us!”, the researchers want to generate novel and original research questions, both from experts and patients, that have previously not been properly addressed in the area of traumatology research. As with the previous project, all “out-of-the-box” questions, ideas and hypotheses will be fed back into the scientific workflow.

The project will begin in early May 2018 and last for two months. If you believe (as I do) that this particular way of using crowdsourcing makes a lot of sense, please, help spread the word about the project. The project website is www.tell-us.online.

I’d like to thank Benjamin Missbach (benjamin.missbach@lbg.ac.at), Project Manager at the Open Innovation in Science Center, for introducing me to the “Tell Us!” project. I’m also grateful to him for his comments on this piece.

The image credit: https://tell-us.online

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Crowdsourcing 2.0

I like to argue, only half-jokingly, that crowdsourcing is very simple. It consists of only two components: a question and a crowd—a question that you present to a crowd and a crowd that you assemble to answer this question.

And yet, the acceptance of crowdsourcing as a practical problem-solving tool has been slow. The explanation might be quite simple: as every tool, crowdsourcing requires certain knowledge and skills to be properly used–and it is this knowledge and skills that are lacked by many organizations.

To begin with the question “part,” there is almost a paralyzing uncertainty over the issue of which technical or business problem can be successfully solved by crowdsourcing. Besides, organizations that are new to crowdsourcing often struggle with the crowd “part,” too, as they feel profoundly confused with a vast variety of available crowdsourcing platforms. Mistakes are very common when novice users try to match their problems with a specific, most suitable platform.

More time and effort will be needed to make the basics of crowdsourcing—Crowdsourcing 1.0, so to speak—a common knowledge among practitioners in the field. At the same time, it’s never too early to start imagining what the future of crowdsourcing—I’d call it Crowdsourcing 2.0—may look like.

I’m particularly interested in a role artificial intelligence (AI) will play in molding the parameters of crowdsourcing of tomorrow. On the “question front,” a large amount of data is already available describing both successful and failed crowdsourcing campaigns. Analyzing the data will help identify types of problems—and/or essential components and features of them–that make any problem more (or less) amenable to solving by applying the wisdom of crowds. Moreover, it’ll be certainly possible to design machine algorithms allowing users to translate their technical or business challenges into specific problem statements with the highest expected levels of success. The same algorithms will tell the users which kind of information they should be ready to provide to the crowd to maximize the odds for the problem to be solved.

AI has a potential to shape the process of matching a specific problem to a “perfect” crowdsourcing platform, too—although this may require some additional effort, given that existing commercially available platforms are often short-lived and have a poor track record of success. By analyzing a set of problems a platform has dealt with in the past, the reported solution rate, and the size and the composition of the crowd behind the platform (where this info is available), it’ll be eventually possible to automatically generate a list of the most promising platforms to tackle any particular problem. AI tools that will allow a rapid creation of sufficiently large and diverse crowds (“crowds-on-demand”) are expected to be developed, too.

As a quote credited to both Abraham Lincoln and Peter Drucker puts it: the best way to predict the future is to create it. The creation of the future of crowdsourcing (Crowdsourcing 2.0) begins today.

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The image credit: https://newevolutiondesigns.com/50-futuristic-city-wallpapers

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Are you innovating? We won’t be paying you today!

A solid body of evidence, from both controlled laboratory experiments and field studies, shows that compensation based on the pay-for-performance (P-f-P) principle—when individuals receive a fixed percentage of the profits resulted from their activities–is effective in inducing higher levels of effort and productivity. However, one must keep in mind that this evidence comes from studying simple routine tasks, in which effort is the main prerequisite for productivity.

But what about activities such as innovation, which requires creativity and exploration of novel approaches? The concern here is that P-f-P, when applied to innovation, could undermine performance because it’ll encourage repetition of what has worked in the past, rather than experimenting with new ways of doing things.

How should executive and managerial compensation be structured if the goal is to foster innovation?

An interesting insight was provided by Florian Ederer and Gustavo Manso in their 2013 paper “Is Pay-for-Performance Detrimental to Innovation?” Ederer & Manso conducted a series of laboratory experiments, in which 379 subjects faced the choice between exploitation and exploration. More specifically, subjects were asked to control the operations of a fictional lemonade stand and had to find the optimal location of the stand as well and the optimal product features. Subjects had a choice to either fine-tune the recommendations given to them by the “previous manager” (exploitation) or select an entirely different strategy (exploration).

There were three different treatment groups, with the only difference between them being the structure of compensation. Subjects in the first treatment group received a fixed wage during the whole experiment. Subjects in the second group were given a standard P-f-P contract (a fixed percentage of the profits produced during the experiment). For the subjects in the third group, the compensation was a fixed percentage of the profits produced during the second half of the experiment. This compensation structure allowed the subjects to fail at no cost in the first half of the experiment while exploring new strategies, but reap the full benefits of their exploration after learning better ways of performing the task.

The authors’ main hypothesis was that subjects in the third group would explore more than subjects in two other groups while trying to find a superior strategy. Experimental results did confirm this assumption. Moreover, the results showed that the subjects in the third group not only explored more; they showed superior performance in finding an optimal performance strategy.

The authors found that risk aversion played a key role in accounting for the poor performance of the subjects in the second (P-f-P contract) group: under the P-f-P contract, more risk-averse subjects were less likely to find the optimal performance strategy; as the result, they obtained lower average profits than less risk-averse subjects.

The above study provides a solid experimental support to a theoretical analysis conducted by Manso in 2011 that showed 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 run. 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. In a series of previous posts, I discussed how tolerance for innovation failures can be institutionalized by the application of the wrongful discharge laws, debtor-friendly bankruptcy codes, and choosing risk-tolerant VC investors.

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. The best way of achieving such an encouragement seems to be via compensation strategies heavily relying on long-term stock option grants. I’ll return to this specific topic in my future posts.

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The image credit: https://gpidesign.com/2015/06/photo-or-faux/

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