Networking Inside & Outside the Firm to Drive Innovation

Linus Dahlander, Siobhan O’Mahony, and David Gann have conducted a fascinating new study regarding researchers at IBM.   They studied more than 600 technical experts at IBM, people responsible for many of the firm's patents.  They paid attention to how these experts networked with others both inside and outside the firm.  In an HBR digital article, the scholars summarize their findings:  

We measured the breadth of each person’s external social network by the different types of external sources they interacted with. Then we assessed how the breadth of each person’s external network was associated with subsequent innovation outcomes at IBM, like the quantity and quality of the patents the individual produced.

Surprisingly, we found that our respondents’ most common sources of inspiration for new ideas were their colleagues inside, rather than outside, the firm. In contrast with current theories of open innovation, people with broader external networks were no more innovative than people with narrow external networks. Many of the experts relied mostly on internal networks and were still innovative. To better understand this puzzle, we examined how people allocated their time among their information sources inside and outside IBM.

We discovered that experts with a broad external network were more innovative only when they devoted enough time and attention to those sources... This is an important finding, as many managers are keen on the idea that networking and forming external ties can boost the flow of ideas that spurs innovation. What we found is, for that to happen, employees need to devote significant time and attention to creating and sustaining their external relationships. In some cases, people who focused on learning from colleagues inside the organization were just as innovative.

About 30% of the respondents who had a broad external network did not allocate enough time to learn from those relationships. These people would have been better off deepening relationships with their colleagues inside the firm. For spending time inside the company is also important to understanding the firm’s innovation needs and knowing how to develop and execute on innovative ideas.

In sum, the best innovators engage in a balance of external and internal networking.  They scour the outside world for ideas, but they do so given a solid understanding of what's happening within their firm.  Moreover, they engage with people in sufficient depth so as to truly learn from them, rather than simply gaining a superficial understanding of the trends and developments in the outside world.  You can't simply have coffee with a hundred people in search of the next big idea. You have to immerse yourself in certain contexts, whether they be internal or external.   Learning takes time, as does relationship building.   

Avoiding Confirmation Bias

Earlier this year, Tom Stafford wrote a column for the BBC's website about how to combat confirmation bias.  In other words, how do we avoid the problem of searching for and relying on data that confirm what we already believe (while dismissing or avoiding data that contradict our pre-existing beliefs)?  

Stafford recalls a famous set of experiments by Charles Lord,  Lee Ross, and Mark Lepper.  In one classic study from several decades ago, they looked at how people's attitudes toward the death penalty changed after being exposed to two contrasting studies - one demonstrating a powerful deterrent effect for the death penalty and another showing the exact opposite finding.   Lord, Ross, and Lepper found that people's attitudes polarized after looking at the two studies.  Why?  They assimilated the data in a biased way, relying heavily on the information that supported their pre-existing beliefs.  

Stafford describes a second experiment that these scholars conducted.  In this subsequent research, they compared two strategies for trying to combat confirmation bias.  They analyzed the impact of two different sets of instructions for people.   They were given these instructions before looking at the data.  Stafford summarizes what these scholars discovered: 

For their follow-up study, Lord and colleagues re-ran the biased assimilation experiment, but testing two types of instructions for assimilating evidence about the effectiveness of the death penalty as a deterrent for murder. The motivational instructions told participants to be "as objective and unbiased as possible", to consider themselves "as a judge or juror asked to weigh all of the evidence in a fair and impartial manner". The alternative, cognition-focused, instructions were silent on the desired outcome of the participants’ consideration, instead focusing only on the strategy to employ: "Ask yourself at each step whether you would have made the same high or low evaluations had exactly the same study produced results on the other side of the issue." So, for example, if presented with a piece of research that suggested the death penalty lowered murder rates, the participants were asked to analyse the study's methodology and imagine the results pointed the opposite way.

They called this the "consider the opposite" strategy, and the results were striking. Instructed to be fair and impartial, participants showed the exact same biases when weighing the evidence as in the original experiment. Pro-death penalty participants thought the evidence supported the death penalty. Anti-death penalty participants thought it supported abolition. Wanting to make unbiased decisions wasn't enough. The "consider the opposite" participants, on the other hand, completely overcame the biased assimilation effect – they weren't driven to rate the studies which agreed with their preconceptions as better than the ones that disagreed, and didn't become more extreme in their views regardless of which evidence they read.

What's the "Optimal" Failure Rate at Netflix?

When Orange is the New Black, House of Cards, and Crown became mega-hits for Netflix, many people credited the analytics capabilities of the company. Mining the customer data had enabled the firm to project the type of original programming that would be highly successful. By this logic, Netflix would achieve a lower failure rate on new shows than the major television networks. After all, broadcasters such as CBS and NBC cancel a substantial share of their new shows each year, some after only a few episodes. 

On the recent Netflix earnings call, many investors were pleased to hear about strong subscriber growth at the firm. However, some investors came away concerned about the amount of spending taking place as the firm acquires or develops new content. Moreover, some observers and analysts have expressed concern about the recent cancellations of some new Netflix original shows. Tom Huddleston Jr. reported on the company's reaction to this criticism in a recent Fortune article:

Meanwhile, also on the Monday earnings call, Netflix's chief content officer Ted Sarandos defended the company's recent cancellations of a handful of expensive, but underperforming, original series. "The more shows we have, the more likely in absolute numbers that you’ll see cancellations, of course," Sarandos said. The executive compared Netflix's recent spate of cancellations—including big-budget series like The Get Down and Sense8—to traditional TV networks that cancel nearly one-third of their new shows after their first seasons. Netflix, he said, has renewed 93% of its original series. With respect to the shows that Netflix opted not to renew, Sarandos argued: "If you’re not failing, maybe you’re not trying hard enough."

This quote from Sarandos raises a fascinating question.  What is the "optimal" failure rate at Netflix?  Surely, we would like the failure rate to be lower than the broadcast networks.  We would like to see the company reaping the benefits of its analytics capabilities.  At the same time, no one should want Netflix's failure rate on original programming to be zero.  We want the firm to take some chances in hopes of landing some surprising breakthrough hits.  Hopefully, the firm isn't simply guessing or drawing on the intuition of the "creatives" in the business.  We would like to see them engaging in "enlightened" experimentation, using big data to guide them while still taking some risks.   If they balance data mining and risk-taking in an effective way, the failure rate won't be zero, but it will be much lower than their broadcast and cable competitors.  

Proxy Fight at P&G: Can Activist Investors Drive Effective Change?

The Wall Street Journal reports today that activist investor Nelson Peltz has launched a proxy fight with Proctor & Gamble.  No company this large has ever faced a proxy fight.  The investor seeks a board seat in hopes of driving change. Peltz has been frustrated with the lackluster revenue and earnings growth at the consumer products giant over the past several years. According to the article, "Mr. Peltz’s Trian Management Fund argues that P&G failed to capitalize on a five-year savings plan that shrank the company by tens of thousands of employees, more than a dozen factories and hundreds of brands. Trian casts doubt on whether a second, five-year, $10-billion savings plan announced by P&G last year will produce results." 

Many observers and analysts have wondered whether activist investors would push for a breakup of P&G. After all, the company does operate a number of businesses including grooming (e.g., Gillette), fabric and home care (e.g., Tide, Cascade), oral and personal care (e.g., Crest, Prilosec), baby and feminine care (e.g., Pampers and Tampax), and household items (e.g., Bounty, Charmin). However, the company already has divested several units that appeared to be somewhat unrelated to their core brands; P&G divested its pet food, battery, coffee, and potato chip businesses in recent years. Peltz has signaled that he's not pushing for further divestitures at this time. 

What's the problem at P&G?  In my mind, the company can't cut its way to enhanced long run performance.  Perhaps costs are bloated, and some efficiencies must be attained.  However, the core problem remains innovation and growth.  In the heyday of A.G. Lafley's first tenure as CEO, P&G excelled because it generated product innovations that drove robust revenue growth (consider the remarkable success of Febreze and Swiffer).   These innovations have not come at the same pace in recent years.  Moreover, customers have traded down from the premium-priced products offered by P&G to more affordable brands.  Consider the success of upstarts in the razor business, as well as the increasing success of private labels in a number of P&G categories.  

What then of the proxy fight led by Peltz?  It seems to me that activist investors can be helpful at times in forcing difficult reorganizations, cost-cutting initiatives, and divestitures that management may be unwilling to undertake.  However, activist investors are not well-equipped to help companies jumpstart innovation and revenue growth.  How will this proxy fight solve the underlying growth problem at P&G?   It won't.   The company has much more challenging work to do than simply fending off an activist investor's attempt to snag a board seat.  

Knowledge Increasingly Generated by Teams Rather than Solo Artists

Do great breakthroughs in knowledge occur as a result of a brilliant mind working alone, perhaps through some brilliant flash of insight? Or, is knowledge creation fundamentally a collaborative endeavor? Has the process of knowledge creation changed in recent years? Has it become more collaborative? Northwestern scholars Stefan Wuchty, Benjamin Jones, and Brian Uzzi studied these questions and have written a paper titled, "The Increasing Dominance of Teams in Production of Knowledge." 

The authors examined nearly 20 million articles from the Institute for Scientific Information Web of Science database. These articles include work from a range of fields including science, engineering, social sciences, the arts, and the humanities. They also studied more than 2 million patents issued during the time that these articles were published. 

The scholars report that, "For science and engineering, social sciences, and patents, there has been a substantial shift towards collective research. In the sciences, team size has grown steadily each year and nearly doubled from 1.9 to 3.5 authors per paper over 45 years." The authors found that solo authors tend to be more prevalent in the arts and the humanities, though collaboration has increased there as well. Moreover, the authors discovered "a broad tendency for teams to produce more highly cited work than individual authors" - a finding true across all fields. In fact, that trend toward higher citations of collaborative work has picked up in recent years. 

In sum, collaboration has become more important and more prevalent over time in the knowledge generation process. This statement represents more than just a well-worn cliche; the empirical data support this claim in clear and convincing fashion.

Sir Ken Robinson: How To Escape Education's Death Valley

Creativity expert Sir Ken Robinson has provided some excellent thinking on the state of education.  I love listening to him describe how we stifle creativity in our children at times, and how we can shift our thinking as educators.  Here's one of his terrific TED talks:

Top Sales People Really Don't Make the Best Managers

The conventional wisdom is straightforward - the top individual performers don't always make the best managers.  That old adage holds true especially in the field of sales.  Many people believe that the best sales people don't make the best managers.   Is it true?  New research examines this assumption, drawing upon one of the most extensive databases ever collected to research this topic.  

Alan Benson, Danielle Li, and Kelly Shue examined data on salespeople at more than 200 firms. These scholars analyzed how individual performers did prior to promotion.  Then, after these individuals were promoted to managers, they examined how the performance of their new subordinates was impacted.   The richness of the data enabled the scholars to compare salesperson performance under this new boss vs. other previous supervisors.   What did they find?  Chicago Booth Review reports:  

The best salespeople did not make the best managers: demonstrated sales skill, as evidenced by managers whose sales doubled before their promotion, corresponded to a 10 percent drop each in the sales performance of new subordinates.  The typical newly minted manager is in charge of five people. Therefore, the doubling of a manager’s sales predicts a total team sales drop equivalent to half the sales of one worker.

Thus, the conventional wisdom is true.  Moreover, the negative impact of promoting the wrong people is substantial.  Companies need to understand the skills and qualities required to become good sales managers, and based on that analysis, they must change their promotion criteria.  Meanwhile, they must find other ways to reward top individual performers, rather than using promotion as a key incentive.