Expansionists, brokers, and conveners

strategy+business, March 31, 2021

by Theodore Kinni


Photograph by John M Lund Photography Inc.

When David Rockefeller, grandson of oil magnate John D. Rockefeller and CEO of Chase Manhattan Bank in the 1970s, liked something, he really liked it. He liked collecting beetles, and left a horde of 150,000 specimens to Harvard on his death. He also liked collecting people. His Rolodex (remember those?) contained more than 100,000 contacts; laid out end to end, its cards would have stretched 16 miles.

In the terminology of personal networks, Rockefeller’s custom-made Rolodex is a good example of an “expansionist network,” according to Marissa King, a professor of organizational behavior at Yale School of Management. In her book Social Chemistry, a wide-ranging but rather unconnected exploration of how we connect with other people, King explains that expansionists “have extraordinarily large networks, are well-known, and have an uncanny ability to work a room.” Expansionists create value by connecting contacts to each other. They are collectors and manipulators of what sociologist Mark Granovetter identified as “weak ties.”

But size doesn’t really matter when it comes to networks, says King. Rockefeller, for example, had to overcome the inherent difficulties of maintaining and leveraging an expansionist network by recording detailed information about his contacts on his Rolodex cards. When the Wall Street Journal got a peek after Rockefeller died at age 101 in 2017, it reported that there were 35 cards documenting his meetings with Henry Kissinger dating back to 1955. What really matters is the quality of your contacts and the structure of your network...read the rest here

The Overlooked Partners That Can Build Your Talent Pipeline

Learned a lot lending an editorial hand here:

MIT Sloan Management Review, March 29, 2021

by Nichola J. Lowe

Image courtesy of Stephanie Dalton Cowan/theispot.com

America has a skill problem. It’s not the result of inadequate educational systems letting down younger workers or a lack of aptitude among older workers, as some claim. The problem is the widespread failure of American companies to share responsibility for skill development. Many employers are simply unwilling — or unable — to invest sufficient resources, time, and energy into work-based learning and the creation of skill-rewarding career pathways that extend economic opportunity to workers on the lowest rungs of the labor market ladder.

This national skills crisis becomes clearest whenever unemployment rates are low. As late as February 2020, most industries in the U.S. showed persistent signs of skills shortages. In manufacturing, for instance, there were 522,000 unfilled job openings in late 2019. There were similar long-standing job vacancies in many other critical industries, including financial and business services, health care, and telecommunications, with executives noting increased skills gaps in data analytics, information technology, and web design, among other areas.

The skills shortage was less obvious during the COVID-19 pandemic, as companies shed millions of jobs, but it persists despite that temporary softening of the labor market. And as hiring picks up along with the economy, employers may increasingly develop workforce strategies that are based not only on skills requirements but on increased commitments to boosting diversity and inclusion.

A better and more enduring skills strategy must begin with the recognition that our national skills crisis rests on a deeply rooted but flawed assumption: namely, that skills are individually held. This view overlooks the collective and context-specific nature of skills — that is, the ways in which they are shared, reinforced, and reproduced through group interactions at work. It also creates a false justification for the bias and hoarding that often accompany employers’ approaches to talent management. That results in more educated workers benefiting from corporate investments in retention, leaving those workers with less formal education underserved and undervalued — a phenomenon that labor scholars call the “great training paradox.” Moreover, it leads to the mistaken categorization of entry-level workers as “unskilled.” This positions them as irrelevant and easy to replace, ignoring the fact that this segment of the workforce — so often women and people of color —not only executes strategy but also has the grounded insights needed to improve organizational processes and practices.

The core assumption that skill is individually held results in supply-side approaches that place the primary burden for skill development on educational institutions and on students within them. These approaches have not and cannot, in isolation, do the trick. Skill shortages are a problem of employment, not education...read the rest here

Seeing, doing, and imagining

strategy+business, March 16, 2021

by Theodore Kinni


Photograph by Laurence Dutton

My vote for the foggiest assertion of the pandemic to date is that the U.S. has an abysmally high number of COVID-19 cases — more than 29 million as of March 8, 2021 — because of testing. “If you don’t test, you don’t have any cases,” former President Donald Trump said during a televised White House roundtable on June 15, 2020. “If we stopped testing right now, we’d have very few cases, if any.” I wonder how many people who heard that had the same first thought as I did: Correlation is not causation.

Though associations gleaned from big data drive recommendation engines and bolster corporate revenues, they have their limitations. Imagine trying to control a viral pandemic by refusing to test people for the virus.This isn’t to say that correlation — the idea that two or more things are associated in some way — isn’t valuable. Indeed, there is big money in correlation. In order to peddle subscriptions, Pandora doesn’t need to know what causes people who listen to The Grateful Dead to also listen to Phish. To bulk up its sales, Amazon doesn’t need to know what causes people who buy a Paleo diet book to also buy beef jerky.

The passive observation of data has limited value, because, as Judea Pearl reminds readers several times in The Book of Why: The New Science of Cause and Effect, data is profoundly dumb. “Data can tell you that the people who took a medicine recovered faster than those who did not take it, but they can’t tell you why,” writes the director of UCLA’s Cognitive Systems Lab. “Maybe those who took the medicine did so because they could afford it and would have recovered just as fast without it.”

Association, which Pearl, a Turing Award winner, identifies as the first of three steps on his ladder of causation, won’t help executives answer many of the questions they need to ask when formulating corporate strategy, making investment decisions, or setting prices. To answer questions such as, “What will raising prices by 10 percent do to revenues?” you need to start climbing Pearl’s ladder. Read the rest here.

The Positive Side of Negative Emotions

Insights by Stanford Business, March 12, 2021

by Theodore Kinni


iStock/Deagreez

The benefits of “cognitive reappraisal” — the widely used self-help strategy of reframing distressing situations to move past the negative emotions they engender — are well established.

Studies have shown that when employees use reappraisal techniques, they are more satisfied with their jobs and are less susceptible to stress and burnout. The research also links reappraisal to higher employee performance.

Given these findings, it’s not surprising that many companies are teaching and encouraging employees to embrace the strategy. Google’s “Search Inside Yourself” training program is a notable example. The program, which includes reappraisal among other practical techniques for mindfulness, self-awareness, and self-management, was created by Chade-Meng Tan, one of the company’s engineers, in 2007. Demand for the program prompted Tan and others to found a nonprofit that went on to teach the techniques to employees in companies ranging from American Express to Volkswagen.

But what if the outcomes of cognitive reappraisal aren’t entirely beneficial? One team of researchers — Matthew Feinberg and Brett Ford at the University of Toronto, along with Francis J. Flynn at Stanford Graduate School of Business — suspected that might be the case.

“Cognitive reappraisal lessens negative emotions by reframing situations in positive terms, but negative emotions serve important social functions,” explains Feinberg, formerly a postdoctoral fellow at Stanford GSB and Stanford Medicine’s Center for Compassion and Altruism Research and Education. “They help ensure that individuals behave in socially acceptable ways and encourage adherence to group norms.” Read the rest here.

How Healthy Is Your Business Ecosystem?

Learned a lot lending an editorial hand here:

MIT Sloan Management Review, March 9, 2021

by Ulrich Pidun, Martin Reeves, and Edzard Wesselink


Image courtesy of Harry Campbell/theispot.com

Companies that start or join successful business ecosystems — dynamic groups of largely independent economic players that work together to create and deliver coherent solutions to customers — can reap tremendous benefits. In the startup phase, ecosystems can provide fast access to external capabilities that may be too expensive or time-consuming to build within a single company. Once launched, ecosystems can scale quickly because their modular structure makes it easy to add partners. Moreover, ecosystems are very flexible and resilient — the model enables high variety, as well as a high capacity to evolve. There is, however, a hidden and inconvenient truth about business ecosystems: Our past research found that less than 15% are sustainable in the long run.

The seeds of ecosystem failure are planted early. Our new analysis of more than 100 failed ecosystems found that strategic blunders in their design accounted for 6 out of 7 failures. But we also found that it can take years before these design failures become apparent — with all the cumulative investment losses in time, effort, and money that failure implies.

Witness Google, which made several unsuccessful attempts to establish social networks. It invested eight years in Google+ before shutting down the service in 2019. One reason for the Google+ failure was its asymmetric follow model, similar to Twitter’s, in which users can unilaterally follow others. This created strong initial growth but did not build relationships, which might have fostered greater engagement on the platform. The downfall of another Google social network, Orkut, was built into its unusually open design, which let users know when their profiles were accessed by others. It turned out that users were uncomfortable with this lack of privacy, and the network went offline in 2014, 10 years after its launch.

Typically, ecosystems are assessed using two kinds of metrics: conventional financial metrics, such as revenue, cash burn rate, profitability, and return on investment; and vanity metrics, such as market size and ecosystem activity (number of subscribers, clicks, or social media mentions). The former are not very useful for assessing the prospects of ecosystems because they are backward-looking. The latter can be misleading because they are not necessarily linked to value creation or extraction. They indicate the current interest in the ecosystem, and presumably its potential, but may also reflect an ecosystem’s ability to spend investors’ money on marketing and other growth tactics more than its ability to generate value.

To improve the odds of success and mitigate the high costs of failure, leaders must be able to assess the health of a business ecosystem throughout its life cycle. They need metrics that indicate performance and potential at the system level and at the level of the individual companies or partners participating in the ecosystem, as well as the ecosystem leader or orchestrator. They need to be able to gauge growth in terms of scale not only in ecosystem participation but also in the underlying operating model. And most critically, they need metrics that reflect the success factors unique to each of the distinct phases of ecosystem development.

This article lays out a set of metrics and early warning indicators that can help you determine whether your ecosystem is on track for success and worthy of continued investment in each development phase. They can also help you identify emerging issues and decide if and when you may need to cut your losses in an ecosystem and/or reorient it. Read the rest here.

Platform Scaling, Fast and Slow

Learned a lot lending an editorial hand here:

MIT Sloan Management Review, March 9, 2021

by Pinar Ozcan and Max Büge


Image courtesy of Michael Glenwood Gibbs/theispot.com

Shortly after its 2009 founding in San Francisco, Uber executed a simple strategy that rapidly led to its expansion on a global scale. To achieve network effects by connecting as many drivers and passengers as quickly as possible, the company prioritized launches in new cities. It hired core teams of general managers, operations managers, and community managers in multiple cities at once. In each city, these teams attracted drivers by offering existing black-car services an app — and sometimes a free smartphone — to monetize their idle time. To attract riders, the teams offered subsidized fares to attendees of large conferences and other high-profile events, signing them up and then gaining thousands more riders through word of mouth.

Rapid scaling, as exemplified by Uber, is a core element of platform strategy, with speed considered the decisive factor in the race to succeed in winner-takes-all and winner-takes-most markets. But we’ve found that rapid scaling may not be the best strategy for all platforms. In some cases, a more careful, incremental, and thus slower approach to scaling is more beneficial.

In studying platform businesses, including Airbnb, Amazon, Apple, Expedia, Facebook (particularly its e-payment project, Libra), Google, Grindr, LinkedIn, Netflix, PayPal, and Uber, we found that regulatory complexity and regulatory risk are two significant but often neglected factors in platform scaling decisions. Moreover, they are likely to become increasingly important in the years ahead as efforts to regulate tech companies gain momentum and as more companies in a greater variety of sectors and markets seek to capture the benefits of platforms. Read the rest here.

Does Your C-Suite Have Enough Digital Smarts?

Learned a lot lending an editorial hand here:

MIT Sloan Management Review, March 3, 2021

by Peter Weill, Stephanie L. Woerner, and Aman M. Shah


Image courtesy of Anna & Elena Balbusso/theispot.com

There’s little doubt that the future of business is digital. Companies that are in the lead implementing digital technologies have radically improved their operational efficiency and their customers’ experiences. And, even more important, the new capabilities unlocked by digital technologies have allowed them to reimagine their purposes and their business models.

Having a digitally savvy top leadership team — that is, a team in which more than half of the executive members are digitally savvy — makes a huge difference. Our latest research shows that large enterprises with digitally savvy executive teams outperformed comparable companies without such teams by more than 48% based on revenue growth and valuation.

Digital savviness is an understanding, developed through experience and education, of the impact that emerging technologies will have on a business’s success over the next decade. Sharing this understanding across the top management team is a key ingredient in the success of corporate transformation. As Jean-Pascal Tricoire, chairman and CEO of energy management company Schneider Electric, told us, “When every business becomes a digital business, every executive needs to take digital transformation personally. The last thing you want in your team is the belief that digital is somebody else’s problem.”

Unfortunately, the demand for digital savviness in the upper echelons of leadership has grown far more quickly than the supply. In 2019, when we studied the boards of directors in 3,228 large U.S.-listed companies with more than $1 billion in annual revenues, we discovered that only 24% of boards were digitally savvy. In 2020, we extended our research to encompass top management teams — C-level executives and leaders of functions and geographic territories — in 1,984 large companies globally. Our new findings indicate that only 7% of companies have digitally savvy executive teams.

In this article, we report the findings of our research into the level of digital savviness among top management teams, the business value it delivers, and the actions that companies can take to increase the digital savviness of their senior executives. Read the rest here.

Arguing your way to better strategy

strategy+business, March 2, 2021

by Theodore Kinni



Illustration by johnwoodcock

There is no shortage of theories regarding the proper basis for a winning corporate strategy. You can set sail on blue oceans with W. Chan Kim and Renée Mauborgne, hone core competences with C.K. Prahalad and Gary Hamel, and get competitive with Michael Porter, to call out just a few of the fashionable options. But how do you transform the theories into a unique strategy capable of driving your company’s long-term success?

This is the question Stanford business school professors Jesper Sørensen and Glenn Carroll address in Making Great Strategy. It’s a book about strategic due diligence. And it fills an important gap in the literature by caring not a whit about a company’s strategy per se, but rather focusing entirely on how rigorously that strategy has been formulated and how thoroughly it has been vetted.

Toward this end, Sørensen and Carroll define strategy as a logical argument that coherently articulates “how the firm’s resources and activities combine with external conditions to allow it to create and capture value.” They further assert that “the development, communication, and maintenance of a strategy argument is best achieved through an open process of actually arguing within the organization, engaging in productive debate.”

Sørensen and Carroll find that in many companies this process of argumentation is either altogether missing or poorly conducted. Instead of using logical argument, decisions about strategy are often dictated by the most powerful people in the room. Or, when they are made more democratically, they are chosen in a rigged or otherwise flawed manner. The authors’ insights help explain the findings of a 2019 survey by Strategy&, PwC’s strategy consulting group, in which only 37 percent of 6,000 executive respondents said that their company had a well-defined strategy, and only 35 percent believed their company’s strategy would lead to success. Read the rest here.

Why So Many Data Science Projects Fail to Deliver

Learned a lot lending an editorial hand here:

MIT Sloan Management Review, March 2, 2021

by Mayur P. Joshi, Ning Su, Robert D. Austin, and Anand K. Sundaram

Image courtesy of Jean Francois Podevin/theispot.com

More and more companies are embracing data science as a function and a capability. But many of them have not been able to consistently derive business value from their investments in big data, artificial intelligence, and machine learning. Moreover, evidence suggests that the gap is widening between organizations successfully gaining value from data science and those struggling to do so.

To better understand the mistakes that companies make when implementing profitable data science projects, and discover how to avoid them, we conducted in-depth studies of the data science activities in three of India’s top 10 private-sector banks with well-established analytics departments. We identified five common mistakes, as exemplified by the following cases we encountered, and below we suggest corresponding solutions to address them. Read the rest here...