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...

Supporting employees working from home

strategy+business, January 29, 2021

by Theodore Kinni


Photograph by Kathrin Ziegler

In mid-December, a light appeared at the end of a long, dark tunnel when the U.S. Food and Drug Administration issued emergency authorizations for the Pfizer-BioNTech and Moderna COVID-19 vaccines. A month later, that light wavered as the death toll in the U.S. reached 400,000 — having reached 300,000 just five weeks earlier — and the outgoing director of the Centers for Disease Control and Prevention warned that the worst of the pandemic was yet to come. As Yogi Berra once said, “It ain’t over till it’s over.”

Even as millions of people are getting vaccinated, many employees won’t be returning to the workplace for months to come. Instead, they will continue to work from home with all the distractions, stresses, and fears that they have experienced over the past year. This is not an insignificant problem: 25 percent of respondents to a PwC Workforce Pulse Survey conducted between January 11 and 13, 2021, said their physical and mental well-being deteriorated during the pandemic; more than 20 percent said their ability to disconnect, their work–life balance, and their workloads worsened. These results could be magnified in the weeks and months ahead by spikes in COVID case rates and deaths and continuing economic uncertainties, especially with regards to job security.

This makes one of the WFH (working from home) challenges that leaders face even more acute: How do you assess employee wellness when your only point of contact is a phone call or a computer screen? For answers, I talked to two experts... read the rest here.

How to Pressure Test Your Strategic Vision

Insights by Stanford Business, January 15, 2021

by Theodore Kinni

iStock/BeholdingEye

There is no shortage of advice regarding the art and craft of business strategy. Yet, in 2019, when the consulting firm Strategy& surveyed 6,000 executives, only 37% said their companies had well-defined strategies and only 35% believed that their strategies would lead to success.

Stanford Graduate School of Business professors Jesper Sørensen and Glenn Carroll peg this lack of confidence in the ability to make sound strategy to a dearth of critical analytical thinking. They find that the strategies that have driven the long-term success of companies such as Apple, Disney, Honda, Southwest Airlines, and Walmart are typically — and insufficiently — attributed to either an innovative vision or the fortuitous discovery of emerging opportunities. In their new book, Making Great Strategy: Arguing for Organizational Advantage, they assert that neither explanation tells the whole story.

“To put it bluntly: Without reasoned analysis, neither vision nor discovery will lead to strategic success,” Sørensen and Carroll write. In their book, which grew out of developing and teaching strategy and organizational design courses at Stanford GSB, Sørensen and Carroll apply the logician’s tools to the creation of successful corporate strategy.
The Importance of Rigorous Logic

Executives need the tools of logic to construct a coherent and valid strategy argument, which Sørensen and Carroll identify as the common core in all successful strategies. They define a strategy argument as “an articulation of how a firm’s resources and activities combine with external conditions to allow it to create and capture value.”

“We tend to venerate and celebrate strategic intuition, but intuition can always be wrong,” explains Sørensen. “Leaders need to buffer themselves against that possibility by being rigorously logical, too. Logic also is easier to communicate accurately than vision. If I articulate a grand vision for the future, you may be inspired by it, but how will you act on it?”

“There’s a distinction between talent and a skill,” adds Carroll. “Steve Jobs had a talent for envisioning the future that can’t be taught. But the skills needed to develop a logical argument that will reveal if the strategy being envisioned has holes in it or is missing things that you haven’t thought about — those skills can be taught.” Read the rest here...

Strategic Incentives: Cash Performance Awards for the Digital Economy

Learned a lot about how private companies can best use long-term cash incentive plans lending an editorial hand here: https://lnkd.in/e9Mpyjk

Do you really want a CEO to be a role model?

strategy+business, January 12, 2021

by Theodore Kinni



Photograph by RgStudio

When Fortune magazine named Elon Musk as the 2020 Businessperson of the Year, its CEO, Alan Murray, announced the news with palpable distaste: “I have never been a Musk fanboy; he is a mix of some of the worst characteristics of today’s leaders — more messianic than Adam Neumann, as allergic to rules and governance as Travis Kalanick, nearly as narcissistic as Donald Trump.” So why honor Musk? He has been extraordinarily successful at turning bold visions into successful companies. As a result, Tesla’s stock is up more than 1,000 percent since the summer of 2019, making its CEO and largest shareholder, with a nearly 20 percent stake, the second richest person in the world.

Musk is the latest in a long line of celebrated leaders who aren’t exactly paragons of good behavior. Steve Jobs was another notable example. In his acclaimed biography of Jobs, Walter Isaacson documented his subject’s famously petulant and abusive interpersonal conduct. But then Isaacson spun it to support his conclusion that Apple’s cofounder and, later, savior was the “the greatest business executive of our era.”

Murray’s ambivalence to Musk and Isaacson’s efforts to excuse Jobs got me thinking about the role that CEOs and other senior leaders inhabit as behavioral exemplars. Programs for corporate or cultural transformation invariably require that senior executives model the behaviors they are trying to encourage in managers and workers. When Jon Katzenbach and his colleagues at the Katzenbach Center (PwC Strategy&’s global institute on organizational culture and leadership) formulated their 10 principles of organizational culture, they specifically called that dictate out. “The people at the top have to demonstrate the change they want to see,” they declared... read the rest here

Four Questions for Appraising Your Alliances

MIT Sloan Management Review, December 22, 2020

by Theodore Kinni



Over the past four years, many of the United States’ geopolitical alliances have been remade with bewildering speed. It’s no surprise that many of those changes created an uproar — some of these relationships dated back a century or more and seemed sacrosanct, until they weren’t. It also prompted Stephen Walt, the Robert and Renée Belfer Professor of International Relations at Harvard University, to cut through the noise with an article in Foreign Policy titled “How to Tell if You’re in a Good Alliance,” which is instructive for business leaders as well as diplomats.

Walt is a pragmatist, so the first thing he points out is the unspoken assumption behind the uproar: that each of a nation’s existing political alliances is actually worth maintaining. “Surely this is not the case, for all allies are not created equal, and the value of any commitment is likely to wax and wane over time,” he writes. “Wise countries choose their allies carefully and do not treat any of them as sacred and inviolable.”

This is as true in business as it is in international relations. Corporate alliances are a means to an end, and they involve costs and obligations. Accordingly, corporate leaders, like the heads of nations, should never take the value of their partnerships for granted. Toward that end, you can conduct a fast review of the value of your company’s alliances by asking the following four questions, derived from the short list of attributes of a good ally that Walt offers in his article.

Does your partner make a meaningful contribution to the alliance? This is a key question when reviewing a partnership. It’s also one that torpedoed the 2009 alliance between Suzuki Motor Corp. and Volkswagen. Volkswagen wanted to gain greater access to the fast-growing Indian market through Suzuki, and Suzuki wanted access to Volkswagen’s hybrid and diesel technologies. The problem, claimed Suzuki chairman Osamu Suzuki less than two years after the companies bought stakes in each other, was that the technology Suzuki sought wasn’t forthcoming. Suzuki didn’t need the technology that VW was willing to provide, and VW wouldn’t provide access to the technology that it did need. If your partner hasn’t made the contributions it promised, you don’t have a good ally. Read the rest here.

Is the gig up?

strategy+business, December 17, 2020

by Theodore Kinni



Photograph by Brothers91

A decade ago, advocates touted the sharing economy as an alternative to corporate capitalism. Digital technology was opening vast, new peer-to-peer marketplaces: TaskRabbit and Airbnb were founded in 2008, Uber in 2009, RelayRides (now Turo) in 2010, Postmates in 2011, Lyft in 2012. These platforms promised that people would be able to make a good living while working when and how they wanted — selling their time and skills, and renting out their cars, spare bedrooms, and that dusty camping gear in the attic.

“You will know by now that things haven’t turned out exactly as expected,” Juliet Schor wryly notes in her new book, After the Gig. Schor, a sociology professor at Boston College, and her team at the Connected Consumption project, funded by the MacArthur Foundation, studied gig workers and platforms of the sharing economy from 2011 to 2017. The result is a more nuanced view than has been offered by previous books on this topic, which typically focus on either how companies can build their own platforms or how platform companies prosper by evading regulation and exploiting workers.

Among the insights: The less you actually need a gig job, the more likely it is that a gig job will work for you. “Workers’ experiences are not uniform, with variation in pay rates, job satisfaction, and how they do the work,” Schor explains. “As we saw these differences playing out at individual companies, we realized that they are explained by how dependent the worker is on income from the platform to pay basic living expenses.” Schor’s team found that supplemental workers — that is, workers who are not financially dependent on their platforms — make more money, have more autonomy, and are more satisfied with their gigs than platform-dependent workers. Moreover, the former group comprises 34 percent of the workers in the sample the team studied; the latter was only 22.5 percent. (The rest, nearly half of platform workers, fall between the two extremes.)

This finding partly contradicts the headlines of worker abuse that have generated a lot of political Sturm und Drang lately. At the same time, it is clear that the gig economy can’t really substitute for a full-time job. As Schor concludes: “With some exceptions, our data suggest that being dependent on a platform is not a viable way to make a living.” Read the rest here.

The Transformational Power of Recommendation

Learned a lot lending an editorial hand here:

MIT Sloan Management Review, November 24, 2020

by Michael Schrage




Image courtesy of Paul Giovanopoulos/theispot.com

Wikipedia defines recommendation engines (and platforms and systems) as “a subclass of information filtering system that seeks to predict the ‘rating’ or ‘preference’ a user would give to an item.” But as a tool, technology, and digital platform, recommendation engines are far more intriguing and important than this definition suggests.

In data-driven markets, the most effective competitors reliably offer the most effective advice. When predictive analytics are repackaged and repurposed as recommendations, they transform how people perceive, experience, and exercise choice. The most powerful — and empowering — engines of commerce are recommendation engines.

Recommendation engines have been essential to the success of digital platforms Alibaba, Amazon, Netflix, and Spotify, according to their founders and CEOs. For companies such as these, recommendation engines aren’t merely marketing or sales tools but drivers of insight, innovation, and engagement. Superior recommendations measurably build superior loyalty and growth; they amplify customer lifetime value. Computing compelling recommendations profitably reshapes human behavior.

The influence and purpose of recommendation engines are not limited to customers or consumption. Large employers, most notably Google, have adopted and adapted recommendation engines as internal productivity platforms to nudge workers to their best decision options. Indeed, in late 2016, Laszlo Bock, the senior vice president of people operations at Google, left the company to launch Humu, a recommendation-engine startup for advising workforce behavior change.

While data remains the essential advisory ingredient, the global recommendations revolution reflects profound and ongoing algorithmic innovation, enabling machine learning and AI to power improvements in deep learning and generative adversarial networks. Successful recommendation engines learn how to learn. The more people use them, the smarter they become; the smarter they become, the more people use them. Done right, recommendation engines enable virtuous cycles of value creation.

The networked nudges and prompts of recommendation engines increasingly influence people’s choices in clothing, entertainment, food, and medicine; they also influence the texts we send, which friends we contact, the customers and prospects we prioritize, the experts we seek, the job candidates we hire, the investments we choose, the memos we edit, and the schedules we follow.

But prompts and nudges shouldn’t obscure the subtle but vital design principle that makes the recommendation-engine value cycle more virtuous: Recommendation is about ensuring better options and choices, not obedience or compliance. Recommendation engines don’t seek to impose optimal, best, or right answers on their users. To the contrary, their point and purpose are greater empowerment and agency. Influence, not control, is the algorithmic aspiration. In this, successful recommendation-engine design depends more on how recommenders seek to influence than on how much they know.

Recommendation engines transform human choice. Much as the steam engine energetically launched an industrial revolution, recommendation engines redefine insight and influence in an algorithmic age. Wherever choice matters, recommenders flourish, and this profound digital transformation of choice will only become more pervasive as recommenders become smarter. Better recommenders invariably mean better choices. Read the rest here.

The New Elements of Digital Transformation

Learned a lot lending an editorial hand here:

MIT Sloan Management Review, November 19, 2020

by Didier Bonnet and George Westerman




Image courtesy of Michael Glenwood Gibbs/theispot.com

Since 2014, when our article “The Nine Elements of Digital Transformation” appeared in these pages, executive awareness of the powerful and ever-evolving ways in which digital technology can create competitive advantage has become pervasive. But acting on that awareness remains a challenging prospect.

It requires that companies become what we call digital masters. Digital masters cultivate two capabilities: digital capability, which enables them to use innovative technologies to improve elements of the business, and leadership capability, which enables them to envision and drive organizational change in systematic and profitable ways. Together, these two capabilities allow a company to transform digital technology into business advantage.

Digital mastery is more important than ever because the risks of falling behind are increasing. In 10 years of research, we have seen digital transformation grow increasingly complex, with a new wave of technological and competitive possibilities arriving before many companies mastered the first. When we began our research, most large traditional enterprises were using digital technologies to incrementally improve parts of their businesses. Since then, this first phase of activity has given way to a new one. Advances in a host of technologies, such as the internet of things, artificial intelligence, virtual and augmented reality, and 5G, have opened new avenues for value creation. More important, leaders now recognize the need for — and the possibility of — truly transforming the fundamentals of how they do business. They understand that they have to move from disconnected technology experiments to a more systematic approach to strategy and execution.

Some companies have successfully graduated from the first phase of digital transformation and are diving into the second. But many are still floundering: In 2018, when we surveyed 1,300 executives in more than 750 global organizations, only 38% of them told us that their companies had the digital capability needed to become digital masters, and only 35% said they had the leadership capability to do so. This has become more worrisome than ever: As COVID-19 accelerates the shift to digital activity, digital masters are widening the gap between their capabilities and those of their competitors.

These conditions prompted us to reexamine the elements of digital transformation that we proposed in 2014. While strong leadership capability is even more essential than ever, its core elements — vision, engagement, and governance — are not fundamentally changed, though they are informed by recent innovations. The elements of digital capability, on the other hand, have been more profoundly altered by the rapid technological advances of recent years. Read the rest here.

When Employees Speak Up, Companies Win

Learned a lot lending an editorial hand here:

MIT Sloan Management Review, November 17, 2020

by Ethan Burris, Elizabeth McCune, and Dawn Klinghoffer





Business headlines suggest that employees are speaking up more than ever. Activist employees are calling out their companies over where and with whom they do business, burned-out employees are asking for more and more unique work-life accommodations, and concerned employees are raising questions about hiring practices and promotion decisions in light of institutional biases. Often, these instances of speaking up — called employee voice behaviors — result in an embarrassingly public airing of organizational issues.

Yet our research reveals that the headlines are not an accurate reflection of the current state of employee voice. We asked 6,000 employees of a Microsoft business unit to tell us how often they spoke up to their managers. In addition, we asked how many of 15 topics they spoke up about, such as their immediate job assignments, the culture of their teams, how employees are treated across the organization, the strategy of the company, and the work-life balance alternatives available to them. We found that relatively few employees consistently share their thoughts and opinions about a multitude of work issues with their managers: Just 13.6% of the surveyed employees said that they speak up on more than 10 of the topics. Slightly more are silent: In fact, 17.5% said they do not speak up at all. The largest group of employees — 47.1% — said they speak up on five or fewer topics, typically on issues related to their jobs.

If we assume that these findings reflect similar tendencies in other organizations, leaders should be concerned, because employee voice is not a voice of complaint or protest per se. It encompasses the willingness of employees to speak up about opportunities for improvement. These efforts are not a prescribed part of employees’ jobs; they are a voluntary communication of constructive ideas to leaders that enable learning and effective change in work groups of all sizes, from teams to entire organizations. Yet these efforts to tell the truth can involve confronting leaders, who can feel challenged or even threatened, especially when the proposed changes involve things that leaders have helped create or for which they are responsible.

More and more, companies are seeking to expand efforts to listen to their employees by inviting them to share their opinions and ideas in areas that are outside of their day-to-day tasks. For instance, in 2014, in the aftermath of a recall of 6 million vehicles for an ignition flaw linked to at least 13 deaths, General Motors launched its Speak Up for Safety program, which asked employees across the company to speak up about anything that might impact customer safety. The growing use of innovation platforms and ecosystems is another example. In addition, during the global COVID-19 pandemic, we’ve seen companies frequently survey employees on topics such as physical and mental health and their working conditions.

The effectiveness of all these efforts depends on employees’ willingness to use their voices. In this study, we sought to examine the benefits of a more expansive employee voice, the factors that determine voice behaviors, and the ways in which companies can encourage those behaviors. Read the rest here.

What people like you like

strategy+business, November 11, 2020

by Theodore Kinni




Photograph by Paper Boat Creative

I don’t set much store in the endless stream of recommendations offered by Amazon, Netflix, Spotify, and most other online businesses. Occasionally, a book, flick, or song pops up that delights me, but most of the suggestions I get either miss the mark or appear suspiciously advantageous to recommendation engine operators and their advertisers.

Michael Schrage, visiting fellow at the MIT Sloan School of Management’s Initiative on the Digital Economy and s+b contributor, awakens us to the potential of delightful discovery in his latest book, Recommendation Engines. “Recommendation inspires innovation: that serendipitous suggestion—that surprise—not only changes how you see the world, it transforms how you see—and understand—yourself. Successful recommenders promote discovery of the world and one’s self,” Schrage writes in its introduction. “Recommenders aren’t just about what we might want to buy; they’re about who we might want to become.”

If this smacks of techno-utopianism, well, there is a strong strain of that ideology running through Recommendation Engines. For the most part, however, Schrage grounds this rosy view in the powerful effects that recommenders are already producing and balances it with acknowledgment of these systems’ potential for abuse. He also provides a short history of recommendations and a suitably technical description of how recommendation engines work and are built.

To date, the powerful effects of recommenders have manifested themselves mostly in commerce. Schrage cites a variety of facts in this regard: a survey that found recommendations account for approximately 30 percent of global e-commerce revenues; another that found online shoppers are 4.5 times more likely to buy after clicking on a recommendation; and research that “strongly suggests” recommendations drive roughly a third of Amazon’s sales. Read the rest here.

The Rising Risk of Platform Regulation

Learned a lot lending an editorial hand on this article:

Sloan Management Review, November 11, 2020 

by D. Daniel Sokol and Marshall Van Alstyne 

On Oct. 6, 2020, the U.S. House Judiciary Committee’s antitrust subcommittee released a 450-page report following a 16-month inquiry into the digital economy. It recommended fundamental changes to antitrust laws generally and targeted the Amazon, Apple, Facebook, and Google technology platforms specifically. Several weeks later, the U.S. Department of Justice filed suit against Google, accusing it of using “anticompetitive tactics to maintain and extend its monopolies in the markets for general search services, search advertising and general search text advertising.” Similar regulatory initiatives aimed at platforms are underway around the world, including in the European Union, United Kingdom, Japan, Korea, and India.

The blizzard of regulatory action swirling around platforms is producing new rules and laws, expanded powers for existing regulatory authorities, and the establishment of new regulatory authorities. These outcomes will not only affect Big Tech but also many other companies, in industries such as construction, health care, finance, energy, and industrial manufacturing, that have adopted or are considering adopting platform business models.

Few platform operators and owners have fully considered how the growing regulatory risk — which includes breakups, line-of-business restrictions, acquisition limits, and interoperability and data portability mandates — could derail their businesses. As a result, they could be caught off guard, just like many companies were caught off guard when the Sarbanes-Oxley Act of 2002 mandated board restructurings and expanded executive financial accountability in the aftermath of accounting scandals. Read the rest here. 

Best Business Books 2020: Management

strategy+business, November 9, 2020

by Theodore Kinni



Illustration by Martin O’Neill; icon by Harry Campbell

This year, COVID-19 upended management-as-usual. Sure, managing is still a matter of getting things done in organizations — divvying up objectives into tasks, ensuring employees have the resources and skills to complete the tasks, overseeing their progress, and helping them when they get bogged down. But where and how people work has changed — radically and overnight in many companies and, in some, maybe permanently.

None of this year’s best business books on management were written for managers per se. But each focuses on capabilities that can help managers identify and cope with pandemic-related challenges.These developments have given rise to new needs and stresses that affect the people you are responsible for managing — needs such as going to work (or going back to work) safely, and stresses such as working while surrounded by kids instead of colleagues — and thus, they’ve also affected your performance as a manager.

In the year’s best business book on management, Tiny Habits, Stanford University professor B.J. Fogg shows how to change your behavior and help others change theirs, too — an essential skill at a time when we are all being called upon to develop new habits. In Acting with Power, Deborah Gruenfeld, also at Stanford, explains how an unconventional view of power can enable you to support people in ways that far exceed the limits of your positional authority. And in You’re Not Listening, journalist Kate Murphy offers an uncommonly insightful exploration of how to actually meet the dictates of an exhortation we’ve all heard before: “Listen!” Read the rest here.