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16 августа, 14:06

When Is Teamwork Really Necessary?

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Caspar Benson/Getty Images Most leaders assume that they need to foster teamwork among the people whom directly and indirectly report to them. Teaming is now seen as the workplace equivalent of motherhood and apple pie — invariably good. The problem is when leaders try to drive the wrong kind of collaboration on their particular teams. The result: wasted time and unnecessary frustration. Consider the example of Nicolas, a regional sales vice president at a medical devices company. When promoted to his new role, he inherited a group of district sales managers responsible for selling to hospital systems in their respective geographies. Although his one-on-one meetings with these reports, which involved progress reviews, motivation, and coaching, were highly productive, his monthly team meetings weren’t. While the group liked getting together and engaging in some joint activities — such as goal setting, best-practice sharing, and talent development — people often wondered why they were in the room. Catherine, a senior marketing director leading a cross-functional product development team at the same company, provides a contrasting case study. Although she sometimes needed to work with team members individually, most productive work happened in weekly meetings, to which she brought focused agendas and effectively facilitated discussions about key issues. The participants rarely felt they were wasting their time. The biggest difference between these two situations lies, of course, in the amount of interdependency among group members and the resulting teamwork necessary. Before embarking on any team-building activities or even setting up team meetings, leaders must ask themselves one question: Am I managing a high-performing group of individuals or a high-performing team? In Nicolas’s case, it was the former. So what he mostly needed was hub-and-spoke, one-on-one leadership, through weekly individual meetings, supplemented by periodic group get-togethers. After realizing this, he moved from monthly to quarterly team meetings and addressed only those issues that actually required teamwork. Perceptions about his overall efficiency and effectiveness rose dramatically. Catherine, meanwhile, had rightly determined that she was leading, even building, a high-performing team. That meant having intensive, two-hour weekly team meetings supplemented with as-needed, one-on-one interactions. Having evaluated the extent to which your reports need to work collectively, the next step is to identify what focus of that teamwork should be and how the work will be accomplished. Here are eight common roles that your reports may need to play together. Agenda Setters: define and communicate strategic direction and priorities Integrators: ensure integration and make tradeoffs across units Execution Drivers: drive planning, execution, and accountability Talent Developers: attract, assess, develop, and retain talent Diplomats: build alliances internally and shape the external environment Role Models: shape the values, behavior, and culture of the organization Architects: design and transform the organization Trailblazers: foster organizational learning, innovation, and adaptation For example, Nicolas’s reports needed to be Agenda Setters (setting and communicating goals), Talent Developers (selecting, assessing, and developing people), and Trailblazers (determining and sharing best practices). The members of Catherine’s product development team, by contrast, had to be Integrators (ensuring integration across the functions), Execution Drivers (committing to and achieving goals within their functions) and Diplomats (communicating, securing resources, and building support with external constituencies). Groups of executives who lead companies or divisions or major functions need to balance leading their own teams and leading the enterprise together. Often, they must play all eight roles to some degree. However, it’s important to clarify which need the most emphasis, given the specifics of the situation. In turnaround situations, for example, the most important roles for the team often are Agenda Setters, Execution Drivers, and Architects (leading required organizational transformation). The table below provides a straightforward way to assess the roles your direct and indirect reports most need to play as a team. Regardless of the amount of time that needs to be devoted to teaming (which, as discussed, is a function of the extent of interdependency), how should that time best be used? Team Roles % of Overall Teaming Effort Agenda setters Integrators Execution drivers Talent developers Diplomats Role models Architects Trailblazers SOURCE Michael Watkins © HBR.org Recognize, too, that the roles the group most needs to play together may shift over time. As an organization moves successfully from turnaround into more-stable growth, for example, the collective focus of senior executives could shift toward being Role Models (shaping behaviors and culture), Talent Developers, and Diplomats. As a team leader, you can certainly start this process by making your own assessment of the required extent and focus of teamwork you need. But then plan to have people give their own opinions on both issues and engage in a group discussion to make sure everyone is on the same page. Teamwork efforts must be tailored to each group and situation. By taking a more limited, focused approach to collaboration, you’ll be able to lead your people much more effectively.

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16 августа, 13:48

Boost Your Emotional Intelligence with These 3 Questions

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Daniel Hurst Photography/Getty Images As the concept of emotional intelligence has gone global, we’ve watched professionals founder as they try to improve their emotional intelligence (or EI) because they either don’t know where to focus their efforts or they haven’t understood how to improve these skills on a practical level. In our work consulting with companies and coaching leaders, we have found that if you’re looking to develop particular EI strengths, it helps to consider areas for improvement others have identified along with the goals you want to achieve — and then to actively build habits in those areas rather than simply relying on understanding them conceptually. To that end, start by asking yourself three questions: What are the differences between how you see yourself and how others see you? The first step, as with all learning, is to get a sense of how your self-perception (how you see yourself) differs from your reputation (how others see you). This is especially true for the development of emotional intelligence because we can be blind to, not to mention biased about, how we express and read the emotional components of our interactions. For example, most of us think that we’re good listeners, but very often that’s really not the case. Without this external reality check, it will be difficult for you to identify the ways that your actions affect your performance. Getting feedback from others can also provide proof of the necessity of shifting our behaviors and an impetus to do so. Furthermore, emotional intelligence can’t be boiled down to a single score, as is done with IQ. You can’t just say that you’re “good” or “bad” at emotional intelligence. There are four separate aspects of it, and we’re all better at some than others: self-awareness, self-management, social awareness, and relationship management. (Within these domains nests a total of 12 learned and learnable competencies). To give you the best sense of where the differences lie between your self-perception and your reputation, then, you should use a 360-degree feedback assessment that takes into account the multiple facets of EI. We use the Emotional and Social Competency Index, or ESCI-360 (a commercially available product one of us —Dan — developed with Richard Boyatzis of Case Western Reserve University and Korn Ferry’s Hay Group), but many organizations have their own assessments. The key is to find one that guarantees confidentiality to those giving you feedback, that is focused on development and not on performance assessment (which skews the feedback), and that can give you a detailed sense of where others gauge you differently than you gauge yourself. Another way to get an outside perspective on how your actions impact your relationships and your work is to work with a coach. A coach can help you delve under the surface and look at how your assumptions and personal narratives may be working against you. To find a well-trained coach, do your due diligence; coaching is not a licensed profession, so it is up to you to get references and tofind out if a prospective coach has gone through a rigorous training program. If working with a coach is not feasible, find a learning partner instead, ideally a colleague whose opinions you trust and who would be willing to talk over how you are doing on a regular basis. What matters to you? When you get your feedback from an assessment or your coach, let that inform what you want to improve. But also consider what your goals are — how you want to get better at what you do now, or where you want to go in the future. When it comes to cultivating strengths in emotional intelligence, you’re at a huge disadvantage if you’re only interested because a colleague, your boss, or someone in HR said you should be. Your emotional intelligence is so tied up in your sense of self that being intrinsically motivated to make the effort matters more when changing longstanding habits than it does when simply learning a skill like budgeting. You and Your Team Series Emotional Intelligence Self-Awareness Can Help Leaders More Than an MBA Can Rasmus Hougaard, Jacqueline Carter, and Marissa Afton Emotional Intelligence Has 12 Elements. Which Do You Need to Work On? Daniel Goleman and Richard E. Boyatzis What Self-Awareness Really Is (and How to Cultivate It) Tasha Eurich That means the areas that you choose to actively work on should lie at the intersection of the feedback you’ve gotten and the areas that are most important to your own aspirations. Ask yourself: Do you want to grow your capacity to take on a leadership position? Be a better team member? Exert greater positive influence? Get better at managing yourself, or keeping the goals that matter in focus? Or — your goals need not be only professional — do you want to have a better connection to your spouse or teenager? Understanding the impacts of your current EI habits relative to your goals will keep you going over the long haul as you do the work of strengthening your emotional intelligence. For example, let’s say you get feedback that you are not a great listener — but you think you are. Instead of taking this assessment as an attack, or simply dismissing it, step back and consider your goals: Perhaps you’ve said that you want to better connect, understand, and communicate with impact. How could listening well help you to do those things? Seeing the feedback in this light can help you position it as an opportunity for developing toward your goals, rather than a threat. What changes will you make to achieve these goals? Once you’ve determined which EI skills you want to focus on, identify specific actions that you’ll take. If you’re working on becoming a better listener, for example, you might decide that when you’re conversing with someone you’ll take the time to pause, listen to what they have to say, and check that you understand before you reply. Keep it specific. That helps you change the target habit. You should also take every naturally occurring opportunity to practice the skill you’re developing, no matter how small. You’re trying to train your brain to react differently in common situations, and the principle of neuroplasticity tells us that as a given brain circuit gets used more often, the connections within it become stronger. And the brain does not distinguish between home and work when it comes to changing your habits: Practice at home as well as at work, with your partner or teenager as you would with your boss or direct reports. Spotting these opportunities to trot out your new habit requires a bit of extra awareness. At first this will take effort (and actually doing it might feel strange). But each time you do it, these new pathways in your brain strengthen their connection, making your new approach easier and more habitual. Soon you’ll find it more natural to pause and listen for a reply, for example, than to cut off the person you’re talking with in your excitement to respond. One day you will reach a neural landmark: The new habit will kick in automatically, without you having to make any effort. That means your new habit has replaced the old as your brain’s default circuit. Here, too, a coach can be useful to you along the way, especially if they are explicitly trained in helping leaders and executives develop their EI strengths. From accessing the right kind of evaluation to observing you in action, a well-trained coach can work with you to identify personal narratives or habitual patterns of mind that undermine your ability to get out of your own way, and instead talk you through those days when life’s pressures force you back into your old, not-so-good habits. By answering these questions and starting to change your routine reactions, you’ll be well on your way to figuring out the old habits that aren’t serving you well and transforming them into new, improved ones that do.

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15 августа, 15:00

How to Avoid Loneliness When You Work Entirely from Home

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Carlo Allegri/Getty Images Working from home can be a coveted perk, allowing you to opt out of rush-hour traffic and eliminate the tedious banalities of office life. But it can also cut you off from the spontaneous interactions that can spark new insights (part of the reason Marissa Mayer famously rescinded Yahoo’s telecommuting policies). And, at times, the solitude may lead to isolation or the feeling that you’re left out at work. How can you combat loneliness and create positive relationships with colleagues when you work from home full-time? I’ve worked from home since 2006, when I launched my consulting and speaking business. Here are three principles I’ve found to be effective in staving off isolation, maintaining productivity, and surrounding oneself with a stimulating cadre of colleagues. First, since you’re not physically interacting with coworkers, it’s important to seek out an online community of like-minded practitioners. The technology changes over time — when I first launched my business, I participated regularly in an online forum for solo consultants; today I maintain an active Facebook community for participants in my “Recognized Expert” online course. These discussion groups allow you to share successes and challenges and ask sensitive questions that, especially because participants are geographically dispersed, can be answered honestly and without feelings of competition. You and Your Team Series Remote Work How to Convince Your Boss to Let You Work from Home Rebecca Knight What Everyone Should Know about Running Virtual Meetings Paul Axtell How to Work Remotely Without Losing Motivation Alison Bukholtz Second, it’s especially useful for at-home workers to leverage video technology. Instead of phone calls, I’ll almost always book Skype or Zoom meetings so that I can see the other person. This helps me read their body language, ensures they’re more likely to remember and recognize me (if we don’t know each other well already), and provides me with a facsimile of in-person interaction. I’m admittedly an introvert, but at the end of a day filled with video calls, I’m often socially exhausted and need downtime, just as I would after a round of in-person meetings — so I think it’s doing the trick. Depending on your preferences, it can be useful to track whether you prefer scheduling a smaller number of daily video interactions with clients and colleagues (a “minimum effective dose”) or instead want to cluster them on the same day. The latter will allow for larger blocks of creative time on the days when you’re not in meetings (as I describe in this post on scheduling meetings when you’re self-employed). Finally, make a concerted effort to learn more about the personal lives of your colleagues. When you work from home, there’s a natural tendency to avoid “wasting time” with small talk; it may seem like a better move to focus exclusively on work-related conversations. But that may be a mistake. As eminent psychologist Robert Cialdini told me, small talk may seem trivial, but it’s actually the cement that creates rapport. “A weakness of Americans,” he says, “is that we tend not to do what is done in many other cultures — spending sociable time interacting with other people so there is a context of commonality recognized by both parties, so subsequent interactions go more smoothly.” Indeed, he cites research showing that when two groups of MBA students who didn’t know each other were asked to perform a negotiation over email, 55% of those who were told to “get straight down to business” made a deal. Meanwhile, a full 90% of those who were encouraged to share personal information and find commonalities with one another beforehand were able to strike one, and their deals were 18% more beneficial to both parties. That’s because, as Cialdini’s research shows, someone is far more persuasive to you when you like them — and knowing more about them and how you’re similar often hastens that process. So before a meeting starts, ask your colleagues about their recent vacation, their daughter’s sports matches, or their upcoming nuptials. These small details can create bonds that enable you to build deeper relationships that are both personally gratifying and professionally beneficial. A bit of loneliness may seem like an unavoidable trade-off when you work from home, away from the buzz of the office. (Though it’s important to note that not all social interactions have to be with humans: A study jointly run by NPR, the Robert Wood Johnson Foundation, and the Harvard T.H. Chan School of Public Health revealed that 87% of those who reported experiencing “a great deal of stress” in the past month were able to reduce it effectively by spending time with a pet, which may be even easier to accomplish when working from home.) But by following these strategies, you can ensure you’re forming meaningful connections with like-minded colleagues, even if you’re not face-to-face with them every day.

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15 августа, 14:25

What Data Scientists Really Do, According to 35 Data Scientists

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burakpekakcan/Getty Images Modern data science emerged in tech, from optimizing Google search rankings and LinkedIn recommendations to influencing the headlines Buzzfeed editors run. But it’s poised to transform all sectors, from retail, telecommunications, and agriculture to health, trucking, and the penal system. Yet the terms “data science” and “data scientist” aren’t always easily understood, and are used to describe a wide range of data-related work. What, exactly, is it that data scientists do? As the host of the DataCamp podcast DataFramed, I have had the pleasure of speaking with over 30 data scientists across a wide array of industries and academic disciplines. Among other things, I’ve asked them about what their jobs entail. Insight Center Adopting AI Sponsored by SAS How companies are using artificial intelligence in their business operations. It’s true that data science is a varied field. The data scientists I’ve interviewed approach our conversations from many angles. They describe a wide range of work, including the massive online experimental frameworks for product development at booking.com and Etsy, the methods Buzzfeed uses to implement a multi-armed bandit solution for headline optimization, and the impact machine learning has on business decisions at Airbnb. That last example came during my conversation with Airbnb data scientist Robert Chang. When Chang was at Twitter, that company was focused on growth. Now that he’s at Airbnb, Chang works on productionized machine-learning models. Data science can be used in a number of different ways, depending not just on the industry but on the business and its goals. But despite all the variety, a number of themes have emerged from these conversations. Here’s what they are: What data scientists do. We now know how data science works, at least in the tech industry. First, data scientists lay a solid data foundation in order to perform robust analytics. Then they use online experiments, among other methods, to achieve sustainable growth. Finally, they build machine learning pipelines and personalized data products to better understand their business and customers and to make better decisions. In other words, in tech, data science is about infrastructure, testing, machine learning for decision making, and data products. Great strides are being made in industries other than tech. I spoke with Ben Skrainka, a data scientist at Convoy, about how that company is leveraging data science to revolutionize the North American trucking industry. Sandy Griffith of Flatiron Health told us about the impact data science has begun to have on cancer research. Drew Conway and I discussed his company Alluvium, which “uses machine learning and artificial intelligence to turn massive data streams produced by industrial operations into insights.” Mike Tamir, now head of self-driving at Uber, discussed working with Takt to facilitate Fortune 500 companies’ leveraging data science, including his work on Starbucks’ recommendation systems. This non-exhaustive list illustrates data-science revolutions across a multitude of verticals. It isn’t all just the promise of self-driving cars and artificial general intelligence. Many of my guests are skeptical not only of the fetishization of artificial general intelligence by the mainstream media (including headlines such as VentureBeat’s “An AI god will emerge by 2042 and write its own bible. Will you worship it?”), but also of the buzz around machine learning and deep learning. Sure, machine learning and deep learning are powerful techniques with important applications, but, as with all buzz terms, a healthy skepticism is in order. Nearly all of my guests understand that working data scientists make their daily bread and butter through data collection and data cleaning; building dashboards and reports; data visualization; statistical inference; communicating results to key stakeholders; and convincing decision makers of their results. The skills data scientists need are evolving (and experience with deep learning isn’t the most important one). In a conversation with Jonathan Nolis, a data science leader in the Seattle area who helps Fortune 500 companies, we posed the question, “Which skill is more important for a data scientist: the ability to use the most sophisticated deep learning models, or the ability to make good PowerPoint slides?” He made a case for the latter, since communicating results remains a critical part of data work. Another recurring theme is that these skills, so necessary today, are likely to change on a relatively short timescale. As we’re seeing rapid developments in both the open-source ecosystem of tools available to do data science and in the commercial, productized data-science tools, we’re also seeing increasing automation of a lot of data-science drudgery, such as data cleaning and data preparation. It has been a common trope that 80% of a data scientist’s valuable time is spent simply finding, cleaning, and organizing data, leaving only 20% to actually perform analysis. But this is unlikely to last. These days even a great deal of machine learning and deep learning is being automated, as we learned when we dedicated an episode to automated machine learning, and heard from Randal Olson, lead data scientist at Life Epigenetics. One result of this rapid change is that the vast majority of my guests tell us that the key skills for data scientists are not the abilities to build and use deep-learning infrastructures. Instead they are the abilities to learn on the fly and to communicate well in order to answer business questions, explaining complex results to nontechnical stakeholders. Aspiring data scientists, then, should focus less on techniques than on questions. New techniques come and go, but critical thinking and quantitative, domain-specific skills will remain in demand. Specialization is becoming more important. While there is no well-defined career path for data scientists, and little support for junior data scientists, we are starting to see some forms of specialization. Emily Robinson described the difference between Type A and Type B data scientists: “Type A is the analysis — sort of a traditional statistician — and Type B is building machine learning models.” Jonathan Nolis breaks data science down into three components: (1) business intelligence, which is essentially about “taking data that the company has and getting it in front of the right people” in the form of dashboards, reports, and emails; (2) decision science, which is about “taking data and using it to help a company make a decision”; and (3) machine learning, which is about “how can we take data science models and put them continuously into production.” Although many working data scientists are currently generalists and do all three, we are seeing distinct career paths emerging, as in the case of machine learning engineers. Ethics is among the field’s biggest challenges. You may gather that the profession offers its practitioners a great deal of uncertainty. When I asked Hilary Mason in our first episode if any other major challenges face the data science community, she said, “Do you think that imprecise ethics, no standards of practice, and a lack of consistent vocabulary are not enough challenges for us today?” All three are essential points, and the first two in particular are front of mind for nearly every DataFramed guest. At a time when so many of our interactions with the world are dictated by algorithms developed by data scientists, what role does ethics play? As Omoju Miller, the senior machine learning data scientist at GitHub, said in our interview: We need to have that ethical understanding, we need to have that training, and we need to have something akin to a Hippocratic oath. And we need to actually have proper licenses so that if you actually do something unethical, perhaps you have some kind of penalty, or disbarment, or some kind of recourse, something to say this is not what we want to do as an industry, and then figure out ways to remediate people who go off the rails and do things because people just aren’t trained and they don’t know. A recurring theme is the serious, harmful, and unethical consequences that data science can have, such as the COMPAS Recidivism Risk Score that has been “used across the country to predict future criminals” and is “biased against blacks,” according to ProPublica. We’re approaching a consensus that ethical standards need to come from within data science itself, as well as from legislators, grassroots movements, and other stakeholders. Part of this movement involves a reemphasis on interpretability in models, as opposed to black-box models. That is, we need to build models that can explain why they make the predictions they make. Deep learning models are great at a lot of things, but they are infamously uninterpretable. Many dedicated, intelligent researchers, developers, and data scientists are making headway here with work such as Lime, a project aimed at explaining what machine learning models are doing. The data science revolution across industries and society at large has just begun. Whether the title of data scientist will remain the “sexiest job of the 21st century,” will become more specialized, or will become a set of skills that most working professionals are simply required to have is unclear. As Hilary Mason told me: “Will we even have data science in 10 years? I remember a world where we didn’t, and it wouldn’t surprise me if the title goes the way of ‘webmaster.’”

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15 августа, 13:00

Research: To Get People to Embrace Change, Emphasize What Will Stay the Same

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Eric Raptosh/Getty Images Common wisdom in management science and practice has it that to build support for a change project, visionary leadership is needed to outline what is wrong with the current situation. By explaining how the envisioned change will result in a better and more appealing future, leaders can overcome resistance to change. But our research, recently published in the Academy of Management Journal, leads us to add a very important caveat to this. A root cause of resistance to change is that employees identify with and care for their organizations. People fear that after the change, the organization will no longer be the organization they value and identify with — and the higher the uncertainty surrounding the change, the more they anticipate such threats to the organizational identity they hold dear. Change leadership that emphasizes what is good about the envisioned change and bad about the current state of affairs typically fuels these fears because it signals that changes will be fundamental and far-reaching. Counterintuitively, then, effective change leadership has to emphasize continuity — how what is central to “who we are” as an organization will be preserved, despite the uncertainty and changes on the horizon. This is a straightforward and actionable notion that we put to the test in two studies. The first study was a survey of 209 employees and their supervisors from a number of organizations that announced organizational change plans (including relocations and business expansions, reorganizations, structural or technical changes, product changes, changes in leadership, and mergers). The focus was on how effective the leadership was in stimulating employee support for the change, measured through supervisor ratings of employee behavior. As predicted, results showed that leadership was more effective in building support for change the more that leaders also communicated a vision of continuity, because a vision of continuity instilled a sense of continuity of organizational identity in employees. These effects were larger when employees experienced more uncertainty at work (as measured by employee self-ratings). In the second study, we tested the same idea using a laboratory experiment so that we could draw conclusions about causality. 208 business school students participated in the study, and the context was potential changes in the school’s curriculum. They received one of two messages allegedly from the dean of the business school. One conveyed a vision of change for the curriculum, and the other conveyed the same vision of change but also conveyed a vision of continuity of identity. Independent of which message they were exposed to, students received one of two versions of background information that suggested either low uncertainty or high uncertainty about change outcomes. We then assessed their sense of continuity of identity and their support for the change as expressed in actual behavior: help in drafting a letter to persuade other students to support the change. The results of this second study were similar to those of the first: Support for change was higher when the vision of change was accompanied by a vision of continuity, because in this case people’s sense of continuity of identity was higher. Again, the effects were stronger when uncertainty about the change was higher. The implications of this research are straightforward. In overcoming resistance to change and building support for change, leaders need to communicate an appealing vision of change in combination with a vision of continuity. Unless they are able to ensure people that what defines the organization’s identity — “what makes us who we are” — will be preserved despite the changes, leaders may have to brace themselves for a wave of resistance.

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15 августа, 12:05

Whether a Husband Identifies as a Breadwinner Depends on Whether He Respects His Wife’s Career — Not on How Much She Earns

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Harry Haysom/Getty Images Professional careers are notorious for demanding that people be single-mindedly devoted to work. It’s a demand that is often especially acute for men, who face rigid expectations that being a successful man requires having a successful career, and that “success” means power and money. Men have traditionally satisfied these expectations by taking on the role of a work-devoted breadwinner, supported by a wife who does not work or who places his career first. But many heterosexual men today are married to women who pursue demanding careers of their own; moreover, many women expect that their husbands will support their careers and be more engaged in family life than previous generations of men have been. The contradiction between the traditional image of the successful man and the reality of men’s lives creates a conundrum: How do men make sense of who they are in relation to their work, given their wives’ careers? My research suggests that while some men fall back on the classic identity of a breadwinner, others respond to this tension by adopting the modern identity of what I call a “breadsharer.” Research on dual-career couples often focuses on how spouses balance their earnings or work hours, but my research showed that these groups of men differed most fundamentally in how they perceived the social status of their wives’ work — its worth and prestige in society. This perception in turn shaped how men described the financial value of their wives’ work. In other words, wages are far more than just dollars: As sociologist Viviana Zelizer has eloquently detailed, money is imbued with meaning, and this meaning shapes how we regard and treat that money. My research reveals how men’s evaluations of the prestige and social worth of their wives’ work shaped how they positioned their wives’ earnings — namely in ways that diminished or that elevated their financial value. These different interpretations of the social status and financial value of their wives’ careers provided men with different ways of approaching their own careers. Breadsharers sought to remain professionally flexible to maximize their ability to respond to their wives’ career opportunities, and were hence uncommitted to any particular pathway and open to leaving their organization. Breadwinners, however, seeing no need to be flexible around their wife’s career, tended to be more committed to achieving success within their organization’s hierarchy. I studied men working at a global strategy consulting firm. As in many such firms, the consultants were expected to be primarily devoted to their work: to be willing to travel far from family and work long days and weekends with very little notice. I interviewed 42 heterosexual married men at the firm. These men ranged in age from their mid-twenties to their early sixties and were employed at different levels of the firm’s hierarchy. Men at this firm generally believed that to be successful, they had to be fully devoted to their employer and willing to prioritize their work over any work a wife did. As evidence of this equation, they pointed to senior men at the firm, who were almost all married to women who did not work outside the home. But this was not true of the men in my sample. When I began this research, I was told by an insider that most men at the firm had stay-at-home wives. In my sample of 42 married men, 23 of their wives worked full-time and 13 worked part-time. Just six of the 42 wives were not working at the time of the interview. There was thus a clear tension in the firm between common beliefs about men’s family lives and the actual characteristics of men’s families. Among the men I interviewed, this tension led to a fair amount of career angst — and marital conflict. Breadsharers  Some men (23 of them, 60% of the sample) conceived of themselves as breadsharers — husbands who valued enabling each partner to pursue their work and family goals. These men described their wives’ work in glowing terms, regarding it as high status and worthy of respect. They spoke at length of how important their wives’ work was, how well it was regarded by others, and of their wives’ many career accomplishments. For example, one man described his wife in the following way: [Her] skills make her stand out in a sea of experts…. She’s an excellent public speaker. And one of her gifts is that she’s able to convey very complex concepts to lay audiences and expert audiences…. Whenever she speaks at any conference, she’s like, nine times out of ten, she’s the top-rated speaker on the evaluation forms. These men used language that elevated the value of their wives’ earnings. One man described his wife as his “gravy train.” Another explained in detail why his wife’s earnings were more important than their dollar value might suggest: “The fact that she doesn’t work full-time is probably what makes us at odds. But otherwise, on a per-day wage, we probably make the same money…. We both do feel quite empowered at our work, because the other works.” Describing a difficult situation at work, he explained that his wife’s earnings had empowered him to stand up for himself because, “I knew, right, there was no need to [worry about being fired]. It’s not like we were going to go hungry or anything — you know, the mortgage will be paid.” Valuing their wives’ work so highly, these men positioned themselves in sharing terms: placing importance on both partners being able to pursue their work and family-related desires, hopes, and dreams. They supported their wives’ work alongside — and sometimes ahead of — their own. One explained: “I want to make sure she continues to be in a professional situation where she can [succeed], and that, in turn, you know, puts pressure back on me to sort of say, “OK, wait. Our life is not going to be the one where I get to do whatever the [expletive] I want job-wise, just because my life is not the center.” This support for their wives’ careers meant that men were uncertain about the direction of their own careers. The firm demanded an unwavering attention to work, which would lead to a pathway to partnership. But these men felt that their wives’ careers required that they themselves remain adaptable and open to changing jobs, cities, or countries. These men were thus not so committed to continuing along the pathway the firm expected of them. They were aware that in this, their own expectations for themselves differed from the expectations they faced at the firm. One told me, “We’re an interesting couple in that I went to business school, I work as a consultant in the professional services space, in a world where in many ways many of the men in the consulting world, right, are the primary breadwinners in the family and I am not that.” Breadwinners A smaller group of men (17 of them, 40% of the sample) positioned themselves in terms consistent with the traditional male breadwinner identity. These men accorded low social status to their wives’ work, which seemed to prime them to view this work as having little financial importance to the family. This happened even when wives seemed — to an external observer — to be quite financially successful. One minimized his wife’s career achievements, saying, “She could have done much more than she has [in her field], but she chose a different path. What I call, you know — being a project manager in the home is the way I describe it….” His wife contributed one-third of the family’s income — about a six-figure salary. Another framed his wife’s (considerable) wages in ways that seemed to disappear them: “I said to her, ‘If you take your job and net out all of the day care expenses and net out all of the extra tax that we have to pay because you work, we’d fundamentally be making the same amount of money between us.’” These men’s characterizations of their wives’ earnings as fundamentally unimportant, somewhat frivolous, and optional echo a longstanding cultural history of the value of women’s work being diminished through labels such as “pin money.” Having diminished the status and financial value of their wives’ work, these men easily laid claim to the identity of a work-devoted breadwinner, which they viewed as essential to their potential career success. One man (whose wife was a full-time professional in a similar role and earned more than he did), put it this way: “Work-life balance is less of an option for the guy if he feels the need to be successful and provide for the family. And I guess that’s the situation that I’m in right now.” These men, unlike the breadsharers, mostly intended to stay at the firm and make partner. And why wouldn’t they? They had made sense of their wives’ careers in ways that freed them to devote themselves to their work as their firm demanded. Yet while some seemed quite happy to be breadwinners, others felt trapped: Even though they claimed a breadwinner identity, it was not always a completely satisfying one. Why should we care about how men identify themselves in relation to their wives’ careers? We often focus on how women’s work lives are shaped by their family lives, but the ways that men’s work lives are shaped by their family circumstances are too often ignored. This study showed that how men in professional careers defined themselves in relation to their work, as well as how they approached their careers, was very much shaped by how they interpreted the social status and financial value of their wives’ work. The importance of status in men’s interpretations was somewhat unexpected; in our conversations about work, career, and couples, we often focus on earnings and work hours. This research shows that social status — worth, esteem, and respect — matters too for couples’ careers. No husband with a wife who was a doctor or lawyer minimized her career or discounted her earnings, no matter how much or little she worked or earned. Finally, this work shows that money matters in couples’ careers, but not in the ways we think. Salaries are more than dollars and cents; they have a social meaning and that meaning is quite malleable.

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14 августа, 19:38

Networking Myths Dispelled

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David Burkus, a professor at Oral Roberts University and author of the book Friend of a Friend, explains common misconceptions about networking. First, trading business cards at a networking event doesn’t mean you’re a phony. Second, your most valuable contacts are actually the people you already know. Burkus says some of the most useful networking you can do involves strengthening your ties with old friends and current coworkers. Download this podcast

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14 августа, 15:00

Managers Think They’re Good at Coaching. They’re Not.

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pbombaert/Getty Images Are you successful at coaching your employees? In our years studying and working with companies on this topic, we’ve observed that when many executives say “yes,” they’re incorrectly answering the question. Why? For one, managers tend to think they’re coaching when they’re actually just telling their employees what to do — and this behavior is often reinforced by their peers. This is hardly an effective way to motivate people and help them grow, and it can result in wasted time, money, and energy. According to Sir John Whitmore, a leading figure in executive coaching, the definition of coaching is “unlocking a person’s potential to maximize their own performance. It is helping them to learn rather than teaching them.” When done right, coaching can also help with employee engagement; it is often more motivating to bring your expertise to a situation than to be told what to do. Recently, my colleagues and I conducted a study that shows that most managers don’t understand what coaching really is — and that also sheds light on how to fix the problem. This research project is still in progress, but we wanted to offer a glimpse into our methodology and initial findings. First, we asked a group of participants to coach another person on the topic of time management, without further explanation. In total, 98 people who were enrolled in an MBA course on leadership training participated, with a variety of backgrounds and jobs. One-third of the participants were female and two-thirds were male; on average, they were 32 years old and had eight years of work and 3.8 years of leadership experience. The coaching conversations lasted five minutes and were videotaped. Later, these tapes were evaluated by other participants in the coaching course through an online peer review system. We also asked 18 coaching experts to evaluate the conversations. All of these experts had a master’s degree or graduate certificate in coaching, with an average of 23.2 years of work experience and 7.4 years of coaching experience. Participants then received face-to-face training in two groups of 50, with breakouts in smaller groups for practice, feedback, and reflection around different coaching skills. At the end, we videotaped another round of short coaching conversations, which were again evaluated by both peers and coaching experts. In total, we collected and analyzed more than 900 recorded evaluations of coaching conversations (pre-training and post-training), which were accompanied by surveys asking participants about their attitudes and experiences with leadership coaching before and after the training. The biggest takeaway was the fact that, when initially asked to coach, many managers instead demonstrated a form of consulting. Essentially, they simply provided the other person with advice or a solution. We regularly heard comments like, “First you do this” or “Why don’t you do this?” This kind of micromanaging-as-coaching was reinforced as good practice by other research participants. In the first coaching exercise in our study, the evaluations peers gave one another were significantly higher than the evaluations from experts. In an organizational setting, you can imagine how a group of executives, having convinced one another of their superior skills, could institutionalize preaching-as-coaching. Our research also looked at how you can train people to be better coaches. We focused on analyzing the following nine leadership coaching skills, based on the existing literature and our own practical experiences of leadership coaching: listening questioning giving feedback assisting with goal setting showing empathy letting the coachee arrive at their own solution recognizing and pointing out strengths providing structure encouraging a solution-focused approach Using the combined coaching experts’ assessments as the baseline for the managers’ abilities, we identified the best, worst, and most improved components of coaching. The skill the participants were the best at before training was listening, which was rated “average” by our experts. After the training, the experts’ rating increased 32.9%, resulting in listening being labeled “average-to-good.” The skills the participants struggled with the most before the training were “recognizing and pointing out strengths” and “letting the coachee arrive at their own solution.” On the former, participants were rated “poor” pre-training, and their rating crept up to only “average” after. Clearly, this is an area managers need more time to practice and work on, and it’s something they likely need to be trained on differently as well. Interestingly, the most improved aspect of coaching was “letting coachees arrive at their own solution.” This concept saw an average increase in proficiency of 54.1%, which moved it from a “poor” rating to a “slightly above average” one. More generally, multiple assessments of participants by experts before and after the training course resulted in a 40.2% increase in overall coaching ability ratings across all nine categories, on average. What can organizations learn from our research? First, any approach to coaching should begin by clearly defining what it is and how it differs from other types of manager behavior. This shift in mindset lays a foundation for training and gives managers a clear set of expectations. The next step is to let managers practice coaching in a safe environment before letting them work with their teams. The good news, as evidenced by our research, is that you don’t necessarily need to invest in months of training to see a difference. You do, however, need to invest in some form of training. Even a short course targeted at the right skills can markedly improve managers’ coaching skills. Regardless of the program you choose, make sure it includes time for participants to reflect on their coaching abilities. In our study, managers rated their coaching ability three times: once after we asked them to coach someone cold, once after they were given additional training, and once looking back at their original coaching session. After the training, managers decreased their initial assessment of themselves by 28.8%, from “slightly good” to “slightly poor.” This change was corroborated by managers’ peers, who reduced their assessment by 18.4%, from “slightly good” to “neither good nor bad,” when looking back at their original observations of others. In other words, if managers have more knowledge and training, they are able to provide a better self-assessment of their skills. Organizations should allocate time for managers to reflect on their skills and review what they have done. What’s working, and what they could do better? Our research also supports the idea of receiving feedback from coaching experts in order to improve. The risk of letting only nonexperts help might reinforce and normalize ineffective behaviors throughout an organization. Specifically, coaching experts could give feedback on how well the coaching skills were applied and if any coaching opportunities have been missed. This monitoring could take the form of regular peer coaching, where managers in an organization come together to practice coaching with each other, or to discuss common problems and solutions they have encountered when coaching others, all in the presence of a coaching expert. Here managers have two advantages: First, they can practice their coaching in a safe environment. Second, coaches can discuss challenges they have experienced and how to overcome them. If you take away only one thing here, it’s that coaching is a skill that needs to be learned and honed over time. Not only does a lack of training leave managers unprepared to undertake coaching, but also it may effectively result in a policy of managers’ reinforcing poor coaching practices among themselves.

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14 августа, 14:39

To Make Self-Driving Cars Safe, We Also Need Better Roads and Infrastructure

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Paul Taylor/Getty Images The big question around self-driving cars, for many people, is: When will the technology be ready? In other words, when will autonomous vehicles be safe enough to operate on their own? But there has been far less attention paid to two equally important questions: When will the driving environment be ready to accommodate self-driving cars? And where will this technology work best? Self-driving cars are the most challenging automation project ever undertaken. Driving requires a great deal of processing and decision making, which must be automated. On top of that, there are many unpredictable external factors that must be accounted for, and therefore many ways in which the driving environment must change. Cars are heavy, fast-moving objects, operating in public spaces. Safety is largely the responsibility of the driver, who must continuously observe, analyze, decide, and act. Not only do drivers have to follow the rules of the road, but they also have to communicate with each other and other road users to navigate ambiguous or contested situations; think about how you wave or nod to someone to signal “You go first.” Insight Center Adopting AI Sponsored by SAS How companies are using artificial intelligence in their business operations. Self-driving systems have to execute all of these functions, and do so accurately, reliably, and safely, across a wide variety of situations and conditions. Currently, the technology is more capable in some situations than in others. Through sensors and detailed mapping software, the systems build representations of their environments and update them many times a second. They classify the objects they see and predict their likely behavior before selecting appropriate responses. The speed and the accuracy of these systems already surpass human responses in many situations. Lasers can see in the dark. Reaction times can be nearly instantaneous. But some conditions still constrain them. Cameras are challenged by strong, low-angle sunlight (important for reading traffic lights), and lasers can be confused by fog and snowfall. Unusual, unfamiliar, and unstructured situations (so-called edge cases), such as accidents, road work, or a fast-approaching emergency response vehicle, can be hard to classify. And self-driving systems are not good at detecting and interpreting human cues, such as gestures and eye contact, that facilitate coordination between cars on the road. Processes and environments that are structured well are much easier to automate than those that are not. Automated systems need to collect, classify, and respond to information, and this is easier to do in a clean, unambiguous environment — which is what many driving environments are not. The designers of self-driving systems simply cannot foresee every possible combination of conditions that will occur on the road. (Though companies are trying: Google’s Waymo team deliberately subjects its cars to “pathological situations” that are unlikely to happen, such as people hiding in bags and then jumping in front of the car.) Over time, learning will take place and the number of situations that systems cannot recognize will decrease. In fact, learning is likely to be better in an automated system, because once an incident has occurred and is understood, the fix can be rolled out across all vehicles. Currently, learning is largely confined to individual drivers, and is not shared across the system as a whole. But novel combinations of conditions will never be eliminated, and sometimes these will have catastrophic consequences — a pattern seen even in the highly disciplined environment of commercial aviation. The problem therefore lies in our period of transition. For the technology to improve, it must be exposed to real, on-road conditions. In the early stages of deployment, it sometimes won’t know the best way to respond and therefore will have to hand over control to a human driver. The issue here, however, is that humans zone out when their full attention is not needed. As self-driving cars improve and humans intervene less, driver inattention and the associated problem of quickly reengaging to respond become even bigger problems. And as the technology becomes more sophisticated, the situations where it requires human assistance are likely to be more complex, ambiguous, and difficult to diagnose. In these cases, a startled human has much less chance of responding correctly. Even in the highly sterile environment of an aircraft cockpit, pilots can be caught by surprise and respond incorrectly when automation has ceded control. Two fatal accidents involving Tesla vehicles operating on their Autopilot systems demonstrate how this space between semi-automated driving and intermittent human control may be the most dangerous place of all. In the Florida 2016 crash, the driver of the Tesla had his hands on the steering wheel for only 25 seconds of the 37 minutes in which he operated the vehicle in automated control mode. In California in 2018, the driver’s hands were not detected on the steering wheel in the six seconds preceding the crash. This problem has led companies such as Waymo and Ford to advocate for fully autonomous cars that get rid of the need for handovers. But this requires a leap: With no driver as backup, there is a risk that the technology will be catapulted into environments that are beyond its ability to handle. Self-driving cars also have to navigate an environment that is shared — with pedestrians who sometimes cross the road without looking, cyclists, animals, debris, inanimate objects, and of course whatever elements the weather brings. Road infrastructure, regulations, and driving customs vary from country to country, even city to city, and are overseen by a multiplicity of bodies. So it’s not clear which institutions have the power and reach to regulate and standardize the driving environment, if they even exist. Roads are very different from airspace, which is governed by powerful global regulatory bodies, has far less traffic, and has very high licensing standards for pilots. This means that we need to think not just about the onboard technology but also about the environment in which it is deployed. We’ll likely start to see a more standardized and active environment as more smart infrastructure is constructed. Think of radio transmitters replacing traffic lights, higher-capacity mobile and wireless data networks handling both vehicle-to-vehicle and vehicle-to-infrastructure communication, and roadside units providing real-time data on weather, traffic, and other conditions. Common protocols and communications standards will have to be devised and negotiated, as they were with internet communication protocols or the Global System for Mobile Communications (GSM) for mobile phones. This transition will take decades, and autonomous vehicles will have to share the roads with human drivers. If rapid, radical change to the driving environment is impractical, what is the alternative? The most likely near-term scenario we’ll see are various forms of spatial segregation: Self-driving cars will operate in some areas and not others. We’re already seeing this, as early trials of the technology are taking place in designated test areas or in relatively simple, fair-weather environments. But we may also see dedicated lanes or zones for self-driving vehicles, both to give them a more structured environment while the technology is refined and to protect other road users from their limitations. We can also expect to see self-driving cars deployed first in relatively controlled environments (such as theme parks, private campuses, and retirement villages), where speeds are lower and the range of situations the vehicles have to deal with is limited. Economics, too, will play an important role in where and how self-driving cars begin to operate. The vehicles will likely appear in environments where it is cost-effective to develop and maintain highly detailed mapping, such as dense urban environments, although of course these also pose other challenges due to their complexity. Although the cost of self-driving cars will fall once they enter mass production, it is currently very high, from $250,000 to $300,000 a vehicle, according to some estimates. So they will first appear in settings where vehicle utilization rates are high and where the cost of a driver’s time matters — imagine robotaxis or ride-hailing vehicles operating in defined, geo-fenced zones. Trials of these are already under way. Robotaxis also point to a way in which humans can support self-driving technology while avoiding the human zone-out problem by interacting with call centers. A self-driving vehicle that cannot get past an obstruction in the road without acting illegally (crossing a white line, for example) can stop and call a human operator for advice, who can then authorize it to act in a nonstandard way. In the long run, driverless cars will help us reduce accidents, save time spent on commuting, and make more people mobile. The onboard technology is developing rapidly, but we’re entering a transition stage in which we need to think carefully about how it will interact with human drivers and the wider driving environment. During this period, the key question we should be asking is not when will self-driving cars be ready for the roads, but rather which roads will be ready for self-driving cars.

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14 августа, 13:00

Why Western Digital Firms Have Failed in China

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Paper Boat Creative/Getty Images Many leading American digital firms, including Google, Amazon, eBay, and Uber, have successfully expanded internationally by introducing their products, services, and platforms in other countries. However, they have all failed in China, the world’s largest digital market. The widely touted reasons for these failures include censorship by the Chinese government and cultural differences between China and the West. While these factors undoubtedly have played a role, such explanations are overly simplistic. Google, for example, has succeeded in dominating many foreign markets that have radically different political systems and cultures (including Indonesia, Thailand, and Saudi Arabia). And these factors have not stopped Western multinationals from succeeding in China in car manufacturing, fast-moving consumer goods, and even sectors where culture plays a key role, such as beer, coffee shops, fast food, and the film industry. There are deeper reasons behind the systematic failure of Western digital firms in China. (The term “digital firms” refers to those companies that from their inception have focused on digital services enabled by the internet and related technologies, including mobile. It does not include traditional IT firms that rely on sales of hardware or software as their main source of revenue.) And yet Western digital firms haven’t given up on trying to tap into China’s rapidly growing market. Google is reentering China by setting up new offices and an AI center, signing new deals with retail heavyweights JD.com and Tencent, rolling out new products (including a controversial local mobile search app that would strictly censor results), and investing in promising local startups. Airbnb, LinkedIn, and WeWork are also expanding their presences in China. Amazon is expanding its China business in cross border e-commerce, Amazon Prime, and Amazon Web Services. The question is, will this be sufficient? What could these firms do differently this time to succeed? “Death by a Thousand Cuts” Based on a comprehensive five-year study, my new research paper, published in the Academy of Management Discoveries this year, systematically identifies the reasons behind the failures of major Western digital firms in China. This study uses two rounds of interviewing to identify what the Nobel laureate Daniel Kahneman describes as the “inside view” and the “outside view” of the phenomenon. First, interviews were conducted with 40 senior business executives from six leading Western digital firms (Google, Yahoo, eBay, Amazon, Groupon, and Uber) and their corresponding direct competitors in China (Baidu, Sohu, Taobao, JD.com, Meituan, and Didi). This was intended to identify the inside view of the phenomenon. The prevailing narrative emerging from these interviews points to a lack of strategic determination and patience by Western digital firms as the main cause of their failure. This is reflected in seven factors: lack of a deep (enough) understanding of the Chinese market poor management of relations with Chinese regulators and the government ill-fated attempts to impose global business models unsuited to the Chinese market failure to cope with the extremely fierce competition in China failure to manage relations effectively with local business partners imposing technological platforms developed for the U.S. market on China overly centralized organizational structure’s leading to slow decision making Second, 185 seasoned expert observers were interviewed in China to identify the outside view on the phenomenon. These interviews highlighted the failure by Western digital firms to acclimate to China’s business environment as the main cause of their failure. This was described in Chinese as Bujie diqi (不接地气), meaning these firms failed to “keep their feet firmly on the ground.” It led to a series of competitive disadvantages, thereby allowing Chinese digital firms to race ahead in the fight for market share. Specific factors identified included: failure to cope with a very large number of local competitors failure to cope with extremely aggressive and determined local competitors underestimating the major differences between digital business and other industries failure to develop and communicate business strategies effectively ineffective innovation strategies failure to fully embed operations in China Despite the differences between the inside view and the outside view, these factors have converged in three clusters: (1) poor understanding of the business environment, (2) ineffective strategy making and communication, and (3) underperformance in operation and execution. The Western firms’ failures in China were not due to one specific factor, but rather to the cumulative effects of multiple factors over time. “It’s death by a thousand cuts!” remarked a former senior executive from eBay. Related Video 5 Principles for Innovation in Emerging Markets Think about consumers' jobs-to-be-done. Save Share See More Videos > See More Videos > To date, Western digital firms have failed to capitalize on their perceived competitive advantage in China. They have failed to understand the complex business environment there, adapt their strategies and business models to the Chinese market, and develop new technologies and services to cater to the preferences of Chinese users. They have underestimated the strength and resilience of Chinese competitors and the enormous challenges involved when trying to dominate the largest digital market in the world. Their successes in other international markets have given them a false sense of safety and invincibility in their perceived competitive advantages. While Western multinationals from other sectors benefit from advanced technologies, established product lines, and global supply chains that may take Chinese firms years of investment to catch up to, digital firms do not. They operate in an environment where the barriers to entry are relatively low and the focus of competition is on product and business model innovations and service delivery, rather than the most advanced technologies. Can They Get Back in the Game? Are Western digital firms forever doomed to fail in the Chinese market? The answer, of course, is no. The problems are not insurmountable, but the size and dynamism of the Chinese digital market suggest that any solutions that focus only on those problems are unlikely to be sufficient. The competitive advantages that have served Western digital firms well in other countries need to be recalibrated for the Chinese market. Three lessons here are particularly important. 1. Doing everything right is not enough. China has huge geographical disparities and socioeconomic variations across its regions. Its institutional environment and market preferences evolve rapidly and sometimes even erratically. Dominating and maintaining dominance in China poses unique challenges. Unlike other digital markets, doing everything right in China is often not enough to guarantee success, due to strong competition. Take Uber. Before entering China, Uber senior leaders did their homework carefully to avoid the mistakes that had derailed many other (digital and not) multinational firms. Uber set up a highly autonomous Chinese subsidiary; partnered with China’s largest search engine, Baidu; committed significant capital and paid out $2 billion in subsidies to win market share; and offered services specially tailored to the Chinese market. Uber’s founder and CEO at the time, Travis Kalanick, took a hands-on role and spent over 20% of his time in China. Despite all this, Uber wound up retreating from the Chinese market. What went wrong? It is hard to pinpoint any individual failure on Uber’s part. One notable challenge, however, is that for the first time, Uber met a genuine competitor: Didi Chuxing, which was more determined, had a larger cash reserve, and focused exclusively on China at that time. Uber sold its operation to Didi Chuxing. This case suggests that simply addressing each of the known mistakes made by other multinationals in the past is often not enough to guarantee success in the future. A more holistic approach is needed. 2. Accumulating incremental advantages in the “winner takes all” digital market. While radical innovations in products, business models, and technologies capture the most headlines, due to their importance to the long-term competitiveness of digital firms, what’s often overlooked is that accumulating incremental advantages across different areas of competition over time is also essential for survival. The digital market is significantly different from other market sectors. Western digital firms had only a short history to establish any inimitable advantages. The focus of digital firms on product and business model innovations, and the relatively low technological entry barriers, allowed a very large number of Chinese competitors to appear. As a culture market, China favors native firms, as they are often better at understanding users and the business environment. Local firms are also more adept at managing relationships with regulatory bodies and thereby influencing and anticipating regulatory changes. The technologies and intellectual property that Western digital firms rely on are often easily imitated and then adapted to local tastes in the digital space. In the “winner takes all” digital market, where usually only one or two players survive in each market niche, incremental advantages can snowball and have increasing returns to scale. The cumulative effect from any such advantage can become what separates winners from losers. 3. Experimental approaches to strategy and innovation. The enormous uncertainties in the rapidly evolving digital markets call for experimental approaches to both strategy and innovation. New ideas can become obsolete before they are fully implemented, requiring frequent recalibration of the course and destination of business strategy. Innovation through experimentation and improvisation is vital for success. But such experimental approaches are only feasible with strong autonomy by local management, local product and technical teams, and, in many cases, business models tailor-made for the Chinese market. Winning in the New Digital Economy Western digital businesses have not given up on China. However, to get back in the game and win, Western digital firms need to bring forward genuine competitive advantages. In addition to customizing their products, platforms, and business models for China, and empowering local management teams to compete for market share, these firms must also learn from Chinese digital firms how to integrate online and offline operations and build new partnerships and ecosystems. As the digital market in China continues to expand rapidly, a growing number of digital startups have joined the ranks of the largest unicorns in the world. And as Chinese digital firms grow larger and more confident, they are actively pursuing new opportunities in other international markets — India, Southeast Asia, Africa, Europe, and even the U.S. So the clashes between Western and Chinese digital firms will continue to escalate both in China and internationally. Beyond digital businesses, similar patterns have been observed in cloud services, mobile communications, fintech, and several non-digital sectors (such as solar energy, electric cars, and high-speed trains). Further, new battle lines have been drawn between Western and Chinese firms for artificial intelligence, driverless vehicles, and industries where Western multinationals have traditionally held major technological advantages (say, pharmaceuticals). The ongoing trade dispute between the U.S. and China is likely to further complicate the situation. To capture a slice of the Chinese market, Western business leaders must recognize and understand the issues highlighted here. These lessons are relevant to more than Western digital businesses in China; they can also shed light on the future competition between Western and Chinese multinationals in other sectors and other international markets.

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14 августа, 12:05

The Best Way for Netflix to Keep Growing

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jakob owens/unsplash Netflix has a lot to gain by becoming a multisided platform. Currently, Netflix is in the business of buying or making content, which it sells consumers access to at prices and on terms it fully controls (a monthly subscription). That’s unlike a platform such as YouTube, which enables myriad content providers to sell directly to users at prices they control, with limited intervention by YouTube other than the enforcement of some content guidelines. Netflix’s model has been undeniably successful to date. However, fighting the blockbuster battle over content acquisition and creation is becoming ever more expensive, and it involves an increasing number of combatants (including Amazon, Apple, Disney, and Google). All these companies already have or will have digital download and streaming services. Furthermore, the growth of Netflix’s subscriber base is slowing down. The company lost more than 15% of its stock market valuation over the past month after its growth numbers disappointed investors. In this context, it seems obvious that Netflix can and should become a platform, using one of the models described in my 2017 HBR article with Liz Altman. Why? Netflix’s big subscriber base (130 million worldwide) and content-delivery infrastructure are potentially very attractive to many third parties. In addition to video content providers, these third parties include marketers and the developers of cloud gaming or other services. How would Netflix become a platform? Simply by allowing these third parties to sell their products or services within Netflix’s service but outside Netflix’s subscription, on terms controlled by the third parties. Becoming a multisided platform in this way would allow Netflix to tap a different dimension of growth: selling more stuff to the same subscribers. And the beauty of the platform model is that Netflix can grow without having to buy or produce the new stuff itself. It just has to attract third parties to develop and sell the content, and then it can take (as is common) a share of the revenue or a transaction fee. Moreover, third parties could experiment with new forms of content, which could be very valuable to Netflix’s content acquisition and production efforts. In this way, Netflix would follow in Amazon’s footsteps. That company started as a pure retailer of products it bought from sellers and sold in its own name, before adding a marketplace where customers purchased directly from third-party sellers. Netflix can aim to become a similarly powerful reseller-platform hybrid, except it will have digital content rather than (for the most part) physical products. Why has Netflix not started on this path already? CEO Reed Hastings and his team must have thought about it. There are only two plausible explanations I can think of: (1) resource allocation and (2) quality control. I don’t find either one very convincing. The resource-allocation argument would be that, given the resources (financial and human) needed to develop and acquire high-quality content, Netflix simply may not have the bandwidth right now to look at platform opportunities. Well, I’d argue it should create the necessary bandwidth, given the huge potential payoff and the danger of getting stuck in the war over content acquisition and creation. Being a platform for content is much more scalable, valuable, and defensible than being just a content creator and reseller. The quality-control argument would run as follows. Becoming a platform — allowing third parties to sell content whose quality is not fully controlled by Netflix, and on terms not completely determined by Netflix — runs the risk of letting low-quality content slip through the cracks and alienating customers, who would then hold Netflix responsible. That is a valid concern, but there are many ways in which Netflix can mitigate this risk — as other companies have done when turning their products into platforms (examples include Amazon, Intuit, and Salesforce). I am not arguing that Netflix should move to an open platform model like YouTube’s, where everyone and their cat can post video content. Rather, Netflix can turn its service into a carefully curated platform, with relatively tight governance rules that can be relaxed over time. My bottom line is that Netflix has little to lose and a lot to gain by shifting from being an aggregator of content under one subscription, to a hybrid aggregator platform on which various content providers sell directly, and at prices of their choosing, to users. Becoming a platform is usually about fear (of competitors) or greed (for new sources of growth and revenues). For Netflix, it should be about both.

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13 августа, 16:00

The Simple Question That Can Make or Break a Startup

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Jonathan Knowles/Getty Images There’s an unassuming restaurant in Dallas called Chop House Burger, home to handspun milkshakes, truffle parmesan french fries, and six innovative burgers. It’s an eight-year-old restaurant in an industry where 80% of new entrants fail in the first five years. And stitched into its origin story is a clue to why some products (and businesses) succeed in the market while most wither and die. The burger place spun out of a The Dallas Chop House, a high-end steakhouse two blocks away. The Chop House was known for its ribeye, filet mignon, and flat iron steaks, dry-aged in the restaurant with Himalayan sea salt. In an effort to diversify their menu, the owners also offered a gourmet burger. Despite how good the steaks were, the burger became one of the most popular items on the menu. So the owners decided to build a restaurant around it. Most of us aren’t in the restaurant industry. We don’t all have an established business to test ideas in. Yet the value of establishing demand before you launch the business is just as important for us, whether we’re launching a new company or simply a new product. In February, venture capitalist database CB Insights conducted an extensive review examining what contributes to the failure of new businesses. After analyzing 101 startup post-mortems, the reviewers found that 42% suffered from a lack of demand for the product or service being offered. They used a harsh phrase to describe this cause of failure: “no market need.” This one flaw harmed significantly more companies than well-known startup challenges such as cash flow (29%), competition (19%), and poor timing (13%), to name a few. The findings raise the question: How can entrepreneurs or new product developers test their ideas before investing the significant time and capital required to actually bring them to life? How can we spot the ideas that are likely to succeed instead of wasting our efforts on those that are prone to failure? Here are three strategies worth trying: Look for successful competitors. When it comes to establishing demand, thriving competitors are a good sign, not the red flag many entrepreneurs view them to be. Being the first mover in a space can produce situational advantages, but showing up late gives you the benefit of added perspective. Today, if you were to release a new photo editing software, beer, or car rental service, you could be confident your offering would be understood and in-demand at some level. Yes, you would still have to get the attention of your target customers. You would also have to differentiate yourself from competitors. And there would still be plenty of ways your product or business could ultimately fail. But you would at least be in charted territory. The food delivery industry contains terrific examples of established companies following the demand validated by early movers, such as Seamless and Grubhub. To better leverage its foodie customer base, Yelp purchased Eat24. Uber applied its successful driver model to meals and created UberEats, which is outgrowing the ride sharing service in some markets. And payment platform Square acquired Caviar, giving restaurant owners a simple way to accept credit cards and deliver meals. Getting outcompeted is the obvious fear associated with entering a space that’s already home to successful companies. It’s a real risk. In the food delivery space, companies differentiated by finding new restaurants to partner with, expanding to new locations, and offering distinct pricing models. But those opportunities won’t exist in every industry. Ultimately, it’s worth considering the numbers. In the CB Insights study, only 19% of the analyzed postmortems claimed their startup had been outcompeted, less than half the number that blamed failure on a lack of demand. Check for search traffic. When people are searching for a product to solve a problem they’re facing, they type what they’re looking for into Google. Through keyword research, entrepreneurs can learn what people are searching for and use the findings to gauge demand for a product or service idea. According to the keyword research tool Ahrefs, 27,000 people per month are searching for “Photoshop alternatives.” 4,000 are searching for “automatic lawn mower.” And 100 are searching for “truckers’ bookkeeping service.” Each of these terms has similar variations that increase the total number of monthly searches. You could also gauge potential demand by looking for broader searches that don’t focus on a specific solution but prove the existence of a problem you can solve. For the examples above, “How to edit photos,” “Don’t have time to mow my lawn,” or “Bookkeeping guidelines for truckers” could be searches worth exploring. There’s no standardized amount of search volume you can use to validate healthy demand. You will have to interpret the data in the context of your specific industry and business goals. But confirming that people are searching for a product or service like yours is a good sign, and through Google AdWords campaigns or SEO, you can work to get in front of these very people if you decide to launch the idea. Test your marketing promise. People don’t buy products. They buy promises. Generally speaking, customers don’t truly know what it’s like to own a product until after they’ve purchased it. They don’t spend money because of any realized benefits. They’re paying for the benefits promised in the sales copy and testimonials. This is a crucial insight for anyone looking to test a new product or service because it suggests that you don’t need a finished product to validate demand for an idea. You can create the marketing copy for your hypothetical offering and test it through surveys or interviews with targeted prospects. Of course, the most accurate test of demand will involve customers getting out their credit cards. Pre-selling models such as Kickstarter are one way to do that. There’s no perfect system to pre-validate demand for a product or service, but that doesn’t mean you shouldn’t do your due diligence. Business failures are costly. They can result in lost capital, wasted time, and damaged confidence. Some of the challenges uncovered by the CB Insights study will be hard to predict — such as timing, whether you’ve hired the right people, and if you’ll make necessary pivots after launching the business. But demand is one key ingredient you can pre-validate, at least partially. Had they done a better job of gauging demand in advance, 42% of the companies in the CB Insights study might have chosen to pursue more reliable ideas, which could have better prepared them to avoid business failure and reach success sooner. After all, the fewer detours we take, the faster we arrive at our goals.