Marina Gorbis's Blog, page 784

Women Act More Ethically Than Men When Representing Themselves — But Not When Representing Others

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Research tells us a lot about why people behave unethically. For example, there is evidence that people tend to be more dishonest later in the day, because they’re more fatigued, and when they’re anxious, because they’re more likely to look out for themselves. Many of these studies, however, only look at the unethical actions people take on behalf of themselves. But what about the many times when we act on behalf of others?

We act on behalf of others in many domains: business, politics, law, the social sector, and others. Managers seek resources for their employees, for example. Lawyers represent clients in negotiations.

My research suggests that people acting on behalf of others can be influenced by the values and perceived expectations of those they’re representing—specifically when it comes to acting ethically. My colleagues and I were especially interested to know how this might apply to women. Research has found that women perform worse in negotiations because they face backlash for acting assertively – but that one way around this backlash is by advocating for others.

We conducted four studies to examine whether people were more likely to lie when negotiating on behalf of others than for themselves. We recruited a total of 1,337 participants to engage in negotiations, and we found that gender played a role in how we negotiate for ourselves and others. Men were more likely than women to lie when they were negotiating for themselves, but not when negotiating for others. But the reverse was true for women – women were more likely than men to lie when they were negotiating on behalf of others.

In one study, we randomly assigned participants to act in a property negotiation as a buyer or as an agent representing the buyer. We told them that buyers wanted to build a commercial high-rise hotel on the property, but that the seller would reject their offer if they knew about this intent. We found that female participants assigned to the role of a buyer’s agent were more likely to lie than those assigned to be the buyer (64.4% vs. 44.4%) about their plans for the property in order to get the deal done. On the other hand, men showed no statistical difference in ethicality when acting for themselves or for others (60.6% vs. 72.2%).

When we asked why participants made the decisions they did, we saw that women were more likely to report feeling guilty about letting down those they were advocating for. They were more willing to engage in questionable behavior because they anticipated feeling more guilt and worried about disappointing others.

Even though our studies focused on women, other research has yielded similar general findings that people tend to act unethically when representing others, if they believe they’re okay with it or prefer it. One set of research studies showed that “utilitarian” individuals, or those who typically engage in conscious cost-benefit analyses when making decisions (e.g., “What do I or society have to gain or lose as a result of my choices and actions?”) are more likely to act unethically if they are acting on behalf of someone else who shares a similar utilitarian approach, verses when working for someone who is more “formalist” (or focused on upholding rules/principles).

Unethical behavior on others’ behalf can spread from minor misconduct here and there to more consequential actions if expectations and norms allow for it. If it’s acceptable to cut minor corners on a client deliverable to make sure a consulting team meets a deadline, for example, that could lead one to engage in more drastic misbehavior such as misrepresenting firm capabilities to ensure the consultancy secures a lucrative account. Research shows that this slippery slope isn’t uncommon.

So how can we combat the tendency to behave unethically when acting on someone else’s behalf? Our research suggests a few approaches:

Aim for intentionality: At the individual level, it’s important to be aware of your motivations when advocating for others. Does your desire to support others lead to a “win at all costs” mentality? Will you feel excessive guilt if you fail to represent them well? Asking yourself such questions in advocacy situations will make you more mindful of your values and intents, and likely keep you in more ethical lanes of behavior.

Ask for clarification: If you’re not sure how ethical those you’re representing are, seek clarification. There can be significant ambiguity in real-world advocacy situations, and that can lead to erroneous assumptions about someone’s ethics and expectations, which in turn may lead to unethical behavior on their behalf. Break through the ambiguity by asking for clarification on expectations related to ethicality (“It’s not about winning at all costs, right?”), while sharing your own expectations.

State your expectations: When you’re representing someone, you should also be upfront about where you are and aren’t willing to go. Similarly, when you’re in a group being represented, make your expectations around ethicality clear to those acting on your behalf (“We need to do this by the book”). Don’t leave space for erroneous assumptions. Moreover, look for signs that a representative may be more likely to act questionably. For example, if someone is expressing feeling guilty about letting you down, step in and assure them that they shouldn’t feel pressure to act untoward.

While the tendency to act unethically on behalf of others exists, the good news is that you can act to prevent such outcomes.

How Bad is Airline Service, Really? And Other Customer Service Complaints

Youngme Moon and Mihir Desai welcome their colleague Ryan Buell to discuss whether airlines deserve their reputation for terrible customer service. They also share other customer service pet peeves, as well as their personal “Customer Experience Picks.”

Download this podcast

For interested listeners:

• Ryan Buell in HBR: The Parts of Customer Service That Should Never Be Automated

You can email Youngme, Mihir, and Felix with your comments and ideas for future episodes at: harvardafterhours@gmail.com.

HBR Presents is a network of podcasts curated by HBR editors, bringing you the best business ideas from the leading minds in management. The views and opinions expressed are solely those of the authors and do not necessarily reflect the official policy or position of Harvard Business Review or its affiliates.

When CEOs Should Speak Up on Polarizing Issues

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CEO activism, the growing trend of top executives speaking out on sensitive social and political issues, has been labeled the “new normal.” But behind the scenes, executives do not feel in control. They are struggling to anticipate and respond to intensifying pressure from the public, investors, and — above all — their employees.

There are conflicting views of how CEOs should proceed. One survey suggests the public wants chief executives to lead on social change without waiting for the government to act. A separate survey shows public support for corporate engagement on such issues as sexual harassment and equal pay — though not on gun control or abortion. A third survey indicates that brands may be punished for even mentioning President Donald Trump, regardless of whether they are being critical or complimentary.

Companies we work with at BSR, a nonprofit sustainability business network and consultancy, feel trapped: Although every conceivable action carries considerable risk, inaction may not be much of an option, either.

What to do, now that the neutral middle ground has become a quicksand? We have been working with businesses to develop a strategic framework for when to take a stand on social issues. It draws on the work of R. Edward Freeman and others regarding stakeholder theory, which offers a way of examining the interests of all groups affected by an organization — not just shareholders but also customers, employees, governments, suppliers, and communities. We started by studying how distinct stakeholder groups view our corporate clients’ positions on social issues. Here is what we learned.

Companies must consider values, not just shared value. Most companies prefer to prioritize their business interests in line with both fiduciary duty and the shared value approach to corporate responsibility, which connects business success with social good. This would suggest that companies should act only when there is a clear business case and an opportunity for direct action. For example, most would assume that it’s easier and more effective for the CEO to cut a company’s climate emissions than to take a stand on immigration. A CEO could be forgiven for thinking it’s safest to only weigh in on political issues that affect operational and strategic goals, industry dynamics, or a company’s regulatory and policy landscape.

But a company’s exposure to a political issue is also determined by its values. Values are determined by the company’s culture, mission, and voluntary commitments, along with the opinions and beliefs of a range of actors — not just customers but also employees, business partners, and civil society organizations.

We were surprised to find that when business interests and values conflict, values are the dominant variable. That’s why, although focusing on core interests may seem sensible, reality shows it to be untenable. Tech companies, for example, had little to gain strategically by opposing the Trump administration’s family separation policy. But Microsoft and Google found it impossible to remain silent in the face of employee demands for a response to what staff regarded as an assault on company values. The same dynamic may now complicate Google’s plans to re-enter China’s search market. Employee pressure can even drive significant turnover in senior leadership ranks, as happened recently at Nike, which earlier this year was sued for sexual discrimination by several former employees.

Employees are now a company’s most powerful interest group. In many ways, corporate power seems to be high: Companies prioritize shareholder value; union membership rates are at an all-time low; employment contracts for some jobs include nondisclosure clauses as standard features; certain sectors of the workforce are moving toward gig economy jobs with diminishing hourly rates and no health care; other sectors are facing unemployment as jobs are automated. So why are leaders responding so readily when employees pressure them to demonstrate integrity? Workers are freely using the tools of this hyper-transparent era — including petitions and email leaks — to land punishing blows against corporate reputations and finances, in the process emerging as companies’ most powerful interest group. At a time when the U.S. economy seems to be approaching full employment, employees have more influence over whether and how their leaders speak out.

Polarization heightens risk. Companies seem to face the greatest peril when an issue is politically polarizing to customers and has more to do with values than with long-term financial consequences. On Jan. 28, 2017, Uber cut congestion pricing to John F. Kennedy International Airport while New York taxi drivers were protesting President Trump’s new immigration policy; although the move was a financial one on Uber’s part, it was perceived as aligning the company with the “Muslim ban,” leading to the #deleteUber hashtag and to hundreds of thousands of riders deleting their accounts. Keurig faced complaints when it pulled advertising from Sean Hannity’s Fox News show. Delta drew outrage and lost tax breaks when it decided to end a travel discount for the National Rifle Association. Still, Target, after undergoing boycotts and petition drives for implementing a transgender bathroom policy in its stores, said it had suffered no material financial impact.

Companies certainly have tools to parse the views of their customers, but fretting over who is yelling the loudest on Twitter does not offer a firm ground for action. Basing decisions on corporate principles and employee values is a better approach than trying to navigate what is likely to be a broad spectrum of customer sentiment.

Your rhetoric has to be aligned with your dollars. Companies face heightened scrutiny over influence-peddling and corruption, which makes it much harder to decouple public rhetoric and private lobbying efforts. The Center for Political Accountability has called out companies on a range of issues, including contraceptive makers that indirectly fund political officials who aim to limit women’s reproductive rights. C-suite hypocrisy is now a media focus, with funding for business associations a particularly vulnerable flank. Climate activists have long highlighted the gap between the oil and gas sector’s softening rhetoric on climate change and the corporate funding for its trade group, the American Petroleum Institute, which has opposed numerous climate change policies.

Opportunities for direct action may be constrained. Companies are not governments, and their customers are not the electorate. So even when they want to take action, there are concrete limits on what businesses can achieve. Companies can accept or decline business, and they can tackle such issues as diversity and climate change in their own operations, but on pure policy matters like trade or immigration, there’s only so much they can do.

Companies know this well, but they struggle to communicate the limits of their ability to drive systemic social change, which leaves them at risk of raising expectations they cannot fulfill. For example, an incident of discrimination by staff at a Starbucks in Philadelphia led to public outcry against the company, which soon stood accused of helping foster gentrification and systemic racism in the U.S. This was a discouraging development, given that the organization has long mounted efforts to drive collaborative action on race and immigration issues, including a pledge to hire 10,000 refugees.

So the paradoxes multiply. While pressure to enhance shareholder value has not relented, companies are listening intently as they try to balance the needs and demands of a broad range of stakeholders. At the same time, powerful sections of the investment community are amplifying demands that companies move beyond empty posturing to better manage their social, political, and environmental efforts. Amid all the fuss, key shareholders seem to be concluding that prioritizing only profits may be neither smart nor sustainable. Behind the roiling divisions on specific issues, a consensus is emerging among markets, employees, and the public: Companies must fundamentally rethink their interactions with society.

Better Ways to Communicate Hospital Data to Physicians

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We recently conducted an in-depth study at Lumere to gain insight into physicians’ perceptions of clinical variation and the factors influencing their choices of drugs and devices. Based on a survey of 276 physicians, our study results show that it’s necessary to consistently and frequently share cost data and clinical evidence with physicians, regardless of whether they’re affiliated with or directly employed by a hospital. This empowers physicians to support the quality and cost goals inherent in a health system’s value-based care model. Below, we offer three recommendations for health systems looking to do this.

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Assess how data is shared with physicians. The reality is that in most health systems, data sharing occurs in irregular intervals and inconsistent formats. Ninety-one percent of respondents to our survey reported that increasing physician access to cost data would have a positive impact on care quality. However, only 40% said that their health systems are working to increase physician access to such data.

While working directly with health systems to reduce clinical variation, Lumere has discovered firsthand that the manner and type of cost and evidence-based data shared with physicians varies dramatically. While some organizations have made great strides in developing robust mechanisms for sharing data, many do little beyond circulating the most basic data from the Centers for Medicare and Medicaid Services’ patient-satisfaction survey (the Hospital Consumer Assessment of Healthcare Providers and Systems, or HCAHPS).

There are multiple explanations as to why health system administrators have been slow to share data with physicians. The two most common challenges are difficulty obtaining accurate, clinically meaningful data and lack of knowledge among administrators about communicating data.

When it comes to obtaining accurate, meaningful data, the reality is that many health systems do not know where to start. Between disparate data-collection systems, varied physician needs, and an overwhelming array of available clinical evidence, it can be daunting to try to develop a robust, yet streamlined, approach.

As for the second problem, many administrators have simply not been trained to effectively communicate data. Health system leaders tend to be more comfortable talking about costs, but physicians generally focus on clinical outcomes. As a result, physicians frequently have follow-up questions that administrators interpret as pushback. It is important to understand what physicians need.

Determine the appropriate amount and type of data to share. Using evidence and data can foster respectful debate, provide honest education, and ultimately align teams.

Physicians are driven by their desire to improve patient outcomes and therefore want the total picture. This includes access to published evidence to help choose cost-effective drug and device alternatives without hurting outcomes. Health system administrators need to provide clinicians with access to a wide range of data (not only data about costs). Ensuring that physicians have a strong voice in determining which data to share will help create alignment and trust. A more nuanced value-based approach that accounts for important clinical and patient-centered outcomes (e.g., length of stay, post-operative recovery profile) combined with cost data may be the most effective solution.

While physicians generally report wanting more cost data, not all physicians have the experience and training to appropriately incorporate it into their decision making. Surveyed physicians who have had exposure to a range of cost data, data highlighting clinical variation, and practice guidelines generally found cost data more influential in their selection of drugs and devices, regardless of whether they shared in savings under value-based care models. This was particularly true for more veteran physicians and those with private-practice experience who have had greater exposure to managing cost information.

Health systems can play a key role in helping physicians use cost and quality data to make cost-effective decisions. We recommend that health systems identify a centralized data/analytics department that includes representatives of both quality-improvement teams and technology/informatics to own the process of streamlining, analyzing, and disseminating data.

Compare data based on contemporary evidence-based guidelines. Physicians would like to incorporate reliable data into their decision-making when selecting drugs and devices. In our survey, 54% of respondents reported that it was either “extremely important” or “very important” that hospitals use peer-reviewed literature and clinical evidence to support the selection of medical devices. Further, 56% of respondents said it was “extremely important” or “very important” that physicians be involved in using data to develop clinical protocols, guidelines, and best practices.

Health systems should ensure that data is organized and presented in a way that is clinically meaningful and emphasizes high-quality patient care. Beginning the dialogue with physicians by asking them to reduce costs does not always inspire collaboration. To get physicians more involved, analyze cost drivers within the clinical context.

Finally, health systems should keep data and communication simple by developing, communicating, and mobilizing a small number of critical key performance indicators (KPIs). These indicators should reflect the voices of health care customers, including patients, care providers, and payers. In some instances, these will overlap — for example, length of stay, infection rates, readmissions, and likelihood to recommend the provider in the future. Consistent, relevant benchmarks will keep physicians focused on organizational goals.

Our survey results paint a vivid picture: Health systems openly and transparently engage with both employed and affiliated physicians and foster a culture that appreciates data and analytics. Only then will we see improved clinical, operational, and financial outcomes.

4 Analytics Concepts Every Manager Should Understand

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Like many professionals, my job doesn’t require expertise in data or analytics. I’m a writer and editor, so I deal with words, not numbers. Still, nearly every knowledge worker today needs to be a regular consumer of data analysis. For example, I need to understand whether and why articles on having a mid-career crisis outperformed ones on receiving feedback or why pieces with particular headlines get more traffic than others.

I also need to be able to read research on the topics I cover and understand whether the findings in those studies are valid and generalizable, and be able to articulate the findings — and their limitations — to you, our readers.

To do all of this, I need a more-than-basic understanding of data analytics. And while the statistics course I took in graduate school was helpful, it didn’t fully equip me to grasp the important concepts and have the conversations I need to around data analysis.

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Fortunately, I had the opportunity to talk with some of the best experts in the field — Tom Redman, author of Data Driven: Profiting from Your Most Important Business Asset, and Kaiser Fung, who founded the applied analytics program at Columbia University — about several critical topics when it comes to data analysis. Here are four refreshers from our archives on data analytics concepts that every manager should understand.

Randomized controlled experiments

One of the first steps in any analysis is data gathering. This often happens via a spectrum of experiments that companies do — from quick, informal surveys, to pilot studies, field experiments, and lab research. One of the more structured types is the randomized controlled experiment. Many people, when they hear this term, immediately think of costly clinical trials but randomized controlled experiments don’t have to be costly or time consuming and they can be used to gather data on things like whether a particular customer service intervention improved customer retention or whether a new, more expensive piece of equipment is more effective than a less costly one. In this refresher, Tom Redman helps me understand what it means for a test to be “controlled” and how you make sure it includes an element of “randomization.” The article also addresses questions like: What are dependent and independent variables? And what are the steps to designing and conducting one of these experiments?

A/B testing

One of the more common experiments companies use these days is the A/B test (which is a type of randomized controlled experiment). At their most basic, these tests are a way to compare two versions of something to figure out which performs better. Companies use it to answer questions like, “What is most likely to make people click? Or buy our product? Or register with our site?” A/B testing is used to evaluate everything from website design to online offers to headlines to product descriptions. It’s critical to understand how to interpret the results and to avoid common mistakes, like ending the experiment too soon before you have valid results or trying to look at a dashboard of metrics when you really should be focusing on a few. You can learn more about A/B tests here.

Regression analysis

Once you have the data, regression analysis helps you make sense of it. Of course, there are many ways to analyze the data, but linear regression is one of the most important. It’s a way of mathematically sorting out whether there’s a relationship between two or more variables. For example, if you are in the business of selling umbrellas, you might want to know how many more items you sell on rainy days. Regression analysis can help you determine whether and how inches of rain impacts sales. It answers the questions: Which factors matter most? Which can we ignore? How do those factors interact with each other? And, perhaps most importantly, how certain are we about all of these factors?

Fortunately, regression is not something you typically do on your own. There are statistics programs for that! But it’s still important to understand the math behind it and the types of mistakes to avoid. In this refresher, I explain how regression works and share a common — but often misunderstood — warning against confusing correlation with causation.

Statistical significance

Once you’ve done the analysis, you need to figure out what your results mean, if anything. This is where statistical significance comes in. This is a concept that is also often misunderstood and misused. And yet because more and more companies are relying on data to make critical business decisions, it’s an essential concept to understand. Statistical significance helps you quantify whether a result from an experiment is likely due to chance or from the factors you were measuring.

This is a concept I sometimes struggled to fully understand myself but, fortunately, the average professional doesn’t need to understand it too deeply. According to Tom Redman, who helped out with this refresher, it’s more important to understand how to not misuse it.

While you’re boning up on these four concepts, it would also be helpful to read this overview on quantitative analysis from my colleague, Walt Frick. It is a nice primer on why data matters, picking the right metrics, and asking the right questions from data. There’s also a great chart on correlation vs. causation to help you make decisions about when to act on analysis and when not to.

Lastly, if you’re interested in analytics because you need to consume social science research, I highly recommend this piece from Eva Vivalt, a research fellow and lecturer at the Australian National University. She gives several tips for determining whether the evidence from a study should be trusted.

Data analytics is ultimately about making good decisions. It doesn’t matter what business you are in or what your role is at your company, we all want to — need to, really — make smart, informed, evidence-based decisions.

To Combat Harassment, More Companies Should Try Bystander Training

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As the wave of #MeToo stories have come to light over the past year, it’s become painfully clear that whatever organizations are doing to try to prevent sexual harassment isn’t working.

Ninety-eight percent of companies say they have sexual harassment policies. Many provide anti-sexual harassment training. Some perpetrators have been fired or fallen from grace. And yet more than four decades after the term “sexual harassment” was first coined, it remains a persistent and pervasive problem in virtually every sector and in every industry of the economy, our new Better Life Lab report finds. It wreaks financial, physical, and psychological damage, keeping women and other targets out of power or out of professions entirely. It also costs billions in lost productivity, wasted talent, public penalties, private settlements, and insurance costs.

So what does work? Or might?

Sadly, there’s very little evidence-based research on strategies to prevent or address sexual harassment. The best related research examines sexual assault on college campuses and in the military. That research shows that training bystanders how to recognize, intervene, and show empathy to targets of assault not only increases awareness and improves attitudes, but also encourages bystanders to disrupt assaults before they happen, and help survivors report and seek support after the fact.

Researchers and workplace experts are now exploring how to prevent sexual harassment in companies by translating that approach. The Equal Employment Opportunity Commission in its 2016 task force report encouraged employers to offer bystander training, for one. And New York City passed a law in May requiring all companies with more than 15 employees to begin providing bystander training by April 2019. It could prove a promising, long-term solution.

But culture change is hard — it can take anywhere from months to several years, experts say. It’s much easier to go for the annual, canned webinar training on sexual harassment that checks the legal-liability box. Yet culture change is exactly why bystander interventions could be powerful: the strategy recognizes that, when it comes to workplace culture, everyone is responsible for creating it, every day, in every interaction.

Jane Stapleton, co-director of the Prevention Innovations Research Center at the University of New Hampshire and an expert in bystander interventions, told me about an all-too-familiar scenario: Say there’s a lecherous guy in the office — someone who makes off-color jokes, watches porn at his cubicle, or hits on younger workers. Everyone knows who he is. But no one says anything. Co-workers may laugh uncomfortably at his jokes, or ignore them. Maybe they’ll warn a new employee to stay away from him. Maybe not. “Everybody’s watching, and nobody’s doing anything about it. So the message the perpetrator gets is, ‘My behavior is normal and natural,’” Stapleton said. “No one’s telling him, ‘I don’t think you should do that.’ Instead, they’re telling the new intern, ‘Don’t go into the copy room with him.’ It’s all about risk aversion — which we know through decades of research on rape prevention, does not stop perpetrators from perpetrating.”

When bystanders remain silent, and targets are the ones expected to shoulder responsibility for avoiding, fending off, or shrugging off offensive behavior, it normalizes sexual harassment and toxic or hostile work environments. So bystander intervention, which Stapleton and others are beginning to develop for workplaces, is designed to help everyone find their voice and give them tools to speak up.

It’s all about building a sense of community. “Bystander intervention is not about approaching women as victims or potential victims, or men as perpetrators, or potential perpetrators” she said. “Rather, it’s leveraging the people in the environment to set the tone for what’s acceptable and what’s not acceptable behavior.”

At the most fundamental level, bystander interventions could begin — long before an incident of harassment — with workers having non-threatening, informal conversations in unstressed moments about how to treat each other, how they can help each other do their jobs or make their days better, and practice giving positive feedback. Normalizing talking about behavior and defining respectful behaviors everyone agrees on may make it easier for coworkers to see and give negative feedback if a worker later crosses a line,  Fran Sepler, who for 30 years has worked as a consultant, trainer, and investigator on workplace harassment prevention, told me in an interview. “So when a co-worker tells an offensive joke, it’s easier to say, ‘Remember how we talked, and we all agreed about what’s OK to say at work? That’s not it.’”

In testimony before the EEOC, Sepler suggested organizations create “feedback rich” environments, where middle managers are trained to respond to complaints and issues in an emotionally intelligent way, and where people feel comfortable speaking up and listening, no matter the issue.

In campus settings, bystanders are trained to recognize when a sexual assault may be imminent and intervene by, for instance, disrupting the environment — turning the lights on at a party, or turning the music off — defusing the situation, with humor perhaps, distracting or interrupting a potential perpetrator, drawing a potential target away, or drawing others in.

But disrupting sexual harassment in the workplace requires a very different set of tools. “Too often people let things slide, concerned that if they get involved, it might affect their own career aspirations,” Alberto Rodríguez. supervising attorney for the New York City Commission on Human Rights, told me.

Because careers and reputations can be on the line, Sepler suggests considering a matrix of questions before acting: “Can I have an impact? Is it safe? What is the best strategy given the culture of the organization and my level of influence?”

Bystanders in the workplace can defuse harassing or offensive language or situations with humor, she said, or verbal or nonverbal expressions of disapproval. They can interrupt a situation by changing the subject, or inserting themselves into the situation. “If it’s the first time you hear someone say something offensive, you might try humor as a way of getting their attention, making a caustic remark, or saying, ‘What year is this? 1970?’ as a way of getting their attention,” Sepler said. Even so, she cautioned that bystanders must weigh whether the colleague has the reputation for being a jerk. Another option bystanders could consider is having a conversation after the fact, when tensions have cooled, laying out why the behavior was offensive.

For a harassing boss or someone who holds power over your career or livelihood, where direct confrontation could be riskier, defusion, distraction, or interruption are still possible tools for bystanders in the moment. And after the fact, bystanders can also seek out a supervisor or influencer, make a report, or help a target make a report.

At a minimum, bystanders can always show support to targets, who often feel isolated, humiliated, diminished, and alone after a harassing incident. “Going to someone and saying, ‘I saw how they were treating you. I didn’t like it. Is there anything I can do to help?’ Or, ‘It’s not your fault, let’s go talk with human resources.’ That might be all you can do,” Sepler said. “That’s not nothing.”

Research: When Getting Fired Is Good for Your Career

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Most leaders are, deep down, afraid of failure. But our 10-year CEO Genome study of over 2,600 leaders showed almost half (45%) suffered at least one major career blow-up — like getting fired, messing up a major deal, or blowing an acquisition. Despite that, 78% of these executives eventually made it to the CEO role.

We conducted additional research on 360 executives, analyzing their careers in depth. While all of them experienced a variety of setbacks, 18% of executives in this dataset faced what many view as the very worst-case scenario: getting fired or laid off. Most of them lost their job at a relatively senior point in their career (only 17% were in their first decade in the workforce at the time they were let go).

What we found is that being fired or laid off doesn’t necessarily have catastrophic effects on leaders’ prospects. We also found that leaders can do some specific things to make sure that a major setback doesn’t become a career-killer.

The good news: 68% of executives who had been let go landed in a new job within six months. An additional 24% had a new job by the end of one year. Even better? 91% of executives who had been fired took a job of similar or even greater levels of seniority.

We even found some signs that the experience of losing a job — when handled the right way — might even make one a stronger candidate for future roles.  In our study, when the interview process included expert third-party assessors engaged by employers to prevent hiring mistakes, 33% of executives who had been previously fired were recommended for hire — compared to 27% of candidates who had never been fired.  Experienced hiring managers know that setbacks are inevitable and want to see how individuals have handled failure in the past. The riskiest hires are the ones who are untested by failure. Executives who have faced failure and learned from it can demonstrate resilience, adaptability, and self-awareness prized in leaders.

About the Research

This article is based on research conducted over 10 years in support of our 2018 book The CEO Next Door. ghSMART has assembled a data set of assessments of over 18,000 C-suite executives across all major industry sectors and company sizes. Each executive assessment includes detailed career and educational histories; performance appraisals; and information on patterns of behavior, decisions, and business results. This data was gathered through structured 4-5 hour interviews with every executive.

That said, executives who had been let go were also more likely to receive a strong “do not hire” recommendation than those who were never fired (46% vs. 36%), indicating that the reason why someone was removed from a role and the way in which they processed that experience did impact their future career potential.

Leaders whose careers soared — not sank — after this setback, did three things differently:

Looked facts in the face… without shame. Those who deflect ownership and instead point to external factors or blame others for failures on their watch don’t do as well. Our data shows that candidates who blamed others cut their chances of being recommended for hire by one-third. Strong performers own their mistakes, and describe what they learned and how they adjusted their behavior and decision making to minimize the chances of making the same mistakes in the future. Having several different types of career blow ups does not derail you. Repeating the same blowup over and over does.

While they own their mistakes, they do so without guilt or shame. Executives who saw their mistakes as failures were 50% less successful than those who took a more learning/growth-oriented approach.

Taking ownership without shame enabled these executives to show themselves as likeable and confident in the interview process for the next role — qualities proven to increase chances of getting the job. Analysis of ghSMART assessments by Kaplan and Sorensen showed that the more likable leaders had higher odds of getting hired for any leadership position. Our research with SAS found that highly confident candidates were 2.5 times more likely to be hired.

Leaned on their professional network to get the next job: Candidates were twice as likely to find a job through a professional network than via recruiters or personal network (59% vs. 28%). While friends may be eager to help and lend their sympathetic ear, ultimately the most powerful support comes from those who have seen the results you can deliver based on their direct working experience with you. Search firms have a wide exposure to available positions but typically play it safe and may be reluctant to put their credibility on the line with their client by presenting a candidate who had been fired before. Proactively reaching out to former bosses, colleagues, customers, or peers for whom you have delivered before proves more fruitful than golfing with friends from university or blasting your CV to the recruiting world — although those most eager do all three.

Relied on their experience: 94% of those who landed a new job within 6 months had prior experience in that industry. Hence, one would be well advised to get experience across 2-3 industries early in one’s career, so that if one gets fired, there are multiple industries to rebound into rather than being pigeonholed.

The most important advice both for those looking to rebound and to prevent getting fired in the first place: Pick jobs in the “bull’s eye” of your skills and motivations.

We hope this offers some hopeful news both to people who’ve been let go, and to managers who are in the position of needing to let someone go. One third of the leaders in our CEO Genome study took too long to make people changes — often with damaging consequences for themselves, their teams, and the executive who is poorly fit to the job. If you are agonizing over the need to move someone out of your team, worried about destroying their career, hopefully this research helps you make the right decision for the wellbeing of your whole team and gives you the tools to support the person you are moving out to help them land in the right next opportunity.

We also hope this is useful research for everyone suffering from the fear of failure. While mistakes and career setbacks are painful, a much bigger mistake, according to our data is not taking risks. When we analyzed careers of executives who got to the top faster than average, what set them apart was taking risks to take messy jobs or smaller jobs that nobody wanted or taking on big leaps that felt way over their head.

More than 20 years of advising and coaching leaders has shown us that when you try to achieve something meaningful, you’ll face blow-ups from time to time. What matters more, is that you address the failure as an opportunity for growth. It can be a real travesty when, by playing defense throughout their careers, so many of us miss a chance to grow to our full potential and to live more meaningful lives.  In the words of Oliver Wendell Holmes “Many people die with their music still in them.”

How to Build Great Data Products

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Products fueled by data and machine learning can be a powerful way to solve users’ needs. They can also create a “data moat” that can help stave off the competition. Classic examples include Google search and Amazon product recommendations, both of which improve as more users engage. But the opportunity extends far beyond the tech giants: companies of a range of sizes and across sectors are investing in their own data-powered products. At Coursera, we use machine learning to help learners find the best content to reach their learning goals, and to ensure they have the support — automated and human — that they need to succeed.

The lifecycle of a so-called “data product” mirrors standard product development: identifying the opportunity to solve a core user need, building an initial version, and then evaluating its impact and iterating. But the data component adds an extra layer of complexity. To tackle the challenge, companies should emphasize cross-functional collaboration, evaluate and prioritize data product opportunities with an eye to the long-term, and start simple.

Stage 1: Identify the opportunity

Data products are a team sport

Identifying the best data-product opportunities demands marrying the product-and-business perspective with the tech-and-data perspective. Product managers, user researchers, and business leaders traditionally have the strong intuition and domain expertise to identify key unsolved user and business needs. Meanwhile, data scientists and engineers have a keen eye for identifying feasible data-powered solutions and a strong intuition on what can be scaled and how.

To get the right data product opportunities identified and prioritized, bring these two sides of the table together. A few norms can help:

Educate data scientists about the user and business needs. Keeping data scientists in close alignment with product managers, user researchers, and business leads, and ensuring that part of their role is to dig in on the data directly to understand users and their needs will help.
Have data scientists serve as data evangelists, socializing data opportunities with the broader organization. This can range from providing the organization with easy access to raw data and model output samples in the early ideation stages, to building full prototypes in the later stages.
Develop the data-savvy of product and business groups. Individuals across a range of functions and industries are upskilling in data, and employers can accelerate the trend by investing in learning programs. The higher the data literacy of the product and business functions, the better able they’ll be to collaborate with the data science and tech teams.
Give data science a seat at the table. Data science can live different places in the organization (e.g., centralized or decentralized), but no matter the structure having data science leaders in the room for product and business strategy discussions will accelerate data product development.

Prioritize with an eye to the future

The best data products get better with age, like a fine wine. This is true for two reasons:

First, data product applications generally accelerate data collection which in turn improves the application. Consider a recommendations product powered by users’ self-reported profile data. With limited profile data today, the initial (or “cold start”) recommendations may be uninspiring. But if users are more willing to fill in a profile when it’s used to personalize their experience, launching recommendations will accelerate profile collection, improving the recommendations over time.

Second, many data products can be built out to power multiple applications. This isn’t just about spreading costly R&D across different use-cases; it’s about building network effects through shared data. If the data produced by each application feeds back to the underlying data foundations, this improves the applications, which in turn drives more utilization and thus data collection, and the virtuous cycle continues. Coursera’s Skills Graph is one example. A series of algorithms that map a robust library of skills to content, careers, and learners, the graph powers a range of discovery-related applications on the site, many of which generate training data that strengthen the graph and in turn improve its applications.

Too much focus on near-term performance can yield underinvestment in promising medium- or long-term opportunities. More generally, the criticality of high-quality data cannot be overstated; investments in collecting and storing data should be prioritized at every stage.

Stage 2: Build the product

De-risk by staging execution

Data products generally require validation both of whether the algorithm works, and of whether users like it. As a result, builders of data products face an inherent tension between how much to invest in the R&D upfront and how quickly to get the application out to validate that it solves a core need.

Teams that over-invest in technical validation before validating product-market fit risk wasted R&D efforts pointed at the wrong problem or solution. Conversely, teams that over-invest in validating user demand without sufficient R&D can end up presenting users with an underpowered prototype, and so risk a false negative. Teams on this end of the spectrum may release an MVP powered by a weak model; if users don’t respond well, it may be that with stronger R&D powering the application the result would have been different.

While there’s no silver bullet for simultaneously validating the tech and the product-market fit, staged execution can help. Starting simple will accelerate both testing and the collection of valuable data. In building out our Skills Graph, for example, we initially launched skills-based search — an application that required only a small subset of the graph, and that generated a wealth of additional training data. A series of MVP approaches can also reduce time to testing:

Lightweight models are generally faster to ship and have the added benefit of being easier to explain, debug, and build upon over time. While deep learning can be powerful (and certainly is trending) in most cases it’s not the place to start.
External data sources, whether open source or buy/partner solutions, can accelerate development. If and when there’s a strong signal from the data the product generates, the product can be adapted to rely on that competitive differentiator.
Narrowing the domain can reduce the scope of the algorithmic challenge to start. For example, some applications can initially be built and launched only for a subset of users or use-cases.
Hand-curation — where humans either do the work you eventually hope the model will do, or at least review and tweak the initial model’s output — can further accelerate development. This is ideally done with an eye to how the hand-curation steps could be automated over time to scale up the product.

Stage 3: Evaluate and iterate

Consider future potential when evaluating data product performance.

Evaluating results after a launch to make a go or no-go decision for a data product is not as straightforward as for a simple UI tweak. That’s because the data product may improve substantially as you collect more data, and because foundational data products may enable much more functionality over time. Before canning a data product that does not look like an obvious win, ask your data scientists to quantify answers to a few important questions. For example, at what rate is the product improving organically from data collection? How much low-hanging fruit is there for algorithmic improvements? What kinds of applications will this unlock in the future? Depending on the answers to these questions, a product with uninspiring metrics today might deserve to be preserved.

Speed of iteration matters.

Data products often need iteration on both the algorithms and the UI. The challenges is to determine where the highest-value iterations will come from, based on data and user feedback, so teams know which functions are on the hook for driving improvements. Where algorithmic iterations will be central — as they generally are in complex recommendation or communication systems like Coursera’s personalized learning interventions — consider designing the system so that data scientists can independently deploy and test new models in production.

By fostering collaboration between product and business leaders and data scientists, prioritizing investments with an eye to the future, and starting simple, companies of all shapes and sizes can accelerate their development of powerful data products that solve core user needs, fuel the business, and create lasting competitive advantage.

Chinese Activists Are Using Blockchain to Document #MeToo Stories

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One of our main jobs when teaching and advising students who are thinking of founding blockchain companies is to get them to question whether or not their idea actually requires it. Data integrity is the main benefit conferred by blockchain technology, and a few questions can help determine whether that’s a particular problem for a given business or use case:

If the data that my business collects is corrupted, how much do people suffer?
Do outsiders (perhaps hackers) have incentives to distort or change the data that my business is based upon?
How much does my business depend on other people being able to trust the data on which it is built?

Take for example, a digital currency — the first use-case for blockchain. There, if data is corrupted or distorted by outsiders, people lose real money, and the outsider who corrupts the data gains money, making such attacks plausible and to be feared. Therefore, no one will adopt a digital currency unless they can trust their data will not be corrupted or distorted. In other words, there’s at least a plausible reason why you’d want blockchain technology managing currency transactions.

However, all too often blockchain startup ideas don’t really need blockchain. Their data really isn’t that valuable or unique in a way that gives outsiders sufficient economic incentives to launch attacks to try and corrupt or otherwise change it. That’s why a recent use of blockchain technology in China in response to the #MeToo movement is so interesting.

In late 2017, increasing number of stories were being shared on Chinese social media surrounding sexual harassment and abuse of position in Chinese universities. At first, the movement was called woyeshi, the Chinese spelling of “Me Too.”  The Chinese government and technology platforms made repeated attempts to filter out such stories by censoring a variety of hashtags and keywords that campaigners used on Weibo and Wechat. First, woyeshi was censored, and then #MeToo, and finally “Rice Bunny”, which has the same pronunciation as “Me Too” in Chinese. As a result, campaigners turned to blockchain technology to record their stories under the name “Every Snowflake.” This website simply uses a blockchain ledger process to record stories about sexual harassment.

This is a use-case that fulfills the three criteria outlined above. Victims desperately want to not be censored; other parties have a deep interest in censoring them; and people can only find value in stories of discrimination if they have not been censored. “Every Snowflake” is a compelling case where blockchain helped people overcome a real problem of data integrity.

However, this project also highlights some of the challenges of using blockchain technology.

The general weakness of using blockchain lies in its interface with other technologies and the rest of the world. I’ve written before about blockchain’s  “last mile problem.” In this case, the “last mile” challenge comes from the fact that it is still possible to restrict access to data built on the blockchain — for example, by banning the website that displays it.

Last, perhaps the biggest challenge to our privacy lives in the fact that digital data usually lives forever unless someone makes strenuous efforts to delete it. Blockchain is even more extreme; it nearly guarantees the data lives forever. Corrections can’t be made. Stories can’t be modified. This raises challenges. What do libel suits look like when records can’t be deleted from the blockchain? What about the “right to be forgotten” that is built into privacy policy in some countries? In the case of sexual harassment, these aren’t just questions of protecting the accused. What if a victim comes to regret making a statement publicly and wants to withdraw it, perhaps to protect their privacy or even their safety?

Nonetheless, “Every Snowflake” hints at the possibilities of blockchain in our “post-truth” world. Not every interesting idea or business proposal requires the blockchain. But where data integrity is essential, it can be transformative.

Stop Initiative Overload

Rose Hollister and Michael Watkins, consultants at Genesis Advisers, argue that many companies today are taking on too many initiatives. Each manager might have their own pet projects they want to focus on, but that trickles down to lower level workers dealing with more projects at a time that they can handle, or do well. This episode also offers practical tips for senior-level leaders to truly prioritize the best initiatives at their company — or risk losing some of their top talent. Hollister and Watkins are the authors of the HBR article “Too Many Projects.”

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