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【速度】
【速度一】
Can You Take Your Strengths Too Far?
by Jack Zenger and Joseph Folkman
For the past decade, leaders have been encouraged to focus on developing their strengths rather than always gravitating to working on a weakness. But is this too much of a good thing? Lately, a number of business thinkers have suggested so.
It's tempting for those of us strongly committed to developing leadership strengths to ignore such dissent on the grounds that any new practice will attract critics. But the debate has practical significance to leaders. How should a hard-driving executive respond when given high scores for his ability to drive for results but low scores on building strong relationships with peers and subordinates? Is this evidence that he's taken his strength too far?
We don't think so. We would absolutely advise this person to keep driving for results; we suspect that his intense drive is what got him this far in the organization. But we don't see this as a zero sum game — we don't think he needs to stop doing one thing to start doing something else. So we'd also recommend he develop additional strengths in relating to people.
Like many of those who are raising doubts about the limits of developing leadership strengths — as Robert E. Kaplan and Robert Kaiser have done in the pages of HBR, and more recently Tony Schwartz has done on this site, we believe that a single strength by itself doesn't serve anyone well. A leader needs several strengths to succeed. And balance is required. Strengths in combination are far more powerful than any one alone, our research has confirmed. Our data show, in fact, that possessing five strengths is a surefooted way to become an exceptional leader. One-trick ponies don't last long in the center ring.
【字数297】
【速度二】
We also strongly agree with them that serious weaknesses should not be ignored. We've called these "fatal flaws," and we certainly advise people to fix them first. That's critical for the roughly one-quarter of leaders our data tell us appear to have such serious defects. We submit, however, that the rest should be working on their strengths.
People can and do behave inappropriately — and they do things to excess. In his blog, Schwartz describes how he learned that his own unbridled candor was hurtful and unproductive. Kaplan and Kaiser similarly described how either "forceful" or "enabling" behaviors could be taken too far and have negative consequences. They observed that if a leader overuses the "forceful" strength by being exceedingly directive — always taking charge, making every decision, and constantly pushing people — the leader's effectiveness diminishes. That's a conclusion that we suspect most would accept. And so do we. They also observed that a leader who is too cautious, gentle, understanding, mild-mannered, and almost exclusively focused on others will also be less effective. We completely concur.
Where we part company is in labeling any those behaviors as a strength.
We find it constructive to use a definition of "a strength" based on the work of psychologist Martin Seligman, among others. By his definition, a strength is a behavior that is:
?Executed effectively
?Broadly used in a variety of situations or settings
?Lasting in its effects over time
?Consistent in producing positive outcomes
?Valued for its intrinsic worth, as well as its positive outcomes
?Not specific to one culture
?Harmonious with, rather than opposed to, other strengths
By these measures, "being forceful," or "exhibiting righteous honesty unmediated by empathy," are not strengths.
【字数279】
【速度三】
Our analysis of behavior that does fit our definition of strengths comes from data generated in the 360-degree evaluations of 30,000 managers by 300,000 of their colleagues. From examining 12 years of such data, we've identified 16 competencies that describe the most effective leaders and distinguish them from average and poor leaders. When done extremely well, these behaviors become leadership strengths. They included qualities like displaying integrity, exhibiting superior problem-solving skills, being highly technically competent, being innovative, taking initiative, inspiring and motivating others to high performance — and, yes, driving for results.
We've found no evidence that extremely high scores on any of these competencies has negative consequences. That is, we haven't found anyone who scored at the 90th percentile for any one of these behaviors who was perceived by their bosses, colleagues, and direct reports as less effective than someone who scored in the 60th or 70th percentiles. We haven't found the business results of any high scorer to be inferior to the people who received lower scores. Nor have we found subordinates and peers writing more negative comments about a higher scorer than about any individual with a more moderate score.
Instead, we find the data tell a consistent story. Those with the lowest scores on a competency receive many negative written comments, and their objective results are inferior. Those with the highest scores produce the best outcomes on everything we've been able to measure. If this is overusing statistics, then so be it. Our profession needs more leadership analytics, not less, in our opinion.
Some might think strengths-based development was discovered by a social scientist or consulting company, but the real credit should go to Peter Drucker, who in his classic 1967 book The Effective Executive made the compelling case for focusing on strengths. In fact, he argued, it is the role of the organization to leverage people's strengths and to make their weaknesses irrelevant.
【字数317】
【速度四】
Managing Risk in Career Decisions
by Bill Barnett
You like your current position, and you're doing well, but you just got a surprisingly attractive job offer. It's exciting. Your friends say they're happy for you. You expect to accept the offer.
But hold on just a minute. Let's assume that if you take that offer, you'll shift to a new function (or a new industry), and you'll enter a very different culture. You'll be required to move halfway across the country, and there'll be more business travel.
Should you take the plunge? Should you make a big bet or play it safe?
Any new job offer is an opportunity. But with opportunity comes risk. Deciding whether to take the new role or stay where you are is a tough choice. What if it doesn't work out?
These risks are prevalent in any career decision, especially those that affect your financial security and personal life. Consider the risk in starting your own enterprise. That's what Brian, 34, did. (All names have been changed.) He left his secure and well-paying job and lived off savings to found a food company whose good-tasting but low-calorie organic products would improve diets.
Brian briefly considered starting his company on nights and weekends without giving up a regular paycheck. He could test the water before going all in. It was safe, but he felt it wouldn't work.
I had to take the full plunge. I knew I wanted investors to commit. If I wanted them to write a check, I had to do it all the way. I couldn't bring on employees if I wasn't fully committed. And if I presented my product to a retailer's buyers, they'd be making a leap of faith, too, so I needed to be fully committed for them.
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【速度五】
Brian's big bet proved to be a wise choice. He built a successful company and sold it at an attractive price.
But big bets aren't for everyone. At 37, corporate SVP James was completing a two-year cost-reduction project that would eliminate his role in the company. He couldn't lead a new cost-driven philosophy and protect his own senior position when it no longer was needed.
James considered his options. He talked with management consultants and private equity firms — the fields where he'd worked a decade before. He talked with other companies in his industry. In the end, he decided to take a smaller role within his company.
James had three reasons for the step downward, all aligned with risk reduction. He wondered whether he'd really succeed back in the trenches with the day-to-day intensity and travel that private equity or consulting required. He wondered whether he'd work well with the people in other companies in his industry. "It's the people," he said, "knowing I stood with people who had my back."
Finally, all these jobs required a move. He'd divorced the year before, and his two young children lived in town with their mother. He wasn't sure how his relationship with his children would develop if he relocated.
James concluded, "It's safest to stay. Even if I didn't want to, I'd be better off crafting my new message from there." He deferred a big commitment and decided to take some time to reflect. Things will change. He may advance in his current company. He'll know more about his relationship with his children. At some point, he'll revisit his strategy.
【字数269】
【剩余部分】
There are occasions when a big bet makes sense. There are occasions when it doesn't. And whatever the occasion, people have different attitudes toward risk. If you're facing a big choice, ask three questions:
1. Does the opportunity require the commitment?
Big bets can create the mentality needed to succeed. Sink or swim situations are energizing. With high aspirations, caution may be more risky than taking the plunge. And risk takers can attract support from others.
Brian knew his prospects were greater if he went all in. He hoped to shape his future, and a full commitment was required to do that. If he'd started the company part-time, it might not have done well, and it would have taken energy away from his day job.
2. Can you accept the risk?
Both James and Brian faced failure risk and personal risk, but they reacted differently. James worried he might fail in the new positions. Staying with his company in a reduced role was safe; he'd be a VP with the associated pay and prestige. At his stage of life, he also considered his relationship with his children.
Brian knew most new ventures fail, but spent no time on that. His current job was okay, but not so good that he worried about giving it up.
3. Is there a contingency plan?
If the business didn't work out, Brian assumed he'd be able to find another good job. He'd always been able to do that. James knew he was safe for the time being, and he could reassess later, once he had a better idea of how his personal and professional lives were progressing.
Big, hard-to-change commitments can shape your environment and increase your prospects for success. But those big commitments often come with a chance of failure. Don't simply let things take their course and hope for the best. Manage that risk by evaluating the opportunity, your situation, and what you can do if things don't work out.
【字数328】
【越障】
The True Measures of Success
by Michael J. Mauboussin
About a dozen years ago, when I was working for a large financial services firm, one of the senior executives asked me to take on a project to better understand the company’s profitability. I was in the equity division, which generated fees and commissions by catering to investment managers and sought to maximize revenues by providing high-quality research, responsive trading, and coveted initial public offerings. While we had hundreds of clients, one mutual fund company was our largest. We shuttled our researchers to visit with its analysts and portfolio managers, dedicated capital to ensure that its trades were executed smoothly, and recognized its importance in the allocation of IPOs. We were committed to keeping the 800-pound gorilla happy.
Part of my charge was to understand the division’s profitability by customer. So we estimated the cost we incurred servicing each major client. The results were striking and counterintuitive: Our largest customer was among our least profitable. Indeed, customers in the middle of the pack, which didn’t demand substantial resources, were more profitable than the giant we fawned over.
What happened? We made a mistake that’s exceedingly common in business: We measured the wrong thing. The statistic we relied on to assess our performance—revenues—was disconnected from our overall objective of profitability. As a result, our strategic and resource allocation decisions didn’t support that goal. This article will reveal how this mistake permeates businesses—probably even yours—driving poor decisions and undermining performance. And it will show you how to choose the best statistics for your business goals.
Ignoring Moneyball’s Message
Moneyball, the best seller by Michael Lewis, describes how the Oakland Athletics used carefully chosen statistics to build a winning baseball team on the cheap. The book was published nearly a decade ago, and its business implications have been thoroughly dissected. Still, the key lesson hasn’t sunk in. Businesses continue to use the wrong statistics.
Before the A’s adopted the methods Lewis describes, the team relied on the opinion of talent scouts, who assessed players primarily by looking at their ability to run, throw, field, hit, and hit with power. Most scouts had been around the game nearly all their lives and had developed an intuitive sense of a player’s potential and of which statistics mattered most. But their measures and intuition often failed to single out players who were effective but didn’t look the role. Looks might have nothing to do with the statistics that are actually important: those that reliably predict performance.
Baseball managers used to focus on a basic number—team batting average—when they talked about scoring runs. But after doing a proper statistical analysis, the A’s front office recognized that a player’s ability to get on base was a much better predictor of how many runs he would score. Moreover, on-base percentage was underpriced relative to other abilities in the market for talent. So the A’s looked for players with high on-base percentages, paid less attention to batting averages, and discounted their gut sense. This allowed the team to recruit winning players without breaking the bank.
Many business executives seeking to create shareholder value also rely on intuition in selecting statistics. The metrics companies use most often to measure, manage, and communicate results—often called key performance indicators—include financial measures such as sales growth and earnings per share (EPS) growth in addition to nonfinancial measures such as loyalty and product quality. Yet, as we’ll see, these have only a loose connection to the objective of creating value. Most executives continue to lean heavily on poorly chosen statistics, the equivalent of using batting averages to predict runs. Like leather-skinned baseball scouts, they have a gut sense of what metrics are most relevant to their businesses, but they don’t realize that their intuition may be flawed and their decision making may be skewed by cognitive biases. Through my work, teaching, and research on these biases, I have identified three that seem particularly relevant in this context: the overconfidence bias, the availability heuristic, and the status quo bias.
Overconfidence. People’s deep confidence in their judgments and abilities is often at odds with reality. Most people, for example, regard themselves as better-than-average drivers. The tendency toward overconfidence readily extends to business. Consider this case from Stanford professors David Larcker and Brian Tayan: The managers of a fast-food chain, recognizing that customer satisfaction was important to profitability, believed that low employee turnover would keep customers happy. “We just know this is the key driver,” one executive explained. Confident in their intuition, the executives focused on reducing turnover as a way to improve customer satisfaction and, presumably, profitability.
As the turnover data rolled in, the executives were surprised to discover that they were wrong: Some stores with high turnover were extremely profitable, while others with low turnover struggled. Only through proper statistical analysis of a host of factors that could drive customer satisfaction did the company discover that turnover among store managers, not in the overall employee population, made the difference. As a result, the firm shifted its focus to retaining managers, a tactic that ultimately boosted satisfaction and profits.
Availability. The availability heuristic is a strategy we use to assess the cause or probability of an event on the basis of how readily similar examples come to mind—that is, how “available” they are to us. One consequence is that we tend to overestimate the importance of information that we’ve encountered recently, that is frequently repeated, or that is top of mind for other reasons. For example, executives generally believe that EPS is the most important measure of value creation in large part because of vivid examples of companies whose stock rose after they exceeded EPS estimates or fell abruptly after coming up short. To many executives, earnings growth seems like a reliable cause of stock-price increases because there seems to be so much evidence to that effect. But, as we’ll see, the availability heuristic often leads to flawed intuition.
Status quo. Finally, executives (like most people) would rather stay the course than face the risks that come with change. The status quo bias derives in part from our well-documented tendency to avoid a loss even if we could achieve a big gain. A business consequence of this bias is that even when performance drivers change—as they invariably do—executives often resist abandoning existing metrics in favor of more-suitable ones. Take the case of a subscription business such as a wireless telephone provider. For a new entrant to the market, the acquisition rate of new customers is the most important performance metric. But as the company matures, its emphasis should probably shift from adding customers to better managing the ones it has by, for instance, selling them additional services or reducing churn. The pull of the status quo, however, can inhibit such a shift, and so executives end up managing the business with stale statistics.
【字数1156】
【剩余部分】
Considering Cause and Effect
To determine which statistics are useful, you must ask two basic questions. First, what is your objective? In sports, it is to win games. In business, it’s usually to increase shareholder value. Second, what factors will help you achieve that objective? If your goal is to increase shareholder value, which activities lead to that outcome?
What you’re after, then, are statistics that reliably reveal cause and effect. These have two defining characteristics: They are persistent, showing that the outcome of a given action at one time will be similar to the outcome of the same action at another time; and they are predictive—that is, there is a causal relationship between the action the statistic measures and the desired outcome.
Statistics that assess activities requiring skill are persistent. For example, if you measured the performance of a trained sprinter running 100 meters on two consecutive days, you would expect to see similar times. Persistent statistics reflect performance that an individual or organization can reliably control through the application of skill, and so they expose causal relationships.
It’s important to distinguish between skill and luck. Think of persistence as occurring on a continuum. At one extreme the outcome being measured is the product of pure skill, as it was with the sprinter, and is very persistent. At the other, it is due to luck, so persistence is low. When you spin a roulette wheel, the outcomes are random; what happens on the first spin provides no clue about what will happen on the next.
To be useful, statistics must also predict the result you’re seeking. Recall the Oakland A’s recognition that on-base percentage told more about a player’s likelihood of scoring runs than his batting average did. The former statistic reliably links a cause (the ability to get on base) with an effect (scoring runs). It is also more persistent than batting average because it incorporates more factors—including the ability to get walked—that reflect skill. So we can conclude that a team’s on-base percentage is better for predicting the performance of a team’s offense.
All this seems like common sense, right? Yet companies often rely on statistics that are neither very persistent nor predictive. Because these widely used metrics do not reveal cause and effect, they have little bearing on strategy or even on the broader goal of earning a sufficient return on investment.
Consider this: Most corporations seek to maximize the value of their shares over the long term. Practically speaking, this means that every dollar a company invests should generate more than one dollar in value. What statistics, then, should executives use to guide them in this value creation? As we’ve noted, EPS is the most popular. A survey of executive compensation by Frederic W. Cook & Company found that it is the most popular measure of corporate performance, used by about half of all companies. Researchers at Stanford Graduate School of Business came to the same conclusion. And a survey of 400 financial executives by finance professors John Graham, Campbell Harvey, and Shiva Rajgopal found that nearly two-thirds of companies placed EPS first in a ranking of the most important performance measures reported to outsiders. Sales revenue and sales growth also rated highly for measuring performance and for communicating externally.
But will EPS growth actually create value for shareholders? Not necessarily. Earnings growth and value creation can coincide, but it is also possible to increase EPS while destroying value. EPS growth is good for a company that earns high returns on invested capital, neutral for a company with returns equal to the cost of capital, and bad for companies with returns below the cost of capital. Despite this, many companies slavishly seek to deliver EPS growth, even at the expense of value creation. The survey by Graham and his colleagues found that the majority of companies were willing to sacrifice long-term economic value in order to deliver short-term earnings. Theory and empirical research tell us that the causal relationship between EPS growth and value creation is tenuous at best. Similar research reveals that sales growth also has a shaky connection to shareholder value. (For a detailed examination of the relationship between earnings growth, sales growth, and value, see the exhibit “The Problem with Popular Measures.”)
Of course, companies also use nonfinancial performance measures, such as product quality, workplace safety, customer loyalty, employee satisfaction, and a customer’s willingness to promote a product. In their 2003 HBR article, accounting professors Christopher Ittner and David Larcker wrote that “most companies have made little attempt to identify areas of nonfinancial performance that might advance their chosen strategy. Nor have they demonstrated a cause-and-effect link between improvements in those nonfinancial areas and in cash flow, profit, or stock price.” The authors’ survey of 157 companies showed that only 23% had done extensive modeling to determine the causes of the effects they were measuring. The researchers suggest that at least 70% of the companies they surveyed didn’t consider a nonfinancial measure’s persistence or its predictive value. Nearly a decade later, most companies still fail to link cause and effect in their choice of nonfinancial statistics.
But the news is not all bad. Ittner and Larcker did find that companies that bothered to measure a nonfinancial factor—and to verify that it had some real effect—earned returns on equity that were about 1.5 times greater than those of companies that didn’t take those steps. Just as the fast-food chain boosted its performance by determining that its key metric was store manager turnover, not overall employee turnover, companies that make proper links between nonfinancial measures and value creation stand a better chance of improving results.
Picking Statistics
The following is a process for choosing metrics that allow you to understand, track, and manage the cause-and-effect relationships that determine your company’s performance. I will illustrate the process in a simplified way using a retail bank that is based on an analysis of 115 banks by Venky Nagar of the University of Michigan and Madhav Rajan of Stanford. Leave aside, for the moment, which metrics you currently use or which ones Wall Street analysts or bankers say you should. Start with a blank slate and work through these four steps in sequence.
1. Define your governing objective. A clear objective is essential to business success because it guides the allocation of capital. Creating economic value is a logical governing objective for a company that operates in a free market system. Companies may choose a different objective, such as maximizing the firm’s longevity. We will assume that the retail bank seeks to create economic value.
2. Develop a theory of cause and effect to assess presumed drivers of the objective. The three commonly cited financial drivers of value creation are sales, costs, and investments. More-specific financial drivers vary among companies and can include earnings growth, cash flow growth, and return on invested capital.
Naturally, financial metrics can’t capture all value-creating activities. You also need to assess nonfinancial measures such as customer loyalty, customer satisfaction, and product quality, and determine if they can be directly linked to the financial measures that ultimately deliver value. As we’ve discussed, the link between value creation and financial and nonfinancial measures like these is variable and must be evaluated on a case-by-case basis.
In our example, the bank starts with the theory that customer satisfaction drives the use of bank services and that usage is the main driver of value. This theory links a nonfinancial and a financial driver. The bank then measures the correlations statistically to see if the theory is correct and determines that satisfied customers indeed use more services, allowing the bank to generate cash earnings growth and attractive returns on assets, both indicators of value creation. Having determined that customer satisfaction is persistently and predicatively linked to returns on assets, the bank must now figure out which employee activities drive satisfaction.
3. Identify the specific activities that employees can do to help achieve the governing objective. The goal is to make the link between your objective and the measures that employees can control through the application of skill. The relationship between these activities and the objective must also be persistent and predictive.
In the previous step, the bank determined that customer satisfaction drives value (it is predictive). The bank now has to find reliable drivers of customer satisfaction. Statistical analysis shows that the rates consumers receive on their loans, the speed of loan processing, and low teller turnover all affect customer satisfaction. Because these are within the control of employees and management, they are persistent. The bank can use this information to, for example, make sure that its process for reviewing and approving loans is quick and efficient.
4. Evaluate your statistics. Finally, you must regularly reevaluate the measures you are using to link employee activities with the governing objective. The drivers of value change over time, and so must your statistics. For example, the demographics of the retail bank’s customer base are changing, so the bank needs to review the drivers of customer satisfaction. As the customer base becomes younger and more digitally savvy, teller turnover becomes less relevant and the bank’s online interface and customer service become more so.
Companies have access to a growing torrent of statistics that could improve their performance, but executives still cling to old-fashioned and often flawed methods for choosing metrics. In the past, companies could get away with going on gut and ignoring the right statistics because that’s what everyone else was doing. Today, using them is necessary to compete. More to the point, identifying and exploiting them before rivals do will be the key to seizing advantage.
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发表于 2012-10-4 20:37:04
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