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|Special Thanks and Acknowledgments||p. iv|
|The Productivity Secrets of the Star Performers|
|What Leads to Star Performance?||p. 3|
|Stars Are Made, Not Born||p. 11|
|Creating the Star Performer Model||p. 24|
|Questions from Interested Readers||p. 36|
|The Nine Work Strategies of the Star Performers|
|Initiative: Blazing Trails in the Organization's White Spaces||p. 49|
|Knowing Who Knows: Plugging into the Knowledge Network||p. 74|
|Managing Your Whole Life at Works: Self-Management||p. 98|
|Getting the Big Picture: Learning How to Build Perspective||p. 121|
|Followership: Checking Your Ego at the Door to Lead in Assists||p. 149|
|Small-L Leadership in a Big-L World||p. 174|
|Teamwork: Getting Real About Teams||p. 194|
|Organizational Savvy: Street Smarts in the Corporate Power Zone||p. 211|
|Show-and-Tell: Persuading the Right Audience with the Right Message||p. 234|
|Become a Star Performer: Making the Program Work for Your||p. 246|
|Some Productive Last Words|
|A Conversation with Women and Minority Employees: Useful Tips on Becoming Stars||p. 257|
|A Message for Managers: Productivity in the Brainpowered Economy||p. 273|
|Conclusion: The Rewards of Star Productivity||p. 283|
|The Research Story Behind the Book: The Hunt for Higher Productivity||p. 291|
|Resources for More Help||p. 309|
|Table of Contents provided by Syndetics. All Rights Reserved.|
WHAT LEADS TO STAR PERFORMANCE?
Whenever people hear about my star performer work--whether sitting next to me on an airplane or at a professional conference--they invariably want the thirty-second sound-bite answer to years of research: "So what's the Big Secret, Kelley? What is `it' that separates star producers from average performers?"
"I'll be glad to share the answer," I tell them. "But before I do, would you mind sharing with me what you think accounts for the difference? Think of someone who is a star performer. Get that person vividly in your mind. Now think of an average performer. Compare these two and tell me what separates them."
I ask these questions because I have found that most people have preconceived notions about the underlying causes of star productivity that leads to success. And most of those notions are as wrong as can be.
I can empathize with them because I was once in their shoes. Ten years ago, I was living a consultant's worst nightmare. For twenty-four months, my investigative team--originally consisting of Janet Caplan from the New School for Social Research, Dick Hayes from Carnegie Mellon University, and me--had been working with executives at one of the world's high-tech giants, Bell Labs, all of us convinced we could crack one of the great mysteries of the modern workplace: what separates the star performers from their average coworkers. When they hire the world's best and brightest, why do only a few become "ten- or twenty-for-oners"--those who outshine and outproduce their peers by factors of ten or twenty?
We had set out to answer the question gnawing at Bell Labs' top management and recruiters: How is it that a company with access to cream-of-the-crop graduates from the world's most prestigious schools ended up with only a sprinkling of star producers in an otherwise solid but mostly average workforce?
For executives at Bell Labs, these were not questions of idle curiosity. As the world's premier R&D organization, Bell Labs' reputation and its future depended on the productivity of star performers. The mid-1980s were a critical time for Bell Labs. Top management was beginning to preach about the need for thinking ahead of the curve, for coming up with ideas for improved productivity.
There was good reason for the new attitude. The monolith known as Ma Bell was no more. In its place after the dust settled from the court-ordered breakup of AT&T were seven Baby Bell companies. Hundreds of sheltered scientists and engineers at the big Bell Labs think tank in New Jersey, and at the satellite centers outside Chicago and Columbus, Ohio, were being thrown into the competitive arena for the first time in their working lives. Forward-thinking managers were concerned about finding ways to prepare them for this sea change by boosting productivity.
The way Bell Labs managers figured it, if they didn't find a way to better measure their workers' productivity and develop a plan to improve it, someone else was going to do it for them--perhaps competitive pressure would force a restructuring, Wall Street would force layoffs, or a new owner would force them all out of the company.
Bell Labs was investing in technical tools to improve productivity, but Janet Nordin, the director of a 250-member lab, and her boss, Don Leonard, Bell's vice president for switching systems, recognized the human side of the equation--much more complex but just as vital. They believed human brainpower, not technological wonders, would provide the longest-lasting productivity advantage.
At the beginning, my investigative team had what I thought was a rare opportunity to do the most definitive research yet on productivity. We had a company with enormous patience and real-world problems, and we had access to a remarkable group of study subjects--workers in the top ranks of the global professional elite, workers who were turning communications products fantasies like pocket-sized cellular telephones into reality.
Bell Labs and I came together because of a mutual belief in the power of brainpowered workers to drive the success of business organizations. Productivity had been my field of research for much of my career. Janet Nordin had read some of my earlier writings in which I first introduced the notion of an emerging, professionally proficient, brainpowered segment of the American workforce. I call them "gold-collar workers," because they are the company's most valuable asset.
Janet and Don wanted a better understanding of the people who did Bell's most important work, and they wanted us to bring it to them. What I didn't realize then was how elusive the answers would be, how many dead-end roads would be traveled following others' research before we finally decided to roll up our sleeves and blaze our own trail.
NAIVE ASSUMPTIONS ABOUT HIGH PRODUCTIVITY
One of the first things we discovered is that most workers and their bosses don't agree on who the star performers are. We first asked managers to list their star performers. We then suggested that they narrow the list to those individuals they would turn to if they had to staff an important new project, if they had a crisis that needed a SWAT team, or if they were going to hire for their own business.
When we showed the list to a group of star performers, they took issue with the managers' picks. "How did Joe get on the list?" they asked incredulously. "Joe hasn't done much for years. And why isn't Maria on the list? She's the one everyone turns to when they hit a brick wall or need new ideas." These reactions gave us pause: managers and their brainpowered workers had different views on who deserved the mantle of "star performer."
We took a step back and asked both groups to nominate those people who greatly outperformed their peers--the cream of the crop--especially those who achieved with methods they admired. We wanted to weed out the high producers whose tactics are to slash and burn their way to greater productivity but whose wake of destruction negates any positive contribution that they make.
The result of this exercise was a 50 percent overlap between the two groups. Brainpowered workers and their managers disagree half the time on who the stars are. In Appendix I, I explain why this disagreement occurs and why it can have devastating productivity effects on individual workers and the entire organization. A goal of this book is to eliminate the definition gap and the productivity deficit that can result.
For our original research at Bell Labs, we chose as star performers only those people who made it to both managers' and coworkers' star lists. In our later work with 3M, we added the requirement that the stars also had to receive a similar approval from customers. At both companies, we also took into account the number of awards, honors, and performance bonuses won, as well as patent or publication credits where applicable. These undisputed stars were the group we studied and the ones whose performance provided the research basis for this book.
Our research team asked top executives, middle managers, brainpowered workers, and other researchers what makes the difference between star performers and middle performers. Our goal was to find out what made the stars so much more productive and valuable. We received these typical responses:
* Stars are smarter; they have higher IQs.
* Stars are better problem solvers and more creative.
* Stars are more driven and ambitious; they have a "will to succeed."
* Stars are more outgoing; they get along well with people.
* Stars are risk takers and mavericks.
When the list was final, we had accumulated forty-five factors that managers and star performers close to the action believed led to star performance. They grouped nicely into three main categories: (1) cognitive factors, such as higher IQ, logic, reasoning, and creativity, (2) personality factors, such as self-confidence, ambition, risk taking, and a feeling of personal control over one's destiny, and (3) social factors, such as interpersonal skills and leadership.
It became clear that most people--managers and brainpowered workers alike--believe that the stars among them are better in a fundamental sense. Average performers, they assume, lack the traits necessary to leap above solid, steady, work--a leap that is the hallmark of stars.
So we put two hundred star and average performers in meeting rooms across the country to administer a two-day battery of tests to figure out which of the forty-five factors on our list separated the stars from the average performers. We even added a few measures that weren't mentioned but that we thought were important, such as the worker's relationship with the boss, job satisfaction, and attitudes toward rewards. We did surveys and developed detailed individual case histories. We interviewed employees and the managers who hired them. Engineers and managers also supplied us with personal biographical information and personnel file material. We became such fixtures in the Bell Labs workplace that employees began to include us in the office gossip mill.
Then came the day when we fed all the scraps of information into a computer, analyzed it for four months in dozens of different ways, and expected to come up with the breakthrough data that would show what Bell's star producers had that separated them from their average coworkers.
AND THE ANSWER IS...
The project meeting to discuss our landing of the Big Kahuna of productivity took place around a large mahogany table in a dimly lit conference room on Bell's sprawling campus outside Chicago. I was surrounded by company executives who had lent reputations and considerable resources to the project. Even though I was flanked by two members of my research team, it had fallen to me to break the news that all our careful gathering of data, our testing, our analyzing had resulted in only one significant finding: We had made a lot of money for the paper industry.
In terms of unlocking productivity secrets, our data showed no appreciable cognitive, personal-psychological, social, or environmental differences between stars and average performers.
"Could this be possible?" we asked ourselves. No IQ difference! No logic or reasoning differences! No creativity differences! No self-confidence, risk-taking, or controlling-your-own-destiny differences! No leadership or motivational differences! No attitudinal differences in views on management, work environment, or company rewards!
For each traditional measure--whether alone or in combination--we had come up empty.
We compared the numbers a dozen different ways, stretching computer analyses to their limits, and with each run the computer spat back what I thought at the time was some kind of terrible methodological mistake: There were no quantifiable differences.
These results also highlighted that no one--not the stars, not their bosses, and not the average workers--knew what actually leads to high productivity or star performance.
Although my gut feeling and experience told me there were differences between stars and their average coworkers, I had no data to back up this intuition. Researchers are trained to let their gut feeling help determine what they should investigate but not, of course, what the outcome should be.
Despite my utter conviction that there were differences to be found out, would it be unfair to continue to spend Bell Labs' money to prove my professional instincts were superior to the reams of data piled up on the conference table? Was I locked into my own tunnel vision, unable to face the hard truth?
I remember thinking, rather bitterly at the time, about the story of Thomas Edison's early attempts to come up with the right material for a lightbulb. He had tried a thousand different elements and all had failed. A colleague asked him if he felt his time had been wasted, since he had discovered nothing. "Hardly," Edison is said to have retorted briskly. "I have discovered a thousand things that don't work"
Lofty anecdotes about the trials of creative geniuses were small comfort to a researcher who went into a meeting to tell the people who hired him that he and his team had found nothing.
"How can this be?" asked Janet Nordin, who had been, and still was, one of the most supportive of the project among Bell Labs executives.
The beginning of that meeting was depressing for the damper it put on Bell managers' hopes for productivity leaps, but it also rubbed salt in the wound I was carrying into the session. No one was more disappointed than I. "Stunned" would be the more accurate description of my reaction to the testing evidence. Our research team had staked our reputations on uncovering a secret--shedding light with insightful reading of clues and leads, uncovering the key differences we were seeking. We would present it on a platter to management and help them find a way to identify those traits in applicants. Productivity problem solved. Publish results. Applause.
No one in our fields of work, in business or academia, makes a long-lived career out of publishing negative findings--"Big news! No news!" doesn't cut it with clients or research journals. But from the depths of that feeling of absolute failure, in the midst of the meeting with justifiably disappointed Bell Labs executives and my depressed colleagues, came glorious revelation. As people were trying to explain why the data didn't uncover differences they knew existed, we flashed on a question that would have made Edison proud:
By recognizing that there were no differences from the factors we collected, had we not discovered something critically important--that the factors we thought were so basic to star performance--cognitive, psychological, and social characteristics--were not the real drivers at all?
And that was followed by perhaps the most significant question of all:
If the differences weren't fundamental, would it be possible for average-performing brainpowered workers to be turned into stars?
While managers and project team members were buzzing among themselves about the possibilities, I quickly pulled out of my briefcase a list of the individuals who scored highest on the cognitive tests. I taped it on a wall.
"What do all these people have in common?" I searched the faces of my colleagues and the management team, all the people who had lived the project for two years, who knew detailed information about these workers. "Very smart engineers but not all of them are stars" is the way one described these individuals. Others in the room nodded, offering reasons why many of these engineers didn't warrant star rank. Then I put up other lists; each list contained only the names of those who scored high on a particular factor that we had previously thought led to star performance--risk taking, reward seeking, company loyalty. Similar assessments were shouted back.
The process started to reveal that other factors were at play. The productivity mystery lay not in the test scores but in patterns of behavior on the job. Day-to-day work strategies and results led to the assessments of the people around the room. It wasn't what these stars had in their heads that made them standouts from the pack, it was how they used what they had.
The meeting eventually turned into the kind of frenzied brainstorming session that happens when people are exploring something completely new. From the threads of Bell Labs executives' work experiences with engineers, the boost of our own intuition, and a willingness to see "no differences" as a path instead of a dead end, we had moved onto a new track.
Thomas Edison was right. The real value of our twenty-four months of selective searching was in everything we eliminated. We had tested and found wanting just about every plausible theory about what leads to high productivity in brainpowered workers. In the process, we eliminated all the pet theories that people hold. We came to call them "naive notions," not because their proponents were naive but because their theories had not undergone rigorous, large-scale testing. With the results of our empirical work, we could silence all the armchair psychologists who thought the answer lay in brain capacity or personality.
The hunt would take off again, this time with the goal of unlocking the work-behavior secrets of the stars and sharing them with managers and with the professionals: the engineers, marketing whizzes, lawyers, software writers, accountants, teachers, science researchers, journalists--people I refer to as "gold-collar workers"--just like you.
Copyright © 1998 Consultants to Executives and Organizations, Ltd.. All rights reserved.