While surfing today,I eventually landed on this thoughtful article in Spectrum.It is very long article so for the sake of readability,breaking it into 2 Parts.
Please share your comments/views/feedback upon this topic.Enjoy the article…
Becoming A Star Engineer
By Robert E. Kelley
IEEE Spectrum, October 1999, Volume 36 – Number 10
While looking for innate factors that separate top-performing engineers from their colleagues, a Carnegie Mellon University professor discovered that stars are made, not born, and reveals how.
IN 1985, I WAS ASKED A SERIES OF QUESTIONS, AND HAVE been tracking down their answers ever since. Bell Laboratories (then part of AT&T Corp. and now mostly belonging to Lucent Technologies Inc.) was perplexed. It hired the best and the brightest from the world’s most prestigious universities, but only a few lived up to their apparent potential for brilliance. Most developed into solid performers of mostly average productivity who did not substantially further Bell Labs’ contribution to AT&T’s competitive advantage in the marketplace.
What the labs wanted to know was: what separates the star from the average performer? Is it innate or canstar performance be learned? Could a program to improve productivity be designed that would help turn average performers into stars?
Not just companies are asking these questions. Since 1985, I have met few professionals who do not want to be more productive. In their own minds, most engineers believe they can be stars. They dislike being outshone by a co-worker and strive constantly to do better than before. In the workplace, they are being forced to do more with less. Global competition, mergers, and downsizings have left them with greater responsibilities and fewer resources. Who among us is not working longer and harder today than five years ago? Who does not have more work piled up in the in-basket or long lists of unanswered e-mail and phone messages? Which of us is not afraid that if we are not more productive, we might get the ax next? Who does not want more control over their lives–a better balance between work and personal lives? Everyone is being told to work smarter, but no one seems to know what that means.
My colleagues and I have been working on these corporate and personal productivity questions ever since. Over a thousand engineers from Bell Laboratories, 3M, and Hewlett-Packard contributed to the original research as both collaborators and subjects. To discover the secrets of star performance, we used paper-and-pencil tests, direct observation, work diaries, focus groups, and individual interviews, drawing upon statistical analyses, content analyses, and iterative model building as appropriate.
Many other companies took part, from those reliant on electrical engineers–such as Analog Devices, Fore Systems, and Air Touch–to those like Shell Oil and Kimberly Clark that are involved in other kinds of engineering. They have used our productivity improvement program to turn their engineers into higher performers and in so doing have also contributed to the growing body of knowledge on star performance.
The path to stardom
Lai and Henry were hired at Bell Laboratories with similar credentials: 3.8 GPAs (grade point averages) from top-ranked undergraduate programs in electrical engineering; summer internships at computer companies; and glowing recommendations from professors. Yet they took distinctly different approaches to their first six-month assignment. Mornings, they took classes in telephone technology and the methods Bell Labs uses to conduct its work. Afternoons were spent on break-in projects–work that needed to be done but that would not jeopardize crucial projects if done badly.
Henry holed up in his office as if writing his dissertation or studying for a law bar exam. He collected volumes of technical documents to acquaint himself with the latest ideas, surfacing only for a bathroom break or a mandatory staff meeting. “What’s going to count,” he remembered thinking at the time, “is whether I can prove to my co-workers how technically smart I am.”
Lai set aside 3 hours each afternoon to work on her assignment and to sharpen her technical skills. In whatever time was left of her workday, she introduced herself to co-workers and asked questions about their projects. If one of them needed a hand or was facing schedule pressures, she volunteered to help. Lai was new to the work place culture, but even so her colleagues warmed to her willingness to pitch in, especially given that their problems were not hers.
One afternoon, a colleague was struggling with a recalcitrant program for a software project due the next week. Lai had picked up a new programming tool in an advanced course, and she thought it could handle the problem. So she offered to work on the program while her colleague focused on the larger project. On another occasion, some sophisticated software tools had to be installed on everyone’s office PC. Standard practice was for each PC user to do the job by trial and error. Having run into the same cumbersome procedure during an internship, Lai thought it more sensible for one person to install the tools in all the machines, and she offered to do the job. But the installations proved unexpectedly tough, requiring two weeks rather than the four days she had planned. Lai could have backed off but she saw it through, even though she had to come in early and stay late for several days so that neither her work assignment nor her class work would suffer.
After six months, Henry and Lai had finished their technical classes and their first assignments. Their projects were successful and judged technically competent. Indeed, Henry’s work may have been slightly more technically proficient than Lai’s.
But in the work place, Henry came up short. While known as a nice guy, he was also pegged as a loner. He was seen as technically adept, but his ability to share his skills with co-workers was questioned. He carried on as if still in school, where the individual’s performance is what counts.
But Lai came across as someone who took initiative, who saw several problems and stepped forward to solve them even though they were not her responsibility. She had created the impression of being in the lab group for far longer than six months. Managers of course noticed she was showing the characteristics of a star engineer and already were viewing her as a candidate for fast-track assignments.
As seen in the quiz on “Understanding star performers”, most people (like Henry) have preconceptions about what causes star productivity, and most of their notions are as wrong as can be. Over the past 14 years, we have debunked many common myths and made some startling discoveries about the outstanding engineer. One of our first findings was that workers and their bosses tend to disagree on who the star performers are. We first asked managers to list their choices. We then suggested narrowing the list to those persons they would turn to if they had to staff an important new project, if they had a crisis that needed a SWAT (Special Weapons and Tactics) team, or if they were going to hire for their own business. When we showed the list to a group of star performers, they pooh-poohed the managers’ selections. “How did Joe get on the list?” they asked incredulously. “Joe hasn’t done much for years. And where’s Maria? Everyone turns to her when they hit a brick wall or need new ideas.”
The difference in their reactions gave us pause. We took a step back and asked managers and brain-powered workers to name those people who greatly out produced and outperformed their peers, especially if they did so with methods others admired. We were after the cream of the crop–we wanted to weed out the high producers who bulldoze their way to greater productivity but whose wake of destruction swamps any positive contribution.
The result of this exercise was only a 50 percent overlap between the two groups. Brain powered workers and their managers disagree half the time on who the stars are.
For our original research at Bell Labs, we refined our sample. We included only people on both managers’ and co-workers’ star lists. (In later work with 3M, we added the requirement that the stars receive customers’ approval, as well.) 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 whose performance was the basis for our research.
To pin down how star performers and solid middle performers differ, our research team asked top executives, middle managers, engineers, and other researchers for their opinions. We accumulated 45 factors that managers and star performers close to the action believed led to outstanding performance. The four main categories were: cognitive factors, such as higher IQ, logic, reasoning and creativity; personality factors, such as self confidence, ambition, courage, and a feeling of personal control over one’s destiny; social factors, such as interpersonal skills and leadership; and work and organizational factors, such as the worker’s relationship with the boss, job satisfaction, and attitudes toward pay and other rewards.
Next, to figure out which of the 45 factors differentiated between the groups, we put hundreds of star and average performers in meeting rooms across the country and administered a two-day battery of tests. We also did surveys, developed detailed case histories, and interviewed employees and the managers who hired them. Engineers and managers also supplied us with biographical information and personnel file material.
Perplexingly, after two years, our data showed no appreciable cognitive, personal or psychological, social, or work or organizational differences between stars and non-stars. For each traditional measure, alone or in combination, we had come up empty. We compared the numbers a dozen ways, stretched computer analyses to their limits, and with each run, found the computer spitting back what we then thought was the result of some terrible methodological mistake: there were no quantifiable differences. between members of the two groups.
Yet, by recognizing this, had we not discovered something critically important? That the four factors we presumed were vital to star performance–cognitive, psychological, social, and organizational characteristics–were not the real drivers at all?
The long-term value of our effort was that it laid to rest the cloud of myths around star performance. And in fact, over the next years of our research, we learned that other factors were at play. Most engineers come to the workplace with more than enough potential to succeed splendidly, but most end up as run-of-the-mill. The stars were not standouts because of what they had in their heads but because of how they used what they had. The productivity mystery lay in learning how to transform their talents into high productivity–much like turning potential energy into kinetic energy. Stars, we saw, are made, not born.
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