Do You Feel Lucky?
How AIMs Affect Performance
Picture this
You are standing before your Board of
Directors asking to rent some equipment. You tell them it costs $75,000 a
year and its price will increase 6% annually. You have no idea if it will
perform like the demo and there is even a 50/50 chance it will be a low
producer. It cannot be upgraded or repaired and might quit working any
time. Furthermore, you might get sued if you terminate its contract. You
conclude by assuring the Board that overall productivity will be
maintained by renting several excess machines to make up for its
inefficiency.
Sound familiar? It should. It happens every time you
hire a knowledge worker!
No organization knowingly hires weak employees, but
separating truth from fiction during the selection process is an enormous
challenge. Experienced managers already know why employees fail - they
just have trouble measuring it. Just look at their hiring tools...
- Interviews: People who pass an interview have
about a 50% chance of
being a high producer.
- First Impressions: Managers who normally
demand detailed cost
justifications for purchasing office equipment
pride themselves on hiring
people based on almost no objective data.
- Bad tests: Many of the so-called "tests" used
for selection have absolutely
no documented relationship with
performance on the job.
Stone Age tools produce Stone Age results
The results of faulty measurement are disastrous. The
numbers should not shock you. Look at sales. Do 20% of the sales people
produce 80% of the business? Now, think about the managers you have worked
for. Have more than 20% been truly competent? How often have you seen
people change based on attendance at a training program? It's amazing to
consider that organizations usually have a more rigorous set of technical
specifications for purchasing a desktop computer computer than for
selecting a $75,000 employee!
Finding Success Patterns
Most people are not fired because they are technically
incompetent, but rather because they didn't "fit" the culture, couldn't
get along with people, or wouldn't do the work. Technical skills are easy
to measure, but they don't cover the full story. Developing a set of
"people" specifications requires an understanding of the traits associated
with both high and low performance in the job. Some people would call
these personality traits and others would call them motivations. I like to
call them AIM's (i.e., for Attitudes, Interests, and Motivations). What
ever you call them, a great deal of your employment success depends on
your ability to measure AIM's during selection. Of course, this kind of
measurement is easier said than done.
Would you be surprised to learn that people say or do
almost anything to get a job? Would you be surprised if people "fibbed" a
little during an interview? Would you be surprised that personal
references are not always honest? Finding traits associated with job
performance takes a special test, a special process to build a unique
answer key and some special scoring tools.
There are two kinds of personality tests around. There
are the basic communication models used in training and the broad-based
general descriptions of personality. Seldom was either of these designs
intended to predict explicit performance on the job. Trying to take a
generic trait like "creative", "intuitive", "wooer", "extroverted",
"supportive", etc., and convert it into job performance can be pure
guesswork. No matter how much fun it might be to play amateur "shrink,"
employers are not in the psychoanalysis business, they want to know if an
applicant can do the job! Period.
What can be learned from all this?
When "hard" skills are about equal, AIM's make a
significant difference between high and low performance ·
One set of MIA factors does not apply to all jobs any
more than a single shoe size fits all people ·
- Different personality factors combine to produce
different performance ratings depending on the task
- Different organizations, jobs and tasks will have
vastly different MIA's.
- The use of any single set of personality items is, to
be polite, not "helpful"
- Adding MIA data to your selection process gives you
an accurate way to predict the "will do"
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