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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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