Posts Tagged ‘predicting preformance’
The executioner climbed up the wooden stairs and took his place on the hangman’s platform, ready to pull the wooden lever on command. A command that would send the man a short distance down to his death, his neck snapped. The accused stood next to the trapdoor on the floor of the platform, the noose snugly around his throat. The day was dry and dusty, and it was hot, really hot. Small eddies of dust danced in the dirt of the town square that contained the mechanism of death by which the hangman performed his work. The relentless sun beat down on the small crowd that had gathered to watch the execution in the center of the rural small town. The accused was urged forward by the executioner’s fingers poking in his back and he shuffled his feet a few paces. He was now centered on the trapdoor, waiting for it to fall away from under his feet and end the nightmare from which he was feverishly hoping to awaken. Just then the cell phone of the executioner rang out through the stagnant air; his ring tone was the melody “I get by with a little help from my friends”. The executioner listened to the small voice at the other end for a few minutes and upon hanging up yelled to the crowd that someone else had confessed to the crime and the man who was about to be executed was innocent. The noose was removed from the man’s neck and as he descended the stairs he collapsed as emotion overwhelmed him.
In addition to our genes we are all products of our experiences and you would have to wonder what the experience above might have on the man who narrowly escaped his demise. What kind of employee or manager would he make? How would he relate to a regimented life in a hierarchical organization? How would he relate to others within the organization? If we postulated at the number of variables that might affect a person and their ability to function in different kinds of organizations we might end up with a very long list in a very short period of time. How about these just for a start? Native intelligence, early socialization factors such as single child or one of many, birth order, number of friends interacted with while growing up, urban, suburban or rural upbringing, whether they were bullied in schooled, were the bully, or just a bystander. Did they have teachers who believed in their ability and pushed them to perform? Were their parent(s) active participants in their early learning? Were they brought up in a single parent household, multiple parents, or divorced parents? Were they raised in a peaceful country or one that had political, economic, social or military upheavals? Was it a struggle to find food and fresh water each day or were they brought up in a “wealthy” environment with their every need provided? Were they loved? Were they ever about to be hanged? The list of factors that can affect who we “are”, how these factors might interact with each other and how we then relate to others is truly staggering and largely unknown.
Industrial Psychologists tend not to look back at these independent variables, these causative factors, but rather tend to say (euphemistically) “look we don’t know how you got there but we are going to measure certain outcomes, for instance whether you are introverted or extroverted, whether you are creative or not, whether you match the existing profile of others who have been successful in the job etc. and use those measures to estimate whether you will succeed on this job”. Developmental Psychologists are more interested in what underlying factors caused you to come out the way you are and Clinical Psychologists try to help you deal with what those underlying factors did to you. So Industrial Psychologists use some simple surrogates to help choose which applicants to an organization should be accepted: type of degree, grades, past work history, references, selection tests, interviews etc. These simple surrogates can only hope to measure a fraction, an approximation of the factors that determine who you are. The goal of the Industrial Psychologist is to utilize the best surrogates that are most predictive of performance outcomes.
Just to compound the problem of determining what is important to measure, what causative factors lead to what outcomes, it is safe to assume that due to individual differences two identical situations on two different individuals may have radically differing impacts and outcomes. Are there other factors that should be utilized other than the commonly used mentioned surrogates above? Possibly, but the enormity of the task and the state of the science makes it a largely untried endeavor.
Let’s say that our goal is to design an optimum organization from both an organizational structural standpoint and a selection standpoint (we will leave optimum processes out for now), optimum here meaning that the structure and who is in each role will lead to a maximization of the performance of the entire entity. Now let’s say we have an organization with 10,000 individuals, not a small but not a huge organization either. Is it possible to appropriately select and configure those 10,000 people to optimize performance based on individual differences, both who is the best fit for what role and how these individuals will interact with the other members of the organization (each with their own complexities), based on the organizational structure that is in place? It becomes a fairly daunting task. We have to assume that each organization out there today has not been able to optimize its performance, to maximize its potential, not from a lack of desire, but from the lack of the state-of-the-art knowledge by which to make these kinds of decisions. Is there any hope on the horizon for organizations desiring to optimize their performance? I think that hope will come from a variety of directions.
While it may be difficult to create the optimum organization with the optimum person filling each and every role, to maximize not only each individual’s performance but also how those individuals interact with others in the organization, there exists a very large number of “good” sub-optimum conditions that allow organizations to function reasonably well. To fulfill the above requirement of the optimum organization with the optimum people in place “perfect knowledge” about each member and potential member would be required and since we are all merely human that is unlikely to happen. You could also argue that the perfect organization could be obtained only if the candidate pool of those you could select into the organization was infinitely large and that is not likely to happen either.
Remember as well that all organizations are working with the same sub-optimum conditions and the same limitations so the winner of the race is not necessarily the organization with the optimum performance but rather the one that out-performs from an organizational standpoint the competition. Your selection procedures and your structure need to be better than the competition rather then “perfect”.
But those words of comfort should not be taken to mean that there is not more that we can and should be doing to maximize our ability to predict and enhance organizational performance. The history of weather forecasting gives provides some insight.
Early weather forecasting or meteorology goes back thousands of years and was initially based upon simple observations of the sky. If it was cloudy and the clouds looked dark it might rain. Early predictions were limited to more or less sunshine in the morning and darkness at night. In 1643 Evangelista Torricelli, an Italian physicist invented the barometer, an instrument designed to measure air pressure. He noted that changes in air pressure often preceded changes in the weather. The hygrometer was invented in 1644, which allowed the moisture content of the air to be measured and in 1714 a German physicist name Daniel Fahrenheit invented the mercury thermometer. These basic tools and those that followed allowed some of the core components that caused weather to be tracked. In 1765 French scientist Laurent Lavoisier began making daily measurements of the weather (temperature, wind speed, pressure, humidity) and that was considered the start of modern meteorology. At first the ability to predict the weather based on these measurements was abysmal.
The next step was taken after World War I when mathematical equations were first attempted to be used to predict the weather by British meteorologist Lewis Richardson. He felt that since the atmosphere is governed by the laws of physics that those laws could be used to predict the weather. At first his complex equations could not be performed quickly enough by hand to allow them to be useful in weather prediction (the storm was over by the time you got the calculation done that a storm was coming). Computers of course solved this dilemma later on, but his initial methodology is intriguing. The earth’s surface was divided up into a grid pattern with each grid length being 80 kilometers long. The atmosphere above each grid has observations of wind, pressure, temperature and humidity recorded at 20 different levels of altitude. Analysis of the data from more than 3,500 observation points on the earth produces a forecast for the next 15 minutes. By tying these observation points together a global forecast can be developed.
Can organizational performance measurement and prediction follow a similar developmental path as weather forecasting? Can we develop the “laws” of organizational performance and then use observations, measurements about the organization to predict future performance? Right now our ability to measure critical components of organizational culture and to predict performance is somewhat better than a prediction of darkness at night, but I am not sure how much better. We seem to be at a point of measuring some of the various attributes of organizations and individuals within organizations but it is still very unclear if we are measuring the “key attributes” and how the history of individual experiences and what is important to measure about them and how they link to organizational performance is still in its infancy. And we are a long way off from having “laws” of organizational performance and using those laws to predict the future of an organization. But maybe we can take a lesson from meteorology and begin our own version of Organizational Meteorology.
© 2010 by Jeffrey M. Saltzman. All rights reserved.