management

1. The Target Model
Human Behavior and Performance in the Workplace
Fred Nickols, CPT
5/9/2010
This paper presents the Target Model of Human Behavior and Performance. It is a framework that has
proven useful in understanding and addressing issues of human behavior and performance in the
workplace. A case from the author’s experience is used to illustrate application of the Target Model and
the model itself is used to identify some requisite conditions of performance. The Target Model affords
those who are interested in improving human performance with some new and different insights into
the nature of behavior and performance in the workplace and some equally new and different
approaches to dealing with such matters. The scientific basis of the Target Model is Perceptual Control
Theory (PCT) as developed and articulated by William T. Powers.

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To Hit a Target…
Performance, whether of an organization or an individual, is often spoken of in terms of “hitting
targets.” Actually hitting a target, whether using rifle, handgun, bow and arrow or cannon, offers some
instructive insights into hitting performance targets. First, there is the shooter, the performer if you will.
Next, is target selection; that is, choosing a target to hit. Third, there is the intent of the shooter (e.g., in
relation to a target in the form of a human silhouette, is it the shooter’s intent to hit the head, the chest,
the mid-section, or perhaps an extremity such as an arm or a leg?). Fourth, there is the act of shooting
itself (e.g., aiming at the desired spot on the target and squeezing the trigger instead of jerking it). Fifth,
there is observation about where the target was actually hit and of subsequently adjusting one’s aim
accordingly. Finally, there are circumstances that can interfere with hitting a target and make it more
difficult to do so (e.g., wind, distance, and any motion of the shooter or of the target itself). As we will
see, these aspects of hitting an actual target have their counterparts in being able to hit a performance
target (see below).
Shooter Performer
Target Target
Intent Goal
Shooting Action
Observation Perception
Circumstances Complicating Conditions
The Target Model
This paper presents “The Target Model of Human Behavior and Performance” (see Figure 1). Its
primary use is as an aid in understanding and addressing issues of human behavior and performance in
the workplace. As is widely known and generally understood, human performance has links to process
and organizational performance and these will become clear as we proceed.
Goal
State
Conditions
Perceived
State
Actions
Target
Figure 1 - The Target Model
Let’s begin with an operational problem that was solved using the Target Model as a guide.

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The Reject Rate Problem
The pie chart in Figure 2 shows the average distribution of
forms processed per run for a registration processing operation
at a major testing company. The division director where the
operation was housed was concerned about the reject rate
which he defined simply as “too high” and his request was to
“get it as low as you can.” No data had been compiled but they
were available and once they were compiled they reflected the
distributions shown in the pie chart.
In a nutshell, the operation was reworking two-thirds of the
forms received. Only one-third of the forms in a run were
processed without error. Of the two-thirds that rejected for
various errors, about half were being resolved at the testing
company and the other half was being returned to registrants
only to be processed again when re-submitted. This was costly rework and both the registrants and the
program client were upset about the high rate of returned forms. Clearly, there was a problem.
Figure 3 shows a basic flowchart view of the operation in question, annotated with the reject figures and
identifying the culprit in question.
Figure 3 - Registration Processing Operation
It was patently obvious that the reject rate owed to registration forms that could not be processed on
the first pass. In systems terms this would be called “faulty input.” In some cases, required fields
contained no entries. In other cases, the entries were invalid, especially in the case of codes identifying
institutions related to the professional field in question. And, in some cases, the forms were so sloppily
filled out that it was hard to reach any conclusion other than carelessness on the part of the registrant.
This was odd because the registrants were registering to take a certification test that they had to pass in
Figure 2

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order to (1) get certified, (2) get licensed, (3) obtain employment and (4) start drawing a paycheck.
Clearly, it was in their best interests to do a good job of filling out the registration form.
What the testing company wanted as input was a “clean and complete” registration form, one in which
all required fields contained valid entries and, consequently, the form could be processed on the first
pass without mishap. The testing company’s ideal situation is illustrated in Figure 4.
Figure 4 - An Idealized View of Registration Preparation
However, the testing company didn’t fill out the registration form, the registrants did, and the reality
was that their performance wasn’t meeting the testing company’s expectations or requirements.
To Realize Desired Performance . . .
The Target Model suggests a set of conditions that must exist in order for a desired performance to
occur. These requisite conditions prove useful in figuring out why a desired performance isn’t occurring.
They also prove useful in specifying the conditions that must be in place to ensure that a desired
performance will occur.
For a desired performance to occur, the performer must :
 focus on the correct target variable;
 be aware of the preferred or goal state for the target;
 genuinely want to achieve that goal state;
 correctly perceive the present or actual state of the target variable (i.e., possess or have access
to current, accurate information about the actual state of the target variable);
 compare the goal state for the target variable with its currently perceived state, identifying any
discrepancies or gaps between the two;

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 act in ways that close or reduce any gap between the goal state and the perceived state (i.e.,
exercise the necessary knowledge and skills as well as vary those actions so as to enable
adjustment and adaptation to reasonable variations in circumstances).
In addition to the performer-related circumstances identified above, there must also exist:
 an absence of any external conditions that would prevent the performer from achieving the goal
state for the target (this means that any required tools, equipment, information and job aids are
available; the performer has access to them, and the working environment supports the
expected performance, which is to say, there are no overwhelming impediments).
Applying the Requisite Conditions of Performance to the Reject Rate Problem
The preceding conditions were used as a guide to investigate the reject rate and this is what was found:
 Nowhere were the registrants advised of the consequences to them of submitting a registration
form that could not be processed upon receipt, nor were they advised as to what constituted a
“clean and complete” registration form. Many if not most of them did not hold the goal of
producing a “clean and complete” registration form.
 Absent criteria for “clean and complete,” there was no way the registrants could themselves tell
whether or not they had correctly completed the form; consequently, many of them completed
the form in ways that satisfied themselves but not the testing company.
 The registrants had the name of an institution and needed to locate the identifying code used by
the testing company. Their expected performance called for a code list that was organized
alphabetically, by institution name.
 At the testing company, the resolution clerks had a code number and needed to determine the
name of the associated institution. Consequently, they used a numerically organized code list.
 The code list provided to the registrants at the time was the same numerically organized code
list used by the testing company. Some registrants would persist in hunting through this list
until they found the institution and its associated code but many others apparently gave up and
entered any old code or left the code field blank. (This is an excellent example of one of those
“complicating conditions” that can interfere with hitting a performance target.)
The solution to the reject rate problem was simple enough and easily implemented. The instructions for
filling out the form were rewritten and they included a sample registration form that was correctly filled
out. A section was added drawing the registrants’ attention to the consequences of not completing the
form properly. And an alphabetically organized code list was provided.
As can be seen from the before and after pie charts in Figure 5 the improvement in the reject rate was
dramatic.

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Figure 5 - Before and After Registration Reject Rate
In this case, what started out as an operational problem quickly led to a problem of human performance
and the solution to that problem also solved the operational problem. The expensive rework was all but
eliminated, the complaint levels went way down, and the testing program client was once again satisfied
with the way things were going. At the heart of it all was some investigative work that ties directly to
the framework of the Target Model.
Review of the Target Model
Now it is time to review the Target Model from a conceptual perspective. Let’s begin our review of the
Target Model with a key element in the model – the performance target – represented by the bulls-eye
in Figures 1 and 4.
A performance target is a variable in “the world out there” that the performer wants to be in some
specified state. Moreover, the performer wants to keep it in that specified state. In other words, a
performance target is a variable that someone wants to control. In the case we just examined, the
target variable was a registration form.
Some other examples of possible performance targets follow. The manager of a particular production
operation might want to achieve and maintain an error rate that is at or below three parts per million.
In this case, the performance target is a variable identified as “error rate.” (We’ll come back to that “at
or below three parts per million” in a moment.) A sales rep might want to control number of sales or
closing rate or some such variable. Someone in Marketing might be concerned about variables like
share of market or account penetration. Someone in Finance might be concerned about a variable like
liquidity as measured by the current ratio or the level of cash reserves or the debt-to-equity ratio.
The Target Model is a model of human behavior and performance so let’s focus for a moment on the
human being in the model – a performer – someone with a genuine interest in hitting the performance
target, whatever it might be. (In case you think the human figure in the Target Model is a male, it is not.
It is a woman with short hair wearing slacks. However, those of you who are male are free to think of it
as a male.) The key points to be made here are that the performer is focused on the right target, knows

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the goal state for it, and genuinely wants to hit the target. As we saw in the reject rate example, the
registrants’ commitment to producing the clean-and-complete registration form wanted by the testing
company was questionable. More important, there was serious doubt as to whether or not they could
tell if they had done so. Perhaps the most important point to be made here is that performers always
control their own performance, no matter how it turns out.
Now let’s go back and re-introduce that “at or below three parts per million” figure we mentioned
earlier. That is a goal, the intended or desired state of the target variable, which in this case is “error
rate.” There is then a difference between a target variable (e.g., error rate) and the intended or goal
state for that target (e.g., at or below three parts per million). In the case we examined, the testing
company had a goal state for the registration form of “clean-and-complete” but that goal was not
initially shared by or attainable by the registrants.
To further illustrate the important distinction between a target variable and the goal state for that
variable, consider once again the familiar example of driving to work. One target variable in this action
is the position of your car in relation to its lane. A goal state for the position of your car might be
defined as “staying between the lines.” Another target variable might be your time of arrival at work,
the goal state for which we’ll define simply as “on time.” The speed of your car is also likely a target and
the goal state for it might be stated as “obey the posted speed limit.”
With target and goal state out of the way, let’s move on to another element of the model – the actual or
perceived state of the target.
Assume the performer wants to hit the performance target, whatever it is, however it has been defined,
and whatever the goal state for it might be. In addition to having some specified goal state for the
target variable, the performer also has a perception of the target variable’s current or actual state.
Maybe the currently perceived state of that error rate mentioned earlier is 15 parts per million instead
of three parts per million. In the case of the registrants, their perception of the actual state of the form
was an evolving one, beginning with a blank form and evolving to whatever state they considered
complete – or at least complete enough to submit. As you drive to work you are aware of your car’s
position, its speed, and your estimate of how much more time it will take to arrive at work. You are
monitoring your own performance.
At this point in our review of the Target Model, we have a performer who holds a goal state for some
target variable and who also has a perception of the current or actual state of that variable. To this we
add the actions of the performer; more specifically, actions aimed at affecting the target. To maintain
control over your car’s position, its speed, and your time of arrival at work, you steer, brake, speed up,
slow down and engage in other driving behaviors. Perhaps the manager of the operation in question
introduces a Six Sigma program as a means of lowering an unacceptably high error rate or perhaps a
new process is installed or the existing one is refined. In any case, these actions result from comparing
the goal state for the target with its perceived state. The registrants started with a blank form and
began filling it out. They kept on doing so until their perception of the form was that it was good
enough to submit (which was a far cry from what the testing company wanted).

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Let’s recap to this point.
 A performer wants to bring some targeted variable to a particular state and keep it there.
 That “particular state” is reflected in the goal for the target – the goal state.
 The performer also perceives the actual or current state of the target.
 The performer compares the perceived state of the target with the goal state for the target and,
if there is an unacceptable difference between the two, the performer acts so as to bring the
perceived state of the target into alignment or correspondence with the goal state.
 If there is no unacceptable difference between the goal state and the perceived state of the
target no corrective action on the part of the performer is required.
Sounds simple enough – and it would be if it weren’t for complicating conditions, other actors and
factors that also affect the target variable and thus might interfere with the performer’s control of the
target variable. So let’s review one last factor – conditions that complicate hitting the target.
We don’t live or work in a vacuum and precious few of us work under tightly controlled, standardized
conditions. For most of us, there are other actors and factors at work that affect the same variables we
wish to control. These complicating conditions can interfere with hitting our performance targets.
Let’s go back to the driving to work example and the target of staying in our lane. Gusts of wind can
interfere with that. So can other drivers and so can road conditions. So could a flat tire if that occurs.
We maintain control by varying our behavior in response to varying conditions; we turn the wheel and
counter the effects of the wind. Unless these complicating conditions overwhelm us we are able to
compensate for them and keep things the way we want them to be. We hit our targets – we stay within
our lane, we keep the error rate where we want it, we make our budget, we hit our sales targets, and
we even show up for work on time.
But, on occasion, these other actors and factors can overwhelm our ability to hit our targets. A reckless
driver forces us out of our lane; shoddy materials from a new supplier drives the error rate through the
roof; and a competitor’s new product is out-performing ours in the marketplace. Control is neither
perfect nor guaranteed. As we saw in our registration form example, the numerically organized code list
hampered the registrants’ ability to provide valid institutional codes.
Some Parting Comments: A Continuum of Performance
Driving a car and filling out a form are hardly what most people would agree are especially difficult or
complicated performances and I am quick to agree. However, in this paper these simple examples serve
primarily to illustrate the Target Model in action and that they do that quite well. But, let’s acknowledge
that there is indeed a continuum of performance complexity or difficulty.
At one end of this continuum are those situations in which the performance of interest consists of the
direct and immediate effects of performer behavior (e.g., correctly completing the registration form or
keeping your car in its lane). These are comparatively simple performances.

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At the other end of the continuum are those situations in which the performance of interest is defined
by the indirect and delayed effects of the performer’s behavior (e.g., the manager who is attempting to
bring the error rate for a process to some pre-determined, acceptable level or perhaps that manager’s
boss who is attempting to turn around a poorly performing division). There are intervening variables
and time delays involved, making these much more complicated performances.
We can call the first kind of performance Proximate, indicating results that are close by in space and
time; and we can call the second kind of performance Ultimate, indicating the presence of intervening
variables and time delays, and acknowledging that the results of interest might be far removed in space
and time (see Figure 6).
Proximate Ultimate
Performance Continuum
Effects of Interest are
Direct and Immediate
Effects of Interest are
Indirect and Delayed
Figure 6 - Continuum of Performance
The second kind of performance still hinges on performer behavior; that is, it requires that the
performer directly and immediately affect things in what some call “the here and now” – however, this
is done with the purpose of affecting things “over there” later on. The success of this second kind of
performance introduces another condition of performance; namely, the ability to identify the linkages
between actions “over here” and results “over there.” In turn, identifying these linkages hinges on
being able to map or envision the structure of the situation in which the direct and immediate effects of
behavior will set in motion a sequence of effects that ripples through the structure of the situation and,
at some other place and time, has the desired effect on the target variable. This structure represents a
“performance architecture” – a network of variables and relationships the performer must manage and
manipulate so as to produce desired results.
Regarding this second kind of performance the performer is perhaps best viewed as an interventionist,
as someone who changes things with some purpose or outcome in mind. But, in the end, whether
performance centers on immediate or downstream results, all performance begins with the actions of
the performer and the Target Model applies to both kinds of performance.
Summary & Conclusions
 Performance occurs – or doesn’t – as the result of hitting – or missing – a performance target.

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 A performance target is some variable the performer intends bringing to and keeping in some
preferred state.
 The target variable might be nearby and directly accessible or far removed and accessible via
indirect avenues.
 The “preferred state” for a target variable is known as the goal state.
 Perceptions of the current or actual state of the target inform the performer as to progress
toward and success in achieving the goal state (i.e., hitting the performance target) – whether
the target is nearby or far distant.
 Other conditions affect the targets and performers must vary their actions so as to compensate
for their effects and to keep the target variable under control. Unless these other conditions
overwhelm them, performers are able to maintain control.
The Target Model presented in this paper serves as a useful guide in examining and addressing issues of
human behavior and performance in the workplace. Managers and others with an interest in improving
human performance would do well to incorporate the Target Model into their toolkits.
Further Reading & Contact Information
The Target Model has solid scientific and research foundations; it is based on Perceptual Control Theory
(PCT) as developed and articulated by William T. Powers and below are listed some key references.
 Behavior: The Control of Perception, 2nd
Edition (2005), William T. Powers. Benchmark
Publications.
 Making Sense of Behavior: The Meaning of Control (1998), Powers, W. T. Benchmark
Publications.
 “A Perceptual Control Theory Primer,” Nickols, F. W. Powers has reviewed this primer and
thinks it is an accurate view of PCT.
Those interested in knowing more about “performance architecture” can refer to my paper bearing that
title by clicking here. My other articles can be found on the web at www.skullworks.com . I can be
reached via email at fred@nickols.us.