SYNCHRONIZED ANALYSIS MODEL (SAM):
LINKING GILBERT'S BEHAVIOR
ENGINEERING MODEL WITH
ENVIRONMENTAL ANALYSIS MODELS
Anthony Marker
Performance technology has many analysis models and selecting which to use can be challenging.
Arguably, the most prestigious and most used HPT model—a cause analysis model—is Gilbert’s
behavior engineering model (BEM). However, even this powerful cause analysis model has its limits;
although it does examine environmental symptoms in general, it doesn’t account for the organizational or environmental levels at which performance problems occur. For data on such levels the
practitioner may turn to environmental analysis models such as those developed by Kaufman,
Langdon, Rummler & Brache, or Rothwell. But the practitioner who uses both a cause analysis model
and an environmental analysis model will be left with two sets of data that do not easily integrate
into a useful guide to action. The model presented here—the synchronized analysis model (SAM)—
is an effort to remedy this situation. By integrated the cause analysis model of Gilbert’s BEM with
levels derived from the environmental analysis models, the SAM offers the practitioner an enhanced
tool for resolving performance problems.
PERFORMANCE TECHNOLOGY HAS MANY analysis
models, and selecting which to use can be challenging.
Arguably, the most prestigious and most used human
performance technology (HPT) model—a cause analysis
model—is Gilbert’s behavior engineering model (BEM).
However, even this model could do a better job at helping practitioners identify relationships among multiple
causes and prioritize interventions. Even though the
BEM is a powerful tool for collecting data on individual
worker behaviors and general organizational factors, it
does not take into account the environmental levels at
which performance problems may be occurring. For
these kinds of data, practitioners may turn to another
class of models—the environmental analysis models. But
the practitioner who uses both a cause analysis and an
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environmental analysis model will be left with two sets of
data, which can be difficult to integrate into a useful
guide to action. The model presented here—the synchronized analysis model—is an effort to remedy this situation. First, Gilbert’s BEM is briefly described. Then the
organizational levels of four leading environmental
analysis models—Kaufman’s organizational elements
model (1991), Rummler and Brache’s organizational
white space (1990), Langdon’s language of work (1999),
and Rothwell’s four environments of human performance (1996)—are examined. Finally, environmental levels are integrated into Gilbert’s cause analysis model. The
resulting synchronized analysis model (SAM) can
enhance the practitioner’s efficiency and effectiveness in
solving performance problems.
TABLE 1
ENVIRONMENT
PERSON
GILBERT’S BEHAVIOR ENGINEERING MODEL
INFORMATION
INSTRUMENTATION
MOTIVATION
• Data
• Support
• Consequences
• Feedback
• Tools
• Rewards
• Resources
• Incentives
• Capacity
• Motives
• Knowledge
• Skills
Source: Adapted from Gilbert, 1978/1996, p. 88.
GILBERT’S BEHAVIOR ENGINEERING
MODEL
Although the field of human performance technology has
many analysis models, perhaps the most prestigious and
enduring of them all is Thomas Gilbert’s cause analysis
model—the behavior engineering model (BEM) (see
Table 1). First offered in 1978 in Human Competence, the
model gained currency when, in 1996, ISPI reissued
Gilbert’s classic book. In this “tribute edition,” forty-seven
outstanding HPT professionals offered declarations of
praise for Gilbert’s contribution. Although other cause
analysis models exist, Gilbert’s BEM is the gold standard
and hence the basic model used here.
The BEM separates performance problems into two
levels: the person and the environment. The person level
consists of performance-supporting factors within the
individual; the environment level consists of performance-supporting factors within the work environment.
The BEM further classifies causes into three categories of
factors that influence performance: information, instrumentation, and motivation. The resulting matrix (Levels
x Influencing Factors) has six cells.
Although the BEM usefully classifies the nature of a
cause (information, instrumentation, or motivation), it
locates it only in the person or—in a general way—in the
environment. When the cause does lie in the environment,
the BEM does not specify where in the environment it
exists. This omission can be costly. For example, many
organizations focus a great deal of effort and money on
improving the performance of the individual worker when
it is in fact the environment that accounts for the lion’s
share of barriers to performance. As Rummler and Brache
(1990) say, “If you pit a good performer against a bad system, the system will win almost every time” (p. 13). Given
the influence the environment has on performance, greater
specificity in this domain is needed. For that information
the practitioner may turn to one of the environmental
analysis models. It should be noted that the models depicted here (with the exception of the SAM) are simplified versions. Although they do not express each original author’s
entire model, they are, I believe, accurate in how they portray the model aspects discussed in this article.
FOUR ENVIRONMENTAL ANALYSIS
MODELS
Among the well-known environmental analysis models
are Kaufman’s organizational elements model (1991),
Rummler and Brache’s organizational white space (1990),
Langdon’s language of work (1999), and Rothwell’s four
environments of human performance (1996). Although
each of these models takes a very different approach to
categorizing causes, needs, or means of performance
problems (and that is not the focus of this article), there
is a significant overlap in the way all four models divide
performance problems into vertical stratifications, or levels, thus addressing the reality that causes of organizational problems range from smaller localized issues (usually at
the level of the individual worker) to larger global issues
(often at the level of the external environment or society
as a whole) (see Figure 1).
The diagram in Figure 1 shows that although the four
models differ somewhat in their specifics—Rummler and
Brache, Langdon, and Rothwell tend to focus on means,
whereas Kaufman focuses on ends—they all propose
hierarchical levels of organizational performance, and as
a careful examination of Figure 1 clearly reveals, these levels have much in common.
As noted earlier, these models offer considerably more
environmental detail than Gilbert’s BEM does. It is the
merger of these models, the expanded view of environmental details, that the proposed synchronized analysis
model takes advantage of. In this way the SAM provides
the practitioner with an improved analysis tool.
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FIGURE 1. Comparing the Levels of Four Analysis Models.
FIGURE 2. The Synchronized Analysis Model (SAM).
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TABLE 2
ENVIRONMENT
GILBERT’S BEM WITH CASE STUDY DATA
INFORMATION
INSTRUMENTATION
MOTIVATION
• A culture valuing speed
over safety
• Dirty work areas
• Increased customer
demands for products
• Additional random
sampling for quality
control
• Poor illumination
• Dirty equipment
• Inadequate rewards
and consequences for
safe behavior
• Increased production
line speed
• Management’s use of
overtime instead of hiring
additional workers
PERSON
(The client’s assumed
cause of the problem
as a knowledge deficit)
THE SYNCHRONIZED ANALYSIS MODEL
In the proposed synchronized analysis model (see Figure 2),
the first and lowest analysis level proposed is that of the
worker. (Rothwell’s term, worker, is chosen over individual,
because it implies active involvement.) At this level the SAM
is identical to the BEM’s original repertoire of personal
behaviors. It includes knowledge, skills, capacity, and motives.
The SAM then expands the BEM’s general environment
level into three levels. The first is the job level. The next—
still within Gilbert’s environment—is the organizational
level. These first two levels are still inside the organization. The last level deals with the external environment. At
this level the focus is on causes that originate outside the
organization’s boundaries: for example, those having to
do with stakeholders, competitors, regulatory agencies,
and society in general.
Note that each environmental level (see Figure 2) still
uses the BEM’s classes of causes (information, instrumentation, and motivation) and the BEM’s breakdown
of factors within each class (the information class still
contains data and feedback; the instrumentation class
contains support, tools, and resources; and the motivation class contains motives), but—with the SAM’s ability
to discriminate at three environmental levels—causes
can now be assigned with precision to the level at which
they actually exist and at which they can be influenced.
For example, environmental information might include
individual production numbers, corporate production
goals, and OSHA industry regulations. In the BEM these
data would all be lumped into one environment-information cell. In the SAM they can be parceled out: indi-
• Workers choosing not to
wear required safety
gear, despite possible fine
• Older age of some workers
• Fatigue caused by working
overtime
vidual production numbers can be assigned to the job
level, where an appropriate intervention can be designed;
corporate production goals can be assigned to the organizational level, where, if it is deemed worthwhile, they
can be revised; and OSHA industry regulations can be
assigned to the external level, where, probably, little can
be done—unless one finds that the cause is not the regulations themselves. For example, one might find that the
regulations have been communicated poorly, in which
case this cause would move into a cell within the organization’s boundaries and the communication problem
could then be addressed.
SAM AT WORK: A CASE STUDY
Advanced graduate students in a needs analysis course were
able to field-test the SAM by applying it to a case in The ID
Casebook (Sebok & Dorin, 2003). The case follows a newly
hired instructional designer conducting a needs analysis at
an asphalt shingle factory that is experiencing an increase
in accidents. The client at the factory is pushing for a training intervention, but to a performance-oriented analyst, a
review of the data suggests that any number of causes
might be responsible for the increase in accidents.
The student analysts applied Gilbert’s original BEM
model. Using a modified cause-and-effect, or fishbone,
diagram, they populated the BEM with data and then
identified these potential causal factors:
• Dirty work areas
• Dirty equipment
• Poor illumination
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FIGURE 3. SAM with Case Study Data.
• Additional random sampling for quality control
requiring additional time
• Increased production line speed
• Increasing customer demands for products
• Workers choosing not to wear required safety gear,
despite possible fine
• Older age of some workers
• Fatigue caused by working overtime
• Inadequate rewards and consequences for safe behavior
The resulting matrix is shown in Table 2. As expected,
Gilbert’s BEM separates environmental causes from individual causes but does not differentiate the locations of
those causes within the environment.
Then the analysts applied the SAM and reclassified the
data. As seen in Figure 3, breaking down the BEM’s environment level allows an analyst to make more direct links
between causes and specific environmental levels. For
example, in the original BEM analysis (Table 2),
“increased customer demands for products” and “inadequate rewards and consequences for safe behavior” are
lumped together. The implication is that they are both
equal candidates for interventions by the practitioner.
However, the SAM analysis assigns the two causes to spe-
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cific environmental levels (Figure 3). This allows the practitioner to discriminate between causes that are likely candidates for effective interventions and causes that may be
realistically beyond the reach of manageable interventions. In this case it would be far easier and more beneficial for the company to rework its reward and consequence structure than to try to curtail customer demands
for its products. It is, however, worth noting that not all
causes in the external environment are beyond organizational influence. For example, note the significant success
some organizations and industries have had in changing
legislation via political lobbying efforts.
The SAM allows the practitioner to retain Gilbert’s powerful framework for classifying causes while he or she also
identifies links between causes and environmental levels.
This added discrimination provides practitioners with several advantages. First, it allows their recommendations to
be tied directly to the organizational structure. It also
allows them to target additional data collection to the most
appropriate organizational level. And finally, and most
important, it allows them to target interventions to the
level of the organization where they have the greatest likelihood of success. In this way the SAM can significantly
improve the efficiency and effectiveness of HPT practice.
FIGURE 4. SAM with Cause Relationships.
GETTING TO ROOT CAUSES: KAIZEN
TQC
Even though the SAM framework helps the practitioner
identify where surface causes occur, it does not by itself
identify the root causes. To uncover the deeper causes and
to relate them to systemic problems, the practitioner can
employ an approach used in a related field. The Kaizen
total quality control process (Imai, 1986) recommends
that—like an inquisitive child—the practitioner ask the
question why five times. Imai describes the rationale
behind the five whys process:
TQC encourages people to go back to the previous
process on the production line to seek out a problem’s
causes. Improvement requires that we be aware of
what comes from the previous process. In the factory,
problem solvers are told to ask “why” not once but five
times. Often the first answer to the problem is not the
root cause. Asking why several times will dig out several causes, one of which is usually the root cause
[Imai, 1986, p. 50].
Returning to the asphalt factory case study, a practitioner might initially decide that the surface cause for
accidents is that workers are not using their safety gear.
The application of SAM reveals alternative causes and
also shows at what level in the organization those causes
exist. Because it is impractical to implement interventions for all these identified causes, the practitioner looks
for the interventions that will have the biggest payoff.
Regardless of other contributing causes, one of the most
critical causes of accidents is that workers are not wearing
their safety gear. Because this is a critical cause, a practitioner may choose to examine it further by applying the
five whys technique. The results might yield something
like the following exchange:
Question 1: Why are the workers not wearing their
safety gear when our data indicate they know better?
Answer 1: Because wearing safety gear slows down
their work.
Question 2: Why is a slightly slower work speed seen as
a problem?
Answer 2: (a) Because there are increased random
sampling demands due to the new quality control system, and (b) because workers are working more overtime than they like.
Question 3: Why do workers have to work more overtime?
Answer 3: Because there aren’t enough workers to
cover the increased production needs.
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Question 4: Why aren’t there enough workers to meet
increased production needs?
Answer 4: Because the factory’s management prefers to
cover increased production demands with current staff
rather than taking on the additional resource burden
of new hires.
Question 5: Why have production demands increased?
Answer 5: Because of an increase in customer orders.
These answers can then be plotted on the SAM (see
Figure 4), using a numbering scheme, so that the practitioner can see the progression from symptoms (an increased
accident rate) to surface causes (workers not using safety
gear) to root causes (increased customer orders), and the
level at which those causes occur. The five whys technique
helps practitioners plot the chain of events from symptom
to root cause on the SAM to understand the system of interrelated causes. By plotting that progression, practitioners
can see the relationships among causes and design interventions at levels where those interventions are likely to have
the greatest impact. This ability is particularly important
because root causes do not necessarily reside in the same cell
as the surface causes arising from them. If a practitioner
were to design a performance intervention for a surface
cause, its effectiveness might be severely limited if the root
cause for the problem is to be found in a different SAM cell.
If practitioners in the case study company were to design an
intervention based solely on workers’ motivation to work
overtime, they would be ignoring the need for an intervention at the organizational level, an intervention that would
address how the company handles sustained increases in
customer demand. In essence they would be treating a
symptom rather than a root cause of the problem.
that might contribute to those problems and the level(s) in
the organization where they reside. The SAM allows practitioners to more easily merge, or synchronize, information
about causes with information about where those causes
reside, to merge environmental analysis with cause analysis.
It stretches Gilbert’s powerful BEM framework to more
accurately display the organizational level where each cause
for a performance problem is found. Most important, when
it has been populated by the five whys technique, the SAM
displays the relationships between causes. Practitioners can
use this additional information to design interventions that
will provide the greatest efficiency and effectiveness in
addressing the true root causes rather than just the symptoms of a performance problem.
References
Gilbert, T.F. (1996). Human competence: Engineering worthy
performance (Tribute ed.). Silver Spring, MD: International
Society for Performance Improvement. (Original work
published 1978)
Imai, M. (1986). Kaizen: The key to Japan’s competitive success.
New York: McGraw-Hill.
Kaufman, R. (1991). Strategic planning plus: An organizational
guide. Upper Saddle River, NJ: Scott Foresman.
Langdon, D. (1999). The language of work. In H. Stolovitch
& E. Keeps (Eds.), Handbook of human performance
technology: Improving individual and organizational
performance worldwide (2nd ed., pp. 260–280). San
Francisco: Jossey-Bass.
Rothwell, W. (1996). Beyond training and development:
State-of-the-art strategies for enhancing human performance. New York: AMACOM.
Rummler, G.A., & Brache, A.P. (1990). Improving perform-
SAM IN SUM
The basis of the synchronized analysis model is Gilbert’s
behavior engineering model, which continues to do a good
job of separating personal performance causes from environmental causes. Several environmental analysis models
provide useful ways of identifying the organizational factors
ance: How to manage the white space on the organizational chart. San Francisco: Jossey-Bass.
Sebok, M., & Dorin, W. (2003). Case study 8: Lynn Dorman. In
P. Ertmer & J. Quinn (Eds.), The ID casebook: Case studies
in instructional design (2nd ed., pp. 40–43). Upper Saddle
River, NJ: Pearson.
ANTHONY MARKER is an assistant professor in the Instructional and Performance Technology
Department at Boise State University and holds a PhD from Indiana University. He teaches graduate
courses in performance technology, needs assessment, evaluation, and instructional design. His
research interests include the current state of research in HPT, change management, nonprofit performance, and discovering ways for HPT practitioners to create and promote environmentally sustainable organizational interventions. He may be reached at [email protected].
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