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behaviourally anchored items across 6 dimensions accounted for 86% of variance in
performance. In summary, these results suggest that to understand and subsequently
enhance command performance, one must focus on the team behaviour.
1.5 Towards a Predictive Model of Performance: The Behaviour
Systems Approach
It should also be noted that this report is part of a longer-term research project that
aims to develop a theoretical framework for predicting performance in C2. The
Behaviour Systems approach adopts an ecological framework and has the advantage
of embedding behaviour within a complex system. When dealing with a complex
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sociotechnical system such as an Army HQ, such an approach is vital in ensuring
valid conclusions are drawn.
It is beyond the scope of this report to provide a detailed overview of the Behaviour
Systems approach. Instead, the following information is provided to give the reader
a general sense of the approach, while the reader is referred to Timberlake (1998) for
greater detail.
By adopting an ecological framework, the analyst seeks to set performance within
the functional context in which it resides. The approach assumes that the subject
comes equipped with organised stimulus sensitivities, processing capacities,
response structures and integrative states that are designed to produce adaptive
behaviour in particular environments. Learning and changes in behaviour occur as
modifications in the operation, inclusion and linkage of different mechanisms. An
ecological analysis of behaviour considers how, where and to what end the operation
of a functioning system is modified by experience. The subject must interpret the
altered environmental stimuli within an evolved and developed functional
framework.
The Behaviour Systems approach attempts to formalise this process into a predictive
model. A more developed model incorporates data from behavioural observations,
physiological characteristics, and experimental manipulations. To begin with,
however, the analyst should focus on developing a baseline of the structure and
processes of the system. In fact, to be able to generate predictions, it is necessary to
have a baseline model of the functioning system. Timberlake (1998) describes the
following steps in developing a predictive model of behaviour:
1. Pose a question about how the subject works in a functional context. For
example, what are the mechanisms involved in the decision making process of
the CO of a Battalion HQ?
2. Assemble behavioural observations into a preliminary model of the structure and
processes that the subject brings to the relevant situation. The purpose of this
model is to provide information about the initial characteristics that the subject
brings to the circumstances under consideration. This model is unlikely to be
perfect, but it is necessary to start with a model of appropriate complexity that
allows analysis and testing in multiple ways. A simple model can limit
consideration of potentially important determinants, while starting off at too
complex a level can make analysis impossible.
3. The third step is to integrate the observations into an initial causal behaviour
system.
4. Step four involves designing an experiment to clarify/test a prediction of the
simple causal system. For example, what effect does “X” have on the decision
making process of the CO? Rather than having to engage the entire system, such
a prediction can be tested in a micro-world simulation of C2, such as Networked
Fire Chief (Thomas, 1999), or the Tactical Land C4I Assessment Capability
(TLCAC) ( Bowden, Gaertner & Williams, 2000).
5. The fifth step is to interpret the outcome of the experiment as the result of the
interaction of the pre-existing causal system with the experimental environment,
rather than as a simple result of the independent variable.
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6. The sixth step is an attempt to store the results of the manipulations in the form
of additions or corrections to the model.
These stages need to be repeated until the model is demonstrated to have reliability
and validity. Once this has occurred, it can be employed to guide such areas as
system design and processes for training. The advantage, as was mentioned, is that
it grounds predictions in an ecologically valid framework.
2. Aims and Objectives
As was mentioned, one aim of this report is to refine methodologies that can be used
to evaluate performance in Army command teams. Specific objectives include the
following:
• Refine observational methodologies designed to assess the implementation and
useability of digitised command support systems in HQ.
　
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