An Information System Approach to the Analysis of Job Design
In many structured job designs, job content can be identified with the complexity of human-machine interfaces, with the computational or logical complexity of decision routines, and with interactive complexities associating with organizational or command structures. We have argued elsewhere(2) that such complexities may be measured or evaluated using established results in the theory of automata(3–5) and methods developed by us(6,7) to yield a practically small set of complexity parameters and class indices descriptive of an entire job and/or routines and tasks within the job. In turn, it is hypothesized that complexity parameters and indices so derived can be used to predict behavioral responses to the job. Appropriate job content can affect attitudes toward work generally, improve performance specifically, and may assist in developing responsibility. In an industrial setting, these effects would be partial determinants of general productivity and individual satisfaction. They may also associate with performance reliability for an operation or system and with the developments of responsibility, efficiency, and sustainable interest for individuals and groups.
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