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Integrative Complexity: An Approach to Individuals and Groups as Information-Processing Systems

Michael J. Driver and Siegfried Streufert
Administrative Science Quarterly
Vol. 14, No. 2, Laboratory Studies of Experimental Organizations (Jun., 1969), pp. 272-285
DOI: 10.2307/2391105
Stable URL: http://www.jstor.org/stable/2391105
Page Count: 14
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Integrative Complexity: An Approach to Individuals and Groups as Information-Processing Systems
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Abstract

Individuals and groups can be viewed as information-processing systems which respond in a curvilinear fashion to three components of input load: complexity of information, noxity (unpleasantness) and eucity (pleasantness). An optimal input load is postulated, at which each system is expected to achieve maximum complexity in information-processing. At similar input levels, some systems are expected to show more complex information-processing than other systems. Research is reviewed which suggests that the model holds for perception, information search, decision-making, and innovation. When productivity criteria are associated with complex information-processing, the model predicts productivity. A more complex phasic theory is then advanced, which argues that perceptual and decision-making junctions are separate and not synchronous.

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