Integrative Complexity: An Approach to Individuals and Groups as Information- Processing Systems Author(s): Michael J. Driver and Siegfried Streufert Source: Administrative Science Quarterly , Jun., 1969, Vol. 14, No. 2, Laboratory Studies of Experimental Organizations (Jun., 1969), pp. 272-285 Published by: Sage Publications, Inc. on behalf of the Johnson Graduate School of Management, Cornell University Stable URL: https://www.jstor.org/stable/2391105 JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact
[email protected]. Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at https://about.jstor.org/terms Sage Publications, Inc. and Johnson Graduate School of Management, Cornell University are collaborating with JSTOR to digitize, preserve and extend access to Administrative Science Quarterly This content downloaded from 67.242.27.73 on Thu, 17 Dec 2020 18:25:09 UTC All use subject to https://about.jstor.org/terms Michael J. Driver and Sieyfried Streufert Integrative Complexity: An Approach to Individuals and Groups as Information-processing Systems 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.