Integrative Complexity: an Approach to Individuals and Groups As Information- Processing Systems Author(S): Michael J
Total Page：16
File Type：pdf, Size：1020Kb
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. 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-malking functions are separate and not synchronous. One of the primary objectives of management bit means the reduction of uncertainty by one science is to understand and control the group half. Although this technical view has yielded processes in highly productive organizations. In some exciting results (Garner, 1962), it seems this discussion productivity is viewed as strongly too restrictive and has led some (Schroder, dependent on the information-processing char- Driver, and Streufert, 1967) to redefine infor- acteristics of individuals, groups, and organiza- mation as anything that alters subjective (or tions. Information can be regarded as the objective) probabilities or utilities. Thus in thread out of which all production decisions this discussion, any environmental change which are woven. The particular way in which organi- could cause a shift in expectations or evalua- zations search for information and handle it is tions will be termed information, regardless of therefore of great importance in developing a whether certainty is decreased, increased or is model for productivity. unaffected, so long as utility (i.e., value times This discussion outlines a new model of probability) is altered. information-processing in systems of all types- A second and crucial concept is system. The individuals, groups, and organizations.1 Some use of the term is based on von Bertalanffy recent experiments testing this model are pre- (1952) and general systems theory (Miller, sented and discussed, and a further extension 1965). Von Bertalanffy, in his important of the model is then proposed. treatise on biology, defined a system as a "complex of elements in mutual interaction." THE INFORMATION-PROCESSING He proposed that all sets of organized elements APPROACH TO PRODUCTIVITY follow certain general rules. These rules he Definitions termed general systems theory. In this dis- cussion, individuals, groups, and organizations First, it is useful to define key concepts. Of are regarded as systems which possess certain central concern is information. In Shannon & general characteristics in common. It is recog- Weaver's (1949) information theory, this term nized that there are serious dangers in making has come to mean something which reduces unwarranted analogies between systems of uncertainty or entropy in a receiver. Informa- different levels of analysis, for biological, per- tion has been quantified as "bits," where each sonality, and social systems differ in many significant ways. Nevertheless, for present 1 This paper is based on an invited address presented at the Institute of Management Science meeting, Feb- purposes, individual or personality and social ruary 1966, Dallas, Texas. systems are treated as having certain basic 272 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 Driver and Streufert: INFORMATION-PROCESSING SYSTEMS 273 similarities in the organization and structure of input to the complexity or organization of their parts, and are therefore expected to re- biological and social systems. He argued that spond in basically similar fashion to certain the more complex the input, the more complex aspects of the environment. the system. In information-processing terms, A third basic concept is input. Most gener- this means that the complexity of the input ally, input is any condition initially external directly determines the organizational capacity to a system which upon contact with a system of a system to process information and produce produces observable change in the organization complex outputs. or behavior of a system. Individuals, groups, Some of the earlier research of Miller (1960) and organizations are considered systems which on information-processing in individuals and operate to process inputs into outputs. The groups seems to offer partial support for Spen- inputs which are of concern here are not defined cer's view. Miller found that as he increased as energy units as in physical systems, but the complexity (i.e., number of units per time rather are defined as information units. An period and amount of information per unit) of information unit is any discrete condition of information input, both individuals and groups the non-system environment capable of altering increased information-handling capacity linearly the utility or probability patterns in an indi- up to an upper limit or asymptotic level. At the vidual or group. The nature of this information asymptotic level, however, the systems began unit in any setting is arbitrary. One could to omit inputs or let them accumulate in a define units as messages, sentences, words, or delayed action "list"-a process termed "queu- the presence or absence of a colored light de- ing." It seemed, therefore, that each system pending on the refinement of measures of had an upper limit for input beyond which change in the utilities or probabilities of the Spencer's linear formulation was not valid. Some information-processing systems involved. In- interesting work of Chapman, Kennedy, Newell, dividuals, groups and organizations are con- and Biel (1959) on a simulated air-defense ceived as information-processing systems, which system showed a similar, general rise in informa- respond in characteristic ways to variations in tion-processing capacity to an asymptotic level. information inputs as they produce outputs. More recently, Miller (1962) found that if The last concept is concerned with a crucial he continued to raise the complexity of the aspect of input, the sheer complexity of the input beyond the previously obtained asymp- information. This is of central importance in totic level, the utilization of information began understanding systematic responses to varia- to decline in both quality and quantity. Chap- tions in input. Complexity of input can be man et al. (1959) found a similar result in defined as the number of utility or probability perception of input, if not in final output. This changes that an input can potentially evoke in led Miller to consider some data which ranges a maximally open system as a result of the from physiological systems to social organiza- informational content of the input. A "maxi- tion systems. For instance, Granit and Philips mally open" system is one in which there are (1956) found that in the Purkinje cell of the no mechanisms blocking the perception of any retina, the response rate first increases with implications in the input. As will be seen, many input, then decreases. Jung and Baumgartner information-processing systems "close out" in- (1955) found a similar curvilinear relation be- formation and do not cope with the full com- tween input complexity and the output of the plexity of the input. optical cortex in the brain. At the individual person level, Quastler and Wulff (1955) found Linear and Curvilinear Relationships that if input complexity (i.e. number of notes Between Input and Output per bar) was steadily increased, the quality of There have been a number of attempts to musical output in accomplished pianists first develop models relating the complexity of the improved, but then deteriorated. Anderson and input to the functioning of human information- Fitts (1958) reported that three input dimen- processing systems. One of the simplest and sions were better than two or four in inducing earliest formulations