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A general systems model for the analysis of organizational change
Author(s) -
Toronto Robert S.
Publication year - 1975
Publication title -
behavioral science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.371
H-Index - 45
eISSN - 1099-1743
pISSN - 0005-7940
DOI - 10.1002/bs.3830200302
Subject(s) - antecedent (behavioral psychology) , proposition , causality (physics) , assertion , causal inference , computer science , inference , set (abstract data type) , causal model , causation , econometrics , cognitive psychology , artificial intelligence , psychology , epistemology , social psychology , mathematics , statistics , philosophy , physics , quantum mechanics , programming language
A model of organizations was developed using the concepts of general systems theory, GST. The model provides a set of logical assertions about causality in organizations and a framework for the detection and analysis of organizational change. Four major propositions derive directly from the model: (1) the level proposition, (2) the constraint proposition, (3) the permanent change proposition and (4) the predominance proposition. The propositions give rise to specific organizational hypotheses There are two major limitations attendant to the systemic hypotheses testing of organizational change. First, dynamic production organizations cannot tolerate experimental controls, making the question of causation more difficult than in traditional designs. Second, testing hypotheses stemming from a general systems model, the basis of which is holistic reasoning, requires the investigator to take an almost clinical approach in his interpretation. This renders his conclusions vulnerable to judgmental error and experimenter bias. Three hypotheses were tested using the methodological orientation of GST—holistic reasoning—to demonstrate three systemic methods of research. (1) The method of weak causal inference makes an assertion of causality from trends in a set of data to a series of antecedent events. (2) The method of strong causal inference compares different kinds of data from different kinds of systems and infers causality to the change interventions. (3) The method of parallel trends compares long interval data, survey indices, with short interval data, business indices, infers a causal relationship between their trends, and infers causality to antecedent events.