Markus and Robey in their paper “Information Technology and Organizational Change: Causal Structure in Theory and Research” examine theories about why and how information technology affects organizational life in terms of their structures. Three dimensions of causal structure are considered, causal agency, logical structure, and level of analysis.
The causal structure of theoretical models comprises three dimensions: causal agency, logical structure, and level of analysis. Causal agency refers to beliefs about the nature of causality: whether external forces cause change (the technological imperative), whether people act purposefully to accomplish intended objectives (the organizational imperative) or whether change emerges from the interaction of people and events (the emergent perspective). Logical structure refers to the time span of theory (static versus dynamic) and to the hypothesized relationships between antecedents and outcomes: whether causes are related to outcomes in an invariant, necessary and sufficient relationship (variance models), or in a recipe of sufficient conditions occurring over time (process models). Level of analysis refers to the entities about which the theory poses concepts and relationships-individuals, collectives, or both. These three dimensions of causal structure are shown in Figure 1.
Causal agency refers to the analyst’s beliefs about the identity of the causal agent, the nature of causal action and the direction of causal influence among the elements in a theory. Building upon the work of (Pfeffer, 1982), the authors have identified three conceptions of causal agency in the literature on information technology and organizational change. They label these: the technological imperative, the organizational imperative and the emergent perspective. In the technological imperative, information technology is viewed as a cause of organizational change. In the organizational imperative, the motives and actions of the designers of information technologies are a cause of organizational change. In the emergent perspective, organizational change emerges from an unpredictable interaction between information technology and its human and organizational users. Each of these perspectives is discussed more fully below and summarized in Figure 2.
The essence of the technological imperative is conveyed by the word “impact”. This perspective views technology as an exogenous force which determines or strongly constrains the behavior of individuals and organizations.
Whereas the technological imperative argues that information technology constrains or determines human and organizational behavior, the organizational imperative assumes almost unlimited choice over technological options and almost unlimited control over the consequences. Information technology is the dependent variable in the organizational imperative, caused by the organization’s information processing needs and manager’s choices about how to satisfy them. The assumption of designer discretion stands in sharp contrast to the external determinism of the technological imperative. The organizational imperative assumes that systems designers can manage the impacts of information systems by attending to both technical and social concerns. This view is shared by management and organization theorists who see information technology as a tool for solving organizational problems. Empirical support for the organizational imperative is limited. The emergent perspective holds that the uses and consequences of information technology emerge unpredictably from complex social interactions. Central concepts in the emergent perspective are the role of the computing infrastructure, the interplay of conflicting objectives and preferences, and the operation of non-rational objectives and choice processes.
The emergent perspective admits greater complexity to the issue of causal agency and to the goal of predicting organizational changes associated with information technology. By refusing to acknowledge a dominant cause of change, emergent models differ qualitatively from the deterministic causal arguments of the two imperatives. Prediction in the emergent perspective requires detailed understanding of dynamic organizational processes in addition to knowledge about the intentions of actors and the features of information technology. This added complexity makes emergent models difficult to construct.
The three perspectives on causal agency presented here differ in their attributions of responsibility for the outcomes observed. These attributions imply that particular interventions will be more or less efficacious in producing or increasing the likelihood of desirable outcomes.
A second dimension of theoretical structure concerns the logical formulation ofthe theoretical argument. Variance theories are concerned with predicting levels’ of outcome from levels of contemporaneous predictor variables; variance theories are concerned with explaining how outcomes develop over time.
(Mohr, 1982) explains the difference between variance theories and process theories in terms of the hypothesized relationships between logical antecedents and outcomes. These are summarized in Figure 3. In variance theories, the precursor is posited as a necessary and sufficient condition for the outcome. In process theories, the precursor is assumed insufficient to “cause” the outcome, but is held to be merely necessary for it to occur. In general, necessary conditions alone cannot constitute a satisfactory theory; outcomes are (partially) predictable from a knowledge of process, not from the level of predictor variables. Variance theories differ from process theories in their assumptions about the relationship between antecedents and outcomes. Variance theories posit an invariant relationship between causes and effects when the contingent conditions obtain. Process theories assert that the outcome can happen only under these conditions, but that the outcome may also fail to happen. Variance and process theories also differ in their conceptualization of outcomes and precursors. In variance theories, these constructs are usually conceptualized as variables: entities which can take on a range of values. This practice allows the prediction of the full range of values of the outcome variable. In process theories, however, outcomes are not conceived as variables that can take on a range of values, but rather as discrete or discontinuous phenomena, that might be called “changes of state.” Process theories cannot be extended, as variance theories can, to explain or predict what happens when there is “more” of a precursor variable.
At first glance, it may appear that all imperative theories are variance theories and all emergent theories are process theories. However, both variance and process models are available to analysts from the perspective of either the technological or the organizational imperative.
The authors also discuss the advantages of process theories. They believe that process theories are useful precisely because, while recognizing and accepting the complexity of causal relationships, they do not abandon the goals of generalizability and prediction. By accepting a more limited definition of prediction, one in which the analyst is able to say only that the outcome is likely (but not certain) under some conditions and unlikely under others, process theorists may be able to accumulate and consolidate findings about the relationship between information technology and organizational change.
The specific theories and research studies discussed concern three different types of entities, or levels of analysis: individuals, organizations, and society. The debate about the appropriate level of analysis centers on two issues:problems of inference and ideological biases (Pfeffer, 1982). Problems of inference arise when concepts are defined and data are collected at levels of analysis inappropriate for the theoretical propositions being examined. Ideological biases originate in the orientations of different disciplinary groups; the customary division of levels of analysis into “macro-level” and “micro-level” theories reflect disciplinary boundaries, each with its favored research questions, acceptable methodologies, and conventions for reporting results. The authors, in contrast to their caution against mixing process and variance theory, they believe that mixing levels of analysis may be useful in research and theory on information technology and organizational change. While the mixed-level strategy preserves macro-level concepts, it grounds these concepts in individual purposes and behavior and so remains “methodologically individualist”. Choice of any level is subject to criticism by proponents of the others, but researchers will be better able to respond to these criticisms after deliberate and thoughtful choice of the appropriate level of analysis for their own work.
By carefully considering each of the dimensions of causal structure discussed in this paper, researcher should be able to construct sounder theories to guide more fruitful research. When assumptions about causal agency, logical structure, and levels of analysis are addressed explicitly, subsequent decisions about research strategy and technique will be better informed. The discussion of causal structure in this paper should facilitate choice and critical thinking both for researchers and for those who apply research findings.
Markus, M. Lynne, and Daniel Robey. “Information Technology and Organizational Change: Causal Structure in Theory and Research.” Management Science 34, no. 5 (1988): pp.583–598.
Mohr, Lawrence B. Explaining Organizational Behavior. Vol. 28. Jossey-Bass, 1982.
Pfeffer, J. Organizations and Organization Theory. Edited by Daniel T. Gilber, Susan T. Fiske, and GardnerEditors Lindsay. Vol. 12. Pitman, 1982.