What is theory?

Jan 27, 2008 by

In The Structure of Scientific Revolutions, Kuhn asserted that science grows when there is a revolutionary change that enables the multiple theories of pre-science to be replaced by a dominant paradigm. After a dominant paradigm is established (which Kuhn refers to as “normal science”), scientists then contribute to the paradigm through producing empirical and theoretical work. Pfeffer stated that organizational science is in a low state of paradigm development, as there is poor consensus on the essential tenets of the field. Unfortunately for us, this would indicate that the field is still in the state of pre-science. As a result, the field cannot be advanced as rapidly as if it were a normal science, as its practitioners may be devoting their time to building upon both fundamental and dead-end theories. Pfeffer cited Kuhn, and in many ways reiterated what Kuhn stated through enumerating the problems caused by organization science being a low-paradigm field. In stating the case against theory, Sutton & Staw argued that the field may have over-emphasized the generation of new theories, citing Van Maanen’s call for a decade-long moratorium on new theories. The strong emphasis in organization science on theory generation may be a symptom of the field’s weak paradigm.

While defining science in Conjectures and Refutations: The Growth of Scientific Knowledge, Popper stated that scientific theories are fundamentally falsifiable. This contrasts with Kuhn’s normal science, which involves consensus around a paradigm. Following the Popperian tradition, Goodson & Morgan examined the nature of theories in more depth, and pronounced that theories are testable, are responsive to empirical data, are internally consistent, have subsumptive power, are parsimonious, are communicable, and have the ability to stimulate the interest of people. The authors also emphasized the importance of a theory being able to both explain observations and improve peoples’ understanding of phenomenon. In contrast, Sutton & Staw explicitly stated that theory is not references, data, lists of variables, diagrams, or hypotheses. To them, theory answers causal questions, and “is about the connections among phenomena, a story about why acts, events, structure, and thoughts occur.” Thus, it appears that Sutton & Staw are either writing within the tradition of Kuhn, or another tradition entirely. Rather than emphasizing the importance of creating falsifiable propositions, they suggest that the role of theory is to explain the answers to questions. These questions are likely couched within the realm of Kuhn’s normal science. In stating the problems caused by relying on their criteria, Goodson & Morgan admit that there are theories (such as Freud’s) which are beneficial to a field although they may have difficulty fulfilling criteria such as falsifiability.

As a person embarking upon an academic career, I must ponder which definition of theory I should operationalize in my papers. It is one thing for a senior academic like Barry Staw to advocate that journals accept more papers that are stronger in theory than in method, and it is another thing for an aspiring academic like me to write such a paper. The problem with such papers, as Sutton & Staw highlight, is that they are more difficult to validate than more data-driven papers. Perhaps, tenured professors are in the best position to advance qualitative theory, as they can lend it credibility through their prior track records. Likewise, their tenured status may give them the luxury of taking riskier projects (with a lower probability of successful publication) than could be endured by younger academics attempting to establish a reputation. Pfeffer mentioned that prominent journals in the field of organization science have an eighty to ninety percent rejection rate. This is all the more reason that junior researchers cannot afford to take such risks.

As a result of the riskiness of purely theoretical papers, I will likely be best served early in my career by composing papers that use highly replicable methods. The field of healthcare systems often employs datasets that are acquired by the researcher, rather than generated by the researcher. This is fortunate, as papers utilizing publicly-available data can be better externally-validated than those using proprietary or confidential datasets. With a level playing field in the realm of data, it is all the more clear that Sutton & Staw are correct that data are not theory, as data are often not even a differentiating factor of a paper. Given the data-driven nature of the field and the isolation of healthcare researchers from their subjects, it makes sense for a Popperian definition of scientific theory to be followed. While it is nice for theories to be interesting and to answer fundamental questions, for the young academic, they should first and foremost be testable.

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