The ordering between data and theory in theory development deserves attention. What drives the process initially? Is it data \u2192 theory \u2192 data (repeating) or theory \u2192 data \u2192 theory (repeating)? What is the foundation of theory development? Direct and indirect commentary are presented in several management theory-oriented articles. Sutton and Staw comment on an example of data driving hypothesis development (and eventually theory) as \u201cbrute empiricism, where hypotheses are motivated by prior data rather than theory\u201d (1995, p. 374). The connotation is that data driving theory is negative.<\/p>\n
Related to the idea of whether data should drive theory or vice versa is the idea that interesting theories should receive more tolerance in regard to empirical support. Sutton and Staw conclude their article with a question of \u201cwhether the evidence provided by people such as Freud, Marx, or Darwin would meet… [current] empirical standards…\u201d (1995, p. 383)? How far should the tolerance surrounding empirical support go? Beyond the confines of falsifiability?<\/p>\n
Freud, Marx, and Darwin were all brilliant, prolific scholars who introduced fascinating theories of gargantuan scale. Yet while philosophically spell-binding and worthy of significant scientific inquiry, some of their theories challenge the call for falsifiability in theories because they are untestable at various, fundamental points. Using Darwin for example\u2014his theory of natural selection is observable, testable and thereby able to be falsified\u2014an important and relevant theory. But the theory of macro-evolution, purporting that planetary life evolved from a single-cell organism or organisms, is inherently unobservable, untestable and thereby not falsifiable. Karl Popper said as much in claiming \u201c\u2026Darwinism is not a testable scientific theory, but a metaphysical research programme<\/em>\u2014a possible framework for testable scientific theories\u201d (1976, p. 168) <\/a> [1] . As a program of research, the ideas purported by evolution are helpful to scientific activity, prompting much study and new ideas. Yet calling an idea beyond falsification a theory seems inconsistent [2] <\/a> . Perhaps we should join Popper and call these ideas “metaphysical research programs” or simply “pseudo-theories.” Attempting to define theory as inherently falsifiable, yet providing tolerance for especially interesting ideas to “fudge” in this regard, especially beyond the confines of testability, is not helpful and only serves to further confound the definition of theory. Otherwise, as a minor case-in-point, the idea that theories must be driven by observable data itself could be broadly defined as a theory. Yet, this should not be so in management theory\u2014the call for falsifiability as a threshold for the establishment of theory precludes it (Bacharach, 1989, p. 501). \u201cTheory should be driven by observables\u201d is simply an idea, it cannot be falsified and should not be titled a theory. Neither should more far-reaching ideas.<\/p>\n Now revisiting my beginning question of what should drive theory development\u2014theory or data? I believe the answer to be data, a further criticism to Sutton and Staw’s remarks against \u201cbrute empiricism.\u201d In the beginning, theory driving data is dangerous as it is more susceptible to individual influence and assumptions than objective theorizing from data followed by retesting with data and further theorizing (and on and on). Bacharach helps this critique in explaining theories as bound by \u201cassumptions about values, time, and space\u201d (1989, p. 499). Further support comes from a response to the Sutton and Staw article by Karl Weick. Weick calls attention to Bill Starbuck\u2019s argument, summarizing that \u201cjust as the best medical doctors treat symptoms directly without relying on diagnosis to determine treatments, the best theorists may make prescriptions based on data alone without introducing theory between data and prescriptions\u201d (1995, p. 387).<\/p>\n In conclusion, data \u2192 theory \u2192 data (repeating) promotes the most believable and objective science. Perhaps we could label such practices as “safe science.” Further, theorizing that does not allow for falsifiability should not be called theory regardless of how interesting it is, or even its likelihood of being true. Incidentally, such ideas cannot even be testable hypotheses, since they cannot be tested. Instead, they should be called what they are: ideas, systems-of-thought or ideologies. Grand and involved as they might be, they are not scientific theories and literature on theory building should clearly distinguish itself from them, not embrace them.<\/p>\n <\/p>\n References<\/strong><\/p>\n Bacharach, S. B. (1989). Organizational Theories: Some Criteria for Evaluation. The Academy of Management Review<\/em>, 14<\/em>(4), 496\u2013515. doi:10.2307\/258555<\/p>\n Popper, K. R. (1976). Unended quest: an intellectual autobiography<\/em> (Revised.). London: Fontana\/Collins.<\/p>\n Sutton, R. I., & Staw, B. M. (1995). What theory is not. Administrative Science Quarterly<\/em>, 40<\/em>, 371\u2013384. doi:10.2307\/2393788<\/p>\n Weick, K. E. (1995). What theory is not, theorizing is. Discussion of R. Sutton and B. Staw\u2019s Article<\/em>, 40<\/em>, 385\u2013390. doi:10.2307\/2393789<\/p>\n
<\/p>\n
\n