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—Assumptions, Explanation, and Prediction in Marketing Science: “It's the Findings, Stupid, Not the Assumptions”

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  • Eric W. K. Tsang

    ()
    (School of Management, University of Texas at Dallas, Richardson, Texas 75083)

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    Abstract

    In his July–August 2007 editorial of , Steven Shugan argues that the realism of assumptions does not matter as long as a theory or model produces satisfactory predictions and claims further that unrealistic assumptions breed good theories. This commentary discusses the problems of his argument and presents a very different view about the realism of assumptions. Assumptions need not be realistic if the only goal of science is prediction. However, a major function of theory is also to explain and not just to predict. The role of explanation is more important in the social sciences because it is far more difficult to produce accurate predictions in the social than the natural sciences. Assumptions, especially core assumptions, often constitute the foundation of the mechanismic explanations provided by a theory. Unrealistic assumptions may lead to faulty explanations and false predictions. Contrary to Shugan's view, the realism of an assumption cannot be assessed just based on the output of a theory. It has to be tested independently of or in conjunction with the hypotheses of the theory. Also, contrary to Shugan's claim, more realistic assumptions result in better theories. As theory development advances, efforts should be directed toward making assumptions more realistic.

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    File URL: http://dx.doi.org/10.1287/mksc.1080.0463
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    Bibliographic Info

    Article provided by INFORMS in its journal Marketing Science.

    Volume (Year): 28 (2009)
    Issue (Month): 5 (09-10)
    Pages: 986-990

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    Handle: RePEc:inm:ormksc:v:28:y:2009:i:5:p:986-990

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    Keywords: assumptions; explanation; prediction; mechanism; realism; research design;

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