From explanation of the past to prediction of the future: A comparative and predictive research design in the Social Sciences

Arjen van Witteloostuijn, Johanna Vanderstraeten, Hendrik Slabbinck, Marcus Dejardin, Julie Hermans, Wim Coreynen

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Abstract

Business and Psychology research (and the Social Sciences, in general) is heavily biased toward explaining the past. The holy grail in such explanation-oriented research is to develop causal theory, and to test this theory with historical data against a null no-effect benchmark. We seek to expand the methodological toolkit by adding a comparative and predictive research design. First, by organizing an inter-theory battle, we move away from classic null hypothesis testing. Second, by predicting the future, we add prediction as a complement to the traditional explanation of the past. By way of illustration, we select a case in the Entrepreneurship field and theorize about the ranked predictions as to the relative growth performance of a sample of Small and Mediumsized Enterprises (SMEs). For this, we adopt two widely acknowledged theories in the literatures of Business and Psychology: The Competitive Strategy theory and the Motive Disposition theory. We use Gamblers’ Ruin or Random Walk theory, arguing that company growth cannot be predicted, as the null benchmark. After identifying key explanatory predictive variables of our basic pair of theories, with Gamblers’ Ruin or Random Walk theory’s non-predictability as our benchmark, we produce ranked predictions as to the relative growth performance of 294 Belgian entrepreneurs and their SMEs. Later in 2023, we will test the predictive accuracy of these two selected theories and their predictive variables by comparing the predictive rankings with realized growth, as well as vis-à-vis randomness.
Original languageEnglish
Article number100269
Pages (from-to)1-10
Number of pages10
JournalSocial Sciences & Humanities Open
Volume6
Issue number1
DOIs
Publication statusPublished - 27 Apr 2022

Keywords

  • Comparative research design
  • Predictive research design
  • Social sciences
  • Entrepreneurship
  • Small and medium-sized enterprises

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