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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.

The rigor-versus-relevance debate in the world of academia is, by now, an old-time classic that does not seem to go away so easily. The grassroots movement Responsible Research in Business and Management, for instance, is a very active and prominent advocate of the need to change current research practices in the management domain, broadly defined. One of its main critiques is that current research practices are not apt to address day-to-day management challenges, nor do they allow such management challenges to feed into academic research. In this paper, we address this issue, and present a research design, referred to as CARE, that is aimed at building a bridge from rigor to relevance, and vice versa. In so doing, we offer a template for conducting rigorous research with immediate impact, contributing to solving issues that businesses are struggling with through a design that facilitates causal inference.