Modeling Levels and Time in Entrepreneurship Research

Abstract

New ventures lack resources, are buffeted by environmental factors, and often experience rapid growth and organizational transformations that can have profound effects on performance and survival. This indicates that factors at multiple levels and across time affect new venture outcomes. Research examining these outcomes often address relationships that cross levels or time, but rarely both. Because scholars potentially can make rich theoretical contributions by simultaneously investigating temporal relationships that cross levels, the authors illustrate multiyear, multilevel model building with random coefficient modeling (RCM) using language that is accessible to entrepreneurship scholars. Specifically, they model the effects of strategic growth actions on new venture performance using a longitudinal data set of young, IPO-stage firms. Their illustration demonstrates the statistical advantages of modeling levels and time simultaneously and offers a roadmap for entrepreneurship scholars interested in examining these effects, including a step-by-step guide with SAS code for working with these data. They also describe some specific research questions to help advance theory development using RCM.