Abstract

The field of entrepreneurship continues to struggle with a crisis of identity or shared purpose. Low and MacMillan (1988: 139) observed, “As a body of literature develops, it is useful to stop occasionally, take inventory of the work that has been done, and identify new directions and challenges for the future”. Since that time, a number of “inventories” have been taken, however we believe that two important questions have yet to be addressed. They are: (1) what are we studying and (2) where are we looking for the answers? In this paper, we examine the dependent variables that are being utilized in empirical entrepreneurship research and the sample frames that are being used to capture the data.

Our detailed examination of 284 papers in ETP and JBV published in the ten-year period form 1996-2005 demonstrates that entrepreneurship scholars are studying firms more than individuals. Firm performance, in particular, is the single class of dependent variable garnering more attention than any other. The data frames are most often drawn from the United States. VC firms (and firms that reached the IPO stage) comprised 12% of the firm level empirical studies and entrepreneurs were the focal subjects in only about a third of the published, individual-level empirical research. Industry was used as a sampling criterion in only a third of instances, with a significant concentration on tech sectors and manufacturing.

Our review suggests that entrepreneurship during the last decade has converged on four topics of inquiry. Two thirds of the studies focused on core strategic management topics of firm performance (49%) and strategy and ownership (17%) while 11% have focused on entry/exit (also a strategic management and organizational theory phenomenon). The remaining 25% of studies examined individual attributes.

Just as firms can experience strategic drift, so too can academic disciplines find themselves without a clear purpose or direction. By taking stock of the ways in which entrepreneurship researchers are framing questions and answering them, this paper provides an opportunity to assess whether we are advancing knowledge in a coherent direction. By overstudying certain phenomena at the expense of others (e.g., firm performance) or oversampling on a particular data frame (e.g., IPOs) we may be learning a great deal about specific trees but little about the forest. We do not presume to know where the field should be going, but we believe there is value in understanding where we are.

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