Mapping the Temporal Structure of Entrepreneurial Start-Up Activities

Brown University, Providence RI

Investigators

Abstract

Entrepreneurship is both an important source of employment and, especially within science and engineering, a powerful force for social and economic renewal. Despite this importance, however, entrepreneurship research has only recently expanded beyond studying precursor conditions (such as entrepreneurial personality traits and untapped market opportunities) to address the process of starting a business--developing a business model, incorporating, registering intellectual property, making first hires, and so on. The present project seeks to map the field of start-up activities and to identify the common pathways that nascent entrepreneurial ventures follow in transiting that field. The PI hypothesizes (a) that start-up trajectories cluster into distinct archetypes; (b) that the choice of archetype depends on identifiable attributes of the founding team, the new firm, and the social context; and (c) that different start-up sequences will yield different outcomes, depending on these team, firm, and context contingencies. If confirmed, these predictions would represent a significant advance over current "one size fits all" models of the start-up process. Such a shift would have a transformative impact on science, teaching, practice and policy in the field of entrepreneurship. This project advances the entrepreneurship literature's recent turn toward "process studies" that explore the contingent, unfolding nature of new firm emergence. Applying a set of under-utilized statistical techniques to data on the timing of 36 start-up activities recorded by the Panel Study of Entrepreneurial Dynamics (PSED I and II), the investigation seeks to identify temporal patterns in the early trajectories of nascent entrepreneurial ventures. First, the project will adapt multi-dimensional scaling (MDS) techniques from psychometry to map the set of start-up activities onto a multi-dimensional activity space, based on various measures of the activities' temporal similarity to one another. Second, the project will adapt sequence analysis (SA) techniques from genomics to cluster the observed start-up sequences into a limited number of archetypal trajectories through the activity space. Third, the project will assess the impact of exogenous conditions such as founder attributes, organization type, and environmental context in determining a new venture's particular trajectory. Fourth, the project will evaluate the interactive impact of these exogenous contingencies and archetypal trajectories in shaping the new venture's survival and performance outcomes. In furtherance of these analyses, the project will refine MDS and SA methodologies to better accommodate the unique features of social-scientific data such as the activity sequences in the PSED. The project will also supplement the existing PSED data sets with new measures of socio-political and economic conditions in respondents' environments. These new methods and variables will enhance the PSED's usefulness as a data infrastructure for exploring social vs. economic, local vs. global, and objective vs. subjective environmental influences on the start-up process, both for the current investigation and for other investigations in years to come.