Multiple successful models: how demographic features of founding teams differ between regions and over time

Abstract

In this study, I ask: (1) is industry evolution or isomorphism theory a better model for understanding the change (lack-there-of) among founding team demographics over time? (2) Does region moderate which founding team demographics are prevalent and valued? To answer these questions, I analyse the demographic features of Boston and San Francisco Bay area biotechnology founding teams formed over a period of more than 30 years. I then examine whether there is a financial benefit -in terms of the value of their first investment -for having certain demographic features. I find that there are significant differences between Boston and San Francisco in (1) who becomes founders, (2) the rate at and ways in which demographic features evolve over time, and (3) the features of the founding teams that are rewarded by venture capitalists through higher initial investments. My results demonstrate the importance of treating region and industry age as moderators rather than controls.

Entrepreneurship policy and the financing of young innovative companies: Evidence from the Italian Startup Act

“…For example, imitation and isomorphic attitudes(DiMaggio and Powell, 1983) towards successful role models emerging in specific geographical areas have been found to largely and permanently affect the differences in the characteristics of startups operating in the biotechnology industry in the US across different regions(Packalen, 2015). While, in our case, observing YICs from their inception and at the beginning of the institutional change brought in by the policy law, should enable us to analyse the matching between fund providers and YICs in a pure and 'untainted' environment.3Benfratello et al (2008), among others, underline the importance of bank support to Italian YICs and show a positive effect of local banking development on firms' innovative activities.…”

Quantifying patterns of research-interest evolution

“…Our quantitative understanding of how scientists choose and shift their research focus over time is highly consequential, because it affects the ways in which scientists are trained, science is funded, knowledge is organized and discovered, and excellence is recognized and rewarded [1][2][3][4][5][6][7][8][9]. Despite extensive investigations of various factors that influence a scientist's choice of research topics [8][9][10][11][12][13][14][15][16][17][18][19][20][21], quantitative assessments of mechanisms that give rise to macroscopic patterns characterizing research interest evolution of individual scientists remain limited. Here we perform a large-scale analysis of publication records, finding that research interest change follows a reproducible pattern characterized by an exponential distribution.…”

“…This becomes ever more so with the accelerating scale and complexity of scientific enterprise [2,26,29,30]. A variety of microscopic factors have been identified that drive a scientist's choice of research problems, ranging from age [10,11] to gender [12,13], to training and mentorship [9,14], from funding or collaboration opportunities [15][16][17], to serendipity [18], to scientist's attitudes and abilities [19], including risk aversion and creativity [8,20,21]. Yet, little is known about the macroscopic patterns underlying the research interest evolution.…”

Human capital of entrepreneurial teams in nascent high-tech sectors: a comparison between Cleantech and Internet

“…However, it could also be argued that a co-evolution dynamics may exist between industrial eco-systems and entrepreneurs' human capital, i.e. that the development of a new high-tech sector is shaped by the human capital 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 of the pioneering founders that initiate specific technology trajectories and determine business conducts which, in turn, imprint the typology and characteristics of the subsequent waves of entrepreneurs (Packalen 2015). Indeed, this perspective would make less compelling the need to control for the life cycle of an industry, but at the same time it also makes the nascent phases the purest to analyze and of key relevance to understand the whole sectoral dynamics.…”