A holistic approach to sustainable growth from entrepreneurship
Reference number | |
Coordinator | Stiftelsen Stockholm School of Economics (SSE) - Stiftelsen Stockholm School of Economics institute for research (SIR) |
Funding from Vinnova | SEK 1 310 400 |
Project duration | January 2018 - July 2022 |
Status | Completed |
Purpose and goal
Increase our knowledge about the development of innovative growth companies and their entrepreneurs over time with links to different financing strategies. Sub-project (i) Follow the entrepreneur: Influence of entrepreneurial experience on individuals´ future careers, (ii) Sustainable growth models: Explanations of sustainable growth in startups with links to financing choices, (iii) Path dependence in new venture´s capital structures: Consequences of early financing choices on future funding strategies Objective: Publish the results in scientific journals.
Expected results and effects
OBJECTIVES ACHIEVED TO A RATHER LARGE EXTENT Sub proj 1: Objective achieved - research completed, research article "Post-entrepreneurial wage-employments: Signaling effects from entrepreneurial experience" submitted in June 2022 to a highly ranked academic journal. Sub proj 2: Delayed - objective not achieved. Prel results presented at research conference 2021 Sub proj 3: Objective achieved - research completed, research article "Path dependence in new ventures´ capital structures" published in Entrepreneurship, Theory and Practice 2021 Other: Six conference papers
Planned approach and implementation
A central part of our research strategy was to create a rich unique database with information about Swedish high-growth innovative startups, their founders and funding sources. We collected both primary data and secondary data. Few other studies in the field have taken such a combined approach with both homogenous innovation driven samples and population samples and simultaneously focused on sustainable growth and financing strategies in startup firms. Statistical methods utilized are hypothesis-driven testing by estimating OLS and logistic regression models.