1. Empirical Taxonomy of Start-Up Firm Growth Trajectories Université catholique de Louvain Mahamadou Biga D., D. François, B. Gailly, M. Verleysen, V. Wertz Centre for Research in Change, Innovation and Strategy Louvain School of Management www.crecis.be Machine Learning Group Engineering Faculty www.ucl.ac.be/mlg

2. 2 Motivation “Promising” firms generate significant wealth and jobs OECD; Audretsch & Turik,; Storey; Birsch… Knowledge about new & small firm growth remains scattered Wiklund & Shepherd; Delmar et al; Davidsson & Wiklund… How do “promising” firms grow and develop over time? Do typical growth trajectories exist? What are the key characteristics of these growth trajectories? Longitudinal approaches generate methodological challenges Garnsey & Heffernan, Garnsey et al. ; Davidsson & Wiklund …

3. 3 Proposed approach Growth is a longitudinal process Garnsey et al., 2006; Busenitz et al., 2003; Chandler & Lyon, 2001; Pettigrew et al., 2001; Davidsson & Wiklund, 2000;… Growth is a multidimensional process Wiklund, 2004; Delmar & et al., 2003; Dess & Lumpkin, 2001; Weinzimmer et al., 1998; Lumpkin & Dess, 1996; Birley & Westhed, 1990; Cameron, 1978. Growth is an ubiquitous phenomenon Delmar & Davidsson, 1998; Delmar et al.,2003 Use of multiple economic data (10 indicators) Focus on growth trajectories Analysis across multiple sectors

4. Methodology 1/2: « Promising firms » Threshold of micro- firm size Excluded firms Sales Employment Assets 10 years Firm age “Promising” firms Sample of 741 “promising” firms with complete trajectory, out of 17,168 firms created between 1992 and 2002 in Belgium.

5. 5 Importance of using multidimensional approach 33% 5% 8% 3% 1% 2% 50% Employment (above 9) Assets (above 2M€) Sales (above 2M€) Share of firms growing beyond micro-firm level based upon criteria selected to measure growth. (n=741)

6. 6 Ubiquity of ‘‘promising firms’’ Sector distribution High-tech vs. low-tech distribution

7. 7 Methodology 2/2: Discretization Multidimensional representation of growth trajectories ●● ●●●● ● ● ●●● ●●●●● ● ●●●● ●●● ● ● ● ● ●● ●● ● ● ● ●● ●●●●● ● ● ● ●● ●●● ●● ● ●● ●● ●●●● ● ●● ●●● ●●●●●●●●●●●●●●●●●● ●●●●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ●●●●● ●● ●●●● ●● ●● ●●● ● ● ● ● ● ● Clustering and discretization ●● ●●●● ● ● ●●● ●●●●● ● ●●●● ●●● ● ● ● ● ●● ●● ● ● ● ●● ●●●●● ● ● ● ●● ●●● ●● ● ●● ●● ●●●● ● ●● ●●● ●●●●●●●●●●●●●●●●●● ●●●●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ●●●●● ●● ●●●● ●● ●● ●●● ● ● ● ● ● ● 1 2 3 4 ----- 21234 ----- Sequence analysis

8. 8 7 Typical growth trajectories identified i.Grow as a question mark ii.Grow as a start-up iii.Grow as a star iv.Grow as a boutique v.Grow from start-up to question mark vi.Grow from start-up to boutique vii.Grow from start-up to star Share of firms adopting one of 7 58 % 50 % 42 % n 602 521 431 i ii iii iv v vi vii Span (years) 4 5 6

9. 9 Conclusions & Further Work  Original empirical approach based on systematic clustering and classification  Relevance of longitudinal and multidimensional approach based on economic data  Identification of several possible growth trajectories beyond classical “start-ups” profiles Further interpretation could rely upon international sample, in- depth case studies or use of more complete data (business model, entrepreneur’s motivation, competitive environment….)