• I thank the editor and the anonymous reviewers as well as Anette Fasang for helpful comments and suggestions. An earlier version of this paper was presented at the Academy of Management Annual Meeting 2010, Montreal, Canada. Direct correspondence to Torsten Biemann, Department of Management, Venloer Strasse 151-153, University of Cologne, 50672 Cologne, Germany; e-mail: biemann@wiso.uni-koeln.de.


Optimal matching (OM) is a method that assesses sequence similarity. It was originally developed to study protein and DNA sequences and was later transferred to the social sciences where it was applied accordingly. However, there is an ongoing debate on the adequacy of its use in the social sciences, as a superficial transfer might not respond to the significant differences between typical sequences in biological and social settings. In this paper, I elaborate on these differences and introduce a distinction between two sequence types—namely, common ancestors and unfolding processes. While the first sequence type is typically found in biological settings (e.g., DNA sequences), the latter applies to most sequences studied in the social sciences (e.g., careers). Based on this distinction, I present a new way of coding sequences as an extension to conventional OM analyses and demonstrate its usefulness in simulated and empirical examples. The paper concludes with a discussion of this new approach and its integration into previous extensions of OM.