A comparative sequence analysis of career paths among knowledge workers in a multinational bank

This study examined two metrics for measuring the distance between sequences (Euclid and OMSpell) and creating distance matrices combined with two types of clustering methods (AGNES and PAM) to analyze the career path clusters of knowledge workers. A regression tree of covariates and career path clusters was used to predict advancement rates. The results indicated that the metric which focused on subsequences (OMSpell) worked best for both clustering methods. Less time as a knowledge worker was associated with greater advancement. Implications for boundaryless careers and social capital formation are discussed.