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Beschreibung:
The emergence of online labor markets calls the validity of traditional career models into question. Given the volatility and digital nature of this environment, short-term employment relationships and heterogeneity of workers, employers and tasks in these markets, it is unclear how careers might unfold - whether they are largely random and accidental or whether there are distinct trajectories and patterns in online careers. We document dominant career paths and develop a taxonomy of novel career patterns in OLMs. This addresses recent calls for research to update and refine our theories and understanding of careers (Rahman et al., 2016). We adopt a quantitative-inductive approach to describe workers' careers in terms of their task and skill specialization. This helps us understand the different types of careers on a continuum between more stable and random, unsystematic careers. Our results provide an innovative way of thinking about career development in OLMs and open up several fruitful areas of future research.