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Paper Type
ERF
Description
The career development patterns in the IT industry remain unrevealed, with existing research only focusing on the lateral job mobility and the job level changes were ignored. Revealing career development patterns necessitates the observation of a considerable number of individual career trajectories. However, data preparation is a demanding and expensive task, and the limited sample size results in coarse-grained career patterns. Digital professional platforms now allow for the accumulation of substantial amounts of real-world career trajectories, creating an unparalleled chance to uncover patterns. This paper develops a data-driven career pattern mining method: Density-Equilibrium Career Paths Pattern Mining (DECPPM), which overcomes the challenges of huge cardinality and job title imbalance that impede effective job title clustering and pattern mining. Using DECPPM, we uncover the career development patterns of IT professionals in terms of both job function and job level changes. Furthermore, we characterize the career patterns of IT individuals in different job levels from a multidimensional and temporal perspective, contributing to the literature on the relationship between career mobility and career success.
Paper Number
1475
Recommended Citation
Wang, Yiwei; Meng, Qingxin; Chong, Alain; and Zhu, Hengshu, "Towards a Better Characterization of IT Career Development Patterns" (2023). AMCIS 2023 Proceedings. 8.
https://aisel.aisnet.org/amcis2023/sig_dsa/sig_dsa/8
Towards a Better Characterization of IT Career Development Patterns
The career development patterns in the IT industry remain unrevealed, with existing research only focusing on the lateral job mobility and the job level changes were ignored. Revealing career development patterns necessitates the observation of a considerable number of individual career trajectories. However, data preparation is a demanding and expensive task, and the limited sample size results in coarse-grained career patterns. Digital professional platforms now allow for the accumulation of substantial amounts of real-world career trajectories, creating an unparalleled chance to uncover patterns. This paper develops a data-driven career pattern mining method: Density-Equilibrium Career Paths Pattern Mining (DECPPM), which overcomes the challenges of huge cardinality and job title imbalance that impede effective job title clustering and pattern mining. Using DECPPM, we uncover the career development patterns of IT professionals in terms of both job function and job level changes. Furthermore, we characterize the career patterns of IT individuals in different job levels from a multidimensional and temporal perspective, contributing to the literature on the relationship between career mobility and career success.
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