Temporal Embeddedness: Unpacking the Dynamics of Network Roles and Organizational Change

Much of the literature (e.g., Li et al. 2023; Fan et al. 2023) treats embeddedness as static or measured at discrete points. But what about the trajectories—organizations that suddenly lose or gain centrality, or whose key ties change rapidly? Drawing inspiration from longitudinal ecological and social network analysis, this project would use dynamic network modeling (e.g., stochastic actor-based models) to study how organizations traverse network positions over time, and whether surprising transitions (e.g., rapid rise or fall in embeddedness) predict innovation, failure, or transformation (see Cheng & Jiang 2022 on recidivism, but extend to positive deviance as well). This dynamic approach could reveal hidden windows of opportunity or risk, challenging static models and informing real-time organizational strategy.

References:

1. Falling in the same predicament again? A network embeddedness perspective of organizational failure recidivism in special treatment firms. Wan Cheng, Yusi Jiang (2022). Management Decision.
2. Research landscape on job embeddedness and organizational commitment: A bibliometric study. Linan Fan, Wei Wu, Hui-Rong Wang, Xiao-Rong Chang, Liusu Yi (2023). African Journal of Business Management.
3. Falling in the same predicament again? A network embeddedness perspective of organizational failure recidivism in special treatment firms. Wan Cheng, Yusi Jiang (2022). Management Decision.