Network Deviance: Mapping and Predicting Positive Outliers in Organizational Embeddedness

While most studies, such as Feng et al. (2025) and Swati Singh et al. (2024), focus on typical patterns of embeddedness and their consequences, very few ask: what about organizations that don’t fit the mold—those that stand out as unexpectedly successful (or resilient) despite low embeddedness, or as struggling despite high embeddedness? By leveraging advanced statistical tools similar to those in Singh et al., we can detect outlier organizations within large-scale networks (e.g., PPP networks, R&D consortia). This research would develop a typology of “network deviants” and use qualitative and quantitative methods to understand their strategies, contexts, and practices. This approach challenges the assumption that greater embeddedness is always optimal, aligning with the “question the norm” heuristic. It could reshape network theory by highlighting when and why deviation—not conformity—yields organizational success.

References:

1. Mathematical Modeling for Non-Linear Behaviorial Analysis of Job Embeddedness on Organization with Improved Statistical Tools. Et al. Swati Singh (2024). Advances in Nonlinear Variational Inequalities.
2. Multi-level relationship patterns among private-sector organizations in public–private partnerships: a network perspective with evidence from China’s transportation sectors. Xiaowei Feng, Jiming Cao, Liang Liu, Yiming Ye, Kaifeng Duan (2025). Engineering Construction and Architectural Management.