期刊
SOCIAL NETWORKS
卷 76, 期 -, 页码 150-159出版社
ELSEVIER
DOI: 10.1016/j.socnet.2023.09.001
关键词
Advice-seeking; Network formation; Status; Cognitive overload; Stochastic actor-oriented models; Agent-based modeling
The study aims to extend previous research on advice-seeking across organizational boundaries through an agent-based model. By utilizing more realistic assumptions and fitting the simulated network to existing data, the findings demonstrate the advantage of exploring multiple generative paths in analyzing network formation.
Advice-seeking typically occurs across organizational boundaries through informal connections. By using Stochastic Actor-Oriented Models (SAOM), previous research has tried to identify the micro-level mechanisms behind these informal connections. Unfortunately, these models assume perfect network information, require agents to perform too cognitively demanding decisions, and do not account for threshold-based critical events, such as simultaneous tie changes. In the context of knowledge-intensive organizations, the shortage of high skilled professionals could determine complex network effects given that many less-skilled professionals would seek advice from a few easily overloaded, selective high-skilled, who are also sensitive to status demotion. To capture these context-specific organizational features, we have elaborated on SAOM with an agent-based model that assumes local information, status-based tie selection, and simultaneous re-direction of multiple ties. By fitting our simulated networks to Lazega's advice network used in previous research, we reproduced the same set of macro-level network metrics with a parsimonious model based on more empirically plausible assumptions than previous research. Our findings show the advantage of exploring multiple generative paths of network formation with different models.
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