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1 Lingting Jiang -- 778 2026-01-07 01:51:50 |
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Jiang, L.; Rundo, J.; Shi, L.; Zhou, N. Network Centrality and Information Cascades in Executive and Director Networks. Encyclopedia. Available online: https://encyclopedia.pub/entry/59406 (accessed on 08 January 2026).
Jiang L, Rundo J, Shi L, Zhou N. Network Centrality and Information Cascades in Executive and Director Networks. Encyclopedia. Available at: https://encyclopedia.pub/entry/59406. Accessed January 08, 2026.
Jiang, Lingting, Janean Rundo, Linna Shi, Nan Zhou. "Network Centrality and Information Cascades in Executive and Director Networks" Encyclopedia, https://encyclopedia.pub/entry/59406 (accessed January 08, 2026).
Jiang, L., Rundo, J., Shi, L., & Zhou, N. (2026, January 07). Network Centrality and Information Cascades in Executive and Director Networks. In Encyclopedia. https://encyclopedia.pub/entry/59406
Jiang, Lingting, et al. "Network Centrality and Information Cascades in Executive and Director Networks." Encyclopedia. Web. 07 January, 2026.
Peer Reviewed
Network Centrality and Information Cascades in Executive and Director Networks
This is a peer-reviewed entry published in Encyclopedia Journal, full entry can be found at 10.3390/encyclopedia6010011

Information cascades refer to a type of learning behavior in social networks where individuals make decisions by observing the actions of others, rather than relying solely on their own private information. Network centrality, which measures the relative importance or influence of a node within a network, plays a significant role in initiating and shaping information cascades across four key dimensions. First, nodes with high degree centrality often initiate information cascades due to their large number of direct connections to other nodes. Second, nodes with high betweenness centrality serve as bridges between different parts of the network, thereby controlling the flow of cascading information. Third, nodes with high closeness centrality can access and disseminate information more quickly, accelerating the spread of cascades throughout the network. Fourth, nodes with high eigenvector centrality augment the impact of information cascades through their visibility and connections to other influential nodes. Synthesizing research findings on executive and director networks from management, finance, and accounting, this entry provides insights into emerging trends in corporate governance by highlighting the interaction between network structure and information dissemination.

network centrality information cascades executive network director network
Network centrality measures the importance or influence of a node (e.g., a person or firm) within a network, and indicates which nodes are most central to the network’s structure—whether because they are highly connected, serve as bridges between otherwise separate groups, or are close to others in the network [1][2]. As a core structural dimension of social networks, network centrality provides insight into how networks function as mechanisms for information diffusion, resource mobilization, and the exercise of power and influence across interpersonal and inter-organizational domains [3][4]. Centrality measures are therefore fundamental to understanding information cascades, which are chain reactions in which information, behaviors, or decisions spread through a network.
Information cascades describe a social learning process in which individuals make decisions based on the observed actions of others rather than relying solely on their private information or judgment [5]. These cascades play a critical role in explaining decision-making under uncertainty, collective behavior, market dynamics, and governance practices.
Network centrality operates as both a megaphone and a lever in shaping information cascades, influencing their initiation, reach, and intensity [6]. Highly central “seed nodes” are most effective in triggering widespread cascades because their structural positions maximize visibility and influence. For instance, nodes with high degree or high eigenvector centrality can initiate ripple effects that accumulate momentum as they move through the network. Nodes with high betweenness centrality facilitate the transmission of information between otherwise disconnected clusters, effectively seeding new cascades in different parts of the network. Nodes with high closeness centrality can quickly observe the actions of others and are often among the first to act on new information; their early adoption is highly visible, accelerating the initiation of cascades.
In the context of undesirable information, like misinformation or fake news, network centrality also plays a pivotal role in cascade suppression. Central actors may exercise gatekeeping functions, delaying action until credible signals emerge, which slows the formation of premature cascades [7]. Contrarian or corrective actions by highly visible nodes can disrupt ongoing cascades, thereby weakening herding pressures and potentially reversing collective misjudgments [5][8]. Strategic interventions aimed at nodes with high betweenness centrality can further impede the flow of harmful information across network clusters, thereby preventing its systemic amplification [9].
Information diffusion within a network is inherently heterogeneous. The structural positions of nodes determine both the breadth and the pattern of diffusion. Bridging nodes, or those with high betweenness centrality, are especially critical for facilitating global spread because they connect otherwise disconnected communities and enable information to “jump” across structural holes [10]. By contrast, high-centrality nodes embedded within a single community primarily drive local dynamics, reinforcing beliefs and behaviors within their immediate social circles [11][12]. Together, these mechanisms illustrate how both global bridges and local reinforcers jointly shape whether information cascades remain confined to subgroups or escalate into network-wide phenomena.
The primary aim of this review is to synthesize and integrate theoretical and empirical insights on how network centrality shapes information cascades, with particular attention to applications in accounting, finance, and management research. Accordingly, this review pursues three main objectives: (i) to clarify the theoretical linkage between network centrality and information cascade theory; (ii) to systematically describe and compare the four commonly used centrality measures, along with their composite or overall centrality scores; and (iii) to evaluate how these measures have been applied in prior accounting, finance, and management literature, including evidence from international contexts. Methodologically, this review adopts a structured narrative approach, systematically categorizing prior theoretical and empirical studies by centrality measure, network type, disciplinary domain, and geographic context to provide a coherent and comparative synthesis of the existing evidence.

References

  1. Freeman, L.C. Centrality in Social Networks: Conceptual Clarification. Soc. Netw. 1979, 1, 215–239.
  2. Borgatti, S.P. Centrality and Network Flow. Soc. Netw. 2005, 27, 55–71.
  3. Brass, D.J.; Galaskiewicz, J.; Greve, H.R.; Tsai, W. Taking Stock of Networks and Organizations: A Multilevel Perspective. Acad. Manag. J. 2004, 47, 795–817.
  4. Larcker, D.F.; So, E.C.; Wang, C.Y. Boardroom Centrality and Firm Performance. J. Account. Econ. 2013, 55, 225–250.
  5. Bikhchandani, S.; Hirshleifer, D.; Welch, I. A Theory of Fads, Fashion, Custom, and Cultural Change as Informational Cascades. J. Polit. Econ. 1992, 100, 992–1026.
  6. Valente, T.W. Network Interventions. Science 2012, 337, 49–53.
  7. Aral, S.; Walker, D. Identifying Influential and Susceptible Members of Social Networks. Science 2012, 337, 337–341.
  8. Banerjee, A.V. A Simple Model of Herd Behavior. Q. J. Econ. 1992, 107, 797–817.
  9. Centola, D. Change: How to Make Big Things Happen; Little, Brown Spark: New York, NY, USA, 2021.
  10. Burt, R.S. Brokerage and Closure: An Introduction to Social Capital; Oxford University Press: Oxford, UK, 2005.
  11. Granovetter, M.S. The Strength of Weak Ties. Am. J. Sociol. 1973, 78, 1360–1380.
  12. Centola, D.; Macy, M. Complex Contagions and the Weakness of Long Ties. Am. J. Sociol. 2007, 113, 702–734.
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Subjects: Operations Research & Management Science
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register : Lingting Jiang , Janean Rundo , Linna Shi , Nan Zhou
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Online Date: 07 Jan 2026
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