Supplier Bottleneck and Information Dissemination

Li, Meng; Li, Yue; Zhang, Yang

doi:10.2139/ssrn.3747717

Cited by 1 publication

(2 citation statements)

References 35 publications

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“…Socially influenced decision-making has also been studied considering the embedding of the information in a social network (Berger et al 2014;Jackson et al 2017;Li et al 2019;Ni et al 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Here, social influence can be directly associated with the interactions (links or edges) among individuals (nodes) conforming a social influence network (Zhang et al 2018). In this context, studies in economics (Jackson et al 2017), finance (Allen et al 2018;Li et al 2019), healthcare (Ni et al 2021;Xie et al 2020), and information systems (Berger et al 2014;Hong et al 2017;Mislove 2009) have found that properties of the social network structures (patterns or regularities in relationships between individuals obtained directly from the network structural summaries) can be associated with changes on the decision-making behaviour. For example, Engel et al (2021) constructed a network containing different economic actors (nodes) and where a link establishes whether an actor holds shares of another economic actor.…”

Section: Introductionmentioning

confidence: 99%

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Implications of social network structures on socially influenced decision-making

Zheng,

Ospina-Forero,

Chen

2024

Decision

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Individuals often make decisions in a social environment where social influence can impact on people’s decision-making domains such as online purchasing, political voting and voluntary vaccine uptake. Social influence can be recognised as the intentional or unintentional change in an individual’s belief, perception, or behaviours caused by an information diffusion process embedded in a social network. However, there is limited research on how this diffusion process is shaped by the topology or structure of the social network. This work provides an exploratory and systematic analysis of how decision-making outcomes in a population can be affected by both the structure of the social network and the starting node of where new information starts to diffuse. Simulation results considering three common network structures highlight how social networks with clear community structures lead to a larger absolute impact on decision-making outcomes and networks where the social connections follow a preferential attachment rule show the largest relative impact than the others. The results also suggest scenarios in which introducing new pieces of information to the social network can facilitate the information diffusion process and produce a more significant impact in terms of the overall population decision-making process.

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Section: Introductionmentioning

confidence: 99%