A yield management strategy for semiconductor manufacturing based on information theory

Online social networks, which have been defined as aggregated organizations that emerge from the Internet when people carry on public discussions, are increasingly becoming the vehicle of influence in social, political and economic discourse. Yet, despite its increasing importance, the nature of influence in online social networks is not really understood. Practitioners openly admit that they lack the experience to make sense of the phenomenon, and extant theory of influence in networks, which extrapolates from observations of the real world, is demonstrably inadequate when it comes to explaining influence online. The paper introduces a novel approach to analyzing influence online, which is based on the premise that knowledge flows rather than connectivity or position determine loci and regions of influence. The authors propose an exploratory, longitudinal population study of 100 highly diverse online social networks. The study will 1) benchmark a set of metrics for influence in these networks to determine which metrics are best suited for measuring influence in a plethora of contexts; 2) characterize the nature and properties of knowledge flows within each network; and 3) determine how knowledge flows impact the (virtual) spatial and temporal distribution of influence within that network.

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A yield management strategy for semiconductor manufacturing based on information theory

Cited by 2 publications

References 19 publications

Demand Management in Semiconductor Manufacturing: A Dynamic Pricing Approach Based on Fast Model Predictive Control

Demand Management in Semiconductor Manufacturing: A Dynamic Pricing Approach Based on Fast Model Predictive Control

Knowledge Flows and Influence in Online Social Networks: Proposing a Research Agenda

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