Statistical Modeling of Multi-Dimensional Knowledge Diffusion Networks: An ERGM-Based Framework

Persistent Link:
http://hdl.handle.net/10150/555946
Title:
Statistical Modeling of Multi-Dimensional Knowledge Diffusion Networks: An ERGM-Based Framework
Author:
Jiang, Shan
Issue Date:
2015
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
Knowledge diffusion networks consist of individuals who exchange knowledge and knowledge flows connecting the individuals. By studying knowledge diffusion in a network perspective, it helps us understand how the connections between individuals affect the knowledge diffusion processes. Existing research on knowledge diffusion networks mostly adopts a uni-dimensional perspective, where all the individuals in the networks are assumed to be of the same type. It also assumes that there is only one type of knowledge flow in the network. This dissertation proposes a multi-dimensional perspective of knowledge diffusion networks and examines the patterns of knowledge diffusion with Exponential Random Graph Model (ERGM) based approaches. The objective of this dissertation is to propose a framework that effectively addresses the multi-dimensionality of knowledge diffusion networks, to enable researchers and practitioners to conceptualize the multi-dimensional knowledge diffusion networks in various domains, and to provide implications on how to stimulate and control the knowledge diffusion process. The dissertation consists of three essays, all of which examine the multi-dimensional knowledge diffusion networks in a specific context, but each focuses on a different aspect of knowledge diffusion. Chapter 2 focuses on how structural properties of networks affect various types of knowledge diffusion processes in the domain of commercial technology. The study uses ERGM to simultaneously model multiple types of knowledge flows and examine their interactions. The objective is to understand the impacts of network structures on knowledge diffusion processes. Chapter 3 focuses on examining the impact of individual attributes and the attributes of knowledge on knowledge diffusion in the context of scientific innovation. Based on social capital theory, the study also utilizes ERGM to examine how knowledge transfer and knowledge co-creation can be affected by the attributes of individual researchers and the attributes of scientific knowledge. Chapter 4 considers the dynamic aspect of knowledge diffusion and proposes a novel network model extending ERGM to identify dynamic patterns of knowledge diffusion in social media. In the proposed model, dynamic patterns in social media networks are modeled based on the nodal attributes of individuals and the temporal information of network ties.
Type:
text; Electronic Dissertation
Keywords:
knowledge diffusion; Multi-dimensionality; Network dynamics; Social media; Social network analysis; Management Information Systems; Exponential random graph model
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Management Information Systems
Degree Grantor:
University of Arizona
Advisor:
Chen, Hsinchun

Full metadata record

DC FieldValue Language
dc.language.isoen_USen
dc.titleStatistical Modeling of Multi-Dimensional Knowledge Diffusion Networks: An ERGM-Based Frameworken_US
dc.creatorJiang, Shanen
dc.contributor.authorJiang, Shanen
dc.date.issued2015en
dc.publisherThe University of Arizona.en
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en
dc.description.abstractKnowledge diffusion networks consist of individuals who exchange knowledge and knowledge flows connecting the individuals. By studying knowledge diffusion in a network perspective, it helps us understand how the connections between individuals affect the knowledge diffusion processes. Existing research on knowledge diffusion networks mostly adopts a uni-dimensional perspective, where all the individuals in the networks are assumed to be of the same type. It also assumes that there is only one type of knowledge flow in the network. This dissertation proposes a multi-dimensional perspective of knowledge diffusion networks and examines the patterns of knowledge diffusion with Exponential Random Graph Model (ERGM) based approaches. The objective of this dissertation is to propose a framework that effectively addresses the multi-dimensionality of knowledge diffusion networks, to enable researchers and practitioners to conceptualize the multi-dimensional knowledge diffusion networks in various domains, and to provide implications on how to stimulate and control the knowledge diffusion process. The dissertation consists of three essays, all of which examine the multi-dimensional knowledge diffusion networks in a specific context, but each focuses on a different aspect of knowledge diffusion. Chapter 2 focuses on how structural properties of networks affect various types of knowledge diffusion processes in the domain of commercial technology. The study uses ERGM to simultaneously model multiple types of knowledge flows and examine their interactions. The objective is to understand the impacts of network structures on knowledge diffusion processes. Chapter 3 focuses on examining the impact of individual attributes and the attributes of knowledge on knowledge diffusion in the context of scientific innovation. Based on social capital theory, the study also utilizes ERGM to examine how knowledge transfer and knowledge co-creation can be affected by the attributes of individual researchers and the attributes of scientific knowledge. Chapter 4 considers the dynamic aspect of knowledge diffusion and proposes a novel network model extending ERGM to identify dynamic patterns of knowledge diffusion in social media. In the proposed model, dynamic patterns in social media networks are modeled based on the nodal attributes of individuals and the temporal information of network ties.en
dc.typetexten
dc.typeElectronic Dissertationen
dc.subjectknowledge diffusionen
dc.subjectMulti-dimensionalityen
dc.subjectNetwork dynamicsen
dc.subjectSocial mediaen
dc.subjectSocial network analysisen
dc.subjectManagement Information Systemsen
dc.subjectExponential random graph modelen
thesis.degree.namePh.D.en
thesis.degree.leveldoctoralen
thesis.degree.disciplineGraduate Collegeen
thesis.degree.disciplineManagement Information Systemsen
thesis.degree.grantorUniversity of Arizonaen
dc.contributor.advisorChen, Hsinchunen
dc.contributor.committeememberChen, Hsinchunen
dc.contributor.committeememberGoes, Paulo B.en
dc.contributor.committeememberBrown, Susan A.en
dc.contributor.committeememberLin, Mingfengen
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