Advisor

Charles M. Weber

Date of Award

Summer 8-6-2015

Document Type

Dissertation

Degree Name

Doctor of Philosophy (Ph.D.) in Technology Management

Department

Engineering and Technology Management

Physical Description

1 online resource (xv, 528 pages)

Subjects

Online social networks -- Marketing, Online social networks -- Influence, Information technology -- Research

DOI

10.15760/etd.2463

Abstract

Traditional marketing models are swiftly being upended by the advent of online social networks. Yet, practicing firms that are engaging with online social networks neither have a reliable theory nor sufficient practical experience to make sense of the phenomenon. Extant theory in particular is based on observations of the real world, and may thus not apply to online social networks. Practicing firms may consequently be misallocating a large amount of resources, simply because they do not know how the online social networks with which they interact are organized.

The purpose of this dissertation is to investigate how online social networks that are in stark contrast to real-world social networks behave and how they get organized. In particular, I explore how network structure and information flow within the network impact each other, and how they affect the phenomenon of influence in online social networks. I have collected retrospective data from Twitter conversations about six YouTube product categories (Music, Entertainment, Comedy, Science, Howto and Sports) in continuous time for a period of three months. Measures of network structure (Scale Free Metric, Assortativity and Small World Metric), network flows (Total Paths, Total Shortest Paths, Graph Diameter, Average Path Length, and Average Geodesic Length) and influence (Eigenvector Centrality/Centralization) were computed from the data. Experimental measures such as power law distributions of paths, shortest paths and nodal eigenvector centrality were introduced to account for node-level structure. Factor analysis and regression analysis were used to analyze the data and generate results.

The research conducted in this dissertation has yielded three significant findings.

1. Network structure impacts network information flow, and conversely; network flow and network structure impact the network phenomenon of influence. However, the impact of network structure and network flow on influence could not be identified in all instances, suggesting that it cannot be taken for granted.

2. The nature of influence within a social network cannot be understood just by analyzing undirected or directed networks. The behavioral traits of individuals within the network can be deduced by analyzing how information is propagated throughout the network and how it is consumed.

3. An increase or decrease in the scale of a network leads to the observation of different organizational processes, which are most likely driven by very different social phenomena. Social theories that were developed from observing real-world networks of a relatively small scale (hundreds or thousands of people) consequently do not necessarily apply to online social networks, which can exhibit significantly larger scale (tens of thousands or millions of people).

The primary contribution of this dissertation is an enhanced understanding of how online social networks, which exhibit contrasting characteristics to social networks that have been observed in the real world, behave and how they get organized. The empirical findings of this dissertation may allow practicing managers that engage with online social networks to allocate resources more effectively, especially in marketing. The primary limitations of this research are the inability to identify the causes of change within networks, glean demographic information and generalize across contexts. These limitations can all be overcome by follow-on studies of networks that operate in different contexts. In particular, further study of a variety of online social networks that operate on different social networking platforms would determine the extent to which the findings of this dissertation are generalizable to other online social networks. Conclusions drawn from an aggregation of these studies could serve as the foundation of a more broadly-based theory of online social networks.

Persistent Identifier

http://archives.pdx.edu/ds/psu/15888

373383_supp_C881E266-2970-11E5-ADE0-3D3FEF8616FA.pdf (583 kB)
Detailed correlations of all the variables in Comedy, Entertainment, Music, Howto, Science and Sports product categories

373383_supp_384283_07FC0514-3166-11E5-9D96-2D73EF8616FA.pdf (322 kB)
Daily values of all variables of Comedy Network in Consumption Phase for 91 days

373383_supp_384284_0E021E6C-3166-11E5-B46B-6373EF8616FA.pdf (320 kB)
Daily values of all variables of Comedy Network in Directed Phase for 91 days

373383_supp_384286_13CB33EC-3166-11E5-88A0-7B73EF8616FA.pdf (323 kB)
Daily values of all variables of Comedy Network in Propagation Phase for 91 days

373383_supp_384287_181228F2-3166-11E5-8122-9473EF8616FA.pdf (317 kB)
Daily values of all variables of Comedy Network in Undirected Phase for 91 days

373383_supp_384288_207C176E-3166-11E5-B393-BA73EF8616FA.pdf (322 kB)
Daily values of all variables of Entertainment Network in Consumption Phase for 91 days

373383_supp_384289_25BE51B0-3166-11E5-AFA4-C873EF8616FA.pdf (323 kB)
Daily values of all variables of Entertainment in Directed Phase for 91 days

373383_supp_384290_2C8AADCC-3166-11E5-BC8E-DE73EF8616FA.pdf (322 kB)
Daily values of all variables of Entertainment Network in Propagation Phase for 91 days

373383_supp_384292_31E283D0-3166-11E5-881B-8674EF8616FA.pdf (317 kB)
Daily values of all variables of Entertainment Network in Undirected Phase for 91 days

373383_supp_384293_39862A06-3166-11E5-80C2-B274EF8616FA.pdf (320 kB)
Daily values of all variables of Howto Network in Consumption Phase for 91 days

373383_supp_384294_3F8D3890-3166-11E5-842D-D774EF8616FA.pdf (320 kB)
Daily values of all variables of Howto in Directed Phase for 91 days

373383_supp_384295_464C9996-3166-11E5-8FA2-EC74EF8616FA.pdf (321 kB)
Daily values of all variables of Howto Network in Propagation Phase for 91 days

373383_supp_384297_4B88CBA0-3166-11E5-B704-0775EF8616FA.pdf (315 kB)
Daily values of all variables of Howto Network in Undirected Phase for 91 days

373383_supp_384298_58E5A7D2-3166-11E5-B821-3575EF8616FA.pdf (326 kB)
Daily values of all variables of Music Network in Consumption Phase for 91 days

373383_supp_384299_5E9DD136-3166-11E5-8729-4175EF8616FA.pdf (325 kB)
Daily values of all variables of Music in Directed Phase for 91 days

373383_supp_384300_634F61D6-3166-11E5-884B-0676EF8616FA.pdf (326 kB)
Daily values of all variables of Music Network in Propagation Phase for 91 days

373383_supp_384301_6AA1921A-3166-11E5-A794-2276EF8616FA.pdf (316 kB)
Daily values of all variables of Music Network in Undirected Phase for 91 days

373383_supp_384302_70F46700-3166-11E5-A423-3776EF8616FA.pdf (322 kB)
Daily values of all variables of Science Network in Consumption Phase for 91 days

373383_supp_384303_79808048-3166-11E5-8CB5-7B76EF8616FA.pdf (322 kB)
Daily values of all variables of Science in Directed Phase for 91 days

373383_supp_384304_7D5DE61A-3166-11E5-9CA1-9876EF8616FA.pdf (322 kB)
Daily values of all variables of Science Network in Propagation Phase for 91 days

373383_supp_384305_8190624E-3166-11E5-889B-A676EF8616FA.pdf (315 kB)
Daily values of all variables of Science Network in Undirected Phase for 91 days

373383_supp_384306_8C762202-3166-11E5-9873-FA76EF8616FA.pdf (323 kB)
Daily values of all variables of Sports Network in Consumption Phase for 91 days

373383_supp_384307_90EFED5E-3166-11E5-96F9-1677EF8616FA.pdf (331 kB)
Daily values of all variables of Sports in Directed Phase for 91 days

373383_supp_384308_96BAF350-3166-11E5-BA3C-5277EF8616FA.pdf (323 kB)
Daily values of all variables of Sports Network in Propagation Phase for 91 days

373383_supp_384310_9FB0C804-3166-11E5-B416-8B77EF8616FA.pdf (317 kB)
Daily values of all variables of Sports Network in Undirected Phase for 91 days

373383_supp_C98466E6-2972-11E5-BC90-610C2E1BA5B1.pdf (7227 kB)
Detailed Factor Analysis output of all the variables in Comedy, Entertainment, Music, Science and Sports product categories

373383_supp_D648A176-2972-11E5-A419-A40C2E1BA5B1.pdf (248 kB)
Daily values of Meta Data for Comedy, Entertainment, Music, Howto, Science and Sports product categories for 91 days

373383_supp_384313_F6972398-3166-11E5-A476-4AF42D1BA5B1.pdf (12460 kB)
Detailed Regression Analysis output of all the variables in Music product category

373383_supp_384314_F1E6D316-3166-11E5-8171-F977EF8616FA.pdf (11494 kB)
Detailed Regression Analysis output of all the variables in Entertainment product category

373383_supp_384315_FB4C9E90-3166-11E5-B1A5-0678EF8616FA.pdf (11593 kB)
Detailed Regression Analysis output of all the variables in Comedy product category

373383_supp_384317_10DE8836-3167-11E5-9F32-8DF42D1BA5B1.pdf (15024 kB)
Detailed Regression Analysis output of all the variables in Sports product category

373383_supp_384318_0D307532-3167-11E5-A697-98F42D1BA5B1.pdf (14698 kB)
Detailed Regression Analysis output of all the variables in Science product category

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