Degree Type

Thesis

Date of Award

2020

Degree Name

Doctor of Philosophy

Department

Apparel, Events and Hospitality Management

Major

Hospitality Management

First Advisor

Liang (Rebecca) Tang

Abstract

In the Internet age, the sheer volume of information can be generated and disseminated through online user-generated content (UGC). Within the context of Twitter, the retweeting function is one of the key mechanisms, which enables the information diffusion process among users in the social network. Stimulated by this concern, the purpose of the current study was to investigate the effects of textual content including the sentiments, emotions, and language style matching (LSM) of Twitter, a series of statistical analyses are conducted to the Twitter dataset with around one million pieces of customer tweet information. The results indicated that sentiments, emotions, and LSM have significant influences on customer retweeting behavior. Besides, significant differences were identified of both sentiments and emotions based on both six periods of the timeline analysis and the geographic distance at the city level, state level, and nationwide level. Discussions and implications interpreted the significance of the most valuable findings and suggested some important insights to both academia and industry.

DOI

https://doi.org/10.31274/etd-20200624-113

Copyright Owner

Xi Wang

Language

en

File Format

application/pdf

File Size

89 pages

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