Full metadata
Title
Digital Media Analytics: Towards an Understanding of Content Design and Social Media Promotion
Description
Digital media refers to any form of media which depends on electronic devices for its creation, distribution, view, and storage. Digital media analytics involves qualitative and quantitative analysis from the business to understand users’ behaviors. This technique brings disruptive changes to many industries and its path of economic disruption is getting wider and wider. Under the context of the increasingly popular digital media market, this dissertation investigates what are the best content delivery strategy and the new cultural phenomenon: Internet Water Army. The first essay proposes a theory-guided computational approach that consolidates distinct data sources spanning unstructured text, image, and video data, systematically measures modes of persuasion, and unveils the multimedia content design strategies for crowdfunding projects. The second essay studies whether using the Internet Water Army helps sales and under what conditions it helps. This study finds that the Internet water army helps product sales at both post-level and fans-level. The effect is largely reflected by changing the number of emotional fans. Furthermore, the earlier to purchase the water armies, more haters, likers, and neutral fans it can attract. The last essay builds a game model to study the trade- off between honestly promoting the product according to their evaluation and catering to the consumer’s prior belief on the product quality to stay on the market as long as possible. It provides insights on the optimum usage of promotion on social media and demonstrate how conventional wisdom about negative reviews will hurt business may be misleading in the presence of social media. These three studies jointly contribute to the crowdfunding and social media studies literature by elucidating the content delivery strategy, and the impact and purchasing strategy of the Internet Water Army.
Date Created
2020
Contributors
- YIN, XUEYAN (Author)
- Chen, Pei-Yu (Thesis advisor)
- Gu, Bin (Committee member)
- Shi, Zhan (Committee member)
- Benjamin, Victor (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
169 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.I.62704
Level of coding
minimal
Note
Doctoral Dissertation Business Administration 2020
System Created
- 2020-12-08 11:58:08
System Modified
- 2021-08-26 09:47:01
- 3 years 3 months ago
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