Thesis Format
Integrated Article
Degree
Doctor of Philosophy
Program
Business
Supervisor
Wang, Shane (xin)
Affiliation
Virginia Tech
Abstract
Studying the design and management of digital content is imperative for individuals and organizations that aim to thrive in the digital age. This dissertation investigates the design of digital content in three aspects: (1) consumer reviews for e-commerce, (2) online news headlines, and (3) multimedia content. By applying diverse methodologies, this dissertation makes a substantial contribution to the understanding of online word-of-mouth and digital content creation. Three essays form the core of this dissertation.
In Essay 1, I investigate how the differential impact of image content in online reviews affects review helpfulness and consumer purchase intentions. I find that images featuring focal products (consumption contexts) are more helpful for hedonic (utilitarian) products. In Essay 2, I examine the effects of opinion- vs. fact-based news headlines on user engagement and discovered a trade-off between volume-based engagement (e.g., intention to read, number of comments) and valence-based engagement (e.g., the ratio of upvote vs. downvote). In Essay 3, I apply machine learning techniques to create a novel metric to quantify the extent of emotional fit between the image and audio content of NFTs. The results show that emotional similarity between image and audio is positively associated with NFT prices, and this effect is moderated by product rarity. In summary, this dissertation provides insights for businesses to assist them in crafting and managing online digital content.
Summary for Lay Audience
For individuals and businesses who want to succeed in today's digital age, understanding how to create and handle digital content is important. This study explores how to create digital content in three different ways: looking at what people say about products online, examining how news headlines are written, and retrieving useful business insights from pictures, videos, and sounds. By combining different approaches and bringing together findings from multiple studies, this research helps us understand how people communicate online and how businesses can improve digital content creation.
This research has three main sections. The first part of the dissertation investigates the impact that pictures in online reviews have not only on whether people find the reviews helpful, but also on the likelihood of their purchasing the products. The results show that pictures showing the products being used, as opposed to pictures showing the context of consumption, are helpful, especially for things that are purchased for pleasure and enjoyment. The second part of the essay explores how news headlines influence people’s decisions to read and discuss news. News headlines with opinions spark more conversation but decrease the readers’ trust and approval of the news. The third part uses advanced machine learning techniques to assess how well pictures and sounds match in digital content. When the emotions of pictures and sounds match, that content tends to be worth more. This effect is also greater for rarer content. In summary, this research provides businesses with feasible advice on how to create and manage online digital content.
Recommended Citation
Ding, Meng Qi, "Online Content Design with Unstructured Data" (2024). Electronic Thesis and Dissertation Repository. 10003.
https://ir.lib.uwo.ca/etd/10003