I cordially invite you to attend my dissertation proposal scheduled for Monday, Sept. 29th, 10:00 AM - 11:00 AM EST. The location will be Exec Ed Room 312, Scheller College of Business.

You are also welcome to join remotely via Zoom: 

The abstract of the proposal can be found below. Copies of the proposal will be available upon request.

Best Regards,

Qixin Ye

PhD student in Information Technology Management

Scheller College of Business | Georgia Institute of Technology

 ____________________________________________ 

Area: Information Technology Management

Committee Members: Dr. Han Zhang (Chair), Dr. Eric Overby, Dr. Dezhi (Denny) Yin

Title: The Impact of Generative AI Applications in Content Generation

Abstract: The rising popularity of generative artificial intelligence (AI) for creating and improving content necessitates a comprehensive understanding of its impact on the perceptions and behaviors of observers and users. My first essay focuses on AI-augmented reviews—online reviews written initially by human consumers and refined by AI tools. We propose that despite the enhancement of review writing quality after using AI augmentation, the disclosure of this use can reduce perceived review authenticity and sway consumer purchase decisions. We further posit that review valence moderates the relative impact of review authenticity (vs. writing quality), such that the impact is greater for positive reviews than for negative reviews. We conducted two experiments and found evidence supporting these propositions. This study represents an initial exploration at the intersection of online reviews and AI augmentation, reveals an unintended consequence of disclosing AI augmentation, and uncovers the surprising persuasive power of authenticity (vs. writing quality). My second essay focuses on the impact of perceived generative variability of AI on user satisfaction. We define perceived generative variability as a user's subjective assessment of the degree of difference among multiple outputs generated by a generative AI system in response to the same or semantically identical prompts. We propose that high (vs. low) perceived generative variability increases perceived AI creativity, which, in turn, enhances user satisfaction. However, high (vs. low) perceived generative variability decreases perceived AI credibility, which, in turn, reduces user satisfaction. We further posit that the perceived task risk moderates the effect of perceived generative variability on user satisfaction. We designed two experiments to test these hypotheses. Unlike previous studies, which primarily advocate for the variability of AI, this research focuses on perceived generative variability and aims to provide a nuanced understanding of its dual effects in different contexts. In sum, these two essays illuminate the complexities of human-AI interaction in content generation and evaluation by revealing the intricate psychological mechanisms and boundary conditions.