Tag Archives: generative AI

News Speed vs. Quality: Investigating Large Language Models’ Impact on Modern Journalism

Zhang, Xiaoke, Myunghwan Lee, Mi Zhou, Gene Moo Lee “News Speed vs. Quality: Investigating Large Language Models’ Impact on Modern Journalism”, Work-in-progress.

  • Presentations: UBC (2024), DS (2024), CIST (2024)

With the advancement of generative artificial intelligence (AI), news outlets are increasingly incorporating large language models (LLMs) into their workflow to increase news productivity and quality. Utilizing a unique empirical setting where two major news organizations in South Korea introduced LLM-based news writing assistants, this study examines how LLM assistance affects news production and consumption. We first developed a novel framework using GPT-4o to extract information sources from news articles. We then constructed a unique dataset of 571 LLM-assisted news articles and 3,489 competing human-generated articles covering the same events. Using the DiNardo-Fortin-Lemieux reweighting method to ensure comparability between the LLM-assisted and human-generated news, our empirical analysis reveals that LLM assistance significantly increases news publication speed but reduces the diversity of information sources in news articles. Furthermore, LLM-assisted news is associated with decreased reader consumption, a trend exacerbated by reduced source diversity even with faster publication speed. Our findings contribute to the broader literature on generative AI’s role in professional content creation.

Unpacking AI Transformation: The Impact of AI Strategies on Firm Performance with a Dual Lens on Product and Process Orientation

Park, Jaecheol, Myunghwan Lee, J. Frank Li, Gene Moo Lee “Unpacking AI Transformation: The Impact of AI Strategies on Firm Performance with a Dual Lens on Product and Process Orientation”, Work-in-Progress.

  • Presentations: UBC (2024), INFORMS (2024)

Artificial intelligence (AI) technologies hold great potential for large-scale economic impact. Aligned with this trend, recent studies explore the adoption impact of AI technologies on firm performance. However, they predominantly measure AI capabilities with input (e.g., labor/job posting), neglecting to consider the strategic use of such AI input in business operations and value creation. In this paper, we empirically examine how firms’ strategic AI orientation affects firm performance with a dual-lens on product and process orientation. We create a novel firm-year-level AI orientation measure by employing a large language model to analyze business descriptions in Form 10-K filings and identify an increasing trend of AI disclosure among U.S. public firms. By further dissecting firms’ AI disclosure into AI mention and AI (product and process) orientation, our long-difference analyses show that AI orientation significantly affects costs, sales, and market value but AI mention does not, showing the importance of strategic deployment of AI to create business value. Moreover, we find the heterogeneous effects across AI product orientation and AI process orientation on performance. This study contributes to the recent AI management literature by providing the strategic role of AI orientation on firm performance.

Xiaoke Zhang’s Master’s Thesis

Xiaoke Zhang (2023). “How Does AI-Generated Voice Affect Online Video Creation? Evidence from TikTok”, Master’s Thesis, University of British Columbia

Supervisors: Gene Moo Lee, Mi Zhou

The rising demand for online video content has fostered one of the fastest-growing markets as evidenced by the popularity of platforms like TikTok. Because video content is often difficult to create, platforms have attempted to leverage recent advancements in artificial intelligence (AI) to help creators with their video creation process. However, surprisingly little is known about the effects of AI on content creators’ productivity and creative patterns in this emerging market. Our paper investigates the adoption impact of AI-generated voice – a generative AI technology creating acoustic artifacts – on video creators by empirically analyzing a unique dataset of 4,021 creators and their 428,918 videos on TikTok. Utilizing multiple audio and video analytics algorithms, we detect the adoption of AI voice from the massive video data and generate rich measurements for each video to quantify its characteristics. We then estimate the effects of AI voice using a difference-in-differences model coupled with look-ahead propensity score matching. Our results suggest that the adoption of AI voice increases creators’ video production and that it induces creators to produce shorter videos with more negative words. Interestingly, creators produce more novel videos with less self-disclosure when using AI voice. We also find that AI-voice videos received less viewer engagement unintendedly. Our paper provides the first empirical evidence of how generative AI reshapes video content creation on online platforms, which provides important implications for creators, platforms, and policymakers in the digital economy.

 

How Does AI-Generated Voice Affect Online Video Creation? Evidence from TikTok

Zhang, Xiaoke, Mi Zhou, Gene Moo Lee “How Does AI-Generated Voice Affect Online Video Creation? Evidence from TikTok”, Working Paper.

The rising demand for online video content has fostered one of the fastest-growing markets as evidenced by the growing popularity of platforms like TikTok. In response to the challenges of video creation, these platforms are increasingly incorporating artificial intelligence (AI) to support creators in their video creation process. However, little is known about how AI integration influences online content creation. Our paper aims to address this gap by investigating the impact of AI-generated voice on video creators’ productivity and creative patterns. Using a comprehensive dataset of 554,252 videos from 4,691 TikTok creators, we conduct multimodal analyses of the video data to detect the adoption of AI voice and to quantify video characteristics. We then estimate the adoption effects using a stacked difference-in-differences model coupled with propensity score matching. Our results suggest that AI voice adoption significantly increases creator productivity. Moreover, we find that the use of AI voice enhances video novelty across image, audio, and text modalities, suggesting its role in reducing workload on routine tasks and fostering creative exploration. Lastly, our study also uncovers a disinhibition effect, where creators tend to conceal their identities with the AI voice and exert more negative sentiments because of diminished social image concerns. Our paper provides the first empirical evidence of how AI reshapes online video creation, providing important implications for creators, platforms, and policymakers in the creator economy.

Seeing the Unseen: The Effects of Implicit Representation in an Online Dating Platform

Kwon, Soonjae, Gene Moo Lee, Dongwon Lee, Sung-Hyuk Park (2024) “Seeing the Unseen: The Effects of Implicit Representation in an Online Dating Platform,” Working Paper.

  • Previous title: Learning Faces to Predict Matching Probability in an Online Dating Market
  • Presentations: DS (2021), AIMLBA (2021), WITS (2021), ICIS (2022)
  • Preliminary version in ICIS 2022 Proceedings
  • Based on an industry collaboration

This study investigates the effects of implicit preference-based representation on user engagement and matching outcomes in two-sided platforms, focusing on an online dating context. We develop a novel approach using explainable AI and generative AI to create personalized representations that reflect users’ implicit preferences. Through extensive matching simulations, we demonstrate that implicit representation significantly enhances both user engagement and matching outcomes across various recommendation algorithms. Our findings reveal heterogeneous effects driven by positive cross-side and same-side network effects, which vary depending on the gender distribution within the platform. This research contributes to understanding implicit representation in two-sided platforms and offers insights into the transformative potential of generative AI in digital ecosystems.