Tag Archives: LLM

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

Zhang, Xiaoke, Myunghwan Lee, Mi Zhou, Gene Moo Lee “News Quality vs. Promptness: Investigating Large Language Models’ Impact on Modern Journalism,” Working Paper.

  • Presentations: UBC (2024), DS (2024), CIST (2024), BIGS (2024)
  • Industry partner: Muhayu

The advancement of digital technologies has transformed the news industry, with large language models (LLMs) introducing new opportunities and challenges for the institutional press. Utilizing a unique setting where two major news institutions in South Korea introduced LLM-based news writing assistants, this study investigates how LLM assistance affects journalists’ news production and readers’ engagement using a mixed-method approach. We first conduct interviews and surveys with journalists to identify key constructs in journalism and develop a research model on the emerging phenomenon. We then empirically examine the impacts of LLM assistance on news production and engagement using a unique dataset of 941 LLM-assisted articles and 7,230 human-written articles on identical events. Notably, we develop a novel framework using GPT-4o to extract information sources from a large number of news articles. Our results show that LLM assistance reduces the diversity and uniqueness of information sources in news articles but increases publication promptness. Furthermore, LLM-assisted news is associated with decreased reader engagement, a trend exacerbated by reduced source diversity and uniqueness despite faster publication. This study contributes to the understanding of generative AI’s role in journalism and provides implications for the institutional press navigating the integration of AI technologies in journalistic practices.

Unpacking AI Transformation: The Impact of AI Strategies on Firm Performance from the Dynamic Capabilities Perspective

Park, Jaecheol, Myunghwan Lee, J. Frank Li, Gene Moo Lee “Unpacking AI Transformation: The Impact of AI Strategies on Firm Performance from the Dynamic Capabilities Perspective,” Work-in-Progress.

  • Presentations: UBC (2024), CIST (2024), INFORMS (2024), SNU (2024), UMass (2024), BIGS (2024), KrAIS (2024), CityU Hong Kong (2025), NTU (2025), AIM (2025)
  • Best Paper Award at BIGS 2024
  • Best Student Paper Award at KrAIS 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 firms’ AI capabilities with input (e.g., labor/job posting) or output (e.g., patents), neglecting to consider the strategic direction toward AI in business operations and value creation. In this paper, we empirically examine how firms’ AI strategic orientation affects firm performance from the dynamic capabilities perspective. We create a novel firm-year AI strategic orientation measure by employing a large language model to analyze business descriptions in Form 10-K filings and identify an increasing trend and changing status of AI strategies among U.S. public firms. Our long-difference analysis shows that AI strategic orientation is associated with greater operating cost, capital expenditure, and market value but not sales, showing the importance of strategic direction toward AI to create business value. By further dissecting firms’ AI strategic orientation into AI awareness, AI product orientation, and AI process orientation, we find that AI awareness is generally not related to performance, that AI product orientation is associated with short-term increased operating expenses and long-term market value, and that AI process orientation is associated with long-term increased costs and sales. Moreover, we find the negative moderating effect of environmental dynamism on AI process orientation. This study contributes to the recent AI strategy and management literature by providing the strategic role of AI orientation on firm performance.