Tag Archives: LLM

Large Language Models in the Institutional Press: Investigating the Effects on News Production and Consumption

Zhang, Xiaoke, Myunghwan Lee, Mi Zhou, Gene Moo Lee. “Large Language Models in the Institutional Press: Investigating the Effects on News Production and Consumption,” R&R, MIS Quarterly.

  • Presentations: UBC (2024), DS (2024), CIST (2024), BIGS (2024), JUSWIS (2025)
  • Industry partner: Muhayu

The rapid advancements of Large Language Models (LLMs) have introduced new opportunities and challenges for the institutional press. Utilizing a mixed-method approach, this paper combines two qualitative studies and two sets of large-scale quantitative studies to theorize and empirically examine how LLM assistance affects news production and consumption. We begin with open-ended surveys and interviews with 12 journalists to identify three key constructs central to the journalistic value–source quality, publication promptness, and reader engagement – and formulate our research questions. To empirically examine these dimensions, we compile a comprehensive dataset of 2,060,894 news articles sampled from 111 major South Korean media outlets. We collaborate with industry experts to fine-tune a Korean-language LLM detector to identify undisclosed LLM usage in the news corpus and leverage GPT-4.1 to label information sources in each article. Our event-level analysis reveals that while LLM assistance expedites news publication, it is associated with a reduction in both the number and quality of sources, as well as a decline in reader engagement. To further investigate the impact of LLM adoption on journalists’ long-term information sourcing behaviors, we conduct a journalist-level analysis using staggered Difference-in-Differences. Results reveal that journalists reduce the use of primary, unaffiliated, and contextual sources after LLM adoptions, and alarmingly, these negative effects are enlarging over time. Drawing on the information foraging theory and a second-wave of qualitative study with 32 journalists, we explore the underlying mechanisms and posit that the negative effects of LLM adoption on source quality are driven by a combination of LLM limitations and long-term shifts in journalists’ behaviors. We conclude by proposing actionable guidelines for the institutional press on combining technical solutions with organizational policies to mitigate the negative effects and facilitate the responsible integration of LLMs. Our findings contribute to the growing literature on the digital transformation of journalism.

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.