Category Archives: Working Papers

The New Industrial Revolution: AI, Labor Unions, and the Future of Work

Park, Jiyong, Myunghwan Lee, Yoonseock Son, Gene Moo Lee “The New Industrial Revolution: AI, Labor Unions, and the Future of Work”, Work-in-Progress.

  • First three authors equally contributed to the work.
  • Presentations: CIST (2024), WISE (2024).

As a general-purpose technology, Artificial Intelligence (AI) has profoundly transformed the human labor landscape with its capability to perform complex tasks. Despite ongoing debates about AI’s role in the future of work, there remains a limited understanding of how a firm’s endeavors to strengthen its AI capabilities interweave with its workforce. Reflecting on the ongoing role of labor unions in responding to technological advancements, we investigate the impact of unionization on AI investments and their subsequent effects on firm value. Utilizing datasets on labor elections and AI-skilled labor, we construct measures for unionization and AI investment. Based on a regression discontinuity design framework, our results indicate that while AI investments lead to an improvement in firm value, unionization decreases AI investments in the following 5 years, implying that labor unions have an indirect negative impact on firm value by hampering AI investments. We also provide suggestive evidence of the competition between firms and labor unions in resource allocation between AI investments and employment as a potential mechanism through which labor unions may hinder AI investments. This research advances our understanding of corporate AI strategies by recognizing labor unions as a crucial stakeholder, enriching the discourse on the literature on the business value of AI and IT.

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.

Exploring the Influence of Machine Learning on Organizational Learning: An Empirical Analysis of Publicly Listed Organizations

Lee, Myunghwan, Timo Sturm, Gene Moo Lee “Exploring the Influence of Machine Learning on Organizational Learning: An Empirical Analysis of Publicly Listed Organizations”, Work-in-Progress.

  • Presentations: JUSWIS 2024, KrAIS Summer 2024
  • Best Short Paper Award at KrAIS Summer Workshop 2024.

We contribute to the literature on the role of machine learning (ML) in organizational learning by examining two key learning tendencies: exploitation and exploration. We analyze the effect of ML investments on organizations’ learning tendency, which in turn influences firm performance and survival. Our findings suggest that ML primarily shifts organizations towards exploration and that ML-induced learning tendency fully mediates the positive relationship between ML investments and organization survival. Notably, we find that non-IT organizations with exploitative tendencies can effectively shift towards exploration through ML investments. To our knowledge, this study provides the first large-scale empirical insights into ML’s impact on organizations’ learning tendency and performance outcomes, offering valuable insights for rethinking organizational learning in the age of ML.

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)

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. 

Anatomy of Phishing Tactics and Susceptibility

Bera, Debalina, Gene Moo Lee, Dan J. Kim “Anatomy of Phishing Tactics and Susceptibility: An Investigation of the Dynamics of Phishing Tactics and Contextual Traits in Susceptibility,” Working Paper.

Phishing is a deceptive tactic to create a front of apparent credibility to fraudulently acquire sensitive personal or financial information from an unsuspecting user or espionage system by infiltrating malware or crimeware. Despite automated technological solutions and training interventions, recent phishing statistics show that specifically few phishing tactics are increasing users’ phishing susceptibility (PS). Further, assessing the moderating role of phishing contextual traits in the relationship between phishing tactics and PS indicates the importance of their trait differences. Based on theoretical postulation, employing a sequential mixed method design, and using two sets of data (simulated phishing penetration testing results and scenario-based experiments), we examine the effect of phishing tactics along with the moderating role of individual phishing contextual traits on PS. This study extends the theoretical boundary relevant to phishing tactics and provides practical guidance to identify the most dangerous phishing tactics that increase PS and phishing contextual traits that help to combat phishing attacks.

The Effect of Mobile Device Management on Work-from-home Productivity: Insights from U.S. Public Firms

Park, Jaecheol, Myunghwan Lee, Gene Moo Lee “The Effect of Mobile Device Management on Work-from-home Productivity: Insights from U.S. Public Firms”, Work-in-Progress.

  • Presentations: UBC 2023, MSISR 2023, KrAIS 2023, WeB 2023, AOM 2024
  • Best Paper Nomination at WeB 2023

The use of mobile IT, providing employees with accessibility, flexibility, and connectivity, has become increasingly vital for businesses, especially for work-from-home during the COVID-19 pandemic. However, despite its prevalence and importance in the industry, the business value of mobile device management (MDM) and its role in establishing digital resilience remain underexplored in the literature. To address this research gap, our study examines the effect of MDM on a firm’s resilience to the pandemic. Drawing on the resource-based view (RBV), we find that firms with MDM have better financial performance during the pandemic, demonstrating greater resilience to the shock. Additionally, we explore the moderating role of external and internal factors, revealing that firms with high environmental munificence or those with low IT capabilities experience greater resilience effects from MDM. Furthermore, we observe heterogeneous effects across industries that firms in industry sectors demanding greater mobility have a greater resilience effect from MDM. This study contributes to the information systems literature by emphasizing the business value of MDM and its crucial role in building digital resilience.

Disrupt with AI: The Impact of Deep Learning Capabilities on Exploratory Innovation

Lee, Myunghwan, Victor Cui, Gene Moo Lee. “Disrupt with AI: The Impact of Deep Learning Capabilities on Exploratory Innovation”, AOM 2023

Given the importance of exploratory innovation in fostering firms’ sustainable competitive advantages, firms often depend on technological assets or inter-firm relationships to pursue exploration. Regarded as a general-purpose technology, deep learning (DL)-based artificial intelligence (AI) can be an exploratory innovation-seeking instrument for firms in searching unexplored resources and thereby broadening their boundary. Drawing on the theories of organizational learning and path dependence, we hypothesize the impact of a firm’s DL capabilities on exploratory innovation and how DL capabilities interact with conventional pathbreaking activities such as technical assets and inter-firm relationships. Our empirical investigations, based on a novel DL capabilities measure constructed from comprehensive datasets on AI conferences and patents, show that DL capabilities have positive impacts on exploratory innovation. The results also show that extant technological assets (i.e., structured data management capabilities) and inter-firm relationships remedy the constraints on a firm’s innovation-seeking behaviors and that these path-breaking activities negatively moderate the positive impact of DL capabilities on exploratory innovation. To our knowledge, this is the first large-scale empirical study to investigate how DL affects exploratory innovation, contributing to the emerging literature on AI and innovation.

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.

  • Presentations: INFORMS DS (2022), UBC (2022), WITS (2022), Yonsei (2023), POSTECH (2023), ISMS MKSC (2023), CSWIM (2023), KrAIS Summer (2023), Dalhousie (2023), CIST (2023), Temple (2024), Santa Clara U (2024), Wisconsin Milwaukee (2024)
  • Best Student Paper Nomination at CIST 2023; Best Paper Runner-Up Award at KrAIS 2023
  • Media coverage: [UBC News] [Global News]
  • API sponsored by Ensemble Data
  • SSRN version: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4676705

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.

Ideas are Easy but Execution is Everything: Measuring the Impact of Stated AI Strategies and Capability on Firm Innovation Performance

Lee, Myunghwan, Gene Moo Lee (2022) “Ideas are Easy but Execution is Everything: Measuring the Impact of Stated AI Strategies and Capability on Firm Innovation Performance”, Work-in-Progress.

Contrary to the promise that AI will transform various industries, there are conflicting views on the impact of AI on firm performance. We argue that existing AI capability measures have two major limitations, limiting our understanding of the impact of AI in business. First, existing measures on AI capability do not distinguish between stated strategies and actual AI implementations. To distinguish stated AI strategy and actual AI capability, we collect various AI-related data sources, including AI conferences (e.g., NeurIPS, ICML, ICLR), patent filings (USPTO), inter-firm transactions related to AI adoption (FactSet), and AI strategies stated in 10-K annual reports. Second, while prior studies identified successful AI implementation factors (e.g., data integrity and intelligence augmentation) in a general context, little is known about the relationship between AI capabilities and in-depth innovation performance. We draw on the neo-institutional theory to articulate the firm-level AI strategies and construct a fine-grained AI capability measure that captures the unique characteristics of AI-strategy. Using our newly proposed AI capability measure and a novel dataset, we will study the impact of AI on firm innovation, contributing to the nascent literature on managing AI.

Do Incentivized Reviews Poison the Well? Evidence from a Natural Experiment at Amazon.com

Park, Jaecheol, Arslan Aziz, Gene Moo Lee. “Do Incentivized Reviews Poison the Well? Evidence from a Natural Experiment at Amazon.com”, Working Paper.

  • Presentations: UBC (2021), KrAIS (2021), WISE (2021), PACIS (2022), SCECR (2022), BU Platform (2022), CIST (2022), BIGS (2022)
  • Preliminary version in PACIS 2022 Proceedings

The rapid growth in e-commerce has led to a concomitant increase in consumers’ reliance on digital word-of-mouth to inform their choices. As such, there is an increasing incentive for sellers to solicit reviews for their products. The literature has examined the direct and indirect effects of incentivized reviews on subsequent organic reviews within consumers who received incentives. However, since incentivized reviews and reviewers are often only a small proportion of a review platform (only 1.2% in our sample), it is important to understand whether their presence and absence on the platform affect the organic reviews from other reviewers who have not received incentives, which are often in the majority. We theorize two underlying effects that incentivized reviews can generate on other organic reviews: the herding effect from imitating incentivized reviews and the disclosure effect from the increased trust or skepticism by explicit incentive disclosure statements. Those two effects make organic reviews either follow or deviate from incentivized reviews. Using Bidirectional Encoder Representations from Transformers (BERT) to identify incentivized reviews and a natural experiment caused by a policy change on Amazon.com in October 2016, we conduct difference-in-differences with propensity score matching analyses to identify the effects of banning incentivized reviews on organic reviews. Our results suggest the disclosure effects are salient: banning incentivized reviews has positive effects on organic reviews in terms of frequency, sentiment, length, image, and helpfulness. Moreover, we find that the presence of incentivized reviews has poisoned the well for organic reviews regardless of the incentivized review ratio and that the effect is heterogeneous to product quality uncertainty. Our findings contribute to the literature on online review and platform design and provide insights to platform managers.