Tag Archives: AI

From Enthusiasm to Reality: Evaluating Generative AI’s Role in Modern Journalism

Zhang, Xiaoke, Myunghwan Lee, Mi Zhou, Gene Moo Lee “From Enthusiasm to Reality: Evaluating Generative AI’s Role in Modern Journalism”, Work-in-progress.

  • Presentations: UBC (2024)

Generative AI (GenAI), initially greeted with enthusiasm for its potential for content creation, encounters challenges when applied in professional settings such as journalism. These challenges, including the generation of inaccurate outputs, inconsistencies, and a reduction in human accountability, may pose conflicts with the core journalistic values of accuracy, transparency, and credibility. Our research investigates the impact of GenAI in news media leveraging a unique empirical setting when a major news outlet in South Korea launched a GenAI-powered news editor to assist its journalists in news production in December 2023. Our preliminary analysis of 196,288 news articles published between June 2023 and April 2024 suggests that GenAI adoption has not led to a significant increase in productivity, indicating persistent challenges in effectively integrating GenAI into journalistic workflows. Our study seeks to further explore this phenomenon by addressing two primary questions. First, we will conduct a survival analysis to identify effective GenAI strategies that lead to consistent GenAI use and positive outcomes in news production. Second, we will examine the impact of GenAI on the overall media news output (e.g., local vs. global; factual vs. opinion news) and discuss its broader implications in ideology formation (e.g., polarization). This research will contribute to the nascent literature on GenAI’s impact on digital platforms by providing a nuanced understanding of the phenomenon.

Unlocking the Impact of Machine Learning on Organizational Learning: Evidence from US Public Firms

Lee, Myunghwan, Timo Sturm, Gene Moo Lee “Unlocking the Impact of Machine Learning on Organizational Learning: Evidence from US Public Firms”, Work-in-Progress.

  • Presentations: KrAIS Summer 2024

Organizational learning is a critical core process that fundamentally controls organizations’ innovation and thus affects organizational performance and long-term survival. Due to their ability to learn, recent research has recognized the far-reaching influence of machine learning (ML) systems’ contributions to organizational learning. So far, however, the emerging discourse on the role of ML in organizational learning has remained largely theoretical, offering helpful initial insights but inconclusive predictions about ML’s impact. To resolve existing conflicts by adding empirical evidence, we explore the real-world innovations of 265 ML and 700 non-ML organizations from 2006 to 2019. Based on a novel ML measure constructed from ML patents, publications, and transaction datasets, we find that ML primarily contributes to shifting organizational learning toward exploration. Our results further show that ML’s influence depends on external environmental factors: ML’s effect increases with higher levels of market concentration and competitors’ strategic orientation towards ML. Lastly, we find that organizations using ML tend to survive longer due to increased performance and more balanced innovation. To the best of our knowledge, this is the first large-scale empirical study of the impact of ML on organizational learning outcomes, contributing to rethinking organizational learning in the era of ML.

Unpacking the AI Blackbox:  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 the AI Blackbox:  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 have become increasingly pervasive and hold great potential for large-scale economic impact. Aligned with this trend, numerous studies explore the adoption and use of AI technologies on firm performance. However, they predominantly focus on AI as an input (e.g., labor/job posting), neglecting to consider the strategic deployment of AI in business operations. Thus, it is crucial to understand how” and “where” to use AI to achieve business value. In this paper, we examine how firms’ strategic AI orientation affects firm performance with a dual-lens on product and process orientation. We measure AI orientation by employing LLM to assess it from strategy descriptions in Form 10-K filings between 2015 and 2022. Our findings show that 13% of firms have an AI orientation, 7% have an AI product orientation, and 3% have an AI process orientation. Additionally, we will provide some preliminary results on the impact 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.

 

Myunghwan Lee’s PhD Proposal: Three Essays on AI Strategies and Innovation

Myunghwan Lee (2023) “Three Essays on AI Strategies and Innovation”, Ph.D. Dissertation Proposal, University of British Columbia. https://sites.google.com/view/myunghwanlee/home

Supervisor: Gene Moo Lee

Artificial Intelligence (AI) technologies, along with the explosive growth of digitized data, are transforming many industries and our society. While both academia and industry consider AI closely intertwined with innovation, we still have limited knowledge of the business and economic values of AI on innovation. This three-essay dissertation seeks to address this gap (i) by proposing a novel firm-level measure to identify strategically innovative firms; (ii) by examining how firm-level AI capabilities affect knowledge innovation; and (iii) by investigating the impact of robotics, embodied AI with a physical presence, on operational innovation.

In the first essay, we propose a novel firm-level measure, Strategic Competitive Positioning (SCP), to identify distinctive strategic positioning (i.e., first-movers, second-movers) and competition relationships. Drawing on network theory, we develop a structural hole-based, dynamic, and firm-specific SCP measure. Notably, this SCP measure is constructed using unsupervised machine-learning and network analytics approaches with minimal human intervention. Using a large dataset of 10-K annual reports from 13,476 public firms in the U.S., we demonstrate the value of the proposed measure by examining the impact of SCP on subsequent IPO performance.

In the second essay, we study the impact of firm-level AI capabilities on exploratory innovation to determine how AI’s value-creation process can facilitate knowledge innovation. Drawing on March and Simon (1958), we theorize how AI capabilities can help firms overcome bounded rationality and pursue exploratory innovation. We compiled a unique dataset consisting of 54,649 AI conference publications, 3 million patent filings, and 1.9 million inter-firm transactions to test the hypotheses. The findings show that a firm’s AI capabilities have a positive impact on exploratory innovation, and interestingly that conventional exploratory innovation-seeking approaches (e.g., traditional data management capabilities and inter-firm technology collaborations) negatively moderate the positive impact of AI capabilities on exploratory innovation.

The impact of AI technologies can be beyond knowledge innovation. Embodied AI technologies, specifically robotics, are driving operational innovation in manufacturing and service industries. While industrial robots designed for pre-defined tasks in controlled environments are extensively studied, little is known about the impact of AI-based service robots designed for customer-facing dynamic environments. In the third essay, we seek to examine how service robots can affect operational efficiency and service quality using the case of the hospitality industry. The preliminary results from a difference-in-differences model using a dataset of 4,610 restaurants in Singapore demonstrate that service robot adoption increases customer satisfaction, specifically through perceived service quality. To validate the initial result and further explore underlying mechanisms, we plan to collect additional datasets from different geographic areas and industries.

 

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
  • 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.

Papers on AI, Automation, and Robotics

Last update: May 17, 2024

In this post, I am gathering AI, automation, and robotics-related papers in information systems and related disciplines. This is by no means an exhaustive list. I will keep updating this list.

  1. Babina, Tania, Anastassia Fedyk, Alex He, James Hodson (2024) Artificial intelligence, firm growth, and product innovation, Journal of Financial Economics 151.
  2. Eloundou T, Manning S, Mishkin P, Rock D. (2023) GPTs are GPTs: An early look at the labor market impact potential of large language models. arXiv preprint arXiv:2303.10130.
  3. Acemoglu, Daron and Pascual Restrepo (2022) Tasks, Automation, and the Rise in U.S. Wage Inequality, Econometrica, 90(5): 1973-2016,
  4. Park, Jiyong, Jongho Kim (2022) A Data-Driven Exploration of the Race between Human Labor and Machines in the 21st Century, Communications of ACM 65(5):79-87.
  5. Koch, Michael, Manuylov Ilya, Marcel Smolka (2021) Robots and Firms, The Economic Journal 131(638):2553-2584.
  6. Ge, Ruyi, Zhiqiang (Eric) Zheng, Xuan Tian, Li Liao (2021) Human–Robot Interaction: When Investors Adjust the Usage of Robo-Advisors in Peer-to-Peer Lending. Information Systems Research 32(3):774-785.
  7. Jain, Hemant, Balaji Padmanabhan, Paul A. Pavlou, T. S. Raghu (2021) Editorial for the Special Section on Humans, Algorithms, and Augmented Intelligence: The Future of Work, Organizations, and Society. Information Systems Research 32(3):675-687.
  8. Berente, Nicholas, Gu, Bin, Recker, Jan, Santhanam, Radhika. (2021) Special Issue Editor’s Comments: Managing Artificial Intelligence. MIS Quarterly (45: 3) pp. 1433-1450.
  9. Dixon, Jay, Bryan Hong, Lynn Wu (2021) The Robot Revolution: Managerial and Employment Consequences for Firms. Management Science 67(9):5586-5605.
  10. Schanke, Scott, Gordon Burtch, Gautam Ray (2021) Estimating the Impact of “Humanizing” Customer Service Chatbots. Information Systems Research 32(3):736-751.
  11. Park, H., Jiang, S., Lee, O. D., Chang, Y. (2021) Exploring the Attractiveness of Service Robots in the Hospitality Industry: Analysis of Online Reviews. Information Systems Frontier
  12. Graetz, G., Michaels, G. 2018. Robots at work. Review of Economics and Statistics (100:5), pp. 753-768.
  13. Luo, Xueming, Siliang Tong, Zheng Fang, Zhe Qu (2019) Frontiers: Machines vs. Humans: The Impact of Artificial Intelligence Chatbot Disclosure on Customer Purchases. Marketing Science 38(6):937-947.

 

Learning Faces to Predict Matching Probability in an Online Dating Market (ICIS 2022)

Kwon, Soonjae, Gene Moo Lee, Dongwon Lee, Sung-Hyuk Park (2024) “Digital Cupid: Empowering Generative AI for Fair and Efficient Matchmaking,” 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

With the increasing prevalence of online transactions, enhancing matching efficiency has emerged as a critical objective for most matching platforms. However, these efforts often lead to decreased fairness, making it challenging to balance these two elements. This study presents a novel generative AI-based approach to increase the platform’s efficiency and fairness simultaneously in the context of online dating. By developing a model that utilizes users’ multimodal features to predict individual preferences, we assess the impact of various matching algorithms on platform efficiency and fairness. Extensive simulations show that our fairness-aware algorithm significantly enhances both metrics, addressing conventional methods’ severe efficiency-fairness tradeoff issue. We also introduce a novel generative AI-based personalization technique that modifies users’ profile images in different directions according to their counterparts, further boosting efficiency without sacrificing fairness. Our matching framework can be applied to platforms with various objectives, contributing to all stakeholders in digital platforms.