Tag Archives: NLP

Jaecheol Park’s PhD Proposal: Strategic Roles of AI and Mobile Management on Performance: Evidence from U.S. Public Firms

Jaecheol Park (2024) “Strategic Roles of AI and Mobile Management on Performance: Evidence from U.S. Public Firms”, Ph.D. Dissertation Proposal, University of British Columbia. https://jaecheol-park.github.io/

Supervisor: Gene Moo Lee

Supervisory Committee Members: J. Frank Li, Jiyong Park (Georgia)

The integration of emerging technologies such as Artificial Intelligence (AI) and mobile IT into the workplace is transforming how businesses operate. Despite the increasing prevalence and importance of AI and mobile IT, there is limited research on how firms can strategically manage these technologies to achieve competitive advantage and enhance performance. This dissertation consists of two large-scale empirical studies on U.S. public firms, aiming to provide new theoretical and managerial insights into how firms can harness the power of these technologies to drive success.

The first chapter investigates the impact of mobile device management (MDM) on firm performance during the recent pandemic, highlighting the importance of MDM in digital resilience. Drawing on the resource-based view and a novel proprietary dataset from a global MDM provider for U.S. public firms, 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. This study contributes to the work-from-home and hybrid work literature by emphasizing the business value of MDM and its crucial role in building digital resilience.

The second chapter investigates the effect of AI strategic orientation on firm performance with a dual lens on product and process orientation. We create a novel measure of AI orientation by employing a large language model to assess business descriptions in Form 10-K filings, and identify an increasing trend of AI disclosure among U.S. public firms. By dissecting firms’ AI disclosure into AI washing and AI (product and process) orientation, our long-difference analyses show that AI orientation significantly affects costs, sales, and market value but AI washing does not, showing the importance of strategic deployment of AI to create business value. Moreover, we find the heterogeneous effects between AI product and process orientation on performance. This study contributes to the recent AI management literature by providing the strategic role of AI orientation on firm performance.

The findings of the dissertation offer valuable insights for academics, practitioners, and policymakers seeking to understand and leverage these emerging technologies’ full potential. From an academic perspective, this dissertation contributes to the literature on the business value of IT and AI by empirically demonstrating the business value of MDM and AI strategies. From an industry perspective, this research provides actionable guidance for businesses looking to leverage the power of MDM and AI to achieve strategic goals and drive success in the digital age.

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. 

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.

Corporate Social Network Analysis: A Deep Learning Approach

Cao, Rui, Gene Moo Lee, Hasan Cavusoglu. “Corporate Social Network Analysis: A Deep Learning Approach,” Working Paper.

Identifying inter-firm relationships is critical in understanding the industry landscape. However, due to the dynamic nature of such relationships, it is challenging to capture corporate social networks in a scalable and timely manner. To address this issue, this research develops a framework to build corporate social network representations by applying natural language processing (NLP) techniques on a corpus of 10-K filings, describing the reporting firms’ perceived relationships with other firms. Our framework uses named-entity recognition (NER) to locate the corporate names in the text, topic modeling to identify types of relationships included, and BERT to predict the type of relationship described in each sentence. To show the value of the network measures created by the proposed framework, we conduct two empirical analyses to see their impacts on firm performance. The first study shows that competition relationship and in-degree measurements on all relationship types have prediction power in estimating future earnings. The second study focuses on the difference between individual perspectives in an inter-firm social network. Such a difference is measured by the direction of mentions and is an indicator of a firm’s success in network governance. Receiving more mentions from other firms is a positive signal to network governance and it shows a significant positive correlation with firm performance next year.

Strategic Competitive Positioning: An Unsupervised Structural Hole-based Firm-specific Measure

Lee, Myunghwan, Gene Moo Lee, Hasan Cavusoglu, Marc-David L. Seidel. “Strategic Competitive Positioning: Unsupervised Operationalization of a Structural Hole-based Firm-specific Construct”, [Latest version: Aug 15, 2023]

  • doc2vec model of 10-K reports: Link
  • Presented at UBC MIS Seminar 2018, CIST 2019 (Seattle, WA), KrAIS 2019 (Munich, Germany), DS 2021 (online), KrAIS 2021 (Austin, TX), UT Dallas 2022, KAIST 2022, Korea Univ 2022, INFORMS 2022 (Indianapolis, IN)
  • Funded by Sauder Exploratory Grant 2019
  • Research assistants: Raymond Situ, Sahil Jain

In this paper, we build on the network structural hole concept of organizational theory to theorize an individual firm-specific strategic competitive positioning (SCP) construct. We use unsupervised document embedding approaches to operationalize the SCP construct by capturing each firm’s relative competitive and strategic positioning in a strategic similarity matrix of all existing U.S. publicly traded firms’ annual corporate filings. This approach enables us to construct a theoretically driven firm-level SCP measure with minimal human expert intervention. Our construct dynamically captures competitive positioning across different firms and years without using artificially bounded and often outdated industry classification systems. We illustrate how the dynamic measure captures industry-level and cross-industry strategic changes. Then, we demonstrate the effectiveness of our construct with an empirical analysis showing the imprinting and dynamic effects of SCP on firm performance. The results show that our dynamic SCP measure outperforms existing competition measures and successfully predicts post-IPO performance. This paper makes significant contributions to the information systems and organizations literatures by proposing an organizational theory-based unsupervised approach to dynamically conceptualize and measure firm-level strategic competitive positioning. The construct can be easily applied to firm-specific, industry-level, and cross-industry research questions in many contexts across many disciplines.

IT Risk and Stock Price Crash Risk (Working Paper)

Song, Victor, Hasan Cavusoglu, Mary L. Z. Ma, Gene Moo Lee (2023) “IT Risk and Stock Price Crash Risk,” Under review.

IT risk, especially cybersecurity risk, has rapidly increased and become a top concern for researchers, regulators, firm managers, and investors. This study creates a novel firm-level IT risk measure applicable to all US-listed firms by applying the BERTopic topic modeling to risk factors reported in Item 1A of the 10-K annual reports. We validate the measure with multiple approaches including cross-validations, presenting illustrative excerpts of IT risk factors, conducting cross-sectional and over-time distribution analyses, and analyzing firm characteristics associated with IT risk. The measure is found to be heightened in IT-intensive industries and for firms with larger sizes, higher profits, and better growth potential, and it can predict future data breaches. Using this ex-ante IT risk measure, we examine the relation between IT risk and stock price crash risk, which reflects a firm’s propensity to stock price crashes. Our findings suggest that IT risk is positively associated with crash risk, and we also identify that downward operating risk and predictability for data breaches are two mechanisms for the crash risk effect of IT risk. By decomposing IT risk into cybersecurity risk and non-cybersecurity IT risk, we find that both types of IT risk increase crash risk, but the effect of cybersecurity risk is stronger than that of non-cybersecurity IT risk, consistent with their different risk natures. We further observe that the novelty and readability of IT risk factors strengthen the crash risk effects of IT risk, consistent with the notion that the novelty represents updated and increased IT risk, and readability improves the understanding of IT risk. Lastly, difference-in-differences analyses reveal that IT risk increases stock price crash risk, not the other way around. We conclude the paper by discussing academic contributions and practical implications in the context of the SEC’s directives on reporting and managing IT risk and cybersecurity risk.

Matching Mobile Applications for Cross Promotion (ISR 2020)

Lee, Gene Moo, Shu He, Joowon Lee, Andrew B. Whinston (2020) Matching Mobile Applications for Cross-Promotion. Information Systems Research 31(3), pp. 865-891.

  • Based on an industry collaboration with IGAWorks
  • Presented in Chicago Marketing Analytics (Chicago, IL 2013), WeB (Auckland, New Zealand 2014), Notre Dame (2015), Temple (2015), UC Irvine (2015), Indiana (2015), UT Dallas (2015), Minnesota (2015), UT Arlington (2015), Michigan State (2016), Korea Univ (2021)
  • Dissertation Paper #3
  • Research assistant: Raymond Situ

The mobile applications (apps) market is one of the most successful software markets. As the platform grows rapidly, with millions of apps and billions of users, search costs are increasing tremendously. The challenge is how app developers can target the right users with their apps and how consumers can find the apps that fit their needs. Cross-promotion, advertising a mobile app (target app) in another app (source app), is introduced as a new app-promotion framework to alleviate the issue of search costs. In this paper, we model source app user behaviors (downloads and postdownload usages) with respect to different target apps in cross-promotion campaigns. We construct a novel app similarity measure using latent Dirichlet allocation topic modeling on apps’ production descriptions and then analyze how the similarity between the source and target apps influences users’ app download and usage decisions. To estimate the model, we use a unique data set from a large-scale random matching experiment conducted by a major mobile advertising company in Korea. The empirical results show that consumers prefer more diversified apps when they are making download decisions compared with their usage decisions, which is supported by the psychology literature on people’s variety-seeking behavior. Lastly, we propose an app-matching system based on machine-learning models (on app download and usage prediction) and generalized deferred acceptance algorithms. The simulation results show that app analytics capability is essential in building accurate prediction models and in increasing ad effectiveness of cross-promotion campaigns and that, at the expense of privacy, individual user data can further improve the matching performance. This paper has implications on the trade-off between utility and privacy in the growing mobile economy.

Predicting Litigation Risk via Machine Learning

Lee, Gene Moo*, James Naughton*, Xin Zheng*, Dexin Zhou* (2020) “Predicting Litigation Risk via Machine Learning,” Working Paper. [SSRN] (* equal contribution)

This study examines whether and how machine learning techniques can improve the prediction of litigation risk relative to the traditional logistic regression model. Existing litigation literature has no consensus on a predictive model. Additionally, the evaluation of litigation model performance is ad hoc. We use five popular machine learning techniques to predict litigation risk and benchmark their performance against the logistic regression model in Kim and Skinner (2012). Our results show that machine learning techniques can significantly improve the predictability of litigation risk. We identify two best-performing methods (random forest and convolutional neural networks) and rank the importance of predictors. Additionally, we show that models using economically-motivated ratio variables perform better than models using raw variables. Overall, our results suggest that the joint consideration of economically-meaningful predictors and machine learning techniques maximize the improvement of predictive litigation models.

On the Spillover Effects of Online Product Reviews on Purchases: Evidence from Clickstream Data (ISR 2021)

Kwark, Young*, Gene Moo Lee*, Paul A. Pavlou*, Liangfei Qiu* (2021) On the Spillover Effects of Online Product Reviews on Purchases: Evidence from Clickstream Data. Information Systems Research 32(3): 895-913. (* equal contribution)

  • Data awarded by Wharton Consumer Analytics Initiative
  • Presented in WCBI (Snowbird, UT 2015), KMIS (Busan, Korea 2016), Minnesota (2016), ICIS (Dublin, Ireland 2016), Boston Univ. (2017), HEC Paris (2017), and Korea Univ. (2018)
  • An earlier version was published in ICIS 2016
  • Research assistants: Bolat Khojayev, Raymond Situ

We study the spillover effects of the online reviews of other covisited products on the purchases of a focal product using clickstream data from a large retailer. The proposed spillover effects are moderated by (a) whether the related (covisited) products are complementary or substitutive, (b) the choice of media channel (mobile or personal computer (PC)) used, (c) whether the related products are from the same or a different brand, (d) consumer experience, and (e) the variance of the review ratings. To identify complementary and substitutive products, we develop supervised machine-learning models based on product characteristics, such as product category and brand, and novel text-based similarity measures. We train and validate the machine-learning models using product pair labels from Amazon Mechanical Turk. Our results show that the mean rating of substitutive (complementary) products has a negative (positive) effect on purchasing of the focal product. Interestingly, the magnitude of the spillover effects of the mean ratings of covisited (substitutive and complementary) products is significantly larger than the effects on the focal product, especially for complementary products. The spillover effect of ratings is stronger for consumers who use mobile devices versus PCs. We find the negative effect of the mean ratings of substitutive products across different brands on purchasing of a focal product to be significantly higher than within the same brand. Lastly, the effect of the mean ratings is stronger for less experienced consumers and for ratings with lower variance. We discuss implications on leveraging the spillover effect of the online product reviews of related products to encourage online purchases.

Does Deceptive Marketing Pay? The Evolution of Consumer Sentiment Surrounding a Pseudo-Product-Harm Crisis (J. Business Ethics 2019)

Song, Reo, Ho Kim, Gene Moo Lee, and Sungha Jang (2019) Does Deceptive Marketing Pay? The Evolution of Consumer Sentiment Surrounding a Pseudo-Product-Harm Crisis. Journal of Business Ethics, 158(3), pp. 743-761.

The slandering of a firm’s products by competing firms poses significant threats to the victim firm, with the resulting damage often being as harmful as that from product-harm crises. In contrast to a true product-harm crisis, however, this disparagement is based on a false claim or fake news; thus, we call it a pseudo-product-harm crisis. Using a pseudo-product-harm crisis event that involved two competing firms, this research examines how consumer sentiments about the two firms evolved in response to the crisis. Our analyses show that while both firms suffered, the damage to the offending firm (which spread fake news to cause the crisis) was more detrimental, in terms of advertising effectiveness and negative news publicity, than that to the victim firm (which suffered from the false claim). Our study indicates that, even apart from ethical concerns, the false claim about the victim firm was not an effective business strategy to increase the offending firm’s performance.