Author Archives: gene lee

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.

Security Defense against Long-term and Stealthy Cyberattacks (DSS 2023)

Kookyoung Han, Choi, Jin Hyuk, Yun-Sik Choi, Gene Moo Lee, Andrew B. Whinston (2023) Security Defense against Long-term and Stealthy Cyberattacks. Decision Support Systems, 166: 113912.

  • Funded by NSF (Award #1718600) and UNIST
  • Best Paper Award at KrAIS 2017
  • Presented at UT Austin (2017), UNIST (2017), INFORMS (Houston, TX 2017), CIST (Houston, TX 2017), WITS (Seoul, Korea 2017), and KrAIS (Seoul, Korea 2017)
  • Previous titles: “Misinformation and Optimal Time to Detect”, “Optimal Stopping and Strategic Espionage”, “To Disconnect or Not: A Cybersecurity Game”

Modern cyberattacks such as advanced persistent threats have become sophisticated. Hackers can stay undetected for an extended time and defenders do not have sufficient countermeasures to prevent advanced cyberattacks. Reflecting on this phenomenon, we propose a game-theoretic model to analyze strategic decisions made by a hacker and a defender in equilibrium. In our game model, the hacker launches stealthy cyberattacks for a long time and the defender decides when to disable a suspicious user based on noisy observations of the user’s activities. Damages caused by the hacker can be enormous if the defender does not immediately ban a suspicious user under certain circumstances, which can explain the emerging sophisticated cyberattacks with detrimental consequences. Our model also predicts that the hacker may opt to be behavioral to avoid worst cases. This is because behavioral cyberattacks are less threatening and the defender decides not to immediately block a suspicious user to reduce cost of false detection.

    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.

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    • For current UBC students (Ph.D., Master, Undergraduate): Thank you for your interest in working with me. There could be research opportunities for exceptional cases: students should have a working knowledge of Python programming and Linux environment. I recommend interested students to take these online courses: Data Science, Machine Learning, Linux, SQL, and NoSQL. Before you contact me, please read my research group’s publications and working papers, then describe the specific research topics you want to pursue. In addition, please send me your transcript, resume, and code samples (e.g., GitHub repository if available).
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    • Regarding the Visiting Associate Program at Centre for Korean Research, Institute of Asian Research: https://ckr.iar.ubc.ca/ckr-visiting-scholar-program/

    A Friend Like Me: Modeling Network Formation in a Location-Based Social Network (JMIS 2016)

    Lee, Gene Moo*, Liangfei Qiu*, Andrew B. Whinston* (2016) A Friend Like Me: Modeling Network Formation in a Location-Based Social Network, Journal of Management Information Systems 33(4), pp. 1008-1033. (* equal contribution)

    • Best Paper Nomination at HICSS 2016
    • Presented in WITS (Auckland, New Zealand 2014), and WISE (Auckland, New Zeland 2014), HICSS (Kauai, HI 2016)
    • Dissertation Paper #2

    This article studies the strategic network formation in a location-based social network. We build an empirical model of social link creation that incorporates individual characteristics and pairwise user similarities. Specifically, we define four user proximity measures from biography, geography, mobility, and short messages. To construct proximity from unstructured text information, we build topic models using Latent Dirichlet Allocation. Using Gowalla data with 385,306 users, 3 million locations, and 35 million check-in records, we empirically estimate the model to find evidence on the homophily effect on network formation. To cope with possible endogeneity issues, we use exogenous weather shocks as our instrumental variables and find the empirical results are robust: network formation decisions are significantly affected by our proximity measures.

    How would information disclosure influence organizations’ outbound spam volume? Evidence from a field experiment (J. Cybersecurity 2016)

    He, Shu*, Gene Moo Lee*, Sukjin Han, Andrew B. Whinston (2016) How Would Information Disclosure Influence Organizations’ Outbound Spam Volume? Evidence from a Field Experiment. Journal of Cybersecurity 2(1), pp. 99-118. (* equal contribution)

    Cyber-insecurity is a serious threat in the digital world. In the present paper, we argue that a suboptimal cybersecurity environment is partly due to organizations’ underinvestment on security and a lack of suitable policies. The motivation for this paper stems from a related policy question: how to design policies for governments and other organizations that can ensure a sufficient level of cybersecurity. We address the question by exploring a policy devised to alleviate information asymmetry and to achieve transparency in cybersecurity information sharing practice. We propose a cybersecurity evaluation agency along with regulations on information disclosure. To empirically evaluate the effectiveness of such an institution, we conduct a large-scale randomized field experiment on 7919 US organizations. Specifically, we generate organizations’ security reports based on their outbound spam relative to the industry peers, then share the reports with the subjects in either private or public ways. Using models for heterogeneous treatment effects and machine learning techniques, we find evidence from this experiment that the security information sharing combined with publicity treatment has significant effects on spam reduction for original large spammers. Moreover, significant peer effects are observed among industry peers after the experiment.

    Toward a Better Measure of Business Proximity: Topic Modeling for Industry Intelligence (MISQ 2016)

    Shi, Zhan, Gene Moo Lee, Andrew B. Whinston (2016) Toward a Better Measure of Business Proximity: Topic Modeling for Industry Intelligence. MIS Quarterly 40(4), pp. 1035-1056.

    In this article, we propose a new data-analytic approach to measure firms’ dyadic business proximity. Specifically, our method analyzes the unstructured texts that describe firms’ businesses using the statistical learning technique of topic modeling, and constructs a novel business proximity measure based on the output. When compared with existent methods, our approach is scalable for large datasets and provides finer granularity on quantifying firms’ positions in the spaces of product, market, and technology. We then validate our business proximity measure in the context of industry intelligence and show the measure’s effectiveness in an empirical application of analyzing mergers and acquisitions in the U.S. high technology industry. Based on the research, we also build a cloud-based information system to facilitate competitive intelligence on the high technology industry.

    The Spillover Effects of User-Generated Online Product Reviews on Purchases: Evidence from Clickstream Data (ICIS 2016)

    Kwark, Y., Lee, G. M., Pavlou, P. A., Qiu, L. (2016). The Spillover Effects of User-Generated Online Product Reviews on Purchases: Evidence from Clickstream Data, Proceedings of International Conference on Information Systems (ICIS 2016), Dublin, Ireland.

    We analyze the spillover effect of online product reviews on purchases using clickstream data from a large retailer by investigating (a) whether the products are complementary/substitutive; (b) whether the products are from the same or a different brand, and (c) which media channel (mobile or PC) is used. To identify complementary/substitutive products, we used a text-mining approach of topic modeling on product descriptions to quantify the functional similarity of pairwise products. Our empirical analysis shows that the mean rating of online reviews of substitutive products has a negative role in purchasing, while the rating of complementary products has a positive role. Also, we find the negative spillover effect among substitutive products of different brands to be significantly greater than those of the same brand and for consumers who used mobile devices versus traditional PCs. Our study has implications on leveraging the spillover effect of online product reviews on substitutive/complementary products.

    Strategic Network Formation in a Location-Based Social Network: A Topic Modeling Approach (HICSS 2016)

    Lee, G. M., Qiu, L., Whinston, A. B. (2016). Strategic Network Formation in a Location-Based Social Network: A Topic Modeling Approach, Proceedings of Hawaii International Conference on System Sciences (HICSS 2016), Kauai, Hawaii. Nominated for Best Paper Award

    This paper studies strategic network formation in a location-based social network. We build a structural model of social link creation that incorporates individual characteristics and pairwise user similarities. Specifically, we define four user proximity measures from biography, geography, mobility, and short messages. To construct proximity from unstructured text information, we build topic models using latent Dirichlet allocation. Using Gowalla data with 385,306 users, three million locations, and 35 million check-in records, we empirically estimate the structural model to find evidence on the homophily effect in network formation.