Category Archives: Working Papers

What Fuels Growth? A Comparative Analysis of the Scaling Intensity of AI Start-ups

Schulte-Althoff, Matthias, Daniel Fuerstenau, Gene Moo Lee, Hannes Rothe, Robert Kauffman. “What Fuels Growth? A Comparative Analysis of the Scaling Intensity of AI Start-ups”. Working Paper. [ResearchGate]

  • Previous title: “A Scaling Perspective on AI startup”
  • Presented at HICSS 2021 (SITES mini-track), Copenhagen Business School 2021, FU Berlin 2021, University of Cologne 2021, University of Bremen 2021, Humboldt Institute for Internet and Society 2021, WITS 2022

We examine how firm revenue scales with labor for revenue-per-employee (RPE) and is moderated by firm-level AI investment. We compare AI start-ups, in which AI provides a competitive advantage, with digital platforms and service start-ups. We use propensity score matching to explain the scaling of start-ups and find evidence for sublinear scaling intensity for revenue as a function of labor. Our study suggests similar scaling intensities between AI and service start-ups, while platform start-ups produce higher scaling intensities. We show that an increase in employee counts is associated with major revenue increases for platform start-ups, while increases were modest for service and AI start-ups.

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.

When Does Congruence Matter for Pre-roll Video Ads? The Effect of Multimodal, Ad-Content Congruence on the Ad Completion

Park, Sungho, Gene Moo Lee, Donghyuk Shin, Sang-Pil Han. “When Does Congruence Matter for Pre-roll Video Ads? The Effect of Multimodal, Ad-Content Congruence on the Ad Completion, Working Paper [Last update: Jan 29, 2023]

  • Previous title: Targeting Pre-Roll Ads using Video Analytics
  • Funded by Sauder Exploratory Research Grant 2020
  • Presented at Southern Methodist University (2020), University of Washington (2020), INFORMS (2020), AIMLBA (2020), WITS (2020), HKUST (2021), Maryland (2021), American University (2021), National University of Singapore (2021), Arizona (2022), George Mason (2022), KAIST (2022), Hanyang (2022), Kyung Hee (2022), McGill (2022)
  • Research assistants: Raymond Situ, Miguel Valarao

Pre-roll video ads are gaining industry traction because the audience may be willing to watch an ad for a few seconds, if not the entire ad, before the desired content video is shown. Conversely, a popular skippable type of pre-roll video ads, which enables viewers to skip an ad in a few seconds, creates opportunity costs for advertisers and online video platforms when the ad is skipped. Against this backdrop, we employ a video analytics framework to extract multimodal features from ad and content videos, including auditory signals and thematic visual information, and probe into the effect of ad-content congruence at each modality using a random matching experiment conducted by a major video advertising platform. The present study challenges the widely held view that ads that match content are more likely to be viewed than those that do not, and investigates the conditions under which congruence may or may not work. Our results indicate that non-thematic auditory signal congruence between the ad and content is essential in explaining viewers’ ad completion, while thematic visual congruence is only effective if the viewer has sufficient attentional and cognitive capacity to recognize such congruence. The findings suggest that thematic videos demand more cognitive processing power than auditory signals for viewers to perceive ad-content congruence, leading to decreased ad viewing. Overall, these findings have significant theoretical and practical implications for understanding whether and when viewers construct congruence in the context of pre-roll video ads and how advertisers might target their pre-roll video ads successfully.

On the Heterogeneity of Startup Tech Stacks

Schulte-Althoff, Matthias, Kai Schewina, Gene Moo Lee, Daniel Fuerstenau. “On the Heterogeneity of Startup Tech Stacks”. Working Paper [HICSS version]

  • Presented in HICSS 2020 (Maui, HI)
  • Previous title: On the Heterogeneity of Digital Infrastructure in Entrepreneurial Ecosystems

Digital infrastructure is the backbone on which digital startups realize business opportunities, and the homogeneity or heterogeneity of the technological base can have significant downstream impacts on business risks, inflexibilities, and growth barriers. On the nexus of digital entrepreneurship and infrastructure studies, we suggest a conception of startup digital infrastructure as organized in tech stacks; tech stacks contain individual technological elements that are combined in a single startup, while the way this is done will be inspired by shared templates within the ecosystem. Given that there is limited understanding of the heterogeneity (or homogeneity) of startup tech stacks, we use public registry datasets from StackShare and Crunchbase to identify common tech stacks of startups. Through our analysis, we identify ten commonly used startup tech stacks, which we use to measure and analyze the heterogeneity of startup tech stacks and its antecedents. OLS regression analysis shows that a startup’s technologies’ interrelatedness, its age, and investor funding are associated with the heterogeneity of startup tech stacks. The overall analysis suggests that while startups may make individual choices regarding technology usage, there could be underlying commonalities and imprinting effects across startups, exposing them to common risks in terms of their digital infrastructures. This could pose important implications for startups, investors, and society at large

Price Competition and Active or Inactive Consumer Search

Koh, Yumi, Gea M. Lee, Gene Moo Lee (2023) “Price Competition and Active or Inactive Consumer Search”. Working Paper. [Latest version: May 31, 2023] [SSRN]

We propose a price-competition model in which prices are dispersed and a fraction of consumers decide whether to make an immediate purchase without actively searching for prices or to search sequentially. We use an incomplete-information setting with heterogeneous production costs and information frictions:  rms’ production cost types are drawn from an interval and are privately observed. The model includes active or inactive consumer search as an equilibrium outcome and allows a competition-induced switch between the two outcomes. We study how firms and consumers interact in determining prices and making an active or inactive search when competition intensifies with more firms.

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 2nd round review at Information Systems Research.

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.

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.