Tag Archives: deep learning

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. 

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.

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.

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.

Enhancing Social Media Analysis with Visual Data Analytics: A Deep Learning Approach (MISQ 2020)

Shin, Donghyuk, Shu He, Gene Moo Lee, Andrew B. Whinston, Suleyman Cetintas, Kuang-Chih Lee (2020) Enhancing Social Media Analysis with Visual Data Analytics: A Deep Learning Approach, MIS Quarterly, 44(4), pp. 1459-1492. [SSRN]

  • Based on an industry collaboration with Yahoo! Research
  • The first MISQ methods article based on machine learning
  • Presented in WeB (Fort Worth, TX 2015), WITS (Dallas, TX 2015), UT Arlington (2016), Texas FreshAIR (San Antonio, TX 2016), SKKU (2016), Korea Univ. (2016), Hanyang (2016), Kyung Hee (2016), Chung-Ang (2016), Yonsei (2016), Seoul National Univ. (2016), Kyungpook National Univ. (2016), UKC (Dallas, TX 2016), UBC (2016), INFORMS CIST (Nashville, TN 2016), DSI (Austin, TX 2016), Univ. of North Texas (2017), Arizona State (2018), Simon Fraser (2019), Saarland (2021), Kyung Hee (2021), Tennessee Chattanooga (2021), Rochester (2021), KAIST (2021), Yonsei (2021), UBC (2022), Temple (2023)

This research methods article proposes a visual data analytics framework to enhance social media research using deep learning models. Drawing on the literature of information systems and marketing, complemented with data-driven methods, we propose a number of visual and textual content features including complexity, similarity, and consistency measures that can play important roles in the persuasiveness of social media content. We then employ state-of-the-art machine learning approaches such as deep learning and text mining to operationalize these new content features in a scalable and systematic manner. For the newly developed features, we validate them against human coders on Amazon Mechanical Turk. Furthermore, we conduct two case studies with a large social media dataset from Tumblr to show the effectiveness of the proposed content features. The first case study demonstrates that both theoretically motivated and data-driven features significantly improve the model’s power to predict the popularity of a post, and the second one highlights the relationships between content features and consumer evaluations of the corresponding posts. The proposed research framework illustrates how deep learning methods can enhance the analysis of unstructured visual and textual data for social media research.

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.