Tag Archives: AI

Learning Faces to Predict Matching Probability in an Online Dating Market

Kwon, Soonjae, Sung-Hyuk Park, Gene Moo Lee, Dongwon Lee (2021) “Learning Faces to Predict Matching Probability in an Online Dating Market”. Work-in-progress.

  • Under review for a conference presentation.
  • Based on an industry collaboration

With the increasing use of online matching markets, predicting the matching probability among users is crucial for better market design. Although previous studies have constructed visual features to predict the matching probability, facial features extracted by deep learning have not been widely used. By predicting user attractiveness in an online dating market, we find that deep learning-enabled facial features can significantly enhance prediction accuracy. We also predict the attractiveness at various evaluator groups and explain their different preferences based on the theory of evolutionary psychology. Furthermore, we propose a novel method to visually interpret deep learning-enabled facial features using the latest deep learning-based generative model. Our work contributes to IS researchers utilizing facial features using deep learning and interpreting them to investigate underlying mechanisms in online matching markets. From a practical perspective, matching platforms can predict matching probability more accurately for better market design and recommender systems for maximizing the matching outcome.

AI Robot Adoption in the Service Industry (KOSEN Report 2020)

Gene Moo Lee (2020) “AI Robot Adoption in the Service Industry”. KOSEN Report 

  • This is an industry report on AI robot adoption in the service industry.

Abstract

디지털 전환(Digital Transformation) 시장은 2020년 기준 3,550억 달러의 가치가 있으며, 2027년까지의 연간 성장률은 22.5%에 이를 것으로 예상되고 있다.  스마트폰의 보급과 무선인터넷의 확산은 디지털생태계가 구축될 수 있는 환경을 조성하였으며, 이용자들의 지속적인 디지털콘텐츠 활용으로 인한 데이터의 폭발적인 증가는 방대한 양의 데이터를 효율적으로 처리할 수 있는 빅데이터 처리 기술이 발달할 수 있는 밑거름이 되었다. 뿐만 아니라 사물인터넷(IoT), Quantum 컴퓨팅, 인공지능 기술의 발달은 기존의 오프라인 시장이 디지털 시장으로 전환할 수 있는 촉매제 역할을 하여 디지털 시장이 성장할 수 있는 원동력이 되었다. 실제로 다양한 산업 영역에서 디지털 시장 내에서 새로운 사업 기회를 포착하고자 하는 시도가 많이 이루어지고 있으며, 이를 바탕으로 오프라인에서 벗어나 온라인 디지털 시장에서 다양한 가치 창출을 가능하게 하였다. 전통산업의 디지털 전환이 가속화되고 있음은 다음과 같은 사례를 통해 파악할 수 있다. 자동차산업에서는 자율주행 서비스를 통해 고객들의 주행 데이터를 디지털화하여 무인 자동차 시대를 위한 준비를 하고 있으며, 의료산업에서는 원격진료를 통해 물리적 한계를 뛰어넘는 의료서비스라는 가치를 창조하고 있고, 제조산업에서는 생산시스템 자동화를 통해 생산 효율성을 높이고 품질을 높이는 활동을 하고 있다.

Trustworthy Face? The Effect and Drivers of Comprehensive Trust in Online Job Market Platform

Kwon, Jun Bum, Donghyuk Shin, Gene Moo Lee, Jake An, Sam Hwang (2020) “Trustworthy Face? The Effect and Drivers of Comprehensive Trust in Online Job Market Platform”. Work-in-progress.

The abstract will appear here.

Robots Serve Humans: Does AI Robot Adoption Enhance Operational Efficiency and Customer Experience?

Lee, Myunghwan, Gene Moo Lee, Donghyuk Shin, Sang-Pil Han (2020) “Robots Serve Humans: Does AI Robot Adoption Enhance Operational Efficiency and Customer Experience?Working Paper.

  • Presented at WITS (2020), KrAIS (2020), UBC (2021)
  • Research assistants: Raymond Situ, Gallant Tang

Service providers have been adopting various robotics technologies to improve operational efficiency and increase customer satisfaction. Robotics technologies bring new restaurant experiences to customers by taking orders, cooking, and serving. While the impact of industrial robots has been well documented in the literature, little is known about the impact of customer-facing service robot adoption. To fill this gap, this work-in-progress study aims to analyze the impact of service robot adoption on restaurant service quality using 4,612 restaurants and their online customer reviews. We analyzed the treated effect of robot adoption using a difference-in-differences approach with propensity score and exact matching. Estimation results show that restaurant robot adoption has a positive impact on customer satisfaction, specifically on perceived food quality and perceived value. This study provides both academic and practical implications on the emerging AI robotics techniques.

A Scaling Perspective in AI Startups

Schulte-Althoff, Matthias, Daniel Fuerstenau, Gene Moo Lee, Hannes Rothe, Robert Kauffman (2021) “A Scaling Perspective in AI Startups”. Working Paper. [ResearchGate]

  • Presented at HICSS 2021 (SITES mini-track)

Digital startups’ use of AI technologies has significantly increased in recent years, bringing to the fore specific barriers to deployment, use, and extraction of business value from AI. Utilizing a quantitative framework regarding the themes of startup growth and scaling, we examine the scaling behavior of AI, platform, and service startups. We find evidence of a sublinear scaling ratio of revenue to age-discounted employment count. The results suggest that the revenue-employee growth pattern of AI startups is close to that of service startups, and less so to that of platform startups. Furthermore, we find a superlinear growth pattern of acquired funding in relation to the employment size that is largest for AI startups, possibly suggesting hype tendencies around AI startups. We discuss implications in the light of new economies of scale and the scope of AI startups related to decision-making and prediction.

Structural Hole-based Measures of Firm’s Strategic Competitive Positioning

Lee, Myunghwan, Gene Moo Lee, Hasan Cavusoglu, Marc-David L. Seidel. “Structural Hole-based Measures of Firm’s Strategic Competitive Positioning”, Working Paper.

The theory of network opportunity emergence holds that as the overall industry network structure becomes centralized, opportunities emerge for new entrants. However, new entrants must correctly strategically position themselves in the market to be properly valued. This creates tensions for entrepreneurial ventures considering going public around how to craft their strategic posture to take advantage of differentiating opportunities in the market structure while still being familiar enough to customers and investors. In this paper, we propose a theory of IPO strategic posture to unpack these dynamics. We empirically test our theory using a machine learning approach called doc2vec to create a similarity matrix of all existing U.S. publicly traded companies based upon self-provided business descriptions provided in their 10-K annual reports. This enables us to measure existing companies’ similarities of strategic postures and identify where industry-level structural holes emerge. We then use these structural hole signatures of potential market entry opportunities to predict how new companies strategically posture an IPO. We then follow the trajectories of those newly listed companies to see how their strategic posture impacts growth and ultimate survival. We conclude with a discussion of how the institutional pressures of the venture capital industry create pressure for ventures to self-present their IPO strategic postures as too distinct for their own long-term survival.

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)

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.

Developing Cyber Risk Assessment Framework for Cyber Insurance: A Big Data Approach (KIRI Research Report 2018)

Lee, G. M. (2018). Developing Cyber Risk Assessment Framework for Cyber Insurance: A Big Data Approach (in Korean)KIRI Research Report 2018-15.

As our society is heavily dependent on information and communication technology, the associated risk has also significantly increased. Cyber insurance has been emerged as a possible means to better manage such cyber risk. However, the cyber insurance market is still in a premature stage due to the lack of data sharing and standards on cyber risk and cyber insurance. To address this issue, this research proposes a data-driven framework to assess cyber risk using externally observable cyber attack data sources such as outbound spam and phishing websites. We show that the feasibility of such an approach by building cyber risk assessment reports for Korean organizations. Then, by conducting a large-scale randomized field experiment, we measure the causal effect of cyber risk disclosure on organizational security levels. Finally, we develop machine-learning models to predict data breach incidents, as a case of cyber incidents, using the developed cyber risk assessment data. We believe that the proposed data-driven methods can be a stepping-stone to enable information transparency in the cyber insurance market.

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.