Category Archives: Publications

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

Gene Moo Lee (2020) “AI Robot Adoption in the Service Industry”. KOSEN Report  DOI: https://doi.org/10.22800/kisti.kosenexpert.2020.588

  • 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.

Information Systems Design and Implementation

Information System Name Related Article Link
Business Proximity MISQ 2016 https://misr.sauder.ubc.ca/bizprox/
SpamRankings.net JC 2016 http://cloud.spamrankings.net/
Korean Cyber Risk Assessment System KIRI 2018 https://misr.sauder.ubc.ca/cyberrisk/
SEC Filings Text Analytics JTI 2018 https://misr.sauder.ubc.ca/edgar_dashboard/
Corporate Social Network WIP 2020 https://misr.sauder.ubc.ca/corporate_network/

 

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)

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.

Understanding Security Vulnerability Awareness, Firm Incentives, and ICT Development in Pan-Asia (JMIS 2020)

Zhuang, Yunhui, Yunsik Choi, Shu He, Alvin Chung Man Leung, Gene Moo Lee, Andrew B. Whinston (2020) “Understanding Security Vulnerability Awareness, Firm Incentives, and ICT Development in Pan-Asia“. Journal of Management Information Systems, 37(3): 668-693.

This paper investigates how the awareness of a security vulnerability index affects firms’ security protection strategy and how the information awareness effect interacts with firm incentives and country-wide IT development level. The security index is constructed based on outgoing spams and phishing website hosting, which may serve as an indicator of a firm’s security controls. To study whether security vulnerability awareness causes firms to improve their security, we conducted a randomized field experiment on 1,262 firms in six Pan-Asian countries and regions. Among 631 randomly selected treated firms, we alerted them of their security vulnerability index and their relative rankings compared to their peers via advisory emails and websites. Difference-in-differences analyses show that compared with the controls, the treated firms improve their security over time, with a statistically significant reduction of outgoing spam volume according to one of the data sources but not phishing website hosting. However, a statistically significant reduction in phishing website hosting was observed among non-web hosting firms, suggesting that firms’ underlying incentives play an important role in the treatment effect. Lastly, exploiting the multi-country nature of the data, we found that firms in countries with high information and communications technology (ICT) development are more responsive to our intervention because they have higher IT capabilities and more resources to resolve security issues. Our study provides cybersecurity policymakers with useful insights on how firm incentives and ICT environments play roles in firms’ security measure adoption.

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.

Development of Topic Trend Analysis Model for Industrial Intelligence using Public Data (J. Technology Innovation 2018)

Park, S., Lee, G. M., Kim, Y.-E., Seo, J. (2018). Development of Topic Trend Analysis Model for Industrial Intelligence using Public Data (in Korean)Journal of Technology Innovation, 26(4), 199-232.

  • Funded by the Korea Institute of Science and Technology Information (KISTI)
  • Demo website: https://misr.sauder.ubc.ca/edgar_dashboard/
  • Presented at UKC (2017), KISTI (2017), WITS (2017), Rutgers Business School (2018)

There are increasing needs for understanding and fathoming of the business management environment through big data analysis at the industrial and corporative level. The research using the company disclosure information, which is comprehensively covering the business performance and the future plan of the company, is getting attention. However, there is limited research on developing applicable analytical models leveraging such corporate disclosure data due to its unstructured nature. This study proposes a text-mining-based analytical model for industrial and firm-level analyses using publicly available company disclosure data. Specifically, we apply LDA topic model and word2vec word embedding model on the U.S. SEC data from the publicly listed firms and analyze the trends of business topics at the industrial and corporate levels.

Using LDA topic modeling based on SEC EDGAR 10-K document, whole industrial management topics are figured out. For comparison of different pattern of industries’ topic trend, software and hardware industries are compared in recent 20 years. Also, the changes in management subject at the firm level are observed with a comparison of two companies in the software industry. The changes in topic trends provide a lens for identifying decreasing and growing management subjects at industrial and firm-level. Mapping companies and products(or services) based on dimension reduction after using word2vec word embedding model and principal component analysis of 10-K document at the firm level in the software industry, companies and products(services) that have similar management subjects are identified and also their changes in decades.

For suggesting a methodology to develop an analytical model based on public management data at the industrial and corporate level, there may be contributions in terms of making the ground of practical methodology to identifying changes of management subjects. However, there are required further researches to provide a microscopic analytical model with regard to the relation of technology management strategy between management performance in case of related to the various pattern of management topics as of frequent changes of management subject or their momentum. Also, more studies are needed for developing competitive context analysis model with product(service)-portfolios between firms.

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.

Security Defense against Long-term and Stealthy Cyberattacks (CIST 2017, WITS 2017, KrAIS 2017)

Kookyoung Han, Choi, Jin Hyuk, Yun-Sik Choi, Gene Moo Lee, Andrew B. Whinston (2021) “Security Defense against Long-term and Stealthy Cyberattacks”. Under Review.

  • Latest version: May 2021
  • 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 these advanced cyberattacks. Reflecting on this phenomenon, we propose a game-theoretic model in which a hacker launches stealthy cyberattacks for a long time and a defender’s actions are to monitor the activities and to disable a suspicious user. Focusing on cases in which the players sufficiently care about future payoffs, we find that if the defender does not immediately ban a suspicious user, damages caused by the hacker can be enormous. Therefore, the defender bans every suspicious user in equilibrium to avoid huge losses, resulting in the worst payoffs for both players. These results explain the emerging sophisticated cyberattacks with detrimental consequences. Our model also predicts that the hacker may opt to be non-strategic. This is because non-strategic cyberattacks are less threatening and the defender decides not to immediately block a suspicious user to reduce false detection, in which case both players become better off.

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 CrisisJournal 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.