Author Archives: gene lee

Do Incentivized Reviews Poison the Well? Evidence from a Natural Experiment at Amazon.com

Park, Jaecheol, Arslan Aziz, Gene Moo Lee (2021) “Do Incentivized Reviews Poison the Well? Evidence from a Natural Experiment at Amazon.comWorking Paper.

  • Presentations: UBC (2021), WISE (2021)

The rapid growth in e-commerce has led to a concomitant increase in consumer’s reliance on digital word-of-mouth to inform their choices. As such, there is an increasing incentive for sellers to solicit reviews for their products. Recent studies have examined the direct effect of receiving incentives or introducing incentive policy on review writing behavior. However, since incentivized reviews are often only a small proportion of the overall reviews on a platform, it is important to understand how their presence on the platform has spillover effects on the unincentivized reviews which are often in the majority. Using a natural experiment caused by a policy change, a ban on the incentivized reviews, on Amazon.com in October 2016, we conducted the difference-in-differences analyses for three different periods after the policy implementation. Our results suggest that there are positive spillover effects on the review sentiment, length, helpfulness, and frequency, suggesting that the policy stimulates more reviews in the short-run, and more positive, longer, and more helpful reviews in the long run.

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.

  • Presentations: DS 2021, AIMLBA 2021, WITS 2021
  • 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.

My thoughts on AI, Big Data, and IS Research

Last update: June 10th, 2021

Recently, I had a chance to share my thoughts on how Big Data Analytics and AI will impact Information Systems (IS) research. Thanks to ever-growing datasets (public and proprietary) and powerful computational resources (cloud API, open-source projects), AI and Big Data will be important in IS research in the foreseeable future. If you are an aspiring IS researcher, I believe that you should be able to embrace this and take advantage of this.

First, AI and Big Data are powerful “tools” for IS research. It could be intimidating to see all the fancy new AI techniques. But they are just tools to analyze your data. You don’t need to reinvent the wheel to use them. There are many open-source projects in Python and R that you can use to analyze your data. Also, many cloud services (e.g., Amazon Rekognition, Google Cloud ML, Microsoft Azure ML) allow you to use pre-trained AI models at a modest cost (that your professors can afford). What you need is some working knowledge in programming languages like Python and R. And a high-level understanding of the idea behind algorithms.

Don’t shy away from hands-on programming. Using AI and Big Data tools may not be a competitive advantage in the long run because of the democratization of AI tools. However, I believe it will be the new baseline. So you need to have it in your research toolbox. Specifically, I believe that IS researchers should have a working knowledge of Python/R programming and Linux environment. I recommend these online courses: Data ScienceMachine LearningLinuxSQL, and NoSQL.

Second, AI and Big Data Analytics are creating a lot of interesting new “phenomenon” in personal lives, firms, and societies. How AI and robots will be adopted in the workplace and how that will affect the labor market? Are we losing our jobs? Or can we improve our productivity with AI tools? How AI will be used in professional services by the experts? What are the unintended consequences (such as biases, security, privacy, misinformation) of AI adoptions in the organization and society? And how can we mitigate such issues? There are so many new and interesting research questions.

In order to conduct relevant research, I think that IS researchers should closely follow the emerging technologies. Again, it could be hard to keep up with all the advances. I try to keep up to date by reading industry reports (from McKinsey and Deloitte) and listening to many podcasts (e.g., Freakonomics Radio, a16 Podcasts by Andreessen Horowitz, Lex Fridman Podcast, Stanford’s Entrepreneurial Thought Leaders, HBR’s Exponential View by Azeem Azhar).

I hope this post may help new IS researchers shape their research strategies. I will try to keep updating this post. Cheers!

 

 

IS / Marketing Papers on Visual Data Analytics (Image, Video)

Last update: July 19, 2021

With the advent of social media and mobile platforms, visual data are becoming the first citizen in big data analytics research. Compared to textual data that require significant cognitive efforts to comprehend, visual data (such as image and video) can easily convey the message from the content creator to the general audience. To conduct large-scale studies on such data types, researchers need to use machine learning and computer vision approaches. In this post, I am trying to organize studies in Information Systems, Marketing, and other management disciplines that leverage large-scale analysis of image and video datasets. The papers are ordered randomly:

  1. Zhou, M., Chen, G. H., Ferreira, P., Smith, M. D. (2021) “Consumer Behavior in the Online Classroom: Using Video Analytics and Machine Learning to Understand the Consumption of Video Courseware,” Journal of Marketing Research, forthcoming.
  2. Shin, D., He, S., Lee, G. M., Whinston, A. B., Cetintas, S., Lee, K.-C. (2020) “Enhancing Social Media Analysis with Visual Data Analytics: A Deep Learning Approach,” MIS Quarterly 44(4): 1459-1492[Details]
  3. Li, Y., Xie, Y. (2020) “Is a Picture Worth a Thousand Words? An Empirical Study of Image Content and Social Media Engagement,” Journal of Marketing Research 57(2): 1-19.
  4. Zhang, Q., Wang, W., Chen, Y. (2020) “Frontiers: In-Consumption Social Listening with Moment-to-Moment Unstructured Data: The Case of Movie Appreciation and Live comments,” Marketing Science 39(2).
  5. Liu, L., Dzyabura, D., Mizik, N. (2020) “Visual Listening In: Extracting Brand Image Portrayed on Social Media,Marketing Science 39(4): 669-686.
  6. Peng, L., Cui, G., Chung, Y., Zheng, W. (2020) “The Faces of Success: Beauty and Ugliness Premiums in E-Commerce Platforms,” Journal of Marketing 84(4): 67-85.
  7. Liu, X., Zhang, B., Susarla, A., Padman, R. (2020) “Go to YouTube and Call Me in the Morning: Use of Social Media for Chronic Conditions,” MIS Quarterly 44(1b): 257-283.
  8. Zhao, K., Hu, Y., Hong, Y., Westland, J. C. (2020) “Understanding Characteristics of Popular Streamers in Live Streaming Platforms: Evidence from Twitch.tv,” Journal of the Association for Information Systems, Forthcoming.
  9. Ordenes, F. V., Zhang, S. (2019) “From words to pixels: Text and image mining methods for service research,” Journal of Service Management 30(5): 593-620.
  10. Wang, Q., Li, B., Singh, P. V. (2018) “Copycats vs. Original Mobile Apps: A Machine Learning Copycat-Detection Method and Empirical Analysis,” Information Systems Research 29(2): 273-291.
  11. Lu, S., Xiao, L., Ding, M. (2016) “A Video-Based Automated Recommender (VAR) System for Garments,” Marketing Science 35(3): 484-510.
  12. Xiao, L., Ding, M. (2014) “Just the Faces: Exploring the Effects of Facial Features in Print Advertising,” Marketing Science 33(3), 315-461.
  13. Lysyhakov M, Viswanathan S (2021) “Threatened by AI: Analyzing users’ responses to the introduction of AI in a crowd-sourcing,” Working Paper.
  14. Park, S., Lee, G. M., Shin, D., Han, S.-P. (2020) “Targeting Pre-Roll Ads using Video Analytics,” Working Paper.
  15. Zhang, S., Lee, D., Singh, P. V., Srinivasan, K. (2018) “How Much is an Image Worth? Airbnb Property Demand Estimation Leveraging Large Scale Image Analytics,” CMU Working Paper.
  16. Choi, A., Ramaprasad, J., So, H. (2021) Does Authenticity of Influencers Matter? Examining the Impact on Purchase Decisions, Working Paper.
  17. Park, J., Kim, J., Cho, D., Lee, B. Pitching in Character: The Role of Video Pitch’s Personality Style in Online Crowdsourcing, Working Paper.
  18. Yang, J., Zhang, J., Zhang Y. (2021) First Law of Motion: Influencer Video Advertising on TikTok, Working Paper.
  19. Davila, A., Guasch (2021) Manager’s Body Expansiveness, Investor Perceptions, and Firm Forecast Errors and Valuation, Working Paper.
  20. Peng, L., Teoh, S. H., Wang, U., Yan, J. (2021) Face Value: Trait Inference, Performance Characteristics, and Market Outcomes for Financial Analysts, Working Paper.
  21. Zhang, S., Friedman, E., Zhang, X., Srinivasan, K., Dhar, R. (2020) Serving with a Smile on Airbnb: Analyzing the Economic Returns and Behavioral Underpinnings of the Host’s Smile,” Working Paper.
  22. Park, K., Lee, S., Tan, Y. (2020) “What Makes Online Review Videos Helpful? Evidence from Product Review Videos on YouTube,” UW Working Paper.
  23. Doosti, S., Lee, S., Tan, Y. (2020) “Social Media Sponsorship: Metrics for Finding the Right Content Creator-Sponsor Matches,” UW Working Paper.
  24. Koh, B., Cui, F. (2020) “Give a Gist: The Impact of Thumbnails on the View-Through of Videos,” KU Working Paper.
  25. Gunarathne, P., Rui, H., Seidman, A. (2019) “Racial Discrimination in Social Media Customer Service: Evidence from a Popular Microblogging Platform,” Rochester Working Paper.
  26. Hou J.R., Zhang J., Zhang K. (2018) Can title images predict the emotions and the performance of crowdfunding projects? Workshop on e-Business.

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.

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.

Observations and Strategies of Online Teaching

Last update: April 21, 2021

All of a sudden, instructors are in the situation to teach online. I am taking this opportunity to develop a hybrid model for effective teaching. In this post, I will summarize my observations, experiences, and possible solutions. A caveat is that I teach “technical” courses in business analytics, so some of the issues I discussed here may not be directly applicable to “qualitative” courses. Also, note that this post is a work-in-progress and may be updated in the future.

  1. Reading the class
    1. One challenge in online lectures is that it is hard to “read the class”.
    2. We can ask students to turn on their videos so that instructors can see their facial expressions and catch non-verbal cues.
    3. We can use the chat/poll features to get instant, short feedbacks (even shy students feel comfortable sharing their thoughts in this textual mode).
    4. Now that all class activities are online, instructors have access to detailed analytics data that can be used to read the class throughout the course (not necessarily an individual class meeting).
  2. Effectively delivering materials
    1. In an online situation, the attention span is really short. Thus we need to chunk lectures into 20-30 min pieces with 10-15 min lecture + 10-15 min individual/group exercise.
    2. The breakout group feature works really well. Students can clarify issues with each other during the breakout group time. TAs can help in this process as well.
    3. Sometimes, students may ask some “out-of-scope” questions. In online sessions, we can let TAs find the relevant information and post it in an online Q&A forum (I use Piazza).
    4. Just like in offline teaching, TAs and instructors can hold virtual office hours. Sharing screen works really well.
  3. Building high-touch community
    1. One of the downsides of having online classes is that students don’t have opportunities to build a personal connection with the professor and with each other.
    2. We can create “introduction videos” to build relationships.
    3. For ice-breaking purposes, when starting the online lecture session, instructors can enter the session 5-10 min before the lecture starts (just like we do in offline lectures).
    4. Online forums (e.g., Piazza) can facilitate peer interactions.
    5. Finally, online environments allow us to invite virtually any guest speakers from all around the world. We can easily invite high-profile speakers and alumni to online class sessions. Universities can create a lot of value by leveraging the alumni network.
  4. Course participation
    1. One challenge I faced was the objective measure of the course participation events. I recorded all the chat history and asked TAs to count how many times each student verbally asked questions or made comments. As I used Piazza as the Q&A forum, I also incorporated the question/comment/endorsement counts from its analytics data.
    2. Some students questioned if we can use in-class chats or virtual office hour visits are counted. Whichever option an instructor chose, it has to be clearly stated in the course outline to avoid any confusion.
  5. Exam
    1. I used an open book/note exam given the nature of the subject.
    2. To avoid the possibility of collusion, I created multiple question banks for each subject and difficulty level. In Canvas, the exam is dynamically generated by picking random questions from the question banks. To implement this, I had to create 3x exam questions than a paper-based exam. In Canvas, the order of multiple choice answers can be randomized as well.
    3. One challenge is to inform students of any clarification issues in the exam. In case one student found an issue with the exam, it is hard to share this information with the whole class. So I decided not to handle any content issues during the exam time.

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/