Author Archives: gene lee

Reflections on conference organizations in 2021

In 2021, I had great opportunities to serve as an organizer for three events: Program Co-Chair for INFORMS Workshop on Data Science 2021, Workshop Co-Chair for KrAIS Research Workshop 2021, and Minitrack Co-Chair for HICSS 2022 TAEM Minitrack. This post is to reflect my experiences in organizing these events. In sum, I am grateful that I had the opportunity to contribute to my academic communities!

1. INFORMS Workshop on Data Science 2021 (Virtual via Zoom) [DS 2021 Program]

This INFORMS workshop is for data science-oriented IS research. Many of the papers are technical in nature, using various computational and machine learning approaches, to solve a variety of business and societal challenges. The previous workshops were collocated with CIST in the INFORMS Annual Meeting locations. Due to the pandemic, the 2021 workshop was held virtually. There are both positive and negative sides to being virtual. Just focussing on the positive side, because there is no travel cost, many participants from all around the world could participate in the event, although there could be some time zone issues. Thankfully, we could invite many prestigious editors to our panel discussion (many thanks to the editors Andrew Burton-Jones, Alok Gupta, Subodha Kumar, Olivia Sheng, D. J. Wu as well as the moderator Ahmed Abbasi). We also had the great honor to have Jon Kleinberg as the keynote speaker. Last but not least, we had great presentations about many cutting-edge papers on recommender systems, algorithm design, deep learning, personalization, pricing, network analytics, and healthcare. Thanks to all the conference co-chairs (Gautam Pant, Wenjun Zhou, Shawn Mankad), program co-chairs (Yong Ge, Jingjing Zhang), and other organizing committee members. It was great teamwork!

2. KrAIS Research Workshop 2021 (Hybrid in Austin, TX & Zoom) [KrAIS 2021 Program]

This post-ICIS workshop is to promote the scholarship and provide networking opportunities for the AIS members with Korean heritage. ICIS 2021 was held in Austin, TX, and I was looking forward to visiting my second home through this opportunity. We managed to secure a great conference venue (OASIS on Lake Travis). However, due to the COVID-19 variant omicron, many international participants (including myself!) had to cancel their travel plans at the very last moment, hence the organizers had to manage many last-minute changes. Managing a hybrid conference brought interesting challenges: the audio-video delivery between the venue and Zoom, the transition between on-site and online, and registration processes. We had a great panel discussion on the issue of EDI (many thanks to panelists Victoria Yoon, Byungjoon Yoo, Min-Seok Pang, and the moderator Dokyun Lee). Also, I appreciate the support from the KrAIS Co-Presidents (Habin Lee, Byungjoon Yoo) and KrAIS Committee members (Wooje Cho, Kyung Young Lee, Youngsok Bang). Many thanks to my fellow workshop co-chairs (Hyeyoung Hah, JaeHong Park)!

3. HICSS 2022 Technology and Analytics in Emerging Markets (TAEM) Mini-track (Virtual via Zoom) [HICSS 2022 TAEM Mini-track]

Starting from HICSS 2021, Sang-Pil Han, Sungho Park, Wonseok Oh, and I are organizing a mini-track at the HICSS conference. The objective of this mini-track is to nurture a vibrant community between academics and industry on the topic of technology and analytics in emerging markets. Of course, in beautiful Hawaii islands. Unfortunately, we had to do virtual conferences for two consecutive years (we are missing Hawaii!). Fortunately, we had many great paper submissions this year (thanks to the authors who submitted their great work). We had a Zoom session to discuss the accepted papers. We all agreed to meet in person again in Hawaii next year!

4. Summary

When I was a participant in conferences, I didn’t realize all the complexities behind the scene. Now I started to appreciate the significant amount of time and effort put by conference organizers to make such events a reality. Thanks to all the organizers of the numerous conferences and workshops that I attended in my academic life! In 2022, I will be serving as a track co-chair (with Ali Shuyaev and Jing Wang) for ICIS 2022 Data Analytics for Business and Societal Challenges, a track co-chair (with Seung Hyun Kim and Dan J. Kim) for PACIS 2022 Cybersecurity, Privacy, and Ethical Issues, and a conference co-chair (with Jingjing Zhang and Yong Ge) for INFORMS Workshop on Data Science 2022. The reward of good work is more work, but I am happy to keep contributing to our academic communities ๐Ÿ™‚

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

Park, Jaecheol, Arslan Aziz, Gene Moo Lee.ย โ€œDo Incentivized Reviews Poison the Well? Evidence from a Natural Experiment at Amazon.comโ€,ย Working Paper.

  • Presentations: UBC (2021), KrAIS (2021), WISE (2021), PACIS (2022), SCECR (2022), BU Platform (2022), CIST (2022), BIGS (2022)
  • Preliminary version in PACIS 2022 Proceedings

The rapid growth in e-commerce has led to a concomitant increase in consumersโ€™ 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. The literature has examined the direct and indirect effects of incentivized reviews on subsequent organic reviews within consumers who received incentives. However, since incentivized reviews and reviewers are often only a small proportion of a review platform (only 1.2% in our sample), it is important to understand whether their presence and absence on the platform affect the organic reviews from other reviewers who have not received incentives, which are often in the majority. We theorize two underlying effects that incentivized reviews can generate on other organic reviews: the herding effect from imitating incentivized reviews and the disclosure effect from the increased trust or skepticism by explicit incentive disclosure statements. Those two effects make organic reviews either follow or deviate from incentivized reviews. Using Bidirectional Encoder Representations from Transformers (BERT) to identify incentivized reviews and a natural experiment caused by a policy change on Amazon.com in October 2016, we conduct difference-in-differences with propensity score matching analyses to identify the effects of banning incentivized reviews on organic reviews. Our results suggest the disclosure effects are salient: banning incentivized reviews has positive effects on organic reviews in terms of frequency, sentiment, length, image, and helpfulness. Moreover, we find that the presence of incentivized reviews has poisoned the well for organic reviews regardless of the incentivized review ratio and that the effect is heterogeneous to product quality uncertainty. Our findings contribute to the literature on online review and platform design and provide insights to platform managers.

Learning Faces to Predict Matching Probability in an Online Dating Market (ICIS 2022)

Kwon, Soonjae, Gene Moo Lee, Dongwon Lee, Sung-Hyuk Park (2024) โ€œDigital Cupid: Empowering Generative AI for Fair and Efficient Matchmaking,โ€ 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 usersโ€™ 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.

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 researchersย should have a working knowledge of Python/R programming and Linux environment. I recommend these online courses: Data Science,ย Machine Learning,ย Linux,ย SQL, andย 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 Multimodal Data Analytics (Image, Video, Audio)

Last update: Sep 7, 2023

With the advent of social media and mobile platforms, visual and multimodal 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 images and videos) 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. Yang, Yi, Yu Qin, Yangyang Fan, Zhongju Zhang (2023). Unlocking the Power of Voice for Financial Risk Prediction: A Theory-Driven Deep Learning Design Approach. MIS Quarterly 47(1): 63-96.
  2. Ceylan, G., Diehl, K., & Proserpio, D. (2023). EXPRESS: Words Meet Photos: When and Why Visual Content Increases Review Helpfulness.ย Journal of Marketing Research, forthcoming.
  3. Alex Burnap, John R. Hauser, Artem Timoshenko (2023) Product Aesthetic Design: A Machine Learning Augmentation. Marketing Science, forthcoming.
  4. Gao, Jia,ย Ying Rong,ย Xin Tian,ย Yuliang Yaoย (2023) Improving Convenience or Saving Face? An Empirical Analysis of the Use of Facial Recognition Payment Technology in Retail. Information Systems Research, forthcoming.
  5. Guan, Yue, Yong Tan, Qiang Wei, Guoqing Chen (2023) When Images Backfire: The Effect of Customer-Generated Images on Product Rating Dynamics. Information Systems Research, Forthcoming.
  6. Son, Y., Oh, W., Im, I. (2022) The Voice of Commerce: How Smart Speakers Reshape Digital Content Consumption and Preference. MIS Quarterly,ย forthcoming.
  7. Hou, J., Zhang, J., & Zhang, K. (2022). Pictures that are Worth a Thousand Donations: How Emotions in Project Images Drive the Success of Crowdfunding Campaigns? An Image Design Perspective. MIS Quarterly, Forthcoming.
  8. Lysyakov, Mikhail, Siva Viswanathan (2022) Threatened by AI: Analyzing Usersโ€™ Responses to the Introduction of AI in a Crowd-Sourcing Platform. Information Systems Research, Forthcoming.
  9. Hanwei Li, David Simchi-Levi, Michelle Xiao Wu, Weiming Zhu (2022) Estimating and Exploiting the Impact of Photo Layout: A Structural Approach. Management Science, Forthcoming.
  10. Bharadwaj, N., Ballings, M., Naik, P. A., Moore, M, Arat, M. M. (2022) “A New Livestream Retail Analytics Framework to Assess the Sales Impact of Emotional Displays,” Journal of Marketing, 86(1): 24-47.
  11. Chen, Z., Liu, Y.-J., Meng, J., Wang, Z. (2022) “What’s in a Face? An Experiment on Facial Information and Loan-Approval Decision“, Management Science, forthcoming.
  12. Lu, T., Wang, A., Yuan, X., Zhang, X. (2020) “Visual Distortion Bias in Consumer Choices,” Management Science, forthcoming.
  13. 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 58(6): 1079-1100.
  14. Zhang, Shunyuan,ย Dokyun Lee,ย Param Vir Singh,ย Kannan Srinivasanย (2021) What Makes a Good Image? Airbnb Demand Analytics Leveraging Interpretable Image Features. Management Science 68(8):5644-5666.
  15. Gunarathne, P., Rui, H., Seidmann, A. (2021) “Racial Bias in Customer Service: Evidence from Twitter,” Information Systems Research 33(1): 43-54.
  16. Shin, D., He, S.,ย Lee, G. M., Whinston, A. B., Cetintas, S., Lee, K.-C. (2020) โ€œEnhancing Social Media Analysis with Visual Data Analytics: A Deep Learning Approach,โ€ย MISย Quarterlyย 44(4): 1459-1492.ย [Details]
  17. 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.
  18. 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).
  19. Liu, L., Dzyabura, D., Mizik, N. (2020) “Visual Listening In: Extracting Brand Image Portrayed on Social Media,Marketing Science 39(4): 669-686.
  20. 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.
  21. 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.
  22. 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.
  23. 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.
  24. 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.
  25. Lu, S., Xiao, L., Ding, M. (2016) “A Video-Based Automated Recommender (VAR) System for Garments,” Marketing Science 35(3): 484-510.
  26. Xiao, L., Ding, M. (2014) “Just the Faces: Exploring the Effects of Facial Features in Print Advertising,” Marketing Science 33(3), 315-461.
  27. Suh, K.-S., Kim, H., Suh, E. K. (2011) “What If Your Avatar Looks Like You? Dual-Congruity Perspectives for Avatar Use,” MIS Quarterly 35(3), 711-729.
  28. Todorov, A., Porter, J. M. (2014) “Misleading First Impressions: Different for Different Facial Images of the Same Person“, Psychological Science 25(7): 1404-1417.
  29. Todorov, A., Madnisodza, A. N., Goren, A., Hall, C. C. (2005) “Inferences of Competence from Faces Predict Election Outcomes“, Science 308(5728): 1623-1626.
  30. Mueller. U., Mazur, A. (1996) “Facial Dominance of West Point Cadets as a Predictor of Later Military Rank“, Social Forces 74(3): 823-850.
  31. Lee, H, Nam, K. “When Machine Vision Meets Human Fashion: Effects of Human Intervention on the Efficiency of CNN-Driven Recommender Systems in Online Fashion Retail”, Working Paper.
  32. Lysyhakov M, Viswanathan S (2021) “Threatened by AI: Analyzing users’ responses to the introduction of AI in a crowd-sourcing,” Working Paper.
  33. Park, S., Lee, G. M., Shin, D., Han, S.-P. (2020) “Targeting Pre-Roll Ads using Video Analytics,” Working Paper.
  34. Choi, A., Ramaprasad, J., So, H. (2021) Does Authenticity of Influencers Matter? Examining the Impact on Purchase Decisions, Working Paper.
  35. Park, J., Kim, J., Cho, D., Lee, B. Pitching in Character: The Role of Video Pitch’s Personality Style in Online Crowdsourcing, Working Paper.
  36. Yang, J., Zhang, J., Zhang Y. (2021) First Law of Motion: Influencer Video Advertising on TikTok, Working Paper.
  37. Davila, A., Guasch (2021) Manager’s Body Expansiveness, Investor Perceptions, and Firm Forecast Errors and Valuation, Working Paper.
  38. Peng, L., Teoh, S. H., Wang, U., Yan, J. (2021) Face Value: Trait Inference, Performance Characteristics, and Market Outcomes for Financial Analysts, Working Paper.
  39. 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.
  40. Park, K., Lee, S., Tan, Y. (2020) “What Makes Online Review Videos Helpful? Evidence from Product Review Videos on YouTube,” UW Working Paper.
  41. Doosti, S., Lee, S., Tan, Y. (2020) “Social Media Sponsorship: Metrics for Finding the Right Content Creator-Sponsor Matches,” UW Working Paper.
  42. Koh, B., Cui, F. (2020) “Give a Gist: The Impact of Thumbnails on the View-Through of Videos,” KU Working Paper.
  43. 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) โ€œAI Robot Adoption in the Service Industryโ€. KOSEN Reportย  DOI: https://doi.org/10.22800/kisti.kosenexpert.2020.588

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

Abstract

๋””์ง€ํ„ธ ์ „ํ™˜(Digital Transformation) ์‹œ์žฅ์€ 2020๋…„ ๊ธฐ์ค€ 3,550์–ต ๋‹ฌ๋Ÿฌ์˜ ๊ฐ€์น˜๊ฐ€ ์žˆ์œผ๋ฉฐ, 2027๋…„๊นŒ์ง€์˜ ์—ฐ๊ฐ„ ์„ฑ์žฅ๋ฅ ์€ 22.5%์— ์ด๋ฅผ ๊ฒƒ์œผ๋กœ ์˜ˆ์ƒ๋˜๊ณ  ์žˆ๋‹ค. ย ์Šค๋งˆํŠธํฐ์˜ ๋ณด๊ธ‰๊ณผ ๋ฌด์„ ์ธํ„ฐ๋„ท์˜ ํ™•์‚ฐ์€ ๋””์ง€ํ„ธ์ƒํƒœ๊ณ„๊ฐ€ ๊ตฌ์ถ•๋  ์ˆ˜ ์žˆ๋Š” ํ™˜๊ฒฝ์„ ์กฐ์„ฑํ•˜์˜€์œผ๋ฉฐ, ์ด์šฉ์ž๋“ค์˜ ์ง€์†์ ์ธ ๋””์ง€ํ„ธ์ฝ˜ํ…์ธ  ํ™œ์šฉ์œผ๋กœ ์ธํ•œ ๋ฐ์ดํ„ฐ์˜ ํญ๋ฐœ์ ์ธ ์ฆ๊ฐ€๋Š” ๋ฐฉ๋Œ€ํ•œ ์–‘์˜ ๋ฐ์ดํ„ฐ๋ฅผ ํšจ์œจ์ ์œผ๋กœ ์ฒ˜๋ฆฌํ•  ์ˆ˜ ์žˆ๋Š” ๋น…๋ฐ์ดํ„ฐ ์ฒ˜๋ฆฌ ๊ธฐ์ˆ ์ด ๋ฐœ๋‹ฌํ•  ์ˆ˜ ์žˆ๋Š” ๋ฐ‘๊ฑฐ๋ฆ„์ด ๋˜์—ˆ๋‹ค. ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์‚ฌ๋ฌผ์ธํ„ฐ๋„ท(IoT), Quantum ์ปดํ“จํŒ…, ์ธ๊ณต์ง€๋Šฅ ๊ธฐ์ˆ ์˜ ๋ฐœ๋‹ฌ์€ ๊ธฐ์กด์˜ ์˜คํ”„๋ผ์ธ ์‹œ์žฅ์ด ๋””์ง€ํ„ธ ์‹œ์žฅ์œผ๋กœ ์ „ํ™˜ํ•  ์ˆ˜ ์žˆ๋Š” ์ด‰๋งค์ œ ์—ญํ• ์„ ํ•˜์—ฌ ๋””์ง€ํ„ธ ์‹œ์žฅ์ด ์„ฑ์žฅํ•  ์ˆ˜ ์žˆ๋Š” ์›๋™๋ ฅ์ด ๋˜์—ˆ๋‹ค. ์‹ค์ œ๋กœ ๋‹ค์–‘ํ•œ ์‚ฐ์—… ์˜์—ญ์—์„œ ๋””์ง€ํ„ธ ์‹œ์žฅ ๋‚ด์—์„œ ์ƒˆ๋กœ์šด ์‚ฌ์—… ๊ธฐํšŒ๋ฅผ ํฌ์ฐฉํ•˜๊ณ ์ž ํ•˜๋Š” ์‹œ๋„๊ฐ€ ๋งŽ์ด ์ด๋ฃจ์–ด์ง€๊ณ  ์žˆ์œผ๋ฉฐ, ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์˜คํ”„๋ผ์ธ์—์„œ ๋ฒ—์–ด๋‚˜ ์˜จ๋ผ์ธ ๋””์ง€ํ„ธ ์‹œ์žฅ์—์„œ ๋‹ค์–‘ํ•œ ๊ฐ€์น˜ ์ฐฝ์ถœ์„ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•˜์˜€๋‹ค. ์ „ํ†ต์‚ฐ์—…์˜ ๋””์ง€ํ„ธ ์ „ํ™˜์ด ๊ฐ€์†ํ™”๋˜๊ณ  ์žˆ์Œ์€ ๋‹ค์Œ๊ณผ ๊ฐ™์€ ์‚ฌ๋ก€๋ฅผ ํ†ตํ•ด ํŒŒ์•…ํ•  ์ˆ˜ ์žˆ๋‹ค. ์ž๋™์ฐจ์‚ฐ์—…์—์„œ๋Š” ์ž์œจ์ฃผํ–‰ ์„œ๋น„์Šค๋ฅผ ํ†ตํ•ด ๊ณ ๊ฐ๋“ค์˜ ์ฃผํ–‰ ๋ฐ์ดํ„ฐ๋ฅผ ๋””์ง€ํ„ธํ™”ํ•˜์—ฌ ๋ฌด์ธ ์ž๋™์ฐจ ์‹œ๋Œ€๋ฅผ ์œ„ํ•œ ์ค€๋น„๋ฅผ ํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, ์˜๋ฃŒ์‚ฐ์—…์—์„œ๋Š” ์›๊ฒฉ์ง„๋ฃŒ๋ฅผ ํ†ตํ•ด ๋ฌผ๋ฆฌ์  ํ•œ๊ณ„๋ฅผ ๋›ฐ์–ด๋„˜๋Š” ์˜๋ฃŒ์„œ๋น„์Šค๋ผ๋Š” ๊ฐ€์น˜๋ฅผ ์ฐฝ์กฐํ•˜๊ณ  ์žˆ๊ณ , ์ œ์กฐ์‚ฐ์—…์—์„œ๋Š” ์ƒ์‚ฐ์‹œ์Šคํ…œ ์ž๋™ํ™”๋ฅผ ํ†ตํ•ด ์ƒ์‚ฐ ํšจ์œจ์„ฑ์„ ๋†’์ด๊ณ  ํ’ˆ์งˆ์„ ๋†’์ด๋Š” ํ™œ๋™์„ ํ•˜๊ณ  ์žˆ๋‹ค.

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) โ€œTrustworthy Face? The Effect and Drivers of Comprehensive Trust in Online Job Market Platformโ€. 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 (2022) โ€œRobots Serve Humans? Understanding the Economic and Societal Impacts of AI Robots in the Service Industryโ€,ย Working Paper.

  • Presented at WITS (2020), KrAIS (2020), UBC (2021), DS (2022)
  • Research assistants: Raymond Situ, Gallant Tang

Service providers, such as restaurants, have been adopting various robotics technologies to improve operational efficiency and increase customer satisfaction. AI 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 4,610 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 service quality. This study provides both academic and practical implications on emerging AI robotics techniques.

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.ย โ€œWhat Fuels Growth? A Comparative Analysis of the Scaling Intensity of AI Start-upsโ€. 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.

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.