Author Archives: gene lee

Papers on AI, Automation, and Robotics

First published: Jan 18, 2022; Last update: May 17, 2024

In this post, I am gathering AI, automation, and robotics-related papers in information systems and related disciplines. This is by no means an exhaustive list. I will keep updating this list.

  1. Babina, Tania, Anastassia Fedyk, Alex He, James Hodson (2024) Artificial intelligence, firm growth, and product innovation, Journal of Financial Economics 151.
  2. Eloundou T, Manning S, Mishkin P, Rock D. (2023) GPTs are GPTs: An early look at the labor market impact potential of large language models. arXiv preprint arXiv:2303.10130.
  3. Acemoglu, Daron and Pascual Restrepo (2022) Tasks, Automation, and the Rise in U.S. Wage Inequality, Econometrica, 90(5): 1973-2016,
  4. Park, Jiyong, Jongho Kim (2022) A Data-Driven Exploration of the Race between Human Labor and Machines in the 21st Century, Communications of ACM 65(5):79-87.
  5. Koch, Michael, Manuylov Ilya, Marcel Smolka (2021) Robots and Firms, The Economic Journal 131(638):2553-2584.
  6. Ge, Ruyi, Zhiqiang (Eric) Zheng, Xuan Tian, Li Liao (2021) Humanโ€“Robot Interaction: When Investors Adjust the Usage of Robo-Advisors in Peer-to-Peer Lending. Information Systems Research 32(3):774-785.
  7. Jain, Hemant, Balaji Padmanabhan, Paul A. Pavlou, T. S. Raghu (2021) Editorial for the Special Section on Humans, Algorithms, and Augmented Intelligence: The Future of Work, Organizations, and Society. Information Systems Research 32(3):675-687.
  8. Berente, Nicholas, Gu, Bin, Recker, Jan, Santhanam, Radhika. (2021) Special Issue Editor’s Comments: Managing Artificial Intelligence. MIS Quarterly (45: 3) pp. 1433-1450.
  9. Dixon, Jay, Bryan Hong, Lynn Wu (2021) The Robot Revolution: Managerial and Employment Consequences for Firms. Management Science 67(9):5586-5605.
  10. Schanke, Scott, Gordon Burtch, Gautam Ray (2021) Estimating the Impact of โ€œHumanizingโ€ Customer Service Chatbots. Information Systems Research 32(3):736-751.
  11. Park, H., Jiang, S., Lee, O. D., Chang, Y. (2021) Exploring the Attractiveness of Service Robots in the Hospitality Industry: Analysis of Online Reviews. Information Systems Frontier
  12. Graetz, G., Michaels, G. 2018. Robots at work. Review of Economics and Statistics (100:5), pp. 753-768.
  13. Luo, Xueming, Siliang Tong, Zheng Fang, Zhe Qu (2019) Frontiers: Machines vs. Humans: The Impact of Artificial Intelligence Chatbot Disclosure on Customer Purchases. Marketing Science 38(6):937-947.

 

Reflections on conference organizations in 2021

Published: Jan 9, 2022

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, Joy Wu, Arslan Aziz, Gene Moo Lee.ย โ€œDo Incentivized Reviews Poison the Well? Evidence from a Natural Experiment at Amazon.comโ€, 3rd round R&R at Information Systems Research..

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

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.

VISAGE: Designing AI Artifacts for Dynamic Self-Presentation in Matching Platforms

Kwon, Soonjae, Gene Moo Lee, Dongwon Lee, Sung-Hyuk Park (2024) โ€œVISAGE: Designing AI Artifacts for Dynamic Self-Presentation in Matching Platforms,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

Online matching platforms constrain users to static profiles, producing a mismatch between the idealized self a user presents and the heterogeneous preferences of potential partners. Drawing on self-discrepancy theory, we conceptualize this mismatch as an interpersonal gap between oneโ€™s presented self and what each partner desires to see, with AI serving as a mediator to help address it. Following the computational design science perspective, we propose VISAGE, an AI system comprising two artifacts grounded in distinct human-AI collaboration principles. The augmentation artifact selects optimal images from usersโ€™ existing assets, whereas the assemblage artifact generates new images tailored to individual partner preferences. Using large-scale operational data from a major online dating platform, we evaluate VISAGE at both the user and platform levels. At the user level, model-predicted ratings suggest that both artifacts improve attractiveness ratings. Relative effectiveness varies with partner-preference heterogeneity and user impression management skill, consistent with theoretical predictions from the human-AI collaboration literature. At the platform level, agent-based simulations suggest that VISAGE can enhance matching efficiency and reduce inequality in matching opportunities, although optimal deployment strategies depend on the platformโ€™s recommendation algorithm. The theoretical contribution of this study is to foreground the interpersonal gap as a key source of matching inefficiency and to illustrate how AI can address it at scale. The design contribution lies in actionable design knowledge, including when to deploy augmentation versus assemblage artifacts based on user and partner characteristics and how to align user-facing AI features with backend algorithmic infrastructure.

My thoughts on AI, Big Data, and IS Research (for Junior Scholars)

First published Jan 10, 2021; Last update: November 13, 2025

Back in 2021, 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: AI Fundamentals, Data Science,ย Machine Learning,ย Linux,ย SQL, andย NoSQL.

Second, AI and Big Data Analytics are creating a lot of interesting new “phenomena” in personal lives, firms, and societies. How AI and robots will be adopted in the workplace and how will that affect the labor market? Are we losing our jobs? Or can we improve our productivity with AI tools? How will experts use AI in professional services? What are the unintended consequences (such as biases, security, privacy, and 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.

From the academic point of view, here are a few great editorials on this:

Also, to stay relevant, I think that IS researchers should closely follow 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).

For UBC current and prospective students, here are some resources:

For educators, I have shared my teaching experience using AI in May 2024. You can find the slide deck here.

I hope this post may help people shape their research, teaching, and career strategies. I will try to keep updating this post. Cheers!

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

Now, visual data analytics is widely adopted in IS and related fields. So, I will stop updating this page (March 3, 2025).

First published: Dec 7, 2020; 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.

Service Robots and Workforce Transformation: Evidence from Restaurant Operations

Lee, Myunghwan, Gene Moo Lee, Donghyuk Shin, Wooje Cho, Sang-Pil Han (2025) โ€œService Robots and Workforce Transformation: Evidence from Restaurant Operationsโ€,ย Working Paper.

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

The introduction of AI-powered service robots, those capable of order taking, table delivery, and busser support, is significantly altering the workflow dynamics within the restaurant industry, fundamentally reshaping operations. Although these robots hold considerable promise for enhancing customer experiences and operational efficiency, their integration can introduce complex and potentially unintended consequences. Successful integration demands a careful balance among customer acceptance, automation efficiency, and worker adaptation. Yet critical questions remain insufficiently explored, particularly how the adoption of robots affects the workforce structures. This study addresses this gap by theorizing and empirically examining the impact of robotic integration on the composition of labor, with emphasis on part-time workers, who represent a significant portion of the restaurant workforce. Increased automation may reduce the number of part-time positions, but among those who remain, service robots may augment their roles by supporting or replacing routine tasks, allowing workers to focus on higher-touch interactions. This dual effectโ€”numerical displacement alongside functional augmentationโ€” illustrates a nuanced form of inequality in which the benefits of automation accrue unevenly even within the same labor group. Such shifts could either exacerbate labor inequalities or create opportunities for workforce adaptation and upskilling. From a systematic analysis of operational and customer review data from 3,636 restaurants, our results uncover asymmetric and unintended consequences of robotic integration on labor costs, workforce distribution, and overall restaurant performance. By shedding light on the intersection of automation, workforce restructuring, and customer reception, our findings contribute to the nascent discourse on the digital transformation of retail operations. The insights offered have important implications for managers and policymakers navigating the evolving landscape of AI-driven automation in customer-facing industries.

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.