Author Archives: gene lee

AI Capability or AI Washing? Measuring the Impact of Stated AI Strategies and AI Executions on Firm Innovation and Market Reaction

Lee, Myunghwan, Gene Moo Lee (2022) “AI Capability or AI Washing? Measuring the Impact of Stated AI Strategies and AI Executions on Firm Innovation and Market Reaction”Work-in-Progress.

Contrary to the promise that AI will transform various industries, there are conflicting views on the impact of AI on firm performance.   We argue that existing AI capability measures have three major limitations, limiting our understanding of the impact of AI in business. First, the definition of AI itself is still elusive in the IS and business literature. With the recognition that AI is a multifaceted problem-solving process different from traditional IT, we present a detailed AI classification scheme using various sources (e.g., PapersWithCode, HuggingFace, ACM). Second, existing measures on AI capability do not distinguish between stated strategies and actual AI implementations.   To distinguish “AI washing” and actual AI capability, we collect various AI-related data sources, including AI conferences (e.g., NeurIPS, ICML, ICLR), AI software projects (GitHub repositories), inter-firm transactions related to AI adoption (FactSet), and AI strategies stated in 10-K annual reports. Third, while prior studies identified successful AI implementation factors (e.g., data integrity and intelligence augmentation) in a general context, little is known about the fit of specific AI technologies and different types of tasks. We draw on the theory of task-technology fit to construct a fine-grained AI capability measure that captures the unique characteristics of different industries. Using our newly proposed AI capability measure and a novel dataset, we will study the impact of AI on firm innovation and market reaction, contributing to the nascent literature on managing AI.

IS papers on Cybersecurity

Last update: Jan 18, 2022

In this post, I gathered recent IS publications (2010-current) on the topic of cybersecurity. It is by no means an exhaustive list of the topic. This does not cover other related topics such as privacy and ethics.

  1. Jacob Haislip, Jee-Hae Lim, Robert Pinsker (2021) The Impact of Executives’ IT Expertise on Reported Data Security Breaches. Information Systems Research 32(2):318-334.
  2. Ahmed Abbasi, David Dobolyi, Anthony Vance, Fatemeh Mariam Zahedi (2021) The Phishing Funnel Model: A Design Artifact to Predict User Susceptibility to Phishing Websites. Information Systems Research 32(2):410-436.
  3. Yunhui Zhuang, Yunsik Choi, Shu He, Alvin Chung Man Leung, Gene Moo Lee & Andrew Whinston (2020) Understanding Security Vulnerability Awareness, Firm Incentives, and ICT Development in Pan-Asia, Journal of Management Information Systems, 37:3, 668-693.
  4. Qian Tang & Andrew B. Whinston (2020) Do Reputational Sanctions Deter Negligence in Information Security Management? A Field Quasi‐Experiment, Production and Operations Management 29(2):410-427.
  5. Yoo, Chul & Goo, Jahyun & Rao, Raghav. (2020). Is Cybersecurity a Team Sport? A Multilevel Examination of Workgroup Information Security Effectiveness. MIS Quarterly. 44. 907-931.
  6. Mohammadreza Ebrahimi, Jay F. Nunamaker Jr. & Hsinchun Chen (2020) Semi-Supervised Cyber Threat Identification in Dark Net Markets: A Transductive and Deep Learning Approach, Journal of Management Information Systems, 37:3, 694-722
  7. Sebastian W. Schuetz, Paul Benjamin Lowry, Daniel A. Pienta & Jason Bennett Thatcher (2020) The Effectiveness of Abstract Versus Concrete Fear Appeals in Information Security, Journal of Management Information Systems, 37:3, 723-757.
  8. Che-Wei Liu, Peng Huang & Henry C. Lucas Jr. (2020) Centralized IT Decision Making and Cybersecurity Breaches: Evidence from U.S. Higher Education Institutions, Journal of Management Information Systems, 37:3, 758-787.
  9. Ravi Sen, Ajay Verma & Gregory R. Heim (2020) Impact of Cyberattacks by Malicious Hackers on the Competition in Software Markets, Journal of Management Information Systems, 37:1, 191-216
  10. John D’Arcy, Idris Adjerid, Corey M. Angst, Ante Glavas (2020) Too Good to Be True: Firm Social Performance and the Risk of Data Breach. Information Systems Research 31(4):1200-1223.
  11. Zan Zhang, Guofang Nan, Yong Tan (2020) Cloud Services vs. On-Premises Software: Competition Under Security Risk and Product Customization. Information Systems Research 31(3):848-864.
  12. Terrence August, Duy Dao, Kihoon Kim (2019) Market Segmentation and Software Security: Pricing Patching Rights. Management Science 65(10):4575-4597.
  13. Seung Hyun Kim, Juhee Kwon (2019) How Do EHRs and a Meaningful Use Initiative Affect Breaches of Patient Information?. Information Systems Research 30(4):1184-1202.
  14. Kai-Lung Hui, Ping Fan Ke, Yuxi Yao, Wei T. Yue (2019) Bilateral Liability-Based Contracts in Information Security Outsourcing. Information Systems Research 30(2):411-429.
  15. Victor Benjamin, Joseph S. Valacich, and Hsinchun Chen (2019) DICE-E: a framework for conducting darknet identification, collection, evaluation with ethics. MIS Quarterly 43(1):1–22.
  16. Indranil Bose and Alvin Chung Man Leung (2019) Adoption of identity theft countermeasures and its short- and long-term impact on firm value. MIS Quarterly 43(1):313–328.
  17. Corey M. Angst, Emily S. Block, John D’Arcy, and Ken Kelley (2017) When do IT security investments matter? Accounting for the influence of institutional factors in the context of healthcare data breaches. MIS Quarterly 41(3):893–916.
  18. Orcun Temizkan, Sungjune Park, Cem Saydam (2017) Software Diversity for Improved Network Security: Optimal Distribution of Software-Based Shared Vulnerabilities. Information Systems Research 28(4):828-849.
  19. Shu He, Gene Moo Lee, Sukjin Han, Andrew B. Whinston (2016) How Would Information Disclosure Influence Organizations’ Outbound Spam Volume? Evidence from a Field Experiment. Journal of Cybersecurity 2(1), pp. 99-118.
  20. Yonghua Ji, Subodha Kumar, Vijay Mookerjee (2016) When Being Hot Is Not Cool: Monitoring Hot Lists for Information Security. Information Systems Research 27(4):897-918.
  21. Karthik Kannan, Mohammad S. Rahman, Mohit Tawarmalani (2016) Economic and Policy Implications of Restricted Patch Distribution. Management Science 62(11):3161-3182.
  22. Chul Ho Lee, Xianjun Geng, Srinivasan Raghunathan (2016) Mandatory Standards and Organizational Information Security. Information Systems Research 27(1):70-86.
  23. Jingguo Wang, Manish Gupta, and H. Raghav Rao (2015) Insider threats in a financial institution: Analysis of attack-proneness of information systems applications. MIS Quarterly 39(1):91–112.
  24. Jingguo Wang, Nan Xiao, H. Raghav Rao (2015) Research Note—An Exploration of Risk Characteristics of Information Security Threats and Related Public Information Search Behavior. Information Systems Research 26(3):619-633.
  25. Sabyasachi Mitra, Sam Ransbotham (2015) Information Disclosure and the Diffusion of Information Security Attacks. Information Systems Research 26(3):565-584.
  26. Debabrata Dey, Atanu Lahiri, and Guoying Zhang (2014) Quality competition and market segmentation in the security software market. MIS Quarterly 38(2):589–606.
  27. Seung Hyun Kim and Byung Cho Kim (2014) Differential effects of prior experience on the malware resolution process. MIS Quarterly 38(3):655–678.
  28. Ryan T. Wright, Matthew L. Jensen, Jason Bennett Thatcher, Michael Dinger, Kent Marett (2014) Research Note—Influence Techniques in Phishing Attacks: An Examination of Vulnerability and Resistance. Information Systems Research 25(2):385-400.
  29. Asunur Cezar, Huseyin Cavusoglu, Srinivasan Raghunathan (2013) Outsourcing Information Security: Contracting Issues and Security Implications. Management Science 60(3):638-657.
  30. Xia Zhao, Ling Xue & Andrew B. Whinston (2013) Managing Interdependent Information Security Risks: Cyberinsurance, Managed Security Services, and Risk Pooling Arrangements, Journal of Management Information Systems, 30:1, 123-152.
  31. Chul Ho Lee, Xianjun Geng, Srinivasan Raghunathan, (2012) Contracting Information Security in the Presence of Double Moral Hazard. Information Systems Research 24(2):295-311.
  32. Ransbotham, S., Mitra, S., & Ramsey, J. (2012). Are Markets for Vulnerabilities Effective? MIS Quarterly36(1), 43–64.
  33. Gupta, A., & Zhdanov, D. (2012). Growth and Sustainability of Managed Security Services Networks: An Economic Perspective. MIS Quarterly36(4), 1109–1130.
  34. Kai-Lung Hui, Wendy Hui & Wei T. Yue (2012) Information Security Outsourcing with System Interdependency and Mandatory Security Requirement, Journal of Management Information Systems, 29:3, 117-156.
  35. Caliendo, M., Clement, M., Papies, D., & Scheel-Kopeinig, S. (2012). Research Note: The Cost Impact of Spam Filters: Measuring the Effect of Information System Technologies in Organizations. Information Systems Research23(3), 1068–1080.
  36. August, T., & Tunca, T. I. (2011). Who Should Be Responsible for Software Security? A Comparative Analysis of Liability Policies in Network Environments. Management Science57(5), 934–959.
  37. Chen, P., Kataria, G., & Krishnan, R. (2011). Correlated Failures, Diversification, and Information Security Risk Management. MIS Quarterly35(2), 397–422.
  38. Mookerjee, V., Mookerjee, R., Bensoussan, A., & Yue, W. T. (2011). When Hackers Talk: Managing Information Security Under Variable Attack Rates and Knowledge Dissemination. Information Systems Research22(3), 606–623.
  39. Galbreth, M. R., & Shor, M. (2010). The Impact of Malicious Agents on the Enterprise Software Industry. MIS Quarterly34(3), 595–612.
  40. Mahmood, M. A., Siponen, M., Straub, D., Rao, H. R., & Raghu, T. S. (2010). Moving Toward Black Hat Research in Information Systems Security: An Editorial Introduction to the Special Issue. MIS Quarterly34(3), 431–433.

Papers on Automation and Robotics

Last update: Jan 18, 2022

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

  1. Ge, Ruyi, Zhiqiang (Eric) Zheng, Xuan Tian, Li Liao (2021) Human–Robot Interaction: When Investors Adjust the Usage of Robo-Advisors in Peer-to-Peer Lending. Information Systems Research 32(3):774-785.
  2. 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.
  3. Berente, Nicholas, Gu, Bin, Recker, Jan, Santhanam, Radhika. (2021) Special Issue Editor’s Comments: Managing Artificial Intelligence. MIS Quarterly (45: 3) pp. 1433-1450.
  4. Dixon, Jay, Bryan Hong, Lynn Wu (2021) The Robot Revolution: Managerial and Employment Consequences for Firms. Management Science 67(9):5586-5605.
  5. Schanke, Scott, Gordon Burtch, Gautam Ray (2021) Estimating the Impact of “Humanizing” Customer Service Chatbots. Information Systems Research 32(3):736-751.
  6. 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
  7. Graetz, G., Michaels, G. 2018. Robots at work. Review of Economics and Statistics (100:5), pp. 753-768.
  8. 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

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 🙂

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

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

  • Presentations: UBC (2021), KrAIS (2021), WISE (2021), PACIS (2022), SCECR (2022), BU Platform (2022)

The rapid growth in e-commerce has led to a concomitant increase in consumers’ 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 whether their presence on the platform has spillover effects on the unincentivized reviews which are often in the majority. Using the state-of-the-art language model, Bidirectional Encoder Representations from Transformers (BERT) to identify incentivized reviews, a document embedding method, Doc2Vec to create matched pairs of Amazon and non-Amazon branded products, and a natural experiment caused by a policy change on Amazon.com in October 2016, we conduct a difference-in-differences analysis to identify the spillover effects of banning incentivized reviews on unincentivized reviews. Our results suggest that there are positive spillover effects of the ban on the review sentiment, length, helpfulness, and frequency, suggesting that the policy stimulates more reviews in the short-run and more positive, lengthy, and helpful reviews in the long run. Thus, we find that the presence of incentivized reviews on the platform poisons the well of reviews for unincentivized reviews.

Learning Faces to Predict Matching Probability in an Online Dating Market

Kwon, Soonjae, Sung-Hyuk Park, Gene Moo Lee, Dongwon Lee. “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: Feb 28, 2022

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. Lu, T., Wang, A., Yuan, X., Zhang, X. (2020) “Visual Distortion Bias in Consumer Choices,” Management Science, forthcoming.
  2. 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.
  3. Zhang, S., Lee, D., Singh, P. V., Srinivasan, K. (2021) “What Makes a Good Image? Airbnb Demand Analytics Leveraging Interpretable Image Features,” Management Science, forthcoming.
  4. Gunarathne, P., Rui, H., Seidmann, A. (2021) “Racial Bias in Customer Service: Evidence from Twitter,” Information Systems Research 33(1): 43-54.
  5. 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]
  6. 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.
  7. 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).
  8. Liu, L., Dzyabura, D., Mizik, N. (2020) “Visual Listening In: Extracting Brand Image Portrayed on Social Media,Marketing Science 39(4): 669-686.
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. Lu, S., Xiao, L., Ding, M. (2016) “A Video-Based Automated Recommender (VAR) System for Garments,” Marketing Science 35(3): 484-510.
  15. Xiao, L., Ding, M. (2014) “Just the Faces: Exploring the Effects of Facial Features in Print Advertising,” Marketing Science 33(3), 315-461.
  16. 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.
  17. 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.
  18. Lysyhakov M, Viswanathan S (2021) “Threatened by AI: Analyzing users’ responses to the introduction of AI in a crowd-sourcing,” Working Paper.
  19. Park, S., Lee, G. M., Shin, D., Han, S.-P. (2020) “Targeting Pre-Roll Ads using Video Analytics,” Working Paper.
  20. Choi, A., Ramaprasad, J., So, H. (2021) Does Authenticity of Influencers Matter? Examining the Impact on Purchase Decisions, Working Paper.
  21. Park, J., Kim, J., Cho, D., Lee, B. Pitching in Character: The Role of Video Pitch’s Personality Style in Online Crowdsourcing, Working Paper.
  22. Yang, J., Zhang, J., Zhang Y. (2021) First Law of Motion: Influencer Video Advertising on TikTok, Working Paper.
  23. Davila, A., Guasch (2021) Manager’s Body Expansiveness, Investor Perceptions, and Firm Forecast Errors and Valuation, Working Paper.
  24. Peng, L., Teoh, S. H., Wang, U., Yan, J. (2021) Face Value: Trait Inference, Performance Characteristics, and Market Outcomes for Financial Analysts, Working Paper.
  25. 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.
  26. Park, K., Lee, S., Tan, Y. (2020) “What Makes Online Review Videos Helpful? Evidence from Product Review Videos on YouTube,” UW Working Paper.
  27. Doosti, S., Lee, S., Tan, Y. (2020) “Social Media Sponsorship: Metrics for Finding the Right Content Creator-Sponsor Matches,” UW Working Paper.
  28. Koh, B., Cui, F. (2020) “Give a Gist: The Impact of Thumbnails on the View-Through of Videos,” KU Working Paper.
  29. 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.