Author Archives: gene lee

My thoughts on AI, Big Data, and IS Research

Last update: May 31, 2024

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, Machine Learning, Linux, SQL, and 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.

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).

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. 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, Ying Rong, Xin Tian, Yuliang Yao (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, 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 Users’ 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, Dokyun Lee, Param Vir Singh, Kannan Srinivasan (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., 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]
  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) “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.

Service Robots and Workforce Transformation: Evidence from Restaurant Operations

Lee, Myunghwan, Gene Moo Lee, Donghyuk Shin, Wooje Cho, Sang-Pil Han (2025) “Service Robots and Workforce Transformation: Evidence from Restaurant Operations”, 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 effect—numerical displacement alongside functional augmentation— 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. “What Fuels Growth? A Comparative Analysis of the Scaling Intensity of AI Start-ups”. 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.

Corporate Social Network Analysis: A Deep Learning Approach

Cao, Rui, Gene Moo Lee, Hasan Cavusoglu. “Corporate Social Network Analysis: A Deep Learning Approach,” Working Paper.

Identifying inter-firm relationships is critical in understanding the industry landscape. However, due to the dynamic nature of such relationships, it is challenging to capture corporate social networks in a scalable and timely manner. To address this issue, this research develops a framework to build corporate social network representations by applying natural language processing (NLP) techniques on a corpus of 10-K filings, describing the reporting firms’ perceived relationships with other firms. Our framework uses named-entity recognition (NER) to locate the corporate names in the text, topic modeling to identify types of relationships included, and BERT to predict the type of relationship described in each sentence. To show the value of the network measures created by the proposed framework, we conduct two empirical analyses to see their impacts on firm performance. The first study shows that competition relationship and in-degree measurements on all relationship types have prediction power in estimating future earnings. The second study focuses on the difference between individual perspectives in an inter-firm social network. Such a difference is measured by the direction of mentions and is an indicator of a firm’s success in network governance. Receiving more mentions from other firms is a positive signal to network governance and it shows a significant positive correlation with firm performance next year.

IS Papers on Big Data, Analytics, and AI

Last update: Jan 22, 2025.

My research involves Big Data Analytics and AI in Information Systems literature. This post tries to keep track of the editorial and seminal articles on the topic of Big Data, Data Science, Analytics, and AI in the Information Systems and Management literature. The papers are listed in chronological order:

  1. Bapna, Goes, Gopal, Marsden (2006) Moving from Data-Constrained to Data-Enabled Research: Experiences and Challenges in Collecting, Validating and Analyzing Large-Scale e-Commerce Data, Statistical Science 21(2): 116-130.
  2. Shmueli and Koppius (2011) Predictive Analytics in Information Systems Research, MIS Quarterly 35(3): 553-572
  3. Chen, Chiang, Storey, (2012) Business Intelligence and Analytics: From Big Data to Big Impact, MIS Quarterly 36(4): 1164-1188
  4. Lin, Lucas Jr., Shmueli (2013) Research Commentary: Too Big to Fail: Large Samples and the p-Value Problem, Information Systems Research 24(4): 906-917.
  5. Agarwal, Dhar (2014) Editorial – Big Data, Data Science, and Analytics: The Opportunity and Challenge for IS Research, Information Systems Research 25(3): 443-448
  6. Varian (2014) Big Data: New Tricks for Econometrics, Journal of Economic Perspectives 28(2): 3-28
  7. Goes (2014) Editor’s Comments: Big Data and IS Research, MIS Quarterly 38(3): iii-viii
  8. AMJ Editors (2016) From the Editors: Big Data and Data Science Methods for Management Research, Academy of Management Journal 59(5): 1493-1507
  9. Abbasi, Sarker, Chiang (2016) Big Data Research in Information Systems: Toward an Inclusive Research Agenda, Journal of the Association for Information Systems 17(2): i-xxxii
  10. Rai (2016) Editor’s Comments: Synergies Between Big Data and Theory, MIS Quarterly 40(2): iii-ix
  11. Baesens, Bapna, Marsden, Vanthienen, Zhao (2016) Transformational Issues of Big Data and Analytics in Networked Business, MIS Quarterly 40(4): 807-818
  12. Athey (2017) Beyond Prediction: Using Big Data for Policy Problems, Science 355(6324): 483-485
  13. Chiang, Grover, Liang, Zhang (2018) Special Issue: Strategic Value of Big Data and Business Analytics, Journal of Management Information Systems 35(2): 383-387
  14. Delen, Ram (2018) Research challenges and opportunities in business analytics, Journal of Business Analytics 1(1): 2-12.
  15. Maass, Parsons, Puraro, Storey, Woo (2018) Data-Driven Meets Theory-Driven Research in the Era of Big Data: Opportunities and Challenges for Information Systems Research, Journal of the Association for Information Systems 19(12): 1253-1273
  16. Yang, Adomavicius, Burtch, Ren (2018) Mind the Gap: Accounting for Measurement Error and Misclassification in Variables Generated via Data Mining, Information Systems Research 29(1): 4-24.
  17. Berente, Seidel, Safadi (2019) Research Commentary: Data-Driven Computationally Intensive Theory Development, Information Systems Research 30(1), 50-64.
  18. Johnson, Gray, Sarker (2019) Revisiting IS Research Practice in the Era of Big Data, Information and Organization 29(1): 41-56
  19. Grover, Lindberg, Benbasat, Lyytinen (2020) The Perils and Promises of Big Data Research in Information Systems, Journal of the Association for Information Systems 21(2): 268-291.
  20. Shmueli (2021) INFORMS Journal of Data Science (IJDS) Editorial #1: What is an IJDS paper?, INFORMS Journal of Data Science.
  21. Ram, Goes (2021) Focusing on Programmatic High Impact Information Systems Research, not Theory, to Address Grand Challenges, MIS Quarterly 45(1): 479-483.
  22. Burton-Jones, Boh, Oborn, Padmanabhan (2021) Editor’s Comments: Advancing Research Transparency at MIS Quarterly: A Pluralistic Approach, MIS Quarterly 45(2): iii-xviii.
  23. Berente, Gu, Recker, Santhanam (2021) Special Issue Editor’s Comments: Managing Artificial Intelligence, MIS Quarterly 45(3): 1433-1450.
  24. Jain, Padmanabhan, Pavlou, 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.
  25. Padmanabhan, Fang, Sahoo, Burton-Junes (2022) Editor’s Comments: Machine Learning in Information Systems Research, MIS Quarterly 46(1): iii-xix.
  26. Abbasi, Parsons, Pant, Sheng, Sarker (2024) Pathways for Design Research on Artificial Intelligence. Information Systems Research 35(2):441-459.

 

When Does Congruence Matter for Pre-roll Video Ads? The Effect of Multimodal, Ad-Content Congruence on the Ad Completion

Park, Sungho, Gene Moo Lee, Donghyuk Shin, Sang-Pil Han. “When Does Congruence Matter for Pre-roll Video Ads? The Effect of Multimodal, Ad-Content Congruence on the Ad Completion“, Working Paper [Last update: Jan 29, 2023]

  • Previous title: Targeting Pre-Roll Ads using Video Analytics
  • Funded by Sauder Exploratory Research Grant 2020
  • Presented at Southern Methodist University (2020), University of Washington (2020), INFORMS (2020), AIMLBA (2020), WITS (2020), HKUST (2021), Maryland (2021), American University (2021), National University of Singapore (2021), Arizona (2022), George Mason (2022), KAIST (2022), Hanyang (2022), Kyung Hee (2022), McGill (2022)
  • Research assistants: Raymond Situ, Miguel Valarao

Pre-roll video ads are gaining industry traction because the audience may be willing to watch an ad for a few seconds, if not the entire ad, before the desired content video is shown. Conversely, a popular skippable type of pre-roll video ads, which enables viewers to skip an ad in a few seconds, creates opportunity costs for advertisers and online video platforms when the ad is skipped. Against this backdrop, we employ a video analytics framework to extract multimodal features from ad and content videos, including auditory signals and thematic visual information, and probe into the effect of ad-content congruence at each modality using a random matching experiment conducted by a major video advertising platform. The present study challenges the widely held view that ads that match content are more likely to be viewed than those that do not, and investigates the conditions under which congruence may or may not work. Our results indicate that non-thematic auditory signal congruence between the ad and content is essential in explaining viewers’ ad completion, while thematic visual congruence is only effective if the viewer has sufficient attentional and cognitive capacity to recognize such congruence. The findings suggest that thematic videos demand more cognitive processing power than auditory signals for viewers to perceive ad-content congruence, leading to decreased ad viewing. Overall, these findings have significant theoretical and practical implications for understanding whether and when viewers construct congruence in the context of pre-roll video ads and how advertisers might target their pre-roll video ads successfully.