{"id":2842,"date":"2020-12-07T10:10:56","date_gmt":"2020-12-07T17:10:56","guid":{"rendered":"https:\/\/blogs.ubc.ca\/genemoolee\/?p=2842"},"modified":"2025-12-04T12:11:00","modified_gmt":"2025-12-04T19:11:00","slug":"papers-on-visual-data-analytics-image-video","status":"publish","type":"post","link":"https:\/\/blogs.ubc.ca\/genemoolee\/2020\/12\/07\/papers-on-visual-data-analytics-image-video\/","title":{"rendered":"IS \/ Marketing Papers on Multimodal Data Analytics (Image, Video, Audio)"},"content":{"rendered":"

Now, visual data analytics is widely adopted in IS and related fields. So, I will stop updating this page (March 3, 2025).<\/strong><\/p>\n

First published: Dec 7, 2020; Last update: Sep 7, 2023<\/p>\n

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:<\/p>\n

    \n
  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<\/a>. MIS Quarterly<\/em><\/strong> 47(1): 63-96.<\/li>\n
  2. Ceylan, G., Diehl, K., & Proserpio, D. (2023). EXPRESS: Words Meet Photos: When and Why Visual Content Increases Review Helpfulness<\/a>.\u00a0Journal of Marketing Research<\/strong><\/em>, forthcoming.<\/li>\n
  3. Alex Burnap, John R. Hauser, Artem Timoshenko (2023) Product Aesthetic Design: A Machine Learning Augmentation<\/a>. Marketing Science<\/em><\/strong>, forthcoming.<\/li>\n
  4. Gao, Jia,\u00a0Ying Rong,\u00a0Xin Tian,\u00a0Yuliang Yao\u00a0(2023) Improving Convenience or Saving Face? An Empirical Analysis of the Use of Facial Recognition Payment Technology in Retail<\/a>. Information Systems Research<\/strong><\/em>, forthcoming.<\/li>\n
  5. Guan, Yue, Yong Tan, Qiang Wei, Guoqing Chen (2023) When Images Backfire: The Effect of Customer-Generated Images on Product Rating Dynamics<\/a>. Information Systems Research<\/strong><\/em>, Forthcoming<\/em>.<\/li>\n
  6. Son, Y.,<\/strong> Oh, W., Im, I. (2022) The Voice of Commerce: How Smart Speakers Reshape Digital Content Consumption and Preference<\/a>. MIS Quarterly<\/strong>,\u00a0<\/i>forthcoming.<\/li>\n
  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<\/a>. MIS Quarterly<\/strong>, Forthcoming.<\/i><\/li>\n
  8. Lysyakov, Mikhail, Siva Viswanathan (2022) Threatened by AI: Analyzing Users\u2019 Responses to the Introduction of AI in a Crowd-Sourcing Platform<\/a>. Information Systems Research<\/em><\/strong>, Forthcoming.<\/li>\n
  9. Hanwei Li, David Simchi-Levi, Michelle Xiao Wu, Weiming Zhu (2022) Estimating and Exploiting the Impact of Photo Layout: A Structural Approach<\/a>. Management Science<\/strong>, Forthcoming<\/em>.<\/li>\n
  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<\/a>,” Journal of Marketing<\/em><\/strong>, 86(1): 24-47.<\/li>\n
  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<\/a>“, Management Science,<\/em><\/strong> forthcoming.<\/li>\n
  12. Lu, T., Wang, A., Yuan, X., Zhang, X. (2020) “Visual Distortion Bias in Consumer Choices<\/a>,” Management Science<\/em><\/strong>, forthcoming.<\/li>\n
  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<\/a>,” Journal of Marketing Research <\/em><\/strong>58(6): 1079-1100.<\/li>\n
  14. Zhang, Shunyuan,\u00a0Dokyun Lee,\u00a0Param Vir Singh,\u00a0Kannan Srinivasan\u00a0(2021) What Makes a Good Image? Airbnb Demand Analytics Leveraging Interpretable Image Features<\/a>. Management Science<\/em><\/strong> 68(8):5644-5666.<\/li>\n
  15. Gunarathne, P., Rui, H., Seidmann, A. (2021) “Racial Bias in Customer Service: Evidence from Twitter<\/a>,” Information Systems Research<\/em><\/strong> 33(1): 43-54.<\/li>\n
  16. Shin, D., He, S.,\u00a0Lee, G. M., Whinston, A. B., Cetintas, S., Lee, K.-C. (2020) \u201cEnhancing Social Media Analysis with Visual Data Analytics: A Deep Learning Approach<\/a>,\u201d\u00a0MIS\u00a0<\/em><\/strong>Quarterly<\/strong><\/i>\u00a044(4): 1459-1492.\u00a0<\/i>[Details<\/a>]<\/li>\n
  17. Li, Y., Xie, Y. (2020) “Is a Picture Worth a Thousand Words? An Empirical Study of Image Content and Social Media Engagement<\/a>,” Journal of Marketing Research<\/em><\/strong> 57(2): 1-19.<\/li>\n
  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<\/a>,” Marketing Science<\/strong><\/em> 39(2).<\/li>\n
  19. Liu, L., Dzyabura, D., Mizik, N. (2020) “Visual Listening In: Extracting Brand Image Portrayed on Social Media,<\/a>” Marketing Science<\/strong><\/em> 39(4): 669-686.<\/li>\n
  20. Peng, L., Cui, G., Chung, Y., Zheng, W. (2020) “The Faces of Success: Beauty and Ugliness Premiums in E-Commerce Platforms<\/a>,” Journal of Marketing<\/strong> <\/em>84(4): 67-85.<\/li>\n
  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<\/a>,” MIS Quarterly<\/em><\/strong> 44(1b): 257-283.<\/li>\n
  22. Zhao, K., Hu, Y., Hong, Y., Westland, J. C. (2020) “Understanding Characteristics of Popular Streamers in Live Streaming Platforms: Evidence from Twitch.tv<\/a>,” Journal of the Association for Information Systems<\/strong>, Forthcoming<\/em>.<\/li>\n
  23. Ordenes, F. V., Zhang, S. (2019) “From words to pixels: Text and image mining methods for service research<\/a>,” Journal of Service Management<\/em><\/strong> 30(5): 593-620.<\/li>\n
  24. Wang, Q., Li, B., Singh, P. V. (2018) “Copycats vs. Original Mobile Apps: A Machine Learning Copycat-Detection Method and Empirical Analysis<\/a>,” Information Systems Research<\/em><\/strong> 29(2): 273-291.<\/li>\n
  25. Lu, S., Xiao, L., Ding, M. (2016) “A Video-Based Automated Recommender (VAR) System for Garments<\/a>,” Marketing Science<\/em><\/strong> 35(3): 484-510.<\/li>\n
  26. Xiao, L., Ding, M. (2014) “Just the Faces: Exploring the Effects of Facial Features in Print Advertising<\/a>,” Marketing Science<\/strong><\/em> 33(3), 315-461.<\/li>\n
  27. Suh, K.-S., Kim, H., Suh, E. K. (2011) “What If Your Avatar Looks Like You? Dual-Congruity Perspectives for Avatar Use<\/a>,” MIS Quarterly<\/em> <\/strong>35(3), 711-729.<\/li>\n
  28. Todorov, A., Porter, J. M. (2014) “Misleading First Impressions: Different for Different Facial Images of the Same Person<\/a>“, Psychological Science<\/em><\/strong> 25(7): 1404-1417.<\/li>\n
  29. Todorov, A., Madnisodza, A. N., Goren, A., Hall, C. C. (2005) “Inferences of Competence from Faces Predict Election Outcomes<\/a>“, Science<\/em><\/strong> 308(5728): 1623-1626.<\/li>\n
  30. Mueller. U., Mazur, A. (1996) “Facial Dominance of West Point Cadets as a Predictor of Later Military Rank<\/a>“, Social Forces<\/em><\/strong> 74(3): 823-850.<\/li>\n
  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.<\/li>\n
  32. Lysyhakov M, Viswanathan S (2021) “Threatened by AI: Analyzing users’ responses to the introduction of AI in a crowd-sourcing<\/a>,” Working Paper.<\/li>\n
  33. Park, S., Lee, G. M., Shin, D., Han, S.-P. (2020) “Targeting Pre-Roll Ads using Video Analytics<\/a>,” Working Paper.<\/li>\n
  34. Choi, A., Ramaprasad, J., So, H. (2021) Does Authenticity of Influencers Matter? Examining the Impact on Purchase Decisions<\/a>, Working Paper.<\/li>\n
  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.<\/li>\n
  36. Yang, J., Zhang, J., Zhang Y. (2021) First Law of Motion: Influencer Video Advertising on TikTok<\/a>, Working Paper.<\/li>\n
  37. Davila, A., Guasch (2021) Manager’s Body Expansiveness, Investor Perceptions, and Firm Forecast Errors and Valuation<\/a>, Working Paper.<\/li>\n
  38. Peng, L., Teoh, S. H., Wang, U., Yan, J. (2021) Face Value: Trait Inference, Performance Characteristics, and Market Outcomes for Financial Analysts<\/a>, Working Paper.<\/li>\n
  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<\/a>,” Working Paper.<\/li>\n
  40. Park, K., Lee, S., Tan, Y. (2020) “What Makes Online Review Videos Helpful? Evidence from Product Review Videos on YouTube<\/a>,” UW Working Paper.<\/li>\n
  41. Doosti, S., Lee, S., Tan, Y. (2020) “Social Media Sponsorship: Metrics for Finding the Right Content Creator-Sponsor Matches<\/a>,” UW Working Paper.<\/li>\n
  42. Koh, B., Cui, F. (2020) “Give a Gist: The Impact of Thumbnails on the View-Through of Videos<\/a>,” KU Working Paper.<\/li>\n
  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<\/em>.<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

    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. […]<\/p>\n","protected":false},"author":51140,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[822902],"tags":[5015,45339,45138,1008963,45300,424636,45301,45302],"class_list":["post-2842","post","type-post","status-publish","format-standard","hentry","category-teaching-materials","tag-analytics","tag-audio","tag-big-data-analytics","tag-computer-vision","tag-image","tag-machine-learning","tag-picture","tag-video"],"_links":{"self":[{"href":"https:\/\/blogs.ubc.ca\/genemoolee\/wp-json\/wp\/v2\/posts\/2842","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs.ubc.ca\/genemoolee\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.ubc.ca\/genemoolee\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.ubc.ca\/genemoolee\/wp-json\/wp\/v2\/users\/51140"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.ubc.ca\/genemoolee\/wp-json\/wp\/v2\/comments?post=2842"}],"version-history":[{"count":58,"href":"https:\/\/blogs.ubc.ca\/genemoolee\/wp-json\/wp\/v2\/posts\/2842\/revisions"}],"predecessor-version":[{"id":4621,"href":"https:\/\/blogs.ubc.ca\/genemoolee\/wp-json\/wp\/v2\/posts\/2842\/revisions\/4621"}],"wp:attachment":[{"href":"https:\/\/blogs.ubc.ca\/genemoolee\/wp-json\/wp\/v2\/media?parent=2842"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.ubc.ca\/genemoolee\/wp-json\/wp\/v2\/categories?post=2842"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.ubc.ca\/genemoolee\/wp-json\/wp\/v2\/tags?post=2842"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}