IDEAS home Printed from https://ideas.repec.org/a/eee/jbrese/v123y2021icp367-379.html
   My bibliography  Save this article

Understanding videos at scale: How to extract insights for business research

Author

Listed:
  • Schwenzow, Jasper
  • Hartmann, Jochen
  • Schikowsky, Amos
  • Heitmann, Mark

Abstract

Video content has become a major component of total internet traffic. Growing bandwidth and computational power conspire with an increasing number of video editing tools, smartphones, and online platforms that have facilitated video production, distribution, and consumption by businesses and consumers alike. This makes video content relevant across business research disciplines. However, analyzing videos can be a cumbersome manual task. Automated techniques are scattered across technical publications and are often not directly accessible to business researchers. This article synthesizes the current state of the art and provides a consolidated tool to efficiently extract 109 video-based variables, requiring no programming knowledge. The variables include structural video characteristics such as colorfulness as well as advanced content-related features such as scene cuts or human face detection. The authors discuss the research potential of video mining, the types of video features of likely interest, and illustrate application using a practical example.

Suggested Citation

  • Schwenzow, Jasper & Hartmann, Jochen & Schikowsky, Amos & Heitmann, Mark, 2021. "Understanding videos at scale: How to extract insights for business research," Journal of Business Research, Elsevier, vol. 123(C), pages 367-379.
    • Handle: RePEc:eee:jbrese:v:123:y:2021:i:c:p:367-379
    DOI: 10.1016/j.jbusres.2020.09.059
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0148296320306445
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jbusres.2020.09.059?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Clement, Michel & Wu, Steven & Fischer, Marc, 2014. "Empirical generalizations of demand and supply dynamics for movies," International Journal of Research in Marketing, Elsevier, vol. 31(2), pages 207-223.
    2. Li Xiao & Min Ding, 2014. "Just the Faces: Exploring the Effects of Facial Features in Print Advertising," Marketing Science, INFORMS, vol. 33(3), pages 338-352, May.
    3. Beth L. Fossen & David A. Schweidel, 2019. "Measuring the Impact of Product Placement with Brand-Related Social Media Conversations and Website Traffic," Marketing Science, INFORMS, vol. 38(3), pages 481-499, May.
    4. Jehoshua Eliashberg & Sam K. Hui & Z. John Zhang, 2007. "From Story Line to Box Office: A New Approach for Green-Lighting Movie Scripts," Management Science, INFORMS, vol. 53(6), pages 881-893, June.
    5. van Reijmersdal, Eva A. & Rozendaal, Esther & Hudders, Liselot & Vanwesenbeeck, Ini & Cauberghe, Veroline & van Berlo, Zeph M.C., 2020. "Effects of Disclosing Influencer Marketing in Videos: An Eye Tracking Study Among Children in Early Adolescence," Journal of Interactive Marketing, Elsevier, vol. 49(C), pages 94-106.
    6. Swani, Kunal & Milne, George R., 2017. "Evaluating Facebook brand content popularity for service versus goods offerings," Journal of Business Research, Elsevier, vol. 79(C), pages 123-133.
    7. Alex Burnap & John R. Hauser & Artem Timoshenko, 2019. "Product Aesthetic Design: A Machine Learning Augmentation," Papers 1907.07786, arXiv.org, revised Nov 2022.
    8. Anuj Kumar & Yinliang (Ricky) Tan, 2015. "The Demand Effects of Joint Product Advertising in Online Videos," Management Science, INFORMS, vol. 61(8), pages 1921-1937, August.
    9. Tobias Kretschmer & Christian Peukert, 2020. "Video Killed the Radio Star? Online Music Videos and Recorded Music Sales," Information Systems Research, INFORMS, vol. 31(3), pages 776-800, September.
    10. Jeon, Yongwoog Andrew & Son, Hyunsang & Chung, Arnold D. & Drumwright, Minette E., 2019. "Temporal Certainty and Skippable In-Stream Commercials: Effects of Ad Length, Timer, and Skip-ad Button on Irritation and Skipping Behavior," Journal of Interactive Marketing, Elsevier, vol. 47(C), pages 144-158.
    11. Bellman, Steven & Schweda, Anika & Varan, Duane, 2012. "Interactive TV advertising: iTV ad executional factors," Journal of Business Research, Elsevier, vol. 65(6), pages 831-839.
    12. Guitart, Ivan A. & Gonzalez, Jorge & Stremersch, Stefan, 2018. "Advertising non-premium products as if they were premium: The impact of advertising up on advertising elasticity and brand equity," International Journal of Research in Marketing, Elsevier, vol. 35(3), pages 471-489.
    13. Catherine E. Tucker, 2015. "The Reach and Persuasiveness of Viral Video Ads," Marketing Science, INFORMS, vol. 34(2), pages 281-296, March.
    14. Shehu, Edlira & Bijmolt, Tammo H.A. & Clement, Michel, 2016. "Effects of Likeability Dynamics on Consumers' Intention to Share Online Video Advertisements," Journal of Interactive Marketing, Elsevier, vol. 35(C), pages 27-43.
    15. Choi, Dongwon & Bang, Hyejin & Wojdynski, Bartosz W. & Lee, Yen-I & Keib, Kate M., 2018. "How Brand Disclosure Timing and Brand Prominence Influence Consumer's Intention to Share Branded Entertainment Content," Journal of Interactive Marketing, Elsevier, vol. 42(C), pages 18-31.
    16. Shasha Lu & Li Xiao & Min Ding, 2016. "A Video-Based Automated Recommender (VAR) System for Garments," Marketing Science, INFORMS, vol. 35(3), pages 484-510, May.
    17. Geuens, Maggie & De Pelsmacker, Patrick & Faseur, Tine, 2011. "Emotional advertising: Revisiting the role of product category," Journal of Business Research, Elsevier, vol. 64(4), pages 418-426, April.
    18. Hautz, Julia & Füller, Johann & Hutter, Katja & Thürridl, Carina, 2014. "Let Users Generate Your Video Ads? The Impact of Video Source and Quality on Consumers' Perceptions and Intended Behaviors," Journal of Interactive Marketing, Elsevier, vol. 28(1), pages 1-15.
    19. Rajagopal, 2014. "The Human Factors," Palgrave Macmillan Books, in: Architecting Enterprise, chapter 9, pages 225-249, Palgrave Macmillan.
    20. Liu-Thompkins, Yuping & Rogerson, Michelle, 2012. "Rising to Stardom: An Empirical Investigation of the Diffusion of User-generated Content," Journal of Interactive Marketing, Elsevier, vol. 26(2), pages 71-82.
    21. Couwenberg, Linda E. & Boksem, Maarten A.S. & Dietvorst, Roeland C. & Worm, Loek & Verbeke, Willem J.M.I. & Smidts, Ale, 2017. "Neural responses to functional and experiential ad appeals: Explaining ad effectiveness," International Journal of Research in Marketing, Elsevier, vol. 34(2), pages 355-366.
    22. Ken Roberts & John H. Roberts & Peter J. Danaher & Rohan Raghavan, 2015. "Practice Prize Paper—Incorporating Emotions into Evaluation and Choice Models: Application to Kmart Australia," Marketing Science, INFORMS, vol. 34(6), pages 815-824, November.
    23. Dessart, Laurence & Pitardi, Valentina, 2019. "How stories generate consumer engagement: An exploratory study," Journal of Business Research, Elsevier, vol. 104(C), pages 183-195.
    24. Hartmann, Jochen & Huppertz, Juliana & Schamp, Christina & Heitmann, Mark, 2019. "Comparing automated text classification methods," International Journal of Research in Marketing, Elsevier, vol. 36(1), pages 20-38.
    25. Ivan Guitart & Jorge Gonzalez & Stefan Stremersch, 2018. "Advertising non-premium products as if they were premium : The impact of advertising up on advertising elasticity and brand equity," Post-Print hal-02312175, HAL.
    26. Ashish Arora & Michelle Gittelman & Sarah Kaplan & John Lynch & Will Mitchell & Nicolaj Siggelkow & Philip Gylfe & Henrika Franck & Curtis Lebaron & Saku Mantere, 2016. "Video methods in strategy research: Focusing on embodied cognition," Strategic Management Journal, Wiley Blackwell, vol. 37(1), pages 133-148, January.
    27. Dessart, Laurence, 2018. "Do ads that tell a story always perform better? The role of character identification and character type in storytelling ads," International Journal of Research in Marketing, Elsevier, vol. 35(2), pages 289-304.
    28. Prithwiraj Choudhury & Dan Wang & Natalie A. Carlson & Tarun Khanna, 2019. "Machine learning approaches to facial and text analysis: Discovering CEO oral communication styles," Strategic Management Journal, Wiley Blackwell, vol. 40(11), pages 1705-1732, November.
    29. Hildebrand, Christian & Efthymiou, Fotis & Busquet, Francesc & Hampton, William H. & Hoffman, Donna L. & Novak, Thomas P., 2020. "Voice analytics in business research: Conceptual foundations, acoustic feature extraction, and applications," Journal of Business Research, Elsevier, vol. 121(C), pages 364-374.
    30. Oded Netzer & Ronen Feldman & Jacob Goldenberg & Moshe Fresko, 2012. "Mine Your Own Business: Market-Structure Surveillance Through Text Mining," Marketing Science, INFORMS, vol. 31(3), pages 521-543, May.
    31. Thales S. Teixeira & Michel Wedel & Rik Pieters, 2010. "Moment-to-Moment Optimal Branding in TV Commercials: Preventing Avoidance by Pulsing," Marketing Science, INFORMS, vol. 29(5), pages 783-804, 09-10.
    32. Li, Xi & Shi, Mengze & Wang, Xin (Shane), 2019. "Video mining: Measuring visual information using automatic methods," International Journal of Research in Marketing, Elsevier, vol. 36(2), pages 216-231.
    33. Sam K. Hui & Tom Meyvis & Henry Assael, 2014. "Analyzing Moment-to-Moment Data Using a Bayesian Functional Linear Model: Application to TV Show Pilot Testing," Marketing Science, INFORMS, vol. 33(2), pages 222-240, March.
    34. Quesenberry, Keith A. & Coolsen, Michael K., 2019. "Drama Goes Viral: Effects of Story Development on Shares and Views of Online Advertising Videos," Journal of Interactive Marketing, Elsevier, vol. 48(C), pages 1-16.
    35. Jehoshua Eliashberg & Mohanbir S. Sawhney, 1994. "Modeling Goes to Hollywood: Predicting Individual Differences in Movie Enjoyment," Management Science, INFORMS, vol. 40(9), pages 1151-1173, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Klostermann, Jan & Meißner, Martin & Max, Alexander & Decker, Reinhold, 2023. "Presentation of celebrities’ private life through visual social media," Journal of Business Research, Elsevier, vol. 156(C).
    2. Yang, Zhengliang & Caputo, Vincenzina, 2023. "Introducing automatic video mining to agricultural economics: A case study from a crowdfunding website," 2023 Annual Meeting, July 23-25, Washington D.C. 335600, Agricultural and Applied Economics Association.
    3. Matthew McGranaghan & Jura Liaukonyte & Kenneth C. Wilbur, 2022. "How Viewer Tuning, Presence, and Attention Respond to Ad Content and Predict Brand Search Lift," Marketing Science, INFORMS, vol. 41(5), pages 873-895, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ngai, Eric W.T. & Wu, Yuanyuan, 2022. "Machine learning in marketing: A literature review, conceptual framework, and research agenda," Journal of Business Research, Elsevier, vol. 145(C), pages 35-48.
    2. Grewal, Dhruv & Herhausen, Dennis & Ludwig, Stephan & Villarroel Ordenes, Francisco, 2022. "The Future of Digital Communication Research: Considering Dynamics and Multimodality," Journal of Retailing, Elsevier, vol. 98(2), pages 224-240.
    3. Ma, Liye & Sun, Baohong, 2020. "Machine learning and AI in marketing – Connecting computing power to human insights," International Journal of Research in Marketing, Elsevier, vol. 37(3), pages 481-504.
    4. Bitty Balducci & Detelina Marinova, 2018. "Unstructured data in marketing," Journal of the Academy of Marketing Science, Springer, vol. 46(4), pages 557-590, July.
    5. Wei, Liyuan & Yang, Yupin, 2022. "An empirical investigation of director selection in movie preproduction: A two-sided matching approach," International Journal of Research in Marketing, Elsevier, vol. 39(3), pages 888-906.
    6. Mengxia Zhang & Lan Luo, 2023. "Can Consumer-Posted Photos Serve as a Leading Indicator of Restaurant Survival? Evidence from Yelp," Management Science, INFORMS, vol. 69(1), pages 25-50, January.
    7. Yang, Zhengliang & Caputo, Vincenzina, 2023. "Introducing automatic video mining to agricultural economics: A case study from a crowdfunding website," 2023 Annual Meeting, July 23-25, Washington D.C. 335600, Agricultural and Applied Economics Association.
    8. Venkatesh Shankar & Sohil Parsana, 2022. "An overview and empirical comparison of natural language processing (NLP) models and an introduction to and empirical application of autoencoder models in marketing," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1324-1350, November.
    9. Beth L. Fossen & Alexander Bleier, 2021. "Online program engagement and audience size during television ads," Journal of the Academy of Marketing Science, Springer, vol. 49(4), pages 743-761, July.
    10. Gupta, Shaphali & Leszkiewicz, Agata & Kumar, V. & Bijmolt, Tammo & Potapov, Dmitriy, 2020. "Digital Analytics: Modeling for Insights and New Methods," Journal of Interactive Marketing, Elsevier, vol. 51(C), pages 26-43.
    11. Wen, Xiaohan (Hannah) & Kim, Shinhye & Bowen, Melanie, 2023. "Doing good by sharing messages: An investigation of “You Share, We Donate” campaigns and how they can attain viral success," Journal of Business Research, Elsevier, vol. 156(C).
    12. Wang, Xin (Shane) & Ryoo, Jun Hyun (Joseph) & Bendle, Neil & Kopalle, Praveen K., 2021. "The role of machine learning analytics and metrics in retailing research," Journal of Retailing, Elsevier, vol. 97(4), pages 658-675.
    13. Huang, Ming-Hui & Rust, Roland T., 2022. "A Framework for Collaborative Artificial Intelligence in Marketing," Journal of Retailing, Elsevier, vol. 98(2), pages 209-223.
    14. Ivan A. Guitart & Guillaume Hervet & Sarah Gelper, 2020. "Competitive advertising strategies for programmatic television," Journal of the Academy of Marketing Science, Springer, vol. 48(4), pages 753-775, July.
    15. Agrawal, Shiv Ratan & Mittal, Divya, 2022. "Optimizing customer engagement content strategy in retail and E-tail: Available on online product review videos," Journal of Retailing and Consumer Services, Elsevier, vol. 67(C).
    16. Carlson, Keith & Kopalle, Praveen K. & Riddell, Allen & Rockmore, Daniel & Vana, Prasad, 2023. "Complementing human effort in online reviews: A deep learning approach to automatic content generation and review synthesis," International Journal of Research in Marketing, Elsevier, vol. 40(1), pages 54-74.
    17. Hye-jin Kim & Yi Wang & Min Ding, 2021. "Brand Voiceprint," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 8(4), pages 123-136, December.
    18. Laura Toschi & Elisa Ughetto & Andrea Fronzetti Colladon, 2023. "The identity of social impact venture capitalists: exploring social linguistic positioning and linguistic distinctiveness through text mining," Small Business Economics, Springer, vol. 60(3), pages 1249-1280, March.
    19. Tarunija Chandra & Vibhuti Tripathi, 2023. "Antecedents and Mediators for Building and Integrated Research Framework towards Successful Brand Revitalisation," Central European Business Review, Prague University of Economics and Business, vol. 2023(1), pages 133-163.
    20. Fan-Osuala, Onochie & Zantedeschi, Daniel & Jank, Wolfgang, 2018. "Using past contribution patterns to forecast fundraising outcomes in crowdfunding," International Journal of Forecasting, Elsevier, vol. 34(1), pages 30-44.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jbrese:v:123:y:2021:i:c:p:367-379. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jbusres .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.