IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v39y2020i2p285-295.html
   My bibliography  Save this article

Frontiers: In-Consumption Social Listening with Moment-to-Moment Unstructured Data: The Case of Movie Appreciation and Live Comments

Author

Listed:
  • Qiang Zhang

    (School of Management and Economics and Shenzhen Finance Institute, The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), 518172 Shenzhen, China)

  • Wenbo Wang

    (Department of Marketing, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong)

  • Yuxin Chen

    (Stern School of Business, New York University, New York, New York 10012)

Abstract

Consumption of entertainment products such as movies, video games, and sports events often lasts a nontrivial time period. During these experiences, consumers are likely to encounter temporal variations in the content of consumption, to which they may react in real time. Compared with existing in-consumption analysis (e.g., eye tracking and neural activity analysis), listening to in-consumption consumers’ voices on social media has great potential. Our paper proposes a new approach for in-consumption social listening and demonstrates its value in the context of online movie watching wherein viewers can react to movie content with live comments. Specifically, we propose to listen to the live comments through a novel measure, moment-to-moment synchronicity (MTMS), to capture viewers’ in-consumption engagement. MTMS refers to the synchronicity between temporal variations in the volume of live comments and those in movie content mined from unstructured video, audio, and text data (i.e., camera motion, shot length, sound loudness, pitch, and spoken lines). We demonstrate that MTMS significantly predicts viewers’ postconsumption appreciation of movies and that it can be evaluated at a finer level to identify engaging content. Finally, we discuss the information value of MTMS with the presence of measures used in the previous literature and the value of integrating supply-side content information into in-consumption analysis.

Suggested Citation

  • Qiang Zhang & Wenbo Wang & Yuxin Chen, 2020. "Frontiers: In-Consumption Social Listening with Moment-to-Moment Unstructured Data: The Case of Movie Appreciation and Live Comments," Marketing Science, INFORMS, vol. 39(2), pages 285-295, March.
  • Handle: RePEc:inm:ormksc:v:39:y:2020:i:2:p:285-295
    DOI: 10.1287/mksc.2019.1215
    as

    Download full text from publisher

    File URL: https://doi.org/10.1287/mksc.2019.1215
    Download Restriction: no

    File URL: https://libkey.io/10.1287/mksc.2019.1215?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
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Samuel B. Barnett & Moran Cerf, 2017. "A Ticket for Your Thoughts: Method for Predicting Content Recall and Sales Using Neural Similarity of Moviegoers," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 44(1), pages 160-181.
    3. Stephan Seiler & Song Yao & Wenbo Wang, 2017. "Does Online Word of Mouth Increase Demand? (And How?) Evidence from a Natural Experiment," Marketing Science, INFORMS, vol. 36(6), pages 838-861, November.
    4. Suresh Ramanathan & Ann L. McGill, 2007. "Consuming with Others: Social Influences on Moment-to-Moment and Retrospective Evaluations of an Experience," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 34(4), pages 506-524, July.
    5. 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.
    6. Jing Wang & Bobby J. Calder, 2006. "Media Transportation and Advertising," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 33(2), pages 151-162, July.
    7. David Godes & Dina Mayzlin, 2004. "Using Online Conversations to Study Word-of-Mouth Communication," Marketing Science, INFORMS, vol. 23(4), pages 545-560, June.
    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. Jordi McKenzie, 2023. "The economics of movies (revisited): A survey of recent literature," Journal of Economic Surveys, Wiley Blackwell, vol. 37(2), pages 480-525, April.
    2. Jiayue Liu & Ziyao Zhou & Ming Gao & Jiafu Tang & Weiguo Fan, 2023. "Aspect sentiment mining of short bullet screen comments from online TV series," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(8), pages 1026-1045, August.
    3. Zecong Ma & Sergio Palacios, 2021. "Image-mining: exploring the impact of video content on the success of crowdfunding," Journal of Marketing Analytics, Palgrave Macmillan, vol. 9(4), pages 265-285, December.
    4. Shasha Lu & Hye-Jin Kim & Yinghui Zhou & Li Xiao & Min Ding, 2022. "Audio and Visual Analytics in Marketing and Artificial Empathy," Foundations and Trends(R) in Marketing, now publishers, vol. 16(4), pages 422-493, April.
    5. Ziwei Cong & Jia Liu & Puneet Manchanda, 2021. "The Role of "Live" in Livestreaming Markets: Evidence Using Orthogonal Random Forest," Papers 2107.01629, arXiv.org, revised Sep 2022.
    6. Wei, Zihan & Zhang, Mingli & Qiao, Tong, 2022. "Effect of personal branding stereotypes on user engagement on short-video platforms," Journal of Retailing and Consumer Services, Elsevier, vol. 69(C).
    7. Song, Danyang & Wang, Shichao & Ou, Carol & Chen, Xi & Liu, Ruitao & Tang, Haihong, 2021. "How do video features matter in visual advertising? An elaboration likelihood model perspective," Other publications TiSEM 37845995-5426-470a-8630-8, Tilburg University, School of Economics and Management.
    8. Liu, Zhenyuan & Geng, Ruoqi & Tse, Ying Kei (Mike) & Han, Shuihua, 2023. "Mapping the relationship between social media usage and organizational performance: A meta-analysis," Technological Forecasting and Social Change, Elsevier, vol. 187(C).

    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. 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.
    2. 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.
    3. Beth L. Fossen & David A. Schweidel, 2019. "Social TV, Advertising, and Sales: Are Social Shows Good for Advertisers?," Marketing Science, INFORMS, vol. 38(2), pages 274-295, March.
    4. Beth L. Fossen & David A. Schweidel, 2017. "Television Advertising and Online Word-of-Mouth: An Empirical Investigation of Social TV Activity," Marketing Science, INFORMS, vol. 36(1), pages 105-123, January.
    5. 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.
    6. Xingyu Chen & Xing Li & Dai Yao & Zhimin Zhou, 2019. "Seeking the support of the silent majority: are lurking users valuable to UGC platforms?," Journal of the Academy of Marketing Science, Springer, vol. 47(6), pages 986-1004, November.
    7. Ronny Behrens & Natasha Zhang Foutz & Michael Franklin & Jannis Funk & Fernanda Gutierrez-Navratil & Julian Hofmann & Ulrike Leibfried, 2021. "Leveraging analytics to produce compelling and profitable film content," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 45(2), pages 171-211, June.
    8. Liu, Angela Xia & Xie, Ying & Zhang, Jurui, 2019. "It's Not Just What You Say, But How You Say It: The Effect of Language Style Matching on Perceived Quality of Consumer Reviews," Journal of Interactive Marketing, Elsevier, vol. 46(C), pages 70-86.
    9. Soumya Mukhopadhyay & V Kumar & Amalesh Sharma & Tuck Siong Chung, 2022. "Impact of review narrativity on sales in a competitive environment," Production and Operations Management, Production and Operations Management Society, vol. 31(6), pages 2538-2556, June.
    10. Guangxin Yang & Yingjie Zhang & Hongju Liu, 2024. "Frontiers: Pirating Foes or Creative Friends? Effects of User-Generated Condensed Clips on Demand for Streaming Services," Marketing Science, INFORMS, vol. 43(3), pages 469-478, May.
    11. Rumpf, Christopher & Boronczyk, Felix & Breuer, Christoph, 2020. "Predicting consumer gaze hits: A simulation model of visual attention to dynamic marketing stimuli," Journal of Business Research, Elsevier, vol. 111(C), pages 208-217.
    12. Jordi McKenzie, 2023. "The economics of movies (revisited): A survey of recent literature," Journal of Economic Surveys, Wiley Blackwell, vol. 37(2), pages 480-525, April.
    13. Nail Kashaev & Natalia Lazzati & Ruli Xiao, 2023. "Peer Effects in Consideration and Preferences," Papers 2310.12272, arXiv.org, revised Jan 2024.
    14. Diwanji, Vaibhav S. & Cortese, Juliann, 2020. "Contrasting user generated videos versus brand generated videos in ecommerce," Journal of Retailing and Consumer Services, Elsevier, vol. 54(C).
    15. Melanie Bowen & Xiaohan Hannah Wen & Shinhye Kim, 2023. "A lure or a turn-off: social media reactions to business model innovation announcements," Marketing Letters, Springer, vol. 34(1), pages 13-33, March.
    16. Yucheng Zhang & Zhiling Wang & Lin Xiao & Lijun Wang & Pei Huang, 2023. "Discovering the evolution of online reviews: A bibliometric review," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-22, December.
    17. Shijie Lu & Xin (Shane) Wang & Neil Bendle, 2020. "Does Piracy Create Online Word of Mouth? An Empirical Analysis in the Movie Industry," Management Science, INFORMS, vol. 66(5), pages 2140-2162, May.
    18. James Agarwal & Wayne DeSarbo & Naresh K. Malhotra & Vithala Rao, 2015. "An Interdisciplinary Review of Research in Conjoint Analysis: Recent Developments and Directions for Future Research," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 2(1), pages 19-40, March.
    19. Vermeer, Susan A.M. & Araujo, Theo & Bernritter, Stefan F. & van Noort, Guda, 2019. "Seeing the wood for the trees: How machine learning can help firms in identifying relevant electronic word-of-mouth in social media," International Journal of Research in Marketing, Elsevier, vol. 36(3), pages 492-508.
    20. Monic Sun, 2012. "How Does the Variance of Product Ratings Matter?," Management Science, INFORMS, vol. 58(4), pages 696-707, April.

    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:inm:ormksc:v:39:y:2020:i:2:p:285-295. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.