IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v38y2019i2p274-295.html
   My bibliography  Save this article

Social TV, Advertising, and Sales: Are Social Shows Good for Advertisers?

Author

Listed:
  • Beth L. Fossen

    (Kelley School of Business, Indiana University, Bloomington, Indiana 47405)

  • David A. Schweidel

    (Goizueta Business School, Emory University, Atlanta, Georgia 30322)

Abstract

Television viewers are increasingly engaging in media-multitasking while watching programming. One prevalent multiscreen activity is the simultaneous consumption of television alongside social media chatter about the programming, an activity referred to as “social TV.” Although online interactions with programming can result in a more engaged and committed audience, social TV activities may distract media multitaskers from advertisements. These competing outcomes of social TV raise the question: are programs with high online social TV activity, so called “social shows,” good for advertisers? In this research, we empirically examine this question by exploring the relationship among television advertising, social TV, online traffic, and online sales. Specifically, we investigate how the volume of program-related online chatter is related to online shopping behavior at retailers that advertise during the programs. We find that advertisements that air in programs with more social TV activity see increased ad responsiveness in terms of subsequent online shopping behavior. This result varies with the mood of the advertisement, with more affective advertisements—in particular, funny and emotional advertisements—seeing the largest increases in online shopping activity. Our results shed light on how advertisers can encourage online shopping activity on their websites in the age of multiscreen consumers.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:ormksc:v:38:y:2019:i:2:p:274-295
    DOI: 10.1287/mksc.2018.1139
    as

    Download full text from publisher

    File URL: https://doi.org/10.1287/mksc.2018.1139
    Download Restriction: no
    File URL: https://libkey.io/10.1287/mksc.2018.1139?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. Celsi, Richard L & Olson, Jerry C, 1988. "The Role of Involvement in Attention and Comprehension Processes," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 15(2), pages 210-224, September.
    2. Eric J. Johnson & Wendy W. Moe & Peter S. Fader & Steven Bellman & Gerald L. Lohse, 2004. "On the Depth and Dynamics of Online Search Behavior," Management Science, INFORMS, vol. 50(3), pages 299-308, March.
    3. Murry, John P, Jr & Lastovicka, John L & Singh, Surendra N, 1992. "Feeling and Liking Responses to Television Programs: An Examination of Two Explanations for Media-Context Effects," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 18(4), pages 441-451, March.
    4. Alan L. Montgomery & Shibo Li & Kannan Srinivasan & John C. Liechty, 2004. "Modeling Online Browsing and Path Analysis Using Clickstream Data," Marketing Science, INFORMS, vol. 23(4), pages 579-595, November.
    5. Mingyu Joo & Kenneth C. Wilbur & Bo Cowgill & Yi Zhu, 2014. "Television Advertising and Online Search," Management Science, INFORMS, vol. 60(1), pages 56-73, January.
    6. 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.
    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.
    8. Guiyang Xiong & Sundar Bharadwaj, 2014. "Prerelease Buzz Evolution Patterns and New Product Performance," Marketing Science, INFORMS, vol. 33(3), pages 401-421, May.
    9. Jura Liaukonyte & Thales Teixeira & Kenneth C. Wilbur, 2015. "Television Advertising and Online Shopping," Marketing Science, INFORMS, vol. 34(3), pages 311-330, May.
    10. Yoo, Changjo & MacInnis, Deborah, 2005. "The brand attitude formation process of emotional and informational ads," Journal of Business Research, Elsevier, vol. 58(10), pages 1397-1406, October.
    11. Calder, Bobby J. & Malthouse, Edward C. & Schaedel, Ute, 2009. "An Experimental Study of the Relationship between Online Engagement and Advertising Effectiveness," Journal of Interactive Marketing, Elsevier, vol. 23(4), pages 321-331.
    12. 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.
    13. Kenneth C. Wilbur & Linli Xu & David Kempe, 2013. "Correcting Audience Externalities in Television Advertising," Marketing Science, INFORMS, vol. 32(6), pages 892-912, November.
    14. Thales Teixeira & Rosalind Picard & Rana el Kaliouby, 2014. "Why, When, and How Much to Entertain Consumers in Advertisements? A Web-Based Facial Tracking Field Study," Marketing Science, INFORMS, vol. 33(6), pages 809-827, November.
    15. Olney, Thomas J & Holbrook, Morris B & Batra, Rajeev, 1991. "Consumer Responses to Advertising: The Effects of Ad Content, Emotions, and Attitude toward the Ad on Viewing Time," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 17(4), pages 440-453, March.
    16. David A. Schweidel & Natasha Zhang Foutz & Robin J. Tanner, 2014. "Synergy or Interference: The Effect of Product Placement on Commercial Break Audience Decline," Marketing Science, INFORMS, vol. 33(6), pages 763-780, November.
    17. Randall A. Lewis & Justin M. Rao, 2015. "The Unfavorable Economics of Measuring the Returns to Advertising," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 130(4), pages 1941-1973.
    18. 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.
    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. Mengdi Wang & Dong Li, 2020. "What motivates audience comments on live streaming platforms?," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-12, April.
    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. Kirk Kristofferson & Lea Dunn, 2023. "The brand that wasn’t there: The impact of brand displacement on viewer engagement and brand attitude," Journal of the Academy of Marketing Science, Springer, vol. 51(3), pages 716-745, May.
    4. 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.
    5. Gil Appel & Lauren Grewal & Rhonda Hadi & Andrew T. Stephen, 2020. "The future of social media in marketing," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 79-95, January.
    6. Vasu Unnava & Ashwin Aravindakshan, 2021. "How does consumer engagement evolve when brands post across multiple social media?," Journal of the Academy of Marketing Science, Springer, vol. 49(5), pages 864-881, September.
    7. Ning Zhong & David A. Schweidel, 2020. "Capturing Changes in Social Media Content: A Multiple Latent Changepoint Topic Model," Marketing Science, INFORMS, vol. 39(4), pages 827-846, July.
    8. Mark Rowland Bruce & Adebayo Felix Adekoya & Samuel Boateng & Peter Appiahene, 2023. "Prevalent User-centered Monetization Techniques in Social Media," International Review of Management and Marketing, Econjournals, vol. 13(1), pages 19-28, January.
    9. Avramova, Yana R. & Dens, Nathalie & De Pelsmacker, Patrick, 2021. "Brand placement across media: The interaction of placement modality and frequency in film versus text," Journal of Business Research, Elsevier, vol. 128(C), pages 20-30.
    10. Koushyar Rajavi & Donald R. Lehmann & Kevin Lane Keller & Alireza Golmohammadi, 2023. "Ad expenditures and perceived quality: a replication and extension," Marketing Letters, Springer, vol. 34(1), pages 161-169, March.

    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. 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.
    2. Beth L. Fossen & Girish Mallapragada & Anwesha De, 2021. "Impact of Political Television Advertisements on Viewers’ Response to Subsequent Advertisements," Marketing Science, INFORMS, vol. 40(2), pages 305-324, March.
    3. Jura Liaukonyte & Thales Teixeira & Kenneth C. Wilbur, 2015. "Television Advertising and Online Shopping," Marketing Science, INFORMS, vol. 34(3), pages 311-330, May.
    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. 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.
    6. Bitty Balducci & Detelina Marinova, 2018. "Unstructured data in marketing," Journal of the Academy of Marketing Science, Springer, vol. 46(4), pages 557-590, July.
    7. Ali Goli & Simha Mummalaneni & Pradeep K. Chintagunta & Sanjay K. Dhar, 2022. "Show and Sell: Studying the Effects of Branded Cigarette Product Placement in TV Shows on Cigarette Sales," Marketing Science, INFORMS, vol. 41(6), pages 1163-1180, November.
    8. 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.
    9. 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.
    10. Jūra Liaukonytė & Alminas Žaldokas, 2022. "Background Noise? TV Advertising Affects Real-Time Investor Behavior," Management Science, INFORMS, vol. 68(4), pages 2465-2484, April.
    11. Yi-Lin Tsai & Elisabeth Honka, 2021. "Informational and Noninformational Advertising Content," Marketing Science, INFORMS, vol. 40(6), pages 1030-1058, November.
    12. Brett R Gordon & Kinshuk Jerath & Zsolt Katona & Sridhar Narayanan & Jiwoong Shin & Kenneth C Wilbur, 2019. "Inefficiencies in Digital Advertising Markets," Papers 1912.09012, arXiv.org, revised Feb 2020.
    13. 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.
    14. Rex Yuxing Du & Mingyu Joo & Kenneth C. Wilbur, 2018. "Advertising and Brand Attitudes: Evidence from 575 Brands over Five Years," Papers 1810.07783, arXiv.org.
    15. Chesnes, Matthew & Jin, Ginger Zhe, 2019. "Direct-to-consumer advertising and online search," Information Economics and Policy, Elsevier, vol. 46(C), pages 1-22.
    16. Ashish Agarwal & Tridas Mukhopadhyay, 2016. "The Impact of Competing Ads on Click Performance in Sponsored Search," Information Systems Research, INFORMS, vol. 27(3), pages 538-557.
    17. He, Chen, 2018. "Essays on the role and effects of advertising," Other publications TiSEM 47a3272a-54f1-4a90-9714-c, Tilburg University, School of Economics and Management.
    18. Kenneth C. Wilbur, 2016. "Advertising Content and Television Advertising Avoidance," Journal of Media Economics, Taylor & Francis Journals, vol. 29(2), pages 51-72, April.
    19. Rex Yuxing Du & Mingyu Joo & Kenneth C. Wilbur, 2019. "Advertising and brand attitudes: Evidence from 575 brands over five years," Quantitative Marketing and Economics (QME), Springer, vol. 17(3), pages 257-323, September.
    20. Randall Lewis & Dan Nguyen, 2015. "Display advertising’s competitive spillovers to consumer search," Quantitative Marketing and Economics (QME), Springer, vol. 13(2), pages 93-115, June.

    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:38:y:2019:i:2:p:274-295. 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.