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The effect of second screening on repeat viewing: Insights from large-scale mobile diary data

Author

Listed:
  • Sarah Gelper

    (University of Luxembourg)

  • Mitchell J. Lovett

    (University of Rochester)

  • Renana Peres

    (Hebrew University of Jerusalem)

Abstract

This paper examines the effect of second screening, the common practice of using another digital device while watching a television show, on repeat show viewing. We leveraged large-scale individual-level data from mobile diaries of 1,702 US TV viewers on 2,755 prime time shows. We used causal forest analysis for estimation, focusing on the moderating role of viewing preferences and show loyalty, and captured heterogeneity in viewer preferences using latent-class segmentation. We found that overall, show-related second screening has a positive effect on the attitude toward the show, as well as on actual repeat viewing. Show-unrelated second screening diminishes the viewer’s attitude. These effects are especially pronounced in the heavy viewer segment and among infrequent show viewers. Interestingly, our analysis did not provide evidence that second screening harms actual repeat viewing, countering potential concerns of negative distraction effects.

Suggested Citation

  • Sarah Gelper & Mitchell J. Lovett & Renana Peres, 2025. "The effect of second screening on repeat viewing: Insights from large-scale mobile diary data," Journal of the Academy of Marketing Science, Springer, vol. 53(3), pages 907-930, May.
  • Handle: RePEc:spr:joamsc:v:53:y:2025:i:3:d:10.1007_s11747-024-01048-3
    DOI: 10.1007/s11747-024-01048-3
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    1. Lemmens, Aurélie & Croux, Christophe & Stremersch, Stefan, 2012. "Dynamics in the international market segmentation of new product growth," International Journal of Research in Marketing, Elsevier, vol. 29(1), pages 81-92.
    2. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    3. Oded Netzer & James M. Lattin & V. Srinivasan, 2008. "A Hidden Markov Model of Customer Relationship Dynamics," Marketing Science, INFORMS, vol. 27(2), pages 185-204, 03-04.
    4. 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.
    5. Robinson, Peter M, 1988. "Root- N-Consistent Semiparametric Regression," Econometrica, Econometric Society, vol. 56(4), pages 931-954, July.
    6. Olivier Toubia & Andrew T. Stephen, 2013. "Intrinsic vs. Image-Related Utility in Social Media: Why Do People Contribute Content to Twitter?," Marketing Science, INFORMS, vol. 32(3), pages 368-392, May.
    7. Ivan Guitart & Guillaume Hervet & Sarah Gelper, 2020. "Competitive advertising strategies for programmatic television," Grenoble Ecole de Management (Post-Print) hal-02312409, HAL.
    8. Murat Unal & Young-Hoon Park, 2023. "Fewer Clicks, More Purchases," Management Science, INFORMS, vol. 69(12), pages 7317-7334, December.
    9. Rust, Roland T. & Kumar, V. & Venkatesan, Rajkumar, 2011. "Will the frog change into a prince? Predicting future customer profitability," International Journal of Research in Marketing, Elsevier, vol. 28(4), pages 281-294.
    10. Mitchell J. Lovett & Renana Peres & Linli Xu, 2019. "Can your advertising really buy earned impressions? The effect of brand advertising on word of mouth," Quantitative Marketing and Economics (QME), Springer, vol. 17(3), pages 215-255, September.
    11. Jonathan Z. Zhang & Chun-Wei Chang, 2021. "Correction to: Consumer dynamics: theories, methods, and emerging directions," Journal of the Academy of Marketing Science, Springer, vol. 49(1), pages 197-197, January.
    12. Hyslop, Dean R & Imbens, Guido W, 2001. "Bias from Classical and Other Forms of Measurement Error," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(4), pages 475-481, October.
    13. 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.
    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. David Godes & Dina Mayzlin, 2004. "Using Online Conversations to Study Word-of-Mouth Communication," Marketing Science, INFORMS, vol. 23(4), pages 545-560, June.
    16. Mitchell J. Lovett & Richard Staelin, 2016. "The Role of Paid, Earned, and Owned Media in Building Entertainment Brands: Reminding, Informing, and Enhancing Enjoyment," Marketing Science, INFORMS, vol. 35(1), pages 142-157, January.
    17. Ivan Guitart & Guillaume Hervet & Sarah Gelper, 2020. "Competitive advertising strategies for programmatic television," Post-Print hal-02312409, HAL.
    18. Bijmolt, T.H.A. & Paas, L.J. & Vermunt, J.K., 2004. "Country and consumer segmentation : Multi-level latent class analysis of financial product ownership," Other publications TiSEM fb506162-d125-4091-9083-9, Tilburg University, School of Economics and Management.
    19. Jonathan Z. Zhang & Chun-Wei Chang, 2021. "Consumer dynamics: theories, methods, and emerging directions," Journal of the Academy of Marketing Science, Springer, vol. 49(1), pages 166-196, January.
    20. Jura Liaukonyte & Thales Teixeira & Kenneth C. Wilbur, 2015. "Television Advertising and Online Shopping," Marketing Science, INFORMS, vol. 34(3), pages 311-330, May.
    21. S. Siddarth & Amitava Chattopadhyay, 1998. "To Zap or Not to Zap: A Study of the Determinants of Channel Switching During Commercials," Marketing Science, INFORMS, vol. 17(2), pages 124-138.
    22. Miao Guo, 2019. "Social Television Viewing with Second Screen Platforms: Antecedents and Consequences," Media and Communication, Cogitatio Press, vol. 7(1), pages 139-152.
    23. 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.
    24. Lemmens, A. & Croux, C. & Stremersch, S., 2012. "Dynamics in international market segmentation of new product growth," Other publications TiSEM 306086bd-670f-48d2-97d1-3, Tilburg University, School of Economics and Management.
    25. Deepa Chandrasekaran & Raji Srinivasan & Debika Sihi, 2018. "Effects of offline ad content on online brand search: insights from super bowl advertising," Journal of the Academy of Marketing Science, Springer, vol. 46(3), pages 403-430, May.
    26. Miao Guo, 2019. "Social Television Viewing with Second Screen Platforms: Antecedents and Consequences," Media and Communication, Cogitatio Press, vol. 7(1), pages 139-152.
    27. Barwise, T Patrick & Ehrenberg, Andrew S C, 1987. "The Liking and Viewing of Regular TV Series," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 14(1), pages 63-70, June.
    28. Sha Yang & Yi Zhao & Ravi Dhar, 2010. "Modeling the Underreporting Bias in Panel Survey Data," Marketing Science, INFORMS, vol. 29(3), pages 525-539, 05-06.
    29. Lovett, Mitchell J. & Peres, Renana, 2018. "Mobile diaries – Benchmark against metered measurements: An empirical investigation," International Journal of Research in Marketing, Elsevier, vol. 35(2), pages 224-241.
    30. Bharadwaj, Neeraj & Ballings, Michel & Naik, Prasad A., 2020. "Cross-Media Consumption: Insights from Super Bowl Advertising," Journal of Interactive Marketing, Elsevier, vol. 50(C), pages 17-31.
    31. Xiao Liu & Param Vir Singh & Kannan Srinivasan, 2016. "A Structured Analysis of Unstructured Big Data by Leveraging Cloud Computing," Marketing Science, INFORMS, vol. 35(3), pages 363-388, May.
    32. 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.
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