Babić Rosario, A., Sotgiu, F., Valck, K. D., & Bijmolt, T. H. A. (2016). The effect of electronic word of mouth on sales: A meta-analytic review of platform, product, and metric factors. Journal of Marketing Research, 53(June), 297–318.
Google Scholar
Benton, A., & S. Hill (2012). The spoiler effect? Designing social TV content that promotes ongoing WOM, Conference on Information Systems and Technology (CIST). Phoenix, AZ.
Berger, J. (2011). Arousal increases social transmission of information. Psychological Science, 22(7), 891–893.
Google Scholar
Berger, J. (2014). Word of mouth and interpersonal communication: A review and directions for future research. Journal of Consumer Psychology, 24(4), 586–607.
Google Scholar
Berger, J., & Iyengar, R. (2013). Communication channels and word of mouth: How the medium shapes the message. Journal of Consumer Research, 40(3), 567–579.
Google Scholar
Berger, J., & Milkman, K. L. (2012). What makes online content viral? Journal of Marketing Research, 49(2), 192–205.
Google Scholar
Berger, J., & Schwartz, E. M. (2011). What drives immediate and ongoing word of mouth? Journal of Marketing Research, 48(5), 869–880.
Google Scholar
Berger, J., Sorensen, A. T., & Rasmussen, S. J. (2010). Positive effects of negative publicity: When negative reviews increase sales. Marketing Science, 29(5), 815–827.
Google Scholar
Bloch, P. H., & Richins, M. L. (1983). A theoretical model for the study of product importance perceptions. Journal of Marketing, 47(3), 69–81.
Google Scholar
Bornemann, T., Hattula, C., & Hattula, S. (2020). Successive product generations: Financial implications of industry release rhythm alignment. Journal of the Academy of Marketing Science, forthcoming, 48, 1174–1191.
Google Scholar
Cadario, R. (2015). The impact of online word-of-mouth on television show viewership: An inverted U-shaped temporal dynamic. Marketing Letters, 26(4), 411–422.
Google Scholar
Calder, B. J., Malthouse, E. C., & Schaedel, U. (2009). An experimental study of the relationship between online engagement and advertising effectiveness. Journal of Interactive Marketing, 23(4), 321–331.
Google Scholar
Celsi, R. L., & Olson, J. C. (1988). The role of involvement in attention and comprehension processes. Journal of Consumer Research, 15(2), 210–224.
Google Scholar
Chintagunta, P. K., Gopinath, S. S., & Venkataraman, S. (2010). The effects of online user reviews on movie box office performance: Accounting for sequential rollout and aggregation across local markets. Marketing Science, 29(5), 944–957.
Google Scholar
Chordia, A. (2018). Programmatic TV future coming into focus slowly, but surely, (May 6). https://www.mediapost.com/publications/article/318812/programmatic-tv-future-coming-into-focus-slowly-b.html. Accessed 30 Dec 2020.
Comscore TV Essentials (2020), Average audience, September-December 2013, US. https://www.comscore.com/.
Crupi, A. (2019). In Two Years, Broadcast C3 Ratings have Shrunk 24 Percent, AdAge (April), https://adage.com/article/media/two-years-broadcast-c3-ratings-have-shrunk-24-percent/2167886. Accessed 30 Dec 2020.
Danaher, P. J. (1995). What happens to television ratings during commercial breaks? Journal of Advertising Research, 35(1), 37–37.
Google Scholar
Deng, Y., & Mela, C. F. (2017). TV viewing and advertising targeting. Journal of Marketing Research, 55(1), 99–118.
Google Scholar
Derrick, J. L. (2013). Energized by television: Familiar fictional worlds restore self-control. Social Psychological and Personality Science, 4(3), 299–307.
Google Scholar
Dichter, E. (1966). How word-of-mouth advertising works. Harvard Business Review, 44((November–December)), 147–166.
Google Scholar
eMarketer (2017). Few viewers are giving the TV set their undivided attention, (Nov. 7), https://www.emarketer.com/Article/Few-Viewers-Giving-TV-Set-Their-Undivided-Attention/1016717?ecid=NL1001. Accessed 30 Dec 2020.
eMarketer (2018). US Programmatic TV Ad spending, 2016–2020, (July 18). https://www.emarketer.com/chart/223921/us-programmatic-tv-ad-spending-2016-2020-billions-change-of-tv-ad-spending. Accessed 30 Dec 2020.
Feltham, T. S., & Arnold, S. J. (1994). Program involvement and ad/program consistency as moderators of program context effects. Journal of Consumer Psychology, 3(1), 51–77.
Google Scholar
Flomenbaum, A. (2016). Exclusive: Nielsen study shows that TV advertising drives earned media for brands on Twitter, The Drum, (February 21). http://www.thedrum.com/news/2016/02/21/exclusive-nielsen-study-shows-tvadvertising-drives-earned-media-brands-twitter. Accessed 30 Dec 2020.
Fossen, B. L., & Schweidel, D. A. (2017). Television advertising and online word-of-mouth: An empirical investigation of social TV activity. Marketing Science, 36(1), 105–123.
Google Scholar
Fossen, B. L., & Schweidel, D. A. (2019). Social TV, advertising, and sales: Are social shows good for advertisers? Marketing Science, 38(2), 274–295.
Google Scholar
Fossen, B. L., G. Mallapragada, A. De (2021) Impact of political television advertisements on viewers’ response to subsequent advertisements. Marketing Science, forthcoming.
Friedman, W. (2012). Why TV networks want to move from C3 to C7 ratings, MediaPost (Nov). https://www.mediapost.com/publications/article/187080/why-tv-networks-want-to-move-from-c3-to-c7-ratings.html. Accessed 30 Dec 2020.
Friedman, W. (2019). Viacom starts CFlight for Upfront TV Marketers, MediaPost (April). https://www.mediapost.com/publications/article/334580/viacom-starts-cflight-for-upfront-tv-marketers.html. Accessed 30 Dec 2020.
Godes, D., & Mayzlin, D. (2004). Using online conversations to study word-of-mouth communication. Marketing Science, 23(4), 545–560.
Google Scholar
Gong, S., Zhang, J., Zhao, P., & Jiang, X. (2017). Tweeting as a marketing tool: A field experiment in the TV industry. Journal of Marketing Research, 54(6), 833–850.
Google Scholar
Guitart, I. A., Gonzalez, J., & Stremersch, S. (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, 35(3), 471–489.
Google Scholar
Gustafson, P., & Siddarth, S. (2007). Describing the dynamics of attention to TV commercials: A hierarchical Bayes analysis of the time to zap an ad. Journal of Applied Statistics, 34(5), 585–609.
Google Scholar
Han, J. A., McDonnell Feit, E., & Srinivasan, S. (2020). Can negative buzz increase awareness and purchase intent? Marketing Letters, 31(1), 89–104.
Google Scholar
Houston, M. J., & Rothschild, M. L. (1978). Conceptual and methodological perspectives on involvement. In Jain, S. (Ed.), Research Frontiers in Marketing: Dialogues and Directions (pp. 184–187). Chicago.
Huang, M., Ali, R., & Liao, J. (2017). The effect of user experience in online games on word of mouth: A pleasure-arousal-dominance (PAD) model perspective. Computers in Human Behavior, 75, 329–338.
Google Scholar
IAB (2015). The changing TV experience: Attitudes and usage across multiple screens, (April). http://www.iab.com/insights/the-changing-tv-experience-attitudes-and-usage-across-multiple-screens/. Accessed 30 Dec 2020.
Katz, H. (2013). The media handbook: A complete guide to advertising media selection, planning, research, and buying. New York: Routledge.
Google Scholar
Kent, R. J., & Schweidel, D. A. (2011). Introducing the ad ECG: How the set-top box tracks the lifeline of television. Journal of Advertising Research, 51(4), 586–593.
Google Scholar
Kilger, M., & Romer, E. (2007). Do measures of media engagement correlate with product purchase likelihood? Journal of Advertising Research, 47(3), 313–325.
Google Scholar
Ladhari, R. (2007). The effect of consumption emotions on satisfaction and word-of-mouth communications. Psychology & Marketing, 24(12), 1085–1108.
Google Scholar
Lafayette, J. (2018a). C3 Primetime ratings dropped 12% during August, Broadcasting+Cable (Sept). https://www.broadcastingcable.com/news/c3-primetime-ratings-dropped-12-during-august. Accessed 30 Dec 2020.
Lafayette, J. (2018b). Most networks plan to use C7 in Upfront, Broadcasting+Cable (Mar). https://www.broadcastingcable.com/news/most-networks-plan-use-c7-upfront-156967. Accessed 30 Dec 2020.
Lamberton, C., & Stephen, A. T. (2016). A thematic exploration of digital, social media, and Mobile marketing: Research evolution from 2000 to 2015 and an agenda for future inquiry. Journal of Marketing, 80(Nov), 146–172.
Google Scholar
Liaukonyte, J., Teixeira, T., & Wilbur, K. C. (2015). Television advertising and online shopping. Marketing Science, 34(3), 311–330.
Google Scholar
Liu, Y. (2006). Word of mouth for movies: Its dynamics and impact on box office revenue. Journal of Marketing, 70(July), 74–89.
Google Scholar
Liu, X., Singh, P. V., & Srinivasan, K. (2016). A structured analysis of unstructured big data by leveraging cloud computing. Marketing Science, 35(3), 363–388.
Google Scholar
Lovett, M. J., & Staelin, R. (2016). The role of paid, earned, and owned Media in Building Entertainment Brands: Reminding, informing, and enhancing enjoyment. Marketing Science, 35(1), 142–157.
Google Scholar
Luminet IV, O., Bouts, P., Delie, F., Manstead, A. S., & Rimé, B. (2000). Social sharing of emotion following exposure to a negatively Valenced situation. Cognition & Emotion, 14(5), 661–688.
Google Scholar
Mathys, J., Burmester, A. B., & Clement, M. (2016). What drives the market popularity of celebrities? A longitudinal analysis of consumer interest in film stars. International Journal of Research in Marketing, 33(2), 428–448.
Google Scholar
Mattes, J., & Cantor, J. (1982). Enhancing responses to television advertisements via the transfer of residual arousal from prior programming. Journal of Broadcasting & Electronic Media, 26(2), 553–566.
Google Scholar
McGrath, J. M., & Mahood, C. (2004). The impact of arousing programming and product involvement on advertising effectiveness. Journal of Current Issues & Research in Advertising, 26(2), 41–52.
Google Scholar
McSherry, J. (1985). The current scope of channel switching. Marketing and Media Decisions, 20(8), 144–146.
Google Scholar
Moorman, M., Neijens, P. C., & Smit, E. G. (2007). The effects of program involvement on commercial exposure and recall in a naturalistic setting. Journal of Advertising, 36(1), 121–137.
Google Scholar
Nielsen (2011). What time is really primetime, (September 14). https://www.nielsen.com/us/en/insights/article/2011/what-time-is-really-primetime. Accessed 30 Dec 2020.
Nielsen (2015a). New America. New Consumers, (July 21). https://www.nielsen.com/us/en/insights/article/2014/whats-empowering-the-new-digital-consumer/. Accessed 30 Dec 2020.
Nielsen (2015b). Brain activity predicts cocial TV engagement, (March 9). https://www.nielsen.com/us/en/insights/report/2015/brain-activity-predicts-social-tv-engagement. Accessed 30 Dec 2020.
Nielsen (2015c). Live TV + Social Media = Engaged Viewers, (April 6). http://www.nielsen.com/us/en/insights/news/2015/live-tv-social-media-engaged-viewers.html. Accessed 30 Dec 2020.
Nielsen (2019). Nielsen total audience report | Q1 2019, (June 28). https://www.nielsen.com/us/en/insights/report/2019/the-nielsen-total-audience-report-september-2019/. Accessed 30 Dec 2020.
Nielsen (2020). Social content ratings, (July 28). https://www.nielsensocial.com/socialcontentratings. Accessed 30 Dec 2020.
Norris, C. E., & Colman, A. M. (1993). Context effects on memory for television advertisements. Social Behavior and Personality, 21(4), 279–296.
Google Scholar
Page, D. (2017). What happens to your brain when you Binge-Watch a TV Series, (November 4). https://www.nbcnews.com/better/amp/ncna816991. Accessed 30 Dec 2020.
Park, S., & Gupta, S. (2012). Handling endogenous Regressors by joint estimation using copulas. Marketing Science, 31(4), 567–586.
Google Scholar
Pavelchak, M. A., Antil, J. H., & Munch, J. M. (1988). The super bowl: An investigation into the relationship among program context, emotional experience, and ad recall. Journal of Consumer Research, 15(3), 360–367.
Google Scholar
Peterson, T. (2019). Agency Ad buyers say there isn’t enough addressable TV inventory, (March 21). https://digiday.com/future-of-tv/agency-ad-buyers-say-isnt-enough-addressable-tv-inventory. Accessed 30 Dec 2020.
Phalen, P. F. (1998). The market information system and personalized exchange: Business practices in the market for television audiences. Journal of Media Economics, 11(4), 17–34.
Google Scholar
Richins, M. L., & Root-Shaffer T. (1988). The role of evolvement and opinion leadership in consumer word-of-mouth: An implicit model made explicit, in Houston. In M. J. (Ed.), Advances in Consumer Research, (vol. 15, pp. 32–36). Provo: Association for Consumer Research. https://www.scienceopen.com/document?vid=ad126ce7-850b-4869-8772-b11d0f66f5f1. Accessed 30 Dec 2020.
Richins, M. L., Bloch, P. H., & McQuarrie, E. F. (1992). How enduring and situational involvement combine to create involvement responses. Journal of Consumer Psychology, 1(2), 143–153.
Google Scholar
Russell, C. A., & Levy, S. J. (2012). The temporal and focal dynamics of volitional Reconsumption: A phenomenological investigation of repeated hedonic experiences. Journal of Consumer Research, 39(2), 341–359.
Google Scholar
Schreiner, T. (2013). Amplifiers study: The Twitter users who are most likely to retweet and how to engage them, Twitter, (January 10). https://blog.twitter.com/2013/amplifiers-study-the-twitter-users-who-are-most-likely-to-retweet-and-how-to-engage-them. Accessed 30 Dec 2020.
Schwarz, T. (2019). TV Long View: A guide to the ever-expanding world of ratings data, The Hollywood Reporter, (October 5). https://www.hollywoodreporter.com/live-feed/tv-ratings-explained-a-guide-what-data-all-means-1245591. Accessed 30 Dec 2020.
Schweidel, D. A., & Kent, R. J. (2010). Predictors of the gap between program and commercial audiences: An investigation using live tuning data. Journal of Marketing, 74(3), 18–33.
Google Scholar
Schweidel, D. A., & Moe, W. W. (2014). Listening in on social media: A joint model of sentiment and venue format choice. Journal of Marketing Research, 51(4), 387–384.
Google Scholar
Schweidel, D. A., Foutz, N. Z., & Tanner, R. J. (2014). Synergy or interference: The effect of product placement on commercial break audience decline. Marketing Science, 33(6), 763–780.
Google Scholar
Seiler, S., Yao, S., & Wang, W. (2017). Does online word-of-mouth increase demand? (and how?): Evidence from a natural experiment. Marketing Science, 36(6), 838–861.
Google Scholar
Siddarth, S., & Chattopadhyay, A. (1998). To zap or not to zap: A study of the determinants of channel switching during commercials. Marketing Science, 17(2), 124–138.
Google Scholar
Statista (2019). TV advertising spending in the United States from 2011 to 2020 (in Billion U.S. Dollars), (February 11). https://www.statista.com/statistics/272404/tv-advertising-spending-in-the-us. Accessed 30 Dec 2020.
Story, L. (2007). Assigning ratings to commercials turns out to be a tricky task, New York Times (March). https://www.nytimes.com/2007/03/13/business/media/13adco.html. Accessed 30 Dec 2020.
Tavassoli, N. T., Shultz II, C. J., & Fitzsimons, G. J. (1995). Program involvement: Are moderate levels best for ad memory and attitude toward the ad? Journal of Advertising Research, 35(5), 61–72.
Google Scholar
Teixeira, T. S., Wedel, M., & Pieters, R. (2010). Moment-to-moment optimal branding in TV commercials: Preventing avoidance by pulsing. Marketing Science, 29(5), 783–804.
Google Scholar
Teixeira, T., Picard, R., & el Kaliouby, R. (2014). Why, when, and how much to entertain consumers in advertisements? A web-based facial tracking field study. Marketing Science, 33(6), 809–827.
Google Scholar
Tuchman, A. E., Nair, H. S., & Gardete, P. M. (2018). Television ad-skipping, consumption complementarities and the consumer demand for advertising. Quantitative Marketing and Economics, 16(2), 111–174.
Google Scholar
Wang, Z., & Lang, A. (2012). Reconceptualizing excitation transfer as motivational activation changes and a test of the television program context effects. Media Psychology, 15(1), 68–92.
Google Scholar
Wangenheim, F. v., & Bayón, T. (2007). The chain from customer satisfaction via word-of-mouth referrals to new customer acquisition. Journal of the Academy of Marketing Science, 35(2), 233–249.
Google Scholar
Wetzel, H. A., Hattula, S., Hammerschmidt, M., & van Heerde, H. J. (2018). Building and leveraging sports brands: Evidence from 50 years of German professional soccer. Journal of the Academy of Marketing Science, 46(4), 591–611.
Google Scholar
Wilbur, K. C. (2016). Advertising content and television advertising avoidance. Journal of Media Economics, 29(2), 51–72.
Google Scholar
Wilbur, K. C., Xu, L., & Kempe, D. (2013). Correcting audience externalities in television advertising. Marketing Science, 32(6), 892–912.
Google Scholar
Zaichkowsky, J. L. (1985). Measuring the involvement construct. Journal of Consumer Research, 12(3), 341–352.
Google Scholar
Zillmann, D. (1971). Excitation transfer in communication-mediated aggressive behavior. Journal of Experimental Social Psychology, 7(4), 419–434.
Google Scholar