Author ORCID Identifier
https://orcid.org/0000-0002-1465-7295
Date of Award
8-15-2022
Degree Type
Dissertation
Degree Name
Doctor of Business Administration (DBA)
Department
Business
First Advisor
Naveen Donthu
Second Advisor
Denish Shah
Third Advisor
Kai Zhao
Abstract
Billions of US dollars in transactions occur each year between media companies and advertisers purchasing commercials on television shows to reach target demographics. This study investigates how consumer enthusiasm can be quantified (via social media posts) as an input to improve forecast models of television series premiere viewership beyond inputs that are typically used in the entertainment industry. Results support that Twitter activity (volume of tweets and retweets) is a driver of consumer viewership of unscripted programs (i.e., reality or competition shows). As such, incorporating electronic word of mouth (eWOM) into forecasting models improves accuracy for predictions of unscripted shows. Furthermore, trend analysis suggests it is possible to calculate a forecast as early as 14 days prior to the premiere date. This research also extends the Diffusion of Innovation theory and diffusion modeling by applying them in the television entertainment environment. Evidence was found supporting Rogers’s (2003) heterophilous communication, also referred to by Granovetter (1973) as “weak ties.” Further, despite a diffusion pattern that differs from other categories, entertainment consumption demonstrates evidence of a mass media (external) channel and an interpersonal eWOM (internal) channel.
DOI
https://doi.org/10.57709/30238345
Recommended Citation
Goodman, Robert Casey, "Using Consumer-Generated Social Media Posts to Improve Forecasts of Television Premiere Viewership: Extending Diffusion of Innovation Theory." Dissertation, Georgia State University, 2022.
doi: https://doi.org/10.57709/30238345
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