The Effects of Online User Reviews on Movie Box Office Performance: Accounting for Sequential Rollout and Aggregation Across Local Markets

Abstract

Our objective in this paper is to measure the impact (valence, volume, and variance) of national online user reviews on designated market area (DMA)-level local geographic box office performance of movies. We account for three complications with analyses that use national-level aggregate box office data: (i) aggregation across heterogeneous markets (spatial aggregation), (ii) serial correlation as a result of sequential release of movies (endogenous rollout), and (iii) serial correlation as a result of other unobserved components that could affect inferences regarding the impact of user reviews. We use daily box office ticket sales data for 148 movies released in the United States during a 16-month period (out of the 874 movies released) along with user review data from the Yahoo! Movies website. The analysis also controls for other possible box office drivers. Our identification strategy rests on our ability to identify plausible instruments for user ratings by exploiting the sequential release of movies across markets--because user reviews can only come from markets where the movie has previously been released, exogenous variables from previous markets would be appropriate instruments in subsequent markets. In contrast with previous studies that have found that the main driver of box office performance is the volume of reviews, we find that it is the valence that seems to matter and not the volume. Furthermore, ignoring the endogenous rollout decision does not seem to have a big impact on the results from our DMA-level analysis. When we carry out our analysis with aggregated national data, we obtain the same results as those from previous studies, i.e., that volume matters but not the valence. Using various market-level controls in the national data model, we attempt to identify the source of this difference. By conducting our empirical analysis at the DMA level and accounting for prerelease advertising, we can classify DMAs based on their responsiveness to firm-initiated marketing effort (advertising) and consumer-generated marketing (online word of mouth). A unique feature of our study is that it allows marketing managers to assess a DMA's responsiveness along these two dimensions. The substantive insights can help studios and distributors evaluate their future product rollout strategies. Although our empirical analysis is conducted using motion picture industry data, our approach to addressing the endogeneity of reviews is generalizable to other industry settings where products are sequentially rolled out.online word of mouth, sequential new product release, endogeneity, instrumental variables, generalized method of moments, motion pictures

The influence of e-word-of-mouth on hotel occupancy rate

“…Recent studies have appeared quantifying the influence online reviews can have on the purchases of movies (Dellarocas et al, 2007;Duan et al, 2008), music (Chintagunta et al, 2010) and, to some extent, hotels (Ye et al, 2011;Anderson, 2012;Öğüt and Onur Taş, 2012;Kim et al, 2015a). This paper intends to measure the association of user-generated content (online reviews) to hotel occupancy rates.…”

How Questions and Answers Shape Online Marketplaces: The Case of Amazon Answer

“…For one, Chintagunta et al study the effect of online reviews on movie box office performance. They find that the valence, not the volume, seems to impact the performance of the movie in designated market area [15]. They argue that their findings can be generalized to other products that are rolled out sequentially too.…”

The role of online buzz for leader versus challenger brands: the case of the MP3 player market

“…In particular, digitized WOM information, online buzz, spreads rapidly through the internet, further increasing its influence on the consumer decision-making process (for a review, see [53]). Accordingly, the role of online buzz has received much attention from researchers [14,16,19,25,41,43,51,57,62].…”

“…Quality perceptions consist of three components: (1) abstract dimensions of intrinsic attributes (e.g., safety, durability), (2) perceived monetary price, and 3) reputation formed through advertising and brand name [61]. 2 A few notable exceptions include [19,34,43,44,56]. But they collect online buzz data from a single source, such as a movie review site (e.g., Yahoo!Movie) [19,43], an online forum [34], or a single firm [56].…”

“…2 A few notable exceptions include [19,34,43,44,56]. But they collect online buzz data from a single source, such as a movie review site (e.g., Yahoo!Movie) [19,43], an online forum [34], or a single firm [56]. adjust their prices up or down by small increments [10].…”