Exploring the “Twitter Effect:” An Investigation of the ... · —The Cluetrain Manifesto...

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Exploring the “Twitter Effect:” An Investigation of the Impact of Microblogging Word of

Mouth on Consumers’ Early Adoption of New Products

Thorsten Hennig-Thurau

Marketing Center Muenster University of Muenster

48143 Muenster, Germany & Cass Business School, City University London

London EC1Y 8TZ, UK Phone (+49) 251 83 29954 Fax (+49) 251 83 22032

Email: [email protected]

Caroline Wiertz Cass Business School

City University London London EC1Y 8TZ, UK

Phone (+44) 20 7040 5183 Fax: (+44) 20 7040 8262


Fabian Feldhaus Marketing Center Muenster

University of Muenster 48143 Muenster, Germany Phone (+49) 251 83 29954


Acknowledgments: The first and second author contributed equally to the project. The authors thank Andre Marchand as well as the participants of research seminars at Cass Business School, the University of Muenster, the University of Hamburg, the Technical University of Munich, HEC Paris, and the 2010 UCLA/Bruce Mallen Scholars and Practitioners Workshop in Motion Picture Industry Studies for their constructive criticism on previous versions of this manuscript. They also thank Benno Stein and Peter Prettenhofer for their help with the WEKA analysis, Mo Musse and Peter Richards for their IT help and Chad Etzel from Twitter for supporting the data collection. Finally, the authors are grateful for research funds provided by Cass Business School and City University London that supported this project. Keywords: Word of mouth communication, microblogging, Twitter, early adoption.

Working Paper, March 5, 2012

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Exploring the “Twitter Effect:” An Investigation of the Impact of Microblogging Word of Mouth on Consumers’ Early Adoption of New Products

ABSTRACT

Microblogging word of mouth (MWOM) through Twitter and similar services constitutes a

new type of word-of-mouth communication that combines the real-time and personal influence

of traditional (offline) word of mouth (TWOM) with electronic word of mouth’s (EWOM)

ability to reach large audiences. MWOM has the potential to increase the speed of dissemination

of post-purchase quality evaluations from consumers and thus has been argued to affect early

product adoption behaviors. For industries that exploit information asymmetries between

producers and consumers when releasing new products, such a “Twitter effect” would threaten

existing business models. This study develops a conceptual model of the impact of MWOM on

early product adoption, including possible moderating forces, and tests it in the context of the

motion picture industry. Studying 105 movies that were widely released in North American

theaters between October 2009 and October 2010, and all 4 million MWOM messages about

them sent via Twitter on their respective opening weekend, the authors find evidence of the

“Twitter effect” and identify boundary conditions. With a matched sample of 105 movies

released in the pre-MWOM era, the authors also demonstrate that the spread of quality-related

information by consumers through MWOM is indeed the cause of this effect. The authors

discuss notable implications for managers of experiential media products and word-of-mouth

scholars.

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Exploring the “Twitter Effect:” An Investigation of the Impact of Microblogging Word of

Mouth on Consumers’ Early Adoption of New Products

“The Internet is enabling conversations among human beings that were simply not possible in

the era of mass media.”

—The Cluetrain Manifesto (Levine et al. 2000, p. XXII)

Recent world events, such as Iran’s last presidential election and the Arab Spring movement,

have compellingly demonstrated the power of microblogging for the rapid spread of information

among networked individuals (Kaplan and Haenlein 2011). Microblogging refers to the

broadcasting of brief messages to some or all members of the sender’s social network through a

specific web-based service. Although various microblogging services exist, Twitter has become

synonymous with the concept; it alone boasts more than 100 million active users (October 2011)

and processes approximately 250 million messages every day, more than 40% of which are

posted “on the go” using mobile devices (Parr 2011a, 2011b).

For marketing, microblogging enables a new type of word-of-mouth communication for

which we introduce the term microblogging word of mouth (MWOM). Such MWOM combines

elements of both traditional (offline) word of mouth (hereafter: TWOM; Katz and Lazarsfeld

1955) and electronic word of mouth (EWOM; Hennig-Thurau et al. 2004; Liu 2006): It reaches a

potentially very large number of consumers with a single message (which EWOM can, but

TWOM cannot), enables real-time information sharing among consumers from any place, and

relies on a personal connection between the sender and receiver (both of which TWOM can, but

EWOM cannot). This unique combination of characteristics implies that MWOM can reach a

vast number of consumers at unprecedented speed.

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The aim of this research is to investigate whether MWOM, due to its unique characteristics,

influences the success of new products by shifting consumers’ early adoption behaviors. Such a

“Twitter effect”1 (Corliss 2009) would have strong implications for products that depend on

instant success upon their release – at a point in time when consumers are unable to judge their

“true” quality and must make adoption decisions mainly on the basis of promotional material.

Examples of such products include experiential media products (e.g., movies, music, electronic

games), but also products that benefit from a hyped release (e.g., Apple’s iPhones and iPads).

Among motion picture industry experts and journalists, for example, proponents of the

“Twitter effect” blame MWOM for the immediate failure of multimillion projects such as Brüno

and G.I. Joe, as well as for the unexpected opening successes of Transformers and The Karate

Kid, despite their negative reviews by professional critics (e.g., Corliss 2009; Lang 2010). If

MWOM does affect the early adoption of new products, investments in risk-intense products

would become even riskier and less attractive, because MWOM threatens to decrease the share

of revenues that remain unaffected by consumers’ quality perceptions of the product. Although

this goes beyond the scope of this research, the importance of action-based cascades, such as

opening weekend box office lists for movies, implies that the “Twitter effect” could also

influence subsequent revenues, because a large number of consumers base their purchase

decisions on such quality-neutral information (Bikhchandani, Hirshleifer, and Welch 1992).

However, despite anecdotal evidence in support of the “Twitter effect”, it is far from certain

that MWOM exerts such an impact. Other industry insiders and journalists (e.g., Goldstein and

Rainey 2009) question its existence, and a large-scale study of the media habits of moviegoers

reveals that approximately half of the respondents self-report that they rely only on TWOM

1 This effect, though named in reference to Twitter as the dominant market leader, is not solely exerted through Twitter’s service but refers to microblogging’s impact in general.

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when making purchase decisions, but ignore MWOM (Lang 2010). Moreover, MWOM’s very

short message content has been criticized for limiting the amount of information that can be

transmitted and thus limiting the impact of that information (Dugan 2010; Goldstein and Rainy

2009; Lang 2010).

In this study, we address this debate and advance the word-of-mouth literature in marketing.

Specifically, we aim to make three contributions: First, we introduce and conceptualize the

concept of MWOM and position it in relation to TWOM and EWOM. Second, we develop a

conceptual model of MWOM’s impact on the early adoption of new experiential media products,

including its boundary conditions. Third, we test our model empirically for the early adoption of

new movies, drawing on a unique data set of all MWOM messages sent via Twitter that

pertained to 105 widely released movies during their respective North American opening

weekend. Based on sentiment analysis and seemingly unrelated regression analyses, we find

support for the “Twitter effect.” Comparing the findings with a second sample of matched

movies from the pre-MWOM era confirms that the “Twitter effect” is indeed a result of the early

availability of post-purchase quality-related information from consumers, as enabled by

MWOM.

THEORETICAL BACKGROUND

One of marketing’s law-like generalizations states that word-of-mouth communication is a

key information source for consumer decision making (Arndt 1967; Godes and Mayzlin 2004).

Over the years, different types of word-of-mouth communication have emerged as a result of

technological innovations. We briefly summarize key concepts and position MWOM within the

word-of-mouth literature.

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The Role of TWOM and EWOM in Consumer Decision Making

Building on initial work on opinion leaders by communications scholars Katz and Lazarsfeld

(1955), Arndt (1967) offered a first discussion of the defining characteristics of word of mouth.

To distinguish this original type of word-of-mouth communication from later types, we refer to it

as traditional word of mouth (TWOM). Arndt (1967) describes TWOM as face-to-face

communication about a commercial entity or offering between consumers, emphasizing its

unbiased and personal character. Although he does not explicitly distinguish between evaluative

post-purchase communication and anticipatory pre-purchase communication, ensuing TWOM

research has focused mainly on the former. Early TWOM research stressed the role of positive

word of mouth (i.e., recommendations or referrals) for product adoption decisions (Bass 1969;

Dodson and Muller 1978); interest in negative word of mouth only arose in relation to the

consumer satisfaction concept in the 1980s, and was considered a consequence of a consumer’s

dissatisfaction with a product (Richins 1983; Singh 1990). In addition to word of mouth as a

diffusion parameter, TWOM research has also dealt with a consumer’s decision to spread

positive or negative word of mouth (de Matos and Rossi 2008), with TWOM as the dependent

variable (Anderson 1998).

The rise of the Internet then enabled a new type of word-of-mouth communication: EWOM

(Godes and Mayzlin 2004). EWOM refers to online posts from mostly anonymous consumers

regarding a commercial entity or offering, that are visible to potentially millions of consumers,

and available for an indefinite period of time (Hennig-Thurau et al. 2004). However, EWOM

lacks the interpersonal connection between the sender and the receiver that is typical of TWOM,

which reduces its persuasive impact (Chatterjee 2001). As a result of EWOM’s observable

nature, scholars can analyze actual messages that consumers post, which has led to a

differentiation between message sentiment (i.e., valence) and the amount of messages (i.e.,

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volume). Findings regarding the role of the valence dimension in driving sales are conflicting

though: Chevalier and Mayzlin (2006) and Chintagunta, Gopinath, and Venkataraman (2010)

report that EWOM valence affects product sales, whereas Liu (2006) finds no relation to success.

The volume dimension of EWOM does not carry any quality information but rather captures the

“buzz” (i.e., awareness and interest) that a commercial offering generates among consumers (Ho,

Dhar, and Weinberg 2009). Most research has found an influence of EWOM volume on product

adoption and sales (e.g., Godes and Mayzlin 2004 and Liu 2006), though Chintagunta, Gopinath,

and Venkataraman (2010) do not.

MWOM as New Type of Word-of-Mouth Communication

Microblogging word of mouth (MWOM) combines key elements of TWOM and EWOM that

are essential for their respective effectiveness. We define MWOM as any brief statement made

by a consumer about a commercial entity or offering that is broadcast to some or all members of

the sender’s social network through a specific web-based service (e.g., Twitter).2 Because

MWOM is sent electronically to a potentially very large network of personal connections, it

could reach audiences of similar size as those available through EWOM. Moreover, receivers are

directly connected to the sender and should be similarly susceptible to the personal influence that

characterizes TWOM messages. Also similar to TWOM, MWOM reaches network participants

in real-time, often sent from smartphones or other mobile devices. It is this real-time character in

combination with the large network of receivers that allows MWOM to spread more rapidly than

any other type of word-of-mouth communication, raising the question whether MWOM affects

product adoption and success earlier in a product’s lifecycle, when neither TWOM nor EWOM

2 We exclude messages sent to a single recipient through web-based services, as there is no conceptual difference between such communication and TWOM (which also includes mediated exchanges, such as phone calls and letters). See Hennig-Thurau and colleagues (2004) for a similar argument for EWOM.

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can yet exert an impact. With Figure 1 we illustrate how MWOM combines characteristics of

both TWOM and EWOM and thus emerges as a new type of word-of-mouth communication.

--------------Figure 1 approx. here--------------

Empirical research on MWOM is still in an embryonic stage. Jansen and colleagues (2009)

collect data from Twitter but limit their insights to descriptive findings. For 24 movies, Asur and

Huberman (2010) use the rate of tweets to predict opening weekend box office, and combine this

measure with the tweets’ valence to predict the subsequent weekend box office. However, they

do not control for other types of word-of-mouth communication (e.g., EWOM) or producer

signals (e.g., advertising) when studying subsequent success, so while their findings might shed

light on the predictive potential of MWOM, they do not reveal whether MWOM causally affects

early adoption and success. To date, no published study has tested the “Twitter effect”—that is,

the impact of quality-related consumer information spread through MWOM messages on early

product adoption.

CONCEPTUAL MODEL AND HYPOTHESES

We summarize our conceptual model and hypotheses in Figure 2. We argue that the quality

judgments of consumers, articulated and spread immediately after the release of a new product

through MWOM, should influence subsequent early product adoption. This impact constitutes

the “Twitter effect.” We posit that two boundary conditions moderate the strength of this

“Twitter effect,” namely, the volume of evaluative MWOM articulated immediately after the

release of a new product and the differing susceptibility of different consumer segments to

MWOM.

--------------Figure 2 approx. here--------------

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The “Twitter effect” has particular economic relevance for industries characterized by short

lifecycles and exponentially decaying adoption patterns, such as experiential media industries

(e.g., movies, music, electronic games), as those industries’ business models rely heavily on

large-scale early adoption. For example, approximately 50% of album sales of hit music (Asai

2009), 46% of movie ticket sales for major movies (Hayes 2002), and 40% of game revenues

(www.vgchartz.com) are generated in the first week of release. Before the advent of MWOM,

consumers faced information asymmetry and had to make early adoption decisions for

experiential media products on the basis of producer-provided quality signals only (e.g.,

advertising; Kirmani and Rao 2000), as very limited quality judgment of other consumers were

available and professional reviews are usually of limited informational value for consumers

(Eliashberg and Shugan 1997).

We argue that it is at this point that MWOM fundamentally challenges the status quo. Its

unique combination of characteristics expedite the spread of evaluative messages from

consumers who have experienced the new product, so quality judgments articulated by

consumers through MWOM can affect others’ product adoptions much earlier in the product’s

life. With MWOM, consumers share their quality evaluations with a vast network of followers

immediately after or even while consuming the new product in question; many consumers tweet

about the quality of a movie while they are still sitting in the theater. In turn, the valence of this

evaluative MWOM (hereafter, MWOM valence) should influence other consumers’ early

adoption decisions and thus the new product’s success. Valence, a concept adapted from emotion

research, describes the positive or negative emotional tone of MWOM, which is based on the

sender’s consumption of the new product (Brosch and Moors 2009).

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If consumers who have experienced the new product spread mostly positive MWOM

messages immediately after its release, the product’s adoption in subsequent hours and days

should increase. If post-consumption MWOM messages are negative though, adoption should be

adversely affected. In other words, we propose the “Twitter effect” to be positive, as expressed in

our first hypothesis:

H1: The valence of MWOM messages spread by consumers who have experienced a new

experiential media product immediately after its release (“MWOM valence”) has a

positive effect on the product’s subsequent early adoption.

Beyond this direct effect, we predict a number of moderators. The first construct that we

argue to moderate the “Twitter effect” is MWOM volume, which we define in the context of this

research as the number of evaluative MWOM messages about a product spread immediately

after its release. The postulated impact of MWOM valence is based on the assumption that

positive or negative information about a new product reaches a large group of consumers, who

then, based on the information provided, make or change their adoption decisions. However, the

MWOM valence construct itself does not contain information about the number of consumers

reached by MWOM. We thus turn to the EWOM literature that has introduced the concept of

EWOM volume as a measure of the number of messages spread (Godes and Mayzlin 2004; Liu

2006), but restrict our MWOM volume construct to those messages sent by consumers who

already have experienced the new product and then spread positive or negative information about

its quality, as our interest is in the spread of such evaluative information (in contrast to mere

expressions of anticipation).

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The interaction between MWOM valence and MWOM volume, articulated immediately after

a new product’s release, should capture the reach of evaluative MWOM messages. This reach in

turn should moderate the “Twitter effect.” Specifically, we expect a high MWOM volume to

amplify the impact of quality information contained in evaluative MWOM messages, whereas a

low volume should reduce its impact. We summarize this argument in our second hypothesis:

H2: The impact of the valence of MWOM messages spread by consumers who have

experienced a new experiential media product immediately after its release (“MWOM

valence”) on the product’s subsequent early adoption varies with the overall volume of

these MWOM messages (“MWOM volume”).

Moreover, we propose that consumer segments vary in their susceptibility to MWOM

influences, which should moderate the “Twitter effect” as a second boundary condition. We

focus on two consumer segments that are nowadays widely considered crucial for the success of

experiential media products, namely, teenagers and families (e.g., Epstein 2010; Fromme 2003).

We are not aware of any research on consumers’ susceptibility to MWOM influence; however,

existing consumer research has demonstrated that consumers vary in their susceptibility to

reference group influences in general (Childers and Rao 1992). Existing consumer research has

shown that teenagers are particularly susceptible to peer influence in the context of shopping

decisions, and that they enjoy their shopping experiences more when they are shared with and

validated by respected peers (Bachmann, Roedder John, and Rao 1993; Mangleburg, Doney, and

Bristol 2004). Building on these findings, we expect that teenagers’ purchase decisions are more

strongly influenced by MWOM messages than are those of average consumers and that the

relationship between MWOM valence and product adoption is stronger for these consumers. As

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a result, the early adoption of experiential media products whose core target group consists of

teenagers therefore should be more strongly affected by the “Twitter effect” than should products

that focus on other target groups.

Families, as a second key target group for experiential media products, are decision-making

entities that must negotiate joint decisions across individual members (Commuri and Gentry

2000). Although parents are the dominant decision-makers, children exert considerable influence

on particular sub-decisions, such as leisure activities like vacations, movie attendance, and

television viewing (Jenkins 1979; Mangleburg 1990). No research, to the best of our knowledge,

has explicitly examined families’ susceptibility to outside influences such as MWOM, but the

negotiated nature of entertainment-related consumption choices suggests that family decisions

should be less affected by MWOM valence than those of average consumers. Consistent with

this argument, recent research on family identity and its role in family decision making has

highlighted the importance of leisure activities as opportunities for family identity enactment

through collective experiences (Epp and Price 2008). The centrality of collective experiences to

family identity implies that the overall joint consumption experience is only partly influenced by

the (anticipated) quality of a new experiential media product, so that valenced information about

product quality transmitted by MWOM should play a lesser role in family decisions.

Consequently, we expect the early adoption of experiential media products whose core target

group are families to be less strongly affected by the “Twitter effect” than the early adoption of

products that focus on other target groups. We offer our third and final hypothesis:

H3: The impact of the valence of MWOM messages spread by consumers who have

experienced a new experiential media product immediately after its release (“MWOM

valence”) on the product’s subsequent early adoption varies for different consumer

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segments according to their susceptibility to MWOM influence, such that the impact is

(a) stronger for experiential media products targeted at teenage consumers, and (b)

weaker for experiential media products targeted at families.

EMPIRICAL STUDY: TESTING THE “TWITTER EFFECT”

Context, Research Design, and Sample

To test our hypotheses, we collected data in the context of the motion picture industry. We

chose movies because (a) they are an economically important category of experiential media

products, (b) the debate about the existence of the “Twitter effect” is prominent for movies, and

(c) we were able to compile data on daily revenues and important controls (e.g., advertising

spending) for major new releases. Since we are interested in early product adoption, we focused

on movies’ opening weekend. Early adoption is critical for movie success and accounts for

approximately 46% of total movie ticket sales (Hayes 2002); research provides evidence of

additional effects on future consumer adoption decisions and distribution choices (e.g., Elberse

and Eliashberg 2003). In North America, movies are generally released in theaters on Fridays.

We thus use the valence of MWOM messages sent within the first 24 hours after a movie’s

release on Friday by consumers who viewed the movie and study its impact on the movie’s

North American theatrical box office revenues for the remainder of the weekend (i.e., Saturday

and Sunday). In other words, Saturday and Sunday box office revenue is our measure of

subsequent early new product adoption. As both TWOM and EWOM for new movies require

more time to spread on a large scale (even popular EWOM sites such as the Internet Movie

Database IMDb generally do not report consumer opinions before Monday), this research design

allows us to isolate the potential impact of MWOM.

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We collected data on all movie titles that were widely released in North American theaters

(i.e., shown simultaneously in more than 800 theaters at their release) between October 2009 and

October 2010; we excluded 11 titles that were released on different days of the week to avoid

any possible bias.3 The final sample consists of 105 movie titles (see the Appendix for a

complete list).

Empirical Model

The dependent variable in the empirical model that we developed to test our hypotheses was

the total North American box office revenue generated by a movie on the Saturday and Sunday

of its release weekend. As independent variables, we included the MWOM valence of evaluative,

post-purchase messages sent within the first 24 hours after a movie’s Friday release; the

interaction of MWOM valence and the overall volume of these evaluative MWOM messages;

and the interactions of MWOM valence with a movie’s target groups of teenagers and families.

We also included a number of control variables derived from extant motion picture research to

rule out alternative explanations and confounding effects.

Specifically, we included the release day (i.e., Friday) revenues as a control, so the model

only focuses on that part of the Saturday and Sunday box office that is not accounted for by the

general appeal of the movie, which already has been reflected in its release day success.

Therefore, our model targets the variation of Saturday and Sunday box office from the release

day success, not the absolute success of the movie. With this model specification, we ensured

Granger (1969) causality when testing for the “Twitter effect.”

We also controlled for potential influences of established determinants of motion picture

success, namely, a movie’s starpower (Elberse 2007), whether it is a sequel (Hennig-Thurau,

3 In addition, technical problems caused by the Twitter API meant 12 movies could not be considered in the analyses.

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Houston, and Heitjans 2009), the production budget (Basuroy, Chatterjee, and Ravid 2003),

advertising spending (Elberse and Eliashberg 2003), and its pre-release buzz (Karniouchina

2011). We also included professional critics’ ratings (Basuroy, Chatterjee, and Ravid 2003), the

only independent quality judgment available upon a movie’s release. To estimate the interaction

effects, we also included the respective main effects, namely, evaluative, post-purchase MWOM

volume and the movie’s target groups (teenagers and families).

In a separate equation, we used release day revenues as the dependent variable and the

aforementioned success factors as independent variables. We did so to account for the

established effects of these variables on release day success. Because the release day success

variable is also included in the Saturday and Sunday box office revenues equation as an

independent variable for the reasons mentioned above, this equation helped us avoid model

misspecifications.

We followed Elberse and Eliashberg (2003) and chose a multiplicative log-linear model

formulation, as shown in Equations 1 and 2:

(1)

0 31 2

5 64

7 8 9

( ) ( )( ) MWOMVAL AUDFAM MWOMVAL AUDTEENS

D ME

REV_SUB e REV_REL MWOMVAL MWOMVOL

MWOMVAL MWOMVOL e e

X e e e

(2) 0 1 2ß DREV_REL e X e e

REV_SUB is the North American theatrical box office revenues generated by a movie during

the Saturday and Sunday after the movie’s release; REV_REL is the North American theatrical

box office revenues generated by a movie on its release day (i.e., Friday); MWOMVAL is our

measure of MWOM valence; and MWOMVOL is our measure of MWOM volume. AUDFAM

and AUDTEENS are dummy variables that indicate whether a movie’s main target group is

families or teenagers, respectively; X is a vector of metric control variables (i.e., a movie’s

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budget, amount of advertising spent for the movie before its release, its pre-release buzz, and its

reception by professional critics); D is a vector of dummy-coded control variables (i.e., the

movie’s starpower and whether it is a sequel to a previous film); and ME is a vector that consists

of the main effects of the two target group variables.

In Table 1 we provide a description of all variables in our empirical model, their

operationalization, and their empirical and intellectual sources. For the key concepts of MWOM

valence and the moderators (i.e., MWOM volume and the two customer segments, teenagers and

family), we provide additional details about their operationalization below.

--------------Table1 approx. here--------------

MWOM valence. For the 105 movies in the sample, we collected all English-language

MWOM messages sent via Twitter on each day of the opening weekend. We used Twitter

messages as a proxy for MWOM messages in general for two reasons. First, Twitter is by far the

largest microblogging platform (Knowlton 2011), regularly being used as a synonym for

microblogging in general (Anamika 2009). Second, Twitter enabled us to download all tweets

sent about a movie during the opening weekend in real time by granting us extended access

rights to their Application Programming Interface (API). This access was essential, because for

major movies, the amount of Twitter chatter often drastically exceeds the API’s normal

download limits. No existing studies on microblogging (e.g., Asur and Huberman 2010; Jansen

et al. 2009) report a similar rights extension.

Every week from October 2009 to October 2010, we developed a list of search terms for each

movie due to be released on the Friday of that respective week. One author generated an initial

set of search terms based on an extensive manual Twitter search, which was then reviewed and

discussed among all authors to ensure completeness. Up to ten search term combinations were

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considered per movie, taking into account Twitter-specific acronyms, exclusion words, and so

on. These search term combinations then were manually entered into a script, which

automatically downloaded all tweets containing the specified search term combinations

throughout the opening weekend, starting on Fridays at 10:00 a.m. Eastern Daylight Time and

ending Sunday at midnight Eastern Daylight Time. Overall, we collected 4,045,350 tweets about

the 105 movies in our sample. Our extended access rights ensured that these tweets include all

English-language MWOM messages spread about the movies in our sample through Twitter.

Although it is not possible to collect information about the number of followers per tweet due to

Twitter’s privacy policy, the Max Planck Institute (2011) estimated that the average number of

followers per Twitter user is approximately 45, which suggests that the tweets in our sample

have reached roughly 182 million consumers total.

Using this information, we operationalized MWOM valence as the quotient of all positive

tweets a movie received on its opening Friday and the following Saturday until noon, divided by

the number of negative tweets for the movie within the same time period. To determine the

valence of the individual tweets, we ran a multistage sentiment analysis. Prior to the actual

analysis, we eliminated all tweets with identical content by the same author and those not written

in Latin script. The sentiment analysis then involved two steps. First, all remaining tweets were

classified into one of three groups: (1) spam, non-English tweets, and tweets not related to the

movie in question, (2) movie-related tweets that contained no post-consumption quality

assessment (mostly anticipatory statements such as “I look forward watching MOVIE A

tonight”), and (3) evaluative, post-consumption tweets (movie “reviews”). Second, we divided

the third group into positively and negatively valenced tweets using sentiment analysis.

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The analysis was executed simultaneously for all movies, employing the open-source data

mining software WEKA (Bouckaert et al. 2010; Hall et al. 2009). Initially we manually coded

51,000 randomly selected tweets into the three aforementioned categories. Using 65% (i.e.,

33,150) of these coded messages as input, we trained the algorithm of a support-vector machine

(SVM) to build a model to classify cases into categories. Through a decomposition of the

manually coded tweets into their elements (i.e., single words and word groups), these elements

were used to calibrate the model, identifying each element’s discriminatory power. More

formally, a vector was assigned to all words and word groups and mapped into a multi-

dimensional space. The SVM then fitted a hyperplane that divides all training points (i.e.,

vectors) into two classes, such that it maximized the distances between the hyperplane and the

nearest training points. Then the SVM identified those words and word groups whose vectors

showed the greatest distance from the hyperplane and assigned a parameter to each, indicating

the strength of association with a particular category. The words and word groups with the

highest discriminatory power were used for further analysis (Pang, Lee, and Vaithyanathan

2002).

We next applied the SVM to classify all other (non-coded) tweets. Using the sequential–

minimal–optimization algorithm, the SVM searched for the previously identified words and

word groups in each of these tweets. The previously determined parameters of the recognized

words and word groups were then used to calculate the degree to which each tweet was

associated with the different categories, resulting in the final classification of all collected tweets

(Keerthi et al. 2001; Platt 1999). To determine the predictive power of this classification

analysis, we ran an out-of-sample test with the remaining 35% (i.e., 17,850) of the manually

coded tweets that were not used to calibrate the model. These tweets were classified as positive

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and negative reviews, with an accuracy level of 90.2%—higher than most other studies that use

sentiment analysis to code online consumer articulations on the web (e.g., Das and Chen 2007),

which may be a result of the brevity of MWOM messages compared with EWOM messages.

Moderator variables. Drawing on the same data and classification results we used to measure

MWOM valence, we operationalized MWOM volume as the number of the evaluative, post-

consumption tweets for a movie (movie “reviews”) sent on a movie’s opening Friday till the

following Saturday until noon. For the customer segment moderators, we classified each movie

according to three nominally scaled variables: “teenagers as main target group,” “family as main

target group,” and “other main target group.” This classification relied on Jinni.com, a site on

which experts assign movies to audience groups. For this research, we reduced an original six-

group classification to three groups by merging Jinni’s “family outing” and “kids” categories,

using its “teens” category, and then including all movies not assigned to one of these groups in

the “other main target group” category.

To create the interaction terms between MWOM valence and MWOM volume, we used

Lance’s (1988) residual centering approach to minimize the potential multicollinearity between

the interaction term and the main effects (Bottomley and Holden 2001; Hennig-Thurau, Houston,

and Heitjans 2009). Residual centering is an effective, conservative test for interaction effects of

metric variables; it assigns only that part of the variance to the interaction term that is not

explained by the main effects and does not suffer from problems associated with mean-centering

(Echambadi and Hess 2007). We used the raw product terms for the interactions between

MWOM valence and the two target group variables, as the latter are dummies which limits the

usefulness of residual centering.

20

Estimation

To estimate the system of Equations 1 and 2, we first linearized these equations, which

resulted in Equations 3 and 4:

(3) 0 1 2 3

4 5

6 7 8 9

( ) ( ) ( ) ( )

( ) ( )

( ) ( )

LN REV_SUB ß ß LN REV_REL ß LN MWOMVAL ß LN MWOMVOL

ß LN MWOMVAL MWOMVOL ß MWOMVAL AUDFAM

ß MWOMVAL AUDTEENS ß LN X ß D ß ME

(4) 0 1 2( ) ( )LN REV_REL ß ß LN X ß D

We then estimated Equations 3 and 4 using seemingly unrelated regression (SUR; Zellner 1962),

which provided unbiased coefficients for the model variables by accounting for correlated errors

across the two equations stemming from the dual role of REV_REL as both an independent

variable in Equation 3 and a dependent variable in Equation 4. For SUR to be effective, the

system of equations must contain at least one regressor that is used in one equation but not the

other (Elberse 2010). In our model, MWOM valence, the moderator main effects, and the

corresponding interaction terms all meet this criterion and are included in Equation 3 but not

Equation 4.

Results

Descriptive statistics. Of the approximately four million tweets we collected, 829,576 were

classified as evaluative, post-consumption MWOM. The number of review tweets per movie

varied; the mean is 38,527. Consistent with previous studies on MWOM (e.g., Asur and

Huberman 2010) and also EWOM (e.g., Chevalier and Mayzlin 2006), the reviews were more

positive than negative. Figure 3 depicts the number of movie-related tweets sent throughout the

opening weekend, differentiating between movie “reviews” and pre-purchase movie-related

tweets that did not contain quality information. Friday was the most active day in terms of

spreading MWOM; approximately 51% of all movie-related tweets (2,072,731) and 65% of

21

movie “reviews” (543,836) were sent from Friday till the following Saturday until noon. Both

kinds of tweets peaked on the opening day and declined thereafter. Across all three days of the

weekend, review tweets peaked at around 11:00 p.m. Eastern Daylight Time, which is about

three hours after the peak of non-review tweets. This distribution appears consistent with our

prediction that most non-review tweets would be sent shortly before the screening (expressing

anticipation), whereas movie “reviews” are mostly sent after the screening (expressing

evaluation). Table 2 reports the basic descriptive statistics and correlations.

--------------Figure 3 and Table 2 approx. here--------------

Hypotheses tests. The overall fit of the model was good, with R-square values of .62 for the

REV_REL equation and .98 for the REV_SUB equation (which included the release day revenues

as a regressor). Multicollinearity was below critical levels and thus not a problem (Hair et al.

2006). The variance inflation factor (VIF) for MWOM valence, our key construct, was 2.6, and

the highest VIF of all regressors was 5.2 (for the interaction between MWOM valence and

teenagers as the main target group).

As we report in Table 3, the results of the SUR estimation provide support for H1 and thus for

the existence of the “Twitter effect.” The MWOM valence parameter in the REV_SUB equation

is positive and significant, consistent with our expectations: MWOM valence spread immediately

after a new movie’s release systematically influences other consumers’ decisions about whether

to attend a screening of the movie during the remainder of its opening weekend.

--------------Table 3 approx. here--------------

For the moderator hypotheses, we found mixed support. The SUR parameter for the

interaction between MWOM valence and MWOM volume is in the expected direction but not

significant (p = .105); we thus do not find support for H2. For H3, the interaction between

22

MWOM valence and teenagers as the main target group is also non-significant (p = .96).

However, the interaction between MWOM valence and families as the main target group is

significant and negative, as theoretically proposed (p < .01). The positive effect that MWOM

valence exerts on subsequent early product adoption is attenuated if families are the main target

group. Thus, though we do not find support for H3a, we do find support for H3b.

The parameters of the controls are generally consistent with extant theory. Anticipatory buzz

about a new movie has the strongest effect on release day revenues, followed by whether the

movie is based on a prominent brand (e.g., sequel) and then its production budget (as an

indicator of the movie’s production values). Critical reviews are only marginally significant,

while pre-release advertising and starpower are not. We might speculate that their effects are

accounted for by both the buzz a movie creates and its production budget.

Regarding Saturday and Sunday revenues, family-targeted movies show a positive main

effect, and the production budget also positively affects the remainder of the opening weekend.

The sequel variable, MWOM volume, buzz, and advertising all exhibit negative coefficients in

this equation (though non-significant for the latter two). All of these variables are skewed toward

release day revenues (compared with the remainder of the weekend) and are particularly high for

movies that drive large audiences into theaters on a movie’s opening night. Because we

controlled for release day revenues in Equations 1 and 3, their negative parameters in the latter

equation would indicate that movies that have strong brands and are accompanied by high

MWOM volume, buzz, and advertising tend to peak on their release day, when the fan base

crowds theaters, and then attract relatively smaller audiences on the days that follow. In other

words, their parameters in this system of equations must not be confused with causal effects.

23

FOLLOW-UP ANALYSIS OF THE ROLE OF QUALITY-RELATED INFORMATION FOR THE “TWITTER EFFECT”

Our analysis thus far has provided empirical support for the existence of the “Twitter effect,”

in that the valence of consumer judgment about a new experiential media product spread through

MWOM affects subsequent early adoption and new product success. A key assumption

embedded in this argument is that the information about a product’s quality, as perceived by

consumers, is responsible for this effect. To provide additional support for our main hypothesis

and rule out alternative explanations, we conducted a follow-up analysis in which we substituted

MWOM valence with a direct aggregate measure of consumers’ quality perceptions of a new

product, a concept referred to in the literature as ordinary evaluation (Holbrook 2005; Holbrook

and Addis 2007). We then compared the effect of this ordinary evaluation on subsequent early

adoption between a “MWOM period” sample and a “pre-MWOM period” sample of movies.

That is, we tested the effect of ordinary evaluation on the box office revenues generated by a

movie during the remainder of its initial weekend (i.e., Saturday and Sunday), when entered into

our early adoption equation (i.e., Equations 2 and 4) as a substitute for MWOM valence for the

two different samples.

The first sample contained the recently released films we used in our preceding analyses; it

represents the “MWOM period” sample. The “Twitter effect” would suggest that ordinary

evaluation has an effect in this sample, because MWOM enables the instant spread of post-

purchase evaluations. The second sample comprised similar movies that were released before

Twitter and other MWOM services became popular and thus before the spread of quality-

information through MWOM was possible for consumers; we thus refer to it as the “pre-MWOM

period” sample. To identify these “similar movies,” we drew on a comprehensive database of

1,202 movies theatrically released in North America between 1998 and 2006 on at least 800

24

screens during their release weekend. Twitter began operations in 2007, when it hosted just

5,000 tweets—a tiny fraction of today’s 190 million daily messages. Because no other means

existed to enable ordinary evaluation to spread as fast, we can assume that ordinary evaluations

should have no discernible effect on subsequent early adoptions in the pre-Twitter era.

As a proxy for ordinary evaluation, we collected each movie’s rating on the video rental site

Netflix, the leading North American provider of DVD-by-mail and VOD movie streaming, for

both the “MWOM period” and “pre-MWOM period” samples. These Netflix ratings reflect the

quality perceptions of its 23 million North American customers, a mainstream audience

(Mullaney 2006) that is consistent with the definition of ordinary evaluation as a measure of the

taste of “ordinary” consumers (i.e., non-experts or members of the mass audience; Holbrook

2005).

Matching Approach

To compare the role of ordinary evaluation on subsequent early adoption for two time periods

(i.e., “MWOM period” and “pre-MWOM period”), we needed to ensure that the samples did not

differ systematically, which might have distorted the results. For example, such differences

might arise from structural changes in the movie industry’s selection process, which currently

focuses more on so-called “tentpole” pictures (e.g., Avatar), and on franchises (e.g., Pirates of

the Caribbean), while producing fewer medium-budgeted, unbranded films.

To generate such a sample for the “pre-MWOM period,” we applied propensity score

matching, a statistical matching procedure (Rosenbaum and Rubin 1983), and used the

propensity scores for each of the movies generated by this approach to identify a “twin” for each

of the 105 movies in our “MWOM period” sample, using nearest neighbor estimation.

Propensity score matching has been developed to remove selection biases between treatment

groups and no-treatment groups in non-experimental settings (for applications in marketing, see

25

Mithas and Krishnan 2009 and von Wangenheim and Bayón 2007). In our case, the treatment is

time and the changes in the movie industry that accompany it. Consequently, the “MWOM

period” movies represent the treatment cases, and the “pre-MWOM period” movies are the no-

treatment cases.

Propensity score matching applies probit regression (with the “MWOM period” variable as

the dependent variable, coded 0 or 1 for each movie) to generate a propensity score for each

sample element, which then provides the basis for the subsequent steps. As the regressors, it uses

a set of variables that should differ systematically between the treatment (i.e., “MWOM period”)

and no-treatment (i.e., “pre-MWOM period”) cases and that also affect the outcome variable

(i.e., subsequent early adoption). Therefore, we used a set of movie characteristics that previous

research has found to influence box office success, as discussed in the context of Equations 1 and

2. We report the model and results of the probit regression estimation in Table 4. The regression

function was highly significant and, with a pseudo-R-square value of .22, able to explain

differences between the two groups.


We used the resulting propensity scores to identify “nearest neighbors” for each movie in the

“MWOM period” sample; the nearest neighbor from the “pre-MWOM period” sample was the

movie with the smallest Euclidean distance to an “MWOM period” movie’s propensity score.

We chose nearest neighbor estimation over alternative algorithms such as Kernel matching, as

the latter does not identify one twin for each sample unit but rather would use a weighting score,

which is inconsistent with our application of seemingly unrelated regression for the subsequent

early adoption equation.

26

As Table 5 shows, all mean differences between the treatment and no-treatment cases that

were significant before the matching process (i.e., budget, advertising, pre-release buzz, ordinary

evaluation, and teenagers as target audience) became insignificant after the matching. As another

proof of matching effectiveness, we find that when we reran the probit regression with the

matched sample, the pseudo-R-square value was substantially smaller after matching (.03) than

before. Thus, propensity score matching successfully removed differences between the “MWOM

period” and the “pre-MWOM period” samples, allowing us to compare the SUR coefficient for

the ordinary evaluation variable in the subsequent early adoption regression.


SUR Results for “MWOM Period” and Matched “Pre-MWOM Period” Samples

In the final step, we estimated our system of equations again using SUR for both samples, so

that we could determine if ordinary evaluation had a significant effect on box office revenue in

either time period. In other words, we tested if the ability to spread quality-related information

has changed over time and can influence subsequent early adoption of movies today, which

would be additional evidence of the proposed “Twitter effect.” We ran SUR separately for the

“MWOM period” and “pre-MWOM period” samples, essentially replicating Equations 3 and 4.

The only changes were that we substituted MWOM valence with ordinary evaluation and

excluded the valence-based interactions. We report the results in Table 6.


Consistent with our theoretical arguments, ordinary evaluation had no significant effect on

subsequent early adoption in the “pre-MWOM period” sample (p = .99). In contrast (but in line

with our arguments), in the “MWOM period” sample, we find that ordinary evaluation exerts a

significant positive effect on subsequent early adoption (p < .05). Thus, our matched sample

follow-up analysis provides evidence that the “Twitter effect” of MWOM valence indeed results

27

from MWOM’s power to spread consumers’ ordinary evaluation (i.e., their post-purchase quality

evaluations of a new product) rapidly among large groups of relevant consumers, which then

affects the new product’s early adoption.

DISCUSSION, IMPLICATIONS, AND RESEARCH OPPORTUNITIES

Summary

This research introduces the concept of MWOM to the marketing literature, extending

previous research on TWOM and EWOM. MWOM differs from these concepts in that it

combines the personal influence element of TWOM with EWOM’s ability to reach very large

audiences, while at the same time drastically increasing the speed of information dissemination.

We argue that MWOM challenges existing business models adopted by producers of experiential

media products, because it reduces the information asymmetry typical for these products, whose

adoption follows an exponential decay pattern and whose initial successes are amplified through

action-based information cascades.

We developed a conceptual model of this “Twitter effect,” including boundary conditions in

the form of moderator effects, and tested the proposed effect using all MWOM messages sent

through Twitter during the opening weekends of 105 movies widely released in North American

theaters between October 2009 and 2010. Our findings support the existence of the “Twitter

effect,” the intensity of which varies with a movie’s target group, such that it is weaker for

family films. Our follow-up analysis with a matched sample of 105 movies released before

MWOM became a mass phenomenon (i.e., between 1998 and 2006) suggests that the “Twitter

effect” is indeed the result of the quick spread of consumers’ post-purchase quality evaluations

of these movies, as enabled by MWOM.

28

Managerial Implications

These findings have substantial implications for marketing managers, particularly those who

are responsible for the success of experiential media products. Most significantly, our findings

demonstrate that the information asymmetry between producers and consumers which

traditionally exists at the release of a major experiential media product is indeed reduced by the

availability of MWOM. We provide evidence that for motion pictures, MWOM helps spread

evaluative post-purchase quality opinions about experiential media products so quickly and

widely that it significantly affects subsequent adoption behaviors already on the next day.

As a result of the rise of MWOM, managers responsible for such products have less of a

“buffer” to insulate them from consumer opinion. Consider the disappointing opening weekend

box office results for the movie Brüno, whose sales plummeted 40% from Friday to Saturday and

lost even more momentum going into Sunday, supposedly due to negative MWOM spread on

Twitter (Van Grove 2009). The reduced information asymmetry between producers and

consumers offers both a chance and a threat to producers; it is mainly a threat to those products

that consumers perceive as low in quality. Our findings thus should motivate producers to

increase their focus on developing high-quality new products that really meet consumer needs,

and then marketing them in a way that truthfully reflects their quality. Such products will benefit

from reduced information asymmetry, because MWOM spreads good news about their quality

quickly among networked consumers.

But the “Twitter effect” also carries more fundamental implications. Because experiential

media product quality results from a complex creative process, producing only high-quality

products is virtually impossible. The “blockbuster” business model that dominates these

industries requires information asymmetry at and shortly after the new product’s release, so that

producers can redeem investments in products although they have turned out to be creative

29

failures. Actually, (movie) producers have systematically transformed their distribution and

production pattern, from one that builds on word-of-mouth communication to one that relies on

mass advertising and mass distribution and exploits information asymmetries (for a review of

this transformation process, see PBS 2001), and other experiential media industries have

followed suit. Because post-purchase, evaluative word of mouth could not spread quickly and

widely enough before the advent of MWOM, this “blockbuster business model” virtually

guaranteed release success, at least for products that were deemed interesting enough to

stimulate strong buzz, mainly in response to heavy advertising. To address the existence of

MWOM and its impact on early adoption, producers will need to adjust their business model to

the changed environment. For example, they might return to a more word-of-mouth dependent

business model, though such an option conflicts with the high budgets allocated to the

production and marketing of blockbuster titles. No company can afford to invest $250 million in

a movie and only show it on very few screens (and write off the investment if TWOM, EWOM,

and MWOM are negative). Such a business model would thus also imply the descent of big

budgets and blockbusters.

An alternative reaction, consistent with the quality imperative, would be to retain the current

focus on brand names and franchises but base decisions about which brands to turn into a movie

more on consumer-perceived quality. This strategy would acknowledge the higher risk of

producing experiential media products in the MWOM era but also allow producers to invest in

blockbusters (and enjoy the advantages of such products in a globalized world).

Finally, producers might try to influence MWOM directly through MWOM marketing or

social media efforts, such as the recommender programs on Facebook or Twitter, which could

might attenuate negative messages shared by other consumers. Although such a strategy seems

30

currently favored by media industries (Singh 2009), it remains unclear whether industry-

produced information can actually influence MWOM valence (compared with buzz and MWOM

volume). It also is imperative for experiential media product managers to devise a

communications strategy that monitors and tries to carefully steer MWOM communication

during the opening weekend, especially on the release day (Moviemarketingmadness 2009).

Such a communications strategy is particularly important for movies targeted at teenagers, who

are, as we find, comparatively more influenced by MWOM messages than are family audiences.

Research Implications

For researchers interested in word-of-mouth communication, it is important to recognize

MWOM as a distinct type of word-of-mouth that is characterized by the unique combination of

TWOM’s immediacy and personal influence and EWOM’s potential to reach large audiences. As

we have demonstrated, the resulting speed of information dissemination has a profound impact

on the adoption of certain products within as little as 24 hours. We therefore recommend

considering MWOM separately to fully understand word-of-mouth influences on product

adoption. How does MWOM interact with other types of word of mouth, such as TWOM and

EWOM, and how and to what extent do they all converge to produce a consensus judgment?

Very limited research has yet focused on the differences and similarities between the different

types of word-of-mouth communication. Such studies would be welcomed to better understand

the different types in general and their respective roles for consumer decision making in

particular (e.g., Berger 2011).

Previous research studying the impact of EWOM on product adoption suggests that especially

“buzz” (often equated with anticipatory, pre-purchase EWOM) influences adoption (Asur and

Huberman 2010; Liu 2006). But MWOM changes these dynamics, because post-purchase

evaluations can be disseminated very quickly and thus affect a product’s lifecycle much earlier

31

than has been previously possible. The focus of studies on personal influence therefore should

shift back from volume to valence in the MWOM era.

Our empirical research focuses on movies and Twitter, but it would be interesting to replicate

our results by studying the impact of MWOM spread through other channels, such as Facebook

status updates, on other experiential media products, such as computer games or books. We

provide arguments that the “Twitter effect” is not industry specific, but further research that

investigates the role of product context for this effect would be desirable.

Furthermore, this research adds to a stream of studies that employ secondary, aggregate-level

data, and we know as yet little about the effect of MWOM messages on individual consumers.

We infer such effects from the adoption behavior of the target audience, but we do not study

teenagers’ or families’ decision processes directly. Focusing on individual consumers’

motivations might also help reveal why there were more positive than negative MWOM

messages overall in our sample.

Whereas existing research has established a negativity bias in consumers, meaning that

consumers are more strongly influenced by negative rather than positive word-of-mouth

messages, recent research points to the possibility that temporal contiguity cues mitigate this

bias. For example, Chen and Lurie (2011) find that EWOM messages that refer to recent

consumption experiences are perceived as less useful. Because MWOM is characterized by its

immediacy, such message framing effects may be insightful and should be explored further.

CONCLUSION

This research introduces MWOM to the marketing literature as a new type of word-mouth

communication. Combining characteristics of TWOM and EWOM, MWOM influences

consumers’ subsequent early adoption of new movies by enabling consumers to spread their

32

post-purchase quality perceptions on large scale and very fast. This “Twitter effect” threatens

existing business models for experiential media and other industries, because it increases the

relevance of product quality for economic success and shrinks the window in which consumers

will adopt a new product without being able to rely on other consumers’ quality judgments.

33

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FIGURE 1 Types of Word of Mouth and Their Characteristics

TRADITIONAL WORD OF MOUTH (TWOM)

• Receiver is an individual person or a small group

• Real-time transmission• Personal influence

ELECTRONICWORD OF MOUTH (EWOM)

• Receiver is a potentially large group

• Asynchronous transmission• Anonymous

MICROBLOGGINGWORD OF MOUTH (MWOM)

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FIGURE 2 Conceptual Model

Subsequent early product

adoption

MWOM valence(immediately after

release)H1

Customer segment susceptibility to

MWOM

H3

MWOM volume (immediately after

release)

H2

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FIGURE 3 Distribution of Tweets throughout the Opening Weekend

Notes: All time data refer to Eastern Daylight Time.

0

10000

20000

30000

40000

50000

60000

70000Number of tweets per hour

Day/Time

Anticipatory, pre-consumption tweets

Evaluative, post-consumption tweets

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TABLE 1 Variable Operationalizations

Variable Label Operationalization Data Source

Revenues on release day REV_REL North American box office revenues generated on Friday, in USD Boxofficemojo.com

Revenues on subsequent days of the opening weekend

REV_SUB Sum of North American box office revenues generated on Saturday and Sunday, in USD Boxofficemojo.com

MWOM valence MWOMVAL Quotient of positive/negative evaluative, post-purchase MWOM messages for a movie sent through Twitter between Friday, 2:00 p.m., and Saturday, 12:00 p.m. Eastern Daylight Time, classification based on sentiment analysis

Twitter.com API

MWOM volume MWOMVOL Number of evaluative, post-purchase MWOM messages for a movie sent through Twitter between Friday, 2:00 p.m., and Saturday, 12:00 p.m. Eastern Daylight Time

Twitter.com API

Family-targeted movie AUDFAM Main audience of movie is family (= 1, 0 otherwise) Jinni.com, own coding

Teenager-targeted movie AUDTEENS Main audience of movie is teenagers (= 1, 0 otherwise) Jinni.com, own coding

Pre-release movie buzz BUZZ Inverted rank in the Movie-Meter on IMDb at a movie’s release IMDb.com

Sequel SEQUEL Movie is a sequel (= 1, 0 otherwise) IMDb.com

Critics rating CRITRAT Average rating of a movie by up to 40 professional critics, weighted according to the influence of the experts as expressed by the Metascore (scale ranges from 1 to 10)

Metacritic.com

Starpower STAR Movie contains a major star (= 1, 0 otherwise) Quigley Publishing

Pre-release advertising spending

AD Advertising spending for a movie before its release, in USD Kantar Media

Production budget BUDGET Production budget of a movie, in USD (inflation corrected) IMDb/ Boxofficemojo

Ordinary evaluation ORDEVAL Average quality rating of a movie by users of Netflix (scale ranges from 1 to 5) Netflix.com

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TABLE 2 Correlations and Descriptive Statistics

Mean SD 1 2 3 4 5 6 7 8 9 10 11

1 REV_SUB 16.171 .852 1.000

2 REV_REL 15.575 .899 .973 1.000

3 MWOMVAL 2.229 .571 .173 .139 1.000

4 MWOMVOL 7.587 1.446 .710 .787 .211 1.000

5 AUDFAM .180 .387 .148 .023 .105 -.204 1.000

6 AUDTEENS .530 .501 .082 .173 -.050 .234 -.502 1.000

7 BUDGET 3.696 .897 .676 .582 .076 .326 .236 -.010 1.000

8 CRITRAT 1.539 .319 .355 .342 .370 .369 .030 -.177 .258 1.000

9 BUZZ .529 .145 .671 .711 .061 .709 -.152 .194 .511 .291 1.000

10 STAR .290 .454 .224 .184 -.061 .031 -.133 -.127 .319 .164 .125 1.000

11 AD 9.770 .668 .442 .377 .001 .197 .103 -.044 .584 .184 .305 .309 1.000

12 SEQUEL .110 .320 .256 .310 .013 .173 .142 .036 .138 .040 .198 -.095 -.198 Notes: All variables (except dummies) are log-transformed, as used in the estimation process.

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TABLE 3 Estimation Results (Seemingly Unrelated Regression)

Coef. Std. Err. z

DV = REV_SUB

REV_REL .944 .0274 34.41**

MWOMVAL .080 .0315 2.55*

MWOMVOL -.036 .0156 -2.34*

AUDFAM .292 .059 4.98**

AUDTEENS -.040 .050 -.80

BUDGET .115 .021 5.58**

CRITRAT .004 .044 .10

BUZZ -.116 .133 -.87

STAR .024 .030 .79

AD -.011 .023 -.46

SEQUEL -.183 .042 -4.41**

MWOMVAL×MWOMVOL .018 .011 1.62

MWOMVAL×AUDFAM -.009 .003 -2.62**

MWOMVAL×AUDTEENS -.000 .004 -.05

Constant 1.098 .381 2.89**

RMSE = .112; R2 = .98; Chi2 = 5763.12

DV = REV_REL

BUDGET .186 .087 2.14*

CRITRAT .317 .1798 1.76

BUZZ 3.113 .451 6.91**

SEQUEL .588 .186 3.17**

STAR .046 .129 .36

AD .176 .107 1.64

Constant 10.957 .923 11.87**

RMSE = .552; R2 = .62; Chi2 = 172.21

Notes: RMSE = root mean standardized error; ** p < .01, * p < .05

45

TABLE 4

Probit Regression Results DV = MWOM period dummy Coef. Std. Err. z

BUDGET .001 .002 .33

AD .000 .000 1.23

BUZZ -1.915 .533 -3.59**

CRTIRAT -.1207 .043 -2.83**

ORDEVAL 1.014 .107 9.47**

SEQUEL -.060 .195 -.31

STAR .043 .143 .30

AUDFAM .091 .184 .49

AUDTEENS .564 .136 4.14**

Constant -8.259 .700 -11.80**

Likelihood regression Chi2(9) = 155.11; Prob > Chi2 < .00; Pseudo R2 = .22; Log-likelihood = –278.685.

Notes: ** p < .01, * p < .05

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TABLE 5 Comparison of Sample Differences Before and After Matching

Variable Mean t-Test

Sample Treated Control %bias %reduct |bias| t BUDGET Unmatched 58.903 50.896 16.9 1.97* Matched 58.903 60.985 -4.4 74.0 -.31 AD Unmatched 20,230 17,506 33.9 3.52** Matched 20,230 21,519 -16.0 52.7 -1.17 BUZZ Unmatched .067 .127 -32.5 -2.84** Matched .067 .099 -17.5 46.1 -1.38 CRITRAT Unmatched 4.895 4.680 13.0 1.20 Matched 4.895 5.125 -13.9 -7.0 -1.06 ORDEVAL Unmatched 7.421 6.698 95.7 9.52** Matched 7.421 7.360 8.1 91.6 .65 SEQUEL Unmatched .114 .116 -.5 -.05 Matched .114 .048 20.8 -439.6 1.78 STAR Unmatched .286 .282 .9 .09 Matched .286 .2952 -2.1 -135.9 -.15 AUDFAM Unmatched .181 .138 11.8 1.22 Matched .181 .133 13.0 -1.0 .95 AUDTEENS Unmatched .533 .369 33.4 3.32** Matched .533 .562 -5.8 82.6 -.41

Notes: ** p < .01, * p < .05

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TABLE 6 Seemingly Unrelated Regression Results for MWOM Period Sample and Matched Pre-MWOM Period Samples

MWOM Period Sample Pre-MWOM Period Sample Coef. Std. Err. z Coef. Std. Err. z P>|z| DV = REV_SUB REV_REL .868 .020 43.38** .968 .034 28.4** .000 AUDFAM .200 .042 4.80** .203 .059 3.43** .001 AUDTEENS .036 .031 -1.16 -.056 .045 -1.24 .214 BUDGET .112 .021 5.39** .0485 .039 1.26 .209 CRITRAT .022 .044 -.50 .090 .059 1.52 .127 BUZZ .118 .184 -.64 .307 .1912 1.61 .107 SEQUEL .157 .044 -3.59** -.058 .083 -.69 .488 STAR .048 .030 1.57 .146 .044 3.31** .001 AD .002 .024 .07 -.165 .072 -2.28* .022 ORDEVAL .326 .129 2.52* -.002 .281 -.01 .994 Constant 1.646 .364 4.53** 2.482 .701 3.54** .000 RMSE = .122; R2 = .98; Chi2= 4943.98 RMSE = .176; R2 = .95; Chi2 = 2198.99 DV = REV_REL

BUDGET .283 .096 2.95** .236 .109 2.17* .030 CRITRAT .444 .200 2.22* .177 .157 1.13 .259 BUZZ 3.447 .854 4.04** 1.175 .569 2.06* .039 SEQUEL .634 .209 3.03** .489 .255 1.92 .055 STAR .024 .145 .16 -.164 .135 -1.21 .225 AD .209 .120 1.74 1.070 .199 5.38** .000 Constant 10.181 1.057 9.63** 3.073 1.764 1.74 .081 RMSE = .620; R2 = .52; Chi2 = 113.87 RMSE = .548; R2 = .53; Chi2 = 120.57 Notes: RMSE = root mean standardized error; ** p < .01, * p < .05

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APPENDIX

Movie Titles (MWOM Sample)

TITLE

RELEASE DATE

2012 13-Nov-09A Christmas Carol 6-Nov-09Alice In Wonderland 5-Mar-10Alpha and Omega 17-Sep-10Amelia 23-Oct-09Armored 4-Dec-09Astro Boy 23-Oct-09Avatar 18-Dec-09Brooklyn's Finest 5-Mar-10Case 39 1-Oct-10Cats & Dogs: The Revenge of Kitty Galore 30-Jul-10Charlie St. Cloud 30-Jul-10Cirque du Freak: The Vampire's Assistant 23-Jan-09Clash of the Titans 2-Apr-10Cop Out 26-Feb-10Couples Retreat 9-Oct-09Date Night 9-Apr-10Daybreakers 8-Jan-10Dear John 5-Feb-10Despicable Me 9-Jul-10Devil 17-Sep-10Diary Of A Wimpy Kid 19-Mar-10Did You Hear About the Morgans? 18-Dec-09Dinner for Schmucks 30-Jul-10Easy A 17-Sep-10Edge of Darkness 29-Jan-10Everybody's Fine 4-Dec-09Extraordinary Measures 22-Jan-10From Paris with Love 5-Feb-10Furry Vengeance 30-Apr-10Get Him to the Greek 4-Jun-10Going the Distance 3-Sep-10Green Zone 12-Mar-10Grown Ups 25-Jun-10Hot Tub Time Machine 26-Mar-10How To Train Your Dragon 26-Mar-10Inception 16-Jul-10Invictus 11-Dec-09Iron Man 2 7-May-10It's Complicated 25-Dec-09Just Wright 14-May-10Leap Year 8-Jan-10Legend of the Guardians: The Owls of Ga'Hoole 24-Sep-10Legion 22-Jan-10Let Me In 1-Oct-10Letters To God 9-Apr-10Letters to Juliet 14-May-10Life As We Know It 8-Oct-10Lottery Ticket 20-Aug-10MacGruber 21-May-10Machete 3-Sep-10Marmaduke 4-Jun-10

TITLE

RELEASE DATE

Nanny McPhee Returns 20-Aug-10New Moon 20-Nov-09Nightmare On Elm Street 30-Apr-10Our Family Wedding 12-Mar-10Percy Jackson & the Olympians: The Lightning Thief 12-Feb-10Piranha 3-D 20-Aug-10Pirate Radio 13-Nov-09Planet 51 20-Nov-09Predators 9-Jul-10Prince of Persia: The Sands of Time 28-May-10Ramona and Beezus 23-Jul-10Remember Me 12-Mar-10Repo Men 19-Mar-10Resident Evil: Afterlife 10-Sep-10Robin Hood 14-May-10Salt 23-Jul-10Saw VI 23-Oct-09Secretariat 8-Oct-10Sherlock Holmes 25-Dec-09She's Out Of My League 12-Mar-10Shrek Forever After 21-May-10Shutter Island 19-Feb-10Step Up 3-D 6-Aug-10Takers 27-Aug-10The A-Team 11-Jun-10The Back-up Plan 23-Apr-10The Blind Side 20-Nov-09The Book of Eli 15-Jan-10The Bounty Hunter 19-Mar-10The Box 6-Nov-09The Crazies 26-Feb-10The Fourth Kind 6-Nov-09The Karate Kid 11-Jun-10The Last Exorcism 27-Aug-10The Losers 23-Apr-10The Lovely Bones 15-Jan-10The Men Who Stare at Goats 6-Nov-09The Other Guys 6-Aug-10The Social Network 1-Oct-10The Spy Next Door 15-Jan-10The Stepfather 16-Oct-09The Switch 20-Aug-10The Town 17-Sep-10The Wolfman 12-Feb-10Tooth Fairy 22-Jan-10Toy Story 3 18-Jun-10Wall Street: Money Never Sleeps 24-Sep-10When in Rome 29-Jan-10Where the Wild Things Are 16-Oct-09Why Did I Get Married Too? 2-Apr-10You Again 24-Sep-10Youth in Revolt

8-Jan-10

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