Pre-release consumer buzz

CONCEPTUAL/THEORETICAL PAPER

Mark B. Houston

& Ann-Kristin Kupfer

& Thorsten Hennig-Thurau

& Martin Spann

Received: 19 May 2017 /Accepted: 14 December 2017 / Published online: 15 January 2018

Academy of Marketing Science 2018

Abstract “Buzz” during the period leading up to commercial

release is commonly cited as a critical success factor for new

products. But what exactly is buzz? Based on an extensive

literature review and findings from a theories-in-use study

(consumer depth interviews and focus groups), the authors

argue that pre-release consumer buzz (PRCB) is not just a

catchword or a synonym for “word of mouth” but is a distinct

construct for which a precise, shared conceptual understand-

ing is notably absent. The authors define PRCB as the aggre-

gation of observable expressions of anticipation by consumers

for a forthcoming new product; they conceptualize the con-

struct as being manifested in three distinct types of behaviors

(communication, search, and participation in experiential ac-

tivities) along two dimensions (amount and pervasiveness).

PRCB is unique because prior to, versus after, a product’s

release, (1) differing information is available, (2) differing

mental processes occur, and (3) consumers’ behaviors have

differing effects on other consumers, affecting diffusion dif-

ferently. A quantitative study using secondary data for 254

new products illustrates the performance of the theory-based

conceptualization.

Keywords Buzz

Theories-in-use

Word of mouth

New

product success

Partial least squares

Secondary data

Communication

Movies

Video games

“Failure to create the right buzz beforehand [i.e., prod-

uct launch] meant less anticipation and ultimately fewer

ticket purchases. [...] Thus, as projecting the right social

media buzz has become more critical, getting that strat-

egy wrong has become even more costly.”

Freedman (2015)

Introduction

For every new product, tangible or intangible, adoption by

consumers is crucial for success, and extensive research has

studied the drivers of new product adoption (e.g., Muller

et al. 2009). Many scholars name the consumer “buzz” leading

up to release (hereafter , pre-release consumer buzz, or PRCB)

as a critical success factor for early adoption of a new product,

and they stress its particular importance for products that have

exponentially decaying lifecycles, such as entertainment, me-

dia, and fashion products (e.g., Karniouchina 201 1a;Xiongand

Bharadwaj 2014;Campbelletal.2017). The popular press also

notes buzz prior to a new product’s release as an important

success driver (e.g., blockbuster movies, Freedman 2015; initial

public offerings of corporate stocks, The Street 2012)—or as-

sociate failure with the lack of such buzz. For example, in

September 2017, journalists predicted soft demand for the

Apple iPhone 8, based on opening-day line length: “But instead

of queues winding down the street there were fewer than 30

people lining up before the store opened on Friday” (Gibbs

2017). Gibbs also noted that mentions of the new iPhone on

Dhruv Grewal served as Area Editor for this article.

* Mark B. Houston

m.b.houston@tcu.edu

Ann-Kristin Kupfer

ann-kristin.kupfer@wiwi.uni-muenster.de

Thorsten Hennig-Thurau

thorsten@hennig-thurau.de

Martin Spann

spann@spann.de

Department of Marketing, Neeley School of Business, Texas

Christian University, TCU Box 298530, Fort Worth, TX 76129, USA

Marketing Center Muenster, University of Muenster,

48143 Muenster, Germany

Institute of Electronic Commerce and Digital Markets,

Ludwig-Maximilians-University Munich, 80539 Munich, Germany

J. of the Acad. Mark. Sci. (2018) 46:338–360

https://doi.org/10.1007/s11747-017-0572-3

Weibo, China’s popular Twitter-style platform, were signifi-

cantly lower than they were for earlier iPhone models.

When the 2015 action movie San Andr eas, which was ini-

tially predicted to flop, eventually generated a profitable $184

million at the domestic box office, this was attributed to the

notably strong and highly visible engagement of fans of the

film’s lead actor on social media; trackin g metrics increased

by 700% in the weeks prior to the film’s release Martin

(2 015). Entertainment experts argue that a successful new prod-

uct launch now ultimately depends on these kinds of pre-release

reactions by consumers, with “buzz [being] stronger than the

studio’s marketing muscle behind it” (D’Alessandro 2015), i.e.,

buzz having its own value. This suggests that marketing

scholars’ ongoing interest in developing a richer understanding

of pre-release consumer responses, to both explain and manage

their outcomes, is well-placed. In response, we focus our paper

on the construct of pre-release consumer buzz (PRCB), which

we, based on the theory-development process laid out in this

article, define as the aggregation of observable expressions of

anticipation by consumers for a forthcoming new product.

Specifically, this article makes four contributions to the study

of consumer buzz, each of which should be useful to schola rs

who work in the domain and to managers who rely on buzz for

the success of their new products. First, we distinguish pre-

release consumer buzz (i. e., PRCB, the buzz that occurs prior

to commercial release of a new product) from other constructs.

This distinction matters because PRCB drives the initial adop-

tions by Innovators that are essential for the eventual diffusion

of new products throughout the market (Bass 1969). Research

often does not systematically distinguish consumer behaviors

that express anticipation for a forthcoming product (which are

the essence of PRCB) from other types of consumer behaviors

that express interest in an already-available product o r share

experiences with it (e.g., word of mouth; recommendations).

We argue that the information embedded in anticipation-based

PRCB behaviors differs from the information that is contained

in consumers’ experience-based, post-release behaviors; it trig-

gers different behaviors, the understanding of which can be

enhanced by separating PRCB from other constructs.

Second, we conduct and report an extensive review of the

academic literature, concluding that scientific progress regard-

ing PRCB is limited by a lack of a shared and precise defini-

tion of the construct. The variety of definitions (and subse-

quent empirical proxies) has left the field somewhat confused.

Some scholars have used variations of the term “buzz” as a

catchphrase (e.g., Wiles and Danielova 2009) without

attempting to define it precisely, and others have used it as a

construct, but employ “buzz” as a synonym for “word of

mouth” (e.g., Campbell et al. 2017). However, a third group

has used it in ways that provide intriguing hints that PRCB is a

much richer construct, but conceptualizations have generally

remained vague and heterogeneous. When studies address the

nature of PRCB, it is referred to, among other uses, as the

amount of interest in a new product (e.g., expressed by search

volume;Hoetal.2009), the contagiousness of a product (e.g.,

via recommendations; Biemans et al. 2010), and also a per-

son’s

probability of knowing about a new product

(Broekhuizen et al. 2011). In sum, we review the extant liter-

ature on the buzz phenomenon, identify key studies regarding

buzz, and synthesize useful insights from them.

For our third contribution, we conduct a theories-in-use in-

vestigation with consumers who participate in PRCB in order

to offer a precise and useful definition and conceptualization of

PRCB that future research can use to build knowledge more

systematically. Further , because managers often track and man-

age buzz, precise definitions have practical utility for guiding

accurate measurement and interventions. Specifically, we con-

duct a theories-in-use investigation of PRCB (i.e., forty depth

intervie ws and three focus groups with consumers) (Zaltman

et al. 1982) and integrate our findings with insights from the

systematic review of the literature regarding buzz into our def-

inition. Consumers’ perspectives are relevant because they are

the parties whose behaviors constitute PRCB and are affected

by it. Beyond the core definition of PRCB as the aggregation of

observable expressions of anticipation by consumers for a

forthcoming new product, we further develop from our inves-

tigation a conceptualiz ation that views PRCB as being mani-

fested in three types of behaviors (communication, search, and

participation in experiential activities) along two dimensions

(amount and pervasiveness across the population).

Fourth, we use our conceptualization to derive implications

for the measurement of PRCB. Specifically, we argue that

PRCB should be measured in ways that capture (1) its multi-

behavioral nature and (2) not only its amount, but its perva-

siveness, i.e., the degree to which the PRCB behaviors are

spread across the population of interest rather than being con-

fined to only a niche of enthusiasts. By adopting a common

definition and by employing richer operationalizations, we

believe that scholarship surrounding PRCB can advance sys-

tematically and that managers can more precisely measure

(and manage) PRCB for their new products. We demonstrate

the value of the implications with an illustrative quantitative

study in which we compile measures of the different PRCB

behaviors (to tap the construct’s multi- beha vio ra l natu re)

across niche and broad channels (to assess the role of perva-

siveness) and connect these measures to the initial commercial

success of 254 wide-release movies. Findings are supportive

of the power of a multi-behavior conceptualization and the

relevance of pervasiveness to both theory and practice.

Contrasting pre-release and post-release contexts

A clear distinction between pre-release and post-release con-

texts is a first step to providing conceptual clarity regarding

what PRCB is. We argue that any conceptualization of PRCB

J. of the Acad. Mark. Sci. (2018) 46:338–360 339

(which occurs prior to a new product’s release) should differ

from constructs that exist after release. We make this assertion

for three reasons: (1) differing information is available to con-

sumers prior to, versus after, a product’s release, (2) differing

mental processes (anticipation-based versus experience-

based) drive consumers’ behaviors prior to, versus after, a

product’s release, and (3) differing effects of consumers’ be-

haviors on other consumers exist prior to, versus after, a prod-

uct’s release (i.e., creating Innovators versus Imitators).

These three reasons demonstrate the uniqueness of the pre-

release period in which no consumers have experienced the

product. To facilitate our discussion, we offer Fig. 1, which

illustrates the different nature and consequences of product-

related consumer buzz behaviors that occur before (i.e., pre-

release region), versus after, a new product’s release (i.e., post-

release region). The top part of the figure illustrates the point

of release and the behaviors of three example consumers. Prior

to release, the period in which PRCB exists, all behaviors are

anticipatory; in the figure, Consumer A engages in PRCB

prior to product release and adopts the new product shortly

after release. After release, two different groups of consumers

will engage in product-related communication and other be-

haviors. One group of consumers, such as Consumer B, who

have already adopted the product, will engage in experience-

based word of mouth and other behaviors afterwards. The

second group is comprised of those consumers (e.g.,

Consumer C) who, despite its availability, have yet to adopt

the product (but who at this point engage in anticipatory post-

release, pre-consumption behaviors that we name post-release

buzz).

PRCB differs from other concepts in terms of psy-

chological and emotional states as well as i ts underlying

motivations. Specifically, different information sources

exist before versus after release: prior to release, the

only types of information available to potential adopters

are signals of quality (inferred from producer and dis-

tributor acti ons), speculations regarding quality shared

by media, critics, and other consumers, and signals of

the social s alien ce of the product provided by PRCB

and by the intensity of media coverage. After release,

these signals still exist, but they b egin to lose their

power, as “true” quality information (Kirmani and Rao

2000) becomes available from (1) the word of mouth of

adopters who offer personal experiences and (2) “suc-

cess-breeds-success” signals from early sales or adoption

results from whic h consumers draw quality inferences

(Elberse and Eliashberg 2003).

Further, prior to consumption, a consumer’s behaviors re-

garding a new product are purely based on anticipation (i.e.,

an individual’s state of felt expectation, visualizing the future

possession and/or consumption of a product), which spurs a

unique set of appraisals, emotions, and decision processes.

After consumption, behaviors are instead determined by

experience with the product, with evaluation-based appraisals,

consumption-driven emotions, and associated responses.

When a consumer cannot consume a product because it is

not yet available, anticipatory responses create persistent long-

ings that cannot be fulfilled—a state of enjoyable discomfort

(Campbell 1987). It is this state that underlies buzz behaviors;

it triggers “emotional, cognitive, self-perception, and social

appraisals”

(Bagozzi et al. 2003,

p. 276). These responses

motivate a consumer’s pursuit of vicarious experiences that

may provide temporary fulfillment (cf. Hirschman 1980).

Anticipation is accompanied by a range of emotions toward

future consumption, which range from hope (MacInnis and de

Mello 2005) to anxiety (Luce 1998) and suspense (with its

mix of hope and fear, cf. Moulard et al. 2012). Intense emo-

tions, and subsequent behavioral responses, are more preva-

lent when the anticipated products are hedonic or experiential

(Hirschman and Holbrook 1982). Because the psychological

processes (e.g., emotions, appraisals, etc.) that comprise an-

ticipation are unique, the nomological network for PRCB, as

an anticipation-based construct, is equally unique.

Third, consumer behaviors that occur pre- vers us post-

release trigger differing consequences in terms of the diffusion

process. Drawing on Bass’ (1969) classic model, PRCB influ-

ences the tendency of Innovators to adopt the forthcoming

product once it becomes available, increasing the model’s

Coefficient of Innovation. In contrast, experience-based infor-

mation shared via word of mouth impacts not the Coefficient

of Innovation, but the Coefficient of Imitation, which has also

been labeled the word-of-mouth parameter. Accordingly, the

segment of consumers that is affected by word of mouth are

Imitators, not Innovators. Some scholars have referred to pre-

release processes in the diffusion context as “shadow diffu-

sion,” in which adoption decisions are essentially made before

a product is available (Peres et al. 2010); PRCB combines

with advertising and other company measures to exert a major

influence on such shadow processes. According to Peres,

Muller, and Mahajan, the concept of shadow diffusion “lacks

thorough treatment” in the literature (2010, p. 103); our work

on PRCB could contribute to a richer development of the idea.

In what follows, we will now explain how we developed a

definition and conceptualization of PRCB, based on extant

literature and an extensive qualitative empirical study. We

then apply the emergent findings in an illustrative quantitative

study that uses movie data.

Conceptualizing and defining pre-release consumer

buzz

Insights from the extant literature on buzz

We conducted an extensive search for scientific articles

that use t he wo rd “buzz” in the context of new product

340 J. of the Acad. Mark. Sci. (2018) 46:338–360

adoption. Specifically, we reviewed all articles in the

leading academic journals across marketing, advertising,

consumer behavior, innovation, and management that

were published between January 1971 and March

Pre-Release Region

Pre-Release Consumer Buzz

(PRCB) Behaviors

Post-Release/Pre-Consumption

Behaviors

Word of Mouth & Other Post-

Consumption Behaviors

Orientation

esab-ecneirepxEdesab-noitapicitnAdesab-noitapicitnA d

Product/Consumer status

Pre-release/Pre-consumption Post-release/Pre-consumption Post-release/Post-consumption

Psychological / emotional states

Anticipation-based interest, hope, longing,

desire for vicarious consumption, expected

quality, excitement

Anticipation-based interest, hope, longing,

desire for vicarious consumption, expected

quality, excitement

Experience-based interest, satisfaction,

dissatisfaction, service/product quality,

perceived value

Motivations for behaviors

Learning, prepare for consumption, social

salience, vicarious innovativeness

Learning, prepare for consumption, social

salience, vicarious innovativeness

Self-relevant expression, other-orientation

(altruism, market mavenism), venting,

social benefits, dissonance reduction,

advice seeking

Size of impact on behaviors of information antecedents:

Impact of quality signals from

producer/distributor actions

elbigilgeNwoLhgiH

Impact of quality speculations

from media and other consumers

who have yet to consume

woLwoLhgiH

Impact of experience-based "true"

quality information from media

and other consumers

muideMhgiHelbaliavatoN

Impact of personal experience hgiHelbaliavatoNelbaliavatoN

Impact of success-breeds-success

signals

muideMhgiHelbaliavatoN

mpact of signals of social

salience (media, other consumers)

muideMhgiHhgiH

Consequences of behaviors

Create initial Innovators/adopters;

influences Coefficient of Innovation at

early stages of the process ("shadow

diffusion")

Potential Imitators may become adopters;

influences Coefficient of Innovation at later

stages of the process

Innovators/adopters influence potential

imitators; influences Coefficient of

Imitation

Sources: Dichter 1966; Engle, Blackwell & Miniard 1993; Hennig-Thurau et al. 2004; Hennig-Thurau, Houston, and Sridhar 2006; Hirschman 1980; Muller, Perez, and

Maha

an 2009; Richins 1983; Sundaram, Mitra, and Webster 1998.

Post-Release Region

Fig. 1 The conceptual uniqueness of pre-release consumer buzz (PRCB)

J. of the Acad. Mark. Sci. (2018) 46:338–360 341

2017.

Many articles mention “buzz” purely as a catchphrase

(e.g., Wiles and Danielova 2009,p.55:“film placements …

can create buzz and top-line consumer demand”), while others

use the term as a synonym for word of mouth (e.g., Liu 2006).

The latter studies usually link the term “buzz” to the volume of

word of mouth for a new product (e.g., Dhar and Chang 2009;

Tang et al. 2014); often these studies focus on online posts by

consumers (e.g., Meenaghan et al. 2013).

Of primary interest to us are studies that treat buzz as a

distinct construct, not a catchphrase or word-of-mouth syno-

nym. Table 1 describes 18 key studies that fall into this cate-

gory. These studies use buzz as a focal construct (e.g.,

Divakaran et al. 2017;XiongandBharadwaj2014), as a part

of their theoretical model (e.g., Biemans et al. 2010;Holbrook

and Addis 2008), or as an important mechanism for new prod-

uct success (e.g., Griskevicius et al. 2009;Okazaki2009).

We do not limit Table 1 to studies that analyze pre-release

consumer buzz, but also include studies regarding buzz that

occurs post-release to broaden our understanding. In line with

our preceding discussion, we explicitly categorize and sort the

articles based on product (pre- versus post-release) and con-

sumer (pr e- versus post-consumption) status. In the table,

PRCB studies are Group 1, post-release, pre-consumption

buzz are Group 2, and post-release, post-consumption behav-

iors are Group 3. In addition, Group 4 contains studies that

intermix the types of behaviors from Groups 1, 2, and/or 3.

The table shows that, among scholars who address buzz as

a unique construct, definitions differ substantially between

studies. Most of these studies assert the importance of buzz

without clearly explicating their conceptual perspective of

buzz, either describing it rather vaguely or using empirical

definitions without discussing the theoretical nature of buzz.

Still, we identified a number of common themes and elements

that can serve as a starting point for our quest for a coherent

conceptualization of the PRCB construct . Specifically,

looking across the studies that treat buzz as a distinct con-

struct, we find the following characteristics to be associated

with buzz.

Forward-looking Severalscholarsstressthatbuzzhasafor-

ward-looking, speculative nature. For example, Xiong and

Bharadwaj (2014) argue that buzz reflects the “interest” of

consumers, “rather than product evaluation” (p. 401),

Divakaran et al. (2017) note that buzz is based on predictions

about “future consumptions” (p.15), and Hennig-Thurau et al.

(20

12) highlight buzz as a signal of interest by people who

have yet to experience a product (see also Ho et al. 2009).

Broekhuizen et al. (2011) consider buzz as the degree of

knowledge people have about a new product’s existence,in-

stead of about its quality.

Although not all scholars restrict buzz to anticipation, we

believe that this forward-looking characteristic is crucial for

distinguishing PRCB from word of mouth and other con-

structs, because word-of-mouth theory is based on the as-

sumption that consumers have personally experienced a prod-

uct. This is illustrated by the major antecedents examined in

word-of-mouth studies (i.e., consumer satisfaction, loyalty,

service quality, trust, perceived value; de Matos and Rossi

2008a). Also, diffusion theory, as noted earlier, views word

of mouth as coming exclusively from Innovators who have

adopted a new product themselves (Muller et al. 2009).

Behaviors Scholars agree that buzz comprises behaviors, but

we find differences across studies regarding the kinds of be-

haviors. Communication is the primary buzz measure, but

other behaviors are also identified. Karniouchina (2011a)adds

search behavior to communication to define buzz

(Karniouchina 2011b and Ho et al. 2009 consider only

searches when measuring buzz). Divakaran et al. (2017), in

addition to comments, tap behaviors that express awareness,

expectations, and adoption intentions. Craig et al. (2015)ar-

gue that buzz for an upcoming movie comprises comments

about its trailer, the number of trailer views, and the percent-

age of people who intend to see the movie in a theater. Further

buzz behaviors named by scholars include viewing online

content (Siefert et al. 2009), sharing music streams through

social media (Dewan and Ramaprasad 2014), and high cita-

tions of journal articles (Biemans et al. 2010). This means that

whereas word-of-mouth theory focuses solely on communica-

tion, buzz has been associated with many different behaviors,

but no agreement exists which behaviors comprise buzz and

whether the different behaviors tap different parts of buzz—or

are substitutes that equally reflect the underlying construct.

Aggregate versus individual level Scholars largely agree that

buzz is an aggregate-level construct comprised of behaviors of

individual consumers. In this vein, several scholars refer to

buzz as the market-level “amount” of some activity

(Holbrook and Addis 2008, p. 87: “of attention”;Hoetal.

2009,p.174:“of interest”), and others use aggregate-level

terms such as “momentum” (Elberse and Eliashberg 2003,p.

331) and “popularity”

(Mizik and Jacobson 20

08, p. 30).

Consistent with this observation, empirical buzz measures

are usually aggregates of some kind and reflect the market-

level nature of buzz, such as Griskevicius et al. 2009,p.391)

use of the words “everyone” and “millions” in their experi-

mental stimuli. Word of mouth, in contrast, is a message-level

phenomenon, although often analyzed in aggregated form.

Our review included: AM J, AMR, Advances in Consumer Research,

Business Horizons, IJRM, Journal of Advertising, Journal of Advertising

Research, JCR, Journal of Cultural Economic s, Journal of Interactive

Marketing, JM, JMR, Journal of Popular Culture, JPIM, JPP&M, Journal

of Retailing, JAMS, Managemen t Science, Marketing Letters, Marketing

Science, MIS Quarterly , Public Relations Quarterly,andQME.

342 J. of the Acad. Mark. Sci. (2018) 46:338–360

Table 1 Key articles on buzz

Authors Definition/ description

of buzz construct

Role of buzz

in article

Operationalization Buzz-related theory

or argument

Data and

methodology

Context and

industry

Buzz-related findings

Group 1: Pre-release consumer buzz

Chen et al. (2017) Buzz can be measured

with pre-release reviews

Buzz as a moderator of

delaying digital

releases of books

Number of pre-print-release

Amazon reviews

•Consumer awareness •Secondary data

•Regression

•Digital distribution

•Books

The decrease in ebook sales

due to a delay is stronger

for

books with less buzz; buzz

predictive of book sales

Craig et al. (2015) Buzz refers to the behaviors of a

multitude of individuals before

product launch such as their

website visits, engagement on

social media, and expressed

purchase intentions

Buzz as a determinant of

movie sales, predictors

of movie buzz

Number of trailer views,

number of pre-release

comments, percentage of

Fandango users indicating

they “can’twait” to

see the upcoming movie

•Consumer awareness •Secondary data

•Regression

•Success predictions,

buzz predictions

•Movies

Buzz is a strong predictor of

movie success over and

above previously known

success factors

Divakaran et al. (2017) Buzz is a single, latent construct

which combines different

pre-release, complementary

consumer behaviors

Buzz as a predictor of

movies’ initial success

Number of members

participating

in pre-release movie

activities, number of

pre-release comments,

average pre-release

rating, percentage of

positive

votes (Fandango)

•W isdom of crowds •Secondary data

•Structural equation

modeling

•Success predictions

•Movies

Buzz is a latent construct

which predicts movies’

initia

l success and

mediates

the effect of generic

predictor variables

Hennig-Thurau et al.

(2012)

(Popular) Buzz is “the level of

anticipation present among

consumers before they see

the film”

Buzz as a contingency

factor

Residual of search behavior

(IMDb MovieMeter) being

regressed on

advertising spending

•Information cascades •Secondary data

•Regression

•Information signals

•Movies

Buzz reduces the influence of

other information cues

(i.e.,

critical evaluations)

Ho et al. (2009)Buzzis“the amount of [pre-launch]

interest in the movie on the

IMDb website”

Buzz as a driver of movie

success

Amount of weekly search

behavior (IMDb

MovieMeter)

•Secondary data

•Regression

•Advertising /

communication

•Movies

Ambiguous results whether or

not buzz has a significant

impact on sales

Xiong and Bharadwaj

(2014)

Buzz is “online blog and forum

postings about products before

their release, i.e., pre-release

buzz, which reflects their interest

in the forthcoming products”

Antecedents and

consequences of

buzz

Online blog and forum

postings

(e.g., MySpace, Blogger);

additional use of search

intensity (Google Trend s)

•Peer influence /

awareness,

exposure,

and quality signal

•Momentum /

bandwagon effects

•Persuasive

argumentation and

cognitive response

theory

•Secondary data

•Functional data

analysis,

regression

•New product

development /

success predictions

•Vi

deo games

Buzz improves sales forecast

accuracy; is reflected in

stock price movements

and

reduces post-release stock

price correction

Group 2: Post-release pre-consumption buzz

Griskevicius et al.

(2009)

Buzz is part of “attitudinal social

proof,” along with excitement,

describing a constellation where

“everybody’s talking about

[a phenomenon]”

Authors test context

effects of how buzz

influences ad

effectiveness

Experimental stimuli: “The

museum that millions are

talking about” and “See

what

everyone is talking about”

•Social proof •Primary data

•ANOVA

•Advertising /

communication

•Museum, city

Romantic desire can lead buzz

appeals to be

counter-persuasive when

used in advertising

Group 3: Post-release post-consumption buzz

Biemans et al. (2010) Buzz consists of multiple facets

and different behaviors: being

highly cited/used, talked about,

recognized by the press,

Buzz as a dimension of

an academic paper that

is regarded as a

“classic”

Six-item scale •Primary data,

secondary data

•Social network

analysis, regression

•New product

development /

network analysis

•Scientific publications

Creating buzz among

academics and

practitioners

J. of the Acad. Mark. Sci. (2018) 46:338–360 343

Table 1 (continued)

Authors Definition/ description

of buzz construct

Role of buzz

in article

Operationalization Buzz-related theory

or argument

Data and

methodology

Context and

industry

Buzz-related findings

recognized as a classic,

recommendedtoothers

is associated with a paper

being regarded as a classic

Dewan and Ramaprasad

(2014)

Buzz includes sharing information

about music through blog

posts and sharing music on

social media

Buzz as determinant of

music sales

(Blog) buzz, as a proxy for

social media buzz, is

equated with volume of

online postings

(GoogleBlogs)

•Social influence

•Free online

consumption

•Secondary data

•Vector autoregression

•Social media research

•Music

(Blog) Buzz has a negative

effect on song sales and

no significant impact on

album sales

Holbrook and Addis (2008) (Popular) Buzz is the “amount

of attention, word of mouth,

or click of mouse,” associated

with the “tendency of …

recommending it”

Determinants of buzz and

effects on market

performance

Volume of media coverage,

word of mouth, or

click-of-mouse; measured

as number of reviews on

several platforms (IMDb,

Yahoo!, Rotten Tomatoes)

•Secondary data

•Regression

•Information signals

•Movies

(Popular) Buzz is driven

by marketing clout; it

positively influences

sales

Siefert et al. (2009)Buzzis“the number of times an

advertisement was commented

on and the number of times it

was viewed online”

Buzz as DVs (split views

and comments as

separate regressions)

Amount of views and

comments of an

advertisement on

MySpace

•Emotional responses •Primary and secondary

data

•Regression

•Advertising /

communication

•Super

Bowl

More emotionally engaging

content leads to more

buzz

Group 4: Overlapping product region and/or consumer status

Broekhuizen et al. (2011) Buzz is defined as the probability

that a consumer knows about

a new movie

Buzz as social influence

on market share

Proxied by pre-release

advertising and post-

release box officesuccess

•Social influence •Primary and secondary

data

•Agent-based model,

simulation

•ANOVA

•Distribution

simulations /

new product

development

•Movies

Buzz as a form of social

influence explains the

dispersion of new movies

Elberse and Eliashberg

(2003)

Buzz is comparable to a momentum

of a product that reflects

consumers pre-release

expectations and that takes the

form of, for example, word of

mouth or media exposure

Buzz as a link between

different special

(movie) markets

Proxied by revenues per

screen in the previous

week

•Success-breeds-

success, cascades,

and bandwagon

effects

•Secondary data

•Regression

•Distribution

patterns /

supply and

demand

•Movies

Buzz for a movie underlies

dynamic patterns, i.e. it

is perishable and fades

over time

Hewett et al. (2016) Consumer-generated buzz emerges

through their “online WOM,

attitudes, and behaviors”

Buzz as a key driver in

the echoverse

Percentage of consumers

who heard or saw

something positive or

negative about the

product

•Secondary data

•Vector autoregression

•Social media

research /

communi-cation

•Financial services

Buzz reverberates and echoes

within the social media

environment

(“echoverse”)

arniouchina (2011a)Buzzis“the consumer excitement,

interest and communication

around a project or a

participating star that is capable

of increasing their visibility

with both moviegoers

and movie industry participants”

Antecedents and

consequences of buzz

Search intensity (IMDb

StarMeter, IMDb

MovieMeter) and word

of mouth intensity

(number of Yahoo! posts

for post-release

movie buzz)

•Quality signal •Secondary data

•Regression

•Social media

research

•Movies

Star buzz drives anticipatory

movie buzz; both types of

buzz impact exhibitor

decisions and positively

affect sales

Karniouchina (2011b) Buzz is characterized by high

visibility

(buzzed celebrities as “highly

visible stars”)

Buzz as a determinant

of movie success in

an analysis of virtual

stock markets

Search intensity (IMDb

StarMeter)

•Information

conspicuousness

•Secondary data

•Regression

•Success predictions /

new product

developments

•Movies

In the case of limited

information, buzz tends

to be overvalued by

observers (i.e., stock

traders)

for success predictions

Mizik and Jacobson (2008) Buzz for a brand exists when

the brand is “gaining in

popularity” among consumers

Part of post-hoc analysis,

used to explain stock

returns of brands

Survey scale asking a

panel of consumers

whether the focal brand

is “gaining in popularity;”

•Secondary data

•Regression

•Brand manage-ment,

market performance

•Financial markets

Buzz is different than

future-oriented brand

energy;

uzz is insignificant in

344 J. of the Acad. Mark. Sci. (2018) 46:338–360

Observability Another buzz characteristic implied by

scholars is that buzz is visible and is perceived by consumers

through social observation (Karniouchina 2011a,

Karniouchina 2011b), whereas word of mouth is exchanged

solely via communication among consumers (either one-to-

one or one-to-many via technology). Such visibility enables

buzz to influence product success by triggering action-based

cascades (e.g., see Hennig-Thurau et al.’s 2012,p.262,argu-

ment that consumers can be interested in a new product “pri-

marily for its buzz;” see also Okazaki 2009). Griskevicius

et al. (2009) that the visibility of buzz offers consumers “atti-

tudinal social proof” of the product’sattractiveness.

Positivity Related to its forward-looking character, buzz is

often seen as being positively valenced, with limited excep-

tions (one example is Hewett et al. 2016). This positivity is

implicit in definitions of buzz as “interest” and its role as

“social proof” for a new product (Griskevicius et al. 2009).

It becomes more explicit in studies describing buzz as “excite-

ment” (Karniouchina 2011a) or linking buzz with a brand’s

popularity (Mizik and Jacobson 2008). Divakaran et al. (2017)

include an “affective expectation-rating” in their buzz measure

that reflects anticipated “enjoyment value” of the future con-

sumption. The positivity of buzz contrasts with word of

mouth , which encompasses the complete range of assess-

ments, from referrals based on positive experiences to warn-

ings based on negative ones, i.e., word-of-mouth valence

(e.g., Liu 2006).

Dynamic Finally, scholars also stress that buzz is dynamic.

Elberse and Eliashberg (2003), for example, refer to its per-

ishable nature (“may quickly f ade ,” p. 351) and Okazaki

(2009)referstobuzz’s “contagious” character. However, it is

not clear whether such dynamics are part of the construct itself

or only describe its development and underlying mechanisms;

Mizik and Jacobson (20

08) highlight the “current-term” ori-

entation of buzz, in contrast to constructs like energy, that are

inherently dynamic. Hewett et al. (2016), furthermore, argue

that buzz plays a key role in the social-media embed ded

echoverse in which it “reverberates” and “echoes.”

In sum, our literature review supports the view that buzz is

more than a catchphrase and is conceptually distinct from

other constructs, such as word of mouth. Moreover, we ex-

tracted a number of characteristics from existing studies which

might be considered as elements, components, or facets of

buzz. However, despite some overlap between studies regard-

ing these characteristics, we also learned that conceptual un-

derstandings of the term “buzz” are not consistent. Scholars

stress different aspects and use different definitions; when

discussing the conceptual nature of the construct, authors of-

ten do not fully elaborate their view of buzz. Few scholars

include an explicit definition, and those who do mostly de-

scribe the empirical operationalizations that they employ but

Table 1 (continued)

Authors Definition/ description

of buzz construct

Role of buzz

in article

Operationalization Buzz-related theory

or argument

Data and

methodology

Context and

industry

Buzz-related findings

buzz is the percentage

of positive answers

stock

return response model if

both

are included

Okazaki (2009)Buzzis“contagious commentary

about products, services, brands,

and ideas” in which consumers

engage “to be a part of their peer

community”

Buzz as a mechanism to

explain consumers’

attitude toward ad

campaigns

None •Consumer

socialization

theory

•Primary data

•Structural equation

model

•Consumer behavior,

advertising/

communica-tion

•Hair care products

Buzz is the reason to share an

experience; buzz is spread

in a structural manner and

can reinforce the

contagious

effect of brand trials

J. of the Acad. Mark. Sci. (2018) 46:338–360 345

do not delineate the nature of the construct. Thus, whereas

research suggests that buzz is a distinct construct, its exact

nature remains unclear, something that applies equally to the

kind of buzz on which we focus in this manuscript, namely

pre-release consumer buzz.

Insights from consumers’ theories-in-use on pre-release

consumer buzz

Given the need for clarity regarding the nature of the PRCB

construct and the divergence in extant literature, we follow the

advice of MacInnis (2011) and turn to consumers, i.e., the actors

whom extant research has identified as crucial for the existence

of PRCB, and whose decisions are impacted by it. Specifically,

we probe consumers’“theories-in-use” regarding PRCB (e.g.,

discovering what a consumer means when stating “there is a lot

of buzz about the latest Avengers sequel”). Zaltman et al. (1982),

p. 118) suggest using a theories-in-use approach to generate

unique insights into a phenomenon compared to traditional de-

ductive approaches to theory building: “If knowledge is the

mapping of experienced reality , an important way of uncovering

knowledge is to learn about the maps that are held by people

with appropriate experiences.” In short, theory is inducted from

the theories (e.g., if–then relationships) held in the minds of

individuals who engage in the phenomenon of interest. The

approach is particularly relevant for substantive phenomena,

having been successfully used in marketing to investigate con-

structs (Tuli et al. 2007) and processes (Bendapudi and Leone

2002). In this research, we uncover theories-in-use regarding

PRCB based on depth interviews with consumers who engage

in the under-theorized phenomenon (MacInnis 2011). We also

run three separate focus groups to test our emerging understand-

ings and to probe the domain of the construct.

Samples of consumers

For the interviews, we sampled consumers likely to have ex-

perienced PRCB (“enthusiasts”) because our goal was to un-

derstand the phenomenon to develop theory, not to estimate

frequency within a population. To enhance generalizability,

we sampled across enthusiasts in one of five different product

categories (automobiles, mobile phones, movies, performing

arts—theater and dance, and video games). These categories

range from hedonic to utilitarian benefits for which consumer

involvement and decision processes differ (Voss et al. 2003).

To discover diverse perspectives (McCracken 1988), both

genders and some variation in ages and geographic regions

within the U.S. were represented in each category. A national

research firm screened potential participants by product and

demographic criteria; of 44 who qualified, 40 (91%) agreed to

participate (sample details in Appendix). The depth interviews

ranged from 20 to 75 min (average: 50); each participant re-

ceived $50. For the focus groups, we sampled adult con-

sumers of both genders and various ages, regardless of prod-

uct enthusiasm. Participants were MBA students (n =15,n =

16 and n = 8, respectively; ages 22 to 55) from an urban U.S.

university and employed in different industries. Focus groups

lasted about 60 min each.

Data collection and analysis

We structured the depth intervie ws along guidelines from

McCracken (1988) and Thompson et al. (1989). The inter-

viewer’s role was reflective, asking generally-worded ques-

tions to avoid leading the participant and then, in the flow of

conversation, using the participant’s phrasing to articula te

probes to uncover personal meanings. We began each inter-

view with a “grand tour” question to orient the participant and

build rapport, asking if there was a recently released product in

the participant’s category of enthusiasm that created “alotof

buzz,” without defining or explaining the expression. We then

asked participants to describe activities or behaviors that they

observed that, in their view, characterized buzz for a new

product. If a participant did not offer an explicit definition in

his or her response, the interviewer asked him or her to explain

what it meant to say that there is buzz for a new product.

To understand the context in which PRCB exists and to

unpack the dual role of consumers (as receivers of and con-

tributors to PRCB), we then asked the participant to think of a

recent new product with “strong buzz” to which he or she

personally contributed, where he or she first learned of the

product, how interest was stimulated, and about specific be-

haviors in which he or she engaged. The focus groups were

conducted after the interviews so that we could probe emer-

gent findings and explore the boundaries of the domain of

PRCB with non-enthusiasts. To not interfere with consumers’

theories-in-use, we did not set any upfront restrictions that

limited answers to pre-release occurrences, but guided the

participants accordingly in those cases where he or she was

referring to pre-consumption actions that occurred after prod-

uct release. The clear majority of responses, regardless, were

about pre-release phenomena, in line with our focus.

Interviews were recorded and transcribed; detailed notes

were taken during focus groups. The authors reviewed these

records, discussed emerging ideas, and developed an under-

standing of PRCB. Analyses were iterative, going back and

forth between reading transcripts and evaluating conclusions;

we made modifications via induction (Thompson 1997). To

Movies, video games, and performing arts represent hedonic product cate-

gories that mainly provide distraction, entertain ment, and social benefits

(Hirschman and Holbrook 1982). Automobiles and mobile phones are more

utilitarian, providing functional benefits of transportation and communication;

certain cars and phones also provide hedonic benefits (e.g., driving pleasure).

Hirschman and Holbrook (1982) observe that experiences differ when con-

suming hedonic products in the fine arts realm versus from popular culture;

thus, we included performing arts.

346 J. of the Acad. Mark. Sci. (2018) 46:338–360

evaluate method rigor and shed light on whether the findings

were open to alternative interpretations, we followed Tuli et al.

(2007) and gave two independent judges—not authors—the

transcripts and (1) the list of specific PRCB behaviors we

identified and (2) descriptions of the three types of behaviors

(communication, search, and participation) into which we cat-

egorized the specific behaviors (e.g., doing a The Dark Knight

scavenger hunt counted as a participatory behavior). We asked

the judges to read each transcript, document any new types of

behavior they believed necessary to capture the data, and cat-

egorize specific behaviors into the behavior types. The judges

identified no new insights; inter-judge reliability was high

(agreement between the two judges and the authors’ original

coding was 100% on the presence of specific behaviors and

96% on the classification of behaviors into types).

Disagreements were resolved by discussion.

Results: consumers’ view on pre-release consumer buzz

Across the interviews and focus groups, participants consis-

tently treated PRCB as a phenomenon that is anticipatory in

nature, i.e., involves expressions of anticipation for a new

product. It also became clear from the data that PRCB is an

aggregate-level phenomenon that involves two dimensions,

namely the amount of observable PRCB behaviors (i.e., their

quantity), as well as the behaviors’ dispersion across consum-

er segments in society, which we ter m “pervasiveness.”

Further, participants described these dimensions of amount

and pervasiveness of PRCB as being manifested across three

types of observable behaviors: communication, search, and

participation in experiential activities.

Insights into the anticipatory nature of PRCB Anticipation

was a fundamental element underlying almost all participants’

descriptions of PRCB. This anticipatory nature combined two

aspects: PRCB was perceived as forward-looking (for some-

thing yet to happen versus something that has happened) and

that, except in limited circumstances, it involves positive (or

hope-tinged) emotions (versus purely negative emotions). For

example, Uriel stated that PRCB means that she and other

people “can’twaittoplay” the console game Marvel

Ultimate Alliance; theater buff Pat B said that PRCB exists

when people are “obviously excited about that [a new play is]

coming,” Similarly, Pamela referred to the announcement of a

new Star Trek movie when asked about a situation with

“strong buzz,” and stated that this gave her and her movie

friends “chills.”

Dustin, a car enthusiast, linked anticipatory interest explic-

itly to PRCB when stating that PRCB means “different peo-

ple’s anticipation of a new car coming out.” Brenda, highly

engaged in the smartphone catego ry, even compared the

PRCB for a new model of the Apple iPhone with the

anticipatory longing and preparation that women experience

in pregnancy:

Brenda: Well I mean all I could compare [the buzz for

the iPhone] to being pregnant and waiting for the baby

to come. When I was pregnant I would go on websites to

read about what other women had gone through at that

stage of the pregnancy and it’s like okay the countdown

begins, ten more days until the phone comes out, that

was like something on Facebook.

Although generally associated with positive anticipatory

emotions, some participants mentioned that PRCB can, under

certain circumstances, also involve negatively-valenced feel-

ings, such as when consumers are anxious that an anticipated

new product might disappoint. Julie and John recalled that

they were among many T

erminator fans who discussed

worries that a sequel film might not live up to expectations.

Similarly, Jim G described a mix of desire and fear for a new

Chevrolet Camaro model—hoping it would be “authentic,”

but being concerned that it would not be. Note that partici-

pants did not link PRCB to negative affect in isolation—neg-

ative feelings, when mentioned, were always mixed with pos-

itive anticipatory interest in a product.

Observable product-related behaviors Participants revealed

that PRCB encompasses three different types of anticipatory

consumer behaviors that can be observed in the marketplace:

communication, search, and participation in experiential

activities.

It became evident that the communication about a new

product is an essential element of PRCB. Specifically, we find

that the consumer communication that our participants ob-

serve is salient for their perception of PRCB. Jayme, a video

game enthusiast, illustrates this:

Interviewer: What is happening that to you indicates that

there is “a lot of buzz” for a new game?

Jayme: A lot of people talk about buying it and getting

into it. In person, online, over the internet, via e-mail, or

possible text from friends, on the phone. For it to be

buzz, there’salotoftalk.

Anticipatory communication that our participants see as a

part of PRCB can be “anywhere from the internet to face-to-

face” (Elliot); it includes communication with acquaintances

(e.g., Shawna G’s auto enthusiast club) and with anonymous

consumers (e.g., the majority of participants post on internet

discussion boards). Participants also noted anticipatory com-

ments posted on general (e.g., Yahoo!, Amazon .com) and

J. of the Acad. Mark. Sci. (2018) 46:338–360 347

category-specific websites (e.g., video game enthusiasts note

GameStop.com) as being part of PRCB. Participants also

named quantitative social media indices, particularly

Twitter’s “trending topics” lists, as indicators of PRCB.

However, and consistent with what we learned from the

literature review, communication was clearly not the only be-

havior that participants identified as PRCB. Participants also

considered other consumers’ search activity as an element of

PRCB; it was interpreted as a signal of consumers’ interest in

and anticipation for a forthcoming product. Although search is

often seen as a “private” activity, participants noted that the

internet, in general, and Google, in particular, have turned

search into an action that others can observe. For example,

car-enthusiast Dustin noticed the search intensity of other con-

sumers and perceived it as part of the buzz for a new car:

Dustin:IfyoudoaGooglesearchandyousearchfor

different things, you pull up how many people have

searched for the certain topic and things like that ... a

lot of the websites will track how many people have

searched there. [I]t’sexcitingto“hear” the buzz....

Several participants reported the use of specific search

rankings that reveal the intensity of other consumers’ search

behavior for a product. Rankings mentioned revealing wheth-

er a new product has “a lot of buzz” included the MovieMeter

and StarMeter rankings by the IMDb, results from Google

Trends (or searchenginewatch.com), and the Yahoo! Buzz in-

dex (for categories such as movies, sports, etc.) that list the

most searched stories on a range of topics.

Finally, in addition to observable communication and

search activities by others, participants described consumers’

participation in a broad range of experiential activities as a

behavioral element of PRCB. Such participatory activities

were often constructed and controlled outside of the new prod-

uct firm; these behaviors expressed anticipation for the new

product. As an example of such anticipatory participation,

Julie described how she interpreted a spike in rentals of

Terminator films prior to the release of its newest sequel

(which she read about in a newspaper article weeks before)

as evidence of PRCB for the upcoming movie. Related activ-

ities included watching movie trailers (as reflected by high

view numbers and ranks on YouTube, noted by Fernando)

and the reading of books in preparation for a movie adaption

(as made observable through a book re-entering a bestseller

list, suggested by a focus group participant).

Sample members also mentioned playful and/or social ac-

tivities as participatory behavior. Adam interpreted people’s

involvement in Star Trek quiz games on “random quiz sites”

as PRCB for an upcoming Star Trek movie, and a focus group

member noted that his involvement with stock trading games

for a n upcomin g film on the vi rtual Hollywood Stock

Exchange gave him “a feel for the amount of buzz for it.”

Melisa took part in a The Dark Knight scavenger hunt and

noted the huge crowd of fellow participants as PRCB for an

upcoming Batman movie. Some attended social events which

they closely associated with PRCB, such as James visiting the

Comic-Con convention (an event for comic and science fic-

tion aficionados) and Shawna G participating in a Mustang-

owners’“cruise night” in anticipation of a new model an-

nouncement. Related, what happens within social communi-

ties around a new product also was indicative of PRCB; par-

ticipants mentioned fan clubs (e.g., Melisa for Star Trek)and

Facebook sites.

wo dimensions of PRCB The PRCB behaviors above illus-

trate that the amount of behaviors that consumers observed in

the marketplace was crucial for sensing PRCB. For all three

behaviors, the level of PRCB our participants perceived was a

function of the amount of behaviors they noticed, with more

talk (e.g., Twitter’s “trending topics” lists), more search activ-

ities (e.g., high rankings on IMDb’s MovieMeter), and more

participation (e.g., many movie trailer views on YouTube)

being interpreted as stronger PRCB.

At the same time, it became clear that the amount of behav-

ior was not the sole information that the participants used to

form perceptions of PRCB. Instead, that the pervasiveness of

these behaviors across segments of the population also influ-

enced the participants’ view of PRCB. This second dimension

of PRCB, which reflects its spread or dispersion across con-

sumer segments, was mentioned in the context of each of the

three PRCB behaviors. Specifically, in the case of communica-

tion, strong PRCB was associated with a sense that the antici-

patory communication about a new product was pervasive

across the population and not limited to narrow consumer

groups or channels. In other words, participants considered as

important for PRCB not only how much communication takes

place, but also to what extent this communication permeates

potential audiences and, more broadly, society.

Pervasiveness was reflected by the dispersion of PRCB

across segments of consumers. These segments were often de-

fined in terms of age; for example, Patrick (theater) used the

term “cross-generational” involvement as an element of strong

PRCB. Another salient, but less clearly distinguishable group

boundary that, if exceeded, was interpreted as a signal of strong

PRCB, was experts versus laymen. Pat B, referring to the PRCB

in anticipation of a new play, noted that “everybody was talking

about it. I mean all of my friends, including people who usually

don’t go to the theater.” Deanna (theater enthusiast) and Adam

(movie buff) shared this view, stressing the importance of the

involvement of non-experts for strong PRCB to exist:

Deanna: I have a lot of friends who are like art teachers

and people that would be expected to follow [the theater

landscape]. But when it goes outside that group, the

people that work for the Fringe Festival, people that

348 J. of the Acad. Mark. Sci. (2018) 46:338–360

are, you know, a lot of sort of film and artsy kind of

people. So once it gets outside of that, I think it’sgetting

pretty popular. Then there is a lot of buzz.

Adam: I know [that there is buzz] if … you’ll have

onliners [talking about that sort of thing] — not just

the big ones but you’ll have people with their own

websites producing that sort of thing. So if you google

... [the new Pixar movie] for instance, you scroll down to

the 20

thing that pops up and you’ll start to get to

people on personal websites that really have nothing to

do with Pixar or anything like that, and you’ll see them

talking about the movie.

Pervasiveness was also named by participants in the con-

text of search. For example, some who mentioned Google

Trends noted the “general” (i.e., not limited to certain groups)

character of the search measure, which might be interpreted as

a proxy for PRCB pervasiveness: “Everybody googles, so if

it’s hot there, it has to be hot everywhere” (focus group

participant).

Finally, participants’ sense of PRCB was also influenced

by the pervasiveness of participatory activities. Specifically,

several participants referred to the pervasiveness of participa-

tion across groups (e.g., “not just hard-core gamers,” Nathan).

Others stressed the signaling function of participation disper-

sion across types of activities: Rachel, for example, argued

that high PRCB for movies exists when “[people are playing]

lots of different quiz and trivia games.”

Findings and discussion of the theories-in-use study

Definition and conceptualization of PRCB Based on our

examination of consumers’ theories-in-use, we define PRCB

as the aggregation of observable expressions of anticipation

by consumers for a forthcoming new product. We conceptu-

alize PRCB as being manifested in three types of behaviors:

anticipatory communication, search and participation in expe-

riential activities. These three behavioral manifestations can

each be characterized along two dimensions: their amount and

the degree to which they are pervasive (i.e., diffused widely)

across the population (versus being confined to a niche).

Boundary conditions In the focus groups, we probed for

boundary conditions of our definition and conceptualization

of the PRCB construct. We learned that the understanding of

PRCB does not seem to differ between enthusiasts and “ordi-

nary consumers.” While the non-enthusiast focus group par-

ticipants, in general, offered less rich and differentiated in-

sights on PRCB, they confirmed the findings we gathered

from the interviews with enthusiasts regarding the nature of

buzz, the behaviors, and the dimensions. Moreover, as the

more utilitarian product categories from our interviews

(automobiles and mobile p hones) may also provide con-

sumers with hedonic benefits (e.g., prestige, enjoyment), we

pushed to discover whether PRCB also existed for starkly

utilitarian products. The discussion confirmed that anticipa-

tion was clearly less prominent in utilitarian contexts, indicat-

ing a close link of anticipation to consumers’ desired psycho-

logical and social benefits (versus functional benefits).

However, anticipation can exist for new utilitarian products

if those products promise to solve key consumer problems via

strong functional benefits. One example from a focus group

participant of a utilitarian product that generated strong PRCB

was a new housecleaning product that would “easily dust

ceiling fan blades” due to a novel material and a lightweight

extender that eliminated the use of a ladder.

Integration with insights from extant literature Several of

the findings from our theories-in-use approach are consistent

with ideas regarding the buzz phenomenon that are implied in

extant research. Our conceptualization of PRCB confirms the

important role of anticipatory communication for buzz (in

contrast to experience-based word of mouth). Also, our find-

ing that consumer anticipation is at the core of PRCB supports

those scholars who have treated buzz as a forward-looking

and positive phenomenon. Our findings on PRCB are also in

ne with the aggregate-level perspective and the observable

character of buzz articulated by scholars.

At the same time, our findings provide clarity and depth

regarding PRCB’s conceptual characteristics that can shed light

on existing contradictions between studies. The three types of

anticipatory consumer behaviors, i.e., communication, search,

and participation, are the first detailed and comprehensive ty-

pology of PRCB behaviors; they substantially refine previous

suggestions that buzz might involve multiple behaviors.

Whereas all three behaviors have been mentioned in extant

research, the majority of scholars have operationalized buzz

with a single behavior , and the kind of specific behavior has

also differed between studies. Related, the identification of par-

ticipation in experiential activities as behavioral category helps

to classify the various PRCB behaviors beyond communication

andsearchthatwereassociatedwithbuzzinpreviousstudies

(e.g., sharing music, citing articles, or watching trailers).

Further, our finding that PRCB goes beyond a single di-

mension (i.e., amount), to include a second dimension (i.e.,

pervasiveness), is largely untapped territory. We are the first to

call for its systematic inclusion in operationalizations of

PRCB; a measure of pervasiveness has been included in two

word-of-mouth studies, but the dimension has never been

conceptualized.

The only study that foreshadows our find-

ings on pervasiveness to a certain degree is Biemans et al.

Godes and Mayzlin (2004) have looked at dispersion of word of mouth

across internet discussion groups and Dellarocas et al. (2007) between age

groups.

J. of the Acad. Mark. Sci. (2018) 46:338–360 349

(2010), who argue, for “classic” journal articles, that strong

(post-release) buzz requires corresponding behaviors across

stakeholder groups (e.g., scholars, practitioners, the press)

and across mediums (e.g., journals, conferences, practitioner

meetings). Finally, our interviews suggest that dynamism is a

mechanism in the development of PRCB over time, rather than

being part of the construct itself.

Implications for measurement Our conceptualization of

PRCB raises questions regarding its measurement. The

multi-dime nsional and multi-behavioral nature of the con-

struct suggests that studies that employ single-behavior (e.g.,

communication) and single-dimension (e.g., amount) mea-

sures leave out parts of the construct’sdomain(“[o]mitting

an indicator is omitting part of the construct,” Bollen and

Lennox 1991, p. 308), which could influence empirical results

on the role of PRCB. This is relevant, as the majority of PRCB

research has focused on such a single behavioral element

(communication, e.g., Liu 2006, or search, Ho et al. 2009)

from a single enthusiast-targeted channel (e.g., IMDb) and

has not captured pervasiveness.

Limitations and next steps A limitation of any theories-in-

use study is that the perspectives of consumers are necessarily

self-focused and may not fully appreciate the complex nature

of the focal phenomenon. Thus, it was critical to integrate

theories-in-use findings with insights from extant research in

defining PRCB and describing its character. Further, our ap-

proach could not reveal whether overlap exists between the

PRCB behaviors (i.e., what do scholars or managers miss if

they only monitor a single behavior?). The same applies to

pervasiveness—what difference does it make if PRCB mea-

sures do not account for pervasiveness? We shed initial light

on this question in the next section, using quantitative data

from the film industry.

A quantitative illustration of pre-release consumer buzz

A key insight of our literature review and theories-in-use study

is that PRCB encompasses different types of observable con-

sumer behaviors (communication, search, and participation in

experiential activities) along two dimensions (amount and per-

vasiveness). Significant work remains to establish specific

guidelines for operationalizing these behaviors and dimen-

sions. Nevertheless, to illustrate the utility of our conceptual-

ization of PRCB, we compiled a dataset of 254 new product

launches (movies) featuring different measures of PRCB and

linked them to initial product success. Our goal was to provide

a preliminary demonstration of our conceptualization—an ini-

tial “proof-of-concept”—not to argue that the specific mea-

sures we use are the only, or even the best, measures to use.

We study movies because previous studies have assigned a

critical role to PRCB for initial movie success and different

PRCB measures have been used (e.g., Ho et al. 20 09;

Karniouchina 2011a). We build a partial least squares struc-

tural equation model that includes well-established drivers of

movie success, in addition to the measures of PRCB (see also

Divakaran et al. 2017). Specifically, to provide suggestive

evidence of the potential usefulness of our new multi-behav-

ior/multi-dimension conceptualization, we (1) take an initial

look at the incremental contribution of PRCB within a frame-

work of established movie success factors, and (2) compare

our operationalization to alternative specifications of PRCB.

Data and PRCB measures

Our dataset consists of all 254 movies that received a wide

release in North American movie theaters (i.e., at least 800

theaters) from January 2010 to December 2011. To show that

PRCB can be measured without primary data, we used online

sources to construct example measures for each behavior. To

incorporate pervasiveness, we contrasted sources that reflect

behaviors of a broad cross-section of the population with those

that contain information only about the behaviors of a niche.

The latter measures do not account for the role of PRCB

pervasiveness because they do not contain any information

about whether PRCB exists among large parts of the popula-

tion (only a niche). Our measures that account for pervasive-

ness, in contrast, reflect the extent to which anticipation exists

across consumer groups. For all measures, we include only

behaviors that occur before product release to insure that our

measures consist exclusively of anticipatory activities and are

not confounded by experience-based word of mouth.

Together, the following three m easures illustrate an

operationalization of PRCB that aligns with our conceptualiza-

tion, accounting for the role of pervasiveness. To tap anticipa-

tory communication behaviors across the general population,

we capture the number of tweets about a movie posted on

witter within the week before a movie’s release. With 313

million monthly active users (T witter 2017), it is able to reflect

the pervasiveness of communication across the broad popula-

tion. The search volume for a movie in the week before its

release on Google serves as our broad search measure because

it captures the search activities of a broad swath of the popula-

tion. Google search registers trillions of annual searches

(Google 2014), making it the most pervasive existing measure

for search activities. As our measure for a broad participatory

behavior, we collect the n umber of page likes of the official

movie Facebook page before a movie’s release. This purposive

action is the method for actively joining an official group or

community surrounding a new product. W ith more than 2 bil-

lion monthly active users (Facebook 2017), Facebook captures

consumers’ engagement across a broad swath of segments.

Our alternate measures of PRCB, which do not account for

pervasiveness, draw data from niche sources used only by

product-category enthusiasts. Specifically, we use the number

350 J. of the Acad. Mark. Sci. (2018) 46:338–360

of posts about a movie on the online movie forum JoBlo

(joblo.com) that were made until the day before movie release

as a niche measure of PRCB communication. According to

the website, its network represents the “ultimate social net-

work for movie fans [that] is packed with geeks” (Movie

Fan Central 2017). The 99,698 registered forum members

(JoBlo 2017), so-called “Schmoes,” make this website a use-

ful source for communication behaviors of niche enthusiasts.

As our measure of niche search, we employ the

MovieMeter of the Internet Movie Database (IMDb; see

IMDb.com). This “mecca for movie buffs” (Wise 2013)isa

website to which movie enthusiasts turn when searching for

movie information, and its MovieMeter metric reflects regis-

tered users’ search behavior within IMDb. The metric ranks

movies based on weekly search volumes; we used a movie’s

MovieMeter rank in the week before its release and inverted

the score so that higher values reflect a higher number of

searches (to make results more intuitive).

Finally, to measure niche participatory behavior, we em-

ploy the number of edits made by authorized Wikipedia users

on a movie’s Wikipedia page prior to release. While

Wikipedia itself is certainly used by the broad mass of con-

sumers, contributing to a movie’s entry requires a deep level

of interest and enthusiasm for the movie category and novel

information about the movie in question. In practice, only a

small fragment of the site’s visitors actually write and edit

pages; those so-called “Wikipedians” represent less than 1 %

of users (currently 70,000 active contributors compared to 374

million unique visitors monthly; Wikipedia 2017).

Table 2 describes data sources for all six PRCB measures

(broad/pervasive and niche/not pervasive for each behavior)

and measures for the other constructs of the nomological

network.

Model and methodology

To illustrate the performance of our conceptualization, we

place PRCB in a nomological network of constructs and de-

termine its role through partial least squares structural equa-

tion modeling (see Divakaran et al. 2017 for a similar

approach; see Hair et al. 2012 for general applications in

marketing).

Our nomological network is based on extant re-

search on movie success drivers. We therein link PRCB to the

box office revenues that a movie generates on its first weekend

of release (“initial success”), our focal outcome variable that

corresponds with our choice to include only pre-release be-

haviors and analyze their effect on initial adoption behaviors.

As drivers of movie success, in addition to PRCB, we ac-

count for the actions of the producing studio and the quality of

the film itself, as suggested by extant research (e.g., Hennig-

Thurau et al. 2006). Studio actions provide information signals

to consumers; we specify the variable as a formative construct

that combines the film’s production budget, type of distribution

strategy, age-restriction rating, whether the movie was a sequel,

remake, and/or bestseller adaptation, and the presence of a star

actor/actress. The quality variable reflects critics’ perceptions of

the movie, which is the only quality information available to

consumers prior to consumption. In this model, we link studio

actions, quality, and PRCB to the initial success of the new

product, assigning PRCB a mediating role between the effects

of studio actions and quality on initial success.

We calibrate the network for our full operationalization of

PRCB and compare the results with those for alternative

PRCB operationalizations (e.g., niche measures; single behav-

iors), thereby probing the relevance of the different PRCB

dimensions and behaviors. We follow previous movie re-

search and log-transform the production budget, the PRCB

measures, and box office revenues. We use SmartPLS3 to

estimate our models, with 5000 bootstrapping samples (no

sign change, bias-corrected and accelerated bootstrap, two-

tailed) to assess statistical significance. When comparing al-

ternative PRCB specifications, we focus on the amount of

explained variance of initial success (measured by the adjust-

ed R

), the prediction error (via the Root Mean Square Error

[RMSE], and the Mean Absolute Percentage Error [MAPE]).

Results

Model-free evidence Table 3 lists bivariate correlations be-

tween the PRCB measures. All measures relate significantly

to one another (at p < .01), consistent with our view that they

tap the same construct. At the same time, the correlations are

far from perfect, ranging from .19 (broad participation—

Facebook and niche communication—JoBlo) to .65 (broad

participation—Facebook and broad communication—

Twitter); the measures are not interchangeable substitutes,

but capture different aspects of PRCB. The table also includes

correlations between PRCB measures and the movies’ initial

box office; all six correlate positively with initial success.

Testing the measurement m odel and structural model

Following our conceptualization, we specify the PRCB con-

struct as formative, consisting of all three behaviors with their

amount measured via broadly-used channels that capture per-

vasiveness (i.e., via Twitter, Google, and Facebook measures).

To assess the formative measurement model, we check for (1)

potential multicollinearity concerns, (2) the performance of

indicator weights, and (3) convergent validity (see Hair et al.

2017); each criteria is met; all formative indicators exhibit VIF

values of below 3 and significant outer weights for each indi-

cator (see Fig. 2). Testing convergent validity of a formative

construct with secondary data is difficult due to the lack of a

This methodological choice is also consistent with our interest in the predic-

tive performance of PRCB and its formative specification (Hair et al. 201 1).

J. of the Acad. Mark. Sci. (2018) 46:338–360 351

Table 2 Empirical measures and data sources used in the quantitative analysis

Construct Empirical measure Data source Examples of similar measures

Pre-release Consumer Buzz (PRCB)

PRCB communication (broad) Number of tweets about a movie that were posted on Twitter

within the week before release

Twitter, compiled by BoxOffice Hennig-Thurau et al. (2015): Tweets

PRCB search (broad) Volume of Google searches for a movie in the week before

release. Scores range from 0 to 100 and were normalized,

using the movie with the highest score (= 100) as benchmark

when drawing data for all other movies in the dataset. T o

determine the search volume for a specific movie, its title to

gether with the word “movie” was used; results were limited

to the pre-defined category “Arts & Entertainment ” and

U.S. users

Google, compiled through Google

T rends by us

Xiong and Bharadwaj (2014): Google

T rends

PRCB participatory behavior (broad) The number of Facebook likes of the official movie page on

Facebook before a movie’s release

Facebook, compiled from PageData by us Srinivasan et al. (2015): Facebook likes

PRCB communication (niche) Number of posts about a movie in a thread on JoBlo.com that

were made until the day before release

JoBlo, compiled by us Godes and Mayzlin (2004): Threads on

Usenet

PRCB search (niche) 1 divided by a movie’s MovieMeter rank in the week before

its release

IMDb, compiled and transformedbyus Hoetal.(2009): MovieMeter

PRCB participatory behavior (niche) The number of edits that were made on a movie’s Wikipedia

page by authorized Wikipedi a users before a movie’s release.

We use the “Toolserver” offe re d by W ik i m ed ia to gather the

cumulated number of edits that were made to a specific

movie’s Wikipedia page before movie release

W ikipedia, compiled through the

“Toolserver” (Wikimedia) by us

Mestyán et al. (2013): Wik i pe dia edits

Studio Actions

Budget U.S. dollar amount of production costs in million IMDb, BoxOfficeMojo Chen et al. (2012)

Distribution strategy 1 if the studio followed a general blockbuster release strategy

(i.e., 1 if number of opening theaters in North America is >

sample median); note that the exact number of theaters is

not available to consumers prior to release

BoxOfficeM ojo Dellarocas et al. (2007)

Age restriction Age-restriction rating by the Motion Picture Association of

America; G = 1, PG = 2, PG-13 = 3, R =4

MPAA Clement et al. (2014)

Sequel 1 if a movie was listed as a sequel to a previous movie IMDb Hennig-Thurau et al. (2009)

Remake 1 if a movie was listed as a remake to a previous movie IMDb Bohnenkamp et al. (2015)

Best

seller adaptation 1 if the movie was the adaptation of a book that was listed on

the “USA Today” bestseller list at least once until 3 months

before the movie was released

IMDb, USA Today Bohnenkamp et al. (2015)

Star 1 if a movie contained a star actor or actress that previously

appeared at least once on Quigley’s annual list of the top 10

moneymaking stars

Quigley Publishing Hennig-Thurau et al. (2015)

Quality

Critics Average quality rating of a movie by up to 40 major North

American professional critics

Metacritic Chen et al. (2012)

Initial Success

Opening weekend box office revenues US-$ amount of box office revenues in millions generated

inNorthAmericantheatersontheopeningweekend

BoxOfficeMojo Ho et al. (2009)

The natural logarithm is taken for the PRCB measures, budget, and success

352 J. of the Acad. Mark. Sci. (2018) 46:338–360

“true ” global measure for PRCB; we instead use the pre-

release expectations of consumers about each movie’sfuture

success (from insidekino.com). As PRCB expresses con-

sumers’ anticipation for a new product, it should be linked

with their expectations about how many will adopt the product

once released (i.e., their expectation of the new product’ssuc-

cess). A path coefficient of .70 from PRCB to consumers’ pre-

release success expectations in a separate model suggests con-

vergent validity.

In Figure 2, we report the path coefficients for the model

featuring our theory-based PRCB operationalization. Model

performance is satisfactory, with all VIF values below 3, sig-

nificant paths between all constructs, and an adjusted R

of .70

for initial success. The Stone-Geisser’sQ

of .67 indicates a

strong predictive relevance for the outcome variable. Studio

actions and quality perform in a manner that is consistent with

previous research; both show a positive link with initial suc-

cess, with the effect of studio action being stronger, consistent

with our conceptual discussion of information signals in the

pre-release region and results reported in Hennig-Thurau et al.

(2006), both in terms of significance and relative size.

Regarding PRCB itself, we find that the standardized coef-

ficient linking PRCB and initial success is sizable (b = .49)

and significant (p < .01). Compared to a baseline model that

includes all studio actions and quality measures but no mea-

sure of PRCB, the adjusted R

of initial success strongly in-

creases from .56 (base model) to .70 (PRCB model), or 25%,

thus supporting the assertion that PRCB is an important driver

of new product success. Concerning the predictive relevance

for initial success, we find a large q

of .45, a criterion which

shows that a sizable part of the model’s predictive relevance

can be attributed to PRCB (Hair et al. 2017). PRCB acts as a

partial mediator of the effects of both studio actions and qual-

ity on initial success; both influence PRCB significantly. The

Variance Accounted For (VAF) furthermore indicate s that

39% of the total effect of studio actions (SA) and 39% of the

total effect of quality (Q) on initial success are explained by

PRCB (IndirectEffect

= .27, [.208, .342];

TotalEffect

= .69, [.622, .743]; IndirectEffect

=.08,[.030,

.132]; TotalEffect

= .19, [.115, .275]; bias corrected 95%

confidence intervals reported).

Comparing our PRCB specification with alternative spec-

ifications We next compare our theory-based PRCB measure to

four alternative specifications: a model for each PRCB behavior

alone (communication, search, participatory behaviors) and one

model that contains all three behaviors measured via niche chan-

nels (i.e., not accounting for pervasiveness). Comparisons to the

first three models test the usefulness of a multi-behavioral con-

ceptualization, whereas the fourth sheds light on the role of

PRCB pervasiveness for explaining initial success.

When comparing the single-behavior specifications to our

PRCB model, we find that the theory-based, multi-behavior

PRCB model outperforms all three single-behavior models on

all criteria. Specifically, the explained variance and both mea-

sures of prediction error favor the theory-based PRCB model

over models that feature only communication (ΔR

adj:

=.06;

ΔRMSE = −.05; ΔMAPE = − 1.88), only search (ΔR

adj:

.02; ΔRMSE = −.02; ΔMAPE = −1.80), and only participato-

ry behavior (ΔR

adj:

=.15;ΔRMSE = − .1 3; ΔMAPE =

−7.93). This finding suggests that our multi-behavioral con-

ceptualization is advantageous for explaining and predicting

initial success of new products. The improvements are sizable,

with prediction accuracy measured via MAPE, for example,

showing improvements from 8% (versus communication only

and versus search only) up to 27% (versus participatory be-

havior only).

Finally, when we operationalize PRCB with all three be-

haviors, but measured via niche channels that only tap the

activities of enthusiasts versus the population at large (JoBlo

posts, IMDb searches, Wikipedia edits), we find that this niche

PRCB model also has a weaker performance than our theory-

based PRCB model which accounts for the pervasiveness di-

mension of the construct. Specifically, the explained variance

(ΔR

adj:

= .12) and prediction accuracy (Δ RMSE = −.10;

ΔMAPE = −6.47) of the theory-based PRCB model are clear-

ly superior to the niche model. These findings lend support to

the notion that it is important to not only consider the amount

Out of the different studio action indicators, budget, distribution strategy,

sequel, and bestseller adaptation all have positive significant outer weights to

studio actions; age-restriction, star, and remake do not (see e.g., Clement et al.

2014, Divakaran et al. 2017, Bohnenkamp et al. 2015 for similar results

patterns).

Table 3 Correlations between

PRCB indicators and initial

success

(1) (2) (3) (4) (5) Initial success

Low pervasiveness (1) PRCB communication .45

(2) PRCB search .42 .44

(3) PRCB participatory behavior .52 .50 .63

High pervasiveness (4) PRCB communication .44 .49 .60 .69

(5) PRCB search .37 .40 .52 .54 .64

(6) PRCB participatory behavior .19 .33 .51 .65 .41 .52

The natural logarithm is taken for the PRCB measures and success. All correlations are significant at p < .01

J. of the Acad. Mark. Sci. (2018) 46:338–360 353

of the different PRCB behaviors, but to also take into account

their pervasiveness in the population when measuring PRCB.

Discussion of quantitative analysis

Although our evidence is of an illustrative nature, results sug-

gests that the theory-based multi-behavioral conceptualization

of PRCB outperforms single-behavior specifications in terms

of explained success and prediction accuracy. Also, the far-

from-perfect correlations between the different PRCB behav-

iors suggest that these PRCB behaviors differ conceptually

and are not interchangeable proxies. In other words, anticipa-

tory communication, search, and participatory behavior ex-

plain different facets of the initial success of new products.

The model with all three PRCB behaviors outperforms models

with single-behavior specifications.

Our findings also highlight the crucial role of PRCB per-

vasiveness. Results suggest that niche PRCB behaviors (i.e.,

activities by enthusiasts) provide only incremental informa-

tion when compared to broad PRCB information (i.e., activi-

ties performed across the population). This is an important

insight because niche segments are easier to track for man-

agers and scholars; however, such convenience may come at

the price of limiting one’s abilities to explain or predict the

initial success of new products. Managers should be cautious

about predicting the success potential of a new product from

niche PRCB, but to look for broader PRCB measures that are

able to reflect PRCB pervasiveness (or, on the level of the

individual product, its absence).

Clearly, some new products are targeted to highly-specific

niches, and PRCB beyond the core niche might not be needed.

However, for products targeted at mainstream consumers (as

is the case for the wide-release films in our data set), high

PRCB among niche enthusiasts only (if not shared by the

masses) might mislead managers. Some examples of product

flops that had strong PRCB might be explained by this con-

clusion. For example, for the movie Scott Pilgr im vs. the

World (which received “enormous buzz” at the Comic-Con

fair prior to its release but failed at the box office), Kaye

(2010) observed in hindsight that “only nerds like movies

about nerds.” Observing a comparable pattern for the hyper-

violent comic book adaptation Kick-Ass which did not gener-

ate substantive revenues at the box office, Kaye (2010)argued

that “general audiences” did not “understand the tone” of the

movie—contrary to the enthusiasts at Comic-Con.

Limitations We do not directly measure pervasiveness, but

instead compare measures that tap broad cross sections of con-

sumers to measures that reflect niche segments. Although it

would be appealing to measure PRCB separately for each seg-

ment of the population and then calculate pervasiveness direct-

ly, useful data sources are not available and accessible for every

segment. Next, the time horizons of our measures are not per-

fectly aligned. Whereas tweets and searches can be conducted

repeatedly, a Facebook page can only be liked once, which

might affect our res ult s. Las t, we do not measure off line

Fig. 2 Pre-release consumer buzz (PRCB) as mediator of the effects of studio actions and quality on initial success. Notes: ***significant at p <.01,

**significant at p < .05, *significant at p <.10

354 J. of the Acad. Mark. Sci. (2018) 46:338–360

PRCB. In reality, PRCB is not limited to the internet, but in-

cludes real-world behaviors such as asking tech-savvy friends

at the bar for information about the upcoming iPhone or chat-

ting with strangers on a train about the next Adele album. Using

PRCB behaviors that are traceable online, however , enables us

to measure the amount and approximate the pervasiveness of

the three different behavioral elements needed for generating

insights via empirical models of PRCB. Further , absent strong

evidence to the contrary, we expect that the magnitudes of

online and offline PRCB behaviors are highly correlated.

Integrative discussion and implications

Through an exploration of existing literature, consumers’

theories-in-use regarding PRCB, and a quantitative analysis,

we conclude that PRCB should be treated by managers and

scholars as a distinct construct, consisting of three types of

observable, anticipatory consumer behaviors regarding a

forthcoming new product that are pervasive across the target

population. We provide evidence that our conceptualization is

of theoretical, empirical, and managerial value. Findings show

that a multi-behavior PRCB operationalization can produce

results which differ from those generated with a single

PRCB measure. Whereas all PRCB behaviors significantly

relate to each other (which makes sense because they belong

to a common construct) and also to initial success, each be-

havior also captures unique aspects of PRCB.

While previous studies equate PRCB with search (e.g., Ho

et al. 2009) or communication (e.g., Liu 2006), we argue that

the overarching concept is more encompassing than any single

element. Also, a model that captures the pervasiveness of

PRCB across the population appears to be best suited for

future research. Non-pervasive niche measures provide little

value, in comparison. One exception in which using the niche

measure might actually be preferable could be for products

targeted explicitly to a niche of enthusiasts, rather than to the

population at large. Future research could probe this issue.

We further emphasize that the different data sources used in

this study were solely selected as exemplary measures to il-

lustrate the performance of our PRCB conceptualizations in

our movie-specif ic context. Where as some (e.g., Google

searches, Twitter tweets) will likely apply to all settings, future

studies and practical applications will need to adapt (especial-

ly niche measures) to their respective products and industries,

as enthusiasts’ channels are highly context-dependent.

Managerial implications Our findings are not only valuable

for scholarly research; the discovery of the different behaviors

and dimensions of PRCB suggests actionable insights for

managers. We highlight five. First, for firms not already doing

so, tracking the PRCB for a forthcoming new product pro-

vides predictive insights into the future success of the product.

Although our quantitative analysis was only illustrative, our

findings suggest that existing forecasting models by which

managers predict the initial sales of a new product based on

product characteristics and marketing investments can be im-

proved by the inclusion of PRCB.

Second, measuring PRCB does not require costly primary

data collection. We have illustrated how usable proxies for

PRCB can be constructed from readily-available secondary

data. Careful thought is required to select indicators that cap-

ture desired behaviors and reflect pervasiveness, but success

can be achieved with investments of time and effort instead of

financial resources.

Third, because the two-dimensional/multi-behavioral ap-

proach improves explanations and predictions of initial new

product success, managers should (1) track more than one

PRCB behavior and (2) do so in a pervasive channel in order

to gain a more precise outlook for a new product’smarket

potential. T ime- and budget-pressed managers may be tempted

to track only the most convenient niche indicator of PRCB that

may reflect only a single type of anticipatory behavior of a

narrow group of known enthusiasts, which implies serious lim-

itations. Managers of products targeted to broader audiences are

well-advised to select and track different behaviors in channels

reflecting the activities of a broad swath of the population.

Fourth, attention to pervasiveness could also give man-

agers insights about wheth er a new product has even the

potential to appeal to broader audiences. For example, if high

amounts of PRCB are evident only in niche PRCB sources

(but not in broad sources despite efforts to spark mainstream

PRCB), managers might tailor their marketing campaigns to

these respective niche targets. Investing in consumer segments

that have no interest could be wasting scarce resources.

Finally, consideration of PRCB may have implications for

distribution strategies for new products that may actually con-

tribute to or harness PRCB. For example, Apple’siPhonewas

initially available only to AT&T customers because of a five-

year exclusive-distribution arrangement. Because this restric-

tion was announced well before the iPhone’s launch, the strat-

egy may have created PRCB among competitors’ customers

whose interest in the iPhone would only be heightened by

pseudo-scarcity (to be solved by switching to AT&T). The

temporal exclusivity may also have spurred additional post-

release anticipatory buzz among consumers who were bound

to their current carrier and could not yet adopt the iPhone.

We believe these implications are highly valuable to man-

agers. This assessment is supported by the emergence of firms

and market offerings that aim to monetize these types of in-

sights. For example, BuzzMetrics and Buzzrank are businesses

built on the measurement of buzz. The A

dobe Digital Index

tracks some form of online PRCB to generate pre-release pre-

dictions about which movies will be blockbusters or failures

(Fahey 20 1 5). Advancing scientific knowledge on which

PRCB behaviors to track and across which channels is there-

fore of strong managerial value.

J. of the Acad. Mark. Sci. (2018) 46:338–360 355

Interest in such buzz measurements and predictions is not

limited to research firms or strategy analysts, but is shared by

journalists and consumers. Published rankings, such as “most

buzzed-about movies” that are provided by Fandango and

MTV, among others, are popular. These rankings are shared

widely across consumers’ social networks and can reinforce

emerging waves of positive PRCB for a forthcoming product

(or doom the product, when ignored) before its release. As

buzz can be better measured, it becomes more concrete to

managers and its impact on new product success clearer; thus,

buzz can become more focal in ongoing strategy discussions.

A research agenda for the overall buzz phenomenon

Our conceptualization here lays the foundation for developing a

general theory of buzz. In this manuscript, we focus on a unique

and important type of buzz, but PRCB is only one element

within the broader phenomenon. Our contributions offer a sig-

nificant step forward, but we still need to learn more about how

PRCB is initiated, develops over time, and affects outcomes

beyond initial success. Even less is known regarding post-

release consumer behaviors that extend beyond traditional

experience-based word of mouth. We thus suggest a future

research agenda for (1) PRCB, (2) post-release consumer buzz,

and (3) other types of buzz with the hope to spur new studies

and scientific insights on the overarching buzz phenomenon.

Pre-release consumer buzz

For a comprehensive theory of PRCB, we need to learn more

about its antecedents, processes, and outcomes. With PRCB

as an important success factor, managers crave strategic levers

they can utilize to start and grow PRCB. Scattered insights

exist on potential drivers of some behavioral facets of PRCB

(Karniouchina 2011a;XiongandBharadwaj2014; Craig et al.

2015), and our empirical illustration also shows that studio

actions and product quality can help to generate PRCB;

however, a systematic and comprehensive understanding of

its drivers is missing.

Even less is known by scholars about the dynamics of

PRCB once it is initiated. Xiong and Bharadwaj (2014)pro-

vide a notable exception by tracking the evolution of PRCB

via a functional data analysis, using the identified shapes of

PRCB to predict new product success. Building on this, we

encourage scholars to further investigate how and why PRCB

evolves over time. For example, how do PRCB activities of

enthusiasts and the broad population differ in their evolution

and relate to each other’s dynamics? Why do some PRCB

waves initiated by a confined niche of fanboys grow into a

population-pervasive movement while most do not?

Our results are supportive of a general positive effect of

PRCB on initial sales, a notion that is well accepted among

managers, but it would be of high interest to discover whether

there is also a ‘dark side’ of PRCB. Can extremely high ex-

pectations and overexposure due to strong PRCB backfire? In

a post-hoc analysis, we find a marginally-significant negative

quadratic effect of PRCB on initial sales (PRCB = .49, p <.01;

PRCB_SQR = −.03, p < .10), suggesting a satiation point of

PRCB. Digging deeper into this intriguing finding offers an

exciting avenue for future research, especially when future

studies also include post-release processes.

Post-release consumer buzz

There are important d ifferences between PR CB and post-

release consumer behaviors as PRCB exists when there is no

word of mouth based on prior consumption experience. After

release, some consumers engage in experience-based word of

mouth and other post-release buzz behaviors (e.g., search and

participat ion), but with different motivations; other factors, be-

yond those that drove PRCB, become influential. Resulting

differences can guide future research. Consider valence: we

conclude that PRCB is inherently positive but can be

intermingled wi th negativity (e.g., t he anticipated product

may not live u p to its promise). This n otion fit s wit h

Divakaran et al. (2017) who found that pre-release product

quality “ratings ” did not significantly relate to their PRCB mea-

sure. Perhaps PRCB is not subject to a conventional valence

scale. For post-release consumer buzz, however , experienced-

based evaluations provide

new si

gnals to consumers, as do

bestseller rankings, which should alter the role of buzz valence.

Evolutionary processes will also differ between PRCB and

post-release consumer buzz. For example, Hewett et al. (2016),

p. 18) suggest the notion of the “echoverse,” in which, for

brands already in the marketplace, consumer buzz, news media,

and company communic ations “reverberate and echo.”

Disentangling these different processes, thereby understanding

how a mixture of speculative pre-consumption buzz and eval-

uative word of mouth affect each other and consumer decisions,

would be of major interest for both researchers and managers.

Consumers’ (dis)satisfaction and dissonance may be espe-

cially key for better understanding buzz processes and their

effects on outcomes in the post-release phase. If negative feed-

back enters the market after product release, strong buzz might

speed product death (Mlodinow 2006 suggests a similar

process). But, also, overly-positive excitement might decrease

product interest for some consumers and thus dampen sales, in

line with our earlier argument that a satiation point might exist

when PRCB is too “big.” This idea was colorfully illustrated

by a participant in our qualitative study who broached the

realm of post-release consumer buzz:

Anna: I never went to see NAPOLEON DYNAMITE

because everyone kept telling me “you’ll never expect

it,”“it’s absolutely hilarious,”“you’ll totally love it.” So

Ididn’t watch that movie until two or three years after it

356 J. of the Acad. Mark. Sci. (2018) 46:338–360

came out. I totally did love it, but I just didn’twantto—all

the buzz annoyed me and so I never went to the theater .

Thus, while our study on PRCB focused on initial sales of a

new product directly after launch, future research on post-

release consumer buzz needs to center on long-term success.

Both the bright and the potential dark side of buzz offer excit-

ing avenues for further research.

Further , does having engaged in PRCB behaviors for a new

product also affect consumer per ceptions of the actual consump-

tion experience? Chun et al. (2017) find that when consumers

“savor” an upcoming experience (i.e., engage in elaborated cog-

nitive processes in anticipation), their subsequent enjoyment of

the experience is improved, as is their later “remembered enjoy-

ment.” Various behaviors that comprise PRCB likely would

spur elaborated cognition in the mind of the consumer regarding

the anticipated new product. Thus, the impact of PRCB behav-

iors on individual experiences and evaluations—not just on ag-

gregate product sales—becomes an exciting avenue for future

work at the level of the individual consumer.

Other types of buzz

Lastly, this article is explicitly centered on consumer buzz. There

is large agreement among scholars that it is the activities and

perceptions of consumers that are essential for buzz to exist and

spread. Our qualitative study aligns with extant research that buzz

is ind eed a consumer phenomenon. Some scholars, however,

separate consumer buzz from other kinds of “buzz,” such as

Hewett et al. (2016) who mention consumer buzz next to “social

media buzz” that can be fueled by news coverage; Holbrook and

Addis (2008)speakof“critical buzz.” Are expressions of interest

in a new product by external actors, such as firms and the media,

unique types of buzz or do they simply function as antecedents

that initiate and energize consumer buzz? Investigating these ad-

ditional types of actors is a possible direction for future research.

Conclusion

In summary, PRCB is more than a “buzzword” and deserves

serious attention from marketing scholars and managers; it

should not be equated with (experience-based) word of mouth.

Our research highlights the critical role of consumers’ observ-

able anticipation for something new and points to the consumer

behaviors that constitute PRCB, namely communication,

search, and participatory experiences. Although the volume of

PRCB is important, its pervasiveness, a new dimension, also

matters. Are we only observing the actions of a niche of enthu-

siasts, or is the forthcoming product anticipated across consumer

segments? We offer a research agenda on PRCB and the larger

buzz phenomenon to stimulate and structure future research.

Acknowledgements The authors thank Bernd Skiera for his contribu-

tions to this project and Peter Bloch, Chris Blocker, Markus Giesler,

Marsha Richins, Marko Sarstedt, Srihari Sridhar, Caroline Wiertz, Chris

White, and Eric Yorkston for constructive criticism on earlier versions of

this manuscript. The first author gratefully acknowledges financial sup-

port from the TCU Neeley School of Business Research Grant fund.

Table 4 Sample characteristics

ID Name Product Category Category Enthusiasm Rating

U.S. Geographic Region

Age Range Gender

1 Marcus S. Automobiles 10 Eastern 30–39 Male

2 Barry W. Automobiles 10 Mountain 50–59 Male

3 Dustin O. Automobiles 8 Central 21–29 Male

4 Shawna G. Automobiles 10 Eastern 30–39 Female

5 Jim G. Automobiles 9 Central 50–59 Male

6 Jared T. Mobile Phones 10 Eastern 21–29 Male

7 Michael C. Mobile Phones 10 Eastern 21–29 Male

8 Brenda C. Mobile Phones 8 Central 40–49 Female

9 Gabriella J. Mobile Phones 10 Eastern 30–39 Female

10 Cindy B. Mobile Phones 9 Central 50–59 Female

11 James D. Movies 10 Central 30–39 Male

12 Adam A. Movies 9 Central 21–29 Male

13 Yvonne W. Movies 10 Eastern 40–49 Female

14 Melisa S. Movies 10 Central 21–29 Female

15 Pamela L. Movies 10 Eastern 40–49 Female

16 Lana L. Movies 10 Central 30–39 Female

17 Anna K. Movies 8 Central 30–39 Female

Appendix

J. of the Acad. Mark. Sci. (2018) 46:338–360 357

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