Journal of Interactive Advertising, 13(1), 1–13

Copyright C 2013, American Academy of Advertising

ISSN: 1525-2019 online

DOI: 10.1080/15252019.2013.768048

Why People Pass Along Online Video Advertising: From the

Perspectives of the Interpersonal Communication Motives
Scale and the Theory of Reasoned Action
Joonghwa Lee
Middle Tennessee State University, Murfreesboro, Tenneessee, USA

Chang-Dae Ham
University of Illinois at Urbana-Champaign, Urbana, Illinois, USA

Mikyoung Kim
Cheil Communication Sciences Institute, Cheil Worldwide, Seoul, South Korea

(hereinafter referred to as online video ads) as new vehicles for

This study employs the theory of reasoned action to explore product promotion by generating viral effects. Online video ad-
factors influencing consumer intention to pass along online video vertising, one type of online advertising, provides viewers with
advertisements. Structural equation modeling test results indi- the combined effects of the branding power of traditional TV
cated that attitude toward passing along online video ads and
subjective norm positively influenced intention. Among the six ex-
commercials and the interactive power of the Internet (Brown
pected outcomes (pleasure, affection, inclusion, escape, relaxation, 2008). In fact, U.S. Internet users watched more than 8.3 billion
and control) identified via the Interpersonal Communication Mo- online video ads in March 2012 (comScore 2012). In addition,
tives (ICM) scale, only pleasure and escape had positive impacts online video advertising spending was nearly US$1.42 billion in
on attitude. Finally, normative beliefs had positive influences on 2010, and projected to be US$7.11 billion by 2015 (eMarketer
subjective norm. Implications regarding pass-along behaviors are
2011).
discussed.
One distinctive feature of online video ads is that users may
choose to share them or pass them along from person to per-
Keywords online video advertising, viral marketing, theory of
reasoned action, Interpersonal Communication Motives son via e-mail or social networking sites such as Facebook and
scale Twitter (e.g., by using preset “Send this to a friend” or “Share”
links or buttons). If users want to share with friends an ad they
have watched, merely one click of a button can achieve this.
Since the emergence of online video sharing in the mid- In terms of online video viewing, pass-along behavior has be-
2000s, the number of online video-sharing website users (e.g., come popular. According to one online survey, about 57% of
YouTube users) has grown dramatically. In 2010, about 65% of online video viewers send video clips to acquaintances; in par-
U.S. Internet users watched online videos, and that number is ex- ticular, 42% of young adults (ages 18 to 29) forward videos to
pected to grow to 76% by 2015 (eMarketer 2011). With the rapid others a few times per month or more (Madden 2007). Similar
growth of online video-sharing websites, many advertisers and to online video clips, online video ads can generally be passed
marketers are interested in using online video advertisements along via e-mail or social networking websites. Therefore, the
current study includes pass-along behavior through e-mails or
social networking websites because they are popular interper-
Address correspondence to Joonghwa Lee, School of Journalism, sonal communication tools.
Middle Tennessee State University, P.O. Box 64, Murfreesboro, TN
37132. E-mail: Joonghwa.Lee@mtsu.edu Despite the potential power of online video ads as an emerg-
Joonghwa Lee (PhD, University of Missouri) is assistant professor ing marketing tool, little is known about what factors lead to
in the School of Journalism, Middle Tennessee State University. consumers’ behavior of passing along online video ads. Given
Chang-Dae Ham (PhD, University of Missouri) is assistant profes- that such passing along is a behavior unique to online video
sor in the Charles H. Sandage Department of Advertising, College of ads, the purpose of this study is to examine what outcomes
Media, University of Illinois at Urbana-Champaign.
Mikyoung Kim (PhD, Michigan State University) is senior research consumers expect from this and to determine how to predict
manager at the Cheil Communication Sciences Institute, Cheil World- consumers’ pass-along behaviors. The theory of reasoned ac-
wide. tion (TRA) (Ajzen and Fishbein 1980; Fishbein and Ajzen
1
2 J. LEE ET AL.

1975)—which is useful to predict and understand behavior—is which refers to “a person’s subjective probability that he will
employed as a theoretical framework. perform some behavior” (Fishbein and Ajzen 1975, p. 288).
Findings from the current study will first help advertisers Many previous studies have found strong and positive relation-
understand consumers’ pass-along behavior regarding online ships between behavioral intention and actual behavior; people
video ads, which in turn will help them create better online tend to perform behaviors that they plan to execute (Bagozzi
video ads with the goal of creating a viral effect. In addition, the 1981; Bagozzi, Baumgartner, and Yi 1992; Choo, Chung,
current study provides advertising researchers with insight into and Pysarchik 2004; Conner and Armitage 1998; Oliver and
the comprehensive use of TRA in the context of passing along Bearden 1985; Ryan and Bonfield 1980). For example, Bagozzi,
online video ads. Baumgartner, and Yi (1992) found a significant influence of
intention to use coupons on actual coupon-usage behaviors.
LITERATURE REVIEW Since consumers’ passing along of online video ads is under
consumers’ volitional control, we predict that the stronger the
The Theory of Reasoned Action and Passing Along Online
consumers’ intention to pass along online video ads, the more
Video Ads
frequently they will pass along such ads.
TRA is a frequently used theory for predicting and under-
standing a particular behavior (Ajzen and Fishbein 1980; Choo,
Chung, and Pysarchik 2004; Fishbein and Ajzen 1975; Fitz- Determinants of Behavioral Intention
maurice 2005; Hsu and Lin 2008; Oliver and Bearden 1985; Attitude Toward Passing Along Online Video Ads. A factor
Ryan 1982; Shimp and Kavas 1984; Wu and Liu 2007). The influencing consumers’ behavioral intention is attitude toward
primary aim of the theory is to predict and explain individuals’ the behavior, referring to the degree to which an individual has a
motivational influences on behavior. It deals with both internal favorable or unfavorable evaluation of the given behavior (Ajzen
factors (e.g., consumers’ perceptions of events or objects) and and Fishbein 1980; Fishbein and Ajzen 1975). Several previous
external factors (e.g., social influences) affecting consumers’ studies have shown that attitudes toward a given behavior influ-
intentions to perform a particular behavior (Ajzen and Fishbein ence behavioral intention (Bagozzi et al. 2000; Bock et al. 2005;
1980). According to TRA, an individual’s behavior is guided by Davis, Bagozzi, and Warshaw 1989; Hsu and Lin 2008; Ryan
behavioral intention to perform a behavior in question. Because and Bonfield 1980). To illustrate, Davis, Bagozzi, and Warshaw
the behavior is under the control of intention, TRA focuses (1989) found that when people had a more positive attitude
only on volitional behavior. Further, this behavioral intention toward using computer technology (i.e., word-processing pro-
is a function of two factors: attitude toward the behavior and grams), they showed a stronger intention to use that technology.
subjective norm (Ajzen and Fishbein 1980; Fishbein and Ajzen In the context of online video ads, Lee and Lee (2011) found
1975). Generally, the more favorable the attitude and subjective a positive impact of attitude toward watching online video ads
norm regarding a behavior, the stronger the intention will be to on intention to watch the ads. Thus, it is predicted that the more
perform that behavior (Ajzen and Fishbein 1980; Albarrancı́n favorable the attitude consumers have toward passing along on-
et al. 2001; Fishbein and Ajzen 1975). line video ads, the stronger their intention will be to pass along
TRA suggests that attitudes toward behavior and subjec- the ads.
tive norm are influenced by its own belief structure: behav-
ioral beliefs and normative beliefs (Ajzen and Fishbein 1980; H1. Attitude toward passing along online video ads will have a
Albarrancı́n et al. 2001; Bock et al. 2005; Fishbein and Ajzen positive impact on intention to pass along online video ads.
1975). We expect that TRA will provide useful explanations in
understanding consumers’ pass-along behavior regarding online Subjective Norm. Another factor that influences behavioral
video ads for the following reasons: First, TRA deals not with intention is subjective norm, referring to the perceived social
attitudes toward an object (e.g., attitude toward an online video pressure to perform (or not to perform) a given behavior (Ajzen
ad) but with attitudes toward a behavior (e.g., attitude toward and Fishbein 1980; Fishbein and Ajzen 1975). Prior studies have
passing along online video ads; Hansen 2008). Second, TRA found a positive relationship between subjective norm and be-
explains the impact of subjective norm on behavioral intention. havioral intention—in other words, the stronger the subjective
For example, consumers often decide to pass along online video norm, the stronger the behavioral intention (Hansen, Jensen,
ads because of the opinions of close others who are important and Solgaard 2004; Ryan and Bonfield 1980; Shimp and Kavas
to them. Finally, because TRA explains people’s voluntary be- 1984; Wu and Liu 2007). For instance, Ryan and Bonfield (1980)
haviors, we can examine the effect of consumers’ voluntary demonstrated that family, friends, and neighbors positively in-
behavior in passing along online video ads. fluenced consumers’ purchase decisions. Thus, it is expected
that consumers’ perceived social pressure to pass along online
Components of TRA and Passing Along Online Video Ads video ads will relate positively to their intention to pass along
the ads.
Consumers’ Intentions to Pass Along Online Video Ads
According to Fishbein and Ajzen (1975), a primary predic- H2. Subjective norm will have a positive impact on intention to pass
tor of volitional behavior is an individual’s behavioral intention, along online video ads.
 WHY PEOPLE PASS ALONG ONLINE VIDEO ADVERTISING 3

Although the TRA suggests the independent effects of deter- Rubin, Perse, and Barbato (1988) identified motivations called
minants of behavioral intention on intention, previous research the Interpersonal Communication Motives (ICM) scale. The
has found interdependencies between those determinants (Bock ICM scale was developed based on various interpersonal com-
et al. 2005; Choo, Chung, and Pysarchik 2004; Lim and Du- munication behavior studies, such as categories of interper-
binsky 2005; Ryan 1982). Besides, examining possible interde- sonal behaviors (e.g., Bochner, Kaminski, and Fitzpatrick 1977),
pendencies between attitude toward a behavior and subjective structure of conversation (e.g., Wish, D’Andrade, and Goodnow
norm enriches the model’s predictive power (Oliver and Bearden 1980; Triandis 1977), dimensions of relational communication
1985). Specifically, Bock and colleagues (2005) discovered that (e.g., Burgoon and Hale 1984), communication functions (e.g.,
when perceiving social pressure to share knowledge, consumers Bochner 1984; Dance and Larson 1976), and the perspective of
were inclined to develop a positive attitude toward such sharing. media uses and gratifications (e.g., Katz, Blumler, and Gurevitch
In accordance with these findings, it is predicted that subjective 1974; Rubin 1981).The purpose of the ICM scale is to discover
norm will affect consumers’ attitudes toward passing along on- “why people initiate communication with other people” (Rubin,
line video ads. Perse, and Barbato 1988, p. 603). Because the ICM scale reflects
a variety of interpersonal and mediated communication types, it
H3. Subjective norm will have a positive impact on attitude toward
passing along online video ads.
is appropriate to adopt the ICM scale in the context of passing
along online video ads.
Initially, Rubin, Perse, and Barbato (1988) developed the
Determinants of Attitudinal and Normative Components ICM scale using 59 interpersonal communication motives
Behavioral Beliefs. TRA suggests that the sum of accessi- through actual conversations, diaries of communication behav-
ble behavioral beliefs, in other words, the subjective probability iors, and television motivations. Finally, 28 items were drawn
that the behavior will produce given expected outcomes, in- from the initial items, which identified six motivations for inter-
fluences attitude toward a given behavior (Ajzen and Fishbein personal communication: (1) pleasure, communicating for fun,
1980; Fishbein and Ajzen 1975). Previous studies have found excitement, good times, and entertainment; (2) affection, com-
a significant impact of behavioral beliefs on attitude toward the municating for helping others, caring about others’ feelings,
behavior (Bock et al. 2005; Hsu and Lin 2008; Oliver and Bear- and showing encouragement; (3) inclusion, communicating by
den 1985; Ryan 1982; Shimp and Kavas 1984; Yoh et al. 2003). talking to someone and feeling less lonely; (4) escape, commu-
For example, Oliver and Bearden (1985) found that among three nicating to avoiding pressure and work; (5) relaxation, commu-
behavioral beliefs about consequences of using a nonprescrip- nicating to have pleasant rest and relaxation; and (6) control,
tion drug product (product features, product benefits, product communicating to have power to gain compliance. The most
problems), product features such as fair price and safety and relevant study of motivations for passing along online video
product problems such as side effects positively influenced atti- ads has been done by Phelps and his colleagues (2004). They
tudes toward using it. explored motivations of e-mail pass-along behavior using the
Identifying a set of consumers’ beliefs regarding passing ICM scale. They found motivations of consumers to pass along
along online video ads enables us to investigate the role of e-mails to be similar to those of the ICM scale such as enjoy-
such behavioral beliefs in affecting attitudes toward passing ment, entertainment, and social connection. In addition to social
along online video ads. To examine behavioral beliefs regard- networking sites, because online video ads are passed along via
ing pass-along behavior, we need to identify the outcomes that e-mail, motivations for e-mail pass-along behavior offer insights
consumers expect from passing along online video ads. Be- into discerning the expected outcomes of passing along online
cause pass-along behavior of online video ads occurs via e-mail video ads. While Phelps and colleagues (2004) simply found
or social networking sites, which are channels of interpersonal and described motivations for passing along e-mail, the current
communication, it is necessary to understand the motivation study seeks to investigate not only motivations but also the re-
of interpersonal communication to identify behavioral beliefs lationships between those motivations and related outcomes of
about passing along online video ads. In media studies, motiva- passing along online video ads in the context of TRA.
tion is defined as “the type of perceived incentives or rewards Building upon the previous studies, it is assumed that con-
that can propel an individual to take action and engage in me- sumers expect several outcomes from passing along online video
dia use” (Lin 1999, p. 203). Those motivations are considered ads. First, behavioral belief about pleasure represents the sub-
as expected outcomes, benefits, or gratifications sought for me- jective probability that passing along online video ads will help
dia usage (Dobos 1992; Lin 1999). Similarly, from the com- consumers feel fun, joy, arousal, and entertainment (Rubin 1981;
munication perspective, Rubin and Windahl (1986) indicated Rubin, Perse, and Barbato 1988). Second, behavioral belief
that communication motivations are not separated from needs, about affection indicates the subjective probability that pass-
and motivations are expectations caused by communication ing along online video ads will help consumers express love
behavior. toward and concern about acquaintances (Bochner, Kaminski,
In the domain of interpersonal communication, which deals and Fitzpatrick 1977; Rubin, Perse, and Barbato 1988; Schutz
with communicating with others regarding decisions about daily 1966). Third, behavioral belief about inclusion refers to the
lives, sharing information, and showing ideas and feelings, subjective probability that passing along online video ads will
4 J. LEE ET AL.

help consumers belong to a particular group of acquaintances or METHOD

friends (Rubin, Perse, and Barbato 1988; Schutz 1966). Fourth,
behavioral belief about escape indicates the subjective probabil- Participants and Procedure
ity that passing along online video ads will allow consumers to To investigate factors influencing consumers’ pass-along be-
get away from work and boredom (Rubin 1981; Rubin, Perse, havior of online video ads and relationships among those factors,
and Barbato 1988). Fifth, behavioral belief about relaxation in- an online survey was conducted. A total of 452 students partic-
dicates the subjective probability that passing along online video ipated at two major Midwestern universities for extra credit.
ads will help consumers unwind, feel less tense, and rest (Rubin Participants’ ages ranged from 18 to 29 (mean age = 20, SD =
1981; Rubin, Perse, and Barbato 1988). Finally, behavioral be- 1.56). Female participants (68.4%, n = 309) outnumbered males
lief about control is the subjective probability that passing along (31.6%, n = 143). College students are considered appropriate
online video ads will help consumers have power over others for this study because about 76% of young Internet users (ages
to gain compliance (Rubin, Perse, and Barbato 1988; Schutz 18 to 29) view or download online videos, indicating the largest
1966). age group to watch or download online video ads (Madden
Considering TRA’s assumption that salient behavioral beliefs 2007). Although many participants came from journalism (28%)
positively influence attitude toward a particular behavior (Ajzen and advertising (11%), majors from other fields of study were
and Fishbein 1980; Fishbein and Ajzen 1975; Madden, Ellen, represented as well, such as hospitality business (15%), retail
and Ajzen 1992; Oliver and Bearden 1985; Shimp and Kavas (13%), business (10%), and others (e.g., political science, En-
1984; Yoh et al. 2003), it is expected that the more consumers glish, economics, psychology, and art). After being informed via
believe that passing along online video ads will produce those the consent form of their rights as study participants, they were
expected outcomes, the more positive attitudes toward passing asked to read the following definition of online video ads: “An
along online video ads they will have. online video advertisement is an advertisement that is displayed
as a streaming video clip and enables you to play, stop, and drag
forward and backward at any time. Examples of online video
H4a. Pleasure belief will have a positive influence on attitude toward advertisements include video advertisements on YouTube.”
passing along online video ads. In addition, participants were told that pass-along behavior
H4b. Affection belief will have a positive influence on attitude to- is the behavior that distributes messages or content through e-
ward passing along online video ads.
H4c. Inclusion belief will have a positive influence on attitude toward
mail or social networking sites such as Facebook and Twitter.
passing along online video ads. Participants then were asked to indicate their opinions about
H4d. Escape belief will have a positive influence on attitude toward passing along online video ads. Among the participants, about
passing along online video ads. 62% (n = 280) reported that they had passed along online video
H4e. Relaxation belief will have a positive influence on attitude ads at least once a month.
toward passing along online video ads.
H4f. Control belief will have a positive influence on attitude toward
passing along online video ads.
Measurement
Intention to Pass Along Online Video Ads. Intention to pass
Normative Beliefs. According to TRA, subjective norm is
along online video ads was measured with three items modified
determined by motivation to comply with given references and
from Ajzen (2002, 2006) and Ajzen and Fishbein (1980) for this
the sum of accessible normative beliefs. The latter refers to
study. The first statement, “I plan to pass along online video ads,”
the perceived behavioral expectations from close and important
was assessed on a 7-point scale ranging from Extremely unlikely
reference individuals or groups (Ajzen and Fishbein 1980; Fish-
(1) to Extremely likely (7). The second statement, “I will make an
bein and Ajzen 1975). In accordance with this TRA assumption,
effort to pass along online video ads,” was assessed on a 7-point
consumer researchers have found that consumers’ behavioral
scale ranging from I definitely will not (1) to I definitely will (7).
expectations from references (e.g., spouse, family, and friends)
The final statement, “I intend to pass along online video ads,”
positively influence their subjective norm of using a new product
was assessed on a 7-point scale ranging from Strongly disagree
(Oliver and Bearden 1985), using coupons (Shimp and Kavas
(1) to Strongly agree (7) (α = .93).
1984), and adopting a new brand (Ryan 1982). Therefore, it is
Attitude Toward Passing Along Online Video Ads. To mea-
assumed that the more consumers perceive behavioral expecta-
sure attitude toward passing along online video ads, the follow-
tions from close or important reference individuals (e.g., friends
ing four items were used (Ajzen and Fishbein 1980; 7-point
and classmates) in terms of passing along or not passing along
scale): “For me to pass along online video ads is . . . ” Extremely
online video ads, the more they feel pressure to pass along (or
bad (1) to Extremely good (7); Extremely worthless (1) to Ex-
not to pass along) the ads.
tremely valuable (7); Extremely unpleasant (1) to Extremely
pleasant (7); and Boring (1) to Interesting (7) (α = .88).
H5. Normative beliefs will have a positive influence on subjective Subjective Norm. Subjective norm was measured by four
norm. items developed by Ajzen and Fishbein (1980) and modified for
 WHY PEOPLE PASS ALONG ONLINE VIDEO ADVERTISING 5

this study. Three items (i.e., “Many people who are important friends think that I need to pass along online video ads if the ads
to me think that I need to pass along online video ads if the ads are meaningful”) and motivation to comply with each reference
are meaningful”; “Many of my close friends think that I need (e.g., “Generally speaking, how much do you care about what
to pass along online video ads if the ads are meaningful”; and your close friends think about you needing to pass along online
“Many of my classmates think that I need to pass along online video ads?”) were measured. For clear understanding, partici-
video ads if the ads are meaningful”) were used on a 7-point pants were asked to read this statement: “When online video
scale ranging from Definitely false (1) to Definitely true (7). The ads provide you with benefits such as fun, entertainment, and
final item, “Many people whose opinions I value would approve information, those ads are meaningful.”
of my passing along online video ads if the ads are meaningful,” Items for measuring the strength of normative beliefs and
was assessed on a 7-point scale ranging from Strongly disagree motivation to comply were adopted from Ajzen and Fishbein
(1) to Strongly agree (7) (α = .87). (1980). The strength of each normative belief was assessed on a
Behavioral Beliefs. To measure six behavioral beliefs in 7-point scale ranging from Extremely unlikely (1) to Extremely
the context of passing along online video ads (i.e., pleasure, af- likely (7). Motivation to comply with each reference was as-
fection, inclusion, escape, relaxation, and control), the strength sessed on a 7-point scale ranging from Not at all (1) to Very
of each behavioral belief and its corresponding outcome eval- much (7). To compute overall normative beliefs following the
uation were measured. All items of six behavioral beliefs were expectancy-value model, the strength of each normative belief
modified from the ICM scale (Rubin, Perse, and Barbato 1988) was multiplied by motivation to comply with each reference,
for this study as recommended by Ajzen and Fishbein (1980). and the resulting products
 were summed across all items of
Specifically, strength of the belief about pleasure (e.g., “Passing reference groups (i.e., ni mi for each reference) (Ajzen and
along online video ads will help me to have fun”) and its out- Fishbein 1980) (α = .95).
come evaluation (e.g., “For me to pass along online video ads
for pleasure is . . . ”) were measured by eight items. The strength RESULTS
of belief about affection (e.g., “Passing along online video ads
will help me to let others know I care about their feelings”) Measurement Model
and its outcome evaluation were measured by five items. The To test how measured items come together to present the
strength of belief about inclusion (e.g., “Passing along online construct of the hypothesized TRA model, including the decom-
video ads will help me to feel less lonely”) and its outcome posed behavioral beliefs by the ICM scale, a confirmatory factor
evaluation were measured by four items. The strength of belief analysis (CFA) was conducted using AMOS 7.0 with maximum
about escape (e.g., “Passing along online video ads will help me likelihood (ML) estimation to handle incomplete data. The ML
to get away from what I’m doing”) and its outcome evaluation estimation method is a highly recommended solution, especially
were also measured by four items. The strength of belief about for resolving missing data problems when running CFA or struc-
relaxation (e.g., “Passing along online video ads will help me to tural equation modeling (SEM) (Arbuckle 1996; Byrne 2001;
be relaxed”) and its outcome evaluation were measured by four Hair et al. 2006; Jamshidian and Bentler 1999; Kline 2005).
items. Finally, the strength of belief about control (e.g., “Pass- CFA use is encouraged when measurement constructs are
ing along online video ads will help me to have someone to do theoretically grounded (Byrne 2001; Hair et al. 2006; Kline
something for me”) and its outcome evaluation were measured 2005). Because the ICM scale is considered a well-developed
by three items. theoretical measurement and has often been used for other in-
The strength of each behavioral belief was assessed on a 7- terpersonal communication studies (e.g., LaRose, Mastro, and
point scale ranging from Extremely unlikely (1) to Extremely Eastin 2001; Perse and Courtright 1993; Westmyer, DiCioccio,
likely (7). The outcome evaluation of each expected outcome and Rubin 2006), conducting CFA is an appropriate way to test
was measured on a 7-point scale ranging from Extremely bad the measurement model of the hypothesized TRA.
(1) to Extremely good (7). To compute the sum of salient behav- CFA results confirmed dimensions of behavioral beliefs ob-
ioral beliefs, following the expectancy-value model, the strength tained by the ICM scale and other components of TRA (i.e.,
of each behavioral belief was multiplied by its corresponding intention, attitude, subjective norm, normative beliefs). Results
outcome evaluation, and the resulting
 products were summed demonstrated that all six dimensions of behavioral beliefs about
for each outcome factor (i.e., bi ei for each outcome factor) passing along online video ads (i.e., pleasure, affection, in-
(Ajzen and Fishbein 1980). clusion, escape, relaxation, and control) were well identified.
Normative Beliefs. Given that the main target of online Also, results indicated that other components of TRA were
video ads is young adults (ages 18 to 29) and that the participants clearly confirmed as measurements. Although the chi-square
are college students, four reference groups that would influence test rejected a perfect absolute fit between data and the model
the participants’ pass-along behavior of online video ads were [χ 2 (772) = 1891.597, p < .001], it is widely known that the chi-
identified: their opinion leaders, close friends, classmates, and square test is sensitive to the influence of sample size (Brown
important people (e.g., family members). To measure normative 2006; Byrne 2001; Hair et al. 2006). Other model fit indices,
beliefs, the strength of each normative belief (e.g., “My close such as comparative fit index (CFI), Tucker-Lewis index (TLI),
6 J. LEE ET AL.

and incremental index of fit (IFI) were greater than .90, in- on attitude toward passing along online video ads. As expected,
dicating a good fit of the CFA model (Bentler 1990, 1992; subjective norm positively influenced attitude toward passing
Bruce 2004; Byrne 2001; Hair et al. 2006). Also, the estimate along online video ads (β = .303, p < .001). Thus, hypothesis
of the root mean square error of approximation (RMSEA) was 1, hypothesis 2, and hypothesis 3 were supported.
.057, indicating a reasonable degree of fit of the measurement Hypotheses 4a to 4f stated that each behavioral belief about
model (Browne and Cudeck 1993; Byrne 2001; Hair et al. 2006; passing along online video ads would have positive influences
Hansen 2008). All standardized factor loadings in the measure- on attitude toward passing along the ads. Among six behavioral
ment model were significant (p < .001) (see Table 1). beliefs, pleasure (γ = .553, p < .001) and escape (γ = .131, p <
All items of behavioral beliefs were loaded on the same di- .05) had positive impacts on attitude toward passing along on-
mensions obtained by the ICM scale. Specifically, the first factor, line video ads. However, affection (γ = −.141, n.s.), inclusion
labeled pleasure belief , consisted of eight items reflecting the (γ = .170, n.s.), relaxation (γ = .091, n.s.), and control (γ =
subjective probability that passing along online video ads will −.232, n.s.) had no significant influences on attitude toward
help consumers feel aroused and entertained (α = .94). The passing along online video ads. Finally, hypothesis 5 stated that
second factor, labeled affection belief , consisted of five items normative beliefs would have a positive influence on subjective
delineating the subjective probability that passing along online norm. As expected, the result showed a significant positive in-
video ads will help consumers express caring and appreciation fluence of normative beliefs on subjective norm (γ = .742, p <
for acquaintances (α = .91). The third factor, labeled inclusion .001). Thus, hypotheses 4a, 4d, and 5 were supported, while
belief , included four items representing the subjective proba- hypotheses 4b, 4c, 4e, and 4f were not. The means and standard
bility that passing along online video ads will help consumers deviations of all constructs used in the hypotheses testing are
decrease loneliness by providing the feeling of talking and being reported in Table 3.
with others (α = .85). The fourth factor, escape belief , consisted
of four items representing the subjective probability that pass-
ing along online video ads will allow consumers to get away
from work and boredom (α = .83). The fifth factor, labeled re- DISCUSSION AND CONCLUSION
laxation belief , included four items representing the subjective Today, online video websites allow Internet users to watch
probability that passing along online video ads will help con- the content of various online videos and pass along the videos
sumers refresh feelings and take a break (α = .91). The final to other users. When considering that users play active roles
factor, labeled control belief , consisted of three items referring in passing along video contents, examining factors influencing
to the subjective probability that passing along online video ads consumers’ pass-along behavior of online video ads can illumine
will help consumers obtain greater compliance (α = .82). this matter for both researchers and practitioners.
In addition, all items of other TRA model components (inten- Using TRA, this study attempts to predict and understand
tion, subjective norm, attitude, normative beliefs) were loaded consumers’ pass-along behavior of online video ads. The im-
on the same dimensions obtained by the original TRA model. portance of this study derives from this being the first attempt
All reliability estimates for each dimension were highly ac- to explore and examine factors that can predict and understand
ceptable, ranging from .87 to .95. In sum, six dimensions of consumers’ pass-along behavior regarding online video ads, as
behavioral beliefs as well as five components of the TRA model well as the inclusion of relationships among those factors. Al-
were confirmed and therefore can be used as the measurement though previously Lee and Lee (2011) explored motivations for
model in the hypothesized model. watching online video ads in the context of TRA, those mo-
tivations are different from the motivations for passing along
Hypotheses Tests online video ads. Unlike simply watching, this study focuses on
To test the proposed hypotheses, a SEM with ML estimation the pass-along behavior of online video ads in terms of TRA.
was conducted using AMOS 7.0. Although NFI value (.89) is Moreover, unlike previous studies that focused on attitude to-
slightly lower than the standard value of .90, overall fit indices ward ads (e.g., Lutz, MacKenzie, and Belch 1983; MacKenzie,
for the model were acceptable [χ 2 (768) = 1911.789, p < .001; Lutz, and Belch 1986; Mitchell and Olson 1981; Phelps and
CFI = .931; TLI = .915; IFI = .932; RMSEA = .057]. Results Thorson 1991), TRA sheds lights on attitude toward a behav-
of all hypothesized paths are reported in Table 2 and Figure 1. ior (Ajzen and Fishbein 1980; Hansen 2008). Typically, adver-
Hypothesis 1 and hypothesis 2 predicted positive impacts of tising scholars found the relationships between attitude toward
two determinants of behavioral intention—attitude toward pass- ads and intention, such as purchase intention (Lutz, MacKenzie,
ing along online video ads and subjective norm—on intention and Belch 1983; MacKenzie, Lutz, and Belch 1986). However,
to pass along online video ads. Results demonstrated that both because TRA allows us to examine the relationship between
attitude toward passing along online video ads (β = .798, p < attitudes toward a voluntary behavior and the intention to per-
.001) and subjective norm (β = .282, p < .001) had significant form the behavior, this study was able to directly examine how
positive influences on intention to pass along online video ads. the attitude toward pass-along behavior affected the pass-along
In addition, hypothesis 3 predicted the impact of subjective norm behavior. Considering that viral effect is the center of consumer
 WHY PEOPLE PASS ALONG ONLINE VIDEO ADVERTISING 7

TABLE 1
CFA Results of Measurement Model for Pass-Along Online Video Advertising
Standardized factor
Construct and Indicators loading t-value Alpha

Behavioral beliefs ( bi ei )b
Pleasure
b1e1. Passing along online video ads will help me to have fun. .860 –a .94
b2e2. Passing along online video ads will help me to be excited. .851 22.113∗∗∗
b3e3. Passing along online video ads will help me to have a good time. .871 21.575∗∗∗
b4e4. Passing along online video ads will help me to be thrilled. .826 18.378∗∗∗
b5e5. Passing along online video ads will help me to be stimulated. .824 18.544∗∗∗
b6e6. Passing along online video ads will help me to be entertained. .774 17.426∗∗∗
b7e7. Passing along online video ads will give me an opportunity to .854 19.793∗∗∗
enjoy.
b8e8. Passing along online video ads will help to pep me up. .858 20.169∗∗∗
Affection
b9e9. Passing along online video ads will give me an opportunity to help .740 –a .91
others.
b10e10. Passing along online video ads will help me to let others know I .851 17.444∗∗∗
care about their feelings.
b11e11. Passing along online video ads will help me to thank others. .800 16.992∗∗∗
b12e12. Passing along online video ads will give me an opportunity to .844 17.584∗∗∗
show others encouragement.
b13e13. Passing along online video ads will help me to be concerned .811 16.964∗∗∗
about others.
Inclusion
b14e14. Passing along online video ads will help me to talk to or be with .687 –a .85
someone.
b15e15. Passing along online video ads will help me just to have .777 16.576∗∗∗
someone to talk to about my interests sometimes.
b16e16. Passing along online video ads will help me to feel less lonely. .720 15.771∗∗∗
b17e17. Passing along online video ads will reassure me to know .779 15.567∗∗∗
someone is there.
Escape
b18e18. Passing along online video ads will help me to put off doing .562 –a .83
something I should be doing.
b19e19. Passing along online video ads will help me to get away from .636 14.115∗∗∗
what I’m doing.
b20e20. Passing along online video ads will give me an opportunity to .804 10.776∗∗∗
do something when I have nothing better to do.
b21e21. Passing along online video ads will help me to get away from .883 11.507∗∗∗
pressures and responsibilities.
Relaxation
b22e22. Passing along online video ads will help me to be relaxed. .838 –a .91
b23e23. Passing along online video ads will allow me to unwind. .862 24.355∗∗∗
b24e24. Passing along online video ads will give me pleasant rest. .809 19.872∗∗∗
b25e25. Passing along online video ads will help me to feel less tense. .847 21.596∗∗∗
Control
b26e26. Passing along online video ads will help me to have someone to .803 –a .82
do something for me.
b27e27. Passing along online video ads will help me to tell others what .784 17.014∗∗∗
to do.
b28e28. Passing along online video ads will help me to get something I .739 15.910∗∗∗
don’t have.
(Continued on next page)
8 J. LEE ET AL.

TABLE 1
CFA Results of Measurement Model for Pass-Along Online Video Advertising (Continued)
Standardized factor
Construct and Indicators loading t-value Alpha

Normative beliefs ( ni mi )c
n1m1. People whose opinions I value think that I need to pass along .915 –a .95
online video ads if the ads are meaningful.
n2m2. My classmates think that I need to pass along online video ads if .943 23.505∗∗∗
the ads are meaningful.
n3m3. My close friends think that I need to pass along online video ads .935 28.126∗∗∗
if the ads are meaningful.
n4m4. People who are important to me think that I need to pass along .905 26.165∗∗∗
online video ads if the ads are meaningful.
Subjective norm
sn1. Many people whose opinions I value would approve of my passing .613 –a .87
along online video ads if the ads are meaningful.
sn2. Many of my classmates think that I need to pass along online video .911 10.395∗∗∗
ads if the ads are meaningful.
sn3. Many of my close friends think that I need to pass along online .850 10.316∗∗∗
video ads if the ads are meaningful.
sn4. Many people who are important to me think that I need to pass .791 10.077∗∗∗
along online video ads if the ads are meaningful.
Attitude toward passing along online video ads
att1. For me to pass along online video ads is (bad/good). .757 –a .88
att2. For me to pass along online video ads is (worthless/valuable). .774 16.720∗∗∗
att3. For me to pass along online video ads is (unpleasant/pleasant). .835 18.239∗∗∗
att4. For me to pass along online video ads is (boring/interesting). .853 18.710∗∗∗
Intention to pass along online video ads
in1. I plan to pass along online video ads. .876 –a .93
in2. I will make an effort to pass along online video ads. .900 27.275∗∗∗
in3. I intend to pass along online video ads. .926 28.813∗∗∗
Goodness-of-fit statistics
χ (degree of freedom)
2
1891.597 (772)
RMSEA .057
CFI .932
TLI .917
IFI .933
NFI .892
Note: bi: strength of each behavioral belief; ei: outcome evaluation; ni: strength of each normative belief; mi: motivation to comply with each
reference; biei: combined statement of strength of each behavioral belief and its outcome evaluation; nimi: combined statement of strength of
each normative belief and motivation to comply with each reference.
a
One item for each construct was fixed at 1.00.
b
Each item in the behavioral beliefs was multiplied by the “it is good” statement.
c
Each item in the normative beliefs was multiplied by the “I care about them” statement.
∗∗∗
p < .001.

engagement in Web 2.0, this examination of active behaviors, attitudes toward passing along online video ads and perceived
which is allowed by TRA, would gain more importance. more social pressure from their important references, they had
Consistent with the explanation of TRA, this study’s find- greater intention to pass along the ads.
ings indicated that attitude toward passing along online video The impact of social pressure (i.e., subjective norm), in
ads and subjective norm positively and significantly influenced the context of pass-along behavior, is critically important be-
intention to pass along the ads. As participants had more positive cause the influential others will be the actual recipients of the
 WHY PEOPLE PASS ALONG ONLINE VIDEO ADVERTISING 9

TABLE 2
The Results of Structural Model
Standardized
Hypotheses Paths path coefficients Standard error t-values
1 APOVA → IPOVA .798 .115 11.834∗∗∗
2 SN → IPOVA .282 .116 4.049∗∗∗
3 SN → APOVA .303 .066 4.503∗∗∗
4a Pleasure → APOVA .553 .014 3.674∗∗∗
4b Affection → APOVA −.141 .030 −.554
4c Inclusion → APOVA .170 .054 .393
4d Escape → APOVA .131 .008 2.167∗
4e Relaxation → APOVA .091 .017 .511
4f Control → APOVA −.232 .015 −1.756
5 NB → SN .742 .009 8.586∗∗∗
Goodness-of-fit statistics
χ 2 (degree of freedom) 1911.789 (768)
RMSEA .057
CFI .931
TLI .915
IFI .932
NFI .891
IPOVA = Intention to pass along online video ads; APOVA = attitude toward passing along online video ads; SN = subjective norm; NB =
normative beliefs.
∗
p < .05; ∗∗∗ p < .001.

passed-along online video ad, instead of someone who will have passing along ads. This would be an important reason why
an opinion about the passers’ behavior. That is to say, the impact subjective norm has significant impact not only on behavioral
of social pressure should be stronger in the context of passing intention but also on attitudes toward pass-along behavior.
along the ad than in the general context of TRA research, be- We also found that between two determinants of behavioral
cause the influential others will directly receive the benefit of intention, attitude toward behavior had a stronger impact on

Pleasure .553***

Affection −.141

Inclusion .170
Attitude toward
passing along OVA
Escape .131* .798***

Relaxation .091
Intention to
.303*** pass along OVA
Control −.232

.282***
Normative .742*** Subjective
beliefs norm

*
p < .05, ***p < .001

FIG. 1. The Results of Hypothesized Model. OVA = online video ads.

10 J. LEE ET AL.

TABLE 3 2008; Stafford and Stafford 2001; Youn and Lee 2005). In the
Means and Standard Deviations of All Constructs context of online video advertising, Lee and Lee (2008) found
that the entertainment motivation had a strong and positive im-
Constructs Number of items Mean SD pact on attitude toward online video ads. Likewise, the findings
IPOVA 3 3.682 1.554 suggest that consumers tend to have a positive attitude toward
APOVA 4 4.378 1.147 passing along online video ads when they believe online video
SN 4 3.732 1.496 ads increase their pleasure. Recent studies have revealed that
Pleasurea 8a 20.209a 9.794a online video ads, which evoked highly aroused positive emo-
Affectiona 5a 18.606a 9.356a tions, such as pleasure (Eckler and Bolls 2011), joy (Teixeira,
Inclusiona 4a 15.943a 8.751a Wedel, and Pieters 2012), and enjoyment (Southgate, Westoby,
Escapea 4a 19.330a 9.516a and Page 2010), have a positive association with users’ pass-
Relaxationa 4a 17.878a 9.981a along intentions. Findings from those studies support the result
Controla 3a 13.255a 8.315a of this study that pleasure, which is the powerful motivation
NBa 4a 11.858a 9.572a to pass along, would be one of the most important expected
outcomes for Internet users.
a
Scale values range from 1 to 49. Escape, another expected outcome, positively affected atti-
IPOVA = Intention to pass along online video ads; APOVA = attitude tude toward passing along online video ads. This finding demon-
toward passing along online video ads; SN = subjective norm; NB =
strates that consumers may pass along online video ads when
normative beliefs.
their pass-along behavior helps them escape from what they are
doing. Further, it is observed that the influence of pleasure belief
intention to pass along than did subjective norm. Particularly, on attitude was stronger than belief about escape.
subjective norm positively influenced attitude, which in turn However, other expected outcomes (e.g., affection, inclusion,
positively influenced intention to pass along online video ads. relaxation, and control) did not significantly influence attitude
These findings are consistent with previous studies in that at- toward passing along online video ads. Specifically, the effect
titude toward a given behavior is the strongest determinant of of the belief about affection on the attitude was not found, rep-
intention to perform the behavior (e.g., Bagozzi et al. 2000; Lim resenting that participants’ expectations about expressing love
and Dubinsky 2005; Oliver and Bearden 1985; Ryan 1982). In or having concern for others are unrelated to their assessment
addition, the impact of attitudes toward passing along was in- of passing along online video ads. The belief about inclusion
fluenced by the respondent’s subjective norm, which is another also did not have any impact on attitude toward passing along
reason why attitudes are the major determinants in formation of the ads. From this result, we infer that participants’ expected
behavioral intention. outcomes, related to social networking via passing along online
These findings suggest that when advertisers create and use video ads, are unrelated to evaluative assessment of pass-along
online video ads for their campaigns in order to trigger con- behavior. Similarly, the belief about relaxation did not show
sumers’ pass-along behaviors, they need to focus on consumers’ a significant influence on attitude toward passing along online
attitudes toward passing along online video ads first and then video ads. Consumers’ beliefs that passing along such ads will
consider the influence of their reference groups (subjective help them have pleasant rest and feel less tense is not necessar-
norm). ily related to their attitude toward passing along online video
This study also identified six distinct outcomes that con- ads. Finally, the belief about control did not show a significant
sumers expect from passing along online video ads: pleasure, influence on attitude. This finding suggests consumers’ beliefs
affection, inclusion, escape, relaxation, and control. According that passing along online video ads will give them feelings of
to previous literature, consumers’ expected outcomes from per- influence over their acquaintances do not influence their attitude
forming a particular behavior do motivate them to perform that toward passing along online video ads.
behavior (Dobos 1992; Lin 1999). This study’s findings support It is assumed that the nonsignificant findings might have oc-
the previous argument by showing that two of the behavioral be- curred because participants did not think these beliefs strongly
liefs the participants held (i.e., pleasure and escape) influenced affect their evaluations of passing along online video ads. Some
their attitudes toward passing along online video ads, which in of the nonsignificant findings are consistent with the findings
turn influenced them to have greater intention to pass along the of previous studies in the context of Internet use. For example,
ads. relaxation and passing time motivations for using the Internet
Specifically, participants who believed that passing along on- did not have significant relationships with Internet affinity and
line video ads would help them gain pleasure were more likely satisfaction (Ferguson and Perse 2000; Papacharissi and Rubin
to have a positive attitude toward passing along the ads. This 2000). However, considering the different results between the
finding is similar to previous studies, which identified that con- hypothesized model and the final model, it is still interesting to
sumers’ expectations about fun and entertainment outcomes are explore why pleasure and escape were significant influencers
a strong predictor of their attitudes toward media (Lee and Lee on attitudes and behavioral intention, while the others were not.
 WHY PEOPLE PASS ALONG ONLINE VIDEO ADVERTISING 11

According to Deci and Ryan (1985), intrinsic motivation is influential reference groups or individuals of their target con-
a more powerful driver than extrinsic motivation. Although sumers and by urging them to serve as volunteer agents for viral
passing-along behavior is managed and controlled by an indi- marketing.
vidual, this interpersonal communication (i.e., pass along online
video ads) could be extrinsically motivated by relationships with Limitations and Future Research
others. Inclusion, affection, and control would be more extrinsi- While this study’s findings have potential implications, there
cally evoked motivations by the social relationship, while plea- are several limitations. First, college students are appropriate
sure and escape could be more intrinsically elicited motivations participants because of their substantial use of online video ads,
by the more self-oriented mind. Future research, however, needs but future research needs to pay attention to wider groups to be
to further investigate the relationships between these expected able to generalize results. Using convenience sampling also lim-
outcomes and attitude toward passing along online video ads. ited the results of this study. Replicating this study with different
We found that normative beliefs positively affected subjective samples (e.g., random probability samples) can present more ex-
norm, which in turn influenced intention to pass along online planatory and predictive power of the results. Second, although
video ads. The positive relationship between normative beliefs the TRA model was employed in this study to understand and
and subjective norm in terms of passing along online video predict why consumers pass along online video ads, this study
ads suggests that consumers are inclined to feel more social did not include actual pass-along behavior. To examine the full
pressure to pass along such ads when they believe that their model, which will improve the explanation thereof, future re-
important references (e.g., their opinion leaders, close friends, search needs to include actual pass-along behavior. Third, it is
and classmates) think they need to. appropriate to employ the ICM scale to measure pass-along be-
The current study has theoretical and practical implications. havior by e-mail or social networking sites, which are the most
First, although several studies have investigated expected out- frequently used methods to pass along online content. How-
comes and their impact on the use of media, few studies have ever, more research would be necessary in the future, as more
employed TRA to predict and understand various factors influ- consumers use not only e-mail or social networking websites
encing intention and actual behavior in the field of advertising but also various tools such as RSS (i.e., really simple syndica-
research. Although this study did not measure actual pass-along tion) or online discussion forums to pass along online video ads.
behavior, this study’s findings will help researchers explore fac- Qualitative research methods, such as focus groups or in-depth
tors and establish a more comprehensive theoretical model in- interviews, will provide more insight into how consumers pass
fluencing consumers’ passing along of online video ads. In ad- along online video ads. Fourth, although we tested normative be-
dition, understanding how consumers pass along such ads will liefs as a single dimension, it is possible that different reference
provide advertisers and marketers with opportunities for viral groups may have different influences on consumers’ pass-along
marketing. Given that passing along online video ads is a form motivations. Thus, decomposing normative beliefs will provide
of word of mouth via the Internet, it is a good tool for adver- more insight into the impact of different reference groups. It is
tisers and marketers to use to reach their consumers (Cruz and also possible that consumers’ pass-along motivations depend on
Fill 2008; Datta, Chowdhury, and Chakraborty 2005; Hennig- the type of content in the ads. Therefore, future research needs
Thurau et al. 2004). The more knowledgeable consumers be- to test the motivations with different types of online video ads.
come about marketing tactics, the more skeptical they become In the end, although TRA is a solid theory to predict a particular
toward traditional marketing tools (Boush, Friestad, and Ross behavior, its linear compensatory (i.e., step-by-step) assump-
1994). Because those who receive online video ads are exposed tion (Taylor and Todd 1995) has limitations in that it does not
to ads via e-mail from acquaintances, they may not be as skepti- allow us to examine other paths between beliefs and intention.
cal about them as compared to ads via e-mail from companies. In Because of this theoretical limitation in the model, it is diffi-
this situation, the findings that the expected outcomes of plea- cult to explain the complicated nature of consumers’ pass-along
sure and escape activate positive attitudes toward pass-along behaviors.
behavior should influence advertisers and make them want to
generate viral effects using online video ads. In addition, when
considering that peers are important reference groups for young REFERENCES
adults (Cheong and Morrison 2008), the spread of online video Ajzen, Icek (2006), “Constructing a TPB Questionnaire: Concep-
tual and Methodological Considerations,” http://people.umass.edu/aizen/
ads will be faster and greater than that of other mass media. tpd.html.
While advertisers cannot control every component that in- Ajzen, Icek, and Martin Fishbein (1980), Understanding Attitudes and Predict-
fluences consumers’ pass-along behavior of online video ads, ing Social Behavior, Englewood Cliffs, NJ: Prentice-Hall.
they can design their online video ads to satisfy the particular Albarrancı́n, Dolores, Blair T. Johnson, Martin Fishbein, and Paige A.
gratifications that consumers expect from passing along the ads. Muellerleile (2001), “Theories of Reasoned Action and Planned Behav-
ior as Models of Condom Use: A Meta-Analysis,” Psychological Bulletin,
This study identified two gratifications that consumers expect to 127 (1), 142–61.
fulfill; advertisers can use this information for their marketing Arbuckle, James L. (1996), “Full Information Estimation in the Presence of
strategies. Advertisers can also take advantage by identifying Incomplete Data,” in Advanced Structural Equation Modeling: Issues and
12 J. LEE ET AL.

Techniques, George A. Marcoulides and Randall E. Schumacker, eds., Mah- Deci, Edward L., and Richard M. Ryan (1985), Intrinsic Motivation and Self-
wah, NJ: Erlbaum, 243–77. Determination in Human Behavior, New York: Plenum.
Bagozzi, Richard P. (1981), “Attitudes, Intentions, and Behavior: A Test of Dobos, Jean (1992), “Gratification Models of Satisfaction and Choice of Com-
Some Key Hypotheses,” Journal of Personality and Social Psychology, 41 munication Channels in Organizations,” Communication Research, 19 (1),
(4), 607–27. 29–51.
———, Hans Baumgartner, and Youjae Yi (1992), “State versus Action Ori- Eckler, Petya, and Paul Bolls (2011), “Spreading the Virus: Emotional Tone of
entation and the Theory of Reasoned Action: An Application to Coupon Viral Advertising and Its Effect on Forwarding Intentions and Attitudes,”
Usage,” Journal of Consumer Research, 18 (4), 505–18. Journal of Interactive Advertising, 11 (2), 1–11.
———, Nancy Wong, Shuzo Abe, and Massimo Bergami (2000), “Cultural and eMarketer (2011), “Top Digital Trends for 2012,” http://www.slideshare.net/
Situational Contingencies and the Theory of Reasoned Action: Application BeatHrlimann/2012-study-e-marketer-trends-top-digital-trends-2012.
to Fast Food Restaurant Consumption,” Journal of Consumer Psychology, Ferguson, Douglas A., and Elizabeth M. Perse (2000), “The World Wide Web
9 (2), 97–106. as a Functional Alternative to Television,” Journal of Broadcasting and
Bentler, Peter M. (1990), “Comparative Fit Indexes in Structural Models,” Psy- Electronic Media, 44 (2), 155–74.
chological Bulletin, 107 (2), 238–46. Fishbein, Martin, and Icek Ajzen (1975), Belief, Attitude, Intention, and Be-
——— (1992), “On the Fit of Models to Covariances and Methodology to the havior: An Introduction to Theory and Research, Reading, MA: Addison-
Bulletin,” Psychological Bulletin, 112 (3), 400–404. Wesley.
Bochner, Arthur P. (1984), “The Functions of Human Communication in Inter- Fitzmaurice, Julie (2005), “Incorporating Consumers’ Motivations into the The-
personal Bonding,” in Handbook of Rhetorical and Communication Theory, ory of Reasoned Action,” Psychology and Marketing, 22 (11), 911–29.
Carroll C. Arnold and John W. Bowers, eds., Boston: Allyn and Bacon, Hair, Joseph F., Jr., Rolph E. Anderson, Ronald L. Tatham, and William C.
544–621. Black (2006), Multivariate Data Analysis, 6th ed., Upper Saddle River, NJ:
———, Edmund P. Kaminski, and Mary A. Fitzpatrick (1977), “The Concep- Prentice-Hall.
tual Domain of Interpersonal Communication Behavior: A Factor-Analytic Hansen, Torben (2008), “Consumer Values, the Theory of Planned Behaviour,
Study,” Human Communication Research, 3 (4), 291–302. and Online Grocery Shopping,” International Journal of Consumer Studies,
Bock, Gee-Woo, Robert W. Zmud, Young-Gul Kim, and Jae-Nam Lee (2005), 32 (2), 128–37.
“Behavioral Intention Formation in Knowledge Sharing: Examining the ———, Jan M. Jensen, and Hans S. Solgaard (2004), “Predicting Online Gro-
Roles of Extrinsic Motivators, Social-Psychological Forces, and Organiza- cery Buying Intention: A Comparison of the Theory of Reasoned Action
tional Climate,” MIS Quarterly, 29 (1), 87–111. and The Theory of Planned Behaviour,” International Journal of Informa-
Boush, David M., Marian Friestad, and Gregory M. Rose (1994), “Adoles- tion Management, 24 (6), 539–50.
cent Skepticism toward Advertising and Knowledge of Advertiser Tactics,” Hennig-Thurau, Thorsten, Kevin P. Gwinner, Gianfranco Walsh, and Dwayne D.
Journal of Consumer Research, 21 (1), 165–75. Gremler (2004), “Electronic Word-of-Mouth via Consumer-Opinion Plat-
Brown, Morgan (2008), “Do’s and Don’ts of Online Video Advertising,” Ad- forms: What Motivates Consumers to Articulate Themselves on the Inter-
vertising Age, July 15. http://adage.com/article/web-video-report-howto- net?,” Journal of Interactive Marketing, 18 (1), 38–52.
articles/s-don-ts-online-video-advertising/129648/. Hsu, Chin-Lung, and Judy Chuan-Chuan Lin (2008), “Acceptance of Blog Us-
Brown, Timothy A. (2006), Confirmatory Factor Analysis for Applied Research, age: The Roles of Technology Acceptance, Social Influence and Knowledge
New York: Guilford Press. Sharing Motivation,” Information and Management, 45 (1), 65–74.
Browne, Michael W., and Robert Cudeck (1993), “Alternate Ways of Assessing Jamshidian, Mortaza, and Peter M. Bentler (1999), “ML Estimation of Mean and
Model Fit,” in Testing Structural Equation Models, Kenneth A. Bollen and Covariance Structures with Missing Data Using Complete Data routines,”
J. Scott Long, eds., Newbury Park, CA: Sage, 136–62. Journal of Educational and Behavioral Statistics, 24, 21–41.
Burgoon, Judee K., and Jerold L. Hale (1984), “The Fundamental Topoi of Katz, Elihu, Jay G. Blumler, and Michael Gurevitch (1974), “Utilization of Mass
Relational Communication,” Communication Monographs, 51 (3), 193–214. Communication by the Individual,” in The Uses of Mass Communications:
Byrne, Barbara M. (2001), Structural Equation Modeling with AMOS: Basic Current Perspectives on Gratifications Research, Jay G. Blumer and Elihu
Concepts, Applications, and Programming, Mahwah, NJ: Erlbaum. Katz, eds., Beverly Hills: Sage, 19–32.
Cheong, Hyuk Jun, and Margaret A. Morrison (2008), “Consumers’ Reliance Kline, Rex B. (2005), Principles and Practice of Structural Equation Modeling,
on Product Information and Recommendations Found in UGC,” Journal of 2nd ed., New York: Guilford Press.
Interactive Advertising, 8 (2), 38–49. LaRose, Robert, Dana Mastro, and Matthew S. Eastin (2001), “Understanding
Choo, HoJung, Jae-Eun Chung, and Dawn T. Pysarchik (2004), “Antecedents to Internet Usage: A Social Cognitive Approach to Uses and Gratifications,”
New Food Product Purchasing Behavior among Innovator Groups in India,” Social Science Computer Review, 19 (4), 395–413.
European Journal of Marketing, 38 (5/6), 608–25. Lee, Joonghwa, and Mira Lee (2008), “Motivations of Watching Online Video
ComScore (2012), “ComScore Releases March 2012 U.S. Online Video Advertising: From a Perspective of Uses and Gratifications,” manuscript
Rankings,” April 19, http://www.comscore.com/Press Events/Press presented at the 2008 conference of the American Academy of Advertising,
Releases / 2012 / 4 / comScore Releases March 2012 U.S. Online Video San Mateo, CA, March.
Rankings. ———, and ——— (2011), “Factors Influencing the Intention to Watch Online
Conner, Mark, and Christopher J. Armitage (1998), “Extending the Theory of Video Advertising,” Cyberpsychology, Behavior, and Social Networking, 14
Planned Behavior: A Review and Avenues for Further Research,” Journal (10), 619–24.
of Applied Social Psychology, 28 (15), 1429–64. Lim, Heejin, and Alan J. Dubinsky (2005), “The Theory of Planned Behavior
Cruz, Danilo, and Chris Fill (2008), “Evaluating Viral Marketing: Isolating the in E-Commerce: Making a Case for Interdependencies between Salient
Key Criteria,” Marketing Intelligence and Planning, 26 (7), 743–58. Beliefs,” Psychology and Marketing, 22 (10), 833–55.
Dance, Frank E. X., and Carl E. Larson (1976), Function of Human Communi- Lin, Carolyn A. (1999), “Uses and Gratifications,” in Clarifying Communica-
cation: A Theoretical Approach, New York: Holt, Rinehart, and Winston. tion Theories: A Hands-On Approach, G. Stone, M. Singletary, and V. P.
Datta, Palto R., Dababrata N. Chowdhury, and Bonya R. Chakraborty (2005), Richmond, eds., Ames: Iowa State University Press, 199–208.
“Viral Marketing: New Form of Word-of-Mouth through Internet,” Business Lutz, Richard J., Scott MacKenzie, and George Belch (1983), “Attitude Toward
Review, 3 (2), 69–75. the Ad as a Mediator of Advertising Effectiveness: Determinants and Con-
Davis, Fred D., Richard P. Bagozzi, and Paul R. Warshaw (1989), “User Accep- sequences,” in Advances in Consumer Research, vol. 10, R. P. Bagozzi and
tance of Computer Technology: A Comparison of Two Theoretical Models,” A. M. Tybout, eds., Ann Arbor, MI: Association for Consumer Research,
Management Science, 35 (8), 982–1003. 532–39.
 WHY PEOPLE PASS ALONG ONLINE VIDEO ADVERTISING 13

MacKenzie, Scott. B., Richard J. Lutz, and George E. Belch (1986), “The Role ———, and E. H. Bonfield (1980), “Fishbein’s Intentions Model: A Test of
of Attitude Toward the Ad as a Mediator of Advertising Effectiveness: A External and Pragmatic Validity,” Journal of Marketing, 44 (2), 82–95.
Test of Competing Explanations,” Journal of Marketing Research, 23 (2), Schutz, William C. (1966), The Interpersonal Underworld, Palo Alto, CA:
130–43. Science and Behavior Books.
Madden, Mary (2007), “Online Video: 57% of Internet Users Have Watched Shimp, Terence A., and Alican Kavas (1984), “The Theory of Reasoned Action
Videos Online and Most of Them Share What They Find with Oth- Applied to Coupon Usage,” Journal of Consumer Research, 11 (3), 795–809.
ers,” Pew Internet and American Life Project July 25, http://www. Southgate, Duncan, Nikki Westoby, and Graham Page (2010), “Creative De-
pewinternet.org/PPF/r/219/report display.asp. terminants of Viral Video Viewing,” International Journal of Advertising,
Madden, Thomas J., Pamela Scholder Ellen, and Icek Ajzen (1992), “A Com- 29 (3), 349–68.
parison of the Theory of Planned Behavior and the Theory of Reasoned Stafford, Thomas F., and Marla R. Stafford (2001), “Identifying Motivations
Action,” Personality and Social Psychology Bulletin, 18 (1), 3–9. for the Use of Commercial Web Sites,” Information Resources Management
Mitchell, Andrew A., and Jerry C. Olson (1981), “Are Product Attribute Beliefs Journal, 14 (1), 2–30.
the Only Mediator of Advertising Effects on Brand Attitude?,” Journal of Taylor, Shirley, and Peter Todd (1995), “Decomposition and Crossover Effects
Marketing Research, 18 (3), 318–32. in the Theory of Planned Behavior: A Study of Consumer Adoption Inten-
Oliver, Richard L., and William O. Bearden (1985), “Crossover Effects in tion,” International Journal of Research in Marketing, 12 (2), 137–55.
the Theory of Reasoned Action,” Journal of Consumer Research, 12 (3), Teixeira, Thales, Michel Wedel, and Rik Pieters (2012), “Emotion-Induced
324–40. Engagement in Internet Video Advertisements,” Journal of Marketing Re-
Papacharissi, Zizi, and Alan M. Rubin (2000), “Predictors of Internet Use,” search, 49 (2), 144–59.
Journal of Broadcasting and Electronic Media, 44 (2), 175–96. Thompson, Bruce (2004), Exploratory and Confirmatory Factor Analysis: Un-
Perse, Elizabeth M., and John A. Courtright (1993), “Normative Images of derstanding Concepts and Applications, Washington, DC: American Psy-
Communication Media: Mass and Interpersonal Channels in the New chological Association.
Media Environment,” Human Communication Research, 19 (4), 485– Triandis, Harry C. (1977), Interpersonal Behavior, Monterey, CA: Brooks/Cole.
503. Westmyer, Stephanie A., Rachel L. DiCioccio, and Rebecca B. Rubin (2006),
Phelps, Joseph, Regina Lewis, Lynne Mobilio, David Perry, and Niranjan Ra- “Appropriateness and Effectiveness of Communication Channels in Com-
man (2004), “Viral Marketing or Electronic Word-of-Mouth Advertising: petent Interpersonal Communication,” Journal of Communication, 48 (3),
Examining Consumer Responses and Motivations to Pass along Email,” 27–48.
Journal of Advertising Research, 44 (4), 333–48. Wish, M., R. G. D’Andrade, and J. E. Goodnow (1980), “Dimensions of Inter-
Phelps, Joseph, and Esther Thorson (1991), “Brand Familiarity and Product personal Communication: Correspondences between Structures for Speech
Involvement Effects on the Attitude Toward an Ad-Brand Attitude Rela- Acts and Bipolar Scales,” Journal of Personality and Social Psychology,
tionship,” Advances in Consumer Research, 18, 202–209. 39 (5), 848–60.
Rubin, Alan M. (1981), “An Examination of Television Viewing Motivations,” Wu, Jiming, and De Liu (2007), “The Effects of Trust and Enjoyment on Inten-
Communication Research, 8 (2), 141–65. tion to Play Online Games,” Journal of Electronic Commerce Research, 8,
———, and Sven Windahl (1986), “The Uses and Dependency Model of Mass 128–40.
Communication,” Critical Studies in Media Communication, 3 (2), 184–99. Yoh, Eunah, Mary Lynn Damhorst, Stephen Sapp, and Russ Laczniak (2003),
Rubin, Rebecca B., Elizabeth M. Perse, and Carole A. Barbato (1988), “Con- “Consumer Adoption of the Internet: The Case of Apparel Shopping,” Psy-
ceptualization and Measurement of Interpersonal Communication Motives,” chology and Marketing, 20 (12), 1095–118.
Human Communication Research, 14 (4), 602–28. Youn, Seounmi, and Mira Lee (2005), “Advergame Playing Motivations and Ef-
Ryan, Michael J., (1982), “Behavioral Intention Formation: The Interdepen- fectiveness: A ‘Uses and Gratifications’ Perspective,” in Advertising, Promo-
dency of Attitudinal and Social Influence Variables,” Journal of Consumer tion, and New Media, Marla R. Stafford and Ronald J. Faber, eds., Armonk,
Research, 9 (3), 263–78. NY: M.E. Sharpe, 320–47.
Copyright of Journal of Interactive Advertising is the property of Routledge and its content
may not be copied or emailed to multiple sites or posted to a listserv without the copyright
holder's express written permission. However, users may print, download, or email articles for
individual use.