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Download by: [Lehigh University] Date: 02 May 2016, At: 05:37
Journal of Interactive Advertising
ISSN: (Print) 1525-2019 (Online) Journal homepage: http://www.tandfonline.com/loi/ujia20
The Effects of Interactivity on Cross-Channel
Communication Effectiveness
Qimei Chen, David A. Griffith & Fuyuan Shen
To cite this article: Qimei Chen, David A. Griffith & Fuyuan Shen (2005) The Effects of
Interactivity on Cross-Channel Communication Effectiveness, Journal of Interactive Advertising,
5:2, 19-28, DOI: 10.1080/15252019.2005.10722098
To link to this article: http://dx.doi.org/10.1080/15252019.2005.10722098
Published online: 01 Jul 2013.
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Citing articles: 1 View citing articles
THE EFFECTS OF INTERACTIVITY ON CROSS-CHANNEL
COMMUNICATION EFFECTIVENESS
Qimei Chen , David A. Griffith, and Fuyuan Shen
ABSTRACT: This study investigated the effects of web site interactivity on consumers' trust in brands and product evaluations,
and their subsequent purchase intentions in a multi-channel context. Results from the experiment indicated that through greater
interactivity, individuals developed greater trust in the vendor and better understanding of its products. Further, it was
demonstrated that trust and product evaluation carried interactivity's influence onto not only online purchase intention, but also
offline purchase intention at a brand-specific business level. These findings indicate that online interactivity can have broad
implications for multi-channel marketing.
As an advertising medium, the Internet is unique in
permitting firms to create interactive online environments that
allow consumers to directly experience products. Scholars
have considered this unique interactivity as an important
feature that differentiates the Internet from other traditional
advertising media (Griffith and Chen 2004; McMillan and
Hwang 2002; Shen 2002). Traditionally, advertising messages
and direct experience have been considered the two primary
sources of information that markers used to communicate
product information to consumers (Singh, Balasubramanian,
and Chakraborty 2000). The Internet bridges these two
information sources by enabling firms to digitalize experiential
attributes in multimedia formats (Alba et al. 1997; Burke 1997;
Griffith and Chen 2004; Hoffman and Novak 1996), thus
endowing advertising messages with attribute experiences.
Through virtual reality (Biocca 1992), telepresence (Steuer
1992), or virtual direct experience (Griffith and Chen 2004),
the Internet can approximate key characteristics of direct
experience when promoting experience products, and in doing
so, provide consumers elements of interactivity previously
unavailable via traditional media. This has broad implications
for both marketers and consumers as prior research has
indicated that direct experience with products can result in a
greater consistency between consumer attitudes and behaviors
than can indirect experience (Fazio and Zanna 1978; Fazio,
Zanna, and Cooper 1978; Sherman 1982). Furthermore,
researchers have found that attitudes based on direct
experiences tend to be better predictors of behavioral
intentions than attitudes formed through indirect experience
alone (Smith 1993; Smith and Swinyard 1982).
In this study, we investigate the effects of interactivity on
consumers' trust and product evaluation, and their subsequent
purchase intentions. Moreover, the purchase intentions under
investigation were examined in a multi-channel context (i.e.,
online and offline). One of the motivations for this study is the
recent call of marketing academics and practitioners to study
multi-channel strategy, and to understand how interactive and
conventional media work together to move consumers
through the purchase process (Burke 2002). An additional
motivation to investigate the effect of interactivity in a multi-
channel setting is that a large number of businesses have
experienced difficulty in synchronizing their online and offline
operations (McKillen 2001). If indeed the online executions
can directly or indirectly influence consumers' online and
offline behaviors, then any disjunction, in a short time, will
hurt the company's sales, and in a long run, will dilute a
company's brand equity and reputation. Therefore an
empirical investigation of the influence of interactivity on
online and offline purchase intentions is warranted.
Conceptual Background
Product experience (i.e., consumer's living through or
observation of a product) can be considered a continuum
anchored by direct and indirect experience (Fazio and Zanna
1978). Seeing a product demonstrated on TV is a more direct
experience than hearing it described on radio. Similarly,
product trial is a more direct experience than watching the
product being demonstrated on the TV (Coyle and Thorson
2001). In an advertising context, one can argue that the more
interactive the medium, the more likely it is to provide more
direct experience, while less interactivity provides a more
indirect experience. According to Griffith and Chen (2004),
product experiences in an online context can be viewed as
virtual direct experiences (VDE), varying from "lean" to "rich"
with the lean VDE approximating an indirect experience, and
rich VDE approximating a direct experience. The central
dimension differentiating lean VDE from rich VDE is the level
JournalofInteractiveAdvertising,Vol5No2(Spring2005),pp.19‐28.
©2010AmericanAcademyofAdvertising,Allrightsreserved
ISSN1525‐2019
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20JournalofInteractiveAdvertisingSpring 2005
of realism, which is determined by interactivity (Coyle and
Thorson 2001).
Interactivity has been credited for helping generate various
benefits for marketers and consumers. Some of these benefits
reported previously include creation of stronger brand identity
(Upshaw 1995), facilitation of relationship marketing (Cuneo
1995), conversion of interested consumers to interactive
customers (Berthon, Pitt, and Watson 1996), and greater
control over information search and acquisition (Hoffman and
Novak 1996). Scholars have studied interactivity from a variety
of perspectives. Researchers, for example, have studied it as a
part of the communication process (e.g., Blattberg and
Deighton 1991; Steuer 1992), medium features (e.g., Hoffman
and Novak 1996) and individual perceptions (e.g. McMillan
and Hwang 2002; Newhagen, Corders, and Levy 1995). Studies
on interactivity generally tap into this construct from either a
system-centered or a user-centered approach (Unz and Hesse
1999). The system-centered approach deals with
objective/functional features of interactive environments such
as Web sites (e.g. Ghose and Dou 1998; Ha and James 1998;
Schlosser 2003; Stout, Villegas, and Kim 2001), whereas the
user-centered approach tends to deal with perception, i.e.
perceived level of interactivity from users' point of view (e.g.
McMillan and Hwang 2002; Newhagen, Cordes, and Levy
1995; Wu 1999).
Although a system-centered approach is helpful in identifying
key design factors, a user-centered perspective is crucial in
helping to gauge the effectiveness of these design factors as
users' perception of the interactivity may be independent of
design features (Lee et al. 2004). Hence, a user-centered
approach complements the system-centered approach by
reflecting the corresponding levels of objective/functional
interactivity in users' minds.
Although there have been several studies focusing on
interactivity in the context of the Internet (e.g. Coyle and
Thorson 2001; Häubl and Trifts 2000; Heeter 2000; Novak,
Hoffman, and Yung 2000), very little attention has been given
to developing a normative (trust) and cognitive (product
evaluation) model of the influence of interactivity in a multi-
channel advertising context. In this research, we conceptualize
interactivity as that of consumers' perception of their
interaction with the medium. We adopted a unique approach
of using functional design factors to manipulate different
levels of objective interactivity as means of increasing the
variance of perceived interactivity in consumers' minds.
Researchers have found that consumer trust is contingent
upon the consumer's perceived level of interactions with the
marketer that provides the consumer information (Sultan and
Mooraj 2001; Yoon 2002). It is therefore possible that online
interactivity can increase consumer information acquisition,
e.g., through the dynamic participation in modifying the form
and content of a mediated trial environment in real time.
Advertisers' efforts in encouraging information flows via
interactivity signal to consumers a concern and willingness on
the part of the advertisers to involve the consumer in the
purchase decision. This signaling of concern and openness for
information flows enhances a consumer's trust. There is also
evidence that direct experience is more effective at influencing
a consumer's cognitive structure than indirect experience
(Smith 1993; Smith and Swinyard 1982). The Internet, with its
ability to incorporate levels of interactivity, allows consumers
to interact with products while gathering product information
(Meeker 1997). Previous research reported that richer VDE
(which offers more direct experience) was more likely to be
effective than leaner ones (which offers more indirect
experience) (Griffith and Chen 2004). What differentiates
richer VDE from leaner VDE is the level of conveyance of
experiential product attributes and consequently the level of
realism provided in the product experience. As one of the
determinants of the level of realism (Coyle and Thorson 2001),
interactivity also becomes a focal attribute to help decide
whether the VDE is "lean" or "rich". Parallel with the advantage
of direct vs. indirect experience, if consumers perceive the
VDE to be more interactive (i.e. richer), they tend to have a
more positive evaluation of the product. In light of the above-
mentioned research evidence, we hypothesize that online
interactivity will have a positive relationship with both brand
trust and product evaluations. Stated formally, our first two
hypotheses are as follows.
H1: Consumers' perceived interactivity has a positive
impact on their trust in the vendor.
H2: Consumers' perceived interactivity has a positive
impact on their evaluations of products.
Considerable disagreement exists within the literature
regarding the influence of interactivity on purchase intentions
and other behavioral changes. Some researchers have found
interactivity to have a direct influence on purchase intention
(e.g., Wu 2000; Yoo, and Stout 2001), whereas others (e.g.,
Ghose and Dou 1998) suggested that interactivity influenced
consumer's decision making through perceived quality of the
Web site. In this study, we propose that interactivity influences
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21JournalofInteractiveAdvertisingSpring 2005
consumers' online purchase intention through normative and
cognitive structures. As the social perspective of trust is usually
emphasized in a cross-channel context (e.g. Ratnasingham
1998), trust is treated as a normative structure in this study
which mediates the effect of interactivity on consumer online
purchase intention. Product evaluation, which is treated as a
cognitive structure, mediates the effect of interactivity on
consumer online purchase intention.
Interactivity also has been identified as a major catalyst in
fostering relationship development. It has been proposed that
the "outcomes of interactivity are engagement in
communication and relationship building between a company
and its target consumers" (Ha and James 1998, p. 459). Central
to relationship development is trust (Hart and Johnson 1999;
Merrilees and Fry 2002; Sirdeshmukh, Singh, and Sabol 2002).
While prior research has added to our understanding of trust
in traditional channels, it has not explored the transferring
effect of trust across channels. More importantly, research has
yet to explore if a consumer's trust toward a firm developed in
one channel influences consumer behavioral intentions in the
firm's other channels.
Trust influences behavioral intent (e.g. Geyskens, Steenkamp,
and Kumar 1999; Singh and Sirdeshmukh 2000). For instance,
Morgan and Hunt (1994) found empirical support for the
relationship between trust and cooperative behavior. Lynch,
Kent, and Srinivasan (2001) note that given the absence of
physical exposure and contact between a firm and its
customers in an online channel, trust might be particularly
important in influencing behavioral intentions. As such, we
propose that consumer trust reduces consumer uncertainty
(e.g., false advertising, not honoring policies, privacy concerns,
etc.), thus enhancing positive behaviors (e.g., online purchase
intentions). In a cross-channel context, because trust is a
normative (interpersonal) structure, the influence of
interactivity from the development of trust is likely to be
transferred to a consumer's in-store/offline purchase
intention.
At a product/brand level, Aaker (1996) argues that a firm's
brand equity can be leveraged as a firm expands its product
line. Extending the branding literature to this study, it is
theorized that trust developed within one of a marketer's
channels will generate positive behavioral implications in a
marketer's other channels. Specifically, we argue that
consumer trust developed in an online channel will transfer to
a marketer's offline channel. As such, we propose the following
hypotheses:
H3: Consumer trust mediates the influence of
interactivity on consumer online and offline purchase
intentions.
H4: Consumer product evaluation mediates the influence
of interactivity on consumer online and offline purchase
intention.
An offline (i.e., in-store) setting is typically perceived as richer
than an online setting. For instance, previous research
contends that consumers will derive greater shopping context
utility from an in-store retail experience than an online retail
experience (Lee and Tan 2003). As the in-store retail setting
approximates the richest media and offers the most direct
experience, we argue that the stimulation of online purchase
intentions will carry-over toward the richer media hence
transfer online purchase intention to offline purchase
intention. As the basic shopping process consists of the
activities of gathering information (e.g. window shopping),
purchasing, and delivery (Salomon and Koppelman 1992),
consumers are likely to utilize the online retail channel as a
shopping venue carrying-over this intention when considering
the richer media setting. Therefore, we propose the next
hypothesis.
H5: Online purchase intention has a positive impact on
offline purchase intentions.
Figure 1 presents a conceptual model of the relationship
between the key variables. As can be seen, while it is expected
that interactivity will have positive associations with brand
trust and product evaluations, we also posit that online
interactivity has an indirect effect on online as well in-store
purchase intentions mediated by trust and product evaluation.
Figure 1: Conceptual Model of Direct and Indirect Effects of
Interactivity
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RESEARCH METHODS
To examine the proposed hypotheses, we decided to cast our
investigation in the context of an online retail site. We used
search engines (e.g., Yahoo, Netscape, Altavista, etc.) to
identify sites that target the 18-35 age segment and also offer
interactive features. An apparel website site by Land's End (the
largest online apparel marketer, generating $1.462 billion in
revenue in 2001) was finally selected as the context for use in
this study. The site was ideal because first it permits a
meaningful interactivity manipulation in a laboratory setting
due to it being an experience product with limited digitizable
experiential product attributes. Second, it is appropriate for
use as stimuli for the intended subject pool (i.e.,
undergraduate students).
Sample
One hundred students (48 male and 52 female) in
undergraduate marketing courses in a major state university
participated in the experiment. Ninety-two percent of the
participants were between the ages of 20 and 25 with the
balance between the ages of 26 and 35. On average, the
participants spent eleven hours online per week. Seventy-seven
percent of participants had purchased products online. Nearly
half of the participants had purchased online once a month or
more frequently (42%), with 24% routinely purchasing clothes
online. Participants were assigned randomly to three
treatment conditions (i.e., degree of interactivity-
low/medium/high) resulting in average cell sizes of 34.
Experimental procedure
We manipulated interactivity using three levels
(high/medium/low). This decision was based on the possibility
of an "inverted-U" relationship between the interactivity and
web-related dependent variables (Liu 2002). One week prior to
the experiment subjects were randomly assigned to the three
treatment conditions (high/medium/low interactivity) and
asked to fill out a short-survey regarding their body features
(body feature information was needed to create virtual models
for the high interactivity treatment). Although only the body
feature information from the subjects assigned to the high
interactivity treatment was used, all participants were asked to
fill out the short-survey to minimize the confounding effect of
the task. The online shopping task employed necessitated
subjects to select casual pants and a casual shirt for personal
use. The levels of interactivity were implemented by
manipulating the availability of the interactive
features/functions, adopted from the marketing-tool
dimension based on Ghose and Dou's (1998) interactivity
index, in an online product trial context. In the low-
interactivity treatment condition only color palate and fabric
choices were used to stimulate interactivity (with opportunity
to increase viewing size of each). The medium interactivity
treatment condition included a color palate, fabric choice, and
a generic body model on which subjects could try the apparel.
Further, each generic virtual model could be manipulated by
the subject (e.g., rotating view, changing color of clothing, etc).
The high interactivity treatment condition consisted of a color
palate and fabric choices for the clothing presented (with
opportunity to increase viewing size of each) and a virtual
model built for each subject (each virtual model was designed
to replicate the subject's body features). All experimental
treatments constrained subjects from accessing additional
product or company information. Efforts were taken to ensure
that male and female subjects were exposed to comparable
apparel in terms of price, style, color, and fabric. Interactivity
was therefore conceptualized as structural features of the
medium that allow immediate feedback within a retail
channel.
Experimental sessions were conducted in a computer lab in
groups ranging from 8 to 12 participants. Male and female
subjects were assigned to different sessions to avoid cross-
gender confounding effects. Experiment administrator's
gender matched the subjects' in each session. Experiment
administrators read the instructions from a script describing
the procedures. Subjects were first asked to complete the
questions measuring their pre-exposure to the brand and their
risk aversion characteristics. Next, subjects were directed to
the computers, preloaded for each treatment. After examining
the apparel subjects were asked to complete the questionnaire.
Subjects were then debriefed.
Measures
Interactivity was measured by asking subjects to fill out a four-
item, seven-point Likert scale similar to Jee and Lee (2002) and
Li, Kuo, and Russell (1999). The scales assessed the
respondent's perception of interactivity. It was modified to fit
into the brand-specific online apparel retail environment. The
four-item, seven-point Likert scale asked: (1) Interacting with
this site is like having a conversation with a sociable,
knowledgeable and warm representative from the company,
(2) I felt as if this web site talked back to me while I was
navigating, (3) I perceive the web site to be sensitive to my
needs for product information, and (4) All of the attributes
about clothes I want to know have been successfully digitized
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23JournalofInteractiveAdvertisingSpring 2005
online (?=.92). A composite perceived interactivity index was
created by averaging scores on the four items.
Trust was assessed using a four-item, seven-point semantic
differential scale similar to the scales used by Ganesan (1994)
and Sirdeshmukh, Singh, and Sabol (2002). This scale was
chosen because of its normative nature. Respondents were
asked to rate their overall trust toward the online marketer: (1)
very undependable-very dependable, (2) very incompetent-
very competent, (3) of very low integrity-of very high integrity,
and (4) very unresponsive to customers-very responsive to
customers (?=.82). A composite index was created by
averaging the scores on each item.
Product Evaluation was assessed using a four-item, seven-
point semantic differential scale similar to the scale proposed
by Petty, Cacioppo, and Schumann (1983). Subjects were
asked to rate their overall impression of the product from: (1)
bad-good, (2) unsatisfactory-satisfactory, (3) unfavorable-
favorable, and (4) not carefully produced-carefully produced
(?=.89). A composite index was created by averaging the
individual items' scores.
Online and In-store Purchase Intentions. As the focus of the
study was the transferring effect of interactivity through trust
in a multi-channel setting, we employed scales similar to
Griffith, Krampf, and Palmer (2001) and Baker and Churchill
(1977). Online purchase intention was assessed using a one-
item seven-point scale (ranging from "not likely" to "very
likely") capturing the subject's intention to buy the clothes
directly from the online marketer. In-store purchase intention
was assessed using a two-item, seven-point scale (ranging
from "not likely" to "very likely") capturing the subject's
intention to (1) buy the product if they saw it in store, and (2)
actively seek out the product in store to purchase it.
Composite indices were created for online and in-store
purchase intentions respectively.
RESULTS
Interactivity Manipulation
Results indicated there was a significant difference in
perceived interactivity between the three treatment groups,
F(2, 95)=49.62, p<.001. The perceived interactivity in the IH
treatment (M=5.48, p < .001) was significantly higher than that
in the IM treatment (M=3.38) and the IL treatment (M=3.11).
The IL and IM treatments were not significantly different, and
were combined into a single low interactivity group in
subsequent analyses. As mentioned previously, the different
levels of functional interactivity were designed to increase
variance of the interactivity in consumers' perception and the
focal variable we are interested in is the perceived interactivity.
To achieve this purpose, we adopted the composite perceived
interactivity index as a continuous variable in our subsequent
analysis which is preferred to dichotomizing when dealing
with predictor variables (Irwin and McClelland 2003).
Test of Hypotheses
Descriptive statistics and correlations between the core
variables are presented in Table 1. The correlation analysis
indicated that perceived interactivity was indeed significantly
correlated with trust (r = .59, p < .01) as well as product
evaluations (r = .20, p < .05). Further path analysis
demonstrated that perceived interactivity significantly impacts
on trust (ß = .59, p < .001) and product evaluation (ß = .20, p <
.05). Hence, both H1 and H2 were supported.
Table 1: Descriptive Statistics and Intercorrelations
(N=100)
Figure 2 is the final model that we derived by removing all
nonsignificant paths (p < .05) from the full model (see Figure
1). Standard parameter estimates for the final model are
presented in Table 2. The model's fit for the data was assessed
in accordance with three criteria (Kelloway 1998; Kline 1998):
a nonsignificant goodness-of-fit chi-square statistics; a value of
.90 or greater for GFI (good of fit index), AGFI (adjusted
goodness of fit index), CFI (comparative fit index), and NFI
(normed fit index); and a value of .10 or less for RMSEA (root
mean squared error of approximation). Results indicated that
the final model produced a good fit to the data (x2 = 6.59, df =
4, p = .16; RMSEA = .08; GFI = .98; AGFI = .91; NFI = .97;
NNFI = .96; CFI = .99). Missing links between the direct paths
indicate they were nonsignificant.
As Table 2 and Figure 2 indicated, despite the zero-order
positive associations between perceived interactivity and
online or offline purchase intentions (see Table 1), the
relationships became nonsignificant when product evaluation
and trust were introduced into the model. Parameter estimates
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24JournalofInteractiveAdvertisingSpring 2005
indicated that product evaluation had a significant influence
on online purchase intention (ß = .53, p < .001), but no
significant effect on in-store purchase intention. Trust had a
significant influence on online purchase intention (ß = .17, p <
.05), as well as in-store purchase intention (ß = .14, p < .05).
These provide evidence that product evaluation and trust
mediated the effects of perceived interactivity on purchase
intentions. H3 and H4 were therefore supported. The direct
path between online purchase intentions and in-store
purchase intentions was also significant after controlling for
other variables (ß = .76, p < .001). The magnitude of the
standard coefficient indicated that the online purchase
intentions accounted for a substantial portion of the variance
in offline purchase intentions. H5 was therefore also
supported.
Figure 2. Direct and Indirect Effects of Interactivity
Note: *p<.05, **p<.05
Table 2: Structural Parameter Estimates for Hypothesized
Model
The above results were based on treating perceived
interactivity as the independent variable. Therefore, the
question arises as to whether using the manipulated functional
interactivity would lead to different results. To check that
possibility, we re-specified our models by using interactivity as
a manipulated, categorical variable, and re-fitted the model
with the adjusted data. Results indicated that the new model's
fit indexes, parameter estimates and their p values did not vary
significantly from our final model (see Figure 2). We therefore
conclude that in the present study, perceived interactivity
highly corresponds to the objective/functional interactivity in
the context of online product trial.
DISCUSSION
The purpose of this study was two-fold. First, our intention
was to examine the impact of perceived interactivity in
advertising on the normative and cognitive evaluation of
consumers' product experience. Second, we wished to develop
a greater understanding of how trust developed in one channel
would influence consumer behavioral intentions in another
channel. The findings indicated that through greater
interactivity, a consumer develops greater trust and
understanding of the business and its products. Further, it was
demonstrated that trust transferred perceived interactivity's
influence not only onto online behavior intention, but also
onto offline purchase intention at a brand-specific business
level. This indicates that the influence of interactivity in online
communication can have significant implications for offline
behaviors. Interestingly, while trust significantly mediated the
influence of perceived interactivity on online and in-store
purchase intentions, product evaluation mediated only the
influence of interactivity on online purchase intention. This
difference might be due to the differences between the nature
of these two structures, i.e., while evaluation is a cognitive
structure, the normative nature of trust may have served to
foster positive relationships in both online and offline venues.
This suggests that while both consumers' cognitive and
normative evaluations of online communication can influence
their online behavior intention, the building and development
of trust can facilitate consumers' offline behavioral intentions.
While this concept has been investigated at the brand level
when examining products (e.g., Aaker 1996), it has not been
extended to the marketing channel context.
Findings from this research suggest that the Internet can be
used as an effective advertising tool to drive brand
understanding and continuity of purchase intentions. As Elkin
and Neff (2002) have noted, the online venue has not yet been
effectively used in the larger mix in advertising campaigns.
They indicated that most marketers only spent 2% to 3% or
less of their media budgets to advertise to consumers on the
Internet despite the fact that the Internet represents 10% to
15% of total media consumption. This lack of utilization of
online advertising may derive from the lack of research
demonstrating the effect of online advertising execution (e.g.
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25JournalofInteractiveAdvertisingSpring 2005
interactivity) on consumers' online and even offline buying
intentions. Our findings highlight the importance of
synchronizing online with offline advertising as online
advertising could influence both online and offline consumer
behaviors intentions.
Although this study provides new insights, it is not without its
limitations. One limitation of the current study is that it was
conducted within a single industry, i.e., the apparel industry.
While apparel is one of the largest and most important
product categories, the findings here are limited to this
context. For instance, while the use of apparel (a product
category higher in experience attributes) may provide new
insights into apparel promotion, the findings may not be
generalizable to product categories such as computers, which
tend to have a higher proportion of search attributes.
Therefore, it can be theorized that the transferring effect
observed is a function of the product category as with search
products a consumer does not necessarily need to experience
the product and thus may be more willing to purchase online.
As such, researchers should be cautious in generalizing these
findings beyond the scope of the current product category.
Future research could expand upon the current findings using
a variety of products within a broader range of product
categories, thus extending the generalizability of the work.
Second, the focus in this study was limited primarily within
the interactive execution under the context of online virtual
product trial (as a form of interactive advertising). Future
research might want to replicate the study under other
interactive advertising contexts, e.g., in-store kiosk, etc.
In conclusion, although changes in the competitive
environment have stimulated marketers to develop strategies
aimed at synchronizing multiple and complementary channels
to service an increasingly diverse consumer marketplace, little
empirical research has been conducted in this area. As such,
academics and practitioners have a limited understanding of
this topic. While this study demonstrated the importance of
interactivity in fostering behavioral intentions both within and
across channels, it provides only a starting point for the
development of more elaborate multi-channel communication
models. As such, a systematic research effort is warranted.
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ABOUT THE AUTHORS
Qimei Chen is an Assistant Professor of Marketing in the
College of Business Administration at the University of
Hawaii. She received her Ph.D. and M.A. from the University
of Minnesota at Twin Cities. Prior to that, she had years of
industry experience in Hong Kong and Germany. Her
research interests include advertising effectiveness, e-
commerce, IT usage, and cross-culture consumer behavior.
She has published in numerous journals, including the Journal
of Advertising, Journal of Advertising Research, and
Marketing Research. Email: qimei@hawaii.edu
David A. Griffith is Assistant Professor of Marketing &
Supply Chain Management at the Eli Broad Graduate School
of Management at Michigan State University. He received his
Ph.D. and MBA from Kent State University and his BSBA
from the University of Akron. His primary research interests
are international marketing strategy and retailing with an
emphasis on technology. He has published in numerous
academic and practitioner journals, including Journal of
International Business Studies, Journal of World Business,
Journal of International Marketing, and International
Marketing Review.
Fuyuan Shen (Ph.D., University of North Carolina-Chapel
Hill) is an assistant professor in the College of
Communications, Pennsylvania State University. He received
his Ph.D. from the University of North Carolina at Chapel
Hill. His teaching and research interests include advertising,
media psychology, and marketing communications. His work
has appeared in such journals as the Journal of Advertising,
Journal of Communication, and International Journal of
Advertising.
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