Advertising
Article
A Meta-Analysis of When and How
Advertising Creativity Works
Sara Rosengren, Martin Eisend, Scott Koslow, and Micael Dahlen
Abstract
Although creativity is often considered a key success factor in advertising, the marketing literature lacks a systematic empirical
account of when and how advertising creativity works. The authors use a meta-analysis to synthesize the literature on advertising
creativity and test different theoretical explanations for its effects. The analysis covers 93 data sets taken from 67 papers that
provide 878 effect sizes. The results show robust positive effects but also highlight the importance of considering both originality
and appropriateness when investing in advertising creativity. Moderation analyses show that the effects of advertising creativity
are stronger for high- (vs. low-) involvement products, and that the effects on ad (but not brand) reactions are marginally stronger
for unfamiliar brands. An empirical test of theoretical mechanisms shows that affect transfer, processing, and signaling jointly
explain these effects, and that originality mainly leads to affect transfer, whereas appropriateness leads to signaling. The authors
also call for further research connecting advertising creativity with sales and studying its effects in digital contexts.
Keywords
advertising, affect transfer, creativity, meta-analysis, processing, signaling
Online supplement: https://doi.org/10.1177/0022242920929288
Creativity is important in marketing and is often considered to
be at the heart of the advertising industry. The importance of
creativity is highlighted, for example, in the popularity of
industry competitions, such as the Cannes Lions International
Festival of Creativity, and the growing academic literature on
its effects (e.g., Reinartz and Saffert 2013; West, Koslow, and
Kilgour 2019). However, the value of creativity is also subject
to longstanding debate (Baack et al. 2015; Levitt 1963), and
recent reports highlight that marketers are increasingly
growing skeptical of advertising creativity (Parsons 2019;
Premutico 2019) and decreasing their investments in it
(Forrester 2019).
When and how is advertising creativity most valuable? Mar-
keters wanting to answer these questions will find little gui-
dance in the academic literature. Although the link between
advertising expenditure and advertising effects has been con-
sistently supported (Joshi and Hanssens 2010; Sridhar et al.
2016), to date, there is no comprehensive account of advertis-
ing creativity and its influence on consumer response. Even
Vakratsas and Ambler (1999) failed to account for creativity
as a factor in their insightful and influential review of how
advertising works.
Several factors seem to hold back scholarship in advertising
creativity: (1) contrasting empirical results on its effects in
terms of ad and brand outcomes (e.g., Lee and Mason 1999;
Smith, Chen, and Yang 2008; Till and Baack 2005), (2) dis-
agreements over what creativity is and how it should be
assessed (e.g., Modig and Dahlen 2019; Smith, Chen, and Yang
2008), (3) limited understanding of moderators of its effect
(e.g., Yang and Smith 2009), and (4) ambiguity about the kind
of theories that best explain how creativity works (e.g., West,
Koslow, and Kilgour 2019; Yang and Smith 2009). Given the
apparent confusion about what advertising creativity is and
when it might benefit a brand, it is not surprising that marketers
often make the wrong decisions when investing in advertising
creativity (Reinartz and Saffert 2013).
In this article, we synthesize the fragmented literature on
consumer response to advertising creativity. Based on a litera-
ture review, we develop a conceptual framework linking adver-
tising creativity to consumer outcome responses in terms of ad,
brand, and sales. Through a meta-analysis, we then integrate
878 effect sizes in the first quantitative empirical overview of
Sara Rosengren is Professor, Center for Retailing, Stockholm School of
Economics, Sweden (email: sara.rosengren@hhs.se). Martin Eisend is
Professor, Marketing Department, European University Viadrina, Germany
(email: eisend@europa-uni.de). Scott Koslow is Professor, Department of
Marketing, Macquarie University, Australia (email: scott.koslow@mq.edu.au).
Micael Dahlen is Professor, Center for Consumer Marketing, Stockholm
School of Economics, Sweden (email: micael.dahlen@hhs.se).
Journal of Marketing
2020, Vol. 84(6) 39-56
ª The Author(s) 2020
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the literature. Thus, we capture the impact of advertising crea-
tivity on 19 different consumer responses taken from 93 data
sets in 67 papers. We thereby contribute a comprehensive and
empirically grounded account of how and when advertising
creativity works, providing researchers with generalized find-
ings that can serve as benchmarks and a common foundation
for future studies of this important topic.
First, we provide an empirically validated account of how
advertising creativity works. The results show robust positive
effects of advertising creativity on consumer responses but also
inform researchers about the relative importance of various
consumer responses to advertising creativity. Overall, effects
are stronger for ad rather than brand responses (r ¼ .491 vs.
.317) and for attitudinal rather than memory outcomes (all
below .140). This suggests that the main advantages of adver-
tising creativity are not grabbing attention and making ads
memorable but rather the ability to foster positive ad and brand
attitudes.
Second, we highlight that advertising creativity is different
from originality. Effects on consumer response are greater
when creativity is measured as a bipartite construct comprising
of originality and appropriateness. Effects of originality only
on ad and brand attitudes are comparatively small (.362 and
.164), suggesting that marketers who view creativity as synon-
ymous with originality will not reap the full benefits of invest-
ments in advertising creativity.
Third, we show that the different theoretical accounts used
in the literature to explain how advertising creativity works are
complementary. Although previously considered separately,
affect transfer, processing, and signaling provide the best
account when considered jointly. The results further show that
processing is a key mediator of the effects, whereas originality
fosters affect transfer and appropriateness signaling. When
marketers invest in bipartite creativity, affect transfer and sig-
naling occur in parallel to processing, which can explain the
stronger effects of creativity compared with originality only.
Fourth, we find that when the three theoretical accounts are
considered jointly, the effects of advertising creativity on brand
response are not dependent on ad responses. This is in line with
the signaling account of advertising creativity and suggests that
to fully understand how advertising creativity works, marketers
should assess consumer responses in terms of brand rather than
ad outcomes.
For managers, the results provide guidance on how, when,
and why to invest in advertising creativity. For example, adver-
tising creativity leads to greater ad responses in high (vs. low)
involvement communication contexts (.653 vs. .340) and (mar-
ginally) for unfamiliar (vs. familiar) brands (.577 vs. .435). The
literature review also highlights the need for more studies on
the relationship between advertising creativity and sales, as
well as its effects in digital media contexts, two areas that are
especially important given the ongoing debate about the value
of advertising creativity in contemporary marketing practice.
Conceptual Framework
Figure 1 presents the conceptual framework guiding the meta-
analysis. We developed this framework on the basis of a review
of the literature on consumer responses to advertising creativ-
ity. The framework focuses on consumer responses that have
been empirically studied and distinguishes between immediate
and outcome responses. The categorization of immediate
responses is based on the three main theoretical accounts of
how advertising creativity works found in the literature: affect
transfer, processing, and signaling (i.e., consumer response in
terms of affect, processing, and perceived signals at the time of
exposure to creative advertising). The categorization of out-
come variables is based on ad and brand responses and char-
acterized in terms of attitudinal or memory responses (i.e.,
longer-lasting responses related to the ad and brand, such as
attitudes, memory effects, and sales). The framework also high-
lights two moderators of these responses: definitions and
Advertising creativity Consumer response
Creativity
moderator
Originality and
appropriateness
Originality only
Contextual
moderator
Involvement
Familiarity
Immediate
response
Affect
Processing
Signals
Outcome
response
Ad
Attitude/memory
Brand
Attitude/memory
Salesa
Figure 1. How and when advertising creativity works: conceptual framework.
aNot tested empirically due to lack of data.
40 Journal of Marketing 84(6)
assessments of advertising creativity and properties of the com-
munication context (involvement and familiarity).
Whereas research on advertising creativity generally has
found positive effects on immediate and outcome responses
(for reviews, see Sasser and Koslow [2008] and Smith, Chen,
and Yang [2008]), empirical findings suggest that the effects
vary between different types of consumer responses. Findings
generally show that advertising creativity has benefits in terms
of immediate responses, such as attention (Pieters, Warlop, and
Wedel 2002; Smith et al. 2007), positive affect (Haberland and
Dacin 1992; Yang and Smith 2009), and signals, such as per-
ceived sender effort (Dahlen, Rosengren, and Törn 2008;
Lange, Rosengren, and Blom 2016), but results are inconsistent
regarding when and how this might also lead to outcome
responses, such as attitudes and intentions (Modig and Roseng-
ren 2013; Smith, Chen, and Yang 2008) or memory effects
(Pieters, Warlop, and Wedel 2002; Till and Baack 2005).
Results also varied regarding whether attitude and memory
outcomes are affected (Baack, Wilson, and Till 2008). In line
with the overall literature, we hypothesize that advertising
creativity has positive effects on immediate and outcome
responses:
H1: Advertising creativity has positive effects on (a) imme-
diate responses and (b) outcome responses.
However, from a managerial perspective, understanding
whether investments in advertising creativity mainly affect ad
rather than brand response, and whether advertising creativity
is better at stimulating attitude or memory outcomes, is also
important. Given the inconsistencies in the literature, we qua-
lify this hypothesis by asking what type of consumer responses
are most affected:
RQ1: Is consumer response stronger for ad versus brand
outcomes?
RQ2: Is consumer response stronger for memory versus
attitude outcomes?
Defining and Measuring Advertising
Creativity
As indicated in the introduction, a key challenge in the litera-
ture is the different views on advertising creativity. Creativity
is a general construct that has been widely researched in fields
such as psychology and organizational behavior, as well as in
marketing (Im and Workman 2004; Sasser and Koslow 2008).
Creativity can be used to describe individuals (e.g., an art
director at an advertising agency), processes (e.g., design think-
ing methods used to brainstorm advertising campaigns), or out-
puts (e.g., the actual ad executions used in a marketing
campaign). In this article, we adopt the output perspective.
Drawing on the creativity literature (Amabile 1996; Hen-
nessey and Amabile 2010; Runco and Jaeger 2012), we define
advertising creativity as advertising execution(s) that is (are)
original and appropriate. This bipartite definition of creativity
has been widely adapted in the marketing literature, in which
the definition has been applied in advertising (Chen, Yang, and
Smith 2016; Kilgour, Sasser, and Koslow 2013), new product
development (Burroughs et al. 2011; Im and Workman 2004),
and consumer behavior (Burroughs and Mick 2004; Moreau
and Engeset 2016). As argued by Amabile (1996), a bipartite
definition of creativity is required, because outputs that are
original or unique but carry no use or meaning are perceived
as weird or bizarre. However, any judgments of creativity are
subjective and likely to vary across context and time. For
example, judgments about originality and appropriateness in
a work of art differ from the same judgments in an advertising
context (even if the actual object being judged is the same).
Similarly, judgments of the creativity of the same object can
vary over time. In the art domain, for example, there are several
artists who are now considered creative but whose art was
controversial or even rejected in their time (e.g., Monet,
Picasso, Dalı́). Such works were initially seen as “weird” or
“bizarre,” mainly because the type of appropriateness expected
of them at the time of creation was a literal representation of
reality. Thus, these artists were redefined as creative later,
when judgment of appropriateness changed.
In the advertising context, the best documented dimension
of creativity is originality. This dimension has also been
referred to as novelty, divergence, unexpectedness, and new-
ness (Kim, Han, and Yoon 2010; Koslow, Sasser, and Riordan
2003; Sheinin, Varki, and Ashley 2012; Smith et al. 2007).
Originality has positive effects on consumer responses to
advertising, as originality makes advertising more likely to
be attended and processed (Pieters, Warlop, and Wedel 2002;
Smith, Chen, and Yang 2008). Originality also has a positive
effect on consumer response, as people have a predisposition to
appreciate divergent stimuli and deem them intrinsically inter-
esting (Yang and Smith 2009). Advertising practitioners typi-
cally view originality as the most defining aspect of advertising
creativity (Koslow, Sasser, and Riordan 2003; Modig and Dah-
len 2019), especially when it comes to advertising creativity
awards (Choi et al. 2018; Smith et al. 2007). Thus, it is not
surprising that many scholars focus primarily or exclusively on
originality when assessing advertising creativity (Krishen and
Homer 2012; Pieters, Warlop, and Wedel 2002).
When it comes to appropriateness, this dimension comple-
ments originality by connecting the advertisement with brand
strategy and consumer problem-solving abilities and goals
(Ang, Lee, and Leong 2007; El-Murad and West 2004; Modig
and Dahlen 2019; Smith et al. 2007). In the advertising litera-
ture, appropriateness is also referred to as relevance and use-
fulness (and when assessed by practitioners as “on strategy”;
cf. Kilgour, Sasser, and Koslow 2013; Sasser and Koslow
2008). Appropriateness as such has received much research
attention (often using the term “relevance”; e.g., Hayes et al.
2020). However, in contrast with originality, scholars rarely
consider appropriateness to be an indicator of creativity in and
of itself. Instead, researchers typically view appropriateness as
a prerequisite for advertising to be interesting to its intended
audience regardless of its level of creativity.
Rosengren et al. 41
Theoretically, it is clear that creativity is both originality and
appropriateness. Some scholars also argue that additional
dimensions could be needed to fully understand advertising
creativity (Ang, Lee, and Leong 2007; Haberland and Dacin
1992). They argue in favor of including a third advertising-
specific dimension of creativity—namely, the quality of the
ad execution, also referred to as artistry or production (Modig
and Dahlen 2019; Smith et al. 2007). In the literature, we
distinguish four approaches to empirically assess advertising
creativity. First, some studies measure advertising creativity as
a holistic perception of the creativity of an ad, typically by
using a single item “creative” or multiple creativity items that
do not refer specifically to different dimensions (e.g., Roseng-
ren, Dahlen, and Modig 2013). Second, other studies rely on
only one dimension of advertising creativity (typically origin-
ality; e.g., Maniu and Zaharie 2014). Third, acknowledging the
bipartite definition of creativity, some researchers use the inter-
action between originality and appropriateness as a creativity
measure (e.g., Smith et al. 2007). Fourth, acknowledging the
multidimensionality of the bipartite definition, some studies
rely on measures of both originality and appropriateness (Kim,
Han, and Yoon 2010), sometimes combined with one or more
additional dimensions related to the production quality or artis-
tic value (Modig and Dahlen 2019; Reinartz and Saffert 2013).
We argue that researchers who focus on originality only
(e.g., Maniu and Zaharie 2014; Pieters, Warlop, and Wedel
2002) are likely to get different results from those who study
creativity in terms of a bipartite (e.g., Modig and Dahlen 2019;
Smith et al. 2007). Although we cannot test the validity of
different assessments, we propose that the best measure of
advertising creativity should explain more variance in outcome
response variables than alternative measures of creativity, lead-
ing to stronger effect sizes (for a similar argument, see Eisend
2015). The approach that has the highest explanatory value
should also be the most managerially relevant. Given that crea-
tivity is defined as a combination of originality and appropri-
ateness, we propose that the effect sizes should be stronger
when both dimensions are considered and weaker when only
originality is used. Thus,
H2: The effect of advertising creativity on (a) ad response
and (b) brand response is stronger when creativity is
assessed as a bipartite versus as originality only.
When Advertising Creativity Works:
Contextual Moderators
Although we expect advertising creativity to generally have
positive effects on consumer response (H1), we also expect
properties of the communication context to moderate these
effects. In the literature review, it was apparent that little atten-
tion has been paid to such contextual moderators in the existing
literature (Yang and Smith 2009). However, we identified two
theoretically relevant moderators: involvement and familiarity.
Both variables have been found to affect consumer response to
advertising in general, but of interest here is how they affect
consumer responses to advertising creativity. Specifically, the
literature suggests that advertising creativity (i.e., a combina-
tion of originality and appropriateness) has benefits regardless
of the type of processing (peripheral vs. central; e.g., Cacioppo
and Petty 1984) depending on these moderators.
Involvement
Consumers’ involvement with advertising reflects their level of
interest in brand evaluation in any given context and has been
found to moderate the effects of advertising processing and
response (e.g., MacInnis and Jaworski 1989; Meyers-Levy and
Malaviya 1999). Specifically, consumer responses to advertis-
ing differ depending on how much effort goes into processing
it. For example, high involvement with a product category
motivates consumers to pay attention to and actively process
advertising. When involvement is low, attention is typically
allocated to other things, and consequently, ad processing is
limited and utilizes few processing resources and peripheral
cues (e.g., Cacioppo and Petty 1984; MacInnis and Jaworski
1989).
Although advertising creativity has typically been thought
of as an attention-grabbing device (e.g., Pieters, Warlop, and
Wedel 2002), implying that it would work best in low-
involvement contexts (where it can foster situational involve-
ment; Cacioppo and Petty 1984), creativity has been found to
have additional processing advantages when it comes to high-
involvement contexts (Smith and Yang 2004; Yang and Smith
2009). In a low-involvement context, any additional processing
stimulated by a creative ad is likely to be shallow (MacInnis
and Jaworski 1989; Yang and Smith 2009). In a high-
involvement context, however, additional processing makes
consumers more likely to actively assess the claims. In this
context, the combination of originality and appropriateness
fosters more open-minded and less defensive processing of
claims made (“willingness to delay closure”; Kardes et al.
2004; Yang and Smith 2009). This means that consumers will
be more open to new information about the brand and less
likely to use defensive mechanisms when processing advertis-
ing messages that are communicated creatively. Whereas
advertising creativity in low-involvement contexts stimulates
more affective processing, in high-involvement contexts crea-
tivity influences affective and cognitive processing (Yang and
Smith 2009). In both contexts, advertising creativity should
have a positive impact on consumer response, but in a high-
involvement context, in-depth processing, coupled with the
willingness to delay closure, makes the effects stronger. Thus,
H3: The effect of advertising creativity on (a) ad response
and (b) brand response is stronger for high-involvement
versus low-involvement products.
Familiarity
Familiarity reflects the extent of consumers’ direct and indirect
experience with a stimulus, such as a product or a brand (Alba
42 Journal of Marketing 84(6)
and Hutchinson 1987; Campbell and Keller 2003). Consumer
response to advertising has been found to vary with familiarity
(Machleit, Allen, and Madden 1993; Sethuraman, Tellis, and
Briesch 2011). Specifically, the effects of advertising are gen-
erally stronger for unfamiliar than familiar brands. This effect
is due to consumers not being able to draw from previous
experiences (neither their own nor the experiences of others)
when evaluating unfamiliar brands, which makes advertising
more important for these brands. However, advertising for
unfamiliar brands wears out faster (Campbell and Keller
2003). For marketers of unfamiliar brands, this poses a chal-
lenge, as they need advertising to build familiarity but also
must be careful about how they advertise to avoid negative
reactions caused by (too much) repetition.
Familiarity has also been found to moderate the effects of
advertising creativity in terms of familiarity with the specific
ad (Chen, Yang, and Smith 2016; Pieters, Warlop, and Wedel
2002). Chen, Yang, and Smith (2016) found that advertising
creativity has two main benefits when it comes to repetition:
(1) it generates more positive effects upon initial exposure,
and (2) it resists wear-out over multiple exposures. The latter
finding is in line with results showing that advertising crea-
tivity (in terms of originality) helps draw attention to familiar
ads that might otherwise be overlooked due to tedium (Pieters,
Warlop, and Wedel 2002). For unfamiliar brands, these
advantages are more important (Campbell and Keller 2003).
Overall, this suggests that advertising creativity should be
beneficial for unfamiliar and familiar brands, but given its
attention-grabbing character (Pieters, Warlop, and Wedel
2002), the immediate wear-in effect that it can generate
(Chen, Yang, and Smith 2016), and the higher impact of
advertising in general (Machleit, Allen, and Madden 1993;
Sethuraman, Tellis, and Briesch 2011), the effects should be
stronger for unfamiliar brands. Thus,
H4: The effect of advertising creativity on (a) ad response
and (b) brand response is stronger for unfamiliar versus
familiar brands.
How Advertising Creativity Works:
Mediators
In the literature, scholars have used three main theories to
explain the effects of advertising creativity on consumer
responses. These accounts focus on different immediate
responses as key mediators of the effects of advertising crea-
tivity on outcome responses. The affect transfer model
focuses on the potential of creativity to evoke positive feel-
ings that spill over into consumer responses to the ad and
brand (i.e., regarding “positive affect” as a key mediator;
Yang and Smith 2009). The processing model focuses primar-
ily on the ability of creativity to get consumers interested in
the ad and brand (i.e., “ad processing” is the key mediator;
Smith et al. 2007). The signaling model proposes that crea-
tivity works as a marketing signal, directly influencing per-
ceptions about the sender and thus, consumer responses to the
brand (i.e., “perceived sender effort” is the key mediator;
Dahlen, Rosengren, and Törn 2008).
1
Affect Transfer Model
A common explanation for the effects of advertising creativity
is based on affect transfer (De Houwer, Thomas, and Bayens
2001; also referred to as affect infusion; Forgas 1995). This
explanation focuses on the ability of affectively loaded infor-
mation to transfer into other, more or less unrelated, targets. In
the advertising creativity context, the affect transfer model
builds on the fact that consumers generally like creative ads
(Rosengren, Dahlen, and Modig 2013; Smith, Chen, and Yang
2008). Processing creative ads is seen as intrinsically motivat-
ing and pleasurable (Rosengren, Dahlen, and Modig 2013;
Yang and Smith 2009), which means that consumers are likely
to experience positive affect while exposed to such advertising.
This positive affect spills over to the ad and brand, leading
them to be evaluated more favorably (Haberland and Dacin
1992; Yang and Smith 2009). According to this explanation,
the positive effects are driven by creative ads being more
enjoyable and liked, and the positive feelings that this stimu-
lates. Although theoretically exceptions might occur, as may be
the case for executions of fear appeal advertising that combines
originality and appropriateness, the reviewed literature on
advertising creativity unanimously provides empirical support
for positive reactions to advertising creativity. Thus,
H5: The effect of advertising creativity on (a) ad response
and (b) brand response is mediated by positive affect.
Processing Model
Explanations of how advertising works often draw on informa-
tion processing models (e.g., MacInnis and Jaworski 1989;
Meyers-Levy and Malaviya 1999). Specifically, they explain
consumer responses to advertising based on different levels of
ad processing. This is also a common explanation for the
effects of advertising creativity. Creativity is said to stand out,
thus making creative ads more likely to be attended to and
processed (Smith et al. 2007; Yang and Smith 2009). This
means that more creative advertising stimulates more ad pro-
cessing, resulting in longer exposure and greater attention
(Haberland and Dacin 1992; Pieters, Warlop, and Wedel
2002), which has positive effects on consumer outcome
response. According to this explanation, the positive effects
of creativity are driven by creativity being more interesting and
therefore, processed more. Thus,
1
The models we use in this article focus on key variables that are discussed in
the extant literature of the three theoretical accounts. We could not include
additional variables presented in Table 1 because of data constraints, which we
explain in detail in the “Methods” section. It should also be noted that all three
accounts have primarily been developed using experimental approaches.
Rosengren et al. 43
H6: The effect of advertising creativity on (a) ad response
and (b) brand response is mediated by ad processing.
Signaling Model
A third explanation for the effects of advertising creativity
focuses on creativity as a signal of brand and company
ability (e.g., Dahlen, Rosengren, and Törn 2008; Lange,
Rosengren, and Blom 2016). This model builds on research
on marketing signals (Kirmani 1997; Kirmani and Rao
2000), showing that certain behaviors on behalf of a firm
(e.g., offering long-lasting warranties) can be used to sig-
nal unobservable quality to consumers. For example,
advertising spending (i.e., monetary investments) has been
found to work as a signal of brand quality (Kirmani 1990;
Kirmani and Wright 1989). Similarly, advertising creativ-
ity has been found to be perceived by consumers as a
signal that the sender has invested effort (in terms of time
and money) in their brand (Dahlen, Rosengren, and Kars-
berg 2018; Dahlen, Rosengren, and Törn 2008). Through
creative advertising, a brand conveys that it is committed
to its advertising and its products, which is interpreted as a
signal that positively affects how the brand is perceived
and evaluated. According to this explanation, the positive
effects of creativity are driven by creativity signaling
effort on behalf of the sender, thus affecting the ad and
brand positively. In contrast to the other two models, this
account considers process reactions to creative advertising
to be about the immediate perceptions of the brand rather
than the ad. Thus,
H7: The effect of advertising creativity on (a) ad response
and (b) brand response is mediated by perceived sender
effort.
Full Model
Although the three theoretical accounts typically have been
used in isolation (for an exception, see Yang and Smith
[2009]), combining the three models should provide a more
comprehensive account of how advertising creativity works.
Furthermore, the omission of any one of the intermediary
effects might lead to the overestimation of the other (Vakratsas
and Ambler 1999). Therefore,
H8: A model of advertising creativity that considers (a)
positive affect, (b) ad processing, and (c) perceived sender
effort jointly better explains the effects of advertising crea-
tivity on ad and brand responses than any of the three mod-
els used separately.
To test this hypothesis, we propose a full model, which incor-
porates all three theoretical mechanisms (see Figure 2). Given
that the initial models focus on different variables, we propose
several additional relationships in the full model. First, ad pro-
cessing is likely to spur stronger perceptions of sender effort.
This is because processing facilitates a more careful under-
standing of the ad (Smith, Chen, and Yang 2008), which
should, in turn, enhance perceptions of sender effort stimulated
by advertising creativity. Second, affective responses can influ-
ence not only the ad and brand but also perceptions of sender
Perceived
sender
effort
Advertising
creativity
Ad
processing
Positive
affect
Attitude
toward
advertisement
Attitude
toward
brand
.293***
.337***
.341***
.373***
.388***
.051***
.351***
.234***
.462***
.304***
.078†
.206*** .085**
.088**
Figure 2. How advertising creativity works: Full model including estimation results (standardized path coefficients).
yp < .10.
*p < .05. **p < .01. ***p < .001.
Notes: Dotted lines indicate paths that were added to the combined model to form the full model.
44 Journal of Marketing 84(6)
effort. The underlying logic is, again, affect transfer (Yang and
Smith 2009). Furthermore, affect and processing should be
positively related, because feelings ease processing (mood the-
ory), and easy processing is experienced as a good feeling
(processing fluency theory).
Method
Data Set
For this meta-analysis, we selected papers that provide estimates
of the effects of advertising creativity on various consumer
responses. According to our bipartite definition of advertising
creativity, advertising creativity comprises originality and appro-
priateness. To be able to assess the relevance of different assess-
ments of creativity, we included all studies that identify as “ad*
creativity” studies independent of their definition and operationa-
lization of advertising creativity. This means that we also included
all studies that relied on advertising stimuli judged to be creative
(even if they did not use the bipartite definition), as well as studies
that investigated the two main dimensions of creativity, even if
they did not use the term “creativity” (Lee and Mason 1999).
To identify relevant papers, we first referred to review arti-
cles that provide an overview of previous research on advertis-
ing creativity (e.g., Sasser and Koslow 2008). We applied an
ancestry tree search by searching all papers that refer to the
review papers that were available in the Web of Science data-
base. Second, we performed a keyword search of electronic
databases (e.g., ABI/INFORM, Emerald, Elsevier, EBSCO,
and ProQuest Dissertation Publishing) using “advertising
creativity,” “ad creativity,” “advertisement creativity,” and
“advertising creative,” “ad creative,” and “advertisement
creative” as key words, followed by a search with key words
that relate to the two main dimensions of advertising creativity
(“original*,” “novel*,” “newn*,” “unexpected*,” “divergen*,”
“innovati*,” “incongru*,” “relevan*,” “appropriate*,”
“useful*,” and “meaningful*” combined with “advertis*”). The
database search was complemented by a search on Google
Scholar. Third, we performed a manual search of the journal
outlets that turned out to be major sources for articles on adver-
tising creativity. Fourth, once we identified a paper, we exam-
ined the references in a search for additional studies. The
search period covered all papers (published and unpublished)
that were available by December 2018. The retrieval approach
was consistent with recommendations in the literature (Hunter
and Schmidt 2004) and closely followed the steps taken in
recent meta-analyses published in marketing (Roschk and Hos-
seinpour 2020; Zlatevska, Dubelaar, and Holden 2014).
After identifying manuscripts for potential inclusion in the
data set, we applied inclusion and exclusion criteria to deter-
mine which manuscripts to retain. We included all empirical
studies that measured or manipulated advertising creativity (as
described previously) and provided estimates on its effects on
consumer responses. We excluded any manuscripts outside this
scope. For instance, we excluded studies that investigated non-
consumer response to creative ads (e.g., advertisers; Wang
et al. 2013), or studies on creative media choice, but not crea-
tive ads (Dahlen, Friberg, and Nilsson 2009). We also excluded
studies that failed to provide sufficient data for the meta-
analysis and for which necessary data could not be retrieved
from the authors.
To avoid duplications in the data set, a document with orig-
inal analyses and findings by the same authors (e.g., journal
article, working paper, conference paper) is called a “paper.”
In some papers, the authors analyzed more than one distinct data
set (e.g., a paper with several experiments), while some data sets
were analyzed in more than one paper (e.g., a study that was
published as a conference paper and a journal paper). The anal-
ysis is based on data sets. Each data set can provide single or
multiple effect sizes that refer to the effect of advertising crea-
tivity on any consumer response variable. The search resulted in
67 usable papers covering 93 data sets (see Web Appendix
Table 1). The sample includes journal articles, book chapters,
working papers, unpublished theses, and conference proceed-
ings, thus reducing the risk of a biased representation of the state
of research because of the source of publication. The variation of
sources is similar, and the number of papers and data sets is
higher than in other major meta-analyses in marketing (Chang
and Taylor 2015; You, Vaddkkepatt, and Joshi 2015).
Coding
We categorized the consumer response variables measured in
the studies based on the conceptual framework (see Figure 1).
Specifically, we classified consumer responses in terms of
immediate responses (affect, processing, and signals) and last-
ing outcomes related to the ad and brand (none of the data sets
provided data for sales). The outcome responses were further
divided based on attitude and memory responses. In addition,
we identified a few consumer responses that did not fit in either
category (e.g., actual creativity, brand familiarity, willingness
to pay). Because these consumer response variables appeared
either in only one or two data sets or in only one paper, we
eliminated them from further analysis.
2
We did this to ensure a
minimum degree of generalizability, because a meta-analysis
should provide a high degree of generalization and thus,
requires more information than a single manuscript or a
single-study manuscript followed by a replication study. This
left 878 effect sizes. For an overview of the consumer response
variables and categorization scheme, see Table 1.
In terms of creativity moderators, we coded the variables at
the effect size level, meaning that multiple effect sizes from
one data set can be assigned different codes. Specifically, we
coded whether creativity was assessed as originality only, as
appropriateness only, as an interaction effect between origin-
ality and appropriateness, as a multidimensional concept
2
We excluded the following variables from further analysis (mean correlations
in parentheses): negative thoughts (�.105, p < .01), other thoughts (.047, n.s.), negative feelings (.083, n.s.), actual creativity (.189, p < .01), brand familiarity (.192, p < .10), presumed influence (.309, p < .01), and willingness to pay (.429, p < .10).
Rosengren et al. 45
(including originality, appropriateness, and potentially more
dimensions), or as a holistic concept (measured with a single
item “creative” or corresponding multiple items or manipulated
as such; this is the base alternative in the model). As an illustra-
tion, Yang and Smith (2009) presented results based on origin-
ality and appropriateness separately, as well as for the interaction
between the two allowing us to code three types of measure-
ments for each of the variables studied. Although our main
interest is in comparing a bipartite view of advertising creativity
with a view of advertising creativity as originality only, this
coding process allows a more complete understanding of how
different assessments of creativity affect consumer response.
In terms of communication context moderators, we dummy
coded the variables on the data-set level. Specifically, we
coded the data sets 1 if the advertised category was a high-
involvement product and if the advertised brand was familiar.
In addition, we added three control variables that captured
substantial differences between studies and that could be
related to the context variables (medium, year, and award).
Two authors independently assigned variables in the primary
studies to consumer responses and coded the moderators and
control variables based on the information available in each
study. The agreement rate was above 98% (Krippendorff’s
alpha ¼ .932), and inconsistencies were resolved by discussion.
Effect Size Computation
The effect size metric selected for the meta-analysis was the
correlation coefficient; higher absolute values of the coefficient
indicate a stronger influence of advertising creativity on con-
sumer responses. For papers that reported other measures (e.g.,
Student’s t, mean differences), we converted those measures
following guidelines for meta-analysis (Lipsey and Wilson
2001; Peterson and Brown 2005).
3
We adjusted all correlations
for unreliability. When a paper did not report the reliability, or
when the paper used a single-item measure, we used the mean
reliability for that construct across all studies, following the
procedure in previous meta-analyses in marketing (e.g., Kirca,
Jayachandran, and Bearden 2005).
We dealt with integrating dependencies between effect sizes
using the following approach. When a data set provided
Table 1. Variables Used in the Meta-Analysis.
Variable Description Coding Scheme (Reliability)
Immediate Responses Consumer responses at the time of
exposure in terms of…
Affect Emotions Positive affect, humor
Processing Processing Attention, interest in ad, ad processing, complexity of ad/difficult to
comprehend, positive thoughts
Signals Perceptions Perceived sender effort, perceived brand value/quality, perceived trust,
perceived credibility
Outcome Responses Lasting consumer responses in terms of…
Ad response Attitude Aad
Memory Ad recall, ad recognition
Brand response Attitude Abrand, purchase/behavioral intention
Memory Brand recall, brand recognition, brand memory (mix recall/recognition)
Sales response Brand/product sales N.A.
Moderators
Creativitya Creativity assessed in terms of…
Originality only 0 ¼ other, 1 ¼ originality only
Appropriateness only 0 ¼ other, 1 ¼ appropriateness only
Originality � appropriateness 0 ¼ other, 1 ¼ interaction only
Multidimensional measure 0 ¼ other, 1 ¼ multidimensional
Familiarity Degree of brand familiarity 0 ¼ unfamiliar/fictitious/mixed, 1 ¼ familiar (AR ¼ 97.3%,
a ¼ .938)
Involvement Degree of product involvement 0 ¼ low involvement/mixed, 1 ¼ high involvement (AR ¼ 94.5%, a ¼
.786)
Control Variables
Medium Type of medium used to convey ad 0 ¼ print/outdoor, 1 ¼ TV/movies
Year Year of publication Continuous
Award Whether the studied ad has won a
creative award
0 ¼ other, 1 ¼ award winning (AR ¼ 94.5%, a ¼ .888)
aThe moderator variable is measured at the effect-size level, while all other moderators are measured at the data-set level.
Notes: Intercoder reliability is provided for all high-inference coding with AR ¼ agreement rate and a ¼ Krippendorff’s alpha.
3
Of 878 effect sizes, we converted 21 from coefficients in multivariate
regressions via the formula suggested by Peterson and Brown (2005). These
parameters were partial correlations, and therefore, we checked whether they
had an influence on the meta-regression results by including a dummy variable
that distinguishes between partial correlations and correlations. Because partial
correlations did not appear in the set of correlations referring to Abrand, the
dummy was included in the Aad model only.
46 Journal of Marketing 84(6)
findings for different consumer response variables, we treated
the findings as independent, because we integrated and analyzed
the estimates for each consumer response variable separately.
Some data sets reported multiple relevant tests for the same
consumer response variable. We accounted for the dependencies
of the effect sizes and the nested structure of the meta-analytic
data by using a mixed-effects multilevel model (Raudenbush
and Bryk 2002). We estimated the following model:
r ij ¼ g 00 þ m 0j þ e ij; ð1Þ
where i ¼ 1, 2, 3 . . . I effect sizes, j ¼ 1, 2, 3 . . . J data sets. This
formula estimates the average effect size g00, the deviation of
the average effect size in a data set from g00 (m0j), and the
deviation of each effect size in the kth data set from g00 (eij).
The latter two terms have variances that follow a normal dis-
tribution and are uncorrelated.
To address publication bias, we computed fail-safe Ns
(Rosenthal 1979), which represents the number of additional
effects with null results needed to render the results for an inte-
grated effect size not statistically significant at p ¼ .05. The fail-
safe Ns were calculated for all statistically significant integrated
effect sizes (p < .05) using the effect size estimates that were adjusted for measurement error. Furthermore, we provided a
homogeneity test as an aid in deciding whether the observed
effect sizes were more variable than would be expected from
sampling error alone. If they are, there is a strong basis for
including moderators. The homogeneity test involves the Q sta-
tistic, in which the distribution is similar to a chi-square with
K � 1 degrees of freedom (Hedges and Olkin 1985).
Moderator Analysis
If the homogeneity test indicated heterogeneity, we proceeded
with a moderator analysis. We added the moderators specified
by the hypotheses and the control variables simultaneously to
Equation 1 and ran multilevel meta-regression models in hier-
archical linear modeling separately for the major outcome vari-
ables. The model was a mixed-effects model, because fixed
effects for the moderators were considered in addition to ran-
dom components. We specified the following model:
r ij ¼ g 00 þ g 01 � involvement j
� �
þ g 02 � familiarity j
� �
þg 04 � medium j
� �
þ g 05 � year j
� �
þ g 06
� award j
� �
þ g 10 � originality ij
� �
þ g 20
� appropriateness ij
� �
þ g 30 � interaction ij
� �
þg 40 � multidimensionality ij
� �
þ g 50
� partial correlation ij
� �
þ u 0j þ e ij;
ð2Þ
where rij is the ith effect size describing the relationship
between advertising creativity and the respective consequence
variable reported within the jth data set.
Assuring the robustness of the model required a sufficient
sample size. The major restriction is often the higher-level
sample size, and the literature recommends a sample of around
50 to avoid biased estimates of the second-level standard errors
(Maas and Hox 2005). Thus, we applied the model only to the
outcome variables in the data set with a sufficiently large sam-
ple of data sets: Aad and Abrand (43 and 44 data sets,
respectively).
Structural Model Estimation
To investigate the different processes that explain how adver-
tising creativity works, we developed a correlation matrix
including integrated effect sizes of the consumer responses
to advertising creativity and added integrated effect sizes for
the interrelationships between the consumer response vari-
ables. We followed recommendations in the literature about
collecting meta-analytic data for the correlation matrix,
deciding about sample size, analytical decisions, and report-
ing (Bergh et al. 2016). We searched the papers in the meta-
analysis for correlations for the interrelationships between
consumer response variables. For a construct to be included
in such analysis, multiple study effects must relate it to every
other construct in the model. Therefore, no additional vari-
ables shown in Table 1 could be considered. For example,
because we did not find correlations between sender effort
and recall or memory measures, the latter could not be
included in the model. We found at least three correlations
for each relationship, which equals or exceeds the require-
ments of other meta-analytic correlation matrices found in the
literature (Geyskens, Steenkamp, and Kumar 1999). We inte-
grated and adjusted the correlations in the same way as the
correlations between advertising creativity and consumer
response variables. That is, we first adjusted all correlations
for unreliability. We accounted for the dependencies of effect
sizes and the nested structure of meta-analytic data by using a
mixed-effects multilevel model as described previously (Rau-
denbush and Bryk 2002).
We then used this correlation matrix (see Web Appendix
Table 2) as input in a structural equation modeling (SEM)
analysis using the maximum likelihood method. The matrix
was based on 449 correlations, and the harmonic mean of the
cumulative sample size for each cell equaled 1,293. Each con-
struct was measured with a single indicator in the structural
model. We fixed the error variances for these indicators to zero
because we had already considered measurement errors when
we integrated the effect sizes. We used the harmonic mean of
the cumulative sample size underlying each integrated effect
size (i.e., effect size cells comprising each entry in the correla-
tion matrix) as the sample size for the analysis.
Results
Table 2 reports the integration of the reliability-corrected cor-
relations between advertising creativity and all consumer
response variables.
Rosengren et al. 47
Looking at immediate responses, we found statistically sig-
nificant effects on affect in terms of positive affect and per-
ceived humor. Interestingly, although positive affect has been
studied more, the effects of humor were significantly stronger
as indicated by nonoverlapping confidence intervals (95% CI
for positive affect [.198, .388] vs. humor [.428, .832]). We also
found significant positive effects on processing in terms of
attention, interest in the ad, and ad processing, but only a mar-
ginal effect on complexity and positive thoughts. The effects on
attention, interest in the ad, and ad processing were comparable
in size (95% CI for attention [.218, .592], interest in ad [.215,
.615], and ad processing [.015, .659]). Furthermore, advertising
creativity had statistically significant positive effects on per-
ceived signals: sender effort, brand value/quality, brand trust,
and brand credibility. These effects were comparable in terms
of size (95% CI for perceived sender effort [.282, .510], value/
quality [.171, .407], brand trust [.171, .603], and brand cred-
ibility [166, .628]).
Turning to outcome responses, advertising creativity had a
statistically significant effect on all ad responses: Aad, ad recall,
and ad recognition. The strongest and most widely studied
effect was that on Aad. In terms of brand responses, the effects
followed a similar pattern: Abrand was the most widely studied
variable and statistically significantly affected. We also found a
statistically significant positive effect on purchase/behavioral
intention and brand memory, but not on brand recall or brand
recognition. Overall, the pattern of results support H1 by high-
lighting that advertising creativity has positive effects on con-
sumer reactions in terms of ad and brand. Answering RQ1, we
found that the effect on Aad was statistically significantly larger
than that on Abrand and purchase intentions (95% CI for Aad
[.407, .575] vs. Abrand [.235, .399]) and purchase intention
[.225, .387]), and that the effects on ad recall ([.214, .408])
were significantly larger than the effects on brand memory
([.072, .208]). This suggests that ad responses are more affected
than brand responses. Related to RQ2, the pattern of results
Table 2. Influence of Advertising Creativity on Consumer Responses (H1).
# Papers
# Data
Sets
# Effect
Sizes
Total
Sample Size Average r
Homogeneity
Test Q Fail-Safe N
Immediate responses Affect
Positive affecta 6 10 32 2,610 .293*** 158.184*** 595
Perceived humor 4 4 10 1,208 .630*** 142.097*** 1,860
Processing
Attention 12 13 30 4,365 .405*** 2,853.202*** 20,410
Interest in ad 7 11 40 1,829 .415** 15,766.431*** 267,621
Ad processinga 3 4 6 1,037 .337* 822.686*** 429
Complexity of ad/difficult to
comprehend
4 6 15 2,357 �.217y 417.683*** —
Positive thoughts 2 3 57 743 .177y 23.194*** —
Signals
Perceived sender efforta 6 7 38 6,310 .396*** 1,042.326*** 1,333
Perceived brand value/quality 8 10 27 2,623 .289*** 724.790*** 9,735
Perceived brand trust 3 4 6 626 .387*** 81.835*** 220
Perceived credibility 5 7 8 2,138 .397*** 848.119*** 2,434
Outcome responses Ad Attitude
Aad
a
37 44 192 19,729 .491*** 23,134.086*** 1,446,838
Ad Memory
Ad recall 18 24 91 2,712 .311*** 3,575.699*** 30,142
Ad recognition 11 15 32 3,334 .252** 2.485.365*** 7,133
Brand Attitude
Abrand
a 35 43 138 11,434 .317*** 12,438.120*** 232,128
Purchase/behavioral intention 29 34 83 28,950 .306*** 3,214.985*** 122,938
Brand Memory
Brand recall 11 14 36 3,825 .129 793.672*** —
Brand recognition 8 10 21 2,148 .052 2,752.091*** —
Brand memory 4 5 16 742 .140* 8.860y 173
yp < .10. *p < .05. **p < .01. ***p < .001.
a
These variables are used to test H2–H7.
Notes: Only relationships for which effects were available in more than one paper and/or more than two independent data sets are shown. The corrected average
correlation coefficients (r) are the sample size-weighted, reliability-corrected estimates of the population correlation coefficients. The fail-safe N indicates the
number of nonsignificant, unpublished (or missing) effects that would need to be added to a meta-analysis to reduce an overall statistically significant (p < .05)
observed result to nonsignificance.
48 Journal of Marketing 84(6)
suggests that effects of advertising creativity are stronger for
attitudes than for memory. Aad was statistically significantly
different from ad recognition (95% CI for Aad [.407, .575] vs.
ad recognition [.107, .307]), and marginally different from ad
recall ([.214, .408]). Similarly, the effect on Abrand was signif-
icantly stronger than the effect on brand memory (95% CI for
Abrand [.235, .399] vs. brand memory [.072, .208]).
All homogeneity tests (except for brand memory) were sta-
tistically significant at p < .05 and showed that the variation in effect sizes cannot be explained by sampling error alone. The
fail-safe N indicates that the statistically significant integrated
correlations do not suffer from publication bias according to
Rosenthal’s (1979) rule of thumb (fail-safe N should be at least
5 times the number of effects plus 10).
Table 3 presents the results for the multilevel moderator
regression model for the relationship between advertising crea-
tivity and Aad and Abrand. To investigate whether the positive
effects of advertising creativity depend on the type of assess-
ment used, we examined the moderating effect of creativity
assessments. The analysis showed that relying only on origin-
ality led to lower effect sizes for Aad and Abrand; thus, H2 was
supported. We found a similar pattern for assessments relying
on appropriateness only and for interaction effects, although
the negative effect was only marginally significant for the latter
when it came to Abrand. The findings also showed that multi-
dimensional measures of advertising creativity led to stronger
effect sizes for Abrand, but not for Aad. Overall, this pattern of
results suggests that assessing advertising creativity only in
terms of (1) originality, (2) appropriateness, or (3) an interac-
tion effect between the two will lead to an underestimation of
the effects. From a managerial perspective, the result also sug-
gests that a multidimensional view of advertising creativity is
the most relevant, as brand responses are more important than
ad responses.
We then turned to the moderating effect of the communica-
tion context. The results showed stronger effects on Aad and
Abrand for high-involvement products; thus, H3 was
supported.
Furthermore, the effects on Aad were marginally stronger for
unfamiliar products, but there was no statistically significant
difference in terms of Abrand. Thus, H4 was only partially sup-
ported. The control variables showed that using a partial cor-
relation coefficient led to smaller effects on Aad. None of the
remaining control variables affected Aad. However, the effects
on Abrand were higher for audiovisual media (TV/movies) and
marginally lower for award-winning ads. We did not find any
statistically significant differences in terms of year of study.
4
To better understand why advertising creativity has positive
effects on consumer responses, we performed a SEM analysis
of the different models using the meta-analytic correlation
matrix (cf. Web Appendix Table 2). Table 4 presents the results
of the SEMs (standardized coefficients and model fit statistics).
Table 3. Influence of Moderator Variables on Effect Sizes: Multivariate Meta-Regression Analysis Results (H2–H4).
Moderator (Hypothesis) Moderator Values
Aad Abrand
b (SE) Predicted b (SE) Predicted
Intercept .625 (.090)*** .308 (.082)***
Creativity (H2) Other vs. originality only �.202 (.052)** .564 vs. .362 �.170 (.074)* .334 vs. .164
Other vs. appropriateness only �.228 (.074)** .549 vs. .320 �.149 (.048)** .304 vs. .154
Other vs. interaction only �.270 (.089)** .510 vs. .240 –.120 (.068)y .275 vs. .156
Other vs. multidimensional .105 (.102) .231 (.013)*** .260 vs. .492
Involvement (H3) Low vs. high involvement .259 (.078)** .340 vs. .653 .223 (.093)* .196 vs. .420
Familiarity (H4) Unfamiliar vs. familiar �.142 (.080)þ .577 vs. .435 �.064 (.085)
Medium (Ctrl) Print/outdoors vs. TV/movies �.049 (.078) .182 (.081)* .206 vs. .388
Year (Ctrl) Continuous �.004 (.005) �.003 (.003)
Award (Ctrl) Others vs. award winning �.093 (.091) �.214 (.110)y .327 vs. .113
Partial correlation (Ctrl) Other vs. effect converted from
multivariate regression coefficient
�.523 (.052)*** .519 vs. �.004 —
y
p < .10.
*p < .05. **p < .01. ***p < .001.
4
To further explore the role of originality and appropriateness in explaining
these effects, we also tested several plausible interactions between creativity
measurements and moderators (similar to the procedure in Sethuraman,
Tellis, and Briesch 2011). For Aad, we tested interactions between
measurements (using dummy variables for originality, appropriateness, and
multidimensions, the cell sizes for interactions with “interaction only” were
too small to provide a robust analysis) and the hypothesized moderating
variables (involvement, familiarity). The analysis showed a significant
interaction effect for appropriateness � familiarity (b ¼ �.239, SE ¼ .062,
t ¼ 3.830, p < .001), suggesting that appropriateness is more important for
unfamiliar brands. There was also a significant interaction effect for
appropriateness � involvement (b ¼ .271, SE ¼ .058, t ¼ 4.643, p < .001), suggesting that appropriateness is more important in high-involvement
contexts. We conducted the same analysis for Abrand, where we were able to
test interactions between measurements focusing on originality and
appropriateness (the cell sizes for “interaction only” and “multidimensional”
were too small to provide a robust analysis). However, we did not find any
statistically significant interactions.
Rosengren et al. 49
As we suggested alternative models implying that the relation-
ship between advertising creativity and Aad is mediated by
more than one mediating variable, we added a path between
advertising creativity and Aad that captured alternative pro-
cesses to each model. All three individual models showed a
very good model fit, and all paths were statistically significant
and in line with the suggested effects; thus, H5, H6, and H7 were
supported.
The model that combines the three individual models
showed a comparatively weak fit but was significantly
improved by adding the proposed relationships between pro-
cessing and perceived sender effort and positive affect
and perceived sender effort suggested by the full model
(Dw2/d.f. ¼ 96.527/5, p < .001; see Figure 2). To determine whether the full model provided a better explanation than the
three parsimonious models that were nested within it, we com-
pared the fit of the full model that was restricted to any of the
nested models with the fit of the full model with unrestricted
paths. The model fit worsened significantly when it was
restricted to the affect transfer model (Dw2/d.f. ¼ 1,629.935/
8, p < .001), the processing model (Dw2/d.f. ¼ 1,733.093/8,
p < .001), or the signaling model (Dw2/d.f. ¼ 1,528.916/8, p <
.001). Thus, the full model provides an explanation that goes
beyond the explanatory power of each nested model; H8 was
empirically supported. Interestingly, in the full model, the med-
iating effect of Aad on Abrand dropped from around .5 in the
individual models to a marginally significant effect of .078
(Dw2/d.f. ¼ 96.512/1, p < .001). This suggests that the effect of advertising creativity on brand response is only weakly
mediated by ad response, which adds additional insight into
RQ1 about the effects of creativity on ad versus brand response.
We performed two additional analyses to further explore
how well the three models explain the effects of creativity on
consumer response. First, we compared how much of the var-
iance in Aad was explained directly by advertising creativity
and indirectly by either process suggested by the three individ-
ual models (we could not apply this comparison to Abrand, as
there was no direct effect of creativity on Abrand in the model).
We computed the proportion of mediation as the ratio of indi-
rect to total effect; that is, the indirect path(s) was/were divided
by the sum of the direct path and indirect path(s) (Iacobucci,
Saldanha, and Deng 2007). The proportion of mediation via
positive affect was 26.8%, via ad processing was 28.3%, and
via perceived sender effort was 33.9%. When we tested the
mediation paths in the full model against each other by restrict-
ing two corresponding paths at a time (see Web Appendix
Table 3), we found no differences between the paths from
advertising creativity to any of the three mediators (positive
affect, ad processing, and sender effort). However, the effect of
sender effort on Aad was significantly different and stronger than
the effect of either positive affect or ad processing on Aad. The
findings indicate that signaling explains more variance in Aad
than the two other models, thus providing the strongest explana-
tion for the effect of creativity on Aad of the three models.
Table 4. Coefficients and Fit Indices of the Meta-Analytic SEMs (H5–H8).
Affect Transfer
Model (H5)
Processing
Model (H6)
Signaling
Model (H7)
Combined
Model Full Model (H8)
Creativity ! Positive affect .293*** .293*** .293***
Creativity ! Ad processing .337*** .337*** .337***
Creativity ! Perceived sender effort .396*** .396*** .341***
Creativity ! Attitude toward the ad .315*** .301*** .250*** .051** .051***
Positive affect ! Perceived sender effort .085**
Positive affect ! Attitude toward the ad .515*** .388*** .388***
Positive affect ! Attitude toward the brand .266*** .373*** .373***
Positive affect +! Ad processing .206***
Ad processing ! Attitude toward the ad .490*** .351*** .351***
Ad processing ! Attitude toward the brand .131*** .234*** .234***
Ad processing ! Perceived sender effort .088**
Perceived sender effort ! Attitude toward the ad .546*** .462*** .462***
Perceived sender effort ! Attitude toward the brand .128*** .304*** .304***
Attitude toward the ad ! Attitude toward the brand .477*** .562*** .556*** .078y .078y
Model Statistics
w2/d.f. 1.840/1 1.067/1 .655/1 97.903/4*** 1.376/1
Goodness-of-fit index .999 1.000 1.000 .975 1.000
Adjusted goodness-of-fit index .993 .996 .997 .867 .993
Comparative fit index 1.000 1.000 1.000 .973 1.000
Root mean square residual .008 .006 .005 .085 .004
Root mean square error of approximation .025 .007 .000 .134 .017
yp < .10. *p < .05. **p < .01. ***p < .001.
50 Journal of Marketing 84(6)
Second, we compared the theoretical explanation offered by
the full model between the two dimensions of creativity by using
correlation matrices that considered the variable relationships
with either originality or appropriateness instead of creativity
(see Web Appendix Table 4). The results showed that the pos-
itive effects on ad processing are equally strong for both dimen-
sions. However, affect transfer mainly explains the effects of
originality as indicated by the fact that the path from creativity
to positive affect was statistically significant for originality, but
not for appropriateness. When it comes to signaling, however,
appropriateness seems more important, as indicated by the sig-
nificantly stronger link between creativity and sender effort.
Discussion
Summary of Findings
In this article, we offer a comprehensive synthesis of the effects
of advertising creativity on consumer responses. The study
highlights the importance of advertising creativity by showing
robust positive effects on a wide range of immediate and out-
come responses. The effects are stronger for ad responses com-
pared with brand responses and for attitudinal compared with
memory outcomes. Moderation analyses show that the effects
of advertising creativity are weaker when creativity is assessed
as originality only, compared with a bipartite comprising ori-
ginality and appropriateness. This suggests that the effects of
advertising creativity go beyond those of originality alone. The
results further show that advertising creativity has stronger
effects in high-involvement contexts, and that effects on ad
response are (marginally) stronger for unfamiliar brands.
Furthermore, we find empirical support for all three theoretical
accounts (affect transfer, processing, and signaling) used in the
literature, but also that a full model (where the three accounts
are considered jointly) best explains the effects of advertising
creativity on consumer outcome response. In the full model, the
effect of the three advertising creativity mediators (positive
affect, ad processing, and perceived sender effort) on brand
response is only marginally mediated by ad response, suggest-
ing that although ad responses are generally more affected than
brand responses, they are not needed for advertising creativity
affect brand response. Additional analyses show that affect
transfer mainly explains the effects caused by originality and
that signaling provides the strongest account of advertising
creativity in terms of ad response.
Theoretical Implications
Although marketing researchers and practitioners tend to agree
that advertising creativity is important, there are contrasting
views on what advertising creativity is, and how and when it
can lead to positive outcomes. Through this meta-analysis, we
provide a synthesis of the growing, but dispersed, literature on
advertising creativity, thus building a common foundation for
future studies of this important topic. The results inform about
important outcome variables and moderators of advertising
creativity effects. The meta-analytic findings can serve as
benchmarks for future advertising creativity studies, as well
as for studies dealing with other ad execution elements. Future
findings can be compared against the meta-analytic results in
terms of explained variance as a measure of advertising effec-
tiveness. The results also have several implications for future
studies of advertising creativity.
First, we offer an empirically validated understanding of
how advertising creativity works. The pattern of results sug-
gests that advertising creativity has a role to play in stimulating
positive consumer responses that goes beyond being a source of
attention. If the attention-grabbing nature of advertising crea-
tivity were the key benefit, its effects should be greater for
memory rather than attitudinal responses, and in communica-
tion contexts where consumers are less likely to attend to and
process ads, such as for low-involvement products and for
unfamiliar brands (Dahlen, Rosengren, and Törn 2008; Pieters,
Warlop, and Wedel 2002), which is not in line with the empiri-
cal results. Although claims that advertising creativity enables
advertising to “cut through clutter” and make advertising more
memorable (Pieters, Warlop, and Wedel 2002) are true, they
risk directing focus away from attitudinal consumer responses,
which are more affected. The fact that advertising creativity
has stronger effects in high-involvement contexts suggests that
processing is important for the effects to occur. It also raises the
question of what to expect from advertising creativity in con-
texts where consumers are unlikely to pay attention to and
process ads, such as digital and mobile media. The meta-
analysis did not include any such studies, but the results suggest
that effects should be weaker in media such as smartphones
where focus is very directed at other focal tasks (Melumad and
Meyer 2020). At the same time, effects should be stronger for
advertising content in own channels and in media where con-
sumers voluntarily seek out advertising (Rosengren and Dahlen
2015). However, future research is needed to explicitly study
the role of advertising creativity in these contexts.
Second, we contribute insights into how to define and assess
advertising creativity. In line with the creativity literature
(Amabile 1996; Runco and Jaeger 2012), the results indicate
that creativity is not just about originality. A bipartite definition
and multidimensional assessments of creativity offer better
explanations of the effects (for a similar argument, see Ang,
Lee, and Leong 2007 and Modig and Dahlen 2019). This sug-
gests that researchers should be mindful when using the term
advertising creativity and restrict it to studies of original and
appropriate ads. When studying original advertising only, the
term creativity should be avoided. It also suggests that the
reliance on advertising awards as an operationalization of
advertising creativity is not valid, as such awards tend to focus
on originality (Choi et al. 2018; Kilgour, Sasser, and Koslow
2013). The fact that empirical studies have found positive
effects of original and award-winning ads, however, is reassur-
ing, as the results suggest that, if anything, those studies under-
estimate the effects.
Third, we contribute to the theoretical understanding of how
advertising creativity works. The findings show that the different
Rosengren et al. 51
theoretical accounts of advertising creativity available in the
literature are complementary, but also that they have different
relationships with creativity dimensions. Our meta-analytic path
analysis show that originality primarily stimulates affect trans-
fer, whereas appropriateness is more important for signaling. We
also find that signaling has the highest explanatory value. Again,
this reinforces the notion that a bipartite view of advertising
creativity is most relevant, as ads that combine originality with
appropriateness allow these mechanisms to work simultane-
ously, whereas original ads do not. It also suggests that future
studies of advertising creativity should include more comprehen-
sive theoretical frameworks than what has previously been the
case. Together, these insights offer the basic building blocks for
a more complete processing model of advertising creativity
called for by West, Koslow, and Kilgour (2019).
Fourth, the finding that the three theoretical mediators of
advertising creativity have direct effects on brand response
(Abrand) that are only weakly mediated through ad response
(Aad) adds further to our understanding of how advertising
creativity works.
5
It shows that although creativity has stronger
effects on ad responses than brand responses, these effects are
not necessarily dependent on ad response. Again, this pattern
can be understood in terms of the combination of (high) origin-
ality and (high) appropriateness in creative ads. In line with
Smith et al.’s (2007) finding that originality has advantages in
terms of attention and that appropriateness stimulates down-
stream effects and brand response, advertising creativity allows
the two to work in parallel, which also has more direct brand
outcomes. This finding is in line with the signaling account of
advertising creativity that suggests a more direct effect on the
brand. For researchers, it suggests that when studying the
effects of advertising creativity, brand (and sales) responses
must be included through direct measures rather than relying
on Aad or other ad responses as proxies of such effects.
Overall, the empirical results provide convincing evidence
of the positive effects of advertising creativity on consumer
responses and thus highlight the need for marketing scholars
to consider not only media investments (ad spend; Joshi and
Hanssens 2010; Sridhar et al. 2016) but also creativity invest-
ments in models of how advertising work.
Managerial Contributions
For marketers, we contribute a systematic account and empiri-
cal evidence of the value of advertising creativity. Specifically,
we offer important insights into how, when, and why to invest
in advertising creativity. Given the ongoing debate about the
value of creativity in advertising (Forrester 2019; Premutico
2019), this contribution is timely and useful. It also shows no
evidence of advertising creativity becoming less (or more)
effective over time.
When it comes to how to invest, Reinartz and Saffert (2013)
found that many marketers make suboptimal decisions regard-
ing investments in advertising creativity. We suggest that a
tendency to focus on originality might be the root of this prob-
lem. Creativity is more than originality, and by incorporating
appropriateness consumer response will be more positive. To
achieve this, marketers must find ways to assess advertising
creativity. This is easier said than done, given that creativity
judgments are subjective and vary across context and time. We
find that award-winning ads lead to marginally weaker brand
response, suggesting that consumer rather than professional
judgments should be used. This supports Ang, Lee, and
Leong’s (2007) argument that marketers should involve con-
sumers more in advertising development. Whereas there is a
growing literature focusing on consumers as cocreators of
advertising (Dahlen and Rosengren 2016; Thompson and
Malaviya 2013), consumers could also be engaged as prejudges
of advertising. A post hoc analysis of the role of ad judges
provided additional support for this notion. Specifically, we
coded a variable that distinguished between ads that were
judged to be creative by either consumers, by experts, or
selected from award shows. As some studies did not provide
details on ad judges, we first ran analysis of variance models
for a combination of all three outcome responses (Aad, Abrand,
and intentions) to ensure sufficiently large sample sizes. We
found significant effects (F(2, 351) ¼ 4.931, p ¼ .008) on
outcome response. The effects were stronger when consumers
judged advertising creativity (.373) compared with experts
(.300) or award shows (.193). When we analyzed the three
responses separately, the effect held for Abrand and intentions,
but not for Aad. As brand outcomes are more valuable for
marketers, this reinforces the potential in allowing consumers
to (pre)judge advertising creativity.
When it comes to when to invest, the results suggest that
advertising creativity has positive effects in general but also
that the effects are stronger for attitudinal rather than memory
response and marginally stronger in audiovisual media (TV/
movies vs. print/outdoor). Furthermore, the effects are stronger
for high-involvement contexts. For marketers, this challenges
the established view of advertising as a tool for gaining atten-
tion and suggests that creativity is especially valuable in con-
texts where consumers are likely to process advertising.
Although we studied product involvement, this logic should
also hold for media context involvement, meaning that creativ-
ity is more likely to work in situations where more focused ad
processing occurs. Thus, advertising creativity should be more
important for media contexts in which consumers voluntarily
direct their attention to, or are forced to focus directly on,
advertising than in in media contexts that rely on incidental
and divided attention (see also Dahlen and Rosengren 2016;
Rosengren and Dahlen 2015).
We also find that advertising creativity has marginally
stronger effects for unfamiliar compared with familiar brands.
However, this effect is related only to ad rather than brand
response. As suggested by Campbell and Keller (2003), ad
response is a strong indicator of brand response for unfamiliar
5
This suppression effect is especially interesting because the Aad and Abrand
relationship is typically very strong, as evidenced in previous meta-analyses
(e.g., Brown and Stayman [1992]: r ¼ .600 [# effect sizes ¼ 33], Eisend [2007]:
r ¼ .581 [4], Eisend and Küster [2011]: r ¼ .624 [11]).
52 Journal of Marketing 84(6)
brands (as consumers have little other information on which to
base evaluations), suggesting that this finding is still manage-
rially important. By investing in advertising creativity, such
brands can increase the value of their advertising to consumers
(“advertising equity”; Rosengren and Dahlen 2015). Taken
together, this suggests that advertising creativity is especially
valuable when establishing a new brand in the market.
When it comes to why advertising creativity works, the
mechanisms underlying its positive effects are more profound
than many marketers might think. An in-depth understanding
of how affect transfer, processing, and signaling jointly con-
tribute to brand response help make investments in advertising
creativity less risky (West, Koslow, and Kilgour 2019).
Although marketers who focus on originality can expect posi-
tive effects due to affect transfer, they will miss out on the
potential effects of signaling and appropriateness. By investing
in bipartite advertising creativity, marketers can increase the
chances of their ads being liked, processed, and interpreted as
signals of what the brand has to offer. It also means that there is
little risk that positive effects will be for ad response only.
From a managerial perspective, the effects of signaling are
especially important to consider, as they offer the strongest
explanation for the effects on ad response and because appro-
priateness is especially important in high-involvement and
low-familiarity contexts, where advertising creativity also has
the strongest effects. It suggests that advertisements can pro-
duce effects by way of the signals they send rather than the
specific messages they convey. Signals are especially impor-
tant in situations where there is information asymmetry
between marketers and their customers (Chase and Murtha
2019; Kirmani and Rao 2000). This is arguably the case for
unfamiliar brands and high-involvement products as well as in
other situations where the decision-making process is complex,
such as in business-to-business, business-to-government, and
recruitment contexts (Chase and Murtha 2019; Dahlen,
Rosengren, and Karsberg 2018). In fact, recent research sug-
gests that the effect of advertising signals extends beyond con-
sumers to other stakeholders, such as employees and investors
(Dahlen, Rosengren, and Karsberg 2018), though this is beyond
the scope of the present study.
Limitations and Further Research
Given the nature of a meta-analysis, we could study only
consumer responses that previous researchers had investi-
gated. This means, for example, that we could not consider
potential negative effects of creativity on, for example, con-
fusion, negative affect, and fear appeals. However, we found a
marginally significant negative effect of complexity, suggest-
ing that the potential downsides of creativity warrant further
investigation.
Similarly, the literature review revealed a lack of studies on
the effects of advertising creativity on sales (for an exception,
see Reinartz and Saffert [2013]) and the effects of advertising
creativity in digital contexts, such as the effects of advertising
creativity on social media influencer engagement (Hughes,
Swaminathan, and Brooks 2019). Future studies are needed
to explore how advertising creativity works in those contexts.
Studies linking the effects of advertising creativity to beha-
vioral measures, such as brand choice or sales, seem especially
important. This could be done by combining quantitative
(advertising spend) and qualitative (advertising creativity)
assessments of advertising investments with behavioral
outcomes, for example, adding advertising creativity in
marketing-mix models or adding sales as a dependent variable
in experimental studies. In such efforts, additional moderators,
such as clutter (Pieters, Warlop, and Wedel 2002) and repeti-
tion (Chen, Yang, and Smith 2016), should also be considered.
As another limitation, the present study focused on con-
sumer responses to advertising creativity only. There are sev-
eral related issues in the literature that could contribute to our
understanding of advertising creativity. For example, there is a
vast literature on creative processes in agencies that foster
creativity in advertising (Goldenberg, Mazursky, and Solomon
1999; Kilgour and Koslow 2009), and synthesizing this litera-
ture should bring additional insights to marketers. Relatedly,
there should be room to further integrate the literature on adver-
tising creativity with creativity research focusing on other mar-
keting contexts (Burroughs et al. 2011; Dean, Griffith, and
Calantone 2016) to allow for a more complete understanding
of how creativity works in marketing more broadly. It is our
hope that this article can contribute to this development.
Acknowledgments
The authors dedicate this article in loving memory of Dr. Sheila Sas-
ser, a brilliant creativity scholar and dear friend. The authors are very
grateful to the JM review team for their insightful and constructive
comments that greatly benefited the article.
Associate Editor
Wayne Hoyer
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to
the research, author
ship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, author-
ship, and/or publication of this article.
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