Content Analysis as a Predictive Methodology: Recall, Readership

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Content Analysis as a Predictive Methodology:
Recall, Readership, and Evaiuations of
Business-to-Business Print Advertising
This article calls for the application of content analytic techniques to advertising as a
method of predicting advertising effectiveness. A comprehensive empirical
investigation examines the effect of both form variables (e.g., headline size, use of
color, illustration placement) and content variables (e.g., subject matter, use of
humor, use of fear appeals) on recall, readership, and evaluations in the context of
business-to-business print advertising. The prediction of four different outcome
variables is successful, with total variance accounted for ranging from 12 percent to
59 percent. Significant predictors vary substantially across the dependent indicators,
indicating that different advertisement characteristics are likely to be needed to
achieve various advertiser goals.
JOHN L. NACCARATO
Liggett-Stashower, Inc.
KIMBERLY A. NEUENDORF
Cleveland State
University
valid measure of whether or not the advertiser's
message has reached the receivers.
Readership studies have been conducted on a
continuing basis for print media since the 1920s
(Hendon, 1973). There are a number of independent organizations conducting readership studies
(e.g.. Starch, Ad-Q, Ad-Chart, and Harvey), plus a
host of publisher-sponsored readership services
seeking to provide advertisers with information
about advertising placement. However, readerADVERTISING SUCCESS
ship studies have come under certain criticisms in
The direct linking of sales to advertising exposure
the past two decades (Edmonston, 1995; Johnson,
is rarely validated in practice. Even the older, clas1982; Rothschild, 1987; Schaefer, 1989; Sekely and
sic models of advertising and marketing (e.g.,
Blakney, 1994; Whipple and McManamon, 1992;
"DAGMAR") have acknowledged the role of interrttediary processes and states (Olshavksky, 1994), Wood, 1989). Additionally, some industry observers have noted the paucity of syndicated readerincluding but not limited to knowledge, [positive]
ship research for industrial or business-to-business
affect, and behavioral intention (cf., Ajzen and
advertising (Morelli, 1986).
Fishbein, 1980). Correspondingly, and for reasons
of practicality and comparability of criteria, adverWhat makes a consumer read a given advertisetising readership studies are viewed as the basic ment? An early Ogilvy pronouncement declared
that "Every advertisement must tell the whole
too! for assessing advertising effectiveness.
sales story.., Every word in the copy must count"
Readership is probably the most frequently
(Ogilvy, 1986). Images, color, and layout factors
used indicator of advertising effectiveness. Unlike
are also of great concern in the industry (Roman
inquiry reports—which count how many readers
and Maas, 1992). A careful examination of adverrequest additional information and are a mainstay
is sales. As the
dean of advertising David Ogilvy notes: "I do not
regard advertising as entertainment or an art form,
but as a medium of information. When I write an
advertisement, I don't want you to tell me that you
find it 'creative.' I want you to find it so interesting
that you buy the product" (Ogilvy, 1983),
THE ULTIMATE GOAL OF ADVERTISING
of business-to-business advertising—readership
studies ask a representative sample of respondents
whether or not they saw the advertisement, if they
read it, and perhaps how much of the advertisement they remember seeing. Sometimes referred
to as recognition or recall studies', readership
studies have traditionally been considered to be a
'The terms recognition and recall are usually intended to refer to
aided and umided recall, respectively: many commercial services cm>er
both. In llw case of busmess-to-business adivrtising, recognition/aided
recall is perhaps the more salient crilerioti. since company or product
im is often a fmidamental pre-aales-catl goal.
M a y . June 1 9 9 8 JDUHURL OF HDUERTISinG RESEflflCtl 1 9
B-tO-B PRINT ADVERTISING
tising content may shed light on the "sales
story." The research exemplar reported
here attempts to develop a practical
schema applicable to a range of advertisement types, focusing on providing a linking mechanism betv^'een the production of
an advertisement and its positive reception by consumers. The chosen exemplar
examines business-to-business advertising in a trade magazine.
The typical experimental investigation
deals with one variable in isolation from
others and tests fairly abstract outcomes
(e.g., positive affect toward a spokesperson in the advertisement) on nonrepresentative samples. The average single-source
study fails to establish audience exposure
to an advertisement and does not even
consider advertisement characteristics.
Other, complementary studies are needed
to provide practicality of prediction and
Advertisement characteristics and
generalization. Research studies that
advertising success
probe naturally occurring variations in
The question ot which advertisement char- message characteristics include those that
acteristics lead to greater recall, readership, content analyze.
and other goals of advertising is understudied. Against the advice of Ogilvy and
Content analysis as a descriptive and
others, agencies often rely on creative
predictive tool
competitions to index the content and perContent analysis may be defined as the
suasive potential of their advertisements,
systematic, objective^, quantitative analybut results of these competitions may bear
sis of message characteristics. The techlittle reiationship to the success of the adnique was initiated by communication, sovertisement, since creative judges are priciology, and journalism scholars some 50
marily the advertisers' professional peers
years ago (Berelson, 1952) and has gained
and not representative of the ranks of the
validation as a research tool in thousands
message targets. Nevertheless, the "con- of studies examining messages ranging
ventional wisdom" concerning successful
from television beer commercials to news
advertisement creation is a powerful and
items on the Greenhouse Effect to puboften highly valid force (OgOvy, 1983; Rolished Republican and Democratic Party
man and Maas, 1992; Schultz, Tannenplatforms (Fan, 1988; Krippendorff, 1980;
baum, and Allison, 1996).
Weber, 1990). There has been a recogniWhile it may seem manifestly beneficial
to designers of advertising to know what
gets the reader's attention, the bulk of
such research has been left in the hands of
academics (e.g., Laskey, Fox, and Crask,
1994; Tellis, 1994). This research frequently
takes the form of an experiment or field
experiment (Gelb, Hong, and Zinkhan,
1985), manipulating such variables as
source credibility, the use of an appeal such
as humor, or the presence of visual imagery or music. Another, type of research has
been the emergent single-source study,
which links a household's potential advertising exposure to actual household buying behavior (Maloney, 1994; Tellis, 1994).
tion of the difference between form variables, those that are linked to the formal
features of the medium and cannot endure transfer to another media modality,
and content or substance variables, those
that may exist independent of the medium^ (Berelson, 1952; Holbrook and Lehmann, 1980; Huston and Wright, 1983).
Most prior examinations of advertising
content have analyzed the compositional
form variables of print advertisements,
e.g., characteristics of headlines, graphics,
and copy, attempting to develop formulas
for successful print advertisements.'
While there is near-consensus that use of
color and large advertisement size are
positive contributors to readership (Hanssens and Weitz, 1980; Marney, 1985;
Standen, 1989; Twedt, 1952; VandenBergh
and Reid, 1980), the evidence about other
form variables is decidedly mixed (Assael,
Kofron, and Burgi, 1967; Reid, Rotfeld,
and Barnes, 1984).
Various content variables such as the
subject of the advertisement or the approach to the subject (e.g., use of humor,
fear, puffery, celebrity endorsement, message complexity) have been analyzed, and
the conclusions presented are also quite
ambiguous (Aaker and Norris, 1982;
Chamblee et al., 1993; Holman and
Hecker, 1983). For example, the typical
study on the use of humor in advertising
concludes that "sometimes it works,
sometimes it doesn't" (Gelb and Pickett,
1983; Madden and Weinberger, 1984;
Markiewicz, 1974). When form and content variables are directly compared, the
more mecharucal form variables prove to
be much more important predictors of
readership and recall (e.g., Hotbrook and
Lehmann, 1980). Importantly, the content/style variables that have been most
''Studies of compositional form variables in print advertisements include: Ne^vspaper Advertising Bureau, 1986; 1989;
Soley and Reid. 1983a: 1983b; Standen. 1989; Wesson,
^While objectivity is tlie acknowledged goat of such a social
7989: Wesson and Stewart. 1987. There have also been
scientific method, it is recognized that what is actually
studies of other form variables—advertisement size, posi-
achieved is more properly termed "intersubjectivity."
tion in tbe publication, use of color, etc.. (Assael, Kofron,
''Typical of icime publications in the advertising literature.
and Burgi. 1967; Industrial Equipment News, 7979;
Rossiter <19SV termed form and content z'ariables "me-
Marney. 1985;
chanical" and "message" variables, respectively.
ing News, 1987: Stuhlfiiut. 1983).
2 0 JOUmiflL OF HDUERTISIOG RESEfmCH May . June 1 9 9 8
Sales & Marketing Digest, 1988;
Market-
B-to-B PRINT ADVERTISING
often studied in the realm of consumer advertising do not generally apply to indusIriti! or business-to-business advertisements (e.g., celebrity endorser, sex appeals).
Most extant content-analytic studies
hcive neglected a comprehensive coverage
of potential important predictive variables, opting instead to look at just one or
a handful of variable{s).'^ Those studies
that have made the attempt at comprehensiveness warrant mention.
Holbrook and Lehmann (1980) tapped
48 message and mechanical variables, predicting over 30 percent of the variance in
Starch readership scores for Neivsweek and
Sports Illustrated. Their most important
predictors included product class and the
vague construct, "creativity." A major
contribution of this study was its clear
finding that both form and content factors
are important in producing recall and
leadership.
The most comprehensive projects to
date are studies of television commercials.
Stewart and Furse (1986) developed a 151itcm content-analysis scheme, which they
related to measures of recall, comprehension, and persuasiveness for 1,059 spots.
They found both recall and persuasion to
be influenced by (a) brand performance
characteristics (e.g., a brand-differentiated
message, convenience of product use),
and (b) attention and memory factors
(e.g., humor, mnemonic devices, frontend impact, bmnd sign-offs). Gagnard
and Morris (1988) content analyzed 121
CLIO award-winning commercials with
cin adaptation of the Stewart and Furse
scheme. They found a unique set of characteristics common to the award-winning
spots: the use of male characters and few
minorities, animals, or children; omnipresent music; the use of humor; and the
use of a strong front-end impact, for example. Most of the variables employed by
these researchers are inapplicable to print
advertisements.
Only one major attempt has been made
at identifying a coniprehevsive set of print
advertisement characteristics that contribute to readership. During the late 1970s
and early 1980s, David P. Forsyth, vicepresident of research at McGraw-Hill
Publicatior;s, analyzed nearly 3,600 print
advertisements covering a five-year span
of McGraw-Hill's Ad Sell Performance
Studies (reported in Donath, 1982, and
Wood, 1989). His research found "significant" contributors to the advertisement
being noticed included use of color and
use of a spread or bleed format. Contributors to "creating awareness," "arousing
interest," and "building preference" were
long copy (>300 words), use of tables or
charts, and showing the product by itself.
However, the McGraw-Hill project was
limited to mechanical form variables; no
attempt was made to measure such content characteristics as product type or persuasive appeals. The readership studies
reported small sample sizes, and, unfortunately, available reports on the project fail
to supply sufficient detail to evaluate
the content-analysis methodology. This
leaves hollow the claims of "significant"
findings.
Indeed, very few of the studies reviewed used all methodological standards
recommended in the content-analysis literature (Krippendorff, 1980; Riffe and Freitag, 1997).^ Flawed methodology is one
potential reason for the wide variation in
findings across content analyses for both
content and form variables. Other possible
reasons include a failure to identify critical variables in a comprehensive fashion
and context specificity (e.g., what is important to the success of an advertisement
in a general interest magazine may not be
the same as the set of elements that lead to
success in business-to-business advertising).
This study
There is a growing recognition that the
rules of good quantitative methodology
ought to apply to analyses of message
content (Krippendorff, 1980; Neuendorf,
1998; Riffe and Freitag, 1997; Zollars,
1994). The study described here has been
conducted with care given to contentanalytic standards. Sampling was systematic random. The sample size was adequate to support a large number of predictor variables. Coder training was
lengthy and rigorous. Variables not
achieving an acceptable level of reliability
were dropped from final analyses.
Neuendorf (1998) proposes the integrative model of content analysis, wherein
message-centered variables tapped by
content analysis are linked with audiencecentered variables or source-centered
variables measured in additional data collections. The study described here follows
that model by linking content analysis
to readership studies. Attempts to link
content and form measures to recall/
readership began in the 1950s (Twedt,
mann'suseofCronbach'salphaasan
indicator of reliability
(s suspect. They also used a very limited, nonprobability
sample and complained about coders becoming "exhausted"
'Vor example, Rossiter 11981) examined the impact of 13
"sf/ntax" variables ort Starch readership scores Jbr adver-
*For example, Rossiter (1981) properly reported intcrcoder
(p. 55) after only 10 hours of coding. The first study of
li^nneuls in one issue of Newsweek. Although Iw ex-
reliabilities and dropped variables that did not meet a siff
advertising content as related to readership (Twedt, 1952)
I'tnined an impressive amount of variance, he looked exclu-
criterion (rho - .60) but luid a very poor, nonprobabiliti/
gave no description of its amtent-anali/sis methodohgy at
•^iivly at picture size and headline characteristics.
sample, making inference impossible. Holbrook und Lch-
ail.
M a y . June 1 9 9 8 JQUHOHL OF HflUERTISinG HESEHRCH 2 1
B-to-B PRINT ADVERTISING
1952) and continued intermittently
throughout the 1970s and 1980s, as indicated in the above review. But, there is a
long gap in the literature after the mid
1980s. This study updates and continues
the quest, with a call for more rigorous
research standards.
For logistic reasons, and in order to
eliminate confounding factors and "masking" effects of uncontrolled context variables, this study has examined businessto-business advertisements in one particular publication. We have gone for depth
over breadth.
This research is guided by a pair of general research questions:
RQs:
To what extent may form and content attributes of print advertisements predict critical outcome variables such as readership, recall, and
perceptions of the advertisement
(when limited to one particular type
of message pool and receiver type)?
Are significant predictors different
across the outcome variables?
METHODOLOGY
The publication and PARR reports
The focus of this study is on both form
and content variables as applied in industrial or business-to-business trade publication advertisements. Specifically, eight issues of Electric Light & Poiver {EL&P)
magazine were randomly chosen for
analysis. All advertisements in these issues were included in the analysis.
EL&P is published by PennWell Publishing Company. It is a tabloid-size
monthly news magazine aimed at management, engineering, operating, and purchasing personnel in all segments of the
electric utility industry. For the advertisements studied here, audience data were
obtained from PennWell Advertising
Readership Research (PARR) Reports,
conducted by PennWell at no charge to
provide advertisers with a means to measure, evaluate, and compare the readership of and response to their advertising.
The PARR surveys asked the following
questions:
1. Did the reader notice the advertisement?
2. If the reader noticed the advertisement,
how much of it was read?
3. What was the reaction to the advertisement?
a. informative
b. attractive, attention-getting
These PARR surveys were conducted
by mail. Approximately three weeks after
the regular mailing of the issue, a random
sample of readers received a duplicate issue. In an enclosed letter, readers were
asked to go through the issue again and
answer the questions attached to each advertisement.^ The representative sample
differed by studied issue, ranging in size
from 200 to 700; response rates ranged
from 10 percent to 50 percent. The publication's circulation is audited by the Business Publication Audit (BPA) bureau,
which indicates its readership as composed largely of electric utility managers,
supervisors, and consultants.*^
^This classifit- I'/c I'ARR Reports as aided recall research,
Coding and analysis
The codebook developed for the content
analysis provides measures of constructs
selected for their potential predictive
value when correlated with readership
scores from the PARR Reports. This comprehensive pool of measured variables
was generated from (a) a review of past
research and professional guidelines, and
(b) a careful examination of idiosyncrasies
of business-to-business advertisementsThe full codebook contains a detailed definition of each of the 190 measured variables and each category within the variable. The pool of variables was reduced to
75 for final inclusion in analyses, via combining variables and eliminating variables
with low reliability'^ or extremely low
variance.
Each construct is classified as either a
form construct (pertinent to the vehicle,
i.e., print magazine) or content construct
(relative to the subject matter and presentation). A list of form and content variables as used in the final analysis is presented in Appendix A (including reliability figures). Coding was conducted by a
team of four trained coders. Coding assignments were made randomly based on
a total sample size of 247 readershipstudied advertisements from the eight issues of EL&P. Average intercoder reliabilities were calculated prior to the initiation of coding and again with a 10 percent
subset of the final data set.
in that respondents hai'e the opportunity to vie^v the adi'erlisemenls.
The final analyses utilized the 54 form
and 21 content independent variables
M summary of the BPA sfatemcit lists rcatlers us: "General and corporate management, including financial and
lutministrativc, engineering managetncnt and supervision,
^Numerous variables were measured as they occurred in la)
engineers, including planning, design, performance, R&D.
the headline, Ih) the visuals, and/or (c) ttu copy. Due to lou-
operations management ami supen'ision; Ofn'ratkins. in-
frequencies of occurrence, these applications were collapsed
cluding construction, maintenance and fleet, purchasing,
across the three before inclusion in the regression analyses.
commercial marketing, customer service, other qualified
Additionally, variables ivith reliability coefficients below 60
functions" (SRD5, 19%, p. 505).
percent or r = .70 were dropped.
2 2 JQUHnHL OF HDOERTISinG HESEHHCH May • June
B-to-B PRINT ADVERTISING
TABLE 1
Stepwise Prediction of Aided Advertisement Recall
Reliabiiity
Independent Variable
Pearson r
{% or f)
Fractional page
-.53
96%
Junior page
-.15
96%
Tabloid spread
.36
96%
Color
.48
.92(0
Copy in bottom half
.10
Copy in right half
Major visual chart/graph
Frequency
Rnal
Beta
SIg.
19.4%
-.47
<.OOO1*
47.4%
-.34
<.OOO1*
.31
<,0001*
NA
.24
<.OOO1*
78%
49.8%
.18
.0002*
-.04
78%
14.2%
-.16
.0005*
-.12
75%
-.10
.0140
Form variables
Average size of subvisuals
6.9%
1.6%
.18
85-100%
NA
.09
.0336
.18
84%
20.6%
.12
.0059
Content variables
Service advertised
Total ff = .59; Adjusted Ff^ = .58
F(9.233) = 37.83; Sig. = .0001
Niitc.- NA hiiiicates the relialnlily or frequeiny h not apylkable because vnriablea in this table have been combined, averaged, or otherit'iae manipulated from the original measurefs).
'^ig. hoiiis at p < .05 using Boriferroni test (criterion - .0007) fur the final 75 independent variables entered in the imtUiple regression.
listed in Appendix A^" and four dependent variables taken from the PARR Reports: Aided Advertisement Recall, Advertisement Readership, Informativeness
of the Advertisement, and Attractiveness
of the Advertisement. A stepwisemul tip le-regression model was developed
fnr each dependent variable. Categorical
independent variables were included via
standard procedures for dummy and
'"Wi? chose not to factor analyze the predictor set, a technique used fiy Twedt (1952) and Holbrook and Irhninnn
(1980). While a reduction in the predictor set is beneficial to
degrees of freedom and power, the collapsing of variables
also washes out individual variances and potential predictive ability. Instead, zt-e included individual variables
iind employed the Boiiferroni adjustment for nniltiple
effect coding {Cohen and Cohen, 1983). Inspection of interitem correlations for the
predictor variables and condition index/VIF coefficients revealed no significant multicoUinearity problems.
RESULTS
Advertisement aided recall
In the prediction of Aided Advertisement
Recall, the step wise-multiple-regression
analysis yields a total of nine predictors
from the list of seventy-five variables—
eight form variables and one content variable. Table 1 displays a summary of the
zero-order correlations, reliabilities, frequencies, final betas, and levels of significance for Aided Recall. The total R^ of .59
indicates a High level of variance explained by the nine predictors.
Final regression coefficients for the pre-
dictor variables for Aided Recall show
four negative predictors—fractional page
(p = -.47), junior page (-.34), copy in the
right half of the advertisement (-.16), and
use of a chart or graph in the major visual
(-.10). Positive contributions to Aided Recall are indicated for tabloid spread O =
.31), color (.24), copy in the bottom half of
the advertisement (.18), service as the subject of the advertisement (.12), and the average size of secondary visuals (.09).
Predictors relating to the size of the advertisement—fractional page, junior page,
Lind tabloid spread—thus provide some
interesting comparisons when all predictors are submitted in a regression. The frequencies indicate that junior pages are the
most-often-used page size (47.4 percent)
followed by fractional pages (19.4 percent). Yet, the final standardized regression coefficients (betas) indicate that both
M a y . June 1998 JOUBOflL OF RDUEflTISinil MflflCH 2 3
B-to-B PRINT ADVERTISING
TABLE 2
Stepwise Prediction of Advertisement Readership
Reliability
Frequency
Rnal
(% or r)
(%)
Beta
.19
75%
60.7%
.20
.0016
Tabloid page
-.18
96%
17.8%
-.22
.0008
Headline in bottom left section
-.08
76%
-.13
.0365
-.10
60-93%
26.3%
-.16
.0127
.11
89-95%
11.3%
.12
.0448
Independent Variable
Pearson r
Sig.
Form variables
Subject apparent in visuals
1.2%
Content variables
Logical argument used
Fear appeal used
Total ff = .12: Adjusted ff = .10
f^[5,242) = 6.37; Sig. = < . 0 0 0 1
have rather strong negative partial relationships to Aided Recall. Both predictors
are also highly significant {p < .0001),
which meets the ;) < .05 criterion and they
stricter Bonferroni test (p < .0007; Hair,
Anderson, Tatham, and Black, 1995) employed throughout the analyses.
On the other hand, tabloid spreads have
a very low frequency in this study (6.9
percent) but hold the strongest positive relationship to Aided Recall, with a final
beta of .31 (p < .0001). Taken as a whole,
these findings indicate that large, tabloid
spread advertising units are best remembered. Unfortunately, it also seems that
the unit favored by advertisers in this
study, the junior page, is poorly recalled.
And, it is only marginally better remembered than the much smaller, less expensive fractional page unit.
Not surprisingly, color is a significant
(p < .0001) positive predictor of Aided Recall." Frequencies indicate the abundant
use of color—frequency for two-color is
10.1 percent, three-color 3.2 percent, and
four-color 64.0 percent, versus black and
white at 21.9 percent.
Having copy in the bottom half of an
advertisement and not having copy in the
right half of the advertisement relate to
greater Recall. On the other hand, having
a major visual chart or graph, and a large
average size of secondary visuals are
weaker predictors (negative and positive,
respectively), not meeting the Bonferroni
criterion.
Another notable point in Table 1 is the
performance of the only significant content variable—subject of the advertisement as service (versus product, institutional, etc.). Service's reliability (84.0 percent) is good, its frequency (20.6 percent)
is second only to product advertisements
(67.6 percent), and its final beta is positive
(.12).
Advertisement readership
The stepwise-multiple-regression analysis
for Advertisement Readership produces
two positive and three negative predictor
variables: subject apparent in the visuals
(3 = .20), fear appeal used (.12), tabloid
page used (-.22), logical argument as an
approach/appeal to the subject (-.16), and
headline in the bottom left half of the advertisement (-.13). Three are form variables, while two are content variabies. All
of the Readership independent variables
meet the p < .05 level of significance, but
none meets the stricter p = .0007 Bonferroni level.
Tbe summary statistics for Advertisement Readership are shown in Table 2. The
total R^ is .12, and while it is not as massive
as that for Aided Recall, it does achieve a
high level of statistical significance.
. . these findings indicate that iarge, tabioid spread advertising units are best remembered. Unfortunateiy, it
aiso seems that the unit favored by advertisers in this
"Color was entered in the regression as black and white =
J, two-color = 2, three-color - 3. and ftiur-color = 4,
study, the junior page, is poorly recaiied.
2 4 JDURimL DP BDUEBTISinG RESEflRCH May . June 1 9 9 8
&-to-B PRINT ADVERTISING
Once again, size of the advertisement
seems to be a significant predictor in the
regression, with tabloid page {p = .0008)
just shy of the Bonferroni criterion for
Readership. Its frequency (17.8 percent)
places it third behind junior page (47.4
percent) and fractional page (19.4 percent). Readership for the tabloid page
shows a negative relationship (P - -.22)
which could indicate the larger format is a
detriment to readability.
The strongest positive relationship for
Readership is having the subject apparent
in the visuals, with a final beta of .20. The
reliability of the predictor is a respectable
75 percent, and its frequency (60.7 percent) shows that more than half the advertisements depict the subject in the visuals.
Placing the headline in the bottom left
portion of the advertisement shows an inverse relationship (P = -.13) to Readership. Overall, being able to divine the subject of the advertisement by looking at the
visuals appears to aid readership.
Two content variables are expressed in
the Readership regression. The first, logical argument as an approach/appeal (p =
-.16) has a frequency (26.3 percent) that
places it near the middle of the other approach/appeal variables. As a predictor,
logical argument is negatively related to
Advertisement Readership—even with an
audience of engineers, logical argument
discourages readership.
The second content variable in the
Readership regression is the use of a fear
appeal. Fear appeals are infrequently used
in these advertisements—at 11.3 percent it
is third from the bottom among the 13 approach/appeal variables submitted to the
regression. Interestingly, the final beta for
fear (.12) indicates it is positively related
to Readership. Therefore, inducing fear in
readers cannot be discounted as a method
of getting them to read advertisements.
Informativeness of the advertisement
Stepwise-multiple-regression analysis for
the third dependent variable, perceived
Informativeness of the advertisement, results in two negative predictors and four
positive predictors; four are form variables, and two are content variables. As in
the case of Readership, all predictors of
Informativeness meet the criterion p < .05,
but none meets the stringent Bonferroni
test (;; = .0007).
Table 3 shows the summary statistics
for Informativeness of the advertisement.
The total K^ of .20 is highly statistically
significant {p < .0001). Once again, advertisement size variables have run the
gauntlet of the stepwise multiple regression, this time to emerge as significant
predictors of Informativeness. Interestingly, the tabloid page, a large and frequently used format, is the strongest
negative predictor (p = -.19). The fractional page, a small format and frequently
used unit, has the second highest positive
relationship (p = .16) for Informativeness.
It seems these readers consider little advertisements more informative than big
ones.
The use of subheads and placement of
the headline in the top half of the advertisement also appear to result in more informative advertisements. Frequencies for
number of subheads vary from 0 to 12
with a mean of just under 1 per advertisement. Headline in the top half of the ad-
TABLE 3
Stepwise Prediction of Advertisement Informativeness
Reliability
Frequency
Rnal
Pearson r
(%or r)
(%)
Beta
Sig.
-.25
96%
17.8%
-.19
.0021
Fractional page
.21
96%
19.4%
.16
.0091
Headline in top half
.19
96%
64.4%
.15
.0119
Number of subheads
.24
.81(0
NA
.19
.0025
Subject apparent in visuals
.22
75%
60.7%
.14
.0265
90-96%
14.6%
-.14
.0236
Independent Variable
Form variables
Tabloid page
Content variables
Altruism appeal used
-.13
Total f^ = .20; Adjusted Ff^ = .18
F(6,24i) = 10.10; Sig. = < .0001
Note: NA indicates the reliability or frequency is not applicable because variables in this table have been combined, overa^^ed. or othenm-ie manipulated from the nrif^inal measiirfdi).
M a y . June 1 9 9 8 JOUROHL OF RDUeRTiSldl)flESEHHCH2 5
8-to-B PRINT ADVERTISING
vertisement has the highest frequency of
all positions (64.4 percent). Both are positively related according to the final betas:
number of subheads (P = .19) and headline in the top of the advertisement
{p = .15). Having the subject apparent in
the visuals is a positive predictor (p = .14)
of Informativeness but is the least significant of the regression predictors {p =
.0265).
Altruism is the only content variable in
the Informativeness regression. Its frequency is relatively low (14.6 percent)
compared to the other 12 approach/appeal constructs. It has the weakest significance of the Informativeness predictors.
And, with a final beta of -.14, its reverse
relationship to Informativeness indicates
that appeals to altruism in an advertisement are not viewed as informative by the
sample of readers.
Attractiveness of the advertisement
Table 4 summarizes the multiple regression for Attractiveness of the advertisement. The overall R^ is a substantial .42,
once again highly statistically significant.
In the stepwise multiple regression for
Attractiveness, size of the advertisement
is again represented by three significant
predictors, all bearing a negative relationship to Attractiveness: fractional page at
3 = -.31, followed by junior page (P =
-.28), and tabloid page (p = -.14).
Two other predictors demonstrate significance that meets the Bonferroni test:
color ip < .0001) and copy in the bottom
half of the advertisement (p = .0005). The
frequency for copy in the bottom half of
the advertisement (49.8 percent) is the
highest for all the copy position variables.
These two predictors also show the
strongest positive relationships: color (p =
.41) and copy in the bottom half of the
advertisement (p = .23). Both having copy
in the right half of the advertisement and
the number of subheads demonstrate a
moderate negative contribution to Attractiveness (P = -.17 and p = -.15, respectively). Attractiveness is most positively
predicted by color and copy placement.
Two content predictors are present for
Attractiveness of the advertisement: fear
and logical argument as approaches/appeals. The frequency for logical argument
(26.3 percent) is in the mid-range while
fear (11.3 percent) is quite low. What is
interesting is that fear (p = ,16) is positively related to Attractiveness. The final
beta for logical argument (p = -.15) shows
it to be negatively related to Attractiveness. What makes fear a positive attribute
for Attractiveness and logical argument
negative is open to speculation. Per-
TABLE 4
Stepwise Prediction of Ad Attractiveness
Reiiabiiity
independent Variabie
Pearson r
(% or 1)
Fractional page
-.41
96%
Junior page
-.02
Tabloid page
Frequency
Rnai
Beta
Sig.
19.4%
-.31
<.OOO1*
96%
47.4%
-.28
<.OOO1*
.11
96%
17.8%
-.14
.0189
Copy in bottom half
.21
78%
49.8%
.23
Copy in right half
.03
78%
14.2%
-.17
Color
.50
.92(r)
NA
.41
-.16
.81(0
NA
-.15
.0070
.08
89-95%
11.3%
.16
.0024
-.08
60-93%
26.3%
.15
.0049
Form variables
Number of subheads
.0005*
.0106
<.OOO1*
Content variables
Fear appeal used
Logical argument used
Total f^ = .42; Adjusted iR^= .40
F,9.238) = 18.44; Sig. = < .0001
Note: NA indicates the reliability or frequency is not applicable because variables in this table have been combined, averaged, or othenvise manipulated from the original measureis).
*Sig. holds at p < .05 using Bonferroni test (criterion = .0007) for the final 75 independent variables entered in the multiple regression.
26
OFflDUEHTISinGRESEeRCH May . June 1 9 9 8
B-to-B PRINT ADVERTISING
haps advertisers who use fear as an approach/appeal present fear in a dynamic
way to draw the reader's attention to the
advertisement. And, perhaps it is difficult
to devise an attractive method of expressing logic in an advertisement.
DISCUSSION
The
utility of content analysis
This research has demonstrated the manifest value of content analysis as a vital
predictive tool in the process of assessing
advertisement success. Our research extends the earlier efforts of researchers
(e.g., Chamblee et al., 1993; Donath, 1982;
Gronhaug, Kvitastein, and Gronmo, 1991;
Soley, 1986) and provides strong evidence
for the efficacy of content analyzing relevant variables for prediction of advertising success. The variance accounted for
both for recall and for advertisement
evaluations exceeds that achieved by
Zinkhan's (1984) innovative effort to predict buying intention from five factors of
immediate audience reactions (15 percent).
In all four regressions, we successfully
predict an important component of variance in the dependent variables from carefully measured content and form variables. For Aided Recall, the figure is .59.
The prediction of Attractiveness is also
quite successful, with 42 percent of the
variance explained. Even the lowest R^,
.12 for Readership, is highly statistically
significant. These findings point to the
value of this methodology for the building
of grounded theory and for application in
commercial settings. The nearly 60 percent variance explained for Aided Recall
is certainly worth even the considerable
effort of a comprehensive content analysis.
We propose that content analysis be con-
sidered as an itUegral part of publisher and
advertiser research agendas.
. . . "design" variables may get noticed but it taices both
"design" and "substance" or "style" variables to get an
advertisement read and taken seriously.
Our content analysis used proper methods. Other fledgling attempts, including
the most comprehensive ones (Donath,
1982), fail to report such essentials as reliabilities and sampling methodologies
(Krippendorff, 1980; Riffe and Freitag,
1997). Thus, it's difficult to compare our
results to others, and we therefore tend to
view our own attempt as benchmark.
Can we identify standard or universal
variables to content analyze in every case?
With the evidence to date, clearly the most
universally significant variables are use of
color and large advertisement size. This
study provides further confirmation of
these two "standards." But beyond this,
our current content-analysis application
provides results specific to a technical audience for a trade publication. We do not
believe that the aggregate approach used
by the McGraw-Hill group (as reported in
Donath, 1982) is optimal, leaving variances untapped, and resulting in depressed predictive ability, "masked" effects and patterns. Thus, we call for replications and extensions across publications
and audiences, eventually allowing for a
meta-analysis (Rosenthal, 1991). This will
be our best shot at bringing resolution to
divergent results and charting useful predictive models for print advertisement development. Meta-anaiysis will allow the statistical tracking of the interaction of relevant
content and form variables zuith audience types.
are not the same as those that predict
aided recall, informativeness, or attractiveness. The disagreement among predictors across the regressions is an important
finding.
Although the content variables as a
w^hole do not perform nearly as well as the
form variables (consistent with much past
research, e.g., Hotbrook and Lehmann,
1980), they do much better for Readership
and Informativeness than for Aided Recall
and Attractiveness. This suggests "design" variables may get noticed but it
takes both "design" and "substance" or
"style" variables to get an advertisement
read and taken seriously.
The absence of common predictors
strongly suggests readership, aided recall,
and advertisement evaluations are fairly
mutually exclusive processes. The implication for advertisers is that they should
set their objectives accordingly. If they
wish simply to have the advertisement
(and their product, service, or company)
remembered, it should be jiesigned for
that purpose. Conversely, if the advertisement is to be carefully read, the design
should reflect that goal. Informativeness should be approached differently
than Attractiveness,^^
'-T/fese differential patterns may be seen quite clearlt/ in
Table 5. And, zi'e may aho note the variables tfial did not
contribute significantly to any of Ihe four outcnme vari-
Diverse advertiser goals
abk's: headline size, tnajor visual size and placement, type
It is apparent that the predictors emerging
for the four dependent variables are not
congruent across regressions. Simply
stated, variables that predict readership
and location of subvisuals, copy length, advertisement location, all five different advertisement approaches (technical, analogy/allegory, aisi- history, spokesperson/expert use.
May . June 1 9 9 8 JOURURL OF flQUERTlSIRG RESERRCH 2 7
B-to-B PRINT ADVERTISING
TABLE 5
Summary of Significant Results, in Light of Practitioners' "Conventional Wisdom"
Practitioner
Characteristics of
Recommendation?
Advertisement
Form Variables:
/
Larger size
/
Subject apparent:
Significant Predictor of:
Recaii
Readership
informativeness
ft Attractiveness
T
.f
-
-
In headline
0
0
0
0
In visuals
0
+
+
0
/
Copy length
0
0
0
0
</
Color(s)
+
0
0
+
/
Location in publication
0
0
0
0
Top
0
0
+
0
Bottom left
0
-
0
0
Number of subheads
0
0
+
-
Major visual—chart or graph
-
0
0
0
Larger size of subvisuals
+
0
0
0
0
0
+
-
0
0
-
Mixed
Headline placement:
Copy placement:
Bottom
Right
Content Variabies:
/
Technical approach
0
0
0
0
/
Case history approach
0
0
0
0
/
Spokesperson approach
0
0
0
0
/
Competitive comparison
0
0
0
0
v'
Question appeal
0
0
0
0
/
Humor appeal
0
0
0
0
/
Status appeal
0
0
0
0
/
Learned motive appeal
0
0
0
0
/
Logical argument appeal
0
-
0
+
/
Problem/solution appeal
0
0
0
0
/
Calls to action
0
0
0
0
Fear appeal
0
+
0
+
Altruism appeal
0
0
-
0
Adv. ^pe—service
+
0
0
0
NOTE: The variables abmv are limited to those that either (a) are consistently recommended in practitioners' texts or (b) prove to be significant predictors in at least one of this study's four
regression equalions. A list of nil variables m the study appears in Appendix A.
2 8 JOURnflL Of lllEflTISIHG IIESEflRCH May . June 1 9 9 8
B-to-B PRINT ADVERTISING
Conventional wisdom and the findings
The experiential Hndings of practitioners
were a strong motivating force behind this
research, and many of the form and content variables were derived from industry
tenets. Table 5 summarizes the findings
for the four regressions in light of practitioner recommendations.
"Conventional wisdom" from the advertising industry tells us to create simple,
orderly print advertisements,'"' with
readily apparent subjects, and attractive
visuals that are more important than
headlines (Roman and Maas, 1992). We
have been urged to use color when possible (Sandage, Fryburger, and Rotzoll,
I'^89) in relatively large advertisements
(Dunn and Barban, 1986). This research
confirms all those recommendations. Roman and Maas also encourage, "don't be
afraid of long copy," and the nonsignificant contribution of copy length in our
study supports the notion that long copy
will not decrease readership or other positive outcomes.
However, other "old salts" from the annals of advertising are not confirmed. Offering a benefit such as status, a learned
motive (e.g., patriotism, friendship), or a
solution to the reader's problem, is not
lompetitive comparison), five persuasive appeals (question,
iiumor. ghilus, Warm-d motives, problem/solution), calls to
• lition. reader orientation, and compmiy name in the Iteattlini: The fad Ihnt many of these variables have been finind
to be significant predidors in other studies indicates that
either (a) in the presence of other, stronger predictors, tiieir
impact is superceded, or (b) this specific application in a
business-tO'business context shows different results for a
technical audience.
'^Htniwer, this study found the number of subheads included in an advertisement to be positively related to adKertisement informativeness (while at the same time being
negatively related to advertisement attractiveness).
found to contribute to positive advertisement outcomes, as some practitioners
would suggest (Roman and Maas, 1992).
Other "recommended" approaches and
styles that do not pan out include: testimonials, use of technical evidence, and
competitive comparisons (Schultz, Tannenbaum, and Allison, 1996), use of questions, case histories, calls to action, and
humorous copy (Dunn and Barban, 1986;
Ogilvy, 1983), and placement of the advertisement within the publication.
Instead, fear, altruism, and logical arguments emerge as important approaches to
consider. Fear and altruism are not usually mentioned in "how-to" lists of recommendations for print advertisements yet
are found to have positive impacts in this
study. The use of logical/rational arguments—^advocated by Ogilvy & Mather
(Dunn and Barban, 1986)—has mixed results. Use of such arguments relates positively to attractiveness but negatively to
readership.
CONCLUSION
This study has renewed the scrutiny of
message variables for clues in the prediction of advertising success. We have demonstrated, in a business-to-business context, the utility of conducting valid and
methodologically rigorous content analyses as an integral part of an applied research plan.
Rather than attempting to identify a
host of universally predictive message
variables, we instead acknowledge the idiosyncracies of (a) varying desired outcome variables, and (b) specialized publications and audiences. We propose the establishment of a line of research using
comprehensive content-analysis techniques with diverse dependent variables
in a variety of contexts. Meta-analysis
seems ideally suited to the task of statistically profiling successful message vari-
ables for divergent publications and audience types.
According to Schultz, Tannenbaum,
and Allison (1996), "advertising [is] just
like the personal salesperson, that is, it delivers or should deliver a sales message
for the product or service being advertised." It is through the selection of content and form characteristics that this selling goal is variously achieved via print
advertising.
This research has developed a practical,
widely applicable scheme for tapping
print advertisement characteristics that
may predict important goals of advertising. An initial database has been constructed, which could be further developed with the addition of data for other
publications. The establishment of a
broad-reaching database would be costeffective; coding could be completed by
only one or two coders, and programming
is simple. And such a data service, if provided commercially, would be an extremely economical addition to current
audience research services. With this, advertisers and their agencies could utilize
the coding results as part of their marketing analysis to help answer the question of
"why" readers pay attention to and like
their advertisements. lEID
JOHN L. NACCARATO is vice president, general
manager of Liggett-Stashower Interactive in Cleveland,
Ohio, and Instructor of public relations and advertising
at Cieveland State University. He received his B.A.
from Kent State University and his M.A. from
Cleveland State University. His 26 years in
advertising, public relations, saies promotion,
research, and media have Included work with
regional and national clients in the fields of power
generation, steei, construction and mining equipment,
medicai equipment and hospitals.
KIMBERLY A. NEUENDORF is associate professor of
communication at Cleveland State University. She
received her Ph.D. from Michigan State University. Her
May • June 1 9 9 8 JDUHnHl OP HBOEHTISinG RESEflHCH 2 9
B-to-B PRINT ADVERTISING
teaching and research interests include media use
APPPiMniY A
and ethnic identity, the sociai impact of advertising,
_
,
Content Analytic Vanables
and research methodologies. She has served as
principal investigator, advisor, or researcher on nearly
,
Frequency
Reiiabiiitv
(% or mean)
(% or r)
100 content analyses. Her work has appeared in such
Form Variabies
publications as Jourrjal of Broadcasting and Eiectronic
„ ^. ,
,.
^
, ,
, ,
1 . Tabloid spread
6.9%
96%
Media. Journalism Quarterly. Journal of
Communication. Communication Monographs, and
2 . TablOid page
17.8%
„„......
96%
Communication Yearbook.
3 . Junior Spread
6.1%
96%
4. Junior page
47.4%
96%
5. Baby spread
1.6%
96%
6. Fractional page
19.4%
96%
AAKFR, D. A., and D. NORRIS. "CharacterisHcs
J- Headline in top half of ad
64.4%
of TV Commercials Perceived as Informa-
8. Headline in bottom half of ad
11.3%
REFERENCES
Hve." Jounml of Advertising Research 22, 2
{1982); 61-70.
9rHeadline in'left half Of ad
10.
AJZEN. I., and M. FISHBHIN. Understanding Atti-
^L^^^^:^.^''.}^}'.}^.^^?''.^.^!^.
tudes ami Predicting Social Bi'hnvior. Englewood
Cliffs, NJi Prentice-Hall, inc., 1980,
Headline in right half of ad
12. Headline in top right section of ad
1^2%'
0.8%
.^.9:?.^
4.5%
76%
76%
76%"
•
76%
!?.^..,
76%
13. Headline in bottom left section of ad
1.2%
76%
14.
1.2%
76%
48.2%
84%
Headline in bottom right section of ad
ASSAEL, R , J. H. KOFRON, and W. BURGT. " A d -
,. . „ ,
^ ,.
,„. ,
vertising Performance as a Function of Prmt
Ad CUaracteristics." lourml of Advertising Research 7, 2 (1967): 20-26.
15. Headline size (>.25")
^9.:..^^.^^.^}'}^..}^!^^^^.}^..'^°^.^^
17. Subject apparent in headline
18. Number of subheads
BERFLSON, B. Content Anali/sis in Coinrmiincation Research. New York: Hafner Press, 1952.
^,[^^,
52.2%
;75
83%
0.98
.81
19.
Major visual—full ad
25.5%
65%
20.
Major visual in top half of ad
34.0%
65%
CHAMBLEE, R., R. GILMORE, G. THOMAS, and G.
2 1 . Major visual in bottom half of ad
Soiixjw. "When Copy Complexity Can Help
2 2 . Major visual in left half Of ad
10.9%
65%
8.1%
65%
5^7%
65%'"
24. Major visual in top left section of ad
2.0%
65%
COHEN, J., and P. COHEN. Applied Multiple Re-
^^.:..^.^}?L.'f!.^.^.^[!^.^'!^.^
2:8%
65%
gression/Correlation Analysis for the Behavioral
2 6 . Major visual in b o t t o m left section of a d
0.8%
65%
Ad Readership." Journal of Advertising Resennrh 33, 3 (1993); 23-28.
Sciences, 2nd ed. Hillsdale, NJ: Uwrence ErIbaum, 1983.
23" M^or visualin'righi^ half of'ad
27. Major visual in bottom right section of ad
0.8%
65%
00 .7 •
• ',
I
i
28. Major visual a photograph
64.8%
75%
r^-,K,,,-., u -^Ar^
r-1- • r^ .AH .A* 1
DoNAiH, B. Ad Copy Clinic: Q: Wliat Makes
^ 9 . Majorvisual an illustration
25.5%
75%
the Perfect Ad? A; It Depends." Industrial
?.?:..f^.^J.°.'!.!^i^H^I..^..P'^^!! ^L^'^^.P.';'.
h^^i.
Z?.^.
Marketing 67, 8 (1982): 89-92.
3 1 . Size of major visual (> half of ad)
32. Subject apparent in visual(s)
DUNN, S. W., and A. M. BARBAN. Advertisins:
Its Role in Modern Marketing. 6th ed. Chicago:
The Dryden Press, 1986.
^.^ „
33. Proportion of subvisuals that are photographs
?.l
3 0 JDUIinflL OFflDUERTISIflGHESEflRCH May . June 1 9 9 8
41.3%
60.7%
.72
83%
75%
84-100%
B-to-B PRINT ADVERTISING
EDMONSTON, J. "Syndicated Research Lifts Me-
APPENDIX A
Continued
Form Variables
dia J^\annme." Advertising Age's Business Markcting, July 1995.
Frequency
Reliability
(% or mean)
(% or 1)
35. Proportion of subvisuals that are
Mass Media: Computer Content Analysis and
Mathematical Modeling. New York: Greenwood
,07
84-100%
36. Average color (1-4) of subvisuals
2.89
83-100%
37, Average size of subvisuals in columns
1.17
85-100%
38. Proportion of subvisuals in top left of ad
,09
75-100%
39. Proportion of subvisuals in top right of ad
.20
75-100%
40. Proportion of subvisuals in bottom left of ad
,18
75-100%
41, Proportion of subvisuals in bottom right of ad
.30
75-100%
9.7%
78%
charts or graphs
FAN, D . P. Predictions of Public Opinion from the
Press, 1988.
GACNARD, A . , and I. R. MORRIS. "CLIO Com-
mercials from 1975-1985: Analysis of 151 Executional Variables, journalism Quarterly 65, 4
GELB, B. D,, J, W, HONC, atld G. M. ZiNKHAN.
"Gommunications Effects of Specific Advertis-
42. Copy in top half of ad
43. Copy in bottom half of ad
44. Copy in left half of ad
45. Copy in right half of ad
49,8%
3.2%
14.2%
ing Elements: An Update. Current Issues &
78%
Research in Advertising 19S5, Vol. 2: Reviews of
78%
Selected Areas (1985): 75-98.
78%
, and C. M. PICKHTT. "Attitude-toward-
46. Copy in top left section of ad
2.4%
78%
47. Copy in top right section of ad
1,6%
78%
Effectiveness."
48. Copy in bottom left section of ad
2,4%
78%
(1983): 34-41.
49. Copy in bottom right section of ad
3,6%
78%
50, Number of paragraphs of copy
4.87
,97
the-Ad: Links to Humor and to Advertising
Journal of Advertising 12, 2
GRONHAUG, K., O . KviTASTEiN, and S. GRONMO.
"Factors Moderating Advertising Effective-
51. Ad located before center spread
46.6%
95%
ness as Reflected in 333 Tested Advertise-
52. Ad located after center spread
50.6%
95%
ments." Journal of Advertising Research 3 1 , 5
53, Ad located in premium position
2.8%
100%
54. Color(s) used in ad (1-4)
3.10
,92
42-50.
HAIR, ]. F., R. E. ANDERSON, R. L. TATHAM, and
Content Variables
W. C. BLACK. Multivariate Data Analysis with
1. Ad for product
67.6%
84%
Readings, 4th ed. Englewood Cliffs, NJ: Pren-
2. Ad for service
20.6%
84%
tice Hall, 1995.
3. Ad for process
2.4%
84%
4. Corporate ad
4.5%
84%
tiveness of Industrial Print Advertising Print
5. Institutional ad
0,8%
84%
Advertisements across Product Categories."
lournal of Marketing Research 17, 4 (1980): 294-
HANSSENS, D . M . , and B. WEITZ, "The Effec-
6. Technical approach
55.5%
63%
7. Analogy/allegorical approach
23.9%
72%
8. Case history approach
14.6%
89%
9, Spokes person/expert approach
3.6%
87%
10. Competitive comparison approach
23.5%
69%
11. Question appeal
10,5%
100%
12, Humor appeal
11,7%
88%
Mechanical Factors AfAd Perception." journal of Advertising Re13,4 (1973): 39-45.
Hoi.BRixiK, M. B., and D. R. LEHMANN. "Form
versus Content in Predicting Stanrh S c o , ^ . "
M a y . June 1 9 9 8 JOUfinilL OFflDUERTISIIlGRESERRCH 3 1
B-to-B PRINT ADVERTISING
APPENDIX A
NEWSI'APER ADVERTISING BUREAU. Research Facts
' Timing and Creativity in hJcjvspaper
New York: Newspaper AdvertisFrequency
Reliability
Content Variables
(% or mean)
{% or r)
13. Fear appeal
11.3%
. ,
,,^ .
,
ing Buredu, 1986.
90%
17^n,'
. Kc}/Facts 1989: Newspapers. Advertising
^^ Marketing. New York: Newspaper Advertis-
14. Altruism appeal
14.6%
96%
15. Status appeal
49.0%
74%
16. Learned motive appeal
...7.
24.3%
73%
r^-,,,,^
-, on Advertising.
AA ^-•
K
T V
.
OGILVV, r,
D. r^
Ogilvy
New
York:
17. Logical argument appeal
26.3%
85%
Vintage Books, 1983.
18. Problem/solution appeal
36.0%
ing Bureau, Inc., 1989.
19. Number of calls to action (e.g., coupons, 800 #s)
67%
2.31
96%
2 0 . Reader/customer orientation in ad
81.0%
62j^7%
2 1 . Company name in headline
22.7%
90%
• '^"' Unpublished David Ogilvy. New
^'""^'^ ^ ' • ^ ^ " Publishers, Inc., 1986.
OlSHAVSKV, R. W. "Attention as an Epiphenomenon: Some Implications for Advertis-
joiirual of Advertising Research 20, 4 (1980): 5362.
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and D. W. Stewart, eds. Hillsdale, N]: Law-
HOLMAN, R. H., and S. HECKER "Advertising
MADDEN, T. J., and M. G. WEINBERGER. " H U -
Impact: Creative Elements Affecting Brand
^.^r in Advertising: A Practitioner View."
Saliency." Current Issues and Research in Advertising 1983: 157-72.
jp,,^^^; of Advertising Research 24, 4 (1984): 2329.
''^"ce Erlbaum, 1994.
RfiiD, L. N., H. R. RoTHia.D, and J. H.
"Attention to Magazine Ads as a Function of
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HUSTON, A. C , and J. C. WRIGHT. "Children's
Processing of Television: The Informative
Functions of Formal Features." In Children's
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MALONEY, J. C. "The First 90 Years of Adver-
(1984): 439-41.
tising Research." In Attention. Attitude, and
Affect in Respotjse to Advertising. E. M. Clark,
RIFFE, D., and A. FREITAG. "A Content Analy-
T. C. Brock, and D. W. Stewart, eds, Hillsdale,
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