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Content Analysis as a Predictive Methodology: Recall,Readership and Evaluations of Business-to-Business PrintAdvertising

John Narrarato and Kimberley A Neuendorf

Journal of Advertising Research

Vol. 37, No. 2, March/April 1997



Content Analysis as a Predictive Methodology: Recall,Readership, and Evaluations of Business-to-Business Print

Advertising

John L. Naccarato

Liggett-Stashower, Inc., USA

and

Kimberly A. Neuendorf

Cleveland State University, USA

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.

The ultimate goal of advertising 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).

ADVERTISING SUCCESS

The direct linking of sales to advertising exposure is rarely validated in practice. Even the older, classic models of advertising

and marketing (e.g., 'DAGMAR') have acknowledged the role of intermediary processes and states (Olshavksky, 1994),

including but not limited to knowledge, [positive] affect, and behavioral intention (cf., Ajzen and Fishbein, 1980).

Correspondingly, and for reasons of practicality and comparability of criteria, advertising readership studies are viewed as the

basic tool for assessing advertising effectiveness.

Readership is probably the most frequently used indicator of advertising effectiveness. Unlike inquiry reports-which count how

many readers request additional information and are a mainstay of business-to-business advertising-readership studies ask a

Title: Content Analysis as a Predictive Methodology: Recall, Readership and Evaluations ofBusiness-to-Business Print Advertising

Author(s): John Narrarato and Kimberley A NeuendorfSource: Journal of Advertising Research

Issue: Vol. 37, No. 2, March/April 1997

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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 studies1, readership studies have

traditionally been considered to be a 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 anumber 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, readership studies have come under certain criticisms in the past two decades (Edmonston, 1995; Johnson, 1982;

Rothschild, 1987; Schaefer, 1989; Sekely and Blakney, 1994; Whipple and McManamon, 1992; Wood, 1989). Additionally,

some industry observers have noted the paucity of syndicated readership research for industrial or business-to-business

advertising (Morelli, 1986).

What makes a consumer read a given advertisement? An early Ogilvy pronouncement declared that 'Every advertisement

must tell the whole sales story ... Every word in the copy must count' (Ogilvy, 1986). Images, color, and layout factors are also

of great concern in the industry (Roman and Maas, 1992). A careful examination of advertising 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 between the production of an advertisement and its positive

reception by consumers. The chosen exemplar examines business-to-business advertising in a trade magazine.

Advertisement characteristics and advertising success

The question of which advertisement characteristics lead to greater recall, readership, and other goals of advertising is

understudied. Against the advice of Ogilvy and others, agencies often rely on creative competitions to index the content and

persuasive potential of their advertisements, but results of these competitions may bear little relationship to the success of the

advertisement, since creative judges are primarily the advertisers' professional peers and not representative of the ranks of themessage targets. Nevertheless, the 'conventional wisdom' concerning successful advertisement creation is a powerful and

often highly valid force (Ogilvy, 1983; Roman and Maas, 1992; Schultz, Tannenbaum, and Allison, 1996).

While 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 -is 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).

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 non-representative 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 generalization. Research studies that probe

naturally occurring variations in message characteristics include those that content analyze.

Content analysis as a descriptive and predictive tool

Content analysis may be defined as the systematic, objective 2, quantitative analysis of message characteristics. The


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technique was initiated by communication, sociology, and journalism scholars some 50 years ago (Berelson, 1952) and has

gained validation as a research tool in thousands of studies examining messages ranging from television beer commercials to

news items on the Greenhouse Effect to published Republican and Democratic Party platforms (Fan, 1988; Krippendorff,

1980; Weber, 1990). There has been a recognition 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 medium3 (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 advertisements4. 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 quiteambiguous (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 mechanical form

variables prove to be much more important predictors of readership and recall (e.g., Holbrook and Lehmann, 1980).

Importantly, the content/style variables that have been most often studied in the realm of consumer advertising do not

generally apply to industrial or business-to-business advertisements (e.g., celebrity endorser, sex appeals).

Most extant content-analytic studies have neglected a comprehensive coverage of potential important predictive variables,

opting instead to look at just one or a handful of variable(s)'.5 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 Newsweck 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 readership.

The most comprehensive projects to date are studies of television commercials. Stewart and Furse (1986) developed a

151item 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,front-end impact, brand sign-offs). Gagnard and Morris (1988) content analyzed 121 CLIO award-winning commercials with an

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 comprehensive set of print advertisement characteristics that

contribute to readership. During the late 1970s and early 1980s, David P. Forsyth, vice-president of research at McGraw-Hill

Publications, analyzed nearly 3,600 print advertisements covering a five-year span of McGraw-Hill's Ad Sell Performance


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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)'.6 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 content-analytic 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 audience-centered 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, 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 business-to-business advertisements in one particular publication. We have gone for depth over breadth.

This research is guided by a pair of general research questions:

1. 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)?

2. 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


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advertisements. Specifically, eight issues of Electric Light & Power (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.7 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.8

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 advertisements. The 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 reliability9 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 presentedin 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 readership studied advertisements from the eight issues of EL&P . Average

inter-coder reliabilities were calculated prior to the initiation of coding and again with a 10 percent subset of the final data set.

The final analyses utilized the 54 form and 21 content independent variables listed in Appendix A10 and four dependent

variables taken from the PARR Reports: Aided Advertisement Recall, Advertisement Readership, Informativeness of the

Advertisement, and Attractiveness of the Advertisement. A stepwise multiple-regression model was developed for each

dependent variable. Categorical independent variables were included via standard procedures for dummy and effect coding


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(Cohen and Cohen, 1983). Inspection of interitem correlations for the predictor variables and condition index/VIF coefficients

revealed no significant multicollinearity 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 R2 of .59 indicates

a high level of variance explained by the nine predictors.

Final regression coefficients for the predictor variables for Aided Recall show four negative predictors-fractional page (b = -

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 ( b = .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, and tabloid spread-thus provide someinteresting 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 have rather strong negative partial relationships to Aided Recall. Both predictors are also

highly significant (p < .0001), which meets the p < .05 criterion and the stricter Bonferroni test ( < .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 (r < .0001). Taken as a whole, these findings indicate that large, tabloid

TABLE 1: STEPWISE PREDICTION OF AIDED ADVERTISEMENT RECALL

Independent Variable Pearson r Reliability(% or r )

Frequency(%)

FinalBeta

Sig.

Form variables

Fractional page -.53 96% 19.4% -.47 <.0001*

Junior page -.15 96% 47.4% -.34 <.0001*

Tabloid spread .36 96% 6.9% .31 <.0001*

Color .48 .92(r ) NA .24 <.0001*

Copy on bottom half .10 78% 49.8% .18 .0002*

Copy in right half -.04 78% 14.2% -.16 .0005*

Major visual chart/graph -.12 75% 1.6% -.10 .0140

Average size of subvisuals .18 85-100% NA .09 .0336

Content variables

Service advertised .18 84% 20.6% .12 .0059

Total R2 = .59; Adjusted R2 = .58

F (9,233) = 37.833; Sig. = .0001

Note: NA indicates the reliability or frequency is not applicable because variables in this table have been combined,

averaged, or otherwise maipulated from the original measure(s).

*Sig. holds at p < .05 using Bonferroni test (criterion = .0007) for the final 75 independent variables entered in the multiple

regression.


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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 (r < .0001) positive predictor of Aided Recall.11 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 ( b = .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 variables. All of the Readership independent variables meet the r < .05 level of significance, but none

meets the stricter r = .0007 Bonferroni level.

The summary statistics for Advertisement Readership are shown in Table 2. The total R2 is .12, and while it is not as massive

as that for Aided Recall, it does achieve a high level of statistical significance.

Once again, size of the advertisement seems to be a significant predictor in the regression, with tabloid page (r = .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 (b = -.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

TABLE 2: STEPWISE PREDICTION OF ADVERTISEMENT READERSHIP

Independent Variable Pearson r Reliability

(% or r )Frequency

(%)FinalBeta

Sig.

Form variables

Subject apparent in visuals .19 75% 60.7% .20 .0016

Tabloid page -.18 96% 17.8% -.22 .0008

Headline in bottom left section -.08 76% 1.2% -.13 .0365

Content variables

Logical argument used -.10 60-93% 26.3% -.16 .0127

Fear appeal used .11 89-95% 11.3% .12 .0448Total R2 = .12; Adjusted R2 = .10

F (5.242) = 6.37; Sig. = <.0001


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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 (b = -.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 (b = -

.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 r < .05, but none meets the stringent Bonferroni test (r

= .0007).

Table 3 shows the summary statistics for Informativeness of the advertisement. The total R2 of .20 is highly statistically

significant (r < .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 (b = -.19). The fractional page, a small format and frequently used unit, has the second

highest positive relationship (b = .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 advertisement has the highest frequency of all positions (64.4 percent). Both are

TABLE 3: STEPWISE PREDICTION OF ADVERTISEMENT INFORMATIVENESS


(% or r )Frequency

(%)FinalBeta

Sig.

Form variables

Tabloid page -.25 96% 17.8% -.19 .0021

Fractional page .21 96% 19.4% .16 .0091

Headline in top half .19 96% 64.4% .15 .0119Number of subheads .24 .81 (r ) NA .19 .0025

Subject apparent in visuals .22 75% 60.7% .14 .0265

Content variables

Altruism appeal used -.13 90-96% 14.6% -.14 .0236

Note: NA indicates the reliability of frequency is not applicable because variables in this table have been combined, averaged, or otherwise manipulated

from the original measure(s).


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positively related according to the final betas: number of subheads (b = .19) and headline in the top of the advertisement ( b

= .15 ). Having the subject apparent in the visuals is a positive predictor (b = .14) of Informativeness but is the least significant

of the regression predictors (r = .0265).

Altruism is the only content variable in the Informativeness regression. Its frequency is relatively low (14.6 percent) comparedto 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 2 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 b = -.31, followed by junior page (b = -.28),

and tabloid page (b = -.14).

Two other predictors demonstrate significance that meets the Bonferroni test: color (r < .0001) and copy in the bottom half of

the advertisement (r = .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 (b = .41) and copy in

the bottom half of the advertisement (b = .23). Both having copy in the right half of the advertisement and the number of

subheads demonstrate a moderate negative contribution to Attractiveness (b = -.17 and b = -.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.

TABLE 4: STEPWISE PREDICTION OF AD ATTRACTIVENESS


(% or )Frequency

(%)FinalBeta

Sig.

Form variables

Fractional page -.41 96% 19.4% -.31 <.0001*

Junior page -.02 96% 47.4% -.28 <.0001*

Tabloid page .11 96% 17.8% -.14 .0189

.21 78% 49.8% .23 .0005

Copy in bottom half .03 78% 14.2% -.17 .0106

Color .50 .92 (r ) NA .41 <.0001*

Number of subheads -.16 .81 (r ) NA -.15 .0070

Content variables

Fear appeal used .08 89-95% 11.3% .16 .0024Logical argument used -.08 60-93% 26.3% .15 .0049

Total R2 = .42; Adjusted R2

F (9.238) = 18.44; Sig. = < .0001

Note: NA indicates the reliability of frequency is not applicable because variables in this table have been combined, averaged, or otherwise manipulated

from the original measure(s).

*Sig. holds at p < .05 using Bonferroni test (criterion = .0007) for the final 75 independent variables entered in the multiple regression.


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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 (b = .16) is positively related to Attractiveness. The final beta for logical argument (b = -.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. Perhaps 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 anadvertisement.

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 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 R2, .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 considered as an integral part of publisher and

advertiser research agendas.

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-analysis will allow the statistical tracking of the interaction of relevant content and

form variables with audience types.

Diverse advertiser goals

It is apparent that the predictors emerging for the four dependent variables are not congruent across regressions. Simply

stated, variables that predict readership are not the same as those that predict aided recall, informativeness, or attractiveness.


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The disagreement among predictors across the regressions is an important finding.

Although the content variables as a whole do not perform nearly as well as the form variables (consistent with much past

research, e.g., Holbrook 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 designed 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.12

Conventional wisdom and the findings

The experiential findings 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.

TABLE 5: SUMMARY OF SIGNIFICANT RESULTS, IN LIGHT OF PRACTITIONERS' 'CONVENTIONAL WISDOM'

Practitioner

Recommendation?Characteristic ofAdvertisement

Form Variables:

Significant Predictor of:- Recall-Readership-Informativeness

-Attractiveness

ü Larger size + - - Mixed

ü Subject apparent:

In headline Ø Ø Ø Ø

In visuals Ø + + Ø

ü Copy length Ø Ø Ø Ø

ü Color(s) + Ø Ø +

ü Location in publication Ø Ø Ø Ø

Headline placement

Top Ø Ø + Ø

Bottom left Ø - Ø Ø

Number of subheads Ø Ø + -

Major visual-chart or graph - Ø Ø Ø

Larger size of subvisuals + Ø Ø Ø

Copy placement:

Bottom Ø Ø Ø Ø

Right Ø Ø Ø Ø

Content Variables:

ü Technical approach Ø Ø Ø Ø

ü Case history approach Ø Ø Ø Ø

ü Spokesperson approach Ø Ø Ø Ø

ü Competitive comparison Ø Ø Ø Ø

ü Question appeal Ø Ø Ø Ø

ü Humor appeal Ø Ø Ø Ø

ü Status appeal Ø Ø Ø Ø

ü Learned motive appeal Ø Ø Ø Ø


12



'Conventional wisdom' from the advertising industry tells us to create simple, orderly print advertisements13, 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, 1989) 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 non-significant 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 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

idiosyncrasies 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 variables 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

ü Logical argument appeal Ø - Ø +

ü Problem/solution appeal Ø Ø Ø Ø

ü Calls to action Ø Ø Ø Ø

Fear appeal Ø + Ø +

Altruism appeal Ø Ø - Ø

Adv. type-service + Ø Ø Ø

Note: The variables above 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 equations. A list of all variables in the study appears in Appendix A.


13



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 cost effective; 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 andlike their advertisements.

Appendices

APPENDIX A: CONTENT ANALYTIC VARIABLES

Form VariablesFrequency

(% or mean)Reliability

(% or r )

1. Tabloid spread 6.9% 96%

2. Tabloid page 17.8% 96%

3. Junior spread 6.1% 96%

4. Junior page 47.4% 96%

5. Baby spread 1.6% 96%

6. Fractional page 19.4% 96%

7. Headline in top half of ad 64.4% 76%

8. Headline in bottom half of ad 11.3% 76%

9. Headline in left half of ad 1.2% 76%

10. Headline in right half of ad 0.8% 76%

11. Headline in top left section of ad 10.5% 76%

12. Headline in top right section of ad 4.5% 76%

13. Headline in bottom left section of ad 1.2% 76%

14. Headline in bottom right section of ad 1.2% 76%

15. Headline size (>.25") 48.2% 84%

16. Headline length on words 9.53 .75

17. Subject apparent in headline 52.2% 83%

18. Number of subheads 0.98 .81

19. Major visual-full ad 25.5% 65%

20. Major visual in top half of ad 34.0% 65%

21. Major visual in bottom half of ad 10.9% 65%

22. Major visual in left half of ad 8.1% 65%

23. Major visual in right half of ad 5.7% 65%

24. Major visual in top left section of ad 2.0% 65%

25. Major visual in top right section of ad 2.8% 65%

26. Major visual in bottom left section of ad 0.8% 65%

27. Major visual in bottom right section of ad 0.8% 65%

28. Major visual a photograph 64.8% 75%

29. Major visual in illustration 25.5% 75%

30. Major visual a chart or graph 1.6% 75%

31. Size of major visual (> half of ad) 41.3% 83%

32. Subject apparent in visual(s) 60.7% 75%

33. Proportion of subvisuals that are photographs .72 84-100%

34. Proportion of subvisuals that are illustrations .20 84-100%


14



Endnotes

1. The terms recognition and recall are usually intended to refer to aided and unaided recall, respectively; many commercial

services cover both. In the case of business-to-business advertising, recognition/aided recall is perhaps the more salient

criterion, since company or product recognition is often a fundamental pre-sales-call goal.

35. Proportion of subvisuals that are charts or graphs .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%

42. Copy in top half of ad 9.7% 78%

43. Copy in bottom half of ad 49.8% 78%

44. Copy in left half of ad 3.2% 78%

45. Copy in right half of ad 14.2% 78%

46. Copy in top left section of ad 2.4% 78%

47. Copy in top right section of ad 1.6% 78%

48. Copy in bottom left section of ad 2.4% 78%

49. Copy in bottom right section of ad 3.6% 78%

50. Number of paragraphs of copy 4.87 .97

51. Ad located before center spread 46.6% 95%

52. Ad located after center spread 50.6% 95%

53. Ad located in premium position 2.8% 100%

54. Color(s) used in ad (1-4) 3.10 .92

Content Variables

1. Ad for product 67.6% 84%

2. Ad for service 20.6% 84%

3. Ad for process 2.4% 84%

4. Corporate ad 4.5% 84%

5. Institutional ad 0.8% 84%

6. Technical approach 55.5% 63%7. Analogy/allegorical approach 23.9% 72%

8. Case history approach 14.6% 89%

9. Spokesperson/expert approach 3.6% 87%

10. Competitive comparison approach 23.5% 69%

11. Question appeal 10.5% 100%

12. Humor appeal 11.7% 88%

13. Fear appeal 11.3% 90%

14. Altruism appeal 14.6% 96%

15. Status appeal 49.0% 74%

16. Learned motive appeal 24.3% 73%

17. Logical argument appeal 26.3% 85%

18. Problem/solution appeal 36.0% 67%

19. Number of calls to action (e.g. coupons, 800 #s) 2.31 96%

20. Reader/customer orientation in ad 81.0% 62-67%

21. Company name in headline 22.7% 90%


15



2. While objectivity is the acknowledged goal of such a social scientific method, it is recognized that what is actually achieved

is more properly termed 'inter-subjectivity'.

3. Typical of some of the publications in the advertising literature, Rossiter (1981) termed form and content variables

'mechanical' and 'message' variables respectively.

4. Studies of compositional form variables in print advertisements include: Newspaper Advertising Bureau, 1986; 1989; Soley

and Reid, 1983a; 1983b; Standen, 1989; Wesson, 1989; Wesson and Stewart, 1987. There have also been studies of other

form variables-advertisement size, position in the publication, use of color, etc. (Assael, Kofron and Burgi, 1967; Industrial

Equipment News , 1979; Marney, 1985; Sales & Marketing Digest , 1988; Marketing News , 1987; Stuhlfaut, 1983).

5. For example, Rossiter (1981) examined the impact of 13 'syntax' variables on Starch readership scores for advertisements in

one issue of Newsweek . Although he explained an impressive amount of variance, he looked exclusively at picture size and

headline characteristics.

6. For example, Rossiter (1981) properly reported inter-coder reliabilities and dropped variables that did not meet a set

criterion (rho = .60) but had a very poor, non-profitability sample, making inference impossible. Holbrook and Lehmann's use of

Cronbach's alpha as an indicator of reliability is suspect. They also used a very limited, non-profitability sample and

complained about decoders becoming 'exhausted' (p. 55) after only 10 hours of coding. The first study of advertising content

as related to readership (Twedt, 1952) gave no description of its content-analysis methodology at all.

7. This classifies the PARR Reports as aided recall research, in that respondents have the opportunity to view the

advertisements.

8. A summary of the BPA statement list readers as: 'General and corporate management, including financial and

administrative, engineering management and supervision, engineers, including planning, design, performance, R & D,

operations management and supervision; Operations, including construction, maintenance and fleet, purchasing, commercial

marketing, customer service, other qualified functions' (SRDS, 1996, p.505).

9. Numerous variables were measured as they occurred in (a) the headline, (b) the visuals, and/or (c) the copy. Due to low

frequencies of occurrence, these applications were collapsed across the three before inclusion in the regression analyses.

Additionally, variables with reliability coefficients below 60 percent or r = .70 were dropped.

10. We chose not to factor analyze the predictor set, a technique used by Twedt (1952) and Holbrook and Lehmann (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, we included individual variables and employed the Bonferroni

adjustment for multiple significance test.

11. Color was entered in the regression as black and white = 1, two-color = 2, three-color = 3, and four-color = 4.

12. These differential patterns may be seen quite clearly in Table 5. And, we may also note the variables that did not

contribute significantly to any of the four outcome variables: headline size, major visual size and placement, type and location

of subvisuals, copy length, advertisement location, all five different advertisement approaches (technical, analogy/allegory,

case history, spokesperson/expert use, competitive comparison), five persuasive appeals (question, humor, status, learned


16



motives, problem/solution), calls to action, reader orientation and company name in the headline. The fact that many of these

variables have been found to be significant predictors in other studies indicates that either (a) in the presence of other,

stronger, predictors their impact is superceded, or (b) this specific application in a business-to-business context shows

different results for a technical audience.

13. However, this study found the number of subheads included in an advertisement to be positively related to advertisement

informativeness (while at the same time being negatively related to advertisement attractiveness).

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NOTES & EXHIBITS

John L NaccaratoJohn L Naccarato is vice president, general manager of Liggett-Stashower Interactive in Cleveland, Ohio, and

Instructor of public relations and advertising at Cleveland 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, sales

promotion, research, and media have included work with regional and national clients in the fields of power

generation, steel, construction and mining equipment, medical equipment and hospitals.


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Kimberly A. Neuendorf

Kimberly A. Neuendorf is associate professor of communication at Cleveland State University. She received her

Ph.D. from Michigan State University. Her teaching and research interests include media use and ethnic identity,the social impact of advertising, and research methodologies. She has served as principal investigator, advisor, or

researcher on nearly 100 content analyses. Her work has appeared in such publications as Journal of Broadcasting

and Electronic Media , Journalism Quarterly , Journal of Communication , Communication Monographs , and

Communication Yearbook .

© Copyright Advertising Research Foundation 1997Advertising Research Foundation432 Park Avenue South, 6th Floor, New York, NY 10016Tel: +1 (212) 751-5656, Fax: +1 (212) 319-526 5

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