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The authors review more than 250 journal articles and books to establish what is and should be known about how advertising affects the consumer—how it works. They first deduce a taxonomy of models, discuss the theoretical principles of each class of models, and summarize their empirical findings. They then synthesize five generalizations about how advertising works and propose directions for further research. Advertising effects are classified into intermediate effects, for example, on consumer beliefs and attitudes, and behavioral effects, which relate to purchasing behavior, for example, on brand choice. The generalizations suggest that there is little support for any hierarchy, in the sense of temporal sequence, of effects. The authors propose that advertising effects should be studied in a space, with affect, cognition, and experience as the three dimensions. Advertising's positioning in this space should be determined by context, which reflects advertising's goal diversity, product category, competition, other aspects of mix, stage of product life cycle, and target market.
How Advertising Works:
What Do We Really Know?
Demetrios Vakratsas*
Tim Ambler**
Our thanks are due to Paddy Barwise for encouragement throughout this project, Simon Broadbent
for the original stimulus, and Susan Keane and Carolyn Boulard for their valuable assistance in
editing this article. We would also like to thank the editor and three anonymous JM reviewers for
their helpful comments and suggestions throughout the review process.
*Visiting Assistant Professor, School of Management, The University of Texas at Dallas
** Senior Fellow, London Business School.
Any correspondence should be addressed to the first author at:
School of Management
The University of Texas at Dallas
P. O. Box 830688, JO 5.1
Richardson, TX 75083-0688
How Advertising Works:
What Do We Really Know?
The authors review over 250 journal articles and books to establish what is and should be known about
how advertising affects the individual - how it works. They first deduce a taxonomy of models, discuss the
theoretical principles of each class of models and summarize their empirical findings. They then synthesize
five generalizations about how advertising works and propose directions for future research. Advertising
effects are classified into intermediate, e.g., on consumer beliefs and attitudes, and behavioral which relate
to purchasing behavior, e.g., on brand choice. The generalizations suggest that there is little support for any
hierarchy, in the sense of temporal sequence, of effects. The authors propose that advertising effects should
be studied in a space, with affect, cognition and experience being the three dimensions. Advertising’s
positioning in this space should be determined by context, which reflects advertising’s goal diversity,
product category, competition, other aspects of mix, stage of product life cycle and target market.
How Advertising Works:
What Do We Really Know?
With much advertising expenditure wasted in ineffective campaigns (Abraham and Lodish 1990, Lodish et
al. 1995a), advertisers should be concerned with how advertising affects individuals, how it works, in
order to formulate more effective advertising strategies. The first formal model is probably AIDA
(AttentionInterestDesire Action), attributed to E. St. Elmo Lewis in 1898 (Strong 1925, p. 76).
These types of “hierarchy of effects” models (Lavidge and Steiner 1961) have dominated the literature
since. Past integrative studies concerning advertising focused on particular models or effects of
advertising (e.g. Frequency of exposure and scheduling - Naples 1979; Market response - Clarke 1976,
Assmus, Farley and Lehmann 1984; Wear-in and Wear-out - Pechmann and Stewart 1988; Hierarchy of
effects - Barry and Howard 1990; Affective responses - Brown and Stayman 1992) rather than evaluating
the full range of different theories and models. One hundred years on, it is time to establish what is, and
what is not but should be, known about how advertising works.
We deduced a taxonomy of the different models as a structure for the discussion of their theoretical
principles and their empirical evidence and then summarized the findings in 25 conclusions, from which
we form five generalizations. In order to identify what should be known, we discuss what issues the
various models in the taxonomy failed to address and propose future research by formulating five
In order to choose among the numerous advertising-related journal articles, proceedings papers, working
papers and books, we first formulated study selection criteria based on a simple framework of how
advertising works (Figure 1). Advertising, of own and competitive brands, is shown as an input for the
consumer. Scheduling of the media, message content and repetition (Singh and Cole 1993) are
components of this input and comprise the advertising strategy which triggers an individual’s response.
Figure 1 about here
The intermediate type of response implies that, consciously or unconsciously, advertising must have some
mental effect (e.g., awareness, memory, attitudes toward the brand) before it can affect behavior.
Cognition, the “thinking” dimension of an individual’s response, and affect, the “feeling” dimension, are
seen as two major intermediate advertising effects. Individual purchasing and product usage behavior, or
changes thereto, in our model represents the consequential, behavioral, effects of advertising. For most
products, and especially the frequently purchased packaged goods with which much research is
concerned, the consumer’s mind is not a blank sheet awaiting advertising but already contains conscious
and non-conscious memories of product purchasing and usage. Thus behavior feeds back to “experience,”
which is our third principal intermediate effect. Individual responses to advertising are mediated by factors
such as motivation and ability to process information (Cacioppo and Petty 1985; MacInnis and Jaworski
1989) and attitudes toward the ad (MacKenzie, Lutz, and Belch 1986). These mediating factors can alter
or radically change response to advertising. They can therefore be considered filters of the initial
advertising input. Our notation, to describe the different theories and models of how advertising works,
follows Holbrook (1986): C for cognition, A for affect. Additionally, we use E for (memories of) prior
experience of brand purchase, usage and advertising.
The studies eventually included were selected from an extensive database of advertising research
constructed primarily by searching the ABI/Inform database using advertising-related keywords, and
consulting Broadbent’s (1992) synopsis of 456 studies of how advertising works, 122 of which were case
histories. Extensive networking also identified candidate studies. The study selection criteria were:
(a) Each study should focus on behavioral and/or intermediate effects (Figure 1). Thus, studies concerned
with general economic and social effects of advertising were excluded;
(b) The study should report empirical results, or discuss (review) empirical results of other studies;
(c) The study should be recent (i.e. after 1960) and reflect the trend the current more systematic approach
to studying advertising effects;
(d) English language;
(e) The majority of the studies were published in marketing journals. Relevant books and unpublished
studies (working papers) were included. While this led to some variation in quality, the key
consideration was whether the study (potentially) contributed to the stock of knowledge of how
advertising works.
We make no claim that this selection is complete. There will be practitioner and academic papers we
missed. Furthermore, practitioners employ models of how their advertising works which they do not
publish. The studies selected do, however, so far as we can determine, include every significant and
current theory of how advertising works. Our study also has an international flavor, since it examines
research by academics and practitioners in the USA, UK, Australia, and the Netherlands.
Our approach to evaluating the models and empirical results involved two steps:
1. Classification of the different models and theories of advertising effects; in other words, developing a
taxonomy of models of how advertising works. We discuss the theoretical principles for each class of
models and their most robust empirical findings which are summarized in Table 2.
2. Generalizations based on the empirical findings. We chose the literature review approach (Bass and
Wind 1995) to forming such generalizations rather than meta-analysis or content analysis, due to the
diverse designs of the studies considered (field, single source, experimental) and the measures
employed (advertising-sales elasticities, purchase intentions, awareness, etc.). Each generalization was
supported by at least two different conclusions.
Our taxonomy, summarized in Table 1, describes the various models and theories of how advertising
works. It builds progressively from models that assume no intermediate effects (market response - “-“
in our notation) to models that assume only one type of intermediate effect (cognitive information-
“C” or pure affect - “A”). Then it describes models that assume more than one type of intermediate
effect in a certain hierarchy (persuasive hierarchy models- “CA”, low involvement hierarchy models-
“CEA”), followed by models with varying hierarchies of effects (integrative models- “(C)(A)(E)”)
and finally models that assume no hierarchy of effects at all (hierarchy-free- NH).
Table 1 about here
“Market response models” (econometric models of market response to advertising, “-” in our notation), do
not consider intermediate effects at all. They typically relate advertising, pricing and promotional measures
to behavioral (sales or brand choice) measures. This has the advantage of employing “objective”
(secondary) data and eliminating intermediate measurement uncertainties.
According to one historian (Nevett 1982), advertising has, from its earliest days, been seen as providing
strictly factual information. Such models of advertising purely as information transfer are termed
“cognitive information” (C). “C” models rely heavily on economics (Nelson 1970; 1974; Robinson 1933;
Stigler 1961; Telser 1964) and assume consumer decisions to be rational only. Conversely, “pure affect
models (A), which appeared later in the literature (e.g., Zajonc 1980), pay little or no attention to cognition
and emotional response.
As noted above, the main stream of advertising research began with AIDA (AttentionInterest
DesireAction). Originally a model of personal selling, it was only later adapted for advertising (Strong
1925, p. 76). From this emerged the class of “persuasive hierarchy” models (Table 1) summarized by
Kotler (1997, p. 611) as “Response Hierarchy Models”: AIDA, Hierarchy-of-Effects (Lavidge and Steiner
1961), and Innovation-Adoption (Rogers 1962). All these follow the Cognitive stage Affective stage
Behavior sequence, or “CA” in our notation, meaning that cognition is followed by affect. Note that C
always appears before A; advertising is seen as persuading the consumer to buy (more), and E does not
feature at all.
Recognition of the importance of product trial and usage experience led to another class of models termed
here “low involvement hierarchy” models (e.g., Ray 1973). In this category, advertising merely serves to
reinforce behavior as distinct from causing it. Advertising may create awareness (C), but affect and brand
preferences are formed after product trial and experience, thus CognitionExperience
AffectBehavior, or CEA in our notation. This class of models is sometimes known as the “weak
theory” of advertising (Jones 1990), to distinguish it from the “strong,” or persuasive hierarchy (CA).
This is similar to operant, or instrumental, conditioning where learning follows performance (Skinner
1938; Thorndike 1911).
More complex hierarchies have been classified as “integrative” (“(C)(A)(E)”) where the brackets indicate
that the order of a particular effect (C, A, or E) in the sequence is not fixed and depends on context. For
example, the FCB grid (Vaughn 1980; 1986) has C, A and E in various sequences, depending on the
individual’s involvement in the product category and whether consumer choice, in that category, was
primarily determined by cognition or affect. Our final category is that of “hierarchy-free” models, where
no particular processing sequence is assumed. This category is the most sparsely populated, but
recent applications of post-modernism and anthropology to advertising effects suggest that this class
can be expanded. Modern neuroscience, as we discuss later, has important insights for advertising
3.1 Market Response Models (-)
Market response models typically relate advertising, price, and promotional measures directly to
purchasing behavior measures such as sales, market share, and brand choice in a regression or logit model
framework. For example, measurement of loyalty would here be based on repeat purchasing behavior
rather than an attitude of mind. Market response models can be further classified into aggregate level
(Bass and Clarke 1972; Blattberg and Jeuland 1981; Hanssens, Parsons, and Schultz 1990; Little 1979;
Rao 1970; Rao and Miller 1975; Rao 1986; Zufryden 1987) and individual level (Deighton, Henderson,
and Neslin 1994; Pedrick and Zufryden 1991; Tellis 1988; Winer 1991). Aggregate-level studies use
market-level data such as brand advertising expenditures or gross rating points and brand sales or market
share. Individual level studies use measures such as individual brand choice and the number of exposures
for an individual (or household), derived from single source data.
Many aggregate-level econometric studies concerned with the long-term, or carryover, effects of
advertising (Bass and Leone 1983; Broadbent 1984; Clarke 1976; Dhalla 1978; Srinivasan and Weir
1988), concluded that the duration of advertising effects depends on the data interval (weekly, biweekly,
monthly, etc.). “Intermediate” interval data (bimonthly, quarterly) appear to provide more realistic results,
though the issue of the appropriate data interval is still open. Clarke (1976) and Assmus, Farley, and
Lehmann (1984), in meta-analytic studies, suggested that 90% of the advertising effects dissipate after
three to fifteen months. Leone (1995), in an empirical generalizations study, suggested that the range be
narrowed to six to nine months. Dekimpe and Hanssens (1995) applied “persistence modeling”, a time
series methodology, to monthly data from a home improvement retail chain and concluded that the effects
of advertising “did not dissipate within a year” (p. 18). This apparent contradiction with Clarke (1976) and
Leone (1995) was attributed to the evolving nature (in terms of sales and advertising) of the industry under
study. Winer (1980), using split cable panel data from an undisclosed frequently purchased category,
found, apart from a transitory effect, no permanent advertising effect on consumption. This transitory effect
was found to last for about 16 weeks for one brand and at least 32 weeks for another, both within the
bounds suggested by Assmus, Farley, and Lehmann (1984). In contrast with the previously cited studies
which used aggregate-level data, Mela, Gupta, and Lehmann (1997) used individual-level data on
purchases of an unnamed product category and concluded that advertising helps a brand by making
consumers less price-sensitive and by decreasing the size of the nonloyal segment. Promotions, on the
other hand, make consumers, especially nonloyals, more price-sensitive.
A large-scale, single-source study by Lodish et al. (1995a) concluded that increased advertising weights
increased sales of established brands in only 33% of cases and in 55% of cases for new brands. The
implication, consistent with empirical results reported by Parsons (1975), Arora (1979), and Parker and
Gatignon (1996), is that advertising elasticities are dynamic and decrease over the product life cycle. Winer
(1979), using Lydia Pinkham data, found that while carryover effects decline over time, current advertising
effects increase over the same period. Although the first result is consistent with the product life-cycle
theory, the second result is attributed to the ability of that particular product to attract more new purchasers
rather than retain them. In a study of similar design to their first, Lodish et al. (1995b) suggested that short-
term effects are prerequisite for the achievement of long-term effects, a conclusion also reached by Jones
(1995a) in another study using single-source data across several product categories. The two Lodish et al.
(1995 a, b) studies used an extensive database compiled by IRI for the “How Advertising Works” projects
(see for example Advertising Research Foundation transcript proceedings in 1991, p. 13). Advertising
elasticities were consistently found to be low, typically in the range 0 to 0.2 (Assmus, Farley, and Lehmann
1984; Lodish et al. 1995a), and short-term promotional effects were shown to be larger than the
advertising ones (Deighton, Henderson, and Neslin 1994; Jones 1995a; Tellis 1988).
The studies by Tellis (1988), Deighton, Henderson, and Neslin (1994) and Jones (1995a, b) along with the
original single-source study conducted by McDonald (1971), suggest that , short-term advertising effects
diminish fast: More specifically, after the third exposure, response to advertising levels off. Once three
exposures per household are therefore achieved, advertisers should focus on reach (see also Pedrick and
Zufryden 1991; 1993). These results are in general agreement with the conclusions of Naples’ (1979)
review of various empirical advertising studies: “An exposure frequency of two within a purchase cycle is
an effective level” (p. 64) and that “by and large, optimal exposure frequency appears to be at least three
exposures within a purchase cycle” (p. 67). The relative effect of media reach and frequency on
purchasing behavior has been the focus of other market response researchers (Danaher 1988;1989; 1991;
Leckenby and Kishi 1984; Metheringham 1964; Pedrick and Zufryden 1991; 1993; Rust 1986; Rust and
Leone 1984).
Market response category findings are summarized as conclusions 1-8 in Table 2.
3.2 Cognitive Information Models (C)
This class of models assumes that consumer preferences, e.g., the relative weights of attribute importance,
are not changed by advertising and consumer decisions are only rational. Advertising provides information
and/or utility in reducing search costs, e.g., shopping time (Bharadwaj, Varadarjan, and Fahy 1993;
Nelson 1970; 1974). An ad in the Yellow Pages saves the customer having to go from store to store.
Goods are classified into two major categories: experience and search (Nelson 1974) with experience sub-
divided into high, where considerable usage is required before quality can be assessed by the consumer,
and low (Davis, Kay, and Star 1991). For search goods, product quality and the truth of the advertising
claim can be judged by inspection (without trial) and evaluation of relevant objective information (e.g.,
price). A third category of credence goods (Darby and Kirni 1973), can be used in order to refine the
above classification. For credence goods, the “average” consumer cannot determine the quality of the good
even after experience, e.g., designer clothes. Advertising is expected to be more effective for experience
and credence than for search goods since it provides information which inspection does not (Nelson 1974;
Verma 1980). Classification of goods into search, and experience (or credence) may be problematic, as
many goods (e.g. autos) consist of both search (e.g., leather seats) and experience (e.g., driving feel)
attributes. A distinction, therefore, between search and experience attributes, rather than goods, appears
more accurate and realistic (Wright and Lynch 1995).
Firms producing high-quality products may have large advertising expenditure to signal their quality to the
consumers, thus achieving long-term advantage (Nelson 1974; Verma 1980). High-quality image and
differential advantages reduce consumer price sensitivity and permit a gradual increase in price, according
to the Market Power theory (Comanor and Wilson 1974; 1979). The Economics of Information theory
(Stigler 1961; Telser 1964), on the other hand, suggests that advertising increases price sensitivity because
it facilitates consumer search (Chiplin and Sturgess 1981; Eskin and Baron 1977). Empirical testing of the
two competing theories brought mixed results. Using aggregate data, Wittink (1977) and Eskin and Baron
(1977) found support for the Economics of Information theory. Using individual-level data, Krishnamurthi
and Raj (1985) supported Market Power, whereas Kanetkar, Weinberg, and Weiss (1992) favored the
Economics of Information explanation. Lambin (1976) used brand-level data to conclude that advertising
leads to lower price sensitivities. Finally, Eastlack and Rao (1986), after analyzing advertising
experimental data for V-8 vegetable juice, concluded that the combined effect of advertising and a price
increase was a temporary increase in price sensitivity, which then came back to historical levels.
Advertising therefore allowed in the long run a price increase, while maintaining the price sensitivity level.
In a meta-analytic study, Kaul and Wittink (1995) concluded that non-price advertising decreases price
sensitivity whereas price advertising increases price sensitivity and ultimately leads to lower prices. This
confirmed prior conjectures about the differential effects of price and non-price advertising by Boulding,
Lee, and Staelin (1992), Farris and Albion (1980), and Krishnamurthi and Raj (1985).
We should note the emphasis given to factual information by practitioners. Reeves (1961) created USP
(Unique Selling Proposition) as part of the long-standing recognition of the idea, now called positioning,
that a brand must both differentiate itself, if possible, through tangible product attributes and then
communicate that differentiation positively. Ogilvy (1983, p. 159) bends the knee to Dr. Johnson’s
“promise, large promise, is the soul of an advertisement” and stresses the informative role of advertising.
Their work and that of other practitioners (e.g., Fletcher 1992) may be largely affective but their
publications emphasize the cognitive.
The findings from the cognitive category are summarized as conclusions 9-12 in Table 2.
3.3 Pure Affect Models (A)
Conversely to the economics paradigm, some theories pay little or no attention to cognition but focus on
affective responses, the familiarity and feelings ads may evoke (Aaker, Stayman, and Hagerty 1986;
Alwitt and Mitchell 1985; Peterson, Hoyer, and Wilson 1986). One class of such theories, the so-called
“mere exposure” theories, suggest that awareness of the advertisement is not necessary, though awareness
of the brand is. According to this approach, individuals form their preferences based on elements such as
liking, feelings and emotions induced by the ad or familiarity triggered by mere exposure to the ad, rather
than product/brand attribute information (Batra and Ray 1986; Gardner 1985; Holbrook and Batra 1987;
Janisewski and Warlop 1993; Mitchell and Olson 1981; Shimp 1981; Srull 1983; Stuart, Shimp, and
Engel 1987; Zajonc 1980; 1984; Zajonc and Markus 1982). Two of these theories, namely response
competition (Harrison 1968) and optimal arousal (Berlyne 1960; 1966; Crandall 1970), suggest that
unfamiliar advertising messages create hostility and take longer to reach their optimal effectiveness. That
may well describe the advertising “wear-in” effect observed frequently in advertising studies (Blair 1987;
Pechman and Stewart 1989): a minimum (threshold) number of exposures is necessary for the ad to have
an effect on the consumer. The “two-factor” theory (Berlyne 1970) also suggests a wear-out effect: after a
number of exposures, the effect of advertising decreases. Thus the advertising response function has an
inverted-U shape.
Affective responses to advertising can be further classified into two types: one that leads to the formation
of an attitude toward the brand and one that leads to the formation of an attitude toward the ad, an
expression of the likability of the ad itself (Mitchell and Olson 1981; Shimp 1981; and, for a meta-
analysis of attitudes toward the ad studies, Brown and Stayman 1992). Empirical evidence concerning
affective responses and ad likability is based both on experimental and field research. Gorn (1982), in a
classical conditioning experiment, found significant effects of background music on preference. Bierly,
McSweeny, and Vannieuwkerk (1985), in another experimental study, also found music effects on
preference ratings, and Janisewski (1988) concluded that affective processes can be formed independently
of cognitive processes. Both the cumulative effect of liking and its correlation with sales seem to generalize
empirically, but not uniformly: The ARF copy research project (Haley and Baldinger 1991; Joyce 1991)
and the study on US prime time commercials by Biel (1990) suggested that ad likability is positively
correlated with behavior (preference). On the other hand, Hall and Maclay (1991) and Stapel (1987)
suggested that the influence of ad likability on brand preference is not strong. Brown (1991) suggested that
ad likability has a long-term effect.
The absence of cognition suggested by pure affect models is difficult to show, because cognition usually
intervenes in measurement. Asking about feelings brings cognitive processes into play and induces
“cognitive bias,” a bias towards cognitive methods and models (Sawyer 1981). Noncognitive measures
have been developed, such as projective techniques and the Facial Action Coding System developed by
Ekman and Friesen (1978). Bogart (1996, p. 73) notes skin conductivity, pupil dilation and “brain waves”
measured by EEG (electroencephalograph). Unfortunately, none of these are yet reliable for advertising
affect measurement purposes (Scherer and Ekman 1982). Rothschild and Hyun (1990) used EEG
technology to show that TV ad recognition was increased when right brain was employed initially but
left hemisphere dominated during the following seconds. Bilateral processing was greatest for
rational commercials and least for emotional, with mixed-appeal commercials between those
extremes. Cognitive bias problems aside, models based purely on affective responses are rather
improbable, as some awareness appears to be a necessary condition for advertising effectiveness (Franzen
1994). Advertising typically works on both the cognitive and affective planes (Agres, Edell, and Dubitsky
1990; Holbrook and O’Shaughnessy 1984). However, this class of models (A) essentially introduced
affective responses to the study of advertising effects, and they have consistently been shown to be
important (Aaker and Stayman 1990a and b).
Conclusions 13, 15 and 16 in Table 2 summarize the findings from the A category.
3.4 Persuasive Hierarchy Models (CA)
The idea that if advertising is to promote sales it must inform and then persuade, has intuitive appeal.
Persuasive models introduced the concept of a hierarchy of effects, i.e., an order in which things happen,
with the implication that the earlier effects, being necessary preconditions, are more important. The
hierarchy concept has played a large part in the development of advertising research. The number of
stages may be increased or refined, (Aaker and Day 1974; Advertising Research Foundation 1961; Colley
1961; Greenwald 1968; Lavidge and Steiner 1961; McGuire 1968; 1978; Robertson 1971; Rogers 1962;
Wright 1973), but the underlying pattern is Cognition Affect Behavior (CA). Two important mediating
factors (“filters” in Figure 1) of individual responses to advertising, involvement and attitude toward the
ad, have been extensively studied within the persuasive hierarchy framework (Batra and Ray 1985; Burke
and Edell 1989; Cacioppo and Petty 1985; Homer 1990; MacKenzie and Lutz 1989; MacKenzie, Lutz,
and Belch 1986; Petty, Cacioppo, and Schuman 1983; Sawyer and Howard 1991). As we have already
discussed the concept of attitude toward the ad in the previous section (3.3), we should first briefly discuss
the concept of involvement before considering persuasive hierarchy models and their empirical results.
Krugman (1965; 1967) operationalized involvement as the number of linkages made between the
advertised product and the individual’s life during exposure to an advertisement. Several definitions since
then followed (e.g., Houston and Rothschild 1978; Lastovicka and Gardner 1979; Mitchell 1981; and, for
reviews, see Greenwald and Leavitt 1984 and McWilliam 1993). Rothschild (1984) defined involvement
as “an unobservable state of motivation, arousal, or interest. It is evoked by a particular stimulus or
situation and has drive properties. Its consequence are types of searching, information-seeking and
decision making.”
One of the most comprehensive persuasive models is the Elaboration Likelihood Model (ELM) (Petty and
Cacioppo 1980; 1981). ELM distinguishes between elaborate (attribute evaluation) and non-elaborate
(paying attention to execution elements, e.g., celebrity endorsers) information evaluation. Elaboration is
essentially cognitive, and the model introduces alternative paths for consumer responses to advertising.
The two alternative paths, however, follow the same CA sequence. Other multidimensional versions of the
persuasive hierarchy paradigm have been proposed by MacInnis and Jaworski (1989), MacInnis,
Moorman, and Jaworski (1991), and Bloom, Edell, and Staelin (1994). More specifically, MacInnis and
Jaworski (1989) proposed a model with six levels of mental processing (intermediate effects): 1) Feature
analysis leading to mood-generated affect, 2) Basic categorization leading to pure affect transfer, 3)
Meaning analysis leading to heuristic evaluation, 4) Information integration leading to message-based
persuasion, 5) Role-taking leading to empathy-based persuasion, and 6) Constructive processes leading to
self-generated persuasion.
The MacInnis and Jaworski model integrates the brand-processing (Gardner, Mitchell and Russo 1978;
Mitchell 1980) and four-level audience involvement model (Greenwald and Leavitt 1984). Greenwald and
Leavitt, after reviewing definitions of involvement, relate levels of involvement to stages of consumer
information processing as follows: 1) Preattention related to sensory buffering and feature analysis, 2)
Focal attention and channel selection, perceptual and semantic processing, 3) Comprehension related to
syntactic analysis, and 4) Elaboration related to conceptual analysis . According to Greenwald and
Leavitt’s model, complex ads, which require inferences on brand quality based on persuasive arguments,
should require a high level of involvement - mainly elaboration. On the other hand, advertising that links a
brand to attractive objects should only require focal attention - a lower-level type of involvement. Bloom,
Edell, and Staelin (1994) use the Fishbein-Ajzen framework of attitude formation, in order to distinguish
among communication, brand, and product category beliefs. These three types of belief lead to
corresponding forms of attitudes (attitude toward the ad, brand and product category), which interact in
affecting behavior.
Batra and Ray (1985), still within the persuasive hierarchy category, suggested an alternative to ELM:
Citing evidence from Bagozzi, Tybout, Craig, and Strenthal (1979), Bagozzi and Burnkrant (1979), and
Bagozzi (1981), they challenged the Fishbein-Ajzen attitude formation model (Fishbein and Ajzen 1975;
Lutz 1975; 1991) adopted by ELM, where attitudes toward the brand are “utilitarian,” i.e., based
exclusively on beliefs about hard product attributes. They suggested that consumers may develop a
“hedonic” effect based on pure liking without evaluation of hard product attributes (Hirschman and
Holbrook 1982; Holbrook and Batra 1987). The multi-dimensionality in consumer response, according to
the Batra and Ray framework, is therefore the result of the different ways attitudes may form (utilitarian
vs. hedonic), rather than the degree of elaboration. This suggests that measures of affect should include
both utilitarian and hedonic components.
Applications of persuasive hierarchy models highlighted the importance of involvement as a moderator of
advertising effects. Using the ELM framework, Petty, Cacioppo, and Schumann (1983) suggested that
highly involved individuals choose an elaborate way to evaluate message information (relying on message
argument quality to form their attitudes and purchase intentions) whereas low-involvement individuals
choose a less elaborate way (relying on celebrity status of the product endorser). Cacioppo and Petty
(1985) concluded that repetitions of different versions of an ad have a positive effect on low-involvement
individuals but no effect for high-involvement individuals. In other words, repetition of a series of ads can
prevent (or delay) wear-out. Similar results on the differential effects of repetition (single vs. series of ads)
were suggested by Zielske (1959), Zielske and Henry (1980), and Rao and Burnkrant (1991) who found
that varied ad executions maintained ad recall at high levels. Batra and Ray (1986) found that in low-
involvement situations, affective responses influence brand attitudes more positively than in high-
involvement situations. Similarly, using the ELM framework, Dröge (1989) showed that attitude toward
the ad positively affects attitudes towards the brand only in low-involvement situations. By enhancing
brand beliefs to include non-utilitarian attributes, Mittal (1990) showed that the (still significant)
contribution of Attitude towards the ad as predictor of behavior is reduced.
Correlations between measures of attitude (affect) and behavior reported in the literature are usually low
(between 0 and 0.30 - Fazio and Zanna 1979; Wicker 1969) which prompted some researchers to reject
the persuasive hierarchy (Heeler 1972; Palda 1966; Ray 1973; Rothschild 1974; Sawyer 1971; Strong
1972). Barry and Howard (1990) report that only two studies have properly tested the sequence (Batra and
Vanhonacker 1986; Zinkhan and Fornell 1988) with inconclusive results. Some support for the persuasive
hierarchy sequence, however, is provided by some earlier work (Assael and Day 1968; O’Brien 1971) but
not since replicated.
Our conclusions from this research are backhanded: While there is little support for the persuasive (CA)
hierarchy per se, there is considerable support for a multipath approach such as ELM; namely, different
people respond to different ads in different ways, depending on their involvement. While attitudes
correlate poorly with behavior, possibly due to cognitive bias (which we discuss later), affect is relatively
more important in low involvement/non-elaborate situations. Cognitive and affective beliefs may be
independent under these circumstances (Wilson et al. 1989).
Conclusions 14-20 in Table 2 summarize the findings from the CA category.
3.5 Low Involvement Hierarchy Models (CEA)
The main alternative to the persuasive approach is Cognition Experience Affect (“CEA”) though
“cognition” may mean no more than passing awareness in categories where the consumer has low
involvement. Ehrenberg’s (1974) ATR model (Awareness Trial Reinforcement) is typical of this class
of models and suggests that product preferences are formed after initial trial. In low-involvement
hierarchies, product experience is seen as the dominant factor, with advertising reinforcing existing habits,
framing experience and defending the brand’s consumer franchise (Pechmann and Stewart 1989;
Ehrenberg 1994). In the notation of this paper, these experiences, habits, and recollections are collectively
“experience.” This category is termed as “low involvement hierarchy” (Harris 1987; Ray 1973; Smith and
Swinyard 1978; 1982; Swinyard and Coney 1978), because it is associated with the routinized choice
behavior more likely to happen in low-involvement situations.
Empirical applications suggest that product usage experience has a larger impact on beliefs, attitude
formation, and choice than advertising which rather reinforces habits or frames usage experience (Alba,
Hutchinson, and Lynch 1991; Deighton, Henderson, and Neslin 1994; Marks and Kamins 1988; Olson
and Dover 1979; Smith 1993; Smith and Swinyard 1983; 1988; Tellis 1988; Winter 1973). More
specifically, Smith (1993) found that advertising tends to mitigate a negative trial effect when it precedes
trial but has no impact on beliefs and attitudes when trial is positive. Levin and Gaeth (1988) and Hoch
and Ha (1986) provided evidence that when exposure precedes usage experience, advertising is relatively
more effective. Both previous empirical results suggest that advertising’s framing effect is more persistent
when it precedes usage experience - or that advertising has a predictive framing effect. Similar evidence
was found by Deighton (1984) and Deighton and Schindler (1988). Hoch and Ha (1986) also suggested
that advertising’s framing effect is stronger when the product category is ambiguous, i.e., quality is hard to
determine. To the extent that high experience goods (attributes) can be characterized as ambiguous, this
result is in accordance with the cognitive information conclusion on advertising effectiveness for search
and experience goods (attributes). The reinforcing role of advertising was supported by Raj (1982), Tellis
(1988), Deighton, Henderson and Neslin (1994), and D’Souza and Rao (1995) in that advertising has
more effect on loyal consumers.
Conclusions 9 and 21-24 in Table 2 summarize the findings from the CEA category.
3.6 Integrative Models {(C)(A)(E)}
In this class of models, different hierarchies of C, A and E are assumed, depending on the context in which
advertising operates. For example, product category and level of involvement may determine the order of
effects. In some of these models, context may also determine the strength of each effect.
The FCB Grid (Vaughn 1980; 1986) uses involvement (high/low), and think/feel (C or A being the
dominant consumer motivator) as the two dimensions for classifying product categories. The type of
involvement used by the FCB grid is therefore category involvement and not brand, personal or situational
involvement. The primary grid validation study was conducted in the USA among 1,800 consumers across
250 products (Vaughn 1986). It was then extended to over 20,000 interviews in 23 countries. The FCB
study carried the operationalization of involvement from laboratory to survey. The managerial implication
was that advertising should be designed according to the quadrant in which the product category belonged.
Frequently purchased packaged goods, for example, were likely to be low-involvement and affect-
motivated. McWilliam (1993) verified that involvement was determined by category, not by brand.
Rossiter and Percy (1997) suggested a development of the FCB grid (the “Rossiter-Percy” grid- see also
Rossiter, Percy, and Donovan 1991) using awareness as a necessary condition for the effectiveness of
advertising and replacing the think/feel dimension with a more directly motivational one
(“informational/transformational”). They also distinguish between product category and brand choices and
prescribe advertising tactics that fit the cells of their grid as defined by involvement and motivation.
Smith and Swinyard (1982) distinguish between “higher-order” (strongly held) and “lower-order” beliefs
(weakly held) to introduce context specificity in the way advertising affects the individual. According to
their Information Integration Response Model (IIRM), for low involvement goods, where trial is easy and
inexpensive (e.g. frequently purchased products), advertising is more likely to affect “lower-order” beliefs,
mostly by increasing awareness and introducing uncertainty. Such beliefs are updated after product trial
and experience. Experience would resolve uncertainty, confirm or disconfirm lower-order beliefs, and
either lead to commitment to the brand or rejection of the brand. Higher-order beliefs are therefore formed
only after a number of trial purchases. Such a scenario is consistent with the CEA or low-involvement
hierarchy suggested by Ray (1973) and Ehrenberg (1974). The distinction between trial and committed
purchases in the IIRM is similar to that proposed by Ehrenberg (1974) and Robertson (1976). For high-
involvement products where trial is risky and expensive (e.g. household appliances), higher order beliefs
are formed immediately, but they need not be based exclusively on advertising. Other external information
sources (word of mouth, magazine articles) and past experience should also affect higher order belief
formation. In this case, advertising’s effects are expected to follow the persuasive hierarchy CA, but due to
the influence of other sources, the effect of advertising may not be strong.
Deighton’s (1984; 1986) two-stage model is conceptually similar to the IIRM of Smith and Swinyard. In
the first stage, advertising provides initial arousal and helps in developing expectations (or hypotheses)
about the advertised brand, similarly to the lower-order belief formation stage in the IIRM. The second
stage is the product trial/experience stage, where initial expectations are confirmed or disconfirmed. As
confirmation may not be immediate (i.e., after the first product trial), these two stages continuously interact
with experience, updating expectations. Conceptually, the model can be perceived as a hierarchy where C,
E and A exchange positions. Using experimental data, Deighton (1984) provided evidence for the
existence of interactions between expectations and usage experience.
The (C)(A)(E) category evidence (mostly based on the application of the FCB Grid) robustly supports the
concept of involvement and the idea that C or A determining choice is dependent on the product category.
The research assumes individual homogeneity, whereas one would expect involvement to be personally as
well as product-driven. No evidence supports the idea that consumers process advertising information in a
hierarchic fashion.
Conclusion 25 in Table 2 summarizes the findings from the (C)(A)(E) category.
3.7 Hierarchy-Free Models (NH)
While most research falls more or less neatly into the previous six categories, we reserved a final section
for all others. This proved to be the smallest category a fact which itself provides some support for the
classification methodology. This last category generally presents a more person-centered view of
advertising, which can be thought of as an extension of a basic reinforcement model. It discounts the
persuasive view of advertising (see CA) and rational decision making, and suggests that advertising is a
part of a brand totality (King 1975; Lannon and Cooper 1983; Lannon 1986; Lannon 1994). The example
of New Coke beating the “real thing” in product tests (attribute evaluation) but not in the marketplace
(where the real thing is an established entity) is suggested as an illustration of the added value framework.
It could also reflect the artificiality, or cognitive bias, of that type of research.
Anthropomorphizing the brand and accommodating it in the consumer's real world (Buttle 1991; Troiano
1996) may help the understanding of marketers and copywriters, and clarify the nature of the
communications they are creating, i.e., brand advertising. The literature does not reveal, however, how
these models actually work nor how the effects of advertising may be measured. In other words, empirical
validation is largely experiential. This category would include relating the meaning of advertising with
brands as myths and marketing as myth-making which was sourced from anthropology (Lannon 1994;
Lévi-Strauss 1963; Stern 1995) and its neighbor, semiotics (Mick 1988). Stern has also analyzed
advertising from the standpoints of feminist literary criticism (1993) and drama criticism (1994). The
latter usefully distinguished sympathy (for the characters in TV commercials), from empathy. The length of
the commercial gave time to recognize the plight of the protagonists, but not to identify with them.
Post-positivist/post-modern researchers (e.g., Hirschman and Holbrook 1986) regard introspection and
experiential learning as a valid methodology alongside the hypothetico-deductive tradition that underlies
most of the work reported here. The flavor of the debate between the various new “isms” and the
traditionalists is given by Hirschman and Holbrook (1986), Hunt (1992), Peter (1992), and Zinkhan and
Hirschheim (1992). Much of this is philosophic; at the pragmatic level of how advertising actually works,
we conclude that the post-positivists have, thus far, broadened the width of our understanding but not the
depth. For example, we have not found research to advise the practitioner as to which measures predict ad
Neuro-science indicates that the brain receives and handles information in a parallel fashion (Rose 1993;
Sutherland 1993). While serial hierarchies exist to pass information from stage to stage, the different
functions of the brain, in this case C and A, receive information in parallel (Martin 1991, p. 335). Sensory
information, apart from smell, reaches the brain through the thalamus which relays it to the cognitive
functions in the neo-cortex and, independently, the affective functions of the limbic system (Kupfermann
1991, p. 737). These in turn are massively interconnected as higher cognitive functions affect feelings and
emotions and vice versa. It would thus seem likely that C and A, if they are both engaged at all, are
engaged simultaneously and interactively. As the human brain has been called the most complex structure
in the universe (Fischbach 1992), steps into this territory have to be rather tentative. From the way the
brain processes (advertising) information, it would appear that the hierarchy of effects concept is deeply
Our generalizations used the following criteria:
(a) quality, objectivity, and consistency (Bass 1995; Bass and Wind 1995); and
(b) scope, precision, parsimony, usefulness, and linkage with theory (Barwise 1995).
Based on conclusions 5, 6, 13, 14, 20-24 of Table 2, cognition (C), affect (A) and experience (E) are
significant when studied in combination with or in isolation. According to conclusions 5 and 6, advertising
carryover requires E; conclusions 13 and 14 suggest that both C and A are required; and conclusions 20-
23 stress the impact of E on beliefs, attitudes, and advertising effectiveness. Furthermore (see conclusion
24), beliefs, attitudes, and choice build cumulatively with awareness, trial, word of mouth,
promotions/distribution, and advertising, any of which can be reinforcing or negative. In sum, the evolution
of models from relatively simple (C) to more complex (C)(A)(E) has shown the persistent significance of
all these three key effects and suggests that omission of any one is likely to overstate the importance of the
others. The key conclusion therefore here is that all three effects should be consistently included in studies
of advertising effectiveness. Thus:
G1. Experience, Affect, and Cognition are three key intermediate advertising effects, and the
omission of any one can lead to overestimation of the effect of the others.
Based on conclusions 1, 3, 5, and 20 of Table 2, short-term advertising elasticities are low (0 - 0.2), about
20 times lower than promotions, and weaker than product usage experience effects. They are, however,
significant for about one third of established brands and half of new brands, suggesting that advertising is
more effective in the beginning of the life of a product. Unfortunately, a similar generalization for the long-
term effects of advertising cannot be made. Although studies (primarily of the market response models
variety) have consistently provided evidence for the significance of such effects (Clarke 1976; Lambin
1976; Assmus, Farley, and Lehmann 1985; Leone 1995; Dekimpe and Hanssens 1995), there has been no
general agreement for their duration. Thus:
G2. Short-term advertising elasticities are small and decrease over the product life cycle.
Based on conclusions 7 and 8 of Table 2, single-source and experimental studies have repeatedly and
independently verified that one to three exposures per purchasing cycle are “enough” to trigger a
consumer purchase. Accordingly, while more exposures would increase effectiveness, they represent poor
value for the advertiser. This agrees with previous conclusions of Krugman (1972), Simon and Arndt
(1980), and Lambin (1976). The latter notes that “doubling the amount of advertising does not double
sales, because the efficiency of increased advertising exposures always decreases beyond the threshold
level.” (pp. 97-98). Krugman (1972), suggested that the third exposure “clinches a decision” and further
exposures have little value. These conclusions empirically reconcile both economic theories about
diminishing returns to advertising and theories of learning and affective response which recognize that the
first exposure is the most influential. The research supporting conclusions 7, 8, 17, and 18 was based
largely on frequently purchased packaged goods where the brand was already known to the consumer.
G3. In mature frequently purchased packaged goods markets, returns to advertising diminish
fast. A small frequency, therefore (one to three reminders per purchase cycle), is sufficient for
advertising an established brand.
Depending on product category, brand, consumer, and time factors, some intermediate effects are more
important than others. Based on conclusions 9, 10, 17, and 21 of Table 2, where product involvement is
low and/or when quality cannot be objectively assessed (e.g., credence or long-term experience goods),
the impact of trial, usage, and the other forms of communication will be relatively low. In those cases,
advertising could be relatively more important. In low-involvement situations (or for low-involvement
products) consumers would not resort to counter-arguing and rather passively receive the advertising
message. As low-involvement individuals do not engage in elaborate information processing, advertising
messages in such situations should emphasize “peripheral”, affective cues (celebrity endorsers, execution
elements, etc.) rather than factual product information. The success of the recent humor-oriented
“Snickers” campaign (Advertising Age 1996) exemplifies this situation. Similarly credence, long-term
experience, and high-quality goods brands should advertise more to resolve ambiguity, enhance their
quality perceptions, and ultimately increase their credibility. Based on conclusion 20, experience is more
important than advertising in mature markets and conclusion 4 suggests that advertising is more effective
in the early stages of the product life cycle. Conclusions 1and 3, point out that in the short-term,
promotions are more effective than advertising.
The evidence quoted in the previous paragraph suggests that the significance of the three key intermediate
effects (C, A, E) depends on the context in which advertising operates: C is more important than A for high
involvement goods, A is more important than C for low involvement goods, E is the most important for
mature, familiar products, etc. This suggests that a three dimensional (C, E, A) space is a more realistic
model of advertising effects than a hierarchical one. In such space the “weight” (coordinate) of each
dimension may vary depending on the advertising context.
The hierarchy framework, where effects take place in a particular sequence, does not allow for interaction
between effects. However interactions between advertising and experience have been well documented as
suggested by conclusion 23. What seems to be significant here is importance (dominance) of each of the
effects rather than any temporal sequence, and the fact that the importance of these effects is influenced by
context-related factors, such as product category, stage of product life cycle. This concept of sequence
lacks evidence in the literature (conclusion 20) and, taking the opposing neuroscience conclusions from the
hierarchy-free section (3.7), cannot be supported.
Taking the above two paragraphs together:
G4. The concept of a space of intermediate effects is supported, but not a hierarchy (sequence).
Based on conclusions 13-16 of Table 2, affect and advertising effectiveness are not exclusively, or at all,
dependent on an individual’s cognitive response. Yet, conventional forms of attitude or other affect
measurement typically involve verbal questioning, which subjects responses to rationality by both
respondent and researcher. At each level of subsequent formal processing, i.e., as it is thought about,
written down, and presented to others, cognition dominates affect.
Wilson et al. (1989) researched attitude-behavior consistency in studies conducted between 1981 and 1988
and concluded that having to give reasons for attitudes disrupted those attitudes. In other words, the
application of C to A disrupted A. They cite a film festival judge who had to give up because the necessary
analysis destroyed his judgmental ability. Where the respondent is an expert, i.e., has beliefs and
knowledge that have long challenged each other interactively, this problem may disappear, though it did
not for the film judge. But in the low-involvement non-elaborate categories discussed in this paper, it
would be most acute. The issue, therefore, is how affect can be measured unobtrusively. We found it
noteworthy that emotion has only surfaced relatively recently in the literature (mainly since 1980), even
though the conclusions of Table 2 suggest that affect can be more important than cognition. We conclude
that cognitive bias tends both to understate the role of affect and, due to measurement problems, misreport
reality. Thus:
G5. Cognitive bias interferes with affect measurement.
G4 suggests that the framework implied by persuasive hierarchy framework cannot be supported because
it assumes a sequence of effects and ignores the role of experience, the way it is affected by advertising,
and the way it affects subsequent behavior. Our discussion of G4 further suggested that context should
receive more attention in future research:
Goal Diversity: Some advertising seeks to convey factual information through cognitive appeals, some
seeks liking and affect, whereas some merely reinforces habit;
Product category: Advertising varies for high, medium, and low-involvement categories, durables and
non-durables, industrial and consumer products; high quality brands should advertise more than their
low quality competitors;
Competition: The extent of competitive advertising impacts its effectiveness;
Marketing mix: The extent of non-advertising promotional activities;
Stage of the product life cycle: The advertising effects sought for new products are different from those
for mature or post-mature products; and
Target market: The target consumers are themselves diverse; consumer may vary with respect to their
We propose five directions for future research based on the above principles:
5.1 Integrating all three intermediate effects
G1 suggests that cognition, affect and experience are the three key intermediate advertising effects which
G4 presents as a space rather than a hierarchy. Beliefs (cognition) for example are not the only, not even a
necessary, condition for the formation of attitudes. We thus propose a three-dimensional space (“EAC
Space”) for the study and measurement of intermediate advertising effects (Figure 2). The “coordinates”
of each dimension (w1, w2, w3, in Figure 2), indicate the relative strength of the corresponding
advertising effect and therefore the position occupied by a particular advertising message.
We propose that advertising’s positioning (i.e., the coordinates in EAC Space) be determined by context.
For example, a classified ad for a second-hand bicycle will require minimal experience and affect, but
extensive factual (cognitive) information (make, age, condition and price). In this case w3 should be
considerably higher than w1 and w2, and advertising should be positioned close to the cognition axis.
Conversely, a TV commercial for laundry detergent might minimally target C and concentrate on A, e.g.,
warmth and liking (Aaker and Stayman 1990b), as well as reinforcement of habit (E). In such a case w1
should be higher than w2 and w3, and advertising should be positioned close to the affect axis. This
positioning of advertising would help clarify the client-agency creative briefing process and track
advertising performance.
5.2 Context
The discussion in the beginning of this section suggested that five factors characterize context: goal,
category, competition, stage of the product life cycle, and target market. Any one of the context
components could serve as a starting point, but we prefer to begin with the goal the advertising is
supposed to achieve. Researchers have conventionally assumed that its task is to increase sales, or market
share, but this is not necessarily true. Advertising may be used to support premium pricing or, due to
competitive activity, simply maintain share. If all brands in a category advertise in order to increase share,
they cannot all succeed and yet any brand that decides to opt out would be likely to lose share. Experience
goods and service brands need more creative and affective advertising and can expect a higher return than
industrial and search goods brands, which have to rely on a rational/informational approach. New
products would need to advertise more than established ones in order to break through the clutter, achieve
target awareness levels, and establish an image. Target market can dictate changes in advertising strategies
even for the same brand. For example, a breakfast cereal brand will use a different appeal (mainly
emotional and experiential) when its target audience is children rather than adults buying for their children.
The UK Institute of Practitioners in Advertising (IPA) biennially publish winning case histories of effective
advertising (IPA 1981-95). These, and similar sources, contain the context within which the advertising
works and may be a suitable foundation for analysis of the effects of the diversity of advertising context.
Future IPA competitors will be required to identify this contextual information.
5.3 Long-term effects
Most of the research we examined in this paper has focused on short-term advertising effects, which are
generally considered weak (G2). Although fewer advertising studies have dealt with long-term effects,
due perhaps to data availability and model complexity, both practitioners (e.g., long-term effects category
in the IPA case histories) and academics (e.g., Leone 1995; Lodish et al. 1995a & b) have identified and
measured them. The study of long-term effects, however, has been primarily at market rather than at the
individual level. One exception, discussed in the market response models section (3.1), is the study by
Mela, Gupta, and Lehmann (1997) which utilized individual-level data, albeit from a single product
category. We need more studies of long-term advertising effects based on individual-level, single-source
data which should build upon and extend the results of previous single source studies (Deighton,
Henderson, and Neslin 1994; Kanetkar, Weinberg, and Weiss 1992; Mela, Gupta, and Lehmann 1997;
Tellis 1988). Future research should also concentrate on developing and comparing different
methodologies for reliable measurement of long-term effects.
5.4 Combining intermediate and behavioral effects
The discussion of econometric market response and conceptual models of intermediate advertising effects
(e.g., hierarchy of effects, affective response, and integrative models) in section 3 suggests that in terms of
knowledge contribution, these two streams of models complement each other. One stream (econometric
studies) focuses on “objective” marketing-mix and purchase behavior measures and studies the effects of
advertising on purchase behavior to provide reliable estimates of the size of behavioral effects. The other
stream (conceptual) focuses on pre-purchase, intermediate effects of advertising using “subjective
measures of cognition (beliefs, recall, awareness) and affect (feelings, emotions, attitudes). Primarily
experimental procedures isolate advertising effects from, say, promotional and competitive effects, and
study the causality of behavioral effects. We propose that the consumer profile information that typically
accompanies single-source household purchase data be augmented by including cognitive, affective, and
experience measures with respect to the particular brands and their advertising. Once such data bases are
compiled, research should focus on the study of long- and short-term, main and interactive effects of
advertising, promotions, cognition, affect, and experience on consumer choice. An opportunity therefore
exists to exploit the advantages of each stream and study both intermediate and behavioral effects in a
single “natural,” non-experimental setting.
5.5 Cognitive bias
More work is needed to calibrate measurement methodologies of affect. Empirical research so far has
suggested that there are at least two components of affective response: utilitarian and hedonic. The
traditional measurement of attitudes through cognitive analysis has been shown to be disruptive (Wilson et
al. 1989) or inadequate (Batra and Ray 1986). Alternatives such as facial, projective and other nonverbal
measures are available, but none has become dominant in practice, an indication that perhaps they are not
entirely satisfactory (Bogart 1996, p. 73). Further recognition needs to be given to the cognitive bias in
subsequent data processing. The problem is not just with the collection of raw data from respondents, but
also with the way it has to be made explicit and verbalized as it is summarized and transmitted from
researcher to manager, and thence through a series of increasingly senior client and agency executives.
Similar considerations arise with academic research.
This paper has classified and reviewed past research of intermediate and behavioral effects of advertising
using a taxonomy of models starting from market response (-), and concluding with integrative ((C)(A)
(E)) and non-hierarchic (NH) models. A major generalization (G4) concerned the persuasive hierarchy
(CA) category of models of advertising effects. Although such models have been actively employed for
100 years, we find them to be flawed on two grounds: the concept of hierarchy (temporal sequence) upon
which they are based cannot be empirically supported, and they exclude experience effects. These
observations led us to our first direction for future research: We propose that advertising be evaluated in a
three-dimensional space with the dimensions being experience, affect and cognition (the EAC Space). The
emphasis of a particular advertising campaign can therefore be determined by the coordinates of the three
dimensions. These coordinates of the EAC Space should be adjusted according to the context: product
category, competitive environment, other marketing mix components, stage of the product life cycle and
target audience. We also suggest that behavioral (brand choice, market share) and cognitive and affective
measures (beliefs, attitudes, awareness) be compiled in single-source data bases to allow researchers both
in academia and industry to test the interaction of context, intermediate effects and long- and short-term
behavior. In this effort we will also need to relieve measures of affective responses from cognitive bias.
This will be especially important for low involvement products where habit and affect are much more
important than cognition.
Table 1: Taxonomy of Models of How Advertising Works
Model Notation Sequence of Effects
Market Response (-) No intermediate advertising
effects considered
Cognitive Information C “Think”
Pure Affect A “Feel”
Persuasive Hierarchy CA “Think”Feel” “Do”
Low Involvement Hierarchy CEA “Think” “Do” “Feel
Integrative (C)(A)(E) Hierarchy not fixed- depends on
product, involvement
Hierarchy-Free NH No particular hierarchy of
effects is proposed
Table 2: Summary of Empirical Findings
Model Topic Conclusion Studies
(-) Short-Term Effects:
Advertising Elasticities
1. Advertising elasticities range
from 0-0.20.
Leone and Schultz 1980;
Assmus, Farley and Lehmann
1984; Lodish et al. 1995a.
2. Advertising elasticities for
durables are higher than those for
Leone and Schultz 1980;
Sethuraman and Tellis 1991.
3. Promotional elasticities are up to
20 times higher than advertising
Sethuraman and Tellis 1991;
Lodish et al. 1995a.
(-) Dynamic Advertising
4. Advertising elasticities are
dynamic and decrease over the
product life cycle. Advertising
elasticities are therefore higher for
new than for established brands.
Parsons 1975; Arora 1979;
Winer 1979; McDonald 1992;
Lodish et al. 1995a;
Parker and Gatignon (1996)
(-) Long-Term Effects:
Advertising Carryover
5. Purchase reinforcement, habitual
loyalty effects are stronger than
advertising carryover effects.
Givon and Horsky 1990.
6. 90% of the advertising effects
dissipate after three to fifteen
Clarke 1976; Assmus et al 1984;
Leone 1995.
(-) Advertising Response
Functions, Reach and
7. Returns to advertising are usually
diminishing: the first exposure is the
most influential for short-term sales
or share gains.
McDonald 1971;
Simon and Arndt 1980;
Tellis 1988; Deighton ,
Henderson and Neslin 1994;
Jones 1995;
Pedrick and Zufryden 1991.
8. For frequently purchased package
goods share returns to advertising
diminish fast, typically after the
third exposure. After the third
exposure, advertisers should focus
on reach rather than frequency.
McDonald 1971;
Krugman 1972;
Naples 1979;
Tellis 1988; Deighton, Henderson
and Neslin 1994;
Pedrick and Zufryden 1991;
(C), (CEA) Advertising for search,
experience and ambiguous
9. Advertising is more effective for
experience than search (ambiguous)
goods. Furthermore ads for search
goods contain more product-oriented
information than experience goods
Nelson 1974;
Verma 1980;
Hoch and Ha 1986.
(C) Advertising as a signal of
product quality
10. Increased advertising signals
high quality when costs of
producing quality are low and
consumers are less responsive to
Tellis and Fornell 1988.
Table 2 (continued)
Model Topic Conclusion Studies
(C) Advertising effects on
consumer price sensitivity
11. Price advertising increases price
sensitivity whereas non-price
advertising decreases price
sensitivity. Furthermore, price
sensitivity leads to lower prices.
Kaul and Wittink 1995.
12. When consumers rely on
memory to retrieve product
information then advertising
increases price sensitivity; When
consumers rely on point-of-purchase
information then advertising
decreases price sensitivity
Mitra and Lynch 1995.
(A) Advertising and affective
13. Advertising need not be
informative to be effective; nor need
be verbal only; emotional and visual
elements enhance preference.
Zajonc 1980;
Sawyer 1981;
Zajonc and Markus 1982;
Gorn 1982;
Rossiter and Percy 1978; 1983;
Resnik and Stern 1977; Aaker
and Norris 1982; Weinberger
and Spotts 1989; Stern and
Resnik 1991; Stern et al. 1981,
Healy and Kassarjian 1977;
Krugman 1977.
(CA) Brand Attitude Formation 14. Brand attitudes are not
exclusively formed based on beliefs
about the product/brand attributes.
They can also be based on emotions.
For example attitude towards the ad
is a significant moderator in the
formation of brand attitudes.
MacKenzie, Lutz and Belch
Aaker, Stayman and Hagerty
Batra and Ray 1986;
Burke and Edell 1989;
Homer 1990;
MacKenzie and Lutz 1989;
Brown and Stayman 1992;
Smith 1993.
(A), (CA) Ad Likability, Attitude
Toward the ad
15. Ad likability highly correlates
with brand preference.
Biel 1990;
Haley and Baldinger 1991.
16. Attitude toward the ad affects
brand attitudes only in non-elaborate
Droge 1989.
Table 2 (continued)
Model Topic Conclusion Studies
(CA) Effects of Message
Repetition on Awareness,
Recall and Attitude
17. In low involvement situations,
repetition of different versions of an
ad prevents early decay of
advertising effects.
18. Recall and attitudes can be
maintained at high level if an ad
campaign consists of a series of ads.
Cacioppo and Petty 1985.
Zielske 1959; Zielske and
Henry 1980; Calder and
Strenthal 1980;
Rao and Burnkrant 1991.
(CA) Attitude-Behavior
19. Attitude-Behavior correlations
range 0 and 0.30.
Wicker 1969;
Fazio and Zanna 1979.
(CA) Sequence of intermediate
20. The concept of a single
hierarchy of effects is not supported.
Heeler 1972; Palda 1966; Ray
1973; Rothschild 1974; Sawyer
1971; Strong 1972; Barry and
Howard 1990.
(CEA) Advertising-Experience
21. Product usage experience
dominates advertising influence on
beliefs attitudes and behavior.
Winter 1973;
Olson and Dover 1979;
Hoch and Ha 1986;
Smith and Swinyard 1983;
1988; Marks and Kamins
Tellis 1988;
Smith 1993.
22. Advertising is superior to
product usage in communicating
quality for credence goods; Product
experience dominates advertising
for search and low experience goods
Wright 1990;
Wright and Lynch 1995.
23. Advertising has a stronger effect
on consumers with high behavioral
Raj 1982;
Tellis 1988;
Deighton , Henderson and
Neslin 1994;
D’Souza and Rao 1995.
24. Advertising is relatively more
effective when it precedes usage
experience (predictive framing), in
particular when such experience is
Deighton 1984; Deighton and
Schindler 1988;
Hoch and Ha 1986;
Levin and Gaeth 1988;
Smith 1993.
Advertising process
25. The relative importance of C
and A depend on context. Beliefs
generally build cumulatively with
awareness, trial word of mouth,
promotions and advertising. These
effects act interactively and
Vaughn 1980;
Vaughn 1986;
Smith and Swinyard 1982,
1983, 1988;
Franzen 1994;
Deighton 1984; 1986
Kupfermann; Martin 1991;
Rose 1993; Sutherland 1993.
Figure 1
A Framework for Studying How Advertising Works
Advertising Input:
Message content, media scheduling,
Cognition Affect Experience
Consumer behavior:
Choice, consumption,
loyalty, habit etc.
Motivation, ability (Involvement)
Figure 2
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... One of the most suitable areas to which machine learning for prediction purposes can be applied is in the field of online customer behavior [6]. Marketing literature has understood the consumer's purchasing process step by step through a conversion funnel model to predict consumer purchasing behavior [7][8][9]. Unlike the offline environment, the online environment opens up a new opportunity to predict customer behavior through the use of machine learning, as it can identify the consumer journey and various click stream data [10,11]. The customer journey represents a series of stages through which the user gradually goes through the recognition stage as they evaluate alternatives to the actual purchase of the product [12]. ...
... In the marketing literature, the conversion funnel model has been studied as an important theory for understanding consumer decision-making and behavior [7][8][9], and is used as a core framework for marketing decision-making [3]. Researchers have tried to understand consumer behavior through various transformations in the basic funnel model structure such as attention, interest, decision, and purchase [14]. ...
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Machine learning technology is recently being applied to various fields. However, in the field of online consumer conversion, research is limited despite the high possibility of machine learning application due to the availability of big data. In this context, we investigate the following three research questions. First, what is the suitable machine learning model for predicting online consumer behavior? Second, what is the good data sampling method for predicting online con-sumer behavior? Third, can we interpret machine learning’s online consumer behavior prediction results? We analyze 374,749 online consumer behavior data from Google Merchandise Store, an online shopping mall, and explore research questions. As a result of the empirical analysis, the performance of the ensemble model eXtreme Gradient Boosting model is most suitable for pre-dicting purchase conversion of online consumers, and oversampling is the best method to mitigate data imbalance bias. In addition, by applying explainable artificial intelligence methods to the context of retargeting advertisements, we investigate which consumers are effective in retargeting advertisements. This study theoretically contributes to the marketing and machine learning lit-erature by exploring and answering the problems that arise when applying machine learning models to predicting online consumer conversion. It also contributes to the online advertising literature by exploring consumer characteristics that are effective for retargeting advertisements.
... the content was based on the literature from marketing, psychology, sociology, and economics literature on media and communication (Cialdini 2001;Bagwell 2007;Berger 2014;Chandy et al. 2001;and Vakratsas and Ambler 1999). Further, Hennig-Thurau et al. (2004) report eight specific factors that motivate consumers to engage with online communities, including (1) venting negative feelings, (2) concern for other consumers, (3) self-enhancement, (4) advice-seeking, (5) social benefits, (6) economic benefits, (7) platform assistance, and (8) helping the company. ...
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This study examines the difference between Facebook-based firm-initiated online brand communities (FIOBCs) and consumer-initiated online brand communities (CIOBCs). A content analysis of 2512 Facebook posts across twelve online brand communities (OBCs) of six brands was conducted using Netnography. This was followed by ten in-depth interviews of community members of these online brand communities. The most engaging posts in the consumer-initiated online brand community provide information and focus on self-enhancement and brand identification. In the firm-initiated online brand community, posts related to a brand, new product launches, greetings, and rewards were perceived as most engaging. Additionally, it was found that the drivers of engagement are informational, economic, and social benefits, brand identification, and self-enhancement. This paper contributes to customer engagement and brand community literature by examining the differences between firm-initiated and consumer-initiated online brand communities, focusing on Facebook-based online brand communities.
... This apprehended by consumers instigated by advertising can be categorized into two phases: cognition and affection. Cognition and affection represent rational and sensitivity, respectively (Vakratsas ,1999). According to Schiffman and Kanuk (2007) advertising appeal may modify consumers' approach. ...
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The socio-economic development of Sargodha city of Pakistan has positive effects on the purchase behavior of citizen of the city. The induction of different brands in this city creates an opportunity to test the purchase behavior influenced by electronic media. The study examines the effects of celebrity endorsement in electronic media advertisements on purchase behavior of citizens of Sargodha city. Television is selected from a broad range of electronic media due to its popularity and access to the majority of population. 320 individuals are selected through multi stage sampling techniques in which participants are categorized demographically by gender, age, education, and as per income level with the ratio of 160 each gender category. Survey research method is used to acquire proper response from selected sample. Statistical analysis using SPSS describes the celebrity endorsed advertisements in TV which has significant positive impact on purchase behavior of the consumers of the study. The study reveals that customers pay more attention towards celebrity endorsed rather than noncelebrity advertisement. It is also learnt that female showbiz celebrities are more adored by customers as compared to male showbiz celebrities. In addition, customers recall level about advertisement with celebrities is higher than the advertisements with absence of celebrities. Findings further show that celebrity endorsed advertisements is more influential on purchase behavior of citizen of Sargodha.
The purpose of the present study was to empirically examine the efficacy of statutory disclosures in the Indian mutual fund industry. Whether the disclaimers aid investors in their decision-making process was investigated. The study made a distinction between type of investors (novice and seasoned investors) as disclaimers affect differently on investor’s belief, attitude and ability to take informed decision. Survey was conducted using a structured questionnaire to evaluate the responses of 388 investors, consisting of 243 novice and 145 seasoned investors. Data was analyzed using mean comparison, independent t-test, and logistic regression model. Results revealed that statutory disclaimers were less effective on seasoned investors compared to novice investors. This suggests seasoned investors process the disclaimer information differently. Novice investors systematically process the disclaimers of mutual fund advertising, and their investment decision was meaningfully affected by the disclaimers. The study offers specific suggestions for stakeholders working in the area of behavioral finance, highlighting the importance of considering the dual process theory of information processing. To the best of authors knowledge, this study is the first of its kind to evaluate the efficacy of mandatory disclaimers in the Indian mutual fund industry, providing unique insights for future research in the field.
This study explores the AIDA model (attention, interest, desire, action) for social media promotion and community engagement for small cultural organizations. The internal situation and external environment were first analyzed with the SWOT analysis augmented with PEST analysis. Then, the authors show how the AIDA model can be used in social media marketing to improve public awareness, engagement, and thus participation in the organization's activities. As the global economy is getting linked to the internet and social media, utilizing the AIDA model for small cultural organizations contributes to effective information dissemination and increases interactions in the targeted community. The rise of social media has also triggered smaller organizations to consider how to survive under dynamic changes and fulfill their mission through better community engagement.
Autonomous vehicles will be a key component of the sustainable cities and transport systems of the future. However, there is little data available on effective methods of communicating with the public about these benefits to optimise uptake and achieve their sustainability potential. The aim of this exploratory study was to assess outcomes associated with exposure to various messages communicating proposed benefits of autonomous vehicles. Australians aged 16+ years (n = 1053) responded to two online surveys administered two weeks apart. The instruments included items relating to demographic attributes, driving factors, and attitudes to autonomous vehicles. Respondents were randomised to one of five messages referring to efficient travel time, economic savings, saving lives, mobility for the elderly and disabled, and job opportunities. Messages relating to saving lives and providing mobility for the elderly and disabled performed best, especially in terms of presenting a strong argument (62% agreement) and believability (67% agreement), respectively. The results provide initial evidence that public awareness communications could favourably influence attitudes to autonomous vehicles, potentially enhancing uptake rates to yield corresponding benefits. This information will be of value in countries where the roll-out of autonomous vehicles is a strategic priority to achieve sustainable cities and transport systems.
One effective way to cope with water scarcity is to enhance water use efficiency by reforming water customer habits. Owing to the semi-arid climate of Iran, the drinking water consumption rate of urban households is greater than the routine average daily per capita consumption of water. With limited water supply and poor consumption habits, the country needs to reform its water management practices. On the other hand, the influence of advertising types, especially mass media, to improve water consumers' behavior in society is undeniable. Herein, the current study examines the effectiveness of using four advertisement strategies (animation, celebrities, social networks, and conceptual advertising) to modify the public's drinking water consumption. The statistical population in this study included urban households in four critical cities subjected to intense water stress, including Tehran, Isfahan, Shiraz, and Kermanshah. In order to evaluate the impact of the advertisement strategy on residents, the most important questions were extracted and used to prepare a questionnaire with 24 indicators. Afterward, a novel Multiple Attribute Decision-making (MADM) based methods was designed to consider the importance weights of age and education level on respondents to estimate the questionnaire accuracy. The dominant strategy by applying age and education level influence for each city was using animation in Tehran, followed by a conceptual advertisement in Isfahan and Shiraz, and the social network in Kermanshah. Further, as a statistical examination using SPSS software, the Spearman test was employed to analyze the questionnaire and its results without the impact of age and education level. The results obtained in the developed technique compared with the analyzed results of statistical test showed that the application of age and education level will significantly impact advertising comprehension and validation of results.
p>Tujuan Penelitian ini untuk mengetahui tanggapan Khalayak terhadap program Dukcapil Keliling Kota Madiun. Dukcapil merupakan salah satu program pemerintah Kota Madiun dalam pelayanan administrasi kependudukan. Program Pelayanan melalui channel dengan tajuk Dukcapil Keliling Kota Madiun yang dalam penelitian ini dilakukan uji ukur tingkat daya tarik pada aspek isi informasi dan bantuk iklan terhadap tanggapan masyarakat untuk memanfaatkan program tersebut. Penelitian ini menggunakan data kuantitatif dengan berdasar studi kasus di lapangan. Berdasarkan hasil uji hipotesis pertama, dinyatakan bahwa hasil statistik uji regresi dengan nilai t<sub>hitung</sub> sebesar 3,034 dengan nilai signifikansi sebesar 0,003 lebih kecil dari 0,05 ( Sig . < 0,05), dan koefisien regresi mempunyai nilai positif sebesar 0,265; maka hipotesis pertama dalam penelitian ini terbukti yaitu daya tarik isi informasi memiliki pengaruh yang positif terhadap tanggapan masyarakat untuk mengunakan layanan tersebut. Hasil statistik uji regresi pada hipotesis kedua, nilai t<sub>hitung</sub> sebesar 3,795 dengan nilai signifikansi sebesar 0,000 lebih kecil dari 0,05 ( Sig . < 0,05), dan koefisien regresi mempunyai nilai positif sebesar 0,332; maka hipotesis kedua dalam penelitian ini terbukti yaitu daya tarik bentuk iklan memiliki pengaruh yang positif terhadap tanggapan masyarakat untuk mengunakan layanan tersebut. Untuk hasil uji hipotesis ketiga, diperoleh nilai F<sub>hitung</sub> sebesar 14,906 dengan signifikansi sebesar 0,000. Oleh karena nilai signifikansi lebih kecil dari 0,05 ( Sig .<0,05), maka dapat disimpulkan bahwa hipotesis ketiga dalam penelitian ini terbukti yaitu daya tarik isi informasi dan bentuk penyampaian iklan memiliki pengaruh yang positif terhadap tanggapan masyarakat pada ILM Dukcapil Keliling Kota Madiun. </p
Dallas Smythe bearbeitet polit-ökonomisch eine Reihe von Fragen, die dann in der späteren Rezeptionsgeschichte des Textes v. a. in der sog. Blindspot-Debatte und in der Digital Labour-Debatte kontroverse Antworten provoziert haben: Warum werden aus ökonomischer Sicht überhaupt massenmediale Inhalte, wie Information und Unterhaltung produziert? Was genau kaufen die Werbetreibenden mit ihren Werbeausgaben? Wie stellen die Werbetreibenden sicher, auch das zu erhalten, wofür sie bezahlen? Welche Rolle spielt das Publikum für die wirtschaftliche Beziehung zwischen Massenmedien und werbetreibender Industrie? Wer produziert die Dienstleistung bzw. Ware, die die Werbetreibenden kaufen? Wie lässt sich die Werbeindustrie im (Monopol-)Kapitalismus im Rahmen einer marxistischen (Arbeits-)Werttheorie verstehen? Haben sowohl kritische als auch orthodoxe wirtschaftswissenschaftliche Ansätze die ökonomische Rolle werbefinanzierter Medien bisher missinterpretiert? Mit der Einführung des Begriffs der audience commodity (Publikumsware) bestimmt der Text die Rolle des Publikums für die kapitalistische Ökonomie als eine produktive, dessen Aktivität unter monopol- oder oligopolartigen Marktbedingungen fortlaufend gesichert werden muss, um sie ausbeuten zu können.
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Attitude toward the ad (Aad) has been postulated to be a causal mediating variable in the process through which advertising influences brand attitudes and purchase intentions. Previous conceptual and empirical research on this topic has suggested four alternative models of the relationships between brand-related cognitive, affective, and conative responses and ad-related cognitive and affective responses. The authors describe a structural equations analysis of these four models, utilizing two data sets generated within a commercial pretest setting. The results suggest that a dual mediation hypothesis, which postulates that Aad influences brand attitude both directly and indirectly through its effect on brand cognitions, is superior to the other three models under the particular set of conditions in the pretest setting.
The authors examine switching and repeat purchase effects of advertising in mature, frequently purchased product categories. They draw on consumer behavior theories of framing and usage dominance to formulate a logit choice model for measuring these effects. They estimate the model using single-source scanner data. Their results suggest that advertising induces brand switching but does not affect the repeat purchase rates of consumers who have just purchased the brand, a result consistent with usage dominance rather than framing. They find the switching influence to be largely confined between the current and previous purchase occasions. They illustrate the magnitude of this effect and explore potential profitability.
MacKenzie, Lutz, and Belch have enhanced our understanding of the mediating role of attitude toward the ad (A Ad ). The current study replicates and extends the structural equation tests of the four competing models they presented. Two independent datasets are used to examine the role of processing involvement. Consistent with the earlier findings, the dual mediation hypothesis model provides the “best” fit of the data in both experiments. However, the hypothesized causal path between brand cognitions and brand attitudes that emerges for each of the datasets conflicts with the earlier findings. Contrary to expectations, processing involvement does not produce substantial differences in the specification or strength of the causal paths.
The effects of price and advertising expenditures on new-product sales are examined across four test-market experiments. All experiments involved two levels of advertising expenditure tested across cities and two or three price levels tested across stores within city. Mean sales results over a 24–week period are reported for each treatment combination. Analysis of variance results are interpreted as supporting a negative price main effect, a positive advertising main effect, a negative price-advertising interaction, and significant but variable time effects. Assessments of these results in terms of design adequacy and their implications for alternative theoretical explanations are offered.
The authors investigate how increased advertising affects consumer price sensitivity. First, a conceptual framework integrating the role of advertising content is presented. Next, a methodology for studying the impact of advertising on consumer price sensitivity to brand purchase quantity and consumption is developed. Analyses of diary panel data for an established, frequently purchased brand from an ADTEL advertising field experiment clearly demonstrate that increased advertising lowers price sensitivity. Further, this effect is strong in the high price sensitivity segment for purchase quantity and consumption. In the low price sensitivity segment the effect is marginal. Additional support for these results was obtained by choosing different cutoff points for high sensitivity segmentation.
Information integration theory and the integrated information response model are used to explore how consumers combine information from advertising and trial. Also investigated is the ability of attitude toward the ad to mediate advertising's effects on brand cognitions and brand attitudes after trial. An experiment is conducted in which three independent variables are manipulated: the information source (ad only, trial only, and ad plus trial), information sequence (ad/trial and trial/ad), and favorability of trial (positive and negative). Results show that (1) advertising can lessen the negative effects of an unfavorable trial experience on brand evaluations, especially when the ad is processed first, (2) when negative trial precedes exposure to advertising, cognitive evaluations of the ad are more negative, (3) the ability of ad attitudes to influence brand cognitions and brand attitudes is significantly reduced after trial, and (4) the ability of brand cognitions to influence brand attitudes is significantly increased after trial. Implications for advertising research and practice are discussed.
In 1988 Danaher showed a log-linear model to be accurate for predicting magazine exposure distributions. However, the model was expensive on computer time and stored array space when the number of magazines and insertions was large. An approximation to that model is developed that is of comparable accuracy yet takes less than one quarter of the computation time and eliminates the need for a large stored array. The approximate log-linear model is compared empirically with Danaher's previous log-linear model and one of Leckenby and Kishi's Dirichlet-multinomial models for equal-insertion schedules. For unequal-insertion schedules, the approximate log-linear model is compared with the log-linear model and the popular Metheringham beta-binomial model. The results show that in accuracy, the approximate log-linear model is between the log-linear and Leckenby and Kishi's model for equal insertions and is about the same as the log-linear for unequal-insertion schedules, but is significantly more accurate than Metheringham's model.
The authors examine the long-term effects of promotion and advertising on consumers’ brand choice behavior. They use 8 1/4 years of panel data for a frequently purchased packaged good to address two questions: (1) Do consumers’ responses to marketing mix variables, such as price, change over a long period of time? (2) If yes, are these changes associated with changes in manufacturers’ advertising and retailers’ promotional policies? Using these results, the authors draw implications for manufacturers’ pricing, advertising, and promotion policies. The authors use a two-stage approach, which permits them to assess the medium-term (quarterly) effects of advertising and promotion as well as their long-term (i.e., over an infinite horizon) effects. Their results are consistent with the hypotheses that consumers become more price and promotion sensitive over time because of reduced advertising and increased promotions.