Employing Brand Equity Theory to Explain Variances in
Ratings Inheritance Effects on 11:00 PM Newscasts
Walter McDowell Ph.D.
Southern Illinois University
College of Mass Communication and Media Arts
Radio - Television Department
Carbondale, IL 62901
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and
John Sutherland Ph.D.
University of Florida
Department of Advertising
College of Journalism and Mass Communication
Gainesville, FL 32611
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Abstract
Employing Brand Equity Theory to Explain Variances in
Ratings Inheritance Effects on 11:00 PM Newscasts
Recognizing the potent influence of lead-in programming or inheritance effects
on the ratings performance of television programs, the purpose of this study was
to explore the plausibility of applying conventional brand equity theory to
electronic media and to offer a tentative explanation of the considerable
variances found in inheritance effects research. Adapting the essential
components of an established brand equity model, the researchers propose that
program brand equity is revealed in the differential ratings response of a
program to its direct competitors and to its lead-in programming. The daily
ratings of three 11:00 Pm newscasts and their respective lead-ins were analyzed
using one station as an equity criterion. Several hypotheses were tested within
a case study format.
The results of this exploratory study were encouraging with all hypotheses
supported to some degree. The study concludes with recommendations for more
generalizable studies using a proposed audience typology.
Applying Brand Equity Theory to Explain Variances in
Ratings Inheritance Effects
The business of commercial broadcasting is the selling of audiences to
advertisers. In addition to developing program content, broadcasters have found
that proper scheduling can be an important factor in attracting audiences.
Within this domain of scheduling strategies resides the notion of inheritance
effects, sometimes referred to as tuning inertia. It is no secret among
television executives that the best predictor of a program's audience size is
usually the size of the audience leading into it. While a programmer would be
foolish not to take advantage of the potent influence of lead-in programming,
there can be a downside in that inheritance effects can mask inherent weaknesses
in a program's supposed popularity. That is, the broadcaster is often not sure
what proportion of a program's audiences arrived as a consequence of (a)
preferred choice due to content or (b) random chance due to lead-in scheduling
(Webster & Lichty, 1991).
While the overall impact of inheritance effects has been well documented by
industry and academic researchers, these studies have failed to explain
adequately the considerable variances in the ratings performance among programs.
That is, some programs appear to capitalize on a lead-in audience better than
other programs. Even when structural variables such as genre and compatibility
have been analyzed, the bulk of the variance has remained a confounding
variable.
A primary assumption of inheritance effects studies is that there are
significant numbers of passive or uncommitted viewers who are not motivated to
change channels (Webster & Lichty, 1991; Cooper, 1996, Rosenstein, 1997; Rubin
1984). In recent years, with the explosive growth of cable programming,
conventional television broadcasters have encountered steady audience erosion.
Where in 1977, ABC, CBS and NBC collectively accounted for over 92 percent of
all primetime viewing, they (including Fox and UPN) now garner only 57 percent
of Nielsen Households (Nielsen, 1998) The decades old, 3-network oligopoly has
given way to a new era of unbridled competition and almost limitless choice for
media consumers (Sims, 1997; Brinkley, 1997, Long, 1979).
A logical conclusion derived from this situation is that given more choice,
television viewers become more selective. This proposition is supported
empirically by Youn (1994). One can also surmise from this trend, that
inheritance effects based on docile audiences with few program options will
become a less effective programming strategy in years to come (Myers, 1997;
Walker, 1988).
While this type of intense competition is relatively new to electronic media, it
is not a surprise to the American consumer goods industry. Cutthroat competition
has been the catalyst for the emergence of the art and science of brand
management. In a cluttered marketplace, where consumer products are often more
similar than they are different, proper brand management increases the
probability of consumer brand choice. Amid the lexicon of brand management
concepts is brand equity - the brand's "added values" perceived by consumers to
be unique to that one brand. Brand equity is the reason why consumers buy Big
Macs rather than hamburgers, Nikes rather than sneakers, Harley Davidsons rather
than motorcycles.
Although there are many divergent conceptualizations of brand equity, there
is universal agreement that equity enhances a product's performance in the
consumer marketplace. That is, equity helps reinforce consumer loyalty, attract
new customers, and insulates the product from competitive attack. Facing the
specter of dozens if not hundreds of new program competitors, broadcasters have
embraced the jargon if not the substance of brand management. In fact, an
editorial from Broadcasting and Cable, the magazine of record for the industry,
proclaimed that "_branding is threatening to supplant 'synergy' or 'convergence'
as the queen bee of TV buzzwords" (Editorial, 1998)
The purpose of this study was to explore the plausibility of applying brand
equity theory to help explain the variances in program inheritance effects.
Television program brand equity was operationalized using standard Nielsen
ratings. Utilizing a case study format, several diagnostic tools were introduced
to evaluate the relationship between three local newscasts and their lead-in
programs.
Although there is considerable professional and scholarly research on brand
equity dealing with conventional consumer goods, there has been little work done
on adapting these theoretical precepts to electronic media audience behavior.
Similarly, there is a body of knowledge addressing inheritance effects but no
studies have approached this issue from the theoretical vantage point of brand
equity.
Late evening local newscasts were chosen for analysis for several reasons.
First, local news is extremely important to local stations, providing as much as
45% of a station's overall sales revenue (Alridge, 1996; Littlejohn, 1996).
While most stations broadcast several news programs throughout the day and
evening, the ratings performance of the 11:00 PM (10:00 PM central) newscast is
considered often a primary indicator of a station's vulnerability to competitive
attack. With a different lead-in program every night, the late news is the most
susceptible time slot for competitive program sampling by docile audiences that
are not particularly committed to any one brand of newscast (Eastman, 1997).
Furthermore, these rival newscasts are scheduled usually at the same time.
Where sitcoms, dramas and talk shows may encounter different program genres on
competing channels, local newscasts almost always compete head to head. The
methods section of this article will elaborate on why this type of daily direct
competition is an ideal experimental crucible for studying program brand equity.
Inheritance Effects
For decades, the single best predictor of a program's ratings performance has
been the ratings of the program scheduled immediately before it. That is,
television programs tend to "inherit" sizable audiences from the program airing
immediately prior to it on the same channel. Goddhart, Ehrenberg, and Collins
(1975) coined the term inheritance effects when they worked on the broader issue
of audience duplication. Based on television viewing in the United Kingdom, the
researchers discovered that when programs were adjacent to each other, an
"inheritance effect" took over that exceeded the predictions derived from their
duplication model. This audience carryover phenomenon is known also as tuning
inertia, meaning that there is a greater likelihood that audiences will remain
tuned to their current channel than deviate from this established pattern
(Cooper, 1996).
Although overall inheritance effects have been found in myriad situations, the
specific impact on programs has been difficult to predict. These differences
have not been explained adequately using structural factors such as lead-out,
number of options, program type compatibility, network affiliation, and cable
penetration (Headen, Klompmaker, and Rust, 1979; Webster, 1985; Tiedge and
Ksobiech, 1986 and 1988; Walker 1988; Cooper, 1996). In all cases, lead-in
ratings completely overwhelmed any other factor in the proposed model. And yet,
this lead-in effect varied considerably among programs.
Boemer (1987) broached the notion of audience loyalty as an explanation when he
took a 2-year look at local late night newscasts in Dallas, Texas. Using
Arbitron sweep ratings, he found high positive correlations between newscasts
and prime time lead-in programming. However, he also found considerable
variances among the three competing stations ranging from .30 to .69.
Additionally, he found that some newscasts delivered better ratings than their
lead-ins, providing "circumstantial evidence of a loyal viewership for local
news in the market" (p 93). To date, Boemer (1987) is the only published
inheritance study concentrating on late evening newscasts. This study intends to
expand and elaborate on Boomer's (1987) work, providing greater insight.
Brand Equity
Consumer loyalty is often equated with consumer-based brand equity in that they
both address the relative strength of a brand. A brand is a name, term, sign,
design, or a unifying combination of them intended to identify and distinguish
the product or service from its competitors. More importantly, brand names
communicate attributes and meaning that are designed to enhance the value of a
product beyond its functional value. (Keller, 1998; de Chernatony & McDonald,
1998). Brand equity is essentially a measure of this added value. The
theoretical underpinning for our exploration of television program equity comes
from Keller (1993) who conceptualizes brand equity according to two kinds of
memory associations, brand awareness and brand image. The two in combination are
called brand knowledge. He conceptualizes brand equity as the differential
effect of brand knowledge on consumer response to the marketing of a brand.
Keller also asserts that "fundamentally high levels of brand knowledge
(awareness and image) increase the probability of choice, as well as produce
greater consumer loyalty and decrease vulnerability to competitive marketing
actions" (p. 3).
From a behavioral viewpoint, Keller describes how these brand associations can
be manifested in the marketplace behavior.
A brand is said to have positive (negative) consumer-based brand equity if
consumers react more (or less) favorably to the marketing mix of the brand than
they do to the same marketing mix element when it is attributed to a
fictitiously named or unnamed version of the product or service...If a brand is
seen by customers to be the same as a prototypical version of the product or
service in the category, their response should not differ from their response to
a hypothetical product or service. If the brand has some salient, unique
associations, these responses should differ. (p. 4)
In other words, if the researcher controls the marketing mix, the attitudinal
components of brand equity will manifest themselves in consumer behavior. In
this study, Nielsen ratings were the units of analysis for measuring audience
behavior.
Using Keller's (1993) conceptualizations of brand equity we can offer a partial
explanation for the variances in inheritance results. A program that is unable
to generate sufficient brand knowledge (ie. familiar, strong, and unique brand
associations) among its potential viewers is vulnerable to the program content
on competing channels. Likewise, a program demonstrating strong equity
characteristics will retain more of its lead-in audience and recruit audiences
from other channels. That is, programs with strong equity would be expected to
optimize lead-in ratings more efficiently than programs with weaker equity. This
optimization would be observed in the differential ratings response between the
two programs. We propose that inheritance effects is influenced by the two
efficiency factors; (a) retention of lead-in audiences and (b) recruitment of
audiences from other sources.
In order to measure properly the differential effect of brand knowledge on
consumer response, Keller (1993 and 1998) and other researchers realize that
certain marketing mix components must be controlled where the target brands must
be (a) direct competitors in terms of product category, (b) identical in price
across all competing brands, (c) equally available in terms of distribution to
consumers, and (d) unaffected by short-term promotion activities. Without these
controls in place it would be impossible to determine whether consumer response
was a function of factors uniquely attributable to the brand or other non-equity
factors such as available inventory, distribution patterns or sales incentives.
Research Hypotheses
From the basic research question, how does brand equity influence program
ratings performance, a number of exploratory hypotheses can be offered that
resonate with our proposed theoretical framework.
The literature review focused on the relationship of a program to its lead-in
(inheritance effects). That is, consumer-based brand equity can be found in the
differential ratings response between two adjacent programs. The following was
hypothesis was offered.
H1: Programs with higher brand equity will reveal a greater difference between
lead-in program audience size and program audience size than programs with lower
brand equity.
Another way of looking at how competing programs optimize their lead-in
audiences would be to index their performance where an index exceeding 100 would
indicate how much a program was exceeding its lead-in. Because available
audiences (Homes Using Television or HUTs) can vary hour to hour, share of
audience is the most appropriate unit of measure.
H2: Programs with higher brand equity will reveal a greater differential share
index than programs with lower brand equity.
Correlational analysis has been used in all work on inheritance effects to
describe the close association or dependence between adjacent programs. However
program brand equity is presumed to be a function of independence. That is, a
program with strong equity is not as dependent on its lead-in for audience
ratings as a program exhibiting lesser equity. Therefore, one would expect a
weaker association (less dependency) for a program with relatively strong
equity.
H3: Programs with higher brand equity will reveal less dependency on their
lead-in program audience than programs with lower brand equity.
Narrowing our focus to the dynamics underlying this differential response, the
literature review proposed that program brand equity is a function of (a) the
power to retain lead-in audiences from the same channel and (b) the power to
recruit audiences from other sources. Accordingly, two additional hypotheses
were offered.
H4: Programs with higher brand equity will retain a greater proportion of
their lead-in audiences than programs with lower brand equity.
H5: Programs with higher brand equity will recruit a greater proportion of
audiences from other sources than programs with lower brand equity.
.
Methodology
Because of the exploratory nature of this work and the need to exercise as much
control over extraneous marketing mix factors as possible, a case study format
was chosen. Adapting Keller's (1993) equity conceptualizations for broadcasting,
a direct competitor would be a program of highly similar content. Equal
availability would be defined as competing programs that are scheduled at the
same day and time in the same market airing on comparable facilities.
With the above criteria in mind, the investigators procured from Nielsen Media
Research, ratings data for January through December 1996 for one major
television market. These data included 365 days of metered overnights ratings
and four diary-based "sweep" reports for February, May, July, and November 1996.
Nielsen Methods (1996) provides terminology and procedures. For reasons of
confidentiality, the name of the market was not disclosed in this study, and the
target stations were identified merely as stations A, B and C.
To assess a program's ability to retain lead-in audiences and recruit audiences
from other sources, Nielsen also provided a custom "Audience Flow Analysis"
based on the May 1996 sweep period. This analysis tracks the source and
destination of the viewing audiences between an adjacent quarter-hour. For this
study, our focal point was the household "flow" from10: 45 PM to 11:00 PM.
These three stations are well-established network-affiliated competitors that
have been offering 11:00 PM newscasts for over 20 years. Their terrestrial
signal coverage and cable penetration levels were highly similar. In fact, two
stations share the same transmitting tower. Furthermore, from an audience
perspective, the average weekly cumulative household delivery (Monday through
Sunday, 6:00 AM to 1:00 AM) was almost identical for these target stations. That
is, over the course of a typical week, each station reached the same number of
individual (unduplicated) households within the market (Nielsen Cume, 1996).
Based on the above criteria, one could presume that the major marketing mix
components were equivalent.
In addition to obvious direct brand competition in terms of program content,
scheduling, and signal coverage, there was the added advantage of each newscast
experiencing a different lead-in seven nights a week. To assure genuine direct
competition, the ratings database was further refined by extracting dates when
the three newscasts did not compete head to head at 11:00 PM. Throughout the
year stations are often forced into "late starts" due to extended movies,
specials events, and sports. For example, the ABC affiliate had to contend with
late starts for a dozen weeks due to Monday Night Football, thus destabilizing
the competitive marketing mix at 11:00 PM. Of the 365 available dates for study,
289 were found to offer the ideal competitive environment where at 11:00 PM news
viewers had three legitimate news options.
In order to test the relative influence of brand equity on program performance,
it was first necessary to identify a benchmark or criterion newscast that
demonstrated superior equity attributes based on information garnered from an
alternative source.
Marshall Marketing and Communications (MM&C). MM&C conducts annual surveys of
consumer purchasing habits for select markets around the county using in-depth
telephone interviews. One specific research area is consumer loyalty to
specific brands. Loyalty is defined conceptually as recurring and exclusive
purchasing. Although MM&C has yet to adopt branding jargon, this measure of
consumer loyalty is consistent in many respects to Keller's (1993)
conceptualizations. In 1996, the same year as the Nielsen ratings data, MM&C
conducted such a survey in the test market. Over 1,000 telephone interviews were
conducted over four weeks. Station A dominated the category by more than a 2 to
1 margin over either competitor. Additionally, a ten-year longitudinal ratings
analysis revealed that this same station had maintained its number one ranking
for over 90 percent of the sweep periods. Consequently, Station A was assigned
tentatively as the test criterion for program brand equity.
Results
Hypotheses One
Hypothesis one stated that programs with higher brand equity would reveal a
significantly greater difference between lead-in program audience size and
program audience size than programs with lower brand equity. Table 1 looks at
the overall household performance of the three stations. In terms of combined
performance, we see that there was a 6% drop in households using television
(HUT) from 10:45 PM to 11:00 PM. However, the data also show that station A, as
expected, witnessed a substantial gain in audience, surpassing its lead-in by
9%. Of the three stations, only station A achieved a positive differential
response to its lead-in, thus supporting the hypothesis. Additionally, an ANOVA
and Scheffee analysis of these differentials (news minus lead-in households)
indicates that these differences were significant among all three stations.
Table 1
Differential Performance Between 10:45 PM Lead-in and 11:00 PM Newscasts
(Overall Nielsen Metered Overnight Households [000])
Station A
Station B
Station
C
A&B&C
(News HUT)
F ratio
Prob.
Scheffee at
.05
10:45 PM
115
127
99
341
33.4
.000
A-B, A-C
11:00 PM
127
110
91
326
126.0
.000
A-B, B-C, C-A
Differential
+11
- 17
- 8
-15
79.2
.000
A-B, B-C, A-C
% change
+ 9
- 14
- 9
- 6
Note. df = 866, p = .05.
Hypothesis two stated that programs with higher equity would reveal a
significantly greater differential share index than programs with lower equity.
Table 2 interprets the newscast/lead-in relationship as a share index. Index
scores exceeding a magnitude of one indicate the degree to which a newscast
outperformed its lead-in. Conversely, scores of less than one infer the degree
to which the program was unable to hold its lead-in audiences. As expected, in
almost all cases, station A (our equity leader) indexed higher than its two
competitors.
Table 2
Share Index of Late Newscasts Compared to Lead-in Programming
Station
Overall
Mon
Tues
Wed
Thurs
Fri
Sat
Sun
A
1.28
1.5 *
1.2 *
1.2 *
1.5 *
1.0
1.5 *
1.2 *
B
1.02
1.1
.97
.99
.83
1.1 *
1.1
1.1
C
1.08
1.0
.94
1.2 *
1.2
1.0
1.0
.96
*Indicates highest index for that evening.
Hypothesis Three
Hypothesis three dealt with the degree of association or dependence between the
two programs and proposed that programs demonstrating strong equity would be
less dependent on their lead-ins and therefore, would exhibit lower correlations
than direct competitors. Unlike prior inheritance studies, the investigators
used seven-day moving averages in computing an overall correlation. Moving
averages are widely used in consumer market research to reduce the "noise" in
data to uncover an underlying pattern (Lehmann & Winer, 1994). According to the
data results presented in Table 3, the hypothesis is supported with station A
exhibiting the lowest overall correlation.
Table 3
Household Correlations Derived from Seven-Day
Moving Averages
Station
Pearson r
A
.59
B
.82
C
.72
Hypotheses Four and Five
Hypotheses four and five take the above notions of differential response a step
further by dividing the phenomenon into two distinct segments: (a) the power to
retain lean-in audiences and (b) the power to recruit audiences from other
sources. Nielsen Media Research provided the investigator with a standard "Flow
Analysis" study of the May 1996 diary sweep period. Using four-week averaged
household ratings, Nielsen tracked weekday audience flow from 10:45 PM to 11:00
PM. The most salient data have been distilled into Table 4.
Table 4
Nielsen Audience Flow Analysis, May Sweep 1996 (10:45 to 11:00 PM, Monday
through Friday, Four-Week Average Households [000])
Row no.
Measure
Station A
Station B
Station C
0
000
%
000
%
000
%
1
HH retained from
lead-in
80
49
77
59
53
56
2
HH recruited from
from other news stations (switchers)
40
25
29
22
23
24
3
HH recruited from non news sources (switcher)
44
26
25
19
18
20
4
Total News HH
164
100
131
100
94
100
5
% HH retained from leads-in *
65
54
51
6
% recruited from total news switchers (92) **
44
31
25
7
% recruited from total non news switchers (87) ***
51
29
20
Note. Chi square p = .05:
*4.49 with stations B and C collapsed, df = 1. ** 5.0 with stations B and C
collapsed, df = 1. *** 12.84 among all three stations, df = 2 and 11.64 with
stations B and C collapsed, df = 2.
Table rows 1 through 4 (shaded area) provide data on the relative audience
composition of each newscast. That is, total news households (row 4) equals the
sum of households retained from lead-ins (row 1), plus households recruited from
other stations providing local news (row 2) plus households recruited from
nonnews sources (row 3). The last two categories deal with audiences that
"switched" channels in order to watch a specific newscast. The percentage
columns within the shaded area give the proportion of the total news audience
that each category holds. These four data rows, however, do not provide
sufficient insight into the power of brand equity. Rows 5, 6, and 7 delve into
dynamics of our final hypotheses.
Hypothesis four predicted that programs with higher brand equity would retain a
greater proportion of their lead-in audiences than programs with lower brand
equity. As expected, row 5 shows station A to be the best performer, retaining
65% of its lead-in households.
Hypothesis five stated that programs with higher brand equity would recruit a
greater proportion of audiences from other sources than programs with lower
brand equity. Rows 6 and 7 of Table 4 address this proposition. The proportion
of households recruited from other news stations (row 6) offers the most
persuasive evidence. Here we have audiences that switched away from a channel
that was about to present a local newscast. Of all the news channel "switchers,"
station A earned the largest proportion (44%). Similarly, station A recruited a
greater proportion (51%) of the total nonnews switchers.
.
Discussion
Presuming that essential marketing mix components are controlled or
neutralized, the program equity model of audiences retained/audiences recruited
appears to have merit in explaining ratings inheritance effects. Instead of
addressing structural factors, such as program type or number of program
options, this approach delved into the ability of a program to earn rather
inherit audiences. A program with strong consumer-based equity can overcome a
poor lead-in and recruit loyal viewers from other sources. Additionally, this
program will be less likely to lose portions of its lead-in audience to direct
competitors.
The case study provided a concrete way to "test" some of these theoretical
propositions with all hypotheses supported to some degree. This exploratory
study opens the door for more generalizable empirical research using multiple
markets and different types of program content. The one caveat is the crucial
need to control important marketing mix components.
While this study was intended to use brand equity theory to better understand
inheritance effects, the findings can also be used to support the reciprocal
notion that the diagnostics of audience flow can be used to determine in part a
program's brand equity. More specifically, programs with strong equity process
lead-in audiences differently than programs with weaker equity.
The following proposed audience member categories might assist future research.
1. Loyalists.
Definition: Viewers who hold strong consumer-based program brand equity and are
found within the lead-in program's total audience.
Disposition: These viewers remain in place on the same channel because of brand
commitment
2. Passives.
Definition: Viewers who do not hold any strong consumer-based program brand
equity towards any direct competitor and are found within the lead-in program's
total audience.
Disposition: These viewers will remain in place on the same channel because of
simple "inertia" rather than true commitment.
3. Converts.
Definition: Viewers who hold strong consumer-based program equity but were
watching on a competing channel and, therefore, are motivated to switch
channels.
Disposition: If necessary, these viewers will abandon a lead-in program channel
and switch over to a more suitable program option (see number 5 defectors
below).
4. Tune-ins.
Definition: "Appointment" viewers who hold very strong consumer-based program
brand equity and make a deliberate effort to turn on the TV set in order to
watch a specific program. It possible to have a subcategory of passive tune-ins,
where the desire is simply to have the set "on" with no regard for specific
programming.
Disposition: These viewers are acquired audiences from what some researchers
call the "off position."
5. Defectors.
Definition: "Converts" seen from an opposing perspective, that is, viewers who
are about to abandon a program channel in order to find a more suitable or
preferred program. Defectors hold strong consumer-based program equity for a
direct competitor. (One program's defector is another program's convert.)
Disposition: At the appropriate time, these viewers leave one program and go to
another on a competing channel.
Using these categories, one could hypothesize that program equity
(a) Increases the number of converts, (b) reduces the number of defectors, (c)
reinforces current loyalists, and (d) transforms passives into converts.
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