The television of abundance arrives: cable choices and interest maximization

The television of abundance arrives: cable choicesand interest maximization q

Kimberly Neuendorf 1, Leo W. Je�res 2, David Atkin *

Department of Communication, Cleveland State University, Cleveland, OH 44115, USA

Abstract

Utility theory o�ers some suggestions for how people react as the so-called ``television of

abundance'' reaches the point where audiences can maximize their interests. Contrasting the

interest maximization outlook with the media substitution hypothesis, this study explores

cable channel repertoire patterns. A random survey of 319 cable subscribers in a major me-

tropolitan area measured exposure to several dozen channel categories. Utilizing factor

analysis, pattern repertoires were developed, comparing social category groupings. Overall,

the ®ndings support an interest maximization perspective, with individual category pattern

repertoires providing insight into the use of abundant cable o�erings to serve specialized

needs. Ó 2000 Elsevier Science Ltd. All rights reserved.

1. Interest maximization

In the early years of American cable television, an expansion in the number ofchannels led largely to increased redundancy rather than program variety. Cablesystems merely imported the signals of distant stations whose programming closelyparalleled that of local independents (Agostino, 1974). The larger number of chan-nels did expand one's choices at any given point in time, but there still was limitedopportunity for optimizing content preferences.

www.elsevier.com/locate/tele

Telematics and Informatics 17 (2000) 169±197

q Revised version of a paper presented to the Communication and Technology Division for presentation

at the annual conference of the International Communication Association, San Francisco, May, 1999.* Corresponding author. Tel.: +1-216-687-4637.

E-mail address: [email protected] (K. Neuendorf).1 Tel.: +1-216-687-3994.2 Tel.: +1-216-687-5088.

0736-5853/00/$ - see front matter Ó 2000 Elsevier Science Ltd. All rights reserved.

PII: S0 73 6 -5 85 3 (0 0) 00 0 07 -1

Television content can be viewed on many dimensions, but it is the traditionalnotion of ``formats'' that best describes the variety available in the eyes of viewers,who have de®nite preferences for soap operas, sitcoms, game shows, police-detectiveshows, and so forth. Utility theory o�ers some suggestions for how people react asthe so-called ``television of abundance'' reaches the point where audiences canmaximize their interests. The theory assumes that a person faced with a set of objectsis able to evaluate and rank order them. Given the opportunity to select one of theelements, the individual will maximize his/her perceived utility by choosing the ele-ment most highly evaluated (Miller, 1972).

Je�res (1978) o�ers the concept of ``interest maximization'' to describe how au-diences, given expanded choices by subscribing to cable TV, may select programs/formats that maximize their interests. As the TV menu grows, each individual'sviewing pattern might become more ``homogeneous'' as she/he avoids those formatsdisliked and selects those preferred. When Je�res ®rst o�ered this notion, cabletelevision was just beginning to penetrate the nation and the limited range ofchannels available provided a poor test of the concept. However, with an expansionof cable o�erings and the inclusion of networks specializing in speci®c formats, it isnow possible to revisit the concept in a more meaningful way.

Although it is possible that someone could devote almost all of their TV viewingtime to a few formats, at some point we might expect diminishing returns to set in asthe individual becomes saturated with sitcoms or game shows, for example. Theindividual would have ful®lled his or her interests in those particular forms and thepercentage of viewing time spent with the desired content would reach asymptote.

Another view is o�ered by Donohew and Tipton (1973), who hold that the in-dividual operates between the boundaries of variety and redundancy, at one timetuning out monotonous information in favor of something new, and at another timetuning out ``new'' information if it reaches a certain threat level. The individual isseen as oscillating between the need for a predictable environment and the need forarousal and stimulation. Similarly, researchers working in experimental psychology(e.g., Berlyne, 1970; 1971) have found that the hedonic value of a stimulus is max-imized at a moderate level of novelty.

Both the tendency to concentrate viewing in valued categories and variety-re-dundancy oscillation may be applicable to TV viewing. TodayÕs cable systems of-fering scores of channels ± typically about 72 ± provide an improved environment fortesting these notions.

We might expect that people who watch a great deal of TV (in terms of number ofhours) to alternate between redundancy and variety more than individuals whowatch relatively little TV. Individual characteristics also may be factors. People wholead relatively busy lives, with many interpersonal contacts and a variety of dailyactivities, would need less stimulation from mass media. We might expect such in-dividuals to seek a few satisfying TV content types, while people who have fewoutside contacts or activities alternate between redundancy and variety as a means ofachieving stimulation in their lives.

170 K. Neuendorf et al. / Telematics and Informatics 17 (2000) 169±197

2. The substitution hypothesis

A broadly tested perspective that provides a di�erent outlook on audience se-lectivity is that of the substitution hypothesis, positing a model of functional dis-placement (Henke and Donohue, 1989). To wit, audience members may substitutethe use of one functionally similar medium for another. A commonly cited illus-tration of this dynamic involves the displacement of radio by television as the mostwidely adopted mass entertainment medium (Lasswell, 1948). Contemporary sub-stitution models characterize pay-per-view or movie purchases on video as re-placements for movie-going activity (e.g., Childers and Krugman, 1987; Henke andDonohue, 1989; LaRose and Atkin, 1991; Lin, 1993), as these new video modalitiesprovide better audience control over the household budget and leisure-time alloca-tions.

Alternately, uses of certain media could be complementary or orthogonal, evenwhen those channels provide similar content. For instance, audience use of cableservices was found to have little e�ect on their patronage of TV, newspapers andother traditional news sources (Grotta and Newson, 1983; Reagan, 1987). Morerecent work (Je�res and Atkin, 1996; Kang and Atkin, 1999; Lin and Je�res, 1998)failed to ®nd any signi®cant correlations between interest in using online services andother mass media, although other studies report a slight reduction of TV viewingtime among online users (e.g., Atkin et al., 1998b; Berniker, 1995; Crispell, 1997;James et al., 1995).

Media usage variables were also included in early studies of cable adoption and,similarly, the results have been inconsistent. For instance, Reagan (1987) found TVand magazine exposure to be positively related to cable subscribership but not radiolistenership. Paradoxically, Collins et al. (1983) found that TV viewership wasnegatively related to cable adoption. Other studies have found the opposite to betrue (Becker et al., 1983; Sparkes, 1983; Sparkes and Kang, 1986; LaRose and Atkin,1988a,b; Lin, 1994).

By contrast, a complementary relation obtains when the uses of two media mu-tually reinforce one another. For instance, the VCR can complement televisionviewing by allowing for time-shifting of program viewing (e.g., Lin, 1993). As Lin(1994) maintains, the onset of a complementing or displacement/substitutionmechanism should depend on whether ``new media'' can e�ectively compete with``old media'' for cost-e�ciency, perceived communication utilities and grati®cationexpectations. It is this potential complementarity mechanism that is highly consistentwith interest maximization.

The literature has focused on complementing or substituting relationships amongmedia, with scant attention paid to multiple content types (e.g., specialized cablechannels) as potential individual sources. In the age of media abundance, we areunable to treat cable TV as a monolithic, homogenous source. Complementing orsubstituting mechanisms may accrue within a system of cable viewership; comple-mentarity or substitution may occur between cable channels rather than between

K. Neuendorf et al. / Telematics and Informatics 17 (2000) 169±197 171

media delivery systems. Bearing this in mind, we examine whether a pattern ofcomplementarity exists for cable channels, and whether this pattern demonstrates aconsistency with the notion of interest maximization ± i.e., are similarly programmedchannels positively related in their patterns of exposure? And, are there importantvariations in such patterns, according to audience interests?

3. Modelling channel repertoire

Heeter (1985) integrated the multichannel media environment into her ad hocchoice process model. In formulating a predictive model, Webster and Lichty(1991) o�ered a model of audience behavior that incorporates audience and mediafactors as determinants of viewing. They found that media factors are moststrongly in¯uenced by programming options. To wit, Ferguson and Melkote (1997,p. 190) de®ne channel repertoire (CR) as ``the number of channels that a viewerchooses to watch, without much regard to the total number of channels available''.They uncovered a relatively modest in¯uence of demographic variables in deter-mining number of cable channels included in oneÕs repertoire, as people who spendthe most time with TV are more likely to include cable channels in their viewingrepertoire.

Researchers (Lin, 1994; Perse et al., 1994; Lin, 1999) developed models that linkthe richer new media (e.g., cable) repertoires with a more active audience. Theyfound some support for their contention that cable-channel repertoire will be higheramong groups who spend more time watching television.

Ferguson and Melkote found that cable CR di�ered between low TV/low otherleisure and high TV/high other leisure groups. They posited that people who aremore active in general have larger (or ``more active'') CRs. Moreover, since elderlyand less educated viewers spend more time with TV (Robinson, 1981), they are likelyto indicate larger CRs; Ferguson and Melkote (1997) found that both education andage were predictive of cable CR.

Focusing on gender, Nathanson et al. (1997) found some support for their sup-position that males had an instrumental viewing style-with goal-directed reasons forwatching and intentional, concentrated and selective use of television. Females, onthe other hand, had a relationship-oriented approach to TV that augured in favor ofa smaller CR.

In fashioning an integrated model of channel selection in a rapidly changingmedia environment, we have begun to extend the concept of CR to accommodate thechanging media environment. We do this in several ways. The traditional CRmeasures, reliant upon simple summative indicators of number of channels accessedin a given time period, are enhanced by such elements as: (1) the use of weightingsre¯ecting frequency of use, (2) the development of both primary repertoires (thosesets of channels used regularly and frequently) and specialized repertoires (those setsof channels accessed by particular audience segments on a regular but non-daily


basis), (3) the examination and use of factor analytic patterns of cable channel use(pattern repertoires), (4) the comparison of such repertoires across achievement andascriptive social categories (e.g., educational level or racial groups).

Elsewhere (Neuendorf et al., 1998), we have reported results relevant to points (1)and (2) above, using both straight additive measures and indicators weighted byexposure frequency. While the traditional simple summative measure of repertoirewas undi�erentiated by any of the social locator variables, the frequency-weightedmeasure of overall repertoire was di�erentiated by race (with Black respondentsreporting a larger repertoire than do White respondents), with near-signi®cant dif-ferences for the two genders (males exhibiting a greater repertoire) and for groupsde®ned by educational level (those with low education reporting a larger frequency-weighted repertoire).

The construction of specialized repertoires has further enhanced our under-standing of selectivity relevant to the plethora of channel options available today.We (Neuendorf et al., 1998) have de®ned three levels of specialized repertoires ±primary repertoire (number of channel categories viewed at least daily), secondaryrepertoire (number of channel categories viewed at least weekly), and tertiary rep-ertoire (number of channel categories viewed weekly or less, but at least occasion-ally). We found support for the notion that CR is positively associated with greaterviewing overall, except in the case of Tertiary Repertoire. In a set of social locatorcomparisons, we discovered a signi®cant education e�ect. Those of low educationhad a signi®cantly higher primary repertoire, and a signi®cantly lower tertiary rep-ertoire.

Given that total repertoire did not di�er between the groups, we concluded thatthose of lower educational achievement have a higher motivation for channel sur-veillance ± they reexamine their repertoire on a much more frequent basis (Neuen-dorf et al., 1998). While this certainly corresponds to a higher level of TV exposurefor those of lower education, it is important to note that the more highly educatedgroup reported just as many channel categories in their total repertoire. And, ®nd-ings reported elsewhere (Atkin et al., 1998a) provide initial evidence of patternrepertoires with high levels of complementarity, as judged by a standard of facevalidity. For example, heavy viewers of the Family Network were also heavierconsumers of the USA Network, the Lifetime Network, and Superstations (TNT,etc.). This factor seems to encompass content relevant to those who are highlypeople-oriented ± those with families, and/or with strong interests in issues of rela-tionships among family and friends.

4. The utility of a combined approach

Utilizing these extended approaches to the measurement of CR, facilitatedthrough data reduction, allows a renewed scrutiny of the possibility of interestmaximization. In this initial, basic test application of a factor analytic approach to


CR as applied to interest maximization, we compare patterns of viewership amonggroups delimited by social categories. Five social category variables are considered,three ascriptive and two achievement: Race, age, and gender, and marital status andeducation level, respectively.

The questions posed earlier may ®nd speci®c application with factor-analytically-derived pattern repertoires for cable viewership. Past work (e.g., Ferguson andMelcote, 1997) o�ers some evidence that ascriptive factors like education encouragelarger repertoires, providing support for a guiding hypothesis that social factorsin¯uence repertoires. However, these social categories have yet to be investigated inthe light of common underlying themes that help de®ne CRs, prompting us to posethe following research questions:

RQ1: Do pattern repertoires display complementarity consistent with the notionof interest maximization?RQ2: Do pattern repertoires di�er among social categories?/Do any emergent dif-ferences support an interest maximization perspective (i.e., showing a concentra-tion of attention in areas attractive to that social grouping)?

5. Methods

Study data were collected from the Communication Research Center at ClevelandState University, utilizing a survey of cable subscribers in an ethnically diversemetropolitan area of the Midwestern US.

The survey, conducted during November of 1997, was administered in conjunc-tion with the City O�ce of Cable TV to assess subscribersÕ satisfaction with theircable system, the pattern of their viewing, and their experience with local origination.Cable TV subscribers were interviewed using a computer-aided telephone inter-viewing (CATI) system at Cleveland State. Telephone numbers were randomlygenerated to insure selection of residents with both listed and unlisted telephonenumbers, generating 319 complete responses. Respondents were told the purpose ofthe survey with the following introduction:

Hello, my name is (FIRST NAME ONLY), and IÕm calling from ClevelandState University, where we're conducting a brief survey about cable TV andother topics for the city of Cleveland Cablevision's franchise agreement withthe city will be up for renewal soon. We're not selling anything, and we'd likeyour views about the current cable system, as well as other topics, which we'llbe summarizing in a report to the city.

Potential respondents were then asked if they were at least 18 years of age, lived inthe city and received cable TV in their house. Those who responded positively werethen asked about their gender so that interviewers could ®lter respondents in a waythat ensured gender balance in the survey.


The substantive sections of the survey were presented in the following order: (1)frequency of viewing for di�erent cable channels (34 items, utilizing a 0±5 scale where0� never watch and 5�watch several times a day); (2) public access experience(PEG ± so-called public/educational/government access programming (Atkin et al.,1998a), 3 items, plus probes); (3) problems with their cable reception or hardware (6items); (4) overall evaluation of the cable system (5 items); (5) TV and other mediause (8 items); (6) a general section seeking public opinion and perceptions (15 items);and (7) social categories (ethnicity, age, etc.). See the authors for a copy of the in-terview schedule.

From the cable viewing measures, we constructed ®ve repertoire indicators:

1. Repertoire: The total number of 37 channel sets ever viewed (corresponding withthe measurement of repertoire in past research).

2. Frequency-weighted repertoire: The sum of all frequency of viewing measures (0±5scale), after each was standardized.

3. Primary repertoire: The number of channel sets viewed at least daily.4. Secondary repertoire: The number of channel sets viewed at least weekly.5. Tertiary repertoire: The number of channel sets viewed weekly or less, but at least

occasionally.

6. Description of sample

Our sample of 319 respondents consisted of 42% males and 58% females. Some40% were married, while 13% were divorced or separated, 13% were widowed and34% had never been married. While 8.3% were ages 18 or 19, 19.5% were 20±29,21.7% 30±39, 16.9% 40±49, 12.8% 50±59, 9.3% 60±69 and 11.5% 70 or older. Sixtypercent were Caucasian, 32% Black, 5% Hispanic, and 4% other. While 6% hadadvanced college degrees, 14% were college graduates, a third had some college, athird were high school graduates and 14% had less formal education than that. Afourth of the sample reported household incomes of $20,000 or less, while 18.5%reported incomes $20,001±$30,000, 21% $30,001±$40,000, 18.2% $40,001±$50,000and 17% a higher income.

7. Results

Table 1 lists all the cable channel categories presented to respondents for possibleendorsement, with corresponding percentages of viewership and mean frequencies ofviewership. The most-frequently viewed channel set is that of broadcast networka�liates (99.4% reporting at least occasional viewership); however, a wide variety ofchannel categories show very broad reach, with three-quarters or more of respon-dents reporting at least occasional viewership-public TV, independent broadcast


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stations, News Channels, Sports Channels, American Movie Classics, DiscoveryChannel, USA Network, Lifetime Network, Superstations (e.g., TNT, WGN), andthe Weather Channel. As background for the subsequent factor analyses, the tablealso reports all signi®cant di�erences in frequency of viewership for the ®ve socialcategories of interest here.

Tables 2±5 present pattern repertoires derived from oblique factor analyses usingthe latent root criterion, and utilizing frequency-weighted measures of viewership.For the sake of brevity, only primary loadings are displayed in each table. Inter-factor correlations of 0.20 or greater are reported near the bottom of each table.Also, at the end of each table, we have included the mean values for our ®ve rep-ertoire indicators by social categories, with signi®cant di�erences noted (Neuendorfet al., 1998).

It is important to note that the patterns displayed in the factor analysis tables arenot in order of frequency of viewership, but rather in order of strength of internalrelationships among exposure items. Thus, interpretations in this section will focusheavily on selected primary factors for each analysis.

Table 2 displays the pattern repertoires for Black and White respondents. Thenumber-one factor for Black viewers is one including Shopping Networks, theComedy Network, and the Sci-Fi Network; notice that none of these channelsare more heavily viewed by Blacks, or for that matter, Whites (see Table 1).However, the pattern of viewership ± the likelihood that a Black viewer who is afan of Shopping Networks will also more regularly watch the Comedy Networkand the Sci-Fi Network ± is quite strong, the strongest apparent pattern forBlack viewers. In contrast, this pattern is non-existent for White viewers ± i.e.,each of the three items is in a separate factor. The second factor for Blackviewers is one that might be interpreted as high culture-viewing Bravo and A&E,and not viewing BET (heavily programmed at present with music videos). Again,viewing of these channels is not strongly related for White respondents. The ®rstfactor for Whites is the basic, earlier-discussed people-oriented repertoire, which,with the addition of AMC, also constitutes a lesser pattern (Factor 4) for Blackrespondents.

Table 3 contains a comparison of younger (under age 40) and older (age 40 andolder) respondents. Among younger viewers, a sort of civic-oriented pattern that isapparent in the ®rst factor ± viewership of all types of public access programming ± islinked with viewership of Court TV. While an earlier analysis (Atkin et al., 1998a)established that age was negatively related to public access sports viewing in par-ticular (e.g., high school football, baseball), the inclusion of Court TV as a minorloader raises the possibility of a broader orientation toward public access channels.The ®rst factor for older respondents is a full version of the previously de®ned``people-oriented'' repertoire; this factor is not at all intact for younger respondents,a ®nding unique among all factor analyses presented here. For younger respondents,the Lifetime Channel is grouped with pay channels, watching USA is related towatching independent channels, and Superstation viewing is correlated with the


Tab

le2

Patt

ern

rep

erto

ires

for

Bla

ckan

dW

hit

ere

spo

nd

ents

Bla

ckre

spo

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Wh

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ts

Fact

or

Ch

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nel

(s)

Pri

ma

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Co

mm

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valu

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%

of

Vari

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ce

Fact

or

Ch

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nel

(s)

Pri

mary

load

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Co

mm

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%

of

vari

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ce

19

Sh

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pin

g0

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0.7

95.6

915.4

%1

18

Fam

ily

0.7

30

.67

5.2

914.3

%

15

Co

med

y0

.62

0.7

624

Su

per

stati

on

s0.7

20.6

1

23

Sci

-Fi

0.4

60.5

817

US

A0.6

60

.64

19

Lif

etim

e0.6

60.6

3

22

1B

rav

o0

.59

0.6

03.1

88.6

%

20

A&

E0

.49

0.7

42

33

Cart

oo

n0.7

80

.74

3.0

38.2

%

30

BE

T)

0.3

40.7

112

Dis

ney

0.7

80.6

5

20

A&

E)

0.3

80.6

0

32

6H

GT

V0

.81

0.8

02.6

87.2

%

25

Fo

od

0.6

60.6

33

26

HG

TV

0.6

30.6

32.2

96.2

%

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viewing of a range of entertainment fare ± Comedy, E!, and Music Channels.Viewing of the Family Channel is linked with News Channel viewing.

The second factor for older respondents is mystifying. While the co-presence ofSpanish language channels and SCOLA indicates a multicultural outlook, the minorloadings of the Sci-Fi Network and the Comedy Channel do not have apparent facevalidity.

In Table 4, we see the results of comparative factor analyses for male and femalerespondents. The ®rst factors for the two groups are similar ± both are variations onthe people-oriented repertoire. However, females seem to have a larger people-ori-ented repertoire (consistent with the Nathanson et al. (1997) ®ndings). The role ofSports Channels is divergent between the male and female groups. For males, SportsChannels are part of a minor factor, in a repertoire with news-oriented program-ming. For females, sports ®t with the Disney and Cartoon Channels ± more of a ``funand games'' framing.

Table 5 displays a comparison of married and unmarried (i.e., divorced, widowed,never married) respondents. The people-oriented repertoire is important for bothgroups, with a slightly larger repertoire for married respondents. Each group alsohas a food/home/garden factor that is prominent. In terms of di�erences, a uniquetype of apparently informational repertoire is represented by the ®rst factor formarried respondents ± Discovery Channel, Learning Channel, Court TV, and (un-expectedly) the Sci-Fi Network constitute the most important viewing pattern forthis group.

A comparison of lower education and higher education respondents (via a mediansplit) is shown in Table 6. The ubiquitous people-oriented repertoire is present as animportant viewing pattern for both educational groups, but is more important forthe higher educated group. The ®rst factor for lower educated respondents is anintriguing mix of seemingly informational/cultural sources ± Court TV, HealthChannel, Bravo, and SCOLA.

8. Discussion

This study has demonstrated the utility of combining a factor analytic approachto examining CRs with an investigation of interest maximization. We see support forthe notion that audiences can be meaningfully segmented across di�erent socialcategories with characteristic tastes. In particular, with regard to Research Question1, the data provide clear evidence of interest maximization manifested in comple-mentary viewing of certain functionally similar channels. That is, at least some setsof functionally similar channels seem to provide reinforcement or complementarity,rather than replacement for every group examined in the analyses. Fans of onechannel do generally tend to seek redundant programming in particular patternsidenti®ed in this investigation. Certain pattern repertoires appear robust across so-cial groupings.


For example, the people-oriented repertoire of the Family Channel, Supersta-tions, USA Network, and Lifetime, sometimes joined by other channels such as A&Eor AMC, is present in nearly every factor solution. The clustering of Home andGarden TV (HGTV), the Health Channel, and the Food Channel is also fairly ro-bust. And, of course, the repertoire of the three measures of public access viewing ±PEG in general, PEG sports, and PEG government ± holds together across socialcategories.

In the context of utility theory, these ®ndings support the notion of an audienceseeking redundancy rather than variety (Donohew and Tipton, 1973), with thehomogenous viewing patterns predicted by interest maximization (Je�res, 1978).Most factor groupings possess high face validity for a complementarity dynamic.However, the exceptions (e.g., Sci-Fi Network grouped with Discovery Channel,Learning Channel, and Court TV for married respondents) introduce a hint ofDonohew and TiptonÕs (1973) variety/redundancy oscillation dynamic, withredundancy winning out over variety in general in this set of aggregate analyses.And, we did anticipate other complementary repertoires that did not emerge. Forexample, we expected to ®nd groupings of similar public-a�airs channels (e.g., PublicTV, PEG), but the clusters observed are rather broader than anticipated (Reaganet al., 1995).

In response to RQ2, we do see some clear di�erences in pattern repertoires be-tween social groupings. Some individual channels apparently are likely to servedi�erent functions for di�erent individuals, and we do see systematic selective clus-tering of channels between di�erent social category groupings. Examples of thistendency for channel ``splitting'' are too numerous to inventory, but some of themore intriguing include:

· The role of E! Entertainment Network varies ± as entertainment grouped withcomedy and music for younger respondents, while grouped with shopping for old-er respondents. Also, E! is grouped with surveillance sources for Black respon-dents (with Discovery, Court TV).

· Sports Channel viewing is related to news viewing for males, while it is related toviewing of adolescent fare (Disney, Cartoon Network) among females.

· The role of BET varies for Black and White respondents ± it is negatively relatedto Bravo and A&E viewing for Blacks, while for Whites it is correlated with view-ing of religious and Spanish-language Channels, in a sort of cultural-exchangerepertoire.

· Viewing of Bravo is all over the place ± sometimes linked with A&E, sometimeswith pay-per-view, at other times with Spanish-language Channels, the HistoryChannel, or even the Weather Channel.

· The Cartoon Network and the Disney Channel most often go together, but as apair are combined variously with other channels ± with Sports Channels foryounger respondents and for females, with premium channels for older respon-dents, with pay-per-view channels for married respondents, for males, and for


those of lower education, and with not viewing A&E for White respondents, forunmarried respondents, and for those of higher education.

We also see some unique repertoires for certain social category groupings: Forexample, the unique informational repertoire of married respondents (Factor 1), andthe Spanish/SCOLA/Sci-Fi/Comedy repertoire of older respondents (Factor 2).Unfortunately, since this study did not examine audience perceptions of channels,nor their goals and motivations for viewing, our interpretations can only be inferredfrom viewership patterns (see e.g., Swanson, 1987). For some repertoires, the in-terpretation is readily apparent. For others, including those just mentioned, a sat-isfying interpretation is not readily at hand.

Thus, for the second part of RQ2, inquiring whether repertoire di�erences supportan interest maximization perspective, the evidence is not clear. Only in isolated in-stances can we point de®nitively to a pattern repertoire that prima facie meets theneeds of a certain social grouping. For example, the grouping of sports with news formales only supports a hegemonic view of a male-dominated sports industry. Thisdynamic could explain why gatekeepers in that group made dysfunctional miscal-culations, such as ABCÕs overbid (and ®nancial loss) for Monday Night Football;male executives failed to anticipate its unpopularity with the audience at large. 3

Limitations of using factor analysis for any purpose ought to be noted. Largeanalyses such as those reported here typically include some relatively low commu-nalities and low loadings (with substantial secondary loadings), and this study is noexception. And, the variance explained by the factor solutions in this study rangesfrom 63.3% to 70.6% for an 11- to 14-factor solution. Thus, up to a third of the totalvariance is left unexplained. Still, the factor analyses present a new and useful way ofexamining CR.

Future research needs to extend this approach beyond the single market studiedhere and employ more social category comparisons over time. Exigencies of theparticular client-oriented data collection reported here prevented the inclusion ofdetailed measures of viewing motives, uses and grati®cations. Studies to followshould include such measures, in order to develop de®nitive links between grati®-cations sought and pattern repertoires accessed. And while the aggregate analysespresented here provide a baseline template for studying the concept of patternrepertoire, an individual-di�erences approach should prove more fruitful in detailingthe relationship between interests and the accessing of content that can maximizethose interests. Later work should also employ structural or regression models to test

3 It could be argued that ABC was willing to overpay for football because, according to a larger

accounting scheme, the prestige of carrying professional football brings with it less tangible bene®ts such

as prestige and a�liate loyalty. Similar dynamics prompted Rupert Murdoch to bid a new record high for

its Sunday NFL franchise, thereby enhancing FoxÕs standing as a ``major'' network and depriving a

competitor (CBS) of quality sports programming.


the applicability of the factors uncovered here. Finally, it would be useful to extendthis analysis to other media such as the Internet as they continue to reshape themedia environment.

In conclusion, the evidence presented here gives us enough support of the exis-tence of interest maximization in the aggregate to allow us to move forward todocument this process at the individual level. The present study supports observa-tions that the age of media abundance is here, but it is not precisely a television ofabundance. The content menu at the turn of the millennium is ®rst of all a cableenvironment of abundance, and for many it is a hyper-world of abundance, repletewith Internet, cable, videotape and videodisc options. We have only begun to tap thisinformation environmentÕs complexity, and the manner in which users organize,access, and make sense of their information world.

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The television of abundance arrives: cable choices and interest maximization

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