Has Lead-in Lost its Punch? An Analysis of Prime Time Inheritance Effects:Comparing 1992 with 2002

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Has Lead-in Lost its Punch? An Analysis of Prime Time Inheritance Effects:Comparing 1992 with 2002
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  The business of commercial televisionis the selling of audiences to advertis-ers, which often translates into thebuying and selling of rating points.Television programmers have longbeen aware of the capacity of a pro-gram to “inherit” sizable ratings fromthe program scheduled immediatelybefore it. Although intervening vari-ables, such as program genre, lead-out,and daypart, have been shown to havesome minor influence on this phenom-enon, by far the most powerful predic-tor of program ratings has been themere size of a program’s lead-in audi-ence. Because most networks and large-market television stations negotiatecommercial rates based on audiencedelivery, the ratings gained or lostfrom program scheduling can amountto subsequent gains or losses in adver-tising revenue (Surmanek, 1996). Onecould argue that managing program-ming essentially is the managing of audiences for sale, or more preciselythe managing of ratings points for sale.Inheritance effects have been reaf-firmed empirically many times duringthe 1970s and 1980s. However, in recent years conventional broadcast television,represented by “the Big Four” of ABC,CBS, NBC and Fox, has experienced somuch audience upheaval, it seems plau-sible to question the potency of lead-inscheduling strategies. Given the circum-stantial evidence of plummeting rat-ings coinciding with ever-increasingprogram competition from cable, satel-lite, and other alternative media over Has Lead-in Lost its Punch? An Analysis of Prime Time Inheritance Effects:Comparing 1992 with 2002 by Walter S. McDowell, University of Miami, U.S.A., and Steven J. Dick,Southern Illinois University, U.S.A. Abstract  Recognizing the recent dramatic increase in the number of channels available to thetypical American household coinciding with an equally dramatic decrease in audi- ence ratings for the major broadcast networks, there was reason to hypothesize that,in recent years, lead-in or inheritance effects have diminished. However, an analysisof prime time ratings comparing 1992 with 2002 for ABC, CBS, NBC and Fox showedno support for this notion, suggesting that, despite the recent upheavals in the televi- sion industry, lead-in has not lost its punch.  Walter McDowell (wmcdowell@miami.edu) is an Assistant Professor at the University of Miami in Coral Cables Florida. His re- search interests include media branding, audience research and broadcasting eco- nomics. Steven Dick (sdick@siu.edu) is an Assistant Professor at Southern Illinois University in Carbondale, Illinois. Hisresearch interests include new technologies and media management. the past decade, one might suspect thataudiences today are more discriminat-ing and therefore, less susceptible tothe powers of lead-in.The purpose of this study was to ascer-tain whether lead-in programmingover the past decade has lost its punchin terms of influencing prime time au-dience ratings. A study comparingNielsen prime time household ratingsof 1992 with 2002 was conducted toanswer this question. To date, therehave been no published studies offer-ing this type of ratings comparison. Inaddition to adding to the existing bodyof work on inheritance effects, thisstudy raises some provocative theoreti-cal concerns about program schedul-ing practices and audience behavior ina multi-channel environment. Literature Review Inheritance Effects The overall ratings impact of lead-inprogramming has been confirmedmyriad times by industry and academicresearchers. Beginning in 1975,Goddhart, Ehrenberg, and Collinscoined the term inheritance effects while working on the broader issue of audience duplication among programs.They discovered a highly predictableflow of audience between adjacent pro-grams. Headen, Klompmaker, and Rust(1979) proposed a more sophisticatedmodel introducing several independent variables including ratings, channel,program type, daypart, and repeat view-ing. Using Simmons Market Researchdata, an examination of over 4,000 com-binations of pairs of programs revealedthat by a substantial margin, ratings were the single best predictor variable. A different model offered by Webster(1985) introduced factors of audience   © 2003 –  JMM – The International Journal on Media Management    – Vol. 5 – No. IV   : (285 – 293) 285     w     w     w  .    m    e      d      i    a      j      o     u     r    n    a      l .    o     r    g    286 © 2003 –  JMM – The International Journal on Media Management    – Vol. 5 – No. IV  w w w.m e d  i     a   j     o ur n a l    . or   g  availability, lead-in program ratings,the number of program options, andprogram content. Using Arbitron rat-ings from one sweep period, Websterconcluded that for adjacent programpairs, lead-in ratings and the numberof program options in combinationexplained 80% of the variance. A mas-sive 22-year study of network primetime programming from 1963-1985conducted by Tiedge and Ksobiech(1986) concluded that programs withhigh-ranked lead-ins scored highershare points than those with lowranked lead-ins. Also, fewer programoptions produced higher lead-in corre-lations and visa versa. In 1988, thesame research team using the identi-cal ratings data set concluded that the“pull” effects of lead-out programs were minimal compared to the stron-ger “push” effects of lead-in programs(Tiedge & Ksobiech, 1988). Looking atnine years of Nielsen ratings from 1976to 1985, Walker (1988) found that thecorrelational relationships among in-heritance effects, lead-in, programtype, and number of options supportedthe earlier findings of Tiedge andKsobiech (1986). Boemer (1987) foundin one television market high positivecorrelations between audience ratingsof local late evening newscasts andtheir respective prime time lead-ins.Davis and Walker (1990) discoveredthat the most effective way to competein prime time against new media (cableand satellite) was to take advantage of lead-in effects. Examining syndicatedrather than network programs, Cooper(1993) correlated the influence of sev-eral variables on program ratings in-cluding lead-in, lead-out, number of options, program type compatibility,network affiliation, and cable penetra-tion. The results from a 50-marketanalysis revealed that lead-in ratingscompletely overwhelmed any other fac-tor in the model. A fairly consistentconclusion found among most but notall of these early studies that includednumber of program options as a vari-able was that as the number of optionsincreased, the correlations betweenlead-in and lead-out programs (i.e. in-heritance effects) weakened. A moredetailed examination of the defini-tional problems surrounding the termprogram options is presented in thediscussion section of this study.In later years, Inheritance effects re-ceived less academic attention but afew studies did keep the topic alive. Forexample, McDowell and Sutherland(2000) discovered in a one- year, singlemarket case study that top ranked lo-cal newscasts took greater advantage of lead-in audiences than lesser-rankednewscasts. From an advertising per-spective Napoli (2001) found that at thebeginning of a new fall premiere sea-son, the ratings of returning lead-inprograms can assist network sales de-partments in reducing the degree of error in forecasting the ratings for newprime time programs. The Art and Science of Scheduling Savvy television programmers will con-cede that the ratings performance of many supposedly successful programsis more a matter of clever schedulingthan compelling content. When ana-lyzing a program’s ratings perfor-mance Webster, Phalen and Lichty(2000) warn that Some people assume the choice of a programcenters upon the active expression of a pref- erence for a program or type of program. However, so called structural factors have tra- ditionally been considered important media- tors of the programs viewers choose and com-  plicate the relationship between viewing  preference and viewing behavior”. (p. 178) Over the years, these structural factorshave acquired their own special jargonas outlined by Eastman & Ferguson(2000). For example, placing a rela-tively weak or unfamiliar program be-tween two strong programs is called“hammocking”. This is a commonstrategy used to stimulate sampling of a new program. Inserting a strong pro-gram between two weaker entries hasbeen dubbed “tent-poling” and is oftenassociated with the notion of salvaginga poor program line-up. Offering sev-eral adjacent programs with highlysimilar content, such as an evening of sitcoms, is called “block program-ming.” The strategy of responding to acompetitor with radically differentprogram content is known as “counter-programming.” Eastman, Newton,Riggs and Neal-Lunsford (1997) ana-lyzed ways the major networks capital-ized on inheritance effects and en-hanced audience flow by positioningcommercial breaks away from thenatural transitions between programs.All of the above-mentioned schedulingtechniques share a common strategicthread in that they attempt to take ad- vantage of the power of lead-in and al-though this strategy is still popular,there have been no nonproprietary lon-gitudinal studies to see if this strategyhas lost some of its potency. A plausiblereason for alleging such a decline is theunprecedented increase over the pastdecade in the number of program op-tions available to audiences. Media and Programming OptionsExplode During the 1990s During the 1970s and 1980s, whenmost of the above mentioned inherit-ance studies were conducted, the me-dia landscape remained relatively con-stant. For over thirty years America wasserviced by a three-network oligopoly(Long, 1979). The 1980s set the stage forthe tumultuous 1990s with the expan-sion of cable capacity and the introduc-tion of new competition. In 1987, Foxbecame a feisty competitor to the “bigThree” but did not become a signifi-cant force until the mid 1990s whenthe network acquired the broadcastrights for NFL football and began topersuade established VHF stations toswitch network affiliations (Litman,1998; Block, 1990). Later, upstart net- works WB and UPN and most recentlyPax have chipped away audiences fromthe larger incumbent networks. Corre-sponding with this increase in the    © 2003 –  JMM – The International Journal on Media Management    – Vol. 5 – No. IV 287     w     w     w  .    m    e      d      i    a      j      o     u     r    n    a      l .    o     r    g   number of broadcast networks was widespread dissemination in the 1990sof remote control tuning devices, which enhanced greatly the physicalease of changing channels. This tech-nology encouraged channel “surfing”and commercial “zapping” (Ching,2001) Also, it should be noted that de-spite much talk and speculation aboutthe potential distractions frominternet usage during this period, theamount of time dedicated to watchingtelevision by the typical Americanhousehold actually increased. Ameri-can households in 2001 watched anaverage of over 53 hours of televisionper week, a substantial increase overthe 48 hours of household watching re-corded in 1990 (Nielsen Report, 2001).The decade of the 1990s witnessed notonly a dramatic increase in the num-ber of broadcast networks but also astunning increase in the number of cable/ satellite networks. The 1992Cable Consumer Protection and Com-petition Act stimulated the growth of a new multi-channel delivery systemresulting in the deployment of DBS sat-ellite services beginning with zero sub-scribers in 1993 to over 17 million in2002. (Carlin, 2002). An analysis of theFederal Communications Commission(FCC) data revealed that a total of 105national and regional cable networksstarted operations prior to 1992. Fol-lowing 1992, the number has jumpedto 344 channels (FCC, 2002). Accordingto the National Cable and Telecommu-nications Association (NCTA), the big-gest growth came between 1997 and1999 when 111 national program ser- vices were launched (NCTA, 2003). Thisgrowth translated into more channelavailability to consumers. According toNielsen Media Research, channel avail-ability for the typical American home(cable and non cable combined) surgedfrom 33.2 channels in 1990 to 102channels in 2002 (Nielsen Report2003). The primary catalyst for thisimpressive growth has been attributedto the 1996 Telecommunications Actthat deregulated competition and fos-tered the development of digital pro-gram tiers. Since 2000, the number of digital subscribers has increased by 100percent (Carlin, 2002).Coinciding with this onslaught of newprogram competition, the 1990s sawthe broadcast networks lose primetime audiences at an alarming rate,culminating in the 1998-99 season when for the first time cable/satelliteprogramming achieved prime timeparity with broadcast networks. Ac-cording to the Cable Advertising Bu-reau (CAB). This spurred the growth of cable advertising (CAB, 2003). Sincethat benchmark season, broadcast rat-ings have continued to slide.In summary, the 1990’s were an age of great competition across all electronicmedia. Broadcast television was threat-ened by growing cable penetration andcable was threatened by growing DBSpenetration and looming in the nearfuture was Internet distribution with wealthy telephone companies gettinginto the electronic marketplace.Spurred on by the often-touted prom-ise of a 500-channel cable system,multi-channel programmers devel-oped a greatly expanded programlineup to answer the call (Burgi, 1996). Audience Disposition  A primary assumption of inheritanceeffects studies has been that there aresignificant numbers of passive or un-committed viewers who are not moti- vated to change channels (Webster,Phalen and Lichty, 2000). The result is what some researchers call tuning in-ertia, whereby the audience disposi-tion is to remain on the same channelunless there is a sufficient externalforce that alters the mindless momen-tum (Cooper, 1996). This is not a newconcept. Rubin (1984) maintained thatthe simple act of watching television,regardless of the specific content,could become a daily ritualistic behav-ior. Some industry observers havecoined the phrase “glow and flow”, re-ferring to the idea that programs are of secondary importance as long as some-thing fills the screen (Head, Spann andMcGregor, 2001). Furthermore, it is nosecret that mere habit is a powerfulforce that often supercedes other moti- vations for seeking alternative programcontent (Rosenstein, 1997). None-the-less, the fact that people knowingly sub-scribe to multi-channel services impliesa heightened awareness and motivationto investigate these viewing options. Interms of published studies, the notionof heightened awareness resulting inmore deliberate channel changing isperplexing. For instance, Heeter (1985)found that channel changing was a signof greater selectivity and reevaluationof programs. However, Perse (1990) con-cluded that channel changing reflectedless attentive use of television.It is certainly plausible to presume thatprime time audiences have changedover time. Census records reveal thatthe population is aging and perhaps,this trend might signify other changesin audience dispositions. Additionally,for years academics and practitionershave speculated whether cable audi-ences are somehow different thanbroadcast audiences. In particular,there has been a running debate overpossible differences in “attentiveness”and channel surfing behavior. The Tele- vision Bureau of Advertising (TVB, 2003)and the Cable Advertising Bureau (CAB,2003) have commissioned audiencestudies that have yielded completelycontradictory results. A second look at InheritanceEffects Given the above-mentioned unprec-edented changes in American televisionduring the past decade, including butnot limited to (a) a nearly four-fold in-crease in the number of program op-tions available to audiences (b) a signifi-cant drop in broadcast networkaudience ratings and (c) a substantial in-crease in viewing via subscription-basedbusiness models, the researchers be-lieved there was sufficient circumstan-  288 © 2003 –  JMM – The International Journal on Media Management    – Vol. 5 – No. IV  w w w.m e d  i     a   j     o ur n a l    . or   g  tial evidence that lead-in probably haslost some of its punch. That is, insteadof passive ritualistic viewing behavior,audiences can also take part in whatRubin (1987) calls instrumental behav-ior, characterized by viewing that isplanned and attentive. With audiencessupposedly becoming more discrimi-nating, the researchers offered a work-ing research hypothesis:H1:Inheritance effects were not asstrong in 2002 as they were in 1992. Methodology For this study, “inheritance effects” were operationalized as the ability of aprogram to retain audience ratingsfrom the program scheduled immedi-ately prior. The sample frame wasprime-time network ratings as reportedby Broadcasting and Cable and Elec-tronic Media magazines throughout1992 and 2002.Coders selected ABC, CBS, NBC and Foxprograms where ratings were availablefor the program immediately prior. Ef-fectively, the first program was skippedand coding started with the secondprogram of the evening. For each se-lected program, coders recorded (a) tar-get program share, and (b) prior pro-gram share. Coders attempted to use allfifty-two weeks of data. However, rat-ings data were not available for seven weeks in 2002. These weeks wereskipped – making the data set for 1992somewhat larger. Data sets for each year were kept separate until the analy-sis began. At that time, a dummy vari-able for year was added. Audienceshares rather than ratings were selectedas the unit of analysis because sharesare a function of HUT (Homes UsingTelevision) levels at a specific time andtherefore, offer a more standardizedmeasure of program-to-program perfor-mance over time.Typically, prior studies have looked atinheritance in context of several other variables. Given overwhelming supportfor inheritance in prior studies, thisstudy simply compares the inheritanceeffect in one time period to the sameeffect in another time period. Priorstudies looked at the correlation(Pearson’s r) between past and targetprogram rating. More advanced studies went on to use a regression or covari-ant analysis to compare inheritance toother possible predictors of audiencesize. This study compared the correla-tion between past and current pro-grams in the two time periods. A regres-sion analysis was used to look for adifference in the predictive value of theinheritance effect.The challenge was to look for signifi-cant differences between two regres-sion lines. Gujarati (1988 and 1970) rec-ommends a dummy variable approach where observations from both regres-sions (1992 and 2002) are pooled into asingle regression. Y  i = α + α 2 D i + α 1 X 1 + α 2 (D i X i ) + ? I The above equation starts with the stan-dard regression equation including adependant variable (Yi), the indepen-dent variable slope ( α 1 X 1 ), intercept ( α 1 ),and error term (u i ). The second regres-sion line was tested with the additional variables α 2 D i for the intercept and α 2 (D i X i ) for the slope — where Di was adummy variable. In this case, “year” isentered as a dummy variable (D i ) with1992 = 0 and 2002 = 1. The second termDi Xi is computed by multiplying thedummy by the dependent variable. If the measures for this second line aresignificant, then the two regressionlines are significantly different and thetwo lines can be determined from thefinal equation. If the measures are notsignificant, then the null hypothesis(no significant difference) can be ac-cepted and one regression line exists.The advantage of this method is thatboth regression lines can be computedfrom the equation (discussed below). Figure1: Program Share Comparison Between 1992 and 2002        P     e     r     c     e     n      t 20021992 1 5 8 11 14 17 20 23 26 29 32 35 39 4302468101214 Rating    © 2003 –  JMM – The International Journal on Media Management    – Vol. 5 – No. IV 289     w     w     w  .    m    e      d      i    a      j      o     u     r    n    a      l .    o     r    g   There was one additional challenge tothe project before continuing with theanalysis. As described in the literaturereview, there have been some dramaticchanges in the television market andthese changes were reflected in the dataset. The average program share in 1992 was 18.0 compared to 10.9 in 2002. An ANOVA was performed to confirm thatthe two data sets were significantly dif-ferent (F = 2133.2, p > 0.001). In FigureOne below you can clearly see the dif-ference between the two histograms.Not only was the mode clearly shiftedbut also the curve for 2002 was moreskewed than in 1992. Left uncorrected,the regression may show significancenot because of a difference in inherit-ance but because of other differencesbetween the years.In order to compare effectively the two years, the share values were standard-ized for each year. Standardization isthe process of converting data to thesame scale by subtracting the samplemean and dividing by the standard de- viation (Malhotra, 1993). Standardiza-tion does not change the correlationbetween variables but simply makes themean equal to zero and the standarddeviation equal to one. In this study,“share” and “previous share” were stan-dardized by year. Once each data set(1992 and 2002), were standardized theanalysis could proceed. Results The data collection resulted in a verylarge data set. As summarized in TableOne, data were collected from 3050 pro-grams in 1992 and 2541 in 2002. There was a shift of about seven share pointsdifference between 1992 and 2002. Notonly did overall shares drop from 18 to10.9 but also the maximum and mini-mum shares dropped about the sameamount. The correlation between targetand previous program (Pearson’s r forshare) for 1992 was 0.618 (2-tailed sig-nificance < 0.001) and in 2002 was 0.664(2-tailed significance < 0.001). Walker(1988) looked at similar data (sweeps Table 1: Descriptive Statistics.  weeks data only) in four years between1976 and 1985. He found a correlationof 0.69 (p ›0.001). This first level of theanalysis shows a strong and similar ef-fect of inheritance despite a drop inoverall share. A single regression analysis was suffi-cient to test the hypothesis. Table Twodisplays the result of the regressionanalysis. The overall regression equa-tion had a reasonably strong adjustedR-square of 0.409 and the F (1289.7) was highly significant (> 0.001). Nowlooking at the individual variables inthe equation, all variables in the equa-tion were significant. The constant (in-tercept) and “previous program share” were both highly significant (> 0.001).The “dummy variable for year” and the“dummy times previous programshare” were both acceptably signifi-cant at the 0.05 level. A likely level of autocorrelation between these vari-ables probably reduced some of the sig-nificance. The first level of analysis was that the regression equation sup-ports the importance of inheritanceeffect and a change in inheritance ef-fect from 1992 to 2002. As a result, thenull hypothesis (no effect) was rejectedbut that is not the end of the story.Figure Two graphically displays thepredicted regression lines based on raw(not standardized) data. The adjusted R-square for raw data regression in 1992 was 0.382 and in 2002 was 0.441 (inter-cept and slope significant > 0.001). Thelines seem similar but what was impor-tant was that the slope of the line for2002 was greater than 1992. This effectis exactly opposite of what was pre-dicted by the operational hypothesis. If the operational hypothesis was sup-ported, the line for 2002 would notcross the line for 1992 and would havea gentler slope. This means that thedata not only fails to support the nullbut also the operational hypothesis.Turning to the interpretation of theregression line summarized in TableTwo. The regression equation predictsthe following. Y = 3.82 – 0.46 D i + 0.62 X i + 0.05 D i X i The resulting regression equations per year would then be:1992Y= 3.82 + 0.62 X2002Y= (3.82 – 0.46) + (0.62 + 0.05)X= 3.36 + 0.67 X Again, the increase is statistically signifi-cant although it is modest. What wasimportant was the direction of thechange. At the very least, this data sup-ports the conclusion that inheritanceeffect is holding its own and quite possi-bly becoming more important. Whilethe increase was modest, it was oppositeto what was predicted from the litera-ture review and the research design. Thefinal analysis of this study must be thatinheritance has not lost its punch. How-ever, it is premature to say that inherit-ance has truly become more powerful. Discussion Based on the results of this study, onecan conclude that despite a decade of plummeting ratings and ever-increas-ing competition from other media, thepower of lead-in among the four ma- Year 19922002 N 30502541 Minimum 51 Maximum 5042 Mean 18.010.9ShareShare Std. Deviation 5.94.6
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