the impact of pre- and post-launch publicity and advertising on new product sales

Intern. J. of Research in Marketing xxx (2015) xxx–xxx

IJRM-01081; No of Pages 10

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Intern. J. of Research in Marketing

j ourna l homepage: www.e lsev ie r .com/ locate / i j resmar

The impact of pre- and post-launch publicity and advertising on new product sales

Alexa B. Burmester a, Jan U. Becker b,⁎, Harald J. van Heerde c, Michel Clement a

a University of Hamburg, Germanyb Kühne Logistics University, Germanyc Massey University, New Zealand | Tilburg University, The Netherlands

⁎ Corresponding author. Tel.: +49 40 328707 221; fax:E-mail addresses: [email protected] (

[email protected] (J.U. Becker), h.vanheerde@[email protected] (M. Clement).

http://dx.doi.org/10.1016/j.ijresmar.2015.05.0050167-8116/© 2015 Elsevier B.V. All rights reserved.

Please cite this article as: Burmester, A.B., etResearch in Marketing (2015), http://dx.doi.o

a b s t r a c t
a r t i c l e i n f o
Article history:First received on August 4, 2014 and was underreview for 8 monthsAvailable online xxxx

Area Editor: Tammo H.A. Bijmolt

Keywords:PublicityAdvertisingLongitudinal analysisBehavioral data

When companies launch new products, they need to understand the impact of publicity and advertising on sales.What is their relative effectiveness? Do they strengthen each other (have a positive interaction effect) or weakeneach other (have a negative interaction effect)? Further, does the timing of these activities (before or afterlaunch) affect their impact on sales? This paper develops hypotheses regarding the elasticities of pre- andpost-launch publicity and advertising on sales. The hypotheses are tested on a large-scale empirical data setthat tracks sales, publicity, and advertising for 3336 video games across 52 weeks covering the pre- and post-launch phases. The results demonstrate that pre-launch publicity is more effective than pre-launch advertisingbut that the reverse is true post-launch. Surprisingly, the analysis reveals a negative interaction effect betweenpre-launch advertising and publicity, which means that publicity becomes less effective when it is accompaniedby higher levels of advertising for the same product. Simulations indicate that companies can gain most sales byfocusing on publicity pre-launch, and that there is little benefit from increasing publicity and advertising duringthe same phase, which is consistent with negative (pre-launch) and zero (post-launch) interaction effects.

© 2015 Elsevier B.V. All rights reserved.

1. Introduction

When companies launch new products, they need to understandthe impact of publicity and advertising on sales. Publicity is defined as“editorial space in media for promotion purposes that does not identifythe message sponsor” (Eisend & Küster, 2011, p. 906). In contrast, thesponsor is clearly identified in advertising. As many managers considerpublicity to bemore advantageous than advertising, companies increas-ingly prefer to stimulate publicity instead of advertising (Ries & Ries,2002). The rationale is clear: many consumers feel overwhelmed by ad-vertisingmessages (specifically, 65% of consumers according to Porter &Golan, 2006) and therefore choose to avoid traditional marketing com-munication (Hann, Hui, Lee, & Png, 2008). Publicity in the form of edito-rial messages offers consumers seemingly objective and unbiasedcontent that has been endorsed by a third party. In contrast, advertisingproduces communications that are obviously paid for and endorsed by acompany. As a result, many consumers regard these communicationswith skepticism and consider them to be less credible (Kotler & Keller,2011).

There is a long tradition of research on the effectiveness of publicityand advertising. Since Preston and Scharbach’s (1971) seminal paper onthis topic, several studies have investigated the trade-offs between

+49 40 328707 209.A.B. Burmester),y.ac.nz (H.J. van Heerde),

al., The impact of pre- and porg/10.1016/j.ijresmar.2015.05

publicity and advertising and provided valuable insights into the effectsof publicity and advertising on consumers. Studies of the effects onconsumer attitude (Wang, 2006), credibility (e.g., Straughan, Bleske, &Zhao, 1996), attention (e.g., Lord & Putrevu, 1998), and cognitiveresponse (e.g., Chaiken &Maheswaran, 1994) help elucidate the funda-mental psychological processes that occur as a result of both types ofcommunication. For an overview of previous studies in this area, pleasesee the online appendix. Despite the progress that the literature hasmade regarding the effects of advertising and publicity on mindsetmetrics such as consumer attitudes, no study to date has analyzedtheir effects on sales.1 This leaves a number of key issues unresolved:Is there a difference between pre- and post-launch advertising andpublicity effectiveness (in terms of elasticities)? What is the relative ef-fectiveness of advertising and publicity on sales? Do they strengtheneach other (have a positive interaction effect) or weaken each other(have a negative interaction effect)?

The objective of this study is to extend the knowledge on the effectsof publicity and advertising in terms of the dependent measure consid-ered (sales), in terms of their timing (pre- versus post-launch) and interms of their combined use (interaction effects). We use new productsales as the dependent variable, which is a highly managerially relevantperformance metric. Thus, this study complements extant studies that

1 An exception is the study byCleeren, VanHeerde, andDekimpe (2013). However, thatpaper examines the effect of publicity and advertising on sales in the specific context ofovercoming a product-harm crisis, in which the publicity is predominantly negative intone.

st-launch publicity and advertising on new product sales, Intern. J. of.005

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2 A.B. Burmester et al. / Intern. J. of Research in Marketing xxx (2015) xxx–xxx

have used surveys or experiments with mindset metrics as the depen-dent variable.

The distinction between pre- and post-launch effects is especiallyrelevant for industries with short life cycles in which many differentnew products are frequently introduced. New products in these indus-tries (e.g., movies, video games, books, music or new technological de-vices such as the iPad) often experience a peak in demand that occursimmediately after the launch of the product. This peak is then followedby a strongly declining sales curve (e.g., Ainslie, Drèze, & Zufryden,2005; Beck, 2007; Jedidi et al., 1998; Tellis, Stremersch, & Yin, 2003).The specific diffusion pattern is the result of a pre-release “shadowdiffusion” (Goldenberg, Libai, Muller, & Moldovan, 2007). This shadowdiffusion is initiated by pre-launch awareness of an innovation, whichis triggered by advertising and/or publicity activities before the productenters themarket. Thus, consumers may decide to adopt a new producteven before it is available, but have to wait until the product is intro-duced to the market (Muller, Perez, & Mahajan, 2009). For productswith these types of diffusion patterns, it is particularly relevant to effec-tively utilize pre-launch advertising and publicity to drive overall de-mand, which is substantially influenced by the product’s performanceduring its launch period (Elberse & Eliashberg, 2003).

In the empirical analysis,we use a unique panel data set on publicity,advertising, and sales for 3336 products in the video games market. Thedata for each product cover a total timespan of 52 weeks, including the26weeks prior to launch and 26weeks after launch. The data allowus toanalyze not only the relative effect of publicity and advertising on salesfor products that are already available on the market but also the effectof pre-launch publicity and advertising on sales during the launchweek.These first-week sales are especially relevant for entertainment goodssuch as video games (e.g., Ainslie et al., 2005).

We use elasticities (the percent change in sales due to a 1% change inadvertising or publicity) to compare the effects of publicity and adver-tising. The benefit of elasticities is that they are unitless and thereforecomparable.We use a consistentmeasure for both advertising and pub-licity (i.e., the number of pages in magazines weighted by circulation).Furthermore, the mean levels of the advertising and publicity variablesare very similar in the empirical application, implying that a 1% changerepresents a comparable absolute change. Because communicationeffects are often not merely instantaneous but may also persist, weallow for dynamic effects, where current publicity and advertisingmay affect future sales. We use an econometric model that controlsfor endogeneity.

In summary, this study contributes to the literature in three ways.First, we provide insights into the relative effectiveness (elasticities) ofpublicity and advertising on a managerially relevant measure of suc-cess: sales. Second, we extend previous knowledge by distinguishingbetween the pre- and post-launch elasticities of publicity and advertis-ing. In the empirical analysis, we observe a substantially stronger effecton first-week sales for pre-launch phase publicity than for pre-launchphase advertising. This result is reversed in the post-launch phase:after the product launch, advertising is more effective than publicity.Third, we account for the effects of a combined-format strategy. Inter-estingly, we observe a negative interaction effect in the pre-launchphase (and an insignificant interaction post-launch), which meansthat publicity becomes less effective when it is accompanied by higherlevels of advertising for the same product. The results allow us to pro-vide recommendations regarding the focus of marketing campaigns(advertising or publicity) before and after the launch of products.

2. Theory and hypothesis development

Although the dynamic effects of advertising are well documented(e.g., Bruce, Foutz, & Kolsarici, 2012; Frison, Dekimpe, Croux, & DeMaeyer, 2014), little knowledge exists on the dynamic effects of public-ity on sales, particularly with respect to the relative effectiveness ofthese communication formats during the pre-launch and post-launch

Please cite this article as: Burmester, A.B., et al., The impact of pre- and poResearch in Marketing (2015), http://dx.doi.org/10.1016/j.ijresmar.2015.05

phases. In both phases, consumers lack reliable information on theproduct. This is particularly the case for entertainment products andother hedonic products, for which consumers will only be able to trulyjudge their enjoyment of the product in question after they have con-sumed it (Hirschman & Holbrook, 1982).

Considering this high consumer uncertainty, which demands credi-ble sources of information, we expect to observe differences betweenadvertising and publicity. According to attribution theory, differentsources may lead recipients of messages to attribute (product-related)information to external factors (e.g., Kelley & Michela, 1980). Becauseconsumers might attribute advertising claims to a profit motive (an ex-ternal factor) rather than the provision of neutral product information(Lord& Putrevu, 1998), the credibility of advertising is negatively affect-ed. Conversely, consumers are less likely to question themotives of pub-licity, and its source credibility would therefore remain intact. Becausesource credibility may serve as a peripheral cue (in the elaboration like-lihoodmodel; Miniard, Dickson, & Lord, 1988), the perceived credibilityof the sourcewould be transferred to themessage, thereby positively af-fecting the consumers’ attitudes towards the product (Lord & Putrevu,1993). Consequently, comparedwith advertising, publicity is associatedwith higher levels of expertness and independence and with lowerlevels of intention to persuade (Hallahan, 1999). Thus, we argue thatpublicity has a greater uncertainty-reducing potential and is perceivedto be more credible than advertising. Therefore, publicity is likely tobemore effective than advertising in reducing the pre-consumption un-certainty regarding a new product, especially during the pre-launchphase, when the product is not yet in the market. This reasoning leadsto the following hypothesis:

H1. The elasticity of pre-launch publicity for first-week sales is largerthan the elasticity of pre-launch advertising.

However, after a product has been launched, consumers can basetheir evaluations of the product on others’ experienceswith the productas it becomes increasingly well-known in the marketplace (Chew,Slater, & Kelly, 1995). Thus, in addition to advertising and publicity, athird source of information, word-of-mouth, becomes available. Giventhe higher source credibility of perceptions of individuals relative tomore distant sources such as advertising and publicity (Hovland &Weiss, 1951),we expect that the effectiveness of advertising and public-ity is lower in the post-launch stage than in the pre-launch stage:

H2. The sales elasticities of publicity and advertising are lower in thepost-launch phase than in the pre-launch phase.

An important question concerns whether the presence of word-of-mouth in the post-launch stage has a more negative effect on the effec-tiveness of publicity or on the effectiveness of advertising. We arguethat it reduces the effectiveness of publicity to a greater extent.Whereasconsumers perceive advertising as a commercial source of information,word-of-mouth and publicity are both categorized as non-commercialsources of information (Hennessey & Anderson, 1990). In that sense,publicity andword-of-mouth are understood as closer information sub-stitutes than are advertising and word-of-mouth. Based on the similarityprinciple (Tversky, 1972), when word-of-mouth from non-commercialsources close to the consumer becomes available, it substitutes for the in-formation previously provided by the similar (non-commercial) sourceof publicity to a greater extent than the more dissimilar (commercial)source of advertising. Thus,we expect that the decline in the effectivenessof publicity is relatively strong in the post-launch phase:

H3. Moving from the pre-launch phase to the post-launch phase, thedecrease in the publicity elasticity is stronger than the decrease in theadvertising elasticity.

We also need to understandwhether the simultaneous use of adver-tising and publicity leads to synergistic or antagonistic effects. Becausetheoretical rationales exist for both directions of effects, we develop



3A.B. Burmester et al. / Intern. J. of Research in Marketing xxx (2015) xxx–xxx

competing hypotheses (see Armstrong, Brodie, & Parsons, 2001). In theevent that the two formats convey information that is largely comple-mentary, there will be a synergistic effect of publicity and advertising.Arguments along this line can also be drawn from the encoding variabil-ity theory (Lord & Putrevu, 1998). According to this theory, consumerswho encounter information in more than one context have multiple re-trieval cues available, leading to stronger, clearer, and more accessibleinformation (Stammerjohan,Wood, Chang, & Thorson, 2005). Such syn-ergies may, of course, evolve from submitting similar informationthrough different formats because one of these formats can producegreater reach, whereas the other format can produce higher communi-cation frequencies (Vakratsas & Ambler, 1999). However, it is not onlythe repetition but also the variation of themessage content that, accord-ing to the repetition-variation theory, affects the consumer (Schumann,Petty, & Clemons, 1990). As a consequence of the exposure to bothformats, the likelihood that the information will be recalled correctlyis higher, andwe thus can hypothesize the following for their combineduse:

H4a. The interaction effect (elasticity) between publicity and advertis-ing on sales is positive.

Conversely, the combination of advertising and publicity might alsohave an antagonistic effect, leading to a decreasing efficiency of bothformats. This effect might occur if the frequent exposure to informationof both formats negatively affects their perceived source credibility. Forinstance, consumers might discount the credibility of publicity that isreceived in temporal proximity to an advertisement (e.g., publicityand an advertisement within the same issue of a magazine). In thatcase, consumersmight question the credibility of the publicity if they at-tribute the same profit motive to the publicity (attribution theory;e.g., Kelley & Michela, 1980). As a consequence, the information pro-cessing that normally favors publicity with respect to source credibility(Lord & Putrevu, 1993) would not differ from advertising. Following thetheory of reactance (Brehm, 1966), an even more negative reaction tocombination could emerge. For instance, consumers could perceive anextensive, simultaneously run publicity and advertising campaign as adeliberate effort to persuade them and, hence, start regarding the infor-mation sources negatively. Equivalently, higher levels of publicity mayreduce the news value and therefore the effectiveness of advertising.Moreover, the marginal effect on demand could decrease at high levelsof communication for either advertising or publicity due to a saturationeffect. Considering these lines of thought, we can also hypothesize:

H4b. The interaction effect (elasticity) between publicity and advertis-ing on sales is negative.

How does the interaction effect change when moving from thepre-launch to the post-launch phase? We expect that the availabilityof word-of-mouth as an information source during the post-launchphasewill not only reduce the effectiveness of publicity and advertising(H2), but it will also attenuate the interaction effect. That is, once word-of-mouth becomes available, consumers devote relatively less attentionto publicity and advertising. This weakens the reasons for synergisticeffects between these sources (e.g., less complementarity due to infor-mation substitution provided by word-of-mouth) but also the reasonsfor antagonistic effects (e.g., less consumer attention devoted to deliber-ate attempts to persuade them). Thus, we argue that:

H5. The interaction effect between publicity and advertising on sales isstronger in magnitude during the pre-launch phase than it is during thepost-launch phase.

3. Data and measures

This study uses longitudinal data on 3336 products releasedbetween 2004 and 2009 in the video games market to investigate the


dynamic effects of pre- and post-launch publicity and advertising. Themarket for video games is particularly suitable for analyzing the relativeperformance of these two communications formats for three reasons.First, the consumption of video games involves multisensory, fantasy,and emotive aspects; therefore, video games provide high levels of he-donic benefits (Hirschman & Holbrook, 1982). Such hedonic marketsfor entertainment goods typically demonstrate strongmarket dynamicsin whichmany different new products are frequently introduced. Videogames also possess relatively short life cycles. Consequently, themarketfor video games not only provides the opportunity to analyze a largenumber of new releases but also permits tracking the performance ofthese products over the course of their entire life cycles.

Second, classical diffusion theory argues that adoption by experts(’innovators’) triggers imitators to follow, resulting in a bell-shaped dif-fusion pattern (e.g., Bass, 1969). In contrast, studies for entertainmentgoods (e.g., movies, books, music) have demonstrated that their diffu-sion curve follows an exponentially declining pattern, as explained inthe introduction. For products with these types of short-lived diffusionpatterns, it is particularly relevant to effectively utilize pre-launch ad-vertising and publicity to drive the product’s performance during itslaunch week (Elberse & Eliashberg, 2003). This makes video games asuitable industry to study these effects.

Third, with a sales volume of USD 93 billion in 2013, the market forvideo games is one of the largest entertainment industry markets in theworld (Entertainment Software Association, 2014). The video gamemarket is also one of the fastest growing entertainment industrymarkets globally (the growth rate from 2009 to 2012 in the U.S. was10%; Entertainment Software Association, 2014). This market is highlycompetitive and dynamic; in particular, the industry releases an averageof more than 1000 new video games per year (see www.vgreleases.com). Thus, we consider data that not only include information on theamount of publicity and advertising regarding a product that occursafter the product’s launch and its subsequent sale but also allow us toconsider the pre-launch marketing activities in both communicationformats.

The study’s sample consists of all PC and console games(e.g., PlayStation or Xbox) that were released in Germany between2004 and 2009 and were listed at least once in the Top 200 weeklysales charts. This approach results in a sample of 3336 games. We col-lected data for these games for the 26 weeks before and the 26 weeksafter the launch of each game. In total, we have 86,620 observationsfor model calibration: 3336 games times 26 weeks of sales per gamefor nearly all of the games. The first week’s sales range from 0 to382,482 units, indicating that the sample captures both blockbustersand poor-selling games.

We use the weekly sales of each examined game over the course ofthe 26 weeks after its launch as the key success metric for this study.We observe that games sell on average 2462 units (SD: 10,822) in thefirst week, amounting to average revenues of EUR 112,036 (detailed in-formation regarding the variables is displayed in Table 1). To capturethe post-launch impact of publicity and advertising, we use the weeklysales from week 2 to week 26. During this time, the average weeklysales per game are 513 units (SD: 1255). The games in the sample ex-hibit the typical steeply declining diffusion pattern of sales that hasbeen found for comparable media products, such as movies (Ainslieet al., 2005; Sawhney & Eliashberg, 1996), music (Moe & Fader, 2002),and books (Beck, 2007).

TheGermanmarket for video games is well-suited for the analysis ofpublicity and advertising because the majority of marketing coveragefor these games appears in special interest magazines. Thus, we wereable to capture the publicity and print advertising efforts of game pro-ducers by examining the 36 relevant gaming magazines. The circula-tions of these magazines range from 10,000 to 678,000 copies perissue. This range demonstrates the broadness of the magazine marketcaptured in the analysis. We account for differences in circulationfigures between the magazines in our measures, as we explain below.


http://www.vgreleases.com

http://www.vgreleases.com


Table 1Operationalization of variables and descriptive statistics.

Variable Operationalization Mean(Median)

SD

Dependent variables (N = 86,620; number of games = 3336)Sales (t = 1) a Sales of week 1 in number of sold units 2462.08 (480.5) 10,821.84Sales (t = 2–26) a Sales of weeks 2–26 in number of sold units 512.74 (226) 1254.77

Focal variables pre-launch (from 26 weeks before launch until launch week; N = 90,072; number of games = 3336)Pre-launch publicity b Weekly publicity in number of pages per magazine weighted by circulation of magazine for pre-launch

phase (for 36 magazines)0.45 (0) 4.50

Pre-launch advertising b Weekly advertising in number of pages per magazine weighted by circulation of magazine for pre-launchphase (for 36 magazines)

0.49 (0) 5.46

Focal variables post-launch (from 2nd till 26th week after launch; N = 83,284; number of games = 3336)Post-launch publicity b Weekly publicity in number of pages per magazine weighted by circulation of magazine after launch

(for 36 magazines)0.55 (0) 9.10

Post-launch advertising b Weekly advertising in number of pages per magazine weighted by circulation of magazine afterlaunch (for 36 magazines)

0.41 (0) 4.29

Note: Data sources: a Media Control, b Gamepress. The reason why the publicity and advertising variables show median values of zero is that we look at a rather long 52-week window(26 weeks pre-launch, 26 weeks post-launch). In many of these weeks there is no publicity or advertising as these activities are mostly concentrated in the weeks directly surroundingthe introduction.


The measures for advertising and publicity were obtained from acommercial service provider. Multiple coders determined whether thepages of the magazines contained advertising or publicity and howmany pages (including fraction of pages) the formats occupied permag-azine. The publicity measure captures neutral editorial content such asnews, tips, tricks & cheats, and game convention reports. Triggering,supporting, and enforcing such messages are generally the activities ofinternal or external public relation units. Existing categories in themag-azines (e.g., reviews, previews, news, etc.)made themeasurement rath-er straightforward for the coders.2

Specifically, we capture publicity as the fraction of each page thatwas devoted to an editorialmention of each game in the sample. For ex-ample, a value of 0.5 was used if editorial information concerning agame occupied half of a page in amagazine. The fractionswere summedfor each game for each magazine and week and weighted by the circu-lation of themagazine (in 10,000) to account for the different impact ofeach examined magazine. Finally, we added each game’s publicityvalues across all of the assessed magazines to obtain a weekly scorefor publicity (an illustrative calculation is presented in the AppendixA). During the pre-launch phase, the games had an average publicityscore of 0.45 (SD: 4.50), which represents an average weekly coverageof nearly one-half of a page in amagazinewith a circulation 10,000 cop-ies. The weekly average (post-launch) publicity score is 0.55 (SD: 9.10).

To measure advertising, we used an identical approach to thatemployed for publicity: weighted weekly fractions of magazines devot-ed to advertising for each game. We observe an average pre-launchadvertising score of 0.49 (SD: 5.46). For post-launch advertising, the av-erage advertising score per week is 0.41 (SD: 4.29). The average scoresfor publicity and advertising are within a comparable range, whichmeans that their elasticities, which is the sales impact of a 1% change,represent similar absolute changes.

A correlation analysis for a subsample of the examined games indi-cates that print advertising efforts are highly correlatedwith companies’overall marketing spending (r = 0.78,p b 0.05).3 Thus, in our analyses,the use of print advertising serves as a good proxy for the other adver-tising efforts of game producers, such as online marketing (industry

2 The publicitymeasure excludes reviews or previews for the games to exclude the crit-ic effect (Boatwright et al., 2007). However, if we include previews and reviews of gamesin our publicity variable, we obtain similar results (see Table 3). This similarity reflects thefact that these two variables are highly correlated, particularly with respect to pre-launchactivities (r = 0.71; p b 0.01); therefore, we decided to exclude reviews or previews.

3 We conducted the analysis for the gamesmarketed by amajor company.We correlat-ed, across its N = 98 games, the annual spending on print advertising per game with theannual marketing spend per game. Thomson Media Control provided the spendinginformation.


experts also confirmed this conclusion). Overall marketing spending isnot available on aweekly basis, which iswhywe opt for the print adver-tising measure.

4. Modeling approach

4.1. Model challenges

Ourmodel specification considers a number ofmethodological chal-lenges. First, we account for the carry-over effects of advertisingand publicity by introducing a stock formulation for both variables.Wemodel the respective stock variables and estimate the carry over ef-fects via grid search procedures (e.g., Dinner, van Heerde, & Neslin,2014).

Second, because we model all weeks simultaneously, we need toseparate the effects of pre-launch frompost-launch publicity and adver-tising. Therefore, we introduce a dummy variable that capturesthe launch week (Launchit = 1 in week 1, 0 otherwise). We model theinteraction effects between this launch dummy and our advertisingand publicity variables for the launch week dummy (Launchit) and forsubsequent weeks (1 – Launchit).

Third, we need to control for the endogeneity of publicity and adver-tising. Our model addresses cross-sectional endogeneity by utilizingfixed effects per game. Doing so controls for the likely scenario that“bigger” games receive more publicity and advertising. The per-gamedummies remove any correlation generated by systematic differencesbetween games in levels in the dependent and independent variables.In other words, our estimates are solely based on longitudinal variationwithin games, similar to how the Scan*Promodel operates (VanHeerde,Leeflang, & Wittink, 2002). Further, to correct for other potentialendogeneity issues, we rely on an instrument-freemethod using Gauss-ian copulas, which was introduced by Park and Gupta (2012). Copulasconstruct the joint distribution function, describing the dependence be-tween random variables (i.e., the “endogenous” component of a regres-sor) and the error term of the focal equation. We use the controlfunction approach and include regressors based on the Gaussian cop-ulas (Park & Gupta, 2012). In ourmodel, these control variables accountfor the effect that the timing of publicity and advertising might not berandom. Compared with 2SLS or 3SLS, the Gaussian copulas approachhas the benefit that we do not need to identify instrumental variables.Finding instrumental variables that are valid and strong is notoriouslydifficult, especially in a setting with numerous different manufacturersas in our case.

Fourth, as entertainment media products typically face very shortlife cycles (Ainslie et al., 2005; Sawhney& Eliashberg, 1996),we account



5 We also estimate models in which we interact the stock variables for advertising andpublicity with the lnTime variable to test for additional changes in their effects over the


for the declining diffusion pattern by including the log of Timeit in ourmodel, which measures the number of weeks that have passed sincelaunch.

4.2. Model specification

We adopt a log–log formulation to directly obtain elasticities and ac-count for non-linear effects (Hanssens, Parsons, & Schultz, 2001, p. 102).Eq. (1) presents our log-logmodel for the sales Salesit of game i in weekt, for all i (=1,.., I) and t (=1,…, T)4:

lnSalesit ¼ β0 þXI−1

i¼1

β0;iDiþβ1Launchit þ β2 lnTimeit

þ β3Pre AdStockit þ β4Pre PubStockitþ β5Pre AdStockit � Pre PubStockitþ β6Post AdStockit þ β7Post PubStockitþ β8Post AdStockit � Post PubStockitþ β9C Pre AdStockit þ β10C Pre PubStockitþ β11C Pre AdStockPubStockit þ β12C Post AdStockitþ β13C Post PubStockit þ β14C Post AdStockPubStockit þ uit:

ð1Þ

The dummy variableDi controls for level differences between games(fixed effects) and equals 1 for game i and zero otherwise (i = 1, 2, …I-1 for I games). Launchit is a dummy variable that equals 1 for the firstweek of the game in the market (launch) and zero otherwise. Theterms in Eq. (1) denoted Pre_ are a result of the multiplication ofthe stock variable and the Launchit variable and capture the effects forthe launchweek (week 1 of the new product). For instance, the variablePre_AdStockit in Eq. (1) equals Launchit x AdStockit. To capture the effectsin the post-launch period (weeks 2-26; denoted Post_), all of the stockvariables for the post-launch phase are multiplied by (1–Launchit). Weadopt this specification (instead of the econometrically equivalentapproach of having a main effect and a separate effect for either thepre- or post- launch period) because it directly provides separate esti-mates for the pre- versus post-launch effects.

AdStockit and PubStockit represent stock variables for game i inweek tand are modeled following Koyck (1954):

AdStockit ¼ λ1AdStockit−1 þ ln Adit þ 1ð Þ; ð2Þ

PubStockit ¼ λ2PubStockit−1 þ ln Pubit þ 1ð Þ; ð3Þ

where Adit is the extent of advertising for game i in week t andPubit is the extent of publicity for game i in week t, where both arecirculation-weighted sums of the coverage across gaming magazines,as explained above. To operationalize the stock variable for the firstweek, we use the value of log advertising (log publicity) in that week,which is appropriate because we observe these variables from thebeginning (26weeks before launch). Note thatwhile Eq. (1) only relatesto weeks 1-26 after launch, Eqs. (2) and (3) are based on the full datacapturing 52 weeks (26 weeks before and 26 weeks after launch).

β3 (β4) is the launch-week advertising elasticity for advertising(publicity). For the post-launch phase, the short-term (= same-week)elasticities are β6 for advertising and β7 for publicity, whereas β6/(1–λ1) and β7/(1–λ2) are the long-term elasticities. Please note thatthe long-term elasticities are only defined for the post-launch period(t = 2, …, 26) because in this period, we can assess the cumulativesales effect of a one-time shock in advertising or publicity over multipleweeks. Because the launch period is only one week (t = 1), its long-term elasticity equals its short-term elasticity. The interaction effectparameter (e.g., β5 for the pre-launch interaction between advertisingand publicity) represents the extent to which the elasticity of one for-mat changes when the stock level of the other format changes.

4 To avoid taking the log of zero, we add 1 to all sales values.


Timeit is a count measure that starts at 1 in the release week of thegame, becomes 2 in the second week, and so forth. We include lnTimeitto account for the typical nonlinear decay in sales after the new releaseweek.

4.3. Copula terms to control for endogeneity

Following Park and Gupta (2012), we adopt additional regressors tocontrol for endogeneity. Eqs. (4)–(9) describe the control variables inthe pre- and post-launch phases that are included in Eq. (1):

C Pre AdStockit ¼ Φ−1 H Pre AdStock Pre AdStockitit

� �� ; ð4Þ

C Pre PubStockit ¼ Φ−1 HPre PubStock Pre PubStockitit

� �� ; ð5Þ

C Pre AdStockPubStockit¼ Φ−1 HPre AdStockPubStock Pre AdStockPubStockit

it� ��

; ð6Þ

C Post AdStockit ¼ Φ−1 HPost AdStock Post AdStockitit

� �� ; ð7Þ

C Post PubStockit ¼ Φ−1 HPost PubStock Post PubStockitit

� �� ; ð8Þ

C Post AdStockPubStockit¼ Φ−1 HPost AdStockPubStock Post AdStockPubStockit

it� ��

; ð9Þ

whereΦ–1 is the inverse of the normal cumulative distribution functionand H(∙) represents the empirical distribution of the respective vari-ables. For identification, it is necessary that the pre- and post-launchAdStockit and PubStockit variables are non-normally distributed (Park &Gupta, 2012). The non-normal distribution is confirmed by a Shapiro-Wilk test (Pre_AdStock = 0.77, p b 0.01; Pre_PubStock = 0.58, p b 0.01;Post_AdStock = 0.53, p b 0.01; Post_PubStock = 0.57, p b 0.01).

4.4. Other checks

Wealso assesswhether advertising and publicity have a causal effecton sales, in the Granger sense of causality. Using the panel data Grangercausality test (implemented in Eviews 8), we conclude that, both pub-licity (p b 0.01) and advertising (p b 0.01) cause sales.

Multicollinearity is not a concern because the VIF values of thevariables in Eq. (1) are all 3.54 or less.5 Eq. (1) is estimated using fixedeffects panel estimation and bootstrap standard errors with 200 repeti-tions, as proposed by Park and Gupta (2012). To estimate the carry-overcoefficients λ1 and λ2, we use a bivariate grid searchmaximizingmodelfit, where λ1 and λ2 vary independently from 0, 0.05, 0.10,…, 0.95. Thegrid search yields a weekly carry-over λ1 = 0.75 for advertising andλ2 = 0.90 for publicity. The overall R2 is 0.785 (and within-R2 is0.437), indicating satisfactory model fit. Fig. 1 illustrates model fit bydepicting actual versus predicted log sales for a random selection ofgames.

Several of the Copula terms are significant (Table 2), emphasizingthe importance of controlling for endogeneity (Park & Gupta, 2012).

5. Estimation results

5.1. Hypothesis tests

WeuseModel 1 (without the interaction effects required for H4 andH5) to test H1-H3, because Model 1 allows us to compare the main

product life cycle. However, these additional terms generate excessive multicollinearity(VIFs up to 24.76), and the estimates become less reliable.



Fig. 1. Actual versus predicted sales.


effects directly. (Wedonote that the outcomes for testingH1-H3 are thesame when based on Model 2 with interaction effects; the results areavailable on request). Regarding the pre-launch phase, H1 postulatesthat the effectiveness of publicity on sales outweighs the correspondingeffectiveness of advertising on sales. Model 1 in Table 2 indicates signif-icant and positive elasticities for both pre-launch publicity (β4,1 =0.210; p b 0.01) and pre-launch advertising (β3,1 = 0.118; p b 0.01)on first-week sales. The elasticity of publicity is significantly strongerthan that of advertising (Wald test, χ2 = 11.78, d.f. = 1, p b 0.01),supporting H1.

H2 predicts a decline in elasticities from the pre-launch phase to thepost-launch phase. In line with H2, the elasticity of pre-launch publicity

Table 2Model estimation results.

Mo

Variables Hypothesis Co

Fixed effect dummies for 3336 games Not shown; available on requestPre-launch elasticities

Pre_AdStock β3 H1, H3 0.1Pre_PubStock β4 H1, H3 0.2Pre_AdStock X Pre_PubStock β5 H4, H5

Post-launch elasticitiesPost_AdStock β6 H2, H3 0.0Post_PubStock β7 H2, H3 0.0Post_AdStock X Post_PubStock β8 H5

CovariatesLaunch week (dummy) β1 −Life cycle (ln) β2 −

Copula-based control variablesC_Pre_AdStock β9 0.0C_Pre_PubStock β10 0.0C_Pre_AdStockPubStock β11

C_Post_AdStock β12 −C_Post_PubStock β13 −C_Post_AdStockPubStock β14

Intercept β0 7.0Overall R2 0.7Within-R2 0.4Wald chi2 (d.f. = 14) 88Observations 86Groups 33

Note: * p b 0.1; **p b 0.05; ***p b 0.01; ns not significant. For the analysis, we used fixed effects


(β4,1=0.210) is significantly stronger than the elasticity of post-launchpublicity,β7,1=0.032 (Wald test,χ2=174.03, d.f.=1, p b 0.01).More-over, the elasticity of pre-launch advertising (β3,1 = 0.118) is strongerthan that of post-launch advertising, β6,1 = 0.082 (Wald test, χ2 =5.81, d.f. = 1, p b 0.05), again confirming H2.

H3 posits that when moving from the pre-launch phase to the post-launch phase, the decrease in elasticity is stronger for publicity than foradvertising. The Wald test for the difference between (i) the change inthe pre- versus post-launch publicity elasticity and (ii) the change inthe pre-launch versus post-launch advertising elasticity (χ2 = 32.34,d.f. = 1, p b 0.01) confirms H3. Empirically, we observe a reversal ofthe relative elasticities in the post-launch phase. Specifically, the post-

del 1 (without interactions) Model 2 (with interactions)

efficient t-Value Coefficient t-Value

18 7.752*** 0.195 9.475***10 14.391*** 0.276 13.449***

−0.028 −7.511***

82 10.410*** 0.078 7.442***32 4.817*** 0.028 3.816***

0.001 0.874ns

1.120 −41.061*** −1.172 −42.107***0.683 −63.273*** −0.684 −69.637***

09 4.013*** 0.007 2.994***07 2.938*** 0.005 1.991**

−0.002 −0.654ns

0.010 −2.363** −0.006 −1.493ns

0.013 −2.103** −0.006 −0.983ns

−0.008 −1.772*36 232.576*** 7.040 234.265***84 0.78535 0.43769.68 (10)*** 9286.02***,620 86,62036 3336

panel estimation and bootstrap standard errors with 200 repetitions.




launch elasticity of advertising (β6,1 = 0.082; p b 0.01) is nearly threetimes larger than the post-launch elasticity of publicity (β7,1 = 0.032;p b 0.01).

As the combination of publicity and advertisingmay have positive ornegative effects, H4a and H4b address their synergistic or antagonisticeffects.Whereas previous experimental research has reported synergiesbetween these communication formats, the results of this study revealthe existence of an antagonistic effect on sales for the pre-launchphase (see Model 2 in Table 2, which is the model with interactions).In this phase, we observe that the interaction effect between publicityand advertising is negative and significant (β5,2= –0.028; p b 0.01). Re-garding the post-launch model, this interaction (β8,2) is not significant(p N .1). For the differences of the interaction between the pre- andpost-launch phases postulated in H5, these results indicate that the an-tagonistic effect of the combined-format strategy is stronger beforelaunch than after launch (Wald test, χ2 = 65.21, d.f. = 1, p b 0.01).Thus, the analyses support H4b (antagonistic interaction) and H5(stronger pre-launch than post-launch interaction).

5.2. Robustness checks

To check the robustness of our findings, we estimate alternative var-iants of the model using the same carryover lambdas for maximumcomparability. First, we estimateModel 3 without using the copula con-trol variables. The results in Table 3 indicate that the coefficients arehighly similar to the coefficients in the original model.

As mentioned in Section 3, we exclude all reviews and previews forthe examined games from our analysis to eliminate the critic effect(Boatwright, Basuroy, & Kamakura, 2007). To determine whether in-cluding reviews and previews affects the results, we estimate a modelwith publicity data that also include reviews and previews. Overall,

Table 3Robustness checks.

Model 3 (without copulascontrols)

Model 4 (includingpreviews & reviews)

Variables Coefficient t-Value Coefficient t-Value

Fixed effect dummies for3336 games

Pre-launch elasticitiesPre_AdStock 0.206 11.027*** 0.128 4.622***Pre_PubStock 0.283 13.282*** 0.149 22.862***Pre_AdStock XPre_PubStock

−0.030 −7.182*** −0.011 −5.247***

Post-launch elasticitiesPost_AdStock 0.074 6.723*** 0.048 3.354***Post_PubStock 0.025 3.525*** 0.033 9.911***Post_AdStock XPost_PubStock

0.001 0.992ns 0.001 0.602ns

CovariatesLaunch week (dummy) −1.164 −42.383*** −1.235 −39.903***Life cycle (ln) −0.684 −72.044*** −0.652 −56.071***

Copula-based controlvariablesC_Pre_AdStock - - 0.005 2.230**C_Pre_PubStock - - 0.006 2.562**C_Pre_AdStockPubStock - - 0.002 1.122ns

C_Post_AdStock - - −0.003 −0.828ns

C_Post_PubStock - - −0.011 −1.569ns

C_Post_AdStockPubStock

- - −0.010 −2.247**

Intercept 7.041 244.791*** 6.900 206.407***Overall R2 0.785 0.789Within-R2 0.436 0.447Wald chi2 (d.f.) 9994.39

(8)***9008.22(14)***

Observations 86,620 86,620Groups 3336 3336

Note: * p b 0.1; **p b 0.05; ***p b 0.01; ns not significant. For the analysis, we used fixed ef-fects panel estimation and bootstrap standard errors with 200 repetitions.


despite somewhat lower coefficients, the results are similar to those ofthe original model (see Model 4, Table 3).

Across the threemodels that include the interaction between public-ity and advertising, we observe a highly consistent pattern (Tables 2 and3). That is, this interaction is significantly negative for pre-launch pub-licity and advertising, whereas it is insignificant in the post-launchphase.

6. Discussion

Despite the economic relevance of publicity and advertising in con-temporary markets, studies employing longitudinal behavioral data toanalyze the relative effectiveness of both types of marketing activitiesare scarce. This study provides insights into how the relative effective-ness of publicity and advertising changes from the pre-launch stage tothe post-launch stage. We use a unique data set that combines marketsales data and information on publicity and advertising campaigns formore than 3300 video games. We investigate the sales elasticities ofboth formats for a total of 52 weeks, which includes the periods priorto and after the launch of each product.

6.1. Research implications

Three implications for academic research can be derived from the re-sults of this study and address a fundamental issue inmarketing theory.Overall, the findings indicate that the decision of whether and when touse publicity or advertising is complex in nature, and companies mustconsider the implications that result from the fact that the effectivenessof these marketing formats varies over the course of product life cycles.

First, the results indicate that rather than a static approach, a time-varying approach based on longitudinal data is necessary to assess theeffectiveness of publicity and advertising efforts. Our study reveals achange in the effects of publicity and advertising before the launch ofa product versus their effects after the launch. This holds not only forthe relative effect of publicity and advertising but also for the combina-tion of both formats. Considering the different effects between the pre-and post-launch phases, the results demonstrate the necessity of ac-counting for time variation in the effects of both formats.

Second, the analyses extend the findings of existing experimentalstudies regarding the combined-format effects of publicity and advertis-ing (e.g., Jin, Suh, &Donavan, 2008; Jin, Zhao, &An, 2006; Stammerjohanet al., 2005). In contrast to previous results, our study indicates that theinteraction of pre-launch advertising and publicity has a negative effecton sales. This new and important finding is in line with the notion thatcustomers exhibit reactance (Brehm, 1966), whereby they discount thecredibility of publicitywhen it is accompanied by higher levels of adver-tising for the same product. The effect may also be due to a saturationeffect, whereby the marginal effect becomes lower at higher levels ofcommunication (for either advertising or publicity). However, this an-tagonistic effect becomes non-significant after the launch of a product.These findings demand further research because we demonstrate thatexperimental studies that typically report positive interaction effectsbetween these formats on attitude, recall, and recognition cannot sim-ply be transferred to behavioral outcomes, such as sales. Future researchcould attempt to identify the boundary conditions and underlyingmechanisms for positive versus negative interaction effects betweenpublicity and advertising.

Third, the product characteristics of video games and their similari-ties to other products suggest the generalizability of our results. For ex-ample, with respect to short product life cycles, high pre-consumptionuncertainty, and substantial pre-launchmarketing, video games resem-ble many technical innovations (e.g., tablet PCs or mobile phones; Dhar& Wertenbroch, 2000). For products with similar characteristics, wewould expect publicity to outperform advertising for the pre-launchphase, whereas advertising should close the gap with publicity (oreven dominate it) after the launch of a product.




6.2. Managerial implications

The study’s findings also advance managerial knowledge with re-spect to the focus of companies’ marketing efforts. While advertisingis under companies’ direct control, publicity is not, althoughmany com-panies have large PR departments with the goal of creating positivepublicity. An key finding of interest is that publicity has longer carry-over effects on sales than does advertising (λ1 = 0.75 for advertisingand λ2=0.90 for publicity). This implies thatwhile the 90%duration in-terval for advertising is 8 weeks, the effects of publicity on sales persistsfor 22 weeks (Clarke, 1976). Compared with advertising, publicity’shigher source credibility (Miniard et al., 1988) and higher perceivedlevels of expertness and independence (Hallahan, 1999b) may lead tomore enduring effects on consumer memory and purchase behavior.

To guide decisions concerning where to focus marketing efforts (onadvertising and/or on publicity), we rely on the normative results onsynergy in multimedia communications derived by Naik and Raman(2003). Two guiding principles from their paper are particularly rele-vant for our case. First, the media budget should be allocated to variousactivities in proportion to their relative effectiveness (Naik & Raman,2003, Proposition 1). Second, as the synergy increases, the advertisershould decrease (increase) the proportion of the media budget allocatedto the more (less) effective communications activity (Naik & Raman,2003, Proposition 3).

In the pre-launch phase, publicity (elasticity = 0.276) is 41% moreeffective than advertising (elasticity = 0.195) in driving first-weeksales. Moreover, there is a negative interaction effect between publicityand advertising (–0.028). FollowingNaik and Raman (2003, Proposition1), it is therefore advisable for companies to focus their marketing ef-forts on publicity during this time. Considering the negative interactionbetween advertising and publicity (which represents the reversal ofNaik and Raman (2003)’s Proposition 3), companies should furtherincrease their focus on the more effective format, publicity, and onlyuse advertising cautiously prior to launch.

However, during the post-launch phase, publicity and advertisingstill have an effect on sales, but the impact of advertisingnowoutweighsthat of publicity. After launch, advertising is nearly three times more ef-fective than publicity in the short term (the elasticities are 0.078 and0.028, respectively). However, their post-launch long-term elasticitiesare very similar. For advertising, the long-term elasticity is β6/(1–λ1) = 0.078/(1–0.75) = 0.310, and for publicity, it is β7/(1–λ2) =0.028/(1–0.90) = 0.280. Because the post-launch interaction is not sig-nificantly different from zero, we can use Naik and Raman (2003)’sProposition 1 to suggest that management should devote similar effortsto publicity and advertising during the post-launch phase of a product.

Table 4Ranked sales predictions for different scenarios of pre- and post-launch publicity and advertisi

Scenario Pre-launch publicity Pre-launch advertising Post-launch publicity Po

0 (base) At mean At mean At mean At1 +20%2 +10% +3 +10% +10%4 +5 +10% +10%6 +5% +5% +5% +7 +10% +8 +10% +9 +20%10 +10% +10%11 +20%

Note: Scenario 0 is the base case for a representative game, where the means of pre- and post(=0.5). The distribution of the amounts over time is based on themean distribution across gampublicity and advertising and across the pre- versus post-launch phase. The increase is appliedmultiplied by 1.20. For each scenario, we calculate the log values for the independent variaEqs. (2) and (3). Next, we predict sales as exp(predicted log sales))*exp(0.5 sigma^2), where sidistribution.


To understand the relative effectiveness of different communicationstrategies, we use the estimated model to simulate sales outcomes fordifferent scenarios (Table 4). In the simulation, we omit the copulaterms because they are only included in the estimation to obtain consis-tent estimates. The baseline scenario holds for the average game (weuse the average fixed effect for the intercept) and for the pre- andpost-launch publicity and advertising that are all at the same weeklylevel (=0.50), which is close to the sample average (Table 1).

Thewhat-if scenarios in Table 4 consider the case inwhich amanag-er is given an opportunity to increase/stimulate advertising and/or pub-licity by+20%. S/he can choose to allocate funding to just one format inonephase (e.g.,+20% for pre-launch publicity) or allocate itmore even-ly across stages and formats (e.g.,+10% pre-launch publicity and+10%post-launch advertising).

Relative to the baseline scenario, Table 4 reports the strongest in-crease in total sales for a 20% increase of pre-launch publicity only(sales+6.5%; Scenario 1). This is in linewith the strong pre-launch pub-licity elasticity and the negative interaction effect with advertising(which means that a joint investment is not fruitful). The second-bestoption is to spend part of the increased funds (+10%) on the highly ef-fective pre-launch publicity and part (+10%) on relatively effectivepost-launch advertising, leading to a 5.4% sales increase in Scenario 2.We find that in all of the top-three scenarios, pre-launch publicity isthe focus: In case of Scenario 3, it is supplemented by post-launch pub-licity (+4.8%). In all of the top four scenarios, there is no benefit fromincreasing publicity and advertising during the same phase, which isconsistent with negative (pre-launch) and zero (post-launch) interac-tion effects. Only in the fifth-best Scenario 5 do we observe that effortis spread across publicity and advertising in the same (pre-launch)phase (sales +4.1%). The main effect of publicity is sufficiently strongto outweigh the negative interaction effect with advertising. Across allscenarios, theworst investmentwould be to focus solely on the relative-ly ineffective pre-launch advertising (Scenario 11; +1.6% sales).

Combining these insights, a very clear picture emerges. Given thenegative interaction effect between publicity and advertising in thepre-launch period, and the relatively stronger effectiveness of publicityon first-week sales, our advice is to adopt a publicity-focused strategypre-launch. Post-launch, a stronger focus on advertising is warranted.

6.3. Limitations and future research

Several limitations should be considered thatmight provide avenuesfor future research. First, we cannot offer a Return on Investment anal-ysis since this requires cost data for both advertising and publicity.For advertising we have been able to obtain advertising cost per page,

ng.

st-launch advertising SalesWeek 1

SalesWeeks 2-26

Total sales Increase rel.to base

mean 1968 10,487 12,455 \\2568 10,751 13,320 6.5%

10% 2271 10,898 13,169 5.4%2299 10,788 13,089 4.8%

20% 1991 11,009 13,001 4.2%2288 10,693 12,982 4.1%

5% 2152 10,815 12,969 3.9%10% 2012 10,926 12,939 3.7%10% 2016 10,825 12,842 3.0%

2031 10,801 12,833 2.9%2036 10,718 12,754 2.3%2038 10,616 12,654 1.6%

-launch publicity advertising are all set to the same amount, close to the sample averagees. In the other scenarios, there is scope for a total of 20% increase in support, spread acrossto each week in that phase. For instance, a 20% increase means that all weekly values arebles to predict log sales for each week, where we use the adverting and publicity stockgma^2 is the variance of the error term, because this expression is themean of a lognormal




which ranges from €6K to €13K across magazines, with a mean that isaround €10K (we received the information from the media agencyPilot). However, for publicity it is nearly impossible to estimate the“cost of one page of publicity” as magazines do not charge fees for pub-licity. However, this does notmean there is such a thing as free publicity,because a firm has to bear several types of cost in order to get publicityin the first place. First, publicity typically only happens if there is some-thing newsworthy to report on, such as a new product release, and anew product costs money to develop. Second, the company has to payfor its PR department to push stories into the media. Third, advertisingin a magazine may lead to additional publicity in the magazine(Rinallo & Basuroy, 2009). This list of cost factors may not even be com-plete. To quantify the cost of an additional page of publicity, a firm couldrun a model regressing the number of pages of publicity on these costcomponents (new product development, PR department, advertising,other cost components). However, in the absence of data, we cannotrun such an analysis to approximate the cost of publicity.

What we can do is a break-even analysis to compare advertising in-vestments to publicity investment. For example, in the post-launchphase, the long-term advertising elasticity is 0.310 and the publicityelasticity is 0.280, and there is no significant interaction effect. Henceadvertising is a factor 0.310/0.280 = 1.1 more effective than publicitypost launch. The cost of advertising is around €10K per page as ex-plained above. To offset its effectiveness disadvantage, publicity wouldhave to cost €10K/1.1 = €9K per page to have the same Return onInvestment as advertising.

Another limitation is that wemeasure publicity and advertising usingprint activities. We discussed this limitation with executives from thegames industry; these executives argued that this study’s approachdoes not impair its results because online and on-air advertising and pub-licity are highly correlated with companies’ print activities. A correlationanalysis for a subsample confirmed the existence of a high correlation be-tween spending for print advertising and overall marketing (r = 0.78,p b 0.05). Nevertheless, we suggest that future studies may wish tomore extensively test the differentiated effects of marketing throughmedia channels that were not examined in the present investigation.


Furthermore, although our results were derived from a single mar-ket, we expect our findings regarding the time-variant effects of public-ity and advertising to be encountered for other products. The focus on asingle market has the advantage of allowing a wide range of new re-leases (in our case, 3336 new products) to be included in the study.However, we suggest that replications of our analyses in other industrycontexts will be fruitful by providing results that can be compared andcontrasted with the findings obtained in this study.

Next, as we rely on market data, we are unable to test psychologicalmechanisms at the individual level. Specifically, testing the underlyingmechanisms of the negative interaction between pre-launch advertis-ing and publicity would provide an interesting avenue for futureresearch.

Finally, we focus on products with a relatively short life cycle andsubstantial pre-launch marketing activities. Although this focus isadvantageous because it allows us to observe the effects of advertis-ing and publicity over the course of the complete life cycle of a prod-uct, it somewhat limits the generalizability of the findings. As videogames are also experiential and associated with high consumeruncertainty, we expect our findings to be generalizable to otherproduct categories with these characteristics such as entertainmentmedia (e.g., movies, books, or music). Even technical innovationswith short life cycles, high pre-launch marketing spending, and con-sumer uncertainty (for example, iPhones) might yield similar effects.Nevertheless, a useful extension of this study would be to verify ourcurrent findings with respect to post-launch activities for productswith longer life cycles.

Despite these limitations, we believe that this study uncovers novelandmanagerially relevant insights on the impact of publicity and adver-tising on sales. Publicity is especially effective pre-launch, more so thanadvertising, and their interaction is negative. However, once the prod-uct has been launched, significant shifts occur: both formats lose effec-tiveness, but publicity does so to the largest degree, even to the extentit becomes less effective than advertising post launch. Companiescan benefit from these shifts by focusing on publicity during the pre-launch and on advertising during the post-launch phase.

Appendix A. Model calculation for advertising score

Game
Week Magazine Circulation (in 10,000) Fraction per page
s.

Sum (per magazine)

t-launch publicity a005

Circulation (in 10,000) x sum

nd advertising on new pro

(Circulated)Weightedscore

The Sims 2
11 PS2 Magazine 5.1458 0.66 1.66 8.5420 11.3601 The Sims 2 11 PS2 Magazine 5.1458 1.00 The Sims 2 11 Gamepro 3.7574 0.75 0.75 2.8181
Note: The example shows adverting for one game in two magazines. In the first magazine, we observe two ads – one full page and 2/3 of a page. Furthermore, ¾ of a page is in the secondmagazine. The total advertising space per magazine is multiplied by the circulation and summed up for all magazines for the respective week. Thus, the resulting (circulated) weightedscore of 11.36 can be interpreted as 11.36 pages of advertising in a magazine with a circulation of 10,000 copies. Multiple coders were used to determine the amount of advertisingand publicity in each magazine.

duct sales, Intern. J. of



Appendix B. Supplementary data

Supplementary data to this article can be found online at http://dx.doi.org/10.1016/j.ijresmar.2015.05.005.

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the impact of pre- and post-launch publicity and advertising on new product sales

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