The Dynamic Interplay Between Recorded Music and Live Concerts

Dominik Papies & Harald J. van Heerde The Dynamic Interplay Between Recorded Music and Live Concerts: The Role of Piracy, Unbundling, and Artist Characteristics The business model for musicians relies on selling recorded music and selling concert tickets. Traditionally, demand for one format (e.g., concerts) would stimulate demand for the other format (e.g., recorded music) and vice versa, leading to an upward demand spiral. However, the market for recorded music is under pressure due to piracy and the unbundling of albums, which also entail threats for the traditional demand spiral. Despite the fundamental importance of recorded music and live concerts for the multibillion-dollar music industry, no prior research has studied their dynamic interplay. This study ﬁlls this void by developing new theory on how piracy, unbundling, artist fame, and music quality affect dynamic cross-format elasticities between record demand and concert demand. The theory is tested with a unique data set covering weekly concert and recorded music revenues for close to 400 artists across more than six years in the world’s third-largest music market, Germany. The cross-format elasticity of record on concert revenue is much stronger than the reverse elasticity of concert on record revenue. The results show the key role of piracy, unbundling, and artist characteristics on these cross-format elasticities, which have implications for the business model of the music industry.

Keywords: music, record sales, concerts, piracy, unbundling

Online Supplement: http://dx.doi.org/10.1509/jm.14.0473

he market for entertainment goods (e.g., music, movies, music in one format would stimulate demand for this artist in the video games) has seen remarkable growth in the past other format, leading to an upward demand spiral. For exam- Tyears, with global revenue approaching $2 trillion in ple, an upcoming artist could give concerts, which would lead 2015 (Eliashberg et al. 2016). Accordingly, entertainment to more albums sold, which would then lead to a greater goods have attracted considerable academic attention in mar- attendance at future concerts, and so on. keting (e.g., Eliashberg et al. 2016; Saboo, Kumar, and Ramani Technological developments, however, challenge many 2016). One key business in the entertainment sector is the music traditional business models (Eliashberg et al. 2016; Shugan industry, with global revenues from recorded music alone worth 2004). The advent of technology changes the way consumers $16.5 billion in 2012 (International Federation of the Phono- consider, purchase, and consume entertainment goods in general graphic Industry 2013). The music industry is an example of an and music in particular. A key development is file sharing, or entertainment good with two major consumption formats: live piracy, which is the process by which “individuals who do not concerts and recorded music (singles and albums, in either own and have not purchased a particular song or movie can physical or digital form). Traditionally, demand for an artist’s nevertheless obtain that song or movie from unknown third parties” (Liebowitz 2006, p. 4). Piracy represents a major threat to recorded music sales, leading to strong decline in revenues (e.g., Browne 2012; Liebowitz 2016). Dominik Papies is Professor of Marketing, University of Tübingen (e-mail: Another key development is unbundling, wherein firms can [email protected]). Harald J. van Heerde is Research “offer individual products that were previously only (or pri- Professor of Marketing and MSA Charitable Trust Chair in Marketing, ” Massey University; and Extramural Fellow, CentER, Tilburg University marily) sold as part of bundles (Elberse 2010, p. 107). The (e-mail: [email protected]). This research was supported by the unbundling of music allows consumers to cherry-pick their German Research Foundation (Grant PA 1975/1-1) and the Marketing favorite songs rather than buy the entire album, with possibly Science Institute (MSI Research Grant 4-1770). The authors thank Ralf van adverse effects on demand for recorded music (Elberse 2010). der Lans, Marnik Dekimpe, and seminar participants at the Marketing Whereas recorded music is under threat, the market for Dynamics Conference at Tilburg University and the Cologne-Hamburg Marketing Camp for valuable feedback. Rajkumar Venkatesan served as live concert performances has seen remarkable growth area editor for this article. (Krueger 2005), and many artists have been able to substitute lost revenues from record sales with revenues from

© 2017, American Marketing Association Journal of Marketing ISSN: 0022-2429 (print) Vol. 81 (July 2017), 67–87 1547-7185 (electronic)67 DOI: 10.1509/jm.14.0473 live performances.1 Two examples illustrate the growing attending a concert would lead to a record purchase, but that importance of concerts. For a concert by Led Zeppelin now, consumers can download the music for free through file with a venue capacity of 18,000, millions of fans entered a sharing. This development would imply that the link between ballot (at more than V180 per ticket; Cheal 2007). In 2007, concert ticket demand and record demand becomes weaker, that Madonna left her music label (Warner) to join Live Nation, a is, cross-buying is inhibited by piracy. We also hypothesize that company that was known as a concert agency (Waddell this link could be weakened by unbundling, which allows 2007). Industry statistics mirror these examples. Artists in consumers to buy just those tracks they enjoyed the most at the the U.S. music industry earned $4 billion in revenue through concert. concerts and sold 100 million tickets in 2007 (Courty and We expect that the cross-buying effects are moderated not Pagliero 2011). In the United Kingdom, revenues from live only by the external environment represented by technological performances exceeded revenues from recorded music for change but also by characteristics of the artist’s music itself. the first time in 2008 (Michaels 2009). These trends suggest We distinguish between the quality of the artist’s music, which that the live experience of music (vs. the consumption of reflects consumers’ quality assessment and satisfaction with the recorded music) has become increasingly important for artist’s current offering, and the artist’s fame, which reflects past many consumers. Artists have capitalized on this trend as billboard successes. We develop and test new theory on how well. Traditionally, artists such as Madonna or U2 have cross-format buying is moderated by music quality and artist gone on world tours to promote their albums, triggering fame. We expect that music quality enhances cross-buying in cross-buying from concert tickets to records. With concerts both directions (from records to concerts and vice versa) but becoming an increasingly important source of income, the that fame especially increases the cross-buy from records to cross-buying behavior from records to concert tickets concerts. may be more relevant (Seabrook 2009). This new focus is We test the hypotheses using a unique weekly panel data illustrated pointedly by the artist Prince, who made albums set. It covers close to 400 of the best-selling artists in Germany, available for free to enhance concert ticket sales (Paine the third-largest music market worldwide (International Fed- 2010). eration of the Phonographic Industry 2013), across more than Despite the fundamental importance of recorded music and six years, for a total of 118,000 artist-week observations. The live concerts for the multibillion-dollar music industry, no prior data cover the period 2004–2010, representing a turbulent time research has studied the interrelationship of these revenue in the music industry given the threat of piracy and the rise of sources. The objective of this article is to shed new light on unbundling. The data set includes a comprehensive list of dynamic cross-format effects: how demand for one format (e.g., concert activities, assembled from PollstarPro and other sources. concerts) is affected by demand for the other format (e.g., On the basis of these data, we estimate an econometric model records). We study the strength of these effects, their symmetry that assesses how the dynamic cross-format elasticities between versus asymmetry, and their moderating factors. We estimate record revenue and concert ticket revenue are moderated by cross-format elasticities that capture how much demand for one technological developments and artist characteristics. The format (e.g., concerts) is affected by a 1% change in demand for econometric model enables us to capture the main and inter- the other format (e.g., records). The main research questions are active effects of marketing activities (new products, advertising, the following: and airplay, i.e., tracks played on TV and radio) on both for- ’ ’ • How strong are the dynamic cross-format elasticities between mats revenues. The model also captures the artist sdecisionto record demand and concert demand and vice versa? give a concert, accounts for unobserved demand shocks as well • How are these cross-format elasticities changing due to as endogeneity in advertising and airplay, and allows for error technological developments? correlations between concert and record revenue. • How are the cross-format elasticities moderated by artist We contribute to the literature by providing evidence for a characteristics? self-enforcing spiral of “success breeds success”: if an artist becomes more successful on the record market, this will A fundamental technological development in society in enhance this artist’s concert revenue, which in turn enhances general is the digitization of information. In the music industry, record revenue. However, the spiral is highly asymmetric, this has led to digital music files that can be transferred via the with a much stronger effect of record demand on concert Internet. This opened key opportunities to develop new business demand than the other way around. In line with our theorizing, models because it meant that albums could be unbundled and we identify technological developments and artist character- the tracks could be sold separately. It also led to a key threat in istics that affect this spiral. In particular, piracy and unbundling the form of illegal file sharing among consumers (i.e., piracy). In weaken the effect of concert demand on record demand, as these this article, we develop and test new theory of how these two developments allow consumers to substitute the full product key technological developments, piracy and unbundling, affect (whole album) with a free alternative (pirated files) or a less cross-buying from concerts to recorded music. One crucial costly alternative (parts of the unbundled album). We also find untested hypothesis is proposed by Krueger (2005). He spec- that artist fame is a double-edged sword. A more famous artist ulates that in the pre–file sharing era, a positive stimulus from enjoys a stronger effect of record demand on concert demand but faces a relatively weak effect of concert demand on record 1In this research, we do not differentiate between an artist, the demand. Finally, music quality enhances the impact of concert management, and the record label; we use the term “artist” to demand on record demand. Our findings allow us to draw new represent their combination. implications for the music industry.

68 / Journal of Marketing, July 2017 Related Literature and Contributions Literature on the Music Industry The other stream this study contributes to is the literature on the Literature on Multiformat Goods music industry. Given the relevance of the music industry and This article contributes to several literature streams (Table 1), the richness in research questions it entails, there is an emerg- including those on multiple formats and cross-buying. Music is ing literature on this industry in economics (e.g., Dewenter, an entertainment good that can be enjoyed across different Haucap, and Wenzel 2012; Peitz and Waelbroeck 2005; channels or formats (e.g., Elberse 2010; Koukova, Kannan, and Thomes 2013), in cultural economics (e.g., Liebowitz 2016), Kirmani 2012; Mortimer, Nosko and Sorensen 2012). The and in marketing (e.g., Elberse 2010; Giesler 2008). Mortimer major formats are recorded music (e.g., Koukova, Kannan, and et al. (2012) study the direct effect of piracy on concert demand Kirmani 2012) and live music (concerts). Similarly, for the and record demand by examining the launch of the file-sharing movie industry, the major formats are watching a movie in website Napster in 1999. They conclude that while record sales cinema versus watching it at home (e.g., Eliashberg et al. 2006; declined in the years after 1999, concert ticket sales increased Hennig-Thurau et al. 2007). We use the term “format” rather for smaller artists. Because these authors do not measure piracy, “ ” than channel to highlight the fact that the different formats they use the potential structural break that occurred in 1999 with share some attributes but may differ on other attributes the introduction of Napster to identify the effect of file shar- (Koukova, Kannan, and Kirmani 2012). Other examples of ingondemandforbothformats.3 Importantly, Mortimer multiformat goods or services are restaurant meals (in-restaurant et al. (2012) study record sales and concert sales as separate dining vs. at home), education (on-campus or online learning), dependent variables, without linking the two; that is, they do 2 news (print vs. online) and books (paper copy vs. e-book). not estimate cross-format elasticities. Consistent with the idea A key challenge in managing multiformat goods is bal- that the two formats may act as complements, our study adds a ancing the relationship between formats to improve over- new perspective by looking at how demand for one format all performance across formats. This can be achieved by dynamically drives demand for the other format. This allows us reducing substitutability and/or improving complementarity to understand the time lag, asymmetries, and moderators of the (e.g., Gentzkow 2007; Geyskens, Gielens, and Dekimpe 2002; cross-format elasticities. As far as we are aware, this is the first Koukova, Kannan, and Kirmani 2012). In some industries, the empirical study to do so. formats are substitutes (e.g., paper book version vs. electronic In a descriptive study on the music industry, Krueger (2005, book version of the same novel), whereas in other industries, the p. 26) speculates that new “technology that allows many po- formats are complements (e.g., consumers may purchase a DVD tential customers to obtain recorded music without purchasing a after having seen a movie in a theater). Koukova, Kannan, and record has severed the link between the two products” (i.e., Kirmani (2012) show that when the formats are equivalent in quality on salient attributes, consumers are more likely to see concert ticket and record demand). Krueger (2005) does not test them as complementary and will be more likely to purchase both this proposition empirically. A second key development ini- (i.e., a cross-buy will occur). Cross-buying will also be enhanced tiated by technological advancements is unbundling. Elberse when the risk associated with the cross-buy is reduced (Kumar, (2010) studies how unbundling affects record demand. How- George, and Pancras 2008). ever, the study does not analyze concert demand or the mod- Managing multiformat goods becomes even more com- erating effect of unbundling on the cross-format elasticity from plicated when there are dynamic consumption effects. Many concert demand to record demand. multiformat goods cannot be consumed simultaneously. For In summary, some prior research has studied the main example, a consumer would typically not attend a concert and effect of piracy on record and concert demand (e.g., Mortimer listen to recorded music at the same time. Instead, a consumer et al. 2012) and some research has studied the main effect of may buy recorded music, listen to it, and then decide to attend unbundling on record demand (e.g., Elberse 2010). Krueger the concert, or vice versa (Krueger 2005), creating dynamic (2005) suggests (but does not test) that piracy severed the link cross-format effects. between concert demand and record demand. However, what is Technology affects business models in various industries unclear is how piracy and unbundling moderate the dynamic (e.g., Elberse 2010; Gentzkow 2007; Geyskens, Gielens, and cross-format elasticities between concert demand and record Dekimpe 2002; Shugan 2004). Technological developments demand. We therefore contribute to the literature by developing such as digitization and interconnectivity due to the Internet and testing hypotheses for these moderating effects. fundamentally change the way consumers search for, buy, and The music literature has identified artist fame and music consume products and services. This may also affect the quality as key demand drivers (e.g., Dewan and Ramaprasad dynamic interplay between multiformat goods. As we argue for 2012; Elberse 2010). We add to this literature by studying how the case of the music industry, technology may affect cross- the cross-format effect is moderated by artist fame and music format demand effects. As such, a focal contribution of this quality. As we argue in the next section, we expect that the impact article is to increase our understanding of how the dynamic of concert demand on recorded music demand and vice versa interplay between two formats is moderated by technology. varies systematically with artist fame and quality of the music.

2There are some similarities between dual-format markets and 3In an additional, more disaggregate analysis, the authors group dual-sided markets. The key difference is that dual-sided markets market areas by broadband penetration under the assumption that the serve two markets, (e.g., readers and advertisers), while dual-format effects should be more pronounced in areas with high broadband markets serve (in principle) the same market (e.g., music consumers). penetration.

The Dynamic Interplay Between Recorded Music and Live Concerts / 69 Table 1 Related Literature and the Incremental Contributions of This Study Some Key Our Incremental Key Issue Description of Issue Main Findings Publications Contribution

Demand for multiformat Multiformat goods offer • New digital channels/ • Gentzkow (2007) Our article contributes to goods consumers two or more formats hurt • Geyskens, Gielens, this literature by studying formats of the same or established channels/ and Dekimpe (2002) how the dynamic similar good. These formats. • Koukova, Kannan, interplay between the formats may be • Overall performance and Kirmani (2012) demand for two formats is substitutes or implications may be • Kumar, George, and moderated by technology complements. The ﬁrm’s positive or negative. Pancras (2008) and by cues that challenge is to manage • Equivalent quality on consumers can utilize to demand across formats. salient attributes leads reduce the purchase risk to products being associated with a cross- perceived as buy. complements. • Cross-buying is enhanced by risk reduction. Opportunities and The digitization of music • Piracy hurts recorded • Elberse (2010) Our article adds the threats in the music and the rise of the music sales. • Krueger (2005) perspective that demand industry Internet have led to two • Live concerts, • Mortimer, Nosko, for one format (e.g., key developments: especially for smaller and Sorensen concert) may affect piracy and unbundling. artists, have thrived (2012) demand for the other since piracy became a format (e.g., recorded mass phenomenon. music), captured by • Unbundling reduces dynamic cross-format revenues from elasticities. We recorded music. contribute to the literature by studying (1) how piracy and unbundling moderate the cross- format demand elasticity between recorded music and live concerts; (2) the moderating effect of artist fame and music quality on the cross-format elasticities; and (3) the effects of the marketing variables advertising, airplay, and new releases on record demand and concert demand.

To keep the wheel of concert and record demand spin- format elasticities between record and concert demand. We ning, artists need marketing activities, in particular, new prod- test new theory on the moderating role of piracy, unbundling, uct innovation (new music releases), advertising, and airplay and artist characteristics on these cross-format elasticities, and (songs played on TV and radio). Some research has looked at a we assess the effects of marketing activities on record and subset of these activities. For example, Lee, Boatwright, and concert demand. Kamakura (2003) and Moe and Fader (2001) conclude that record sales peak at the time of a new release and quickly decline afterward, and they ﬁnd that airplay enhances a record’s market potential. However, no prior study has measured the Conceptual Framework and effects of new product innovation, advertising, and airplay on Hypotheses record demand and concert demand. We do this and test not The focal outcome variables are the demand for records and only for main effect but also for interaction effects. concerts for an artist (see Figure 1). We conceptualize these two In sum, this article’s contribution to the literature is a consumption formats as a wheel or spiral. A main source of theoretical and empirical assessment of the dynamic cross- power to keep the wheel spinning comes from the marketing

70 / Journal of Marketing, July 2017 Figure 1 Conceptual Framework for the Music Demand Spiral Between Concert Revenue and Record Revenue

• Piracy (H1: –) Control Variables • Unbundling (H2: –) • Fixed artist effects • Music Quality (H3: +) • Piracy • Fame (H6: –) • Unbundling • Fame • Music quality • Seasonal effects + • Trends • Google searches • DRM removal

Marketing Variables • New releases Concert Revenue • Advertising Record Revenue • Airplay • Interactions

Control Variables • Fixed artist effects • Piracy • Unbundling • Fame • Music quality • Seasonal effects + • Trends • Google searches • Concert revenue • Piracy • Other genres • Unbundling • Music Quality (H4: +) • Fame (H5: +)

variables, which we discuss in more detail after the hypotheses. format (e.g., concerts) from the same artist. The rationale is that In addition, artist characteristics drive demand for concerts and entertainment goods such as music are experience goods for records; for example, better artists are likely to produce better which the utility or quality can only be fully assessed during records and concerts. As we outline in more detail in the consumption (Dewan and Ramaprasad 2012; Nelson 1970; “Model” section, we control for time-invariant artist charac- Saboo, Kumar, and Ramani 2016). This means that consumers teristics by including artist fixed effects and by including time- experience uncertainty that needs to be resolved before they varying artist characteristics as control variables. make a cross-buy purchase (Nelson 1970). If the consumer is The central feature of the conceptualization is that we satisfied with the consumption of one format, a cross-buy of expect a positive dynamic effect of concert demand on record the other format is more likely. demand and vice versa, even after we control for artist char- These arguments are supported by the literature on multi- acteristics, fixed effects, and marketing variables. These format goods (e.g., Koukova, Kannan, and Kirmani 2012). This dynamic cross-format effects (visualized in Figure 1 by curved literature has established that when the two formats of a good arrows with plus signs along them) mean that an increase in have unique attributes (as is the case for recorded and live demand for either format at time t will enhance demand for the music), these two formats are more likely to be complements other format at a time later than t. We expect these positive because these two different formats are valuable in different dynamic effects due to cross-buying effects (Kumar, George, usage situations. In particular, recorded music allows con- and Pancras 2008) where a consumers’ experience in one sumers to relive the music they may have enjoyed at the concert format (e.g., recorded music) triggers purchases from the other at any time and in any place. Conversely, when consumers have

The Dynamic Interplay Between Recorded Music and Live Concerts / 71 enjoyed an artist’s recorded music, they may become interested consumers who have downloaded tracks illegally may de- in attending a concert. Seeing the artist live strengthens the velop a desire to experience a live concert by the artist. Thus, we human element of music and enhances the bond between the expect a positive main effect of piracy on concert demand. consumer and the artist (Park et al. 2010). A concert also brings Because piracy is a phenomenon that shapes the record market a mass social element to enjoying the music, which is absent and because theory does not provide strong arguments for an when listening to recorded music alone or in smaller groups interaction effect between record demand and piracy on concert (Raghunathan and Corfman 2006). Thus, we expect a positive demand, we do not state a hypothesis for this interaction. In a effect of record demand on concert ticket demand and vice versa robustness check, we establish that its inclusion does not change because consumption of one format will reduce the risk asso- the results for the other hypothesis tests. ciated with purchasing the other format and may even create the desire to purchase the other format. Unbundling The Moderating Effects of Technological Unbundling means that consumers no longer have to buy the Developments and Artist Characteristics full album if they are interested in just a few tracks. This may bring in new consumers who otherwise would not have pur- The tenet of this study is that the dynamic effects between chased the album, potentially enhancing demand. However, demand for the two formats (concerts and records) are sys- Elberse (2010) finds that the flexibility to purchase individ- tematically moderated by technological developments and artist ual tracks instead of the entire albums reduces overall re- characteristics. We will develop formal hypotheses for these cord demand, and thus we expect a negative main effect of moderating effects in the following sections. As we argued unbundling on record demand. previously, the digitization of music and the rise of the Internet Our main interest is the question of how unbundling have enabled two developments that continue to shape the moderates the dynamic cross-buying effect of concert demand market, namely, piracy as a key illegal force, and unbundling as on record demand. As we have discussed, attending a concert is the major change in the legal market. We therefore focus on an experience that may trigger a cross-buy of recorded music these two technological developments. There are associated because the consumer has experienced the product and lowered developments we do not study, such as the rise of online radio the risk associated with purchasing additional products from the stations and mobile apps, and the sales of MP3 players and same artist (Kumar, George, and Pancras 2008). Unbundling, smartphones. We view these as corollaries of the unbundling of however, gives consumers the flexibility to purchase just those music, which is the focal variable representing a new way of tracks that they enjoyed the most (Elberse 2010), so a cross-buy buying recorded music. Before we discuss the hypotheses, we may still occur, but at a lower rate: clarify that we measure the dynamic demand effects between the two formats using cross-format elasticities: when demand for H2: As unbundling increases, the impact of concert demand on one format increases by 1% in period t, by how much does the record demand decreases. demand for the other format increase across periods t + 1, t + 2, Unbundling means that recorded music can more easily t + 3, and so on? Thus, the hypotheses are about how moderators spread among a wider audience. This may enhance demand for affect the dynamic cross-format elasticity. Please note that we do concerts, and thus we anticipate a positive main effect of un- not propose formal hypotheses for main (direct) effects. bundling on concert demand. However, because unbundling is a phenomenon that shapes the record market and there are Piracy no strong arguments for an interaction effect between record Piracy has led to a sharp decline in recorded music revenue over demand and unbundling on concert demand, we do not state a time (Liebowitz 2016), and we thus expect a negative main hypothesis for this interaction. One of the robustness checks effect on record demand. Krueger (2005) speculates (but does shows that its inclusion does not change the results of other not empirically test) that the effect of concert ticket demand on hypothesis tests. record demand has been severed by file sharing. The reason is We now discuss the moderating effects of music quality and that consumers who have attended a concert would in the past artist fame. Music quality is an evaluation of how highly the have bought a record because of a positive concert experience; current music is rated, whereas fame represents the artist’s in other words, cross-buying would occur (Kumar, George, and proven track record of producing music with mass appeal, Pancras 2008). With the advent of file sharing, however, the reflecting the artist’s entire oeuvre, possibly going back de- desire to savor the experience can also be accommodated by cades. These constructs are not necessarily correlated; empir- downloading the music from a file sharing network; that is, the ically, the correlation between the respective measures cross-buying behavior is inhibited by piracy. We therefore (discussed in the next section) is only .02. hypothesize the following:

H1: As piracy increases, the impact of concert demand on record Music Quality demand decreases. Music is an experience good, for which quality is difﬁcult to While piracy represents a phenomenon that directly com- assess prior to consumption (e.g., Nelson 1970). Therefore, petes with record demand, it may increase concert demand as consumers look for ways to reduce their purchase uncertainty, well because it leads to a greater pool of consumers who are and one way to achieve this is through quality assessments by exposed to the artist’s recorded music (Mortimer et al. 2012). other consumers (e.g., Liu 2006). These quality assessments Like consumers who have acquired the recorded music legally, reﬂect and signal peers’ satisfaction with the music. We expect a

72 / Journal of Marketing, July 2017 positive main effect of music quality (as rated through consumer Marketing Variables reviews) on both record demand and concert demand. We identify new releases, airplay, and advertising as the mar- Once consumers have attended a concert, a cross-buy of keting drivers of music demand. The rationale is that releas- recorded music is more likely if consumers are satisﬁed with the ing new products and communicating about them will drive quality of the music. Satisfaction is mirrored in positive quality demand for both records and concerts. We expect that the evaluations (Zhu and Zhang 2010). Similarly, for consumers marketing variables will have not only main effects but who have bought the record, the decision to attend a concert also interaction effects. We primarily anticipate positive (i.e., a cross-buy) is facilitated when their satisfaction with the interaction effects, or synergistic effects, for example, that music quality is higher. Thus: advertising enhances the effectiveness of new releases.

H3: The impact of concert demand on record demand is stronger However, we can also expect antagonistic effects, for for music of higher quality. example, that advertising becomes less effective for higher

H4: The impact of record demand on concert demand is stronger levels of airplay due to saturation effects (e.g., Burmester for music of higher quality. et al. 2015).

Artist Fame Data and Measures An artist’s fame serves as a cue to reduce the uncertainty that Sample is associated with purchasing music as an experience good (Dewan and Ramaprasad 2012). This uncertainty will be lower To test the conceptual model (Figure 1), we analyze the German for a more famous artist because the artist has a proven track music market for both domestic and international artists. We record of producing music with mass appeal. Thus, consumers focus on artists who earn revenues from both record sales conjecture that the artist’s recorded music and live concert and concert sales and thus exclude artists who no longer present more enjoyable experiences. We therefore expect a perform live (e.g., ABBA, the Beatles). We identified all positive main effect of fame on both record demand and concert artists who had given at least one concert and who appeared ticket demand. at least once in the German music charts (album or single top Consumers who purchase an artist’s recorded music are 100) between January 2003 and June 2010. We selected 410 more likely to cross-buy a concert ticket if they perceive a low of the most successful artists according to their cumulative consumption risk. This will be the case for famous artists. In chart placements of singles and albums during the obser- other words, more famous artists may leverage their brand vation period. We dropped 16 artists because sales data were strength to enhance cross-buying from records to concerts; thus: unavailable and 7 artists because they did not have at least one full year of observations (i.e., they entered the market at the H5: The more famous the artist, the stronger the impact of record end of the observation period). For the remaining 387 artists, demand on concert demand. we collected weekly data on the relevant variables. Impor- For the reverse cross-effect (from concerts to records), we tantly, the sample includes international superstars in the expect that fame will play a different role. We conceptualize top deciles but also new and relatively unknown artists in fame as previous record market success (i.e., cumulative past the bottom deciles. The estimation period covers January chart placements). While the theory implies that the risk asso- 2004–June 2010 because some variables (e.g., Google ciated with cross-buying a record from a famous artist is low, searches) are only available as of 2004. Table 2 summarizes there is an opposing force that does not apply to the concert thedataset. market: fame implies prior record chart success, which means that consumers who attend a concert of a more famous artist are Dependent Variables ’ likely to already own most of the artist s recorded music. Thus, We measure both recorded music demand and concert demand for a famous artist, there is less potential for converting con- in revenue rather than units because this puts both formats on a certgoers into record sales because these fans probably already common denominator, which would not be the case if we used 4 own these records. This limitation does not apply to concerts units such as the number of albums or tracks sold for recorded because each new concert tour offers consumers new ways to fl music and the number of tickets sold for concerts. Revenue has experience the music. The ip side of the argument is that a less also more direct managerial relevance than unit sales. We note famous artist has a bigger potential record market to win over, that in one of the robustness checks we obtain very similar andonewaytodothatisbygivingconcerts,whichinspire results if we use unit sales. We also note that for more popular consumers to start buying records. Thus, while we expect that artists, concert tickets and album prices tend to be higher than cross-buying will still occur for famous artists, it will be relatively for less popular artists, which will drive up their revenues weak for more famous artists compared with less famous artists: compared with those of less popular artists. The analysis fi H6: The more famous the artist, the weaker the impact of concert employs a log-log model with xed effects for artists, which demand on record demand. means that any revenue-level differences are filtered out and that the cross-format effects are unitless elasticities, which can be 4We emphasize that this reasoning also holds in the presence of a compared across artists. positive main effect of fame on record revenue because the negative interaction only implies that it becomes more difficult for famous Record revenues. One dependent variable is the revenue artists to convert concert ticket sales into record sales. earned in Germany from recorded music, observed per artist

The Dynamic Interplay Between Recorded Music and Live Concerts / 73 Table 2 Variables and Operationalizations Variable Deﬁnition Source M (SD) Min Max

Dependent Variables RecordRevenuesit Record revenues in thousand euros for GfK 22.29 (58.08) 0 3,490.49 artist i (i = 1, …, 387) in week t (t = 1, …, 337) Entertainment

ConcertRevenuesit Concert revenues in thousand euros for PollstarPro; 29.40 (408.39) 0 21,549.81 artist i (i = 1, …, 387) in week t (t = 1, …, 337) various websites

zit Equals 1 if artist i gives a concert in week t, 0 Own calculation .04 (.20) 0 1 otherwise from concert observations Independent and Moderator Variablesa Piracyt Number of German Internet users (in GfK 3.64 (.49) 2.90 4.40 millions) having illegally downloaded music Brennerstudie from the Internet

Unbundlingt Single sales divided by the sum of single Own calculation .35 (.09) .12 .57 and album sales in week t from record revenue (Elberse 2010)

Fameit Cumulative number of album chart Ofﬁziellecharts.de 4.21 (6.43) 0 35.81 placements (=chart position-1) from 1995 (Dewan and until week t for artist i Ramaprasad 2012)

MusicQualityit Average number of Amazon review stars Amazon.de 4.26 (.63) 0 5.00 across all products for artist i in week t

NewReleaseit AlbumNewReleaseit +:40 Musicbrainz; .02 (.12) 0 1.71 · SingleNewReleaseit +:31·ReReleaseit Wikipedia +:25·DVDNewReleaseit,where AlbumNewReleaseit, SingleNewReleaseit, ReReleaseit, and DVDNewReleaseit are dummies for artist i that equal 1 in the week of the new release, and 0 otherwise.

Advertisingit Thousands of euros spent on advertising on Ebiquity 2.95 (25.34) 0 2,199.74 artist i in week t

Airplayit RadioPlaysit + VideoPlaysit for artist i in Nielsen Music 88.08 (267.43) 0 5,777 week t Control

GoogleSearchesit Google search volume (index) for artist i in Google.de .56 (1.78) 0 270 week t

DRMt Step dummy for the removal of DRM (= 0 Lischka (2009) .21 (.41) 0 1 before April 1, 2009, and 1 after)

IVConcertit Concert revenue from all artists from other Own calculation 9,842.58 (21,034.43) 0 191,966.18 genres and other labels from concert revenue aNonlogarithmic form, excluding interactions.

per week. Through an industry partner that wishes to remain recorded music across all artists per year) to obtain weekly anonymous, we obtained weekly unit sales data for all artists in record revenue for each artist across formats.5 the sample for all records (i.e., physical records such as CDs, as well as paid downloads through commercial download stores, as tracked by GfK). Following Elberse (2010), we multiply the 5The analysis does not include streaming because it was still at its sales volume by the average prices of the respective formats infancy during the observation period and did not contribute to an of recorded music (e.g., average price for a given format of artist’s revenue from recorded music.

74 / Journal of Marketing, July 2017 Concerts. The other dependent variable is concert revenue content that had been on the market before (e.g., “greatest hits” earned in Germany per artist per week. Artists tend to choose albums).6 We measure airplay by the cumulative number of the capacity of the venue as well as the number of concerts to airplays (on the radio) and video plays (on TV) that an artist match the expected demand for their concerts. We undertook a received in a given week in Germany, across all radio stations comprehensive search on when each artist gave a concert in and across the two main music video channels. These data were which venue (sources included Last.fm, Laut.de, MLK.de, tracked by Nielsen Music Control. While we do not have access venue and artist websites). This search resulted in a master set of to data on online airplay, we expect it to be strongly correlated 10,277 concerts performed by the 387 artists in Germany with our airplay measure, because songs/artists that are popular between 2004 and 2010. We believe this represents a (near) on the radio and TV are also likely to be popular online. census of all concert activity. We observe for each artist, for each week, whether a concert took place and, if so, the capacity Cross-format demand effects. To capture cross-format of the concert venue. To complement the concert data, we also demand effects, we use the lag of the stock of cross-format purchased data from PollstarPro, which collects data on concert revenue as independent variables. These stock variables, which activities including capacity, crowd size, and ticket prices. For we define in the “Model” section, allow us to directly estimate the sample of 387 artists, PollstarPro has records in the form dynamic cross-format elasticities. of artist tour reports for 2,199 concerts in Germany during the observation period, representing just 21.4% of all concerts. To combine the strengths of the master data set with more Moderating variables. The moderating variables enter the than 10,000 concerts (completeness) and the PollstarPro data models for concert demand and record demand as both main (revenue information), we fuse the data sets. As we establish in effects and interaction effects. For the piracy moderator, we use the Web Appendix, the PollstarPro data and the master data set data from an anonymized survey (“Brennerstudie”;seehttp:// have very similar distributions of observable variables. We also www.musikindustrie.de/sonstige-studien/) conducted by GfK utilize the fact that the size of the venue (that we observe for all in Germany, which measures the number of people who ille- concerts) is the primary driver of crowd size, with a correlation gally download music in a given year.7 We operationalize of .97 in the PollstarPro data. As explained in more detail in the unbundling as a market-level variable by dividing, for a given Web Appendix, using the PollstarPro data, we regress atten- week, the number of singles sold by the total sales volume (i.e., dance rate on attendance rates per artist, attendance rates per single plus album sales). venue, and venue size. We then use this model to calculate Music quality is the average number of Amazon stars predicted attendance rates for concerts not contained in the (from 1 to 5; e.g., Chevalier and Mayzlin 2006) across all PollstarPro data. The correlation between the predicted versus products for artist i in week t. This measure reflects con- actual concert attendance for the PollstarPro data for which sumers satisfaction with the artist’s music (Zhu and Zhang we have full data is again .97, confirming the validity of the 2010), which is mirrored by the fact that retailers such as approach. We proceed in a similar way to calculate the expected Amazon send out postpurchase e-mails asking consumers ticket prices (details in the Web Appendix). Combining these whether they are satisfied with the product and encouraging with the number of tickets sold allows us to calculate concert them to share satisfaction ratings. Similar to Dewan and revenues for each artist for each week. Ramaprasad (2012) and Elberse (2010), we measure fame We acknowledge that ideally, we would have had firsthand as the cumulative number of the inverse of album chart records of revenues for all 10,000+ concerts, but these are not placements until week t for artist i. The measure uses the available. Given the research questions, we believe that ana- inverse to ensure that, for example, a number-two hit lyzing all concerts that took place is preferable to using just 21% (inverse = 1/2) counts more than a number-ten hit (inverse = of the concert observations where revenue is observed (the 1/10). As mentioned before, music quality and fame are only PollstarPro data). Nevertheless, we ran a robustness check in weakly correlated, at .02 (see the Web Appendix). This allows which we included only those artists in the estimation sample us test the hypotheses on the moderating effects of music quality that are also included in the PollstarPro data. The results are very and fame without multicollinearity concerns. similar, as the next section reports. 6We obtain the weights by an auxiliary regression of revenues on the full set of independent variables and the four new-release dummies. Independent Variables The weights are such that a new album gets the highest weight of 1 and the Marketing. We obtained advertising information from the other releases (singles, DVDs, rereleases) obtain lower weights. Table 2 fi provides details. Using weights to combine the effects of similar inde- market research rm Ebiquity and use weekly advertising ex- pendent variables is often done in response modeling to combine the penditures (in euros) per artist across all major vehicles, such as cross-effects of multiple competitors (e.g., Cleeren, Van Heerde, and TV, print, radio, outdoor, and Internet. Television accounts for Dekimpe 2013). This allows for a more parsimonious model. In this case, 92% of all advertising expenditure. In line with previous research the alternative (less parsimonious) model would require four separate (e.g., Dinner, Van Heerde, and Neslin 2014), we aggregate the dummy variables for the four categories, plus eight interactions with the weekly advertising expenditures per artists across advertising other marketing instruments. In previous analyses, we also estimated models with alternative weights of 1 for albums and .1 for all other vehicles. To account for new product releases, we use a weighted categories. There is no material difference in the substantive outcomes. new-release variable. This is the weighted combination of 7Musicmetric is another potential source for measurement of piracy. four categories of new releases: (1) new album releases, (2) However, it is not an option for this study because it provides data only new single releases, (3) new DVD releases, and (4) rereleases of as of December 2011, not for the required period 2004–2010.

The Dynamic Interplay Between Recorded Music and Live Concerts / 75 Control variables. Demand for an artist’s products may be use this approach to capture the current and dynamic effect of an influenced by unobserved shocks such as positive or negative independent variable x, and we use the term “stock” to convey publicity. To capture otherwise unobserved time-varying artist the analogy with AdStock. We define the stock variable for demand shocks, we measure the extent to which an artist is variable x as subject to online search (Archak, Ghose, and Ipeirotis 2011), À Á (1) x–stock =l x–stock + 1 -l lnðx + 1Þ. in line with the recommended data-rich approach to prevent it x,y it-1 x,y it endogeneity in Rossi (2014) and Germann, Ebbes, and Grewal The carry-over coefficient lx,y varies between 0 and .9. If it is 0, (2015). We collect the weekly Google Trends search volume in the effect of x on y is instantaneous; if it is .9, the effect of x Germany for all artists across the observation period. Google decays very slowly. We allow each stock variable to have its indexes search volume for a keyword (i.e., artist) per week own carry-over coefficient lx,y for independent variable x and relative to the week with the highest search volume. To account dependent variable y. In the calculation of the log values of for an upward trend in consumer usage of search engines, we variables, we add 1 to the original variable to avoid taking the regress the artist-specific search volume on a time trend and use logof0(e.g.,advertisingmaybezeroinagivenweek).A the residuals in the model. We also include a time trend to benefit of the stock variable as specified in Equation 1 is that its safeguard against the possibility that the regressors of interest regression coefficient in a model for a log-dependent variable is pick up general trends. Quarter dummy variables capture the long-term elasticity: the cumulative impact across time of a seasonality within the year (e.g., Christmas). 1% shock in the input variable (Dinner, Van Heerde, and Neslin 2014). When we interact x stockit with a moderator, we directly Descriptive statistics. Table 2 displays the descriptive obtain the moderating effect on the long-term elasticity.9 statistics together with the variable definitions. On average per week, an artist earns V22,290 in revenue from record sales and V29,400 from concerts. Both variables have substantial var- Model for Record Revenue iation, with a maximum of almost V3.5 million for weekly We use log-log models to be able to interpret the coefficients as record revenue and V21.5 million for weekly concert revenue. elasticities. The model for log revenue from recorded music for The average advertising budget per week is V2,950, with a artist i in week t is range between V0andV2.2 million. On average, an artist is played on radio and TV 88 times per week. The mean value of (2) ln RecordRevenuesit =b0i +b1 ln ConcertRevenueStockit-1 the new-release variable is .02, corresponding with an average +b2NewReleaseStockit of one new album per year. +b3 ln AdvertisingStockit

+b4 ln AirplayStockit Model +b5 ln Piracyt To test the conceptual framework, we need a model that +b6 ln Unbundlingt addresses the following challenges. It needs to account for (1) +b7 ln MusicQualityit the current and dynamic effects of the marketing variables and +b8Fameit the dynamic cross-format revenue effects; (2) the truncated +b ln ConcertRevenueStockit-1 nature of the concert variable, equal to 0 when an artist does not 9 · ln Piracy give a concert, positive otherwise; (3) the possible endogeneity t in advertising and airplay; and (4) correlated errors across +b10 ln ConcertRevenueStockit-1 dependent variables to account for shocks induced by omitted · ln Unbundlingt variables. To address these challenges, we build a system of +b11 ln ConcertRevenueStockit-1 equations with correlated errors, using one equation for rev- · ln MusicQualityit enues from recorded music, one for concert revenues, a selection +b ln ConcertRevenueStockit-1 equation (Tobit Type II) to model the artist’s decision to give a 12 · Fame +b ln AdvertisingStock concert, and equations to account for potential endogeneity in it 13 it advertising and airplay.8 · ln AirplayStockit To model the dynamic effects of the key independent +b14 ln AdvertisingStockit variables, we use variables that are analogous to AdStock · NewReleaseStockit variables (Broadbent 1984; Dinner, Van Heerde, and Neslin +b ln AirplayStock ﬂ 15 it 2014). AdStock is a exible yet parsimonious way of account- · NewReleaseStock ing for advertising effects that carry over to future periods. We it +b16 ln GoogleStockit +b17Trendt 8Requirements 1, 3, and 4 would naturally lead to a vector +b18Quarter2t +b19Quarter3t r autoregressive (VAR) model. However, in this case, we have an +b20Quarter4t +b21DRMt +eit, additional requirement 2, concerning the distribution of the concert variable because in most weeks, an artist does not give a concert. A 9 VAR model cannot readily accommodate this type of limited For new releases, we adopt NewReleaseStockit =lx,y dependent variable, which is why we use a Tobit Type II model for NewReleaseStockit-1 + ð1 -lx,yÞNewReleaseit because of the dis- the concert revenue equation and integrate it into a system of crete nature of the new-release variable. Its coefﬁcient is a quasi- equations with correlated errors. long-term elasticity.

76 / Journal of Marketing, July 2017 fi p where the variables are as de nedinTable2.Thetermb0i is an (4) zit = 1ifzit > 0, and zit = 0 otherwise, artist-specific intercept that controls for artist differences. The p where the latent variable z is a linear function of a vector of dynamic cross-format elasticity for concert revenue on recorded it independent variables (Zit): music revenue is b1. The hypotheses are tested through the p interactions between lag of concert revenue stock and (1) piracy (5) zit =y0i + Zity+uit. (H : b < 0), (2) unbundling (H : b < 0), (3) music quality 1 9 2 10 The Web Appendix details the full model and the additional (H : b > 0), and (4) fame (H : b < 0). The model controls 3 11 6 12 independent variables that enter Z for identification purposes for the main effects of new releases, advertising, and airplay, and it their interactions, as well as for the main effects of the mod- and that are not part of Equation 3 (e.g., Wooldridge 2002, r p. 564). This concert incidence equation is a selection equation erators and control variables. The term eit is the error term. In the interactions, we mean-center the artist-specific variables by artist that accounts for the fact that artists may strategically choose the means and market-level variables by their grand means before timing of their concerts. To account for unobserved depend- calculating the product term. This allows us to interpret the main encies, we allow for correlation in the error terms of Equations effects to hold at the mean level of the independent variables. 2, 3, and 5.

Identiﬁcation and Model Estimation Model for Concert Revenue Endogeneity. To safeguard against unobserved demand Equation 3 models concert revenues per artist, conditional on shocks that may be correlated with the predictors, we follow a giving a concert: three-pronged approach. First, we include artist ﬁxed effects in

(3) ln ConcertRevenuesit =g0i +g1 ln RecordRevenueStockit-1 all equations, for example, b0i for recorded music (Equation 2) and g for concert revenue (Equation 3). Fixed effects con- +g NewReleaseStockit 0i 2 trol for endogeneity due to unobserved artist characteristics +g ln AdvertisingStock 3 it simultaneously determining demand and the drivers of demand +g4 ln AirplayStockit +g5 ln Piracyt (Germann, Ebbes, and Grewal 2015). Second, as discussed +g6 ln Unbundlingt before, we include Google searches to control for otherwise

+g7 ln MusicQualityit unobserved demand shocks (Archak, Ghose, and Ipeirotis 2011). Third, advertising and airplay may be strategically set +g8Fameit or pushed to capitalize on additional, unobserved demand +g ln RecordRevenueStockit-1 9 shocks, which makes these variables potentially endogenous. · ln MusicQuality it We therefore use a simultaneousequationapproachwithcor- +g10 ln RecordRevenueStockit-1 related errors (e.g., Ataman, Van Heerde, and Mela 2010). We · Fameit +g11 ln AdvertisingStockit model the endogenous regressors as a function of the exogenous · ln AirplayStockit variables and instrumental variables (IVs); the specifications are in the Web Appendix. One IV is the log of total advertising +g12 ln AdvertisingStockit · NewReleaseStock expenditure by artists from different labels and from different it music genres (for a similar approach, see Cleeren, Van Heerde, +g13 ln AirplayStockit and Dekimpe 2013). This variable is unlikely to be correlated · NewReleaseStockit with artist-specific unobserved demand shocks, but it is cor- +g14 ln GoogleStockit +g15Trendt related with the focal artist’s advertising behavior because it fl +g16Quarter2t +g17Quarter3t re ects general advertising drivers (e.g., changing advertising costs). As in other sectors of the entertainment industry, +g18Quarter4t advertising in the music industry typically peaks around the time +g IVConcert +ec . 19 it it of a new release (Moe and Fader 2001; Joshi and Hanssens fi c In this model, g0i is an artist-speci c intercept and eit is an artist 2009). We therefore include as IVs dummy variables that equal i– and time t–specific error term. The dynamic cross-format 1 in the week prior to a new album (or single) release. This elasticity for record revenue on concert revenue is g1.The captures the advertising spike caused by a new release but is hypotheses are tested through the interaction between lag of unrelated to a potential unobserved demand shock because the record revenue stock and (1) music quality (H4: g9 > 0) and (2) new release is not yet on the market. To compute statistical tests fame (H5: g10 > 0). on the suitability of the instruments, we construct models in 2SLS Many artists give only a couple of concerts per year, leading that mimic the full model as closely as possible. The multivariate to zeroes in the observations for concert revenue (e.g., Courty Sanderson–Windmeijer F-test in a model with both advertis- and Pagliero 2011; Krueger 2005; Mortimer et al. 2012). To ing and airplay as endogenous variables shows that the IVs are accommodate this and to control for selection effects, we model sufficiently strong, with an F-value of 221.30 (d.f.1 = 2; d.f.2 = both the incidence of concerts and the revenue conditional on 118,218; p < .001) for advertising and an F-value of 221.90 giving a concert with a (Bayesian) Tobit Type II model with (d.f.1 = 2; d.f.2 = 118,218; p < .001) for airplay (Sanderson correlated errors (e.g., Chib 1992; Van Heerde, Gijsbrechts, and and Windmeijer 2016). The Sargan test shows that the exclusion Pauwels 2008). The incidence of artist i giving a concert in week restriction is satisfied (c2 = .113, d.f. = 1, p = .74). The Hausman– t is captured by a probit model: Wu test rejects the null of no systematic differences in

The Dynamic Interplay Between Recorded Music and Live Concerts / 77 coefficients between a model that corrects for endogeneity and Results a model that does not (c2 = 170.48, d.f. = 3, p < .001). Preliminaries Identification of Equations 2 and 3. We also need to The correlation between actual and predicted log record revenue establish the identification of the two revenue equations. is high (.88), indicating a good fit for this model. The hit rate for Models 2 and 3 capture the dynamic cross-format effects concert incidence is .72 (with a sensitivity of .80 and a spe- by including the lagged stock variable of the other format cificity of .72). Conditional on an artist giving a concert, the on the right-hand side. Note that this is different from model does an adequate job explaining concert revenue (cor- including a lagged dependent variable (of the same format) relation is .55). Across equations, the system R2 is .823. The on the right-hand side; if that were the case, panel data correlations between the independent variables are modest generalized method of moments estimators would need to be (most variance inflation factors are below 2, and all are below considered (e.g., Baltagi 2013). 8), mitigating multicollinearity concerns. The error covariances Note also that we do not include the current dependent between the equations are nonzero, showing the need to allow variable of the other format on the right-hand side; rather, we for these (see the Web Appendix). use the lag of a stock variable of the other format, which helps to Table 3 summarizes the posterior parameter distribution for mitigate simultaneity concerns. Empirically, the cross-format the concert model and the record model (the Web Appendix stock variables obtain carry-over coefficients of l = :9, in x,y contains the results for the other equations). Throughout the both Equations 2 and 3 (see next section). This means there is analysis, we discuss the medians of the posterior parameter only a 10% weight on lagged concert revenue and a 90% weight distributions. We use the 95% posterior density intervals to on earlier concert revenue. In other words, there is a very much assess the significance of the estimates (expressed by “p < .05,” delayed effect of concert revenue on record revenue and vice even though p-values are not strictly Bayesian). versa, with a 90% duration interval of 23 weeks. This time separation reduces simultaneity concerns substantially. To be sure that the focal estimates are not affected by simultaneity, Hypothesis Tests we include one identifying (unique) variable on the right-hand As expected, we find evidence for a spiral of success breeding side of each revenue equation. We need a variable that affects success; that is, if an artist becomes more successful in the demand for a given format but that is uncorrelated with concert market, it enhances the artist’s record revenue, and vice unobserved demand shocks in the other format. For the record versa. However, we find that the cross-format elasticity is much market, we use a step dummy for the introduction of downloads stronger for the effect of records on concert revenue (.237; p < (tracks and albums) that were freed of restrictions imposed by .05) than for the effect of concerts on records (.030; p < .05). digital rights management (DRM). This move was initiated by These elasticities do not yet incorporate cross-equation feed- Apple’s Steve Jobs and was widely implemented in the German back effects; we assess these in the section “Estimating Dynamic market on April 1, 2009. Step dummy DRMt in Equation 2 Effect Sizes.” equals 0 before this week and 1 afterward. It has a negative effect In line with H1,wefind that as piracy increases, the effect of (shown in Table 3) reflecting overall adverse effects on physical concert on record revenue becomes significantly weaker (-.118; music sales. For the concert market, there is no equivalent p < .05). This offers empirical support for the expectation that supply shock. Instead, we use the log of concert revenue from all the link between these two formats has been weakened by file artists from other genres and other labels as an instrument sharing (Krueger 2005). H2 states that the cross-format elasticity (IVConcertitÞ in Equation 3. Importantly, however, the results from concert revenue to record revenue is impeded by do not depend on whether we include or exclude these addi- unbundling. The analysis supports this notion, as we find a tional identifying variables, as the robustness checks show. significant negative interaction between concert revenue and unbundling (-.068; p < .05). H3 suggests that the cross-format Hierarchical Bayes estimation. We use hierarchical elasticity from concert revenue to record revenue is enhanced by Bayes with uninformative priors to estimate the system of music quality. The significant positive interaction (.200; p < .05) equations (Chib and Greenberg 1995; Web Appendix). There supports this hypothesis. H6 argues that cross-buying from are 118,627 observations across 387 artists and 337 weeks concerts to records may be less effective for more famous artists. (some artists enter the market later in the observation period). Indeed, we find that this part of the spiral works significantly We run the Gibbs sampler for 100,000 draws, retaining every worse for more famous artists (-.005; p < .05). tenth of the last 50,000 draws. The chain is well converged We now move to the cross-format elasticity from record within the burn-in sample of 50,000 draws (convergence plots revenue to concert revenue. H4 posits that this cross-format are in the Web Appendix).10 elasticity should be higher for more famous artists. Indeed, we find a significant positive interaction (.018; p < .05), in line with 10We account for artist heterogeneity by estimating artist-specific the prediction that the more famous the artist, the stronger the intercepts. We attempted estimating a model with slope parameter impact of record revenue on concert revenue. heterogeneity, but it was not feasible because for many artists there is H posits that the cross-format elasticity should increase insufficient variation in the concert variable to reliably estimate 5 artist-specific slope parameters. One additional option would arise as music quality increases. While the median of the posterior through a finite-mixture modeling approach, in which latent artist parameter distribution is positive, the 95% posterior density segments are uncovered. We leave this as a potential avenue for interval includes 0, which means that H5 is not supported. In future research. summary, the data provide support for five out of six hypotheses.

78 / Journal of Marketing, July 2017 TABLE 3 Estimation Results Percentiles of Posterior Parameter Draws Hypothesis 2.5 Median 97.5

Record Revenue Model Lagged concert revenue .021 .030 .038 · Piracy H1: - 2.186 2.118 2.049 · Unbundling H2: - 2.095 2.068 2.040 · Music quality H3: + .153 .200 .246 · Fame H6: - 2.006 2.005 2.005 Piracy 2.599 2.502 2.406 Unbundling 2.352 2.323 2.295 Fame .038 .041 .044 Music quality 1.108 1.148 1.187 New release 10.224 10.452 10.686 Advertising .251 .258 .266 Airplay .217 .224 .230 New release · Advertising .328 .481 .633 New release · Airplay -.070 .089 .249 Advertising · Airplay 2.031 2.028 2.025 Trend 2.001 2.001 2.001 Quarter 2 2.090 2.076 2.063 Quarter 3 .002 .016 .030 Quarter 4 .111 .127 .142 Google searches 1.091 1.125 1.159 DRM 2.101 2.083 2.066 Fixed effects Included Concert Revenue Model Lagged record revenue .182 .237 .301 · Music quality H4: +-.017 .097 .222 · Fame H5: + .012 .018 .025 Piracy -.424 .394 1.209 Unbundling -.129 .119 .373 Fame .041 .051 .063 Music quality -.415 .150 .731 New release -1.078 .205 1.481 Advertising -.079 -.030 .016 Airplay .124 .214 .284 New release · Advertising 2.521 2.270 2.019 New release · Airplay -.370 .318 .971 Advertising · Airplay 2.025 2.013 2.001 Trend .001 .002 .003 Quarter 2 .095 .230 .364 Quarter 3 .058 .192 .322 Quarter 4 -.048 .109 .263 Google searches .801 1.133 1.343 Other artist’s concert revenue .346 .384 .413 Fixed effects Included Number of artists 387 Number of weeks 337 Observations 118,627

Notes: Boldface indicates the parameters whose 95% highest posterior density excludes 0. New release, advertising, airplay, Google searches, lagged record revenue, and lagged concert revenue are stock variables.

Marketing Variables and record demand drives concert demand, there is an indirect effect of advertising on concert demand (to be discussed in more Advertising. We estimate a long-term advertising elasticity of .258 (p < .05) for record revenue, which is similar to the meta- detail subsequently). analytic long-term elasticity of .24 reported by Sethuraman, Tellis, and Briesch (2011). In the concert equation, we find an Airplay. For airplay, we find a long-term elasticity of .224 insignificant main effect of advertising, which is not unexpected (p < .05) on record revenues, which is close to the estimate for because advertising in this market is typically directed at records advertising elasticity. The airplay elasticity (.214; p < .05) on and not at concerts. Given that advertising drives record demand concert demand model is similar in magnitude.

The Dynamic Interplay Between Recorded Music and Live Concerts / 79 New releases. New releases are a strong driver of record piracy and unbundling, respectively, and record revenue, as revenues, with a long-term effect of 10.452 (p < .05). If a new drivers of concert revenue. Both interactions are insignificant, album is released, the new-release variable changes from 0 to 1, and all other results remain virtually unchanged. Finally, we and the coefficient captures the cumulative long-term increase estimate the focal model excluding all the marketing inter- in ln RecordRevenue after a new album is released. The strong actions, which leaves all results with respect to the hypothesis effect is in line with the notion that new releases are the lifeblood testing unchanged. In the Web Appendix, we demonstrate the of the record market, and without new releases, many artists sell strong robustness of the findings. Across six hypotheses and few records. While new releases do not significantly affect five robustness checks, only one hypothesis in one test is not concert revenues directly, they do have an indirect effect via consistent with the focal model (1 out of 30 cases). record demand. Finally, we estimate the model excluding the last 52 weeks We also obtain insights on marketing interactions. For the of the data as a holdout sample. We next predict the dependent record model, the interaction between new releases and ad- variables for this holdout sample. In the Web Appendix, we vertising is positive (.481; p < .05), suggesting that informing show that the hypothesized model performs similarly well in the consumers about new albums has synergistic effects. Inter- estimation sample and the holdout sample, mitigating over- estingly, the interaction between advertising and airplay is fitting concerns. negative (-.028; p < .05). This suggests a saturation effect (Burmester et al. 2015): when consumers are exposed to a lot of airplay by one artist, the marginal effectiveness of advertising Estimating Dynamic Effect Sizes weakens (and vice versa). We find a similar negative interaction The focus of this study is the cross-format elasticities between effect (-.013; p < .05) between advertising and airplay and concert and record revenue and how they change due to between new releases and airplay in the concert model (-.270, technological developments and artist characteristics. We have p < .05).11 established statistical significance for most of the hypotheses (five out of six), but we also would like to assess effect sizes to Control Variables establish managerial significance using dynamic simulations. The effects of the control variables are in line with expectations. The approach enables us to quantify how the effect of the cross- The main effect of piracy on record revenue is significantly format elasticity changes in response to the moderators, while negative (-.502; p < .05), as expected, and the same applies for taking all interactions and potential feedbacks between formats the main effect of unbundling (-.323; p < .05) and the general into account, as well as parameter uncertainty. trend (-.001; p < .05). In line with the expectations, fame (.041; We start by simulating the impact of a 1% shock in concert p < .05) and music quality (1.148; p < .05) have significant revenue for an average observation at the sample mean. To this positive effects on record revenue. Google search volume is end, we add .01 to ln ConcertRevenue, compute the new value significantly positively associated with record revenue (1.125; for the corresponding stock variable, and use the model esti- p < .05). The quarterly dummies pick up seasonality in record mates to predict the resulting changes in record revenue. These revenue (e.g., a spike in quarter 4 due to Christmas). changes in record revenue then carry over to the concert Google searches (1.133; p < .05) are also positively as- incidence and concert revenue of the next period, which again ’ sociated with concert revenue, as are fame (.051; p < .05) and affects next period s record revenue. We then cumulate the the general trend (.002; p < .05). The quarterly dummies capture effects over 52 weeks to assess the full long-term impact of seasonality in concert revenue (e.g., spikes in quarters 2 and 3 this shock. For each moderator, we repeat this process for a due to outdoor concerts). range of values from low (one standard deviation [SD] below the mean) to high (one SD above the mean). To account for Robustness Checks parameter uncertainty, we repeat the simulation for 250 random draws from the Gibbs sampler in the Bayesian esti- We conduct five robustness checks. First, we estimate the model mation and use these to assess the posterior distribution of the on those artists who are part of the PollstarPro data, and we drop effect size. all other artists. Despite the substantial loss of data, the results Figure 2, Panel A, shows how piracy affects the cross- are very similar (for details of all robustness checks, see the Web format elasticity from concert revenue to record revenue. The Appendix). The only difference is that the interaction between elasticity essentially halves when piracy increases from low to piracy and concert demand is insignificant in this robustness high. This again supports the notion that the cross-buying link check. Second, we estimate the full model but replace revenue between these two formats has been attenuated by the preva- with unit sales throughout. The substantive results are very lence of online file sharing (Krueger 2005). The attenuating similar to the focal results. Third, we run a model variant in effect is similarly strong due to unbundling (see Figure 2, Panel which we exclude the additional identifying variables (DRM t B). The implication is that while technological developments from Equation 3 and IVConcert from Equation 4), with again it brought about by the Internet weaken the impact of concert very similar results. Fourth, we include the interactions between revenue on record revenue, this is due not only to piracy but also to unbundling. 11We speculate that the latter finding could be due to a lack-of- familiarity effect: if there is a lot of advertising for the artist’s new The cross-format elasticity from concert demand to record music, this could lead to adverse responses among those consumers demand also changes substantially due to artist fame (Figure 2, who primarily attend concerts to experience the artist’s existing Panel C). An artist low on fame has a cross-format elasticity music. Future research could address this angle. (around .08) that is about twice as strong as that for an average

80 / Journal of Marketing, July 2017 FIGURE 2 Simulated Effects of a 1% Shock in Concert Revenue on Record Revenue A: As Piracy Increases, the Impact B: As Unbundling Increases, the Impact of Concert Revenue on Record of Concert Revenue on Record Revenue Decreases Revenue Decreases

.06 .06

.05 .05

.04 .04

.03 .03 Cross-Format Elasticity Cross-Format Elasticity

.02 .02

Low Mean High Low Mean High Piracy Unbundling

C: As Fame Increases, the Impact D: As Music Quality Increases, the Impact of Concert Revenue on Record of Concert Revenue on Record Revenue Decreases Revenue Increases

1 .08

.06

.5 .04

.02

0 Cross-Format Elasticity 0 Cross-Format Elasticity –.02

Low Mean High Low Mean High Fame Music Quality

artist, and for famous artists, the cross-format elasticity is essentially revenue on concert revenue, from about .1 to .5 (Figure 3, Panel zero. Thus, a less famous artist can use concerts much more as a B). Thus, for a famous artist there is a much stronger spillover driver of record revenue than a more famous artist. Figure 2, effect from the record market to the concert market than for a Panel D, shows that music quality has a managerially strong less famous artist. positive moderating effect on the impact of concert revenue on record revenue. The cross-format elasticity of record revenue on concert Discussion and Implications revenue (Figure 3) has a mean around .27, which is much This study fits in a broader stream of articles looking at marketing stronger than the reverse elasticity of concert revenue on record issues in the entertainment industry, including movies (e.g., revenue (Figure 2), with a mean of .04. Figure 3, Panel A, shows Eliashberg et al. 2006), theme parks (Van Oest, Van Heerde, and that as music quality increases, the cross-format elasticity from Dekimpe 2010), and music (e.g., Dewan and Ramaprasad 2012; record revenue to concert revenue increases. However, the Elberse 2010; Saboo, Kumar, and Ramani 2016). The enter- confidence interval shows substantial uncertainty for this effect, tainment industry continues to see substantial changes due to in line with the lack of support for H5. Interestingly, as fame new technologies. The empowerment of consumers through increases, there is a fivefold increase in the impact of record new technology has strongly impacted many firms’ business

The Dynamic Interplay Between Recorded Music and Live Concerts / 81 FIGURE 3 Simulated Effects of a 1% Shock in Record Revenue on Concert revenue A: As Music Quality Increases, the Impact B: As Fame Increases, the Impact of Record Revenue on Concert Revenue of Record Revenue on Concert Increases, but with a Wide Confidence Interval Revenue increases

.5 .6

.4 .4

.2 .2 Cross-Format Elasticity Cross-Format Elasticity

.1 0

Low Mean High Low Mean High Music Quality Fame

Notes: The heavy black line is the mean across all draws for a given level of the moderator; the gray band indicates the area between the 2.5th and the 97.5th percentile. Low level of moderator = one SD below mean; high = one SD above mean. models in several sectors of the industry. In particular, rapid run into the millions of dollars for some artists, so even small advancements in (Internet) technology have caused changes in elasticities represent a lot of revenue. how consumers obtain music (legally or illegally) and how they The third caveat is that the demand spiral changes in re- enjoy music. At the same time, concerts are thriving and have sponse to technological advancements. The analysis uncovers surpassed records as the main revenue stream in some key severe threats to the cross-format elasticity from concert rev- markets, such as the United Kingdom (Michaels 2009), and for enue to record revenue. Piracy allows consumers to download the artists we study in the German market. records illegally, weakening this cross-format elasticity, in line This research contributes to the literature by providing a with our hypotheses. On top of that, unbundling allows con- theoretical and empirical analysis of the dynamic and changing sumers to cherry-pick the tracks they like the most. This again relationship between recorded music and concert revenue. Our attenuates the cross-format elasticity, consistent with our the- findings represent an important extension of the literature (e.g., orizing. While piracy is a phenomenon that sparked the Krueger 2005; Mortimer et al. 2012), which has so far assumed attention of academic research (e.g., Krueger 2005) and worries that concerts and records have cross-format effects. To the best many industry players, we find that the attenuating effect is of our knowledge, ours is the firstempiricalstudytoshowthat equally strong for unbundling. In addition, we find the mod- these cross-format effects exist, how strong they are, that they erating effect of unbundling to be very stable in all robustness are asymmetric, and that they are moderated by technological checks, whereas the interaction with piracy is insignificant advancements and artist characteristics. in one robustness check. This highlights that while we have The analysis of close to 400 musicians across more than six evidence that piracy attenuates the link from concerts to records, years of weekly data shows evidence for a reinforcing spiral artists should be at least as concerned about unbundling. This of “success breeds success.” If an artist is successful on the finding is new and contributes to the literature’s understanding record market, this will enhance concert revenue, which in turn of how these two formats interact. Furthermore, this finding enhances demand for the artist’s recorded music. However, this extends our knowledge about the role of unbundling. Elberse conclusion comes with several caveats. First, there is a general (2010) introduces this concept into the literature and highlights downward trend in record revenue and a general upward trend in the negative main effect on demand. We extend this finding concert revenue, so the cross-format effects we discuss operate by showing that—in addition to the negative main effect— at the margin: a marginal increase in the revenue of one format unbundling weakens the cross-format elasticity from concerts to enhances the revenue of the other format. records. The second caveat is that this spiral is asymmetric. The What are the bright spots? One is that piracy seems to be on asymmetry is that the dynamic cross-format elasticity from the decline in some markets, such as Germany (see Figure 4, record revenue to concert revenue (with a mean of .27) is much Panel B), which could undo the negative impact of piracy on stronger than the reverse cross-format elasticity from concert the cross-format elasticity from concert revenue to record rev- revenue to record revenue (with a mean of .04). Thus, record enue. The upward movement of unbundling is also slowing success breeds concert success, but the reverse is much less the down (Figure 4, Panel A), which means that its harmful effect case. At the same time, revenue streams from both sources does not get stronger. Figure 4, Panel C, shows the joint impact of

82 / Journal of Marketing, July 2017 FIGURE 4 The Observed Levels of Unbundling and Piracy over Time and the Resulting Cross-Format Elasticity from Concert Revenue on Record Revenue A: Development of Unbundling in Germany over Time

.4 Unbundling

2004 2005 2006 2007 2008 2009 2010 Year B: Development of Piracy in Germany over Time

3.5

Piracy 3

2.5

2004 2005 2006 2007 2008 2009 2010 Year C: Concert-to-Record Revenue Cross-Format Elasticity over Time as a Function of Unbundling and Piracy

.08

.06

.04

.02 Cross−Format Elasticity 0

2004 2005 2006 2007 2008 2009 2010 Time

Notes: Unbundling is the ratio of unbundled sales (singles) to total record sales (singles + albums). Piracy is the number of German Internet users (in millions) having illegally downloaded music from the Internet in a given year. In Panel C, the heavy black line is the mean across all draws for a given level of the two moderators; the gray band indicates the area between the 2.5th and the 97.5th percentile. piracy and unbundling on cross-format elasticity by calculating format elasticity from concert revenue to record revenue has, on this elasticity across the observed levels of piracy and unbundling balance, stayed stable across the observation period. The fact that across the observation period 2004–2010. On the basis of the piracy appears to be declining while unbundling is increasing joint impact of both moderators, we conclude that the cross- results in two opposing forces, and the cross-format elasticity

The Dynamic Interplay Between Recorded Music and Live Concerts / 83 would be much lower today if piracy were on the level that we who have learned about the artist through airplay and listening to saw, for example, in 2004. This substantially extends the findings recorded music. A key insight from this research is therefore that by Krueger (2005), who does not consider unbundling or artists across the spectrum should have a strong interest in giving foresee a scenario in which piracy might actually decline. concerts, albeit for different reasons depending on their fame. For A silver lining also applies for artists who are (still) rela- famous artists, concerts represent a core format through which to tively low on fame. They benefit from giving concerts, which reap what has been sown through selling records and producing stimulate record revenue to a larger extent than those of more hits. For less famous artists, concerts represent a seed that can be famous artists, in line with the hypothesis. In a sense, a road to harvested through selling records. success for a relatively new artist is still through giving concerts, This finding also implies that labels, especially the ones although, of course, this is not the only path in today’s online promoting famous artists, should intensify their attempts to world. Most likely, these findings do not apply for completely benefit from revenue earned on the concert market. The fact that new or unknown artists, who are not contained in our sample. records stimulate demand for concerts means that labels’ mar- Furthermore, we find that quality does pay off for record keting efforts indirectly contribute to revenues that the labels do revenue, not only in a direct way (significant main effect) but not benefit from under standard agreements. From this per- also as a facilitator of the cross-format elasticity from concert spective, labels’ attempts to install deals that cover both record revenue to record revenue. The better the peer-rated quality of and concert revenues are justified. Madonna is a prominent the music, the greater the impact of concert revenue on record example of an artist whose activities in both recorded music and revenue, as we hypothesized. The magnitude of the two artist- concerts are covered by one agency (Live Nation). related moderating effects (Figure 1, Panels C and D) on the cross-format elasticity from concert revenue to record revenue is Marketing actions: advertising, new releases, and airplay. sufficiently strong to counter the negative moderating effects of We find that marketing actions have much more of a direct piracy and unbundling (Figure 2, Panels A and B). We do not impact on record revenue than on concert revenue. New releases find support for a moderating role of music quality on the effect are the lifeblood of the record market, confirmed by a strong of record on concert revenue. One potential explanation could effect on record revenue, but they do not impact concert revenue be that we measure music quality as the satisfaction with the directly. Advertising in this empirical context is primarily recorded music and not as the quality of the concert. focused on selling recorded music, not on promoting concerts. Aseeminglyconflicting finding is that music quality Accordingly, we find a positive main effect of advertising on strengthens the cross-format effect from concert revenue to recorded revenue but not on concert revenue. We also find that record revenue, whereas fame weakens it. However, the latter the record revenue impact of a new release is enhanced through finding is in line with the notion that a more famous artist has advertising. In short, there is little to no direct gain from new experienced past sales success (in line with the artist’sbill- releases and advertising on the concert market, whereas the board hits, which we use to operationalize fame) as well as record market does benefit. Airplay is the only variable that has a current sales success (in line with the positive main effect of positive main effect on both record revenue and concert revenue, fame on record demand). This means that a lot of consumers but it comes with negative interaction effects with advertising. already own the current and past music of a more famous artist However, once we consider indirect effects (i.e., via the and that cross-buy of the artist’s recorded music after a other format), the picture changes. The strong increases in consumer attends a concert is less likely. Importantly, the record revenue that can be accrued due to new releases, ad- correlation between fame and music quality is very low (.02), vertising, and airplay will benefit the concert market through the suggesting that an artist’s ability to create high-quality music is cross-format elasticity from records to concerts. Consider the distinct from the artist’s ability to create music that appeals to case of a 1% increase in advertising. The estimation results the masses. (Table 3) indicate that this 1% increase lifts record revenue by For the other side of the demand spiral, the cross-format .26%. For the average artist, the cross-format elasticity from elasticity from record revenue to concert revenue, the implica- records to concerts is .27, which means that concert revenues tions are even more upbeat. Not only is it stronger to begin with increase by .07% (.27 · .26% = .07%). This indirect effect is than the reverse elasticity, but there is no evidence for tech- even more pronounced for more famous artists, for whom a 1% nological developments threatening this cross-format elas- increase in advertising leads to a .13% increase in concert ticity. We find that the more famous an artist, the stronger the revenues. Thus, artists on the concert market can indirectly cross-format elasticity from record revenue to concert revenue, benefit from their marketing activities. The fact that the direct which is line with the expectation. Thus, we uncover the novel effects of marketing actions (except airplay) are less beneficial to and interesting finding that fame is a double-edged sword: it concerts than the indirect effects means that only artists who are makes it easier to convert record revenue into concert revenue, but successful in the record market can reap the benefits of mar- it makes it harder to use concerts as a driver of record revenue. keting in the concert market. Technology is constantly evolving, so the music industry is by no means in a new equilibrium. Rather, technological Managerial Implications developments will continue to shape the relations that we This research has important additional implications for artists and analyze in this article. On the one hand, piracy is on the decline labels. Their attempts to stimulate record revenue (e.g., through (Figure 4, Panel B), and theory indicates that the link between new releases or advertising) translate into revenues from the concert revenue and record revenue should therefore at least concert market. Concerts capitalize on the base of potential fans, not decline any further. On the other hand, music labels use

84 / Journal of Marketing, July 2017 streaming services as a new business model to combat piracy, Limitations and streaming can be seen as yet another variant of recorded As is true for any research, this study has limitations that offer music that is even further unbundled. In the observation period, opportunities for future research. One limitation is that we streaming music was not yet relevant, but today it is becoming a cannot assess profitability, since we do not have access to cost major phenomenon (e.g., Wlomert¨ and Papies 2016). Hence, and profit margin data. Organizing concerts is expensive, streaming may further reduce the positive cross-format elas- especially in the light of ever-increasing expectations for ticity from concerts to record sales. spectacular shows and ever more stringent safety requirements. In addition, artists generate revenue from other sources during Other implications for the entertainment industry. Our concerts, such as merchandising, which we do not observe. findings highlight that many artists have found a way of meeting Another limitation is that we only observe concert revenue at the challenges fueled by technological developments. Concerts the moment the concert took place, which is a limitation that become increasingly relevant as a revenue source as record this research shares with other research on concerts (e.g., Courty revenues decline. This shows that consumers are strongly and Pagliero 2011; Krueger 2005; Mortimer et al. 2012). interested in the core product of the artists: the music. The Many concert tickets are sold weeks or months in advance of consumption mode for music is, however, constantly changing. the concert. In the analyses, this is accounted for through the Similar developments may be relevant for other sectors of the observations on the drivers in the lead-up period to the entertainment industry. Consider the book industry, wherein concert as drivers of concert demand. In addition, we observe illegal e-books are becoming a threat. The results suggest that concert revenue only for a subset of all concerts. We therefore consumers will still be interested in the core product—authors impute the attendance rate for the remaining observations. and books—but may also seek new ways of consuming. Live The results are fairly robust even if we estimate the model that events (e.g., authors reading from their books for an audience, in only considers artists contained in the PollstarPro sample. person or online) may be a powerful way of reaching the However, we acknowledge that it would be desirable to use a audience and triggering future sales. The strategic challenge is data set that contains complete information on all concerts. that firms need to properly define the formats that are relevant Furthermore, the sample includes close to 400 of the most for consumer enjoyment, as well as the resulting business successful artists during the observation period, rather than a model. A music label that defines its business model as pro- random sample. All variables show substantial variance (e.g., moting and delivering records to consumers will most likely some artists spend very little on advertising or receive little not survive the changes that were fueled by the technological airplay). Combined with the fact that we consider artist-level developments analyzed in this study because its business model heterogeneity through the moderator of fame, we believe the and competitors will be too narrowly defined (Levitt 1960). results can be generalized. However, it may be desirable to When piracy and unbundling started to unfold their impact, the test the hypotheses on an even larger random sample of artists music industry initially struggled to address consumer prefer- with more unknown artists. ences. The reaction of some artists to these new demands shows Finally, we study a specific dual-format industry (music) how firms can actively seek new opportunities when techno- and moderating effects of technological developments that logical innovations force business models to change. It would are most germane to that industry (piracy and unbundling) have been easy to interpret the decline of record sales as an and cross-sectional characteristics that are key in this con- indication that consumers were losing interest in music and text (quality and fame). We cannot generalize all moderators engaging in alternative entertainment instead. However, many to other multiformat goods, although at a meta-level, quality artists recognized that the interest in the product was still high, is an asset that facilitates cross-buying, and the digitization but consumers were looking for new ways to enjoy it. This of information represents a major opportunity and threat may serve as an encouraging example for industries that see for many (single- and multiformat) industries. We hope consumer interest in their products slip, for example, tradi- this article stimulates new research on how technological tional newspapers, video rental stores, and travel agents. The developments impact the business model of other multi- underlying consumer needs are stronger than ever before, but format (entertainment) goods and services. Despite these the preferred delivery format is changing. Firms that can rec- limitations, we believe this study makes an important step ognize the underlying consumer need but anticipate or even toward an understanding of how the music industry, with orchestrate the change in format will lead the industry. its multiple formats, is evolving.

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