Do Artists Benefit From Online Music Sharing?

Transcription

1 Do Artists Benefit From Online Music Sharing? Ram D. Gopal* School of Business University of Connecticut 2100 Hillside Road Unit 1041 IM Storrs, CT , USA Ph: (860) Fax: (860) Sudip Bhattacharjee School of Business University of Connecticut 2100 Hillside Road Unit 1041 IM Storrs, CT , USA Ph: (860) Fax: (860) G. Lawrence Sanders 310A Jacobs Management Center State University of New York at Buffalo Buffalo, NY 14260, USA Ph: (716) Fax: (716) *Corresponding author Revised February 2004

2 Do Artists Benefit From Online Music Sharing? Abstract Music is an information good, and more specifically, an experience good, whose true value is realized only after its consumption. At its fundamental form, artists create (or produce) the music that consumers pay to listen. Digital technologies and network based sharing/distribution mechanisms have created tremendous opportunities and challenges for producers and consumers of such goods. This paper models the economics of online music sharing and addresses the fundamental question: who benefits? The model incorporates economic and technological incentives to sample, and analyzes the effect of consumers potential purchasing and pirating decisions. The analysis reveals several key insights. The revenue impact will be more closely related to the intrinsic value of the music to consumers, as these technologies enable users to discover the true value of music more easily. Contrary to conventional wisdom, lowering the cost to sample music will propel more consumers to purchase music online, as the total cost of evaluation and acquisition decreases. Attempts to prevent sampling will be counter productive in the long run. Reducing the cost to sample may encourage some consumers to pirate music; but it also enhances the legitimate customer base by decreasing the total cost of music acquisition. This, along with increasing enforcement aimed at individual consumers and effective pricing schemes, will enable artists to effectively leverage the emerging technologies. The model also sheds light on the conflicting opinions by the artist community on the impact of sharing technologies. We find that sharing technologies erode the superstar phenomenon widely prevalent in the music business. Extensive empirical investigations, based on surveys and Billboard Ranking Charts, lend support to the economic model and validate the key results. Implications from this study are generally applicable to buyers and sellers of other similar digital experience goods.

3 Do Artists Benefit From Online Music Sharing? 1. Introduction Several recent high profile legal cases have focused a renewed interest on digitized products, intellectual property and related copyright and pricing issues in an increasingly Internet-enabled world (Clark 1999, Bravin 2000, Gomes and Mathews 2001, Gomes 2001, Federal Trade Commission 2000, Crombie 2001). Downloading, sampling and sharing digital goods by Internet users who do not own it in other forms has become a major issue. For the music recording and distribution industry, for example, this problem has turned quite acute 1. According to a study (Pew 2000), about 14% of Internet users have downloaded digitized music files from the Internet for free. This number is likely to grow rapidly, and illegal online music sharing is estimated to result in annual sales losses of $3.1 billion by 2005 (Clark 2000). The technology that facilitates such online sampling of digital audio and other digital goods is improving rapidly. Various software packages make it increasingly easier for consumers to search, download and subsequently share music files online with others (Ahlberg 2000). These use variations of peer-to-peer network models to share music in compressed formats 2, with comparatively minor losses in sound quality. This phenomenon of digital music sharing was predicted by Alexander (1994b). With an increase in Internet connection speed, and availability of better search techniques, search and download times for these digital goods is being cut down significantly 3. Experts forecast that decreasing prices for data storage and faster connection 1 MP3, the most popular format for compressed music, is one of the most searched for term on the internet ( 2 MP3, for example, compresses original audio sources up to 12:1. 3 The entire file acquisition (searching, downloading, and listening) procedure is merely a matter of several mouse clicks and 1-2 minutes of waiting. 2

4 speeds will allow consumers to use in the near future to send entire disks of music (Clark 2000). Music (and other goods such as digitized photographs and video clips) is an information good, and specifically, an experience good, whose true value to a consumer is revealed only after its consumption (Nelson 1970). It needs relatively little time (and skills) to consume. In addition, it has the characteristics of a quasi-public good, in that once the good is provided to some consumers, it is very difficult to preclude other consumers from consuming it. In this context, the so-called free-rider, an individual that consumes a public good without actually paying for it, can undermine market efficiencies (Alexander, 2002). Further, music items are also affected by network externalities, such as fan clubs which share information. Digital technology undoubtedly makes such information sharing, and sampling of such goods, easier 4 (Barua, et. al. 2001) and less costly (Cunningham, et. al. 2003), as the positive externality created by more samples lead to more sharing, and further reduce the cost of information sharing and sampling. However, from a long-term perspective, it is unclear whether such technology hurts intellectual property and related revenues. Intellectual property has potentially been under threat ever since the development of the printing press (Shapiro and Varian 1999); however, historically, new business models have consistently embraced new technology to generate increased revenues from intellectual property. The central motivation for our study stems from the fact that digital technology and products provide potential new avenues for revenue generation. Our primary focus is on the economic principles for such experience goods that potentially guide consumers and producers into new business models to reap benefits from the new medium. At a fundamental level, artists create (or produce) music which consumers pay to listen (and enjoy). The dissemination of music has used various forms and technologies over time. The 3

5 effect of online sharing technologies on music sales is inconclusive, and is largely based on anecdotal journalistic evidence (King, 2000a, King 2000b, Mathews 2000a, Mathews and Peers 2000, Peers and Gomes 2000, Evangelista 2000). The proponents of online sharing argue that a lot of consumers download music to sample it and then subsequently purchase a CD if they like the music, and that sharing serves a useful marketing function by broadening the market for music (Boston 2000). Some have claimed that there is little evidence that online music sampling has actually decreased overall sales (Mathews and Peers 2000, Peers and Gomes 2000). It also potentially benefits artists by helping new artists to become known. Proponents also argue that digital compression decreases the quality of music in relation to a CD 5, hence consumers with a high value for music would eventually purchase the higher quality CD 6. Opponents of online music sharing, in particular the recording industry and some artists, argue that it undermines CD sales. Their fundamental concern is ( How can you build a business when the product you have developed is being cloned and given away on a mass scale for free? They argue that piracy threatens the future of artists, composers, and record producers. Many experts are critical about the long-term effectiveness of focusing on Internet piracy facilitators (Garber 1996, Hardie et al. 1999, Jerry 1987, Mason 1990). The common refrain is that the genie is now out of the bottle and that simply shutting down such services will have limited effect (Hansen et. al. 2000, McCarthy 2000). Despite the scope and publicity that music sharing technology has triggered, little research exists on the effect that digital music sampling 7 has on subsequent music sales. A substantially larger body of research has examined the related problem of software piracy. While software and music are both information goods 4 Online fan clubs exist for numerous popular performers. 5 In subsequent discussions, references to CD does not exclude other high quality recording media Sampling may or may not lead to piracy. 4

6 in that the marginal cost of production is virtually zero, certain key characteristics differentiate the two - i) quality of an original CD song is better than that of its electronically transferable compressed version. Software, on the other hand, requires a loss-less compression for proper functioning, ii) music files are much smaller than a typical software application, hence they take much less time to transfer and consume, iii) consumption of music requires little specific skills as compared to software, hence the increased consumer base adds significant dynamics to the issue, iv) consumers closely relate a performer with a music product, unlike developers with a software product, which create issues of personalized valuations for that musical product that depend on the performer, and v) the volume of available music is significantly larger than the existing volume of software products. This provides a far greater product sampling base, compared to software products, which introduces additional levels of dynamics in music sampling and its analysis. The focus of this research is on the economic dynamics of online digital music sampling. In particular we study i) potential consumer benefits from digital music sampling technologies; ii) the effect of such technologies on their purchasing and pirating behavior; and iii) the impact of new sampling technologies on sales of music superstars. First, we develop an economic model that incorporates incentive structures for producers and consumers of music items, and derive their implications for consumer surplus and producer profits. We then identify various market scenarios that affect the incentive structures and study the economic outcomes. Important propositions derived from the analyses state that i) with the advent of new technology, consumers incentive for sampling and buying a music item is closely related to the value of the item to an individual consumer, ii) the producer, via economic and technological instruments, can effectively combat piracy and increase revenues, and iii) 5

7 sampling technologies threaten the phenomenon of superstars, and this threat is proportional to the cost of sampling. Extensive empirical investigations, based on surveys and Billboard Ranking Charts, lend support to the economic model and validate the key results. The remainder of the paper is organized as follows. Section 2 discusses the related literature. Section 3 develops an analytical model of the economic factors, and Section 4 presents the empirical study. A discussion of the key results and concluding remarks are the subject of Section Related Research Digital goods are expensive to produce for the first copy (high fixed costs), and inexpensive to reproduce and distribute for subsequent copies (near zero variable costs). Digital products also exhibit the fundamental characteristics of a public good in that sharing with others does not reduce the consumption utility of the product. These traits of digital products facilitate the widespread and often illegal distribution worldwide. The growing importance of digital piracy has spurred research on the behavioral and economic understandings of piracy activity, especially in the area of software piracy (Conner and Rummelt, 1991, Givon, et. al. 1995, Gopal and Sanders, 1997, Gopal and Sanders, 1998, Gopal and Sanders, 2000, Eining and Christensen 1991; Glass and Wood 1996; Solomon and O'Brien 1991). Studies have reported that females, older individuals, and individuals with an ethical predisposition towards legal justice tend to pirate less. However, as mentioned earlier, digital music sampling has several unique characteristics, and little research exists on its effect on future sales. The literature on software piracy suggests that economic factors play a key role in an individual s decision to pirate. A number of studies have reported that the price of software has a 6

8 significant impact on piracy (Cheng, et. al. 1997, Gopal and Sanders, 1997, Gopal and Sanders, 2000). As the price of the product increases, the net value from obtaining an illegal product increases, and hence the negative impact of price on piracy. Gopal and Sanders (1998) highlight the income effect on the national piracy levels. Their main recommendation is that the price of the product should be indexed with the affordability levels. In a similar result for global audio piracy, Burke (1996) finds that economic development, rather than copyright regulations, differentiate high and low piracy nations. The value of the product also plays a crucial role on an individual s decision higher valued consumers typically tend to purchase rather than pirate because their realize higher surplus from consumption (Cheng, et. al., 1997, Conner and Rummelt, 1991, Gopal and Sanders, 1998). Technology that facilitates (or hinders) the piracy activity also plays an important role in piracy behavior (Conner and Rummelt, 1991, Gopal and Sanders 1997, Bhattacharjee, et. al. 2003). Conventional wisdom suggests that easier access to pirated software increases piracy activity. Software firms have employed piracy protection technologies that raise the cost of pirating software as a weapon to combat piracy. Technologies such as encryption have been used to prevent easy piracy, with the intent that the prohibitive cost would cause would-be pirates to legally purchase the product. However, Gopal and Sanders (1997) argue that in the face of increasing preventive controls, individuals who do not legitimately acquire a digital good would simply do-without it, and this behavior represents a drain on software publisher profits. They advocate the use of deterrent controls that rely educational and legislative schemes to thwart piracy and increase seller revenues. Some studies have argued that software piracy may not necessarily harm software publishers. Conner and Rummelt (1991) argue that in the presence of network externalities 7

9 (where the value a user derives depends on the size of the user base), the utility of the software increases with piracy because it increases the number of other individuals using it. The utility of product consumption increases with the total number of individuals using it. Givon et. al (1995), using innovation diffusion models, suggest that piracy provides a word-of-mouth advertising for the software product and thus represents an efficient form of sampling that leads to a future purchase. asymmetries, is: The economic argument, as it relates to incomplete information or information If consumers do not have accurate information about market prices or product quality, the market system will not operate efficiently. This lack of information may give producers an incentive to supply too much of some products and too little of others. In other cases, some consumers may not buy a product, even though they would benefit from doing so, while other consumers may buy products that leave them worse off. For example, consumers may buy pills that guarantee weight loss, only to find that the pills have no medical value. (Pindyck and Rubinfield, p. 612, Microeconomics, fourth edition, Prentice-Hall, 1995) In essence, music consumers do not have accurate information on the quality of the music, because it is an experience good. Music publishers, because of the delay in obtaining market information for all of their music, may over-invest in certain music genre and underinvest in others. A typical strategy to overcome the inefficiencies and uncertainties in the market is to focus on the superstars. Music sampling has the potential to reduce this uncertainty, increase market efficiencies and to permit a broader-base of talented musicians and singers to be successful. The economics of music includes studies in the market structure of firms in the industry (Alexander 1994a, 1997), and the effect of chart success of an album on future sales (Strobl and Tucker 2000). The phenomenon of the superstar effect in music, and its related economics, has 8

10 been well-documented. A superstar owes his or her existence to intrinsic elements of talent (Rosen 1981), extrinsic elements of circumstance, or luck, and user expectations based on past performance (Adler 1985, Hamlen 1991, MacDonald 1988, Towse 1992). Models of superstardom have been presented by Chung and Cox (1994), Ravid (1999) and Crain and Tollison (2002), among others. At the heart of the superstar phenomenon lies the desire by consumers to minimize their search and sampling costs by choosing the most popular artist (Adler, 1985). Music as an experience good requires information (or knowledge) about the particular item before its consumption. The search for information is costly, especially for relatively unknown artists. In such a case, consumers balance their additional search costs for unknown artists or items of music with their existing knowledge of a known popular artist. In a statistical sense, consumers correlate past performance with future outcomes (MacDonald, 1988), and try to minimize the variability in their expectations of individual performances. There is also evidence that new release recordings have higher demand elasticities than older or wellknown music items (Mixon and Ressler 2000). Behavioral models of digital piracy are important for initiating educational and legal campaigns to reduce piracy (Mathews 2000b). These constitute indirect methods to modify consumer ethics and attitudes. They are investigated in detail in (Gopal et. al. 2001), which also reports that deterrent strategies used successfully in anti-software piracy campaigns have limited effect on music piracy. Additionally, music publishers are acknowledging that they have to make buying music easier than stealing music

11 3. Analytical Modeling of Sharing Technologies Do Artists Benefit from Online Music Sharing? In this section, we present an economic model that analyzes consumer incentives to sample, pirate and purchase music offerings, and the ensuing impact on seller s revenues. The model also captures the economic implications of sharing technologies on music offerings by well-known and relatively unknown artists. This is followed by an empirical model that is designed to validate the key results of the economic analysis, via both primary and secondary data sources. Commercially offered music is a quintessential experience good whose true value is revealed to the consumer only after the initial consumption (i.e. experiencing or sampling the music). The volume of music that is commercially available is vast, and spans panoply of genres, languages, artists, and themes. Consider a music item 9 that a consumer can legally purchase at a price of P. Let the function f i (v) denote consumer i s probability density function of the value for the music offered, and let 0 v V i. According to this denotation, V i represents the maximum value attached by consumer i to music in general. Of course, this varies across consumers, and those with larger values of V i display a higher affinity for music consumption. The function f K i (v) captures a priori expectations of music item K by consumer i, and it is a proper p.d.f. in that it satisfies the V i K i condition f ( v ) dv = 1. 0 The uncertainty depicted in f K i (v) above arises from the a priori expectation of the value of music item K based on the artist s reputation and information available about K. Given the large volume of available music, no consumer has all information on all music items. Further, 9 In the model development, the unit of analysis is an item of music. This can represent a single piece of music if it can be bought solo or a CD that contains a collection of songs. 10

12 music is an experience good whose true consumption utility can only be determined through a costly knowledge acquisition process. Hence the functional form relates to a consumer s prior exposure to the particular music, and can vary across consumers, and also across music items under consideration. The variance of v is the consumer s uncertainty regarding the underlying value of the music item. Traditionally, variance reduction for a music item occurs through coverage in the press, live performances, music-oriented radio stations, music television outlets, and other mechanisms that advertise the value of the music item to consumers. An important determinant of the variance is the artist(s) who created the music item. This arises from the prevalence of stardom in the music industry. From an economic standpoint, stardom is a market device used by consumers to economize on the learning and information acquisition costs. Instead of diversifying indefinitely across a large number of artists which may necessitate significantly large costs of searching and learning, consumers may prefer to patronize a limited number of stars (Adler, 1985). Being a known entity whose music is widely listened to and enjoyed amongst other users, a consumer would place a lower uncertainty on the music released by the superstars. A consumer who decides to directly purchase the music item K realizes the following expected benefit. E direct buy = Vi vf 0 K i ( v )dv P (1) 3.1 Consumer Incentives with Sharing Technologies Internet-enabled sharing technologies enable consumers to reduce the information uncertainty regarding commercially available music. Much to the chagrin of the recording industry, they also permit users to obtain illegal copies of the music without paying proper remuneration to the 11

13 recording industry, the artists, and other entities involved in the creation and distribution of the music. A consumer who considers using online sharing technologies to experience the music without first paying for it incurs a cost that we denote as C sample. This captures the time and effort expended by the user in searching, downloading, and listening to the illegal copy of the music 10. If a consumer decides to sample, C sample becomes a sunk cost. The following discussion details the economic benefits to a consumer from sampling 11. A consumer decides to sample if the net expected benefit from sampling is positive and larger than the benefit from direct buying (equation 1). After downloading a song, a consumer faces three subsequent choices: buy a legitimate version of the song, keep the illegal copy (this constitutes piracy), or discard the downloaded song. Let the actual value of music item K to consumer i be λv i. Clearly, λ assumes a value between 0 and 1. This value is revealed to the consumer only after she has experienced the music item. The decisions a consumer makes prior to experiencing a music item are governed by uncertainties regarding the value of λ. We capture this uncertainty via variable x (0 x 1). Thus, prior to downloading the music item, the consumer is unaware of its true value 12 and therefore the sampling decision is driven primarily by the expectations captured in f i K (xv i ). The expected benefit (ignoring the sunk cost C sample ) of purchasing the item after sampling is: xv i - P (2) Similarly, if the consumer decides to pirate the item, the realized value is 10 Among other factors, user s online connection speed that determines the time to search and download, and prevalence of sites on the internet that make music available for sharing and downloading, have a significant impact on the cost to sample. 11 If a particular music item is not available online, the sampling option simply does not exist for that item. This scenario can be depicted in the model by assigning a large value for C sample. 12 Unless the variance of the function f i (v) is 0. 12

14 xv i F ke (3) In the above expression, F (0 F 1) captures the reduction in the consumption value of pirated music. The value deterioration can result from reasons such as poorer quality of downloaded music 13, availability of only partial music online for sampling 14, and other services that a legitimate seller might offer 15. E denotes the enforcement penalty due to copyright violations of owning an illegal music item, and k is the probability of enforcement. While the current legal strategy employed by the recording industry targets only entities that facilitate illegal sharing of music (such as Napster), consumers remain liable for owning pirated music. If the consumer decides to delete the music item after sampling, the realized value is zero. From (2) and (3), it follows that a consumers who samples will subsequently buy if xv i P ke (4) ( 1 F ) On the other hand, a consumer would pirate the item if xv i F ke 0. This implies ke xv i (5) F Finally, a consumer whose realized value is lower than ke/f would discard the downloaded music item. The decision to sample music prior to purchasing is driven by the distribution f i K (v), the only information a consumer has regarding the value of x. Hence the net expected benefit from sampling is as follows. E sample = V r i K K ( v P) f ( v) dv + F ( v s) f ( v) i r s i dv C sample (6) where r = (P-kE)/(1-F) and s = ke/f. 13 Music available online for download is typically compressed to reduce the file size, which loses some detail. 14 Due to download problems, unreliable Internet connections, etc. 13

15 The decision made by the consumer to buy directly or sample before buying is based on the higher of the expected value estimations given by (1) and (6). As defined before, λv i is the actual or true value of the music item revealed to the consumer who decides to sample, where the value of λ lies between 0 and 1. The post-sampling decision made by a consumer (buy, pirate or discard the downloaded file) is driven by the surplus derived from the actions, and is determined by λ. Figure 1 depicts the consumer s decision-making strategy. Pre-sampling decision Post-sampling decision E direct buy E sample and E direct buy >0 E sample E direct buy and E sample >0 Sample Direct Buy Buy V i P ke λ ( 1 F ) ke Pirate V i λ F P ke < λ ( 1 F ) E sample <0 and E direct buy <0 Do Nothing Discard V i λ < ke F Figure 1: Consumer Decision Making A number of factors play into the decision by consumers regarding sampling, and subsequent purchase or illegal acquisition of music. In the following discussion we present the impact of the function f K i (v). We present two cases, with high and low degrees of uncertainty in terms of prior expectations. The former relates to music offerings by relatively unknown artists, and the latter to releases by well-known superstars in the music industry. In order to make consumer benefit and seller profit calculations determinate we employ specific functional forms 15 These may include discounted tickets for concerts and other musical performances, discounts on music-related paraphernalia of interest to consumers, and other useful information that consumers may value. 14

16 of f i K (v) and assume uniformity of expectations across consumers. The generalizability of the results from this analysis is tested in the empirical methodology. 3.2 Value Uncertainty We model the case of consumer uncertainty regarding the actual consumption value by specifying the function f K i (v) to be uniformly distributed i.e. U[0 v V i ]. Let V max = max(v i ; i I), where I is the set of consumers, and let V i be continuous in the range [0, V max ]. Note that we make no specific assumptions regarding the distribution of V i other than that it is continuous. Thus the results presented in the discussion that follows should apply for any customer demand function. The expected benefits from direct buy and sampling are computed as per Eqns. 1 and 6. E direct buy = V i 2 P (7) E sample = Vi 2 1 P + 2V i ( P ke) 1 F 2 ( ke) + F 2 C sample (8) For ease of exposition, let γ = 2 1 C sample ( P ke) 1 F 2 ( ke) + F 2 (9) From the above equations it follows that the consumer makes the following choices: Direct Buy if V i 2P and V i γ 2 2 Sample if V i < γ and Vi P + Csample ( P + Csample ) γcsample 2 2 Do nothing if Vi < P + Csample ( P + Csample ) γcsample Amongst the samplers, the subsequent buy/pirate/discard decision is dictated by 15

17 equations (4) and (5), and it is also depicted in Figure 1. Two cases of interest arise in this context: γ 2P and γ < 2P, illustrated in Figure 2. In the first case, some of these consumers sample first prior to making the purchase decision. When γ 2P, two interesting scenarios arise: P ke 2P (1 F) and P ke (1 F) > 2P. In the first scenario, some of the consumers with V i 2P will continue to buy the music item even if they sample music first. In the second scenario, some of these consumers resort to piracy. When γ < 2P, consumers with V i 2P continue to buy direct and remain unaffected by the sharing technologies. In this case, consumers with V i in [γ, 2P] represent a lost sales segment they would not buy directly, given their lower valuation for music compared to aficionados. They would also not sample a product with a diminished value (δv), since it would not provide them with positive expected surplus. Proposition 1: The amount of piracy is non-decreasing in P and F, and non-increasing in ke. Proof: Consumers whose maximum value for music is in the interval ke P ke, pirate music. The interval shrinks with lower P and F, and higher values λf λ(1 F) of ke. QED. The above proposition illustrates that price, enforcement and value-added services provided to legitimate customers are useful instruments to fight piracy. The popular press has frequently criticized the pricing policies adopted by the music industry, and a number of reports have claimed that high prices contribute to piracy in the music industry (Hoover 2001, Federal Trade Commission 2000, Crombie 2001). The seller can eliminate piracy by setting the value of P at ke/f. 16

18 (a) γ 2P and P ke 2P λ(1 F) do nothing discard pirate samplers 0 Ω ke P ke 2P γ λf λ( 1 F) buy direct buy V max (b) γ 2P and P ke > 2P λ(1 F) do nothing 0 Ω do nothing discard discard ke λf samplers samplers pirate buy 2P buy P ke λ( 1 F) pirate direct buy γ direct buy Lost sales V max (c) γ < 2P 0 Ω ke P ke γ 2P λf λ( 1 F ) V max where Ω = P + C sample ( ) C 2 P + 2 γc sample sample Fig. 2: Potential decisions of consumers with varying V i Consider the impact of C sample. Note that C sample is inversely related to γ (equation 9). When C sample is substantially large, only a smaller proportion of samplers subsequently make a legitimate purchase (Figure 2). The underlying rationale is that consumers who incur a substantial cost to search and locate a music item will be loathe to further spend P to obtain a legitimate copy. As the C sample decreases, the total cost to sample and buy decreases, and as a result consumers are more likely to purchase after sampling music. These key results are highlighted in the following propositions. 17

19 Proposition 2a: The proportion of consumers that sample is inversely related to C sample Proposition 2b: The proportion of samplers who subsequently buy is inversely related to C sample As the market price of the music item increases, consumers are more likely to sample first, rather than directly purchase. This follows because only those consumers with V i 2P and V i γ make direct purchases, and the right-hand side of both inequalities increase with P. This implies that as the market price of a music item increases, more consumers would spend a smaller amount of additional resources to experience the music item before they spend a larger amount to buy it. An alternate explanation is that as the price increases, buyers exhibit a more risk-averse attitude towards new music, since they do not know its actual value (Mixon and Ressler 2000). This rationale is captured in the following proposition. Proposition 3: The proportion of consumers that buy directly is inversely related to P. Sampling, in essence, is a truth-revelation mechanism that allows consumers who sample to make the purchase decision on the actual value of the music item. A lower actual value (λ) results in a smaller proportion of the samplers that purchase the music item. This result is captured in the following proposition. Proposition 4: The proportion of samplers that do not buy is inversely related to λ. Proof: Amongst the samplers those with the maximum value for music lower P ke than do not then purchase the music item. The proportion of these consumers λ( 1 F) who either pirate or discard the music increases as λ decreases. QED. We now consider the impact of sharing technologies on seller revenues. Note that, absent sharing technologies, consumers with V i 2P legally purchase the music. As the sampling costs decrease with increasing availability of free music online, more consumers engage in sampling 18

20 prior to the purchase decision. A significant concern for the music industry is that this customer base will resort to sampling and then piracy, and this can negatively impact their bottom line. The following proposition highlights the impact of sharing technologies on the revenues of the music industry. Proposition 5: Sharing technologies have a positive impact on revenues when P ke 2P. λ(1 F) P ke Proof: When 2P, consumers with V i 2P will purchase the music item. Thus, λ(1 F) the same set of customers that would buy in the absence of sharing technologies will continue to do so. Additionally, sharing technologies provide an incentive to new P ke customers whose value is in the range, γ when γ 2P, and those in the range λ( 1 F) P ke,2p when γ > 2P, to also purchase the music after sampling. QED. λ(1 F) An important result from the above proposition is that the revenue impact of sharing technologies depends on the actual value of the music to consumers. Music that is highly valued by the consumers will stand to gain from the availability of sharing technologies. Lower valued music will see decreased revenues as customers increasingly attempt to decipher the true value of the music via sampling. The proposition also suggests that efforts by the producer to increase ke (enforcement) and lower F (increasing the value-added for legitimate consumers) constitute a more effective strategy for the music industry than the attempts to make C sample high for consumers. A high value of C sample might shore up the existing the consumers by dissuading them from downloading and sampling music. However, this strategy will not attract new customers who can potentially enhance the revenues of the music industry, as it has the potential of dissuading customers from engaging in any type of music transaction. 19

21 Overall, the propositions provide important welfare implications of online music sharing technologies. Clearly, consumer surplus is always increased by sharing technologies, only when it adds to the surplus. Pirates gain as they obtain music without paying for it. For samplers, the surplus is enhanced as their purchasing behavior is based on informed choices. It follows immediately from Proposition 5 that when P ke 2P, overall welfare is higher with the λ(1 F) presence of online sharing networks than without. These provide useful instruments for the music business to sustain and grow in the presence of new sharing technologies. 3.3 Stardom and Sharing Technologies The analysis presented thus far applies to unknown artists, as consumers are assumed uncertain on the true value of the music offerings from these artists. A relatively large amount of information available about superstar artists significantly reduces the variability in consumer expectations of their music. We now analyze the impact of sharing technologies and stardom on sales. In what follows, superscript s denotes a superstar and n an unknown artist. We consider a comparative analysis where the true value of a music item offered by a superstar is the same as that from an unknown artist, i.e. λ s =λ n. Proposition 6: When C sample >> 0 and λ s =λ n, a superstar reaps higher sales than the unknown artist. Proof: When C sample >> 0, a consumer would not choose to spend extra to sample and determine the true value of a music item, but would aim to maximize surplus by choosing the item with higher expected value and lower purchasing cost (which may include sampling cost of the unknown artist s music). The rationale is that given music items from a superstar and an unknown artist (with equal true values, i.e. λ s =λ n ), the consumer would directly choose the superstar s music, because it has a higher expected value and lower variability, rather than sample and subsequently buy the unknown artist s music. This leads to higher sales for the superstar. QED. 20

22 Proposition 7: When λ s = λ n, as C sample 0, (superstar revenue - unknown artist revenue) 0. Proof: As C sample 0, the negligible sampling cost allows a consumer to sample and determine the true value of a music item from an unknown artist (λ n ) and a superstar (λ s ) before purchase. Since λ s =λ n, consumer surplus is maximized by either music item, ceteris paribus, hence the difference in sales between a superstar s music and an unknown artist is negligible. QED. An immediate observation is that current price premiums on superstar music (Strobl and Tucker 2000) will begin to decrease as sampling costs decrease. As an illustration of Propositions 6 and 7, we consider the extreme case of a perfect superstar where the consumer variance in expectations is Such a superstar gets wide exposure in the press, and significant playtime on radio and television. In essence, consumers do not have to expend additional individual effort to learn and determine the value offered by the star s music. By employing the methodology detailed before, the consumer decision-making strategy is: Direct Buy if V i P ke s λ C sample s ( 1 F ) λ ( 1 F ) Sample and Pirate if ke + C s λ F sample V i P ke < s λ C sample s ( 1 F ) λ ( 1 F ) Do Nothing if V i < ke + C s λ F sample do nothing sample & pirate direct buy ke + C s λ F sample λ P ke s C sample s ( 1 F ) λ ( 1 F ) γ V max Fig. 3: Consumer decisions based on star status (a priori expectation of music value) 16 This assumption does not hinder the generalization of our results, as long as the variance in the priors for a superstar is lower. 21

23 This is illustrated in Figure 3. Note that a consumer will never sample and subsequently buy a perfect superstar s music item, since she is aware of the true value a priori. Thus, downloading music of superstars is more likely to result in piracy than the downloading of music from relatively unknown artists. This offers a possible explanation for the high concern expressed by some popular artists (such as rock group Metallica and rapper Dr. Dre) regarding the online sharing of music via mechanisms such as Napster (Jenkins 2000). The above discussion suggests that low sampling costs present a significant disadvantage for current superstars, and aids the discovery of equally valued music items by less known artists. This has potentially far-reaching consequences in music publishing and advertising, and should be accounted for in designing new selling strategies. The next section of the paper provides an empirical validation of the key analytical results discussed so far. 4. Empirical Methodology In this section, we discuss the empirical evidence used to examine the key propositions enumerated in the analytical model. The data used to investigate the propositions was drawn from two sources. The first data set consisted of primary data collected via a survey and it was used to validate consumer choices under various technological and economic parameter settings. A second data set was developed using the Billboard Ranking Charts and it was used to evaluate the propositions related to the superstar phenomenon and sharing technologies. 4.1 Intention Survey The survey instrument studies consumer attitudes toward online music. The primary data collection was completed in two major phases. An initial pilot study was conducted with a sample of 76 graduate students. Subjects were assured complete anonymity. The measurement 22

24 scales derived from the analytical model were tested for validity, clarity and consistency. Based on the feedback, a revised survey questionnaire was administered to 200 respondents. The survey was targeted towards collage students as online music copying and sharing phenomenon is rampant among students (Fine 2000, Jay 2000). Furthermore, this demographic constitutes a significant portion of the music fan base (Holbrook and Schindler 1989), and consequently the analysis on this segment is directly relevant to the music industry. The ages ranged from 19 to 54 years with an average of 23, with 61% males; 15% worked full-time and 54% part-time. 52% reported a very high level of interest in music, while another 37% listened to music regularly. The sample group is sufficiently diverse in terms of demographic, economic and social aspects Measures To evaluate the impact of economic and technological factors that influence sampling and piracy, the respondents were asked to reveal their online music experiences, provide demographic information, and specify preferences for decisions relating online music activities under varied settings. Choices Action Decision Code Download and sample, delete from computer, buy CD A 1 Download and sample, delete from computer, do not buy CD B 2 Download and sample, keep in computer, buy CD C 3 Download and sample, keep in computer, do not buy CD D 4 Do not download, buy CD E 5 Do not download, do not buy CD F 6 Figure 4: Illustration of Intention Survey Choices 23

25 Price and Sampling Cost: The price variable indicates the retail price of a music CD, and was set at $5 and $15. The sampling cost was measured via Internet connection speeds. A high sampling cost scenario involves respondents searching and downloading music via a 56 kbps modem which, on average, can take up to an hour to download a typical song. A low sampling cost scenario involves a fast Internet connection such as cable modem or DSL that offers significantly lower download times. Price and Sampling Cost are coded as categorical variables. The pilot study suggested that other technological factors did not have a significant influence. Decision Choice: For a given sampling cost and price setting, the respondents were asked to take one of the six actions described in Figure 4. If actions A, B, C or D are selected, it constitutes sampling. Among these, Action D constitutes piracy, actions A or C constitute sample and buy, and action B constitutes sample and discard. The traditional direct buy option is captured from the action E. Value uncertainty regarding the music offering is captured as follows. If a respondent has listened to a particular music before, its value is assumed to be known to her, otherwise it is unknown. A user is assumed to be uncertain about the true value of unknown music. The respondents were presented with a total of five types of music choices as depicted in Table 1. Table 1: Choices of Music Music Choice Description Known Music 1 One of their top 5 known music items 2 One of their top 50 known music items Unknown Music 3 Unheard music item from favorite artist 4 Unheard music item from the genre of music I like (classical, rock, pop, etc.) 5 Unheard music item recommended by a friend or colleague 24

26 4.1.2 Hypotheses Table 2 highlights the hypothesis tested, and the corresponding proposition developed in the analytical model. Table 2: Hypotheses Hypothesis Support provides validation for H1: For unknown music, increasing the market price decreases sampling and buying. Partial support for Proposition 1 H2a: For unknown music, decreasing the sampling cost increases sampling. Proposition 2a H2b: For unknown music, decreasing the sampling cost leads more samplers to buy. Proposition 2b H3: For unknown music, increasing the market price decreases direct buying Proposition 3 H4: For known music,higher valued music is sampled and bought more than lower Proposition 4 valued music Data Analysis To test hypotheses H1, H2a, H2b and H3, we estimate a multinomial logit model 17 where: Decision = f(price, sampling cost, age, gender, income), for all unknown music choices. To test H4, we estimate a multinomial logit model: Decision = f(music value, age, gender, income), for the two types of known music choices. In these models, price, sampling cost, gender and music value are categorical variables, with their low values coded as 0 and high values coded as 1. For gender, males are coded as 0. Table 3a presents the results for the unknown music choices, and Table 3b presents those for known music choices. For both models, income has little or no significant effect. The lack of income effect is perhaps due to the low cost of a typical music item. In contrast, software, which is much more expensive, exhibits significant income effect (Gopal and Sanders 2000). Females generally have 17 For this and the next estimation, the Hausman test validated the IIA (Independence from Irrelevant Alternatives) property, which states that for a specific individual, the ratio of choice probabilities of any two alternatives is entirely unaffected by the systematic utilities of any other alternatives. This suggests that there is no latent nested structure that needs to be explicitly modeled and evaluated. 25

27 a negative correlation with overall sampling decisions (decisions 1 through 4), especially with piracy behavior (decision 4), but show a positive effect on sample and buy decisions for unknown music (Table 3a). This suggests that females primarily sample unknown music with an intention to buy. Table 3a: Multinomial Logit Model of Unknown Music Choices Decision Implication Variable Estimate Std. Err. t-statistic 1 Sample, Price Delete, and Sampling Cost Buy Age Gender Income Sample and Discard 3 Sample, Keep, and Buy 4 Sample and Pirate 5 Direct Buy Const Price Sampling Cost Age Gender Income Const Price Sampling Cost Age Gender Income Const Price Sampling Cost Age Gender Income Const Price Sampling Cost Age Gender Income Const (Outcome decision = 6 is the comparison group) Log likelihood = (Estimation was performed using Stata) 26

28 Table 3b: Multinomial Logit Model of Known Music Choices Decision Implication Variable Estimate Std. Err. t-statistic 1 Sample, Value Delete, and Age Buy Gender Income Sample and Discard 3 Sample, Keep, and Buy 4 Sample and Pirate 5 Direct Buy Const Value Age Gender Income Const Value Age Gender Income Const Value Age Gender Income Const Value Age Gender Income Const (Outcome decision = 6 is the comparison group) Log likelihood = (Estimation was performed using Stata) The results from unknown music choices (Table 3a) show that price has a statistically significant negative correlation with music sampling and buying (decisions 1 and 3). This validates hypothesis H1. It also suggests that sampling costs are negatively correlated with the buying intention of unknown music. For unknown music, sampling cost has a significant negative effect on sampling behavior (decision 1 through 4), which validates H2a. Sampling costs also have a negative correlation with the decision to sample and buy (decisions 1 and 3), 19 It is not clear whether this sampling technique and analysis method provided robust results. However, since the 27

29 signifying that decreasing sampling costs leads to increased sampling and buying of unknown music. Hence H2b is valid. Further, market price has a significant negative correlation to a consumer s direct buy decision (decision 5) for unknown music. This validates H3, and also suggests that sampling costs positively affect direct buying. Results for known music (Table 3b) show that the decision to sample and buy (decisions 1 and 3) is positively related to the music value, which validates H Superstar Analysis To test actual consumer buying behavior based on information on a particular music item, linear regression analysis was used to empirically evaluate the Billboard Rankings Charts archival data. This data set consists of weekly rankings of the published Billboard Top 200 album charts. The rankings are calculated from the total retail sales in the United States for the previous week, and are usually published on Saturdays. Hence these rankings directly reflect actual sales of albums. Billboard rankings are widely used as a marketing tool by the music industry to denote the success of an album a high ranking suggests a high value of a particular music item to potential customers. In an earlier study of similar data, Strobl and Tucker (2000) used a sampling technique where they included only those data where artists first names began with the letters A through D. Their analysis included a simple count of the number of times an artist appeared in the Top 30 rankings, and did not employ a weighted ranking of albums to account for the impact of actual rankings on future sales 19. Sample data for our analysis was collected for the top 200 album rankings for each week, analysis was performed on a different data set (U.K. Chart rankings), it was not possible to verify the results here. 28