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The future of online

music marketing

Sharing playlists will save your life

Bianca Harms

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THE FUTURE OF ONLINE MUSIC

MARKETING

Sharing playlists will save your life

Master Thesis

Master of Business Administration

Faculty of Economics and Business

University of Groningen

The Netherlands

By:

Bianca Harms

De Skieding 38

9222 LB Drachtstercompagnie

Student Number: S1760920

e-mail:

biancaharms@gmail.com

Supervision University of Groningen, Department of Marketing

Supervisor: Dr. W. Jager

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Abstract 

The future of online music marketing lies within the sharing of playlists in the online environment. The rapidly increasing use of on-demand streaming music services and sharing of playlists has an enormous impact on current music consumption behavior. Nowadays, the decisive power of this music sharing seems to have a greater impact on music consumers than traditional marketing activities. Therefore, it is important to identify the factors affecting the sharing of playlists in an online environment for anybody who has an interest in selling or promoting music online.

This study is one of the first attempts to gain insight in current online music consumption behavior and to identify factors that affect the intention and behavior of sharing playlists in an online environment.

The study successfully uses a (decomposed) Theory of Planned Behavior (Ajzen, 1991) as the foundation for its’ research model. Based on extensive literature study self-identity related to music, involvement with music and psychological ownership were identified as possible predictors of attitude. Ingoing and outgoing normative and informational influences, as well as tie strength were identified as possible predictors of subjective norms. Finally, self-efficacy was identified as possible predictor of perceived behavioral control.

196 usable questionnaires were collected through snowball sampling via highly involved music fans on Facebook.

Multiple regression analyses identified the direct measures attitude, subjective norm and perceived behavioral control as significant predictors for the intention to share playlists in an online environment. Only subjective norm proved to be a significant predictor for the number of playlists respondents have.

Self-identity related to music proved the sole significant predictor for attitude, confirming previous research results. While a great part of the respondents experience a feeling of ownership towards their playlists, this does not affect the attitude towards sharing playlists online.

In line with previous research, outgoing normative and informational influence was tested significant as predictors for subjective norm. However, tie strength proved to have the strongest predictive value. Finally, self-efficacy is a significant predictor for perceived behavioral control.

These results stress the importance for beneficiaries to realize that social influence is an important factor affecting both intention and behavior of sharing playlists in an online environment especially from strong ties. Furthermore, attempts should be made to influence the consumers’ attitude towards online playlist sharing including making an appeal to the enhancement of self-identity through playlist sharing. Since the experienced capability of consumers to share playlists online is important, marketers should gain insight in and anticipate on this aspect.

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Preface 

Herewith I proudly present my thesis, which I wrote to complete the Master of Science in Business Administration at the University of Groningen. It has taken me longer than expected to complete the studies due to several personal circumstances, but I made it. Besides my past working experience in several marketing positions within the music industry it was mainly my strong interest in music consumption behavior and the effect of the digital developments on this consumption behavior that led to the topic of this thesis.

Being a mother of three young children (of which two arrived during my studies) and having full-time position, it was impossible to complete the studies without the support of my wonderful husband Hans. My studies asked a lot from him and I am forever thankful for his love, care, backup, strength, flexibility and insights!

Also, I would like to express my thankfulness to my mother for caring for my children, when needed. I am very thankful to Wouter Kerkdijk, Olga Heijns, Dagmar Heijmans, Maaike de Jong and Nick Stevens for their input and/or critical questions during the process of writing this thesis.

A special word of thanks goes out to Wander Jager, who was willing to supervise me on a very short notice throughout the summer. His enthusiasm,

knowledge, encouragement and (quick) feedback have been extremely valuable to me. Also, I like to thank Liane Voerman who was willing to step in as co-assessor. Both of them made it possible for me to meet the final deadline!

And last but not least, I want to express my thankfulness to my employer Stenden University, who facilitated me in completing these studies.

Bianca Harms

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3.2.7. Involvement ... 37  3.2.8 Normative/informative influence ... 37  3.2.9 Tie strength ... 38  3.2.10 Psychological ownership ... 38  3.2.11 Self‐Efficacy ... 38  3.2.12 Control variables ... 38  3.3 Population and Sample development ... 39  3.4 Statistical analysis methods and procedures ... 40  4. Results and Analysis ... 41  4.1 Descriptive Statistics ... 41  4.1.1 Sample Population ... 41  4.1.2 Music Consumption Behavior ... 42  4.1.3 Collecting and sharing online playlists ... 45  4.1.4 Playlist sharing ... 47  4.2  Test of conceptual model ... 49  4.2.1 Reliability ... 49  4.3 Hypothese Testing ... 51  4.3.1 Direct Measures ... Error! Bookmark not defined.  4.3.2 Indirect measures ... Error! Bookmark not defined.  5. Conclusions & Recommendations ... 62  References ... 68  Appendices ... 75  1. Overview of the Behavioral Intention Models and Theories ... 75  2: Dutch Questionnaire (Survey Monkey) ... 78  3. KMO & Bartlett’s test of Sphericity ... 81  4: Reliability scores items within constructs ... 82 

List of Figures 

Figure 1: Decomposed TPB as theoretical framework for this study ... 11 

Figure 2: Structure of the thesis ... 19 

Figure 3: Theory of Reasoned Action ... 21 

Figure 4: Theory of Planned Behavior ... 21 

Figure 5: Technology Acceptance Model (Taylor and Todd, 1995) ... 22 

Figure 6: Theory of Interpersonal Behavior (Triandis, 1980) ... 23 

Figure 7: Unified Theory of Acceptance and Use of Technology (Venkatesh et al., 2003) ... 24 

Figure 8: Research Model ... 35 

Figure 9: Regression model 1 ... 52 

Figure 10: Regression model 2 ... 53 

Figure 11: Regression model 3 ... 55 

Figure 12: Regression model 4 ... 55 

Figure 13: Regression model 5 ... 57 

Figure 14: Regression model 6 ... 59 

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List of Tables 

Table 1: Overview of definitions constructs ... 33 

Table 2: Overview of Hypotheses ... 35 

Table 3: Sample demographics ... 42 

Table 4: Used devices for experiencing music ... 43 

Table 5: Music preferences ... 44 

Table 6: Music listening per gender ... 44 

Table 7 Music listening per age group ... 45 

Table 8: Average number of playlists per category ... 46 

Table 9: Number of playlists ... 46 

Table 10: Average number of playlists ... 46 

Table 11: Number of playlists per category/age group ... 47 

Table 12: Number of playlists shared in the past three months ... 48 

Table 13: Number of playlists shared in the past three months per age group and gende ... 48 

Table 14: Motives for sharing playlists online ... 49 

Table 15: Reliability scores Self-identity ... 50 

Table 16: Reliability results ... 51 

Table 17: Correlations regression model 1 ... 52 

Table 18: Tolerance and VIF scores regression model 1 ... 53 

Table 19: Significant variables regression model 1 ... 53 

Table 20: Correlations regression model 2 ... 54 

Table 21: Tolerance and VIF scores regression model 2 ... 54 

Table 22: Significant variables regression model 2 ... 54 

Table 23: Correlations regression model 4 ... 56 

Table 24: Tolerance and VIF-scores regression model 4 ... 56 

Table 25: Significant variables regression model 4 ... 56 

Table 26: Correlations regression model 5 ... 57 

Table 27: Tolerance and VIF scores regression model 5 ... 58 

Table 28: Significant variables regression model 5 ... 58 

Table 29: Correlations regression model 6 ... 59 

Table 30: Tolerance and VIF scores regression model 6 ... 59 

Table 31: Significant variables regression model 6 ... 60 

Table 32: Correlations regression model 7 ... 60 

Table 33: Tolerance and VIF scores regression model 7 ... 61 

Table 34: Significant variable regression model 7 ... 61 

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1. Introduction 

The way people consume music and act around this music consumption has changed dramatically over the past decade. Under the influence of various technical and sociological developments, consumption of music has become an important part of daily life and a main part of the cultural input through which social action is constructed and organized (O’Hara and Brown, 2006).

In 2005, David Kusek published an article in Forbes Magazine explaining the concept “Music like Water” promoting a complete online catalogue of music available for everyone to listen to, download and share against a flat fee transforming it essentially into a public utility. His vision (which he developed together with Gerd Leonard) had got its first proof of credibility since the introduction and the diffusion of the peer-assisted music streaming service Spotify in 2008. Spotify changed the experience of music distribution and consumption irreversibly. Spotify offers subscribers an unique experience of music consumption by offering users a huge on-demand catalogue of online music tracks for personal use for free or against a flat fee offering many extra services. At present Spotify has a library containing over 13 million tracks and over 10 million users (Goldman, M. and Kreitz, G, 2011). These numbers are increasing rapidly as the catalogue expands daily and Spotify is spreading its’ activities successfully to more countries.

After previous focus of academics towards music Mp3-file sharing behavior, current consumer behavior patterns identify the need to gain more insight in the impact of streaming music services on music consumption. This knowledge is not only important because of the increasing number of people experiencing music through these on-demand streaming services, but also as these services bring new marketing opportunities for various parties in the music industry.

As an important feature of streaming on-demand music services is the possibility to integrate playlists and music listening behavior with personal activities on social network sites, blogs and communities, music consumption behavior is spreading to a large group of people within short periods of time. The behavior of sharing personally created playlists is rising. An important consequence of individuals sharing information is that this can ultimately impact consumption (Ramaprasad and Dewan, 2011).

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The intentions underlying playlist sharing within an online environment

In order to understand this sharing behavior, the Theory of Planned Behavior (TPB) is used as the basic framework for this study (Ajzen, 1991). The TPB presumes that the intention to perform any behavior (empowered by existing perception) is a good predictor of actual behavior The TPB is widely used in the academic field and has predicted a variety of behaviors of individuals with some degree of success (Conner and Armitage, 1998). TPB is based on the proposition that behavior is a function of three kinds of salient beliefs relevant to the behavior.

-Behavioral beliefs, which influence attitude;

-Normative beliefs, that constitute underlying determinants of subjective norms; -Control beliefs, which provide the basis for perceptions of behavioral control (Ajzen, 1991).

Several studies were dedicated to gain understanding on the relationships between belief structures and the antecedents of intentions. Taylor et al. (1995a) found that decomposing belief structures into multidimensional constructs and allowing for crossover effects improved predictability of the TPB. This study includes decomposing belief structures as it adds new constructs to the basic model.

Behavioral beliefs affecting Attitude

In this study attitude is defined as an individual’s evaluation of sharing a playlist in an online environment. In other words how does a person feel about sharing playlists on-line. The proposition is that how a person feels about sharing playlists online will be based on that individuals’ self-identity, the persons’ level of music involvement and psychological ownership, which refers to the feeling of ownership. Already in 1959, Levy constituted that products are not only bought because of their functionality, but also for what these products mean to the buyer.

Particularly, music is considered as a means to convey a message to a relevant audience and people form preferences in a social environment where they want to express their identity. This symbolic meaning is used to define a customer’s concept of “Self”. The self is a sense of who we are (Kleine and Kurnan, 1993), and how we think that others view us (Hoyer, McInnis, 2008). Music is consumed driven by the need for distinction and self-definition (Belk, 1982;1988). As sharing playlists in an online environment results in exposure towards at least the connections of an individual, self-identity is most probably a strong behavioral believe, influencing people to engage in this sharing behavior.

Involvement refers to the perceived level of importance of an object to an individual (Antil, 1984). Users of the Internet are confronted with WoM through the exposure of an information overload. The enormous amount of information stemming from status updates and content shared by connections on social network sites, blogs and so on, includes a lot of information about music consumption, making this a relevant factor within this study. The level of involvement with any music that is seen to be consumed will at least partly determine if shared music will catch the attention of connections within the information overload. Also, previous studies found that the level of involvement impacts attitude-formation (Zaichowsky, 1985). Individuals with a high level of music involvement will most probably not only notice shared playlists, but also have a more positive attitude towards sharing playlists in an online environment.

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ownership does no longer only relate to possession of physical carriers or digital files but also to the sense of ownership users experience when accessing music online.

In particular the ability to create personal playlists, listen to the music off-line, sharing it within any persons network (large or not), and paying fees for accessing the database must influence the level of ownership one experiences. While possessions can be defined as things we consider as being our own (Belk, 1988) the concept of psychological ownership is broader and refers to the concept of emotional attachment that entails the degree of attachment with a product on one hand and the affective reaction or emotion on the other hand (Shu and Peck, 2011). Since the trending topic within the industry of recorded music is the shift from music ownership towards music access, it is essential to gain understanding of the ownership concept and the effect on the attitude towards music sharing. Considering the construct of psychological ownership it might be very possible that customers experience psychological ownership with on-demand streaming music, while not actually having legal ownership. Consequently, the feeling of psychological ownership of personal playlists could result in a positive effect on the attitude towards sharing playlists in an online environment. As consumption of content is becoming more cloud-based the reality is that the difference between owning and consuming is becoming smaller and smaller. Effectively the difference will disappear in the future making the distinction irrelevant.

Normative beliefs affecting the subjective norm

Consumer decision-making is highly affected by social influence (Van den Bulte & Stremersch, 2004). In this study, the subjective norm is defined as an individual’s own estimate of the social pressure to perform or not perform the target behavior (Ajzen, 1991). In other words the degree to which an individual perceives sharing playlists in an online environment as a norm among people who are important to them. The construct of the subjective norm is not a single construct (Ten Kate et al., 2010). The subjective norm within TPB refers to social influence, which can be subdivided in normative influence and informational influence. Normative influence is the tendency to conform to the expectations of others (Burnkrant and Cousineau, 1975), which can be exerted both ingoing and outgoing. Ingoing normative influence results from the behavior of other people. If connections with strong normative influence share playlists in an online environment, one might feel it is expected of him to do so as well. Outgoing normative influence occurs if this person has strong normative influence toward others, and he is perceived as norm setting for this sharing behavior. Informational social influence is based on the acceptance of information from others as evidence about reality (Kaplan and Miller, 1987), which is also exerted both ingoing and outgoing. Searching for musical preferences and playlists of opinion leaders or observing playlists sharing behavior online, is ingoing informational influence. Also, opinion leaders have strong outgoing influence both normative by setting the behavioral norm as well as informational by diffusing information about for instance new music.

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Information from strong ties is more influential than information from weak ties (Brown and Reingen, 1987), however also weak ties are critical for success as both types provide access to different kind of resources (Haythornthwaite, 2001).

Without weak ties, social networks would only consist of disjoint subgroups (Brown & Reingen, 1987) since these weak ties have a bridging function insuring that new information comes into a network of strong ties. Opinion leaders might rely on weak ties for the discovery of new music for instance. The level of tie- strength determines the strength of influence on the subjective norm towards sharing playlists in an online environment. When strong ties engage in sharing playlists online, the influence on the subjective norm is expected to be stronger.

Control beliefs affecting perceived behavioral control

Perceived behavioral control (PBC) is the extent to which an individual thinks he is able to perform the behavior (Ajzen, 1991). Hence, how difficult or easy an individual feels it is to share playlists in an online environment.

Two dimensions for these control beliefs discussed in previous research are facilitating conditions relating to resources, and self-efficacy relating to the ability on an individual to perform a behavior (Taylor et al., 1995a). Facilitating conditions are defined as the objective environmental factors that observers agree make an act easy to do (Thompson et al. 1991). In case of playlist sharing in an online environment, this construct is not included in this research.

Self-efficacy is defined as the self-confidence of an individual in his or her ability to perform a behavior (Hill et al. 1986). People intend to engage in behavior when they feel they are capable to do so. On one hand, this refers to the difficulty of performing the behavior and on the other hand, this refers to the confidence in their ability. As the extent to which digital technologies are integrated in the users life is affecting online behavior and thus online sharing behavior is an important factor to consider.

Based on the above-mentioned argumentation the following decomposed TPB-model is the framework on which this study is based:

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1.1 Problem Statement 

An increasing number of people use online streaming music services, enabling them to share playlists with their peers. This results in the need to gain more understanding of the factors driving the behavior of sharing playlists online. This insight is imperative for many parties in the music industry, in particular where this sharing of playlists leads to the creation of eWoM, as, besides the traditional marketing and publicity (even if it is online), this is the only way to reach the consumer and attempt to persuade them to consume their product.

This leads to the following problem statement:

Which factors affect the intention and behavior to share playlists in an online environment?

1.2. Background to the problem statement 

One cannot understand current music consumption behavior without an insight in the technological developments and resulting impact on music consumption and music sharing. Even though historical music consumption information is available from around 3000 BC. (Ogden, J.R. et al., 2011), the most significant developments within the music industry are discussed with the focus on the past two decades as these developments create understanding of current music consumption and playlist sharing.

1.2.1 Developments within the industry of recorded music and consumer  behavior in the pre‐digital era 

Popular music is produced within a social context (Longhurst, B. 2007). Before the 20th century, music was only experienced through live performances or bought on sheets. The phonograph introduced by Edison in 1877 enabled consumers to listen to recorded music and new genres of music besides classical music. Sharing music entailed sharing the actual musical experience by enjoying performances together or communicating about music.

The introduction of the gramophone was successful due to greater quality of the music and larger barriers against counterfeiting. This invention enabled mechanically reproduced music, which turned music into an actual product. Customers became able to buy and possess recorded music resulting in the possibility to share music within their social circle both together as well as separately by lending out or borrowing records.

The introduction of AM radio in the 1920s increased the popularity of different genres of music. Radio impacted the music industry by substantially decreasing phonograph sales, sheet music sales and piano sales. The introduction of royalty payment on broadcasted songs corrected this decrease of income. Starting from the 1930s, the production and marketing of records provided a basic business model that would last for decades (Ogden, J.R. et al., 2011).

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Advances of radio- and television technology, allowed business to speak to large audiences (Ogden, J.R. et al., 2011), which turned music into a mass marketed product. The cassette tapes and the eight-track player, that reproduced music magnetically, were introduced in the 1960s and enabled customers to record their own songs from vinyl or radio. Philips introduced these so called mix tapes in 1963. This innovation was the actual start of the music-sharing concept, as we know it today. The mix tapes became popular within a short time, particularly in music subcultures such as the Hip Hop scene. As within these subcultures many accomplished records were not legally available, the mix tapes were used as a means to develop a collective sense of identity founded on these shared musical interests (O’Hare & Brown, 2006).

These new music carriers resulted in increasing industry revenues, as customers perceived these advanced products as a chance to get higher quality music material (Robbins, 2008) resulting in repurchasing behavior of customers who already bought the same music on vinyl.

The rise of the DJ-culture following the Disco-hype in the 70s and Hip-Hop subculture is relevant as technology enabled the mixing of different tracks into a DJ-set which was an extra tool to express yourself and display existing music in a new form. Clubs were the medium for people experiencing a variety of music in a format different from the album format. Club culture was also an outlet for identity sharing by expressing personality, for instance in the gay culture, techno or Hip Hop.

The introduction of the first television station dedicated to music in 1981 proved that music videos were entertainment in their own right, resulting in great promotional opportunities for performing artists.

The compact disc (CD) was introduced in the early 1980s, for the first time music was transformed into digital content, however at that time indissoluble connected to a physical carrier (O’Hare & Brown, 2006). This introduction impacted the revenues of the recorded music industry beneficially, as customers encountered into repurchasing behavior again.

Throughout the 1990s the record industry was blooming due to the peaking sales of CDs, CD-singles and music DVDs. Also music television was flourishing with MTV reaching 57 million households in 1993 (Ogden, J.R. et al., 2011) and other music channels entered the market successfully. As with the mix tapes, music sharing was primarily about social bonding only the physical carrier changed from mix tape to CD-R (O’Hare & Brown, 2006).

1.2.2 The digital revolution and its effect on the industry of recorded music and  music consumption 

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However, the success of Napster and the resulting spinoffs were the start of the revenue downfall of the recording industry to such an extent that “Napsterization” is currently used as a definition to describe an “immense change in an industry where customers using online networks disrupt the status quo, hierarchies and distribution systems (Madden, 2009). Often, these consumers are expecting digitized versions of products such as music to be available for free (Madden, 2009). This finding is certainly applicable to recorded music witnessing the pricing decrease of CD’s and the current online music consumption patterns.

Music sharing changed its face completely when the first peer-to-peer file-sharing services appeared, and the behavior was based on different factors (O’Hare & Brown, 2006). Services like Gnutella, KazaA and Napster offered a huge catalogue of music. This enormous supply made it difficult to find music without searching for a specific artist or track. The search for music, led users to music catalogues with overlapping musical interest and not to libraries including different kinds of music (O’Hare & Brown, 2006). Also, the online music sharing activities remained an individual and even anonymous activity comparable to the first digital sharing through Napster.

Following these developments, Apple impacted the industry by introducing the iPod in 2001 enabling consumers to have access to their paid MP3-songs, downloaded from the iTunes platform, from wherever they wanted. Even though several labels such as Sony Music tried and failed to launch digital music platforms, as the competing labels did not want to collaborate, Apple was the first company that arranged deals with the recording industry in order to make the downloadable files legitimate. The MP3-files became available for 99 cents each. iTunes became the largest legitimate download platform on a global level in the following years. The hesitation of the recording industry to anticipate on the online developments therefore led to the peculiar situation that software company Apple became the largest online music supplier without owning any actual content rights. With iTunes Apple succeeded in convincing artists and labels to make large parts of their content, albums in particular, available as single tracks. Something the traditional parties always resisted because of the underlying vision that customers should consume the music as it was meant to be, a continuous, complete and sequential format. Labels shared this vision as it led to commercial exploitation opportunities by releasing the same album multiple times as “limited edition-versions” including additional tracks. For iTunes users this led to new ways of experiencing music.

A large gap existed between the strong social bonds among individuals via mix tapes and CDR’s in the early days and the anonymous experience of online file sharing (Brown et al., 2001). This changed with the introduction of iTunes, which enabled sharing music not limited to favorites but also to divergent music tastes (O’Hare & Brown, 2006). Also, music sharing through iTunes was and still is not as anonymous as the earlier massive P2P-systems.

Even now, the recording industry does not have a clear view on what business model makes their activities more sustainable. Besides the fact that the debate on content rights is ongoing, the major labels appear to be wavering in anticipating on new technological developments and opportunities.

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However as the market evolved, customers no longer accepted having to pay for downloads and not being able to do anything with the tracks. This made DRM-technology obsolete and most record companies stopped using the DRM-technology. Also, online music suppliers such as iTunes and Amazon.com removed DRM-technologies from their supply of music tracks. Currently, besides the legal alternatives, many online platforms still enable users to download unauthorized MP3-tracks.

As online music distribution is developing in such a velocity, available research data is quickly outdated. In 2010 digital music revenues accounted for approximately 30 per cent of the overall recorded music revenues (Ifpi, 2011). While a relatively low percentage of customers said to frequently purchase music online, 16.5% in the UK and 14% in the US (Ifpi, 2011), no specific statistics are available on illegal downloading. The estimation is that these statistics will out number the figures of legal downloads by far.

Currently, streaming music through cloud services is starting to lead the discussion within the record industry. This discussion is fueled by the introduction and success of Spotify.

The online availability of music apart from downloadable files is not new. In 2001 the first online radio station, Last.fm was introduced. This radio station provided online radio with streaming music making it possible for customers to listen to their own individualized playlists. Last.fm was free of charge, after 2009 listeners were obliged to pay a fee in order to use most of its’ services. Furthermore, early 2000 the social network MySpace was launched. MySpace offered a web space that could be personalized and made it possible to share music, photos and videos with other users. Therefore, this platform enabled users to share their identity within a stronger social context. The network became popular particularly among artists and music fans. When the English band the Arctic Monkeys gained worldwide acknowledgement through MySpace in 2005, the popularity of the platform accelerated even further. This success was also one of the two main reasons for the downfall of the platform. The number of bands and artists on MySpace became countless and users could not 'see the wood for the trees' anymore because of this unlimited choice. Furthermore, the new design that was introduced in 2006 decreased the usability for the users considerable, which resulted in the current weak position of this platform.

In 2007, Soundcloud was introduced as a challenger of MySpace. This service offers, like Myspace, a platform for artists to share their music. Soundcloud is growing due to specific features Myspace does offer. For instance, artists can have their own distinct URL and technologies enable integration with social networks such as Facebook and Twitter.

Also, YouTube founded in 2005, affected music consumption considerably. The service offers the possibility to upload and share videos online. Currently, YouTube is localized in 39 countries in 54 languages. The channel counted more than 1 trillion views in 2011 (www.youtube.com) and is now considered as one of the most popular online music providers. Opinion leaders from the music industry identified YouTube as the number one music service, which was consistent with and earlier study conducted by Nielsen (Lidy and van der Linden, 2011; Nielsen, 2010). Recent research states that 93% of people between 12 and 19 years old actually listen to music through online video platforms led by YouTube (www.trouw.nl) while only 18% listen to traditional radio channels. The results of these studies also indicated that on-demand streaming services such as Spotify and Last.fm are becoming far more popular than iTunes (Lidy and van der Linden, 2011).

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The service was launched in 2008 and it currently contains a library with over 13 million tracks. Over 10 million users signed up for a Spotify account (Goldman, M. and Kreitz, G, 2011). These numbers are rapidly increasing due to the geographical expanding of their services.

New initiatives are arising: Sony launched Music Unlimited in April 2012. This on-demand streaming music platform carries a catalogue of over 15 million tracks that can be listened to on Sony hardware. It is interesting that the catalogue consists not only of Sony Music tracks but also tracks from the other major record companies. Furthermore, Mobile brand Samsung introduced Samsung Music Hub recently. This services enables users with a Samsung phone or browser access to 19 million tracks against a flat monthly fee.

Most on-demand streaming music services offer users the choice between a free account with limited possibilities and exposure to advertising or a paid subscription enabling access to more services such as for instance unlimited access to songs both online and offline.

1.2.3 Current Music Consumption behavior  

The developments of the past two decades resulted in a wide variety of music consumption options. These options come down to the choice between legal music ownership by buying physical carriers or download (authorized or unauthorized) tracks, and music access by using an on-demand streaming music service, or YouTube to watch the music videos on demand.

Online sharing of music is gaining popularity rapidly and might surpass legal music ownership in the near future. Music sharing has never been so prevalent as today. Users are able to share single plays, build personal playlists and share these on their social network sites, blogs and forums or share playlists from labels, artists and so on. This resulted in a changing role of music consumers by enabling them to be more active an interactive due to the rise of these new media channels like Twitter, Google, Facebook and YouTube.

Hargreaves and North (1999) identified emotional expression, aesthetic enjoyment, entertainment, communication and symbolic self-representation as the main reasons for music consumption. These motives did not change due to the digital developments. However, the new consumption options and their possibilities resulted in an increasing impact as the speed of interaction, diffusion of information and the diminishing geographical boundaries led to a much higher reach and impact of the information shared.

The high degree of connectiveness within online communities resulted in a changing role of music consumers by enabling them to be more active an interactive due to the rise of new media channels like Twitter, Google, Facebook and YouTube. This led to the loss of marketing power of the music industry since this power is taken over by eWom through extensive interaction within communities about music and music consumption.

Music has been and still is an experience product. The actual consumption of music is the experience of using the product, as opposed to just owning the carrier. This characteristic is fundamentally important to help understand the consumption of music in general (Shankar, A., 2000). Since music has a high degree of experience qualities, it becomes more interesting for customers to engage in word-of-mouth behavior (Eck, van, Jager, Leeflang, 2012).

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Accordingly, sharing playlists is considered as WoM-communication. WoM is identified as an important determinant of consumer behavior (Hennig-Thurau and Walsh, 2003). Within the fields of marketing and consumer behavior a lot of attention has been paid to WoM not only because of its high incidence rate, but also because WoM is takes a very persuasive part in affecting the attitudes and purchase behavior of consumers (Sundaram et al., 1998). WoM in general and the motives for engaging in WoM in particular have been widely researched (Trusov et al., 2009). WoM occurring online is referred to as electronic WoM or eWoM. This form of communication is defined as any positive or negative statement made by potential, actual, and former customers about a product or a company via the Internet (Hennig-Thurau et al., 2004). Traditional WoM and eWoM are very much alike, however some differences can be found. To the contrary of traditional WoM, eWom:

• Involves multiple ways to exchange information among communicators (Goldsmith, 2006).

• Is more persistent and accessible (Cheung, 2010) • Is better measurable (Lee, Park and Han, 2008)

However, the most important difference between WoM and eWom is that the sender and receiver are separated by time and space (Steffes and Burgee, 2009). eWoM can take place in a variety of settings: opinions including recommendations and reviews are expressed on blogs, forums, newsgroups and social network sites.

This study focuses on eWoM taking place in the online environment. Consumer interaction in the online environment has become extremely popular the past years. Insites Consultancy (2011) reported in their global social media study which included results from over 9000 respondents from 35 countries that over 73% from the European Internet users are using social networks. This implies that approximately 347 million individuals in Europe are active on a social network. A study of AOL/Nielsen revealed in their Content Sharing Study (2010) that people spend 30% of their time online in places where they can share content. Social networking sites offer a platform for users to build and maintain a network of for instance friends, family or co-workers. Users communicate their profiles by adding personal information, photo’s, videos, music and other interests.

1.3 The practical and academic relevance of this study 

As described in the former paragraph, the recording industry is extremely challenged by technological developments that resulted in substantial changes in consumer behavior. For companies and artists who for decades have been dependent on revenues from distribution of recorded music and ancillary income such as airplay and synchronization, it is essential to gain more insight in current music consumption behavior.

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Even though the global digital music revenues rose with more than 1000% from 2004 to an estimated value of US$4.6 billion in 2010, the overall global recorded music revenues declined by 31 per cent over the same period (IFPI 2011). These figures underpin that the increase in digital revenues were not the least able to correct the downfall of the total revenues of the recorded industry.

Opinion leaders from the music industry identified personalized recommendation, social recommendation, cloud services, audio-visual search and content-based recommendation as the main enabling technologies until 2020 (Chorus+, 2011). Obviously these enabling technologies are strongly related to the changing music consumption behavior. Different parties operating in the music industry such as artists, music festivals and record labels are exploring their marketing opportunities through streaming music services by creating playlists for promotional purposes. Music Festivals such as Eurosonic Noorderslag and South-by-South-West started to promote their programming on social media using online playlists. Record label Arcade is currently working together with music streaming service Deezer in order to co-create commercial playlists based on successful CD-concepts from the early days such as Dance Classics and Turn up the Bass.

Since the interaction about music in the online world including the sharing of music and playlists is currently replacing the recommendation function of the traditional record stores and the influence of WoM that is not different from the early days, hence so much stronger due to the speed of information diffusion and surpassing of geographical boundaries, it is crucial for these parties to gain understanding in the personal factors affecting consumers to adopt these playlists or songs distributed by them.

The academic relevance of this study is evident. While the impact of technological developments on the recording industry and its customers has certainly gained academic attendance, only minimal research included the use of on-demand streaming music services. Previous research focused on DRM-technology (Sinha et al., 2010) and piracy (Bender and Wang, 2009). Furthermore, the impact of sharing Mp3-downloads on industry revenues has been studied from different perspectives (Bhattacharjee et al. 2007; Anderson, Frenz, 2010; Goel, S. et al., 2010; Elberse, 2010). The available research on online music consumption is mainly directed at the sharing of downloads (Huang, 2005; Plowman and Goode, 2009; Kwong and Park, 2008; LaRose and Kim, 2007).

This study is directed specifically towards playlist sharing behavior in an online environment in order to fill this gap in academic research at least partly.

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1.4 Structure of the thesis 

The thesis is organized in five main chapters as is shown below in figure 2.

Figure 2: Structure of the thesis

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2. Literature Review 

2.1 Intentions to share playlists in an online environment 

This study aims to gain understanding of a specific part of music sharing behavior in an online environment, which is sharing playlists online. The focus of this research is to gain insight in the psychological variables that affect the sharing of playlists in an online environment. These psychological variables will determine for a large part why some people share playlists and others do not.

An important body of research used to explain and predict consumer behavior, are the behavioral-intention models (Fishbein and Ajzen, 1975; Triandis, 1980). These cognitive models are based on the assumption that behavior toward a particular object is approximated by the intention to perform that behavior (Fishbein and Ajzen, 1975). Intention can be defined as “an individual’s conscious plan or self-instruction to carry out a behavior” (Triandis, 1980; Woon & Pee, 2004. p 81). A variety of studies found that intentions are strong predictors of actual behavior (Woon & Pee, 2004. Thus, the intention to share playlists in an online environment will for a great part determine if an individual will actually engage in this playlist sharing. The behavioral intention models to be reviewed for this study, are: The Theory of Reasoned Action (Ajzen, 1980), Theory of Planned behavior (Ajzen, 1991), Technology Acceptance Model (Davis, 1989), Theory of Interpersonal Relations (Triandis, 1980) and Unified Theory of acceptance and Use of Technology, (UTAUT) (Venkatesh, 2003). The next section discusses the different models in order to establish the usefulness for this research purpose.

2.1.1 Theory of Reasoned action (TRA)  

Ajzen and Fishbein developed TRA in 1975 (figure 3). The model builds on the belief that individuals consciously decide on the behavior they engage in or not (Bauer et al, 2005). Individuals form beliefs about a product or service by associating it with certain attributes. Each belief links the behavior to a certain outcome (Ajzen, 1991). Individuals learn to favour behaviors of which they believe they have largely desirable consequences and forming unfavourable attitudes toward behaviors they associate with mostly undesirable consequences (Fishbein and Ajzen, 1975). Behavioral beliefs can therefore be described as the subjective probability that behavior will result in a certain outcome. Within TRA, attitude is the antecedent of these behavioral beliefs. Normative beliefs are concerned with the likelihood that important referent individuals or groups approve or disapprove of performing a given behavior (Ajzen, 1991). Within TRA, subjective norm is the antecedent of these normative beliefs.

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Figure 3: Theory of Reasoned Action

2.1.2 Theory of Planned behavior (TPB) 

The TPB is an expansion of the TRA (Ajzen, 1991). Identical to TRA, TPB proposes that the intention to perform a behavior is together with perceptions a considerable predictor of actual behavior (Ajzen, 1991). As with the TRA, TPB states that the stronger the intention to engage in a behavior, the more likely should be its performance (Ajzen, 1991). The TPB is widely used in the academic field and has predicted a variety of behaviors of individuals with some degree of success (Conner and Armitage, 1998).

The TPB-model as is shown in figure 4 also proposes that attitude as the antecedent of behavioral beliefs and subjective norm as antecedent of normative beliefs predict the intention to perform a certain behavior. The TRA was extended by including a third antecedent of intention; perceived behavioral control. This additional construct refers to the antecedent of control beliefs. Control beliefs deal with the presence or absence of requisite resources and opportunities and relate to the perception of an individual of the difficulty of a task. These beliefs can be based on past experience with the behavior or influenced by the experiences of others (Ajzen, 1991).

Figure 4: Theory of Planned Behavior

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2.1.3 Decomposed Theory Of Planned behavior 

As the TPB is a widely used theoretical model for academic research, a variety of studies were dedicated to gain understanding on the relationships between these belief structures and the antecedents of intentions (Bagozzi, 1981; Taylor & Todd, 1995). Bagozzi (1981) found that constructs combined into a uni-dimensional construct results in invalid results. Decomposition makes the role of each structure better understandable and improves predictability of the TPB (Taylor et al., 1995).

2.1.4 Technology Acceptance Model (TAM) 

The TAM-model, developed by Davis (1989) predicts the acceptance of information systems as can be seen in figure 5 TAM is a variety of TRA, including two beliefs comprising attitude but no role for subjective norm (Taylor and Todd, 1995). TAM proposes that the motivation of a user is explained by perceived usefulness, perceived ease of use and attitude towards using the technology. Davis excluded subjective norm as he found this construct not having predictive influence on the intention to use technology (Davis, 1989).

TAM proposes that the acceptance of technology by an individual is determined by the intention of that individual to use the technology (Yousafzai et al., 2010). The ease of use and usefulness affect the attitude towards the usage of the technology, which in turn influences the intention to use the technology.

Perceived usefulness is defined as, “the degree to which a person believes that using a particular system would enhance his or her job performance” (Davis, 1989). Ease of use is defined as “the degree to which a person believes that using a particular system would be free of effort” (Davis, 1989).

Figure 5: Technology Acceptance Model (Taylor and Todd, 1995)

2.1.5 Theory of interpersonal behavior (TIB) 

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However, additional constructs were added: habits and facilitating conditions. According to Triandis (1980), habits mediate behavior whereas the facilitating conditions moderate these influences. Furthermore, the model includes affective factors on behavioral intentions. The intention to perform a certain behavior is determined by the following three factors: social factors, affect, and perceived consequences (Triandis, 1980). Habit, behavioral intention and facilitating conditions in turn, will determine the probability that a particular behavior will occur (Triandis, 1977). Comparing to TRA and TPB, TIB is less used in academic research (Woon and Pee, 2004).

Figure 6: Theory of Interpersonal Behavior (Triandis, 1980)

 

2.1.6 Unified Theory of acceptance and Use of Technology (UTAUT) 

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Figure 7: Unified Theory of Acceptance and Use of Technology (Venkatesh et al., 2003)

2.2 Comparison of behavioral intention models 

Appendix 1 includes an overview of the above mentioned behavioral intention models and theories. The conceptual framework for this study is based on TPB. As the sharing of playlists in an online environment entails the use of technological devices and the Internet, TPB is preferred over TRA as it includes control beliefs that are expected to be relevant for this research.

The TPB is preferred over the Technology Acceptance Model (TAM) as the latter only provides very general information on user’s opinions. Even though TPB is more difficult to apply, it provides more specific information (Mathieson, 1991). In particular since TAM does not include any social variables it makes it less useful for explaining music consumption behavior that is highly affected by social variables. Furthermore, the difference between TAM and TPB is that the direct path from perceived usefulness to intention is not in line as TPB claims that attitude completely mediates the relationship between these types of beliefs and intention (Taylor and Todd, 1995). TIB also includes the two constructs habits and facilitating conditions that can enable or hinder the actual behavior (Milhausen, Reece & Perera, 2006) while TPB indicates behavior as a direct function of behavioral intention. Affect towards behavior is included in TIB, whereas TPB proposes that affect is the result of perceived consequences by the value attached to these consequences (Triandis, 1977). Furthermore, to the contrary of TPB, TIB assumes interpersonal agreements, roles and self-image as separate factors in the model. However, TPB takes the influences of these factors in account as part of the belief structure affecting the attitude towards the behavior (Triandis, 1977).

TIB is considered more complex than TPB, which can be an explanation for its limited use in academic research (Godin, 2008). Unlike TPB, the variables within TIB are not clearly defined and measuring guidelines are lacking.

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The focus on the use of computer and information technology within organizations led to the inclusion of constructs, which are less relevant for studying the sharing of playlists within an online environment. Performance expectancy, which is referred to as ““The degree to which an individual believes that using the system will help him or her to attain gains in job performance” (Venkatesh et al., 2003)” is not relevant since the sharing of playlists in an online setting is not related to job performance. Also the constructs of facilitating conditions, which are defined by Venkatesh et al. (2003) as “The degree to which an individual believes that an organizational and technical infrastructure exists to support use of the system” (Venkatesh et al., 2003) do not fit this current research topic.

The moderating factor voluntariness of use is not relevant to take into account when establishing the predictive power of variables affecting the intention and behavior to share playlists in an online environment since this behavior is for the largest part voluntary. However, since decomposition improves the understanding of the structure’s role predictability of the TPB (Taylor et al., 1995). A decomposed TPB is used for this research.

The model states that the stronger the intention is to share playlists in an online environment, the more likely it is that the online sharing of playlists is performed (Ajzen, 1991). The TPB-model as is shown in figure 4 proposes attitude as the antecedent of behavioral beliefs, subjective norm as antecedent of normative beliefs and perceived behavioral control as antecedent of control beliefs.

In this study these belief structures are decomposed in a variety of constructs to improve predictability of the model.

2.3 Attitude (A) 

In this study attitude is defined as an individual’s evaluation of sharing a playlist online. Attitude is related to behavioral intention as users form intentions towards performing behavior to which they might have positive feelings (Ndubish, 2004). Individuals that have positive feelings towards sharing playlists online will therefore have a stronger intention to do so. Hence, the first hypothesis is:

H1: A positive attitude towards sharing playlists in an online environment will positively affect individuals’ behavioral intentions to sharing playlists in an online environment.

However, attitude is also expected to affect the actual behavior of sharing playlists in an online environment directly.

H2: A positive attitude towards sharing playlists in an online environment will positively affect individuals’ behavior to share playlists in an online environment.

Different dimensions for behavioral beliefs are considered in previous literature: Within this study, the dimensions measured are self-identity, involvement with music and psychological ownership.

2.3.1 Self‐Identity  

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In the past the role of self-identity in relation to the TPB was studied in several contexts (Sparks and Shepherd, 1992; Sparks et al., 1995; Theodorakis et al. 1995). These studies indicate that the effects of self-identity may dependent on the studied behavior (Connor and Armitage, 1998). Sparks and Sheperd (1992) proposed self-identity as a part of TPB as self-identity is the salient part of an individuals’ self that relates to a particular behavior (Conner and Armitage, 1998). According to Belk (1988) the terms “self”, “sense of self” and “identity” are synonyms. Jenkins (2008) proposes the definition of the self as “an individual’s reflexive sense of his own particular identity, constituted vis-à-vis others in terms of similarity and difference, without which he wouldn’t know who they are and hence wouldn’t be able to act”.

Kleine and Kurnan (1993) also constitutes the self as “a sense of who we are” in which “who we are” is identical to the “own identity” mentioned in Jenkins’ definition. Hoyer and McInnis (2008) add to this definition “how we think that others view us” similar to “vis-à-vis others” from the definition of Jenkins. While the self, related to consumer behavior emphasized the correspondence between the perceived characteristics of acquired objects and the perceived characteristics of the self in early research, Belk (1988) presents the concept of the extended self (Belk, 1988). He suggests that the extended self includes a complete set of consumption objects that represent the whole range of the total self and therefore the extended self includes also persons, places, and group possessions (Belk, 1988). Thus, symbols of group identity do not need to be individually owned products, these can also be things such as landmarks, places, leaders or media “stars” (Belk, 1988).

One of the key ways of expressing and defining group membership is through shared consumption symbols. These help to define an individual sense of self-indicating group identity and express belonging to a group (Belk, 1988). The symbolic meaning of products is therefore used to define a customer’s self-concept.

Already in 1959, Levy constituted that products are not only bought because of their functionality, but also for what these products mean. Music is consumed motivated by the need for distinction and self-definition (Belk, 1982; 1988) and is considered as a means to convey a message to a relevant audience and people form preferences in a social environment where they want to express their identity.

Academic research focusing on the symbolic consumption of music (Larsen et al. 2010; O’Hara and Brown, 2006) demonstrated that music is a rich and important site of symbolic consumption and included the important role of self-identity related to music consumption. However, these authors did not include the current trend of sharing playlists and music in an online environment.

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Therefore it is expected that individuals who indicate that sharing playlists in an online environment is important to their self-identity, will be more likely to engage in sharing playlists online. This leads to the following hypothesis.

H-A1: Self-identity related to music will positively affect individual’s attitude towards sharing playlists in an online environment.

2.3.2 Involvement with music 

For decades, the construct of involvement has been subject to academic research in the field of marketing. Involvement refers to the perceived level of importance of an object to an individual (Antil 1984). Peter and Olson (2008) refer to involvement as “a motivational state that energizes and directs consumer’s decisions, affective processes and behaviors”. These and other definitions refer to customer involvement as personal importance or relevance.

In an online environment, users are confronted with unsolicited eWoM through the exposure of information from others. For instance, on social network sites the enormous amount of status updates and other information shared by connections make involvement a relevant factor in this study. Without involvement with music, individuals would not pay attention to shared playlists due to this information overload.

Involvement with music is considered enduring involvement, as it comprises an ongoing concern with the product (Huang, 2006). Empirical studies indicated that the level of involvement impacts many aspects of consumer consumption behavior including attitude-formation (Zaichkowsky, 1985). A highly involved customer will search and process more product information which can lead to attitude change (Petty and Cacioppo, 1986). With respect to music consumption, this involvement will also lead to the discovery of new music. This will not necessarily affect the attitude towards current music preferences, but expands product perception leading to new music preferences and attitudes. Furthermore, customers who are highly involved could experience the vision that their lives might change without music (Garcia et al, 1996). As individuals are more likely to notice playlists online and engage in sharing playlists if they are involved in music, it is stated that:

H-A2 Involvement with music will positively affect the individual’s attitude towards sharing playlists in an online environment.

2.3.3 Psychological ownership 

Attitudes can be defined as the degree of belief that some consequence will occur (Ajzen & Fishbein, 1980; Ajzen, 1991). Therefore, in case of music sharing behavior, ownership is a relevant construct to take into account as antecedent of attitude. Recorded music is classified as an experience product. While customers are buying a physical carrier, the true value lies in the experience of using it (Huygen, 2007). This characteristic impacts current music consumption developments.

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This sense of ownership refers to psychological ownership. Beggan (1992) defined “psychological ownership” as the state in which individuals feel an object or a piece of one object as “theirs.” Psychological ownership (Pierce et al. 2003) is characterized by the feeling that something is “mine” (Shu and Peck, 2009).

The concept of psychological ownership is broader than legal ownership and refers to the concept of emotional attachment that entails the degree of attachment with a product on one hand and the affective reaction or emotion on the other hand (Shu and Peck, 2011). The current discussion within the music industry on the shift from music ownership to access of music can be discerned. While the industry focuses on legal ownership issues, the consumers of on-demand streaming music services possibly experience ownership of the streamed tracks and created playlists. Furthermore the proposition is that when an individual experiences psychological ownership of its created playlist a positive correlation exists with the attitude towards playlist sharing in an online environment. This leads to the following hypothesis: H-A3: Experienced psychological ownership of playlists will positively affect the individual’s attitude towards sharing playlists in an online environment.

2.4 Subjective norm (SN) 

Mundane consumption both facilitates and mediates social interaction (Douglas and Isherwood, 1979). The construct of subjective norm is a main representation of social influences (Venkatesh et. al., 2003). Consumer decision-making is highly affected by social influence (Van den Bulte & Stremersch, 2004). Earlier, academic researchers proposed that more complex facets of normative conduct should be encapsulated in an extended TPB (Terry & Hogg, 1996, Conner et al. 1996).

The construct of the subjective norm is not a single construct (Ten Kate et al., 2010). Srite and Karahanna (2006) proposed that; “social norms need to be conceptualized in a more distinguishing manner to capture the nuances of the social environment”. Salient normative beliefs underpin subjective norms (Connor and Armitage, 1998). Subjective norm refer to “the persons’ perception that most people who are important to him think he should or should not perform the behavior in question” (Fishbein and Ajzen, 1975, p302), in other words, the estimation of social pressure to perform or not perform a certain behavior. Therefore the subjective norm refers to the degree to which an individual perceives sharing playlists as a norm among people who are important to them.

Taylor and Todd (1995) propose that subjective norms have been found to be more important in early stages of innovation implementation. The sharing of playlists online is a relatively new activity, which indicates an increased importance of subjective norm. Therefore the following hypothesis is formulated:

H3: A Positive subjective norm towards sharing playlists in an online environment will positively affect individuals’ behavioral intentions to share playlists in an online environment.

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2.4.1 Social Influence 

The subjective norm construct within the TPB refers to social influence. Social influence can be subdivided in normative influence and informational influence. While normative influence is typically considered within the construct of subjective norm, this study also includes informational influence. The type of influence is dependent on the product and the situation (Grewal, Mehta, and Kardes, 2000). Normative influence is defined as “the tendency to conform to the expectations of others” (Burnkrant and Cousineau, 1975). Normative influence does not only refer to what is right or wrong, but also to what music is “in” and what is not. Normative influence affects brand choice congruence and conformity (Hoyer & McInnis, 2008). Informational influence is defined as, “The tendency to accept information from others as evidence about reality” (Deutsch and Gerard, 1955). Informational influence is more important in case of privately consumed goods, whereas both types of influence are essential for publicly consumed goods (Van Eck et al., 2011). Hence, as online music sharing behavior is considered a public activity, both constructs are important to take into account when aiming to gain insight in the role of social influence in online playlist sharing behavior.

People are not just influenced by others they also exert influence towards others. Therefore, this construct is differentiated in ingoing influence and outgoing influence. Both types of influence can be normative or informational. Regarding playlist sharing behavior, all four types of influences are relevant. Whilst the subjective norm construct within TPB refers only includes ingoing social influence. The decomposed TPB-model used for this study also includes outgoing social influence.

The users’ role is important to consider when studying the effect of social influence on playlist sharing behavior. Opinion leaders are very influential. They are highly centralized within a network and play an important role in the diffusion process, as they are considered more innovative and knowledgeable. Opinion leadership is defined as the extent to which individuals tend to give information or advice to others (Rogers, 1995). Opinion leaders for this study could be music experts, artists, journalists but also on-line connected friends who are considered innovative in their musical knowledge. This can result in individuals sharing playlists from opinion leaders while not even listening to these playlists themselves. Van Eck et al. (2011) studied the role opinion leaders play in the adoption process of new products. They found that opinion leaders cause a higher speed of information diffusion and product diffusion because their better judgment of product quality.

Ingoing normative influence (IN) & informational influence (II)

Individuals share playlists from significant others as they feel it is expected of them to do so. This normative influence results in public compliance. Ingoing normative influence is present when the focus is on the person who is exerting the influence when deciding on sharing a playlist. Opinion leaders exert normative influence as they set norms for others to belong to a group. In case of playlist sharing behavior it refers to the likelihood that users will listen to or share what others in their online environment share.

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Informational influence is partly reflected by the number of people who responded, listened to or shared playlists. This information might trigger a connection to respond to the playlist or even share the playlist. Informational influence results in private acceptance of the information or behavior. Based on the expected contributing role of ingoing normative and informational influence, the next hypotheses are:

H-SN1: IN will positively affect the individual’s subjective norm towards sharing playlists in an online environment.

H-SN2: II will positively affect the individual’s subjective norm towards sharing playlists in an online environment.

Outgoing normative influence (ON) and informational influence (OI)

Normative and informative elements are difficult to distinct in case of outgoing influence. The actual motives people have to share playlists are important to consider. The motives for sharing playlists will impact the actual compilation of the playlists. People who might compile and share playlists in an attempt to get as many shares as possible (ON) will make different content decisions than people who just share their own personal favorite tracks (OI).

Individuals who attempt to reach their inner circle with their playlists have normative motives while individuals attempting to share their playlists among as many people as possible will have more informational motives. Music sharers can exert informational influence when considered expert sources or opinion leaders by others. These opinion leaders typical have more connections within a network and more actively search for information. In case of playlist sharing, opinion leaders share music from both known as well as unknown sources. Both types of outgoing social influence are expected to affect subjective norm towards playlists sharing in an online environment. The next hypotheses state that:

H-SN 3: ON will positively affect the individual’s subjective norm towards sharing playlists in an online environment.

H-SN 4: OI will positively affect the individual’s subjective norm towards sharing playlists in an online environment.

2.4.2. Tie strength 

Sharing playlists takes place in an online environment on social network sites, blogs or communities such as Yahoo-groups. These online environments have a degree of structure (Rogers 2003), which affect sharing behavior. Understanding of the network structure creates insight in how communication flows. Networks consist of members (actors) and relational ties that link the members (Vilpponen et al. 2006). The network of customers determines with whom they communicate and the strength of the relation between people in a network determines the extent of communication between them. This relation between people connected in a social network is referred to as a social tie. In case of social network sites where much sharing takes place, people are intentionally connected resulting a social tie.

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